diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..ce3c767 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,19 @@ +# Normalize line endings for text files +* text=auto +# Markdown — always LF +*.md text eol=lf +*.txt text eol=lf +*.csv text eol=lf +# LFS — binary files +*.pdf filter=lfs diff=lfs merge=lfs -text +*.xlsx filter=lfs diff=lfs merge=lfs -text +*.pptx filter=lfs diff=lfs merge=lfs -text +*.docx filter=lfs diff=lfs merge=lfs -text +*.key filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text +*.webp filter=lfs diff=lfs merge=lfs -text +*.svg filter=lfs diff=lfs merge=lfs -text +*.ipynb filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pkpass filter=lfs diff=lfs merge=lfs -text +*.url filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore index b0adf45..7932d27 100644 --- a/.gitignore +++ b/.gitignore @@ -1,15 +1,5 @@ -# to exclude Obsidian's settings (including plugin and hotkey configurations) -#.obsidian/ - -# to only exclude plugin configuration. Might be useful to prevent some plugin from exposing sensitive data -#.obsidian/plugins - -# OR only to exclude workspace cache .obsidian/workspace.json - -# to exclude workspace cache specific to mobile devices .obsidian/workspace-mobile.json - -# Add below lines to exclude OS settings and caches +.obsidian/cache .trash/ -.DS_Store +thumbnails/ diff --git a/.obsidian/appearance.json b/.obsidian/appearance.json index 8242c9e..3a2262b 100644 --- a/.obsidian/appearance.json +++ b/.obsidian/appearance.json @@ -1,10 +1,10 @@ { "theme": "obsidian", "cssTheme": "Catppuccin", - "interfaceFontFamily": "Bahnschrift", - "textFontFamily": "Source Code Pro", + "interfaceFontFamily": "FiraCode Nerd Font", + "textFontFamily": "FiraCode Nerd Font Propo", "accentColor": "", - "monospaceFontFamily": "Source Code Pro", + "monospaceFontFamily": "FiraCode Nerd Font Mono", "baseFontSizeAction": true, "baseFontSize": 16, "enabledCssSnippets": [ diff --git a/.obsidian/graph.json b/.obsidian/graph.json index af12f56..90b9e02 100644 --- a/.obsidian/graph.json +++ b/.obsidian/graph.json @@ -17,6 +17,10 @@ "repelStrength": 17.61474609375, "linkStrength": 0.2745361328125, "linkDistance": 213, +<<<<<<< HEAD "scale": 0.012181151280878372, +======= + "scale": 0.015189448464788776, +>>>>>>> refs/remotes/origin/main "close": true } \ No newline at end of file diff --git a/.obsidian/plugins/extended-graph/data.json b/.obsidian/plugins/extended-graph/data.json index 2bc7df8..3faf49a 100644 --- a/.obsidian/plugins/extended-graph/data.json +++ b/.obsidian/plugins/extended-graph/data.json @@ -97,7 +97,11 @@ "repelStrength": 17.61474609375, "linkStrength": 0.2745361328125, "linkDistance": 213, +<<<<<<< HEAD "scale": 0.012181151280878372, +======= + "scale": 0.015189448464788776, +>>>>>>> refs/remotes/origin/main "close": true }, "openInNewTab": true, diff --git a/.obsidian/plugins/extended-graph/manifest.json b/.obsidian/plugins/extended-graph/manifest.json index a82be45..f873a71 100644 --- a/.obsidian/plugins/extended-graph/manifest.json +++ b/.obsidian/plugins/extended-graph/manifest.json @@ -1,11 +1,11 @@ -{ - "id": "extended-graph", - "name": "Extended Graph", - "version": "2.7.6", - "minAppVersion": "1.7.0", - "description": "Extends the features of the core Graph view, display images, manage states, remove links, change node shapes, and more.", - "author": "Kapirklaa", - "authorUrl": "https://github.com/ElsaTam", - "fundingUrl": "https://github.com/sponsors/ElsaTam", - "isDesktopOnly": true +{ + "id": "extended-graph", + "name": "Extended Graph", + "version": "2.7.6", + "minAppVersion": "1.7.0", + "description": "Extends the features of the core Graph view, display images, manage states, remove links, change node shapes, and more.", + "author": "Kapirklaa", + "authorUrl": "https://github.com/ElsaTam", + "fundingUrl": "https://github.com/sponsors/ElsaTam", + "isDesktopOnly": true } \ No newline at end of file diff --git a/.obsidian/plugins/extended-graph/styles.css b/.obsidian/plugins/extended-graph/styles.css index f60fea5..8d50775 100644 --- a/.obsidian/plugins/extended-graph/styles.css +++ b/.obsidian/plugins/extended-graph/styles.css @@ -1,1444 +1,1444 @@ -body { - --ext-graph-background-container: var(--background-primary); - --ext-graph-background-container-opacity: 0.9; - --ext-graph-layers-max-width: 5em; - --ext-graph-layers-with-labels-max-width: 10em; -} - -/* Container */ -.graph-legend-container, -.graph-states-container, -.extended-graph-layers { - background-color: rgb(from var(--ext-graph-background-container) r g b / var(--ext-graph-background-container-opacity)); - border: 1px solid var(--background-modifier-border); - box-shadow: var(--shadow-s); - border-radius: var(--radius-m); - - &:hover { - --ext-graph-background-container-opacity: 1; - } - - >.setting-item:last-child { - padding-bottom: 0; - } -} - -/* LEGEND */ - -.graph-legend-container { - - position: absolute; - bottom: var(--size-4-3); - left: var(--size-4-3); - margin-inline: auto; - width: fit-content; - max-width: calc(100% - 2 * var(--size-4-3)); - overflow: auto; - display: flex; - flex-direction: column; - - .graph-legend-rows { - --input-height: 1.7em; - display: flex; - flex-grow: 1; - align-self: stretch; - } - - &.is-closed { - display: none; - } - - .setting-item { - padding: var(--size-2-2) var(--size-2-2) var(--size-2-2); - } - - .setting-item-name { - --icon-size: 16px; - font-variant: small-caps; - text-overflow: ellipsis; - white-space: nowrap; - overflow: hidden; - padding: var(--size-2-2); - line-height: 1; - - svg { - padding-inline-start: var(--size-2-2); - vertical-align: text-bottom; - padding-block: 0; - } - } - - @media (hover: hover) { - .setting-item-name.mod-clickable:hover { - background-color: var(--background-modifier-hover); - color: var(--text-normal); - border-radius: var(--radius-s); - } - } - - .setting-item-info { - flex-grow: 0; - margin-right: 0; - margin-inline-end: 0; - } - - .setting-item-control { - justify-content: flex-start; - } - - .graph-legend { - background-color: rgb(var(--legend-color-rgb)); - color: var(--legend-text-color); - padding: var(--size-2-2) var(--size-4-2); - border-radius: var(--radius-s); - line-height: 1; - max-width: 140px; - overflow: hidden; - text-overflow: ellipsis; - white-space: nowrap; - display: inline-block; - } - - .graph-legend.is-hidden { - background-color: rgba(var(--legend-color-rgb), 0.1); - color: var(--text-faint); - font-weight: unset; - } - - .graph-legend-none { - order: 1; - } - - .and-or-group { - display: flex; - justify-content: center; - - button { - padding: var(--size-2-2) var(--size-4-2); - } - } - - .is-collapsed { - display: flex; - background-color: var(--background-secondary); - overflow: hidden; - - .graph-legend, - .and-or-group button { - font-size: 0; - height: 12px; - width: 12px; - border-radius: 50%; - padding: 0; - align-self: center; - } - } - - .setting-item.is-hidden { - display: none; - } - - .graph-legend-hide-rows-container { - --input-height: 1.7em; - --icon-size: 14px; - --icon-stroke: 1px; - - display: flex; - padding: var(--size-4-2); - gap: var(--size-4-2); - border-top: 1px solid var(--background-modifier-border); - - button { - display: flex; - align-items: center; - gap: var(--size-2-2); - padding: var(--size-4-1) var(--size-4-2); - overflow: hidden; - text-overflow: ellipsis; - white-space: nowrap; - display: inline-block; - line-height: 1; - - span { - margin-right: 4px; - } - } - - .is-inactive { - color: var(--text-faint); - } - - svg { - vertical-align: text-bottom; - } - } -} - -.graph-legend-container.horizontal-layout { - right: var(--size-4-3); - - .graph-legend-rows { - flex-direction: column; - min-width: fit-content; - } - - .is-collapsed { - padding-block: 0; - - .setting-item-info svg { - transform: rotate(-90deg); - } - } - - .setting-item:nth-child(1 of .setting-item:not(.is-hidden)) { - border-top: 0; - } - - .setting-item-info { - min-width: unset; - } - - .setting-item-name { - width: 10ch; - } - - .and-or-group { - flex-direction: column; - align-items: stretch; - gap: var(--size-2-1); - padding: var(--size-2-1); - } - - .is-collapsed { - .setting-item-info svg { - vertical-align: middle; - } - } - - .graph-legend-hide-rows-container { - justify-content: center; - } -} - -.graph-legend-container:not(.horizontal-layout) { - top: calc(var(--input-height) + 50px); - - .setting-item { - flex-direction: column; - border-top: 0; - border-left: 1px solid var(--background-modifier-border); - } - - .setting-item:nth-child(1 of .setting-item:not(.is-hidden)) { - border-left: 0; - } - - .setting-item-info { - text-align: center; - } - - .setting-item-control { - flex-direction: column; - align-items: stretch; - } - - .and-or-group { - gap: var(--size-2-1); - padding: var(--size-2-1); - } - - .is-collapsed { - flex-grow: 1; - padding-inline: 0; - row-gap: var(--size-2-2); - - .setting-item-info { - writing-mode: vertical-lr; - text-align: left; - - .setting-item-name { - height: 10ch; - padding-inline-end: 0; - } - } - } - - .graph-legend-hide-rows-container { - flex-direction: column; - } -} - -/* STATES */ - -@media (width > 800px) { - .graph-states-container { - max-width: calc(100% - var(--graph-controls-width) - 3 * var(--size-4-3)); - } -} - -@media (width < 800px) { - .graph-states-container { - max-width: calc(100% - var(--icon-size) - 2 * var(--size-2-3) - 3 * var(--size-4-3)); - } -} - -.graph-states-container { - position: absolute; - top: var(--size-4-3); - left: var(--size-4-3); - padding: var(--size-4-2); - display: flex; - flex-wrap: wrap; - align-items: center; - gap: var(--size-4-3); -} - -.graph-states-container.is-closed { - display: none; -} - - -.graph-states-container .setting-item-name { - font-variant: small-caps; -} - -/* LAYERS */ - -.extended-graph-layers { - position: absolute; - bottom: 35px; - inset-inline-end: var(--size-4-3); - padding: var(--size-2-2); - display: flex; - flex-direction: column-reverse; - max-width: var(--ext-graph-layers-max-width); - - &.show-labels { - max-width: var(--ext-graph-layers-with-labels-max-width); - } - - .layers { - display: flex; - flex-direction: column; - align-items: stretch; - text-align: center; - margin-block: var(--size-2-2); - } - - .layers>* { - padding-block: var(--size-2-1); - padding-inline: var(--size-2-2); - } - - .layer { - display: flex; - gap: var(--size-4-2); - } - - .layer-level { - width: 3em; - padding-inline: var(--size-2-2); - border: 1px solid var(--background-modifier-border); - border-radius: var(--radius-s); - flex-shrink: 0; - } - - .layer-labels { - display: flex; - align-items: center; - color: var(--text-faint); - overflow-x: auto; - } - - .layer-labels>* { - padding-inline: var(--size-2-2); - border: 1px solid var(--background-modifier-border); - border-radius: var(--radius-s); - } - - .current { - border-radius: var(--radius-m); - background-color: hsla(var(--accent-h), var(--accent-s), var(--accent-l), 0.1); - color: var(--color-accent); - - >.layer-level { - font-weight: var(--font-bold); - } - } - - .current:not(:last-child) { - border-bottom: 1px dashed var(--color-accent); - padding-bottom: calc(var(--size-2-1) - 1px); - } - - .active-layers-border { - pointer-events: none; - position: absolute; - inset-inline: var(--size-2-2); - border: 2px solid var(--color-accent); - border-radius: var(--radius-m); - transition: top 0.1s ease-out, bottom 0.1s ease-out; - } -} - -.extended-graph-layers~.graph-legend-container.horizontal-layout { - --layers-max-width: var(--ext-graph-layers-max-width); - right: calc(var(--size-4-3) * 2 + var(--layers-max-width)); - max-width: calc(100% - 3 * var(--size-4-3) - var(--layers-max-width)); -} - -.extended-graph-layers.show-labels~.graph-legend-container.horizontal-layout { - --layers-max-width: var(--ext-graph-layers-with-labels-max-width); -} - -.extended-graph-layers.is-closed~.graph-legend-container.horizontal-layout { - --layers-max-width: calc(2 * var(--size-2-2) + 2 * var(--size-2-3) + var(--icon-size)); -} - -/* MODAL NEW */ - -.graph-modal-new .modal-content { - display: flex; - gap: var(--size-4-4); -} - -.graph-modal-new input { - font-size: var(--font-ui-medium); -} - -/* MODAL GRAPH STATE */ - -.graph-modal-graph-state { - --dialog-width: 80vw; - font-family: var(--font-interface); - - .tag { - text-decoration: none; - border-radius: var(--tag-radius); - padding: var(--tag-padding-y) var(--tag-padding-x); - line-height: 1; - font-size: var(--tag-size); - border: 1px solid rgba(var(--interactive-color), 0.6); - display: inline-block; - margin: 0.2em; - - &:not(.is-disabled) { - background-color: rgba(var(--interactive-color), 0.2); - color: var(--text-normal); - } - - &.is-disabled { - border: 1px dashed rgba(var(--interactive-color), 0.2); - color: var(--text-faint); - background: none; - } - } - - code { - color: var(--code-normal); - font-family: var(--font-monospace); - background-color: var(--code-background); - border-radius: var(--code-radius); - font-size: var(--code-size); - padding: 0.15em 0.3em; - border: var(--code-border-width) solid var(--code-border-color); - box-decoration-break: clone; - -webkit-box-decoration-break: clone; - } - - table { - border-collapse: separate; - border-spacing: 0; - line-height: var(--table-line-height); - word-break: normal; - width: 100%; - - border-left: 1px solid var(--background-modifier-border); - border-top: 1px solid var(--background-modifier-border); - border-radius: var(--radius-l); - } - - td { - padding: var(--size-2-2) var(--size-4-2); - border-right: 1px solid var(--background-modifier-border); - border-bottom: 1px solid var(--background-modifier-border); - } - - thead td:not(:last-child) { - border-right: none; - } - - thead tr:first-child td:first-child { - border-top-left-radius: var(--radius-l); - } - - thead tr:first-child td:last-child { - border-top-right-radius: var(--radius-l); - } - - thead:last-child tr:last-child td:first-child, - tbody:last-child tr:last-child td:first-child { - border-bottom-left-radius: var(--radius-l); - } - - thead:last-child tr:last-child td:last-child, - tbody:last-child tr:last-child td:last-child { - border-bottom-right-radius: var(--radius-l); - } - - thead { - background-color: var(--background-secondary); - text-transform: capitalize; - - --icon-color: var(--text-faint); - - .clickable-icon { - display: inline-block; - vertical-align: middle; - padding: 2px; - margin-left: var(--size-4-2); - } - - .sorted-asc, - .sorted-desc { - --icon-color: var(--text-normal); - } - - .sorted-desc svg { - transform: scaleY(-1); - } - } - - .col-enabled { - width: 7em; - } - - .col-selected { - width: 7em; - } - - .col-shape { - width: 7em; - } - - .col-size { - width: 7em; - } - - - /* PAGINATION */ - .pagination { - display: flex; - justify-content: center; - padding: 0.75em; - align-items: stretch; - - button { - border-radius: 0; - } - - button:not(.mod-cta) { - background: none; - box-shadow: none; - } - - button:not(.mod-cta):hover { - background-color: var(--background-modifier-hover); - border-color: var(--background-modifier-border-hover); - } - - button.mod-cta { - pointer-events: none; - } - - .first-page, - .last-page { - border: 1px solid var(--background-modifier-border); - height: unset; - } - - .first-page { - border-top-left-radius: var(--button-radius); - border-bottom-left-radius: var(--button-radius); - border-right: 0; - } - - .last-page { - border-top-right-radius: var(--button-radius); - border-bottom-right-radius: var(--button-radius); - border-left: 0; - } - - } - - .pagination-inner { - display: flex; - border: 1px solid var(--background-modifier-border); - align-items: baseline; - - > :not(button) { - padding-inline: var(--size-4-3); - } - } - - .pagination-inner:first-child { - - &, - &> :first-child { - border-top-left-radius: var(--button-radius); - border-bottom-left-radius: var(--button-radius); - } - } - - .pagination-inner:last-child { - - &, - &> :last-child { - border-top-right-radius: var(--button-radius); - border-bottom-right-radius: var(--button-radius); - } - } - - /* CONTROLS */ - .number-of-rows { - display: flex; - justify-content: flex-end; - flex: unset; - gap: 0.75em; - align-items: center; - padding: var(--size-4-3); - font-size: var(--font-ui-small); - font-weight: var(--font-weight); - - input { - width: 4em; - font-size: var(--font-ui-small); - } - } - - .setting-item-heading { - justify-content: space-between; - } -} - -/* MODAL OPEN EXTERNAL LINKS */ - -.graph-modal-open-external-link .modal-content { - display: flex; - flex-direction: column; - gap: var(--size-4-2); -} - -/* GRAPH CONTROLS */ - -.graph-controls-extended-graph { - z-index: 1; -} - -.separator-exended-graph { - --hr-thickness: 1px; - margin-block: 0.5rem; -} - -.graph-controls.is-close .mod-extended-graph-toggle { - --icon-color-active: var(--text-normal); - display: flex; -} - -.mod-extended-graph-toggle.is-active { - background-color: var(--interactive-accent); -} - -.mod-states.is-active, -.mod-legend.is-active { - background-color: hsla(var(--accent-h), var(--accent-s), var(--accent-l), 0.2); -} - -.graph-control-section:is(.mod-extended-graph-options, .mod-extended-graph-folders) .setting-item .setting-item-info { - display: block; -} - -.graph-controls:not(.is-close) .separator-exended-graph { - display: none; -} - -.graph-controls.is-close .graph-controls-button.mod-extended-graph-reset, -.graph-controls.is-close .graph-controls-button.mod-states, -.graph-controls.is-close .graph-controls-button.mod-legend { - display: flex; - margin-top: var(--size-4-2); -} - -.graph-controls.is-close .mod-extended-graph-toggle.is-disabled, -.graph-controls.is-close .mod-extended-graph-reset.is-disabled { - --icon-color-active: var(--text-muted); - --icon-color: var(--text-faint); - pointer-events: none; -} - -.graph-control-section:nth-last-child(1 of .graph-control-section) { - border-bottom: none; -} - -.graph-controls .mod-extended-graph-folders .tree-item-children { - max-height: 300px; - overflow-y: auto; -} - -.graph-controls .mod-extended-graph-folders .setting-item { - --interactive-accent: rgb(var(--folder-color-rgb)); -} - -/* SETTINGS */ - -.extended-graph-settings { - - textarea { - resize: vertical; - width: 100%; - } - - - .color-palette canvas { - border-radius: var(--radius-s); - } - - >.setting-item-heading { - background-color: var(--background-secondary-alt); - margin-top: 0.75em; - padding-inline: 0.75em; - border-radius: var(--radius-s); - } - - >.setting-item-heading:not(.is-collapsed) { - border-bottom-left-radius: 0; - border-bottom-right-radius: 0; - z-index: 1; - position: relative; - } - - .toggle-labelled { - display: flex; - gap: var(--size-4-2); - align-items: center; - } - - .setting-item-heading { - .setting-item-control { - font-size: var(--font-ui-small); - font-weight: var(--font-weight); - } - - .setting-header-fold-icon { - display: inline-block; - vertical-align: middle; - } - } - - .setting-item-heading:not(.is-collapsed) { - .setting-header-fold-icon { - transform: rotate(90deg); - } - } - - >.setting-item-heading~.setting-item:not(.setting-item-heading):not(.no-section) { - background-color: var(--background-secondary); - padding-inline: 0.75em; - } - - >.setting-item-heading+.setting-item { - border-top: 0; - } - - .setting-item:not(.no-section)+.setting-item.no-section { - border-top: 0; - margin-top: 0.75em; - } - - .setting-item-heading.setting-automation.is-collapsed~.setting-automation, - .setting-item-heading.setting-tags.is-collapsed~.setting-tags, - .setting-item-heading.setting-properties.is-collapsed~.setting-properties, - .setting-item-heading.setting-property-key.is-collapsed~.setting-property-key, - .setting-item-heading.setting-links.is-collapsed~.setting-links, - .setting-item-heading.setting-folders.is-collapsed~.setting-folders, - .setting-item-heading.setting-images.is-collapsed~.setting-images, - .setting-item-heading.setting-icons.is-collapsed~.setting-icons, - .setting-item-heading.setting-focus.is-collapsed~.setting-focus, - .setting-item-heading.setting-names.is-collapsed~.setting-names, - .setting-item-heading.setting-shapes.is-collapsed~.setting-shapes, - .setting-item-heading.setting-layers.is-collapsed~.setting-layers, - .setting-item-heading.setting-arrows.is-collapsed~.setting-arrows, - .setting-item-heading.setting-elements-stats.is-collapsed~.setting-elements-stats, - .setting-item-heading.setting-zoom.is-collapsed~.setting-zoom, - .setting-item-heading.setting-local-graph.is-collapsed~.setting-local-graph, - .setting-item-heading.setting-display.is-collapsed~.setting-display, - .setting-item-heading.setting-filter.is-collapsed~.setting-filter, - .setting-item-heading.setting-inputs.is-collapsed~.setting-inputs, - .setting-item-heading.setting-performances.is-collapsed~.setting-performances, - .setting-item-heading.setting-beta.is-collapsed~.setting-beta { - display: none; - } - - .settings-colors-container, - .settings-properties-container { - flex-direction: column; - align-items: stretch; - } - - .setting-item.setting-color { - justify-content: flex-start; - - .setting-item-info { - display: none; - } - - .setting-item-control { - justify-content: flex-start; - } - - input[type="text"] { - border-color: color-mix(in srgb, var(--background-modifier-border), var(--interactive-color) 15%); - background: color-mix(in srgb, var(--background-modifier-form-field), var(--interactive-color) 2%); - } - } - - .settings-properties-container { - flex-grow: 1; - } - - .setting-item:not(.setting-item-heading).setting-shapes { - --svg-size: var(--icon-size); - - .setting-item-info { - flex: 0 1 9em; - } - - .setting-item-name { - text-transform: capitalize; - } - - .setting-item-control { - overflow: hidden; - } - - .query-string { - flex-grow: 1; - text-align: start; - overflow: hidden; - text-overflow: ellipsis; - white-space: nowrap; - - color: var(--text-muted); - font-size: var(--font-ui-smaller); - line-height: var(--line-height-tight); - } - - svg.shape-svg { - flex-shrink: 0; - } - - .move-buttons { - display: flex; - } - - .move-buttons>* { - padding-inline: 0; - } - } - - .setting-local-graph { - .shape-icon { - padding: var(--size-4-1); - border-radius: var(--radius-s); - display: flex; - } - - .shape-icon.is-active { - color: var(--color-accent); - background-color: hsla(var(--accent-h), var(--accent-s), var(--accent-l), 0.1); - outline: 1px solid hsla(var(--accent-h), var(--accent-s), var(--accent-l), 0.2); - } - } - - - .setting-warning, - .setting-additional-info { - border-top: 0; - padding-top: 0; - - .setting-item-info { - display: flex; - justify-content: flex-end; - align-items: center; - } - - - .setting-item-description { - padding-top: 0; - } - - .setting-item-control { - display: none; - } - - &.is-hidden { - display: none; - } - } - - .setting-warning { - .setting-item-info { - gap: 0.75em; - } - - svg { - color: var(--color-orange); - } - } - - .setting-additional-info { - svg { - --icon-size: var(--icon-xs); - color: var(--text-faint); - } - } - - .control-warning { - --icon-size: var(--icon-xs); - margin-inline-start: auto; - display: flex; - align-items: center; - gap: var(--size-4-2); - color: var(--text-muted); - font-size: var(--font-ui-smaller); - padding-top: var(--size-4-1); - line-height: var(--line-height-tight); - - svg { - color: var(--color-orange); - } - - &.is-hidden { - display: none; - } - } - - .extended-graph-nav-settings { - display: flex; - padding-bottom: 0.75em; - gap: 0.75em; - justify-content: flex-start; - align-items: center; - overflow-x: auto; - flex-wrap: wrap; - - >.clickable-icon { - background-color: var(--background-secondary); - - &:hover { - background-color: var(--background-modifier-hover); - } - } - - >*:last-child { - margin-right: auto; - } - - >* { - color: var(--text-faint); - font-size: var(--font-ui-smaller); - } - - .nav-label { - color: var(--text-muted); - } - } - - .setting-layer-info { - &:not(:nth-child(1 of .setting-layer-info)) { - border: none; - padding-top: 0; - } - - .setting-item-info { - display: none; - } - - .setting-item-control { - justify-content: space-between; - } - - .setting-item-control> :last-child { - margin-left: auto; - } - - .level-origin-icon { - --icon-size: var(--icon-xs); - color: var(--text-faint); - } - } - - input.number { - max-width: 5em; - } - - .error { - color: var(--color-red); - } -} - -.graph-modal-nodes-query { - .modal-title { - display: flex; - gap: 15px; - vertical-align: middle; - } - - .rule-setting { - align-items: flex-start; - - .setting-item-info { - display: none; - } - - .setting-item-control { - justify-content: flex-start; - max-width: 100%; - - >*:not(.clickable-icon) { - min-width: 20px; - flex-shrink: 1; - } - } - - &.query-invalid { - input[type="text"] { - background-color: color-mix(in srgb, var(--background-modifier-form-field), var(--color-red) 5%); - border-color: color-mix(in srgb, var(--background-modifier-border), var(--color-red) 50%); - } - } - } - - .buttons-container { - display: flex; - justify-content: space-between; - border-top: 1px solid var(--background-modifier-border); - padding: 0.75em 0; - } -} - - - -.graph-modal-interactives-selection { - .setting-item-control { - textarea { - width: 100%; - } - - input { - width: 10ch; - } - } - - .items-container { - display: flex; - gap: var(--size-4-2); - padding: 0.75em 0; - flex-wrap: wrap; - justify-content: flex-start; - - label { - border: 1px solid var(--background-modifier-border); - border-radius: var(--radius-s); - padding: var(--size-2-2); - margin: 0; - display: flex; - align-items: center; - gap: var(--size-2-2); - color: var(--text-faint); - - input[type=checkbox] { - margin: 0; - } - } - - label:hover { - border-color: var(--background-modifier-border-hover); - } - - label.is-active { - color: var(--text-normal); - border-color: var(--background-modifier-border-focus); - background-color: var(--background-modifier-form-field); - } - } -} - - -.graph-modal-shape-picker .modal-content { - --svg-size: 70px; - display: grid; - grid-template-columns: repeat(auto-fill, var(--svg-size)); - gap: 30px; - text-align: center; - - .setting-item { - flex-direction: column-reverse; - gap: 5px; - border: 0; - padding: 0; - } - - .setting-item>* { - margin: 0; - } - - .setting-item-name { - text-transform: capitalize; - } - - svg { - width: var(--svg-size); - } -} - -@media (hover: hover) { - .graph-modal-shape-picker .modal-content .setting-item-control:hover { - box-shadow: none; - opacity: var(--icon-opacity-hover); - color: var(--icon-color-hover); - background-color: var(--background-modifier-hover); - } -} - - -/* PALETTES */ - -.graph-modal-palette-picker { - .setting-item-heading { - display: block; - } - - .setting-item-heading:not(:first-child) { - border-top: 1px solid var(--background-modifier-border); - } - - .setting-item-heading:not(:last-child)>.setting-item-info { - margin-bottom: 0.75em; - display: flex; - justify-content: space-between; - align-items: center; - } - - .setting-item-heading:not(:last-child)>.setting-item-info>.setting-item-name { - display: flex; - gap: var(--size-4-2); - } - - .setting-item-heading:last-child .setting-item-info { - display: flex; - align-items: center; - gap: var(--size-4-2); - } - - .setting-item-name { - display: contents; - } -} - -.graph-modal-palette-maker { - .canvas-gradient-maker-container { - --swatch-height: 100%; - position: relative; - height: 80px; - padding-block: 3px; - } - - canvas { - height: 100%; - width: 100%; - } - - input[type="color"]::-webkit-color-swatch-wrapper { - padding: 0; - } - - input[type="color"] { - position: absolute; - display: inline; - top: 0; - bottom: 0; - left: calc(var(--handle-position) * 100%); - transform: translateX(-50%); - width: 15px; - height: 100%; - } - - input[type="color"]::-webkit-color-swatch { - border: 2px solid var(--background-secondary); - } - - .error { - color: var(--color-red); - } - - .setting-item-heading:last-child .setting-item-name { - display: flex; - align-items: center; - gap: var(--size-4-2); - - svg { - vertical-align: bottom; - } - } -} - -.palette-group { - display: grid; - grid-template-columns: repeat(auto-fill, 120px); - text-align: center; - - .setting-item { - flex-direction: column-reverse; - gap: 5px; - border: 0; - padding: 10px; - } - - .setting-item>* { - margin: 0; - } -} - -.custom-palette { - .setting-item-info { - display: flex; - align-self: stretch; - } - - :nth-child(1 of .clickable-icon) { - margin-left: auto; - } -} - -.palette-canvas.reversed { - scale: -1 1; -} - -@media (hover: hover) { - .graph-modal-palette-picker .palette-group .setting-item:hover { - box-shadow: none; - opacity: var(--icon-opacity-hover); - color: var(--icon-color-hover); - background-color: var(--background-modifier-hover); - border-radius: var(--radius-s); - } -} - -.graph-modal-setting-properties { - .properties-list { - display: flex; - flex-wrap: wrap; - row-gap: var(--size-4-2); - column-gap: var(--size-4-6); - padding-top: var(--size-4-4); - } - - .property-value { - display: flex; - } - - .extended-graph-add-button { - margin-inline-start: var(--size-4-2); - } -} - -/* SHAPES */ - -svg.shape-svg { - width: var(--svg-size, var(--icon-size)); -} - -/* BUTTONS */ - -.extended-graph-delete-button { - --text-color: var(--color-red); - color: var(--text-color); -} - -.extended-graph-add-button { - --text-color: var(--color-green); - color: var(--text-color); -} - -/* PLUGIN ICON */ -.tree-item-header-icon { - align-self: center; - margin-right: var(--size-4-2); - color: var(--icon-color); - opacity: var(--icon-opacity); - - svg { - width: var(--icon-size); - height: var(--icon-size); - } -} - - -/* STATUS BAR */ - -.status-bar-item.plugin-extended-graph.mod-clickable { - padding-block: 1px; -} - - -/* RADIAL MENU */ - -.extended-graph-radial-menu { - - background: none; - border: none; - display: block; - border-radius: 0; - box-shadow: none; - filter: drop-shadow(var(--menu-shadow)); - transform: translate(-50%, -50%); - - .menu-scroll { - position: relative; - width: 200px; - aspect-ratio: 1; - overflow: visible; - } - - .menu-item:not(.back) { - position: absolute; - top: 0; - right: 0; - width: 50%; - aspect-ratio: 1; - transform-origin: 0% 100%; - background-image: radial-gradient(circle at 0% 100%, - transparent, - transparent 29.5%, - rgb(var(--color-rgb), 0.5) 30%, - rgb(var(--color-rgb), 0.5) 30.5%, - rgb(var(--color-rgb)) 31%, - rgb(var(--color-rgb)) 50%, - rgb(var(--color-rgb), 0.5) 50.25%, - rgb(var(--color-rgb), 0.5) 51.5%, - transparent 51.75%, - transparent); - clip-path: polygon(0 0, 0 99%, 99% 0); - transform: rotate(var(--rotation)); - - .menu-item-icon, - .menu-item-title { - font-family: var(--font-interface); - font-size: var(--font-ui-smaller); - position: absolute; - top: 40%; - left: 15%; - transform: rotate(calc(var(--rotation) * -1)); - transform-origin: center center; - color: color-mix(in hsl, var(--color-base-00), var(--color-base-100) var(--dark-text-interp)); - } - - .menu-item-title { - left: 10%; - } - } - - .menu-item.selected:not(.is-label):not(.is-disabled) { - filter: brightness(1.2) saturate(1.5); - - &:not(.back) { - background-color: transparent; - } - - &.back { - background-color: var(--color-base-50); - } - } - - .menu-item.back { - position: absolute; - top: 35%; - left: 35%; - width: 30%; - aspect-ratio: 1; - background-color: var(--color-base-50); - border-radius: 50%; - z-index: 1; - justify-content: center; - - .svg-icon { - --icon-size: 100%; - } - } - - .menu-item-icon:not(:empty)+.menu-item-title { - display: none; - } - - .interactives-list { - position: absolute; - top: 10%; - left: 50%; - transform: translate(-50%, -100%); - - display: flex; - flex-direction: column; - justify-content: stretch; - gap: var(--size-2-2); - font-size: var(--font-ui-small); - padding: var(--size-2-2); - background-color: var(--menu-background); - border: var(--menu-border-width) solid var(--menu-border-color); - border-radius: var(--menu-radius); - box-shadow: var(--menu-shadow); - - .interactive-item { - max-width: 140px; - overflow: hidden; - text-overflow: ellipsis; - white-space: nowrap; - display: inline-block; - line-height: 1; - - &::before { - content: ""; - display: inline-block; - width: 0.7em; - aspect-ratio: 1; - border-radius: 50%; - background-color: rgb(var(--bg-color)); - vertical-align: baseline; - margin-right: 0.4em; - } - - &.is-hidden { - color: var(--text-faint); - - ::before { - background-color: rgba(var(--bg-color), 0.1); - } - } - } - } - - .tooltip { - top: 50%; - left: 0; - right: 0; - margin-inline: auto; - width: fit-content; - transform: translateX(50%); - } -} - -/* SUGGESTERS */ - -.suggestion-container .extended-graph-duplicate { - color: var(--text-faint); +body { + --ext-graph-background-container: var(--background-primary); + --ext-graph-background-container-opacity: 0.9; + --ext-graph-layers-max-width: 5em; + --ext-graph-layers-with-labels-max-width: 10em; +} + +/* Container */ +.graph-legend-container, +.graph-states-container, +.extended-graph-layers { + background-color: rgb(from var(--ext-graph-background-container) r g b / var(--ext-graph-background-container-opacity)); + border: 1px solid var(--background-modifier-border); + box-shadow: var(--shadow-s); + border-radius: var(--radius-m); + + &:hover { + --ext-graph-background-container-opacity: 1; + } + + >.setting-item:last-child { + padding-bottom: 0; + } +} + +/* LEGEND */ + +.graph-legend-container { + + position: absolute; + bottom: var(--size-4-3); + left: var(--size-4-3); + margin-inline: auto; + width: fit-content; + max-width: calc(100% - 2 * var(--size-4-3)); + overflow: auto; + display: flex; + flex-direction: column; + + .graph-legend-rows { + --input-height: 1.7em; + display: flex; + flex-grow: 1; + align-self: stretch; + } + + &.is-closed { + display: none; + } + + .setting-item { + padding: var(--size-2-2) var(--size-2-2) var(--size-2-2); + } + + .setting-item-name { + --icon-size: 16px; + font-variant: small-caps; + text-overflow: ellipsis; + white-space: nowrap; + overflow: hidden; + padding: var(--size-2-2); + line-height: 1; + + svg { + padding-inline-start: var(--size-2-2); + vertical-align: text-bottom; + padding-block: 0; + } + } + + @media (hover: hover) { + .setting-item-name.mod-clickable:hover { + background-color: var(--background-modifier-hover); + color: var(--text-normal); + border-radius: var(--radius-s); + } + } + + .setting-item-info { + flex-grow: 0; + margin-right: 0; + margin-inline-end: 0; + } + + .setting-item-control { + justify-content: flex-start; + } + + .graph-legend { + background-color: rgb(var(--legend-color-rgb)); + color: var(--legend-text-color); + padding: var(--size-2-2) var(--size-4-2); + border-radius: var(--radius-s); + line-height: 1; + max-width: 140px; + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; + display: inline-block; + } + + .graph-legend.is-hidden { + background-color: rgba(var(--legend-color-rgb), 0.1); + color: var(--text-faint); + font-weight: unset; + } + + .graph-legend-none { + order: 1; + } + + .and-or-group { + display: flex; + justify-content: center; + + button { + padding: var(--size-2-2) var(--size-4-2); + } + } + + .is-collapsed { + display: flex; + background-color: var(--background-secondary); + overflow: hidden; + + .graph-legend, + .and-or-group button { + font-size: 0; + height: 12px; + width: 12px; + border-radius: 50%; + padding: 0; + align-self: center; + } + } + + .setting-item.is-hidden { + display: none; + } + + .graph-legend-hide-rows-container { + --input-height: 1.7em; + --icon-size: 14px; + --icon-stroke: 1px; + + display: flex; + padding: var(--size-4-2); + gap: var(--size-4-2); + border-top: 1px solid var(--background-modifier-border); + + button { + display: flex; + align-items: center; + gap: var(--size-2-2); + padding: var(--size-4-1) var(--size-4-2); + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; + display: inline-block; + line-height: 1; + + span { + margin-right: 4px; + } + } + + .is-inactive { + color: var(--text-faint); + } + + svg { + vertical-align: text-bottom; + } + } +} + +.graph-legend-container.horizontal-layout { + right: var(--size-4-3); + + .graph-legend-rows { + flex-direction: column; + min-width: fit-content; + } + + .is-collapsed { + padding-block: 0; + + .setting-item-info svg { + transform: rotate(-90deg); + } + } + + .setting-item:nth-child(1 of .setting-item:not(.is-hidden)) { + border-top: 0; + } + + .setting-item-info { + min-width: unset; + } + + .setting-item-name { + width: 10ch; + } + + .and-or-group { + flex-direction: column; + align-items: stretch; + gap: var(--size-2-1); + padding: var(--size-2-1); + } + + .is-collapsed { + .setting-item-info svg { + vertical-align: middle; + } + } + + .graph-legend-hide-rows-container { + justify-content: center; + } +} + +.graph-legend-container:not(.horizontal-layout) { + top: calc(var(--input-height) + 50px); + + .setting-item { + flex-direction: column; + border-top: 0; + border-left: 1px solid var(--background-modifier-border); + } + + .setting-item:nth-child(1 of .setting-item:not(.is-hidden)) { + border-left: 0; + } + + .setting-item-info { + text-align: center; + } + + .setting-item-control { + flex-direction: column; + align-items: stretch; + } + + .and-or-group { + gap: var(--size-2-1); + padding: var(--size-2-1); + } + + .is-collapsed { + flex-grow: 1; + padding-inline: 0; + row-gap: var(--size-2-2); + + .setting-item-info { + writing-mode: vertical-lr; + text-align: left; + + .setting-item-name { + height: 10ch; + padding-inline-end: 0; + } + } + } + + .graph-legend-hide-rows-container { + flex-direction: column; + } +} + +/* STATES */ + +@media (width > 800px) { + .graph-states-container { + max-width: calc(100% - var(--graph-controls-width) - 3 * var(--size-4-3)); + } +} + +@media (width < 800px) { + .graph-states-container { + max-width: calc(100% - var(--icon-size) - 2 * var(--size-2-3) - 3 * var(--size-4-3)); + } +} + +.graph-states-container { + position: absolute; + top: var(--size-4-3); + left: var(--size-4-3); + padding: var(--size-4-2); + display: flex; + flex-wrap: wrap; + align-items: center; + gap: var(--size-4-3); +} + +.graph-states-container.is-closed { + display: none; +} + + +.graph-states-container .setting-item-name { + font-variant: small-caps; +} + +/* LAYERS */ + +.extended-graph-layers { + position: absolute; + bottom: 35px; + inset-inline-end: var(--size-4-3); + padding: var(--size-2-2); + display: flex; + flex-direction: column-reverse; + max-width: var(--ext-graph-layers-max-width); + + &.show-labels { + max-width: var(--ext-graph-layers-with-labels-max-width); + } + + .layers { + display: flex; + flex-direction: column; + align-items: stretch; + text-align: center; + margin-block: var(--size-2-2); + } + + .layers>* { + padding-block: var(--size-2-1); + padding-inline: var(--size-2-2); + } + + .layer { + display: flex; + gap: var(--size-4-2); + } + + .layer-level { + width: 3em; + padding-inline: var(--size-2-2); + border: 1px solid var(--background-modifier-border); + border-radius: var(--radius-s); + flex-shrink: 0; + } + + .layer-labels { + display: flex; + align-items: center; + color: var(--text-faint); + overflow-x: auto; + } + + .layer-labels>* { + padding-inline: var(--size-2-2); + border: 1px solid var(--background-modifier-border); + border-radius: var(--radius-s); + } + + .current { + border-radius: var(--radius-m); + background-color: hsla(var(--accent-h), var(--accent-s), var(--accent-l), 0.1); + color: var(--color-accent); + + >.layer-level { + font-weight: var(--font-bold); + } + } + + .current:not(:last-child) { + border-bottom: 1px dashed var(--color-accent); + padding-bottom: calc(var(--size-2-1) - 1px); + } + + .active-layers-border { + pointer-events: none; + position: absolute; + inset-inline: var(--size-2-2); + border: 2px solid var(--color-accent); + border-radius: var(--radius-m); + transition: top 0.1s ease-out, bottom 0.1s ease-out; + } +} + +.extended-graph-layers~.graph-legend-container.horizontal-layout { + --layers-max-width: var(--ext-graph-layers-max-width); + right: calc(var(--size-4-3) * 2 + var(--layers-max-width)); + max-width: calc(100% - 3 * var(--size-4-3) - var(--layers-max-width)); +} + +.extended-graph-layers.show-labels~.graph-legend-container.horizontal-layout { + --layers-max-width: var(--ext-graph-layers-with-labels-max-width); +} + +.extended-graph-layers.is-closed~.graph-legend-container.horizontal-layout { + --layers-max-width: calc(2 * var(--size-2-2) + 2 * var(--size-2-3) + var(--icon-size)); +} + +/* MODAL NEW */ + +.graph-modal-new .modal-content { + display: flex; + gap: var(--size-4-4); +} + +.graph-modal-new input { + font-size: var(--font-ui-medium); +} + +/* MODAL GRAPH STATE */ + +.graph-modal-graph-state { + --dialog-width: 80vw; + font-family: var(--font-interface); + + .tag { + text-decoration: none; + border-radius: var(--tag-radius); + padding: var(--tag-padding-y) var(--tag-padding-x); + line-height: 1; + font-size: var(--tag-size); + border: 1px solid rgba(var(--interactive-color), 0.6); + display: inline-block; + margin: 0.2em; + + &:not(.is-disabled) { + background-color: rgba(var(--interactive-color), 0.2); + color: var(--text-normal); + } + + &.is-disabled { + border: 1px dashed rgba(var(--interactive-color), 0.2); + color: var(--text-faint); + background: none; + } + } + + code { + color: var(--code-normal); + font-family: var(--font-monospace); + background-color: var(--code-background); + border-radius: var(--code-radius); + font-size: var(--code-size); + padding: 0.15em 0.3em; + border: var(--code-border-width) solid var(--code-border-color); + box-decoration-break: clone; + -webkit-box-decoration-break: clone; + } + + table { + border-collapse: separate; + border-spacing: 0; + line-height: var(--table-line-height); + word-break: normal; + width: 100%; + + border-left: 1px solid var(--background-modifier-border); + border-top: 1px solid var(--background-modifier-border); + border-radius: var(--radius-l); + } + + td { + padding: var(--size-2-2) var(--size-4-2); + border-right: 1px solid var(--background-modifier-border); + border-bottom: 1px solid var(--background-modifier-border); + } + + thead td:not(:last-child) { + border-right: none; + } + + thead tr:first-child td:first-child { + border-top-left-radius: var(--radius-l); + } + + thead tr:first-child td:last-child { + border-top-right-radius: var(--radius-l); + } + + thead:last-child tr:last-child td:first-child, + tbody:last-child tr:last-child td:first-child { + border-bottom-left-radius: var(--radius-l); + } + + thead:last-child tr:last-child td:last-child, + tbody:last-child tr:last-child td:last-child { + border-bottom-right-radius: var(--radius-l); + } + + thead { + background-color: var(--background-secondary); + text-transform: capitalize; + + --icon-color: var(--text-faint); + + .clickable-icon { + display: inline-block; + vertical-align: middle; + padding: 2px; + margin-left: var(--size-4-2); + } + + .sorted-asc, + .sorted-desc { + --icon-color: var(--text-normal); + } + + .sorted-desc svg { + transform: scaleY(-1); + } + } + + .col-enabled { + width: 7em; + } + + .col-selected { + width: 7em; + } + + .col-shape { + width: 7em; + } + + .col-size { + width: 7em; + } + + + /* PAGINATION */ + .pagination { + display: flex; + justify-content: center; + padding: 0.75em; + align-items: stretch; + + button { + border-radius: 0; + } + + button:not(.mod-cta) { + background: none; + box-shadow: none; + } + + button:not(.mod-cta):hover { + background-color: var(--background-modifier-hover); + border-color: var(--background-modifier-border-hover); + } + + button.mod-cta { + pointer-events: none; + } + + .first-page, + .last-page { + border: 1px solid var(--background-modifier-border); + height: unset; + } + + .first-page { + border-top-left-radius: var(--button-radius); + border-bottom-left-radius: var(--button-radius); + border-right: 0; + } + + .last-page { + border-top-right-radius: var(--button-radius); + border-bottom-right-radius: var(--button-radius); + border-left: 0; + } + + } + + .pagination-inner { + display: flex; + border: 1px solid var(--background-modifier-border); + align-items: baseline; + + > :not(button) { + padding-inline: var(--size-4-3); + } + } + + .pagination-inner:first-child { + + &, + &> :first-child { + border-top-left-radius: var(--button-radius); + border-bottom-left-radius: var(--button-radius); + } + } + + .pagination-inner:last-child { + + &, + &> :last-child { + border-top-right-radius: var(--button-radius); + border-bottom-right-radius: var(--button-radius); + } + } + + /* CONTROLS */ + .number-of-rows { + display: flex; + justify-content: flex-end; + flex: unset; + gap: 0.75em; + align-items: center; + padding: var(--size-4-3); + font-size: var(--font-ui-small); + font-weight: var(--font-weight); + + input { + width: 4em; + font-size: var(--font-ui-small); + } + } + + .setting-item-heading { + justify-content: space-between; + } +} + +/* MODAL OPEN EXTERNAL LINKS */ + +.graph-modal-open-external-link .modal-content { + display: flex; + flex-direction: column; + gap: var(--size-4-2); +} + +/* GRAPH CONTROLS */ + +.graph-controls-extended-graph { + z-index: 1; +} + +.separator-exended-graph { + --hr-thickness: 1px; + margin-block: 0.5rem; +} + +.graph-controls.is-close .mod-extended-graph-toggle { + --icon-color-active: var(--text-normal); + display: flex; +} + +.mod-extended-graph-toggle.is-active { + background-color: var(--interactive-accent); +} + +.mod-states.is-active, +.mod-legend.is-active { + background-color: hsla(var(--accent-h), var(--accent-s), var(--accent-l), 0.2); +} + +.graph-control-section:is(.mod-extended-graph-options, .mod-extended-graph-folders) .setting-item .setting-item-info { + display: block; +} + +.graph-controls:not(.is-close) .separator-exended-graph { + display: none; +} + +.graph-controls.is-close .graph-controls-button.mod-extended-graph-reset, +.graph-controls.is-close .graph-controls-button.mod-states, +.graph-controls.is-close .graph-controls-button.mod-legend { + display: flex; + margin-top: var(--size-4-2); +} + +.graph-controls.is-close .mod-extended-graph-toggle.is-disabled, +.graph-controls.is-close .mod-extended-graph-reset.is-disabled { + --icon-color-active: var(--text-muted); + --icon-color: var(--text-faint); + pointer-events: none; +} + +.graph-control-section:nth-last-child(1 of .graph-control-section) { + border-bottom: none; +} + +.graph-controls .mod-extended-graph-folders .tree-item-children { + max-height: 300px; + overflow-y: auto; +} + +.graph-controls .mod-extended-graph-folders .setting-item { + --interactive-accent: rgb(var(--folder-color-rgb)); +} + +/* SETTINGS */ + +.extended-graph-settings { + + textarea { + resize: vertical; + width: 100%; + } + + + .color-palette canvas { + border-radius: var(--radius-s); + } + + >.setting-item-heading { + background-color: var(--background-secondary-alt); + margin-top: 0.75em; + padding-inline: 0.75em; + border-radius: var(--radius-s); + } + + >.setting-item-heading:not(.is-collapsed) { + border-bottom-left-radius: 0; + border-bottom-right-radius: 0; + z-index: 1; + position: relative; + } + + .toggle-labelled { + display: flex; + gap: var(--size-4-2); + align-items: center; + } + + .setting-item-heading { + .setting-item-control { + font-size: var(--font-ui-small); + font-weight: var(--font-weight); + } + + .setting-header-fold-icon { + display: inline-block; + vertical-align: middle; + } + } + + .setting-item-heading:not(.is-collapsed) { + .setting-header-fold-icon { + transform: rotate(90deg); + } + } + + >.setting-item-heading~.setting-item:not(.setting-item-heading):not(.no-section) { + background-color: var(--background-secondary); + padding-inline: 0.75em; + } + + >.setting-item-heading+.setting-item { + border-top: 0; + } + + .setting-item:not(.no-section)+.setting-item.no-section { + border-top: 0; + margin-top: 0.75em; + } + + .setting-item-heading.setting-automation.is-collapsed~.setting-automation, + .setting-item-heading.setting-tags.is-collapsed~.setting-tags, + .setting-item-heading.setting-properties.is-collapsed~.setting-properties, + .setting-item-heading.setting-property-key.is-collapsed~.setting-property-key, + .setting-item-heading.setting-links.is-collapsed~.setting-links, + .setting-item-heading.setting-folders.is-collapsed~.setting-folders, + .setting-item-heading.setting-images.is-collapsed~.setting-images, + .setting-item-heading.setting-icons.is-collapsed~.setting-icons, + .setting-item-heading.setting-focus.is-collapsed~.setting-focus, + .setting-item-heading.setting-names.is-collapsed~.setting-names, + .setting-item-heading.setting-shapes.is-collapsed~.setting-shapes, + .setting-item-heading.setting-layers.is-collapsed~.setting-layers, + .setting-item-heading.setting-arrows.is-collapsed~.setting-arrows, + .setting-item-heading.setting-elements-stats.is-collapsed~.setting-elements-stats, + .setting-item-heading.setting-zoom.is-collapsed~.setting-zoom, + .setting-item-heading.setting-local-graph.is-collapsed~.setting-local-graph, + .setting-item-heading.setting-display.is-collapsed~.setting-display, + .setting-item-heading.setting-filter.is-collapsed~.setting-filter, + .setting-item-heading.setting-inputs.is-collapsed~.setting-inputs, + .setting-item-heading.setting-performances.is-collapsed~.setting-performances, + .setting-item-heading.setting-beta.is-collapsed~.setting-beta { + display: none; + } + + .settings-colors-container, + .settings-properties-container { + flex-direction: column; + align-items: stretch; + } + + .setting-item.setting-color { + justify-content: flex-start; + + .setting-item-info { + display: none; + } + + .setting-item-control { + justify-content: flex-start; + } + + input[type="text"] { + border-color: color-mix(in srgb, var(--background-modifier-border), var(--interactive-color) 15%); + background: color-mix(in srgb, var(--background-modifier-form-field), var(--interactive-color) 2%); + } + } + + .settings-properties-container { + flex-grow: 1; + } + + .setting-item:not(.setting-item-heading).setting-shapes { + --svg-size: var(--icon-size); + + .setting-item-info { + flex: 0 1 9em; + } + + .setting-item-name { + text-transform: capitalize; + } + + .setting-item-control { + overflow: hidden; + } + + .query-string { + flex-grow: 1; + text-align: start; + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; + + color: var(--text-muted); + font-size: var(--font-ui-smaller); + line-height: var(--line-height-tight); + } + + svg.shape-svg { + flex-shrink: 0; + } + + .move-buttons { + display: flex; + } + + .move-buttons>* { + padding-inline: 0; + } + } + + .setting-local-graph { + .shape-icon { + padding: var(--size-4-1); + border-radius: var(--radius-s); + display: flex; + } + + .shape-icon.is-active { + color: var(--color-accent); + background-color: hsla(var(--accent-h), var(--accent-s), var(--accent-l), 0.1); + outline: 1px solid hsla(var(--accent-h), var(--accent-s), var(--accent-l), 0.2); + } + } + + + .setting-warning, + .setting-additional-info { + border-top: 0; + padding-top: 0; + + .setting-item-info { + display: flex; + justify-content: flex-end; + align-items: center; + } + + + .setting-item-description { + padding-top: 0; + } + + .setting-item-control { + display: none; + } + + &.is-hidden { + display: none; + } + } + + .setting-warning { + .setting-item-info { + gap: 0.75em; + } + + svg { + color: var(--color-orange); + } + } + + .setting-additional-info { + svg { + --icon-size: var(--icon-xs); + color: var(--text-faint); + } + } + + .control-warning { + --icon-size: var(--icon-xs); + margin-inline-start: auto; + display: flex; + align-items: center; + gap: var(--size-4-2); + color: var(--text-muted); + font-size: var(--font-ui-smaller); + padding-top: var(--size-4-1); + line-height: var(--line-height-tight); + + svg { + color: var(--color-orange); + } + + &.is-hidden { + display: none; + } + } + + .extended-graph-nav-settings { + display: flex; + padding-bottom: 0.75em; + gap: 0.75em; + justify-content: flex-start; + align-items: center; + overflow-x: auto; + flex-wrap: wrap; + + >.clickable-icon { + background-color: var(--background-secondary); + + &:hover { + background-color: var(--background-modifier-hover); + } + } + + >*:last-child { + margin-right: auto; + } + + >* { + color: var(--text-faint); + font-size: var(--font-ui-smaller); + } + + .nav-label { + color: var(--text-muted); + } + } + + .setting-layer-info { + &:not(:nth-child(1 of .setting-layer-info)) { + border: none; + padding-top: 0; + } + + .setting-item-info { + display: none; + } + + .setting-item-control { + justify-content: space-between; + } + + .setting-item-control> :last-child { + margin-left: auto; + } + + .level-origin-icon { + --icon-size: var(--icon-xs); + color: var(--text-faint); + } + } + + input.number { + max-width: 5em; + } + + .error { + color: var(--color-red); + } +} + +.graph-modal-nodes-query { + .modal-title { + display: flex; + gap: 15px; + vertical-align: middle; + } + + .rule-setting { + align-items: flex-start; + + .setting-item-info { + display: none; + } + + .setting-item-control { + justify-content: flex-start; + max-width: 100%; + + >*:not(.clickable-icon) { + min-width: 20px; + flex-shrink: 1; + } + } + + &.query-invalid { + input[type="text"] { + background-color: color-mix(in srgb, var(--background-modifier-form-field), var(--color-red) 5%); + border-color: color-mix(in srgb, var(--background-modifier-border), var(--color-red) 50%); + } + } + } + + .buttons-container { + display: flex; + justify-content: space-between; + border-top: 1px solid var(--background-modifier-border); + padding: 0.75em 0; + } +} + + + +.graph-modal-interactives-selection { + .setting-item-control { + textarea { + width: 100%; + } + + input { + width: 10ch; + } + } + + .items-container { + display: flex; + gap: var(--size-4-2); + padding: 0.75em 0; + flex-wrap: wrap; + justify-content: flex-start; + + label { + border: 1px solid var(--background-modifier-border); + border-radius: var(--radius-s); + padding: var(--size-2-2); + margin: 0; + display: flex; + align-items: center; + gap: var(--size-2-2); + color: var(--text-faint); + + input[type=checkbox] { + margin: 0; + } + } + + label:hover { + border-color: var(--background-modifier-border-hover); + } + + label.is-active { + color: var(--text-normal); + border-color: var(--background-modifier-border-focus); + background-color: var(--background-modifier-form-field); + } + } +} + + +.graph-modal-shape-picker .modal-content { + --svg-size: 70px; + display: grid; + grid-template-columns: repeat(auto-fill, var(--svg-size)); + gap: 30px; + text-align: center; + + .setting-item { + flex-direction: column-reverse; + gap: 5px; + border: 0; + padding: 0; + } + + .setting-item>* { + margin: 0; + } + + .setting-item-name { + text-transform: capitalize; + } + + svg { + width: var(--svg-size); + } +} + +@media (hover: hover) { + .graph-modal-shape-picker .modal-content .setting-item-control:hover { + box-shadow: none; + opacity: var(--icon-opacity-hover); + color: var(--icon-color-hover); + background-color: var(--background-modifier-hover); + } +} + + +/* PALETTES */ + +.graph-modal-palette-picker { + .setting-item-heading { + display: block; + } + + .setting-item-heading:not(:first-child) { + border-top: 1px solid var(--background-modifier-border); + } + + .setting-item-heading:not(:last-child)>.setting-item-info { + margin-bottom: 0.75em; + display: flex; + justify-content: space-between; + align-items: center; + } + + .setting-item-heading:not(:last-child)>.setting-item-info>.setting-item-name { + display: flex; + gap: var(--size-4-2); + } + + .setting-item-heading:last-child .setting-item-info { + display: flex; + align-items: center; + gap: var(--size-4-2); + } + + .setting-item-name { + display: contents; + } +} + +.graph-modal-palette-maker { + .canvas-gradient-maker-container { + --swatch-height: 100%; + position: relative; + height: 80px; + padding-block: 3px; + } + + canvas { + height: 100%; + width: 100%; + } + + input[type="color"]::-webkit-color-swatch-wrapper { + padding: 0; + } + + input[type="color"] { + position: absolute; + display: inline; + top: 0; + bottom: 0; + left: calc(var(--handle-position) * 100%); + transform: translateX(-50%); + width: 15px; + height: 100%; + } + + input[type="color"]::-webkit-color-swatch { + border: 2px solid var(--background-secondary); + } + + .error { + color: var(--color-red); + } + + .setting-item-heading:last-child .setting-item-name { + display: flex; + align-items: center; + gap: var(--size-4-2); + + svg { + vertical-align: bottom; + } + } +} + +.palette-group { + display: grid; + grid-template-columns: repeat(auto-fill, 120px); + text-align: center; + + .setting-item { + flex-direction: column-reverse; + gap: 5px; + border: 0; + padding: 10px; + } + + .setting-item>* { + margin: 0; + } +} + +.custom-palette { + .setting-item-info { + display: flex; + align-self: stretch; + } + + :nth-child(1 of .clickable-icon) { + margin-left: auto; + } +} + +.palette-canvas.reversed { + scale: -1 1; +} + +@media (hover: hover) { + .graph-modal-palette-picker .palette-group .setting-item:hover { + box-shadow: none; + opacity: var(--icon-opacity-hover); + color: var(--icon-color-hover); + background-color: var(--background-modifier-hover); + border-radius: var(--radius-s); + } +} + +.graph-modal-setting-properties { + .properties-list { + display: flex; + flex-wrap: wrap; + row-gap: var(--size-4-2); + column-gap: var(--size-4-6); + padding-top: var(--size-4-4); + } + + .property-value { + display: flex; + } + + .extended-graph-add-button { + margin-inline-start: var(--size-4-2); + } +} + +/* SHAPES */ + +svg.shape-svg { + width: var(--svg-size, var(--icon-size)); +} + +/* BUTTONS */ + +.extended-graph-delete-button { + --text-color: var(--color-red); + color: var(--text-color); +} + +.extended-graph-add-button { + --text-color: var(--color-green); + color: var(--text-color); +} + +/* PLUGIN ICON */ +.tree-item-header-icon { + align-self: center; + margin-right: var(--size-4-2); + color: var(--icon-color); + opacity: var(--icon-opacity); + + svg { + width: var(--icon-size); + height: var(--icon-size); + } +} + + +/* STATUS BAR */ + +.status-bar-item.plugin-extended-graph.mod-clickable { + padding-block: 1px; +} + + +/* RADIAL MENU */ + +.extended-graph-radial-menu { + + background: none; + border: none; + display: block; + border-radius: 0; + box-shadow: none; + filter: drop-shadow(var(--menu-shadow)); + transform: translate(-50%, -50%); + + .menu-scroll { + position: relative; + width: 200px; + aspect-ratio: 1; + overflow: visible; + } + + .menu-item:not(.back) { + position: absolute; + top: 0; + right: 0; + width: 50%; + aspect-ratio: 1; + transform-origin: 0% 100%; + background-image: radial-gradient(circle at 0% 100%, + transparent, + transparent 29.5%, + rgb(var(--color-rgb), 0.5) 30%, + rgb(var(--color-rgb), 0.5) 30.5%, + rgb(var(--color-rgb)) 31%, + rgb(var(--color-rgb)) 50%, + rgb(var(--color-rgb), 0.5) 50.25%, + rgb(var(--color-rgb), 0.5) 51.5%, + transparent 51.75%, + transparent); + clip-path: polygon(0 0, 0 99%, 99% 0); + transform: rotate(var(--rotation)); + + .menu-item-icon, + .menu-item-title { + font-family: var(--font-interface); + font-size: var(--font-ui-smaller); + position: absolute; + top: 40%; + left: 15%; + transform: rotate(calc(var(--rotation) * -1)); + transform-origin: center center; + color: color-mix(in hsl, var(--color-base-00), var(--color-base-100) var(--dark-text-interp)); + } + + .menu-item-title { + left: 10%; + } + } + + .menu-item.selected:not(.is-label):not(.is-disabled) { + filter: brightness(1.2) saturate(1.5); + + &:not(.back) { + background-color: transparent; + } + + &.back { + background-color: var(--color-base-50); + } + } + + .menu-item.back { + position: absolute; + top: 35%; + left: 35%; + width: 30%; + aspect-ratio: 1; + background-color: var(--color-base-50); + border-radius: 50%; + z-index: 1; + justify-content: center; + + .svg-icon { + --icon-size: 100%; + } + } + + .menu-item-icon:not(:empty)+.menu-item-title { + display: none; + } + + .interactives-list { + position: absolute; + top: 10%; + left: 50%; + transform: translate(-50%, -100%); + + display: flex; + flex-direction: column; + justify-content: stretch; + gap: var(--size-2-2); + font-size: var(--font-ui-small); + padding: var(--size-2-2); + background-color: var(--menu-background); + border: var(--menu-border-width) solid var(--menu-border-color); + border-radius: var(--menu-radius); + box-shadow: var(--menu-shadow); + + .interactive-item { + max-width: 140px; + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; + display: inline-block; + line-height: 1; + + &::before { + content: ""; + display: inline-block; + width: 0.7em; + aspect-ratio: 1; + border-radius: 50%; + background-color: rgb(var(--bg-color)); + vertical-align: baseline; + margin-right: 0.4em; + } + + &.is-hidden { + color: var(--text-faint); + + ::before { + background-color: rgba(var(--bg-color), 0.1); + } + } + } + } + + .tooltip { + top: 50%; + left: 0; + right: 0; + margin-inline: auto; + width: fit-content; + transform: translateX(50%); + } +} + +/* SUGGESTERS */ + +.suggestion-container .extended-graph-duplicate { + color: var(--text-faint); } \ No newline at end of file diff --git a/.obsidian/plugins/obsidian-advanced-slides/plugin/chalkboard/README.md b/.obsidian/plugins/obsidian-advanced-slides/plugin/chalkboard/README.md index 804cec0..c14e18c 100644 --- a/.obsidian/plugins/obsidian-advanced-slides/plugin/chalkboard/README.md +++ b/.obsidian/plugins/obsidian-advanced-slides/plugin/chalkboard/README.md @@ -1,160 +1,160 @@ -# Chalkboard - -With this plugin you can add a chalkboard to reveal.js. The plugin provides two possibilities to include handwritten notes to your presentation: - -- you can make notes directly on the slides, e.g. to comment on certain aspects, -- you can open a chalkboard or whiteboard on which you can make notes. - -The main use case in mind when implementing the plugin is classroom usage in which you may want to explain some course content and quickly need to make some notes. - -The plugin records all drawings made so that they can be play backed using the `autoSlide` feature or the `audio-slideshow` plugin. - -[Check out the live demo](https://rajgoel.github.io/reveal.js-demos/chalkboard-demo.html) - -The chalkboard effect is based on [Chalkboard](https://github.com/mmoustafa/Chalkboard) by Mohamed Moustafa. - -## Installation - -Copy the file `plugin.js` and the `img` directory into the plugin folder of your reveal.js presentation, i.e. `plugin/chalkboard` and load the plugin as shown below. - -```html - - - - -``` - -The following stylesheet -```html - - -``` -has to be included to the `head` section of you HTML-file. - - -In order to include buttons for opening and closing the notes canvas or the chalkboard you should make sure that `font-awesome` is available. The easiest way is to include -``` - -``` -to the ```head``` section of you HTML-file. - -## Usage - -### Mouse or touch -- Click on the pen symbols at the bottom left to toggle the notes canvas or chalkboard -- Click on the color picker at the left to change the color (the color picker is only visible if the notes canvas or chalkboard is active) -- Click on the up/down arrows on the left to the switch among multiple chalkboardd (the up/down arrows are only available for the chlakboard) -- Click the left mouse button and drag to write on notes canvas or chalkboard -- Click the right mouse button and drag to wipe away previous drawings -- Touch and move to write on notes canvas or chalkboard -- Touch and hold for half a second, then move to wipe away previous drawings - -### Keyboard -- Press the 'BACKSPACE' key to delete all chalkboard drawings -- Press the 'DEL' key to clear the notes canvas or chalkboard -- Press the 'c' key to toggle the notes canvas -- Press the 'b' key to toggle the chalkboard -- Press the 'd' key to download drawings -- Press the 'x' key to cycle colors forward -- Press the 'y' key to cycle colors backward - -## Playback - -If the `autoSlide` feature is set or if the `audio-slideshow` plugin is used, pre-recorded chalkboard drawings can be played. The slideshow plays back the user interaction with the chalkboard in the same way as it was conducted when recording the data. - -## Multiplexing - -The plugin supports multiplexing via the [`multiplex` plugin](https://github.com/reveal/multiplex) or the [`seminar` plugin](https://github.com/rajgoel/reveal.js-plugins/tree/master/seminar). - -## PDF-Export - -If the slideshow is opened in [print mode](https://revealjs.com/pdf-export/), the chalkboard drawings in the session storage (see `storage` option - print version must be opened in the same tab or window as the original slideshow) or provided in a file (see `src` option) are included in the PDF-file. Each drawing on the chalkboard is added after the slide that was shown when opening the chalkboard. Drawings on the notes canvas are not included in the PDF-file. - - -## Configuration - -The plugin has several configuration options: - -- ```boardmarkerWidth```: an integer, the drawing width of the boardmarker; larger values draw thicker lines. -- ```chalkWidth```: an integer, the drawing width of the chalk; larger values draw thicker lines. -- ```chalkEffect```: a float in the range ```[0.0, 1.0]```, the intesity of the chalk effect on the chalk board. Full effect (default) ```1.0```, no effect ```0.0```. -- ```storage```: Optional variable name for session storage of drawings. -- ```src```: Optional filename for pre-recorded drawings. -- ```readOnly```: Configuation option allowing to prevent changes to existing drawings. If set to ```true``` no changes can be made, if set to false ```false``` changes can be made, if unset or set to ```undefined``` no changes to the drawings can be made after returning to a slide or fragment for which drawings had been recorded before. In any case the recorded drawings for a slide or fragment can be cleared by pressing the 'DEL' key (i.e. by using the ```RevealChalkboard.clear()``` function). -- ```transition```: Gives the duration (in milliseconds) of the transition for a slide change, so that the notes canvas is drawn after the transition is completed. -- ```theme```: Can be set to either ```"chalkboard"``` or ```"whiteboard"```. - -The following configuration options allow to change the appearance of the notes canvas and the chalkboard. All of these options require two values, the first gives the value for the notes canvas, the second for the chalkboard. - -- ```background```: The first value expects a (semi-)transparent color which is used to provide visual feedback that the notes canvas is enabled, the second value expects a filename to a background image for the chalkboard. -- ```grid```: By default whiteboard and chalkboard themes include a grid pattern on the background. This pattern can be modified by setting the color, the distance between lines, and the line width, e.g. ```{ color: 'rgb(127,127,255,0.1)', distance: 40, width: 2}```. Alternatively, the grid can be removed by setting the value to ```false```. -- ```eraser```: An image path and radius for the eraser. -- ```boardmarkers```: A list of boardmarkers with given color and cursor. -- ```chalks```: A list of chalks with given color and cursor. -- ```rememberColor```: Whether to remember the last selected color for the slide canvas or the board. - -All of the configurations are optional and the default values shown below are used if the options are not provided. - -```javascript -Reveal.initialize({ - // ... - chalkboard: { - boardmarkerWidth: 3, - chalkWidth: 7, - chalkEffect: 1.0, - storage: null, - src: null, - readOnly: undefined, - transition: 800, - theme: "chalkboard", - background: [ 'rgba(127,127,127,.1)' , path + 'img/blackboard.png' ], - grid: { color: 'rgb(50,50,10,0.5)', distance: 80, width: 2}, - eraser: { src: path + 'img/sponge.png', radius: 20}, - boardmarkers : [ - { color: 'rgba(100,100,100,1)', cursor: 'url(' + path + 'img/boardmarker-black.png), auto'}, - { color: 'rgba(30,144,255, 1)', cursor: 'url(' + path + 'img/boardmarker-blue.png), auto'}, - { color: 'rgba(220,20,60,1)', cursor: 'url(' + path + 'img/boardmarker-red.png), auto'}, - { color: 'rgba(50,205,50,1)', cursor: 'url(' + path + 'img/boardmarker-green.png), auto'}, - { color: 'rgba(255,140,0,1)', cursor: 'url(' + path + 'img/boardmarker-orange.png), auto'}, - { color: 'rgba(150,0,20150,1)', cursor: 'url(' + path + 'img/boardmarker-purple.png), auto'}, - { color: 'rgba(255,220,0,1)', cursor: 'url(' + path + 'img/boardmarker-yellow.png), auto'} - ], - chalks: [ - { color: 'rgba(255,255,255,0.5)', cursor: 'url(' + path + 'img/chalk-white.png), auto'}, - { color: 'rgba(96, 154, 244, 0.5)', cursor: 'url(' + path + 'img/chalk-blue.png), auto'}, - { color: 'rgba(237, 20, 28, 0.5)', cursor: 'url(' + path + 'img/chalk-red.png), auto'}, - { color: 'rgba(20, 237, 28, 0.5)', cursor: 'url(' + path + 'img/chalk-green.png), auto'}, - { color: 'rgba(220, 133, 41, 0.5)', cursor: 'url(' + path + 'img/chalk-orange.png), auto'}, - { color: 'rgba(220,0,220,0.5)', cursor: 'url(' + path + 'img/chalk-purple.png), auto'}, - { color: 'rgba(255,220,0,0.5)', cursor: 'url(' + path + 'img/chalk-yellow.png), auto'} - ] - }, - customcontrols: { - controls: [ - { icon: '', - title: 'Toggle chalkboard (B)', - action: 'RevealChalkboard.toggleChalkboard();' - }, - { icon: '', - title: 'Toggle notes canvas (C)', - action: 'RevealChalkboard.toggleNotesCanvas();' - } - ] - }, - // ... - -}); -``` - - -## License - -MIT licensed - -Copyright (C) 2021 Asvin Goel +# Chalkboard + +With this plugin you can add a chalkboard to reveal.js. The plugin provides two possibilities to include handwritten notes to your presentation: + +- you can make notes directly on the slides, e.g. to comment on certain aspects, +- you can open a chalkboard or whiteboard on which you can make notes. + +The main use case in mind when implementing the plugin is classroom usage in which you may want to explain some course content and quickly need to make some notes. + +The plugin records all drawings made so that they can be play backed using the `autoSlide` feature or the `audio-slideshow` plugin. + +[Check out the live demo](https://rajgoel.github.io/reveal.js-demos/chalkboard-demo.html) + +The chalkboard effect is based on [Chalkboard](https://github.com/mmoustafa/Chalkboard) by Mohamed Moustafa. + +## Installation + +Copy the file `plugin.js` and the `img` directory into the plugin folder of your reveal.js presentation, i.e. `plugin/chalkboard` and load the plugin as shown below. + +```html + + + + +``` + +The following stylesheet +```html + + +``` +has to be included to the `head` section of you HTML-file. + + +In order to include buttons for opening and closing the notes canvas or the chalkboard you should make sure that `font-awesome` is available. The easiest way is to include +``` + +``` +to the ```head``` section of you HTML-file. + +## Usage + +### Mouse or touch +- Click on the pen symbols at the bottom left to toggle the notes canvas or chalkboard +- Click on the color picker at the left to change the color (the color picker is only visible if the notes canvas or chalkboard is active) +- Click on the up/down arrows on the left to the switch among multiple chalkboardd (the up/down arrows are only available for the chlakboard) +- Click the left mouse button and drag to write on notes canvas or chalkboard +- Click the right mouse button and drag to wipe away previous drawings +- Touch and move to write on notes canvas or chalkboard +- Touch and hold for half a second, then move to wipe away previous drawings + +### Keyboard +- Press the 'BACKSPACE' key to delete all chalkboard drawings +- Press the 'DEL' key to clear the notes canvas or chalkboard +- Press the 'c' key to toggle the notes canvas +- Press the 'b' key to toggle the chalkboard +- Press the 'd' key to download drawings +- Press the 'x' key to cycle colors forward +- Press the 'y' key to cycle colors backward + +## Playback + +If the `autoSlide` feature is set or if the `audio-slideshow` plugin is used, pre-recorded chalkboard drawings can be played. The slideshow plays back the user interaction with the chalkboard in the same way as it was conducted when recording the data. + +## Multiplexing + +The plugin supports multiplexing via the [`multiplex` plugin](https://github.com/reveal/multiplex) or the [`seminar` plugin](https://github.com/rajgoel/reveal.js-plugins/tree/master/seminar). + +## PDF-Export + +If the slideshow is opened in [print mode](https://revealjs.com/pdf-export/), the chalkboard drawings in the session storage (see `storage` option - print version must be opened in the same tab or window as the original slideshow) or provided in a file (see `src` option) are included in the PDF-file. Each drawing on the chalkboard is added after the slide that was shown when opening the chalkboard. Drawings on the notes canvas are not included in the PDF-file. + + +## Configuration + +The plugin has several configuration options: + +- ```boardmarkerWidth```: an integer, the drawing width of the boardmarker; larger values draw thicker lines. +- ```chalkWidth```: an integer, the drawing width of the chalk; larger values draw thicker lines. +- ```chalkEffect```: a float in the range ```[0.0, 1.0]```, the intesity of the chalk effect on the chalk board. Full effect (default) ```1.0```, no effect ```0.0```. +- ```storage```: Optional variable name for session storage of drawings. +- ```src```: Optional filename for pre-recorded drawings. +- ```readOnly```: Configuation option allowing to prevent changes to existing drawings. If set to ```true``` no changes can be made, if set to false ```false``` changes can be made, if unset or set to ```undefined``` no changes to the drawings can be made after returning to a slide or fragment for which drawings had been recorded before. In any case the recorded drawings for a slide or fragment can be cleared by pressing the 'DEL' key (i.e. by using the ```RevealChalkboard.clear()``` function). +- ```transition```: Gives the duration (in milliseconds) of the transition for a slide change, so that the notes canvas is drawn after the transition is completed. +- ```theme```: Can be set to either ```"chalkboard"``` or ```"whiteboard"```. + +The following configuration options allow to change the appearance of the notes canvas and the chalkboard. All of these options require two values, the first gives the value for the notes canvas, the second for the chalkboard. + +- ```background```: The first value expects a (semi-)transparent color which is used to provide visual feedback that the notes canvas is enabled, the second value expects a filename to a background image for the chalkboard. +- ```grid```: By default whiteboard and chalkboard themes include a grid pattern on the background. This pattern can be modified by setting the color, the distance between lines, and the line width, e.g. ```{ color: 'rgb(127,127,255,0.1)', distance: 40, width: 2}```. Alternatively, the grid can be removed by setting the value to ```false```. +- ```eraser```: An image path and radius for the eraser. +- ```boardmarkers```: A list of boardmarkers with given color and cursor. +- ```chalks```: A list of chalks with given color and cursor. +- ```rememberColor```: Whether to remember the last selected color for the slide canvas or the board. + +All of the configurations are optional and the default values shown below are used if the options are not provided. + +```javascript +Reveal.initialize({ + // ... + chalkboard: { + boardmarkerWidth: 3, + chalkWidth: 7, + chalkEffect: 1.0, + storage: null, + src: null, + readOnly: undefined, + transition: 800, + theme: "chalkboard", + background: [ 'rgba(127,127,127,.1)' , path + 'img/blackboard.png' ], + grid: { color: 'rgb(50,50,10,0.5)', distance: 80, width: 2}, + eraser: { src: path + 'img/sponge.png', radius: 20}, + boardmarkers : [ + { color: 'rgba(100,100,100,1)', cursor: 'url(' + path + 'img/boardmarker-black.png), auto'}, + { color: 'rgba(30,144,255, 1)', cursor: 'url(' + path + 'img/boardmarker-blue.png), auto'}, + { color: 'rgba(220,20,60,1)', cursor: 'url(' + path + 'img/boardmarker-red.png), auto'}, + { color: 'rgba(50,205,50,1)', cursor: 'url(' + path + 'img/boardmarker-green.png), auto'}, + { color: 'rgba(255,140,0,1)', cursor: 'url(' + path + 'img/boardmarker-orange.png), auto'}, + { color: 'rgba(150,0,20150,1)', cursor: 'url(' + path + 'img/boardmarker-purple.png), auto'}, + { color: 'rgba(255,220,0,1)', cursor: 'url(' + path + 'img/boardmarker-yellow.png), auto'} + ], + chalks: [ + { color: 'rgba(255,255,255,0.5)', cursor: 'url(' + path + 'img/chalk-white.png), auto'}, + { color: 'rgba(96, 154, 244, 0.5)', cursor: 'url(' + path + 'img/chalk-blue.png), auto'}, + { color: 'rgba(237, 20, 28, 0.5)', cursor: 'url(' + path + 'img/chalk-red.png), auto'}, + { color: 'rgba(20, 237, 28, 0.5)', cursor: 'url(' + path + 'img/chalk-green.png), auto'}, + { color: 'rgba(220, 133, 41, 0.5)', cursor: 'url(' + path + 'img/chalk-orange.png), auto'}, + { color: 'rgba(220,0,220,0.5)', cursor: 'url(' + path + 'img/chalk-purple.png), auto'}, + { color: 'rgba(255,220,0,0.5)', cursor: 'url(' + path + 'img/chalk-yellow.png), auto'} + ] + }, + customcontrols: { + controls: [ + { icon: '', + title: 'Toggle chalkboard (B)', + action: 'RevealChalkboard.toggleChalkboard();' + }, + { icon: '', + title: 'Toggle notes canvas (C)', + action: 'RevealChalkboard.toggleNotesCanvas();' + } + ] + }, + // ... + +}); +``` + + +## License + +MIT licensed + +Copyright (C) 2021 Asvin Goel diff --git a/.obsidian/plugins/obsidian-advanced-slides/plugin/chalkboard/img/blackboard.png b/.obsidian/plugins/obsidian-advanced-slides/plugin/chalkboard/img/blackboard.png index 50a2f64..09ff2bb 100644 Binary files a/.obsidian/plugins/obsidian-advanced-slides/plugin/chalkboard/img/blackboard.png and b/.obsidian/plugins/obsidian-advanced-slides/plugin/chalkboard/img/blackboard.png differ 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a/.obsidian/plugins/obsidian-advanced-slides/plugin/chalkboard/plugin (copy).js +++ b/.obsidian/plugins/obsidian-advanced-slides/plugin/chalkboard/plugin (copy).js @@ -1,1812 +1,1812 @@ -/***************************************************************** -** Author: Asvin Goel, goel@telematique.eu -** -** A plugin for reveal.js adding a chalkboard. -** -** Version: 1.5.0 -** -** License: MIT license (see LICENSE.md) -** -** Credits: -** Chalkboard effect by Mohamed Moustafa https://github.com/mmoustafa/Chalkboard -** Multi color support by Kurt Rinnert https://github.com/rinnert -** Compatibility with reveal.js v4 by Hakim El Hattab https://github.com/hakimel -******************************************************************/ - -window.RevealChalkboard = window.RevealChalkboard || { - id: 'RevealChalkboard', - init: function(deck) { - initChalkboard(deck); - }, - configure: function(config) { configure(config); }, - toggleNotesCanvas: function() { toggleNotesCanvas(); }, - toggleChalkboard: function() { toggleChalkboard(); }, - colorIndex: function() { colorIndex(); }, - colorNext: function() { colorNext(); }, - colorPrev: function() {colorPrev(); }, - clear: function() { clear(); }, - reset: function() { reset(); }, - resetAll: function() { resetAll(); }, - updateStorage: function() { updateStorage(); }, - getData: function() { return getData(); }, - download: function() { download(); }, -}; - -function scriptPath() { - // obtain plugin path from the script element - var src; - if (document.currentScript) { - src = document.currentScript.src; - } else { - var sel = document.querySelector('script[src$="/chalkboard/plugin.js"]') - if (sel) { - src = sel.src; - } - } - var path = (src === undefined) ? "" : src.slice(0, src.lastIndexOf("/") + 1); -//console.log("Path: " + path); - return path; -} -var path = scriptPath(); - -const initChalkboard = function(Reveal){ -//console.warn(path); - /* Feature detection for passive event handling*/ - var passiveSupported = false; - - try { - window.addEventListener("test", null, Object.defineProperty({}, "passive", { get: function() { passiveSupported = true; } })); - } catch(err) {} - - -/***************************************************************** -** Configuration -******************************************************************/ - var background, pen, draw, color; - var grid = false; - var boardmarkerWidth = 3; - var chalkWidth = 7; - var chalkEffect = 1.0; - var rememberColor = [true, false]; - var eraser = { src: path + 'img/sponge.png', radius: 20}; - var boardmarkers = [ - { color: 'rgba(100,100,100,1)', cursor: 'url(' + path + 'img/boardmarker-black.png), auto'}, - { color: 'rgba(30,144,255, 1)', cursor: 'url(' + path + 'img/boardmarker-blue.png), auto'}, - { color: 'rgba(220,20,60,1)', cursor: 'url(' + path + 'img/boardmarker-red.png), auto'}, - { color: 'rgba(50,205,50,1)', cursor: 'url(' + path + 'img/boardmarker-green.png), auto'}, - { color: 'rgba(255,140,0,1)', cursor: 'url(' + path + 'img/boardmarker-orange.png), auto'}, - { color: 'rgba(150,0,20150,1)', cursor: 'url(' + path + 'img/boardmarker-purple.png), auto'}, - { color: 'rgba(255,220,0,1)', cursor: 'url(' + path + 'img/boardmarker-yellow.png), auto'} - ]; - var chalks = [ - { color: 'rgba(255,255,255,0.5)', cursor: 'url(' + path + 'img/chalk-white.png), auto'}, - { color: 'rgba(96, 154, 244, 0.5)', cursor: 'url(' + path + 'img/chalk-blue.png), auto'}, - { color: 'rgba(237, 20, 28, 0.5)', cursor: 'url(' + path + 'img/chalk-red.png), auto'}, - { color: 'rgba(20, 237, 28, 0.5)', cursor: 'url(' + path + 'img/chalk-green.png), auto'}, - { color: 'rgba(220, 133, 41, 0.5)', cursor: 'url(' + path + 'img/chalk-orange.png), auto'}, - { color: 'rgba(220,0,220,0.5)', cursor: 'url(' + path + 'img/chalk-purple.png), auto'}, - { color: 'rgba(255,220,0,0.5)', cursor: 'url(' + path + 'img/chalk-yellow.png), auto'} - ]; - var keyBindings = { - toggleNotesCanvas: { keyCode: 67, key: 'C', description: 'Toggle notes canvas' }, - toggleChalkboard: { keyCode: 66, key: 'B', description: 'Toggle chalkboard' }, - clear: { keyCode: 171, key: '+', description: 'Clear drawings on slide' }, - reset: { keyCode: 46, key: 'DEL', description: 'Reset drawings on slide' }, - resetAll: { keyCode: 8, key: 'BACKSPACE', description: 'Reset all drawings' }, - colorNext: { keyCode: 88, key: 'X', description: 'Next color' }, - colorPrev: { keyCode: 89, key: 'Y', description: 'Previous color' }, - download: { keyCode: 68, key: 'D', description: 'Download drawings' } - }; - - - var theme = "chalkboard"; - var color = [0, 0]; - var toggleChalkboardButton = true; - var toggleNotesButton = true; - var colorButtons = true; - var boardHandle = true; - var transition = 800; - - var readOnly = false; - var messageType = 'broadcast'; - - var config = configure( Reveal.getConfig().chalkboard || {} ); - if ( config.keyBindings ) { - for (var key in config.keyBindings) { - keyBindings[key] = config.keyBindings[key]; - }; - } - - function configure( config ) { - - if ( config.boardmarkerWidth || config.penWidth ) boardmarkerWidth = config.boardmarkerWidth || config.penWidth; - if ( config.chalkWidth ) chalkWidth = config.chalkWidth; - if ( config.chalkEffect ) chalkEffect = config.chalkEffect; - if ( config.rememberColor ) rememberColor = config.rememberColor; - if ( config.eraser ) eraser = config.eraser; - if ( config.boardmarkers ) boardmarkers = config.boardmarkers; - if ( config.chalks) chalks = config.chalks; - - if ( config.theme ) theme = config.theme; - switch ( theme ) { - case "whiteboard": - background = [ 'rgba(127,127,127,.1)' , path + 'img/whiteboard.png' ]; - draw = [ drawWithBoardmarker , drawWithBoardmarker ]; - pens = [ boardmarkers, boardmarkers ]; - grid = { color: 'rgb(127,127,255,0.1)', distance: 40, width: 2}; - break; - case "chalkboard": - default: - background = [ 'rgba(127,127,127,.1)' , path + 'img/blackboard.png' ]; - draw = [ drawWithBoardmarker , drawWithChalk ]; - pens = [ boardmarkers, chalks ]; - grid = { color: 'rgb(50,50,10,0.5)', distance: 80, width: 2}; - } - - if ( config.background ) background = config.background; - if ( config.grid != undefined ) grid = config.grid; - - if (config.toggleChalkboardButton != undefined) toggleChalkboardButton = config.toggleChalkboardButton; - if (config.toggleNotesButton != undefined) toggleNotesButton = config.toggleNotesButton; - if (config.colorButtons != undefined) colorButtons = config.colorButtons; - if (config.boardHandle != undefined) boardHandle = config.boardHandle; - if (config.transition) transition = config.transition; - - if (config.readOnly != undefined) readOnly = config.readOnly; - if (config.messageType) messageType = config.messageType; - - if ( drawingCanvas && ( config.theme || config.background || config.grid ) ) { - var canvas = document.getElementById( drawingCanvas[1].id ); - canvas.style.background = 'url("' + background[1] + '") repeat'; - clearCanvas( 1 ); - drawGrid(); - } - - return config; - } -/***************************************************************** -** Setup -******************************************************************/ - - function whenReady( callback ) { - // wait for drawings to be loaded and markdown to be parsed - if ( document.querySelectorAll(".pdf-page").length && loaded !== null ) { - callback(); - } - else { -console.log("Wait for pdf pages to be created and drawings to be loaded"); - setTimeout( whenReady, 500, callback ) - } - } - - - if ( toggleChalkboardButton ) { -//console.log("toggleChalkboardButton") - var button = document.createElement( 'div' ); - button.className = "chalkboard-button"; - button.id = "toggle-chalkboard"; - button.style.visibility = "visible"; - button.style.position = "absolute"; - button.style.zIndex = 30; - button.style.fontSize = "24px"; - - button.style.left = toggleChalkboardButton.left || "30px"; - button.style.bottom = toggleChalkboardButton.bottom || "30px"; - button.style.top = toggleChalkboardButton.top || "auto"; - button.style.right = toggleChalkboardButton.right || "auto"; - - button.innerHTML = '' - document.querySelector(".reveal").appendChild( button ); - } - if ( toggleNotesButton ) { -//console.log("toggleNotesButton") - var button = document.createElement( 'div' ); - button.className = "chalkboard-button"; - button.id = "toggle-notes"; - button.style.position = "absolute"; - button.style.zIndex = 30; - button.style.fontSize = "24px"; - - button.style.left = toggleNotesButton.left || "70px"; - button.style.bottom = toggleNotesButton.bottom || "30px"; - button.style.top = toggleNotesButton.top || "auto"; - button.style.right = toggleNotesButton.right || "auto"; - - button.innerHTML = '' - document.querySelector(".reveal").appendChild( button ); - } -//alert("Buttons"); - - var drawingCanvas = [ {id: "notescanvas" }, {id: "chalkboard" } ]; - setupDrawingCanvas(0); - setupDrawingCanvas(1); - - var mode = 0; // 0: notes canvas, 1: chalkboard - var board = 0; // board index (only for chalkboard) - - var mouseX = 0; - var mouseY = 0; - var xLast = null; - var yLast = null; - - var slideStart = Date.now(); - var slideIndices = { h:0, v:0 }; - var event = null; - var timeouts = [ [], [] ]; - var touchTimeout = null; - var slidechangeTimeout = null; - var playback = false; - - function createPalette( colors, length ) { - if ( length === true || length > colors.length ) { - length = colors.length; - } - var palette = document.createElement( 'div' ); - palette.classList.add('palette'); - var list = document.createElement( 'ul' ); - // color pickers - for (var i = 0; i < length; i++ ) { - var colorButton = document.createElement( 'li' ); - colorButton.setAttribute("data-color",i); - colorButton.innerHTML = ''; - colorButton.style.color = colors[i].color; - colorButton.addEventListener("click", function(e) { - colorIndex(e.target.parentElement.getAttribute("data-color")); - }); - list.appendChild( colorButton ); - } - palette.appendChild( list ); - return palette; - }; - - function setupDrawingCanvas( id ) { - var container = document.createElement( 'div' ); - container.id = drawingCanvas[id].id; - container.classList.add( 'overlay' ); - container.setAttribute( 'data-prevent-swipe', '' ); - container.oncontextmenu = function() { return false; } - container.style.cursor = pens[ id ][ color[id] ].cursor; - - drawingCanvas[id].width = window.innerWidth; - drawingCanvas[id].height = window.innerHeight; - drawingCanvas[id].scale = 1; - drawingCanvas[id].xOffset = 0; - drawingCanvas[id].yOffset = 0; - - if ( id == "0" ) { - container.style.background = 'rgba(0,0,0,0)'; - container.style.zIndex = 24; - container.style.opacity = 1; - container.style.visibility = 'visible'; - container.style.pointerEvents = "none"; - - var slides = document.querySelector(".slides"); - var aspectRatio = Reveal.getConfig().width / Reveal.getConfig().height; - if ( drawingCanvas[id].width > drawingCanvas[id].height*aspectRatio ) { - drawingCanvas[id].xOffset = (drawingCanvas[id].width - drawingCanvas[id].height*aspectRatio) / 2; - } - else if ( drawingCanvas[id].height > drawingCanvas[id].width/aspectRatio ) { - drawingCanvas[id].yOffset = ( drawingCanvas[id].height - drawingCanvas[id].width/aspectRatio ) / 2; - } - - if ( colorButtons ) { - var palette = createPalette( boardmarkers, colorButtons ); - palette.style.visibility = 'hidden'; // only show palette in drawing mode - container.appendChild(palette); - } - } - else { - container.style.background = 'url("' + background[id] + '") repeat'; - container.style.zIndex = 26; - container.style.opacity = 0; - container.style.visibility = 'hidden'; - - if ( colorButtons ) { - var palette = createPalette( chalks, colorButtons ); - container.appendChild(palette); - } - if ( boardHandle ) { - var handle = document.createElement( 'div' ); - handle.classList.add('boardhandle'); - handle.innerHTML=''; - handle.querySelector("#previousboard").addEventListener("click", function(e) { - e.preventDefault(); - setBoard(board-1,true); - }); - handle.querySelector("#nextboard").addEventListener("click", function(e) { - e.preventDefault(); - setBoard(board+1,true); - }); - - container.appendChild(handle); - } - } - - - var sponge = document.createElement( 'img' ); - sponge.src = eraser.src; - sponge.id = "sponge"; - sponge.style.visibility = "hidden"; - sponge.style.position = "absolute"; - container.appendChild( sponge ); - drawingCanvas[id].sponge = sponge; - - var canvas = document.createElement( 'canvas' ); - canvas.width = drawingCanvas[id].width; - canvas.height = drawingCanvas[id].height; - canvas.setAttribute( 'data-chalkboard', id ); - canvas.style.cursor = pens[ id ][ color[id] ].cursor; - container.appendChild( canvas ); - drawingCanvas[id].canvas = canvas; - - drawingCanvas[id].context = canvas.getContext("2d"); - - - document.querySelector( '.reveal' ).appendChild( container ); - drawingCanvas[id].container = container; - } - - -/***************************************************************** -** Storage -******************************************************************/ - - var storage = [ - { width: Reveal.getConfig().width, height: Reveal.getConfig().height, data: []}, - { width: Reveal.getConfig().width, height: Reveal.getConfig().height, data: []} - ]; - - var loaded = null; - - if ( config.storage ) { - // Get chalkboard drawings from session storage - loaded = initStorage( sessionStorage.getItem( config.storage ) ); - } - - if ( !loaded && config.src != null ) { - // Get chalkboard drawings from the given file - loadData( config.src ); - } - - /** - * Initialize storage. - */ - function initStorage( json ) { - var success = false; - try { - var data = JSON.parse( json ); - for (var id = 0; id < data.length; id++) { - if ( drawingCanvas[id].width != data[id].width || drawingCanvas[id].height != data[id].height ) { - drawingCanvas[id].scale = Math.min( drawingCanvas[id].width/data[id].width, drawingCanvas[id].height/data[id].height); - drawingCanvas[id].xOffset = (drawingCanvas[id].width - data[id].width * drawingCanvas[id].scale)/2; - drawingCanvas[id].yOffset = (drawingCanvas[id].height - data[id].height * drawingCanvas[id].scale)/2; - } - if ( config.readOnly ) { - drawingCanvas[id].container.style.cursor = 'default'; - drawingCanvas[id].canvas.style.cursor = 'default'; - } - } - success = true; - storage = data; - } - catch ( err ) { - console.warn( "Cannot initialise storage!" ); - } - return success; - } - - - /** - * Load data. - */ - function loadData( filename ) { - var xhr = new XMLHttpRequest(); - xhr.onload = function() { - if (xhr.readyState === 4 && xhr.status != 404 ) { - loaded = initStorage(xhr.responseText); -console.log("Drawings loaded from file"); - } - else { - config.readOnly = undefined; - readOnly = undefined; - console.warn( 'Failed to get file ' + filename +". ReadyState: " + xhr.readyState + ", Status: " + xhr.status); - loaded = false; - } - }; - - xhr.open( 'GET', filename, true ); - try { - xhr.send(); - } - catch ( error ) { - config.readOnly = undefined; - readOnly = undefined; - console.warn( 'Failed to get file ' + filename + '. Make sure that the presentation and the file are served by a HTTP server and the file can be found there. ' + error ); - loaded = false; - } - } - - - function updateStorage() { - var json = JSON.stringify( storage ) - if ( config.storage ) { - sessionStorage.setItem( config.storage, json ) - } - return json; - } - - /** - * Get data as json string. - */ - function getData() { - // cleanup slide data without events - for (var id = 0; id < 2; id++) { - for (var i = storage[id].data.length-1; i >= 0; i--) { - if (storage[id].data[i].events.length == 0) { - storage[id].data.splice(i, 1); - } - } - } - - return updateStorage(); - } - - /** - * Download data. - */ - function downloadData() { - var a = document.createElement('a'); - document.body.appendChild(a); - try { - a.download = "chalkboard.json"; - var blob = new Blob( [ getData() ], { type: "application/json"} ); - a.href = window.URL.createObjectURL( blob ); - } catch( error ) { - a.innerHTML += " (" + error + ")"; - } - a.click(); - document.body.removeChild(a); - } - - /** - * Returns data object for the slide with the given indices. - */ - function getSlideData( indices, id ) { - if ( id == undefined ) id = mode; - if (!indices) indices = slideIndices; - var data; - for (var i = 0; i < storage[id].data.length; i++) { - if (storage[id].data[i].slide.h === indices.h && storage[id].data[i].slide.v === indices.v && storage[id].data[i].slide.f === indices.f ) { - data = storage[id].data[i]; - return data; - } - } - storage[id].data.push( { slide: indices, events: [], duration: 0 } ); - data = storage[id].data[storage[id].data.length-1]; - return data; - } - - /** - * Returns maximum duration of slide playback for both modes - */ - function getSlideDuration( indices ) { - if (!indices) indices = slideIndices; - var duration = 0; - for (var id = 0; id < 2; id++) { - for (var i = 0; i < storage[id].data.length; i++) { - if (storage[id].data[i].slide.h === indices.h && storage[id].data[i].slide.v === indices.v && storage[id].data[i].slide.f === indices.f ) { - duration = Math.max( duration, storage[id].data[i].duration ); - break; - } - } - } -//console.log( duration ); - return duration; - } - -/***************************************************************** -** Print -******************************************************************/ - var printMode = ( /print-pdf/gi ).test( window.location.search ); -//console.log("createPrintout" + printMode) - - function createPrintout( ) { -console.warn(Reveal.getTotalSlides(),Reveal.getSlidesElement()); - if ( storage[1].data.length == 0 ) return; -console.log( 'Create printout(s) for ' + storage[1].data.length + " slides"); - drawingCanvas[0].container.style.opacity = 0; // do not print notes canvas - drawingCanvas[0].container.style.visibility = 'hidden'; - - var patImg = new Image(); - patImg.onload = function () { - var slides = getSlidesArray(); -//console.log(slides); - for (var i = storage[1].data.length-1; i>=0; i--) { -console.log( 'Create printout for slide ' + storage[1].data[i].slide.h + "." + storage[1].data[i].slide.v ); - var slideData = getSlideData( storage[1].data[i].slide, 1 ); - var drawings = createDrawings( slideData, patImg ); - var slide = slides[ storage[1].data[i].slide.h][ storage[1].data[i].slide.v ]; -//console.log("Slide:", slide); - addDrawings( slide, drawings ); - - } -// Reveal.sync(); - }; - patImg.src = background[1]; - } - - function getSlidesArray() { - var horizontal = document.querySelectorAll('.slides > div.pdf-page > section, .slides > section'); - var slides = []; - var slidenumber = undefined; - for ( var i=0; i < horizontal.length; i++) { - if ( horizontal[i].parentElement.classList.contains("pdf-page") ) { - // Horizontal slide - if ( horizontal[i].getAttribute("data-slide-number") != slidenumber ) { - // new slide - slides.push([]); - slides[slides.length-1].push(horizontal[i]); - slidenumber = horizontal[i].getAttribute("data-slide-number"); - } - else { - // fragment of same slide - slides[slides.length-1][slides[slides.length-1].length-1] = horizontal[i]; - } - } - else { - // Vertical slides - var vertical = horizontal[i].querySelectorAll('section'); - slides.push([]); - var slidenumber = undefined; - for ( var j=0; j < vertical.length; j++) { - if ( vertical[j].getAttribute("data-slide-number") != slidenumber ) { - // new slide - slides[slides.length-1].push(vertical[j]); - slidenumber = vertical[j].getAttribute("data-slide-number"); - } - else { - // fragment of same slide - slides[slides.length-1][slides[slides.length-1].length-1] = vertical[j]; - } - } - } - } -//console.log("Slides:", slides); - return slides; - } - - function cloneCanvas(oldCanvas) { - //create a new canvas - var newCanvas = document.createElement('canvas'); - var context = newCanvas.getContext('2d'); - //set dimensions - newCanvas.width = oldCanvas.width; - newCanvas.height = oldCanvas.height; - //apply the old canvas to the new one - context.drawImage(oldCanvas, 0, 0); - //return the new canvas - return newCanvas; - } - - function getCanvas( template, container, board ) { - var idx = container.findIndex(element => element.board === board); - if ( idx === -1 ) { - var canvas = cloneCanvas(template); - if ( !container.length ) { - idx = 0; - container.push({ board, canvas }); - } - else if ( board < container[0].board ) { - idx = 0; - container.unshift({ board, canvas }); - } - else if ( board > container[container.length-1].board ) { - idx = container.length; - container.push({ board, canvas }); - } - } - - return container[idx].canvas; - } - - function createDrawings( slideData, patImg ) { - var width = Reveal.getConfig().width; - var height = Reveal.getConfig().height; - var scale = 1; - var xOffset = 0; - var yOffset = 0; - if ( width != storage[1].width || height != storage[1].height ) { - scale = Math.min( width/storage[1].width, height/storage[1].height); - xOffset = (width - storage[1].width * scale)/2; - yOffset = (height - storage[1].height * scale)/2; - } - mode = 1; - board = 0; -console.log( 'Create printout(s) for slide ', slideData); - - var drawings = []; - var template = document.createElement('canvas'); - template.width = width; - template.height = height; - - var imgCtx = template.getContext("2d"); - imgCtx.fillStyle = imgCtx.createPattern( patImg ,'repeat'); - imgCtx.rect(0,0,width,height); - imgCtx.fill(); - - for (var j = 0; j < slideData.events.length; j++) { - switch ( slideData.events[j].type ) { - case "draw": - for (var k = 1; k < slideData.events[j].curve.length; k++) { - draw[1]( getCanvas(template,drawings,board).getContext("2d"), - xOffset + slideData.events[j].curve[k-1].x*scale, - yOffset + slideData.events[j].curve[k-1].y*scale, - xOffset + slideData.events[j].curve[k].x*scale, - yOffset + slideData.events[j].curve[k].y*scale - ); - } - break; - case "erase": - for (var k = 0; k < slideData.events[j].curve.length; k++) { - eraseWithSponge( getCanvas(template,drawings,board).getContext("2d"), - xOffset + slideData.events[j].curve[k].x*scale, - yOffset + slideData.events[j].curve[k].y*scale - ); - } - break; - case "setcolor": - setColor(slideData.events[j].index); - break; - case "setboard": - // Todo: create new canvas for each new index - setBoard(slideData.events[j].index); -//board = 0; - break; - case "clear": - getCanvas(template,drawings,board).getContext("2d").clearRect(0,0,width,height); - getCanvas(template,drawings,board).getContext("2d").fill(); - break; - default: - break; - } - } - - drawings = drawings.sort((a, b) => a.board > b.board && 1 || -1); - - mode = 0; - - return drawings; - } - - function addDrawings( slide, drawings ) { - var parent = slide.parentElement.parentElement; - var nextSlide = slide.parentElement.nextElementSibling; - - for (var i = 0; i < drawings.length; i++) { - var newPDFPage = document.createElement( 'div' ); - newPDFPage.classList.add('pdf-page'); - newPDFPage.style.height = Reveal.getConfig().height; -// newPDFPage.innerHTML = '

Drawing should be here!

'; - newPDFPage.append(drawings[i].canvas); -//console.log("Add drawing", newPDFPage); - if ( nextSlide != null ) { - parent.insertBefore( newPDFPage, nextSlide ); - } - else { - parent.append( newPDFPage ); - } - } - } - -/***************************************************************** -** Drawings -******************************************************************/ - - function drawWithBoardmarker(context,fromX,fromY,toX,toY){ - context.lineWidth = boardmarkerWidth; - context.lineCap = 'round'; - context.strokeStyle = boardmarkers[color[mode]].color; - context.beginPath(); - context.moveTo(fromX, fromY); - context.lineTo(toX, toY); - context.stroke(); - } - - function drawWithChalk(context,fromX,fromY,toX,toY) { - var brushDiameter = chalkWidth; - context.lineWidth = brushDiameter; - context.lineCap = 'round'; - context.fillStyle = chalks[color[mode]].color; // 'rgba(255,255,255,0.5)'; - context.strokeStyle = chalks[color[mode]].color; - /*var opacity = Math.min(0.8, Math.max(0,color[1].replace(/^.*,(.+)\)/,'$1') - 0.1)) + Math.random()*0.2;*/ - var opacity = 1.0; - context.strokeStyle = context.strokeStyle.replace(/[\d\.]+\)$/g, opacity + ')'); - context.beginPath(); - context.moveTo(fromX, fromY); - context.lineTo(toX, toY); - context.stroke(); - // Chalk Effect - var length = Math.round(Math.sqrt(Math.pow(toX-fromX,2)+Math.pow(toY-fromY,2))/(5/brushDiameter)); - var xUnit = (toX-fromX)/length; - var yUnit = (toY-fromY)/length; - for(var i=0; i (Math.random() * 0.9)) { - var xCurrent = fromX+(i*xUnit); - var yCurrent = fromY+(i*yUnit); - var xRandom = xCurrent+(Math.random()-0.5)*brushDiameter*1.2; - var yRandom = yCurrent+(Math.random()-0.5)*brushDiameter*1.2; - context.clearRect( xRandom, yRandom, Math.random()*2+2, Math.random()+1); - } - } - } - - function eraseWithSponge(context,x,y) { - context.save(); - context.beginPath(); - context.arc(x, y, eraser.radius, 0, 2 * Math.PI, false); - context.clip(); - context.clearRect(x - eraser.radius - 1, y - eraser.radius - 1, eraser.radius * 2 + 2, eraser.radius * 2 + 2); - context.restore(); - if ( mode == 1 && grid) { - redrawGrid(x,y,eraser.radius); - } - } - - - - /** - * Show an overlay for the chalkboard. - */ - function showChalkboard() { -//console.log("showChalkboard"); - clearTimeout(touchTimeout); - touchTimeout = null; - drawingCanvas[0].sponge.style.visibility = "hidden"; // make sure that the sponge from touch events is hidden - drawingCanvas[1].sponge.style.visibility = "hidden"; // make sure that the sponge from touch events is hidden - drawingCanvas[1].container.style.opacity = 1; - drawingCanvas[1].container.style.visibility = 'visible'; - mode = 1; - } - - - /** - * Closes open chalkboard. - */ - function closeChalkboard() { - clearTimeout(touchTimeout); - touchTimeout = null; - drawingCanvas[0].sponge.style.visibility = "hidden"; // make sure that the sponge from touch events is hidden - drawingCanvas[1].sponge.style.visibility = "hidden"; // make sure that the sponge from touch events is hidden - drawingCanvas[1].container.style.opacity = 0; - drawingCanvas[1].container.style.visibility = 'hidden'; - xLast = null; - yLast = null; - event = null; - mode = 0; - } - - /** - * Clear current canvas. - */ - function clearCanvas( id ) { - if ( id == 0 ) clearTimeout( slidechangeTimeout ); - drawingCanvas[id].context.clearRect(0,0,drawingCanvas[id].width,drawingCanvas[id].height); - if ( id == 1 && grid ) drawGrid(); - } - - /** - * Draw grid on background - */ - function drawGrid() { - var context = drawingCanvas[1].context; - - drawingCanvas[1].scale = Math.min( drawingCanvas[1].width/storage[1].width, drawingCanvas[1].height/storage[1].height ); - drawingCanvas[1].xOffset = (drawingCanvas[1].width - storage[1].width * drawingCanvas[1].scale)/2; - drawingCanvas[1].yOffset = (drawingCanvas[1].height - storage[1].height * drawingCanvas[1].scale)/2; - - var scale = drawingCanvas[1].scale; - var xOffset = drawingCanvas[1].xOffset; - var yOffset = drawingCanvas[1].yOffset; - - var distance = grid.distance*scale; - - var fromX = drawingCanvas[1].width/2 - distance/2 - Math.floor( (drawingCanvas[1].width - distance)/2 / distance ) * distance; - for( var x=fromX; x < drawingCanvas[1].width; x+=distance ) { - context.beginPath(); - context.lineWidth = grid.width*scale; - context.lineCap = 'round'; - context.fillStyle = grid.color; - context.strokeStyle = grid.color; - context.moveTo(x, 0); - context.lineTo(x, drawingCanvas[1].height); - context.stroke(); - } - var fromY = drawingCanvas[1].height/2 - distance/2 - Math.floor( (drawingCanvas[1].height - distance)/2 / distance ) * distance ; - - for( var y=fromY; y < drawingCanvas[1].height; y+=distance ) { - context.beginPath(); - context.lineWidth = grid.width*scale; - context.lineCap = 'round'; - context.fillStyle = grid.color; - context.strokeStyle = grid.color; - context.moveTo(0, y); - context.lineTo(drawingCanvas[1].width, y); - context.stroke(); - } - } - - function redrawGrid(centerX,centerY,diameter) { - var context = drawingCanvas[1].context; - - drawingCanvas[1].scale = Math.min( drawingCanvas[1].width/storage[1].width, drawingCanvas[1].height/storage[1].height ); - drawingCanvas[1].xOffset = (drawingCanvas[1].width - storage[1].width * drawingCanvas[1].scale)/2; - drawingCanvas[1].yOffset = (drawingCanvas[1].height - storage[1].height * drawingCanvas[1].scale)/2; - - var scale = drawingCanvas[1].scale; - var xOffset = drawingCanvas[1].xOffset; - var yOffset = drawingCanvas[1].yOffset; - - var distance = grid.distance*scale; - - var fromX = drawingCanvas[1].width/2 - distance/2 - Math.floor( (drawingCanvas[1].width - distance)/2 / distance ) * distance; - - for( var x=fromX + distance* Math.ceil( (centerX-diameter-fromX) / distance); x <= fromX + distance* Math.floor( (centerX+diameter-fromX) / distance); x+=distance ) { - context.beginPath(); - context.lineWidth = grid.width*scale; - context.lineCap = 'round'; - context.fillStyle = grid.color; - context.strokeStyle = grid.color; - context.moveTo(x, centerY - Math.sqrt( diameter*diameter - (centerX-x)*(centerX-x) )); - context.lineTo(x, centerY + Math.sqrt( diameter*diameter - (centerX-x)*(centerX-x) ) ); - context.stroke(); - } - var fromY = drawingCanvas[1].height/2 - distance/2 - Math.floor( (drawingCanvas[1].height - distance)/2 / distance ) * distance ; - for( var y=fromY + distance* Math.ceil( (centerY-diameter-fromY) / distance); y <= fromY + distance* Math.floor( (centerY+diameter-fromY) / distance); y+=distance ) { - context.beginPath(); - context.lineWidth = grid.width*scale; - context.lineCap = 'round'; - context.fillStyle = grid.color; - context.strokeStyle = grid.color; - context.moveTo(centerX - Math.sqrt( diameter*diameter - (centerY-y)*(centerY-y) ), y ); - context.lineTo(centerX + Math.sqrt( diameter*diameter - (centerY-y)*(centerY-y) ), y ); - context.stroke(); - } - } - - /** - * Set the color - */ - function setColor( index, record ) { - // protect against out of bounds (this could happen when - // replaying events recorded with different color settings). - if ( index >= boardmarkers[mode].length ) index = 0; - color[mode] = index; - drawingCanvas[mode].canvas.style.cursor = pens[mode][color[mode]].cursor; - if ( record ) { - recordEvent( { type: "setcolor", index: index, begin: Date.now() - slideStart } ); - updateStorage(); - } - } - - /** - * Set the board - */ - function setBoard( index, record ) { - board = index; - redrawChalkboard( board ); - - if ( record ) { - recordEvent( { type: "setboard", index: board, begin: Date.now() - slideStart } ); - updateStorage(); - } - } - - function redrawChalkboard( board ) { - clearCanvas( 1 ); - var slideData = getSlideData( slideIndices, 1 ); - var index = 0; - var play = ( board == 0 ); - while ( index < slideData.events.length && slideData.events[index].begin < Date.now() - slideStart) { - if ( slideData.events[index].type == "setboard" ) { - play = ( board == slideData.events[index].index ); - } - else if ( play || slideData.events[index].type == "setcolor" ) { - playEvent( 1, slideData.events[index], Date.now() - slideStart ); - } - index++; - } - } - - - /** - * Forward cycle color - */ - function cycleColorNext() { - color[mode] = (color[mode] + 1) % pens[mode].length; - return color[mode]; - } - - /** - * Backward cycle color - */ - function cycleColorPrev() { - color[mode] = (color[mode] + (pens[mode].length - 1)) % pens[mode].length; - return color[mode]; - } - -/***************************************************************** -** Broadcast -******************************************************************/ - - var eventQueue = []; - - document.addEventListener( 'received', function ( message ) { - if ( message.content && message.content.sender == 'chalkboard-plugin' ) { - // add message to queue - eventQueue.push(message); - } - if ( eventQueue.length == 1 ) processQueue(); - }); - -//console.log(JSON.stringify(message)); - function processQueue() { - // take first message from queue - var message = eventQueue.shift(); - - // synchronize time with seminar host - slideStart = Date.now() - message.content.timestamp; - switch ( message.content.type ) { - case 'showChalkboard': - showChalkboard(); - break; - case 'closeChalkboard': - closeChalkboard(); - break; - case 'startDrawing': - startDrawing(message.content.x, message.content.y, message.content.erase); - break; - case 'startErasing': - if ( message.content ) { - message.content.type = "erase"; - message.content.begin = Date.now() - slideStart; - eraseWithSponge(drawingCanvas[mode].context, message.content.x, message.content.y); - } - break; - case 'drawSegment': - drawSegment(message.content.x, message.content.y, message.content.erase); - break; - case 'stopDrawing': - stopDrawing(); - break; - case 'clear': - clear(); - break; - case 'setcolor': - setColor(message.content.index, true); - break; - case 'setboard': - setBoard(message.content.index, true); - break; - case 'resetSlide': - resetSlide(true); - break; - case 'init': - storage = message.content.storage; - for (var id = 0; id < 2; id++ ) { - drawingCanvas[id].scale = Math.min( drawingCanvas[id].width/storage[id].width, drawingCanvas[id].height/storage[id].height ); - drawingCanvas[id].xOffset = (drawingCanvas[id].width - storage[id].width * drawingCanvas[id].scale)/2; - drawingCanvas[id].yOffset = (drawingCanvas[id].height - storage[id].height * drawingCanvas[id].scale)/2; - } - clearCanvas( 0 ); - clearCanvas( 1 ); - if ( !playback ) { - slidechangeTimeout = setTimeout( startPlayback, transition, getSlideDuration(), 0 ); - } - if ( mode == 1 && message.content.mode == 0) { - setTimeout( closeChalkboard, transition + 50 ); - } - if ( mode == 0 && message.content.mode == 1) { - setTimeout( showChalkboard, transition + 50 ); - } - mode = message.content.mode; - break; - default: - break; - } - - // continue with next message if queued - if ( eventQueue.length > 0 ) { - processQueue(); - } - else { - updateStorage(); - } - } - - document.addEventListener( 'welcome', function( user ) { - // broadcast storage - var message = new CustomEvent(messageType); - message.content = { sender: 'chalkboard-plugin', recipient: user.id, type: 'init', timestamp: Date.now() - slideStart, storage: storage, mode: mode }; - document.dispatchEvent( message ); - }); - -/***************************************************************** -** Playback -******************************************************************/ - - document.addEventListener('seekplayback', function( event ) { -//console.log('event seekplayback ' + event.timestamp); - stopPlayback(); - if ( !playback || event.timestamp == 0) { - // in other cases startplayback fires after seeked - startPlayback( event.timestamp ); - } -//console.log('seeked'); - }); - - - document.addEventListener('startplayback', function( event ) { -//console.log('event startplayback ' + event.timestamp); - stopPlayback(); - playback = true; - startPlayback( event.timestamp ); - }); - - document.addEventListener('stopplayback', function( event ) { -//console.log('event stopplayback ' + (Date.now() - slideStart) ); - playback = false; - stopPlayback(); - }); - - document.addEventListener('startrecording', function( event ) { -//console.log('event startrecording ' + event.timestamp); - startRecording(); - }); - - function recordEvent( event ) { - var slideData = getSlideData(); - var i = slideData.events.length; - while ( i > 0 && event.begin < slideData.events[i-1].begin ) { - i--; - } - slideData.events.splice( i, 0, event); - slideData.duration = Math.max( slideData.duration, Date.now() - slideStart ) + 1; - } - - function startRecording() { - resetSlide( true ); - slideStart = Date.now(); - } - - function startPlayback( timestamp, finalMode ) { -//console.log("playback " + timestamp ); - slideStart = Date.now() - timestamp; - closeChalkboard(); - mode = 0; - board = 0; - for ( var id = 0; id < 2; id++ ) { - clearCanvas( id ); - var slideData = getSlideData( slideIndices, id ); -//console.log( timestamp +" / " + JSON.stringify(slideData)); - var index = 0; - while ( index < slideData.events.length && slideData.events[index].begin < (Date.now() - slideStart) ) { - playEvent( id, slideData.events[index], timestamp ); - index++; - } - - while ( playback && index < slideData.events.length ) { - timeouts[id].push( setTimeout( playEvent, slideData.events[index].begin - (Date.now() - slideStart), id, slideData.events[index], timestamp ) ); - index++; - } - } -//console.log("Mode: " + finalMode + "/" + mode ); - if ( finalMode != undefined ) { - mode = finalMode; - } - if( mode == 1 ) showChalkboard(); -//console.log("playback (ok)"); - - }; - - function stopPlayback() { -//console.log("stopPlayback"); -//console.log("Timeouts: " + timeouts[0].length + "/"+ timeouts[1].length); - for ( var id = 0; id < 2; id++ ) { - for (var i = 0; i < timeouts[id].length; i++) { - clearTimeout(timeouts[id][i]); - } - timeouts[id] = []; - } - }; - - function playEvent( id, event, timestamp ) { -//console.log( timestamp +" / " + JSON.stringify(event)); -//console.log( id + ": " + timestamp +" / " + event.begin +" / " + event.type +" / " + mode ); - switch ( event.type ) { - case "open": - if ( timestamp <= event.begin ) { - showChalkboard(); - } - else { - mode = 1; - } - - break; - case "close": - if ( timestamp < event.begin ) { - closeChalkboard(); - } - else { - mode = 0; - } - break; - case "clear": - clearCanvas( id ); - break; - case "setcolor": - setColor(event.index); - break; - case "setboard": - setBoard(event.index); - break; - case "draw": - drawCurve( id, event, timestamp ); - break; - case "erase": - eraseCurve( id, event, timestamp ); - break; - - } - }; - - function drawCurve( id, event, timestamp ) { - if ( event.curve.length > 1 ) { - var ctx = drawingCanvas[id].context; - var scale = drawingCanvas[id].scale; - var xOffset = drawingCanvas[id].xOffset; - var yOffset = drawingCanvas[id].yOffset; - - var stepDuration = ( event.end - event.begin )/ ( event.curve.length - 1 ); -//console.log("---"); - for (var i = 1; i < event.curve.length; i++) { - if (event.begin + i * stepDuration <= (Date.now() - slideStart)) { -//console.log( "Draw " + timestamp +" / " + event.begin + " + " + i + " * " + stepDuration ); - draw[id](ctx, xOffset + event.curve[i-1].x*scale, yOffset + event.curve[i-1].y*scale, xOffset + event.curve[i].x*scale, yOffset + event.curve[i].y*scale); - } - else if ( playback ) { -//console.log( "Cue " + timestamp +" / " + (Date.now() - slideStart) +" / " + event.begin + " + " + i + " * " + stepDuration + " = " + Math.max(0,event.begin + i * stepDuration - timestamp) ); - timeouts.push( setTimeout( - draw[id], Math.max(0,event.begin + i * stepDuration - (Date.now() - slideStart)), ctx, - xOffset + event.curve[i-1].x*scale, - yOffset + event.curve[i-1].y*scale, - xOffset + event.curve[i].x*scale, - yOffset + event.curve[i].y*scale - ) - ); - } - } - } - - }; - - function eraseCurve( id, event, timestamp ) { - if ( event.curve.length > 1 ) { - var ctx = drawingCanvas[id].context; - var scale = drawingCanvas[id].scale; - var xOffset = drawingCanvas[id].xOffset; - var yOffset = drawingCanvas[id].yOffset; - - var stepDuration = ( event.end - event.begin )/ event.curve.length; - for (var i = 0; i < event.curve.length; i++) { - if (event.begin + i * stepDuration <= (Date.now() - slideStart)) { - eraseWithSponge(ctx, xOffset + event.curve[i].x*scale, yOffset + event.curve[i].y*scale); - } - else if ( playback ) { - timeouts.push( setTimeout( - eraseWithSponge, Math.max(0,event.begin + i * stepDuration - (Date.now() - slideStart)), ctx, - xOffset + event.curve[i].x * scale, - yOffset + event.curve[i].y * scale - ) - ); - } - } - } - - }; - - - function startDrawing( x, y, erase ) { - var ctx = drawingCanvas[mode].context; - var scale = drawingCanvas[mode].scale; - var xOffset = drawingCanvas[mode].xOffset; - var yOffset = drawingCanvas[mode].yOffset; - xLast = x * scale + xOffset; - yLast = y * scale + yOffset; - if ( erase == true) { - event = { type: "erase", begin: Date.now() - slideStart, end: null, curve: [{x: x, y: y}]}; - drawingCanvas[mode].canvas.style.cursor = 'url("' + eraser.src + '") ' + eraser.radius + ' ' + eraser.radius + ', auto'; - eraseWithSponge(ctx, x * scale + xOffset, y * scale + yOffset); - } - else { - event = { type: "draw", begin: Date.now() - slideStart, end: null, curve: [{x: x, y: y}] }; - } - } - - - function showSponge(x,y) { - if ( event ) { - event.type = "erase"; - event.begin = Date.now() - slideStart; - // show sponge image - drawingCanvas[mode].sponge.style.left = (x - eraser.radius) +"px" ; - drawingCanvas[mode].sponge.style.top = (y - eraser.radius) +"px" ; - drawingCanvas[mode].sponge.style.visibility = "visible"; - eraseWithSponge(drawingCanvas[mode].context,x,y); - // broadcast - var message = new CustomEvent(messageType); - message.content = { sender: 'chalkboard-plugin', type: 'startErasing', timestamp: Date.now() - slideStart, x: (mouseX - xOffset)/scale, y: (mouseY-yOffset)/scale }; - document.dispatchEvent( message ); - } - } - - function drawSegment( x, y, erase ) { - var ctx = drawingCanvas[mode].context; - var scale = drawingCanvas[mode].scale; - var xOffset = drawingCanvas[mode].xOffset; - var yOffset = drawingCanvas[mode].yOffset; - if ( !event ) { - // safeguard if broadcast hickup - startDrawing( x, y, erase ); - } - event.curve.push({x: x, y: y}); - if(y * scale + yOffset < drawingCanvas[mode].height && x * scale + xOffset < drawingCanvas[mode].width) { - if ( erase ) { - eraseWithSponge(ctx, x * scale + xOffset, y * scale + yOffset); - } - else { - draw[mode](ctx, xLast, yLast, x * scale + xOffset, y * scale + yOffset); - } - xLast = x * scale + xOffset; - yLast = y * scale + yOffset; - } - } - - function stopDrawing() { - if ( event ) { - event.end = Date.now() - slideStart; - if ( event.type == "erase" || event.curve.length > 1 ) { - // do not save a line with a single point only - recordEvent( event ); - updateStorage(); - } - event = null; - } - } - - -/***************************************************************** -** User interface -******************************************************************/ - - -// TODO: check all touchevents - document.addEventListener('touchstart', function(evt) { -//console.log("Touch start"); - if ( !readOnly && evt.target.getAttribute('data-chalkboard') == mode ) { -// var ctx = drawingCanvas[mode].context; - var scale = drawingCanvas[mode].scale; - var xOffset = drawingCanvas[mode].xOffset; - var yOffset = drawingCanvas[mode].yOffset; - - evt.preventDefault(); - var touch = evt.touches[0]; - mouseX = touch.pageX; - mouseY = touch.pageY; - startDrawing( (mouseX - xOffset)/scale, (mouseY-yOffset)/scale, false ); - // broadcast - var message = new CustomEvent(messageType); - message.content = { sender: 'chalkboard-plugin', type: 'startDrawing', timestamp: Date.now() - slideStart, x: (mouseX - xOffset)/scale, y: (mouseY-yOffset)/scale, erase: false }; - document.dispatchEvent( message ); -/* - xLast = mouseX; - yLast = mouseY; - event = { type: "draw", begin: Date.now() - slideStart, end: null, curve: [{x: (mouseX - xOffset)/scale, y: (mouseY-yOffset)/scale}] }; -*/ - touchTimeout = setTimeout( showSponge, 500, mouseX, mouseY ); - } - }, passiveSupported ? {passive: false} : false); - - document.addEventListener('touchmove', function(evt) { -//console.log("Touch move"); - clearTimeout( touchTimeout ); - touchTimeout = null; - if ( event ) { -// var ctx = drawingCanvas[mode].context; - var scale = drawingCanvas[mode].scale; - var xOffset = drawingCanvas[mode].xOffset; - var yOffset = drawingCanvas[mode].yOffset; - - var touch = evt.touches[0]; - mouseX = touch.pageX; - mouseY = touch.pageY; - if (mouseY < drawingCanvas[mode].height && mouseX < drawingCanvas[mode].width) { - evt.preventDefault(); - // move sponge - if ( event.type == "erase" ) { - drawingCanvas[mode].sponge.style.left = (mouseX - eraser.radius) +"px" ; - drawingCanvas[mode].sponge.style.top = (mouseY - eraser.radius) +"px" ; - } - } - - drawSegment( (mouseX - xOffset)/scale, (mouseY-yOffset)/scale, ( event.type == "erase" ) ); - // broadcast - var message = new CustomEvent(messageType); - message.content = { sender: 'chalkboard-plugin', type: 'drawSegment', timestamp: Date.now() - slideStart, x: (mouseX - xOffset)/scale, y: (mouseY-yOffset)/scale, erase: ( event.type == "erase" ) }; - document.dispatchEvent( message ); -/* - if (mouseY < drawingCanvas[mode].height && mouseX < drawingCanvas[mode].width) { - evt.preventDefault(); - event.curve.push({x: (mouseX - xOffset)/scale, y: (mouseY-yOffset)/scale}); - if ( event.type == "erase" ) { - drawingCanvas[mode].sponge.style.left = (mouseX - eraser.radius) +"px" ; - drawingCanvas[mode].sponge.style.top = (mouseY - eraser.radius) +"px" ; - eraseWithSponge(ctx, mouseX, mouseY); - } - else { - draw[mode](ctx, xLast, yLast, mouseX, mouseY); - } - xLast = mouseX; - yLast = mouseY; - } -*/ - } - }, false); - - - document.addEventListener('touchend', function(evt) { - clearTimeout( touchTimeout ); - touchTimeout = null; - // hide sponge image - drawingCanvas[mode].sponge.style.visibility = "hidden"; - stopDrawing(); - // broadcast - var message = new CustomEvent(messageType); - message.content = { sender: 'chalkboard-plugin', timestamp: Date.now() - slideStart, type: 'stopDrawing' }; - document.dispatchEvent( message ); -/* - if ( event ) { - event.end = Date.now() - slideStart; - if ( event.type == "erase" || event.curve.length > 1 ) { - // do not save a line with a single point only - recordEvent( event ); - } - event = null; - } -*/ - }, false); - - document.addEventListener( 'mousedown', function( evt ) { -//console.log("Mouse down"); -//console.log( "Read only: " + readOnly ); - if ( !readOnly && evt.target.getAttribute('data-chalkboard') == mode ) { -//console.log( "mousedown: " + evt.button ); -// var ctx = drawingCanvas[mode].context; - var scale = drawingCanvas[mode].scale; - var xOffset = drawingCanvas[mode].xOffset; - var yOffset = drawingCanvas[mode].yOffset; - - mouseX = evt.pageX; - mouseY = evt.pageY; - startDrawing( (mouseX - xOffset)/scale, (mouseY-yOffset)/scale, ( evt.button == 2 || evt.button == 1) ); - // broadcast - var message = new CustomEvent(messageType); - message.content = { sender: 'chalkboard-plugin', type: 'startDrawing', timestamp: Date.now() - slideStart, x: (mouseX - xOffset)/scale, y: (mouseY-yOffset)/scale, erase: ( evt.button == 2 || evt.button == 1) }; - document.dispatchEvent( message ); -/* - xLast = mouseX; - yLast = mouseY; - if ( evt.button == 2) { - event = { type: "erase", begin: Date.now() - slideStart, end: null, curve: [{x: (mouseX - xOffset)/scale, y: (mouseY-yOffset)/scale}]}; - drawingCanvas[mode].canvas.style.cursor = 'url("' + path + 'img/sponge.png") ' + eraser.radius + ' ' + eraser.radius + ', auto'; - eraseWithSponge(ctx,mouseX,mouseY); - } - else { - event = { type: "draw", begin: Date.now() - slideStart, end: null, curve: [{x: (mouseX - xOffset)/scale, y: (mouseY-yOffset)/scale}] }; - } -*/ - } - } ); - - document.addEventListener( 'mousemove', function( evt ) { -//console.log("Mouse move"); - if ( event ) { -// var ctx = drawingCanvas[mode].context; - var scale = drawingCanvas[mode].scale; - var xOffset = drawingCanvas[mode].xOffset; - var yOffset = drawingCanvas[mode].yOffset; - - mouseX = evt.pageX; - mouseY = evt.pageY; - drawSegment( (mouseX - xOffset)/scale, (mouseY-yOffset)/scale, ( event.type == "erase" ) ); - // broadcast - var message = new CustomEvent(messageType); - message.content = { sender: 'chalkboard-plugin', type: 'drawSegment', timestamp: Date.now() - slideStart, x: (mouseX - xOffset)/scale, y: (mouseY-yOffset)/scale, erase: ( event.type == "erase" ) }; - document.dispatchEvent( message ); -/* - event.curve.push({x: (mouseX - xOffset)/scale, y: (mouseY-yOffset)/scale}); - if(mouseY < drawingCanvas[mode].height && mouseX < drawingCanvas[mode].width) { - if ( event.type == "erase" ) { - eraseWithSponge(ctx,mouseX,mouseY); - } - else { - draw[mode](ctx, xLast, yLast, mouseX,mouseY); - } - xLast = mouseX; - yLast = mouseY; - } -*/ - } - } ); - - - document.addEventListener( 'mouseup', function( evt ) { - drawingCanvas[mode].canvas.style.cursor = pens[mode][color[mode]].cursor; - if ( event ) { - stopDrawing(); - // broadcast - var message = new CustomEvent(messageType); - message.content = { sender: 'chalkboard-plugin', type: 'stopDrawing', timestamp: Date.now() - slideStart }; - document.dispatchEvent( message ); -/* if(evt.button == 2){ - } - event.end = Date.now() - slideStart; - if ( event.type == "erase" || event.curve.length > 1 ) { - // do not save a line with a single point only - recordEvent( event ); - } - event = null; -*/ - } - } ); - - - window.addEventListener( "resize", function() { -//console.log("resize"); - // Resize the canvas and draw everything again - var timestamp = Date.now() - slideStart; - if ( !playback ) { - timestamp = getSlideDuration(); - } - -//console.log( drawingCanvas[0].scale + "/" + drawingCanvas[0].xOffset + "/" +drawingCanvas[0].yOffset ); - for (var id = 0; id < 2; id++ ) { - drawingCanvas[id].width = window.innerWidth; - drawingCanvas[id].height = window.innerHeight; - drawingCanvas[id].canvas.width = drawingCanvas[id].width; - drawingCanvas[id].canvas.height = drawingCanvas[id].height; - drawingCanvas[id].context.canvas.width = drawingCanvas[id].width; - drawingCanvas[id].context.canvas.height = drawingCanvas[id].height; - - drawingCanvas[id].scale = Math.min( drawingCanvas[id].width/storage[id].width, drawingCanvas[id].height/storage[id].height ); - drawingCanvas[id].xOffset = (drawingCanvas[id].width - storage[id].width * drawingCanvas[id].scale)/2; - drawingCanvas[id].yOffset = (drawingCanvas[id].height - storage[id].height * drawingCanvas[id].scale)/2; -//console.log( drawingCanvas[id].scale + "/" + drawingCanvas[id].xOffset + "/" +drawingCanvas[id].yOffset ); - } -//console.log( window.innerWidth + "/" + window.innerHeight); - startPlayback( timestamp, mode, true ); - - } ); - - Reveal.addEventListener( 'ready', function( evt ) { -//console.log('ready'); - if ( !printMode ) { - slideStart = Date.now() - getSlideDuration(); - slideIndices = Reveal.getIndices(); - if ( !playback ) { - startPlayback( getSlideDuration(), 0 ); - } - if ( Reveal.isAutoSliding() ) { - var event = new CustomEvent('startplayback'); - event.timestamp = 0; - document.dispatchEvent( event ); - } - updateStorage(); - } - else { -console.log("Create printouts when ready"); - whenReady( createPrintout ); - } - }); - Reveal.addEventListener( 'slidechanged', function( evt ) { -// clearTimeout( slidechangeTimeout ); -//console.log('slidechanged'); - if ( !printMode ) { - slideStart = Date.now() - getSlideDuration(); - slideIndices = Reveal.getIndices(); - closeChalkboard(); - clearCanvas( 0 ); - clearCanvas( 1 ); - if ( !playback ) { - slidechangeTimeout = setTimeout( startPlayback, transition, getSlideDuration(), 0 ); - } - if ( Reveal.isAutoSliding() ) { - var event = new CustomEvent('startplayback'); - event.timestamp = 0; - document.dispatchEvent( event ); - } - - updateStorage(); - } - }); - Reveal.addEventListener( 'fragmentshown', function( evt ) { -// clearTimeout( slidechangeTimeout ); -//console.log('fragmentshown'); - if ( !printMode ) { - slideStart = Date.now() - getSlideDuration(); - slideIndices = Reveal.getIndices(); - closeChalkboard(); - clearCanvas( 0 ); - clearCanvas( 1 ); - if ( Reveal.isAutoSliding() ) { - var event = new CustomEvent('startplayback'); - event.timestamp = 0; - document.dispatchEvent( event ); - } - else if ( !playback ) { - // - startPlayback( getSlideDuration(), 0 ); -// closeChalkboard(); - } - } - }); - Reveal.addEventListener( 'fragmenthidden', function( evt ) { -// clearTimeout( slidechangeTimeout ); -//console.log('fragmenthidden'); - if ( !printMode ) { - slideStart = Date.now() - getSlideDuration(); - slideIndices = Reveal.getIndices(); - closeChalkboard(); - clearCanvas( 0 ); - clearCanvas( 1 ); - if ( Reveal.isAutoSliding() ) { - document.dispatchEvent( new CustomEvent('stopplayback') ); - } - else if ( !playback ) { - startPlayback( getSlideDuration() ); - closeChalkboard(); - } - } - }); - - Reveal.addEventListener( 'autoslideresumed', function( evt ) { -//console.log('autoslideresumed'); - var event = new CustomEvent('startplayback'); - event.timestamp = 0; - document.dispatchEvent( event ); - }); - Reveal.addEventListener( 'autoslidepaused', function( evt ) { -//console.log('autoslidepaused'); - document.dispatchEvent( new CustomEvent('stopplayback') ); - - // advance to end of slide -// closeChalkboard(); - startPlayback( getSlideDuration(), 0 ); - }); - - function toggleNotesCanvas() { - if ( !readOnly ) { - if ( mode == 1 ) { - toggleChalkboard(); - notescanvas.style.background = background[0]; //'rgba(255,0,0,0.5)'; - notescanvas.style.pointerEvents = "auto"; - } - else { - if ( notescanvas.style.pointerEvents != "none" ) { - // hide notes canvas - if ( colorButtons) { - notescanvas.querySelector(".palette").style.visibility = "hidden"; - } - event = null; - notescanvas.style.background = 'rgba(0,0,0,0)'; - notescanvas.style.pointerEvents = "none"; - } - else { - // show notes canvas - if ( colorButtons) { - notescanvas.querySelector(".palette").style.visibility = "visible"; - } - notescanvas.style.background = background[0]; //'rgba(255,0,0,0.5)'; - notescanvas.style.pointerEvents = "auto"; - - var idx = 0; - if (color[mode]) { - idx = color[mode]; - } - - setColor(idx, true); - - // broadcast - var message = new CustomEvent(messageType); - message.content = { sender: 'chalkboard-plugin', type: 'setcolor', timestamp: Date.now() - slideStart, index: idx }; - document.dispatchEvent( message ); - } - } - } - }; - - function toggleChalkboard() { -//console.log("toggleChalkboard " + mode); - if ( mode == 1 ) { - event = null; - if ( !readOnly ) { - recordEvent( { type:"close", begin: Date.now() - slideStart } ); - updateStorage(); - } - closeChalkboard(); - - // broadcast - var message = new CustomEvent(messageType); - message.content = { sender: 'chalkboard-plugin', type: 'closeChalkboard', timestamp: Date.now() - slideStart }; - document.dispatchEvent( message ); - } - else { - showChalkboard(); - if ( !readOnly ) { - recordEvent( { type:"open", begin: Date.now() - slideStart } ); - // broadcast - var message = new CustomEvent(messageType); - message.content = { sender: 'chalkboard-plugin', type: 'showChalkboard', timestamp: Date.now() - slideStart }; - document.dispatchEvent( message ); - - var idx = 0; - - if (rememberColor[mode]) { - idx = color[mode]; - } - - setColor(idx, true); - - // broadcast - message = new CustomEvent(messageType); - message.content = { sender: 'chalkboard-plugin', type: 'setcolor', timestamp: Date.now() - slideStart, index: idx }; - document.dispatchEvent( message ); - - } - } - }; - - function clear() { - if ( !readOnly ) { - recordEvent( { type:"clear", begin: Date.now() - slideStart } ); - clearCanvas( mode ); - // broadcast - var message = new CustomEvent(messageType); - message.content = { sender: 'chalkboard-plugin', type: 'clear', timestamp: Date.now() - slideStart }; - document.dispatchEvent( message ); - } - }; - - function colorIndex( idx ) { - if ( !readOnly ) { - setColor(idx, true); -// recordEvent( { type: "setcolor", index: idx, begin: Date.now() - slideStart } ); - // broadcast - var message = new CustomEvent(messageType); - message.content = { sender: 'chalkboard-plugin', type: 'setcolor', timestamp: Date.now() - slideStart, index: idx }; - document.dispatchEvent( message ); - } - } - - function colorNext() { - if ( !readOnly ) { - let idx = cycleColorNext(); - setColor(idx, true); -// recordEvent( { type: "setcolor", index: idx, begin: Date.now() - slideStart } ); - // broadcast - var message = new CustomEvent(messageType); - message.content = { sender: 'chalkboard-plugin', type: 'setcolor', timestamp: Date.now() - slideStart, index: idx }; - document.dispatchEvent( message ); - } - } - - function colorPrev() { - if ( !readOnly ) { - let idx = cycleColorPrev(); - setColor(idx, true); -// recordEvent( { type: "setcolor", index: idx, begin: Date.now() - slideStart } ); - // broadcast - var message = new CustomEvent(messageType); - message.content = { sender: 'chalkboard-plugin', type: 'setcolor', timestamp: Date.now() - slideStart, index: idx }; - document.dispatchEvent( message ); - } - } - - function resetSlide( force ) { - var ok = force || confirm("Please confirm to delete chalkboard drawings on this slide!"); - if ( ok ) { -//console.log("resetSlide "); - stopPlayback(); - slideStart = Date.now(); - event = null; - closeChalkboard(); - - clearCanvas( 0 ); - clearCanvas( 1 ); - - mode = 1; - var slideData = getSlideData(); - slideData.duration = 0; - slideData.events = []; - mode = 0; - var slideData = getSlideData(); - slideData.duration = 0; - slideData.events = []; - - updateStorage(); - // broadcast - var message = new CustomEvent(messageType); - message.content = { sender: 'chalkboard-plugin', type: 'resetSlide', timestamp: Date.now() - slideStart }; - document.dispatchEvent( message ); - } - }; - - function resetStorage( force ) { - var ok = force || confirm("Please confirm to delete all chalkboard drawings!"); - if ( ok ) { - stopPlayback(); - slideStart = Date.now(); - clearCanvas( 0 ); - clearCanvas( 1 ); - if ( mode == 1 ) { - event = null; - closeChalkboard(); - } - - storage = [ - { width: Reveal.getConfig().width, height: Reveal.getConfig().height, data: []}, - { width: Reveal.getConfig().width, height: Reveal.getConfig().height, data: []} - ]; -/* - storage = [ - { width: drawingCanvas[0].width - 2 * drawingCanvas[0].xOffset, height: drawingCanvas[0].height - 2 * drawingCanvas[0].yOffset, data: []}, - { width: drawingCanvas[1].width, height: drawingCanvas[1].height, data: []} - ]; -*/ - if ( config.storage ) { - sessionStorage.setItem( config.storage, null ) - } - // broadcast - var message = new CustomEvent(messageType); - message.content = { sender: 'chalkboard-plugin', type: 'init', timestamp: Date.now() - slideStart, storage: storage, mode: mode }; - document.dispatchEvent( message ); - } - }; - - -/* - this.drawWithBoardmarker = drawWithBoardmarker; - this.drawWithChalk = drawWithChalk; - this.startRecording = startRecording; -*/ - this.toggleNotesCanvas = toggleNotesCanvas; - this.toggleChalkboard = toggleChalkboard; - this.colorIndex = colorIndex; - this.colorNext = colorNext; - this.colorPrev = colorPrev; - this.clear = clear; - this.reset = resetSlide; - this.resetAll = resetStorage; - this.download = downloadData; - this.updateStorage = updateStorage; - this.getData = getData; - this.configure = configure; - - - for (var key in keyBindings) { - if ( keyBindings[key] ) { - Reveal.addKeyBinding( keyBindings[key], RevealChalkboard[key] ); - } - }; - - return this; -}; +/***************************************************************** +** Author: Asvin Goel, goel@telematique.eu +** +** A plugin for reveal.js adding a chalkboard. +** +** Version: 1.5.0 +** +** License: MIT license (see LICENSE.md) +** +** Credits: +** Chalkboard effect by Mohamed Moustafa https://github.com/mmoustafa/Chalkboard +** Multi color support by Kurt Rinnert https://github.com/rinnert +** Compatibility with reveal.js v4 by Hakim El Hattab https://github.com/hakimel +******************************************************************/ + +window.RevealChalkboard = window.RevealChalkboard || { + id: 'RevealChalkboard', + init: function(deck) { + initChalkboard(deck); + }, + configure: function(config) { configure(config); }, + toggleNotesCanvas: function() { toggleNotesCanvas(); }, + toggleChalkboard: function() { toggleChalkboard(); }, + colorIndex: function() { colorIndex(); }, + colorNext: function() { colorNext(); }, + colorPrev: function() {colorPrev(); }, + clear: function() { clear(); }, + reset: function() { reset(); }, + resetAll: function() { resetAll(); }, + updateStorage: function() { updateStorage(); }, + getData: function() { return getData(); }, + download: function() { download(); }, +}; + +function scriptPath() { + // obtain plugin path from the script element + var src; + if (document.currentScript) { + src = document.currentScript.src; + } else { + var sel = document.querySelector('script[src$="/chalkboard/plugin.js"]') + if (sel) { + src = sel.src; + } + } + var path = (src === undefined) ? "" : src.slice(0, src.lastIndexOf("/") + 1); +//console.log("Path: " + path); + return path; +} +var path = scriptPath(); + +const initChalkboard = function(Reveal){ +//console.warn(path); + /* Feature detection for passive event handling*/ + var passiveSupported = false; + + try { + window.addEventListener("test", null, Object.defineProperty({}, "passive", { get: function() { passiveSupported = true; } })); + } catch(err) {} + + +/***************************************************************** +** Configuration +******************************************************************/ + var background, pen, draw, color; + var grid = false; + var boardmarkerWidth = 3; + var chalkWidth = 7; + var chalkEffect = 1.0; + var rememberColor = [true, false]; + var eraser = { src: path + 'img/sponge.png', radius: 20}; + var boardmarkers = [ + { color: 'rgba(100,100,100,1)', cursor: 'url(' + path + 'img/boardmarker-black.png), auto'}, + { color: 'rgba(30,144,255, 1)', cursor: 'url(' + path + 'img/boardmarker-blue.png), auto'}, + { color: 'rgba(220,20,60,1)', cursor: 'url(' + path + 'img/boardmarker-red.png), auto'}, + { color: 'rgba(50,205,50,1)', cursor: 'url(' + path + 'img/boardmarker-green.png), auto'}, + { color: 'rgba(255,140,0,1)', cursor: 'url(' + path + 'img/boardmarker-orange.png), auto'}, + { color: 'rgba(150,0,20150,1)', cursor: 'url(' + path + 'img/boardmarker-purple.png), auto'}, + { color: 'rgba(255,220,0,1)', cursor: 'url(' + path + 'img/boardmarker-yellow.png), auto'} + ]; + var chalks = [ + { color: 'rgba(255,255,255,0.5)', cursor: 'url(' + path + 'img/chalk-white.png), auto'}, + { color: 'rgba(96, 154, 244, 0.5)', cursor: 'url(' + path + 'img/chalk-blue.png), auto'}, + { color: 'rgba(237, 20, 28, 0.5)', cursor: 'url(' + path + 'img/chalk-red.png), auto'}, + { color: 'rgba(20, 237, 28, 0.5)', cursor: 'url(' + path + 'img/chalk-green.png), auto'}, + { color: 'rgba(220, 133, 41, 0.5)', cursor: 'url(' + path + 'img/chalk-orange.png), auto'}, + { color: 'rgba(220,0,220,0.5)', cursor: 'url(' + path + 'img/chalk-purple.png), auto'}, + { color: 'rgba(255,220,0,0.5)', cursor: 'url(' + path + 'img/chalk-yellow.png), auto'} + ]; + var keyBindings = { + toggleNotesCanvas: { keyCode: 67, key: 'C', description: 'Toggle notes canvas' }, + toggleChalkboard: { keyCode: 66, key: 'B', description: 'Toggle chalkboard' }, + clear: { keyCode: 171, key: '+', description: 'Clear drawings on slide' }, + reset: { keyCode: 46, key: 'DEL', description: 'Reset drawings on slide' }, + resetAll: { keyCode: 8, key: 'BACKSPACE', description: 'Reset all drawings' }, + colorNext: { keyCode: 88, key: 'X', description: 'Next color' }, + colorPrev: { keyCode: 89, key: 'Y', description: 'Previous color' }, + download: { keyCode: 68, key: 'D', description: 'Download drawings' } + }; + + + var theme = "chalkboard"; + var color = [0, 0]; + var toggleChalkboardButton = true; + var toggleNotesButton = true; + var colorButtons = true; + var boardHandle = true; + var transition = 800; + + var readOnly = false; + var messageType = 'broadcast'; + + var config = configure( Reveal.getConfig().chalkboard || {} ); + if ( config.keyBindings ) { + for (var key in config.keyBindings) { + keyBindings[key] = config.keyBindings[key]; + }; + } + + function configure( config ) { + + if ( config.boardmarkerWidth || config.penWidth ) boardmarkerWidth = config.boardmarkerWidth || config.penWidth; + if ( config.chalkWidth ) chalkWidth = config.chalkWidth; + if ( config.chalkEffect ) chalkEffect = config.chalkEffect; + if ( config.rememberColor ) rememberColor = config.rememberColor; + if ( config.eraser ) eraser = config.eraser; + if ( config.boardmarkers ) boardmarkers = config.boardmarkers; + if ( config.chalks) chalks = config.chalks; + + if ( config.theme ) theme = config.theme; + switch ( theme ) { + case "whiteboard": + background = [ 'rgba(127,127,127,.1)' , path + 'img/whiteboard.png' ]; + draw = [ drawWithBoardmarker , drawWithBoardmarker ]; + pens = [ boardmarkers, boardmarkers ]; + grid = { color: 'rgb(127,127,255,0.1)', distance: 40, width: 2}; + break; + case "chalkboard": + default: + background = [ 'rgba(127,127,127,.1)' , path + 'img/blackboard.png' ]; + draw = [ drawWithBoardmarker , drawWithChalk ]; + pens = [ boardmarkers, chalks ]; + grid = { color: 'rgb(50,50,10,0.5)', distance: 80, width: 2}; + } + + if ( config.background ) background = config.background; + if ( config.grid != undefined ) grid = config.grid; + + if (config.toggleChalkboardButton != undefined) toggleChalkboardButton = config.toggleChalkboardButton; + if (config.toggleNotesButton != undefined) toggleNotesButton = config.toggleNotesButton; + if (config.colorButtons != undefined) colorButtons = config.colorButtons; + if (config.boardHandle != undefined) boardHandle = config.boardHandle; + if (config.transition) transition = config.transition; + + if (config.readOnly != undefined) readOnly = config.readOnly; + if (config.messageType) messageType = config.messageType; + + if ( drawingCanvas && ( config.theme || config.background || config.grid ) ) { + var canvas = document.getElementById( drawingCanvas[1].id ); + canvas.style.background = 'url("' + background[1] + '") repeat'; + clearCanvas( 1 ); + drawGrid(); + } + + return config; + } +/***************************************************************** +** Setup +******************************************************************/ + + function whenReady( callback ) { + // wait for drawings to be loaded and markdown to be parsed + if ( document.querySelectorAll(".pdf-page").length && loaded !== null ) { + callback(); + } + else { +console.log("Wait for pdf pages to be created and drawings to be loaded"); + setTimeout( whenReady, 500, callback ) + } + } + + + if ( toggleChalkboardButton ) { +//console.log("toggleChalkboardButton") + var button = document.createElement( 'div' ); + button.className = "chalkboard-button"; + button.id = "toggle-chalkboard"; + button.style.visibility = "visible"; + button.style.position = "absolute"; + button.style.zIndex = 30; + button.style.fontSize = "24px"; + + button.style.left = toggleChalkboardButton.left || "30px"; + button.style.bottom = toggleChalkboardButton.bottom || "30px"; + button.style.top = toggleChalkboardButton.top || "auto"; + button.style.right = toggleChalkboardButton.right || "auto"; + + button.innerHTML = '' + document.querySelector(".reveal").appendChild( button ); + } + if ( toggleNotesButton ) { +//console.log("toggleNotesButton") + var button = document.createElement( 'div' ); + button.className = "chalkboard-button"; + button.id = "toggle-notes"; + button.style.position = "absolute"; + button.style.zIndex = 30; + button.style.fontSize = "24px"; + + button.style.left = toggleNotesButton.left || "70px"; + button.style.bottom = toggleNotesButton.bottom || "30px"; + button.style.top = toggleNotesButton.top || "auto"; + button.style.right = toggleNotesButton.right || "auto"; + + button.innerHTML = '' + document.querySelector(".reveal").appendChild( button ); + } +//alert("Buttons"); + + var drawingCanvas = [ {id: "notescanvas" }, {id: "chalkboard" } ]; + setupDrawingCanvas(0); + setupDrawingCanvas(1); + + var mode = 0; // 0: notes canvas, 1: chalkboard + var board = 0; // board index (only for chalkboard) + + var mouseX = 0; + var mouseY = 0; + var xLast = null; + var yLast = null; + + var slideStart = Date.now(); + var slideIndices = { h:0, v:0 }; + var event = null; + var timeouts = [ [], [] ]; + var touchTimeout = null; + var slidechangeTimeout = null; + var playback = false; + + function createPalette( colors, length ) { + if ( length === true || length > colors.length ) { + length = colors.length; + } + var palette = document.createElement( 'div' ); + palette.classList.add('palette'); + var list = document.createElement( 'ul' ); + // color pickers + for (var i = 0; i < length; i++ ) { + var colorButton = document.createElement( 'li' ); + colorButton.setAttribute("data-color",i); + colorButton.innerHTML = ''; + colorButton.style.color = colors[i].color; + colorButton.addEventListener("click", function(e) { + colorIndex(e.target.parentElement.getAttribute("data-color")); + }); + list.appendChild( colorButton ); + } + palette.appendChild( list ); + return palette; + }; + + function setupDrawingCanvas( id ) { + var container = document.createElement( 'div' ); + container.id = drawingCanvas[id].id; + container.classList.add( 'overlay' ); + container.setAttribute( 'data-prevent-swipe', '' ); + container.oncontextmenu = function() { return false; } + container.style.cursor = pens[ id ][ color[id] ].cursor; + + drawingCanvas[id].width = window.innerWidth; + drawingCanvas[id].height = window.innerHeight; + drawingCanvas[id].scale = 1; + drawingCanvas[id].xOffset = 0; + drawingCanvas[id].yOffset = 0; + + if ( id == "0" ) { + container.style.background = 'rgba(0,0,0,0)'; + container.style.zIndex = 24; + container.style.opacity = 1; + container.style.visibility = 'visible'; + container.style.pointerEvents = "none"; + + var slides = document.querySelector(".slides"); + var aspectRatio = Reveal.getConfig().width / Reveal.getConfig().height; + if ( drawingCanvas[id].width > drawingCanvas[id].height*aspectRatio ) { + drawingCanvas[id].xOffset = (drawingCanvas[id].width - drawingCanvas[id].height*aspectRatio) / 2; + } + else if ( drawingCanvas[id].height > drawingCanvas[id].width/aspectRatio ) { + drawingCanvas[id].yOffset = ( drawingCanvas[id].height - drawingCanvas[id].width/aspectRatio ) / 2; + } + + if ( colorButtons ) { + var palette = createPalette( boardmarkers, colorButtons ); + palette.style.visibility = 'hidden'; // only show palette in drawing mode + container.appendChild(palette); + } + } + else { + container.style.background = 'url("' + background[id] + '") repeat'; + container.style.zIndex = 26; + container.style.opacity = 0; + container.style.visibility = 'hidden'; + + if ( colorButtons ) { + var palette = createPalette( chalks, colorButtons ); + container.appendChild(palette); + } + if ( boardHandle ) { + var handle = document.createElement( 'div' ); + handle.classList.add('boardhandle'); + handle.innerHTML=''; + handle.querySelector("#previousboard").addEventListener("click", function(e) { + e.preventDefault(); + setBoard(board-1,true); + }); + handle.querySelector("#nextboard").addEventListener("click", function(e) { + e.preventDefault(); + setBoard(board+1,true); + }); + + container.appendChild(handle); + } + } + + + var sponge = document.createElement( 'img' ); + sponge.src = eraser.src; + sponge.id = "sponge"; + sponge.style.visibility = "hidden"; + sponge.style.position = "absolute"; + container.appendChild( sponge ); + drawingCanvas[id].sponge = sponge; + + var canvas = document.createElement( 'canvas' ); + canvas.width = drawingCanvas[id].width; + canvas.height = drawingCanvas[id].height; + canvas.setAttribute( 'data-chalkboard', id ); + canvas.style.cursor = pens[ id ][ color[id] ].cursor; + container.appendChild( canvas ); + drawingCanvas[id].canvas = canvas; + + drawingCanvas[id].context = canvas.getContext("2d"); + + + document.querySelector( '.reveal' ).appendChild( container ); + drawingCanvas[id].container = container; + } + + +/***************************************************************** +** Storage +******************************************************************/ + + var storage = [ + { width: Reveal.getConfig().width, height: Reveal.getConfig().height, data: []}, + { width: Reveal.getConfig().width, height: Reveal.getConfig().height, data: []} + ]; + + var loaded = null; + + if ( config.storage ) { + // Get chalkboard drawings from session storage + loaded = initStorage( sessionStorage.getItem( config.storage ) ); + } + + if ( !loaded && config.src != null ) { + // Get chalkboard drawings from the given file + loadData( config.src ); + } + + /** + * Initialize storage. + */ + function initStorage( json ) { + var success = false; + try { + var data = JSON.parse( json ); + for (var id = 0; id < data.length; id++) { + if ( drawingCanvas[id].width != data[id].width || drawingCanvas[id].height != data[id].height ) { + drawingCanvas[id].scale = Math.min( drawingCanvas[id].width/data[id].width, drawingCanvas[id].height/data[id].height); + drawingCanvas[id].xOffset = (drawingCanvas[id].width - data[id].width * drawingCanvas[id].scale)/2; + drawingCanvas[id].yOffset = (drawingCanvas[id].height - data[id].height * drawingCanvas[id].scale)/2; + } + if ( config.readOnly ) { + drawingCanvas[id].container.style.cursor = 'default'; + drawingCanvas[id].canvas.style.cursor = 'default'; + } + } + success = true; + storage = data; + } + catch ( err ) { + console.warn( "Cannot initialise storage!" ); + } + return success; + } + + + /** + * Load data. + */ + function loadData( filename ) { + var xhr = new XMLHttpRequest(); + xhr.onload = function() { + if (xhr.readyState === 4 && xhr.status != 404 ) { + loaded = initStorage(xhr.responseText); +console.log("Drawings loaded from file"); + } + else { + config.readOnly = undefined; + readOnly = undefined; + console.warn( 'Failed to get file ' + filename +". ReadyState: " + xhr.readyState + ", Status: " + xhr.status); + loaded = false; + } + }; + + xhr.open( 'GET', filename, true ); + try { + xhr.send(); + } + catch ( error ) { + config.readOnly = undefined; + readOnly = undefined; + console.warn( 'Failed to get file ' + filename + '. Make sure that the presentation and the file are served by a HTTP server and the file can be found there. ' + error ); + loaded = false; + } + } + + + function updateStorage() { + var json = JSON.stringify( storage ) + if ( config.storage ) { + sessionStorage.setItem( config.storage, json ) + } + return json; + } + + /** + * Get data as json string. + */ + function getData() { + // cleanup slide data without events + for (var id = 0; id < 2; id++) { + for (var i = storage[id].data.length-1; i >= 0; i--) { + if (storage[id].data[i].events.length == 0) { + storage[id].data.splice(i, 1); + } + } + } + + return updateStorage(); + } + + /** + * Download data. + */ + function downloadData() { + var a = document.createElement('a'); + document.body.appendChild(a); + try { + a.download = "chalkboard.json"; + var blob = new Blob( [ getData() ], { type: "application/json"} ); + a.href = window.URL.createObjectURL( blob ); + } catch( error ) { + a.innerHTML += " (" + error + ")"; + } + a.click(); + document.body.removeChild(a); + } + + /** + * Returns data object for the slide with the given indices. + */ + function getSlideData( indices, id ) { + if ( id == undefined ) id = mode; + if (!indices) indices = slideIndices; + var data; + for (var i = 0; i < storage[id].data.length; i++) { + if (storage[id].data[i].slide.h === indices.h && storage[id].data[i].slide.v === indices.v && storage[id].data[i].slide.f === indices.f ) { + data = storage[id].data[i]; + return data; + } + } + storage[id].data.push( { slide: indices, events: [], duration: 0 } ); + data = storage[id].data[storage[id].data.length-1]; + return data; + } + + /** + * Returns maximum duration of slide playback for both modes + */ + function getSlideDuration( indices ) { + if (!indices) indices = slideIndices; + var duration = 0; + for (var id = 0; id < 2; id++) { + for (var i = 0; i < storage[id].data.length; i++) { + if (storage[id].data[i].slide.h === indices.h && storage[id].data[i].slide.v === indices.v && storage[id].data[i].slide.f === indices.f ) { + duration = Math.max( duration, storage[id].data[i].duration ); + break; + } + } + } +//console.log( duration ); + return duration; + } + +/***************************************************************** +** Print +******************************************************************/ + var printMode = ( /print-pdf/gi ).test( window.location.search ); +//console.log("createPrintout" + printMode) + + function createPrintout( ) { +console.warn(Reveal.getTotalSlides(),Reveal.getSlidesElement()); + if ( storage[1].data.length == 0 ) return; +console.log( 'Create printout(s) for ' + storage[1].data.length + " slides"); + drawingCanvas[0].container.style.opacity = 0; // do not print notes canvas + drawingCanvas[0].container.style.visibility = 'hidden'; + + var patImg = new Image(); + patImg.onload = function () { + var slides = getSlidesArray(); +//console.log(slides); + for (var i = storage[1].data.length-1; i>=0; i--) { +console.log( 'Create printout for slide ' + storage[1].data[i].slide.h + "." + storage[1].data[i].slide.v ); + var slideData = getSlideData( storage[1].data[i].slide, 1 ); + var drawings = createDrawings( slideData, patImg ); + var slide = slides[ storage[1].data[i].slide.h][ storage[1].data[i].slide.v ]; +//console.log("Slide:", slide); + addDrawings( slide, drawings ); + + } +// Reveal.sync(); + }; + patImg.src = background[1]; + } + + function getSlidesArray() { + var horizontal = document.querySelectorAll('.slides > div.pdf-page > section, .slides > section'); + var slides = []; + var slidenumber = undefined; + for ( var i=0; i < horizontal.length; i++) { + if ( horizontal[i].parentElement.classList.contains("pdf-page") ) { + // Horizontal slide + if ( horizontal[i].getAttribute("data-slide-number") != slidenumber ) { + // new slide + slides.push([]); + slides[slides.length-1].push(horizontal[i]); + slidenumber = horizontal[i].getAttribute("data-slide-number"); + } + else { + // fragment of same slide + slides[slides.length-1][slides[slides.length-1].length-1] = horizontal[i]; + } + } + else { + // Vertical slides + var vertical = horizontal[i].querySelectorAll('section'); + slides.push([]); + var slidenumber = undefined; + for ( var j=0; j < vertical.length; j++) { + if ( vertical[j].getAttribute("data-slide-number") != slidenumber ) { + // new slide + slides[slides.length-1].push(vertical[j]); + slidenumber = vertical[j].getAttribute("data-slide-number"); + } + else { + // fragment of same slide + slides[slides.length-1][slides[slides.length-1].length-1] = vertical[j]; + } + } + } + } +//console.log("Slides:", slides); + return slides; + } + + function cloneCanvas(oldCanvas) { + //create a new canvas + var newCanvas = document.createElement('canvas'); + var context = newCanvas.getContext('2d'); + //set dimensions + newCanvas.width = oldCanvas.width; + newCanvas.height = oldCanvas.height; + //apply the old canvas to the new one + context.drawImage(oldCanvas, 0, 0); + //return the new canvas + return newCanvas; + } + + function getCanvas( template, container, board ) { + var idx = container.findIndex(element => element.board === board); + if ( idx === -1 ) { + var canvas = cloneCanvas(template); + if ( !container.length ) { + idx = 0; + container.push({ board, canvas }); + } + else if ( board < container[0].board ) { + idx = 0; + container.unshift({ board, canvas }); + } + else if ( board > container[container.length-1].board ) { + idx = container.length; + container.push({ board, canvas }); + } + } + + return container[idx].canvas; + } + + function createDrawings( slideData, patImg ) { + var width = Reveal.getConfig().width; + var height = Reveal.getConfig().height; + var scale = 1; + var xOffset = 0; + var yOffset = 0; + if ( width != storage[1].width || height != storage[1].height ) { + scale = Math.min( width/storage[1].width, height/storage[1].height); + xOffset = (width - storage[1].width * scale)/2; + yOffset = (height - storage[1].height * scale)/2; + } + mode = 1; + board = 0; +console.log( 'Create printout(s) for slide ', slideData); + + var drawings = []; + var template = document.createElement('canvas'); + template.width = width; + template.height = height; + + var imgCtx = template.getContext("2d"); + imgCtx.fillStyle = imgCtx.createPattern( patImg ,'repeat'); + imgCtx.rect(0,0,width,height); + imgCtx.fill(); + + for (var j = 0; j < slideData.events.length; j++) { + switch ( slideData.events[j].type ) { + case "draw": + for (var k = 1; k < slideData.events[j].curve.length; k++) { + draw[1]( getCanvas(template,drawings,board).getContext("2d"), + xOffset + slideData.events[j].curve[k-1].x*scale, + yOffset + slideData.events[j].curve[k-1].y*scale, + xOffset + slideData.events[j].curve[k].x*scale, + yOffset + slideData.events[j].curve[k].y*scale + ); + } + break; + case "erase": + for (var k = 0; k < slideData.events[j].curve.length; k++) { + eraseWithSponge( getCanvas(template,drawings,board).getContext("2d"), + xOffset + slideData.events[j].curve[k].x*scale, + yOffset + slideData.events[j].curve[k].y*scale + ); + } + break; + case "setcolor": + setColor(slideData.events[j].index); + break; + case "setboard": + // Todo: create new canvas for each new index + setBoard(slideData.events[j].index); +//board = 0; + break; + case "clear": + getCanvas(template,drawings,board).getContext("2d").clearRect(0,0,width,height); + getCanvas(template,drawings,board).getContext("2d").fill(); + break; + default: + break; + } + } + + drawings = drawings.sort((a, b) => a.board > b.board && 1 || -1); + + mode = 0; + + return drawings; + } + + function addDrawings( slide, drawings ) { + var parent = slide.parentElement.parentElement; + var nextSlide = slide.parentElement.nextElementSibling; + + for (var i = 0; i < drawings.length; i++) { + var newPDFPage = document.createElement( 'div' ); + newPDFPage.classList.add('pdf-page'); + newPDFPage.style.height = Reveal.getConfig().height; +// newPDFPage.innerHTML = '

Drawing should be here!

'; + newPDFPage.append(drawings[i].canvas); +//console.log("Add drawing", newPDFPage); + if ( nextSlide != null ) { + parent.insertBefore( newPDFPage, nextSlide ); + } + else { + parent.append( newPDFPage ); + } + } + } + +/***************************************************************** +** Drawings +******************************************************************/ + + function drawWithBoardmarker(context,fromX,fromY,toX,toY){ + context.lineWidth = boardmarkerWidth; + context.lineCap = 'round'; + context.strokeStyle = boardmarkers[color[mode]].color; + context.beginPath(); + context.moveTo(fromX, fromY); + context.lineTo(toX, toY); + context.stroke(); + } + + function drawWithChalk(context,fromX,fromY,toX,toY) { + var brushDiameter = chalkWidth; + context.lineWidth = brushDiameter; + context.lineCap = 'round'; + context.fillStyle = chalks[color[mode]].color; // 'rgba(255,255,255,0.5)'; + context.strokeStyle = chalks[color[mode]].color; + /*var opacity = Math.min(0.8, Math.max(0,color[1].replace(/^.*,(.+)\)/,'$1') - 0.1)) + Math.random()*0.2;*/ + var opacity = 1.0; + context.strokeStyle = context.strokeStyle.replace(/[\d\.]+\)$/g, opacity + ')'); + context.beginPath(); + context.moveTo(fromX, fromY); + context.lineTo(toX, toY); + context.stroke(); + // Chalk Effect + var length = Math.round(Math.sqrt(Math.pow(toX-fromX,2)+Math.pow(toY-fromY,2))/(5/brushDiameter)); + var xUnit = (toX-fromX)/length; + var yUnit = (toY-fromY)/length; + for(var i=0; i (Math.random() * 0.9)) { + var xCurrent = fromX+(i*xUnit); + var yCurrent = fromY+(i*yUnit); + var xRandom = xCurrent+(Math.random()-0.5)*brushDiameter*1.2; + var yRandom = yCurrent+(Math.random()-0.5)*brushDiameter*1.2; + context.clearRect( xRandom, yRandom, Math.random()*2+2, Math.random()+1); + } + } + } + + function eraseWithSponge(context,x,y) { + context.save(); + context.beginPath(); + context.arc(x, y, eraser.radius, 0, 2 * Math.PI, false); + context.clip(); + context.clearRect(x - eraser.radius - 1, y - eraser.radius - 1, eraser.radius * 2 + 2, eraser.radius * 2 + 2); + context.restore(); + if ( mode == 1 && grid) { + redrawGrid(x,y,eraser.radius); + } + } + + + + /** + * Show an overlay for the chalkboard. + */ + function showChalkboard() { +//console.log("showChalkboard"); + clearTimeout(touchTimeout); + touchTimeout = null; + drawingCanvas[0].sponge.style.visibility = "hidden"; // make sure that the sponge from touch events is hidden + drawingCanvas[1].sponge.style.visibility = "hidden"; // make sure that the sponge from touch events is hidden + drawingCanvas[1].container.style.opacity = 1; + drawingCanvas[1].container.style.visibility = 'visible'; + mode = 1; + } + + + /** + * Closes open chalkboard. + */ + function closeChalkboard() { + clearTimeout(touchTimeout); + touchTimeout = null; + drawingCanvas[0].sponge.style.visibility = "hidden"; // make sure that the sponge from touch events is hidden + drawingCanvas[1].sponge.style.visibility = "hidden"; // make sure that the sponge from touch events is hidden + drawingCanvas[1].container.style.opacity = 0; + drawingCanvas[1].container.style.visibility = 'hidden'; + xLast = null; + yLast = null; + event = null; + mode = 0; + } + + /** + * Clear current canvas. + */ + function clearCanvas( id ) { + if ( id == 0 ) clearTimeout( slidechangeTimeout ); + drawingCanvas[id].context.clearRect(0,0,drawingCanvas[id].width,drawingCanvas[id].height); + if ( id == 1 && grid ) drawGrid(); + } + + /** + * Draw grid on background + */ + function drawGrid() { + var context = drawingCanvas[1].context; + + drawingCanvas[1].scale = Math.min( drawingCanvas[1].width/storage[1].width, drawingCanvas[1].height/storage[1].height ); + drawingCanvas[1].xOffset = (drawingCanvas[1].width - storage[1].width * drawingCanvas[1].scale)/2; + drawingCanvas[1].yOffset = (drawingCanvas[1].height - storage[1].height * drawingCanvas[1].scale)/2; + + var scale = drawingCanvas[1].scale; + var xOffset = drawingCanvas[1].xOffset; + var yOffset = drawingCanvas[1].yOffset; + + var distance = grid.distance*scale; + + var fromX = drawingCanvas[1].width/2 - distance/2 - Math.floor( (drawingCanvas[1].width - distance)/2 / distance ) * distance; + for( var x=fromX; x < drawingCanvas[1].width; x+=distance ) { + context.beginPath(); + context.lineWidth = grid.width*scale; + context.lineCap = 'round'; + context.fillStyle = grid.color; + context.strokeStyle = grid.color; + context.moveTo(x, 0); + context.lineTo(x, drawingCanvas[1].height); + context.stroke(); + } + var fromY = drawingCanvas[1].height/2 - distance/2 - Math.floor( (drawingCanvas[1].height - distance)/2 / distance ) * distance ; + + for( var y=fromY; y < drawingCanvas[1].height; y+=distance ) { + context.beginPath(); + context.lineWidth = grid.width*scale; + context.lineCap = 'round'; + context.fillStyle = grid.color; + context.strokeStyle = grid.color; + context.moveTo(0, y); + context.lineTo(drawingCanvas[1].width, y); + context.stroke(); + } + } + + function redrawGrid(centerX,centerY,diameter) { + var context = drawingCanvas[1].context; + + drawingCanvas[1].scale = Math.min( drawingCanvas[1].width/storage[1].width, drawingCanvas[1].height/storage[1].height ); + drawingCanvas[1].xOffset = (drawingCanvas[1].width - storage[1].width * drawingCanvas[1].scale)/2; + drawingCanvas[1].yOffset = (drawingCanvas[1].height - storage[1].height * drawingCanvas[1].scale)/2; + + var scale = drawingCanvas[1].scale; + var xOffset = drawingCanvas[1].xOffset; + var yOffset = drawingCanvas[1].yOffset; + + var distance = grid.distance*scale; + + var fromX = drawingCanvas[1].width/2 - distance/2 - Math.floor( (drawingCanvas[1].width - distance)/2 / distance ) * distance; + + for( var x=fromX + distance* Math.ceil( (centerX-diameter-fromX) / distance); x <= fromX + distance* Math.floor( (centerX+diameter-fromX) / distance); x+=distance ) { + context.beginPath(); + context.lineWidth = grid.width*scale; + context.lineCap = 'round'; + context.fillStyle = grid.color; + context.strokeStyle = grid.color; + context.moveTo(x, centerY - Math.sqrt( diameter*diameter - (centerX-x)*(centerX-x) )); + context.lineTo(x, centerY + Math.sqrt( diameter*diameter - (centerX-x)*(centerX-x) ) ); + context.stroke(); + } + var fromY = drawingCanvas[1].height/2 - distance/2 - Math.floor( (drawingCanvas[1].height - distance)/2 / distance ) * distance ; + for( var y=fromY + distance* Math.ceil( (centerY-diameter-fromY) / distance); y <= fromY + distance* Math.floor( (centerY+diameter-fromY) / distance); y+=distance ) { + context.beginPath(); + context.lineWidth = grid.width*scale; + context.lineCap = 'round'; + context.fillStyle = grid.color; + context.strokeStyle = grid.color; + context.moveTo(centerX - Math.sqrt( diameter*diameter - (centerY-y)*(centerY-y) ), y ); + context.lineTo(centerX + Math.sqrt( diameter*diameter - (centerY-y)*(centerY-y) ), y ); + context.stroke(); + } + } + + /** + * Set the color + */ + function setColor( index, record ) { + // protect against out of bounds (this could happen when + // replaying events recorded with different color settings). + if ( index >= boardmarkers[mode].length ) index = 0; + color[mode] = index; + drawingCanvas[mode].canvas.style.cursor = pens[mode][color[mode]].cursor; + if ( record ) { + recordEvent( { type: "setcolor", index: index, begin: Date.now() - slideStart } ); + updateStorage(); + } + } + + /** + * Set the board + */ + function setBoard( index, record ) { + board = index; + redrawChalkboard( board ); + + if ( record ) { + recordEvent( { type: "setboard", index: board, begin: Date.now() - slideStart } ); + updateStorage(); + } + } + + function redrawChalkboard( board ) { + clearCanvas( 1 ); + var slideData = getSlideData( slideIndices, 1 ); + var index = 0; + var play = ( board == 0 ); + while ( index < slideData.events.length && slideData.events[index].begin < Date.now() - slideStart) { + if ( slideData.events[index].type == "setboard" ) { + play = ( board == slideData.events[index].index ); + } + else if ( play || slideData.events[index].type == "setcolor" ) { + playEvent( 1, slideData.events[index], Date.now() - slideStart ); + } + index++; + } + } + + + /** + * Forward cycle color + */ + function cycleColorNext() { + color[mode] = (color[mode] + 1) % pens[mode].length; + return color[mode]; + } + + /** + * Backward cycle color + */ + function cycleColorPrev() { + color[mode] = (color[mode] + (pens[mode].length - 1)) % pens[mode].length; + return color[mode]; + } + +/***************************************************************** +** Broadcast +******************************************************************/ + + var eventQueue = []; + + document.addEventListener( 'received', function ( message ) { + if ( message.content && message.content.sender == 'chalkboard-plugin' ) { + // add message to queue + eventQueue.push(message); + } + if ( eventQueue.length == 1 ) processQueue(); + }); + +//console.log(JSON.stringify(message)); + function processQueue() { + // take first message from queue + var message = eventQueue.shift(); + + // synchronize time with seminar host + slideStart = Date.now() - message.content.timestamp; + switch ( message.content.type ) { + case 'showChalkboard': + showChalkboard(); + break; + case 'closeChalkboard': + closeChalkboard(); + break; + case 'startDrawing': + startDrawing(message.content.x, message.content.y, message.content.erase); + break; + case 'startErasing': + if ( message.content ) { + message.content.type = "erase"; + message.content.begin = Date.now() - slideStart; + eraseWithSponge(drawingCanvas[mode].context, message.content.x, message.content.y); + } + break; + case 'drawSegment': + drawSegment(message.content.x, message.content.y, message.content.erase); + break; + case 'stopDrawing': + stopDrawing(); + break; + case 'clear': + clear(); + break; + case 'setcolor': + setColor(message.content.index, true); + break; + case 'setboard': + setBoard(message.content.index, true); + break; + case 'resetSlide': + resetSlide(true); + break; + case 'init': + storage = message.content.storage; + for (var id = 0; id < 2; id++ ) { + drawingCanvas[id].scale = Math.min( drawingCanvas[id].width/storage[id].width, drawingCanvas[id].height/storage[id].height ); + drawingCanvas[id].xOffset = (drawingCanvas[id].width - storage[id].width * drawingCanvas[id].scale)/2; + drawingCanvas[id].yOffset = (drawingCanvas[id].height - storage[id].height * drawingCanvas[id].scale)/2; + } + clearCanvas( 0 ); + clearCanvas( 1 ); + if ( !playback ) { + slidechangeTimeout = setTimeout( startPlayback, transition, getSlideDuration(), 0 ); + } + if ( mode == 1 && message.content.mode == 0) { + setTimeout( closeChalkboard, transition + 50 ); + } + if ( mode == 0 && message.content.mode == 1) { + setTimeout( showChalkboard, transition + 50 ); + } + mode = message.content.mode; + break; + default: + break; + } + + // continue with next message if queued + if ( eventQueue.length > 0 ) { + processQueue(); + } + else { + updateStorage(); + } + } + + document.addEventListener( 'welcome', function( user ) { + // broadcast storage + var message = new CustomEvent(messageType); + message.content = { sender: 'chalkboard-plugin', recipient: user.id, type: 'init', timestamp: Date.now() - slideStart, storage: storage, mode: mode }; + document.dispatchEvent( message ); + }); + +/***************************************************************** +** Playback +******************************************************************/ + + document.addEventListener('seekplayback', function( event ) { +//console.log('event seekplayback ' + event.timestamp); + stopPlayback(); + if ( !playback || event.timestamp == 0) { + // in other cases startplayback fires after seeked + startPlayback( event.timestamp ); + } +//console.log('seeked'); + }); + + + document.addEventListener('startplayback', function( event ) { +//console.log('event startplayback ' + event.timestamp); + stopPlayback(); + playback = true; + startPlayback( event.timestamp ); + }); + + document.addEventListener('stopplayback', function( event ) { +//console.log('event stopplayback ' + (Date.now() - slideStart) ); + playback = false; + stopPlayback(); + }); + + document.addEventListener('startrecording', function( event ) { +//console.log('event startrecording ' + event.timestamp); + startRecording(); + }); + + function recordEvent( event ) { + var slideData = getSlideData(); + var i = slideData.events.length; + while ( i > 0 && event.begin < slideData.events[i-1].begin ) { + i--; + } + slideData.events.splice( i, 0, event); + slideData.duration = Math.max( slideData.duration, Date.now() - slideStart ) + 1; + } + + function startRecording() { + resetSlide( true ); + slideStart = Date.now(); + } + + function startPlayback( timestamp, finalMode ) { +//console.log("playback " + timestamp ); + slideStart = Date.now() - timestamp; + closeChalkboard(); + mode = 0; + board = 0; + for ( var id = 0; id < 2; id++ ) { + clearCanvas( id ); + var slideData = getSlideData( slideIndices, id ); +//console.log( timestamp +" / " + JSON.stringify(slideData)); + var index = 0; + while ( index < slideData.events.length && slideData.events[index].begin < (Date.now() - slideStart) ) { + playEvent( id, slideData.events[index], timestamp ); + index++; + } + + while ( playback && index < slideData.events.length ) { + timeouts[id].push( setTimeout( playEvent, slideData.events[index].begin - (Date.now() - slideStart), id, slideData.events[index], timestamp ) ); + index++; + } + } +//console.log("Mode: " + finalMode + "/" + mode ); + if ( finalMode != undefined ) { + mode = finalMode; + } + if( mode == 1 ) showChalkboard(); +//console.log("playback (ok)"); + + }; + + function stopPlayback() { +//console.log("stopPlayback"); +//console.log("Timeouts: " + timeouts[0].length + "/"+ timeouts[1].length); + for ( var id = 0; id < 2; id++ ) { + for (var i = 0; i < timeouts[id].length; i++) { + clearTimeout(timeouts[id][i]); + } + timeouts[id] = []; + } + }; + + function playEvent( id, event, timestamp ) { +//console.log( timestamp +" / " + JSON.stringify(event)); +//console.log( id + ": " + timestamp +" / " + event.begin +" / " + event.type +" / " + mode ); + switch ( event.type ) { + case "open": + if ( timestamp <= event.begin ) { + showChalkboard(); + } + else { + mode = 1; + } + + break; + case "close": + if ( timestamp < event.begin ) { + closeChalkboard(); + } + else { + mode = 0; + } + break; + case "clear": + clearCanvas( id ); + break; + case "setcolor": + setColor(event.index); + break; + case "setboard": + setBoard(event.index); + break; + case "draw": + drawCurve( id, event, timestamp ); + break; + case "erase": + eraseCurve( id, event, timestamp ); + break; + + } + }; + + function drawCurve( id, event, timestamp ) { + if ( event.curve.length > 1 ) { + var ctx = drawingCanvas[id].context; + var scale = drawingCanvas[id].scale; + var xOffset = drawingCanvas[id].xOffset; + var yOffset = drawingCanvas[id].yOffset; + + var stepDuration = ( event.end - event.begin )/ ( event.curve.length - 1 ); +//console.log("---"); + for (var i = 1; i < event.curve.length; i++) { + if (event.begin + i * stepDuration <= (Date.now() - slideStart)) { +//console.log( "Draw " + timestamp +" / " + event.begin + " + " + i + " * " + stepDuration ); + draw[id](ctx, xOffset + event.curve[i-1].x*scale, yOffset + event.curve[i-1].y*scale, xOffset + event.curve[i].x*scale, yOffset + event.curve[i].y*scale); + } + else if ( playback ) { +//console.log( "Cue " + timestamp +" / " + (Date.now() - slideStart) +" / " + event.begin + " + " + i + " * " + stepDuration + " = " + Math.max(0,event.begin + i * stepDuration - timestamp) ); + timeouts.push( setTimeout( + draw[id], Math.max(0,event.begin + i * stepDuration - (Date.now() - slideStart)), ctx, + xOffset + event.curve[i-1].x*scale, + yOffset + event.curve[i-1].y*scale, + xOffset + event.curve[i].x*scale, + yOffset + event.curve[i].y*scale + ) + ); + } + } + } + + }; + + function eraseCurve( id, event, timestamp ) { + if ( event.curve.length > 1 ) { + var ctx = drawingCanvas[id].context; + var scale = drawingCanvas[id].scale; + var xOffset = drawingCanvas[id].xOffset; + var yOffset = drawingCanvas[id].yOffset; + + var stepDuration = ( event.end - event.begin )/ event.curve.length; + for (var i = 0; i < event.curve.length; i++) { + if (event.begin + i * stepDuration <= (Date.now() - slideStart)) { + eraseWithSponge(ctx, xOffset + event.curve[i].x*scale, yOffset + event.curve[i].y*scale); + } + else if ( playback ) { + timeouts.push( setTimeout( + eraseWithSponge, Math.max(0,event.begin + i * stepDuration - (Date.now() - slideStart)), ctx, + xOffset + event.curve[i].x * scale, + yOffset + event.curve[i].y * scale + ) + ); + } + } + } + + }; + + + function startDrawing( x, y, erase ) { + var ctx = drawingCanvas[mode].context; + var scale = drawingCanvas[mode].scale; + var xOffset = drawingCanvas[mode].xOffset; + var yOffset = drawingCanvas[mode].yOffset; + xLast = x * scale + xOffset; + yLast = y * scale + yOffset; + if ( erase == true) { + event = { type: "erase", begin: Date.now() - slideStart, end: null, curve: [{x: x, y: y}]}; + drawingCanvas[mode].canvas.style.cursor = 'url("' + eraser.src + '") ' + eraser.radius + ' ' + eraser.radius + ', auto'; + eraseWithSponge(ctx, x * scale + xOffset, y * scale + yOffset); + } + else { + event = { type: "draw", begin: Date.now() - slideStart, end: null, curve: [{x: x, y: y}] }; + } + } + + + function showSponge(x,y) { + if ( event ) { + event.type = "erase"; + event.begin = Date.now() - slideStart; + // show sponge image + drawingCanvas[mode].sponge.style.left = (x - eraser.radius) +"px" ; + drawingCanvas[mode].sponge.style.top = (y - eraser.radius) +"px" ; + drawingCanvas[mode].sponge.style.visibility = "visible"; + eraseWithSponge(drawingCanvas[mode].context,x,y); + // broadcast + var message = new CustomEvent(messageType); + message.content = { sender: 'chalkboard-plugin', type: 'startErasing', timestamp: Date.now() - slideStart, x: (mouseX - xOffset)/scale, y: (mouseY-yOffset)/scale }; + document.dispatchEvent( message ); + } + } + + function drawSegment( x, y, erase ) { + var ctx = drawingCanvas[mode].context; + var scale = drawingCanvas[mode].scale; + var xOffset = drawingCanvas[mode].xOffset; + var yOffset = drawingCanvas[mode].yOffset; + if ( !event ) { + // safeguard if broadcast hickup + startDrawing( x, y, erase ); + } + event.curve.push({x: x, y: y}); + if(y * scale + yOffset < drawingCanvas[mode].height && x * scale + xOffset < drawingCanvas[mode].width) { + if ( erase ) { + eraseWithSponge(ctx, x * scale + xOffset, y * scale + yOffset); + } + else { + draw[mode](ctx, xLast, yLast, x * scale + xOffset, y * scale + yOffset); + } + xLast = x * scale + xOffset; + yLast = y * scale + yOffset; + } + } + + function stopDrawing() { + if ( event ) { + event.end = Date.now() - slideStart; + if ( event.type == "erase" || event.curve.length > 1 ) { + // do not save a line with a single point only + recordEvent( event ); + updateStorage(); + } + event = null; + } + } + + +/***************************************************************** +** User interface +******************************************************************/ + + +// TODO: check all touchevents + document.addEventListener('touchstart', function(evt) { +//console.log("Touch start"); + if ( !readOnly && evt.target.getAttribute('data-chalkboard') == mode ) { +// var ctx = drawingCanvas[mode].context; + var scale = drawingCanvas[mode].scale; + var xOffset = drawingCanvas[mode].xOffset; + var yOffset = drawingCanvas[mode].yOffset; + + evt.preventDefault(); + var touch = evt.touches[0]; + mouseX = touch.pageX; + mouseY = touch.pageY; + startDrawing( (mouseX - xOffset)/scale, (mouseY-yOffset)/scale, false ); + // broadcast + var message = new CustomEvent(messageType); + message.content = { sender: 'chalkboard-plugin', type: 'startDrawing', timestamp: Date.now() - slideStart, x: (mouseX - xOffset)/scale, y: (mouseY-yOffset)/scale, erase: false }; + document.dispatchEvent( message ); +/* + xLast = mouseX; + yLast = mouseY; + event = { type: "draw", begin: Date.now() - slideStart, end: null, curve: [{x: (mouseX - xOffset)/scale, y: (mouseY-yOffset)/scale}] }; +*/ + touchTimeout = setTimeout( showSponge, 500, mouseX, mouseY ); + } + }, passiveSupported ? {passive: false} : false); + + document.addEventListener('touchmove', function(evt) { +//console.log("Touch move"); + clearTimeout( touchTimeout ); + touchTimeout = null; + if ( event ) { +// var ctx = drawingCanvas[mode].context; + var scale = drawingCanvas[mode].scale; + var xOffset = drawingCanvas[mode].xOffset; + var yOffset = drawingCanvas[mode].yOffset; + + var touch = evt.touches[0]; + mouseX = touch.pageX; + mouseY = touch.pageY; + if (mouseY < drawingCanvas[mode].height && mouseX < drawingCanvas[mode].width) { + evt.preventDefault(); + // move sponge + if ( event.type == "erase" ) { + drawingCanvas[mode].sponge.style.left = (mouseX - eraser.radius) +"px" ; + drawingCanvas[mode].sponge.style.top = (mouseY - eraser.radius) +"px" ; + } + } + + drawSegment( (mouseX - xOffset)/scale, (mouseY-yOffset)/scale, ( event.type == "erase" ) ); + // broadcast + var message = new CustomEvent(messageType); + message.content = { sender: 'chalkboard-plugin', type: 'drawSegment', timestamp: Date.now() - slideStart, x: (mouseX - xOffset)/scale, y: (mouseY-yOffset)/scale, erase: ( event.type == "erase" ) }; + document.dispatchEvent( message ); +/* + if (mouseY < drawingCanvas[mode].height && mouseX < drawingCanvas[mode].width) { + evt.preventDefault(); + event.curve.push({x: (mouseX - xOffset)/scale, y: (mouseY-yOffset)/scale}); + if ( event.type == "erase" ) { + drawingCanvas[mode].sponge.style.left = (mouseX - eraser.radius) +"px" ; + drawingCanvas[mode].sponge.style.top = (mouseY - eraser.radius) +"px" ; + eraseWithSponge(ctx, mouseX, mouseY); + } + else { + draw[mode](ctx, xLast, yLast, mouseX, mouseY); + } + xLast = mouseX; + yLast = mouseY; + } +*/ + } + }, false); + + + document.addEventListener('touchend', function(evt) { + clearTimeout( touchTimeout ); + touchTimeout = null; + // hide sponge image + drawingCanvas[mode].sponge.style.visibility = "hidden"; + stopDrawing(); + // broadcast + var message = new CustomEvent(messageType); + message.content = { sender: 'chalkboard-plugin', timestamp: Date.now() - slideStart, type: 'stopDrawing' }; + document.dispatchEvent( message ); +/* + if ( event ) { + event.end = Date.now() - slideStart; + if ( event.type == "erase" || event.curve.length > 1 ) { + // do not save a line with a single point only + recordEvent( event ); + } + event = null; + } +*/ + }, false); + + document.addEventListener( 'mousedown', function( evt ) { +//console.log("Mouse down"); +//console.log( "Read only: " + readOnly ); + if ( !readOnly && evt.target.getAttribute('data-chalkboard') == mode ) { +//console.log( "mousedown: " + evt.button ); +// var ctx = drawingCanvas[mode].context; + var scale = drawingCanvas[mode].scale; + var xOffset = drawingCanvas[mode].xOffset; + var yOffset = drawingCanvas[mode].yOffset; + + mouseX = evt.pageX; + mouseY = evt.pageY; + startDrawing( (mouseX - xOffset)/scale, (mouseY-yOffset)/scale, ( evt.button == 2 || evt.button == 1) ); + // broadcast + var message = new CustomEvent(messageType); + message.content = { sender: 'chalkboard-plugin', type: 'startDrawing', timestamp: Date.now() - slideStart, x: (mouseX - xOffset)/scale, y: (mouseY-yOffset)/scale, erase: ( evt.button == 2 || evt.button == 1) }; + document.dispatchEvent( message ); +/* + xLast = mouseX; + yLast = mouseY; + if ( evt.button == 2) { + event = { type: "erase", begin: Date.now() - slideStart, end: null, curve: [{x: (mouseX - xOffset)/scale, y: (mouseY-yOffset)/scale}]}; + drawingCanvas[mode].canvas.style.cursor = 'url("' + path + 'img/sponge.png") ' + eraser.radius + ' ' + eraser.radius + ', auto'; + eraseWithSponge(ctx,mouseX,mouseY); + } + else { + event = { type: "draw", begin: Date.now() - slideStart, end: null, curve: [{x: (mouseX - xOffset)/scale, y: (mouseY-yOffset)/scale}] }; + } +*/ + } + } ); + + document.addEventListener( 'mousemove', function( evt ) { +//console.log("Mouse move"); + if ( event ) { +// var ctx = drawingCanvas[mode].context; + var scale = drawingCanvas[mode].scale; + var xOffset = drawingCanvas[mode].xOffset; + var yOffset = drawingCanvas[mode].yOffset; + + mouseX = evt.pageX; + mouseY = evt.pageY; + drawSegment( (mouseX - xOffset)/scale, (mouseY-yOffset)/scale, ( event.type == "erase" ) ); + // broadcast + var message = new CustomEvent(messageType); + message.content = { sender: 'chalkboard-plugin', type: 'drawSegment', timestamp: Date.now() - slideStart, x: (mouseX - xOffset)/scale, y: (mouseY-yOffset)/scale, erase: ( event.type == "erase" ) }; + document.dispatchEvent( message ); +/* + event.curve.push({x: (mouseX - xOffset)/scale, y: (mouseY-yOffset)/scale}); + if(mouseY < drawingCanvas[mode].height && mouseX < drawingCanvas[mode].width) { + if ( event.type == "erase" ) { + eraseWithSponge(ctx,mouseX,mouseY); + } + else { + draw[mode](ctx, xLast, yLast, mouseX,mouseY); + } + xLast = mouseX; + yLast = mouseY; + } +*/ + } + } ); + + + document.addEventListener( 'mouseup', function( evt ) { + drawingCanvas[mode].canvas.style.cursor = pens[mode][color[mode]].cursor; + if ( event ) { + stopDrawing(); + // broadcast + var message = new CustomEvent(messageType); + message.content = { sender: 'chalkboard-plugin', type: 'stopDrawing', timestamp: Date.now() - slideStart }; + document.dispatchEvent( message ); +/* if(evt.button == 2){ + } + event.end = Date.now() - slideStart; + if ( event.type == "erase" || event.curve.length > 1 ) { + // do not save a line with a single point only + recordEvent( event ); + } + event = null; +*/ + } + } ); + + + window.addEventListener( "resize", function() { +//console.log("resize"); + // Resize the canvas and draw everything again + var timestamp = Date.now() - slideStart; + if ( !playback ) { + timestamp = getSlideDuration(); + } + +//console.log( drawingCanvas[0].scale + "/" + drawingCanvas[0].xOffset + "/" +drawingCanvas[0].yOffset ); + for (var id = 0; id < 2; id++ ) { + drawingCanvas[id].width = window.innerWidth; + drawingCanvas[id].height = window.innerHeight; + drawingCanvas[id].canvas.width = drawingCanvas[id].width; + drawingCanvas[id].canvas.height = drawingCanvas[id].height; + drawingCanvas[id].context.canvas.width = drawingCanvas[id].width; + drawingCanvas[id].context.canvas.height = drawingCanvas[id].height; + + drawingCanvas[id].scale = Math.min( drawingCanvas[id].width/storage[id].width, drawingCanvas[id].height/storage[id].height ); + drawingCanvas[id].xOffset = (drawingCanvas[id].width - storage[id].width * drawingCanvas[id].scale)/2; + drawingCanvas[id].yOffset = (drawingCanvas[id].height - storage[id].height * drawingCanvas[id].scale)/2; +//console.log( drawingCanvas[id].scale + "/" + drawingCanvas[id].xOffset + "/" +drawingCanvas[id].yOffset ); + } +//console.log( window.innerWidth + "/" + window.innerHeight); + startPlayback( timestamp, mode, true ); + + } ); + + Reveal.addEventListener( 'ready', function( evt ) { +//console.log('ready'); + if ( !printMode ) { + slideStart = Date.now() - getSlideDuration(); + slideIndices = Reveal.getIndices(); + if ( !playback ) { + startPlayback( getSlideDuration(), 0 ); + } + if ( Reveal.isAutoSliding() ) { + var event = new CustomEvent('startplayback'); + event.timestamp = 0; + document.dispatchEvent( event ); + } + updateStorage(); + } + else { +console.log("Create printouts when ready"); + whenReady( createPrintout ); + } + }); + Reveal.addEventListener( 'slidechanged', function( evt ) { +// clearTimeout( slidechangeTimeout ); +//console.log('slidechanged'); + if ( !printMode ) { + slideStart = Date.now() - getSlideDuration(); + slideIndices = Reveal.getIndices(); + closeChalkboard(); + clearCanvas( 0 ); + clearCanvas( 1 ); + if ( !playback ) { + slidechangeTimeout = setTimeout( startPlayback, transition, getSlideDuration(), 0 ); + } + if ( Reveal.isAutoSliding() ) { + var event = new CustomEvent('startplayback'); + event.timestamp = 0; + document.dispatchEvent( event ); + } + + updateStorage(); + } + }); + Reveal.addEventListener( 'fragmentshown', function( evt ) { +// clearTimeout( slidechangeTimeout ); +//console.log('fragmentshown'); + if ( !printMode ) { + slideStart = Date.now() - getSlideDuration(); + slideIndices = Reveal.getIndices(); + closeChalkboard(); + clearCanvas( 0 ); + clearCanvas( 1 ); + if ( Reveal.isAutoSliding() ) { + var event = new CustomEvent('startplayback'); + event.timestamp = 0; + document.dispatchEvent( event ); + } + else if ( !playback ) { + // + startPlayback( getSlideDuration(), 0 ); +// closeChalkboard(); + } + } + }); + Reveal.addEventListener( 'fragmenthidden', function( evt ) { +// clearTimeout( slidechangeTimeout ); +//console.log('fragmenthidden'); + if ( !printMode ) { + slideStart = Date.now() - getSlideDuration(); + slideIndices = Reveal.getIndices(); + closeChalkboard(); + clearCanvas( 0 ); + clearCanvas( 1 ); + if ( Reveal.isAutoSliding() ) { + document.dispatchEvent( new CustomEvent('stopplayback') ); + } + else if ( !playback ) { + startPlayback( getSlideDuration() ); + closeChalkboard(); + } + } + }); + + Reveal.addEventListener( 'autoslideresumed', function( evt ) { +//console.log('autoslideresumed'); + var event = new CustomEvent('startplayback'); + event.timestamp = 0; + document.dispatchEvent( event ); + }); + Reveal.addEventListener( 'autoslidepaused', function( evt ) { +//console.log('autoslidepaused'); + document.dispatchEvent( new CustomEvent('stopplayback') ); + + // advance to end of slide +// closeChalkboard(); + startPlayback( getSlideDuration(), 0 ); + }); + + function toggleNotesCanvas() { + if ( !readOnly ) { + if ( mode == 1 ) { + toggleChalkboard(); + notescanvas.style.background = background[0]; //'rgba(255,0,0,0.5)'; + notescanvas.style.pointerEvents = "auto"; + } + else { + if ( notescanvas.style.pointerEvents != "none" ) { + // hide notes canvas + if ( colorButtons) { + notescanvas.querySelector(".palette").style.visibility = "hidden"; + } + event = null; + notescanvas.style.background = 'rgba(0,0,0,0)'; + notescanvas.style.pointerEvents = "none"; + } + else { + // show notes canvas + if ( colorButtons) { + notescanvas.querySelector(".palette").style.visibility = "visible"; + } + notescanvas.style.background = background[0]; //'rgba(255,0,0,0.5)'; + notescanvas.style.pointerEvents = "auto"; + + var idx = 0; + if (color[mode]) { + idx = color[mode]; + } + + setColor(idx, true); + + // broadcast + var message = new CustomEvent(messageType); + message.content = { sender: 'chalkboard-plugin', type: 'setcolor', timestamp: Date.now() - slideStart, index: idx }; + document.dispatchEvent( message ); + } + } + } + }; + + function toggleChalkboard() { +//console.log("toggleChalkboard " + mode); + if ( mode == 1 ) { + event = null; + if ( !readOnly ) { + recordEvent( { type:"close", begin: Date.now() - slideStart } ); + updateStorage(); + } + closeChalkboard(); + + // broadcast + var message = new CustomEvent(messageType); + message.content = { sender: 'chalkboard-plugin', type: 'closeChalkboard', timestamp: Date.now() - slideStart }; + document.dispatchEvent( message ); + } + else { + showChalkboard(); + if ( !readOnly ) { + recordEvent( { type:"open", begin: Date.now() - slideStart } ); + // broadcast + var message = new CustomEvent(messageType); + message.content = { sender: 'chalkboard-plugin', type: 'showChalkboard', timestamp: Date.now() - slideStart }; + document.dispatchEvent( message ); + + var idx = 0; + + if (rememberColor[mode]) { + idx = color[mode]; + } + + setColor(idx, true); + + // broadcast + message = new CustomEvent(messageType); + message.content = { sender: 'chalkboard-plugin', type: 'setcolor', timestamp: Date.now() - slideStart, index: idx }; + document.dispatchEvent( message ); + + } + } + }; + + function clear() { + if ( !readOnly ) { + recordEvent( { type:"clear", begin: Date.now() - slideStart } ); + clearCanvas( mode ); + // broadcast + var message = new CustomEvent(messageType); + message.content = { sender: 'chalkboard-plugin', type: 'clear', timestamp: Date.now() - slideStart }; + document.dispatchEvent( message ); + } + }; + + function colorIndex( idx ) { + if ( !readOnly ) { + setColor(idx, true); +// recordEvent( { type: "setcolor", index: idx, begin: Date.now() - slideStart } ); + // broadcast + var message = new CustomEvent(messageType); + message.content = { sender: 'chalkboard-plugin', type: 'setcolor', timestamp: Date.now() - slideStart, index: idx }; + document.dispatchEvent( message ); + } + } + + function colorNext() { + if ( !readOnly ) { + let idx = cycleColorNext(); + setColor(idx, true); +// recordEvent( { type: "setcolor", index: idx, begin: Date.now() - slideStart } ); + // broadcast + var message = new CustomEvent(messageType); + message.content = { sender: 'chalkboard-plugin', type: 'setcolor', timestamp: Date.now() - slideStart, index: idx }; + document.dispatchEvent( message ); + } + } + + function colorPrev() { + if ( !readOnly ) { + let idx = cycleColorPrev(); + setColor(idx, true); +// recordEvent( { type: "setcolor", index: idx, begin: Date.now() - slideStart } ); + // broadcast + var message = new CustomEvent(messageType); + message.content = { sender: 'chalkboard-plugin', type: 'setcolor', timestamp: Date.now() - slideStart, index: idx }; + document.dispatchEvent( message ); + } + } + + function resetSlide( force ) { + var ok = force || confirm("Please confirm to delete chalkboard drawings on this slide!"); + if ( ok ) { +//console.log("resetSlide "); + stopPlayback(); + slideStart = Date.now(); + event = null; + closeChalkboard(); + + clearCanvas( 0 ); + clearCanvas( 1 ); + + mode = 1; + var slideData = getSlideData(); + slideData.duration = 0; + slideData.events = []; + mode = 0; + var slideData = getSlideData(); + slideData.duration = 0; + slideData.events = []; + + updateStorage(); + // broadcast + var message = new CustomEvent(messageType); + message.content = { sender: 'chalkboard-plugin', type: 'resetSlide', timestamp: Date.now() - slideStart }; + document.dispatchEvent( message ); + } + }; + + function resetStorage( force ) { + var ok = force || confirm("Please confirm to delete all chalkboard drawings!"); + if ( ok ) { + stopPlayback(); + slideStart = Date.now(); + clearCanvas( 0 ); + clearCanvas( 1 ); + if ( mode == 1 ) { + event = null; + closeChalkboard(); + } + + storage = [ + { width: Reveal.getConfig().width, height: Reveal.getConfig().height, data: []}, + { width: Reveal.getConfig().width, height: Reveal.getConfig().height, data: []} + ]; +/* + storage = [ + { width: drawingCanvas[0].width - 2 * drawingCanvas[0].xOffset, height: drawingCanvas[0].height - 2 * drawingCanvas[0].yOffset, data: []}, + { width: drawingCanvas[1].width, height: drawingCanvas[1].height, data: []} + ]; +*/ + if ( config.storage ) { + sessionStorage.setItem( config.storage, null ) + } + // broadcast + var message = new CustomEvent(messageType); + message.content = { sender: 'chalkboard-plugin', type: 'init', timestamp: Date.now() - slideStart, storage: storage, mode: mode }; + document.dispatchEvent( message ); + } + }; + + +/* + this.drawWithBoardmarker = drawWithBoardmarker; + this.drawWithChalk = drawWithChalk; + this.startRecording = startRecording; +*/ + this.toggleNotesCanvas = toggleNotesCanvas; + this.toggleChalkboard = toggleChalkboard; + this.colorIndex = colorIndex; + this.colorNext = colorNext; + this.colorPrev = colorPrev; + this.clear = clear; + this.reset = resetSlide; + this.resetAll = resetStorage; + this.download = downloadData; + this.updateStorage = updateStorage; + this.getData = getData; + this.configure = configure; + + + for (var key in keyBindings) { + if ( keyBindings[key] ) { + Reveal.addKeyBinding( keyBindings[key], RevealChalkboard[key] ); + } + }; + + return this; +}; diff --git a/.obsidian/plugins/obsidian-advanced-slides/plugin/chart/README.md b/.obsidian/plugins/obsidian-advanced-slides/plugin/chart/README.md index df13e26..b9e3972 100644 --- a/.obsidian/plugins/obsidian-advanced-slides/plugin/chart/README.md +++ b/.obsidian/plugins/obsidian-advanced-slides/plugin/chart/README.md @@ -1,129 +1,129 @@ -# Chart - -A plugin for [Reveal.js](https://github.com/hakimel/reveal.js) allowing to easily add charts using [Chart.js](http://www.chartjs.org/). - -[Check out the live demo](https://rajgoel.github.io/reveal.js-demos/chart-demo.html) - -## Installation - -Copy the file `plugin.js` into the plugin folder of your reveal.js presentation, i.e. `plugin/chart`. - -Add the plugin and Chart.js to the dependencies in your presentation, as below. - -```html - - - - -``` - -## Configuration - -The plugin has several parameters that you can set for your presentation by providing an `chart` option in the reveal.js initialization options. Note that all configuration parameters are optional and the defaults of Chart.js will be used for parameters that are not specified. - - -```javascript -Reveal.initialize({ - // ... - chart: { - defaults: { - color: 'lightgray', // color of labels - scale: { - beginAtZero: true, - ticks: { stepSize: 1 }, - grid: { color: "lightgray" } , // color of grid lines - }, - }, - line: { borderColor: [ "rgba(20,220,220,.8)" , "rgba(220,120,120,.8)", "rgba(20,120,220,.8)" ], "borderDash": [ [5,10], [0,0] ] }, - bar: { backgroundColor: [ "rgba(20,220,220,.8)" , "rgba(220,120,120,.8)", "rgba(20,120,220,.8)" ]}, - pie: { backgroundColor: [ ["rgba(0,0,0,.8)" , "rgba(220,20,20,.8)", "rgba(20,220,20,.8)", "rgba(220,220,20,.8)", "rgba(20,20,220,.8)"] ]}, - }, - // ... -}); -``` -The `defaults` parameter will overwrite `Chart.defaults`. Furthermore, for any chart type, e.g. line, bar, etc., the parameters for the individual datasets can be specified. Where Chart.js allows to specify a single parameter for a particular dataset, the plugin allows to specify an array of values for this parameter, which will automatically be assigned to the different datasets. Note that if there are more datasets than elements in the array, the plugin will start again with the first value in the array. - - - -## Usage - -A chart can be included in a slide by adding a `canvas` element with the `data-chart` attribute set to the desired chart type. - -The chart can be configured within the canvas body by a JSON string embedded into an HTML comment. - -```html - - - -``` -It is possible to provide the chart data by comma separated values and use the JSON string within the HTML comment to configure the chart layout. - -```html - -My first dataset, 65, 59, 80, 81, 56, 55, 40 - -My second dataset, 28, 48, 40, 19, 86, 27, 90 - - -``` - -The layout configuration provided in `chart` parameter (see Configuration) will be used by default and only those parameters that are specified in a JSON string are used to overwrite the default values. If no JSON string is provided to configure the chart layout the default configuration is used. Note, that if no labels for the data points are provided by a JSON string, the plugin expects that the first row provides table headers. - -```html - -Month, January, February, March, April, May, June, July -My first dataset, 65, 59, 80, 81, 56, 55, 40 -My second dataset, 28, 48, 40, 19, 86, 27, 90 - -``` - -The chart data can also be provided in an external CSV file. To include external data, the filename must be specified using the `data-chart-src` attribute of the `canvas` element. The CSV file is expected to only contain data values, whereas options for drawing the chart can be given as described above. - -```html - - - -``` - - -## License - -MIT licensed - -Copyright (C) 2021 Asvin Goel +# Chart + +A plugin for [Reveal.js](https://github.com/hakimel/reveal.js) allowing to easily add charts using [Chart.js](http://www.chartjs.org/). + +[Check out the live demo](https://rajgoel.github.io/reveal.js-demos/chart-demo.html) + +## Installation + +Copy the file `plugin.js` into the plugin folder of your reveal.js presentation, i.e. `plugin/chart`. + +Add the plugin and Chart.js to the dependencies in your presentation, as below. + +```html + + + + +``` + +## Configuration + +The plugin has several parameters that you can set for your presentation by providing an `chart` option in the reveal.js initialization options. Note that all configuration parameters are optional and the defaults of Chart.js will be used for parameters that are not specified. + + +```javascript +Reveal.initialize({ + // ... + chart: { + defaults: { + color: 'lightgray', // color of labels + scale: { + beginAtZero: true, + ticks: { stepSize: 1 }, + grid: { color: "lightgray" } , // color of grid lines + }, + }, + line: { borderColor: [ "rgba(20,220,220,.8)" , "rgba(220,120,120,.8)", "rgba(20,120,220,.8)" ], "borderDash": [ [5,10], [0,0] ] }, + bar: { backgroundColor: [ "rgba(20,220,220,.8)" , "rgba(220,120,120,.8)", "rgba(20,120,220,.8)" ]}, + pie: { backgroundColor: [ ["rgba(0,0,0,.8)" , "rgba(220,20,20,.8)", "rgba(20,220,20,.8)", "rgba(220,220,20,.8)", "rgba(20,20,220,.8)"] ]}, + }, + // ... +}); +``` +The `defaults` parameter will overwrite `Chart.defaults`. Furthermore, for any chart type, e.g. line, bar, etc., the parameters for the individual datasets can be specified. Where Chart.js allows to specify a single parameter for a particular dataset, the plugin allows to specify an array of values for this parameter, which will automatically be assigned to the different datasets. Note that if there are more datasets than elements in the array, the plugin will start again with the first value in the array. + + + +## Usage + +A chart can be included in a slide by adding a `canvas` element with the `data-chart` attribute set to the desired chart type. + +The chart can be configured within the canvas body by a JSON string embedded into an HTML comment. + +```html + + + +``` +It is possible to provide the chart data by comma separated values and use the JSON string within the HTML comment to configure the chart layout. + +```html + +My first dataset, 65, 59, 80, 81, 56, 55, 40 + +My second dataset, 28, 48, 40, 19, 86, 27, 90 + + +``` + +The layout configuration provided in `chart` parameter (see Configuration) will be used by default and only those parameters that are specified in a JSON string are used to overwrite the default values. If no JSON string is provided to configure the chart layout the default configuration is used. Note, that if no labels for the data points are provided by a JSON string, the plugin expects that the first row provides table headers. + +```html + +Month, January, February, March, April, May, June, July +My first dataset, 65, 59, 80, 81, 56, 55, 40 +My second dataset, 28, 48, 40, 19, 86, 27, 90 + +``` + +The chart data can also be provided in an external CSV file. To include external data, the filename must be specified using the `data-chart-src` attribute of the `canvas` element. The CSV file is expected to only contain data values, whereas options for drawing the chart can be given as described above. + +```html + + + +``` + + +## License + +MIT licensed + +Copyright (C) 2021 Asvin Goel diff --git a/.obsidian/plugins/obsidian-advanced-slides/plugin/customcontrols/README.md b/.obsidian/plugins/obsidian-advanced-slides/plugin/customcontrols/README.md index 1486d8d..8b9b203 100644 --- a/.obsidian/plugins/obsidian-advanced-slides/plugin/customcontrols/README.md +++ b/.obsidian/plugins/obsidian-advanced-slides/plugin/customcontrols/README.md @@ -1,72 +1,72 @@ -# Custom controls - -This plugin allows to add responsive custom controls to reveal.js which allow arbitrary positioning, layout, and behaviour of the controls. - -[Check out the live demo](https://rajgoel.github.io/reveal.js-demos/customcontrols-demo.html) - - -## Installation - -Copy the files `plugin.js` and `style.css` into the plugin folder of your reveal.js presentation, i.e. ```plugin/customcontrols``` and load the plugin as shown below. - -```html - - - - -``` - -Note, without configuration you need to add - -```javascript - -``` - -between `````` and `````` of your HTML file because the defaults use [Font Awesome](http://fontawesome.io/). - - - -## Configuration - -The plugin can be configured by adding custom controls and changing the layout of the slide number, e.g., by: - - -```javascript -Reveal.initialize({ - // ... - customcontrols: { - controls: [ - { - id: 'toggle-overview', - title: 'Toggle overview (O)', - icon: '', - action: 'Reveal.toggleOverview();' - }, - { icon: '', - title: 'Toggle chalkboard (B)', - action: 'RevealChalkboard.toggleChalkboard();' - }, - { icon: '', - title: 'Toggle notes canvas (C)', - action: 'RevealChalkboard.toggleNotesCanvas();' - } - ] - }, - // ... - -}); -``` - -The `id` and `title` are optional. The configuration should be self explaining and any number of controls can be added. The style file can be altered to control the layout and responsiveness of the custom controls. - -## License - -MIT licensed - -Copyright (C) 2020 Asvin Goel +# Custom controls + +This plugin allows to add responsive custom controls to reveal.js which allow arbitrary positioning, layout, and behaviour of the controls. + +[Check out the live demo](https://rajgoel.github.io/reveal.js-demos/customcontrols-demo.html) + + +## Installation + +Copy the files `plugin.js` and `style.css` into the plugin folder of your reveal.js presentation, i.e. ```plugin/customcontrols``` and load the plugin as shown below. + +```html + + + + +``` + +Note, without configuration you need to add + +```javascript + +``` + +between `````` and `````` of your HTML file because the defaults use [Font Awesome](http://fontawesome.io/). + + + +## Configuration + +The plugin can be configured by adding custom controls and changing the layout of the slide number, e.g., by: + + +```javascript +Reveal.initialize({ + // ... + customcontrols: { + controls: [ + { + id: 'toggle-overview', + title: 'Toggle overview (O)', + icon: '', + action: 'Reveal.toggleOverview();' + }, + { icon: '', + title: 'Toggle chalkboard (B)', + action: 'RevealChalkboard.toggleChalkboard();' + }, + { icon: '', + title: 'Toggle notes canvas (C)', + action: 'RevealChalkboard.toggleNotesCanvas();' + } + ] + }, + // ... + +}); +``` + +The `id` and `title` are optional. The configuration should be self explaining and any number of controls can be added. The style file can be altered to control the layout and responsiveness of the custom controls. + +## License + +MIT licensed + +Copyright (C) 2020 Asvin Goel diff --git a/.obsidian/plugins/obsidian-columns/styles.css b/.obsidian/plugins/obsidian-columns/styles.css index 07c29f8..e17b29d 100644 --- a/.obsidian/plugins/obsidian-columns/styles.css +++ b/.obsidian/plugins/obsidian-columns/styles.css @@ -1,127 +1,127 @@ -:root { - --obsidian-columns-gap: 20px; - --obsidian-columns-padding: 0 20px; - --obsidian-columns-min-width: 100px; - --obsidian-columns-def-span: 1; -} - -div[data-callout="col"].callout, -div[data-callout^="col-md"].callout { - background-color: rgba(0, 0, 0, 0); - padding: 0 0 0 0; - border-style: none; -} - -.columnParent, div[data-callout="col"].callout > div.callout-content { - display: flex !important; - padding: var(--obsidian-columns-padding); - flex-wrap: wrap; - gap: var(--obsidian-columns-gap); - white-space: normal; -} - -div[data-callout="col"].callout > div.callout-title, div[data-callout^="col-md"].callout > div.callout-title { - display: none; -} - -.cm-preview-code-block .admonition-content .columnParent p { - white-space: pre-wrap; -} - -.columnChild, div[data-callout="col"].callout > div.callout-content > * { - flex-grow: var(--obsidian-columns-def-span); - flex-basis: calc(var(--obsidian-columns-min-width) * var(--obsidian-columns-def-span)); - width: calc(var(--obsidian-columns-min-width) * var(--obsidian-columns-def-span)); -} - -div[data-callout="col"].callout > div.callout-content > div[data-callout^="col-md" ].callout { - flex-grow: var(--obsidian-columns-custom-span); - flex-basis: calc(var(--obsidian-columns-min-width) * var(--obsidian-columns-custom-span)); - width: calc(var(--obsidian-columns-min-width) * var(--obsidian-columns-custom-span)); - margin: 0; - - background-color: rgba(0, 0, 0, 0); - padding: 0 0 0 0; - border-style: none; -} - -div[data-callout="col"].callout > div.callout-content > div[data-callout$="-0.5" ].callout { - --obsidian-columns-custom-span: 0.5 -} - -div[data-callout="col"].callout > div.callout-content > div[data-callout$="-1" ].callout, -div[data-callout="col"].callout > div.callout-content > div[data-callout="col-md" ].callout { - --obsidian-columns-custom-span: 1 -} - -div[data-callout="col"].callout > div.callout-content > div[data-callout$="-1.5" ].callout { - --obsidian-columns-custom-span: 1.5 -} - -div[data-callout="col"].callout > div.callout-content > div[data-callout$="-2" ].callout { - --obsidian-columns-custom-span: 2 -} - -div[data-callout="col"].callout > div.callout-content > div[data-callout$="-2.5" ].callout { - --obsidian-columns-custom-span: 2.5 -} - -div[data-callout="col"].callout > div.callout-content > div[data-callout$="-3" ].callout { - --obsidian-columns-custom-span: 3 -} - -div[data-callout="col"].callout > div.callout-content > div[data-callout$="-3.5" ].callout { - --obsidian-columns-custom-span: 3.5 -} - -div[data-callout="col"].callout > div.callout-content > div[data-callout$="-4" ].callout { - --obsidian-columns-custom-span: 4 -} - -div[data-callout="col"].callout > div.callout-content > div[data-callout$="-4.5" ].callout { - --obsidian-columns-custom-span: 4.5 -} - -div[data-callout="col"].callout > div.callout-content > div[data-callout$="-5" ].callout { - --obsidian-columns-custom-span: 5 -} - -div[data-callout="col"].callout > div.callout-content > div[data-callout$="-5.5" ].callout { - --obsidian-columns-custom-span: 5.5 -} - -div[data-callout="col"].callout > div.callout-content > div[data-callout$="-6" ].callout { - --obsidian-columns-custom-span: 6 -} - -div[data-callout="col"].callout > div.callout-content > div[data-callout$="-6.5" ].callout { - --obsidian-columns-custom-span: 6.5 -} - -div[data-callout="col"].callout > div.callout-content > div[data-callout$="-7" ].callout { - --obsidian-columns-custom-span: 7 -} - -div[data-callout="col"].callout > div.callout-content > div[data-callout$="-7.5" ].callout { - --obsidian-columns-custom-span: 7.5 -} - -div[data-callout="col"].callout > div.callout-content > div[data-callout$="-8" ].callout { - --obsidian-columns-custom-span: 8 -} - -div[data-callout="col"].callout > div.callout-content > div[data-callout$="-8.5" ].callout { - --obsidian-columns-custom-span: 8.5 -} - -div[data-callout="col"].callout > div.callout-content > div[data-callout$="-9" ].callout { - --obsidian-columns-custom-span: 9 -} - -div[data-callout="col"].callout > div.callout-content > div[data-callout$="-9.5" ].callout { - --obsidian-columns-custom-span: 9.5 -} - -div[data-callout="col"].callout > div.callout-content > div[data-callout$="-10" ].callout { - --obsidian-columns-custom-span: 10 +:root { + --obsidian-columns-gap: 20px; + --obsidian-columns-padding: 0 20px; + --obsidian-columns-min-width: 100px; + --obsidian-columns-def-span: 1; +} + +div[data-callout="col"].callout, +div[data-callout^="col-md"].callout { + background-color: rgba(0, 0, 0, 0); + padding: 0 0 0 0; + border-style: none; +} + +.columnParent, div[data-callout="col"].callout > div.callout-content { + display: flex !important; + padding: var(--obsidian-columns-padding); + flex-wrap: wrap; + gap: var(--obsidian-columns-gap); + white-space: normal; +} + +div[data-callout="col"].callout > div.callout-title, div[data-callout^="col-md"].callout > div.callout-title { + display: none; +} + +.cm-preview-code-block .admonition-content .columnParent p { + white-space: pre-wrap; +} + +.columnChild, div[data-callout="col"].callout > div.callout-content > * { + flex-grow: var(--obsidian-columns-def-span); + flex-basis: calc(var(--obsidian-columns-min-width) * var(--obsidian-columns-def-span)); + width: calc(var(--obsidian-columns-min-width) * var(--obsidian-columns-def-span)); +} + +div[data-callout="col"].callout > div.callout-content > div[data-callout^="col-md" ].callout { + flex-grow: var(--obsidian-columns-custom-span); + flex-basis: calc(var(--obsidian-columns-min-width) * var(--obsidian-columns-custom-span)); + width: calc(var(--obsidian-columns-min-width) * var(--obsidian-columns-custom-span)); + margin: 0; + + background-color: rgba(0, 0, 0, 0); + padding: 0 0 0 0; + border-style: none; +} + +div[data-callout="col"].callout > div.callout-content > div[data-callout$="-0.5" ].callout { + --obsidian-columns-custom-span: 0.5 +} + +div[data-callout="col"].callout > div.callout-content > div[data-callout$="-1" ].callout, +div[data-callout="col"].callout > div.callout-content > div[data-callout="col-md" ].callout { + --obsidian-columns-custom-span: 1 +} + +div[data-callout="col"].callout > div.callout-content > div[data-callout$="-1.5" ].callout { + --obsidian-columns-custom-span: 1.5 +} + +div[data-callout="col"].callout > div.callout-content > div[data-callout$="-2" ].callout { + --obsidian-columns-custom-span: 2 +} + +div[data-callout="col"].callout > div.callout-content > div[data-callout$="-2.5" ].callout { + --obsidian-columns-custom-span: 2.5 +} + +div[data-callout="col"].callout > div.callout-content > div[data-callout$="-3" ].callout { + --obsidian-columns-custom-span: 3 +} + +div[data-callout="col"].callout > div.callout-content > div[data-callout$="-3.5" ].callout { + --obsidian-columns-custom-span: 3.5 +} + +div[data-callout="col"].callout > div.callout-content > div[data-callout$="-4" ].callout { + --obsidian-columns-custom-span: 4 +} + +div[data-callout="col"].callout > div.callout-content > div[data-callout$="-4.5" ].callout { + --obsidian-columns-custom-span: 4.5 +} + +div[data-callout="col"].callout > div.callout-content > div[data-callout$="-5" ].callout { + --obsidian-columns-custom-span: 5 +} + +div[data-callout="col"].callout > div.callout-content > div[data-callout$="-5.5" ].callout { + --obsidian-columns-custom-span: 5.5 +} + +div[data-callout="col"].callout > div.callout-content > div[data-callout$="-6" ].callout { + --obsidian-columns-custom-span: 6 +} + +div[data-callout="col"].callout > div.callout-content > div[data-callout$="-6.5" ].callout { + --obsidian-columns-custom-span: 6.5 +} + +div[data-callout="col"].callout > div.callout-content > div[data-callout$="-7" ].callout { + --obsidian-columns-custom-span: 7 +} + +div[data-callout="col"].callout > div.callout-content > div[data-callout$="-7.5" ].callout { + --obsidian-columns-custom-span: 7.5 +} + +div[data-callout="col"].callout > div.callout-content > div[data-callout$="-8" ].callout { + --obsidian-columns-custom-span: 8 +} + +div[data-callout="col"].callout > div.callout-content > div[data-callout$="-8.5" ].callout { + --obsidian-columns-custom-span: 8.5 +} + +div[data-callout="col"].callout > div.callout-content > div[data-callout$="-9" ].callout { + --obsidian-columns-custom-span: 9 +} + +div[data-callout="col"].callout > div.callout-content > div[data-callout$="-9.5" ].callout { + --obsidian-columns-custom-span: 9.5 +} + +div[data-callout="col"].callout > div.callout-content > div[data-callout$="-10" ].callout { + --obsidian-columns-custom-span: 10 } \ No newline at end of file diff --git a/.obsidian/plugins/obsidian-csv-table/main.js b/.obsidian/plugins/obsidian-csv-table/main.js index 518d400..223481e 100644 --- a/.obsidian/plugins/obsidian-csv-table/main.js +++ b/.obsidian/plugins/obsidian-csv-table/main.js @@ -12,41 +12,41 @@ function _interopDefaultLegacy (e) { return e && typeof e === 'object' && 'defau var require$$0__default = /*#__PURE__*/_interopDefaultLegacy(require$$0); -/*! ***************************************************************************** -Copyright (c) Microsoft Corporation. - -Permission to use, copy, modify, and/or distribute this software for any -purpose with or without fee is hereby granted. - -THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH -REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY -AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, -INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM -LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR -OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR -PERFORMANCE OF THIS SOFTWARE. -***************************************************************************** */ - -function __rest(s, e) { - var t = {}; - for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0) - t[p] = s[p]; - if (s != null && typeof Object.getOwnPropertySymbols === "function") - for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) { - if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i])) - t[p[i]] = s[p[i]]; - } - return t; -} - -function __awaiter(thisArg, _arguments, P, generator) { - function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); } - return new (P || (P = Promise))(function (resolve, reject) { - function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } } - function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } } - function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); } - step((generator = generator.apply(thisArg, _arguments || [])).next()); - }); +/*! ***************************************************************************** +Copyright (c) Microsoft Corporation. + +Permission to use, copy, modify, and/or distribute this software for any +purpose with or without fee is hereby granted. + +THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH +REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY +AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, +INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM +LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR +OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR +PERFORMANCE OF THIS SOFTWARE. +***************************************************************************** */ + +function __rest(s, e) { + var t = {}; + for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0) + t[p] = s[p]; + if (s != null && typeof Object.getOwnPropertySymbols === "function") + for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) { + if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i])) + t[p[i]] = s[p[i]]; + } + return t; +} + +function __awaiter(thisArg, _arguments, P, generator) { + function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); } + return new (P || (P = Promise))(function (resolve, reject) { + function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } } + function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } } + function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); } + step((generator = generator.apply(thisArg, _arguments || [])).next()); + }); } class ResizeableBuffer{ diff --git a/.obsidian/plugins/obsidian-csv-table/manifest.json b/.obsidian/plugins/obsidian-csv-table/manifest.json index 8a9a3d2..3702cd4 100644 --- a/.obsidian/plugins/obsidian-csv-table/manifest.json +++ b/.obsidian/plugins/obsidian-csv-table/manifest.json @@ -1,10 +1,10 @@ -{ - "id": "obsidian-csv-table", - "name": "CSV Table", - "version": "1.2.0", - "minAppVersion": "0.11.10", - "description": "Render CSV data as a table within your notes.", - "author": "Adam Coddington ", - "authorUrl": "https://coddingtonbear.net/", - "isDesktopOnly": false -} +{ + "id": "obsidian-csv-table", + "name": "CSV Table", + "version": "1.2.0", + "minAppVersion": "0.11.10", + "description": "Render CSV data as a table within your notes.", + "author": "Adam Coddington ", + "authorUrl": "https://coddingtonbear.net/", + "isDesktopOnly": false +} diff --git a/.obsidian/plugins/obsidian-excalidraw-plugin/manifest.json b/.obsidian/plugins/obsidian-excalidraw-plugin/manifest.json index e0ab0a6..9ffe455 100644 --- a/.obsidian/plugins/obsidian-excalidraw-plugin/manifest.json +++ b/.obsidian/plugins/obsidian-excalidraw-plugin/manifest.json @@ -1,12 +1,12 @@ -{ - "id": "obsidian-excalidraw-plugin", - "name": "Excalidraw", - "version": "2.17.1", - "minAppVersion": "1.5.7", - "description": "An Obsidian plugin to edit and view Excalidraw drawings", - "author": "Zsolt Viczian", - "authorUrl": "https://excalidraw-obsidian.online", - "fundingUrl": "https://ko-fi.com/zsolt", - "helpUrl": "https://github.com/zsviczian/obsidian-excalidraw-plugin#readme", - "isDesktopOnly": false +{ + "id": "obsidian-excalidraw-plugin", + "name": "Excalidraw", + "version": "2.17.1", + "minAppVersion": "1.5.7", + "description": "An Obsidian plugin to edit and view Excalidraw drawings", + "author": "Zsolt Viczian", + "authorUrl": "https://excalidraw-obsidian.online", + "fundingUrl": "https://ko-fi.com/zsolt", + "helpUrl": "https://github.com/zsviczian/obsidian-excalidraw-plugin#readme", + "isDesktopOnly": false } \ No newline at end of file diff --git a/.obsidian/plugins/obsidian-icon-folder/main.js b/.obsidian/plugins/obsidian-icon-folder/main.js index 271b2ff..f354c58 100644 --- a/.obsidian/plugins/obsidian-icon-folder/main.js +++ b/.obsidian/plugins/obsidian-icon-folder/main.js @@ -10,36 +10,36 @@ var view = require('@codemirror/view'); var state = require('@codemirror/state'); var language = require('@codemirror/language'); -/****************************************************************************** -Copyright (c) Microsoft Corporation. - -Permission to use, copy, modify, and/or distribute this software for any -purpose with or without fee is hereby granted. - -THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH -REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY -AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, -INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM -LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR -OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR -PERFORMANCE OF THIS SOFTWARE. -***************************************************************************** */ -/* global Reflect, Promise, SuppressedError, Symbol, Iterator */ - - -function __awaiter(thisArg, _arguments, P, generator) { - function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); } - return new (P || (P = Promise))(function (resolve, reject) { - function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } } - function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } } - function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); } - step((generator = generator.apply(thisArg, _arguments || [])).next()); - }); -} - -typeof SuppressedError === "function" ? SuppressedError : function (error, suppressed, message) { - var e = new Error(message); - return e.name = "SuppressedError", e.error = error, e.suppressed = suppressed, e; +/****************************************************************************** +Copyright (c) Microsoft Corporation. + +Permission to use, copy, modify, and/or distribute this software for any +purpose with or without fee is hereby granted. + +THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH +REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY +AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, +INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM +LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR +OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR +PERFORMANCE OF THIS SOFTWARE. +***************************************************************************** */ +/* global Reflect, Promise, SuppressedError, Symbol, Iterator */ + + +function __awaiter(thisArg, _arguments, P, generator) { + function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); } + return new (P || (P = Promise))(function (resolve, reject) { + function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } } + function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } } + function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); } + step((generator = generator.apply(thisArg, _arguments || [])).next()); + }); +} + +typeof SuppressedError === "function" ? SuppressedError : function (error, suppressed, message) { + var e = new Error(message); + return e.name = "SuppressedError", e.error = error, e.suppressed = suppressed, e; }; const iconPacks$1 = { diff --git a/.obsidian/plugins/obsidian-leaflet-plugin/styles.css b/.obsidian/plugins/obsidian-leaflet-plugin/styles.css index 9017be1..edccd9a 100644 --- a/.obsidian/plugins/obsidian-leaflet-plugin/styles.css +++ b/.obsidian/plugins/obsidian-leaflet-plugin/styles.css @@ -1,643 +1,643 @@ -/* required styles */ - -.leaflet-pane, -.leaflet-tile, -.leaflet-marker-icon, -.leaflet-marker-shadow, -.leaflet-tile-container, -.leaflet-pane > svg, -.leaflet-pane > canvas, -.leaflet-zoom-box, -.leaflet-image-layer, -.leaflet-layer { - position: absolute; - left: 0; - top: 0; - } -.leaflet-container { - overflow: hidden; - } -.leaflet-tile, -.leaflet-marker-icon, -.leaflet-marker-shadow { - -webkit-user-select: none; - -moz-user-select: none; - user-select: none; - -webkit-user-drag: none; - } -/* Prevents IE11 from highlighting tiles in blue */ -.leaflet-tile::selection { - background: transparent; -} -/* Safari renders non-retina tile on retina better with this, but Chrome is worse */ -.leaflet-safari .leaflet-tile { - image-rendering: -webkit-optimize-contrast; - } -/* hack that prevents hw layers "stretching" when loading new tiles */ -.leaflet-safari .leaflet-tile-container { - width: 1600px; - height: 1600px; - -webkit-transform-origin: 0 0; - } -.leaflet-marker-icon, -.leaflet-marker-shadow { - display: block; - } -/* .leaflet-container svg: reset svg max-width decleration shipped in Joomla! (joomla.org) 3.x */ -/* .leaflet-container img: map is broken in FF if you have max-width: 100% on tiles */ -.leaflet-container .leaflet-overlay-pane svg, -.leaflet-container .leaflet-marker-pane img, -.leaflet-container .leaflet-shadow-pane img, -.leaflet-container .leaflet-tile-pane img, -.leaflet-container img.leaflet-image-layer, -.leaflet-container .leaflet-tile { - max-width: none !important; - max-height: none !important; - } - -.leaflet-container.leaflet-touch-zoom { - -ms-touch-action: pan-x pan-y; - touch-action: pan-x pan-y; - } -.leaflet-container.leaflet-touch-drag { - -ms-touch-action: pinch-zoom; - /* Fallback for FF which doesn't support pinch-zoom */ - touch-action: none; - touch-action: pinch-zoom; -} -.leaflet-container.leaflet-touch-drag.leaflet-touch-zoom { - -ms-touch-action: none; - touch-action: none; -} -.leaflet-container { - -webkit-tap-highlight-color: transparent; -} -.leaflet-container a { - -webkit-tap-highlight-color: rgba(51, 181, 229, 0.4); -} -.leaflet-tile { - filter: inherit; - visibility: hidden; - } -.leaflet-tile-loaded { - visibility: inherit; - } -.leaflet-zoom-box { - width: 0; - height: 0; - -moz-box-sizing: border-box; - box-sizing: border-box; - z-index: 800; - } -/* workaround for https://bugzilla.mozilla.org/show_bug.cgi?id=888319 */ -.leaflet-overlay-pane svg { - -moz-user-select: none; - } - -.leaflet-pane { z-index: 400; } - -.leaflet-tile-pane { z-index: 200; } -.leaflet-overlay-pane { z-index: 400; } -.leaflet-shadow-pane { z-index: 500; } -.leaflet-marker-pane { z-index: 600; } -.leaflet-tooltip-pane { z-index: 650; } -.leaflet-popup-pane { z-index: 700; } - -.leaflet-map-pane canvas { z-index: 100; } -.leaflet-map-pane svg { z-index: 200; } - -.leaflet-vml-shape { - width: 1px; - height: 1px; - } -.lvml { - behavior: url(#default#VML); - display: inline-block; - position: absolute; - } - - -/* control positioning */ - -.leaflet-control { - position: relative; - z-index: 800; - pointer-events: visiblePainted; /* IE 9-10 doesn't have auto */ - pointer-events: auto; - } -.leaflet-top, -.leaflet-bottom { - position: absolute; - z-index: 1000; - pointer-events: none; - } -.leaflet-top { - top: 0; - } -.leaflet-right { - right: 0; - } -.leaflet-bottom { - bottom: 0; - } -.leaflet-left { - left: 0; - } -.leaflet-control { - float: left; - clear: both; - } -.leaflet-right .leaflet-control { - float: right; - } -.leaflet-top .leaflet-control { - margin-top: 10px; - } -.leaflet-bottom .leaflet-control { - margin-bottom: 10px; - } -.leaflet-left .leaflet-control { - margin-left: 10px; - } -.leaflet-right .leaflet-control { - margin-right: 10px; - } - - -/* zoom and fade animations */ - -.leaflet-fade-anim .leaflet-tile { - will-change: opacity; - } -.leaflet-fade-anim .leaflet-popup { - opacity: 0; - -webkit-transition: opacity 0.2s linear; - -moz-transition: opacity 0.2s linear; - transition: opacity 0.2s linear; - } -.leaflet-fade-anim .leaflet-map-pane .leaflet-popup { - opacity: 1; - } -.leaflet-zoom-animated { - -webkit-transform-origin: 0 0; - -ms-transform-origin: 0 0; - transform-origin: 0 0; - } -.leaflet-zoom-anim .leaflet-zoom-animated { - will-change: transform; - } -.leaflet-zoom-anim .leaflet-zoom-animated { - -webkit-transition: -webkit-transform 0.25s cubic-bezier(0,0,0.25,1); - -moz-transition: -moz-transform 0.25s cubic-bezier(0,0,0.25,1); - transition: transform 0.25s cubic-bezier(0,0,0.25,1); - } -.leaflet-zoom-anim .leaflet-tile, -.leaflet-pan-anim .leaflet-tile { - -webkit-transition: none; - -moz-transition: none; - transition: none; - } - -.leaflet-zoom-anim .leaflet-zoom-hide { - visibility: hidden; - } - - -/* cursors */ - -.leaflet-interactive { - cursor: pointer; - } -.leaflet-grab { - cursor: -webkit-grab; - cursor: -moz-grab; - cursor: grab; - } -.leaflet-crosshair, -.leaflet-crosshair .leaflet-interactive { - cursor: crosshair; - } -.leaflet-popup-pane, -.leaflet-control { - cursor: auto; - } -.leaflet-dragging .leaflet-grab, -.leaflet-dragging .leaflet-grab .leaflet-interactive, -.leaflet-dragging .leaflet-marker-draggable { - cursor: move; - cursor: -webkit-grabbing; - cursor: -moz-grabbing; - cursor: grabbing; - } - -/* marker & overlays interactivity */ -.leaflet-marker-icon, -.leaflet-marker-shadow, -.leaflet-image-layer, -.leaflet-pane > svg path, -.leaflet-tile-container { - pointer-events: none; - } - -.leaflet-marker-icon.leaflet-interactive, -.leaflet-image-layer.leaflet-interactive, -.leaflet-pane > svg path.leaflet-interactive, -svg.leaflet-image-layer.leaflet-interactive path { - pointer-events: visiblePainted; /* IE 9-10 doesn't have auto */ - pointer-events: auto; - } - -/* visual tweaks */ - -.leaflet-container { - background: #ddd; - outline: 0; - } -.leaflet-container a { - color: #0078A8; - } -.leaflet-container a.leaflet-active { - outline: 2px solid orange; - } -.leaflet-zoom-box { - border: 2px dotted #38f; - background: rgba(255,255,255,0.5); - } - - -/* general typography */ -.leaflet-container { - font: 12px/1.5 "Helvetica Neue", Arial, Helvetica, sans-serif; - } - - -/* general toolbar styles */ - -.leaflet-bar { - box-shadow: 0 1px 5px rgba(0,0,0,0.65); - border-radius: 4px; - } -.leaflet-bar a, -.leaflet-bar a:hover { - background-color: #fff; - border-bottom: 1px solid #ccc; - width: 26px; - height: 26px; - line-height: 26px; - display: block; - text-align: center; - text-decoration: none; - color: black; - } -.leaflet-bar a, -.leaflet-control-layers-toggle { - background-position: 50% 50%; - background-repeat: no-repeat; - display: block; - } -.leaflet-bar a:hover { - background-color: #f4f4f4; - } -.leaflet-bar a:first-child { - border-top-left-radius: 4px; - border-top-right-radius: 4px; - } -.leaflet-bar a:last-child { - border-bottom-left-radius: 4px; - border-bottom-right-radius: 4px; - border-bottom: none; - } -.leaflet-bar a.leaflet-disabled { - cursor: default; - background-color: #f4f4f4; - color: #bbb; - } - -.leaflet-touch .leaflet-bar a { - width: 30px; - height: 30px; - line-height: 30px; - } -.leaflet-touch .leaflet-bar a:first-child { - border-top-left-radius: 2px; - border-top-right-radius: 2px; - } -.leaflet-touch .leaflet-bar a:last-child { - border-bottom-left-radius: 2px; - border-bottom-right-radius: 2px; - } - -/* zoom control */ - -.leaflet-control-zoom-in, -.leaflet-control-zoom-out { - font: bold 18px 'Lucida Console', Monaco, monospace; - text-indent: 1px; - } - -.leaflet-touch .leaflet-control-zoom-in, .leaflet-touch .leaflet-control-zoom-out { - font-size: 22px; - } - - -/* layers control */ - -.leaflet-control-layers { - box-shadow: 0 1px 5px rgba(0,0,0,0.4); - background: #fff; - border-radius: 5px; - } -.leaflet-control-layers-toggle { - background-image: url(images/layers.png); - width: 36px; - height: 36px; - } -.leaflet-retina .leaflet-control-layers-toggle { - background-image: url(images/layers-2x.png); - background-size: 26px 26px; - } -.leaflet-touch .leaflet-control-layers-toggle { - width: 44px; - height: 44px; - } -.leaflet-control-layers .leaflet-control-layers-list, -.leaflet-control-layers-expanded .leaflet-control-layers-toggle { - display: none; - } -.leaflet-control-layers-expanded .leaflet-control-layers-list { - display: block; - position: relative; - } -.leaflet-control-layers-expanded { - padding: 6px 10px 6px 6px; - color: #333; - background: #fff; - } -.leaflet-control-layers-scrollbar { - overflow-y: scroll; - overflow-x: hidden; - padding-right: 5px; - } -.leaflet-control-layers-selector { - margin-top: 2px; - position: relative; - top: 1px; - } -.leaflet-control-layers label { - display: block; - } -.leaflet-control-layers-separator { - height: 0; - border-top: 1px solid #ddd; - margin: 5px -10px 5px -6px; - } - -/* Default icon URLs */ -.leaflet-default-icon-path { - background-image: url(images/marker-icon.png); - } - - -/* attribution and scale controls */ - -.leaflet-container .leaflet-control-attribution { - background: #fff; - background: rgba(255, 255, 255, 0.7); - margin: 0; - } -.leaflet-control-attribution, -.leaflet-control-scale-line { - padding: 0 5px; - color: #333; - } -.leaflet-control-attribution a { - text-decoration: none; - } -.leaflet-control-attribution a:hover { - text-decoration: underline; - } -.leaflet-container .leaflet-control-attribution, -.leaflet-container .leaflet-control-scale { - font-size: 11px; - } -.leaflet-left .leaflet-control-scale { - margin-left: 5px; - } -.leaflet-bottom .leaflet-control-scale { - margin-bottom: 5px; - } -.leaflet-control-scale-line { - border: 2px solid #777; - border-top: none; - line-height: 1.1; - padding: 2px 5px 1px; - font-size: 11px; - white-space: nowrap; - overflow: hidden; - -moz-box-sizing: border-box; - box-sizing: border-box; - - background: #fff; - background: rgba(255, 255, 255, 0.5); - } -.leaflet-control-scale-line:not(:first-child) { - border-top: 2px solid #777; - border-bottom: none; - margin-top: -2px; - } -.leaflet-control-scale-line:not(:first-child):not(:last-child) { - border-bottom: 2px solid #777; - } - -.leaflet-touch .leaflet-control-attribution, -.leaflet-touch .leaflet-control-layers, -.leaflet-touch .leaflet-bar { - box-shadow: none; - } -.leaflet-touch .leaflet-control-layers, -.leaflet-touch .leaflet-bar { - border: 2px solid rgba(0,0,0,0.2); - background-clip: padding-box; - } - - -/* popup */ - -.leaflet-popup { - position: absolute; - text-align: center; - margin-bottom: 20px; - } -.leaflet-popup-content-wrapper { - padding: 1px; - text-align: left; - border-radius: 12px; - } -.leaflet-popup-content { - margin: 13px 19px; - line-height: 1.4; - } -.leaflet-popup-content p { - margin: 18px 0; - } -.leaflet-popup-tip-container { - width: 40px; - height: 20px; - position: absolute; - left: 50%; - margin-left: -20px; - overflow: hidden; - pointer-events: none; - } -.leaflet-popup-tip { - width: 17px; - height: 17px; - padding: 1px; - - margin: -10px auto 0; - - -webkit-transform: rotate(45deg); - -moz-transform: rotate(45deg); - -ms-transform: rotate(45deg); - transform: rotate(45deg); - } -.leaflet-popup-content-wrapper, -.leaflet-popup-tip { - background: white; - color: #333; - box-shadow: 0 3px 14px rgba(0,0,0,0.4); - } -.leaflet-container a.leaflet-popup-close-button { - position: absolute; - top: 0; - right: 0; - padding: 4px 4px 0 0; - border: none; - text-align: center; - width: 18px; - height: 14px; - font: 16px/14px Tahoma, Verdana, sans-serif; - color: #c3c3c3; - text-decoration: none; - font-weight: bold; - background: transparent; - } -.leaflet-container a.leaflet-popup-close-button:hover { - color: #999; - } -.leaflet-popup-scrolled { - overflow: auto; - border-bottom: 1px solid #ddd; - border-top: 1px solid #ddd; - } - -.leaflet-oldie .leaflet-popup-content-wrapper { - -ms-zoom: 1; - } -.leaflet-oldie .leaflet-popup-tip { - width: 24px; - margin: 0 auto; - - -ms-filter: "progid:DXImageTransform.Microsoft.Matrix(M11=0.70710678, M12=0.70710678, M21=-0.70710678, M22=0.70710678)"; - filter: progid:DXImageTransform.Microsoft.Matrix(M11=0.70710678, M12=0.70710678, M21=-0.70710678, M22=0.70710678); - } -.leaflet-oldie .leaflet-popup-tip-container { - margin-top: -1px; - } - -.leaflet-oldie .leaflet-control-zoom, -.leaflet-oldie .leaflet-control-layers, -.leaflet-oldie .leaflet-popup-content-wrapper, -.leaflet-oldie .leaflet-popup-tip { - border: 1px solid #999; - } - - -/* div icon */ - -.leaflet-div-icon { - background: #fff; - border: 1px solid #666; - } - - -/* Tooltip */ -/* Base styles for the element that has a tooltip */ -.leaflet-tooltip { - position: absolute; - padding: 6px; - background-color: #fff; - border: 1px solid #fff; - border-radius: 3px; - color: #222; - white-space: nowrap; - -webkit-user-select: none; - -moz-user-select: none; - -ms-user-select: none; - user-select: none; - pointer-events: none; - box-shadow: 0 1px 3px rgba(0,0,0,0.4); - } -.leaflet-tooltip.leaflet-clickable { - cursor: pointer; - pointer-events: auto; - } -.leaflet-tooltip-top:before, -.leaflet-tooltip-bottom:before, -.leaflet-tooltip-left:before, -.leaflet-tooltip-right:before { - position: absolute; - pointer-events: none; - border: 6px solid transparent; - background: transparent; - content: ""; - } - -/* Directions */ - -.leaflet-tooltip-bottom { - margin-top: 6px; -} -.leaflet-tooltip-top { - margin-top: -6px; -} -.leaflet-tooltip-bottom:before, -.leaflet-tooltip-top:before { - left: 50%; - margin-left: -6px; - } -.leaflet-tooltip-top:before { - bottom: 0; - margin-bottom: -12px; - border-top-color: #fff; - } -.leaflet-tooltip-bottom:before { - top: 0; - margin-top: -12px; - margin-left: -6px; - border-bottom-color: #fff; - } -.leaflet-tooltip-left { - margin-left: -6px; -} -.leaflet-tooltip-right { - margin-left: 6px; -} -.leaflet-tooltip-left:before, -.leaflet-tooltip-right:before { - top: 50%; - margin-top: -6px; - } -.leaflet-tooltip-left:before { - right: 0; - margin-right: -12px; - border-left-color: #fff; - } -.leaflet-tooltip-right:before { - left: 0; - margin-left: -12px; - border-right-color: #fff; - } +/* required styles */ + +.leaflet-pane, +.leaflet-tile, +.leaflet-marker-icon, +.leaflet-marker-shadow, +.leaflet-tile-container, +.leaflet-pane > svg, +.leaflet-pane > canvas, +.leaflet-zoom-box, +.leaflet-image-layer, +.leaflet-layer { + position: absolute; + left: 0; + top: 0; + } +.leaflet-container { + overflow: hidden; + } +.leaflet-tile, +.leaflet-marker-icon, +.leaflet-marker-shadow { + -webkit-user-select: none; + -moz-user-select: none; + user-select: none; + -webkit-user-drag: none; + } +/* Prevents IE11 from highlighting tiles in blue */ +.leaflet-tile::selection { + background: transparent; +} +/* Safari renders non-retina tile on retina better with this, but Chrome is worse */ +.leaflet-safari .leaflet-tile { + image-rendering: -webkit-optimize-contrast; + } +/* hack that prevents hw layers "stretching" when loading new tiles */ +.leaflet-safari .leaflet-tile-container { + width: 1600px; + height: 1600px; + -webkit-transform-origin: 0 0; + } +.leaflet-marker-icon, +.leaflet-marker-shadow { + display: block; + } +/* .leaflet-container svg: reset svg max-width decleration shipped in Joomla! (joomla.org) 3.x */ +/* .leaflet-container img: map is broken in FF if you have max-width: 100% on tiles */ +.leaflet-container .leaflet-overlay-pane svg, +.leaflet-container .leaflet-marker-pane img, +.leaflet-container .leaflet-shadow-pane img, +.leaflet-container .leaflet-tile-pane img, +.leaflet-container img.leaflet-image-layer, +.leaflet-container .leaflet-tile { + max-width: none !important; + max-height: none !important; + } + +.leaflet-container.leaflet-touch-zoom { + -ms-touch-action: pan-x pan-y; + touch-action: pan-x pan-y; + } +.leaflet-container.leaflet-touch-drag { + -ms-touch-action: pinch-zoom; + /* Fallback for FF which doesn't support pinch-zoom */ + touch-action: none; + touch-action: pinch-zoom; +} +.leaflet-container.leaflet-touch-drag.leaflet-touch-zoom { + -ms-touch-action: none; + touch-action: none; +} +.leaflet-container { + -webkit-tap-highlight-color: transparent; +} +.leaflet-container a { + -webkit-tap-highlight-color: rgba(51, 181, 229, 0.4); +} +.leaflet-tile { + filter: inherit; + visibility: hidden; + } +.leaflet-tile-loaded { + visibility: inherit; + } +.leaflet-zoom-box { + width: 0; + height: 0; + -moz-box-sizing: border-box; + box-sizing: border-box; + z-index: 800; + } +/* workaround for https://bugzilla.mozilla.org/show_bug.cgi?id=888319 */ +.leaflet-overlay-pane svg { + -moz-user-select: none; + } + +.leaflet-pane { z-index: 400; } + +.leaflet-tile-pane { z-index: 200; } +.leaflet-overlay-pane { z-index: 400; } +.leaflet-shadow-pane { z-index: 500; } +.leaflet-marker-pane { z-index: 600; } +.leaflet-tooltip-pane { z-index: 650; } +.leaflet-popup-pane { z-index: 700; } + +.leaflet-map-pane canvas { z-index: 100; } +.leaflet-map-pane svg { z-index: 200; } + +.leaflet-vml-shape { + width: 1px; + height: 1px; + } +.lvml { + behavior: url(#default#VML); + display: inline-block; + position: absolute; + } + + +/* control positioning */ + +.leaflet-control { + position: relative; + z-index: 800; + pointer-events: visiblePainted; /* IE 9-10 doesn't have auto */ + pointer-events: auto; + } +.leaflet-top, +.leaflet-bottom { + position: absolute; + z-index: 1000; + pointer-events: none; + } +.leaflet-top { + top: 0; + } +.leaflet-right { + right: 0; + } +.leaflet-bottom { + bottom: 0; + } +.leaflet-left { + left: 0; + } +.leaflet-control { + float: left; + clear: both; + } +.leaflet-right .leaflet-control { + float: right; + } +.leaflet-top .leaflet-control { + margin-top: 10px; + } +.leaflet-bottom .leaflet-control { + margin-bottom: 10px; + } +.leaflet-left .leaflet-control { + margin-left: 10px; + } +.leaflet-right .leaflet-control { + margin-right: 10px; + } + + +/* zoom and fade animations */ + +.leaflet-fade-anim .leaflet-tile { + will-change: opacity; + } +.leaflet-fade-anim .leaflet-popup { + opacity: 0; + -webkit-transition: opacity 0.2s linear; + -moz-transition: opacity 0.2s linear; + transition: opacity 0.2s linear; + } +.leaflet-fade-anim .leaflet-map-pane .leaflet-popup { + opacity: 1; + } +.leaflet-zoom-animated { + -webkit-transform-origin: 0 0; + -ms-transform-origin: 0 0; + transform-origin: 0 0; + } +.leaflet-zoom-anim .leaflet-zoom-animated { + will-change: transform; + } +.leaflet-zoom-anim .leaflet-zoom-animated { + -webkit-transition: -webkit-transform 0.25s cubic-bezier(0,0,0.25,1); + -moz-transition: -moz-transform 0.25s cubic-bezier(0,0,0.25,1); + transition: transform 0.25s cubic-bezier(0,0,0.25,1); + } +.leaflet-zoom-anim .leaflet-tile, +.leaflet-pan-anim .leaflet-tile { + -webkit-transition: none; + -moz-transition: none; + transition: none; + } + +.leaflet-zoom-anim .leaflet-zoom-hide { + visibility: hidden; + } + + +/* cursors */ + +.leaflet-interactive { + cursor: pointer; + } +.leaflet-grab { + cursor: -webkit-grab; + cursor: -moz-grab; + cursor: grab; + } +.leaflet-crosshair, +.leaflet-crosshair .leaflet-interactive { + cursor: crosshair; + } +.leaflet-popup-pane, +.leaflet-control { + cursor: auto; + } +.leaflet-dragging .leaflet-grab, +.leaflet-dragging .leaflet-grab .leaflet-interactive, +.leaflet-dragging .leaflet-marker-draggable { + cursor: move; + cursor: -webkit-grabbing; + cursor: -moz-grabbing; + cursor: grabbing; + } + +/* marker & overlays interactivity */ +.leaflet-marker-icon, +.leaflet-marker-shadow, +.leaflet-image-layer, +.leaflet-pane > svg path, +.leaflet-tile-container { + pointer-events: none; + } + +.leaflet-marker-icon.leaflet-interactive, +.leaflet-image-layer.leaflet-interactive, +.leaflet-pane > svg path.leaflet-interactive, +svg.leaflet-image-layer.leaflet-interactive path { + pointer-events: visiblePainted; /* IE 9-10 doesn't have auto */ + pointer-events: auto; + } + +/* visual tweaks */ + +.leaflet-container { + background: #ddd; + outline: 0; + } +.leaflet-container a { + color: #0078A8; + } +.leaflet-container a.leaflet-active { + outline: 2px solid orange; + } +.leaflet-zoom-box { + border: 2px dotted #38f; + background: rgba(255,255,255,0.5); + } + + +/* general typography */ +.leaflet-container { + font: 12px/1.5 "Helvetica Neue", Arial, Helvetica, sans-serif; + } + + +/* general toolbar styles */ + +.leaflet-bar { + box-shadow: 0 1px 5px rgba(0,0,0,0.65); + border-radius: 4px; + } +.leaflet-bar a, +.leaflet-bar a:hover { + background-color: #fff; + border-bottom: 1px solid #ccc; + width: 26px; + height: 26px; + line-height: 26px; + display: block; + text-align: center; + text-decoration: none; + color: black; + } +.leaflet-bar a, +.leaflet-control-layers-toggle { + background-position: 50% 50%; + background-repeat: no-repeat; + display: block; + } +.leaflet-bar a:hover { + background-color: #f4f4f4; + } +.leaflet-bar a:first-child { + border-top-left-radius: 4px; + border-top-right-radius: 4px; + } +.leaflet-bar a:last-child { + border-bottom-left-radius: 4px; + border-bottom-right-radius: 4px; + border-bottom: none; + } +.leaflet-bar a.leaflet-disabled { + cursor: default; + background-color: #f4f4f4; + color: #bbb; + } + +.leaflet-touch .leaflet-bar a { + width: 30px; + height: 30px; + line-height: 30px; + } +.leaflet-touch .leaflet-bar a:first-child { + border-top-left-radius: 2px; + border-top-right-radius: 2px; + } +.leaflet-touch .leaflet-bar a:last-child { + border-bottom-left-radius: 2px; + border-bottom-right-radius: 2px; + } + +/* zoom control */ + +.leaflet-control-zoom-in, +.leaflet-control-zoom-out { + font: bold 18px 'Lucida Console', Monaco, monospace; + text-indent: 1px; + } + +.leaflet-touch .leaflet-control-zoom-in, .leaflet-touch .leaflet-control-zoom-out { + font-size: 22px; + } + + +/* layers control */ + +.leaflet-control-layers { + box-shadow: 0 1px 5px rgba(0,0,0,0.4); + background: #fff; + border-radius: 5px; + } +.leaflet-control-layers-toggle { + background-image: url(images/layers.png); + width: 36px; + height: 36px; + } +.leaflet-retina .leaflet-control-layers-toggle { + background-image: url(images/layers-2x.png); + background-size: 26px 26px; + } +.leaflet-touch .leaflet-control-layers-toggle { + width: 44px; + height: 44px; + } +.leaflet-control-layers .leaflet-control-layers-list, +.leaflet-control-layers-expanded .leaflet-control-layers-toggle { + display: none; + } +.leaflet-control-layers-expanded .leaflet-control-layers-list { + display: block; + position: relative; + } +.leaflet-control-layers-expanded { + padding: 6px 10px 6px 6px; + color: #333; + background: #fff; + } +.leaflet-control-layers-scrollbar { + overflow-y: scroll; + overflow-x: hidden; + padding-right: 5px; + } +.leaflet-control-layers-selector { + margin-top: 2px; + position: relative; + top: 1px; + } +.leaflet-control-layers label { + display: block; + } +.leaflet-control-layers-separator { + height: 0; + border-top: 1px solid #ddd; + margin: 5px -10px 5px -6px; + } + +/* Default icon URLs */ +.leaflet-default-icon-path { + background-image: url(images/marker-icon.png); + } + + +/* attribution and scale controls */ + +.leaflet-container .leaflet-control-attribution { + background: #fff; + background: rgba(255, 255, 255, 0.7); + margin: 0; + } +.leaflet-control-attribution, +.leaflet-control-scale-line { + padding: 0 5px; + color: #333; + } +.leaflet-control-attribution a { + text-decoration: none; + } +.leaflet-control-attribution a:hover { + text-decoration: underline; + } +.leaflet-container .leaflet-control-attribution, +.leaflet-container .leaflet-control-scale { + font-size: 11px; + } +.leaflet-left .leaflet-control-scale { + margin-left: 5px; + } +.leaflet-bottom .leaflet-control-scale { + margin-bottom: 5px; + } +.leaflet-control-scale-line { + border: 2px solid #777; + border-top: none; + line-height: 1.1; + padding: 2px 5px 1px; + font-size: 11px; + white-space: nowrap; + overflow: hidden; + -moz-box-sizing: border-box; + box-sizing: border-box; + + background: #fff; + background: rgba(255, 255, 255, 0.5); + } +.leaflet-control-scale-line:not(:first-child) { + border-top: 2px solid #777; + border-bottom: none; + margin-top: -2px; + } +.leaflet-control-scale-line:not(:first-child):not(:last-child) { + border-bottom: 2px solid #777; + } + +.leaflet-touch .leaflet-control-attribution, +.leaflet-touch .leaflet-control-layers, +.leaflet-touch .leaflet-bar { + box-shadow: none; + } +.leaflet-touch .leaflet-control-layers, +.leaflet-touch .leaflet-bar { + border: 2px solid rgba(0,0,0,0.2); + background-clip: padding-box; + } + + +/* popup */ + +.leaflet-popup { + position: absolute; + text-align: center; + margin-bottom: 20px; + } +.leaflet-popup-content-wrapper { + padding: 1px; + text-align: left; + border-radius: 12px; + } +.leaflet-popup-content { + margin: 13px 19px; + line-height: 1.4; + } +.leaflet-popup-content p { + margin: 18px 0; + } +.leaflet-popup-tip-container { + width: 40px; + height: 20px; + position: absolute; + left: 50%; + margin-left: -20px; + overflow: hidden; + pointer-events: none; + } +.leaflet-popup-tip { + width: 17px; + height: 17px; + padding: 1px; + + margin: -10px auto 0; + + -webkit-transform: rotate(45deg); + -moz-transform: rotate(45deg); + -ms-transform: rotate(45deg); + transform: rotate(45deg); + } +.leaflet-popup-content-wrapper, +.leaflet-popup-tip { + background: white; + color: #333; + box-shadow: 0 3px 14px rgba(0,0,0,0.4); + } +.leaflet-container a.leaflet-popup-close-button { + position: absolute; + top: 0; + right: 0; + padding: 4px 4px 0 0; + border: none; + text-align: center; + width: 18px; + height: 14px; + font: 16px/14px Tahoma, Verdana, sans-serif; + color: #c3c3c3; + text-decoration: none; + font-weight: bold; + background: transparent; + } +.leaflet-container a.leaflet-popup-close-button:hover { + color: #999; + } +.leaflet-popup-scrolled { + overflow: auto; + border-bottom: 1px solid #ddd; + border-top: 1px solid #ddd; + } + +.leaflet-oldie .leaflet-popup-content-wrapper { + -ms-zoom: 1; + } +.leaflet-oldie .leaflet-popup-tip { + width: 24px; + margin: 0 auto; + + -ms-filter: "progid:DXImageTransform.Microsoft.Matrix(M11=0.70710678, M12=0.70710678, M21=-0.70710678, M22=0.70710678)"; + filter: progid:DXImageTransform.Microsoft.Matrix(M11=0.70710678, M12=0.70710678, M21=-0.70710678, M22=0.70710678); + } +.leaflet-oldie .leaflet-popup-tip-container { + margin-top: -1px; + } + +.leaflet-oldie .leaflet-control-zoom, +.leaflet-oldie .leaflet-control-layers, +.leaflet-oldie .leaflet-popup-content-wrapper, +.leaflet-oldie .leaflet-popup-tip { + border: 1px solid #999; + } + + +/* div icon */ + +.leaflet-div-icon { + background: #fff; + border: 1px solid #666; + } + + +/* Tooltip */ +/* Base styles for the element that has a tooltip */ +.leaflet-tooltip { + position: absolute; + padding: 6px; + background-color: #fff; + border: 1px solid #fff; + border-radius: 3px; + color: #222; + white-space: nowrap; + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; + pointer-events: none; + box-shadow: 0 1px 3px rgba(0,0,0,0.4); + } +.leaflet-tooltip.leaflet-clickable { + cursor: pointer; + pointer-events: auto; + } +.leaflet-tooltip-top:before, +.leaflet-tooltip-bottom:before, +.leaflet-tooltip-left:before, +.leaflet-tooltip-right:before { + position: absolute; + pointer-events: none; + border: 6px solid transparent; + background: transparent; + content: ""; + } + +/* Directions */ + +.leaflet-tooltip-bottom { + margin-top: 6px; +} +.leaflet-tooltip-top { + margin-top: -6px; +} +.leaflet-tooltip-bottom:before, +.leaflet-tooltip-top:before { + left: 50%; + margin-left: -6px; + } +.leaflet-tooltip-top:before { + bottom: 0; + margin-bottom: -12px; + border-top-color: #fff; + } +.leaflet-tooltip-bottom:before { + top: 0; + margin-top: -12px; + margin-left: -6px; + border-bottom-color: #fff; + } +.leaflet-tooltip-left { + margin-left: -6px; +} +.leaflet-tooltip-right { + margin-left: 6px; +} +.leaflet-tooltip-left:before, +.leaflet-tooltip-right:before { + top: 50%; + margin-top: -6px; + } +.leaflet-tooltip-left:before { + right: 0; + margin-right: -12px; + border-left-color: #fff; + } +.leaflet-tooltip-right:before { + left: 0; + margin-left: -12px; + border-right-color: #fff; + } /* Settings */ .icon > .suggestion-flair { diff --git a/.obsidian/plugins/obsidian-plugin-todo/main.js b/.obsidian/plugins/obsidian-plugin-todo/main.js index b0570f3..f140d2f 100644 --- a/.obsidian/plugins/obsidian-plugin-todo/main.js +++ b/.obsidian/plugins/obsidian-plugin-todo/main.js @@ -6,29 +6,29 @@ function _interopDefaultLegacy (e) { return e && typeof e === 'object' && 'defau var obsidian__default = /*#__PURE__*/_interopDefaultLegacy(obsidian); -/*! ***************************************************************************** -Copyright (c) Microsoft Corporation. - -Permission to use, copy, modify, and/or distribute this software for any -purpose with or without fee is hereby granted. - -THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH -REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY -AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, -INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM -LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR -OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR -PERFORMANCE OF THIS SOFTWARE. -***************************************************************************** */ - -function __awaiter(thisArg, _arguments, P, generator) { - function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); } - return new (P || (P = Promise))(function (resolve, reject) { - function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } } - function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } } - function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); } - step((generator = generator.apply(thisArg, _arguments || [])).next()); - }); +/*! ***************************************************************************** +Copyright (c) Microsoft Corporation. + +Permission to use, copy, modify, and/or distribute this software for any +purpose with or without fee is hereby granted. + +THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH +REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY +AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, +INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM +LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR +OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR +PERFORMANCE OF THIS SOFTWARE. +***************************************************************************** */ + +function __awaiter(thisArg, _arguments, P, generator) { + function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); } + return new (P || (P = Promise))(function (resolve, reject) { + function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } } + function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } } + function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); } + step((generator = generator.apply(thisArg, _arguments || [])).next()); + }); } const VIEW_TYPE_TODO = 'online.larslockefeer.obsidian-plugin-todo'; diff --git a/.obsidian/plugins/obsidian-plugin-todo/styles.css b/.obsidian/plugins/obsidian-plugin-todo/styles.css index cf8773b..51ad0bc 100644 --- a/.obsidian/plugins/obsidian-plugin-todo/styles.css +++ b/.obsidian/plugins/obsidian-plugin-todo/styles.css @@ -1,77 +1,77 @@ -div.todo-item-view-container { -} - -div.todo-item-view-toolbar { - display: flex; - justify-content: center; - align-items: center; - border-bottom: 1px solid #cccccc; - border-top: 1px solid #cccccc; -} - -div.todo-item-view-toolbar-item { - padding-left: 15px; - padding-right: 15px; - padding-top: 8px; - padding-bottom: 2px; - cursor: pointer; -} - -div.todo-item-view-items { - display: flex; - flex-direction: column; - justify-content: flex-start; -} - -div.todo-item-view-item { - display: flex; - padding-top: 10px; - padding-bottom: 10px; -} - -div.todo-item-view-item p { - margin: 0; -} - -div.todo-item-view-item span.due-date { - padding: 5px 8px 5px 8px; - font-size: 0.6em; - border-radius: 12px; - background-color: #fbb034; - border-color: #fbb034; -} - -div.todo-item-view-item span.due-date.overdue { - background-color: #ff2400; - border-color: #ff2400; -} - -div.todo-item-view-item span.due-date.future-due { - background-color: #6fdf94; - border-color: #6fdf94; -} - -div.todo-item-view-item-checkbox { - flex-basis: 25px; - flex-shrink: 0; -} - -div.todo-item-view-item-description { - flex-grow: 1; - padding-left: 4px; -} - -div.todo-item-view-item-link { - flex-basis: 25px; - flex-shrink: 0; - cursor: pointer; - padding-left: 8px; -} - -div.todo-item-view-item-link svg, div.todo-item-view-toolbar-item svg { - fill: #cccccc; -} - -div.todo-item-view-toolbar-item.active svg { - fill: #7f6df2; -} +div.todo-item-view-container { +} + +div.todo-item-view-toolbar { + display: flex; + justify-content: center; + align-items: center; + border-bottom: 1px solid #cccccc; + border-top: 1px solid #cccccc; +} + +div.todo-item-view-toolbar-item { + padding-left: 15px; + padding-right: 15px; + padding-top: 8px; + padding-bottom: 2px; + cursor: pointer; +} + +div.todo-item-view-items { + display: flex; + flex-direction: column; + justify-content: flex-start; +} + +div.todo-item-view-item { + display: flex; + padding-top: 10px; + padding-bottom: 10px; +} + +div.todo-item-view-item p { + margin: 0; +} + +div.todo-item-view-item span.due-date { + padding: 5px 8px 5px 8px; + font-size: 0.6em; + border-radius: 12px; + background-color: #fbb034; + border-color: #fbb034; +} + +div.todo-item-view-item span.due-date.overdue { + background-color: #ff2400; + border-color: #ff2400; +} + +div.todo-item-view-item span.due-date.future-due { + background-color: #6fdf94; + border-color: #6fdf94; +} + +div.todo-item-view-item-checkbox { + flex-basis: 25px; + flex-shrink: 0; +} + +div.todo-item-view-item-description { + flex-grow: 1; + padding-left: 4px; +} + +div.todo-item-view-item-link { + flex-basis: 25px; + flex-shrink: 0; + cursor: pointer; + padding-left: 8px; +} + +div.todo-item-view-item-link svg, div.todo-item-view-toolbar-item svg { + fill: #cccccc; +} + +div.todo-item-view-toolbar-item.active svg { + fill: #7f6df2; +} diff --git a/.obsidian/plugins/obsidian-reminder-plugin/manifest.json b/.obsidian/plugins/obsidian-reminder-plugin/manifest.json index 2c30b1e..4b6a9a7 100644 --- a/.obsidian/plugins/obsidian-reminder-plugin/manifest.json +++ b/.obsidian/plugins/obsidian-reminder-plugin/manifest.json @@ -1,10 +1,10 @@ -{ - "id": "obsidian-reminder-plugin", - "name": "Reminder", +{ + "id": "obsidian-reminder-plugin", + "name": "Reminder", "version": "1.1.21", - "minAppVersion": "1.0.3", - "description": "Reminder plugin for Obsidian. This plugin adds feature to manage TODOs with reminder.", - "author": "uphy", - "authorUrl": "https://obsidian.md/about", - "isDesktopOnly": false -} + "minAppVersion": "1.0.3", + "description": "Reminder plugin for Obsidian. This plugin adds feature to manage TODOs with reminder.", + "author": "uphy", + "authorUrl": "https://obsidian.md/about", + "isDesktopOnly": false +} diff --git a/.obsidian/plugins/obsidian-style-settings/data.json b/.obsidian/plugins/obsidian-style-settings/data.json index 0d97e87..a477dab 100644 --- a/.obsidian/plugins/obsidian-style-settings/data.json +++ b/.obsidian/plugins/obsidian-style-settings/data.json @@ -1,6 +1,6 @@ { "catppuccin-theme-settings@@catppuccin-theme-dark": "ctp-mocha", - "catppuccin-theme-settings@@catppuccin-theme-accents": "ctp-accent-sky", + "catppuccin-theme-settings@@catppuccin-theme-accents": "ctp-accent-red", "catppuccin-interface-styles@@ctp-bold-folder-title": true, "catppuccin-heading-settings@@ctp-page-title": "var(--ctp-lavender)", "catppuccin-heading-settings@@ctp-h1": "var(--ctp-green)", @@ -18,9 +18,8 @@ "obsidian-solve@@solve-result-text@@light": "#D20F39", "obsidian-solve@@solve-result-margin": "0 0 0 20px", "obsidian-solve@@solve-result-padding": "0 10px 0 10px", - "catppuccin-icon-styles@@ctp-icon-hide": false, - "catppuccin-pdf@@ctp-seamless-pdf": true, - "catppuccin-pdf@@ctp-pdf-darken": true, - "catppuccin-pdf@@ctp-pdf-blend": false, - "pdf-plus@@pdf-plus-vim-hint-inverted": true + "catppuccin-icon-styles@@ctp-icon-hide": true, + "catppuccin-pdf@@ctp-seamless-pdf": false, + "catppuccin-pdf@@ctp-pdf-darken": false, + "catppuccin-pdf@@ctp-pdf-blend": false } \ No newline at end of file diff --git a/.obsidian/plugins/pdf-plus/data.json b/.obsidian/plugins/pdf-plus/data.json index 71fd339..57cb991 100644 --- a/.obsidian/plugins/pdf-plus/data.json +++ b/.obsidian/plugins/pdf-plus/data.json @@ -187,7 +187,7 @@ ], "updateColorPaletteStateFromContextMenu": true, "mobileCopyAction": "pdf-plus", - "showContextMenuOnTablet": false, + "showContextMenuOnTablet": true, "executeBuiltinCommandForOutline": true, "executeBuiltinCommandForZoom": true, "executeFontSizeAdjusterCommand": true, diff --git a/.obsidian/plugins/rich-foot/manifest.json b/.obsidian/plugins/rich-foot/manifest.json index acef5bb..0cb1d16 100644 --- a/.obsidian/plugins/rich-foot/manifest.json +++ b/.obsidian/plugins/rich-foot/manifest.json @@ -1,11 +1,11 @@ -{ - "id": "rich-foot", - "name": "Rich Foot", - "version": "1.10.9", - "minAppVersion": "1.5.0", - "description": "Adds backlink tags and created/modified dates to the footer of your notes.", - "author": "Justin Parker (eQui\\\\ Labs)", - "authorUrl": "https://www.equilllabs.com", - "fundingUrl": "https://ko-fi.com/jparkerweb", - "isDesktopOnly": false -} +{ + "id": "rich-foot", + "name": "Rich Foot", + "version": "1.10.9", + "minAppVersion": "1.5.0", + "description": "Adds backlink tags and created/modified dates to the footer of your notes.", + "author": "Justin Parker (eQui\\\\ Labs)", + "authorUrl": "https://www.equilllabs.com", + "fundingUrl": "https://ko-fi.com/jparkerweb", + "isDesktopOnly": false +} diff --git a/.obsidian/plugins/solve/manifest.json b/.obsidian/plugins/solve/manifest.json index 3257a2e..d466d2b 100644 --- a/.obsidian/plugins/solve/manifest.json +++ b/.obsidian/plugins/solve/manifest.json @@ -1,14 +1,14 @@ -{ - "id": "solve", - "name": "Solve", - "version": "1.1.0", - "minAppVersion": "0.15.0", - "description": "Supercharge your notes with real-time calculations without AI fuss. From dates ('Now + 20 days'), percentages ('10% of 120'), units of measurement ('100cm + 2m'), arithmetic ('10 + 5') and more!", - "author": "Liam Riddell", - "authorUrl": "https://github.com/LiamRiddell", - "fundingUrl": { - "Buy Me a Coffee": "https://www.buymeacoffee.com/liamriddell", - "GitHub Sponser": "https://github.com/sponsors/LiamRiddell" - }, - "isDesktopOnly": false +{ + "id": "solve", + "name": "Solve", + "version": "1.1.0", + "minAppVersion": "0.15.0", + "description": "Supercharge your notes with real-time calculations without AI fuss. From dates ('Now + 20 days'), percentages ('10% of 120'), units of measurement ('100cm + 2m'), arithmetic ('10 + 5') and more!", + "author": "Liam Riddell", + "authorUrl": "https://github.com/LiamRiddell", + "fundingUrl": { + "Buy Me a Coffee": "https://www.buymeacoffee.com/liamriddell", + "GitHub Sponser": "https://github.com/sponsors/LiamRiddell" + }, + "isDesktopOnly": false } \ No newline at end of file diff --git a/.obsidian/plugins/solve/styles.css b/.obsidian/plugins/solve/styles.css index 771d55f..8108a05 100644 --- a/.obsidian/plugins/solve/styles.css +++ b/.obsidian/plugins/solve/styles.css @@ -1,67 +1,67 @@ -/* @settings -name: Solve -id: obsidian-solve -settings: - - - id: solve-result-margin - title: Margin - type: variable-text - default: 0 0 0 4px - - - id: solve-result-padding - title: Padding - type: variable-text - default: 0 0 0 4px - - - id: solve-result-text - title: Text Color - type: variable-themed-color - opacity: true - format: hex - default-light: '#29ce99e8' - default-dark: '#29ce99e8' - - - id: solve-result-background - title: Background Color - type: variable-themed-color - opacity: true - format: hex - default-light: '#22CE9900' - default-dark: '#22CE9900' - - - id: solve-result-border-color - title: Border Color - type: variable-themed-color - opacity: true - format: hex - default-light: '#00000000' - default-dark: '#00000000' - - - id: solve-result-border-style - title: Border Style - type: variable-text - default: solid - - - id: solve-result-border-width - title: Border Width - type: variable-number - default: 0 - format: px - - - id: solve-result-border-radius - title: Border Radius - type: variable-number - default: 4 - format: px - - - id: solve-result-hover-underline-color - title: Text Underline Hover Color - type: variable-themed-color - opacity: true - format: hex - default-light: '#00000000' - default-dark: '#00000000' -*/ +/* @settings +name: Solve +id: obsidian-solve +settings: + - + id: solve-result-margin + title: Margin + type: variable-text + default: 0 0 0 4px + - + id: solve-result-padding + title: Padding + type: variable-text + default: 0 0 0 4px + - + id: solve-result-text + title: Text Color + type: variable-themed-color + opacity: true + format: hex + default-light: '#29ce99e8' + default-dark: '#29ce99e8' + - + id: solve-result-background + title: Background Color + type: variable-themed-color + opacity: true + format: hex + default-light: '#22CE9900' + default-dark: '#22CE9900' + - + id: solve-result-border-color + title: Border Color + type: variable-themed-color + opacity: true + format: hex + default-light: '#00000000' + default-dark: '#00000000' + - + id: solve-result-border-style + title: Border Style + type: variable-text + default: solid + - + id: solve-result-border-width + title: Border Width + type: variable-number + default: 0 + format: px + - + id: solve-result-border-radius + title: Border Radius + type: variable-number + default: 4 + format: px + - + id: solve-result-hover-underline-color + title: Text Underline Hover Color + type: variable-themed-color + opacity: true + format: hex + default-light: '#00000000' + default-dark: '#00000000' +*/ @charset 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:after{content:"BETA";font-size:var(--font-ui-smaller);font-weight:var(--font-semibold);vertical-align:top;background-color:var(--interactive-accent);margin-left:8px;height:20px;width:34px;display:inline-flex;justify-content:center;align-items:center;border-radius:var(--radius-s)}.osf--result-eol .os-result{position:absolute;right:0}.os-result{display:inline-flex;margin:var(--solve-result-margin, 0 0 0 4px);padding:var(--solve-result-padding, 0 0 0 4px);line-height:var(--line-height-normal);color:var(--solve-result-text, #29ce99e8);background-color:var(--solve-result-background, #22ce9900);font-family:var(--font-text);font-size:var(--font-text-size, 16px);border-color:var(--solve-result-border-color, transparent);border-style:var(--solve-result-border-style, solid);border-width:var(--solve-result-border-width, 0);border-radius:var(--solve-result-border-radius, 4px);text-decoration:dotted;text-decoration-line:underline;text-underline-offset:2px;text-decoration-color:transparent;transition:all .15s;-webkit-font-smoothing:antialiased;backface-visibility:hidden;transform:translateZ(0)}.os-result:hover{cursor:pointer;text-decoration-color:var( --solve-result-hover-underline-color, currentColor )} /*! Bundled license information: diff --git a/.obsidian/themes/Catppuccin/manifest.json b/.obsidian/themes/Catppuccin/manifest.json index 78a39de..5f7e09b 100644 --- a/.obsidian/themes/Catppuccin/manifest.json +++ b/.obsidian/themes/Catppuccin/manifest.json @@ -1,7 +1,7 @@ { - "name": "Catppuccin", - "version": "0.4.44", - "minAppVersion": "1.0.0", - "author": "Marshall Beckrich", - "authorUrl": "https://github.com/catppuccin/obsidian" + "name": "Catppuccin", + "version": "0.4.47", + "minAppVersion": "1.0.0", + "author": "Catppuccin", + "authorUrl": "https://github.com/catppuccin/obsidian" } diff --git a/.obsidian/themes/Catppuccin/theme.css b/.obsidian/themes/Catppuccin/theme.css index ef7cb57..ba5b626 100644 --- a/.obsidian/themes/Catppuccin/theme.css +++ b/.obsidian/themes/Catppuccin/theme.css @@ -2111,7 +2111,6 @@ body { .mod-left-split .workspace-tab-header, .mod-right-split .workspace-tab-header { margin: 0 2px; - transition: var(--transition-hover-bg), var(--transition-hover-color); } .clickable-icon:hover, .mod-left-split .workspace-tab-header:hover, @@ -2136,7 +2135,6 @@ body { .mod-right-split .workspace-tab-header.is-active { box-shadow: inset 0 0 4px rgb(var(--ctp-crust)), inset 0 0 6px rgb(var(--ctp-base)); outline: 1px solid rgb(var(--ctp-surface1)); - transition: var(--transition-hover-bg), var(--transition-hover-color); } .clickable-icon.is-active svg, .mod-left-split .workspace-tab-header.has-active-menu svg, @@ -2144,7 +2142,6 @@ body { .mod-left-split .workspace-tab-header.is-active svg, .mod-right-split .workspace-tab-header.is-active svg { color: rgb(var(--ctp-accent)); - transition: background-color 3s ease-in-out; } .clickable-icon.is-active:hover, .clickable-icon.is-active:hover svg, .mod-left-split .workspace-tab-header.has-active-menu:hover, @@ -2167,7 +2164,6 @@ body { background-color: rgb(var(--ctp-red)); box-shadow: 0 0 2px 1px rgb(var(--ctp-surface1)); color: rgb(var(--ctp-base)); - transition: var(--transition-hover-bg), var(--transition-hover-color); } .workspace-tab-header-inner-close-button:hover { @@ -2187,11 +2183,6 @@ body { /* * Windows and Linux min, max, and close buttons */ -.mod-linux .titlebar-button, -.mod-windows .titlebar-button { - transition: var(--transition-hover-bg), var(--transition-hover-color); -} - .mod-linux .titlebar-button:hover, .mod-windows .titlebar-button:hover { background-color: rgb(var(--ctp-pink)); @@ -2217,7 +2208,6 @@ body { */ .nav-folder.is-collapsed .nav-folder-title .nav-folder-title-content::before { -webkit-mask-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24' fill='none' stroke='currentColor' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'%3E%3Cpath d='M4 20h16a2 2 0 0 0 2-2V8a2 2 0 0 0-2-2h-7.93a2 2 0 0 1-1.66-.9l-.82-1.2A2 2 0 0 0 7.93 3H4a2 2 0 0 0-2 2v13c0 1.1.9 2 2 2Z'%3E%3C/path%3E%3Cpath d='M2 10h20'%3E%3C/path%3E%3C/svg%3E"); - transition: -webkit-mask-image 0.2s ease; } .nav-folder.is-collapsed .nav-folder-title .nav-folder-title-content::before, @@ -2248,7 +2238,6 @@ body { background-size: 16px 16px; content: ""; -webkit-mask-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24' fill='none' stroke='currentColor' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'%3E%3Cpath d='m6 14 1.45-2.9A2 2 0 0 1 9.24 10H20a2 2 0 0 1 1.94 2.5l-1.55 6a2 2 0 0 1-1.94 1.5H4a2 2 0 0 1-2-2V5c0-1.1.9-2 2-2h3.93a2 2 0 0 1 1.66.9l.82 1.2a2 2 0 0 0 1.66.9H18a2 2 0 0 1 2 2v2'%3E%3C/path%3E%3C/svg%3E"); - transition: -webkit-mask-image 0.2s ease; } /* @@ -2269,15 +2258,17 @@ body { color: var(--text-on-accent); } +/* Fix for #141 */ +.mod-left-split .workspace-tab-header-container .workspace-tab-header-container-inner, +.mod-right-split .workspace-tab-header-container .workspace-tab-header-container-inner { + overflow: visible; +} + select:focus, .dropdown:focus { box-shadow: 0 0 0 2px rgb(var(--ctp-accent), 70%); } -.dropdown { - transition: var(--transition-hover-bg), var(--transition-hover-box-shadow); -} - textarea:active, input[type=text]:active, input[type=search]:active, @@ -2349,7 +2340,6 @@ input[type=datetime-local]::-webkit-datetime-edit-second-field:focus { text-decoration-color: transparent; text-decoration-line: underline; text-decoration-style: solid; - transition: var(--transition-text-decoration-color); } .cm-s-obsidian .cm-formatting-link.cm-url:hover, .cm-s-obsidian .cm-url:hover, @@ -2372,7 +2362,6 @@ body:not(.ctp-tag-pill) .cm-hashtag.cm-hashtag-end { text-decoration-color: transparent; text-decoration-line: underline; text-decoration-style: solid; - transition: var(--transition-text-decoration-color); } body:not(.ctp-tag-pill) .cm-hashtag.cm-hashtag-end:hover { text-decoration: underline 1px rgb(var(--ctp-tag-pill-color, var(--ctp-accent))); @@ -2421,7 +2410,6 @@ body:not(.ctp-tag-pill) .cm-hashtag::selection { .empty-state-action { border-radius: var(--radius-s); - transition: var(--transition-hover-bg), var(--transition-hover-color); } @media (hover: hover) { @@ -2608,10 +2596,6 @@ body:not(.ctp-tag-pill) .cm-hashtag::selection { background-color: rgb(var(--ctp-accent)); } -.search-result-file-match { - transition: var(--transition-hover-bg), var(--transition-hover-color); -} - .search-result-file-match:hover .search-result-file-matched-text { background-color: rgb(var(--ctp-rosewater), 60%); color: var(--text-on-accent); @@ -2651,28 +2635,16 @@ body:not(.ctp-tag-pill) .cm-hashtag::selection { --interactive-accent: rgb(var(--ctp-mauve)); } -.status-bar-item { - transition: var(--transition-hover-bg), var(--transition-hover-color); -} - .status-bar-item.mod-clickable:hover { --background-modifier-hover: rgb(var(--ctp-accent)); color: var(--text-on-accent); } -.view-header-title-parent .view-header-breadcrumb { - transition: var(--transition-hover-bg), var(--transition-hover-color); -} .view-header-title-parent .view-header-breadcrumb:hover { background-color: rgb(var(--ctp-accent)); color: var(--text-on-accent); } -.horizontal-tab-nav-item, -.vertical-tab-nav-item { - transition: var(--transition-hover-bg), var(--transition-hover-color); -} - .horizontal-tab-nav-item:hover, .vertical-tab-nav-item:hover { background-color: rgb(var(--ctp-accent)); @@ -2688,20 +2660,10 @@ body:not(.ctp-tag-pill) .cm-hashtag::selection { background-color: rgb(var(--ctp-accent)); } -.nav-folder-title, -.nav-file-title { - transition: var(--transition-hover-bg), var(--transition-hover-color); -} - .search-result:not(.is-collapsed) .search-result-file-title { --nav-item-color-active: var(--text-normal); } -.tree-item-self, -.embedded-backlinks .backlink-pane .tree-item-flair { - transition: var(--transition-hover-bg), var(--transition-hover-color); -} - :not(.is-grabbing) .nav-file-title:hover, :not(.is-grabbing) .nav-folder-title:hover, .nav-file-title.is-active, @@ -2763,12 +2725,6 @@ body:not(.ctp-tag-pill) .cm-hashtag::selection { color: var(--text-on-accent); } -body:not(.is-grabbing) .tree-item-self.is-active, -.tree-item-self.is-active, -.tree-item-self.is-clickable { - transition: var(--transition-hover-bg), var(--transition-hover-color); -} - body:not(.is-grabbing) .tree-item-self.is-active:hover, .tree-item-self.is-active { --nav-item-color-active: var(--text-on-accent); @@ -2784,10 +2740,6 @@ body:not(.is-grabbing) .tree-item-self.is-active:hover .tree-item-icon, --icon-color: var(--text-on-accent); } } -.tree-item-icon { - transition: var(--transition-hover-color); -} - .mod-vertical .workspace-tab-header:not(.is-active):hover .workspace-tab-header-inner { --background-modifier-hover: rgb(var(--ctp-accent)); --background-modifier-active-hover: rgb(var(--ctp-accent)); @@ -2799,10 +2751,6 @@ body:not(.is-grabbing) .tree-item-self.is-active:hover .tree-item-icon, color: var(--text-on-accent); } -.workspace-tab-header:not(.is-active):hover .workspace-tab-header-inner { - transition: var(--transition-hover-bg), var(--transition-hover-color); -} - /* Stacked tabs */ /* TODO disentangle all the tabs css */ .mod-vertical .workspace-tabs.mod-stacked .workspace-tab-header:not(.is-active):hover .workspace-tab-header-inner { @@ -2848,10 +2796,6 @@ body:not(.is-grabbing) .tree-item-self.is-active:hover .tree-item-icon, opacity: 40%; } -.canvas-control-item { - transition: var(--transition-hover-bg), var(--transition-hover-color); -} - @media (hover: hover) { .canvas-control-item:hover { background-color: rgb(var(--ctp-pink)); @@ -2906,10 +2850,6 @@ body:not(.is-grabbing) .tree-item-self.is-active:hover .tree-item-icon, background-color: rgb(var(--ctp-green)); } -button:not(.clickable-icon) { - transition: var(--transition-hover-bg), var(--transition-hover-box-shadow); -} - button.mod-cta { border: 0; border-radius: var(--button-radius); @@ -2925,7 +2865,6 @@ button.mod-cta { } button.mod-cta:not(.clickable-icon) { background-color: var(--interactive-normal); - transition: var(--transition-hover-bg), var(--transition-hover-box-shadow); } button.mod-cta:hover { background-color: var(--interactive-hover); @@ -2949,6 +2888,20 @@ button.mod-cta:hover { text-transform: lowercase; } +body:not(.is-phone) .horizontal-tab-nav-item.is-active, +body:not(.is-phone) .vertical-tab-nav-item.is-active { + --ctp-accent: var(--ctp-mauve); + background-color: rgb(var(--ctp-accent)); + color: var(--text-on-accent); +} + +.horizontal-tab-nav-item:hover .vertical-tab-nav-item-icon, +.vertical-tab-nav-item:hover .vertical-tab-nav-item-icon, +.horizontal-tab-nav-item.is-active .vertical-tab-nav-item-icon, +.vertical-tab-nav-item.is-active .vertical-tab-nav-item-icon { + color: var(--text-on--accent); +} + /** * Some tweaks for the light/Catppuccin Latte theme. */ @@ -2964,45 +2917,19 @@ button.mod-cta:hover { --indentation-guide-width: 2px; --list-marker-color: rgb(var(--ctp-sapphire)); --checklist-done-color: rgb(var(--ctp-green)); - /* - * Nav bar - */ - /* - * Icons - */ - /* - * Tabs - */ - /* - * Prompts - */ - /* - * Search - */ - /* - * Dropdowns - */ - /* - * Checkboxes - */ - /* - ? I think !important is necessary to overwrite app.css - */ - /* - * settings slider - */ - /* - * Canvas - */ - /* - * Bookmarks - */ } .theme-dark:not(.css-settings-manager) .workspace-tab-header-inner-close-button:hover, .theme-light:not(.css-settings-manager) .workspace-tab-header-inner-close-button:hover, .ctp-full-palette .workspace-tab-header-inner-close-button:hover { box-shadow: none; } +.theme-dark:not(.css-settings-manager), +.theme-light:not(.css-settings-manager), +.ctp-full-palette { + /* + * Nav bar + */ +} .theme-dark:not(.css-settings-manager) .nav-file-title.is-active, .theme-dark:not(.css-settings-manager) .nav-folder-title.is-active, .theme-light:not(.css-settings-manager) .nav-file-title.is-active, @@ -3029,6 +2956,13 @@ button.mod-cta:hover { background-color: transparent; color: rgb(var(--text-normal)); } +.theme-dark:not(.css-settings-manager), +.theme-light:not(.css-settings-manager), +.ctp-full-palette { + /* + * Icons + */ +} .theme-dark:not(.css-settings-manager) .clickable-icon.is-active, .theme-dark:not(.css-settings-manager) .mod-left-split .workspace-tab-header.has-active-menu, .theme-dark:not(.css-settings-manager) .mod-right-split .workspace-tab-header.has-active-menu, @@ -3046,7 +2980,6 @@ button.mod-cta:hover { .ctp-full-palette .mod-right-split .workspace-tab-header.is-active { box-shadow: inset 0 0 4px rgb(var(--ctp-crust)), inset 0 0 6px rgb(var(--ctp-base)); outline: 1px solid rgb(var(--ctp-surface1)); - transition: var(--transition-hover-bg), var(--transition-hover-box-shadow); } .theme-dark:not(.css-settings-manager) .clickable-icon.is-active svg, .theme-dark:not(.css-settings-manager) .mod-left-split .workspace-tab-header.has-active-menu svg, @@ -3064,7 +2997,6 @@ button.mod-cta:hover { .ctp-full-palette .mod-left-split .workspace-tab-header.is-active svg, .ctp-full-palette .mod-right-split .workspace-tab-header.is-active svg { color: rgb(var(--ctp-pink)); - transition: var(--transition-hover-bg), var(--transition-hover-color); } .theme-dark:not(.css-settings-manager) .clickable-icon.is-active:hover, .theme-dark:not(.css-settings-manager) .clickable-icon.is-active:hover svg, .theme-dark:not(.css-settings-manager) .mod-left-split .workspace-tab-header.has-active-menu:hover, @@ -3099,17 +3031,6 @@ button.mod-cta:hover { box-shadow: none; color: var(--text-on-accent); } -.theme-dark:not(.css-settings-manager) .clickable-icon, -.theme-dark:not(.css-settings-manager) .mod-left-split .workspace-tab-header, -.theme-dark:not(.css-settings-manager) .mod-right-split .workspace-tab-header, -.theme-light:not(.css-settings-manager) .clickable-icon, -.theme-light:not(.css-settings-manager) .mod-left-split .workspace-tab-header, -.theme-light:not(.css-settings-manager) .mod-right-split .workspace-tab-header, -.ctp-full-palette .clickable-icon, -.ctp-full-palette .mod-left-split .workspace-tab-header, -.ctp-full-palette .mod-right-split .workspace-tab-header { - transition: var(--transition-hover-bg), var(--transition-hover-color); -} .theme-dark:not(.css-settings-manager) .clickable-icon:hover, .theme-dark:not(.css-settings-manager) .mod-left-split .workspace-tab-header:hover, .theme-dark:not(.css-settings-manager) .mod-right-split .workspace-tab-header:hover, @@ -3153,6 +3074,13 @@ button.mod-cta:hover { .ctp-full-palette .mod-vertical .workspace-tabs.mod-stacked .workspace-tab-header:not(.is-active) .workspace-tab-header-inner-close-button:hover svg { color: var(--text-on-accent); } +.theme-dark:not(.css-settings-manager), +.theme-light:not(.css-settings-manager), +.ctp-full-palette { + /* + * Tabs + */ +} .theme-dark:not(.css-settings-manager) .workspace .mod-root .workspace-tabs.mod-stacked .workspace-tab-header-inner:hover .workspace-tab-header-inner-title, .theme-light:not(.css-settings-manager) .workspace .mod-root .workspace-tabs.mod-stacked .workspace-tab-header-inner:hover .workspace-tab-header-inner-title, .ctp-full-palette .workspace .mod-root .workspace-tabs.mod-stacked .workspace-tab-header-inner:hover .workspace-tab-header-inner-title { @@ -3163,6 +3091,13 @@ button.mod-cta:hover { .ctp-full-palette .mod-vertical .workspace-tabs.mod-stacked .workspace-tab-header:not(.is-active):hover .workspace-tab-header-inner .workspace-tab-header-inner-title { color: rgb(var(--ctp-pink)); } +.theme-dark:not(.css-settings-manager), +.theme-light:not(.css-settings-manager), +.ctp-full-palette { + /* + * Prompts + */ +} .theme-dark:not(.css-settings-manager) .suggestion-item.is-selected, .theme-light:not(.css-settings-manager) .suggestion-item.is-selected, .ctp-full-palette .suggestion-item.is-selected { @@ -3174,6 +3109,13 @@ button.mod-cta:hover { .ctp-full-palette .suggestion-item.is-selected:hover .suggestion-flair { color: var(--text-on-accent); } +.theme-dark:not(.css-settings-manager), +.theme-light:not(.css-settings-manager), +.ctp-full-palette { + /* + * Search + */ +} .theme-dark:not(.css-settings-manager) .suggestion .suggestion-item.mod-group, .theme-light:not(.css-settings-manager) .suggestion .suggestion-item.mod-group, .ctp-full-palette .suggestion .suggestion-item.mod-group { @@ -3198,6 +3140,13 @@ button.mod-cta:hover { .ctp-full-palette .search-suggest-item.is-selected .search-suggest-info-text { color: var(--text-on-accent); } +.theme-dark:not(.css-settings-manager), +.theme-light:not(.css-settings-manager), +.ctp-full-palette { + /* + * Dropdowns + */ +} .theme-dark:not(.css-settings-manager) select:focus, .theme-dark:not(.css-settings-manager) .dropdown:focus, .theme-light:not(.css-settings-manager) select:focus, @@ -3262,6 +3211,13 @@ button.mod-cta:hover { .ctp-full-palette input[type=number]:focus-visible { box-shadow: 0 0 0 2px rgb(var(--ctp-blue), 70%); } +.theme-dark:not(.css-settings-manager), +.theme-light:not(.css-settings-manager), +.ctp-full-palette { + /* + * Checkboxes + */ +} .theme-dark:not(.css-settings-manager) input[type=checkbox]:not(:checked), .theme-light:not(.css-settings-manager) input[type=checkbox]:not(:checked), .ctp-full-palette input[type=checkbox]:not(:checked) { @@ -3386,7 +3342,8 @@ button.mod-cta:hover { .theme-light:not(.css-settings-manager) .vertical-tab-nav-item.is-active, .ctp-full-palette .horizontal-tab-nav-item.is-active, .ctp-full-palette .vertical-tab-nav-item.is-active { - background-color: rgb(var(--ctp-mauve)); + background-color: rgb(var(--ctp-accent)); + color: var(--text-on-accent); } .theme-dark:not(.css-settings-manager) .horizontal-tab-nav-item.is-active:hover, .theme-dark:not(.css-settings-manager) .vertical-tab-nav-item.is-active:hover, @@ -3396,12 +3353,26 @@ button.mod-cta:hover { .ctp-full-palette .vertical-tab-nav-item.is-active:hover { background-color: rgb(var(--ctp-accent)); } +.theme-dark:not(.css-settings-manager), +.theme-light:not(.css-settings-manager), +.ctp-full-palette { + /* + ? I think !important is necessary to overwrite app.css + */ +} .theme-dark:not(.css-settings-manager) .HyperMD-header .cm-formatting-header, .theme-light:not(.css-settings-manager) .HyperMD-header .cm-formatting-header, .ctp-full-palette .HyperMD-header .cm-formatting-header { color: rgb(var(--ctp-sapphire)); font-size: 75% !important; } +.theme-dark:not(.css-settings-manager), +.theme-light:not(.css-settings-manager), +.ctp-full-palette { + /* + * settings slider + */ +} .theme-dark:not(.css-settings-manager) .mod-settings input.slider, .theme-light:not(.css-settings-manager) .mod-settings input.slider, .ctp-full-palette .mod-settings input.slider { @@ -3412,11 +3383,25 @@ button.mod-cta:hover { .ctp-full-palette .mod-settings input[type=color]::-webkit-color-swatch:hover { box-shadow: inset 0 0 0 1px rgb(var(--mono-rgb-100), 25%), 0 0 0 2px rgb(var(--ctp-surface1)); } +.theme-dark:not(.css-settings-manager), +.theme-light:not(.css-settings-manager), +.ctp-full-palette { + /* + * Canvas + */ +} .theme-dark:not(.css-settings-manager) .canvas-controls button:hover, .theme-light:not(.css-settings-manager) .canvas-controls button:hover, .ctp-full-palette .canvas-controls button:hover { background-color: rgb(var(--ctp-pink)); } +.theme-dark:not(.css-settings-manager), +.theme-light:not(.css-settings-manager), +.ctp-full-palette { + /* + * Bookmarks + */ +} .theme-dark:not(.css-settings-manager) body:not(.is-grabbing) .tree-item-self.is-active:hover, .theme-dark:not(.css-settings-manager) .tree-item-self.is-active, .theme-light:not(.css-settings-manager) body:not(.is-grabbing) .tree-item-self.is-active:hover, @@ -3530,9 +3515,6 @@ body:not(.is-mobile) .mk-main-menu-button:hover { color: var(--text-on-accent); } -.ctp-full-palette .mk-sidebar-item { - transition: var(--transition-hover-bg), var(--transition-hover-color); -} .ctp-full-palette .mk-sidebar-item:hover { background-color: rgb(var(--ctp-pink)); color: var(--text-on-accent); @@ -3544,11 +3526,9 @@ body:not(.is-mobile) .mk-main-menu-button:hover { .ctp-full-palette .mk-sidebar-item-active { box-shadow: inset 0 0 4px rgb(var(--ctp-crust)), inset 0 0 6px rgb(var(--ctp-base)); outline: 1px solid rgb(var(--ctp-surface1)); - transition: var(--transition-hover-bg), var(--transition-hover-box-shadow); } .ctp-full-palette .mk-sidebar-item-active svg { color: rgb(var(--ctp-pink)) !important; - transition: var(--transition-hover-bg), var(--transition-hover-color); } .ctp-full-palette .mk-sidebar-item-active:hover svg, .ctp-full-palette .mk-sidebar-item-active:hover { background-color: rgb(var(--ctp-pink)); @@ -3556,9 +3536,6 @@ body:not(.is-mobile) .mk-main-menu-button:hover { color: var(--text-on-accent) !important; } -.mk-sidebar-item { - transition: var(--transition-hover-bg), var(--transition-hover-color); -} .mk-sidebar-item:hover { background-color: rgb(var(--ctp-accent)); color: var(--text-on-accent); @@ -3570,11 +3547,9 @@ body:not(.is-mobile) .mk-main-menu-button:hover { .mk-sidebar-item-active { box-shadow: inset 0 0 4px rgb(var(--ctp-crust)), inset 0 0 6px rgb(var(--ctp-base)); outline: 1px solid rgb(var(--ctp-surface1)); - transition: var(--transition-hover-bg), var(--transition-hover-box-shadow); } .mk-sidebar-item-active svg { color: rgb(var(--ctp-accent)) !important; - transition: var(--transition-hover-bg), var(--transition-hover-color); } .mk-sidebar-item-active:hover svg, .mk-sidebar-item-active:hover { background-color: rgb(var(--ctp-accent)); @@ -3582,14 +3557,6 @@ body:not(.is-mobile) .mk-main-menu-button:hover { color: var(--text-on-accent) !important; } -.mk-tree-item { - transition: var(--transition-hover-bg), var(--transition-hover-color); -} - -.mk-tree-item svg { - transition: var(--transition-hover-bg), var(--transition-hover-color); -} - body:not(.is-mobile) .mk-tree-item:hover { background-color: rgb(var(--ctp-accent)); color: var(--text-on-accent); @@ -3599,10 +3566,6 @@ body:not(.is-mobile) .mk-tree-item:hover { color: var(--text-on-accent) !important; } -.mk-options-menu__suggestions li { - transition: var(--transition-hover-bg), var(--transition-hover-color); -} - .mk-options-menu__suggestions li.is-active, .mk-options-menu__suggestions li:hover { background-color: rgb(var(--ctp-accent)); @@ -3619,10 +3582,6 @@ body:not(.is-mobile) .mk-tree-item:hover { color: rgb(var(--ctp-accent)) !important; } -body:not(.is-mobile) .mk-tree-wrapper button { - transition: var(--transition-hover-bg), var(--transition-hover-color), var(--transition-hover-box-shadow); -} - body:not(.is-mobile) .mk-folder-buttons button { margin-left: 2px; background-color: transparent !important; @@ -3720,6 +3679,17 @@ body:not(.is-grabbing) .tree-item-self.fn-is-active:hover { font-size: var(--font-smaller); } +/* Fix for #134 */ +.suggestion-item.is-selected .EA-shortcode span.EA-hl { + color: var(--text-on-accent); +} + +/* Make Style Settings accent selection use choice for active items */ +body.css-settings-manager:not(.is-phone, .ctp-full-palette) .horizontal-tab-nav-item.is-active, +body.css-settings-manager:not(.is-phone, .ctp-full-palette) .vertical-tab-nav-item.is-active { + --ctp-accent: unset ; +} + /** * 1. Checklist icons implemented for #91. This scss is from Kepano's Minimal Theme under its MIT License. Thanks Kepano! */ diff --git a/Dailys/2026/04 April/29.04.2026.md b/Dailys/2026/04 April/29.04.2026.md index 7cb806d..068f2d6 100644 --- a/Dailys/2026/04 April/29.04.2026.md +++ b/Dailys/2026/04 April/29.04.2026.md @@ -10,8 +10,8 @@ tags: # Tasks - [x] Kunstwerk oder Maschine? Stadtideale der Moderne 9.15-10.45 01/304. Hörsaal KUWI -- [ ] Grund- und Unbegrifflichkeiten (Übung) 13.15-14.45 01/305. Seminarraum KUWI -- [ ] Öffentliche Kritik 13.15-14.45 01/304. Hörsaal KUWI +- [x] Grund- und Unbegrifflichkeiten (Übung) 13.15-14.45 01/305. Seminarraum KUWI +- [x] Öffentliche Kritik 13.15-14.45 MOGI - [ ] Zwischen Politik und Ästhetik: Stadttheorien seit 1800 15.00-16.30 01/304. Hörsaal KUWI - [ ] Ringvorlesung des Instituts für Kunstwissenschaft 18.30-20.00 02/003. Aula diff --git a/Dailys/2026/04 April/30.04.2026.md b/Dailys/2026/04 April/30.04.2026.md index 68c86b7..2b34716 100644 --- a/Dailys/2026/04 April/30.04.2026.md +++ b/Dailys/2026/04 April/30.04.2026.md @@ -9,8 +9,8 @@ tags: # 30.04.2026 # Tasks -- [ ] „Kunst ist schön, macht aber viel Arbeit.“ 8.00-9.30 01/305. Seminarraum KUWI -- [ ] Dis_Ability Pride 11.30-13.00 01/305. Seminarraum KUWI +- [x] „Kunst ist schön, macht aber viel Arbeit.“ 8.00-9.30 01/305. Seminarraum KUWI +- [x] Dis_Ability Pride 11.30-13.00 01/305. Seminarraum KUWI - [ ] Grund- und Unbegrifflichkeiten (Vorlesung) 13.15-14.45 01/304. Hörsaal KUWI - [ ] Grund- und Unbegrifflichkeiten (Vorlesung) 15.00-16.30 01/304. Hörsaal KUWI diff --git a/Dailys/2026/05 Mai/04.05.2026.md b/Dailys/2026/05 Mai/04.05.2026.md index b31c144..f076595 100644 --- a/Dailys/2026/05 Mai/04.05.2026.md +++ b/Dailys/2026/05 Mai/04.05.2026.md @@ -5,10 +5,10 @@ reason: tags: - Calendar --- -![logo](logo.png) # 04.05.2026 # Tasks -- [ ] Becoming independent 13.15-14.45 01/305. Seminarraum KUWI +- [x] Becoming independent 13.15-14.45 01/305. Seminarraum KUWI# +- [x] Kino 20.30h --- diff --git a/Dailys/2026/05 Mai/06.05.2026.md b/Dailys/2026/05 Mai/06.05.2026.md index b1faaf2..8486eb0 100644 --- a/Dailys/2026/05 Mai/06.05.2026.md +++ b/Dailys/2026/05 Mai/06.05.2026.md @@ -11,6 +11,7 @@ tags: # Tasks - [x] Kunstwerk oder Maschine? Stadtideale der Moderne 9.15-10.45 01/304. Hörsaal KUWI - [ ] Grund- und Unbegrifflichkeiten (Übung) 13.15-14.45 01/305. Seminarraum KUWI + - [ ] [[02. Was ist Erkenntnis]] - [x] Zwischen Politik und Ästhetik: Stadttheorien seit 1800 15.00-16.30 01/304. Hörsaal KUWI - [ ] Ringvorlesung des Instituts für Kunstwissenschaft 18.30-20.00 02/003. Aula diff --git a/Dailys/2026/05 Mai/07.05.2026.md b/Dailys/2026/05 Mai/07.05.2026.md index e07965e..88a8172 100644 --- a/Dailys/2026/05 Mai/07.05.2026.md +++ b/Dailys/2026/05 Mai/07.05.2026.md @@ -5,14 +5,14 @@ reason: Onlinetermin - Kunst ist Schön tags: - Calendar --- -![logo](logo.png) +![logo|611](logo.png) # 07.05.2026 # Tasks -- [ ] „Kunst ist schön, macht aber viel Arbeit.“ 8.00-9.30 01/305. Seminarraum KUWI -- [ ] Dis_Ability Pride 11.30-13.00 01/305. Seminarraum KUWI -- [ ] Grund- und Unbegrifflichkeiten (Vorlesung) 13.15-14.45 01/304. Hörsaal KUWI +- [x] „Kunst ist schön, macht aber viel Arbeit.“ 8.00-9.30 01/305. [Online](https://vc2.sonia.de/b/rooms/urs-dwz-wme-wal/join) +- [x] Dis_Ability Pride 11.30-13.00 01/305. Seminarraum KUWI +- [x] Grund- und Unbegrifflichkeiten (Vorlesung) 13.15-14.45 01/304. Hörsaal KUWI - [ ] Grund- und Unbegrifflichkeiten (Vorlesung) 15.00-16.30 01/304. Hörsaal KUWI diff --git a/Dailys/2026/05 Mai/11.05.2026.md b/Dailys/2026/05 Mai/11.05.2026.md index 8af885b..bda47f3 100644 --- a/Dailys/2026/05 Mai/11.05.2026.md +++ b/Dailys/2026/05 Mai/11.05.2026.md @@ -10,9 +10,12 @@ tags: # Tasks - [x] Becoming independent 13.15-14.45 01/305. Seminarraum KUWI +<<<<<<< HEAD - [ ] Ausschreibung erstellen - [ ] Adorno, Beaudelaire Text lesen und Notizen machen - [ ] Vorbesprechung Tag der Niedersachsen 19.00 [Online](https://teams.microsoft.com/meet/381780687133178?p=DKUyKTi8sfihBHhN86 "Meeting join") [https://teams.microsoft.com/meet/381780687133178?p=DKUyKTi8sfihBHhN86](https://teams.microsoft.com/meet/381780687133178?p=DKUyKTi8sfihBHhN86 "Meeting join") +======= +>>>>>>> refs/remotes/origin/main --- diff --git a/Dailys/2026/05 Mai/12.05.2026.md b/Dailys/2026/05 Mai/12.05.2026.md new file mode 100644 index 0000000..4cee643 --- /dev/null +++ b/Dailys/2026/05 Mai/12.05.2026.md @@ -0,0 +1,14 @@ +--- +title: "12.05.2026 - Calendar" +timestamp: "12.05.2026 - 11:47" +reason: +tags: + - Calendar +--- +![logo](logo.png) +# 12.05.2026 + +# Tasks +- [ ] Friseur 13h + +--- diff --git a/Dailys/2026/05 Mai/13.05.2026.md b/Dailys/2026/05 Mai/13.05.2026.md index d9fed0c..d269b0f 100644 --- a/Dailys/2026/05 Mai/13.05.2026.md +++ b/Dailys/2026/05 Mai/13.05.2026.md @@ -14,4 +14,5 @@ tags: - [x] Zwischen Politik und Ästhetik: Stadttheorien seit 1800 15.00-16.30 01/304. Hörsaal KUWI - [x] Ringvorlesung des Instituts für Kunstwissenschaft 18.30-20.00 02/003. Aula + --- diff --git a/Dailys/2026/05 Mai/16.05.2026.md b/Dailys/2026/05 Mai/16.05.2026.md new file mode 100644 index 0000000..362ca9c --- /dev/null +++ b/Dailys/2026/05 Mai/16.05.2026.md @@ -0,0 +1,15 @@ +--- +title: 16.05.2026 - Calendar +timestamp: 16.05.2026 - 23:34 +reason: +tags: + - Calendar + - Football +--- +![logo](logo.png) +# 16.05.2026 + +# Tasks +- [ ] Kiel Baltic Hurricans Stadion 16h + +--- diff --git a/Dailys/2026/07 July/10.07.2026.md b/Dailys/2026/07 July/10.07.2026.md new file mode 100644 index 0000000..a4d7a65 --- /dev/null +++ b/Dailys/2026/07 July/10.07.2026.md @@ -0,0 +1,14 @@ +--- +title: "10.07.2026 - Calendar" +timestamp: "10.07.2026 - 13:50" +reason: +tags: + - Calendar +--- +![logo](logo.png) +# 10.07.2026 + +# Tasks +- [ ] Kuratorische Praxis (noch offen) Architekturpavillon Braunschweig PK4 TU BS + +--- diff --git a/Dailys/2026/12 Dezember/18.12.2026.md b/Dailys/2026/12 Dezember/18.12.2026.md new file mode 100644 index 0000000..b08b767 --- /dev/null +++ b/Dailys/2026/12 Dezember/18.12.2026.md @@ -0,0 +1,16 @@ +--- +title: 18.12.2026 - Calendar +timestamp: 18.12.2026 - 23:44 +reason: The Hub Presentation +tags: + - Calendar +--- +![logo](logo.png) +# 18.12.2026 + +# Tasks +- [ ] Präsentation Pornhub + - [ ] Vorbereiten mit Nova (Canva) + - [ ] Matrix Einladung verschicken + +--- diff --git a/Dailys/2026/Veranstaltungen - 2026.md b/Dailys/2026/Veranstaltungen - 2026.md index 6ac51d3..49081ec 100644 --- a/Dailys/2026/Veranstaltungen - 2026.md +++ b/Dailys/2026/Veranstaltungen - 2026.md @@ -35,6 +35,8 @@ marker: Konzert, 53.556498523196574, 9.970009790796112, [[20.03.2026|6€90]] marker: Party, 52.27346894031721, 10.525974496905546, [[24.04.2025|IFN Cocktailparty]] +marker: Veranstaltung, 52.267077870685455, 10.517817962304639, [[04.05.2026|Der Teufel trägt Prada 2]] + marker: Veranstaltung, 52.37314221005079, 9.740101002500348, [[23.05.2026|CSD Hannover]] marker: Party, 52.258800059415925, 10.48277036498852, [[09.08.2026|Holy]] diff --git a/Dailys/2026/04 April/Wohnorte.md b/Dailys/2026/Wohnorte.md similarity index 100% rename from Dailys/2026/04 April/Wohnorte.md rename to Dailys/2026/Wohnorte.md diff --git a/Documents/Aktuelles Änderungsangebot Instant Payments Verordnung 2025.pdf b/Documents/Aktuelles Änderungsangebot Instant Payments Verordnung 2025.pdf index 61e0c40..6edb14a 100644 Binary files a/Documents/Aktuelles Änderungsangebot Instant Payments Verordnung 2025.pdf and b/Documents/Aktuelles Änderungsangebot Instant Payments Verordnung 2025.pdf differ diff --git a/Documents/Aktuelles Änderungsangebot.pdf b/Documents/Aktuelles Änderungsangebot.pdf index 28b05a8..22074b1 100644 Binary files a/Documents/Aktuelles Änderungsangebot.pdf and b/Documents/Aktuelles Änderungsangebot.pdf differ diff --git a/Documents/Arbeit/IFN/2023_musterhiwi_arbeitsstunden_arbeitszeitkkonto.pdf b/Documents/Arbeit/IFN/2023_musterhiwi_arbeitsstunden_arbeitszeitkkonto.pdf index 7070a58..e55b006 100644 Binary files a/Documents/Arbeit/IFN/2023_musterhiwi_arbeitsstunden_arbeitszeitkkonto.pdf and b/Documents/Arbeit/IFN/2023_musterhiwi_arbeitsstunden_arbeitszeitkkonto.pdf differ diff --git a/Documents/Arbeit/IFN/2023_musterhiwi_arbeitsstunden_arbeitszeitkkonto.xlsx b/Documents/Arbeit/IFN/2023_musterhiwi_arbeitsstunden_arbeitszeitkkonto.xlsx index deea7c7..524b9b9 100644 Binary files a/Documents/Arbeit/IFN/2023_musterhiwi_arbeitsstunden_arbeitszeitkkonto.xlsx and b/Documents/Arbeit/IFN/2023_musterhiwi_arbeitsstunden_arbeitszeitkkonto.xlsx differ diff --git a/Documents/Arbeit/IFN/Arbeitsstunden.xlsx b/Documents/Arbeit/IFN/Arbeitsstunden.xlsx index a77faaf..bc50b61 100644 Binary files a/Documents/Arbeit/IFN/Arbeitsstunden.xlsx and b/Documents/Arbeit/IFN/Arbeitsstunden.xlsx differ diff --git a/Documents/Arbeit/IFN/Architecture.svg b/Documents/Arbeit/IFN/Architecture.svg index 3a46ef2..df639f3 100644 --- a/Documents/Arbeit/IFN/Architecture.svg +++ b/Documents/Arbeit/IFN/Architecture.svg @@ -1 +1,3 @@ -
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- - - diff --git a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/1.Tutorial.ipynb b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/1.Tutorial.ipynb index 10f42ea..c1e95d6 100644 --- a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/1.Tutorial.ipynb +++ b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/1.Tutorial.ipynb @@ -1,2068 +1,3 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "92fe3a94-61b2-47f9-9485-02aa8b103d5c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-e72aa2f84c4b1cb7", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# 1. Programmierübung: Python Tutorial\n", - "\n", - "
\n", - "
\n", - " Willkommen zur ersten Programmierübung Einführung in Python 3.\n", - "
\n", - " \n", - "
\n", - " \n", - "Python ist eine universelle Programmiersprache, die aufgrund ihrer Einfachheit sehr leicht zu lernen und zu bedienen ist. Die Funktionalität kann durch den Import von Bibliotheken erweitert werden. Im Folgenden werden wir Ihnen zeigen, wie man hier im Jupyter Notebook Python Code ausführen kann. Die grundlegenden Konzepte und Strukturen in Python werden mit Hilfe von externen Quellen gezeigt. Die Übungsaufgaben dienen zum Testen und der Hands-on-Praxis des gelernten Wissens. \n", - "\n", - "In diesem Jupyter Notebook werden die grundlegende Funktionen und Konzepte in Python vorgestellt. Dazu wird es kleine Programmierübungen um das gelernte Wissen in Beispielen anzuwenden. (Objekt Orientierte Programmierung werden wir in diesem Kurs nicht behandeln!)\n", - "\n", - "Das Jupyter Notebook ist in Zellen unterteilt, die durch Boxen gekennzeichnet sind, die einzeln ausgeführt werden können (entweder über `Shift + Enter` oder den `Run`-Knopf). Sie können auch alle Zellen im Notebook ausführen über `Kernel > Restart & Run All` oder dem \"Vorspulen\"-Zeichen.\n", - "\n", - "Bitte beachten Sie, dass alle Zellen im Notebook ein gemeinsamen Workspace nutzen. Das bedeutet, dass Bibliotheken nur einmal importiert werden müssen und dann innerhalb des Notebooks genutzt werden können. Es können jedoch auch Variablen überschrieben werden, wenn diese nicht richtig gekapselt werden (z.B. über Funktionen).\n", - "\n", - "Viel Spaß und Erfolg!\n", - "\n", - "Es gibt _sehr_ viele weitere Python-Tutorials online, z.B. auf [Youtube](https://youtu.be/kqtD5dpn9C8), mit denen Sie die benötigten Grundlagen für Python lernen können.\n", - "\n", - "Wenn Sie Fragen oder Verbesserungsvorschläge zum Inhalt oder Struktur der Notebooks haben, dann können Sie eine E-Mail an Phil Keier([p.keier@hbk-bs.de](mailto:p.keier@hbk-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) oder Martin Le ([le@tu-bs.de](mailto:martin.le@tu-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) schreiben.\n", - "\n", - "Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n", - "\n", - "Der Großteil des Python-Tutorials stammt aus der Veranstaltung _Deep Learning Lab_ und von [www.python-kurs.eu](https://www.python-kurs.eu/python3_kurs.php) und wurden für _Signale und Systeme_, sowie _Einführung in die Programmierung für Nicht Informatiker_ angepasst.\n", - "\n", - "---\n" - ] - }, - { - "cell_type": "markdown", - "id": "6ac5a9e3-7190-4db6-859b-3cb1511fe29f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c8293a61ad8fb19c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Printing\n", - "\n", - "Für viele Anwendungsfälle ist es wichtig, dass der Computer mit uns als Mensch kommunizieren kann. Zu diesem Zweck lernen wir zuerst wie wir eine Ausgabe erzeugen können. Hierzu verwenden wir die Funktion `print()`.\n", - "\n", - "[print()](https://www.w3schools.com/python/ref_func_print.asp) ist eine BuiltIn Funktion, zu diesen später mehr. Es soll aber gesagt sein, dass keinerlei anstrengungen notwendig sind um die Print-Funktion zu verwenden, da Python sie von Haus aus kennt.\n", - "\n", - "## Hello World\n", - "\n", - "Schauen wir uns nun folgend ein einfaches Programm an:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "3733ea3b-12b5-4c1c-9b03-0f1c397babfc", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5f71f94cf9d603e2", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hello World\n" - ] - } - ], - "source": [ - "print(\"Hello World\")" - ] - }, - { - "cell_type": "markdown", - "id": "708d34f1-a479-4277-90e7-14d67a8c688a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d49c16a44b1d3984", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Der Teil in den Klammern ist der Wert den wir ausgeben wollen in diesem Fall eine einfache Zeichenkette (auch dazu später mehr).\n", - "\n", - "Damit zu ersten **Aufgabe**: Geben Sie den Text `Hallo Python` aus. *1 Punkt*" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "88b8d4db-6f4c-411c-b8ad-d2d6ca0b427c", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-cd36c0330024bfe5", - "locked": false, - "points": 1, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hallo Python\n" - ] - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "print(\"Hallo Python\")\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "ad8d06df-5d3d-4203-b17c-227a663c8e7b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-95b07b67b0ede718", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Datentypen und Variablen\n", - "\n", - "Python unterstützt verschiedene Datentypen. Zu diesen Zählen :\n", - "1. Integer (Ganze Zahlen) $$\\mathbb{Z} = \\{1,-1,2,-2,3,-3,\\dots\\}$$\n", - "2. Floatings Point Numbers (Fließkommazahlen) $$\\pi = 3.141592653589793$$\n", - "3. Strings (Zeichenketten)\n", - "> \"Ich bin eine Zeichenkette\"\n", - "\n", - "4. Listen\n", - "> [Objekt1, Objekt2, 42]\n", - "\n", - "5. Dictionaries\n", - "> {\"Schlüssel1\": \"Wert1\", \"Schlüssel2\": \"Wert2\",}\n", - "\n", - "6. Sets\n", - "> {\"Wert1\", 7, \"Zeichenkette\"}\n", - "\n", - "7. Tupel\n", - "> (42, 7)" - ] - }, - { - "cell_type": "markdown", - "id": "313d37b0-52ad-4de4-9fc5-0f362bfe7284", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-12a63250c85469f1", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Zahlentypen (Floats, Integers)\n", - "\n", - "### Aufgabe 1-1: Zuweisungen von Variablen" - ] - }, - { - "cell_type": "markdown", - "id": "65dae625-2f0b-46d2-803c-c3d6c54000f7", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-782aef1600674714", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Eine Definition und Zuweisung eines Wertes zu einer Variablen erfolgt über den `=` Operator." - ] - }, - { - "cell_type": "markdown", - "id": "fca50dd5-4a56-451a-9a1a-6cbd8d76519a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7b71098cec169f0f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Aufgabe** *2 Punkte*: \n", - "\n", - "Definieren Sie zunächst die zwei Variablen `a` und `b` und initialisieren diese mit einem Integerwert ungleich `0`:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d7d24e44-8581-4c5c-a723-83b9f7e418ac", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-7be930fd387f1043", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "scrolled": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "a = 1\n", - "b = -2\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8cfd850c-02c7-4b32-b20c-e37f5eb4fe8b", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-d44ec6114b65557c", - "locked": true, - "points": 2, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "assert isinstance(a, int)\n", - "assert isinstance(b, int)\n", - "\n", - "assert a != 0\n", - "assert b != 0" - ] - }, - { - "cell_type": "markdown", - "id": "337fbcc5-c960-4206-83d1-605d05a51d5d", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-2f79b7b52775db8b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Aufgabe** *2 Punkte*:\n", - "\n", - "Definieren Sie zwei Variablen `s` und `t` und initialisieren diese mit einem Floatwert ungleich `0`:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "03b7469a-4f59-437f-aa8f-797d200b41a1", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-7d48f9bed0df944d", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "s = 1.5\n", - "t = -2.7\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "45404bbe-2026-491d-9aef-9d3ee8894adf", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-3b426f39262c1e03", - "locked": true, - "points": 2, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "assert isinstance(s, float)\n", - "assert isinstance(t, float)\n", - "\n", - "assert s != 0\n", - "assert t != 0" - ] - }, - { - "cell_type": "markdown", - "id": "5452589d-5997-4e8f-b8bb-363925c6166a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8690aecc1748ad4a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe 1-2: Operationen auf Zahlen\n", - "\n", - "Aus der Schule sollten die folgenden Grunlegenden Operationen die sich auf Zahlen ausführen lassen bekannt sein:\n", - "\n", - "1. Addition $\\Rightarrow a+b=c$\n", - "2. Subtraktion $\\Rightarrow a-b=c$\n", - "3. Multiplikation $\\Rightarrow a\\cdot b=c$\n", - "4. Division $\\Rightarrow\\frac{a}{b} = c$\n", - "> Teilt man zwei Integer durcheinander werden diese erst in Floats umgewandelt und dann als Float gespeichert: $$10/3=3.3333333333$$\n", - "> Die Integer Division (Ganzzahl Division $\\lfloor\\frac{a}{b}\\rfloor$) (Notiert mit \"//\") zweier Zahlen schneidet den Rest nach dem Komma ab: $$10//3\\equiv 3$$\n", - "\n", - "5. Modulus $\\Rightarrow a\\mod b \\equiv c$\n", - "> \"Teilen mit Rest\" (in Python notiert mit \"%\" hat nichts mit Prozenten zutun) funktioniert genauso wie man die Uhr lesen würde. Ist es 15 Uhr sagt man im Sprachgebrauch 3 Uhr (Mittags). Der Modulus Operator funktioniert genauso. $$15 \\mod 12 \\equiv 3$$\n", - "\n", - "6. Exponentation $\\Rightarrow a^b = c$\n", - "> In Python notiert mit \"a**b\"" - ] - }, - { - "cell_type": "markdown", - "id": "9e408960-4180-4e92-ba88-ebf39441dfa7", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c4551eabf148e18e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Aufgabe** *2 Punkte*:\n", - "\n", - "Addieren Sie die Werte der Variablen `a` und `b` und speichern Sie das Ergebnis in der Variable `c`:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "6aaa0c05-ae16-4c48-b841-79931dae94bd", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-2ff97153b6652687", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "-1" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "c = a + b\n", - "# END SOLUTION\n", - "c" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "2fa4d7f2-235d-411e-956c-4ff335705124", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-3ba3833c7220bbb7", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "assert isinstance(c, int)\n", - "### BEGIN HIDDEN TESTS\n", - "assert a + b == c\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "9fccb90e-1f22-46b9-99a1-14b64274ece5", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-f80a3165c27dc297", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Aufgabe** *5 Punkte*:\n", - "\n", - "Nutzen Sie die Variablen `a` & `b` und Speichern Sie die Ergebnisse für die Multiplikation, Division, Ganzzahldivision, Exponentiation und den Modulo-Operator in den unten angegebenen Variablen:\n", - "\n", - "\\begin{align}\n", - "m &= a\\cdot b\\\\\n", - "d &= \\frac{a}{b}\\\\\n", - "i &= \\lfloor\\frac{a}{b}\\rfloor\\\\\n", - "e &= a^b\\\\\n", - "r &= a\\; \\textrm{mod}\\; b\n", - "\\end{align}\n", - "\n", - "\n", - "Die Ausführung der anderen arithmetischen Operationen in Python erfolgt analog. Eine Übersicht können Sie [hier](https://www.python-kurs.eu/operatoren.php) entnehmen." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "30ce28b3-b97d-4b3f-bac0-6d01356dcc60", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-3f3640eaf7ee2dd3", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "### BEGIN SOLUTION\n", - "m = a*b\n", - "d = a/b\n", - "i = a//b\n", - "e = a**b\n", - "r = a%b\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "95108d9d-4cba-489b-bdbc-ed5c71316ac8", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-804a957c4a02e824", - "locked": true, - "points": 5, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "### BEGIN HIDDEN TESTS\n", - "assert m == a*b\n", - "assert d == a/b\n", - "assert i == a//b\n", - "assert e == a**b\n", - "assert r == a%b\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "444c4633-9186-497f-88a7-e2ec0013dff4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7ac5c4d8e6463b16", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Sequentielle Datentypen\n", - "\n", - "Sequentielle Datentypen sind ein wichtiger Bestandteil in der Programmierung. Dazu gehören Listen, Tupel und Strings.\n", - "\n", - "Wichtige Eigenschaften dieser Datentypen sind:\n", - "- Die Elemente von Listen, Strings oder Tupeln sind in einer bestimmten Reihenfolge angeordnet (Diese entspricht der Ordnung in der die Elemente eingefügt worden).\n", - "- Der Zugriff (Lesen und Schreiben) dieser Objekte erfolgt über Indizes (Das erste Element eines Sequentiellen Datentypes ist immer `0`).\n", - "- Zugriff auf Elemente kann auch Rückwärts erfolgen das letzte Element wird dann mit `-1` ausgelesen. \n", - "\n", - "Beispiel für eine Liste:\n", - "`some_list = [\"a\", \"b\", \"c\"]`\n", - "\n", - "Beispiel für ein Tupel:\n", - "`some_tuple = (1, 2, 3)`\n", - "\n", - "Beispiel für ein String:\n", - "`some_string = \"Python ist cool!\"`\n", - "\n", - "### Aufgabe 2-1: Strings\n", - "\n", - "Zeichenketten, Text oder Strings lassen sich in Python mit `'Text'`, `\"Text\"` oder der Funktion `str()` definieren.\n", - "\n", - "**Aufgabe** *2 Punkte*:\n", - "\n", - "Ein String-Objekt (Text) können sie mit Hilfe von `'Some Text'` oder `\"Some Text2\"` definieren. Definieren sie die Variable `text` mit einem beliebigen Text." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "f8028cf5-0dc4-4e72-a98e-3e18705c8c98", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-73a9beb04648359b", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "text = \"Hi Mom, I am on TV!\"\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "7a6832ae-e6c7-4230-b3ca-b1f1f90345fb", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-1677fa4f3b4eec12", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "assert isinstance(text, str)" - ] - }, - { - "cell_type": "markdown", - "id": "8e10cd2a-53dc-485f-bbdd-aa2709574660", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-05f0b0cd1211c396", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Python Strings lassen sich mit verschiedenen mitteln formatieren. Dazu zählt die [format-Funktion](https://www.w3schools.com/python/ref_string_format.asp) \n", - "\n", - "**Aufgabe** *1 Punkte*:\n", - "\n", - "Geben Sie die Variablen `a` & `b` aus Aufgabe 1 im format `\"a = 12 und b = 12\"` (Die Werte sollen dann den Werten aus ihrer Definition entsprechen. 12 ist hier nur ein Beispiel) aus." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d3efbadb-3c33-40eb-9260-8f8b11faaf75", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-c94a5b5e9f73479e", - "locked": false, - "points": 1, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a = 1 und b = -2\n" - ] - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "print(\"a = {} und b = {}\".format(a, b))\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "d860edba-749c-41c0-827b-0c68ba6fd00a", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-e4c5420224d04f6a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe 2-2: Listen \n", - "\n", - "Listen lassen sich mit der Funktion `list()` oder `[]` definieren und können eine \"unendliche\" Menge an Elementen unterschiedlichen Datentyps speichern. Die Liste `[420, \"Hallo Jupyter\", 0.222]` ist eine Korrekt definierte Liste. Im Allgemeinen ist es Ratsam listen mit gleichem Datentyp zu füllen, da dies bei der Verarbeitung zu Problemen führen kann." - ] - }, - { - "cell_type": "markdown", - "id": "51416edc-4c96-437c-a1ff-c458f06c9e8a", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-36d12824040df91e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Aufgabe** *1 Punkte*: \n", - "\n", - "Definieren Sie die Variable `l` und weisen Sie dieser Variable eine Liste mit aufsteigenden Integerwerten von `0` bis `4` zu." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "b361ee09-cd48-4c16-89ea-714ee8ab541f", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-89d74b5c210fc331", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "l = list(range(5))\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "65fcfdb4-58ff-47d3-bebd-d3786a971af2", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-589caab43851d55a", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "### BEGIN HIDDEN TESTS\n", - "assert isinstance(l, list)\n", - "assert l == [0, 1, 2, 3, 4]\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "a6dfba7d-4bb5-4530-bb75-689843a718a8", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-5ca56027cd6a5698", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Aufgabe** *1 Punkte*:\n", - "\n", - "Hängen Sie der Liste `l` noch den Wert `42` an.\n", - "\n", - "Hinweis: Nutzen Sie dafür die Methode [.append](https://www.w3schools.com/python/ref_list_append.asp)." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "e39e50dc-3d97-4579-aeb4-04ec2de3dbb3", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-853db222010bee68", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "l.append(42)\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "53000a6c-1187-48b0-b038-129d434cc45a", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-c1aca9603460d1de", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "### BEGIN HIDDEN TESTS\n", - "assert l == [0, 1, 2, 3, 4, 42]\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "4510d2f3-3386-4d33-baa5-1d684ab52370", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c58e5530e380c09a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Zugriff auf Elemente eines Sequentiellen Datentypes lassen sich über `[]` realisieren.\n", - "\n", - "Beispiel - Zugriff auf das erste Element einer Liste:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "259d73e8-eca3-4172-8a0a-1efbbe3b527b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-cb1b7e8055910efc", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "l[0]" - ] - }, - { - "cell_type": "markdown", - "id": "bf6d1243-9650-4c27-b3dc-86a2d48b3abc", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1d8edfe975ed19bf", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Aufgabe** *1 Punkte*:\n", - "\n", - "Geben Sie das dritte Element der Liste `l` aus.\n", - "\n", - "Hinweis: Achten Sie darauf das der erste Index immer `0` ist. " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "358a0b51-3bfa-4e65-9cd4-ee2e7f8bc9d5", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-a386250119dc89fb", - "locked": false, - "points": 1, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2\n" - ] - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "print(l[2])\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "2f25fa6d-9a72-464b-ada6-d2630dd03e92", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0ff369c64d2f8c24", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Aufgabe** *1 Punkte*:\n", - "\n", - "Geben Sie das vorletzte Element der Liste `l` aus.\n", - "\n", - "Hinweis: Achten Sie darauf das der letzte Index mit `-1` ausgegeben wird" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "16ec2e20-e28e-41de-8f85-1091e41bb401", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-2394235b49ebb749", - "locked": false, - "points": 1, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4\n" - ] - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "print(l[-2])\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "291522bc-9c4d-4348-b2cb-99f9653168fa", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c8fe8cb9d2ca1028", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe 2-3: Dictionaries\n", - "\n", - "Das Dictionary ist ein Datentyp, welcher Schlüssel-Werte-Paare speichert. Dabei wird ein Dictionary mit `dict()` oder `{\"Schlüssel1\": \"Wert1\"}` initalisiert. Wichtig ist hierbei das ein Dictionary nicht mit `{}` initialisiert werden kann da dies die Notation für das **Set** Objekt ist.\n", - "\n", - "**Aufgabe** *1 Punkte*:\n", - "\n", - "Initialisieren Sie die Dictionary Variable `my_dict` mit folgendem Mapping:\n", - "\n", - "| Key | Value |\n", - "|:----|:------|\n", - "| `\"apple\"` | `\"Apfel\"` |\n", - "| `\"banana\"` | `\"Banane\"` |\n", - "| `\"cherry\"` | `\"Kirsche\"` |" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "c17338bb-c6df-493c-9d88-0e9ea36a755d", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-86ce3695bf3f6780", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "### BEGIN SOLUTION\n", - "my_dict = {\"apple\": \"Apfel\", \"banana\": \"Banane\", \"cherry\": \"Kirsche\"}\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "a367442e-2c8c-4d32-8acb-5bccf94d64fb", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-969a9415b60857a8", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "assert isinstance(my_dict, dict)\n", - "### BEGIN HIDDEN TESTS\n", - "assert my_dict == {\"apple\": \"Apfel\", \"banana\": \"Banane\", \"cherry\": \"Kirsche\"}\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "8ec10cc1-4b8b-4b53-b1d1-991d6287abda", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0f5df3b99a4774ba", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Aufgabe** *1 Punkte*:\n", - "\n", - "Fügen Sie nun das Key-Value Paar `\"pear\": \"Birne\"` zu `my_dict` hinzu." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "d3aac185-2d6e-4b30-b247-89be8aeeab7d", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-ed3cf3b9d6a8ad58", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "### BEGIN SOLUTION\n", - "my_dict[\"pear\"] = \"Birne\"\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "c377ec37-b382-4f83-a9cc-829a44b7682e", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-9735fc9ff4416c4c", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "### BEGIN HIDDEN TESTS\n", - "assert my_dict == {\"apple\": \"Apfel\", \"banana\": \"Banane\", \"cherry\": \"Kirsche\", \"pear\": \"Birne\"}\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "2b3cfdf2-6864-402c-9ded-9fd0c7c489ee", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-957ca6c50c1cfb70", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Für gewisse Anwendungen reicht es nur die Schlüssel (oder Werte) aus einem Dictionary zu haben. Dazu bietet das Dictionary die Funktionen `.keys()` (für eine Liste der Schlüssel) und `.values()` (für eine Liste der Werte).\n", - "\n", - "**Aufgabe** *1 Punkte*:\n", - "\n", - "Speichere die Werte des Dictionaries `my_dict` in der Variablen `values`." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "03f2c31a-04b4-4dc7-ab00-476cec6922ad", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-f190c63e28ae9e82", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "### BEGIN SOLUTION\n", - "values = my_dict.values()\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "441997b9-3d46-4d2f-bb64-56ebbe4f693c", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-73d4d3b4007a89c6", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Hier werden ihre Lösungen getestet ...\n", - "### BEGIN HIDDEN TESTS\n", - "for v1, v2 in zip(values, my_dict.values()):\n", - " assert v1 == v2\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "6e774e49-895b-4bb2-9436-cddb75a3d46d", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5bd0f8a189d6db1c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Wichtiger für die meisten Probleme ist die Dictionary Funktion `.items()` diese gibt eine Liste an Tupeln mit den Schlüssel Werte Paaren aus.\n", - "\n", - "**Aufgabe** *1 Punkte*:\n", - "\n", - "Speichern Sie die Elemente des Dictionaries `my_dict`, welche mit der Funktion `.items()` ausgegeben werden, in der Variablen `items`. " - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "a399cf66-43eb-4749-8864-18c5e4202f79", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-03afb00cc074d1ef", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "### BEGIN SOLUTION\n", - "items = my_dict.items()\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "ceebb41b-1e38-458a-9afd-e9f898ef4268", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-727afc0671fb1f19", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Hier werden ihre Lösungen getestet...\n", - "### BEGIN HIDDEN TESTS\n", - "for t1, t2 in zip(items, my_dict.items()):\n", - " assert t1 == t2\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "a8d62b7e-ae53-4bd7-a930-84508c3948f9", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-83b1e45bc901dc68", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Funktionen\n", - "\n", - "Mit einigen Funktionen haben wir uns bereits befasst dazu zählen `print()`, `.keys()` und alle weiteren die diesem Schema folgen.\n", - "\n", - "In diesem Kapitel wollen wir uns mit dem Aufbau von Funktionen befassen. Dabei folgt jede Funktion folgendem Aufbaue:\n", - "\n", - "```python\n", - "def some_function_name(param1, param2):\n", - " a = do_something1(param1)\n", - " b = do_something2(a, param2)\n", - " do_something3(b)\n", - " return b\n", - "```\n", - "\n", - "Das `def`-Schlüsselwort leitet die Definition einer Funktion ein, gefolgt von dem Funktionsnamen, den Eingabeparametern der Funktion in runden Klammern und einem Doppelpunkt. Wichtig ist, dass die Anweisungen innerhalb der Funktion eingerückt sein müssen. Das Ergebnis (oder die Ergebnisse) werden mit Hilfe des `return`-Schlüsselworts gekennzeichnet.\n", - "\n", - "**Aufgabe** *1 Punkte*:\n", - "\n", - "Schreibe eine Funktion `successor` die auf jede Eingabe `+1` rechnet." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "1a151db2-617c-48f4-969b-3bafb45b1fd1", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-c6a731a4a13b2bbc", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "def successor(n):\n", - " return n+1\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "615a98b6-139e-485b-874e-d0a70cd22517", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-00693d8d9c92af76", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "assert successor(1) == 2" - ] - }, - { - "cell_type": "markdown", - "id": "d6f47a61-b692-4f46-8ccc-83af10189f93", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-9c358751403a1986", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Aufgabe** *1 Punkte*:\n", - "\n", - "Schreibe eine Funktion `add` mit den Eingabeparametern `a` & `b`, welche die Werte von `a` & `b` miteinander addiert." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "3f101d21-aa1a-4bf3-aadf-bb4e41d8fe12", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-2b72cf583fed9b8c", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "def add(a,b):\n", - " return a+b\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "1d155c26-0875-4a71-8564-a4a0e0e3bb70", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-7a24b5cfd7fc9990", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "assert add(1,2) == 3" - ] - }, - { - "cell_type": "markdown", - "id": "d77ab363-9fe0-4504-b43b-6f7894666525", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0fd1dbfed99faa8a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Kontrollstruckturen\n", - "\n", - "## Aufgabe 3-1: Conditionals - If-Else\n", - "\n", - "Um Entscheidungen treffen zu können nutzt man in Python das Kommando `if ` ist der Ausdruck wahr wird der darauf folgende Code ausgeführt.\n", - "\n", - "Liste von möglichen Ausdrücken:\n", - "\n", - "- `a == b` checkt ob die Werte `a` & `b` gleich sind\n", - "- `a != b` checkt ob die Werte `a` & `b` **nicht** gleich sind\n", - "- `a > b` checkt ob der Wert `a` größer als `b` ist (Analog dazu \"größer gleich\" `a >= b`)\n", - "- `a < b` checkt ob der Wert `a` kleiner als `b` ist (Analog dazu \"kleiner gleich\" `a <= b`)\n", - "- `not ` invertiert das Ergebnis des Ausdruckes, also aus einem wahren Ausdruck wird ein falscher und andersherum.\n", - "\n", - "Zur Verkettung von Ausdrücken:\n", - "\n", - "- ` and ` checkt ob die Ausdrücke `ausdruck1` & `ausdruck2` wahr also erfüllt sind\n", - "- ` or ` checkt ob einer der Ausdrücke `ausdruck1` & `ausdruck2` wahr also erfüllt sind\n", - "\n", - "Beispiel:" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "4363cda5-98c5-4424-8fb3-7c92e1994993", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-020d46673782a358", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "You're the Number One\n" - ] - } - ], - "source": [ - "zahl = 1\n", - "if zahl == 1:\n", - " print(\"You're the Number One\")\n", - "\n", - "if zahl == 2:\n", - " print(\"You Lose\")" - ] - }, - { - "cell_type": "markdown", - "id": "21a23914-d0b7-492d-bcfe-8ecde9c20c85", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-aa2d59d677afd5a3", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Das Kommando `else` funktioniert nur zusammen mit dem `if` Kommando und bietet dem Programm eine Art \"Fall Back\":" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "3eaf3062-ae81-48c0-802d-88a72db587be", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-f4124dd62687158f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "You're not the Number One\n" - ] - } - ], - "source": [ - "zahl = 5\n", - "if zahl == 1:\n", - " print(\"You're the Number One\")\n", - "else:\n", - " print(\"You're not the Number One\")" - ] - }, - { - "cell_type": "markdown", - "id": "7e35f339-3f59-4fd6-a162-e6eb0379c778", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-167fb232c7163fe6", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Um auf mehrere Ausdrücke zu checken kann das `elif` verwendet werden. Es findet seinen Platz zwischen `if` & `else`:" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "e37dd53c-e375-4548-9563-c8c6664dfdd0", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-0c0312666fa7648f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "You're the Number Two\n" - ] - } - ], - "source": [ - "zahl = 2\n", - "if zahl == 1:\n", - " print(\"You're the Number One\")\n", - "elif zahl == 2:\n", - " print(\"You're the Number Two\")\n", - "elif zahl == 2:\n", - " print(\"You're the Number Three\")\n", - "else:\n", - " print(\"You're not the Number One\")" - ] - }, - { - "cell_type": "markdown", - "id": "5248404d-4717-462d-a67f-57431e599945", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-060bd6eb927fa8b1", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Aufgabe** *1 Punkte*:\n", - "\n", - "Schreibe eine Funktion `is_odd` mit einem Eingabeparameter `n` die prüft ob die eingegebene Zahl ungerade ist.\n", - "\n", - "Wenn die Zahl gerade ist gebe den Text `\"Gerade Zahl\"` und bei ungerade `\"Ungerade Zahl\"` zurück." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "d4b1cef3-6222-438d-bbc3-243431fad0cb", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-77afd241bc69d6b1", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "def is_odd(n):\n", - " if n % 2 == 0:\n", - " return \"Gerade Zahl\"\n", - " else:\n", - " return \"Ungerade Zahl\"\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "141d73da-90e6-401a-a651-ad14635de5b7", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-d8541ba8c61147c3", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "assert is_odd(2).lower() == \"Gerade Zahl\".lower()\n", - "assert is_odd(3).lower() == \"Ungerade Zahl\".lower()" - ] - }, - { - "cell_type": "markdown", - "id": "c0086b8c-da02-4fdf-8341-9c0518bb6406", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-69409d8dcfe070e1", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Aufgabe 3-2: Sequentielles - While Loop\n", - "\n", - "*7 Punkte*\n", - "\n", - "Schleifen sind wichtig um eine Aufgabe öfter zu wiederholen. \n", - "\n", - "Schauen wir uns dazu zunächst den `while`-loop an. Die Syntax schaut wie folgt aus:\n", - "\n", - "```python\n", - "while :\n", - " do_something()\n", - "```\n", - "\n", - "Solange der Ausdruck nach dem `while` wahr ist wird die Schleife ausgeführt. **!Vorsichtig!** solange der Ausdruck wahr bleibt und nie falsch wird hört die Schleife nie auf zu laufen.\n", - "\n", - "**Aufgabe**: Schreibe eine Funktion `fubar` mit Eingabeparameter `n`.\n", - "Die Funktion soll wie folgt definiert sein:\n", - "\n", - "- Der Eingabeparameter `n` ist ein Integer, Floats geben `False` zurück\n", - "- Negative zahlen & 0 beenden die Funktion und geben `False` zurück\n", - "- Die Funktion zählt bis einschließlich dem Eingabeparameter\n", - " bsp.: $n=9 \\rightarrow 1, 2, 3, \\dots, 9$\n", - "- Bei jedem Schleifendurchlauf soll die Zahl bei der sich die Schleife gerade befindet mittels `print` ausgegeben werden werden.\n", - "- Ist der zurzeitige Schleifendurchlauf durch `3` teilbar, gebe mittels `print` denn String `Foo` aus.\n", - "- Ist der zurzeitige Schleifendurchlauf durch `5` teilbar, gebe mittels `print` denn String `Bar` aus.\n", - "- Ist der zurzeitge Schleifendurrchlauf durch `3 & 5` teilbar, gebe mittels `print` den String `FooBar` aus.\n", - "\n", - "**Tipp**: Implementiere nicht alles aufeinmal sollte Schritt für Schritt und teste deine Lösung nach jedem Schritt.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "d1f074e2-c036-445c-97f6-618f5aa4cedb", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-0796f3b2cbac6f8e", - "locked": false, - "points": 4, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "def fubar(n: int):\n", - " if isinstance(n, float) or n < 1:\n", - " return False\n", - "\n", - " count = 1\n", - " while count <= n:\n", - " msg = count\n", - " if count % 3 == 0:\n", - " msg = \"Foo\"\n", - " if count % 5 == 0:\n", - " msg = \"Bar\"\n", - " if count % 15 == 0:\n", - " msg = \"FooBar\"\n", - " \n", - " count += 1\n", - " print(msg, end=', ')\n", - " \n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "22784528-9205-4575-84ef-0060732cd053", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-f7774d4246e958a6", - "locked": true, - "points": 3, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fubar to 4\n", - "1, 2, Foo, 4, \n", - "Fubar to 6\n", - "1, 2, Foo, 4, Bar, Foo, \n", - "Fubar to 16\n", - "1, 2, Foo, 4, Bar, Foo, 7, 8, Foo, Bar, 11, Foo, 13, 14, FooBar, 16, \n", - "Fubar to 200\n", - "1, 2, Foo, 4, Bar, Foo, 7, 8, Foo, Bar, 11, Foo, 13, 14, FooBar, 16, 17, Foo, 19, Bar, Foo, 22, 23, Foo, Bar, 26, Foo, 28, 29, FooBar, 31, 32, Foo, 34, Bar, Foo, 37, 38, Foo, Bar, 41, Foo, 43, 44, FooBar, 46, 47, Foo, 49, Bar, " - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "print(\"Fubar to 4\")\n", - "fubar(4)\n", - "print(\"\\nFubar to 6\")\n", - "fubar(6)\n", - "print(\"\\nFubar to 16\")\n", - "fubar(16)\n", - "print(\"\\nFubar to 200\")\n", - "fubar(50)\n", - "### BEGIN HIDDEN TESTS\n", - "assert fubar(-1) == False\n", - "assert fubar(0) == False\n", - "assert fubar(.1) == False\n", - "### END HIDDEN TESTS" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +version https://git-lfs.github.com/spec/v1 +oid sha256:f3527efa4266b5cecc53586cf3c7069c1be83372893f624156d6b68c5976eff2 +size 51917 diff --git a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/10.Data_Analysis.ipynb b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/10.Data_Analysis.ipynb index 9ceb7d0..c817f54 100644 --- a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/10.Data_Analysis.ipynb +++ b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/10.Data_Analysis.ipynb @@ -1,1221 +1,3 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0362a1eb-1e46-462a-b22c-f972c398741d", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-e3006ef8308b2f34", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# 10. Programmierübung: Datenanalyse\n", - "\n", - "
\n", - "
\n", - " Willkommen zur zehnten Programmierübung Einführung in Python 3.\n", - "
\n", - " \n", - "
\n", - "\n", - "Wenn Sie Fragen oder Verbesserungsvorschläge zum Inhalt oder Struktur der Notebooks haben, dann können sie eine E-Mail an Phil Keier ([p.keier@hbk-bs.de](mailto:p.keier@hbk-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) oder Martin Le ([martin.le@tu-bs.de](mailto:martin.le@tu-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) schreiben.\n", - "\n", - "Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n", - "\n", - "Der Großteil des Python-Tutorials stammt aus der Veranstaltung _Deep Learning Lab_ und von [www.python-kurs.eu](https://www.python-kurs.eu/python3_kurs.php) und wurde für _Signale und Systeme_, sowie _Einführung in die Programmierung für Nicht Informatiker_ angepasst.\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "0a97a824-bc2f-4f9d-a730-42ff05828cc8", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a5071e148ad2227c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Data Analysis\n", - "\n", - "In dieser & letzter Aufgabe ist dir das Datenset `survey.csv` gegeben. Diese Übung dient als Vorbereitung auf die abschließende Prüfung und wird dieshingehend auch streng bewertet. Jede Aufgabe setzt sich aus 6 Punkten zusammen von denen jeweils 4 auf den Schriftteil und 2 Punkte auf den Programmatischen Teil fallen. \n", - "\n", - "Gegeben ist immer eine Hypothese, beweise/widerlege die Hypothese und zeige auch die Gegenhypothese auf. Stelle zu jeder Aufgabe Annahmen an, Erkläre kurz dein Vorgehen, Interpretiere dein Ergebniss, Schließe mit einem angemessenen Fazit ab und erkläre jede von dir selbst erstellte Grafik. Gebe auch alle verwendeten Quellen die benutzt wurden an. \n", - "\n", - "Da zahlen nicht Aussagen gilt auch, dass sofern der Schriftteil nicht beantwortet wurde die Aufgabe mit 0 Punkten bewertet wird.\n", - "\n", - "Antworten sind in Englisch & Deutsch möglich andere Sprachen werden nicht akzeptiert.\n", - "\n", - "Die Daten stammen aus folgender Umfrage [Survey](https://forms.gle/JVKq6FrSUE8kN7Jq6).\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "ad9d17d9-dd59-4766-bb46-de4df822ce0f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3da446eb91bdd35d", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Hypothesis 1\n", - "\n", - "Der Kurs besteht überwiegend aus männlichen Individuum, welche überwiegend Windows verwenden und sich überpropotional als gute Programmierer einschätzen." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8e24289e-a738-4ae4-a6d4-a936263120fc", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-a6a23c7c132e44d9", - "locked": false, - "points": 2, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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ZeitstempelWie Alt bist du?Bewerte deine bisherige Erfahrung mit Gruppenarbeiten an der UniversitätWelches Geschlecht hast du?Was studieren du?Auf einer Skala von 1-5 (1 - absolut unzufrieden, 5 - absolut zufrieden), wie beurteilst du deinen Studiengang?Nutzt du ein Smartphone?Wenn Ja, von welcher Marke ist ihr Smartphone?Bewerte die allgemeine Politische Situation in Deutschland.Auf einer Skala von 1-5 (1 - Sehr gut, 5 - Garnicht), wie gut schätzen du dich selbst ein die Programmiersprache Python zu beherrschen ?Besitzen du einen Heimrechner (Laptop, Standrechner, etc.; Kein Smartphone!)Wenn Ja, welches Betriebsystem verwendst du?Ich habe mich (in meinem bisherigen leben) mindestens einmal strukturell benachteiligt gefühlt.Auf einer Skala von 1-5 (5 Sehr gut, 1 Garnicht), wie gut schätz du dich selbst ein Programmieren zu können?Ich komme aus...
02025/01/17 3:01:58 PM MEZ223WeiblichMedienwissenschaften2JaApple13JaWindows 11Ja2dem normalem Mittelstand
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152025/01/17 3:03:26 PM MEZ213WeiblichMedienwissenschaften3JaApple24JaWindows 10Nein2dem normalem Mittelstand
162025/01/17 3:03:28 PM MEZ212WeiblichMedienwissenschaften2JaApple32JaMac OSNein3Einem sehr gut situierten Haushalt
172025/01/17 3:03:31 PM MEZ224WeiblichMedienwissenschaften5JaApple34JaWindows 10Ja2dem normalem Mittelstand
182025/01/17 3:03:31 PM MEZ203WeiblichMedienwissenschaften3JaApple24JaWindows 10Nein2dem normalem Mittelstand
192025/01/17 3:04:07 PM MEZ213WeiblichSonstige4JaApple23JaWindows 11Ja2dem normalem Mittelstand
202025/01/17 3:04:11 PM MEZ233WeiblichSonstige4JaApple13JaMac OSJa3dem normalem Mittelstand
212025/01/17 3:04:13 PM MEZ263MännlichSonstige4JaApple23JaWindows 10Ja3dem normalem Mittelstand
222025/01/17 3:07:39 PM MEZ214WeiblichMedienwissenschaften4JaApple23JaMac OSJa2dem normalem Mittelstand
232025/01/17 3:11:27 PM MEZ2043WeiblichMedienwissenschaften3JaApple24JaMac OSJa1ärmlichen Verhältnissen
242025/01/17 3:11:54 PM MEZ213WeiblichMedienwissenschaften4JaXiaomi33JaWindows 11Nein2dem normalem Mittelstand
252025/01/17 3:49:36 PM MEZ193WeiblichMedienwissenschaften3JaSamsung34JaWindows 11Nein3dem normalem Mittelstand
\n", - "
" - ], - "text/plain": [ - " Zeitstempel Wie Alt bist du? \\\n", - "0 2025/01/17 3:01:58 PM MEZ 22 \n", - "1 2025/01/17 3:02:20 PM MEZ 23 \n", - "2 2025/01/17 3:02:20 PM MEZ 30 \n", - "3 2025/01/17 3:02:24 PM MEZ 21 \n", - "4 2025/01/17 3:02:26 PM MEZ 27 \n", - "5 2025/01/17 3:02:31 PM MEZ 21 \n", - "6 2025/01/17 3:02:43 PM MEZ 22 \n", - "7 2025/01/17 3:02:44 PM MEZ 23 \n", - "8 2025/01/17 3:02:46 PM MEZ 21 \n", - "9 2025/01/17 3:02:58 PM MEZ 22 \n", - "10 2025/01/17 3:02:59 PM MEZ 21 \n", - "11 2025/01/17 3:03:00 PM MEZ 24 \n", - "12 2025/01/17 3:03:07 PM MEZ 25 \n", - "13 2025/01/17 3:03:14 PM MEZ 24 \n", - "14 2025/01/17 3:03:18 PM MEZ 21 \n", - "15 2025/01/17 3:03:26 PM MEZ 21 \n", - "16 2025/01/17 3:03:28 PM MEZ 21 \n", - "17 2025/01/17 3:03:31 PM MEZ 22 \n", - "18 2025/01/17 3:03:31 PM MEZ 20 \n", - "19 2025/01/17 3:04:07 PM MEZ 21 \n", - "20 2025/01/17 3:04:11 PM MEZ 23 \n", - "21 2025/01/17 3:04:13 PM MEZ 26 \n", - "22 2025/01/17 3:07:39 PM MEZ 21 \n", - "23 2025/01/17 3:11:27 PM MEZ 204 \n", - "24 2025/01/17 3:11:54 PM MEZ 21 \n", - "25 2025/01/17 3:49:36 PM MEZ 19 \n", - "\n", - " Bewerte deine bisherige Erfahrung mit Gruppenarbeiten an der Universität \\\n", - "0 3 \n", - "1 3 \n", - "2 3 \n", - "3 4 \n", - "4 4 \n", - "5 2 \n", - "6 2 \n", - "7 3 \n", - "8 3 \n", - "9 2 \n", - "10 4 \n", - "11 3 \n", - "12 4 \n", - "13 3 \n", - "14 3 \n", - "15 3 \n", - "16 2 \n", - "17 4 \n", - "18 3 \n", - "19 3 \n", - "20 3 \n", - "21 3 \n", - "22 4 \n", - "23 3 \n", - "24 3 \n", - "25 3 \n", - "\n", - " Welches Geschlecht hast du? Was studieren du? \\\n", - "0 Weiblich Medienwissenschaften \n", - "1 Weiblich Medienwissenschaften \n", - "2 Männlich Medienwissenschaften \n", - "3 Weiblich Medienwissenschaften \n", - "4 Weiblich Medienwissenschaften \n", - "5 Weiblich Sonstige \n", - "6 Weiblich Medienwissenschaften \n", - "7 Weiblich Sonstige \n", - "8 Weiblich Medienwissenschaften \n", - "9 Weiblich Medienwissenschaften \n", - "10 Weiblich Medienwissenschaften \n", - "11 Weiblich Medienwissenschaften \n", - "12 Weiblich Medienwissenschaften \n", - "13 Weiblich Medienwissenschaften \n", - "14 Weiblich Medienwissenschaften \n", - "15 Weiblich Medienwissenschaften \n", - "16 Weiblich Medienwissenschaften \n", - "17 Weiblich Medienwissenschaften \n", - "18 Weiblich Medienwissenschaften \n", - "19 Weiblich Sonstige \n", - "20 Weiblich Sonstige \n", - "21 Männlich Sonstige \n", - "22 Weiblich Medienwissenschaften \n", - "23 Weiblich Medienwissenschaften \n", - "24 Weiblich Medienwissenschaften \n", - "25 Weiblich Medienwissenschaften \n", - "\n", - " Auf einer Skala von 1-5 (1 - absolut unzufrieden, 5 - absolut zufrieden), wie beurteilst du deinen Studiengang? \\\n", - "0 2 \n", - "1 4 \n", - "2 3 \n", - "3 3 \n", - "4 2 \n", - "5 4 \n", - "6 3 \n", - "7 3 \n", - "8 3 \n", - "9 3 \n", - "10 3 \n", - "11 3 \n", - "12 3 \n", - "13 3 \n", - "14 3 \n", - "15 3 \n", - "16 2 \n", - "17 5 \n", - "18 3 \n", - "19 4 \n", - "20 4 \n", - "21 4 \n", - "22 4 \n", - "23 3 \n", - "24 4 \n", - "25 3 \n", - "\n", - " Nutzt du ein Smartphone? Wenn Ja, von welcher Marke ist ihr Smartphone? \\\n", - "0 Ja Apple \n", - "1 Ja Apple \n", - "2 Ja Sonstige \n", - "3 Ja Apple \n", - "4 Ja Samsung \n", - "5 Ja Apple \n", - "6 Ja Apple \n", - "7 Ja Sonstige \n", - "8 Ja Apple \n", - "9 Ja Apple \n", - "10 Ja Apple \n", - "11 Ja Apple \n", - "12 Ja Apple \n", - "13 Ja Apple \n", - "14 Ja Apple \n", - "15 Ja Apple \n", - "16 Ja Apple \n", - "17 Ja Apple \n", - "18 Ja Apple \n", - "19 Ja Apple \n", - "20 Ja Apple \n", - "21 Ja Apple \n", - "22 Ja Apple \n", - "23 Ja Apple \n", - "24 Ja Xiaomi \n", - "25 Ja Samsung \n", - "\n", - " Bewerte die allgemeine Politische Situation in Deutschland. \\\n", - "0 1 \n", - "1 2 \n", - "2 3 \n", - "3 2 \n", - "4 2 \n", - "5 3 \n", - "6 2 \n", - "7 2 \n", - "8 2 \n", - "9 2 \n", - "10 2 \n", - "11 1 \n", - "12 2 \n", - "13 1 \n", - "14 3 \n", - "15 2 \n", - "16 3 \n", - "17 3 \n", - "18 2 \n", - "19 2 \n", - "20 1 \n", - "21 2 \n", - "22 2 \n", - "23 2 \n", - "24 3 \n", - "25 3 \n", - "\n", - " Auf einer Skala von 1-5 (1 - Sehr gut, 5 - Garnicht), wie gut schätzen du dich selbst ein die Programmiersprache Python zu beherrschen ? \\\n", - "0 3 \n", - "1 4 \n", - "2 3 \n", - "3 4 \n", - "4 4 \n", - "5 4 \n", - "6 5 \n", - "7 3 \n", - "8 4 \n", - "9 4 \n", - "10 4 \n", - "11 3 \n", - "12 3 \n", - "13 3 \n", - "14 4 \n", - "15 4 \n", - "16 2 \n", - "17 4 \n", - "18 4 \n", - "19 3 \n", - "20 3 \n", - "21 3 \n", - "22 3 \n", - "23 4 \n", - "24 3 \n", - "25 4 \n", - "\n", - " Besitzen du einen Heimrechner (Laptop, Standrechner, etc.; Kein Smartphone!) \\\n", - "0 Ja \n", - "1 Ja \n", - "2 Ja \n", - "3 Ja \n", - "4 Ja \n", - "5 Ja \n", - "6 Ja \n", - "7 Ja \n", - "8 Ja \n", - "9 Ja \n", - "10 Ja \n", - "11 Ja \n", - "12 Ja \n", - "13 Ja \n", - "14 Ja \n", - "15 Ja \n", - "16 Ja \n", - "17 Ja \n", - "18 Ja \n", - "19 Ja \n", - "20 Ja \n", - "21 Ja \n", - "22 Ja \n", - "23 Ja \n", - "24 Ja \n", - "25 Ja \n", - "\n", - " Wenn Ja, welches Betriebsystem verwendst du? \\\n", - "0 Windows 11 \n", - "1 Mac OS \n", - "2 Windows 11 \n", - "3 Mac OS \n", - "4 Mac OS \n", - "5 Mac OS \n", - "6 Mac OS \n", - "7 Mac OS \n", - "8 Windows 11 \n", - "9 Windows 11 \n", - "10 Windows 11 \n", - "11 Windows 10 \n", - "12 Windows 10 \n", - "13 Mac OS \n", - "14 Mac OS \n", - "15 Windows 10 \n", - "16 Mac OS \n", - "17 Windows 10 \n", - "18 Windows 10 \n", - "19 Windows 11 \n", - "20 Mac OS \n", - "21 Windows 10 \n", - "22 Mac OS \n", - "23 Mac OS \n", - "24 Windows 11 \n", - "25 Windows 11 \n", - "\n", - " Ich habe mich (in meinem bisherigen leben) mindestens einmal strukturell benachteiligt gefühlt. \\\n", - "0 Ja \n", - "1 Ja \n", - "2 Nein \n", - "3 Ja \n", - "4 Ja \n", - "5 Nein \n", - "6 Ja \n", - "7 Ja \n", - "8 Ja \n", - "9 Ja \n", - "10 Ja \n", - "11 Nein \n", - "12 Ja \n", - "13 Ja \n", - "14 Ja \n", - "15 Nein \n", - "16 Nein \n", - "17 Ja \n", - "18 Nein \n", - "19 Ja \n", - "20 Ja \n", - "21 Ja \n", - "22 Ja \n", - "23 Ja \n", - "24 Nein \n", - "25 Nein \n", - "\n", - " Auf einer Skala von 1-5 (5 Sehr gut, 1 Garnicht), wie gut schätz du dich selbst ein Programmieren zu können? \\\n", - "0 2 \n", - "1 2 \n", - "2 3 \n", - "3 4 \n", - "4 3 \n", - "5 4 \n", - "6 1 \n", - "7 3 \n", - "8 2 \n", - "9 2 \n", - "10 2 \n", - "11 1 \n", - "12 3 \n", - "13 3 \n", - "14 2 \n", - "15 2 \n", - "16 3 \n", - "17 2 \n", - "18 2 \n", - "19 2 \n", - "20 3 \n", - "21 3 \n", - "22 2 \n", - "23 1 \n", - "24 2 \n", - "25 3 \n", - "\n", - " Ich komme aus... \n", - "0 dem normalem Mittelstand \n", - "1 Einem sehr gut situierten Haushalt \n", - "2 dem normalem Mittelstand \n", - "3 dem normalem Mittelstand \n", - "4 ärmlichen Verhältnissen \n", - "5 dem normalem Mittelstand \n", - "6 dem normalem Mittelstand \n", - "7 dem normalem Mittelstand \n", - "8 dem normalem Mittelstand \n", - "9 ärmlichen Verhältnissen \n", - "10 dem normalem Mittelstand \n", - "11 dem normalem Mittelstand \n", - "12 dem normalem Mittelstand \n", - "13 dem normalem Mittelstand \n", - "14 dem normalem Mittelstand \n", - "15 dem normalem Mittelstand \n", - "16 Einem sehr gut situierten Haushalt \n", - "17 dem normalem Mittelstand \n", - "18 dem normalem Mittelstand \n", - "19 dem normalem Mittelstand \n", - "20 dem normalem Mittelstand \n", - "21 dem normalem Mittelstand \n", - "22 dem normalem Mittelstand \n", - "23 ärmlichen Verhältnissen \n", - "24 dem normalem Mittelstand \n", - "25 dem normalem Mittelstand " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "### BEGIN SOLUTION\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "80cf1c27-644a-4538-abbc-7d5f524251d7", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-9cbb6c488a06eb18", - "locked": false, - "points": 4, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "source": [] - }, - { - "cell_type": "markdown", - "id": "171340b9-0302-4c7c-ac85-f81d1e57a9da", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3fc34b3f943dfa4b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Hypothesis 2\n", - "\n", - "Geisteswissenschaftliche Studierende neigen dazu das Betriebsystem Mac Os zu verwenden. Unteranderem wird auch angenommen, dass diese Gruppe überwiegend das Smartphone Betriebsystem Android verwendet. Daraus ableitend ist anzunehmen das diese Gruppe sich selbst als mittelmäßige Python Programmierer einschätzt." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "98fb0abc-0d50-4f4c-aeb0-16b056aeeabf", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-b28aca63b8048f70", - "locked": false, - "points": 2, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [], - "source": [ - "### BEGIN SOLUTION\n", - "\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "7ae161df-8dd8-4828-99cc-3ba28d2bf9dd", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-016f2b30651bea18", - "locked": false, - "points": 4, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "source": [] - }, - { - "cell_type": "markdown", - "id": "3b77bda1-5851-4394-81c7-fd649cb980e5", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-fae767f8aab85e92", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Hypothesis 3\n", - "\n", - "Studierende aus gut situierten Haushalten neigen dazu Gruppenarbeiten an der Uni als schlecht einzuschätzen. Insbesondere dann wenn Sie sich selbst als Gute Programmierer einschätzen." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "0d74bd0d-e83e-4211-bc58-b9615e36b848", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-34a9cdeebcf2d7b9", - "locked": false, - "points": 2, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [], - "source": [ - "### BEGIN SOLUTION\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "b9bb8c23-e161-492c-b1ee-9cc4c21c2325", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-81264718d58ae0f6", - "locked": false, - "points": 4, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "source": [] - }, - { - "cell_type": "markdown", - "id": "3fa617bb-77dd-4c43-b30b-b422eac770ef", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d532947366781d90", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Hypothesis 4\n", - "\n", - "Personen die bereits strukturelle Benachteiligung erfahren haben sind meistens älter als der Durschnitt der Samplegruppe und in ärmlicheren Verhältnissen aufgewachsen. Ihr bevorzugtes Smartphone ist ein Samsung." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "18467418-586b-4153-9066-0de730994e8e", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-5284bedc957436b3", - "locked": false, - "points": 2, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [], - "source": [ - "### BEGIN SOLUTION\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "3f5dace2-5a13-4806-946c-0d0e4c9bc307", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-90f0108fe83268c7", - "locked": false, - "points": 4, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "source": [] - }, - { - "cell_type": "markdown", - "id": "c6fdcfa2-5507-4c85-b262-c83e6b1074ee", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-6ace0ffb9a32d3b1", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Hypothesis 5\n", - "\n", - "Jüngere Studierende betrachten die zurzeitige Politische Situation in Deutschland als schlecht. Unteranderem deshalb bewerten Sie Gruppenarbeiten als etwas gutes, da so ihr sozialer Zusammenhalt gestärkt wird" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "5a76e273-a649-4bdf-ba8d-bc1351d0ec08", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-18c2e79244ca1beb", - "locked": false, - "points": 2, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [], - "source": [ - "### BEGIN SOLUTION\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "8bd2c8fa-801a-4c0a-8754-61bdc2f8127b", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-a421f0cc1a31c697", - "locked": false, - "points": 4, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +version https://git-lfs.github.com/spec/v1 +oid sha256:2c687fc764c6e58646872fa0c70e6e949984116102fd7516ae6d5f72e1ce7885 +size 58305 diff --git a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/2.Tutorial.ipynb b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/2.Tutorial.ipynb index 886e2a1..c3bf1a0 100644 --- a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/2.Tutorial.ipynb +++ b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/2.Tutorial.ipynb @@ -1,1462 +1,3 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "079afb70-639e-4955-8ca7-1c290cbf08a9", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7057e40105900012", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# 2. Programmierübung: Python Tutorial\n", - "\n", - "
\n", - "
\n", - " Willkommen zur zweiten Programmierübung Einführung in Python 3.\n", - "
\n", - " \n", - "
\n", - "\n", - "Wenn Sie Fragen oder Verbesserungsvorschläge zum Inhalt oder Struktur der Notebooks haben, dann können sie eine E-Mail an Phil Keier ([p.keier@hbk-bs.de](mailto:p.keier@hbk-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) oder Martin Le ([martin.le@tu-bs.de](mailto:martin.le@tu-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) schreiben.\n", - "\n", - "Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n", - "\n", - "Der Großteil des Python-Tutorials stammt aus der Veranstaltung _Deep Learning Lab_ und von [www.python-kurs.eu](https://www.python-kurs.eu/python3_kurs.php) und wurde für _Signale und Systeme_, sowie _Einführung in die Programmierung für Nicht Informatiker_ angepasst." - ] - }, - { - "cell_type": "markdown", - "id": "85fd88de-a9ee-4149-8bed-1b8ebc0bbad4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-26e0f96baeb79aac", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Kontrollstruckturen\n", - "\n", - "## Sequentielles - For Loop\n", - "\n", - "Python verwendet eine spezielle Form des 'for-loops' dabei handelt es sich sprachlich um den 'for-each loop'.\n", - "\n", - "Mittlerweile hat jede nennenswerte Programmiersprache das Konzept des 'for-each loops' auf seine Weise implementiert. Python hingegen nutzt diesen als Standard. Sprachen wie JavaScript, C/C++, etc. verwenden standardmässig eine 'Zählschleife', dabei wird meist von '0' angefangen bis zu einem Grenzwert 'n' gezählt.\n", - "\n", - "Ein schönes beispiel bietet hierfür JavaScript:\n", - "\n", - "```js\n", - "for (let i = 0; i < arr.length; i++) {\n", - " // do something\n", - "} \n", - "```\n", - "\n", - "Zu lesen ist dies wie folgt: \"Für ein i mit dem Wert 0 (let i = 0), zähle bis i größer die Länge von Array arr (i < arr.length) und erhöhe nach jedem Schleifendurchlauf den Wert von i um 1 (i++)\"\n", - "\n", - "In Python sehe selbiger Code wie folgt aus:\n", - "\n", - "```python\n", - "for i in range(0,len(arr)):\n", - " # do something\n", - "```\n", - "\n", - "Zu lesen ist dies wie folgt: \"Für jedes (for each) i in dem Intervall/Menge 0 bis arr.length mach etwas\"\n", - "\n", - "Der Unterschied besteht darin das Python jedes Element einer Menge durchläuft, der Catch liegt darin das es absolut unabhängig davon ist wie die Menge aussieht. Widmen wir uns zunächst einer Aufgabe:" - ] - }, - { - "cell_type": "markdown", - "id": "7215a3e7-a240-43ec-914c-10221d8b28b0", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-80add6da9914f961", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe \n", - "\n", - "*3 Punkte*\n", - "\n", - "Schreibe eine Funktion `sum_up` mit Eingabeparameter `n`, welcher die Zahlen von `1...n` aufsummiert.\n", - "\n", - "Nutze dafür einen `for-loop`.\n", - "\n", - "**Beispiel**:\n", - "\n", - "$$n = 5$$ \n", - "$$sum\\_up(5) \\rightarrow 1 \\rightarrow 1 + 2 = 3 \\rightarrow 3 + 3 = 6 \\rightarrow 6 + 4 = 10 \\rightarrow 10 + 5 = 15$$\n", - "\n", - "Hinweis: die Funktion `range()` zählt standardmässig von `0...n-1`. Schauen Sie sich gerne dazu die offizielle Dokumentation an [PEP 204](https://peps.python.org/pep-0204/#list-ranges)." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "5426ddf1-2d2f-4c92-b007-2f6eca61703f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d43ef87a62b03cdf", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "def sum_up(n: int) -> int:\n", - " count = 0\n", - " for i in range(1,n+1):\n", - " count += i\n", - " return count\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "3c38a839-3ab0-466c-98f9-189c35fc5025", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-cff511e86dce0377", - "locked": true, - "points": 3, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "15\n" - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "print(sum_up(5))\n", - "### BEGIN HIDDEN TESTS\n", - "for n in range(3,12):\n", - " assert sum(range(n+1)) == sum_up(n)\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "4e6dfa94-18b9-4fb2-830a-83202d034752", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-02370acddb67290d", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Nachdem wir nun gelernt haben wie man mit der Built-In Funktion 'range' zählen kann, schauen wir uns folgend ein paar Beispiele an wie in Python eigentlich Iteriert werden soll.\n", - "\n", - "#### Beispiel 1 - Iterieren über eine Liste:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "db89c7c5-6efc-49bb-be92-414a7334ed84", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-dd3ea63dd3b1d927", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "square_numbers = [1,4,9,16,25,36]\n", - "for number in square_numbers:\n", - " print(number)" - ] - }, - { - "cell_type": "markdown", - "id": "6413a239-c334-491e-8062-7f78f75182fe", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-9bc7f123a8fb7680", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "#### Beispiel 2 - Iterieren über ein Dictionary:\n", - "\n", - "Erweitern wir Beispiel 1 und arbeiten nun mit einem Dictionary. Dieses Besteht wie Sie noch aus dem ersten Tutorial Wissen immer aus 'key-value' paaren. Mit der Built-In Funktion `.items()` bekommen wir ein Tuple an Werten zurück, welches erst entpackt werden muss. Dazu behilft uns der 'for-loop' indem einfach 2 variabeln gleichzeitig deklariert werden. (Achtung! Mit `.items()` werden die 'key-value' paare als '(key, value)' zurückgegeben)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "116ce552-a5c0-4c9c-8d89-fe1f3e40bdba", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-72122af8e519273b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "square_numbers_dict = {1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36}\n", - "\n", - "for key, value in square_numbers_dict.items():\n", - " print(key, \"->\" , value)" - ] - }, - { - "cell_type": "markdown", - "id": "b4e748de-0603-41c9-8282-86e92923e358", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-52b4d0167c7fb9ba", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "#### Beispiel 3 - Iteration mit Zählen:\n", - "\n", - "Die Built-In Funktion `enumerate()` [PEP 279](https://peps.python.org/pep-0279/) ermöglicht das Zählen und gleichzeitige iterieren über eine Liste.\n", - "Dabei wird wieder ein Tuple zurückgegeben welches die Form '(index, value)' annimmt." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8cbf9142-2cf3-4579-9e19-799ee9b25a54", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-29953c29ed4bcdcf", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "alphabet = [\"a\", \"b\", \"c\", \"d\"]\n", - "for index, buchstabe in enumerate(alphabet):\n", - " print(index, \"->\", buchstabe)" - ] - }, - { - "cell_type": "markdown", - "id": "4add1ce5-e462-4be3-8bd7-9960d86ae780", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b64ce270167d3025", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Mit den traditionellen Mitteln lässt sich der absolut Selbe Output generieren. Das verwenden von `enumerate()` ist jedoch eleganter:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d2e8274b-00d4-4042-adbf-937aea8f0e7e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8b2e3cb4e0c977f2", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "alphabet = [\"a\", \"b\", \"c\", \"d\"]\n", - "for index in range(len(alphabet)):\n", - " print(index, \"->\", alphabet[index])" - ] - }, - { - "cell_type": "markdown", - "id": "b504f072-53ce-4d03-9f72-b4d4ba85ae74", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5e8d9fc47a709ba4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe\n", - "\n", - "*2 Punkte*\n", - "\n", - "Ihnen ist das Dictionary `dict2` gegeben. Ändern Sie jeden Wert in dem Dictionary nach der Formel $f(x) = x^3-1$ mittels `for-loop`.\n", - "\n", - "Tipp: Lassen Sie sich nicht von den Schlüsseln verwirren." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "abd323c0-5e1b-4c14-a65a-9d54a06f3a80", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0361320c63b2cb03", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "dict2 = {\"a\": 56, 5: 12, \"python\": 9, 3.14: 1.141414}\n", - "### BEGIN SOLUTION\n", - "dict2 = {key: value**3-1 for key, value in dict2.items()}\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "7e0f9ac4-c5d4-44b6-a2fc-db98e2b46356", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-82eec3cba623ab8f", - "locked": true, - "points": 2, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'a': 175615, 5: 1727, 'python': 728, 3.14: 0.48706374396146557}\n" - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "print(dict2)\n", - "### BEGIN HIDDEN TESTS\n", - "d = {\"a\": 56, 5: 12, \"python\": 9, 3.14: 1.141414}\n", - "d = {key: value**3-1 for key, value in d.items()}\n", - "assert d == dict2\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "ecc21f6a-2ea0-41a0-9e56-04faae5a0fc6", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-9ffa970f1cdac592", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Mit dem Unterstrich `_` als Zählvariable werden `for`-loops gekennzeichnet die ihre Zählvariable nicht verwenden:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "786461b8-ecf9-4afb-8b93-6186f53f6e97", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8316e1e4eaa0ea0b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Python is Nice!\n", - "Python is Nice!\n", - "Python is Nice!\n" - ] - } - ], - "source": [ - "for _ in range(3):\n", - " print(\"Python is Nice!\")" - ] - }, - { - "cell_type": "markdown", - "id": "7fd320aa-6bb5-4b9b-9593-2df69cb2ca1e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-cb8f33d1ae55a6a4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## List Comprehension\n", - "\n", - "Seit dem Proposal von [PEP 202](https://peps.python.org/pep-0202/) gibt es in Python die List Comprehension.\n", - "\n", - "Für diese Vorlesung ist es nicht nötig das Sie die Syntax anwenden können, Sie sollten zumindest verstehen wie diese funktioniert.\n", - "\n", - "Angenommen wir haben folgende Mathematische beschreibung einer Menge $$\\{x^2 \\vert x \\in \\mathbb{N}\\}$$\n", - "\n", - "Dies beschreibt die Funktion $f(x) = x^2$ für alle natürlichen Zahlen.\n", - "\n", - "In Python ist es möglich genau diese Menge in einer einzigen Zeile Abzubilden. Dazu wird die List Comprehension verwendet deren Syntax im allgemeinen folgende Form hat:\n", - "\n", - "```python\n", - "[ for value in ]\n", - "```\n", - "\n", - "Schauen wir uns dazu an wie wir die Quadrat Zahlen von `1...6` also `1...36` erzeugen." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7bcdc9d9-cc5b-49be-8cfb-ca40eb3b7796", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-4fd6f801463c5ea6", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "squared = [n*n for n in range(1,7)]\n", - "print(squared)" - ] - }, - { - "cell_type": "markdown", - "id": "b6dcb24c-e01e-4522-8ac4-074adfe6105a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c6922240434c9d3a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Probieren Sie sich gerne selber aus.\n", - "\n", - "### Zusatzaufgabe \n", - "\n", - "*Keine Punkte*\n", - "\n", - "Erstellen Sie eine List mittels List Comprehension, welche die Zahlen `1...6` auf deren kubische Zahl `1...216` also der Funktion $f(x) = x^3$ abbildet." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "522f3228-1797-4ca2-9103-44fce48dfd4a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1dc645632c5f653a", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "cubics = []\n", - "### BEGIN SOLUTION\n", - "cubics = [n**3 for n in range(1,7)]\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "7dc19b9f-116b-4741-a798-a66d495d477e", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-4e441b6db861559e", - "locked": true, - "points": 0, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1, 8, 27, 64, 125, 216]\n" - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "print(cubics)\n", - "### BEGIN HIDDEN TESTS\n", - "c = [n**3 for n in range(1,7)]\n", - "assert c == cubics\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "5b1355f0-29ed-4318-92f2-51f151c7946e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7d9eebd920496d59", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# System Interactions\n", - "\n", - "Im folgenden Abschnitt beschäftigen wir uns mit dem Eingeben von Daten in ein Programm. Dies geschieht entweder 'von Hand', also über den Benutzer, oder über Dateien." - ] - }, - { - "cell_type": "markdown", - "id": "976a7ad0-856d-4fb6-bba8-485668df22a2", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-e2df7221e04e8c54", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Im Normalfall wollen wir größere Datenmengen einlesen. Dazu nutzen wir die Built-In Funktion `open` ([Python Docs - Open](https://docs.python.org/3/library/functions.html?highlight=open#open)). Auch wenn in der Doku viele Parameter stehen benötigt man im Normalfall nur zwei. Der erste ist der Name der File, der zweite im welchem Modus die Datei geöffnet werden soll (Angegeben als String).\n", - "\n", - "Zum bearbeiten der Datei nutzen wir den Python Kontext Manager - das `with`-Statement. Bei externen Daten ist es immer wichtig die Datei auch wieder zu schließen, damit es nicht zu Datenverlust oder Arbeitsspeichermüll kommt. Daher ist der Lebenzyklus einer Datei in einem Programm immer `Datei öffnen` -> `Datei Bearbeiten` -> `Datei schließen`. Kommt es in einer der Drei schritte zu einem Fehler, bleibt die Datei im RAM hängen und der Computer muss neu gestartet werden um diesen Speicher wieder Frei zu geben.\n", - "\n", - "Daher gibt es Kontext Manager. Dieser versichert dem Programmierer, dass das schließen der Datei immer (!!) passiert. Die Syntax folgt folgender Strucktur:\n", - "\n", - "```python\n", - "with as :\n", - " # do something\n", - "```\n", - "\n", - "Dabei ist `` ein Objekt (für uns eine Datei) und `` die Zuweisung zu einer Variablen. Für die Funktion `open` (im Lesemodus) sieht der Kontext wie folgt aus:\n", - "\n", - "```python\n", - "with open(\"filename.txt\", \"r\") as f:\n", - " f.readlines() # do something with f\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "a345cdf5-d1a0-4bd8-9f77-06e6d4562e00", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-58fb9b7e476f3ef2", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe\n", - "\n", - "*2 Punkte*\n", - "\n", - "Erstellen und Öffnen sie eine Datei `testfile.txt` mit der `open` Funktion, nutzen Sie dafür das `with`-Statement.\n", - "\n", - "Schreiben Sie in diese Datei 100 mal den String `\"Python\\n\"`. " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "f11f7a0b-bcca-4db0-a6ca-bf95c70c7303", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0735bb589edcc6a8", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "with open('testfile.txt', 'w') as f:\n", - " for _ in range(100):\n", - " f.write(\"Python\\n\")\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "c91e07bb-fc41-42c1-8b42-0aca56c57c35", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-2592f8b51914455e", - "locked": true, - "points": 2, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "### BEGIN HIDDEN TESTS\n", - "with open('testfile.txt', 'r') as f:\n", - " lines = f.readlines()\n", - " assert len(lines) == 100\n", - " for line in lines:\n", - " assert line == 'Python\\n'\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "4adb5400-8749-4790-ae49-68a8622b4a3d", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-39769ee8cbf2157d", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe\n", - "\n", - "*2 Punkte*\n", - "\n", - "Öffnen Sie die zuvor erstellte Datei `testfile.txt` im Lesemodus und weißen Sie den Inhalt der `.readlines()` Funktion der Variabeln `lines` zu. " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "adf300e9-ee63-4da1-a6f3-c768fa2b5fc9", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0a3b9e01dd66e134", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "lines = None\n", - "# BEGIN SOLUTION\n", - "with open('testfile.txt', 'r') as f:\n", - " lines = f.readlines()\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "229499d6-bb33-492b-af5d-a26ab3b3f5d4", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-aa7c104b5f3f2572", - "locked": true, - "points": 2, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Anzahl der gelesenen Zeilen: 100\n" - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "print(\"Anzahl der gelesenen Zeilen:\", len(lines))\n", - "### BEGIN HIDDEN TESTS\n", - "with open('testfile.txt', 'r') as f:\n", - " assert f.readlines() == lines\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "47d92dde-16a1-4c11-a452-cadd74255db2", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-be695e1423a6ccf4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Import Statement\n", - "\n", - " \n", - "\n", - "Da wir nicht immer das Rad neu erfinden wollen nutzen wir Bibliotheken von anderen Entwicklern.\n", - "\n", - "Dazu nutzt man das Keyword `import` gefolgt von dem Modul welches man Importieren möchte. Nutzen wir als Beispiel `numpy`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ced37c65-87d2-48d0-a0ba-4674dcaf104c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7d19506e181bcda9", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import numpy" - ] - }, - { - "cell_type": "markdown", - "id": "f5506975-e1cd-434a-8ef1-05a7a06992df", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b7d981325bea8b84", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Möchte man nun eine Funktion aus dem Modul nutzen folgt die Syntax der Strucktur `.`. Dazu folgendes Numpy Beispiel:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "69c68b31-d0f7-469b-ae54-eef871ec6ad6", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-25ad372a576a793e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "numpy.array(range(100))" - ] - }, - { - "cell_type": "markdown", - "id": "7c5d65db-d1e8-4cad-884e-bb595c57d445", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0176c541098f7c21", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe\n", - "\n", - "*3 Punkte*\n", - "\n", - "Importiere Python Built-In Library `random` und rufe zuerst aus dem Modul die Funktion `seed` auf mit dem Eingabewert `42`, und weiße danach der Variable `rand` den Wert des Funktionsaufrufes von `randint(1,100)` zu. " - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "0d80bc9f-6923-4e3f-8a70-909548e693a6", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8ccc5fe1848176c8", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "rand = None\n", - "# BEGIN SOLUTION\n", - "import random\n", - "random.seed(42)\n", - "rand = random.randint(1,100)\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "ada0211b-03bf-463a-b932-2bad621d5559", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-d7817c3dd1ee34f9", - "locked": true, - "points": 3, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "82\n" - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "print(rand)\n", - "### BEGIN HIDDEN TESTS\n", - "assert rand == 82\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "f7b3bdcc-825e-4607-8804-a5ce3d39ace5", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-2cc1d2ed682d1b0e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Das Keyword `as` ist bereits bekannt, dieses kann auch verwendet werden um Module beim import umzubennen. Dies ist dann Hilfreich wenn Module lange Namen haben.\n", - "\n", - "Numpy wird im Internet immer mit np abgekürzt. Beispiel:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f115eae9-500b-448e-a1be-e3b43ffad0fd", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-60f357d6dda4a8d3", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "np.array(range(100))" - ] - }, - { - "cell_type": "markdown", - "id": "f7570244-0fea-4683-a19c-069f6ba619dd", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-94fb7e92492a12f7", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe\n", - "\n", - "*1 Punkt*\n", - "\n", - "Importieren Sie die Built-In Library `datetime` als `dt`." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "e7e6c246-6dc4-4555-a202-73887b5f8249", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-59dc2ded4f59471f", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "import datetime as dt\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "b03ee34b-8520-4106-a23f-4419e54dcfcc", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-d77ffdb7f9ba178d", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-11-01 19:32:22.691479\n" - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "print(dt.datetime.now()) # UTC Time also Standard Greenwich Zeit\n", - "### BEGIN HIDDEN TESTS\n", - "assert 'dt' in dir()\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "1cc39ffb-1207-4aa8-a4fe-51df2335fea6", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c55653efe0a77419", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Möchte man nur eine Bestimmte Funktion aus einem Modul haben nutzt man die `import from` Syntax. Beispiel Pretty Print:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "638e6c20-7bba-4862-9acc-34031bae8514", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-f84e2596969633f5", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{0: 1,\n", - " 1: 2,\n", - " 2: 4,\n", - " 3: 8,\n", - " 4: 16,\n", - " 5: 32,\n", - " 6: 64,\n", - " 7: 128,\n", - " 8: 256,\n", - " 9: 512,\n", - " 10: 1024,\n", - " 11: 2048,\n", - " 12: 4096,\n", - " 13: 8192,\n", - " 14: 16384,\n", - " 15: 32768,\n", - " 16: 65536,\n", - " 17: 131072,\n", - " 18: 262144,\n", - " 19: 524288}\n" - ] - } - ], - "source": [ - "from pprint import pprint\n", - "pprint({n: 2**n for n in range(20)})" - ] - }, - { - "cell_type": "markdown", - "id": "c1e077c1-59a5-411b-bf71-32ea66dc731d", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0ac2347c47ff5774", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe\n", - "\n", - "*2 Punkte*\n", - "\n", - "Importieren Sie die Funktion `sqrt` aus dem Built-In Modul `math`.\n", - "Berechnen Sie $\\sqrt4$. Speichern Sie das Ergebnis in der variablen `s4`." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "81838f9b-558c-49b3-90ef-647e28380a97", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7c1ea8bca61d5c12", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "from math import sqrt\n", - "s4 = sqrt(4)\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "6d984a74-a93d-4865-b6d1-d2dec8586907", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-83c667de468e9ef8", - "locked": true, - "points": 2, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.0\n" - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "print(s4)\n", - "### BEGIN HIDDEN TESTS\n", - "assert 'sqrt' in dir()\n", - "assert int(s4) == 2\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "6edf5dce-17d2-4a5f-9d96-2c7c3091de88", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-814b3bfa4b6e049a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Ein letzter Hinweis: Es gibt auch die Möglichkeit in der `import from` Syntax das Keyword `as` zu verwenden.\n", - "\n", - "Beispiel aus dem Modul `dataclasses` importiert `dataclass` als `dclass`:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "b707bf52-546f-4689-8216-c5ad0b9665a7", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-9961359e9d09d79b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "from dataclasses import dataclass as dclass\n", - "print(dclass)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +version https://git-lfs.github.com/spec/v1 +oid sha256:1d9b05d2b9278073660f8e3b84e31eac2d53c4362c1c9567c2f669757f9225dd +size 36048 diff --git a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/3.Extended_Applications.ipynb b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/3.Extended_Applications.ipynb index 8838baf..3f7dbc6 100644 --- a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/3.Extended_Applications.ipynb +++ b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/3.Extended_Applications.ipynb @@ -1,1661 +1,3 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "1ddc11f3-0a8a-47ee-b7b7-6dfd22ac40d9", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-8c0ecbebdebcad39", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Extended Applications: Python Übung\n", - "\n", - "
\n", - "
\n", - " Willkommen zur dritten Programmierübung Einführung in Python 3\n", - "
\n", - " \n", - "
\n", - "\n", - "In dieser Übung werden erweiterte Konzepte gelernt, welche das Konzipieren von Programmen stark vereinfacht.\n", - "\n", - "Wenn Sie Fragen oder Verbesserungsvorschläge zum Inhalt oder Struktur der Notebooks haben, dann können sie eine E-Mail an Phil Keier ([p.keier@hbk-bs.de](mailto:p.keier@hbk-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) oder Martin Le ([martin.le@tu-bs.de](mailto:martin.le@tu-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) schreiben.\n", - "\n", - "Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n", - "\n", - "Der Großteil des Python-Tutorials stammt aus der Veranstaltung _Deep Learning Lab_ und von [www.python-kurs.eu](https://www.python-kurs.eu/python3_kurs.php) und wurde für _Signale und Systeme_, sowie _Einführung in die Programmierung für Nicht Informatiker_ angepasst." - ] - }, - { - "cell_type": "markdown", - "id": "f47e9f08-663b-4c2f-85c7-8b6c7f654b45", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-40ca946ad65b66b6", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Konventionen\n", - "\n", - "Python hat einen Grundlegenden Styleguide 2001 von Guido van Rossum festgelegt in [PEP 8](https://peps.python.org/pep-0008/).\n", - "\n", - "Dazu nur ein paar Anmerkungen:\n", - "\n", - "1. Variabel- & Funktionsnamen werden immer kleingeschrieben und mittels \"snake case\" geschrieben. Bsp.: `is_alive`\n", - "2. Zum Einrücken sollten 4 Leerzeichen verwendet werden die mit Tab eingeleitet werden. (Jupyter macht dies Automatisch richtig)\n", - "3. Beim schreiben von Kommentaren folgt nach `#` immer ein Leerzeichen. Bsp.: `# Kommentar`\n", - "4. Importe aus Modulen sollen getrennt sein.\n", - "\n", - " Richtig:\n", - " ```python\n", - " import os\n", - " import sys\n", - " ```\n", - " \n", - " Falsch:\n", - " ```python\n", - " import os, sys\n", - " ```\n", - "\n", - "5. Mehrfach importe aus einem Modul sind dennoch mit `,` gern gesehen.\n", - "\n", - " Bsp.:\n", - " ```python\n", - " from dataclass import dataclass, field, asdict\n", - " ```\n", - "\n", - "6. Nach jedem `,`, `:`(außer beim slicing) und operator `+, =, etc.` folgt ein Leerzeichen.\n", - " \n", - " Bsp.:\n", - " ```python\n", - " x = 4 + 2\n", - " arr[5:10]\n", - " ```\n", - "\n", - "7. Kein unnötiges Ausrichten von Variablen.\n", - "\n", - " So nicht:\n", - " ```python\n", - " x = 4\n", - " y = 5\n", - " name = \"Lisa\"\n", - " ```\n", - "\n", - "8. Die Variablnamen `l` (Kleingeschriebenes L), `O` (Großes o) und `I` (Großes i) sollten niemals als Einzelvariablennamen verwendet werden, da diese Schwer von `i` (Kleines i), `0`(Die Zahl Null) und `L` (Großes L) in einigen Schriftarten zu unterscheiden sind. (Sollte mit Jupyter kein Problem darstellen)" - ] - }, - { - "cell_type": "markdown", - "id": "4abb5645-0172-4fd7-8f79-02af5140b234", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ef37f36145723997", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Generatoren\n", - "\n", - "Nachdem wir bereits Funktionen kennengelernt haben, welche einen Rückgabewert haben, lernen wir nun ein Python Konzept kennen, dass \"On the fly\" Daten zur Verfügung stellt.\n", - "\n", - "Bisher sind wir davon ausgegangen das beispielweise die Funktion `range` eine Liste zurückgibt. Dies ist vom Gedanken her richtig, dennoch nicht in der Umsetzung.\n", - "\n", - "Mit einem einfachen `print` lässt sich dies auch bestätigen:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f8f4aee6-ce57-41f8-af18-7256b0126515", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1e78bfa1751878c9", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "range(0, 10)\n" - ] - } - ], - "source": [ - "print(range(10))" - ] - }, - { - "cell_type": "markdown", - "id": "dc9f0f52-0a0d-4e5c-8b6f-d7a2b7c5b2e3", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1c1a0f19a370c840", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Statt wie vielleicht zu erwarten die Liste von werten `0...9` als Ausgabe zu bekommen, gibt uns Python lediglich `range(0, 10)` zurück.\n", - "\n", - "Möchte man die Werte direkt evaluiert haben muss der `*`-Operator verwendet werden:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "89986045-bb47-4bca-b561-bab9bb2b988b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-723d591c32fda1a7", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 1 2 3 4 5 6 7 8 9\n" - ] - } - ], - "source": [ - "print(*range(10))" - ] - }, - { - "cell_type": "markdown", - "id": "2248b888-aa12-4367-b1f4-bb4befc43df4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-19e02ebb63f72a88", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Dabei verändert `range(10)` die `print` Funktion indem es alle Generator Werte als Parameter einsetzt `print(0,1,2,3,4,5,6,7,8,9)` und danach aufruft.\n", - "\n", - "Um selber einen Generator zu definieren benötigt man das Python Keyword `yield`. Im Gegensatz zum Normalen `return` wird die Berechnung nur gestoppt und zu einem späteren Zeitpunkt ausgeführt. Sozusagen hat der Generator ein veränderbaren Zustand.\n", - "\n", - "Die Syntax hierzu ist im Allgemeinen:\n", - "\n", - "```python\n", - "def ():\n", - " # do something\n", - " yield \n", - "```\n", - "\n", - "Eine rudimentäre Funktion `count_to` lässt sich dementsprechend wie folgt definieren:" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "e4686bab-6686-4464-9015-54d420087e5a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-795ddd785249ca9d", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is a Generator: \n", - "This Generator evaluates to: 0 1 2 3 4 5 6 7 8 9\n" - ] - } - ], - "source": [ - "def count_to(n):\n", - " count = 0\n", - " while count < n:\n", - " yield count\n", - " count += 1\n", - "\n", - "print(\"This is a Generator:\", count_to(10))\n", - "print(\"This Generator evaluates to:\", *count_to(10))" - ] - }, - { - "cell_type": "markdown", - "id": "7adfeae1-bab7-42b8-b9f7-39c91cb5db0b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ca4e8d2864d7cff7", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Selbiges mit einem For Loop:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "a19b17c4-144b-4197-8a71-f0554b467564", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-131bc31d10dd26c9", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is a Generator: \n", - "This Generator evaluates to: 0 1 2 3 4 5 6 7 8 9\n" - ] - } - ], - "source": [ - "def count_to(n):\n", - " for i in range(n):\n", - " yield i\n", - "\n", - "print(\"This is a Generator:\", count_to(10))\n", - "print(\"This Generator evaluates to:\", *count_to(10))" - ] - }, - { - "cell_type": "markdown", - "id": "7cbdb90b-bedf-4197-97b8-8baf58c92190", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c2c489efaeeb75dd", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Aufgabe\n", - "\n", - "*3 Punkte*\n", - "\n", - "Schreibe einen Generator `square_count` mit einem Eingabeparameter `n`, welcher die Quadratzahlen von $1\\dots n^2$ ausgibt.\n", - "\n", - "Die Aufgabe gibt 0 Punkte, wenn die Funktion `square_count` kein Generator ist!\n", - "\n", - "Hinweis: Bei Eingabe von `5` soll die Ausgabe `1 4 9 16` sein." - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "2df62e85-d5a5-4664-bdf6-c544ed8fb0d1", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-a037e576943e230b", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "def square_count(n): \n", - " for i in range(1, n):\n", - " yield i*i\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "234d782c-88f4-4bc2-a872-15a592ee1248", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-a6c43a5365ad736d", - "locked": true, - "points": 3, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Square Numbers from 0 to 1: 1\n", - "Square Numbers from 0 to 2: 1 4\n", - "Square Numbers from 0 to 3: 1 4 9\n", - "Square Numbers from 0 to 4: 1 4 9 16\n", - "Square Numbers from 0 to 5: 1 4 9 16 25\n" - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "\n", - "# Check if the generator has the right name\n", - "assert \"square_count\" in dir() # 1 Punkt\n", - "\n", - "# Check if square_count is a generator\n", - "import types\n", - "assert isinstance(square_count(1), types.GeneratorType) # 1 Punkt\n", - "\n", - "# Check if the generator generates the right output\n", - "for n in range(10): \n", - " assert [i*i for i in range(1,n)] == [i for i in square_count(n)] # 1 Punkt\n", - "\n", - "# print\n", - "for n in range(2, 7):\n", - " print(f\"Square Numbers from 0 to {n-1}:\", *square_count(n))" - ] - }, - { - "cell_type": "markdown", - "id": "8e1cb8b2-cdaa-422a-9889-c4e0794d7d81", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5f6f32fed19a1933", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Generatoren können auch eine unendliche Menge an Daten zurückgeben. Dieses Ziel kann man erreichen indem der Generator unendlich oft ausgeführt wird. Da die Daten zur Laufzeit berechnet werden kann man von einer unendlichen Menge sprechen.\n", - "\n", - "Um eine Berechnung nie enden zu lassen muss die Bedingung der Schleife immer `wahr` bleiben.\n", - "Dies erreicht man durch die Syntax `while True:`, aber Python ist eben Python und die Syntax `while 1:` ist Laufzeit effizienter.\n", - "\n", - "Schauen wir uns nun das Beispiel eines unendlichen Generator an der Fortwährend die nächste Fakultät ausgibt:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "e7a2e825-b67d-4618-9358-4ee06ef5cc38", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-55717dfe7800e3fb", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "def faktoriel_gen():\n", - " curr = 1\n", - " count = 1\n", - " while 1:\n", - " curr = count * curr\n", - " count += 1\n", - " yield curr" - ] - }, - { - "cell_type": "markdown", - "id": "496d59e9-e3c1-47d1-982d-8c5688cb7893", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7d21c6426cee23f0", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Vorsicht (!!) wertet man diesen Generator nun aus würde die Berechnung niemals enden. Um den nächsten Wert der Berechnung zu erhalten hat Python die Funktion `next`, welche den nächsten Zustand des Generators ausgibt. Mit einem `for`-loop und `next` lassen sich dann die Fakultäten der Zahlen bis `5` einfach ausgeben:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "4249c820-f03c-4393-bb8f-5078f9a9a0b8", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-166ede3f392e88e7", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n", - "2\n", - "6\n", - "24\n", - "120\n" - ] - } - ], - "source": [ - "gen = faktoriel_gen()\n", - "\n", - "for _ in range(5):\n", - " print(next(gen))" - ] - }, - { - "cell_type": "markdown", - "id": "8b0d160f-d445-4877-a634-2de69336e70a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-2e5778830ac74399", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Da der Zustand des Generator gespeichert ist lässt sich mit einem weiteren aufruf in nächster Zelle auf $6! = 720$ ausgeben:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "32e5eb93-507a-490e-9395-717bc7717973", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-30acb8e9a68a7794", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "720\n" - ] - } - ], - "source": [ - "print(next(gen))" - ] - }, - { - "cell_type": "markdown", - "id": "9901cb74-faf9-47f1-b87e-60d9683778d3", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-3f6a4841c593371f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe\n", - "\n", - "*3 Punkte*\n", - "\n", - "Schreibe einen Generator `naturals`, welcher mit jedem Aufruf die nächste natürliche Zahl ausgibt. (Angefangen mit 1)\n", - "\n", - "- Es sind keine Eingabeparameter notwendig.\n", - "- Ist die Funktion kein generator, wird diese Aufgabe mit 0 Punkten bewertet\n", - "\n", - "*Hinweis*: Orientiere dich an dem Beispiel `faktoriel_gen`" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "a8b2697c-706b-4744-a4b3-00682d6c95c3", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-09dd94e802cad9bc", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "def naturals():\n", - " curr = 1\n", - " while 1:\n", - " yield curr\n", - " curr += 1\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "403c70b9-3ac3-4a85-a302-d9c7d0585cbc", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-e065f7326fb561ad", - "locked": true, - "points": 3, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, " - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "\n", - "# Check if generator is named properly\n", - "assert \"naturals\" in dir() # 1 Punkt\n", - "\n", - "# Check if naturals is a generator\n", - "import types\n", - "assert isinstance(naturals(), types.GeneratorType) # 1 Punkt\n", - "\n", - "# Test if generator works as intended\n", - "import random\n", - "test_n = random.randint(5, 17)\n", - "test_gen = naturals()\n", - "for i in range(1, test_n):\n", - " number = next(test_gen)\n", - " print(number, end=', ')\n", - " assert i == number # 1 Punkt" - ] - }, - { - "cell_type": "markdown", - "id": "41bb0337-7194-4482-a5b9-af1243d4809c", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-bbe0bad0c47d41ae", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Type Hints\n", - "\n", - "Mit [PEP 484](https://peps.python.org/pep-0484/) wurden in Python die `type hints` eingeführt. Die Motivation dafür war es eine Standard Syntax zu definieren, welche einem die Möglichkeit gibt den In- und Output von Funktionen besser zu bestimmen. Unter anderem verbessert sich die Testbarkeit von Python Programmen ungemein wenn Type Hints vorhanden sind. Im Allgemeinen werden `type hints` daher verwendet, um von Variablen und Funktionen die Typen (`int`, `float`, `dict`, etc.) anzuzeigen.\n", - "\n", - "Die Allgemeine Syntax dafür:\n", - "```python\n", - "def (: , : ) -> :\n", - " # do something\n", - " return \n", - "```\n", - "\n", - "Python ist eine dynamische typisierte Sprache. Das heißt, dass der Typ einer Variable immer wieder überprüft werden muss. So kann eine Ganzzahl `int` mit einer einfachen addition in eine Flieskommazahl `float` überführt werden:" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "id": "d2148990-b5cb-4fe5-8751-bac8338fa581", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-560aa9d85c5a4383", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3 \n", - "3.14 \n" - ] - } - ], - "source": [ - "number = 3\n", - "print(number, type(number))\n", - "number += 0.14\n", - "print(number, type(number))" - ] - }, - { - "cell_type": "markdown", - "id": "56c143f5-1c67-4999-8656-dc68d5912fb4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a85903e6bffa1ff1", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Um diese unerwünschten Typenwechsel zu vermeiden, kann man type hints verwenden. Type hints sind nur eine Info keine Garantie das der Typ einer Variable sich ändert!\n", - "\n", - "Eine nutzlose Funktion die den Eingabewert als `int` in einen `str` umwandelt sieht wie folgt aus:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "642dc60b-fa8d-4655-bc47-815f39365506", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-94708ecde4287ff7", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number 42\n" - ] - } - ], - "source": [ - "def useless(number: int) -> str:\n", - " return \"Number {}\".format(number)\n", - "\n", - "print(useless(42))" - ] - }, - { - "cell_type": "markdown", - "id": "ac0a9f33-2e9a-4779-8bf8-1004e6ed7177", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-36dc004fdb6c6682", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Um anzuzeigen das eine Variable einen bestimmten Datentypen zugeordnet ist wird folgende Syntax verwendet:" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "id": "e25d2f29-5857-4f0a-93d7-609201e8a0da", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-fdc340bdc4a2000e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "name: str = \"Peter Parker\"" - ] - }, - { - "cell_type": "markdown", - "id": "777904ec-3567-41c8-a619-2c38ad6b6c51", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ea3aeed85ff0587c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Dataclasses \n", - "\n", - "Allgemein auf Klassen wird hier nicht eingegangen jedoch ein Konzept, welches mit [PEP 557](https://peps.python.org/pep-0557/) eingeführt wurde, soll folgend stärker beleuchtet werden.\n", - "\n", - "Datenstruckturen wie `dict`, `set`, `list`, etc. sind mächtige Werkzeuge und ermöglichen dem Programmierer Daten in vielen Formen akkurat dazustellen. Möchte man jedoch feste Datenstrukturen mit genau definierten Werten verwenden eignet sich das Modul `dataclasses`.\n", - "\n", - "Dazu wird eine Klasse mit dem Keyword `class` definiert und dem Decorator `dataclasses.dataclass` ausgesattet. Folglich können feste Datenobjekte mit definierter Struktur erstellt werden.\n", - "\n", - "Zunächst wird das Modul aus der Standard Bibliothek importiert:" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "24392aea-8ba8-4f9e-ba95-5bf5c99a127b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-fa6710d8dd259c2d", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "from dataclasses import dataclass" - ] - }, - { - "cell_type": "markdown", - "id": "abb46684-015f-48c7-98b8-f2955e71de2d", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-36842ee26321c3ec", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Danach kann eine Klasse erstellt werden. Erstellen wir zunächst eine Klasse `Person`, welche die Werte `vorname` und `nachname` als Strings bereitstellen soll:\n", - "\n", - "Wichtig: Python Klassen fangen immer mit einem Großbuchstaben an. Mit Ausnahme der Standard Bibliothek. Die `range` Funktion lässt sich zwar verwenden wie eine Funktion, ist aber eigentlich eine Klasse!" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "b70f39ca-6e57-4b03-a3be-c3b5ac604a9c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8b8b2e316c36725c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "@dataclass\n", - "class Person:\n", - " vorname: str\n", - " nachname: str" - ] - }, - { - "cell_type": "markdown", - "id": "94901847-a849-463b-968a-b498a9f84edc", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-846dcecf70fd8d14", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Möchten wir nun eine Person erstellen sieht dies wie folgt aus:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "27320011-ee5d-43c9-8e07-fbd3036680f7", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1cf940f3240bf428", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Person(vorname='Eduard', nachname='Jorswieck') \n" - ] - } - ], - "source": [ - "person = Person(\"Eduard\", \"Jorswieck\")\n", - "print(person, type(person))" - ] - }, - { - "cell_type": "markdown", - "id": "be0c2b13-937e-4073-8dfa-a6d305ed9c6f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8338f70364284fc7", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Wie dem Output zu entnehmen ist die Variable `person` ein Objekt vom Typ `Person` und hält die Werte `vorname='Eduard'` und `nachname='Jorswieck'` vor.\n", - "\n", - "Auf die einzelnen Werte innerhalb der Dataclass kann nun per `.` Operator zugegriffen werden:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "191bb87c-06ab-40a4-b5b3-56e9c533b930", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b6da422f93431723", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vorname: Eduard\n", - "Nachname: Jorswieck\n" - ] - } - ], - "source": [ - "print(\"Vorname:\", person.vorname)\n", - "print(\"Nachname:\", person.nachname)" - ] - }, - { - "cell_type": "markdown", - "id": "3e85dee5-09ea-4c00-9730-05fb006ced2b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d80fa3ef35e81dab", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Dataclasses bieten auch den Vorteil, dass ihre Werte direkt über die Variablennamen definiert werden können. Dabei spielt die Reihenfolge dann keine Rolle mehr." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "a5ea4ef9-dcba-4411-a2e8-1a1a811e6cac", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-20f0d6f2cb9ef5df", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Person(vorname='Martin', nachname='Le') \n" - ] - } - ], - "source": [ - "person2 = Person(nachname=\"Le\", vorname=\"Martin\")\n", - "print(person2, type(person2))" - ] - }, - { - "cell_type": "markdown", - "id": "68db3f9c-b6ad-4454-87be-71a78a209a40", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-2489e8269aa6414d", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Nicht immer sind alle Werte vorhanden und damit dies nicht zum Problem wird können Standardwerte vergeben werden:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "ce05d4eb-747b-41ad-9fea-5430b8310e8c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-92f8a3cacfda2c15", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "@dataclass\n", - "class Person:\n", - " vorname: str = \"Max\"\n", - " nachname: str = \"Mustermann\"" - ] - }, - { - "cell_type": "markdown", - "id": "e4def98c-60a9-4dcf-a71b-6be49f853b3b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-009056608941f805", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Wird nun eine Dataclass ohne Eingabeparameter erstellt, werden ihr ihre Standardwerte zugewiesen:" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "6d5e0c5d-0f70-4303-83cb-234e9523500c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-dbc29451821056d9", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Aufruf mit print: Person(vorname='Max', nachname='Mustermann')\n" - ] - } - ], - "source": [ - "nameless_person = Person()\n", - "print(\"Aufruf mit print:\", nameless_person) " - ] - }, - { - "cell_type": "markdown", - "id": "ccee5184-2f5e-4b90-99ae-2702405c0ec9", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-1c171ff9c43179be", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe\n", - "\n", - "*6 Punkte*\n", - "\n", - "Schreiben Sie eine Dataclass `Student`\n", - "\n", - "- Die dataclass soll die Werte `vorname`, `nachname`, `semester` und `mat_nr` speichern, vergebe hierzu selber den !!geeigneten!! Datentypen. Mache dir dazu Gedanken ob es Sinnvoll beispielweise die Semesteranzahl als Float zu speichern.\n", - "\n", - "- importiere aus dem dataclasses modul die Funktion `asdict`, erstelle ein Objekt mit den Werten aus dem Beispielstundent, weiße den rückgabewert aus `asdict` aufgerufen mit dem Beispielstudenten der Variablen `stud` zu.\n", - "\n", - "- Die Aufgabe wird mit 0 Punkten bewertet, wenn `Student` keine dataclass ist!\n", - "\n", - "- Hat einer der Attribute keinen geeigneten Datentypen, führt dies nicht zu Punktabzug bei zwei oder mehr schon.\n", - "\n", - "Beispielstudent:\n", - "\n", - "|Attribut|Wert|\n", - "|-|-|\n", - "|vorname|Martin|\n", - "|nachname|Le|\n", - "|mat_nr|520420|\n", - "|semester|5|" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "d17b07db-7340-47c0-ab1d-38d75b3194c0", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-ac7d4ba80c4a0341", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "from dataclasses import asdict\n", - "\n", - "@dataclass\n", - "class Student:\n", - " vorname: str\n", - " nachname: str\n", - " mat_nr: int\n", - " semester: int \n", - "\n", - "stud = asdict(Student(vorname='Martin', nachname='Le', mat_nr=520420, semester=5))\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "id": "ab8dcc00-fc3f-4ae3-a18d-73f97e166ae3", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-10b27d53a9659696", - "locked": true, - "points": 6, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'vorname': 'Martin', 'nachname': 'Le', 'mat_nr': 520420, 'semester': 5}\n" - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "\n", - "# Check if asdict is imported\n", - "assert \"asdict\" in dir() # 1 Punkt\n", - "\n", - "# Check if Student is named properly\n", - "assert \"Student\" in dir() # 1 Punkt\n", - "\n", - "# Check if Student is a Dataclassimport dataclasses\n", - "from dataclasses import is_dataclass\n", - "assert is_dataclass(Student) # 1 Punkt\n", - "\n", - "# Check if stud is properly converted from Dataclass to dict\n", - "# 3 Punkt\n", - "assert stud == {'vorname': 'Martin', 'nachname': 'Le', 'mat_nr': 520420, 'semester': 5}\n", - "print(stud)" - ] - }, - { - "cell_type": "markdown", - "id": "448b2019-405f-4f3d-87d4-afa183363a35", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-b8ee343f30f40e1e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe\n", - "\n", - "*6 Punkte*\n", - "\n", - "Gegeben sind zwei Listen `colorn` & `colorv_hex`, welche zueinander Index Sortiert sind.\n", - "\n", - "Schreiben nun eine Dataclass `Color` mit den Attributen `name` & `value` und vergebe geeignete Type Hints.\n", - "\n", - "Erstelle danach eine Liste, welche die Werte aus `colorn` & `colorv_hex` in die Dataclass `Color` umwandeln, und speicher die Liste in der variablen `colors`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "id": "27555b53-51e1-48b8-8962-528c22c85b02", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-8fb05db31c5a091d", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "colorn = ['RED', 'GREEN', 'BLUE', 'YELLOW', 'PURPLE']\n", - "colorv_hex = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#FF00FF']" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "id": "be9ea7b1-45c4-418d-9a5f-0d101c9fd245", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-dbfa5551c836768a", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Color(name='RED', value='#FF0000'), Color(name='GREEN', value='#00FF00'), Color(name='BLUE', value='#0000FF'), Color(name='YELLOW', value='#FFFF00'), Color(name='PURPLE', value='#FF00FF')]\n" - ] - } - ], - "source": [ - "colors = None\n", - "# BEGIN SOLUTION\n", - "@dataclass\n", - "class Color:\n", - " name: str\n", - " value: str\n", - "\n", - "colors = [Color(n, w) for n, w in zip(colorn, colorv_hex)]\n", - " \n", - "print(colors)\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "id": "cbf53cfd-7b06-443c-8aca-55463d77aa57", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-a720d5d5ba2ea35c", - "locked": true, - "points": 6, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Color(name='RED', value='#FF0000'), Color(name='GREEN', value='#00FF00'), Color(name='BLUE', value='#0000FF'), Color(name='YELLOW', value='#FFFF00'), Color(name='PURPLE', value='#FF00FF')]\n" - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "\n", - "# Check if Color is named properly\n", - "assert \"Color\" in dir() \n", - "\n", - "# Check if Color is a Dataclassimport dataclasses\n", - "from dataclasses import is_dataclass\n", - "assert is_dataclass(Color) # 1 Punkt\n", - "\n", - "# Check if colors is a list\n", - "assert isinstance(colors, list) # 1 Punkt\n", - "\n", - "# Check if colors contains only Color Classes \n", - "for c in colors:\n", - " assert is_dataclass(c) # 1 Punkt\n", - "\n", - "### BEGIN HIDDEN TEST\n", - "@dataclass\n", - "class Color:\n", - " name: str\n", - " value: str\n", - "\n", - "c = [Color(n, w) for n, w in zip(colorn, colorv_hex)]\n", - "for r, s in zip(c, colors):\n", - " assert r.name == s.name\n", - " assert r.value == s.value\n", - "### END HIDDEN TEST\n", - "\n", - "print(colors) # 3 Punkte" - ] - }, - { - "cell_type": "markdown", - "id": "afe6543a-b41b-4d3c-9929-525a0463c6af", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-a65f2c871406072c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Walrus Operator - Assingment Expressions\n", - "\n", - "Der Grund warum Guido van Rossum das Python Projekt verlassen hat, ist der Walrus Operator `:=`, zu finden unter [PEP 572](https://peps.python.org/pep-0572/). \n", - "\n", - "Das Offizielle Statement:\n", - "\n", - "> \"The straw that broke the camel's back was a very contentious Python enhancement proposal, where after I had accepted it, people went to social media like Twitter and said things that really hurt me personally. And some of the people who said hurtful things were actually core Python developers, so I felt that I didn't quite have the trust of the Python core developer team anymore.\"\n", - "> - Guido van Rossum\n", - "\n", - "Das Problem der Operator `:=` fügt keinerlei neue Funktionalität hinzu und erlaubt einzig und allein eine Zuweisung während einer Auswertung zu erlauben.\n", - "\n", - "Daher ein paar kurze Beispiele, lesen Sie ansonsten gerne PEP 572.\n", - "\n", - "Zuweisung mittels Walrus Operator: (Machen Sie das bitte nicht nach, Niemand wirklich Niemand möchte das sehen!)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "8055d0b2-eb9f-4a1a-ac74-4bfcd6cbee81", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-860f5501722f78aa", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n", - "False\n" - ] - } - ], - "source": [ - "# Normale Zuweisung\n", - "walrus = True\n", - "print(walrus)\n", - "\n", - "# Walrus Zuweisung\n", - "(walrus := False)\n", - "print(walrus)" - ] - }, - { - "cell_type": "markdown", - "id": "af5f2aab-0e6d-41d8-b3e1-96969a5ad264", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-2fb74418e2478ef5", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Berechnung und Zuweisung in einer Zeile.\n", - "\n", - "Walrus soll verwendet werden, wenn man vermeiden möchte das eine Berechnung zweimal ausgeführt wird.\n", - "\n", - "Beispiel Klassisch `n+1` wird zweimal berechnet:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "b41cae11-20f2-4500-b32a-77775502f6ea", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-85ffa763f1c26ced", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5\n" - ] - } - ], - "source": [ - "n = 4\n", - "if (n + 1) > 3:\n", - " print(n+1)" - ] - }, - { - "cell_type": "markdown", - "id": "5ad0b0b7-5d85-4a1d-9083-30790c494035", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-da3163fec3bf5f09", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Mit Walrus lässt sich im `if` die Berechnung `n+1` der Variablen `out` zuweisen:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "cde3f827-477c-46d4-8057-af35b694ebef", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-664ab7388a3cb90c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5\n" - ] - } - ], - "source": [ - "n = 4\n", - "if (out := n + 1) > 3:\n", - " print(out)" - ] - }, - { - "cell_type": "markdown", - "id": "16375c7c-ef2e-44b7-a5f1-2156248f616c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-11c8ad1c8ab2ef15", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Ohne Walrus lässt sich dennoch immer vermeiden die Berechnung `n+1` zweimal auszuführen:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "c1bf6d7e-8ce8-4770-8060-a6d18ab8d85e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-2dbb33acbcf4f8a4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5\n" - ] - } - ], - "source": [ - "n = 4\n", - "out = n + 1\n", - "if out > 3:\n", - " print(out)" - ] - }, - { - "cell_type": "markdown", - "id": "302a6dfd-2e3d-401d-93c9-1335e081e837", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-3254f96c16f5c246", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Persönliche Meinung**: Ich rate davon ab Walrus `:=` zu verwenden. In meinen Augen macht es den Code Grundsätzlich unlesbar und spart im besten Fall 2-3 Zeilen Code. In meiner eigenen Programmiererfahrung gab es nie einen Grund den Operator zu verwenden, er fügte nie einen realen Nutzen in irgendeine meiner Berechnungen ein. Dennoch wollt ich dir einmal Demonstrieren wie Walrus funktioniert." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +version https://git-lfs.github.com/spec/v1 +oid sha256:03c527abe6d052f081f020cf377dee44c436f78c6062622b175b761bffcf73f9 +size 43605 diff --git a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/4.NumPy_MatPlotLib.ipynb b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/4.NumPy_MatPlotLib.ipynb index bf826d4..9aec640 100644 --- a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/4.NumPy_MatPlotLib.ipynb +++ b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/4.NumPy_MatPlotLib.ipynb @@ -1,2792 +1,3 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "40366dea-bec8-4128-b8e9-a7c10536a613", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-44b468fb46efb002", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# 4. Programmierübung: NumPy\n", - "\n", - "
\n", - "
\n", - " Willkommen zur vierten Programmierübung Einführung in Python 3.\n", - "
\n", - " \n", - "
\n", - "\n", - "Wenn Sie Fragen oder Verbesserungsvorschläge zum Inhalt oder Struktur der Notebooks haben, dann können sie eine E-Mail an Phil Keier ([p.keier@hbk-bs.de](mailto:p.keier@hbk-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) oder Martin Le ([martin.le@tu-bs.de](mailto:martin.le@tu-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) schreiben.\n", - "\n", - "Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n", - "\n", - "Der Großteil des Python-Tutorials stammt aus der Veranstaltung _Deep Learning Lab_ und von [www.python-kurs.eu](https://www.python-kurs.eu/python3_kurs.php) und wurde für _Signale und Systeme_, sowie _Einführung in die Programmierung für Nicht Informatiker_ angepasst.\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "4d4eed4c-36ff-4643-bbc8-6c5440f73065", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5ac025a1f69e3f31", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Was ist NumPy\n", - "\n", - "NumPy steht für *Numerical Python*, ist OpenSource und wird mittlerweile von nahezu jedem Python Entwickeler verwendet. Dabei ist das Core Feature von NumPy seine effiziente Implementierung eines n-dimensionales Arrays in C, welches in Python verwendet werden kann. Hinzu kommt eine Hülle an Funktionen wie effiziente Zufallsalgorithmen und mathematische Funktionen aus den unterschiedlichten Bereichen der Statistik und numerischen Berechnung, welche alle für NumPy Arrays optimiert sind. Im Folgenden wollen wir den Umgang mit NumPy Arrays lernen. \n", - "\n", - "__Für dieses Notebook schauen Sie bitte in die [NumPy Docs](https://numpy.org/doc/stable/reference/index.html)!!!__ Dort sind alle Funktionen beschrieben die wir hier bearbeiten und noch mehr!\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "ee26bbc0-d6a6-4721-b371-5cade59879ea", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d2be4b14819d46e9", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Was ist Matplotlib\n", - "\n", - "Matplotlib ist eine Python Bibliothek zum (interaktiven) Visualisieren von Daten. Die Bibliothek integriert sich gut mit anderen viel benutzten Python Bibliotheken wie NumPy. Der Vorteil der Kombination mit Jupyter besteht in der direkten Ausgabe eines Plots auf dem Bildschirm.\n", - "\n", - "__Nutzen Sie für diese Aufgabe gerne die [Matplotlib Reference](https://matplotlib.org/stable/users/index.html)__\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "8f6a28a6-9ef0-4f80-9432-80375cf3876b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b8d545ee5d6cabe7", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Das gesamte Internet importiert NumPy mit dem Kürzel `np` & das pyplot Objekt mit `plt`.\n", - "\n", - "Um uns diesen ungeschriebenen Gesetz anzuschließen importieren wir in nächster Zelle NumPy als np:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "20dc6e2f-9b96-4f0c-85a8-8763e6722ce8", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3d1fe74859052932", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "id": "57f719db-3269-49e2-acd2-a676911c0470", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-39e898b8ebe45728", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "id": "9e008318-4d2a-4244-ac91-63d7b8806491", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b910cff04746aa1d", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Was ist ein Array?\n", - "\n", - "Ein Array ist eine kontinuierliche Datenstruktur. Dabei werden die Daten in Reihe im Arbeitsspeicher hinterlegt, vergleichbar mit der Python Liste.\n", - "\n", - "## Erstellen von Arrays\n", - "\n", - "Alle folgenden Beispiele finden Sie im [Beginners Guide](https://numpy.org/doc/stable/user/absolute_beginners.html).\n", - "\n", - "Für unser erstes Beispiel erstellen wir aus einer Python Liste ein [NumPy Array](https://numpy.org/doc/stable/reference/arrays.html)." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "b44876d4-2376-4651-b489-9708a02f86bd", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1adaa95f01483572", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 2, 3, 4, 5])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "arr = np.array([1,2,3,4,5])\n", - "arr" - ] - }, - { - "cell_type": "markdown", - "id": "a08e52ec-5148-4304-93da-064369cab9f8", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ea5067ebbb1c99bc", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Der Zugriff auf Elemente des Arrays erfolgt analog zu Pythons Liste:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "68679f58-495f-483e-a394-0cb3016dd488", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-cb73ac88e9fa5d93", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "np.int64(5)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "arr[4]" - ] - }, - { - "cell_type": "markdown", - "id": "3f9f7d47-b9d6-47b2-85e3-bdb015cc72d8", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ec0a814ecfda8547", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "NumPy Arrays sind n-dimensional, heißt Arrays in NumPy können aus geschachtelten Listen bestehen. Beispiel für ein 2-Dimensionales Array:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "04452b0d-ca06-4dd8-9b6a-47aa00b38011", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ff72c8352626ac82", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1, 2, 3, 4, 5],\n", - " [6, 7, 8, 9, 8],\n", - " [7, 6, 5, 4, 3]])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "arr = np.array(\n", - " [\n", - " [1,2,3,4,5],\n", - " [6,7,8,9,8],\n", - " [7,6,5,4,3],\n", - " ])\n", - "arr" - ] - }, - { - "cell_type": "markdown", - "id": "a4d10e8e-6856-4f14-a18c-b4d0d4cfee96", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-413fcd639649e39a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Diese Datenstruktur wird Allgemein auch Matrix gennant. Der Zugriff auf ein Element einer Matrix folgt nach dem Prinzip \"Spalte->Reihe\". Die erste Spalte ist demnach:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "9539a428-4cb1-426e-9ff0-bb10f9e5dd75", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c7a59ce293c8e402", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 2, 3, 4, 5])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "arr[0]" - ] - }, - { - "cell_type": "markdown", - "id": "eb7b82f2-c8ba-4336-b3f9-505d89010e3b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-fb0cf79581b45e5e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Der Zugriff auf ein einzelnes Element erfolgt dann Analog:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "c940ad92-43b4-4783-be50-687b2be51541", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-2f107dba2b747fbb", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "np.int64(5)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "arr[0][4]" - ] - }, - { - "cell_type": "markdown", - "id": "37cd7d69-9a6f-4136-95bc-3470ea512f45", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-617d777cf3216789", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Oder mit NumPys eigener Syntax `arr[, ]`" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "630ba433-c2e5-4faf-980a-ae34e57d470a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3e1e7323c57088ad", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "np.int64(5)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "arr[0, 4]" - ] - }, - { - "cell_type": "markdown", - "id": "2b1ad36a-eeec-410f-82f1-768bb134accd", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-62701cdd045c7c4c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Platzreservierung\n", - "\n", - "Falls bekannt ist, wie viele Elemente ein Array im späteren Programmverlauf haben soll, bietet einem NumPy die Möglichkeit diesen Platz im Speicher gewissermaßen zu reservieren.\n", - "Hierfür gibt es einige Funktionen.\n", - "\n", - "### Ones\n", - "\n", - "1 Dimensionales NumPy Array der Größe 10 mit 1 gefüllt:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "1da0eee9-4aa6-4a2e-91ab-7f9110ba5961", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d3aeedf2a30a9b30", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.ones(10)" - ] - }, - { - "cell_type": "markdown", - "id": "dec9de1d-0415-4ce1-8182-cdf5f50cb8cd", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-6e683d4afe40b7ff", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Zeros\n", - "\n", - "Analog dazu mit 0 gefüllt:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "22b81912-0f2b-4ea6-b085-938e8f835daa", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-450f40270416767a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.zeros(10)" - ] - }, - { - "cell_type": "markdown", - "id": "794bfa8c-2c62-4d13-b2cf-65165b9f66d8", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ef1e55c1165e2de5", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Empty\n", - "\n", - "Analog mit zufälligen Werten (bzw. Werte, die bereits an der Speicherstelle waren, meistens 0):" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "5bede573-fda6-4065-bc05-2238cdcd7dc5", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-896e48c096be9062", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.empty(10)" - ] - }, - { - "cell_type": "markdown", - "id": "f8543f2c-37a1-45c7-9e6f-387126d2e0c4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-947ed3289815694d", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Arange\n", - "\n", - "Analog dazu die `arange` Funktion (die Paramter sind die selben wie bei der Python `range` Funktion):" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "57a83964-bf61-4db8-8358-d415d92e49bb", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5329dd48e6129b33", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.arange(10)" - ] - }, - { - "cell_type": "markdown", - "id": "14461c48-a442-41d8-a9dc-bd411beb6b14", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-299417e99c41e05f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Linspace\n", - "\n", - "Um später mit Matplotlib besser arbeiten zu können hat Numpy die [linspace](https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy-linspace) Funktion. Welche die Werte von einem Start und End Punkt Linear berechnet, zusätzlich kann noch die Anzahl der Elemete mit dem `num` Parameter angegeben werden:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "1defeafc-b02a-4e84-a1d3-30878fc69071", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-036bdec449f35dc5", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0. , 2.5, 5. , 7.5, 10. ])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.linspace(0,10, num=5)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "72416872-9e66-4ff8-88ad-9244b6e67827", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-9ac9d3f215fc0237", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 400. , 466.66666667, 533.33333333, 600. ,\n", - " 666.66666667, 733.33333333, 800. , 866.66666667,\n", - " 933.33333333, 1000. ])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.linspace(400, 1000, num=10)" - ] - }, - { - "cell_type": "markdown", - "id": "53cd285e-fe83-4c8b-9ac5-ee65db57107d", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-522faf35a6c76300", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe\n", - "\n", - "*1 Punkt*\n", - "\n", - "Erstellen Sie ein NumPy Array, welches 6 Nullen reserviert und speichern Sie das Array in der Variablen `only_zeros`. " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "8f64d227-64cc-4b26-b076-b9ea3db3d798", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-176f6befb5f45c58", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [], - "source": [ - "only_zeros = None\n", - "# BEGIN SOLUTION\n", - "only_zeros = np.zeros(6)\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "ca435e4c-e686-4e64-b796-6f6884708ce3", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-8ad1e3a41d459d55", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0. 0. 0. 0. 0. 0.]\n" - ] - } - ], - "source": [ - "print(only_zeros)\n", - "\n", - "# Check i length is correct\n", - "assert len(only_zeros) == 6\n", - "\n", - "# Check if every element contains a zero\n", - "for el in only_zeros:\n", - " assert el == 0" - ] - }, - { - "cell_type": "markdown", - "id": "07012ea7-35b0-4e24-83d4-5732853761bd", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0108e88f3110e70f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe\n", - "\n", - "*1 Punkt*\n", - "\n", - "Erstellen Sie ein NumPy Array mit 11 Elementen mittels `linspace`. Dabei soll der Startwert = -4 und der Endwert = 17 sein. Speichern Sie das Ergbniss in der Variablen `x_scale`. " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "41f68397-24db-44d8-a2cf-4e7aec431b57", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3231ee937ba8ab7a", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [], - "source": [ - "x_scale = None\n", - "# BEGIN SOLUTION\n", - "x_scale = np.linspace(-4, 17, num=11)\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "2439440f-f8f6-4ee1-ae2b-3bca19992675", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-e5d66ef7599f7b91", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-4. -1.9 0.2 2.3 4.4 6.5 8.6 10.7 12.8 14.9 17. ]\n" - ] - } - ], - "source": [ - "# Hier werden ihre Lösungen getestet\n", - "print(x_scale)\n", - "\n", - "# Check if length is correct\n", - "assert len(x_scale) == 11\n", - "### BEGIN HIDDEN TESTS\n", - "s = np.linspace(-4, 17, num=11)\n", - "### END HIDDEN TESTS\n", - "\n", - "# Test if elements are correct\n", - "for el1, el2 in zip(x_scale, s):\n", - " assert el1 == el2" - ] - }, - { - "cell_type": "markdown", - "id": "dfdb02ea-44b4-4368-9b46-235ca1dca667", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5d8e19cc802e33a4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "id": "9276ba6e-30a0-4803-ace7-14147cd214ee", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-30de8243b097dfdc", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# First plot\n", - "\n", - "Wie dem [Getting Started](https://matplotlib.org/stable/users/getting_started/index.html#getting-started) Beispiel zu entnehmen, wollen wir einmal die Sinus Funktion plotten.\n", - "\n", - "Dazu brauchen wir zwei Attribute:\n", - "1. Die x-Skala - Dies kann die Länge eines Datensets sein, oder ein allgemeiner Linespace. Auf jeden Fall eine Liste bzw. Array.\n", - "2. Die y-Skala - Im Allgemeinen die Werte eines zu plottenden Datensets. Auf jeden Fall auch eine Liste bzw. Array.\n", - "\n", - "Plotten wir im Folgenden die Sinus Funktion. Eine der schönen Eigenschaften der Sinus Funktion ist, dass diese sich nach dem Intervall $[0...2\\pi]$ wiederholt. Daher enthält die x-Skala einen linespace von $[0...2\\pi]$. Als Wert für $\\pi$ wird die NumPy Konstante [np.pi](https://numpy.org/doc/stable/reference/constants.html#numpy.pi) verwendet.\n", - "\n", - "Auf der y-Skala plotten wir im folgenden das zuvor berechnete Array mit den Sinus Werten. Die Hierfür verwendete Funktion ist [np.sin](https://numpy.org/doc/stable/reference/generated/numpy.sin.html). \n", - "\n", - "Das `plt` Objekt hat mehrere Funktionen die in einer bestimmten Reihenfolge aufgerufen werden müssen. Dabei können zuerst mehrere plots mit [plt.plot](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html) definiert werden. Zum Schluss wird zur Ausgabe [plt.show](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html) aufgerufen." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "63f7bd92-7a44-4fe0-b03e-d13b1374be1c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-2eaf77b2d04abff1", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.linspace(0, 2*np.pi, num=200) # Definiere einen linearen Bereich von 0 bis 2pi\n", - "y = np.sin(x) # Berechne den Sinus mit den Werten von x\n", - "\n", - "plt.plot(x, y) # Setze für die X-Achse x und für die Y-Achse y\n", - "plt.show() # Zeige den Plot" - ] - }, - { - "cell_type": "markdown", - "id": "9dc5f266-8956-431d-aa8e-cb33afc8fc34", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1dd3b7172bc39b59", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Zwei Funktionen \n", - "\n", - "Wie bereits zuvor erwähnt lässt sich `plt.plot` mehr als einmal aufrufen. Wollen wir im folgenden den Kosinus mittels [np.cos](https://numpy.org/doc/stable/reference/generated/numpy.cos.html) dazu plotten. Dafür werden die Werte aus dem bereits definierten x wiederverwendet. Die Variabelen `y1 = np.sin(x)` & `y2 = np.cos(x)` enthälten die jeweiligen y werte. " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "7f63b65d-875d-4ee2-998b-fc6f39935ab0", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-2e9cc2ce95f1e20e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y1 = np.sin(x) # Sinus Werte mittels x berechnen\n", - "y2 = np.cos(x) # Kosinus Werte mittels x berechnen\n", - "\n", - "plt.plot(x, y1) # Plotte den Sinus\n", - "plt.plot(x, y2) # Plotte den Kosinus\n", - "\n", - "plt.show() # Zeige das Diagramm" - ] - }, - { - "cell_type": "markdown", - "id": "c1e5749a-8f46-41ae-817a-19e5103a9a7a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d1d50ca1d203ac29", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe - Erster eigener Plot Square Root\n", - "\n", - "Analog zu voheriger Erklärung plotten Sie im folgenden die Funktion Square Root, Mathematisch definiert als $f(x) = \\sqrt x; \\quad x \\geq 0$.\n", - "\n", - "Gehen Sie dabei wie folgt vor:\n", - "1. Definieren Sie einen **geeigneten** [Linespace](https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy-linspace) für die Zahlenraum 0...100. (Tipp: Achten Sie auf die Definition! Die Wurzel ist nur für positive Zahlen definiert.)\n", - "2. Berechnen Sie mittels der Funktion [np.sqrt](https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html#numpy.sqrt) die Werte für die Wurzel.\n", - "3. Plotten Sie das Ergebnis" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "c36ff699-9c30-448c-9d97-0a75e00888fd", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-bae73642cf0a866a", - "locked": false, - "points": 3, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "xs = np.linspace(0, 100, num=200)\n", - "ys = np.sqrt(xs)\n", - "plt.plot(xs, ys)\n", - "plt.show()\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "a98f7855-0bca-4769-aaf5-fa27f0404b23", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-998243908406c7d4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Styling\n", - "\n", - "Da bei mehreren Plots der Überblick schnell verloren geht, beschäftigen wir uns im folgenden mit dem Styling. Dabei gehen wir im Schnelldurchlauf durch alle Parameter.\n", - "\n", - "Die Grundlage für alle folgenden Plots werden in der nächsten Zelle gesetzt." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "dd074404-20be-450e-a187-d992c9d804cf", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a86ddac229c0bbbb", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "x = np.linspace(0, 2*np.pi, num=200)\n", - "s = np.sin(x)\n", - "c = np.cos(x)" - ] - }, - { - "cell_type": "markdown", - "id": "7e541a27-5fb0-48a5-98a6-175842a59f31", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-e27c575962048d7b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Farbe ändern\n", - "\n", - "Die Standard Farbe für den ersten Plot ist immer Blau. Um die Farbe zu verändern wird `plt.plot` der Parameter `color` übergeben. Dieser erwartet einen String. Für eine genauere Erläuterung, lesen Sie die Dokumentation zu [Specifying color](https://matplotlib.org/stable/users/explain/colors/colors.html). Für dieses Notebook werden die Beispiele mit den \"Single Character Shorthands\" (Aus der Dokumentation zu entnehmen) ausgestattet.\n", - "\n", - "Plotten wir den Sinus nun in Rot:" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "01d03aa6-22c3-4843-8cf0-33155b78740e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1ec79feac73af81f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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fpUuX5gw83r17N/5/G7uxbds2vvrqKz777LOzrhcQEMCPP/7I7NmzOXLkCBERETRr1owRI0YQFBTk6rcjJ0+aDR3B7DnVsKHdPJI/TzxhNj7dsgWefNIMQBYR8WJ+Tqfv3ZR3OByEhoaSlpam8TgF9cILZlp4hQpm0KrWH/IcK1fCnXeanrdvvtFGqCLicQry+a1pL5J/u3fDs8+a9ksvqbjxNHfcAXFxZqBxjx5mjRwRES+lAkfyr29fOH4cbr8dHn7Ydhq5FGPGmML0hx/M4GMRES+lAkfy55NPYMECCAgwex1pOwbPVLFi7v5UTz8N59kTTkTE06nAkYvLyIDevU378cehbl27eeTydOkCN90EaWkwZIjtNCIiLqECRy7utdfg11/NyrhPP207jVyugIDcFY6nTYPvv7ebR0TEBVTgyIWlpsJzz5n2qFFQurTdPFI4brvNbMjpdEKfPlrhWES8jgocubAhQ+DoUWjUSAOLvc2LL0KJEvDVV/Duu7bTiIgUKhU4cn7JyTB9ummPHavNNL1NZKRZ0whgwAAzQ05ExEvoE0vOzek008KdTmjbFpo0sZ1IXGHAAKhcGfbsMT06IiJeQgWOnNvChWbl2+Bgs3qxeKcSJcyijWD+O+/ZYzePiEghUYEjZ8vIMHsXgXmsUsVuHnGt++4zizeeOJG7KaeIiIdTgSNnGzcOduww08IHDbKdRlzNzy+3F+fNN80qxyIiHk4FjuR16BCMHGnaI0fCFVfYzSNFo3FjM9bK6YSBA22nERG5bCpwJK9Ro+DIEbNasaaF+5aRI6F4cfjsM3OIiHgwFTiSa/duGD/etEePNiveiu+oXh3i40174EDIzLSbR0TkMqjAkVxDh5oBxnfeCS1a2E4jNgwZAqGhZhzO22/bTiMicslU4Ijxww/w1lum/eKL2i3cV115JTz5pGkPGQJ//WU3j4jIJVKBI0Ziohlg+sADcPPNttOITX36mMX/9u7N3ZRTRMTDqMARWL4cPvkEihWD55+3nUZsCw7O3WD1hRfg8GG7eURELoEKHF935rTgHj2gRg27ecQ9PPQQ3HCDmVGXvUaOiIgHUYHj6xYsgO++M+vdaBVbyRYQkNuLM3YspKZajSMiUlAqcHxZZmZuUdOvH1SsaDePuJd77jELAB4/nrv4o4iIh1CB48vmzYOffoIyZSAhwXYacTd+frljsiZNMuskiYh4CBU4vurUKRg+3LQHDjRFjsjfNW0K//wnnDwJzz5rO42ISL6pwPFVs2fDr7+a21J9+thOI+7qzF6cWbNg2zarcURE8ksFji/KyMj913hiIpQqZTePuLfoaLj7bjNmK7vXT0TEzanA8UVTpsCePXDVVWZquMjFZM+omj/frHotIuLmVOD4muPHc285DB1qFnUTuZgbb4S2bU37mWfsZhERyQcVOL5mwgSzpkn16vDII7bTiCcZNsyMyVmwAJKTbacREbkgFTi+5Ngxs5EmmA+r4sXt5hHPUqeOenFExGOowPElEyfCwYNmO4b27W2nEU+U3YuzcCF8/73tNCIi51UkBc6ECROoWrUqwcHBREVFsXbt2vOeO2vWLPz8/PIcwX8bJ+J0Ohk2bBiVKlWiRIkSxMTE8Msvv7j6bXi29HQYM8a0hwwxG2uKFFTt2tCunWmrF0dE3JjLC5z58+eTkJDA8OHD2bBhA/Xq1SM2NpYDBw6c9zUhISHs378/59i1a1ee77/44ouMGzeOSZMmsWbNGkqVKkVsbCwnTpxw9dvxXJMmwR9/wDXXqPdGLk92L86HH6oXR0TclssLnFdeeYVu3brRuXNn6tSpw6RJkyhZsiQzZsw472v8/PwIDw/POcLCwnK+53Q6GTt2LEOGDKF169bceOONvPnmm+zbt4+FCxe6+u14puPHc8fePPWUem/k8tSqBQ8+aNpPP201iojI+bi0wDl58iTr168nJiYm9wf6+xMTE8Pq1avP+7pjx45RpUoVIiMjad26NZs3b8753s6dO0lJSclzzdDQUKKios57zYyMDBwOR57Dp0yaBAcOQLVq0KGD7TTiDYYOBX9/WLQINmywnUZE5CwuLXAOHjxIZmZmnh4YgLCwMFJSUs75mpo1azJjxgw+/PBD3n77bbKysrj11lvZu3cvQM7rCnLNUaNGERoamnNERkZe7lvzHGf23gwZoplTUjjO7MXRWBwRcUNuN4sqOjqauLg46tevzx133MEHH3xAhQoVmDx58iVfMzExkbS0tJxjz549hZjYzU2ZYta9qVYNHn7YdhrxJkOHmrE4ixbBxo2204iI5OHSAqd8+fIEBASQmpqa5/nU1FTCw8PzdY3ixYvToEEDtm/fDpDzuoJcMygoiJCQkDyHT/jrL3jhBdN+8kn13kjhqlkT7r/ftEeOtJtFRORvXFrgBAYG0rBhQ5KSknKey8rKIikpiejo6HxdIzMzk40bN1KpUiUAqlWrRnh4eJ5rOhwO1qxZk+9r+oyZMyElBapUgbg422nEGz35pHl8913QUg0i4kZcfosqISGBqVOnMnv2bLZs2ULPnj1JT0+nc+fOAMTFxZGYmJhz/rPPPstnn33Gjh072LBhAx06dGDXrl107doVMDOs+vbty3PPPceiRYvYuHEjcXFxRERE0KZNG1e/Hc9x6lTu2JuBAyEw0G4e8U716sG//w1ZWTB6tO00IiI5XD5fuG3btvzxxx8MGzaMlJQU6tevz9KlS3MGCe/evRt//9w66/Dhw3Tr1o2UlBTKli1Lw4YN+eabb6hTp07OOQMHDiQ9PZ3u3btz5MgRbrvtNpYuXXrWgoA+be5c2LULwsLgf8WkiEs89RQsWQJvvgnDh0PlyrYTiYjg53Q6nbZDFDWHw0FoaChpaWneOR4nKwuuvx62bjVjcAYOtJ1IvN1dd8Hy5dCrF4wfbzuNiHipgnx+u90sKikECxea4qZMGejRw3Ya8QVPPWUep00zs/ZERCxTgeNtnM7cGS29e4M39lCJ+7nrLoiKghMn4JVXbKcREVGB43WWLYP166FkSejTx3Ya8RV+frm9OG+8AYcO2c0jIj5PBY63ye69efRRKF/ebhbxLf/+N9x4Ixw7pnE4ImKdChxv8vXXsHKlWdCvf3/bacTX+Pnlrovz2mtw9KjdPCLi01TgeJNRo8xjp05w1VVWo4iPuu8+uO46OHwYLmN7FRGRy6UCx1skJ8NHH5kdnjUtXGwJCIDBg0375ZfNoGMREQtU4HiL7FVkH3gAatSwm0V8W4cOZrG/lBSYMcN2GhHxUSpwvMHPP5u9gADO2PZCxIrixXN7EV9+GU6ftptHRHySChxv8OKLZv2b7FksIrZ17gxXXgk7dsAHH9hOIyI+SAWOp/v9d7MHEKj3RtxHyZJmoUnILcBFRIqQChxPN26c2Tn89tvh1lttpxHJFR8PJUqYhSdXrLCdRkR8jAocT+ZwwKRJpj1ggN0sIn9Xvjw88ohpv/ii3Swi4nNU4HiyqVNNkVOrFrRqZTuNyNkSEszSBUuXwo8/2k4jIj5EBY6nOnUKxo417SeeMB8iIu6menW4/37THjPGbhYR8Sn6VPRU8+bB3r0QHm7WHRFxV9m3T+fNg9277WYREZ+hAscTOZ25/xru0weCguzmEbmQhg3hrrvMejjZvY4iIi6mAscTffYZbNwIpUpBjx6204hcXPbCf1OmmH2qRERcTAWOJ8ruvenWDcqWtZtFJD+aNTOLUKanw8SJttOIiA9QgeNpNmyApCSzqWHfvrbTiOSPn1/uWJxx47QJp4i4nAocT/PSS+axbVuoUsVuFpGCaNsWIiMhNRXeest2GhHxcipwPMmuXbmbamphP/E0xYubdXHAFOqZmXbziIhXU4HjSV591XwoxMRA/fq204gUXNeuUKYM/PwzLFpkO42IeDEVOJ7i8GGYNs201XsjnuqKK6BnT9N+9VW7WUTEq6nA8RQTJ5oZKPXqwb/+ZTuNyKXr1cvcrvryS1i3znYaEfFSKnA8QUYGjB9v2k88YWakiHiqiAho18601YsjIi6iAscTzJ8PKSlw1VVmJoqIp+vXzzy++y7s2WM3i4h4JRU47s7pzP1XbnbXvoina9AA/vlPM2g+u3dSRKQQqcBxdytXQnIylCgB3bvbTiNSeLKnjE+ZAkeP2s0iIl5HBY67y+696dgRypWzm0WkMLVsCdddB2lpMHOm7TQi4mVU4Liz7dth8WLT1rYM4m38/XPH4owdq4X/RKRQFUmBM2HCBKpWrUpwcDBRUVGsXbv2vOdOnTqV22+/nbJly1K2bFliYmLOOr9Tp074+fnlOZo3b+7qt1H0XnvNjMFp2RJq1rSdRqTwxcWZnsmdO+HDD22nEREv4vICZ/78+SQkJDB8+HA2bNhAvXr1iI2N5cCBA+c8f8WKFTz44IMsX76c1atXExkZSbNmzfj999/znNe8eXP279+fc8ydO9fVb6VoHTmS222f/a9cEW9TsmTuwn+vvGI3i4h4FT+n0+l05Q+Iiori5ptv5vXXXwcgKyuLyMhIevfuzeDBgy/6+szMTMqWLcvrr79OXFwcYHpwjhw5wsKFCy8pk8PhIDQ0lLS0NEJCQi7pGi43ZgwMHAh168IPP2jtG/Fe+/ebjWNPnYJvv4WoKNuJRMRNFeTz26U9OCdPnmT9+vXExMTk/kB/f2JiYli9enW+rnH8+HFOnTpFub8NsF2xYgUVK1akZs2a9OzZkz///PO818jIyMDhcOQ53Nrp07lTZ/v2VXEj3q1SJXjwQdPWwn8iUkhcWuAcPHiQzMxMwsLC8jwfFhZGSkpKvq4xaNAgIiIi8hRJzZs358033yQpKYkXXniBlStX0qJFCzLPM0hx1KhRhIaG5hyRkZGX/qaKwv/9n1n8rGJFeOgh22lEXC/7Nuz778OuXXaziIhXcOtZVKNHj2bevHksWLCA4ODgnOfbtWvHPffcQ926dWnTpg1Llixh3bp1rFix4pzXSUxMJC0tLefY4+4rp2b/K7ZnTzjjfYt4rfr14a67tPCfiBQalxY45cuXJyAggNTU1DzPp6amEh4efsHXvvTSS4wePZrPPvuMG2+88YLnVq9enfLly7N9+/Zzfj8oKIiQkJA8h9tavRrWrIHAwNzBlyK+IHvhv6lTwd1vI4uI23NpgRMYGEjDhg1JSkrKeS4rK4ukpCSio6PP+7oXX3yRESNGsHTpUho1anTRn7N3717+/PNPKlWqVCi5rcruvWnfHv52a0/Eq7VoYZZDcDhg1izbaUTEw7n8FlVCQgJTp05l9uzZbNmyhZ49e5Kenk7nzp0BiIuLIzExMef8F154gaFDhzJjxgyqVq1KSkoKKSkpHDt2DIBjx44xYMAAvv32W3777TeSkpJo3bo1NWrUIDY21tVvx7V27TLjb0BTw8X3+PtDnz6mPX48ZGXZzSMiHs3lBU7btm156aWXGDZsGPXr1yc5OZmlS5fmDDzevXs3+/fvzzl/4sSJnDx5kvvuu49KlSrlHC+99BIAAQEB/Pjjj9xzzz1cd911dOnShYYNG/Lll18SFBTk6rfjWtn/U2/a1EwPF/E1cXEQGmpW8f74Y9tpRMSDuXwdHHfkluvgHD0KV19tuueXLIFWrWwnErHjiSfg5ZchJgaWLbOdRkTciNusgyMFMHOmKW5q1jRjEUR8Va9e5nbV55/D5s2204iIh1KB4w4yM82+UwCPP27+5y7iq6pWhdatTXvcOKtRRMRz6ZPUHSxeDDt2QNmyZgyCiK97/HHz+NZbcOiQ3Swi4pFU4LiD7Knhjz4KpUrZzSLiDv7xD7P4319/mXVxREQKSAWObcnJsGoVBARAfLztNCLuwc8vtxdnwgSzP5uISAGowLEte1n6++4zs6hExGjXDipUMPuyLVhgO42IeBgVODYdPAjvvGPa2QuciYgRHAw9epj22LFWo4iI51GBY9PUqZCRAQ0bwgW2rhDxWT17QvHi8M038N13ttOIiAdRgWPLqVPwxhum3aePGXMgInlVqgQPPGDa2UspiIjkgwocWxYuhL17oWJFaNvWdhoR95U92Hj+fDhjWxcRkQtRgWNL9gJmjz4Knr6Hlogr3XyzuYV76hRMmmQ7jYh4CBU4NmzYAF99BcWK5Q6iFJHz69vXPE6aZMatiYhchAocG7Knht9/P0RE2M0i4gn+8x+zjMKBAzBvnu00IuIBVOAUtQMHYM4c09bUcJH8KV48dyHM114Dp9NuHhFxeypwitrUqXDypBlXEBVlO42I5+jWDUqUgO+/N7d4RUQuQAVOUdLUcJFLd+WV0KGDaWvKuIhchAqcovTBB7BvH4SFmfE3IlIw2bd1FyyAXbvsZhERt6YCpyhlTw3v0UNTw0UuxQ03QNOmkJWV2xsqInIOKnCKynffmeXmixc3a9+IyKXJXvhv6lQ4ftxuFhFxWypwikr21PAHHjDLz4vIpWnZEqpXh8OHczerFRH5GxU4RSE1NXftDk0NF7k8AQHQq5dpjxunKeMick4qcIrClClmanhUFDRubDuNiOfr3BlKlYJNm2DFCttpRMQNqcBxtZMnYeJE01bvjUjhKFMGOnY0bU0ZF5FzUIHjav/3f2YH5PBwuO8+22lEvEf2bapFi2DnTrtZRMTtqMBxteyp4T17QmCg3Swi3qR2bWjWzIzBmTDBdhoRcTMqcFxp7Vr49ltNDRdxlezbvtOmwbFjdrOIiFtRgeNK2VPD27UzqxeLSOFq0QJq1IC0NHj7bdtpRMSNqMBxlZQUmD/ftHv3tptFxFv5++f+/dKUcRE5gwocV5k82WyuGR1tdg4XEdfo1AmuuAK2bIGkJNtpRMRNqMBxBU0NFyk6ISFmXRzIHdQvIj5PBY4rvPeeWb04IgLuvdd2GhHvlz1lfMkS+PVXu1lExC0USYEzYcIEqlatSnBwMFFRUaxdu/aC57/33nvUqlWL4OBg6taty8cff5zn+06nk2HDhlGpUiVKlChBTEwMv/zyiyvfQsGcOTW8eHG7WUR8wXXXmQHHTie8/rrtNCLiBlxe4MyfP5+EhASGDx/Ohg0bqFevHrGxsRw4cOCc53/zzTc8+OCDdOnShe+//542bdrQpk0bNm3alHPOiy++yLhx45g0aRJr1qyhVKlSxMbGcuLECVe/nYtbs8ZMDw8MhO7dbacR8R3Zt4NnzICjR+1mERH7nC7WuHFjZ3x8fM7XmZmZzoiICOeoUaPOef4DDzzgbNWqVZ7noqKinI8++qjT6XQ6s7KynOHh4c4xY8bkfP/IkSPOoKAg59y5c/OVKS0tzQk409LSCvp2Lu6hh5xOcDo7diz8a4vI+WVmOp3XXWf+/r3+uu00IuICBfn8dmkPzsmTJ1m/fj0xMTE5z/n7+xMTE8Pq1avP+ZrVq1fnOR8gNjY25/ydO3eSkpKS55zQ0FCioqLOe82MjAwcDkeewyX27YN33zVtTQ0XKVr+/rm9OOPHQ1aW3TwivmrzZvjvf2HlSqsxXFrgHDx4kMzMTML+tshdWFgYKSkp53xNSkrKBc/PfizINUeNGkVoaGjOERkZeUnv56ImT4bTp6FJE2jY0DU/Q0TOLy7OzKratg2WLbOdRsQ3jR8PCxbkLnZriU/MokpMTCQtLS3n2LNnj2t+UMeOkJAAAwa45voicmGlS8Mjj5i2poyLFL3Dh+Gtt0zb8p0MlxY45cuXJyAggNTU1DzPp6amEh4efs7XhIeHX/D87MeCXDMoKIiQkJA8h0tUrw4vvwytW7vm+iJycfHx4OcHH38MP/9sO42Ib5kxA44fhxtvhH/8w2oUlxY4gYGBNGzYkKQzVhfNysoiKSmJ6Ojoc74mOjo6z/kAy5Ytyzm/WrVqhIeH5znH4XCwZs2a815TRHxIjRrQqpVpa8q4SNHJzMz9O9enj/mHhkUuv0WVkJDA1KlTmT17Nlu2bKFnz56kp6fT+X8rj8bFxZGYmJhz/uOPP87SpUt5+eWX2bp1K08//TTfffcdvf63kJefnx99+/blueeeY9GiRWzcuJG4uDgiIiJo06aNq9+OiHiC7MHGM2eCqyYViEheS5bAb79BuXLw0EO201DM1T+gbdu2/PHHHwwbNoyUlBTq16/P0qVLcwYJ7969G3//3Drr1ltvZc6cOQwZMoQnn3ySa6+9loULF3LDDTfknDNw4EDS09Pp3r07R44c4bbbbmPp0qUEBwe7+u2IiCeIiYHatc3+VLNmacsUkaKQPai4WzcoUcJuFsDP6fS97XcdDgehoaGkpaW5bjyOiNg1cSI89pi5ZbVtm5lGLiKusXkz3HCD+Xu2cydUruySH1OQz2/9jRcR7xQXB6GhsH07LF1qO42Id8vuvWnTxmXFTUGpwBER71SqFHTtatqaMi7iOmdODXej28EqcETEe2VPGf/0U9i61XYaEe+UPTW8bl3rU8PPpAJHRLxXtWpwzz2mbXlVVRGv5GZTw8+kAkdEvFt2l/ns2XDkiNUoIl7no4/camr4mVTgiIh3++c/4frrIT3drIsjIoUne3xb165QsqTdLH+jAkdEvJufX24vzuuvmy51Ebl8mzdDUpKZGv7YY7bTnEUFjoh4v/btoWxZ2LHD7FElIpcve+xNmzZQpYrVKOeiAkdEvJ+mjIsUrsOH4c03TdvyruHnowJHRHxDfLzpSv/8c/jpJ9tpRDzbzJm5U8PvuMN2mnNSgSMivqFKFdOVDurFEbkcZ04N793braaGn0kFjoj4juzBxm++abrYRaTgPvrI7DdVtqwZ3+amVOCIiO/4xz/gxhvhr79g+nTbaUQ805m7hrvZ1PAzqcAREd+hKeMil+enn8w4NjedGn4mFTgi4lseesisurprFyxebDuNiGfJ7r1p3dotp4afSQWOiPiWEiWge3fT1mBjkfw7dMhseQJutWv4+ajAERHf89hjEBAAy5fDxo2204h4hmnTzPi1evXcdmr4mVTgiIjviYyE//7XtNWLI3Jxp0/nTg1//HG3nRp+JhU4IuKbsrvY334b/vzTbhYRd7dgAezZAxUqwIMP2k6TLypwRMQ3NWkCDRrAiROm611Ezu+118xjjx4QHGw3Sz6pwBER33TmlPEJE0wXvIic7bvv4OuvoXhx6NnTdpp8U4EjIr6rXTsoX950vX/4oe00Iu4pu/embVuoVMlulgJQgSMivis4GB591LQ12FjkbPv3w/z5pv3443azFJAKHBHxbT17minjq1ZBcrLtNCLuZeJEOHUKbr0VGjWynaZAVOCIiG+76iq47z7Tzl6lVUTMAPxJk0y7b1+rUS6FChwRkeyu93fegT/+sJtFxF3MnWv+PkRGwn/+YztNganAERG55RbT/Z6RAVOn2k4jYp/TmTu4uFcvKFbMbp5LoAJHROTMKeNvvGHGHIj4slWr4IcfzN5tXbvaTnNJVOCIiAA88ABUrAi//25WbRXxZWPHmse4OChXzmqUS6UCR0QEICjIrNIKmjIuvm3Hjtx1oTxg1/DzUYEjIpKtRw8z1uDrr2H9ettpROx4/XUzBqdZM6hTx3aaS+bSAufQoUO0b9+ekJAQypQpQ5cuXTh27NgFz+/duzc1a9akRIkSVK5cmT59+pCWlpbnPD8/v7OOefPmufKtiIgvqFTJ3KoCTRkX33T0KEyfbtoeODX8TC4tcNq3b8/mzZtZtmwZS5YsYdWqVXTv3v285+/bt499+/bx0ksvsWnTJmbNmsXSpUvp0qXLWefOnDmT/fv35xxt2rRx4TsREZ+R3SU/dy6kptrNIlLUZs0ChwOuuw5iY22nuSx+TqfT6YoLb9myhTp16rBu3Toa/W/1w6VLl9KyZUv27t1LREREvq7z3nvv0aFDB9LT0yn2v2lqfn5+LFiw4JKLGofDQWhoKGlpaYSEhFzSNUTEi91yC6xZA8OGwTPP2E4jUjSysqBWLfjlF3ObKj7edqKzFOTz22U9OKtXr6ZMmTI5xQ1ATEwM/v7+rFmzJt/XyX4Txf42Bz8+Pp7y5cvTuHFjZsyYwYXqtIyMDBwOR55DROS8EhLM4xtvwF9/2c0iUlQ++cQUN6Gh0LGj7TSXzWUFTkpKChUrVszzXLFixShXrhwpKSn5usbBgwcZMWLEWbe1nn32Wd59912WLVvGvffey2OPPcb4C9wvHzVqFKGhoTlHZGRkwd+QiPiO//4XKleGgwfh7bdtpxEpGq++ah67doUrrrCbpRAUuMAZPHjwOQf5nnls3br1soM5HA5atWpFnTp1ePrpp/N8b+jQoTRp0oQGDRowaNAgBg4cyJgxY857rcTERNLS0nKOPXv2XHY+EfFixYrlbt/w6qum617EmyUnQ1KS2Xi2d2/baQpFgdde7t+/P506dbrgOdWrVyc8PJwDBw7kef706dMcOnSI8PDwC77+6NGjNG/enNKlS7NgwQKKFy9+wfOjoqIYMWIEGRkZBAUFnfX9oKCgcz4vInJeXbrA00/Dli3w6afQooXtRCKuk917c999UKWK3SyFpMAFToUKFahQocJFz4uOjubIkSOsX7+ehg0bAvDFF1+QlZVFVFTUeV/ncDiIjY0lKCiIRYsWERwcfNGflZycTNmyZVXEiEjhCQ01XfWvvmoOFTjirfbtM7MGIXf8mRdw2Ric2rVr07x5c7p168batWv5+uuv6dWrF+3atcuZQfX7779Tq1Yt1q5dC5jiplmzZqSnpzN9+nQcDgcpKSmkpKSQmZkJwOLFi5k2bRqbNm1i+/btTJw4kZEjR9LbS7rURMSN9OkD/v6wbBls3Gg7jYhrvP662X/tttugcWPbaQqNS7cHfeedd+jVqxdNmzbF39+fe++9l3FnLIF+6tQptm3bxvHjxwHYsGFDzgyrGjVq5LnWzp07qVq1KsWLF2fChAn069cPp9NJjRo1eOWVV+jWrZsr34qI+KKqVeHee+G990wvzowZthOJFK70dJg0ybT797ebpZC5bB0cd6Z1cEQk3779FqKjITAQdu2Ci4whFPEoEyZAr15wzTWwbZsZZOzG3GIdHBERr3DLLabAOXnSrIsj4i0yM3MHF/fr5/bFTUGpwBERuRgt/CfeaPFi+PVXKFsWLjI72hOpwBERuZg2bcx4nD//hLfesp1GpHC8/LJ57NEDSpWym8UFVOCIiFyMFv4Tb7N2LXz1FRQvbsbgeCEVOCIi+fHIIxASAlu3wtKlttOIXJ5XXjGPDz4I+dz82tOowBERyY+QEMhejiK7a1/EE+3aBe+/b9petLDf36nAERHJrz59zEyTL76ADRtspxG5NOPGmRlUTZtCvXq207iMChwRkfyqXBnatjXtC2zwK+K2HA6YOtW0vWxhv79TgSMiUhADBpjHd9+FnTvtZhEpqKlT4ehRqF0bYmNtp3EpFTgiIgVRvz40a2ZmUmUvkibiCU6ezP0z27+/2WfNi3n3uxMRcYWBA83jtGlw8KDdLCL5NWcO/P47VKoEHTrYTuNyKnBERArqrrugQQOzqrG2bxBPkJWVO26sb18ICrIapyiowBERKSg/v9xenPHjtX2DuL+PPoKffjLLHTz6qO00RUIFjojIpbjvPrN9w8GDMGuW7TQiF/bii+axRw8IDbWbpYiowBERuRTFiuUukvbSS2ZdERF39M03ZluGwMDcLUd8gAocEZFL9cgjUK4c7NgBH3xgO43IuWX33jz8sNduy3AuKnBERC5VqVK5GxWOGQNOp908In+3ZQt8+KEZN5a9hpOPUIEjInI5evWC4GBYtw5WrrSdRiSvl14yj61bQ82adrMUMRU4IiKXo0IF6NzZtLNvBYi4g99/h7feMu1Bg+xmsUAFjojI5UpIMKvCfvIJbNxoO42I8dprcOoU3H473HKL7TRFTgWOiMjlqlED/vtf09YmnOIOjhyBSZNM2wd7b0AFjohI4cj+EJkzR5twin2TJplNNW+4AVq2tJ3GChU4IiKFoVEj+Ne/zHo4GosjNp04YW5PgZk55ednN48lKnBERArLU0+ZxxkzYN8+u1nEd82aBSkpcPXV8OCDttNYowJHRKSw/OMf0KQJnDwJL79sO434olOnYPRo0x44EIoXt5vHIhU4IiKFxc8vtxdn0iSzT5VIUZozB3btgooVoWtX22msUoEjIlKYmjeHm26C48dzx0GIFIXMTBg50rSfeAJKlLCbxzIVOCIihcnPD5580rTHj4e0NLt5xHe8/z78/LPZH61HD9tprFOBIyJS2P7zH6hd2xQ3b7xhO434gqwseO450+7bF0qXthrHHajAEREpbP7+kJho2q++am5XibjS4sWwaROEhEDv3rbTuAUVOCIirvDgg1CtGvzxB0ydajuNeDOnM7f3plcvKFPGahx34dIC59ChQ7Rv356QkBDKlClDly5dOHbs2AVfc+edd+Ln55fn6PG3e4m7d++mVatWlCxZkooVKzJgwABOnz7tyrciIlIwxYrlrm48ZoyZOi7iCp99Bt99ByVLmttTAri4wGnfvj2bN29m2bJlLFmyhFWrVtG9e/eLvq5bt27s378/53jxjFVBMzMzadWqFSdPnuSbb75h9uzZzJo1i2HDhrnyrYiIFFynThARYXZ1fvNN22nEGzmdMGKEaT/6qNndXgDwczqdTldceMuWLdSpU4d169bRqFEjAJYuXUrLli3Zu3cvERER53zdnXfeSf369Rk7duw5v//JJ5/w73//m3379hEWFgbApEmTGDRoEH/88QeBgYEXzeZwOAgNDSUtLY2QkJBLe4MiIvnx6qtmt/FrroGtW03PjkhhWbkS7rwTAgPNHmjn+Wz1FgX5/HZZD87q1aspU6ZMTnEDEBMTg7+/P2vWrLnga9955x3Kly/PDTfcQGJiIsfPGKC3evVq6tatm1PcAMTGxuJwONi8efM5r5eRkYHD4chziIgUie7doXx5+PVXmD/fdhrxNtljb7p08fripqBcVuCkpKRQsWLFPM8VK1aMcuXKkZKSct7XPfTQQ7z99tssX76cxMRE3nrrLTp06JDnumcWN0DO1+e77qhRowgNDc05IiMjL/VtiYgUTKlS0K+faT/3nFmMTaQwfPstfP656RUcONB2GrdT4AJn8ODBZw0C/vuxdevWSw7UvXt3YmNjqVu3Lu3bt+fNN99kwYIF/Prrr5d8zcTERNLS0nKOPXv2XPK1REQKrFcvs/ja1q3qxZHC8/zz5vHhh6FqVatR3FGBbwb379+fTp06XfCc6tWrEx4ezoEDB/I8f/r0aQ4dOkR4eHi+f15UVBQA27dv55prriE8PJy1a9fmOSc1NRXgvNcNCgoiKCgo3z9TRKRQhYRA//5mn6pnn4W2bSEgwHYq8WTr18OSJWbNpcGDbadxSwUucCpUqECFfIzSjo6O5siRI6xfv56GDRsC8MUXX5CVlZVTtORHcnIyAJUqVcq57vPPP8+BAwdyboEtW7aMkJAQ6tSpU8B3IyJSRHr3NjuMb9sG8+ZB+/a2E4knGz7cPLZvD9ddZzeLm3LZGJzatWvTvHlzunXrxtq1a/n666/p1asX7dq1y5lB9fvvv1OrVq2cHplff/2VESNGsH79en777TcWLVpEXFwc//jHP7jxxhsBaNasGXXq1OHhhx/mhx9+4NNPP2XIkCHEx8erl0ZE3Ffp0mYDRDC9OBqLI5dqzRr46CPTCzh0qO00bsul6+C888471KpVi6ZNm9KyZUtuu+02pkyZkvP9U6dOsW3btpxZUoGBgXz++ec0a9aMWrVq0b9/f+69914WL16c85qAgACWLFlCQEAA0dHRdOjQgbi4OJ599llXvhURkcvXqxdceaXZEHHuXNtpxFNl9948/DBce63dLG7MZevguDOtgyMi1owebfapuu462LxZ6+JIwXzzDTRpYnpvfv4Zqle3nahIucU6OCIicg7x8erFkUuX3XvTubPPFTcFpQJHRKQolS4NAwaY9ogRoH30JL9Wrcpd9+app2yncXsqcEREilp8vFnd+JdftEeV5F92702XLlr3Jh9U4IiIFLUrroAnnzTtp5+GjAyrccQDLF8OK1aYPaey/+zIBanAERGxoWdPuPpq2LMHJk+2nUbcmdOZe0uqa1eoXNluHg+hAkdExIbgYBg2zLSffx6OHbObR9zX4sWwejWUKAFDhthO4zFU4IiI2NKpE9SoAQcOwLhxttOIO8rMzO29efxx+N+q/nJxKnBERGwpXtysagzw4otw+LDdPOJ+5s6FTZugTBntGF5AKnBERGxq2xbq1oW0NBgzxnYacScnT+bexhw0CMqWtZvHw6jAERGxyd8fnnvOtF97DVJS7OYR9zF1KuzcaW5L9eljO43HUYEjImLb3XdDVBQcP24W/xNJT8/9szB0KJQsaTePB1KBIyJim5+f2aMKYMoUs42D+LbXXoPUVLjmGjM1XApMBY6IiDu4805o1cps3ZCYaDuN2PTnn2bQOZhB6MWL283joVTgiIi4ixdeMGNyPvgAvv7adhqx5dlnzaDzevWgXTvbaTyWChwREXdx/fXwyCOmPWCAWcFWfMsvv8Abb5j2yy+bglcuiX5zIiLu5JlnzIDS1atNT474lkGDzG3Kli2haVPbaTyaChwREXcSEQH9+5v24MFw6pTdPFJ0vvwSFiwwvTZaE+myqcAREXE3AwZAxYqwfbs24vQVWVm5hW23blCnjt08XkAFjoiIuyldGp5+2rSfecYMOBXvNn8+rFsHV1xh/pvLZVOBIyLijrp2hVq14OBBLf7n7U6cyF0aYPBgCAuzm8dLqMAREXFHxYvDq6+a9muvwbZtdvOI67z2GuzaBVddBf362U7jNVTgiIi4q+bNcxf/S0iwnUZcYd++3L3IRo7UlgyFSAWOiIg7e+UV05vz8cfmEO8yaBAcOwa33AIdOthO41VU4IiIuLPrroPHHzftfv3g5Em7eaTwfP01vP222Yts3Dgt6lfI9NsUEXF3Q4eaaeM//wyvv247jRSGzEzo3du0H3kEbr7Zbh4vpAJHRMTdhYTAqFGm/cwzcOCA3Txy+aZPh++/h9BQM/ZGCp0KHBERT9CpEzRsCA6Hdhv3dIcPw5NPmvYzz5jeOSl0KnBERDyBvz+MH2/aM2bAV1/ZzSOXbtgw+PNPs7nqY4/ZTuO1VOCIiHiK6GizjD9Ajx7ap8oTJSfn7hY+bpyZIScuoQJHRMSTjB4N5cvD5s25CwGKZ8jMNAVqVhY88ADcdZftRF5NBY6IiCcpVw5eesm0n3nGrIArnuGNN+C778zA4rFjbafxei4tcA4dOkT79u0JCQmhTJkydOnShWPHjp33/N9++w0/P79zHu+9917Oeef6/rx581z5VkRE3EdcHNxxBxw/Dn362E4j+bF3b+7A4tGjoVIlu3l8gJ/T6XS66uItWrRg//79TJ48mVOnTtG5c2duvvlm5syZc87zMzMz+eOPP/I8N2XKFMaMGcP+/fu54oorTGg/P2bOnEnz5s1zzitTpgzBwcH5yuVwOAgNDSUtLY2QkJBLfHciIhZt2QL16plxOAsXQuvWthPJhfz3v7BggRlH9dVXWtTvEhXk89tlBc6WLVuoU6cO69ato1GjRgAsXbqUli1bsnfvXiIiIvJ1nQYNGnDTTTcxffr03NB+fixYsIA2bdpcUjYVOCLiFZ580qyPExkJP/0E//tHoLiZDz+ENm2gWDGz9s0NN9hO5LEK8vntshJy9erVlClTJqe4AYiJicHf3581a9bk6xrr168nOTmZLl26nPW9+Ph4ypcvT+PGjZkxYwYXqtMyMjJwOBx5DhERjzdkCFSrBnv2wODBttPIuRw9Cr16mfaAASpuipDLCpyUlBQq/m3xomLFilGuXDlSUlLydY3p06dTu3Ztbr311jzPP/vss7z77rssW7aMe++9l8cee4zx2etDnMOoUaMIDQ3NOSIjIwv+hkRE3E3JkjB1qmlPmAArVliNI+fw1FNm/E316mbLDSkyBS5wBg8efN6BwNnH1q1bLzvYX3/9xZw5c87ZezN06FCaNGlCgwYNGDRoEAMHDmTMmDHnvVZiYiJpaWk5x549ey47n4iIW2jaFLp3N+0uXSA93W4eybViRe7ijJMmQYkSVuP4mmIFfUH//v3p1KnTBc+pXr064eHhHPjbfimnT5/m0KFDhIeHX/TnvP/++xw/fpy4uLiLnhsVFcWIESPIyMggKCjorO8HBQWd83kREa8wZgx88gns2GF6DDQF2b5jx6BzZ9Pu3h3+9S+7eXxQgQucChUqUKFChYueFx0dzZEjR1i/fj0NGzYE4IsvviArK4uoqKiLvn769Oncc889+fpZycnJlC1bVkWMiPimkBBzq6p5c7M67n33wW232U7l2wYMgN9+gypVctctkiLlsjE4tWvXpnnz5nTr1o21a9fy9ddf06tXL9q1a5czg+r333+nVq1arF27Ns9rt2/fzqpVq+jatetZ1128eDHTpk1j06ZNbN++nYkTJzJy5Eh6Z287LyLii2Jj4ZFHwOk0j8eP207ku5YtM7ekwOwbVrq03Tw+yqUT8d955x1q1apF06ZNadmyJbfddhtTpkzJ+f6pU6fYtm0bx//2F3HGjBlcffXVNGvW7KxrFi9enAkTJhAdHU39+vWZPHkyr7zyCsOHD3flWxERcX8vvwwREfDLL2aGlRS9tDQzFgogPl7bMVjk0oX+3JXWwRERr/Xxx9CqlWl/9pnGfhS1Ll1Mr0316vDjj1CqlO1EXsUt1sERERELWraExx4z7bg4+Nvq8OJCH35oihs/P5g1S8WNZSpwRES8zUsvQZ06kJJiehR8r6O+6O3ZkztrKiEBbr/dbh5RgSMi4nVKlIC5cyEwEBYvhokTbSfybqdPw0MPweHD0KgRjBxpO5GgAkdExDvdeCO88IJp9+8PmzfbzePNRowwG2iWLg3z5pnCUqxTgSMi4q369DFr45w4YXoYTpywncj7rFwJzz1n2pMmwTXX2M0jOVTgiIh4K39/M9i1QgUzoyc+XuNxCtOff0L79pCVBZ06mSJS3IYKHBERbxYWBnPmmGJnxgyYNs12Iu+QmWlmqf3+O9SsmbvnlLgNFTgiIt4uJib3NkqvXvC31ePlEgwfbtYcCg42426uuMJ2IvkbFTgiIr5g8GBo0wZOnjR7VWl9nEv3/vvw/POmPW0a1K9vNY6cmwocERFfkL343HXXmTVb2rUz05ulYDZuNONtwKx307691ThyfipwRER8RWgofPCBWWH3iy9Mr47k36FDphcsPR2aNs2dhi9uSQWOiIgvuf56M9gYzOackyfbzeMpTp+GBx+EHTugalWYPx+KFbOdSi5ABY6IiK954AF4+mnTfuwxM1hWzs/pNFPsP/sMSpaEhQvhyittp5KLUIEjIuKLhg0zY0myskzBs2GD7UTua8QImDLFjGN6+22oV892IskHFTgiIr7Iz8/cnmra1IwpadUKdu+2ncr9TJtmpoQDTJgA//mP3TySbypwRER8VWAg/N//wQ03mJ3HW7Y0G0aKsXgxPPqoaT/1FPTsaTePFIgKHBERXxYaCh99BJUqmQ05Y2MhLc12KvtWr4a2bc0tvM6dzW0q8SgqcEREfF3lyvDpp2bg7Lp1ZoNOh8N2KnvWrIEWLeCvv0yv1uTJ5paeeBQVOCIiAnXrwuefQ9my8O235oP92DHbqYreN9/Av/5lerFuvx3efReKF7edSi6BChwRETHq1zdFTpky8PXXZuBxerrtVEXnq6/MLbqjR+HOO+GTT8yiiOKRVOCIiEium24y672EhMCqVaYnxxfG5KxcaW7NHTtmZpZ99JGKGw+nAkdERPK6+WYzJie7yLnzTkhNtZ3KdT7+2BRy6enm9tTixWZBP/FoKnBERORst9wCK1ZAxYqQnAxNmsC2bbZTFb5p0+Cee+D4cdOD8+GHUKKE7VRSCFTgiIjIuTVoYMbiVKsGv/4K0dGwfLntVIUjMxMSE6FbN9OOi1Nx42VU4IiIyPnVqGFmVd1yi1kEsFkzs6Kv02k72aVLS4PWrWH0aPP1U0/BrFlm4UPxGipwRETkwipWhC++gHbtzK7avXqZHg9PnGGVnAyNG5tBxMHBZm+p557TOjdeSAWOiIhcXIkSMGcOvPwyBASYwqBRI8/ZpNPphDfeMD1RP/8MV18NX34J7dvbTiYuogJHRETyx88PEhIgKcls7bB1qykYRo2CU6dspzu/vXvNysTx8ZCRAf/+t+nJadTIdjJxIRU4IiJSMHfcARs3mp21T52CJ580U8u/+852srwyM02vzQ03mGnvQUHwyiuwaJHZlkK8mgocEREpuCuvNDuRz5pltnf44QcztqVrV7MzuW1ffmnyxMebQcWNG5tem379NN7GR6jAERGRS+PnBx07mltVDz1kxrlMnw7XXmtmJv35Z9Fn+v57s8XEP/5hxgeFhsLrr5s9pmrVKvo8Yo3LCpznn3+eW2+9lZIlS1KmTJl8vcbpdDJs2DAqVapEiRIliImJ4ZdffslzzqFDh2jfvj0hISGUKVOGLl26cMwXN4QTEXEXFSvCO++YNXMaNzbbHYwcCVWrmh6Tv/1/vNBlZZntJZo1M1tNfPyxGQjdvbtZnDA+3nwtPsVlBc7Jkye5//776dmzZ75f8+KLLzJu3DgmTZrEmjVrKFWqFLGxsZw4cSLnnPbt27N582aWLVvGkiVLWLVqFd27d3fFWxARkYK49VZYvRoWLDCLBB47BmPHwnXXmS0QZs4s3H2ttm6FZ5+Fa64xm2QuWwb+/vDgg/DTTzB5MoSFFd7PE4/i53S6drWmWbNm0bdvX44cOXLB85xOJxEREfTv358nnngCgLS0NMLCwpg1axbt2rVjy5Yt1KlTh3Xr1tHof6Pfly5dSsuWLdm7dy8RERH5yuRwOAgNDSUtLY2QkJDLen8iInIOTqcZ2Dt+vNmVO/ujpnhxUwg1a2ZWRr7pJnMbKT/X27MH1qwx42s++yzv1hEhIeZ2Wb9+ZuVl8UoF+fwuVkSZLmrnzp2kpKQQExOT81xoaChRUVGsXr2adu3asXr1asqUKZNT3ADExMTg7+/PmjVr+M9//nPOa2dkZJCRkZHztcPhcN0bERERMz6neXNz7NgBc+ea21hbtpidu1euzD03IsIUJZUqmWKnRAmzoOCJE/DHH7B/v1m75u/DEYoXh7vuMmvZ3HuvNsiUPNymwEn536j7sL91J4aFheV8LyUlhYoVK+b5frFixShXrlzOOecyatQonnnmmUJOLCIi+VK9uhl0/NRTsH276X1ZvtxMK//tN9i3zxwXU6wYXH893H67GUQcG2t6bkTOoUAFzuDBg3nhhRcueM6WLVuo5WYj1RMTE0lISMj52uFwEBkZaTGRiIiPqlHDHI89Zr4+dMhs5Lljh+mtcTjgr79MMRMYCBUqmHE011xjZmcVL243v3iMAhU4/fv3p1OnThc8p3r16pcUJDw8HIDU1FQqVaqU83xqair169fPOefAgQN5Xnf69GkOHTqU8/pzCQoKIigo6JJyiYiIC5UrZ46bb7adRLxMgQqcChUqUKFCBZcEqVatGuHh4SQlJeUUNA6HgzVr1uTMxIqOjubIkSOsX7+ehg0bAvDFF1+QlZVFVFSUS3KJiIiI53HZNPHdu3eTnJzM7t27yczMJDk5meTk5Dxr1tSqVYsFCxYA4OfnR9++fXnuuedYtGgRGzduJC4ujoiICNq0aQNA7dq1ad68Od26dWPt2rV8/fXX9OrVi3bt2uV7BpWIiIh4P5cNMh42bBizZ8/O+bpBgwYALF++nDvvvBOAbdu2kXbGmggDBw4kPT2d7t27c+TIEW677TaWLl1KcHBwzjnvvPMOvXr1omnTpvj7+3Pvvfcybtw4V70NERER8UAuXwfHHWkdHBEREc9TkM9v7UUlIiIiXkcFjoiIiHgdFTgiIiLidVTgiIiIiNdRgSMiIiJeRwWOiIiIeB0VOCIiIuJ1VOCIiIiI11GBIyIiIl7HZVs1uLPsxZsdDoflJCIiIpJf2Z/b+dmEwScLnKNHjwIQGRlpOYmIiIgU1NGjRwkNDb3gOT65F1VWVhb79u2jdOnS+Pn5Feq1HQ4HkZGR7NmzR/tc/Y1+Nxem38+F6fdzYfr9nJ9+NxfmSb8fp9PJ0aNHiYiIwN//wqNsfLIHx9/fn6uvvtqlPyMkJMTt/6DYot/Nhen3c2H6/VyYfj/np9/NhXnK7+diPTfZNMhYREREvI4KHBEREfE6KnAKWVBQEMOHDycoKMh2FLej382F6fdzYfr9XJh+P+en382FeevvxycHGYuIiIh3Uw+OiIiIeB0VOCIiIuJ1VOCIiIiI11GBIyIiIl5HBU4hmjBhAlWrViU4OJioqCjWrl1rO5LbWLVqFXfffTcRERH4+fmxcOFC25HcxqhRo7j55pspXbo0FStWpE2bNmzbts12LLcxceJEbrzxxpxFyKKjo/nkk09sx3JLo0ePxs/Pj759+9qO4haefvpp/Pz88hy1atWyHcut/P7773To0IErr7ySEiVKULduXb777jvbsQqFCpxCMn/+fBISEhg+fDgbNmygXr16xMbGcuDAAdvR3EJ6ejr16tVjwoQJtqO4nZUrVxIfH8+3337LsmXLOHXqFM2aNSM9Pd12NLdw9dVXM3r0aNavX893333HXXfdRevWrdm8ebPtaG5l3bp1TJ48mRtvvNF2FLdy/fXXs3///pzjq6++sh3JbRw+fJgmTZpQvHhxPvnkE3766SdefvllypYtazta4XBKoWjcuLEzPj4+5+vMzExnRESEc9SoURZTuSfAuWDBAtsx3NaBAwecgHPlypW2o7itsmXLOqdNm2Y7hts4evSo89prr3UuW7bMeccddzgff/xx25HcwvDhw5316tWzHcNtDRo0yHnbbbfZjuEy6sEpBCdPnmT9+vXExMTkPOfv709MTAyrV6+2mEw8UVpaGgDlypWznMT9ZGZmMm/ePNLT04mOjrYdx23Ex8fTqlWrPP8PEuOXX34hIiKC6tWr0759e3bv3m07kttYtGgRjRo14v7776dixYo0aNCAqVOn2o5VaFTgFIKDBw+SmZlJWFhYnufDwsJISUmxlEo8UVZWFn379qVJkybccMMNtuO4jY0bN3LFFVcQFBREjx49WLBgAXXq1LEdyy3MmzePDRs2MGrUKNtR3E5UVBSzZs1i6dKlTJw4kZ07d3L77bdz9OhR29Hcwo4dO5g4cSLXXnstn376KT179qRPnz7Mnj3bdrRC4ZO7iYu4q/j4eDZt2qRxAn9Ts2ZNkpOTSUtL4/3336djx46sXLnS54ucPXv28Pjjj7Ns2TKCg4Ntx3E7LVq0yGnfeOONREVFUaVKFd599126dOliMZl7yMrKolGjRowcORKABg0asGnTJiZNmkTHjh0tp7t86sEpBOXLlycgIIDU1NQ8z6emphIeHm4plXiaXr16sWTJEpYvX87VV19tO45bCQwMpEaNGjRs2JBRo0ZRr149XnvtNduxrFu/fj0HDhzgpptuolixYhQrVoyVK1cybtw4ihUrRmZmpu2IbqVMmTJcd911bN++3XYUt1CpUqWz/pFQu3Ztr7mNpwKnEAQGBtKwYUOSkpJynsvKyiIpKUnjBOSinE4nvXr1YsGCBXzxxRdUq1bNdiS3l5WVRUZGhu0Y1jVt2pSNGzeSnJycczRq1Ij27duTnJxMQECA7Yhu5dixY/z6669UqlTJdhS30KRJk7OWpPj555+pUqWKpUSFS7eoCklCQgIdO3akUaNGNG7cmLFjx5Kenk7nzp1tR3MLx44dy/Ovpp07d5KcnEy5cuWoXLmyxWT2xcfHM2fOHD788ENKly6dM24rNDSUEiVKWE5nX2JiIi1atKBy5cocPXqUOXPmsGLFCj799FPb0awrXbr0WWO1SpUqxZVXXqkxXMATTzzB3XffTZUqVdi3bx/Dhw8nICCABx980HY0t9CvXz9uvfVWRo4cyQMPPMDatWuZMmUKU6ZMsR2tcNiexuVNxo8f76xcubIzMDDQ2bhxY+e3335rO5LbWL58uRM46+jYsaPtaNad6/cCOGfOnGk7mlt45JFHnFWqVHEGBgY6K1So4GzatKnzs88+sx3LbWmaeK62bds6K1Wq5AwMDHReddVVzrZt2zq3b99uO5ZbWbx4sfOGG25wBgUFOWvVquWcMmWK7UiFxs/pdDot1VYiIiIiLqExOCIiIuJ1VOCIiIiI11GBIyIiIl5HBY6IiIh4HRU4IiIi4nVU4IiIiIjXUYEjIiIiXkcFjoiIiHgdFTgiIiLidVTgiIiIiNdRgSMiIiJeRwWOiIiIeJ3/B6tf5nsfKmQJAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(x, s, color='r') # Plot mit der Farbe Rot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "e229b04c-8688-4457-982c-7a3f27c4254c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-6d559d4604922bd9", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Titel für den Plot setzen\n", - "\n", - "Dafür wird [plt.title](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.title.html) der Paramter als String übergeben:" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "ca46e07c-2f69-40cb-b58b-5278e13a048e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-106786a4fca81b67", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(x, s)\n", - "plt.title(\"Sinus Plot\") # Titel Setzen\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "6d0a0200-ac39-467c-a3e8-66e6a3ba3818", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8d7bed3592e18530", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Legende und Labels\n", - "\n", - "Um eine Legende anzuzeigen, muss vor `plt.show` die Funktion [plt.legend](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html) aufgerufen werden. Damit dies Wirkung zeigt, muss jeder Plot mit dem Parameter `label` (als String) ausgezeichnet werden. Plotten wir im Folgenden den Sinus und Kosinus mit entsprechenden Labels." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "4d58a327-a11e-408b-a998-ef03c7cfaf87", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b1d037b9a275622c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(x, s, label=\"Sinus\") # Plotte Sinus mit label Sinus\n", - "plt.plot(x, c, label=\"Kosinus\") # Plotte Kosinus mit label Kosinus\n", - "plt.legend() # Füge die Legende ein\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "fb03ce2f-5639-402a-9b32-0b351113b5d6", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-63bbc82ff5e6892a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Linestyle\n", - "\n", - "Die letze wichtige Eigenschaft ist das Setzen eines Linestyles. Dazu wird `plt.plot` der Parameter `linestyle` als String übergeben. Entnehmen Sie die verschiedenen Linestyles bitte der Dokumentation zu [Linestyles](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html).\n", - "\n", - "Sinus als `dashed` line:" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "90be449a-1d18-416f-b22b-782081d1b007", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-402e40ea2ceafc35", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(x, s, linestyle=\"dashed\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "b465a6e5-06ab-4fc0-8238-f4f05593a9d1", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-88e04ff7645c08cd", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe - Multiplot\n", - "\n", - "*8 Punkte*\n", - "\n", - "In der nächsten Aufgabe wollen wir gleich zwei Funktionen plotten. $f(x) = \\sqrt x; x \\geq 0$ und $g(x) = x^2$.\n", - "\n", - "Gehen Sie dabei wie folgt vor:\n", - "1. Definieren Sie einen geeigneten [Linespace](https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy-linspace) für die Zahlenraum 0...3. (Tipp: Achten Sie auf die Definition! Die Wurzel ist nur für positive Zahlen definiert.)\n", - "2. Berechnen Sie mittels der Funktion [np.sqrt](https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html#numpy.sqrt) die Werte für die Wurzel.\n", - "3. Berechnen Sie mittels der Funktion [np.square](https://numpy.org/doc/stable/reference/generated/numpy.square.html#numpy-square) die Werte für die Quadratzahlen\n", - "4. Geben Sie den beiden Plots die Farben Grün & Rot. Nutzen Sie gerne die [Color Shorthands](https://matplotlib.org/stable/users/explain/colors/colors.html) aus der Dokumentation.\n", - "5. Plotten Sie die Square Funktion mit dem Linestyle `dashdot`, wie der Dokumentation zu entnehmen [Linestyles](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html)\n", - "6. Geben Sie den beiden Plots angemessene Labels.\n", - "7. Fügen Sie die Legende hinzu.\n", - "8. Plotten Sie das Ergebnis." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "41c07973-1717-48f1-94a5-c6930b0089ec", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-6bb6ab1d60fffde5", - "locked": false, - "points": 8, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "xt = np.linspace(0, 3, num=200)\n", - "sqrt = np.sqrt(xt)\n", - "square = np.square(xt)\n", - "\n", - "plt.plot(xt, sqrt, color='r', label=\"Square Root\")\n", - "plt.plot(xt, square, color='g', label=\"Square Function\", linestyle=\"dashdot\")\n", - "plt.legend()\n", - "plt.show()\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "d33d372a-ed48-4a31-a1fc-4b325e910d27", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-6d0019ce99d7aacb", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "id": "7dd33f6a-1e5d-4c8f-8778-7820d13fa357", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c7e6f6e837a94d41", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Warum Zufall?\n", - "\n", - "Für statistische Analysen jeglicher Art ist es wichtig seine Werkzeuge zu verstehen. Da nicht immer direkt ein Dataset vorliegt, oder dieses zurzeit noch im Erstellungsprozess ist, gibt es die Möglichkeit, die mathematischen und programmatischen Werzeuge zuvor an nachvollziebaren Zufallsdaten zu testen. Dabei wollen wir in dieser Übung lernen, was Zufall ist, wie Zufallsgeneratoren funktionieren und wie der Zufall auf bestimmte Art manipuliert werden kann.\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "6536feb0-7612-4bad-b797-3960ed3c832c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-f387eeef09033ea3", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Was ist Zufall?\n", - "\n", - "Für Zufall gibt es viele Definitionen. Aber was genau ist Zufall? Das Würfeln eines Würfels zum Beispiel ist nicht zufällig. Jeder Würfelwurf kann vorhergesagt werden, vorausgesetzt, alle erforderlichen Daten sind vorhanden. Auch das Wetter ist nicht zufällig. Alle Ereignisse lassen sich vorhersagen, wenn genügend Daten vorhanden sind, das Problem ist nur, dass es nie genug Daten gibt. \n", - "\n", - "Jedes andere Beispiel kann mit der gleichen Argumentation als \"zufällig\" widerlegt werden. Daher werden diese Phänomene als pseudo-zufällig bezeichnet. \n", - "\n", - "Aber das nur am Rande. In der Physik gibt es ein Phänomen, das völlig unvorhersehbar ist. Wenn ein einzelnes Photon auf einen halbtransparenten Spiegel geschossen wird, kann __nie__ vorhergesagt werden, auf welchen der beiden Detektoren das Photon treffen wird. Diese Form der wirklich echten Zufalls wird von der Firma [ID Quntique](https://www.idquantique.com/random-number-generation/overview/) genutzt und verkauft Geräte, die das zuvor erklärte Quantenphänomen nutzen.\n", - "\n", - "Aber genug von der echten Koinzidenz und zurück zum Pseudo-Zufall und wie Computer damit umgehen.\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "73c86ab1-9f06-4dc5-861b-6558e8315bb3", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-fde25777024719e0", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Pseudo Randomness\n", - "\n", - "Zufallszahlen in Computern werden über Formeln berechnet. Einer dieser Generatoren ist der _Linear Congruent Generator (LCG)_, dessen mathematische Grundlage leicht verdaulich ist.\n", - "\n", - "$$ X_{n+1} = (aX_n + c) \\;mod\\; m; \\quad n \\geq 0 $$\n", - "\n", - "Wenn $ c = 0 $ dann nennt man den Generator auch _Multiplicative Congruent Generator (MCG)_.\n", - "\n", - "Die Werte haben folgenden Nutzen in der Funktion:\n", - "\n", - "- $X_n$ ist der Startwert oder seed\n", - "- $X_{n+1}$ ist der Folgewert der im nächsten Schritt für $X_n$ eingesetzt wird\n", - "- $a$ ist der Vorfaktor vom Startwert. Dieser wird skaliert, deshalb wird er Skalar gennant\n", - "- $c$ ist das hinzuaddierte Offset\n", - "- $m$ ist der Restklassenring oder auch Modulus genannt" - ] - }, - { - "cell_type": "markdown", - "id": "39b18b10-a831-4fbe-a50b-d8177108bca1", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a1c6ad2b24a97f6b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe\n", - "\n", - "*6 Punkte*\n", - "\n", - "Schreibe einen _Linear Congruent Generator_ mit dem funktionsnamen `lcg`.\n", - "\n", - "- Nutze die oben gegebene Definition\n", - "- Prüfe auch, das Werte nicht verwendet werden dürfen (Bsp. $n \\geq 0$)\n", - "- `lcg` muss ein unendlicher Generator sein" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "224134b7-6ab5-4438-ad46-6cb77261680c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d5c7ad13eb813b34", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "def lcg(seed: int, scalar: int, modulus: int, offset: int) -> int:\n", - " ''' \n", - " Linear Congruential Generators\n", - "\n", - " X(n+1) = (a X(n) + c) mod m; n >= 0\n", - "\n", - " m > 0; \n", - " 0 <= a < m;\n", - " c > 0; a > 0\n", - "\n", - " '''\n", - " assert modulus > 0, \"Modulus must be greater than 0\"\n", - " assert 0 <= scalar and scalar < modulus, \"Scalar must be in range 0 <= a < m\"\n", - "\n", - " while seed > 1:\n", - " seed = (scalar*seed+offset) % modulus\n", - " assert seed >= 0\n", - " yield seed\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "853a70eb-7dd1-43b5-af99-ed1b3126846c", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-35fbf31fbee439df", - "locked": true, - "points": 6, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Lcg using Cocktailshaker Numbers: 3089810780120156248\n", - "Correct should be: 3089810780120156248\n", - "\n", - "Lcg using Cocktailshaker Numbers: 8356396685252565260\n", - "Correct should be: 8356396685252565260\n", - "\n", - "Lcg using Cocktailshaker Numbers: 1921117399837525548\n", - "Correct should be: 1921117399837525548\n", - "\n", - "Lcg using Cocktailshaker Numbers: 14806858147081821235\n", - "Correct should be: 14806858147081821235\n", - "\n", - "Lcg using Cocktailshaker Numbers: 2557599628047639428\n", - "Correct should be: 2557599628047639428\n", - "\n", - "Lcg using Cocktailshaker Numbers: 16453652254840064460\n", - "Correct should be: 16453652254840064460\n", - "\n", - "Lcg using Cocktailshaker Numbers: 15995401842808378843\n", - "Correct should be: 15995401842808378843\n", - "\n", - "Lcg using Cocktailshaker Numbers: 681272290641816305\n", - "Correct should be: 681272290641816305\n", - "\n", - "Lcg using Cocktailshaker Numbers: 10955466795170118648\n", - "Correct should be: 10955466795170118648\n", - "\n", - "Lcg using Cocktailshaker Numbers: 13714992071537968180\n", - "Correct should be: 13714992071537968180\n", - "\n" - ] - } - ], - "source": [ - "# Hier werden ihre Lösungen getestet ...\n", - "\n", - "# Check if lcg is a properly defined\n", - "assert 'lcg' in dir()\n", - "\n", - "# Check if lcg is a generator\n", - "import types\n", - "assert isinstance(lcg(1,1,1,1), types.GeneratorType) # 1 Punkt\n", - "\n", - "# Using Cocktailshaker numbers :)\n", - "s = lcg(3935559000370003845, 3203021881815356449, 2**64-1, 11742185885288659963)\n", - "### BEGIN HIDDEN TESTS\n", - "def lcg_test(seed: int, scalar: int, modulus: int, offset: int) -> int:\n", - " assert modulus > 0, \"Modulus must be greater than 0\"\n", - " assert 0 <= scalar and scalar < modulus, \"Scalar must be in range 0 <= a < m\"\n", - "\n", - " while seed > 1:\n", - " seed = (scalar*seed+offset) % modulus\n", - " assert seed >= 0\n", - " yield seed\n", - "\n", - "t = lcg_test(3935559000370003845, 3203021881815356449, 2**64-1, 11742185885288659963)\n", - "### END HIDDEN TESTS\n", - "\n", - "# Check if Generator works\n", - "for _ in range(10):\n", - " stud = next(s)\n", - " instructor = next(t)\n", - " print(\"Lcg using Cocktailshaker Numbers:\", stud)\n", - " print(\"Correct should be:\", instructor, end='\\n\\n')\n", - " assert stud == instructor\n" - ] - }, - { - "cell_type": "markdown", - "id": "8e516bd4-c203-4b11-9880-31eabba13c2e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-88eadd4386332f26", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "---\n", - "\n", - "# A Family of Better Random Number Generators\n", - "\n", - "
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\n", - " Melissa E. O’Neill\n", - "

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Linear Congruent Generators zeichnen sich durch ihre Stabilität und Geschwindigkeit als Hervorragende Zufallsgeneratoren. Doch 2014 gelang Melissa E. O'Neil ein neuer Durchbruch in der Konzeption von Pseudozufallsgeneratoren. Das Problem mit existierenden Zufallsgeneratoren ist entweder ihre Stabilität (Wie vorhersehbar die Zufallszahlen sind) oder ihre Geschwindigkeit (Wie lange der Zufallsgenerator braucht um die nächste Zufallszahl zu errechnen).

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Ihr Durchbruch gelang, indem Sie die Vorteile eines Linear Congruent Generators mit dem eines XorShift Generators verband. Dadurch erreichte Sie nicht nur eine Normalverteilung in den generierten Zufallszahlen (und eine damit einhergende Stabilität), Sie hatte auch eine Family von schnellen einfachen Algorithmen entwickelt. Diese nennen sich PCG - Permuted Congruential Generator.

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Die Implementierungsdetails lassen wir im nächsten Schritt aus, da diese nicht einfach in Python umzusetzen sind. Auf der Webseite pcg-random.org lassen sich Implemtierungen für C & C++ finden. Als weiterführende Literatur ist PCG: A Family of Simple Fast Space-Efficient Statistically Good Algorithms for Random Number Generation - Melissa E. O’Neill als Literatur angegeben.

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\n", - "
\n", - "\n", - "Zum Glück hat _Numpy_ eine Implementierung des _PCG_. Diese findet sich unter [Numpy PCG64](https://numpy.org/doc/stable/reference/random/index.html)" - ] - }, - { - "cell_type": "markdown", - "id": "81959fe2-2f2f-4f57-9360-4e96dc0674ba", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3cfb52b705a8d147", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe\n", - "\n", - "*6 Punkte*\n", - "\n", - "Plote die Zufallszahlen eines _Permuted Congruent Generators_ mittels NumPy & MatPlotLib.\n", - "\n", - "- Gegeben ist der Anfangszustand des Generators.\n", - "- Nutze die Dokumentation und rufe den `default_rng` aus dem `numpy.random` Modul, **20** mal auf speichere die Werte in der variablen `pcgs`. *(Tipp: Nutze ein NumPy Array)*\n", - "- Sortiere im nächsten Schritt die in `pcgs` gespeicherten Werte und speichere diese in `pcgs_sorted`\n", - "- Plotte sinnvoll beide Array. Gestalte den Plot angemessen." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "a2fbf6d5-9460-48bc-8183-b2afb9c5c186", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-9e88f0a0a4a77c47", - "locked": false, - "points": 3, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "np.random.seed(42) # Setting a fixed start Value for the Generator\n", - "pcgs: np.array = None\n", - "pcgs_sorted: np.array = None\n", - "\n", - "# BEGIN SOLUTION\n", - "gen = np.random.default_rng()\n", - "pcgs = np.array([gen.random() for _ in range(20)])\n", - "pcgs_sorted = np.sort(pcgs)\n", - "\n", - "# Plot\n", - "plt.plot(np.arange(len(pcgs_sorted)), pcgs, color='r', label='PCGs')\n", - "plt.plot(np.arange(len(pcgs_sorted)), pcgs_sorted, color='g', label='Sortierte PCGs')\n", - "plt.title(\"PCG Random Numbers\")\n", - "plt.legend()\n", - "plt.show()\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "91d763ff-71ac-4940-9cf8-54ca7ea6b8ca", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-69197dfdc3015ec5", - "locked": true, - "points": 3, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PCGs [0.83138478 0.46249136 0.44121926 0.42622691 0.42466013 0.3974171\n", - " 0.98723244 0.5836077 0.7404793 0.53513345 0.7691253 0.52569606\n", - " 0.87456503 0.78567245 0.44853762 0.95138405 0.65358711 0.33559832\n", - " 0.19727424 0.39672796]\n", - "Sorted PCGs [0.19727424 0.33559832 0.39672796 0.3974171 0.42466013 0.42622691\n", - " 0.44121926 0.44853762 0.46249136 0.52569606 0.53513345 0.5836077\n", - " 0.65358711 0.7404793 0.7691253 0.78567245 0.83138478 0.87456503\n", - " 0.95138405 0.98723244]\n" - ] - } - ], - "source": [ - "# Hier werden ihre Lösungen getestet ...\n", - "print(\"PCGs\", pcgs)\n", - "print(\"Sorted PCGs\", pcgs_sorted)\n", - "### BEGIN HIDDEN TESTS\n", - "np.random.seed(42)\n", - "test_gen = np.random.default_rng()\n", - "test_pcgs = np.array([gen.random() for _ in range(20)])\n", - "test_pcgs_sorted = np.sort(pcgs)\n", - "### END HIDDEN TESTS\n", - "\n", - "# Check if pcgs are correctly generated\n", - "assert test_pcgs.all() == pcgs.all()\n", - "\n", - "# Check if pcgs are correctly sorted\n", - "assert test_pcgs_sorted.all() == pcgs_sorted.all()\n", - "\n", - "# Plot is manually checked" - ] - }, - { - "cell_type": "markdown", - "id": "9283cd7a-b8a6-4090-84c3-da70e61b2d2e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-486bea96505ad0a6", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "---\n", - "\n", - "# Plot Types\n", - "\n", - "Im folgenden Kapitel beschäftigen wir uns mit verschiedensten Plot Typen." - ] - }, - { - "cell_type": "markdown", - "id": "8fe0a0f7-a836-4279-bc7c-e993004f97aa", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-79e43d78c9874975", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Line Plots\n", - "\n", - "Line Plots haben wir im voherigen Kapitel bereits kennengelernt. Diese können mittels `plt.plot` augerufen werden.\n", - "\n", - "Beispiel Sinus:" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "19b25d9b-0ee5-4eff-b290-8513f7948f50", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b48b1eec8fe65537", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.linspace(0, 2*np.pi, num=200)\n", - "y = np.sin(x) \n", - "\n", - "plt.plot(x, y) \n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "10d13d03-03f5-44c4-a62b-b68bf8dcaaf9", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d81b654168ec4bc8", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Bar Charts\n", - "\n", - "Beliebt sind Barcharts. Dazu werden aber mehrere Parameter benötigt, da einfache mathematische Funktionen bei dieser Art Plot keinen Sinn ergeben.\n", - "\n", - "Konsultieren wir dafür folgendes Beispiel:\n", - "\n", - "Wir wollen wissen wie viele Kinder an einer Grundschule in jeder Klassenstufe sind.\n", - "Dazu benötigen wir 2 Listen.\n", - "1. Die Klassenstufen\n", - "2. Die Anzahl an Kinder in der Klassenstufe" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "06b54439-c88c-438a-9870-896280151491", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3767e976a92e292a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "classes = [\"1. Klasse\", \"2. Klasse\", \"3. Klasse\", \"4. Klasse\"]\n", - "kids = [42, 30, 26, 45]" - ] - }, - { - "cell_type": "markdown", - "id": "55022635-12f7-4666-991b-e802ce67c9a5", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a702a6b994c5809e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Plotten wir die Werte nun als Bar Chart mit der Funktion `plt.bar`:" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "d73faeb6-2b83-495f-b246-2e4b4c6ff784", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8c604c68ae96c752", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.bar(classes, kids)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "a641132d-cf1a-4e92-bdc3-55f5425b9388", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c2444cb0f1af6626", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Bessere Datenrepresentation\n", - "\n", - "Da die Daten aus `classes` & `kids` miteinander eine Verbindung teilen wäre, die Repräsentation mittels Dictionary die klügere Wahl, um keine Fehler in den Plot zu bringen.\n", - "\n", - "Mittels der `.keys` & `.values` Funktion auf dem Dictionary lassen sich dann die Daten gezielt plotten.\n", - "\n", - "Schauen Sie sich daher folgendes Beispiel an:" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "d21c9c59-2b84-4e87-a2f2-1388070b2419", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1777b8fcd5bd30c4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Definiere ein Dictionary welches die Anzahl der Schüler ihrer Klasse zuweist \n", - "school = { \n", - " \"1. Klasse\": 42,\n", - " \"2. Klasse\": 30,\n", - " \"3. Klasse\": 26,\n", - " \"4. Klasse\": 45,\n", - "}\n", - "\n", - "plt.bar(school.keys(), school.values()) # Plotte mit den Werten des Dictionarys\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "a921963b-94df-4c6a-81f7-c3a3fb09b7c0", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d2c659803a58f15e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Styling\n", - "\n", - "Bar plots können auch gestyled werden. Hierzu wird dem Parameter `color` eine Liste mit farbwerten übergeben:" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "00e9f1c6-5ccd-4ea3-bcec-b2265602917f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a3604899d50585d2", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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d9jc3NxeOfVBZWVlUVVV12ACA/qtb42PcuHExcuTI2LRpU2Ffa2trvPDCC1FbW9udNwUA9FGd/m2Xd999N958883Cx3v37o2dO3fG0KFDY+zYsXHzzTfH7bffHuPHj49x48bFX/3VX8WoUaNi/vz53TlvAKCP6nR87NixI2bMmFH4eMWKFRERsXjx4li3bl38+Z//ebS1tcXSpUvj4MGD8eu//uvx1FNPxaBBg7pv1gBAn1WUUkq9PYmf19raGtXV1dHS0tIzr/8oKur+MTk1PfytZml7R0/fgxSttrC9Jd3W0z8erG3v6f617czP717/bRcA4BeL+AAAshIfAEBW4gMAyEp8AABZiQ8AICvxAQBkJT4AgKzEBwCQlfgAALISHwBAVuIDAMhKfAAAWYkPACAr8QEAZCU+AICsxAcAkJX4AACyEh8AQFbiAwDISnwAAFmJDwAgK/EBAGQlPgCArMQHAJCV+AAAshIfAEBW4gMAyEp8AABZiQ8AICvxAQBkJT4AgKzEBwCQlfgAALISHwBAVuIDAMhKfAAAWYkPACAr8QEAZCU+AICsxAcAkJX4AACyEh8AQFbiAwDISnwAAFmJDwAgK/EBAGQlPgCArMQHAJCV+AAAshIfAEBW4gMAyEp8AABZiQ8AICvxAQBkJT4AgKzEBwCQlfgAALISHwBAVuIDAMhKfAAAWYkPACAr8QEAZCU+AICsxAcAkJX4AACyEh8AQFY9Fh933313/NIv/VIMGjQoPvWpT8WLL77YUzcFAPQhPRIfjzzySKxYsSJuu+22eOmll2LKlCkxe/bseOutt3ri5gCAPqRH4mPt2rXxR3/0R3HDDTfExIkT41vf+lacddZZ8Xd/93c9cXMAQB9S0t0Dvvfee9HY2BgrV64s7CsuLo5Zs2bFtm3bTji/vb092tvbCx+3tLRERERra2t3T43eZk37pR5f1sM9PD4fyv1wf9b9a/v+90tK6WPP7fb4+O///u84duxY1NTUdNhfU1MTb7zxxgnnNzQ0xOrVq0/YP2bMmO6eGr2turq3Z0APsKz9V/Uai9t/9dzaHjp0KKo/5o6h2+Ojs1auXBkrVqwofHz8+PH46U9/GsOGDYuioqJenNmZpbW1NcaMGRP79++Pqqqq3p4O3cja9l/Wtn+yrieXUopDhw7FqFGjPvbcbo+Pc845JwYMGBDNzc0d9jc3N8fIkSNPOL+srCzKyso67BsyZEh3T6vfqKqq8s3eT1nb/sva9k/W9UQf94jH+7r9BaelpaUxderU2LRpU2Hf8ePHY9OmTVFbW9vdNwcA9DE98rTLihUrYvHixXHxxRfHpZdeGnfddVe0tbXFDTfc0BM3BwD0IT0SH7/3e78Xb7/9dqxatSqampri137t1+Kpp5464UWonLqysrK47bbbTniKir7P2vZf1rZ/sq6nryidyu/EAAB0E3/bBQDISnwAAFmJDwAgK/FxBvvRj34URUVFsXPnzt6eCt1s8+bNUVRUFAcPHuztqdDNrG3/ZF27l/jogmeffTauvvrqGDVqVBQVFcXjjz/epXE+eO2RI0fi2muvjfPOOy9ee+217pksndLQ0BCXXHJJVFZWxogRI2L+/Pmxe/fuTo1xsmg8dOhQzJgxIyZOnBg//vGPu3nWnIp77703Jk+eXHhjqNra2vj+97/fqTGs7ZltzZo1UVRUFDfffHOnrrOu+YmPLmhra4spU6bE3Xff3W1j/u///m989rOfje3bt8fWrVtj0qRJ3TY2p27Lli1RX18fzz//fDzzzDNx5MiRuPLKK6Otra3LY7799tsxY8aMaGtri+eeey5Gjx7djTPmVI0ePTrWrFkTjY2NsWPHjrj88stj3rx58frrr3d5TGt75ti+fXvcd999MXny5NMey7r2PPHRBXPmzInbb789fud3fqdbxjt48GBcccUVceDAgdi6dWuMGzfupOcdO3YslixZEuPGjYvBgwfHJz/5yfj617/e4ZzNmzfHpZdeGuXl5TFkyJC47LLL4j//8z8jIuLll1+OGTNmRGVlZVRVVcXUqVNjx44dhWu3bt0an/nMZ2Lw4MExZsyY+NM//dPT+qHbFz311FNx/fXXx6/+6q/GlClTYt26dbFv375obGzs0nj79++Pz3zmM1FdXR0/+MEPYtiwYSc975133ik86nXWWWfFhRdeGA8//HCHcx577LG48MILY/DgwTFs2LCYNWtWYX0+at0jIp544om46KKLYtCgQfHLv/zLsXr16jh69GiXPqe+6uqrr47f/u3fjvHjx8cnPvGJuOOOO6KioiKef/75Lo1nbc8c7777blx33XXx7W9/O84+++zTGsu65iE+ellTU1NMnz49Iv7vf90n+/s37zt+/HiMHj06Hn300di1a1esWrUq/uIv/iK++93vRkTE0aNHY/78+TF9+vR45ZVXYtu2bbF06dLCH+i77rrrYvTo0bF9+/ZobGyMW2+9NQYOHBgREf/+7/8ev/VbvxV1dXXxyiuvxCOPPBJbt26NZcuW9fBX4MzW0tISERFDhw7t9LW7d++Oyy67LCZOnBj/+I//GBUVFR967uHDh2Pq1Knxve99L1577bVYunRpfO5zn4sXX3wxIiJ+8pOfxLXXXhs33nhj/Ou//mts3rw5FixYECmlj1335557LhYtWhQ33XRT7Nq1K+67775Yt25d3HHHHV34ivQPx44diw0bNkRbW1uX/uyDtT2z1NfXx9y5c2PWrFmnNY51zShxWiIibdy4scvXlpaWpgkTJqS2trYTju/duzdFRPqXf/mXDx2jvr4+1dXVpZRSeuedd1JEpM2bN5/03MrKyrRu3bqTHluyZElaunRph33PPfdcKi4uTj/72c9O8TPqX44dO5bmzp2bLrvssk5d9/66lZaWphkzZqSjR4+ecM4///M/p4hI//M///Oh48ydOzf92Z/9WUoppcbGxhQR6Uc/+tEJ533cus+cOTN9+ctf7rDvoYceSueee24nPqv+4ZVXXknl5eVpwIABqbq6On3ve9/r1PXW9szz8MMPp0mTJhXup6ZPn55uuummTo1hXfMTH6fpdONjwYIFqbi4OK1du/aE4yeLj29+85vpoosuSuecc04qLy9PAwcOTJdccknh+PXXX5/KysrSVVddle6666504MCBwrHbbrstlZSUpJkzZ6aGhob05ptvFo5dfPHFqbS0NJWXlxe2s846K0VE2rVrV5c+v77uj//4j9P555+f9u/f36nr3l+3hQsXppKSkvTd7373hHM+eEd29OjR9KUvfSlNmjQpnX322am8vDyVlJSk3/3d3y0cnzlzZqqsrEwLFy5M999/f/rpT39aGO+j1v2cc85JgwYN6rC2gwYNShFx0ujtz9rb29OePXvSjh070q233prOOeec9Prrr5/y9db2zLJv3740YsSI9PLLLxf2nU58WNd8xMdpOt342LhxY1q3bl0qLi5Of/3Xf93h+Afj4+GHH06DBg1Kd999d3rppZfSnj170tKlS9OUKVM6XPfSSy+lL3/5y6m2tjZVVFSkbdu2FY7t3r07rV27Nl1xxRWptLQ0/f3f/31KKaUJEyak5cuXpz179pywtbe3d+nz68vq6+vT6NGj03/8x390+tqfX7cvfelLqaSkJD3yyCMdzvngHVlDQ0MaNmxYeuihh9LOnTvTnj170ty5c9O8efMK1xw/fjxt3bo1rVq1Kl144YVp+PDhHeb3Yes+aNCg9JWvfOWka3vs2LHOf3H6kZkzZ57wiN9HsbZnlo0bN6aISAMGDChsEZGKiorSgAEDTvoIxslY1/zEx2nqjvhIKaUHH3wwDRgwIH3ta18rHP9gfCxbtixdfvnlHcaYOXPmCfHx86ZNm5aWL19+0mPXXHNNuvrqq1NKKf3+7/9+mjlzZpc+j/7k+PHjqb6+Po0aNSr927/9W5fG+OC63X777amkpCRt2LChcM4H78iuuuqqdOONNxaOHzt2LI0fP77DHdnPO3r0aDrvvPNOCNb3/fy6f/rTn+4wNv/fjBkz0uLFi0/5fGt7ZmltbU2vvvpqh+3iiy9Of/AHf5BeffXVUx7HuubXI3/Vtr97991348033yx8vHfv3ti5c2cMHTo0xo4dGxERK1eujP/6r/+KBx988JTG/NznPhfFxcWxePHiSCnF5z//+RPOGT9+fDz44IPx9NNPx7hx4+Khhx6K7du3F347Zu/evXH//ffHZz/72Rg1alTs3r079uzZE4sWLYqf/exn8fnPfz4WLlwY48aNix//+Mexffv2qKuri4iIL3zhCzFt2rRYtmxZ/OEf/mGUl5fHrl274plnnolvfvObp/sl6zPq6+tj/fr18cQTT0RlZWU0NTVFRER1dXUMHjw4IiIWLVoU5513XjQ0NJzSmH/5l38ZAwYMiOuuuy6OHz8e11577QnnjB8/Ph577LH44Q9/GGeffXasXbs2mpubY+LEiRER8cILL8SmTZviyiuvjBEjRsQLL7wQb7/9dlxwwQUfue4REatWrYqrrroqxo4dGwsXLozi4uJ4+eWX47XXXovbb7+9O75sfcLKlStjzpw5MXbs2Dh06FCsX78+Nm/eHE8//XThHGvbt1RWVp7wtgTl5eUxbNiwDvut6xmot+unL3q/gD+4/fz/oBYvXpymT5/+kePESR41Wb9+fRowYEBas2bNCTV++PDhdP3116fq6uo0ZMiQ9Cd/8ifp1ltvLTzy0dTUlObPn5/OPffcVFpams4///y0atWqdOzYsdTe3p6uueaaNGbMmFRaWppGjRqVli1b1uHFpC+++GK64oorUkVFRSovL0+TJ09Od9xxRzd8xfqOk61rRKQHHnigcM706dM/8n/LH/ZC4a985StpwIAB6Tvf+c4J/4t655130rx581JFRUUaMWJE+uIXv5gWLVpU+F/Url270uzZs9Pw4cNTWVlZ+sQnPpH+5m/+JqX00ev+vqeeeip9+tOfToMHD05VVVXp0ksvTffff393fMn6jBtvvDGdf/75qbS0NA0fPjzNnDkz/dM//VOHc6xt33ey13xY1zNPUUopZe4dAOAXmPf5AACyEh8AQFbiAwDISnwAAFmJDwAgK/EBAGQlPgCArMQHAJCV+AAAshIfAEBW4gMAyEp8AABZ/T8jzNwa/6NbLgAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "school = { \n", - " \"1. Klasse\": 42,\n", - " \"2. Klasse\": 30,\n", - " \"3. Klasse\": 26,\n", - " \"4. Klasse\": 45,\n", - "}\n", - "\n", - "bar_colors = [\"red\", \"blue\", \"green\", \"yellow\"] # Farben definieren\n", - "\n", - "plt.bar(school.keys(), school.values(), color=bar_colors) # Farben übergeben\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "a74afec9-34fd-4cfb-bcd3-db6be64ef35f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-964f579ce46a2882", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Y-Label\n", - "\n", - "Mit `plt.ylabel` (als String) lässt sich die y-Achse beschriften:" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "e9f4c9a3-e39c-4eb8-95fd-77a3b40fa249", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-21cee3bf50f011e1", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "school = { \n", - " \"1. Klasse\": 42,\n", - " \"2. Klasse\": 30,\n", - " \"3. Klasse\": 26,\n", - " \"4. Klasse\": 45,\n", - "}\n", - "\n", - "bar_colors = [\"red\", \"blue\", \"green\", \"yellow\"]\n", - "\n", - "plt.bar(school.keys(), school.values(), color=bar_colors)\n", - "\n", - "plt.ylabel(\"Anzahl Kinder\") # Beschriften der Y-Achse\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "9d90f3b3-adc9-498e-9dbe-13e0c577b886", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-32dabb34444f6190", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### X-Label\n", - "\n", - "Analog dazu die Beschriftung der X-Achse mit `plt.xlabel`." - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "2644cc38-238e-4cec-b951-3ff88b80011e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3737280b071f9d91", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "school = { \n", - " \"1. Klasse\": 42,\n", - " \"2. Klasse\": 30,\n", - " \"3. Klasse\": 26,\n", - " \"4. Klasse\": 45,\n", - "}\n", - "\n", - "bar_colors = [\"red\", \"blue\", \"green\", \"yellow\"]\n", - "\n", - "plt.bar(school.keys(), school.values(), color=bar_colors)\n", - "\n", - "plt.ylabel(\"Anzahl Kinder\") # Beschriften der Y-Achse\n", - "plt.xlabel(\"Klassenstufen\") # Beschriften der X-Achse\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "16e05c9d-80ef-47b8-a17f-df7a96398ab5", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-230328a26793cddb", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Aufgabe\n", - "\n", - "*5 Punkte*\n", - "\n", - "Ihnen ist ein Datenset `sec_school` einer Hauptschule gegeben, welches die Klassenstufen von 5 bis 9 auf die Anzahl ihrer Schüler im Jahrgang mappt. \n", - "\n", - "Definieren Sie einen Barplot. Gehen Sie dabei wie folgt vor:\n", - "1. Definieren Sie ein geeignetes Farbschema zur Darstellung der Daten.\n", - "2. Extrahieren Sie die Schlüssel und Werte aus dem Datenset und übergeben Sie diese zusammen mit den Farbwerten an die Funktion `plt.bar`.\n", - "3. Setzen Sie geeignete Werte für die X & Y-Achse.\n", - "4. Setzen Sie einen geeigneten Titel für den Plot.\n", - "5. Plotten Sie den Werte" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "0608b1ea-6275-47c7-82a8-fed86e5e5973", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b954e989a8bbc2fa", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "sec_school = {\n", - " '5. Klasse': 29,\n", - " '6. Klasse': 35,\n", - " '7. Klasse': 25,\n", - " '8. Klasse': 28,\n", - " '9. Klasse': 31\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "ae63d6f9-cd8c-4603-9a15-7ee336d905e9", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-8caba57a6ad34b87", - "locked": false, - "points": 5, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "colors = [\"red\", \"blue\", \"green\", \"yellow\", \"pink\"]\n", - "plt.bar(sec_school.keys(), sec_school.values(), color=colors)\n", - "plt.xlabel(\"Klassenstufe\")\n", - "plt.ylabel(\"Anzahl Kinder\")\n", - "plt.title(\"Verteilung Kinder einer Hauptschule pro Klassenstufe\")\n", - "plt.show()\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "bf9e917c-6949-4bb1-8184-270747e8cb4e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-42bf44a09515d0fd", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Horizontales Bar Chart\n", - "\n", - "Analog zum Barchart erzeugt `plt.barh` einen Horizontales Barchart.\n", - "\n", - "Beispiel:" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "id": "146615f7-1315-41a3-a690-73c98ee465e3", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-54166820b406e29e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "school = { \n", - " \"1. Klasse\": 42,\n", - " \"2. Klasse\": 30,\n", - " \"3. Klasse\": 26,\n", - " \"4. Klasse\": 45,\n", - "}\n", - "\n", - "plt.barh(list(school.keys()), list(school.values()), color=\"maroon\") # barh statt bar\n", - "\n", - "plt.xlabel(\"Anzahl Kinder\") \n", - "plt.ylabel(\"Klassenstufen\") \n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "df9117c3-0ca8-4e0d-8779-9b8826044a3a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3adde3f53176bcb0", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Pie Chart\n", - "\n", - "Kommen wir als nächstes zu der besten, tollsten und schönsten Darstellung von Daten.\n", - "\n", - "![](https://flowingdata.com/wp-content/uploads/2014/12/Pie-Pyramid-620x311.png)\n", - "\n", - "Kuchendiagramme können mittels `plt.pie` erstellt werden. Nehmen wir dazu wieder das Beispiel aus voherigem Kapitel. Dabei verlangt das Pie Chart nur die Werte (`school.values`) des Datensets:" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "bb311d1b-978d-41f8-9648-874b4a862bc5", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-46cde6d166912ad0", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "school = { \n", - " \"1. Klasse\": 42,\n", - " \"2. Klasse\": 30,\n", - " \"3. Klasse\": 26,\n", - " \"4. Klasse\": 45,\n", - "}\n", - "\n", - "plt.pie(school.values()) # Pie Chart\n", - "\n", - "plt.title(\"Klassenverteilung einer Grundschule\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "76e19a82-7aa7-416d-beae-667b77497cf3", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-aec08dc408437049", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Styling\n", - "\n", - "Alle Parameter wie `title`, `color`, `xlabel`, `ylabel`, etc. lassen sich auch für das Pie Chart setzen. Die Beschriftung der einzelnen Stücke jedoch Funktioniert etwas anders.\n", - "\n", - "Dazu wird der Parameter `label` mit den dazugehörigen Werten ausgestattet:" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "id": "09124a3e-1f76-4f32-8638-19e7efc234b0", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c742155fd484b71b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "school = { \n", - " \"1. Klasse\": 42,\n", - " \"2. Klasse\": 30,\n", - " \"3. Klasse\": 26,\n", - " \"4. Klasse\": 45,\n", - "}\n", - "\n", - "plt.pie(school.values(), labels=school.keys()) # Setzen der Labels\n", - "\n", - "plt.title(\"Klassenverteilung einer Grundschule\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "d3e68330-6319-4a87-acda-91abd4f98900", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5336af155ef45527", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Zum Setzen von Prozentwerten wird der Parameter `autopct` verwendet. Dieser nutzt einen Format String oder eine Funktion zum Definieren der Werte. Schaue dazu für mehr in die Dokumentation für [autpct](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.pie.html#matplotlib-pyplot-pie), eine Dokumentation zu Formatstrings findest du [hier](https://www.geeksforgeeks.org/format-specifiers-in-c/).\n", - "\n", - "Beispiel für Prozentwerte:" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "974a71d0-552c-45d4-bb01-88edd6be608c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-183044afa87a0492", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "school = { \n", - " \"1. Klasse\": 42,\n", - " \"2. Klasse\": 30,\n", - " \"3. Klasse\": 26,\n", - " \"4. Klasse\": 45,\n", - "}\n", - "\n", - "plt.pie(school.values(), labels=school.keys(), autopct='%1.1f%%') # Setzen von Prozentwerten\n", - "\n", - "plt.title(\"Klassenverteilung einer Grundschule\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "a0a13fc0-3ae1-4ab5-a488-8c5247cf74c5", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-430a11a7f58f4fa5", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Pie Charts haben auch einen Parameter `shadow`. Dieser ist Standardmässig `False`. Setzt man den Wert auf `True` sieht man einen Schatten:" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "id": "62971d68-8cdd-48c4-8f60-209bffe64e84", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-408e63a1464d64ea", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "school = { \n", - " \"1. Klasse\": 42,\n", - " \"2. Klasse\": 30,\n", - " \"3. Klasse\": 26,\n", - " \"4. Klasse\": 45,\n", - "}\n", - "\n", - "plt.pie(school.values(), labels=school.keys(), autopct='%1.1f%%', shadow=True) # Zeige einen Schatten\n", - "\n", - "plt.title(\"Klassenverteilung einer Grundschule\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "59e8be26-c070-4205-8260-f4597074ec6f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-376fc818f2a3d818", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Zum Herausnehmen von Kuchenstücken gibt es den Parameter `explode`. Dieser erwartet eine Liste mit Fließkommazahlen die zwischen 0.0 - Standardwert und 1.0 - absoluter Explode liegen.\n", - "\n", - "Beispiel Klasse 3 ist vom Ursprung 20% entfernt:" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "id": "bf1ea228-1547-4c1d-975e-d7f49111486c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-e3921561732c3895", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "school = { \n", - " \"1. Klasse\": 42,\n", - " \"2. Klasse\": 30,\n", - " \"3. Klasse\": 26,\n", - " \"4. Klasse\": 45,\n", - "}\n", - "\n", - "plt.pie(school.values(), labels=school.keys(), autopct='%1.1f%%', explode=[0, 0, 0.2, 0]) # Zeige einen Schatten\n", - "\n", - "plt.title(\"Klassenverteilung einer Grundschule\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "cbe9aba9-529d-4d4c-87fa-ff46e3a24e5b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ed3d080835960776", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe\n", - "\n", - "*5 Punkte*\n", - "\n", - "Ihnen ist ein Datenset `sec_school` einer Hauptschule gegeben, welches die Klassenstufen von 5 bis 9 auf die Anzahl ihrer Schüler im Jahrgang mappt. \n", - "\n", - "Definieren Sie einen Pieplot. Gehen Sie dabei wie folgt vor:\n", - "1. Definieren Sie ein geeignetes Farbschema zur Darstellung der Daten.\n", - "2. Extrahieren Sie die Schlüssel und Werte aus dem Datenset und übergeben Sie diese zusammen mit den Farbwerten an die Funktion `plt.pie`. (Nutzen Sie zum Anzeigen der Prozentwerte autopct='%1.1f%%')\n", - "3. Lassen Sie die 6. Klasse 25% und die 9. Klasse 40% explodieren.\n", - "4. Setzen Sie einen geeigneten Titel für den Plot.\n", - "5. Plotten Sie den Werte." - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "id": "5a2e4c2b-051d-4e9d-ae93-0e6fe4b25003", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-bf48088c515caf5c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "sec_school = {\n", - " '5. Klasse': 29,\n", - " '6. Klasse': 35,\n", - " '7. Klasse': 25,\n", - " '8. Klasse': 28,\n", - " '9. Klasse': 31\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "id": "fdebbc40-17eb-48e5-89fa-f8d12ec65688", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-d201bd3e919fcf1c", - "locked": false, - "points": 5, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "plt.pie(\n", - " sec_school.values(),\n", - " labels=sec_school.keys(),\n", - " autopct='%1.1f%%',\n", - " explode=[0, 0.25, 0, 0, 0.4]\n", - ")\n", - "\n", - "plt.title(\"Klassenverteilung einer Grundschule\")\n", - "\n", - "plt.show()\n", - "# END SOLUTION" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +version https://git-lfs.github.com/spec/v1 +oid sha256:ba5a8492cf3779cd4d04b0d30b166a332feffd2a2044f6e95eef6e7723cfd9d5 +size 710639 diff --git a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/5.SciPy.ipynb b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/5.SciPy.ipynb index eb6a8d9..1920305 100644 --- a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/5.SciPy.ipynb +++ b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/5.SciPy.ipynb @@ -1,1328 +1,3 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fd2f3bf7-b314-4449-95dc-f07d1a13f1c6", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-04300aa5f61f7f12", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# 5. Programmierübung: SciPy\n", - "\n", - "
\n", - "
\n", - " Willkommen zur fünften Programmierübung Einführung in Python 3.\n", - "
\n", - " \n", - "
\n", - "\n", - "Wenn Sie Fragen oder Verbesserungsvorschläge zum Inhalt oder Struktur der Notebooks haben, dann können sie eine E-Mail an Phil Keier ([p.keier@hbk-bs.de](mailto:p.keier@hbk-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) oder Martin Le ([martin.le@tu-bs.de](mailto:martin.le@tu-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) schreiben.\n", - "\n", - "Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n", - "\n", - "Der Großteil des Python-Tutorials stammt aus der Veranstaltung _Deep Learning Lab_ und von [www.python-kurs.eu](https://www.python-kurs.eu/python3_kurs.php) und wurde für _Signale und Systeme_, sowie _Einführung in die Programmierung für Nicht Informatiker_ angepasst.\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "bb677cea-65c2-486d-9442-6495b5063bd2", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-110034393ecbe731", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Was ist SciPy\n", - "\n", - "SciPy steht für Scientific Python und ist eine Open-Source-Bibliothek, die auf der bewährten Architektur von NumPy aufbaut. Sie bietet eine Vielzahl von Funktionen, die speziell für ingenieurtechnische und wissenschaftliche Anwendungen entwickelt wurden. In diesem Zusammenhang möchten wir uns insbesondere mit Teilen des Statistikmoduls von SciPy vertraut machen.\n", - "\n", - "__Für dieses Notebook schauen Sie bitte in die [SciPy Docs](https://docs.scipy.org/doc/scipy/tutorial/index.html)!!!__ Dort sind alle Funktionen beschrieben die wir hier bearbeiten und noch mehr!\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "3dde4289-bc10-49fc-875d-ebc44fbf807a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-82f61f58224c5db9", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "SciPys wird meist als `sp` importiert da für diese Aufgabe nur das Statistik modul nötig ist wird einfach dieses importiert. Aufgrund des kurzen schlüssigen namens findet keine umbenennung statt:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "e058b67a-ab34-4685-b023-084bfa80f171", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-fe3ab9d39498717f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "from scipy import stats" - ] - }, - { - "cell_type": "markdown", - "id": "57ada7f0-5eea-45cc-b8d0-85f52c7f6b55", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-eec41b75718f0e4e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "id": "5983dc91-91f3-4b6b-b6c2-9873b9ea8db9", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-65e8f06a2b373c8c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Lineare Regression\n", - "\n", - "## Motivation\n", - "\n", - "Die **lineare Regression** ist eine grundlegende Methode zur Modellierung von Beziehungen zwischen Variablen. Sie hilft, Zusammenhänge zu verstehen und ermöglicht Vorhersagen auf Basis vorhandener Daten. Durch die Bestimmung einer linearen Beziehung zwischen einer abhängigen und einer oder mehreren unabhängigen Variablen können wir Trends identifizieren und zukünftige Werte schätzen. Ihre Einfachheit, Effizienz und die Möglichkeit, auch bei großen Datensätzen präzise Ergebnisse zu erzielen, machen sie zu einem wertvollen Werkzeug in der Datenanalyse und ein Fundament für komplexere Modelle.\n", - "\n", - "Dafür geht man davon aus, dass es zwei unabhängige Variablen $x_1$ & $x_2$ gibt, für die eine Zukünftige Aussage getätigt werden soll. Hierfür wird eine fehlerminimierte Gerade $g(x)$ zwischen die Datenpunkte gelegt. Daraus resultierenden lässt sich mit $g(x)$ eine zukünftige Vorhersage in einem bestimmten Fehlerbereich tätigen.\n", - "\n", - "Aus der Schule sollte die Geraden Gleichung $g(x) = m\\cdot x+b$ bekannt sein. Dabei beschreibt $m$ die Steigung (Slope) & $b$ den Schnittpunkt mit der y-Achse (interception). Die **lineare regression** bietet daher die Möglichkeit diese beiden Parameter herauszufinden.\n", - "\n", - "Für das folgende Beispiel werden zwei (pseudo-)zufällig erzeugte Datensets generiert, auf welche dann die lineare regression angewandt wird. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "60712a1d-7a55-44f4-89e9-cae5ceab952a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-fbb9bfd35036d55e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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9Wr58ue655x5NnTpVoaGhmj17tiZOnKibbrpJd9xxh/bt26eXXnqpWmtkKnLLLbfoyiuv1Pz58zVp0iQlJiaqc+fOmjp1qoqKiuRyufTaa68FrJWpjuHDh6tfv36aNm2avvjiC3Xr1k35+fkVBsenn35at9xyi2644Qb95Cc/0bfffqsFCxaoWbNmmjFjxiV91+oYO3as/vznP+vdd9/VmDFj/M4EVed3db5mzZpp5MiRWrBggRwOhzp37qy33nqrwps9Pv/88+rfv7969Oihu+++W5dddpmOHDmijRs36uDBg9q+fbskqVu3bkpJSVGvXr3UsmVLffjhh8rLy9PkyZNr+KgA5Zh92RRgVe+8847x4x//2EhMTDSaNm1qNG7c2Lj88suN++67zzhy5EhA/z/+8Y/GNddcY4SFhRktWrQwBg4caBQUFPi2//Of/zR+8IMfGOHh4UaHDh18l3OffwnsmTNnjNGjRxvNmzc3JPldMrtixQqjW7duRqNGjQIukf3oo4+M9PR0o1WrVkZYWJjRsWNHY9SoUcbq1at9fcouFT527FiF3/m1114z+vfvb0RGRhqRkZFGYmKiMWnSJGP37t1+/X73u98ZCQkJRlhYmNG7d29jw4YNxsCBA4O+/LqyS3SXLFni97127txpDB482GjatKnRunVr4+677za2b98e8N0zMzONyMjIgP2Vfd/yTpw4YYwbN85wuVxGs2bNjHHjxhkfffRRhZe2/+Mf/zD69etnhIeHGy6Xyxg+fLixc+fOCj/j/GNaWU0DBw40unfvfsHjVOa7774zoqOjDUnG22+/HbA92N/V+ZdfG4ZhHDt2zPjRj35kREREGC1atDAmTpxofPzxxxUei88//9y46667jPbt2xuhoaFGTEyMceuttxp5eXm+PrNnzzauu+46o3nz5kZ4eLiRmJhozJkzJ+CWBUBNchjBrjoDAACwGNbIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA26r3N8Tzer366quvFBUVVekt3gEAgLUYhqHTp0+rQ4cOCgmp/LxLvQ8yX331leLi4swuAwAAXIQDBw4oNja20u31PshERUVJ3x8Il8tldjn1VmlpqVatWqWbb75ZoaGhZpfToDEW1sA4WAdjYR3VGQu32624uDjf3/HKmBpkNmzYoKefflpbtmzRoUOHtHz5co0YMcK33TAMPfbYY3rxxRd18uRJ9evXTwsXLlSXLl2C/oyy6SSXy0WQqUWlpaWKiIiQy+XiXxQmYyysgXGwDsbCOi5mLC60LMTUxb5nz57V1Vdfreeff77C7fPmzdOzzz6rRYsW6YMPPlBkZKRSU1P13//+t85rBQAA1mPqGZlbbrlFt9xyS4XbDMNQTk6OHnnkEd9j6//85z+rXbt2ev3113XnnXfWcbUAAMBqLLtGZt++fTp8+LAGDx7sa2vWrJmuv/56bdy4sdIgU1JSopKSEt9rt9stfX86q7S0tA4qb5jKji3H2HyMhTUwDtbBWFhHdcYi2PGybJA5fPiwJKldu3Z+7e3atfNtq8jcuXM1c+bMgPZVq1YpIiKiFipFeQUFBWaXgO8xFtbAOFgHY2EdwYxFcXFxUPuybJC5WA899JCmTJnie1226vnmm29msW8tKi0tVUFBgYYMGcJiOpMxFtbAOFgHY2Ed1RmLshmVC7FskGnfvr0k6ciRI4qOjva1HzlyRD179qz0fWFhYQoLCwtoDw0N5QdcBzjO1sFYWAPjYB2MhXUEMxbBjpVlH1GQkJCg9u3ba/Xq1b42t9utDz74QDfccIOptQEAAGsw9YzMmTNn9Nlnn/le79u3T9u2bVPLli0VHx+v7OxszZ49W126dFFCQoKmT5+uDh06+N1rBgAANFymBpkPP/xQN954o+912dqWzMxMLVmyRL/61a909uxZ3XPPPTp58qT69++vlStXqkmTJiZWDQAArMLUIJOSkiLDMCrd7nA4NGvWLM2aNatO6wIAAFXzeD0q3F+oQ6cPKToqWsnxyXKGOOu8Dssu9gUAABevNoNG/q58Za3M0kH3QV9brCtWuUNzlZ6UXiOfESyCDAAA9UxtBo38XfnKWJYhQ/4zKkXuImUsy1DeqLw6DTOWvWoJAABUX1nQKB9iVC5o5O/Kv+h9e7weZa3MCggxknxt2Suz5fF6LvozqosgAwBAPVHbQaNwf2FAQDr/Mw64D6hwf+FF7f9iEGQAAKgnajtoHDp9qEb71QSCDAAA9URtB43oqOggegXfryYQZAAAqCdqO2gkxycr1hUrhxwVbnfIoThXnJLjky9q/xeDIAMAQD1R20HDGeJU7tBc377O37ck5QzNqdP7yRBkAACoJ+oiaKQnpStvVJ5iXDF+7bGu2Dq/9FrcRwYAgPqlLGhUdB+ZnKE5NRI00pPSldY1jTv7AgCAmlcXQcMZ4lRKp5Qa29/FIsgAAFAPWSVo1DbWyAAAANsiyAAAANsiyAAAANsiyAAAANsiyAAAANsiyAAAANsiyAAAANsiyAAAANsiyAAAANsiyAAAANsiyAAAANsiyAAAANsiyAAAANsiyAAAANsiyAAAANtqZHYBAADUNx6vR4X7C3Xo9CFFR0UrOT5ZzhCn2WXVSwQZAABqUP6ufGWtzNJB90FfW6wrVrlDc5WelG5qbfURU0sAANSQ/F35yliW4RdiJKnIXaSMZRnK35VvWm31FUEGAIAa4PF6lLUyS4aMgG1lbdkrs+Xxekyorv4iyAAAUAMK9xcGnIkpz5ChA+4DKtxfWKd11XcEGQAAasCh04dqtB+CQ5ABAKAGREdF12g/BIcgAwBADUiOT1asK1YOOSrc7pBDca44Jccn13lt9RlBBgCAGuAMcSp3aK70fWgpr+x1ztAc7idTwwgyAADUkPSkdOWNylOMK8avPdYVq7xRedxHphZwQzwAAGpQelK60rqmcWffOkKQAQCghjlDnErplGJ2GQ0CU0sAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2LB1kPB6Ppk+froSEBIWHh6tz5856/PHHZRiG2aUBAAALaGR2AVV56qmntHDhQv3pT39S9+7d9eGHH2rChAlq1qyZ7r//frPLAwAAJrN0kHn//feVlpamYcOGSZI6deqkV155RZs2bTK7NAAAYAGWDjJ9+/bV4sWLtWfPHl1xxRXavn273nvvPc2fP7/S95SUlKikpMT32u12S5JKS0tVWlpaJ3U3RGXHlmNsPsbCGhgH62AsrKM6YxHseDkMCy848Xq9+vWvf6158+bJ6XTK4/Fozpw5euihhyp9z4wZMzRz5syA9qVLlyoiIqKWKwYAADWhuLhYo0eP1qlTp+RyuSrtZ+kg89e//lW//OUv9fTTT6t79+7atm2bsrOzNX/+fGVmZlb4norOyMTFxen48eNVHghcmtLSUhUUFGjIkCEKDQ01u5wGjbGwBsbBOhgL66jOWLjdbrVu3fqCQcbSU0u//OUvNW3aNN15552SpB49eujLL7/U3LlzKw0yYWFhCgsLC2gPDQ3lB1wHOM7WwVhYA+NgHYyFdQQzFsGOlaWDTHFxsUJC/K8Qdzqd8nq9ptUEAKj/PF6PCvcX6tDpQ4qOilZyfLKcIU6zy0IFLB1khg8frjlz5ig+Pl7du3fXRx99pPnz5+vHP/6x2aUBAOqp/F35ylqZpYPug762WFescofmKj0p3dTaEMjSQWbBggWaPn26fv7zn+vo0aPq0KGDJk6cqEcffdTs0gAA9VD+rnxlLMuQIf/lo0XuImUsy1DeqDzCjMVYOshERUUpJydHOTk5ZpcCAKjnPF6PslZmBYQYSTJkyCGHsldmK61rGtNMFmLpRxQAAFBXCvcX+k0nnc+QoQPuAyrcX1indaFqBBkAACQdOn2oRvuhbhBkAACQFB0VXaP9UDcIMgAASEqOT1ZsVGyl2x1yKM4Vp+T45DqtC1UjyAAAIGnF7hX69rtvK9zmkEOSlDM0h4W+FmPpq5YAAKgLlV12XaZleEstHr6YS68tiDMyAIAGrarLrsuENwpXWte0Oq0LwSHIAAAatAtddi1JB08f5LJri2JqCQBga5f6XCQuu7Y3ggwAwLZq4rlIXHZtb0wtAQBsqWyB7vnTQmXPRcrflR/UfpLjkxXrivVdmXQ+Lru2NoIMAMB2LvRcJEnKXpktj9dzwX05Q5zKHZorlbvMugyXXVsfQQYAYDs1/Vyk9KR05Y3KU4wrxq891hXLE68tjjUyAADbqY0FuulJ6UrrmnZJC4dR9wgyAADbqa0Fus4Qp1I6pVxkVTADU0sAANthgS7KEGQAALbDAl2UIcgAAGyJBboQa2QAAHbGAl0QZAAAtsYC3YaNqSUAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbBBkAAGBbPKIAAGALHq+HZyohAEEGAGB5+bvylbUySwfdB31tsa5Y5Q7N5SnXDRxTSwAAS8vfla+MZRl+IUaSitxFyliWofxd+abVBvMRZAAAluXxepS1MkuGjIBtZW3ZK7Pl8XpMqA5WQJABAFhW4f7CgDMx5RkydMB9QIX7C+u0LlgHa2QAAJZSflHvzmM7g3rPodOHar0uWBNBBgBgGRUt6g1GdFR0rdUEayPIAAAsoWxRb0XrYSrjkEOxrlglxyfXam2wLtbIAABMV9Wi3so45JAk5QzN4X4yDRhBBgBgugst6q1IrCtWeaPyuI9MA8fUEgDAdMEu1n0k+RF1a9ONO/vChyADADBdsIt1B102SCmdUmq9HtgHU0sAANMlxycr1hXrW/dyPoccinPFsagXAQgyAADTOUOcyh2aK5VbxFuGRb2oCkEGAGAJ6UnpyhuVpxhXjF87i3pRFdbIAAAsIz0pXWld03x39mVRLy6EIAMAsBRniJMFvQgaU0sAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2CDIAAMC2LB9kioqKNHbsWLVq1Urh4eHq0aOHPvzwQ7PLAgAAFtDI7AKq8s0336hfv3668cYb9c4776hNmzbau3evWrRoYXZpAADAAiwdZJ566inFxcXppZde8rUlJCSYWhMAALAOSweZN954Q6mpqRo5cqTWr1+vmJgY/fznP9fdd99d6XtKSkpUUlLie+12uyVJpaWlKi0trZO6G6KyY8sxNh9jYQ2Mg3UwFtZRnbEIdrwchmEYl1xZLWnSpIkkacqUKRo5cqQ2b96srKwsLVq0SJmZmRW+Z8aMGZo5c2ZA+9KlSxUREVHrNQMAgEtXXFys0aNH69SpU3K5XJX2s3SQady4sXr37q3333/f13b//fdr8+bN2rhxY4XvqeiMTFxcnI4fP17lgcClKS0tVUFBgYYMGaLQ0FCzy2nQGAtrYBysg7GwjuqMhdvtVuvWrS8YZCw9tRQdHa1u3br5tSUlJem1116r9D1hYWEKCwsLaA8NDeUHXAc4ztbBWFgD42AdjIV1BDMWwY6VpS+/7tevn3bv3u3XtmfPHnXs2NG0mgAAgHVYOsg88MAD+te//qUnnnhCn332mZYuXarFixdr0qRJZpcGAAAswNJBpk+fPlq+fLleeeUVXXnllXr88ceVk5OjMWPGmF0aAACwAEuvkZGkW2+9VbfeeqvZZQAAAAuy9BkZAACAqhBkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbQUdZL766qvarQQAAKCagg4y3bt319KlS2u3GgAAgGoIOsjMmTNHEydO1MiRI/X111/XblUAAABBCDrI/PznP9e///1vnThxQt26ddObb75Zu5UBAABcQKPqdE5ISNCaNWv03HPPKT09XUlJSWrUyH8XW7durekaAQAAKlStICNJX375pfLz89WiRQulpaUFBBkAAIC6Uq0U8uKLL+oXv/iFBg8erE8++URt2rSpvcoAAAAuIOggM3ToUG3atEnPPfec7rrrrtqtCgAAIAhBBxmPx6N///vfio2Nrd2KAAAAghR0kCkoKKjdSgAAAKqJRxQAAADb4pIjAIAfj9ejwv2FOnT6kKKjopUcnyxniNPssoAKEWQAAD75u/KVtTJLB90HfW2xrljlDs1VelK6qbUBFWFqCQAgfR9iMpZl+IUYSSpyFyljWYbyd+WbVhtQGYIMANiEx+vRui/W6ZUdr2jdF+vk8XpqdN9ZK7NkyAjYVtaWvTK7Rj8TqAlMLQGAxVS0RmXF7hW1OuVTuL8w4ExMeYYMHXAfUOH+QqV0SrnkzwNqCkEGACykojUqrcJb6cS3JwL6lk355I3Ku+Qwc+j0oRrtB9QVppYAwCIqW6NSUYhRDU/5REdF12g/oK4QZADAAqpao1KV8lM+lyI5Plmxrlg55Khwu0MOxbnilByffEmfA9Q0ggwAWMCF1qhcyKVO+ThDnModmit9H1rKK3udMzSH+8nAcggyAGABlxpEamLKJz0pXXmj8hTjivFrj3XF1sg6HKA2sNgXACzgYoOIQw7FumJrbMonPSldaV3TuLMvbIMgAwAWULZGpchdFPQ6mdqa8nGGOLnEGrbB1BIAWEAwa1Rahbfya2fKB+CMDABYRtkalYpufJczNIcpH6ACBBkAsJALrVFhygfwR5ABAIthjQoQPNbIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA2yLIAAAA27JVkHnyySflcDiUnZ1tdikAAMACbBNkNm/erBdeeEFXXXWV2aUAAACLsEWQOXPmjMaMGaMXX3xRLVq0MLscAABgEY3MLiAYkyZN0rBhwzR48GDNnj27yr4lJSUqKSnxvXa73ZKk0tJSlZaW1nqtDVXZseUYm4+xsAbGwToYC+uozlgEO16WDzJ//etftXXrVm3evDmo/nPnztXMmTMD2letWqWIiIhaqBDlFRQUmF0CvsdYWAPjYB2MhXUEMxbFxcVB7cthGIZRAzXVigMHDqh3794qKCjwrY1JSUlRz549lZOTU+F7KjojExcXp+PHj8vlctVZ7Q1NaWmpCgoKNGTIEIWGhppdToPGWFgD42AdjIV1VGcs3G63WrdurVOnTlX599vSZ2S2bNmio0eP6tprr/W1eTwebdiwQc8995xKSkrkdDr93hMWFqawsLCAfYWGhvIDrgMcZ+tgLKyBcbAOxsI6ghmLYMfK0kFm0KBB2rFjh1/bhAkTlJiYqAcffDAgxAAAgIbF0kEmKipKV155pV9bZGSkWrVqFdAOAAAaHltcfg0AAFARS5+Rqci6devMLgFAPeHxelS4v1CHTh9SdFS0kuOT5QxhyhqwE9sFGQCoCfm78pW1MksH3Qd9bbGuWOUOzVV6UrqptQEIHlNLABqc/F35yliW4RdiJKnIXaSMZRnK35VvWm0AqocgA6BB8Xg9ylqZJUOBt9Aqa8temS2P12NCdQCqiyADoEEp3F8YcCamPEOGDrgPqHB/YZ3WBeDisEYGQNDqw+LYQ6cP1Wg/AOYiyAAISn1ZHBsdFV2j/QCYi6klABdUnxbHJscnK9YVK4ccFW53yKE4V5yS45PrvDYA1UeQAVCl+rY41hniVO7QXOn70FJe2eucoTm2mzIDGiqCDIAq1cfFselJ6coblacYV4xfe6wrVnmj8mw1VQY0dKyRAVCl+ro4Nj0pXWld02y/eBlo6AgyAKpUnxfHOkOcSumUYnYZAC4BU0sAqsTiWABWRpABUCUWxwKwMoIMgAticSwAq2KNDICgsDgWgBURZABUqT48lgBA/UWQAVCp+vJYAgD1F2tkAFSoPj2WAED9RZABEKC+PZYAQP1FkAEQoD4+lgBA/USQARCgvj6WAED9Q5ABEKA+P5YAQP1CkAEQgMcSALALggyAADyWAIBdEGQAVIjHEgCwA26IB6BSPJYAgNURZABUyRniVEqnFLPLAIAKMbUEAABsiyADAABsiyADAABsiyADAABsiyADAABsiyADAABsiyADAABsiyADAABsiyADAABsiyADAABsiyADAABsiyADAABsiyADAABsiyADAABsiyADAABsiyADAABsiyADAABsiyADAABsiyADAABsiyADAABsiyADAABsiyADAABsiyADAABsiyADAABsq5HZBQCwJo/Xo8L9hTp0+pCio6KVHJ8sZ4jT7LIAwA9BBkCA/F35ylqZpYPug762WFescofmKj0p3dTaAKA8ppYA+Mnfla+MZRl+IUaSitxFyliWofxd+abVBgDnI8gA8PF4PcpamSVDRsC2srbsldnyeD0mVAcAgQgyAHwK9xcGnIkpz5ChA+4DKtxfWKd1AUBlCDIAfA6dPlSj/QCgthFkAPhER0XXaD8AqG0EGQA+yfHJinXFyiFHhdsdcijOFafk+OQ6rw0AKkKQAeDjDHEqd2iu9H1oKa/sdc7QHO4nA8AyCDIA/KQnpStvVJ5iXDF+7bGuWOWNyuM+MgAsxdI3xJs7d67y8/P16aefKjw8XH379tVTTz2lrl27ml0aUK+lJ6UrrWsad/YFYHmWDjLr16/XpEmT1KdPH3333Xf69a9/rZtvvlk7d+5UZGSk2eUBtcIqjwZwhjiV0imlzj8XAKrD0kFm5cqVfq+XLFmitm3basuWLRowYIBpdQG1hUcDAED1WDrInO/UqVOSpJYtW1bap6SkRCUlJb7XbrdbklRaWqrS0tI6qLJhKju2wR5jj9ejjQc36vCZw2rftL1uiL2hwU9bvLn7TY1bPk6GDIWHhPvavz7ztcbljZNul4Z3HX7B/VR3LFA7GAfrYCysozpjEex4OQzDCLwXuQV5vV7ddtttOnnypN57771K+82YMUMzZ84MaF+6dKkiIiJquUoAAFATiouLNXr0aJ06dUoul6vSfrYJMvfee6/eeecdvffee4qNja20X0VnZOLi4nT8+PEqDwQuTWlpqQoKCjRkyBCFhoZW2q/8WYfyyi7tffn2l4M661DfvLf/PQ1bOuyC/f4++u/qH9+/yj7BjgVqF+NgHYyFdVRnLNxut1q3bn3BIGOLqaXJkyfrrbfe0oYNG6oMMZIUFhamsLCwgPbQ0FB+wHWgquPs8XqUVZClYm9xhdsdcii7IFtp3dIa3DTT4eLD+tb7bVD9gv0d85u3BsbBOhgL6whmLIIdK0vfR8YwDE2ePFnLly/XmjVrlJCQYHZJuAQ8kLByPBoAAC6OpYPMpEmT9Je//EVLly5VVFSUDh8+rMOHD+vbby/8X66wHh5IWDkeDQAAF8fSQWbhwoU6deqUUlJSFB0d7fvn1VdfNbs0XATOOlSORwMAwMWxdJAxDKPCf8aPH292abgInHWoGo8GAIDqs8ViX9QPZWcdMpZlyCGH35VLnHX4PzwaAACqhyCDOlV21qGiu9fmDM3hrAOPBgCAaiHIoM5x1gEAUFMIMjAFZx0AADXB0ot9AQAAqsIZGRvyeD1MywAAQJCxn/xd+RUulM0dmstCWQBAg8PUko3k78pXxrKMgNv8F7mLlLEsQ/m78k2rDQAAMxBkbMLj9ShrZVbAU6P1/TOKJCl7ZbY8Xo8J1QEAYA6CjE3wwEUAAAIRZGyCBy4CABCIIGMTPHARAIBABBmb4IGLAAAEIsjYRNkDF1XuAYtleOAiAKChIsjUMY/Xo3VfrNMrO17Rui/WVesqo7IHLsa4YvzaY12xyhuVx31kAAANDjfEq0M1cTM7HrgIAMD/R5CpI2U3szv/PjBlN7OrzhkVHrgIAMD/YWqpDnAzOwAAagdBpg5wMzsAAGoHQaYOcDM7AABqB0GmDnAzOwAAagdBpg5wMzsAAGoHQaYOcDM7AABqB0GmjnAzOwAAah73kalD3MwOAICaRZCpY9zMDgCAmsPUEgAAsC2CDAAAsC2CDAAAsC2CDAAAsC2CDAAAsC2CDAAAsC2CDAAAsC2CDAAAsC2CDAAAsC2CDAAAsC2CDAAAsC2CDAAAsC2CDAAAsC2CDAAAsC2CDAAAsC2CDAAAsC2CDAAAsC2CDAAAsC2CDAAAsK1GZhdgRx6vR4X7C3Xo9CFFR0UrOT5ZzhCn2WUBANDgEGSqKX9XvrJWZumg+6CvLdYVq9yhuUpPSje1NgAAGhqmlqohf1e+MpZl+IUYSSpyFyljWYbyd+WbVhsAAA0RQSZIHq9HWSuzZMgI2FbWlr0yWx6vx4TqAABomAgyQSrcXxhwJqY8Q4YOuA+ocH9hndYFAEBDRpAJ0qHTh2q0HwAAuHQEmSBFR0XXaD8AAHDpCDJBSo5PVqwrVg45KtzukENxrjglxyfXeW0AADRUBJkgOUOcyh2aK30fWsore50zNIf7yQAAUIcIMtWQnpSuvFF5inHF+LXHumKVNyqP+8gAAFDHuCFeNaUnpSutaxp39gUAwAIIMhfBGeJUSqcUs8sAAKDBY2oJAADYFkEGAADYFkEGAADYFkEGAADYFkEGAADYli2CzPPPP69OnTqpSZMmuv7667Vp0yazSwIAABZg+SDz6quvasqUKXrssce0detWXX311UpNTdXRo0fNLg0AAJjM8kFm/vz5uvvuuzVhwgR169ZNixYtUkREhP74xz+aXRoAADCZpW+Id+7cOW3ZskUPPfSQry0kJESDBw/Wxo0bK3xPSUmJSkpKfK/dbrckqbS0VKWlpXVQdcNUdmw5xuZjLKyBcbAOxsI6qjMWwY6XpYPM8ePH5fF41K5dO7/2du3a6dNPP63wPXPnztXMmTMD2l9//XVFRETUWq34PytWrDC7BHyPsbAGxsE6GAvrCGYsiouLJUmGYVTZz9JB5mI89NBDmjJliu91UVGRunXrpp/+9Kem1gUAAKrv9OnTatasWaXbLR1kWrduLafTqSNHjvi1HzlyRO3bt6/wPWFhYQoLC/O9btq0qQ4cOKCoqCg5HI5ar7mhcrvdiouL04EDB+Ryucwup0FjLKyBcbAOxsI6qjMWhmHo9OnT6tChQ5X9LB1kGjdurF69emn16tUaMWKEJMnr9Wr16tWaPHlyUPsICQlRbGxsLVeKMi6Xi39RWARjYQ2Mg3UwFtYR7FhUdSamjKWDjCRNmTJFmZmZ6t27t6677jrl5OTo7NmzmjBhgtmlAQAAk1k+yNxxxx06duyYHn30UR0+fFg9e/bUypUrAxYAAwCAhsfyQUaSJk+eHPRUEswRFhamxx57zG99EszBWFgD42AdjIV11MZYOIwLXdcEAABgUZa/sy8AAEBlCDIAAMC2CDIAAMC2CDIAAMC2CDK4aHPnzlWfPn0UFRWltm3basSIEdq9e7fZZUHSk08+KYfDoezsbLNLaZCKioo0duxYtWrVSuHh4erRo4c+/PBDs8tqcDwej6ZPn66EhASFh4erc+fOevzxxy/47B5cug0bNmj48OHq0KGDHA6HXn/9db/thmHo0UcfVXR0tMLDwzV48GDt3bv3oj6LIIOLtn79ek2aNEn/+te/VFBQoNLSUt188806e/as2aU1aJs3b9YLL7ygq666yuxSGqRvvvlG/fr1U2hoqN555x3t3LlTzzzzjFq0aGF2aQ3OU089pYULF+q5557Trl279NRTT2nevHlasGCB2aXVe2fPntXVV1+t559/vsLt8+bN07PPPqtFixbpgw8+UGRkpFJTU/Xf//632p/F5deoMceOHVPbtm21fv16DRgwwOxyGqQzZ87o2muv1e9+9zvNnj1bPXv2VE5OjtllNSjTpk3TP//5TxUWFppdSoN36623ql27dvrDH/7ga/vRj36k8PBw/eUvfzG1tobE4XBo+fLlvkcNGYahDh066Be/+IWmTp0qSTp16pTatWunJUuW6M4776zW/jkjgxpz6tQpSVLLli3NLqXBmjRpkoYNG6bBgwebXUqD9cYbb6h3794aOXKk2rZtq2uuuUYvvvii2WU1SH379tXq1au1Z88eSdL27dv13nvv6ZZbbjG7tAZt3759Onz4sN+/p5o1a6brr79eGzdurPb+bHFnX1if1+tVdna2+vXrpyuvvNLschqkv/71r9q6das2b95sdikN2n/+8x8tXLhQU6ZM0a9//Wtt3rxZ999/vxo3bqzMzEyzy2tQpk2bJrfbrcTERDmdTnk8Hs2ZM0djxowxu7QG7fDhw5IU8Kihdu3a+bZVB0EGNWLSpEn6+OOP9d5775ldSoN04MABZWVlqaCgQE2aNDG7nAbN6/Wqd+/eeuKJJyRJ11xzjT7++GMtWrSIIFPHli1bpv/93//V0qVL1b17d23btk3Z2dnq0KEDY1GPMLWESzZ58mS99dZbWrt2rWJjY80up0HasmWLjh49qmuvvVaNGjVSo0aNtH79ej377LNq1KiRPB6P2SU2GNHR0erWrZtfW1JSkvbv329aTQ3VL3/5S02bNk133nmnevTooXHjxumBBx7Q3LlzzS6tQWvfvr0k6ciRI37tR44c8W2rDoIMLpphGJo8ebKWL1+uNWvWKCEhweySGqxBgwZpx44d2rZtm++f3r17a8yYMdq2bZucTqfZJTYY/fr1C7gNwZ49e9SxY0fTamqoiouLFRLi/2fO6XTK6/WaVhOkhIQEtW/fXqtXr/a1ud1uffDBB7rhhhuqvT+mlnDRJk2apKVLl2rFihWKioryzW02a9ZM4eHhZpfXoERFRQWsTYqMjFSrVq1Ys1THHnjgAfXt21dPPPGERo0apU2bNmnx4sVavHix2aU1OMOHD9ecOXMUHx+v7t2766OPPtL8+fP14x//2OzS6r0zZ87os88+873et2+ftm3bppYtWyo+Pl7Z2dmaPXu2unTpooSEBE2fPl0dOnTwXdlULQZwkSRV+M9LL71kdmkwDGPgwIFGVlaW2WU0SG+++aZx5ZVXGmFhYUZiYqKxePFis0tqkNxut5GVlWXEx8cbTZo0MS677DLj4YcfNkpKSswurd5bu3ZthX8fMjMzDcMwDK/Xa0yfPt1o166dERYWZgwaNMjYvXv3RX0W95EBAAC2xRoZAABgWwQZAABgWwQZAABgWwQZAABgWwQZAABgWwQZAABgWwQZAABgWwQZAABgWwQZALbi8XjUt29fpaen+7WfOnVKcXFxevjhh02rDUDd486+AGxnz5496tmzp1588UWNGTNGknTXXXdp+/bt2rx5sxo3bmx2iQDqCEEGgC09++yzmjFjhj755BNt2rRJI0eO1ObNm3X11VebXRqAOkSQAWBLhmHopptuktPp1I4dO3TffffpkUceMbssAHWMIAPAtj799FMlJSWpR48e2rp1qxo1amR2SQDqGIt9AdjWH//4R0VERGjfvn06ePCg2eUAMAFnZADY0vvvv6+BAwdq1apVmj17tiTpH//4hxwOh9mlAahDnJEBYDvFxcUaP3687r33Xt144436wx/+oE2bNmnRokVmlwagjnFGBoDtZGVl6e2339b27dsVEREhSXrhhRc0depU7dixQ506dTK7RAB1hCADwFbWr1+vQYMGad26derfv7/fttTUVH333XdMMQENCEEGAADYFmtkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbRFkAACAbf0/kPZHq7vOABYAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "rand = np.random.default_rng(42) # RNG with fixed seed\n", - "\n", - "# Generate, Scale, round and sort random numbers for an increasing slope\n", - "scalar: int = 10\n", - "x: np.array = np.round(np.sort(rand.random(20) * scalar), decimals=2)\n", - "y: np.array = np.round(np.sort(rand.random(20) * scalar), decimals=2)\n", - "\n", - "# Plot these values\n", - "plt.title(\"Scattered Random Values\")\n", - "plt.grid()\n", - "plt.xlabel(\"X\")\n", - "plt.ylabel(\"Y\")\n", - "plt.scatter(x,y, color='g')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "083ba4ea-8e6f-4286-8049-1c8e3f22c004", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-caa85fe6a76214bf", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Aus den erzeugten Daten ist klar ersichtlich, dass diese einem Trend folgen. Mittels SciPy wollen wir diesen Trend darstellen.\n", - "\n", - "Dazu wird die Funktion `linregress` verwendet. Diese verlangt, wie aus der [Dokumentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html#scipy.stats.linregress) zu entnehmen, zwei arrays mit Daten. \n", - "\n", - "Die Ausgabe ist aufgespalten in 5 Parameter:\n", - "- slope: die Steigung `m` der Geraden\n", - "- intercept: der Punkt an dem die Gerade die y-Achse trifft oder das `b`\n", - "- rvalue: der Pearson Korrelations Koefficient, welcher voerst ignoriert wird\n", - "- pvalue: der p-Wert das die Nullhypothese stimmt, wird auch ignoriert\n", - "- stderr: der Standard Error der Steigung, unter der Annahme einer Normalverteilung der Daten *(PCGs sind Normalverteilt)*" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9c1959d8-5bac-45fa-9aa2-47fa79943d72", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-553a583b633273d5", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "slope, intercept, _, _, stderr = stats.linregress(x,y)" - ] - }, - { - "cell_type": "markdown", - "id": "2c2bd060-bb4c-440f-9a04-72382c9a33eb", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-111845c39238eeee", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Aus `slope` & `intercept` lässt sich folglich eine Gerade definieren:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1e30868c-f11f-43bd-a60b-200ec00e903f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-49d79f2b8dcf7315", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "def line(x: float) -> float:\n", - " '''\n", - " Evaluates the rounded line from the previous\n", - " evaluated linear regression model\n", - "\n", - " Note: Output rounded to 2 decimal places\n", - " '''\n", - " res: float = slope * x + intercept\n", - " rounded: np.float64 = np.round(res, decimals=2)\n", - " return float(rounded)" - ] - }, - { - "cell_type": "markdown", - "id": "8fdd9724-5c61-471d-a121-787f2d56f132", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a63e440ba0b57186", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Diese kann über den gesatme bereich dargestellt werden. Dazu werden die bereits bekannten x-Werte verwendet:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "0a59d146-4ae3-429a-a67e-7c158d5ebac7", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5239bfcad5788ae3", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Calculate the Line using vectorization\n", - "regline: np.array = np.vectorize(line)(x)\n", - "\n", - "# Plot tvalues\n", - "plt.title(\"Scattered Random Values\")\n", - "plt.grid()\n", - "\n", - "plt.xlabel(\"X\")\n", - "plt.ylabel(\"Y\")\n", - "\n", - "plt.scatter(x,y, color='g', label=\"Original Data\")\n", - "plt.plot(x, regline, color='m', label=\"Fitted line\")\n", - "\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "802bf60a-e402-4912-96bf-baa6cd398c6e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-e0da661ffad2086e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Mit diesem Model lässt sich dementsprechend die \"Zukunft\" vorhersagen. Hierfür können wir im folgenden einfach die Werte für `-1` & `11` berechnen und diese dem Plot hinzufügen:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e62f6248-5813-4df0-9715-a72d3a5467f8", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-92a5bbb041e28a56", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Future Values\n", - "fut: np.array = np.array([line(-1), line(11)])\n", - "\n", - "# Calculate extended Line using vectorization\n", - "ext_range: np.array = np.arange(-1,12)\n", - "regline: np.array = np.vectorize(line)(ext_range)\n", - "\n", - "# Plot values\n", - "plt.title(\"Scattered Random Values\")\n", - "plt.grid()\n", - "\n", - "plt.xlabel(\"X\")\n", - "plt.ylabel(\"Y\")\n", - "\n", - "plt.scatter(x,y, color='g', label=\"Original Data\")\n", - "plt.plot(ext_range, regline, color='m', label=\"Fitted Line\")\n", - "plt.scatter((-1, 11), fut, color='r', label=\"Predicted Data\")\n", - "\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "04f002a5-4339-4225-9515-848513347697", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-6c8576c776d11325", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Wie zu erwarten liegen beide Werte auf der Geraden.\n", - "\n", - "Um deren Werte zu ermitteln lassen sich diese mittels print einfach ausgeben, dies sollte immer mit Angabe des Standard Errors erfolgen. Sonst ist unklar wie genau die Daten sind:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "563d452b-350c-4628-a97d-5d9ce268f4c4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7677ec5dda3e8e13", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Prediction of f(11) = 10.16, with standard deviation 0.07\n" - ] - } - ], - "source": [ - "print(\"Prediction of f(11) = {}, with standard deviation {}\".format(line(11), np.round(stderr, decimals=2)))" - ] - }, - { - "cell_type": "markdown", - "id": "5e81b1a9-4155-4bfd-8613-2530dbe61f03", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-facd8e8ef9f4aed1", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Aufgabe\n", - "\n", - "*7 Punkte*\n", - "\n", - "Bestimme mittels Linearer Regression die *best fit* Funktion für die beiden gegebenen Datensets `x_data` & `y_data`, unter beachtung folgender Punkte:\n", - "\n", - "- Plotte das Ergebnis angemessen\n", - "- Nutze SciPys `linregress` Funktion, speichere den Output vor dem entpacken in der Variablen `l`\n", - "- Definiere die Funktion `reg_line` mit einem Eingabeparameter\n", - "- Bestimme die Werte für `-0.3` & `3.4` speichere diese als liste in variablen `future`\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f5b801bc-450e-417b-aeea-9b69d638fb64", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-cb5b277089c75c40", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "random = np.random.default_rng(420)\n", - "\n", - "# 2 scuffed up One-Liners :)\n", - "x_data: np.array = np.sort(np.round(random.random(40)*np.pi, decimals=2))\n", - "y_data: np.array = np.flip(np.sort(np.round(random.random(40)*np.sqrt(2), decimals=2)))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "2645541f-c30f-487e-be65-3ec5ac22d427", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-12b5b631856ffe41", - "locked": false, - "points": 1, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "l = stats.linregress(x_data,y_data)\n", - "slope, intercept, _, _, stderr = l\n", - "\n", - "def reg_line(x: float) -> float:\n", - " return float(np.round(slope*x+intercept,decimals=2))\n", - "\n", - "ext: tuple = (-0.3, 3.4)\n", - "rl: np.array = np.vectorize(reg_line)(ext)\n", - "\n", - "future: list = [reg_line(ext[0]), reg_line(ext[1])]\n", - "# Plot values\n", - "plt.title(\"Scattered Random Values\")\n", - "plt.grid()\n", - "\n", - "plt.xlabel(\"X\")\n", - "plt.ylabel(\"Y\")\n", - "\n", - "plt.scatter(x_data,y_data, color='g', label=\"Original Data\")\n", - "plt.plot(ext, rl, color='m', label=\"Fitted Line\")\n", - "plt.scatter(ext, future, color='r', label=\"Predicted Data\")\n", - "\n", - "plt.legend()\n", - "\n", - "plt.show()\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "aa34ed84-f200-4863-be0b-40ff5492c654", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-8f20c3645d0a9b46", - "locked": true, - "points": 6, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Hier werden ihre Lösungen getestet\n", - "\n", - "# Check if reg_line is defined\n", - "assert 'reg_line' in dir()\n", - "\n", - "# Check if reg_line gives right output\n", - "assert reg_line(10) == -3.04\n", - "\n", - "# Check if future values are calculated right\n", - "assert future == [1.51, -0.12]\n", - "\n", - "### BEGIN HIDDEN TESTS\n", - "sl, ip, _, _, se = stats.linregress(x_data,y_data)\n", - "slope, intercept, _, _, stderr = l\n", - "\n", - "assert slope == sl\n", - "assert intercept == ip\n", - "assert stderr == se\n", - "\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "8369e788-5862-488b-a003-d3cd31ebaa99", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-59872a1e95590378", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "id": "02656b99-d250-4a21-bc2d-46007806d81d", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-455ed6a04d0d701f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Verteilungen\n", - "\n", - "## Probability Density Function - Normal Verteilung\n", - "\n", - "### Motivation\n", - "\n", - "Es wurden Daten über die Körperlänge eines Bienenvolkes erhoben, dementsprechend liegen diskrete Werte über die deren Körperlängen vor. Logischerweise repräsentiert dies nur das erhobene Bienenvolk, dieses arbeitet aber kontinuierlich weiter und erzeugt nachkommen. Die Population verändert sich. \n", - "\n", - "Daher ist anzunehmen, dass auch Werte zwischen den bereits Erhobenen auftreten können. Um dies zu Modellieren wird eine Normalverteilung benötigt.\n", - "\n", - "Schauen wir uns dazu erst die Gaussche Normalverteilung an, diese hat ihren Mittelpunkt bei $x=0$, bezeichnet als Erwartungswert $\\mu$, und eine Standardabweichung $\\sigma = 1$. Mathematisch ist sie definiert als $$p(x|\\mu,\\sigma)=\\frac{1}{\\sqrt(2\\pi\\sigma^2)}e^{-\\frac{(x-\\mu)^2}{2\\sigma^2}}$$\n", - "\n", - "SciPy bietet hierfür das `norm` objekt aus dem `stats` Modul an, dieses verlangt die beiden Parameter $\\mu$ & $\\sigma$, das auf dem `norm` Objekt kann dann die Funktion `pdf` mit einem Paramter als Stepsize oder einem Array aufgerufen werden. Nach Plotten ergibt sich folglich die Normalverteilung (von -4 bis 4):" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "2803dcac-bb3b-4aff-a3c9-c0085148c376", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-54174b5f7d8309db", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.arange(-4, 4, 0.01) # Plot between -4 and 4 with stepsize 0.01\n", - "y = stats.norm(0,1).pdf(x) # Calculate pdf with mu=0, sigma=1\n", - "\n", - "# Plot\n", - "plt.title(\"Gaussche Normalverteilung\")\n", - "plt.plot(x, y)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "ca2f768f-16ff-47ff-b898-fe0b8cf3511f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-259b8d6cb9d76981", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Um herauszufinden wie viel Prozent einer Population innerhalb dieser Normalverteilung fallen wird die Funktion `ppf` (Percent Point Function) verwendet.\n", - "\n", - "Am Beispiel 90% der Population:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "0e5e7709-90a2-4a30-b49f-cbb243aa4e4b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5a9ed9588ce2ea27", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "90% of the Population fall into the range 1.282.\n" - ] - } - ], - "source": [ - "percentile = stats.norm(0,1).ppf(0.9)\n", - "print(f\"90% of the Population fall into the range {percentile:0.3f}.\")" - ] - }, - { - "cell_type": "markdown", - "id": "96afa424-d697-4c60-aa9b-4655491f4f13", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5102a53fda328278", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Um dies zu veranschaulichen:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "4f5d7f54-856f-4e12-8251-d0cdc5c0f002", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5d84b05b2808a672", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(9,6))\n", - "ax.plot(x,y, color='r')\n", - "\n", - "# filling under the curve\n", - "x_fill = np.arange(-4, percentile, 0.01)\n", - "y_fill = stats.norm(0,1).pdf(x_fill)\n", - "ax.fill_between(x_fill, y_fill, 0, color='g')\n", - "plt.title(\"Filled Percentile under Normal Curve\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "1f7a26f6-36d8-4561-ad68-db6c19c7720e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c469fe12a69725b1", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Machen wir dies alles am Beispiel der Fuchsrote Lockensandbiene fest. Diese ist laut [Bundesministerium für Ernährung und Landwirtschaft](https://www.bmel.de/DE/themen/landwirtschaft/artenvielfalt/bienen-fuettern/wildbienen-honigbienen-und-co.html) 12-14 mm groß. Daher lässt sich annehmen das die meisten Bienen $\\mu=13mm$, mit einer Standardabweichung von $\\sigma=1mm$ haben. Wir wollen nun wissen wie groß 95% der Bienen sind, rechnen wir dies mittels `ppf` aus:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "6d57bdf0-8e75-4760-8159-d42effd9ea62", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-360e11812d2e9932", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mu = 13 # Mean\n", - "sigma = 1 # std deviation in mm\n", - "\n", - "x_norm = np.linspace(10, 16, 100) # Normaldistribution range from 10-16mm \n", - "y_norm = stats.norm(mu, sigma).pdf(x_norm) # Calculate normal\n", - "\n", - "# Height of 95th percentile of bees\n", - "percentile = stats.norm(mu,sigma).ppf(0.95)\n", - "\n", - "x_percentile = np.arange(x_norm[0], percentile, 0.01)\n", - "y_percentile = stats.norm(mu,sigma).pdf(x_percentile)\n", - "\n", - "# Plot\n", - "fig, ax = plt.subplots(figsize=(9,6))\n", - "ax.plot(x_norm, y_norm, color='r')\n", - "\n", - "# filling under the curve\n", - "ax.fill_between(x_percentile, y_percentile, 0, alpha=0.2, color='#FE0000')\n", - "\n", - "\n", - "# Set text\n", - "ax.text(0.5,0.25,\n", - " f\"95th percentile of bees fall under {percentile:.2f}mm\",\n", - " ha='center', va='center', transform=ax.transAxes,\n", - " bbox={'facecolor':'#fafafa','alpha':1,'edgecolor':'none','pad':1},\n", - " color='#de2e0b'\n", - " )\n", - "\n", - "# Show\n", - "plt.title(\"Filled Percentile under Normal Curve\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "e9c59eb8-bfb1-46f2-bf1f-af1ffe59e190", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7718d285f36c2fad", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Aufgabe \n", - "\n", - "*6 Punkte*\n", - "\n", - "Gegeben sind die nach Altersgruppe aufgeschlüsselten Durschnittskörpergrößen (in cm) von Frauen in Deutschland. (Zu finden beim [Statistischen Bundesamt](https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Gesundheit/Gesundheitszustand-Relevantes-Verhalten/Tabellen/koerpermasse-frauen.html))\n", - "\n", - "Gehe wie folgt vor:\n", - "\n", - "- Berechne das arithmetische Mittel nutze dafür NumPy. und speichere das Ergebnis mit einer Genauigkeit von 1 Dezimalstelle nach dem Komma in der Variablen `avg_height`.\n", - "- Gegeben ist auch die Standardabweichung von 15cm, stelle die Normalverteilung mittels `norm.pdf` auf. Speichere den Wert in `norm_height` und finde einen geeigneten linespace zum plotten.\n", - "- Berechne folgend die Körpergröße unter die 80% aller Frauen (nach Datenset) fallen. Speichere den Wert in der Variablen `avg_percentile`.\n", - "- Plotte das Ergebnis. Orientiere dich gerne an dem Bienenbeispiel. Finde eine geeignete Darstellung. *Tipp: Da die Y-Achse in diesem Beispiel keinen Sinn ergibt kannst du sie einfach austellen mit `plt.yticks([])`*\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "3983d224-e885-43bd-aadd-4e276a56a370", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-048d8330d555432a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Given\n", - "avg_height_per_woman = {\n", - " \"18 - 20\": 167.6,\n", - " \"20 - 25\": 167.7,\n", - " \"25 - 30\": 167.3,\n", - " \"30 - 35\": 167.2,\n", - " \"35 - 40\": 167.3,\n", - " \"40 - 45\": 167.5,\n", - " \"45 - 50\": 167.1,\n", - " \"50 - 55\": 167.1,\n", - " \"55 - 60\": 166.9,\n", - " \"60 - 65\": 165.4,\n", - " \"65 - 70\": 164.5,\n", - " \"70 - 75\": 163.9,\n", - " \"75+\": 162.8\n", - "}\n", - "\n", - "avg_height = None\n", - "norm_height = None\n", - "avg_percentile = None" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "2c2e2cf6-c743-4cb0-a329-2240205fc7da", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-a59f2b3230aad3d6", - "locked": false, - "points": 3, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "avg_height = np.round(np.mean(list(avg_height_per_woman.values())), decimals=1)\n", - "std_sigma = 15\n", - "\n", - "norm_x = np.linspace(120, 220, 1000)\n", - "norm_height = stats.norm(avg_height, std_sigma).pdf(norm_x)\n", - "\n", - "# Height of 80th percentile of woman heights\n", - "avg_percentile = stats.norm(avg_height, std_sigma).ppf(0.8)\n", - "\n", - "x_percentile = np.arange(norm_x[0], avg_percentile, 0.01)\n", - "y_percentile = stats.norm(avg_height, std_sigma).pdf(x_percentile)\n", - "\n", - "# Plot\n", - "fig, ax = plt.subplots(figsize=(9,6))\n", - "ax.plot(norm_x, norm_height, color='r')\n", - "\n", - "# filling under the curve\n", - "ax.fill_between(x_percentile, y_percentile, 0, alpha=.5, color='#fa0000')\n", - "\n", - "\n", - "# Set text\n", - "ax.text(0.4,0.18,\n", - " f\"80th percentile of Womens heigth\\n fall under {avg_percentile:.1f}cm\",\n", - " ha='center', va='center', transform=ax.transAxes,\n", - " bbox={'facecolor':'#fafafa','alpha':1,'edgecolor':'none','pad':1},\n", - " color='#de2e0b'\n", - " )\n", - "\n", - "# Show\n", - "plt.title(\"Woman Height Normal Distribution\")\n", - "plt.xlabel(\"Height\")\n", - "plt.yticks([]) # hide y\n", - "plt.show()\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "d9f8aeae-6b51-4645-b578-19693a07ece3", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-26f141bc8b12d7fe", - "locked": true, - "points": 3, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Hier werden ihre Lösungen getestet...\n", - "import math \n", - "\n", - "# Check if average height is close to real value\n", - "assert math.isclose(avg_height, 166.3, rel_tol=.2) # 1 Punkt\n", - "\n", - "# Check if norm height is close to real value\n", - "assert math.isclose(np.round(np.sum(norm_height), decimals=1), 10, rel_tol=.2) # 1 Punkt\n", - "\n", - "# Check if percentile is close to real value\n", - "assert math.isclose(avg_percentile, 179, rel_tol=.2) # 1 Punkt" - ] - }, - { - "cell_type": "markdown", - "id": "f50b8883-e018-4a44-831a-1a3c14640955", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-6dac9e79dc305781", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Probabillity Mass Function - Binomial Verteilung\n", - "\n", - "Im Gegensatz zur Normal Verteilung folgen Binomiale Verteilungen Diskreten Werten. Dies lässt sich am besten am Beispiel zweier unabhängiger Würfel zeigen.\n", - "\n", - "Wir wollen im folgenden also aufzeigen wie die Wahrscheinlichkeits Verteilung zweier 6 seitiger Würfel ist. Da wir die Augenzahl beider Bestimmen führen wir eine Addition aller möglichen Werte vor. Da uns die Auftrittswahrscheinlichkeit interessiert eignet sich ein dict zum speichern der Daten. Dabei ist der Schlüssel die Augenzahl und der Wert die Autrittsanzahl:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "7a58ad07-9593-4c58-9a86-23a002ef5e94", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d852ddb9a826faa3", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6, 8: 5, 9: 4, 10: 3, 11: 2, 12: 1}\n" - ] - } - ], - "source": [ - "# Two Dices\n", - "first_dice: np.array = np.arange(1,7)\n", - "second_dice: np.array = np.arange(1,7)\n", - "\n", - "# Creating a dict with all keys\n", - "dist: dict = {el: 0 for el in range(2,13)}\n", - "\n", - "# Summing all possible combinations and store them inside the dict\n", - "for el1 in first_dice:\n", - " for el2 in second_dice:\n", - " dice_roll: int = el1 + el2\n", - " dist[dice_roll] += 1\n", - "\n", - "# Print the result\n", - "print(dist)" - ] - }, - { - "cell_type": "markdown", - "id": "0b2ff2d3-e8b0-45e9-9429-8db1acc5e526", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-591fc5a2d228a903", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Wie wir erstmal sehen können folgen die Zahl einem Muster, dieses lässt sich mittels Balkendiagramm visualisieren:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "f7915dad-d12a-4db1-b691-3fd53ead4027", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b70e902f730c6e98", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.bar(dist.keys(), dist.values(), color='#F00a00')\n", - "plt.xticks(list(dist.keys()))\n", - "plt.title(\"Double Dice role distribution\")\n", - "plt.xlabel(\"Points rolled\")\n", - "plt.ylabel(\"Number of occurrences\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "f3dc3bc4-b796-4ac9-be15-03c780673084", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-f6d18a41bf8cf643", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Aufgabe\n", - "\n", - "*7 Punkte*\n", - "\n", - "Gegeben sind zwei Würfel aus dem Spiel **Super Mario Party**. Es wird im folgenden angenommen das die Charaktere ihre Würfel würfeln und deren Augenzahl addiert wird.\n", - "\n", - "- Stelle alle Kombinationen der Gegebenen Daten auf, speichere diese als Dictionary in der Variablen `dist_mp`.\n", - "- Exthrahiere den am dritt häufigsten Aufkommenden Wert und speicher diesen in der Variablen `best3` als tuple in der Form `(, )`.\n", - "- Plotte eine geeignete binomiale Verteilung. *Tipp: schaue dir an wie du den Plot mittels matplotlib vergrößerst*\n", - "\n", - "Einige der Würfel gebem dem Charakter Münzen. Finde eine logische Schlussfolgerung diese mit anderen Werten zu ersetzen. Überlege dabei welche Auswirkungen es auf die Augenzahl hat das der Charakter Münzen bekomment.\n", - "\n", - "|Charakter|Dice|\n", - "|-|-|\n", - "|Mario|1, 3, 3, 3, 5, 6|\n", - "|Peach|0, 2, 4, 4, 4, 6|\n", - "|Boo|-2 Coins, -2 Coins, 5, 5, 7, 7\n", - "|Donkey Kong|+5 Coins, 0, 0, 0, 10, 10|" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "41b90981-ef18-40b4-bb03-9cd3407869b4", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-3e706ac49be47a70", - "locked": false, - "points": 3, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'plt' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 27\u001b[0m\n\u001b[1;32m 24\u001b[0m best3: \u001b[38;5;28mtuple\u001b[39m \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m10\u001b[39m, dist_mp[\u001b[38;5;241m10\u001b[39m])\n\u001b[1;32m 26\u001b[0m \u001b[38;5;66;03m# Plot\u001b[39;00m\n\u001b[0;32m---> 27\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241m.\u001b[39mbar(dist_mp\u001b[38;5;241m.\u001b[39mkeys(), dist_mp\u001b[38;5;241m.\u001b[39mvalues(), color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m#F00a00\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 28\u001b[0m plt\u001b[38;5;241m.\u001b[39mxticks(\u001b[38;5;28mlist\u001b[39m(dist_mp\u001b[38;5;241m.\u001b[39mkeys()))\n\u001b[1;32m 29\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDouble Dice role distribution\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined" - ] - } - ], - "source": [ - "dist_mp: dict = None\n", - "best3: tuple = None\n", - "# BEGIN SOLUTION\n", - "# define dices\n", - "mario = [1, 3, 3, 3, 5, 6]\n", - "peach = [0, 2, 4, 4, 4, 6]\n", - "boo = [0, 0, 5, 5, 7, 7]\n", - "dk = [0, 0, 0, 0, 10, 10]\n", - "\n", - "# Create Keys\n", - "keys: set = {m+p+b+d for m in mario for p in peach for b in boo for d in dk}\n", - "\n", - "# Creating a dict with all keys\n", - "dist_mp: dict = {k: 0 for k in keys}\n", - "\n", - "# Summing all possible combinations and store them inside the dict\n", - "for m in mario:\n", - " for p in peach:\n", - " for b in boo:\n", - " for d in dk:\n", - " dice_roll: int = m+p+b+d\n", - " dist_mp[dice_roll] += 1\n", - "\n", - "best3: tuple = (10, dist_mp[10])\n", - "\n", - "# Plot\n", - "plt.bar(dist_mp.keys(), dist_mp.values(), color='#F00a00')\n", - "plt.xticks(list(dist_mp.keys()))\n", - "plt.title(\"Double Dice role distribution\")\n", - "plt.xlabel(\"Points rolled\")\n", - "plt.ylabel(\"Number of occurrences\")\n", - "plt.show()\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "9b142059-c19b-49c4-bc4b-02ca875b1fbf", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-cd335bd7d9a4cd4f", - "locked": true, - "points": 4, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden ihre Lösungen getestet...\n", - "# Check dist_mp 1 Punkt\n", - "assert isinstance(dist_mp, dict), \"dist_mp isn't a dict\"\n", - "assert len(dist_mp) == 27, \"dist_mp too small\"\n", - "\n", - "# Check if there is a zero value\n", - "for key, el in dist_mp.items():\n", - " assert el != 0, f\"Key: {key} in dist_mp is zero, thats impossible\"\n", - "\n", - "### BEGIN HIDDEN TESTS\n", - "test_dist_mp = {1: 8,\n", - " 3: 32,\n", - " 5: 56,\n", - " 6: 16,\n", - " 7: 88,\n", - " 8: 48,\n", - " 9: 48,\n", - " 10: 112,\n", - " 11: 20,\n", - " 12: 152,\n", - " 13: 32,\n", - " 14: 136,\n", - " 15: 60,\n", - " 16: 64,\n", - " 17: 76,\n", - " 18: 32,\n", - " 19: 32,\n", - " 20: 56,\n", - " 21: 8,\n", - " 22: 76,\n", - " 23: 8,\n", - " 24: 68,\n", - " 25: 16,\n", - " 26: 28,\n", - " 27: 16,\n", - " 28: 4,\n", - " 29: 4}\n", - "\n", - "# 3 Punkte\n", - "for test, answer in zip(test_dist_mp.items(), dist_mp.items()):\n", - " test_k, test_v = test\n", - " k, v = answer\n", - " assert test_k == k, f\"Key missmatch should: {test_k} | is: {k}\"\n", - " assert test_v == v, f\"Value missmatch should: {test_v} | is: {v}\"\n", - "\n", - "# 1 Punkt\n", - "assert best3[0] == 10\n", - "assert best3[1] == 112\n", - "### END HIDDEN TESTS" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +version https://git-lfs.github.com/spec/v1 +oid sha256:abd4dd0591f378048e5c247705bfb43116a755a920549ded126bea70bf366558 +size 363282 diff --git a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/6.Monte_Carlo-Copy1.ipynb b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/6.Monte_Carlo-Copy1.ipynb index 6f9987e..2b12f72 100644 --- a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/6.Monte_Carlo-Copy1.ipynb +++ b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/6.Monte_Carlo-Copy1.ipynb @@ -1,1070 +1,3 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "839f9f95-d3ea-477f-96b6-b23eea60be3c", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-94f52a4a45c90a6a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# 6. Programmierübung: Monte Carlo Simulationen\n", - "\n", - "
\n", - "
\n", - " Willkommen zur sechsten Programmierübung Einführung in Python 3.\n", - "
\n", - " \n", - "
\n", - "\n", - "Wenn Sie Fragen oder Verbesserungsvorschläge zum Inhalt oder Struktur der Notebooks haben, dann können sie eine E-Mail an Phil Keier ([p.keier@hbk-bs.de](mailto:p.keier@hbk-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) oder Martin Le ([martin.le@tu-bs.de](mailto:martin.le@tu-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) schreiben.\n", - "\n", - "Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n", - "\n", - "Der Großteil des Python-Tutorials stammt aus der Veranstaltung _Deep Learning Lab_ und von [www.python-kurs.eu](https://www.python-kurs.eu/python3_kurs.php) und wurde für _Signale und Systeme_, sowie _Einführung in die Programmierung für Nicht Informatiker_ angepasst.\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "53c75485-1cd4-4ac7-9092-e14b3c400980", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-107d356336d8d991", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## (Continues) Uniform Distribution\n", - "\n", - "Die gleichmäßige Verteilung zeigt sich durch ihre Charakteristische Form eines Rechteckes mit Flächeninhalt 1 = 100%. \n", - "Technisch gesehen ist diese Form der Verteilung bereits bekannt da PCGs dieser folgen.\n", - "\n", - "Eine Uniform Distribution setzt sich aus den selben zwei Parametern zusammen wie die Normalverteilung, $\\mu$ der Mittelwert und $\\sigma$ die Standardabweichung vom Mittelwert. \n", - "\n", - "Jedoch nehmen die Libraries NumPy & SciPy die Werte anders auf, $\\mu$ ist hierbei der kleinste Wert und $\\sigma$ der Größte Wert.\n", - "\n", - "Folgende Grafik verdeutlicht die Verteilung mit $\\mu=0$ & $\\sigma=1$:" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "52d292d3-66ee-4d00-b5eb-e65497839fba", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-654d073e308d12e3", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "from scipy.stats import uniform # Import scipys uniform\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Define the distribution parameters to be plotted\n", - "sigma = 1\n", - "mu = 0\n", - "x = np.linspace(-0.5, 1.5, 1000)\n", - "\n", - "\n", - "# plot the distributions\n", - "fig, ax = plt.subplots(figsize=(8, 5))\n", - "\n", - "dist = uniform(mu, sigma)\n", - "\n", - "plt.plot(x, dist.pdf(x), c='black', label=r'$\\mu=%i,\\ \\sigma=%i$' % (mu, sigma))\n", - "\n", - "plt.xlim(-0.5, 1.5)\n", - "plt.ylim(0, 1.2)\n", - "\n", - "plt.xlabel('$x$')\n", - "plt.ylabel(r'$p(x|\\mu, \\sigma)$')\n", - "plt.title('Uniform Distribution')\n", - "\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "55a561f8-46dc-4a72-a4e3-c7711aede96b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0ccc61b247199a19", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Random Sampling\n", - "\n", - "Wie man von Verteilungen sampled ist bereits bekannt aus dem Kapitel zu den PCGs diese sind uniformiert verteilt.\n", - "\n", - "Folgendes Beispiel zieht 3 zufällige Zahlen aus einer PCG uniformierten Verteilung mittels NumPy:" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "id": "a312483e-058b-4efc-a92a-bc9e370a3559", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-c4c29720ffba7a48", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Parameters\n", - "mu = 0\n", - "sigma = 1\n", - "\n", - "# Get a 3 random samples\n", - "rand = np.random.default_rng(5000)\n", - "uniform_samples = rand.uniform(mu, sigma, 3)\n", - "\n", - "# Linespace\n", - "x = np.linspace(-0.5, 2, 1000)\n", - "\n", - "# Get uniform from linespace\n", - "dist = uniform(mu, sigma)\n", - "\n", - "# Plot Uniform\n", - "fig, ax = plt.subplots(figsize=(8, 5))\n", - "plt.plot(x, dist.pdf(x), c='black', label=r'$\\mu=%i,\\ \\sigma=%i$' % (mu, sigma))\n", - "\n", - "# Plot samples\n", - "step = 0.1\n", - "for u in uniform_samples:\n", - " u = np.round(u, decimals=2)\n", - " plt.axvline(u, color='r')\n", - " plt.text(u, .5+step, u, color='g')\n", - " step += 0.1\n", - "\n", - "# Cosmetics\n", - "plt.xlim(-0.5, 1.5)\n", - "plt.ylim(0, 1.2)\n", - "\n", - "plt.xlabel('$x$')\n", - "plt.ylabel(r'$p(x|\\mu, \\sigma)$')\n", - "plt.title('3 Samples from a Uniform Distribution')\n", - "\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "a87b1a90-adff-4eb4-b91b-2de5284f64d3", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-42065ce8f33baff7", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Random sampling funktioniert auch auf Normalverteilungen. Beispiel:" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "id": "f1981ddf-87e1-4010-a39c-8a89d172d771", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-d87cc7a18f64858f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Import scipys norm\n", - "from scipy.stats import norm \n", - "\n", - "#Parameters\n", - "mu = 0\n", - "sigma = 1\n", - "\n", - "# Get a 3 random samples\n", - "rand = np.random.default_rng(5000)\n", - "uniform_samples = rand.normal(mu, sigma, 3)\n", - "\n", - "# Linespace\n", - "x = np.linspace(-4, 4, 1000)\n", - "\n", - "# Get uniform from linespace\n", - "dist = norm(mu, sigma)\n", - "\n", - "# Plot Uniform\n", - "fig, ax = plt.subplots(figsize=(8, 5))\n", - "plt.plot(x, dist.pdf(x), c='black', label=r'$\\mu=%i,\\ \\sigma=%i$' % (mu, sigma))\n", - "\n", - "# Plot samples\n", - "step = 0.1\n", - "for u in uniform_samples:\n", - " u = np.round(u, decimals=2)\n", - " plt.axvline(u, color='r')\n", - " plt.text(u, .1+step, u, color='g')\n", - " step += 0.1\n", - "\n", - "# Cosmetics\n", - "plt.xlim(-4, 4)\n", - "plt.ylim(0, .5)\n", - "\n", - "plt.xlabel('$x$')\n", - "plt.ylabel(r'$p(x|\\mu, \\sigma)$')\n", - "plt.title('3 Samples from a Normal Distribution')\n", - "\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "541dd4a5-8849-43fe-8e8c-305b3b99a350", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-de3ef99af1cebfe1", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Monte Carlo Simulationen\n", - "\n", - "Monte Carlo Simulationen werden in viel Wissenschaftlichen & Finzanziellen (Forschungs-)Feldern angewandt um Stochhastisch relevante Ergebnisse einfach zu lösen. Damit lassen sich insbesondere Zufallsereignisse verschiedenster Verteilungen mit einander Kombinieren. So lässt sich Beispielweise die Zahl $\\pi$ approximieren.\n", - "\n", - "Dazu betrachte folgende Darstellung:" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "id": "cd6000b8-cb79-4513-8cae-dc9752e47f93", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-3b11e71ec9f993b5", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "circle = plt.Circle((0, 0), 1, color='black', fill=False)\n", - "fig, ax = plt.subplots(figsize=(5,5))\n", - "ax.add_patch(circle)\n", - "ax.plot(np.linspace(0,1), np.zeros(50), color='black')\n", - "ax.text(0.5, 0.05, \"$r = 1$\")\n", - "\n", - "plt.xlim(-1, 1)\n", - "plt.ylim(-1, 1)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "ad96fd0a-3e95-4670-966b-6defe5dbade4", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-c67a16c39ab32478", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Vorliegend der Einheitskreis mit Radius=1 dieser hat den Flächeninhalt $A_{circle} = \\pi$. Das Umgebende Rechteck hat die Seitenlänge $a=r*2=2$ darauß ergibt sich ein Flächeninhalt $A_{square}=2*2=4$. Daraus ergibt sich die Differenz $$\\frac{A_{circle}}{A_{square}} = \\frac{\\pi}{4}$$\n", - "\n", - "Jeder Punkt $(x,y)$ in dem Diagramm ist gleichwahrscheinlich, also uniform Verteilt. Mittels Monte Carlo können wir nun den Wert von $\\pi$ bestimmen. Nach Umstellen ergibt sich: $$\\pi = 4 \\cdot \\frac{A_{circle}}{A_{square}}$$\n", - "\n", - "Nacheinander werden folgend zufällig gleichverteilte x,y Werte gezogen.\n", - "\n", - "Für das Beispiel nutzen wir sehr wenig Werte um die Darstellung nicht zu verunstalten:" - ] - }, - { - "cell_type": "code", - "execution_count": 159, - "id": "082ce292-4b01-4b93-abaa-c810232f0d0a", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-1c33d4a20d47764c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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GbtXc4FnbE5cvX4ajoyOmTp2KIUOG4MWLF5gwYQK0tLQUlo2Q8tLgayBocBC+q/cdGpo3xKAmg7752fVq1aph06ZNuHfvHszMzNCpUyf0798fb958vR8DKV1cZhx+u/UbVtxcgbcZb7mOUwTdo63E90lYlsWLtBfIF+Wjnlk9aPIVOzbr7bjbuPjyIkx1TdHZrDNmz5iNQ4cOoVWrVli7di2cnJwUmocQZcOyLPbt24cpU6YgMzMTs2bNwuTJk+mLZznFpsfCZbMLPuR/AAAYaRkhbFRYma7i0T1aUiEMw8De1B5NLJoovMgCQIvqLTCtxTRkXMqAUyMnXLt2Dbt378b169epyBLy6d+on58fnj9/jrFjx2LevHlo1KgR/v77b66jqZQ1oWuQUZABCSuBhJUgqzALq0NXcx1LigotkZvLly+jUaNGmDNnDkaPHo3nz5/jhx9+oI5OcqSGF6gqBUNDQ6xYsQKRkZGoWbMmevTogV69eiEuLo7raCqhpAlVCkTKM8kKFVoic5mZmRg9ejQ6duwIa2trRERE4Pfff6/Ul+m5lpCVgLbb20JzoSZs/rBBcGww15HIN6hfvz4uXryIw4cPIywsDA0bNsTWrVvpC9RXDGw8ECzLgsfwwGN4kLASDGw8kOtYUnSPln75y9TFixcxYsQIpKamYvny5Rg9ejR4PPo+J28eOzxw8+1NiCQi8MCDjoYOXk14hap6VbmORr7Rhw8fMGnSJOzYsQNdunTBli1bUKNGDa5jKa3g2GBsCNsACSvBGJcx6GDXoUz70T1aojIyMjIwcuRIdOnSBXXq1MHDhw8xduxYKrIKcjvutvRxLgkkyBHm4EnKE65jkQqoUqUKtm3bhn0n9yHyVSQaNmqIzZs309ltKTxsPXDw+4M43O9wmYusotBvQVJh586dQ6NGjXDgwAFs2rQJly5dgp2dHdex1EoNoxrgMf/8c2bAwMbQhtNMpGKEYiEGHB2AgfcHIvGHRJiPMcf/xvwPnTt3RmxsLNfxSDlQoSXfLDMzE8OGDUO3bt1Qv359PHr0CKNGjaLOThzY02cPjLSMgE/Dba7qugq1TWpzHYtUwNo7a3Ho8SHpcqxuLEZuHYmoqCg0atQIGzZsoLNbFfHlQToJKUVYWBh8fHyQkpKCv/76C8OGDaMCK0fZhdmYfGEyrr2+hlrGtbCm+5oizwg2r9YcMRNi8CLtBSz1LVHdsDqneUnFPX3/FHweX3pLgM/jQ2IiwaNHjzB16lSMHTsW58+fx7Zt22BiYsJ1XPIFdEZLykUikWDlypVo2bIlTE1N8eDBAwwfPpyKrJwNPj4YW+9txbP3z3Dh1QW029EO2YXZRbYx0jaCi7ULFdlKwtHCscgwqmKJGE0smsDQ0BCbNm3CiRMnpM+k37hxg9Os5MvUutBm5GfgxLMTOP38dJkGB1d3KSkp8PLywuTJkzFhwgTcuHEDtWopbvxkdcWyLM5EnYGYFQMARBIR4jLj8Cj5EdfRiByNdhmNEc1GSJeHNR2Gca7jpMu9evXCgwcPULNmTbRr1w4LFy6EWCzmKC35ErV+vMdmiQ3eFnwcE7OJRRPc8L8BAy0DruMppeDgYPj5+aGwsBA7d+5E9+7duY6kUmLTY/E4+THsqtihgXmDcu9vttwMqXmpRdY9G/cMDmYOMkxJlFGuMBcAoKuhW+L7IpEIv/76KxYuXIj27dtjz549sLKyUnBK1UWP98hZfGa89L8fJT/C2jtrOc2jjEQiEebNm4cOHTrAwcEBERERVGTL6fjT47BfY4+e+3ui0fpG+PP2n+U+xoYeG8Bn/pnZ6OcWPytlkQ2NC8XYs2Mx/u/xeJrylOs4pXqd/hoXXl7Aqw+vuI7yVboauqUWWQAQCAT49ddfERQUhKdPn8LR0RHnzp1TaEbyZWp9RssEMGC1Pn58AU+An5r/hN89f+c6ntJITk5G//79cf36dcyfPx8BAQE0jV05sSwLo6VGyC7MBouPf9d4DA/vJr2Dpb7lV/f/tycpTxAWH4aaRjXRzradnBJ/u1tvb6Hdjn9yafI1cW/UPaX7QnDg0QEMOj4IIokIfIaPrd5bMcRpCNexZCI5ORlDhw5FYGAgAgICsGDBAvo3+xV0Ritn//6OIZKI0K1uN07zKJP79+/DxcUFz549w5UrVzB79mz6B/sNCsQFyCrMkhZZAJCwErzPfV/uYzUwb4DBjoOVssgCwKbwTWBZFiKJCCKJCIXiQuyK2MV1rCIKxYUYemKotJORmBVj5OmRxTqWqaqqVavizJkzWLZsGZYtWwZvb29kZBSftJ4olloX2mWdl6GeWT00Mm+EfX32oVOtTlxHUgoHDhxAq1atULVqVYSFhaFt27ZcR1JZ2gJttKjWAgLmnwnDqxlUQ+0qle8ZVwbFe54rW2/09Pz0YgPQCyVCpOWlcZZJ1ng8HqZNm4a///4bN2/ehJubG54/f851LLWm1oV2tMtoPB33FA/HPsSAxgO4jsM5sViMgIAADBgwAH369MH169dRvTo9KlJRJ3xPoEudLjDXNYdbdTcEDQ6CjoYO17FkbpzrOPAYHgQ8AQQ8AXQEOvB38uc6VhHmuuZwMHWQ3u/mM3zYGdvB2sCa62gy5+npiTt37oBhGLi5uSEwMJDrSOqLVYC1a9eyNWvWZLW0tNjmzZuzoaGhpW7brl07FkCxV/fu3aXbDBkypNj7np6eZc6TkZHBAmAzMjIq/Nkqi/T0dLZHjx4sj8djV6xYwUokEq4jERV0P+E+O+ncJHbahWlsdGo013FKFPshlm23vR1bZWkVtuXWlkqbU1YyMjLYnj17sgzDsMuWLaN/2/+hiHog90J74MABVlNTk922bRv7+PFjduTIkayxsTGblJRU4vapqalsQkKC9PXo0SOWz+ez27dvl24zZMgQtmvXrkW2S0tLK3MmZSm0IrGIFYqFnGZgWZZ9/vw5W69ePdbIyIgNDAzkOg4hRMZEIhEbEBDAAmAHDhzI5ubmch1JaSiiHsj90vHKlSsxcuRI+Pv7o0GDBti4cSN0dXWxbdu2Erc3MTGBpaWl9HXx4kXo6uqiX79+RbbT0tIqsl2VKlXk/VFkhmVZzL0yFzqLdKC1UAsjTo0oMgKMIl2+fBnNmzcHy7K4c+cOunbtykkOQoj88Pl8LF68GAcOHMDx48fRtm1bJCUlcR1Lbci10BYWFiI8PBydOv3TyYjH46FTp04ICQkp0zG2bt0KX19f6OnpFVkfHByMqlWrwsHBAWPGjEFqamqpxygoKEBmZmaRF5cOPj6IBdcWQCgRQsJKsO3+Nvx+S/GPFR08eBBdu3aFm5sbQkNDYW9vr/AMhBDF8fHxwc2bN/Hu3Tu0bNkSL1684DqSWpBroX3//j3EYjEsLCyKrLewsEBiYuJX979z5w4ePXqEESNGFFnftWtX7Nq1C0FBQVi2bBmuXr2Kbt26lTr82JIlS2BkZCR92dhwO31YWHwYNHga0mWGYRCWEKbQDKtWrYKvry98fHxw+vRpGBkZKbR9Qgg3mjZtilu3bkFDQwOtWrVCWJhif/eoI6Xudbx161Y0btwYzZs3L7Le19cX3t7eaNy4MXr37o0zZ87g7t27CA4OLvE4AQEByMjIkL7evn2roE9QsromdYtcKmbAoK5JXYW0zbIsZsyYgYkTJ2Lq1KnYuXMnNDU1FdI2IUQ52NraSscq9/DwwPnz57mOVKnJtdCamZmBz+cXuxeQlJQES8svj4qTk5ODAwcOYPjw4V9tp1atWjAzM0N0dHSJ72tpacHQ0LDIi0sjmo2AT0Mf6XLrGq0xq80subcrFAoxdOhQLFu2DCtXrsTy5cvB4yn1dy1CiJyYmZkhKCgIHh4e6NmzJ/bs2cN1pEpLrr9lNTU14ezsjKCgIOk6iUSCoKAguLu7f3Hfw4cPo6CgAD/88MNX24mLi0NqaqrKDKTN5/Gxr+8+xE6IRfT4aAQNDoKepl4Z9vx22dnZ8Pb2xv79+7F//378/PPPcm2PEKL89PT0cPz4cQwaNAiDBg3Cb7/9RpPJy4Pc+jN/cuDAAVZLS4vdsWMH++TJE3bUqFGssbExm5iYyLIsyw4aNIidMWNGsf1at27N+vj4FFuflZXFTpkyhQ0JCWFjYmLYS5cusc2aNWPr1q3L5ufnlymTsjzeoyjJycmsq6srq6+vz168eFHu7X3I+8DGZ8azYolY7m0RQipOIpGws2bNYgGwP//8MysWq8+/XUXUA4G8C7mPjw9SUlIwd+5cJCYmwsnJCefOnZN2kHrz5k2xy5fPnz/HjRs3cOHChWLH4/P5iIyMxM6dO5Geng5ra2t06dIFCxYsgJaWlrw/jspJTExEx44d8f79e1y7dg1NmzaVW1ssy2L25dlYfGMxAKBl9ZY463cWxtrGcmuTEFJxDMNg4cKFsLKywvjx45GVlYVNmzbRrSUZUevZe+Q5W4MyiI+PR4cOHZCVlYXLly/DwUG+s6icjTqLnvt7Spf5DB8jmo3Axp4b5douIUR2du3aBX9/fwwaNAhbt26t9JOJKKIeyP2MlnDj7du36NChAwoKCnD16lXUqVNH7m0+ff8UfIYPMfvxMSsxK8bDpIdyb5cQIjuDBw+GQCDA4MGDIRQKsXPnTggEVCoqgn56ldDbt2/h4eEBsViMq1evws7OTiHtNq7aWFpk8emMtqmV/C5VE0LkY+DAgdDQ0MDAgQMhFouxZ88eKrYVQBfgK5m4uDh4eHhAIpEotMgCgGcdTyxsvxA85uNfq/a27bGk4xKFtU8IkZ1+/frh0KFDOHr0KAYPHgyRiJthYisDukdbie7Rvnv3Dh4eHigsLMTVq1dha2vLSY4CUQHyRfkw0qbRpghRdUePHoWPjw98fX2xc+fOSnfPlu7RkjJLTExE+/btpfdkuSqyAKAl0IKWgHqAE1IZ9O3bF/v378eAAQMgEAiwbds26o1cTlRoK4GMjAx07doVOTk5uH79ukIvFxNCKr9+/fpBLBZj4MCBMDExwe+//w6GYbiOpTKo0Kq4/Px89OrVC69fv8b169dRq1YtriMRQiohX19fpKWlYdy4cbCwsMD06dO5jqQyqNCqsM/fMENDQ3Hp0iU0atSI60hqJzknGWHxYTDVMUXzas3pWz6p1MaOHYvk5GTMmDED5ubmGDZsGNeRVAIVWhXFsizGjBmDU6dO4cSJE2jVqhXXkdTOvYR76LirI9Lz0wEAI5uNxKaem9S62EpYCZbfXI6jT4/CRNsEizsuhrO1M9exiAzNmzcPSUlJGDlyJMzMzODt7c11JKVHvY5VtNfxnDlzsHDhQmzfvh1Dhw6Ve3tXY6/ifuJ91DGpgx51e6h1MfmsxV8tEBYfVuTZ4StDrsDD1oPTXFxadG0RZl+ZDXx6jlpHQwcPxzyErTF3nfOI7InFYvj6+uLMmTO4cOEC2rRpw3Wkb6aIekBdx1TQmjVrsHDhQixfvlwhRfbP23/CY6cHJl+YDK/9Xvj5PM38AwAJWQlFiiwAJGYncpZHGRx4fED632JWjOzCbFyOucxpJiJ7fD4fe/bsQcuWLeHl5YXIyEiuIyk1KrQq5uDBg5gwYQImT56MqVOnyr09kUSEaRenAZ8uCwLAqtBVeJf5Tu5tK7uudbtKB+fgMTzoCHTQonoLrmNxyljbWPoz+cxQSzWvGpEv09LSwvHjx1G7dm107doVsbGxXEdSWlRoVcidO3cwZMgQDBw4EMuXL1dIm0KxEEKJsNj6HGGOQtpXZn96/olhTsNgoWeBBuYNcO6Hc2p/iXR5p+XQ4v/zDLWHrQd6OfTiNBORH0NDQ/z999/Q0dFBr169kJ2dzXUkpUT3aFXkHu27d+/g6uqKmjVr4sqVK9DW1lZY2977vXH2xVlIWAn4DB9NLJrgzsg7EPCoLx0p7mXaS1yJvQIjLSP0rtcbGnwNriMROXv8+DHc3d3RsWNHHD16VKUGtFBEPaBCqwKFNi8vD23btkViYiLu3r0LS0tLhbafK8zF7MuzcTf+LuqZ1sPSTkthqmuq0AyEEOV25swZeHt7Y9asWViwYAHXccqMhmAkYFkWw4cPx+PHj3Hjxg2FF1kA0NXQxUrPlQpvlxCiOnr27IklS5ZgxowZaNiwIXx9fbmOpDSo0Cq5pUuXYv/+/Th06BCaNWvGdRyl9STlCU48OwEdgQ6GOA2BiY4J15EIUTvTpk3Do0eP4O/vjzp16sDFxYXrSEqBLh0r8aXjU6dOoXfv3pgzZw7mz5/PdRyldefdHbTd3hYiiQgsWNQwqoHwUeFUbAnhQH5+Pjw8PBAXF4e7d+/CysqK60hfRM/RqrGHDx/Cz88Pffr0wbx587iOo9SW3FgCoUQIMSuGhJXgTcYb7I7YzXUsQtSStrY2jh8/DgDo3bs38vPzuY7EOSq0SigzMxN9+vRB7dq1sXPnTpXqwceFAlEB/n1hhgGDAnEBp5kIUWdWVlY4efIkIiMj8dNPP3Edh3P0G1zJsCyLUaNGITk5GUePHoWenh7XkZTeUKehYMGCAQM+w4eWQAvf1fuO61iEqDVnZ2esXbsWW7Zswf79+7mOwynqDKVktmzZgoMHD+LgwYOoXbs213FUQv+G/cFn+Nj3cB90NHQwrdU01DWty3UsQtTesGHDcPnyZYwaNQouLi6oW1c9/11SZygl6gwVGRkJNzc3DB06FBs2bOA6DiGEVFhWVhacnZ2hp6eHkJAQhQ62UxbUGUqNZGdno3///nBwcMAff/zBdRxCCJEJAwMDHDp0CE+fPsXkyZO5jsMJKrRK4PPcsnFxcTh06JDSfeMjhJCKcHJywh9//IH169fjyJEjXMdROCq0SmDHjh3Ys2cPNm3aBHt7e67jEEKIzI0ePRr9+vXD8OHD8erVK67jKBTdo+X4Hu2TJ0/g4uKCgQMH4q+//uI0iyyExoVi3d11YMFiZLORaFuzLdeRCCFKIiMjA82aNYOJiQlu3rwJTU1NriPRpALyoiyFVigUwt3dHVl5WTh64ShszW2hr6nPWZ6KCo8Ph/tWd+m8tSxYBA8JRpuabbiORghREnfv3oW7uztmz56NX375hes41Bmqslu+fDnuJd5D8g/JaPxXY5ivMMexp8e4jvXN9kTuAQsWYlYMMSsGDzzsitjFdSxCiBJxdXXFzJkzsWjRIty/f5/rOApBhZYjDx8+xPz582EwwgBZwiwAQL4oHwOODkBKTgrX8b6JBl8D+Pf1EQY0FykhpJjZs2ejQYMGGDJkCAoLC7mOI3dUaEvwIPEBZgXNwvzg+UjISpD58YVCIYYMGYK6DnWRyWRCzIql7xWKC/Em443M21SEMS5jYKBlAD7DB5/hQ1ugjZ/caPg1QkhRmpqa2LlzJ54+fapSc9d+KxoZ6j9uvb0Fjx0eYMGCZVmsD1uPB/97ACsD2c1AsWTJEkRGRiI0NBR+IX6ITouGmBWDAQNdDV3UqlKryPZvM97i1YdXqGNSB9UMq8ksh6zZVbHDg9EPsDtiNySsBAMbD0RtExrdihBSnJOTE+bMmYNff/0VvXr1qtRT6lFnqP/c/O53qB+OPTsm7dDDY3hY3GExpreeLpO2Hzx4AFdXV8yYMQMLFizAs/fP4LXfC9Fp0aiiXQWH+x1Gx1odpdvvjdyLoSeHQiQRQYOngYPfH8R39WkcX0KI6hMKhXBzc0NhYSHCw8OhpaWl8AzUGYoD/76Mi08zwfx33bcqLCzE0KFD0aBBA8yZMwcAUM+sHqJ+jELmjEykTkstUmSzC7Mx7OQwiCQiAIBQIoTfMT8IxUKZ5CGEEC5paGhg586diIqKqtRzblOh/Y8RzUaAZVnwGT4EjAA6Gjro37C/TI69ZMkSPH78GDt27Cjy/BjDMDDQMgDDMEW2f5/7HoWSoh0F8kR5SM9Pl0keQgjhWuPGjTFv3jwsW7YMd+/e5TqOXNCl4xIuFVx8eRF7H+6FFl8LE1tMRH3z+hVuMzo6Gg0bNsSUKVOwaNGiMu0jFAtRe3VtxGfFQ8yKwWf4qFWlFp7/+LxYUSaEa9mF2bj06hIAoFOtTir9TDhRLJFIBFdXVwgEAty+fRt8Pl9hbdOAFXKi6AErWJZFjx498OTJEzx58gS6urpl3vfZ+2f44dgPePb+GRpVbYR9ffcV6yxFCNdSc1PhvtUdL9JeAADqmtRFyPAQmOqach2NqIhbt26hVatW2LhxI/73v/8prN1Kc4923bp1sLW1hba2Ntzc3HDnzp1St92xYwcYhiny+u8g+yzLYu7cubCysoKOjg46deqEFy9eKOCTfJuTJ08iMDAQq1atKleRxad7uGGjwpA9Mxu3R9ymIkuU0m+3fsOrD/+MX/vqwyusDFnJaSaiWlq2bAl/f38EBATg/fv3XMeRKbkX2oMHD2LSpEmYN28e7t27B0dHR3h6eiI5ObnUfQwNDZGQkCB9vX79usj7y5cvx+rVq7Fx40aEhoZCT08Pnp6eyM/Pl/fHKbecnBxMmDAB3bt3h7e3N9dxCJGL1LzUIrczGIZBal4qp5mI6lm6dClYlsWMGTO4jiJTci+0K1euxMiRI+Hv748GDRpg48aN0NXVxbZt20rdh2EYWFpaSl8WFhbS91iWxZ9//onZs2ejV69eaNKkCXbt2oX4+HicOHFC3h+n3BYvXoykpCSsXr2a7quSSqtH3R7S3vEAIJKI0L1ud04zVUbh8eGYfXk2Fl9fjPe5leusDwCqVq2KxYsXY+vWrbh9+zbXcWRGroX287NRnTp1+qdBHg+dOnVCSEhIqftlZ2ejZs2asLGxQa9evfD48WPpezExMUhMTCxyTCMjI7i5uX3xmFyIiorCihUrMGPGDNSuze3ADRn5GRh6Yijs19ijx94eiE2P5TQPqVx61euFbd7b0Mi8ERqZN8I2723wdqArOLIUHBuMFltbYNnNZZh7ZS6abWqG1NzKd9Vg1KhRaNasGcaOHQuxWDaPVnJNroX2/fv3EIvFRc5IAcDCwgKJiYkl7uPg4IBt27bh5MmT2LNnDyQSCVq2bIm4uDgAkO5XnmMWFBQgMzOzyEveWJbF+PHjUb16dUyfLpvBLiqi/5H+2BO5By/SXuD8y/Nov7M98oR5XMcilYh/U388HPsQD8c+hH9Tf67jVDrLby6HhJVAJBFBzIoRlxmHA48OcB1L5vh8PtavX4/79+9j06ZNXMeRCaV7jtbd3R2DBw+Gk5MT2rVrh2PHjsHc3LxCP/AlS5bAyMhI+rKxsZFp5pIcO3YMFy5cwOrVq6GjoyP39r5EKBbi4suL0oE3xKwYsemxeJ76nNNchJCyE0qE0hHr8OkWm1BSOQevcXNzw4gRIzBr1iykpKjmJCv/JtdCa2ZmBj6fj6SkpCLrk5KSYGlpWaZjaGhooGnTpoiOjgYA6X7lOWZAQAAyMjKkr7dv337jJyoboVCIGTNmoFu3bujZs6dc2yoLAU8APU29YuuraFfhJA8hpPxGNRsFAB8H0+EJYKBpgD71+3AdS26WLFkCiUSChQsXch2lwuRaaDU1NeHs7IygoCDpOolEgqCgILi7u5fpGGKxGA8fPoSV1cdB/e3s7GBpaVnkmJmZmQgNDS31mFpaWjA0NCzykqe//voLL1++xLJly+TaTlkxDIPNPTeDx/zzxz2n7RzUNK7JaS5CSNn1a9gPp3xPYUDjAfB38sfdkXdRw6gG17HkxszMDNOnT8eGDRsQExPDdZyKYeXswIEDrJaWFrtjxw72yZMn7KhRo1hjY2M2MTGRZVmWHTRoEDtjxgzp9vPnz2fPnz/Pvnz5kg0PD2d9fX1ZbW1t9vHjx9Jtli5dyhobG7MnT55kIyMj2V69erF2dnZsXl5emTJlZGSwANiMjAyZf96srCzWwsKCHTx4sMyPXVGPkh6xeyL2sDff3OQ6CiGEfFV2djZrZWXF+vn5ya0NedaDz+Q+TZ6Pjw9SUlIwd+5cJCYmwsnJCefOnZN2Znrz5g14vH/OtD58+ICRI0ciMTERVapUgbOzM27duoUGDRpIt5k2bRpycnIwatQopKeno3Xr1jh37lyxgS248Oeff+LDhw/49ddfuY5STMOqDdGwakOuYxBCSJno6elh3rx5GDNmDKZMmQInJyeuI30TGoJRhpeRU1JSULt2bYwYMQIrVyp+VJyYDzFIzUtFPbN6NM4sIaRSEAqFaNiwIWrXro3AwECZH7/SDMGoLhYtWgSGYTBz5kyFtz3vyjzUWl0LrltcYb/GHs/eP1N4BkIIkTUNDQ0sXrwY586dw5UrV7iO803ojFZG32BiYmLg4OCAefPmYdasWTI5ZlmFxoWixdYW0mU+w0dLm5a45n9NoTkIIUQeWJaFm5sbACA0NFSmo+zRGa0KmTt3LkxNTTFx4kSk5qbibNRZXI29WuS5N3n57yhPYlZcZIB3QghRZQzDSOerPXr0KNdxyk3unaHUwfPnz7F3716sW7cOcXlxaLO9DVJyPz5k3dO+J074nACfJ7/5FZtZNYOAJ4BYIgaLj5PWt67RWm7tEUKIorVv3x5dunTBL7/8gj59+hTpRPtfYokYwbHByBHmoJVNK86na6QzWhlYtmwZLC0tMWzYMEy7OA1peWnS985EncGRJ0fk2n5d07o41v8YqupVhQZPA93qdMPGnhvl2iYhhCjanDlz8PjxY5w+fbrUbYRiIbrt7YZOuzuh14FeaLC+AaLTohWa87+o0FbQmzdvsHv3bkyZMgVaWlqIz46XDnUIAAwYJOeUPiWgrHg5eCFxSiIKZhfg9MDTMNY2lnubhBCiSK1bt0abNm2wePFilNa9aP+j/bj46qJ0OTU3FTMucTvtHhXaCvr9999haGiIUaM+Do/WvU53MPh4o54BAz6Pj3a27RSWh6biI4RUZjNnzsSdO3dK7YGckpNSZBQ8MStGUk5SidsqChXaCkhOTsaWLVvw008/QV//43Orc9rNwZSWU2BjaIP65vVx0vckmlg04ToqIYRUCp6enmjatCkWL15c4vvt7dqD+fS/z7rX4XZuZHq8pwLduWfNmoVVq1bhzZs3MDExkWlGQgghJTt8+DD69++P0NBQNG/evNj7Z6LOICAoANkF2fBr4of5HvNL7ZCqiMd7qNB+4w82IyMDNWrUwMiRI/Hbb7/JPCMhhJCSicViNGjQAA0aNMDx48crdCx6jlaJbdiwAfn5+Zg0aRLXUUgl9Dj5MfyO+aHXgV7YG7mX6ziEKBU+n48ZM2bgxIkTePz4MddxvorOaL/hG0x+fj5q1qyJ7777Dhs30mM0RLZi02PRZEMT5ApzIWElYMFiq/dWDGs6jOtohCiNwsJC1KlTB+3bt8fOnTu/+Th0RqukDh8+jOTkZPz8889cRyGV0Onnp5FdmA0x+3EAEgDY8WAH17EIUSqampr46aefcODAAaSkpHAd54uo0H6DdevWoXPnznBwcOA6CqmEtAVFp3vkMbxi6wghgL+/P3g8HrZu3cp1lC+iQltO4eHhCA0NxdixY7mOQiop30a+sDe1//gcNsMHn+FjTts5XMciROmYmprC19cXGzduhFgsLsMe3KB7tOW8Jj98+HBcvHgRr169gkBAQ0UT+cgsyMTOBzuRI8xBT/ueaFS1EdeRCFFKYWFhcHV1xcmTJ+Ht7V3u/enxHjn51h9sWloaqlWrhjlz5nAy5ywhhJDi3NzcYGxsjPPnz5d7X+oMpWS2b98OsViM4cOHcx2FEELIJ2PHjsWFCxfw4sULrqOUiAptGUkkEmzYsAH9+vWDhYUF13EIIYR84uPjA1NTU2zYsIHrKCWiQltGFy5cwMuXLzFu3DiuoxBCCPkXbW1tDB8+HNu3b0dubi7XcYqhQltGmzdvRpMmTeDu7s51FEKIGioUFyLmQwyyC7O5jqKURo8ejYyMDBw+fJjrKMVQoS2DtLQ0nDlzBv7+/jQNHSFE4R4mPYTtn7aotboWTJebYv/D/VxHUjp2dnZo164ddu/ezXWUYqjQlsHhw4chFovh6+vLdRRCiBryOeKD5Jxk4NOZ7ZATQ/Au8x3XsZTODz/8gMuXL+PdO+X62VChLYM9e/agS5cusLS05DoKIUTNsCyLF2kvIGb/GZBBKBEiJj2G01zKqG/fvtDU1MT+/cp1xk+F9itiYmJw48YN/PDDD3I5fr4oH/mifLkcmxCi+hiGQeOqjcFnPs6nyoCBtkAbdUzqcB1N6RgbG8Pb2xt79uzhOkoRVGi/Yu/evdDT00Pv3r1lelyxRIyRp0dCZ5EOdBfpYszZMZCwEpm2QYgySM1Nxb2Ee0jLS+M6iso61O8QapvUBgDoa+rjSL8jsNSnK2wl+eGHHxAREYGHDx9yHUWKRob6wkggLMuifv36aN68OXbt2iXTDKtDV2PiuYnS2VkAYEOPDRjtMlqm7RDCpRPPTsD3iC8KxAXQFejicP/D6F63O9exVBLLssgqzIK+pj54DJ0jlaawsBBWVlYYMWIEli1b9tXtaWQojoWHh+P58+dyuWz8IPEB+Dy+dFnAE+BB4gOZt0MIV3KFuRhwdAAKxAUAgDxRHnyO+EAoFnIdTSUxDANDLUMqsl+hqakJHx8f7N27FxKJclwlpD+xL9i9ezcsLS3RoUMHmR+7nlm9IpeKxRIxHExp2j1SeaTkpBTpf8CCRXZhNj7kf+A0F6n8Bg0ahHfv3iE4OJjrKAAV2tJJJBIcPnwYvr6+cpmlZ2KLifB2+Gemie8bfI8fm/8o83ZI+bEsi30P92Hulbk4/Pgw1PDuikxYGVjBUt9S2omHz/BhY2gDM10zrqORSq5Fixaws7PDwYMHuY4CAKB53koRHh6OhIQEfPfdd3I5viZfE8f6H0NCdgJ4DA8WehY0GIaSGPf3OGwI2wABTwCRRIRpLadhWeev3+shRWnyNXH+h/Pod7gfotOiYW9qjyP9j9ClTyJ3DMOgd+/eOHjwICQSCXg8bv/OUWeoUm5+z549Gxs2bEBSUhLNO6tGknOSYfFb8UkjsgOyoaepx0mmykDCSqjAEoUKDg5G+/btcefOHbi6upa6HXWG4tCpU6fQo0cPKrJqplBcWOJ6kUSk8CyVCRVZomitW7dGlSpVcOrUKa6jUKEtSUxMDB4+fIhevXpxHYUomLWBNTrZdQKP4YEBAx7DQy+HXjDSNuI6GiGkHAQCAXr06KEUhZZO10pw6tQpaGpqokuXLlxHIQrGY3g4OeAkFl5biGfvn6GJRRMEtA7gOhYh5Bt8HiUqJiYGdnZ2nOWge7QlXJPv2LEjNDU1ERgYyEk+QgghFZeZmQkzMzP89ttv+Omnn0rdhu7RKtiHDx9w9epVumxMCCEqztDQEB06dOD88rFCCu26detga2sLbW1tuLm54c6dO6Vuu2XLFrRp0wZVqlRBlSpV0KlTp2LbDx06FAzDFHl17dpVJlkDAwMhFovRs2dPmRyPEEIId7y9vXH16lWkp6dzlkHuhfbgwYOYNGkS5s2bh3v37sHR0RGenp5ITk4ucfvg4GAMGDAAV65cQUhICGxsbNClS5di8wt27doVCQkJ0pespkUKDAxE06ZNUb16dZkcjxAiPyzLYtmNZWi4viGab2mO89HnuY5ElEzPnj0hEolw6dIlzjLI/R6tm5sbXF1dsXbtWuDTiEs2NjYYP348ZsyY8dX9xWIxqlSpgrVr12Lw4MHApzPa9PR0nDhx4psylXZNnmVZ2NjYYMCAAVixYsU3HZsQojirbq/CxPMTgU/Tx/F5fNwZcQdNrZpyHY0okbp166JLly5Yt25dsfdU/h5tYWEhwsPD0alTp38a5PHQqVMnhISElOkYubm5EAqFMDExKbI+ODgYVatWhYODA8aMGYPU1NQK542Ojsa7d+/g4eFR4WMRQuTvVNQ/995YsGBZFhdeXuA0E1E+7du3x5UrVzhrX66F9v379xCLxbCwKDrSjoWFBRITE8t0jOnTp8Pa2rpIse7atSt27dqFoKAgLFu2DFevXkW3bt0gFotLPEZBQQEyMzOLvEoSHBwMPp+PNm3alOtzEkK4YapjKh1LGZ9GoDLRMfniPkT9eHh44OnTp0hKSuKkfaXudbx06VIcOHAAx48fh7a2tnS9r68vvL290bhxY/Tu3RtnzpzB3bt3S52pYcmSJTAyMpK+bGxsStzuypUrcHZ2ltvlA0KIbC3ssBDG2sbSZffq7hjkOIjTTET5tG/fHvh0MsUFuRZaMzMz8Pn8Yt8ikpKSYGlp+cV9f/vtNyxduhQXLlxAkyZNvrhtrVq1YGZmhujo6BLfDwgIQEZGhvT19u3bYtuwLIsrV65I/0AIIcrP3tQeT8Y9wYG+B3DS9ySuDL0CbYF2GfYk6sTKygoODg6cXT6Wa6HV1NSEs7MzgoKCpOskEgmCgoLg7u5e6n7Lly/HggULcO7cObi4uHy1nbi4OKSmpsLKyqrE97W0tGBoaFjk9V/Pnz9HYmIi3Z8lRMVU1asKn0Y+8HbwhiZfk+s4RElxeZ9W7peOJ02ahC1btmDnzp14+vQpxowZg5ycHPj7+wMABg8ejICAf4a4W7ZsGebMmYNt27bB1tYWiYmJSExMRHZ2NgAgOzsbU6dOxe3btxEbG4ugoCD06tULderUgaen5zfnvHLlCgQCAVq3bi2DT00IIUSZtG/fHlFRUYiPj1d423IvtD4+Pvjtt98wd+5cODk54cGDBzh37py0g9SbN2+QkJAg3X7Dhg0oLCzE999/DysrK+nrt99+AwDw+XxERkbC29sb9vb2GD58OJydnXH9+nVoaWl9c84rV67A1dUV+vr6MvjUhBBClMnnq5VcnNXSWMeGhmBZFtbW1hg6dCiWLFnCdTxCCCFy0LBhQ7Rp0wYbN26UrlP552hVRXx8PBITE9G8eXOuoxAlJBQLkZqbCjX8TkpIpeLq6oqwsDCFt0uFFkB4eDgAlKnjFVEvuyN2Q3+JPsxWmKHxhsZ4m1G8xzohRDW4uLggMjISBQUFCm2XCi2AsLAwmJub0/jGpIio1CgMPTkUheJCAMDz988x9MRQrmMRQr6Rs7MzhEIhHj16pNB2qdB+OqN1cXEBwzBcRyFK5GnKU0hYiXRZxIrwIOkBp5kIId/O0dERPB5PehVTUdS+0LIsi7CwMDg7O3MdhSgZBzMHMPjny5eAEaCReSNOM5Xm2NNjcNnsgqYbm2JL+Bau4xCilHR1ddGwYUOF36cVKLQ1JfTu3TskJyfT/VlSTD2zetjitQWjz46GSCKCXRU77PxuJ9exirkScwXfH/oe+DSw/qgzo2CgZQDfRr5cR1NJl2Mu42zUWVTRqYJxruNQRacK15GIDDk7Oyv8jFbtC+3nbzZUaElJhjcbjgGNByA9Px0Wehbg8/hl2Euxzr44Cz6PD5FEBADgMTycjjpNhfYbHHp8CL5HfMHn8cGyLHZF7EL4qHAYaBnItd1CcSGevX8GPQ091DapLde21J2Liwv27t2LgoKCCo29UB5qf+k4PDwcFhYWsLa25joKkTOWZaUdm8pDV0MX1gbWSllkAcBY27jIo0c8hgdjLeMv7kNKtvTGUrBgIZKIIGbFeJH2AmdfnJVrm8k5yWi2qRkcNzqizpo6GHl6JD1KJkefO0Q9fPhQYW2qfaG9d+8enJ2dqSNUJXc++jzMV5hDa6EWmm1qVqke0/mx+Y+wN7WXLlvoWWBmm5mcZlJVYknxqTZLWidL0y9Nx7P3z6TLf937C0eeHJF5Oxn5GTgbdRYXXl74pi+clYWjoyP4fL5CLx+r/aXjp0+f4vvvv+c6BpGjhKwE9D7YGwWij8/OPUx+CJ8jPrg1/BbX0WTCWNsYd0feRWB0IMQSMTrX7kxzsn6jn9x+wojTI8Bn+GAYBhZ6FuhWt5tc24xOi4aY/aeY8xk+YtJjZNpGfFY8Wm5tidcZr4FP0wkGDQ6CjoaOTNtRBTo6OqhVqxaePXtWhq1lQ60LbUFBAWJjY2Fvb1+GrYmqevb+GfJF+dJlkUSEewn3OM0ka3qaevi+AX1hrKjhzYajik4VnIk6A2NtY0xtOVXuX1paVm+JW29vSR8lE7NiNK8m21Hqfgn+Be8y30mXQ9+FYnP4ZkxoMUGm7agKBwcHREVFKaw9tS60r169AsuycHBw4DoKkaGcwhwcenwIeaI8dKvTDbbGtuAxPOkvMj7Dpw4npFR96vdBn/p9FNbe/PbzEZ8Vj4OPD0JLoIUVnVfAw1a203Wm5KYUOWvmMTyk5KbItA1VYm9vj5MnTyqsPbUutJ8niqcz2sojuzAb7lvd8Sj5ERgw0NXQxY1hN7C552b878z/IGbFMNM1w74++7iOSggAQFugjd19dmPndzvBgJFLfxHP2p448eyEdFksEaNTrU4yb0dVODg4ICYmBoWFirlXrdaF9sWLFzAyMkLVqlW5jkJkZE/kHjxOfgx8eqY0X5SPX4J/wQnfE+hdrzeSc5Jha2yrlvemiHLjMfLrm/o/5//hQ94HbLm3BZp8TcxrN0/mZ82qxN7eHhKJBC9fvkS1atXk3p5aF9ro6GjY29tTj+NKJLswGwzDSB+PELNiZBdmAwBMdU1hqmvKcUJCFI9hGAS0CUBAmwCuoyiFz7cLo6KiFFJo1frxns+FllQePer2gCZfs8jZAQ3cQAj5N0tLS+jr6yusQ5RaF9oXL15QR6hKpr55fQQPCUa3Ot3gUdMDW7y2YHjT4VzHIoQoEYZh4ODggOfPnyukPbW+dJyWlkZntJWQW3U3nBl4husYhBAlZm9vr7BCq9ZntABgZ2fHdQRCSAWtDl0Ni98sYLbcDHMuz/nqEIYHHh3AsJPDMP3idKTmpiosJ1EetWrVwuvXrxXSllqf0QJQyI1wQoj8nHh2AhPO/TPwwsLrC2FtYI0xrmNK3H516GpMODcBAp4ALMvi+LPjCpk4gCgXa2trJCYmQiKRlGHrilH7M1oLCwuuIxBCKuD66+vQ4GlIl3kMD9feXCt1+z9v/wl8GiHs88QBwbHBCslKlIeVlRWEQiHS0tLk3pZaF1pzc3MIBGp/Uk+ISrM2sC4y6hEDBtUMSr9SVdLjfPJ8hpUop88ztiUmJsq9LbX+22Vpacl1BEJIBY1rPg5tarSRLjcwb4BZbWaVun1A64/PkmrwNMBjeGhctTE62HVQSFYuXLt2DV5eXrC2tgbDMDhx4kQZ9qr8rKysAAAJCQlyb0utT+eo0BKi+rQF2rg0+BLC48MhZsVoZtUM2gLtUrcf0WwErA2scfHlRZjrmePH5j8q7UhhIpGowlfdcnJy4OjoiGHDhqFPH8WN4azsPv/+T0pKkntbal1oP3+jIYSoNgFPALfqbmXevnvd7uhet7tcM5VXbGws7OzscPDgQaxZswZ37tzB/v37K1wcu3Xrhm7d5DvVnyrS1NSEubm5Qs5o6dIxIYQogYiICADAihUrMHfuXDx+/BgdO3aUvr948WLo6+t/8fXmzRsOP4HqsbKyojNaeaNCSwhRFg8ePICenh4OHz4MW1vbYu+PHj0a/fv3/+IxPnfwIWVjbW1N92jljWbtIYQoi4iICHh7e5dYZAHAxMQEJibynYRe3VhaWuLx48dyb0etLx0bGhpyHYEQQoBPZ7QeHqVPXUeXjmXP0NAQWVlZcm9Hrc9o9fX1uY5ACCHIzMxEbGwsmjZtWuo2dOlY9gwMDJCdnS33dtS60BoY0JBrhBDuRUREgM/no3HjxqVu862XjrOzsxEdHS1djomJwYMHD2BiYoIaNWp8c+bKwMDAADk5OXJvR60vHdMZrWphWRZ/3v4TLbe2RNc9XXHn3R2uIxEiExEREXBwcIC2dunP/34LCStB0I0gNG3aVHq2PGnSJDRt2hRz586VaVuqyMDAQCGXjhn2a9NcVEKZmZkwMjLC27dvUb16da7jkDL6I+QPTLowCfg0ZJ4WXwuRYyJRx6QO19EIUTph8WHw2ueFxJxEmOiY4ITPCbSp2aYMe6qP3bt3Y/DgwQCAjIwMufXbUeszWj09Pa4jkHI48PiA9L8lrAT5onxceHmB00xllSfMw8JrCzH85HBsCtsECSv/GUOI+pKwEvTc1xPJuckAgPS8dHjt90KeMI/raEpFUVc11foeLZ/P5zoCKQcjLSPwGJ60SLFgYaCp/PfZxRIxuu3thutvroPH8LDtwTY8T32OlZ4ruY5GKqm0vDQk5fwzEIMEEmQUZCA+Kx61TWpzmk2ZKKqfjlqf0RLVsqjDoiJj2Lao1gL9GvbjNFNZPH3/FFdfX4WElUAkEQEA1t5ZS2e1RG5MdExgpmsmnZWIAQN9TX1YGdCws/+mqEKr1me0RLW4VnPFozGPcPHVRRhoGqBP/T7QEmhxHeurGBSflq2kqdoIkRUew8Mp31Pw2u+F1LxUGGgZ4Fj/Y9DV0OU6mlKhQktICeyq2GGU8yiuY5RLffP66FK7Cy69ugQ+w4dQIsRk98k0ByqRK3cbdyRMTkBSThKq6lWFJl+T60hKR0tLMV/UFfIvfd26dbC1tYW2tjbc3Nxw586XH8s4fPgw6tWrB21tbTRu3Bh///13kfdZlsXcuXNhZWUFHR0ddOrUCS9evJDzpyDk23w+u1jReQVGu4zGrt67sKjDIq5jETWgwddAdcPqVGRLoah+OnIvtAcPHsSkSZMwb9483Lt3D46OjvD09ERycnKJ29+6dQsDBgzA8OHDcf/+ffTu3Ru9e/fGo0ePpNssX74cq1evxsaNGxEaGgo9PT14enoiPz9f3h+HkG+iJdDCJPdJWN1tNQY5DqJLx4QoAR5PMVeV5P4crZubG1xdXbF27VoAgEQigY2NDcaPH48ZM2YU297Hxwc5OTk4c+aMdF2LFi3g5OSEjRs3gmVZWFtbY/LkyZgyZQrw6fknCwsL7NixA76+vl/N9Pk5Wnk+N0WIMmNZFrm5uVzHIIRT7969g4ODAyDn52jleo+2sLAQ4eHhCAgIkK7j8Xjo1KkTQkJCStwnJCQEkyZNKrLO09MTJ06cAD4NH5aYmIhOnTpJ3zcyMoKbmxtCQkJKLLQFBQUoKCiQLmdmZsrk81V2LMvi2NNjiEyKRH3z+vBp6ENnYpVEbm4ujYxGiILItdC+f/8eYrEYFhYWRdZbWFjg2bNnJe6TmJhY4vaJiYnS9z+vK22b/1qyZAnmz59foc+ijmZcmoHlt5ZDwBNAJBHhxpsbWNt9LdexCCFEpahFr+OAgIAiZ8mZmZmwsbHhNJOyyynMwfJbywFA+uznurvr8IvHLzDTNeM4HakoXV1dhcxaQpTT8/fP0W1vNyTnfOwrM8Z1DJZ3Wq52V6zi4uJQr149ubcj10JrZmYGPp+PpKSkIuuTkpJgaWlZ4j6WlpZf3P7z/yclJcHKyqrINk5OTiUeU0tLS2HduCuLQnFhudYT2ckT5mHR9UV4kvIEDc0bYlbbWUUG6pAFhmFoCFI1NuXIFKSKUoFPnZE3RGxAf6f+8LAtfT7cykjWkziURq5drjQ1NeHs7IygoCDpOolEgqCgILi7u5e4j7u7e5HtAeDixYvS7e3s7GBpaVlkm8zMTISGhpZ6TFJ+VXSqwMveCzyGBwYM+AwfnWp1gpU+jSwjTyzLos/BPlhyYwlOPDuBxTcW4/tD36O8fRbFErHcMhLV9zr9NcRs0b8jbzPecpaHKxKJYkZnk/ul40mTJmHIkCFwcXFB8+bN8eeffyInJwf+/v4AgMGDB6NatWpYsmQJAGDChAlo164dfv/9d/To0QMHDhxAWFgYNm/eDHz6Jj5x4kQsXLgQdevWhZ2dHebMmQNra2v07t1b3h9HrRzqdwiLry/Gw+SHqG9WH7Pbzla7S0uKlpyTjHMvz0mXWZbF2RdnkZqXWqZL9qFxoeh/pD/eZLxB46qNccznGM1uRIrxsPXA64iPxZYBAz6PD9dqrlzHUjiRSKSQduReaH18fJCSkoK5c+ciMTERTk5OOHfunLQz05s3b4o8y9SyZUvs27cPs2fPxsyZM1G3bl2cOHECjRo1km4zbdo05OTkYNSoUUhPT0fr1q1x7tw5hV0GUBfaAm382v5XrmOoldIGFijLgAPZhdnotrcbMgoyAABPUp6g576eePZjyR0Pifpa1W0VsguzcfbFWVTRqYL13dejnpn871UqG0VM+g51n4+WnqMlymh84HisvbMWGjwNCCVC/Nzi5zLN9PMw6SGabGxSbH3OzBwa45aQEly/fh1t27YFVPk5WmWnht8xiApY3XU1Wtm0wpOUJ2hUtRH6NSjbDEXWBtbS4oxPQz+a6JhAR6Aj58RE1UhYCf669xfuvLsDO2M7THKfBB0N9ft7kpWVpZB21LrQFhZSD1qifBiGgW+jr49w9l+muqbY9d0uDD4+GEKJEPqa+jjS7wjdVyfFTL0wFStvr4SAJ4CEleBSzCVcGnQJfJ56zdGtqEfc1LrQZmdnw9zcnOsYhMiMbyNfdKrVCe8y38Guih0MtejWCClKwkqw5s4a4F/PyAfHBuPp+6doVLXRV/auXOiMVgEU9UMmRJHMdM1oUBFSbuo4bWNWVhZ0dHSQl5cn13bU7yf7LzQyDiFE3fAYHqa0/DghiwZPAzyGB8/anmrZ6zgrK0shY36r9Rmtorp2E0KIMlnUYRHqmdXD3Xd3YWtsix+b/6iWZ7TZ2dkwMDBASkqKXNtR60JLl44JIeqIYRgMdhyMwY6DuY7CKUWd0arfV5h/SU1N5ToCUSFiiRiLri2Cy2YXdNndBfcS7nEdiRBSAampqTA2NpZ7O2p9RvvfyQsI+ZKF1xbil6u/AAD4DB/tdrTDozGPUNO4JtfRCCHfID4+HtbW1nJvR63PaEubv5aQkhx6ckj632JWjOzCbFyJvcJpJlJ55IvycS76HM5GnUV2IXXUVISEhIQis8DJi1qf0VKhJeVhrGUMHsODhP1nxg8jLSNOM5HKIasgC222t0FEUgQAoHaV2ggZHgJzPXrOX15YlkV8fHypU7bKEp3RElJGK7qsgBb/n3mNO9p1hJeDF6eZSOWwOnQ1HiY/lC7HpsdiyY0lnGaq7DIzM5GXl6eQQqvWZ7QJCQlcRyAqpKVNSzwc8xDBscEw1jZGr3q9IOCp9T8hIiMpuSngM/wiV0tScuX7yIm6i4+PBwAqtPJGZ7SkvGqb1EZtk9pcxyByFPMhBo9THqNWlVpoYN5AIW12qd0Fq0JXSZfFrBietT0V0ra6UmShVetLx7m5ufQsLSFE6siTI7Bfaw+v/V5otL4RVt1eVYa9Kq573e7Y3ms77E3tUbtKbfzh+Qf8GvsppG119fmKJhVaBfj8rYaoDpZlsezGMjRc3xDNtzTH+ejzXEcilYCElcD/pL90oH0WLCZdmITknGSFtD/UaSie//gc0T9FY2KLiTTrkpzFx8fDyMgIurryn6tZ7QttXFwc1xFIOa0OXY0ZQTPwJOUJwuLD0HN/T9xPuM91LKLi8kX5xR6rkbASvM99z1kmedj/cD98jvhg5KmRePXhFddxOPPu3TuFPEMLdS+0PB4PL1684DoGKaeTz09K/5sFC5ZlcfHVRU4zEdWnq6ELt2puEDAfu67wGT5sDG1Qq0otrqPJzF/3/sLAYwNx5MkR7HiwA823NEdClnp2Co2KikLdunUV0pZaF1pbW1tERUVxHYOUk5muGfjMPxNUS1gJTHRMOM1EKocTvifQpU4XVNWrCvfq7rg0+BK0Bdpcx5KZTeGbgE//ZkSsCKl5qfj7xd9cx+JEVFQU7O3tFdKWWvc6rlu3LhVaFbSg/QJcjrmM1LyPY1W7V3fHD01+4DoWqQQs9S1xduBZrmPIjSZPEwwYsGCl6zT4Gpxm4kJBQQFiY2Ph4OCgkPbUutDWqVMH589TRxpV42DmgMdjHyM4Nhg6GjroWqcrNPmaXMciROnNbjsbXvu9wDAMJKwE9qb26FO/D9exFO7ly5eQSCR0RqsIdevWxcaNG1FYWAhNTfpFrUos9C3g08iH6xiEqJRudbvh1vBbOBt1FoZahhjpPBL6mvKfJk7ZPH/+HADojFYR6tSpA7FYjFevXqFevXpcxyGEELlrXq05mldrLtc2CsWFOP70OLIKs9DRriPsqtjJtb3yioqKgqGhIapWraqQsRTUvtDi0w+dCi0hhFRcgagAHXZ2wK24W8Cn3txXh16Fi7UL19Gknj9/Dnt7e4U9q6zWvY4tLS2hr68vvYxACCGkYg4/OSwtsvj0fPLMoJmcZvqvqKgohV02hroXWoZhYG9vT4WWEEJkJLMgEwz+OVOUsBKk56dzmunfWJaVntEqiloXWgBo3LgxIiIiuI5BCCGVQpfaXaAt0AaP+ae89G/Yn9NM/xYfH4/379+jcePGCmtT7Quti4sLIiMjUVhYyHUUQghReXVM6uDKkCvoaNcRzas1x4rOKzDJfRLXsaTCwsKAT7/7FUWtO0MBgLOzMwoLC/Ho0SM0a9aM6ziEEKLy3Kq74cKgC1zHKFF4eDjMzc1RvXp1hbWp9me0jo6O4PP50m85hBBCKq/w8HC4uLgodHYktS+0urq6aNCgAcLDw7mOQgghRI5YlkVYWBicnZ0V2q7aF1p8ulZPZ7SEEFK5vXv3DsnJyQq9PwsqtB85Ozvj4cOHKCgo4DoKIYQQOfl8QkVntBxwcXGBUCjEw4cPuY5CCCFETsLDw2FhYYFq1aoptF0qtACaNGlCHaIIIaSS+3x/VpEdoUCF9iMdHR04Ojri1q1bZdiaEEKIqhGLxbh9+zbc3NwU3jYV2k88PDxw5coVsCxbhq0JIYSokoiICKSnp8PDw0Phbcu10KalpcHPzw+GhoYwNjbG8OHDkZ2d/cXtx48fDwcHB+jo6KBGjRr46aefkJGRUWQ7hmGKvQ4cOFChrO3bt0dcXBxevnxZoeMQQghRPleuXIG2tjYnZ7RyHRnKz88PCQkJuHjxIoRCIfz9/TFq1Cjs27evxO3j4+MRHx+P3377DQ0aNMDr168xevRoxMfH48iRI0W23b59O7p27SpdNjY2rlDWNm3agMfjITg4WDp9HiGElIeEleDVh1dgWRa1TWoXGe+XcOvKlSto1aoVtLS0FN84KydPnjxhAbB3796VrgsMDGQZhmHfvXtX5uMcOnSI1dTUZIVCoXQdAPb48ePfnC0jI4MFwGZkZBRZ7+rqyg4cOPCbj0uIOisUFbKxH2LZnMIcrqNwIl+Yz/bY24PFL2DxC9guu7uwecI8rmMRlmWFQiFrYGDALly4sNh7pdUDWZLb162QkBAYGxsXeTC4U6dO4PF4CA0NLfNxMjIyYGhoCIGg6Mn3uHHjYGZmhubNm2Pbtm1fvLdaUFCAzMzMIq+StG/fnu7TEvINIhIjUOPPGrBdZQuTZSY48uRIGfaqXFaHrkZgdKB0+dKrS/j91u+cZiIf3bt3D1lZWWjfvj0n7cut0CYmJqJq1apF1gkEApiYmCAxMbFMx3j//j0WLFiAUaNGFVn/66+/4tChQ7h48SL69u2LsWPHYs2aNaUeZ8mSJTAyMpK+bGxsStzOw8MDCQkJiIqKKlM+QshH3x/+Hik5KQCAAnEBBh4diKTsJK5jKdTLDy/BZ/jSZR7Dw8sP1OdDGQQHB0NXV1fhI0J9Vu5CO2PGjBI7I/379ezZswoHy8zMRI8ePdCgQQP88ssvRd6bM2cOWrVqhaZNm2L69OmYNm0aVqxYUeqxAgICkJGRIX29ffu2xO1at24NPp+P4ODgCucnRF1IWAlepr2EmBVL1wklQsSmx3KaS9FcrF0glAilyyKJCK7WrpxmIh9duXIFrVu3hqamJiftl7vQTp48GU+fPv3iq1atWrC0tERycnKRfUUiEdLS0mBpafnFNrKystC1a1cYGBjg+PHj0NDQ+OL2bm5uiIuLK3UIRS0tLRgaGhZ5lcTAwACurq64cuXKV38OhJCPeAwP9c3rS8/mGDDQEeigVpVaXEdTqOFNh2Nqy6nQ4GlAg6eBiW4T8T+X/3EdS+0JhUJcv36ds8vG+JZex+bm5jA3N//qdu7u7khPT0d4eLh0XMnLly9DIpF8sXt1ZmYmPD09oaWlhVOnTkFbW/urbT148ABVqlSRSW+yjh07YuPGjRCJRMXuCxNCSnas/zF039cdrz68gr6mPg5+fxDmel//PVGZMAyD5Z2XY2mnpWBZFnwevwx7EXm7desWcnJy0LFjR84yyK2S1K9fH127dsXIkSOxceNGCIVC/Pjjj/D19YW1tTXwaSaFjh07YteuXWjevDkyMzPRpUsX5ObmYs+ePUU6Lpmbm4PP5+P06dNISkpCixYtoK2tjYsXL2Lx4sWYMmWKTHJ7eXlh0aJFCAkJQZs2bWRyTEIqOwczB0SPj0ZGQQYMtQzV+rEWHsMDFDvCH/mCU6dOwcrKSuETCfybXE/Z9u7dix9//BEdO3YEj8dD3759sXr1aun7QqEQz58/R25uLvCpZ9jnHsn/fZY1JiYGtra20NDQwLp16/Dzzz+DZVnUqVMHK1euxMiRI2WS2dXVFZaWljh16hQVWkLKgWEYGGtX7Hl2QmSJZVmcPHkSXl5e4PG4+/LHsGr4LEtmZiaMjIykjw7916hRoxAcHIznz58rfPBpQgghsvHkyRM0bNgQZ86cQY8ePUrc5mv1QBbU9/rOF3h7e+PFixd4/vw511EIIYR8o1OnTkFXVxcdOnTgNAcV2hJ07NgRurq6OHXqFNdRiBLJKsjCkBNDUPOPmmizrQ0eJT/iOhIh5AtOnToFT09P6OjocJqDCm0JdHR00KVLF5w8eZLrKESJDD05FHsj9+JN5huExIWg/c72+JD3getYhJASJCUl4fbt2/D29uY6ChXa0nh7eyMkJKTYs8BEfQW+CJQOyiBmxXif+x4RSRFcxyKElODMmTMAUOq9WUWiQluKz384Z8+e5ToKURJmumZg/vPchrmuej0rSoiqOHXqFFq2bFmmcR/kjQptKapWrYrWrVvj0KFDXEchSmKz12Zo8v8Zwm2K+xQ0rNqQ00yEkOIyMjJw/vx59O7dm+sogLyfo1V1fn5+GDduHJKSkmBhYcF1HMKxrnW64um4p7iXcA/VDavDrbriJ5AmhHzd0aNHUVhYiAEDBnAdBaAz2i/r168feDweDhw4wHUUoiTsqtihb4O+xYrsiWcnYPmbJbQXauO7g98hs6DkqRjlIbswG4EvAnHh5QUUiEoe75sQdbJ792506NAB1apV4zoKQIX2y0xMTNCjRw/s2bOH6yhEiT1NeYp+h/shOScZBeICnH5+GuMDxyuk7eScZDhtdEL3fd3huccTLbe1RHZhtkLaJkQZvXnzBsHBwfjhhx+4jiJFhfYrfvjhB4SFhclk6j9SOYUnhEMkEYHFx0HWxKwY119fV0jbi64twuuM19LlB4kPsO7OOoW0TYgy2r9/P7S1tdGnTx+uo0hRof2KHj16wNjYmM5qSalqGtUsssxn+LAztlNI2ym5KZCwEukyj+EhOYceSSPqiWVZ7N69G71795bbcIrfggrtV2hra6Nfv37Ys2cPJBJJGfYg6qZNzTaY2nKqdNlS3xIbem5QSNtdancpUmhFEhG61O6ikLYJUTYRERF4/PixUl02BvU6/roPeR9g2tEUr8Ne4/SV0+jVsRfXkYgSWt55OUY5j0JaXhoamDeAvqa+Qtod4jgE73PfY/3d9eDz+JjVZhY863gqpG1ClM2ePXtgZmaGLl2U68smzd7zhcsLKTkpcN3i+vEeGAtoS7TxfPJz1DCqodC8spQnzMP/zvwPx58dh4GmAVZ3W43vG3zPdSxCCKkQsVgMGxsb9O3bF2vWrCnzfjR7D8fW3lmLuMy4jwsMkI98LLyykOtYFTLt0jTsfbgX2YXZSMxOhM8RH0Qk0jCChBDVdvbsWSQkJGDIkCFcRymGLh1/QVZh1sf5aD+f8zNA5LNIjlNVTHBssPSeHgsWLMvidtxtOFo6ch2NEELKhWVZrAxZiQOPD+DVk1eo36U+XFxcuI5VDJ3RfsF39b6DWCL+Z3xbHhAfFA9Vvtpua2wLPsMvss7GyIazPITIg0giQlxmHPJF+VxHIXL0x+0/MOXiFITFhyHNMA3RraLx/L3yzSNOhfYL2tRsg9MDTsPD1gNtarTBLIdZeHvpLa5du8Z1tG+2qusqWBlYSZdHNhuJbnW6cZqJEFmKSIyAzR82sPnDBqbLTXHqOc0rXVkdeXLknwUeIGJFOP/yPJeRSkSdocpx85tlWdSvXx9NmjRR6ckGsguzEZkUCSMtIxoUn1Q6tVfXxuv019IpDbX4WoibFAczXTOuoxEZ67a3Gy6+vCj9swaAHb12YIhT2e/TUmcoJcMwDMaOHYvjx48jPj6e6zjfTF9THy1tWlKRJZWOSCLCqw+vivziLRAXIDY9ltNcRD4Wd1gMwb+6GrlXd4dvI19OM5WECm05DRkyBFpaWtiyZQvXUQgh/yHgCWBvai/th8CAga5AV2EjdRHFcrJ0Qu1ztdEkpgkOfn8QwUODoSXQ4jpWMVRoy8nIyAg//PADNm3aBKFQyHUcQsh/HOt/DNUMP87aoq+pj6M+R2Gqa8p1LCIHt2/fxpOQJ1jqsxT9G/YvMl+0MqF7tN9wTT4yMhKOjo44cOAAfHx85JJRFbAsi+tvriMtLw1u1dyKdLIihEssyyItLw3G2sbg8/hl2IOoIj8/P4SGhiIqKgo83redN9I9WiXVpEkTdOzYEcuWLVPpR30qgmVZDD4xGO12tMN3B79DvXX1EBYfxnUsQoBP/SlMdU2pyFZir169wsGDBzFhwoRvLrKKotzplNjMmTNx//59nD+vfF3JFeHSq0vYE/nPjEY5hTkY/7di5mAlhJAVK1bA1NQUI0aM4DrKV1Gh/Ubt27eHm5sbFi9ezHUUTqTkphRZFrNiJOUkcZaHEKI+EhISsG3bNkycOBE6Ojpcx/kqKrTfiGEYBAQE4Pr167hx4wbXcRSupU1L6Ah0wGM+/hXiMTz0tO/JdSy1cefdHUwInIApF6bgZdpLruMQolArV66EtrY2xo4dy3WUMqHOUBW4+S2RSNCkSRPUrFkTZ8+elWlGVXDjzQ1MODcB73Pew7ueN37r/JtSdq2vbG6+uQmPnR7SZV0NXdz/333UqlKL01yEKEJaWhpq1KiBCRMmYNGiRRU+HnWGUnI8Hg8BAQH4+++/8eDBA67jKFzrGq0RPiocr39+jTXd1lCRVZD1YevBsixEEhFEEhFyCnOwO2I317EIUYg1a9ZALBZjwoQJXEcpMyq0FeTj4wM7OzssWbKE6yhETZR0EYqF2l2YUltCsRCx6bHILMjkOorCZWdnY/Xq1Rg5ciSqVq3KdZwyo0JbQQKBANOnT8fhw4cRFRXFdRyiBsa6jgXDMBDwBBDwBNDT1MNgx8FcxyIKEJ0WDYe1DrBbZQeTZSbYFLaJ60gKtXnzZmRmZmLKlClcRykXukcrg2vy+fn5qF27Njw8PLB3716ZZCTkS0LjQrEncg80+BoY6zoWdUzqcB2JKEDrba0R+i4UIokI+DTE5OOxj1HfvD7w6WpHal4qBDwBjLWNOU4rW9nZ2ahduzZ69OiBbdu2yey4dI9WRWhra+OXX37Bvn37cP/+fa7jEDXgVt0Na7qvwUrPlVRk1cjTlKfSIotPtwyi06IBAAWiAvQ+2BvmK8xRZVkV/O/M/yBhJRVuc9/Dfeh/uD9GnBqBVx9eVfh432rlypXIyMjAL7/8wlmGb0WFVkb8/f3h4OCAgIAArqMQ8lUSVoJz0eewN3IvzWyjQppaNYWA+We2GgFPID2bXXpjKc5EnZG+tzl8M3Y+2Fmh9raEb4HfMT8cfXIUOyN2ovmW5kjISqjQMb9FcnIyVqxYgXHjxqFGjRoKb7+iqNDKiEAgwOLFi3H+/HlcvnyZ6ziElErCSvD9oe/RbW83/HD8BzRY1wA339zkOhYpgx29d0int9QR6GD3d7ulVzQepzwusq0GT6PYuvLaFP7xHrAEEogkIqTmpeLvF39X6JjfYtGiReDxeJg5c6bC25YFKrQy9N1338HNzQ3Tp09X2zGQifK7+PIijj87Ll0uEBdg4vmJnGYiZVPdsDoejH6AjBkZyArIKjL3auOqjYtsK5QI0ahqowq1p8XXAgOmyDpFz5Dz6tUrbNiwAdOnT4epqWrOwkSFVoYYhsGyZcsQFhaGI0eOcB2HkBKl5aUVWZawEqTmpnKWh5SfoZZhsQkTprWaht71ekuXx7mOwxDHId90fKFYiCkXpiD6QzRYsOAxPPAYHuqZ1cN39b+rcP7ymDt3LszMzFTqudn/kmuhTUtLg5+fHwwNDWFsbIzhw4cjOzv7i/t4eHiAYZgir9GjRxfZ5s2bN+jRowd0dXVRtWpVTJ06FSKRqNRjKlK7du3QrVs3zJo1i+arJUqpTc020NfULzI5ep/6fbiORSpIS6CFo/2PIn16OrICsrC2+1owDFOGPYubc2UOVoasRHJOMphP/5vWchpCR4RCX1Nf5tlL8+DBA+zduxfz5s2Dnp6ewtqVNbkWWj8/Pzx+/BgXL17EmTNncO3aNYwaNeqr+40cORIJCQnS1/Lly6XvicVi9OjRA4WFhbh16xZ27tyJHTt2YO7cufL8KOWyZMkSREdHY+vWrVxHIaSY6obVcWXIFbSq0Qr1zepjWqtpWNKRBlypLIy0jSpcDM9EnZEOgsKChZgVo7ZJbRhqyefxl9IEBATA3t4ew4YNU2i7MsfKyZMnT1gA7N27d6XrAgMDWYZh2Hfv3pW6X7t27dgJEyaU+v7ff//N8ng8NjExUbpuw4YNrKGhIVtQUFCmbBkZGSwANiMjo8yfp7wGDRrEWlhYsOnp6XJrgxBC5KHDzg4sfz6fxS+Qvk48PaHQDOfPn2cBsIcPH5ZrO4qoB3I7ow0JCYGxsTFcXFyk6zp16gQej4fQ0NAv7rt3716YmZmhUaNGCAgIQG5ubpHjNm7cGBYWFtJ1np6eyMzMxOPHJfewKygoQGZmZpGXvC1evBjZ2dmYN2+e3NsihBBZ+sPzjyJnr9/V+w5eDl4Ka7+goADjx49Hu3bt0LdvX4W1Ky+CMmzzTRITE4uNRSkQCGBiYoLExMRS9xs4cCBq1qwJa2trREZGYvr06Xj+/DmOHTsmPe6/iywA6XJpx12yZAnmz58vg09VdtWrV8e8efMwY8YM+Pv7w9HRUaHtE0Jk60PeBwh4AhhoGXAdRe6aWDTB03FPERIXAiMtI7SzbSedElMRVq5ciZcvX+LYsWPffJ9ZmZT7JzdjxoxinZX++3r27Nk3Bxo1ahQ8PT3RuHFj+Pn5YdeuXTh+/Dhevvz2OTcDAgKQkZEhfb19+/abj1UeEyZMgIODA8aNGweJpOIjtBBCFC9flA/v/d4wWW4Cw6WGmHBuglo8vmehb4He9XqjvV17hRbZ169fY8GCBZg4cSIaNmyosHblqdxntJMnT8bQoUO/uE2tWrVgaWmJ5OTkIutFIhHS0tJgaWlZ5vbc3NwAANHR0ahduzYsLS1x586dItskJSUBQKnH1dLSgpaW4qdw09TUxLp169ChQwfs3r0bQ4Z8W1d7Qgh3Fl5biLMv/plvenXoajS3bg6/Jn6c5qqsfv75Z1SpUqVS3XYrd6E1NzeHubn5V7dzd3dHeno6wsPD4ezsDAC4fPkyJBKJtHiWxed5Xq2srKTHXbRoEZKTk6WXpi9evAhDQ0M0aNCgvB9H7tq3b48BAwZg6tSp6NWrF4yNK9dA34RUdo+SHxU5g9XgaeBR8iNOM1VWgYGBOH78OA4cOAADg8pziV5u1wPq16+Prl27YuTIkbhz5w5u3ryJH3/8Eb6+vrC2tgYAvHv3DvXq1ZOeob58+RILFixAeHg4YmNjcerUKQwePBht27ZFkyZNAABdunRBgwYNMGjQIEREROD8+fOYPXs2xo0bx8lZa1n89ttvyMvLw5w5c7iOQggpp0ZVGxW5TyiUCKXDIBLZyc/Px/jx49GhQwf079+f6ziyJbf+zCzLpqamsgMGDGD19fVZQ0ND1t/fn83KypK+HxMTwwJgr1y5wrIsy75584Zt27Yta2JiwmppabF16tRhp06dWqzbdWxsLNutWzdWR0eHNTMzYydPnswKhcIy51JEd+7/+v3331kej8feu3dPYW0SwqWt97ay5svNWYPFBuzYs2NZobjs/0aVSW5hLttjbw/pYy7j/x7PSiQSrmNVOgsWLGAFAgH75MkThbariHpA89HKaf7B/xIKhWjatCl0dXVx69YtCARy6/BNCOeuxl6Fx04P6TIDBr94/IK57ZRnYJnyYFkWaXlp0OBrKHzQBnXw4sULNGnSBD/99BOWLVum0LZpPtpKRENDA3/99RfCw8Px+++/cx2HELm6+famdIhHfBpd6Orrq5xmqgiGYWCqa0pFVg7EYjH8/f1RrVo1pRrhT5ao0CpQixYtMGXKFMydO7fUwTUIqQxsDG0gZsXSZT7DR02jmpxmIspp1apVuHXrFrZv367S4xl/CV06VtCl48/y8/PRrFkz6OnpISQkhC4hk0pJLBHD54gPjj49CgCwN7XH1aFXYalf9kf7SOX37NkzNG3aFGPGjMHKlSs5yaCIekCFVsGFFgDu3LkDd3d3LFiwQGUnMibka1iWRWRSJPJEeXC0cISOhg7XkYgSEYvFaNWqFdLS0vDgwQPo6upykkMR9YBOpzjQvHlzTJ8+Hb/88gu8vLzQuHHjMuxFiGphGAaOljT0KCnZ77//jjt37uDGjRucFVlFoTNaDs5o8WnQbGdnZ2hqaiI0NBQaGhqc5CCEEEV78uQJmjZtip9++gkrVqzgNAv1Oq7EtLS0sHPnTkRGRmLJEpoLlBCiHkQiEYYMGYJatWrh119/5TqOQlCh5ZCzszNmzpyJBQsW4Pbt21zHIYQQufv1119x79497NixAzo66nHfngotx+bMmQNXV1f4+vriw4cPXMchhBC5CQoKwsKFCzF//vwSx7x/9v4ZbsfdRnZhNif55IUKLcc0NDSwf/9+ZGZmYtiwYWox/RYhRP0kJibCz88PHTt2REBAQJH3WJbF+MDxqL+uPty3uqP+uvp4mfbtU6MqGyq0SqBmzZrYvn07Tpw4gTVr1nAdhxBCZEosFuOHH34AAOzZswd8Pr/I+5deXcLaO2ulywlZCfjx7x8VnlNeqNAqiV69emHixImYMmUKwsLCirxXICrAiFMjYLjEENVWVsPBRwc5y0kIIeW1ZMkSXL58GXv37oWFhUWx999kvCmyLGbFiM2IVWBC+aJCq0SWLVsGR0dH+Pj4ICMjQ7p+zpU52P5gO7IKsxCfFY8BRwcgLD7si8cihBBlcPXqVcybNw9z5sxBx44dS9ymebXm4DN8MPg4HSGP4aFdzXYKTio/VGiViKamJg4ePIj3799jxIgR0vu1V2KuQMJKimwb8jaEo5SEEFI2KSkpGDhwINq0afPFCQMaWzTGwe8PwkLfAjoCHfRr0A+/d6k8k69QoVUytWrVwrZt23DkyBGsX78eAFDDqEaxmVCqG1bnMCUhhHyZWCzG4MGDIRQKsW/fvmL3Zf+rb4O+SJicgNxZuTjw/QHoaVaeCQao0Cqhvn374qeffsLEiRNx9epVrPRcWaSwDmoyCL3q9eI0IyGEfMmsWbNw4cIF7NmzB9bW1lzH4RQNwcjREIxfIxQK0bVrV0RERODu3bswr2aOiMQIGGgZoHHVxmAYhuuIhBBSoj179mDQoEH4/fffMWnSJK7jfBHN3iMnqlBoASAtLQ3NmzeHjo4Obt26BQMDA64jEaJUWJbFrbe3EJ8VDxdrF9hVseM6ktoLDQ1Fu3btMGDAAGzbtk3pTwporGM1Z2JiglOnTuH169f44YcfIJFIyrAXqUze577Hvof7cOTJEeQJ87iOo3QmnJuA1ttbo/+R/qi/rj4uvrzIdSS1FhcXh969e8PZ2RkbN25U+iKrKFRolVyDBg1w4MABnD59GrNnz+Y6DlGg2PRYNFzfEH7H/NDvcD+4/eWGrIIsrmMpjfsJ97Hmzj8DvAglQvzvzP84zaTOcnNz0bt3b2hoaODYsWPQ0tLiOpLSoEKrArp3745ly5ZhyZIl2LdvH9dxiIIsuLoAaXlp0uXHKY+xOXwzp5mUSWpeapFlCSvB+9z3nOUpj/8+rqfqWJbF8OHD8fTpU5w8ebLEQSnUGRVaFTFlyhQMGjQIw4cPx927d7mOQxQgvSAdYolYusxjePiQTxNPfNbUsilMdEykj77xGT561O3BdawvSslJQfud7aGxQAMWKyzw94u/uY4kE4sXL8aBAwewa9cuNG3alOs4SocKrYpgGAabN2+Gk5MTvLy88PJl5Rlwm5TM294bLD72VWTAQMJKlL6QKJKprimChwSjpU1L2Bnbwd/JH395/8V1rC8acmIIrr++DgkrQUpuCvoc7IPX6a+5jlUhe/bswezZs/HLL7+gb9++XMdRStTrWEa9zCSsBGvvrMXlmMuw1LfELx6/wFLfUibH/reUlBS0bt0aIpEIN2/ehKWl7NsgyoFlWWwM24gt97ZAk6+J2W1no6d9T65jkQowWWZS7KrE6QGnVfbPNTAwEN7e3hg8eDD++usvlez8RI/3yIk8frCzgmZh8Y3FYMCAx/BQw6gGIkZ/fO5V1mJjY9GqVStUrVoVwcHBMDIyknkbhBDZc97sjIjECIjZf24JPBzzEI2qNuI017e4ffs2OnbsiE6dOuHo0aMQCARcR/om9HiPCtlybwvwaXhEMStGTHoMQuLkMx6xra0tzp07h9jYWPTq1Qv5+flyaYcQIls7e++Ema4Z8Ol2wIrOK1SyyD59+hQ9evRAs2bNcODAAZUtsopCPx0Z0RIU78quxZdf9/bGjRvj9OnT6Ny5M/z8/HDo0KGvjiVKCOFWo6qN8PKnl3iR9gLmuuaoZliN60jl9vbtW3Tp0gXVqlXD6dOnoaOjw3UkpUdntDKysP1CAICAJwADBh1sO6BVjVZybbN169Y4dOgQTp48ibFjx0IN7wIAADLyM3Dq+Sn8/eJvFIgKuI5DyBfpaerBydJJJYtsamoqPD09IRAIcO7cORgbG3MdSSXQGa2MDHEaAltjW1x9fRWW+pYY6jQUAp78f7xeXl7YsmULhg0bBgsLC/z6669yb1OZxGfFw32ru3Ti6GZWzXBt6LVKNfMHIcogJycHPXv2REpKCm7evKn2EwWUBxVaGWpn2w7tbBU/WbG/vz9SUlIwffp06OvrY9q0aQrPwJUFVxcgPjNeuvwg8QE2hG3AlJZTOM1FSGWSl5eHXr164dGjR7hy5Qrs7e25jqRSqNBWElOnTkV2djamT58OHo+HKVPUo9C8z3tfZJQdHsNTmdGBCFEFn4tsSEgIAgMD4eLiwnUklUP3aL/Bn7f/hMkyExguMcTk85OVYjg1hmEwf/58zJo1C1OnTsUff/zBdSSF6FanGyT45+cvlojRpXYXTjMRUlnk5+ejd+/euHHjBs6ePYu2bdtyHUkl0RltOZ1+fho/n/9Zurzy9sdJ2X92//mL+ykCwzBYsGABxGKxdA7In3/mPpc8+Tv5Iz0/HRvDNkoHdehg14HrWITITb4oH1djr0LCStC2Zlu59UfIy8tDnz59cP36dZw5cwYeHh5yaUcdUKEtp5tvb0KDpwGhRAh8ehbuxtsbSlFo8anYLl68GAAwadIkCIXCSn3PlmEYTHKfhEnuyj25NCGykFWQhTbb2yAiKQIAYG9qj1vDbsFU11Sm7eTm5qJXr164efMmTp8+jQ4d6MtrRVChLScbQxuIJCLpMo/hoYZhDU4z/dfnYquhoYHp06ejsLCQptgjpBJYFboKj5IfSZdfpr3E0htLsaLLCpm1kZ2dDS8vL9y9exeBgYFo107xHTwrGyq05TTSeSTOvjiLwOhA4NMD6HPbzeU6VjEMw+DXX3+FhoYG5syZg8LCQsyfP18lxyIlhHyUnJP88d/wvx6ZT85NltnxMzMz0aNHDzx48ADnzp1D69atZXZsdUaFtpw0+Zo4M/AMIpMiIRQL0cSiSYmjQimLOXPmQFNTEzNmzEBqaipWr15NI0gRoqK61O5SZLJ7MStGl1qy6fyXkJCAbt26ITY2FhcvXkSLFi1kclwi517HaWlp8PPzg6GhIYyNjTF8+HBkZ2eXun1sbCwYhinxdfjwYel2Jb1/4MABeX6UIngMD06WTnCt5qrURfaz6dOnY8uWLdi4cSP69+9PYyMToqJ62vfEFq8tqGNSB7Wq1MIfnn/Ar4lfhY8bFRWFli1bIiUlBdevX6ciK2Nynb2nW7duSEhIwKZNmyAUCuHv7w9XV1fs27evxO3FYjFSUlKKrNu8eTNWrFiBhIQE6OvrfwzNMNi+fTu6du0q3c7Y2Bja2tplyqWI2RqU0alTp+Dj44PmzZvj5MmTNHwaIQR37txBjx49YGZmhvPnz6NGDeXqcyJvKj1N3tOnT9GgQQPcvXtX+oDzuXPn0L17d8TFxZV5+K6mTZuiWbNm2Lp16z+hGQbHjx9H7969vymbuhZaALh16xa8vLxgbW2Nc+fOoVo11RtvVd08THqInRE7wYDB8GbDUc+sHteRSCURGBiI77//Ho6Ojjh9+jRMTWXbe1kVqPQ0eSEhITA2Ni4yikinTp3A4/EQGhpapmOEh4fjwYMHGD58eLH3xo0bBzMzMzRv3hzbtm374oD6BQUFyMzMLPJSVy1btsSNGzeQkZEBd3d3PH36lOtI5AsiEiPQ/K/mWBW6Cn+G/gnnzc54mkJ/ZqTidu3aBW9vb3Ts2BGXLl1SyyKrKHIrtImJiahatWqRdQKBACYmJkhMTCzTMbZu3Yr69eujZcuWRdb/+uuvOHToEC5evIi+ffti7NixWLNmTanHWbJkCYyMjKQvGxubb/xUlUP9+vUREhICIyMjtGrVCrdu3eI6EinFxrCNEElE0lehuBB/3fuL61hEhbEsi2XLlmHIkCEYOnQojh07Bl1dXa5jVWrlLrQzZswotcPS59ezZ88qHCwvLw/79u0r8Wx2zpw5aNWqFZo2bYrp06dj2rRpWLGi9OfIAgICkJGRIX29ffu2wvlUXbVq1XD9+nU0btwYHTt2xP79+7mORErAoviVmpLWEVIWhYWFGDNmDGbMmIG5c+di8+bNNGm7ApT7Jzx58mQMHTr0i9vUqlULlpaWSE4u+nyXSCRCWloaLC0tv9rOkSNHkJubi8GDB391Wzc3NyxYsAAFBQXQ0iphAnYtrRLXqztjY2OcP38eo0aNwsCBAxEZGYmFCxfS4z9KZGSzkdh2f5t0ykUBTwB/J3+uYxEVlJycjL59+yI0NBRbt27FsGHDuI6kNspdaM3NzWFubv7V7dzd3ZGeno7w8HA4OzsDAC5fvgyJRAI3N7ev7r9161Z4e3uXqa0HDx6gSpUqVEy/gba2Nnbu3AlHR0dMmzYNkZGR2LdvH4yMjLiORgA4Wzvj9ojb2HZ/G3gMDyObjURji8Zcx1J7OYU5SMtLg5WBlULmna6o+/fvo1evXigsLERwcHCx23FEzlg56tq1K9u0aVM2NDSUvXHjBlu3bl12wIAB0vfj4uJYBwcHNjQ0tMh+L168YBmGYQMDA4sd89SpU+yWLVvYhw8fsi9evGDXr1/P6urqsnPnzi1zroyMDBYAm5GRUcFPWLmcO3eONTY2Zh0cHNhnz55xHYcQuboQfYFtva0167LZhV13Zx0rkUjKtN+eiD2s5gJNFr+ArfFHDfZpylO5Z62I/fv3szo6OqyLiwv79u1bruMoHUXUA7kW2tTUVHbAgAGsvr4+a2hoyPr7+7NZWVnS92NiYlgA7JUrV4rsFxAQwNrY2LBisbjYMQMDA1knJydWX1+f1dPTYx0dHdmNGzeWuG1p1KnQiiVi9m3GWzYlJ6VM20dFRbH169dnjYyM2L///lvu+QjhQti7MJY/n88yvzAsfgGLX8Buvbf1q/vFfohlBb8KpPvw5/NZp41OCslcXiKRiJ0xYwYLgPXz82Nzc3O5jqSUFFEP5DpghbJSl+doMwsy0XNfT1x/cx0A8JPbT/jT88+vjnecmZkJPz8/nD17FkuXLsXUqVNpjGRSqfx69Vf8evVXiFkx8GkWru51u+PMwDNf3O9yzGV03NWxyDptgTbyZuXJNW95ZWRkwM/PD4GBgVi2bBkmT55M/4ZLodLP0RLuzbk8B7fe/vPozurQ1Tjy5MhX9zM0NMTJkycREBCA6dOno1+/fkhPT5dzWkIUx0jLqEjvbR7DQxWdKl/dz97UHho8Dekyn+GjcVXlumd+//59uLq6SidrnzJlChVZjlGhVVGPkx9j3Z11OPDoQJFp+/7t6fun0m/s+NRj9dn7sj16xePxsGjRIhw9ehRBQUFwcnLC7du3ZZafEC4NbzYcDc0bSpeNtI0wt+3XZ+GqblgdB78/CF2Nj8+d1japjQPfK26c9S9hWRarV69GixYtoK+vj7t37xYZppZwR/m7y5FizkefR8/9PSGWiMGCxbb72xDoFwg+r+hjOU0tmyIoJggSVgIAEElEcLR0LFdbffr0QbNmzTBw4EC0bt0aixYtwtSpU8Hj0Xc0roXHh2Pl7ZUQioXwd/JHt7rduI6kMvQ19XF7xG2cfn4aBeICdKndBZb6X3/sEAC+q/8dMh0ykVmQCWNtY6U4W0xNTcWwYcNw6tQpTJgwAcuWLaOnMJQI3aNVwXu0TTY0waPkR0Uuff098O9iv2jzhHnwPeqLU89PgcfwMKftHPzi8cs3tSkUCjFv3jwsXboUnTt3xq5du2BhYVHhz0K+zcOkh3Dd4gqRRAQWLCSsBGcHnkX3ut25jkYU7Pr16xg4cCByc3Oxfft2eHt7cx1JpdA9WlKirIKsYqMDZRcWn35QR0MHJ31PIisgC3mz8r65yAKAhoYGFi9ejPPnz+PBgwdwdHTEpUuXvvl4pGIOPT4EMSuGmBVDwkrAY3jYGbGT61hEgcRiMRYsWAAPDw/Y2dkhIiKCiqySokKrgv49/ySf4cNExwRta7YtdXt9TX1o8jVl0nbnzp0RERGBJk2aoEuXLpg5cyYKCwtlcmxSdloCrSITaTBgoMWnS4Xq4t27d+jcuTPmzZuH2bNn4/Lly6hevTrXsUgpqNCqoPke87G4w2K0smkFL3sv3Bx2Exb6iruMa2lpiXPnzmHJkiVYsWIFXF1dcf/+fYW1T4DhTYfDXM8cPIYHPsOHlkALk90ncx1L5eQKc/Ek5Qne577nOkqZsCyL7du3o2HDhnj27BmCgoIwf/58Gq/4k7vv7qLN9jaov64+ZlyaAaFYyHUkgO7RquY9WmVy//59+Pv749GjRwgICMDs2bOpE4aCJGUnYVfELgglQnzf4HvYm9pzHUmlRCRGoMueLkjOSQaf4WNDjw0Y6TyS61ilevv2LUaOHInz589j8ODB+OOPP2BiYsJ1LKXxJuMNGqxrgDxRHiSsBAwYTGk5Bcs7L//ifio98bsyo0IrW4WFhVi6dCkWLFgABwcH7Nixo8g8xIQoo4brG+L5++fSR+B4DA8vxr9ArSq1uI5WBMuy+OuvvzB58mQYGhpi06ZN6NGjB9exlM7eyL344fgPRdbVrlIb0T9Ff3E/6gxFVIKmpibmzp2LsLAwaGpqokWLFggICEB+fj7X0Qgp1asPr4o8Zy5hJXid/prTTP/1+vVreHp6YtSoUejXrx8ePXpERbYU/x1whMfwYKZrxlmef6NCS2TG0dERoaGhmD9/Pn7//Xc0a9YMoaGhXMcipEQuVi4QMB/vbTJgoCPQQT2zelzHAgBIJBJs2rQJjRo1wtOnTxEYGIitW7fC2NiY62hKq2udrujt0Fu6rC3Qxh+ef3Ca6TMqtESmNDQ0MGvWLNy7dw96enpwd3fH6NGjkZqaynU0QorY13efdAAXEx0THPM5BisDK65j4d69e2jdujVGjx6NgQMH4vHjxzTCUxnwGB6O+hxFoF8g9vbZiydjn8Ddxp3rWADdo6V7tPIkEomwfv16zJkzBwKBAIsXL8aIESNoYnmiVIRiIQQ8AecjPKWlpWH27NnYuHEjGjZsiDVr1sDDw4PTTOqA7tESlSYQCPDTTz8hKioKvXr1wujRo9G8eXOEhIRwHY0QKQ2+BqdFViwWY/PmzbC3t8fevXvxxx9/4N69e1RkKxEqtETuLCwssG3bNmmBbdmyJfz9/ZGUlMR1NEI4dfv2bbi5ueF///sfvLy8EBUVhQkTJkBDQ6MMexNVQYWWKEyLFi1w584dbNy4EadOnYK9vT1WrVpFI0sRtZOYmIjhw4fD3d0dEokEN2/exPbt22n88EqKCi1RKD6fj//973+IiorCwIED8fPPP6N+/frYt28fJBIJ1/EIkauMjAzMnj0btWvXxvHjx7FhwwbcvXsXLVu25DoakSMqtIQTpqam2LBhAyIjI9GoUSP4+fmhadOmOHv2LNSwfx6p5PLy8rBixQrUqlULK1euxE8//YSXL19i9OjR1DlQDVChJZxq1KgRTp48iVu3bsHY2Bg9e/ZE27ZtcePGDa6jEVJhIpEIW7ZsQd26dTFz5kz4+Pjg5cuXWLJkCapUqVKGI5DKgAotUQru7u4IDg5GYGAgsrOz0aZNG3h5eSEyMpLraISUm0QiweHDh9GwYUOMGjUKbdu2xdOnT7F+/XpYWXH/rC5RLCq0RGkwDIOuXbsiPDwc+/fvx7Nnz+Dk5IT+/fsjPDyc63iEfJVYLMbBgwfh7OyM/v37o06dOrh//z727duHOnXqcB2PcIQKLVE6PB4Pvr6+ePLkCTZt2oTbz27DZaYLbEbbYOXxlXQPlyid/Px8bNq0CQ4ODvD19YW5uTmuXr2Ks2fPwsnJiet4hGNUaInS0tDQQHef7sj5IQe8ljzEWcRhcsRk1POqh+PHj1MvZcK5zMxMLF++HHZ2dhgzZgyaNWuGsLAwXLhwAW3btpVZO28y3uDw48MIjg2mL5oqiGYLJkrt0ONDSM9PhwQSgPdx8Pf3td6jT58+qFevHqZPn46BAwdCU1OT66hEjSQlJWHVqlVYv349cnNzMWTIEEydOhX29rKfE/jGmxvosrsL8kR5AIDBjoOxo9cOzoeMJGVHZ7REqZX0y6RFixa4desWHBwc4O/vj1q1amHBggVISEjgJCNRH2FhYRg2bBhsbW2xZs0ajBw5EjExMdiyZYtMimyuMBfTL06H5x5PTAicgMyCTIz7exwKxAXSbXZF7ML1N9cr3BZRHJpUgCYVUGoJWQlw3OiID3kfAObjnKGBfoHoUrsLAODJkyf4448/sG/fPhQWFuK7777DmDFj4OHhQd/4iUzk5ubiwIED2LBhA8LCwlCjRg2MHj0ao0ePlvkjOj339URgdCAkrAR8hg+3am549eEVEnMSi2x3tP9R9KnfR6ZtqytF1AMqtFRold67zHdYe2ct8kR56NegH1rVaFVsm4yMDOzatQvr16/Hs2fPUK9ePYwZMwaDBw+mOTzJN4mKisKGDRuwY8cOZGRkoGvXrhgzZgy6d+8ul0EmMgsyYbTUqNj6AY0G4ODjg9Liq6eph6fjnsLawFrmGdQRFVo5oUJbebEsi6tXr2LDhg04duwYNDU1MXDgQIwaNQouLi50lku+qKCgAGfOnMGGDRsQFBQEMzMzDBs2DP/73/9Qq1YtubadL8qH7iJdsCj6K/n1xNdYcWsF/o76G5b6lljVbRVcrF3kmkWdUKGVEyq06iEhIQFbt27F5s2b8fbtW9jb28PPzw9+fn6oXbs21/GIkpBIJLh+/Tr27t2Lw4cPIz09HS1btsSYMWPw/fffQ1tbW2FZFlxdgLnBc8Fn+BCzYkx0m4g/uv6hsPbVERVaOaFC+3UiiQgpOSkw1zOHgKfandNFIhGuXLmCPXv24NixY8jOzoabmxv8/Pzg4+ODqlWrch2RcCAyMhJ79+7F/v378fbtW9ja2mLgwIHw8/NDgwYNOMsV+CIQEUkRqGdWD70cetFVGDmjQisnlbHQJmUnYdnNZUjNS0W3Ot3g28j3m48V8jYE3vu98T7vPYy1jXHC5wTa2baTaV6u5Obm4vTp09i7dy8CAwPBsiw6d+4MPz8/9O7dG/r6+lxHJHL0+vVr7N+/H3v37sWjR49gamqK/v37w8/PDy1btqSipoao0MpJZSu0GfkZcNzoiHdZ78CyLMSsGGu6rcGPzX8s97HEEjEsfrPAh/wPkLAS8BgeDDQNkDglEdoCxV1CU4TU1FQcPnwYe/fuxY0bN6CpqYkOHTrAy8sLXl5esLGx4ToiqSCJRILw8HCcPn0ap0+fxoMHD6Cjo4PevXtj4MCB8PT0pEnW1RwVWjmpbIX2yJMj6He4X5F19qb2eP7j83IfKyk7CZa/WxZbHzshFjWNa1YopzJ7/fo1Tpw4gdOnT+Pq1asQiURwcnKSFl1nZ2fwePTYuSrIzc1FUFAQTp8+jTNnziAhIQFVqlRB9+7d4eXlhe7du8PAwIDrmERJKKIeqPbNNwIA4DPFHzUQMN/2R2umawYzXTOk5aV9PKMFDwZaBrDUL158K5OaNWtiwoQJmDBhAjIyMnDu3DmcPn0aa9euxYIFC2BpaYmePXuie/fuaNu2LUxNTbmOTD5hWRYvX77E5cuXcebMGVy6dAl5eXmoW7cuBg4cCC8vL7Rq1QoCAf26I9ygM9pKcEabK8yF219ueJLyBDyGB5FEhD3f7YFfE79vOt7tuNvw2u+F97nvUUW7Ck74nkDbmrIbt1WViEQi3Lx5E6dPn8apU6fw4sULAECTJk3g4eEBDw8PKrwK9rmwBgcHS1/v3r0Dj8dDq1at4OXlBW9vbzg4OHAdlagAunQsJ5Wt0OLTw+7r765Ham4qPOt4olOtThU6nlgiRkpuCsx0zVS+17EsvX79GlevXpX+go+JiQH+U3jbtGkDMzMzrqNWGizLIjo6usjP/XNhdXZ2lv7cW7VqBSOj4gM+EPIlVGjlpDIWWsKN0gpvzZo14ezsDGdnZzRr1gzOzs4wNzfnOq7Sk0gkePXqFcLDw6Wve/fuIT09nQorkQuVLrSLFi3C2bNn8eDBA2hqaiI9Pf2r+7Asi3nz5mHLli1IT09Hq1atsGHDBtStW1e6TVpaGsaPH4/Tp0+Dx+Ohb9++WLVqVbkey6BCS+Tl9evXuHXrFu7duyctEhkZGQAAGxubIoW3fv36qFGjhlyG81MFeXl5iI6OxqNHj6RF9f79+8V+Xs7OznBxcYG7uzsVViJzKl1o582bB2NjY8TFxWHr1q1lKrTLli3DkiVLsHPnTtjZ2WHOnDl4+PAhnjx5Ih2dpVu3bkhISMCmTZsgFArh7+8PV1dX7Nu3r8zZqNASRfnSGRoAaGpqok6dOrC3ty/2qlq1qso/1ykSifD69WtERUUVe71580a63b+vAHz+MkJXAIgiqHSh/WzHjh2YOHHiVwsty7KwtrbG5MmTMWXKFODTQPEWFhbYsWMHfH198fTpUzRo0AB3796Fi8vHsT7PnTuH7t27Iy4uDtbWZRtkmwot4RLLsnj9+jWeP39erPi8fv1aOrG3oaEhbG1tYWVlJX1ZW1sXWbayslLoEIH//gyZmZlISEhAfHw8EhISirzi4+MRHx+P2NhYCIVCAICWlhbq1q1b7AtFvXr1qDMZ4YxaPd4TExODxMREdOr0TyceIyMjuLm5ISQkBL6+vggJCYGxsbG0yAJAp06dwOPxEBoaiu+++67EYxcUFKCg4J/5HD9fmsrMzJTrZyKkNCYmJnB3d4e7u3uR9fn5+YiJiUF0dDSio6MRFxeHxMREPHr0CEFBQUhMTJQWrs+MjIxgYGAAPT096Ovrl/jS09ODtrY2eDwe+Hw+eDweGIYBy7JgWRYSiQQSiQRisRi5ubnIzs5GdnY2cnJykJWVhZycHGRnZ0v/Oz09Hfn5+UVy6Ovrw8LCAlZWVrC0tESjRo1ga2uLOnXqoE6dOqhevXqpzyLTv0XClc9/9+R5zqk0hTYx8eN8ixYWFkXWW1hYSN9LTEwsNi6tQCCAiYmJdJuSLFmyBPPnzy+2nkb+IZVBRkaG9Msjlz4X55cvX3IdhZByS01NlVsfgHIV2hkzZmDZsmVf3Obp06eoV69eRXPJVEBAACZNmiRdTk9PR82aNfHmzRuV7VyRmZkJGxsbvH37ViUvf1N+7qn6Z1D1/KgEn0HV8+PTF9UaNWrAxMREbm2Uq9BO/n975x8Tdf3H8ed5cgcIB7RDgVLiVyfJIdgGwcxzwfSMimZbgk2hHJTJzDRUKiHBFiXTLUflHD/8w2LqQNgCdBqsZAhJhyIg4wglW9GA+E3mwev7R91n9/FOuIP7AMf3/dhu8/P+vD6f9/P1fn3en5djr/fnvW8fEhMTJ7WZ7p6NHh7/fnmou7sbnp6eXHt3dzdCQkI4mz///JN3nU6nQ19fH3e9KaRSKaRSqVG7i4uLzT4cemQymU37wPTPPbbug63rxwLwwdb1AxD0E6sWJVp3d3fBKgF9fHzg4eGBK1eucIl1cHAQdXV12LlzJwAgIiIC/f39aGhowDPPPAMA+P777zExMYHw8HBBdDEYDAaDMRMES+FdXV1obGxEV1cXxsfH0djYiMbGRgwPD3M2K1euRElJCQBAJBJhz549OHLkCMrKytDU1ITt27fDy8sLr7zyCgAgMDAQarUaSUlJqK+vR01NDVJSUhAXF2d2xTGDwWAwGLOJYMVQ6enpOH36NHccGhoKAKiqqsL69esBAG1tbbwijv3792NkZATJycno7+/H2rVrUVlZyVu+cObMGaSkpCAqKor7YMUXX3xhkTapVIqMjAyTf062FWzdB6Z/7rF1H2xdPxaAD7auH7Pkw//lJxgZDAaDwZgt2AabDAaDwWAICEu0DAaDwWAICEu0DAaDwWAICEu0DAaDwWAIyIJMtJ988gkiIyPh6OgIV1dXs64hIqSnp8PT0xMODg6Ijo5Ge3s7z6avrw+vv/46ZDIZXF1dsWPHDt5yJWtiaV937tyBSCQy+Tt37hxnZ+p8UVHRnOsHgPXr1xtpe/vtt3k2XV1diImJgaOjI5YuXYrU1FTodDqr65+OD/otHBUKBRwcHLBixQrs3r3b6POIQsUgNzcXTz75JOzt7REeHo76+vpJ7c+dO4eVK1fC3t4eSqUS5eXlvPPmzAlrY4kPp06dwnPPPQc3Nze4ubkhOjrayD4xMdForNVq9bzQX1hYaKTt4Q0i5nsMTM1ZkUiEmJgYzmY2Y/DDDz/gpZdegpeXF0QiES5cuDDlNdXV1VizZg2kUin8/f1RWFhoZGPp3DKCFiDp6el07Ngx2rt3L7m4uJh1TXZ2Nrm4uNCFCxfoxo0b9PLLL5OPjw+NjY1xNmq1mlavXk3Xrl2jH3/8kfz9/Sk+Pl4QHyztS6fT0e+//877HT58mJycnGhoaIizA0AFBQU8O0Mf50o/EZFKpaKkpCSetoGBAZ6PQUFBFB0dTRqNhsrLy0kul1NaWprV9U/Hh6amJtq8eTOVlZWRVqulK1euUEBAAL366qs8OyFiUFRURBKJhPLz86m5uZmSkpLI1dWVuru7TdrX1NSQWCymzz//nFpaWuijjz4iOzs7ampq4mzMmRPWxFIftm7dSrm5uaTRaKi1tZUSExPJxcWF7t27x9kkJCSQWq3mjXVfX9+80F9QUEAymYyn7Y8//uDZzPcY9Pb28vTfunWLxGIxFRQUcDazGYPy8nL68MMPqbi4mABQSUnJpPa//PILOTo60t69e6mlpYVOnDhBYrGYKisrORtLx8QUCzLR6ikoKDAr0U5MTJCHhwcdPXqUa+vv7yepVErffvstERG1tLQQAPrpp584m4qKChKJRPTbb79ZVbe1+goJCaE333yT12bOwzdTpqtfpVLRu++++8jz5eXltGjRIt7L6KuvviKZTEb379+3ogfWi8HZs2dJIpHQgwcPuDYhYhAWFka7du3ijsfHx8nLy4s+/fRTk/avvfYaxcTE8NrCw8PprbfeIjJzTlgbS314GJ1OR87OznT69GmuLSEhgWJjYwXR+zCW6p/q/WSLMTh+/Dg5OzvT8PAw1zabMTDEnHm2f/9+WrVqFa9ty5YttHHjRu54pmNCRLQg/3RsKVNt0Qdgyi36rIk1+mpoaEBjYyN27NhhdG7Xrl2Qy+UICwtDfn6+1beHmon+M2fOQC6XIygoCGlpaRgdHeXdV6lU8nZ42rhxIwYHB9Hc3DxvfDBEv8fl4sX8b8NYMwb//PMPGhoaeM/vokWLEB0dzT2/D1NbW8uzx39jqbc3Z05Yk+n48DCjo6N48OCB0cfhq6ursXTpUigUCuzcuRO9vb3zRv/w8DC8vb2xfPlyxMbG8p5jW4xBXl4e4uLisGTJEl77bMRgOkw1D6wxJphP2+TNJUJu0TddPTPtKy8vD4GBgYiMjOS1Z2Zm4vnnn4ejoyMuXbqEd955B8PDw9i9e/ec69+6dSu8vb3h5eWFmzdv4sCBA2hra0NxcTF3X1MxgkEM59oHQ3p6epCVlYXk5GReu7Vj0NPTg/HxcZNjc/v2bZPXPGosDZ93TDEnrMl0fHiYAwcOwMvLi/dSVKvV2Lx5M3x8fNDR0YEPPvgAmzZtQm1tLcRi8ZzqVygUyM/PR3BwMAYGBpCTk4PIyEg0NzfjiSeesLkY1NfX49atW8jLy+O1z1YMpsOj5sHg4CDGxsbw119/zfi5hC0lWlvdos8Qc32YKWNjY/jmm29w6NAho3OGbaGhoRgZGcHRo0fNeskLrd8wISmVSnh6eiIqKgodHR3w8/Ob9n0Nma0YDA4OIiYmBk8//TQ+/vhj3rmZxIBhmuzsbBQVFaG6uppXUBQXF8f9W6lUIjg4GH5+fqiurkZUVNQcqf2XiIgIREREcMeRkZEIDAzEyZMnkZWVNafapkNeXh6USiXCwsJ47fM5BrOFzSRaW92izxBzfZhpX+fPn8fo6Ci2b98+pW14eDiysrJw//79Kb/1OVv6DbUBgFarhZ+fHzw8PIyq/bq7uwGDGE7FbPgwNDQEtVoNZ2dnlJSUwM7OblJ7S2JgCrlcDrFYzI2Fnu7u7kdq9fDwmNTenDlhTabjg56cnBxkZ2fj8uXLCA4OntTW19cXcrkcWq3Wqi/5mejXY2dnh9DQUGi1WsDGYjAyMoKioiJkZmZO2Y9QMZgOj5oHMpkMDg4OEIvFM44rsECrjvVYWgyVk5PDtQ0MDJgshrp+/Tpnc/HiRUGLoabbl0qlMqp0fRRHjhwhNze3Gel9GGuN1dWrVwkA3bhxg8igGMqw2u/kyZMkk8no77//nhc+DAwM0LPPPksqlYpGRkbM6ssaMQgLC6OUlBTueHx8nB5//PFJi6FefPFFXltERIRRMdRkc8LaWOoDEdFnn31GMpmMamtrzerj119/JZFIRKWlpVbRbMh09Bui0+lIoVDQe++9R2RDMaD/3rVSqZR6enqm7EPIGBhibjFUUFAQry0+Pt6oGGomcaWFWnV89+5d0mg03PIWjUZDGo2Gt8xFoVBQcXExd5ydnU2urq5UWlpKN2/epNjYWJPLe0JDQ6muro6uXr1KAQEBgi7vmayve/fukUKhoLq6Ot517e3tJBKJqKKiwuieZWVldOrUKWpqaqL29nb68ssvydHRkdLT0+dcv1arpczMTLp+/Tp1dnZSaWkp+fr60rp167hr9Mt7NmzYQI2NjVRZWUnu7u6CLu+xxIeBgQEKDw8npVJJWq2Wt5xBp9MRCRiDoqIikkqlVFhYSC0tLZScnEyurq5chfa2bdvo4MGDnH1NTQ0tXryYcnJyqLW1lTIyMkwu75lqTlgTS33Izs4miURC58+f5421fp4PDQ3R+++/T7W1tdTZ2UmXL1+mNWvWUEBAgNX/YzYd/YcPH6aLFy9SR0cHNTQ0UFxcHNnb21NzczPPx/kcAz1r166lLVu2GLXPdgyGhoa49z0AOnbsGGk0Grp79y4RER08eJC2bdvG2euX96SmplJrayvl5uaaXN4z2ZiYw4JMtAkJCQTA6FdVVcXZ6Ncy6pmYmKBDhw7RsmXLSCqVUlRUFLW1tfHu29vbS/Hx8eTk5EQymYzeeOMNXvK2JlP11dnZaeQTEVFaWhotX76cxsfHje5ZUVFBISEh5OTkREuWLKHVq1fT119/bdJ2tvV3dXXRunXr6LHHHiOpVEr+/v6UmprKW0dLRHTnzh3atGkTOTg4kFwup3379vGWzsylD1VVVSafOwDU2dlJJHAMTpw4QStWrCCJREJhYWF07do17pxKpaKEhASe/dmzZ+mpp54iiURCq1atou+++4533pw5YW0s8cHb29vkWGdkZBAR0ejoKG3YsIHc3d3Jzs6OvL29KSkpyaIXpJD69+zZw9kuW7aMXnjhBfr5559595vvMSAiun37NgGgS5cuGd1rtmPwqDmo15yQkEAqlcrompCQEJJIJOTr68vLC3omGxNzYNvkMRgMBoMhIGwdLYPBYDAYAsISLYPBYDAYAsISLYPBYDAYAsISLYPBYDAYAsISLYPBYDAYAsISLYPBYDAYAsISLYPBYDAYAsISLYPBYDAYAsISLYPBYDAYAsISLYPBYDAYAsISLYPBYDAYAsISLYPBYDAYAvI/RCD0epnMLYcAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Get Uniform x, y samples\n", - "rand = np.random.default_rng(42) # Set fixed Value\n", - "x_rand = rand.uniform(-1, 1, 100)\n", - "y_rand = rand.uniform(-1, 1, 100)\n", - "\n", - "# Plot \n", - "circle = plt.Circle((0, 0), 1, color='black', fill=False)\n", - "fig, ax = plt.subplots(figsize=(5,5))\n", - "ax.add_patch(circle)\n", - "ax.plot(np.linspace(0,1), np.zeros(50), color='black')\n", - "ax.text(0.5, 0.05, \"$r = 1$\")\n", - "plt.xlim(-1, 1)\n", - "plt.ylim(-1, 1)\n", - "\n", - "# Plot samples as small circles\n", - "for x, y in zip(x_rand, y_rand):\n", - " c = plt.Circle((x,y), 0.01, color='green')\n", - " ax.add_patch(c)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "162d35ab-bd1d-4304-8302-cf96b493740d", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-102b588af1bbdfe3", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Um zu bestimmen ob einer der Werte im Kreis liegt Nutzen wir die parametrisierte Kreisgleichung: $$x^2+y^2=r,\\quad r=1$$\n", - "Mittels dieser lassen sich die Werte sortieren nach \"im Kreis `circle`\" & \"im Rechteck `square`\":" - ] - }, - { - "cell_type": "code", - "execution_count": 170, - "id": "400c3fb5-6e2b-48c1-9b0b-9b4aa4504da2", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-856462e1221faa4c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# reserve two arrays\n", - "circle_coords = []\n", - "square_coords = []\n", - "\n", - "for x,y in zip(x_rand, y_rand):\n", - " # calculate distance from origin\n", - " dist_from_origin = x**2 + y**2\n", - "\n", - " # if distance smaller or equal than 1, the point is in the circle\n", - " if dist_from_origin <= 1:\n", - " circle_coords.append((x,y))\n", - "\n", - " # by definition is every point in the square\n", - " square_coords.append((x,y))" - ] - }, - { - "cell_type": "markdown", - "id": "fcdc0830-f68f-4b28-b5d8-3c15271900ad", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-19f6113a9887beef", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Mittels der länge der Listen lässt sich dementsprechend $\\pi$ approxiemieren:" - ] - }, - { - "cell_type": "code", - "execution_count": 171, - "id": "86d96bbb-9877-4fcc-af03-8b5b1898dda5", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-f8c1f8a2e8c65aae", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pi = 3.12\n", - "Difference to real Value 0.02159265358979301\n" - ] - } - ], - "source": [ - "pi = 4 * len(circle_coords) / len(square_coords)\n", - "print(f\"Pi = {pi}\")\n", - "print(f\"Difference to real Value {np.pi - pi}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9f83c18b-189b-4090-9080-c3bb74207418", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-204384254c42936a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Plotten wir im folgenden die 100 zufällig gleichverteilten Werte:" - ] - }, - { - "cell_type": "code", - "execution_count": 172, - "id": "18862b4a-cdb5-4799-9c32-68b411da3d58", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-cb4b3ecde6b0ab70", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot \n", - "circle = plt.Circle((0, 0), 1, color='black', fill=False)\n", - "fig, ax = plt.subplots(figsize=(5,5))\n", - "ax.add_patch(circle)\n", - "ax.plot(np.linspace(0,1), np.zeros(50), color='black')\n", - "ax.text(0.5, 0.05, \"$r = 1$\")\n", - "plt.xlim(-1, 1)\n", - "plt.ylim(-1, 1)\n", - "\n", - "# Plot samples not in the circle\n", - "for coord in square_coords:\n", - " c = plt.Circle(coord, 0.01, color='blue')\n", - " ax.add_patch(c)\n", - "\n", - "# Plot samples in the circle\n", - "for coord in circle_coords:\n", - " c = plt.Circle(coord, 0.01, color='red')\n", - " ax.add_patch(c)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "704b68d6-4210-4f90-a1ae-9a0078fc8796", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b0626976cd84b809", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Um einen genaueren Wert für $\\pi$ zu erhalten muss dementsprechend nur die Anzahl an Samples erhöht werden (Hier 1 Million).\n", - "\n", - "Da im folgenden auf einen Plot verzichtet wird lässt sich die Berechnung darauf vereinfachen, dass nur gezählt werden muss welche Punkte im Kreis liegen die koordinaten sind nicht mehr vom Nutzen:" - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "id": "38ca2a0d-3198-48c2-8af0-26516a01c9d9", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-0512fe6559228e5c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pi = 3.14178\n", - "Difference to real Value -0.00018734641020667908\n" - ] - } - ], - "source": [ - "# Get Uniform x, y samples\n", - "sample_size = 1_000_000\n", - "rand = np.random.default_rng(42) # Set fixed Value\n", - "x_rand = rand.uniform(-1, 1, sample_size)\n", - "y_rand = rand.uniform(-1, 1, sample_size)\n", - "\n", - "# reserve two arrays\n", - "in_circle = 0\n", - "in_square = 0\n", - "\n", - "for x,y in zip(x_rand, y_rand):\n", - " # calculate distance from origin\n", - " dist_from_origin = x**2 + y**2\n", - "\n", - " # if distance smaller or equal than 1, the point is in the circle\n", - " if dist_from_origin <= 1:\n", - " in_circle += 1\n", - "\n", - " # by definition is every point in the square\n", - " in_square += 1\n", - "\n", - "pi = 4 * in_circle / in_square\n", - "print(f\"Pi = {pi}\")\n", - "print(f\"Difference to real Value {np.pi - pi}\")" - ] - }, - { - "cell_type": "markdown", - "id": "167e5e63-254b-4158-9e14-f3c9ddf0259f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-95ca10cecf8fe36e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Aufgabe - Arbeitszeit abschätzen\n", - "\n", - "*7 Punkte*\n", - "\n", - "Du befindest dich in der Situation wieder das dein Chef dich damit Beauftragt zwei wichtige Aufgaben bis zum Ende des Arbeitstages zu bearbeiten. Im Gegennsatz dazu bist du Abends auf einem Grillfest mit deinen Freunden verabredet. Dieses findet um 18 Uhr statt. Da du dich nun im zwiespalt befindest deinen Beruflichen Aufgaben nachzukommen und auf dem Grillfest zu erscheinen, fragst du dich im nächsten Schritt wie hoch die Chance ist, dass in 9 Stunden anstehende Grillfest pünktlich zu erreichen und gleichzeitig deinen Beruflichen Verpflichtungen nachzukommen.\n", - "\n", - "Nach einigen gut durchdachten Überlegungen stellst du folgendes fest:\n", - "\n", - "- An der ersten Aufgabe sitzt du zwischen 1-5 Stunden.\n", - "- An der zweiten Aufgabe sitzt du zwischen 2-6 Stunden.\n", - "- Egal wie schnell du eine Aufgabe löst es gibt keine Auswirkungen auf die andere Aufgabe. (Sie sind unabhängig voneinander)\n", - "\n", - "Stell im folgenden fest wie hoch die Warscheinlichkeit ist das du pünktlich beim Grillfest angelangst.\n", - "\n", - "Gehe dabei wie folgt vor:\n", - "\n", - "- Nehme an das beide Aufgaben gleichmäßig Verteilt sind, und speichere die Verteilungen in den Variablen `exc1` & `exc2`.\n", - "- Verwende eine geeignete Anzahl an samples. Speichere den gewählten Wert in der variablen `sims`.\n", - "- Speichere die Wahrscheinlichkeit mit der du es zum Grillfest schaffst in der Variablen `chance`, mit einer genauigkeit von zwei nachkommmastellen.\n", - "- Plotte geeignet. Beziehe auch die Variable `chance` mit ein." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "cb2bda83-a3f7-4c46-a2be-f809ec55558c", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-65cc1054d6bb7253", - "locked": false, - "points": 2, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "rand = np.random.default_rng(42) # Use this rng!\n", - "# BEGIN SOLUTION\n", - "import matplotlib.pyplot as plt\n", - "sims = 1_000 # 1 Punkt\n", - "\n", - "# 2 Punkte\n", - "exc1 = rand.uniform(1,5,sims) \n", - "exc2 = rand.uniform(2,6,sims)\n", - "\n", - "duration = exc1 + exc2 # 1 Punkt\n", - "chance = float(np.round((duration > 9).sum()/sims, decimals=2)) # 1 Punkt\n", - "\n", - "plt.figure(figsize=(10,5))\n", - "plt.hist(duration, density=True) # 1 Punkt\n", - "plt.axvline(9, color='r') # 1 Punkt\n", - "plt.show()\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "0910695e-4c60-4608-9152-519db84687d9", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-9fd6737ce69879f1", - "locked": true, - "points": 5, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Hier werden ihre Lösungen getestet...\n", - "import math\n", - "assert math.isclose(chance, 0.13, rel_tol=0.1)\n", - "\n", - "### BEGIN HIDDEN TESTS\n", - "assert sims > 999\n", - "assert sims < 10_000_000\n", - "\n", - "trand = np.random.default_rng(42)\n", - "\n", - "texc1 = trand.uniform(1,5,sims) \n", - "texc2 = trand.uniform(2,6,sims)\n", - "\n", - "assert np.array_equal(exc1, texc1)\n", - "assert np.array_equal(exc2, texc2)\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "aaaf624d-63ed-4645-a793-0ef2aebcb44d", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8eb45ec252910d85", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Aufgabe - Simulation einer Körpergewichtszunahme\n", - "\n", - "*13 Punkte*\n", - "\n", - "Gegeben sind die nach Altersgruppe aufgeschlüsselten Durschnittskörpergewichte (in kg) von Männern in Deutschland. (Zu finden beim [Statistischen Bundesamt](https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Gesundheit/Gesundheitszustand-Relevantes-Verhalten/Tabellen/koerpermasse-maenner.html))\n", - "\n", - "Nutze die Monte Carlo Methode um den mittleren Gewichtszuwachs der Bezugsgrößen zu bestimmen.\n", - "Die mittlere Gewichtsschwankung eines Durschnittsmannes beträgt 2.5kg über den Tag verteilt. \n", - "Finde die Wahrscheinlichkeit mit der ein Durchschnittsmann pro Woche (7 Tage) 3 kilogramm abnimmt und speichere das Ergebniss in der Variablen `gain_percent`.\n", - "\n", - "Beachte:\n", - "\n", - "- Bestimmt das arithmetische Mittel der Durchschnittsgewichte über die Altersgruppen. Speichere den Wert in `avg_weight` mit einer Präsizion von 1 Dezimalstelle.\n", - "- Verwende eine geeignete Anzahl an samples. Speichere den gewählten Wert in der variablen `sims`.\n", - "- Nehme an das das Durchschnittsgewicht Normal Verteilt ist und mit einer Standardabweichung von 5% zum Durschnittswert. Speichere deine Samples in der Variablen `men_normal`.\n", - "- Nehme an das die Gewichtsschwankung pro Tag Gleich Verteilt ist. Speichere die simulierten zunahmen für eine Woche in der Liste `gain_week`.\n", - "- Berechne die Durschnittliche Gewichtszunahme/abnahme und speichere das Ergebnis in der Variablen `duration`.\n", - "- Plotte Angemessen. Beziehe dabei alle relevanten Werte mit ein.\n", - "- Begründe mit einem kurzen Text dein Vorgehen und erkläre dein Ergebnis, beschreibe als letztes die Aussage deines Plottes." - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "id": "b8b21830-be8e-43f2-ae68-7e1867aa46e4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a9d02e6e26d7f2eb", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Given\n", - "avg_weight_per_men = {\n", - " \"18 - 20\": 77.9,\n", - " \"20 - 25\": 80.5,\n", - " \"25 - 30\": 83.3,\n", - " \"30 - 35\": 85.6,\n", - " \"35 - 40\": 86.7,\n", - " \"40 - 45\": 88.1,\n", - " \"45 - 50\": 89.8,\n", - " \"50 - 55\": 89.0,\n", - " \"55 - 60\": 88.8,\n", - " \"60 - 65\": 87.9,\n", - " \"65 - 70\": 86.7,\n", - " \"70 - 75\": 85.3,\n", - " \"75+\": 81.0\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "id": "926897fc-25a8-470c-b06b-946f7370a048", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-ef59ae554f2f4540", - "locked": false, - "points": 2, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.31\n" - ] - } - ], - "source": [ - "rand = np.random.default_rng(420) # Use this rng!\n", - "\n", - "# BEGIN SOLUTION\n", - "avg_weight = np.round(np.mean(list(avg_weight_per_men.values())), decimals=1) # 1 Punkt\n", - "\n", - "sims = 1_000 # 1 Punkt\n", - "\n", - "# 3 Punkte\n", - "sigma = np.round(avg_weight * 0.05, decimals=1)\n", - "men = rand.normal(avg_weight, sigma, sims)\n", - "gain_week = [\n", - " rand.uniform(-2.5, 2.5, sims)\n", - " for _ in range(7)\n", - "]\n", - "\n", - "# Sum up 2 Punkte\n", - "duration = np.zeros(sims)\n", - "for gain in gain_week:\n", - " duration += gain\n", - "duration += men\n", - "\n", - "gain_percent = float(np.round((duration < avg_weight-3).sum()/sims, decimals=2)) # 1 Punkt\n", - "\n", - "plt.figure(figsize=(10,5))\n", - "plt.hist(duration, density=True) # 1 Punkt\n", - "plt.axvline(avg_weight-3, color='r') # 1 Punkt\n", - "plt.show()\n", - "print(gain_percent)\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "01d0d2ee-af6a-46b4-8cdf-652024bb468d", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-089ff54f3c3db43b", - "locked": false, - "points": 3, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "source": [ - "**Begründe dein Vorgehen:**\n", - "\n", - "**Erkläre dein Ergebnis:**\n", - "\n", - "**Interpretiere & Erkläre deinen Plot:**" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "dfe81dfc-5096-4c5b-8e84-81267e212cfa", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-87149b330de46548", - "locked": true, - "points": 8, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Hier werden ihre Lösungen getestet...\n", - "import math\n", - "assert math.isclose(float(gain_percent), 0.3, rel_tol=0.1)\n", - "\n", - "### BEGIN HIDDEN TESTS\n", - "assert sims > 499\n", - "assert sims < 2_001\n", - "\n", - "trand = np.random.default_rng(420)\n", - "\n", - "t_sigma = np.round(avg_weight * 0.05, decimals=1)\n", - "t_men = trand.normal(avg_weight, sigma, sims)\n", - "t_gain_week = [\n", - " trand.uniform(-2.5, 2.5, sims)\n", - " for _ in range(7)\n", - "]\n", - "\n", - "assert math.isclose(sigma, t_sigma, rel_tol=0.1)\n", - "\n", - "for el1, el2 in zip(gain_week, t_gain_week):\n", - " assert np.array_equal(el1, el2)\n", - "\n", - "assert np.array_equal(t_men, men)\n", - "\n", - "t_duration = np.zeros(sims)\n", - "for gain in t_gain_week:\n", - " t_duration += gain\n", - "t_duration += t_men\n", - "\n", - "assert np.array_equal(t_duration, duration)\n", - "### END HIDDEN TESTS" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +version https://git-lfs.github.com/spec/v1 +oid sha256:c91a09b9bd16ba1abfe3163198d50147519a3952e76cb8caf064de4ba779250e +size 290381 diff --git a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/7.Pandas_Seaborn.ipynb b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/7.Pandas_Seaborn.ipynb index 9328033..744021e 100644 --- a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/7.Pandas_Seaborn.ipynb +++ b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/7.Pandas_Seaborn.ipynb @@ -1,3662 +1,3 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c2734eb9-d9b9-43b2-a9fb-c25729a0f3a3", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-5fff5091c9d505d4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# 7. Programmierübung: Pandas & Seaborn\n", - "\n", - "
\n", - "
\n", - " Willkommen zur siebten Programmierübung Einführung in Python 3.\n", - "
\n", - " \n", - "
\n", - "\n", - "Wenn Sie Fragen oder Verbesserungsvorschläge zum Inhalt oder Struktur der Notebooks haben, dann können sie eine E-Mail an Phil Keier ([p.keier@hbk-bs.de](mailto:p.keier@hbk-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) oder Martin Le ([martin.le@tu-bs.de](mailto:martin.le@tu-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) schreiben.\n", - "\n", - "Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n", - "\n", - "Der Großteil des Python-Tutorials stammt aus der Veranstaltung _Deep Learning Lab_ und von [www.python-kurs.eu](https://www.python-kurs.eu/python3_kurs.php) und wurde für _Signale und Systeme_, sowie _Einführung in die Programmierung für Nicht Informatiker_ angepasst.\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "8af16cfd-827f-4e7d-91a3-970e920ad212", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-fa7bc65ef436bf2f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Was ist Pandas?\n", - "\n", - ">_pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis/manipulation tool available in any language. It is already well on its way toward this goal._\n", - "\n", - "Pandas Keyfeature sind die beiden Datenstruckturen [Series](https://pandas.pydata.org/docs/reference/series.html) und [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame).\n", - "\n", - "Diesem Notebook liegt zu Lernzwecken das Datenset `unis_nd.csv` bei, mit dem die Grundlegenden Funktionen Pandas gezeigt werden.\n", - "\n", - "__Für dieses Notebook schauen Sie bitte in die [Pandas Docs](https://pandas.pydata.org/docs/)!!!__ Dort sind alle Funktionen beschrieben die wir hier bearbeiten und noch mehr!\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "496ec5e1-2fdb-4075-853b-c91a6144ac82", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-7fc3ec9d80a10c43", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Was ist Seaborn?\n", - "\n", - ">_Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics._\n", - "\n", - "Seaborn bietet dabei keine neue Funktionalität gegenüber dem bekannten MatPlotLib, jedoch abstrahiert es Darstellungen die Aufwendig mit MatPlotLib darzustelln wären. Schaue dir daher gerne die [Gallerie](https://seaborn.pydata.org/examples/index.html) an.\n", - "\n", - "__Für dieses Notebook schauen Sie bitte in die [Searborn Docs](https://seaborn.pydata.org/api.html)!!!__ Dort sind alle Funktionen beschrieben die wir hier bearbeiten und noch mehr!\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "62286603-158e-4448-98e5-e8f4aa60448a", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-d7a24465d2b7b67c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Achtung\n", - "\n", - "Falls es zu Problemen kommen sollte das Seaborn nicht bekannt sei führe bitte vor **jedem** bearbeiten folgende Zeile aus:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "7198387d-3fcc-4501-9460-d07e84384a75", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-a2a83048f4c8f0b8", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting seaborn\n", - " Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n", - "Collecting numpy!=1.24.0,>=1.20 (from seaborn)\n", - " Downloading numpy-2.3.3-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (62 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.1/62.1 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hCollecting pandas>=1.2 (from seaborn)\n", - " Downloading pandas-2.3.3-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl.metadata (91 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m91.2/91.2 kB\u001b[0m \u001b[31m19.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hCollecting matplotlib!=3.6.1,>=3.4 (from seaborn)\n", - 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"Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "pip install seaborn" - ] - }, - { - "cell_type": "markdown", - "id": "8c6fa552-7e9c-4561-b7c3-e72268db9afb", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-430939b1ccd3736f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Import Pandas\n", - "\n", - "Pandas wird vom Internet mit der Abkürzung `pd` importiert.\n", - "\n", - "Führen Sie die nächste Zelle beim neustart des Notebooks bitte immer aus." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "64315664-4ab4-46db-9600-b0dd58f95174", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-44f74bf2f8803769", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "id": "2ea5a1da-05f7-41a1-96cc-aaba1c74cf0d", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-53884904925c1212", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Data Frame\n", - "\n", - "Ein Pandas Data Frame `pd.DataFrame` ist eine 2-Dimensionale Datenstrucktur, vergleichbar mit einer Excel Tabelle.\n", - "\n", - "![](https://pandas.pydata.org/docs/_images/01_table_dataframe.svg)\n", - "\n", - "Um aus einem Dictionary einen Pandas DataFrame zu erstellen wird das Objekt `pd.DataFrame` verwendet. Dabei ist es wichtig das das Dicitionary einer Ordnung folgt, bei dem die Schlüssel die Namen der Spalten sind. Die Reihe im Data Frame wird dann einem Schlüssel als Liste mit Werten zugeordnet.\n", - "\n", - "Schauen Sie sich dazu Folgendes Dictionary an, welches ein Subset aus dem beiliegenden Datenset `unis_nd.csv`:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "30d2f79a-3f54-4397-a1f3-626b58fdf67e", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-e30f1b0549eb4481", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "unis_data = {\n", - " \t\"University name\": [ \n", - " \"Hochschule für Bildende Künste Braunschweig\",\n", - " \"Technische Universität Carolo-Wilhelmina zu Braunschweig\",\n", - " \"Hochschule 21\",\n", - " \"Technische Universität Clausthal\",\n", - " \"Hochschule Emden/Leer\",\n", - " \"PFH – Private Hochschule Göttingen\",\n", - " \"Georg-August-Universität Göttingen\"\n", - " ],\n", - " \n", - " \"Type of university\": [\n", - " \"Artistic university\",\n", - " \"University\",\n", - " \"University of Applied Sciences\",\n", - " \"University\",\n", - " \"University of Applied Sciences\",\n", - " \"University of Applied Sciences\",\n", - " \"University\"\n", - " ]\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "ba23542f-9030-45a3-a7bc-9c3841ae843e", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-08a43ccd460568f5", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Um aus dem Dictionary ein DataFrame zu erstellen, wird es einfach als Input für `pd.DataFrame` verwendet.\n", - "\n", - "Anschließend lassen wir es ausgeben. Nehmen Sie sich gerne die Zeit uns inspizieren Sie die Strucktur des beispielhaften DataFrames:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "074d682c-e936-40c3-97e1-2208b4e86a79", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-b519f770ad46c82b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " University name \\\n", - "0 Hochschule für Bildende Künste Braunschweig \n", - "1 Technische Universität Carolo-Wilhelmina zu Br... \n", - "2 Hochschule 21 \n", - "3 Technische Universität Clausthal \n", - "4 Hochschule Emden/Leer \n", - "5 PFH – Private Hochschule Göttingen \n", - "6 Georg-August-Universität Göttingen \n", - "\n", - " Type of university \n", - "0 Artistic university \n", - "1 University \n", - "2 University of Applied Sciences \n", - "3 University \n", - "4 University of Applied Sciences \n", - "5 University of Applied Sciences \n", - "6 University " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unis = pd.DataFrame(unis_data)\n", - "unis" - ] - }, - { - "cell_type": "markdown", - "id": "e732d3ff-463e-4411-b023-47f2fe02e4c5", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-6ed4bfb85a3f95bb", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Zugriffs Operationen\n", - "\n", - "Wie bereits vom Dicitonary bekannt lässt sich mittels Schlüssel (im Beispiel `University name`) auf eine Spalte zugreifen. Dabei ist wichtig zu erwähnen das jede Spalte in einem `DataFrame` eine `Series` ist. Eine `Series` ist dabei das 1-Dimensionale äquivalent zum 2-Dimensionales `DataFrame`.\n", - "\n", - "Im Folgenden Beispiel Selektieren wir die Series `University name` aus dem DataFrame `unis_nd`:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "cfa48421-7e56-4c10-a4fc-3d907f429309", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-ef292cd51db1030b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 Hochschule für Bildende Künste Braunschweig\n", - "1 Technische Universität Carolo-Wilhelmina zu Br...\n", - "2 Hochschule 21\n", - "3 Technische Universität Clausthal\n", - "4 Hochschule Emden/Leer\n", - "5 PFH – Private Hochschule Göttingen\n", - "6 Georg-August-Universität Göttingen\n", - "Name: University name, dtype: object" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unis[\"University name\"]" - ] - }, - { - "cell_type": "markdown", - "id": "62a92dad-506a-434f-b343-c020f89b19a1", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-dfdff5bf7e9be2ad", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Um eine `Series` manuel zu definieren wird `pd.Series` verwendet. Dabei kann mittels Parameter `name` ein label für die Series gesetzt werden:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "7d54c159-119d-4350-9cf9-081ecc8f5712", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-2a1d8c078f0249d8", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 Hochschule für Bildende Künste Braunschweig\n", - "1 Technische Universität Carolo-Wilhelmina zu Br...\n", - "2 Hochschule 21\n", - "3 Technische Universität Clausthal\n", - "4 Hochschule Emden/Leer\n", - "5 PFH – Private Hochschule Göttingen\n", - "6 Georg-August-Universität Göttingen\n", - "Name: University name, dtype: object" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "uni_names = pd.Series([ \n", - " \"Hochschule für Bildende Künste Braunschweig\",\n", - " \"Technische Universität Carolo-Wilhelmina zu Braunschweig\",\n", - " \"Hochschule 21\",\n", - " \"Technische Universität Clausthal\",\n", - " \"Hochschule Emden/Leer\",\n", - " \"PFH – Private Hochschule Göttingen\",\n", - " \"Georg-August-Universität Göttingen\"\n", - " ], name=\"University name\")\n", - "\n", - "uni_names" - ] - }, - { - "cell_type": "markdown", - "id": "97930782-3e33-4036-8be2-f40c10f18794", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-e989fac8445abfe4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Wie Sie sehen ist der Output identisch.\n", - "\n", - "Um auf einzelne Elemente in der `Series` zuzugreifen werden, wie bereits bekannt von Listen, Index zugriffe verwendet.\n", - "\n", - "Beispiel; Selektion des 2 Elementes:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "3947baa1-1a3d-41fa-866d-a8acbd07a89a", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-07bcadf8f88e6603", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'Technische Universität Carolo-Wilhelmina zu Braunschweig'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "uni_names[1]" - ] - }, - { - "cell_type": "markdown", - "id": "d8541372-36fa-4f59-b131-9c737b1e3457", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-0662b5716d2ffbf6", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Analog dazu für den Data Frame. Bei dem zuerst die Spalte und dann Reihe selektiert wird:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "69e57b94-06ea-445b-a8ae-63952dd95358", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-d17cf7e2161c24e3", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'Technische Universität Carolo-Wilhelmina zu Braunschweig'" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unis[\"University name\"][1]" - ] - }, - { - "cell_type": "markdown", - "id": "720262aa-1d99-469b-896e-1bf5da4b712b", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-a5f5d2b918c6704b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Wie beim Dictionary lassen sich auch die bekannten Funktionen `.values`, `.keys` & `.items` ausgeben.\n", - "\n", - "Beispiel `.keys`:\n", - "\n", - "Achtung die Ausgabe ist keine Liste auch wenn es den Anschein erwirkt!" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "84809ffa-64a2-4c9d-b3b1-3ff792ebda86", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-0a4876a0ab9f0672", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Schlüssel:\n", - "Index(['University name', 'Type of university'], dtype='object')\n", - "\n", - "Rückgabetype:\n", - "\n" - ] - } - ], - "source": [ - "print(\"Schlüssel:\", unis.keys(), \"\\nRückgabetype:\", type(unis.keys()), sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "id": "88957f18-bbc0-4ce3-981d-9b6735ea437b", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-7708becc41b11601", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Aufgabe - Erstellen eines Dataframes\n", - "\n", - "*5 Punkte*\n", - "\n", - "Erstellen Sie einen Pandas Data Frame mit dem namen `uni_addr`, nachdem Schema folgender Tabelle:\n", - "\n", - "| Address | plz |\n", - "|--------------------------|----------------------------|\n", - "| Johannes-Selenka-Platz 1 | 38118 Braunschweig |\n", - "| Universitätspl. 2 | 38106 Braunschweig |\n", - "| Harburger Str. 6 | 21614 Buxtehude |\n", - "| Adolph-Roemer-Straße 2A | 38678 Clausthal-Zellerfeld |\n", - "| Constantiapl. 4 | 26723 Emden |\n", - "| Weender Landstraße 3-7 | 37073 Göttingen |\n", - "| Wilhelmsplatz 1 | 37073 Göttingen |" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "2a6555ac-97ce-4d78-8d19-359b1eeb5a1a", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-6ea306cdf2a57ea3", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "uni_addr = None\n", - "\n", - "### BEGIN SOLUTION\n", - "uni_addr = pd.DataFrame({\n", - " \"Address\": [\"Johannes-Selenka-Platz 1\", \"Universitätspl. 2\", \"Harburger Str. 6\", \"Adolph-Roemer-Straße 2A\", \"Constantiapl. 4\", \"Weender Landstraße 3-7\", \"Wilhelmsplatz 1\"],\n", - " \"plz\": [\"38118 Braunschweig\", \"38106 Braunschweig\", \"21614 Buxtehude\", \"38678 Clausthal-Zellerfeld\", \"26723 Emden\", \"37073 Göttingen\", \"37073 Göttingen\",]\n", - "})\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "1936705b-949b-47ad-95e1-67d987662efc", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-daa317b892c6c606", - "locked": true, - "points": 5, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Hier werden ihre Lösung gestestet ...\n", - "assert isinstance(uni_addr, pd.DataFrame)\n", - "assert len(uni_addr[\"Address\"]) == 7\n", - "assert len(uni_addr[\"plz\"]) == 7\n", - "### BEGIN HIDDEN TESTS\n", - "uni_addr_test = pd.DataFrame({\n", - " \"Address\": [\"Johannes-Selenka-Platz 1\", \"Universitätspl. 2\", \"Harburger Str. 6\", \"Adolph-Roemer-Straße 2A\", \"Constantiapl. 4\", \"Weender Landstraße 3-7\", \"Wilhelmsplatz 1\"],\n", - " \"plz\": [\"38118 Braunschweig\", \"38106 Braunschweig\", \"21614 Buxtehude\", \"38678 Clausthal-Zellerfeld\", \"26723 Emden\", \"37073 Göttingen\", \"37073 Göttingen\",]\n", - "})\n", - "\n", - "for el1, el2 in zip(uni_addr[\"Address\"], uni_addr_test[\"Address\"]):\n", - " assert el1 == el2\n", - "\n", - "for el1, el2 in zip(uni_addr[\"plz\"], uni_addr_test[\"plz\"]):\n", - " assert el1 == el2\n", - "\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "8f25a6fb-3f9b-497a-a966-3e53c2f86b9c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8871c67f03f141dd", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe - Extrahieren einer Series\n", - "\n", - "*1 Punkte*\n", - "\n", - "Exthahieren Sie die Series `plz` aus dem zuvor erstelltem Data Frame `uni_addr` und speichern Sie ihr Ergebnis in `uni_plz`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "2de2487d-158e-4dec-a5fa-ccd7478c161c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-240ce62f624ce706", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [], - "source": [ - "uni_plz = None\n", - "\n", - "### BEGIN SOLUTION\n", - "uni_plz = uni_addr[\"plz\"]\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "613f9082-d963-4129-a70c-acfab56759e0", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-dbd86892a80c1f08", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 38118 Braunschweig\n", - "1 38106 Braunschweig\n", - "2 21614 Buxtehude\n", - "3 38678 Clausthal-Zellerfeld\n", - "4 26723 Emden\n", - "5 37073 Göttingen\n", - "6 37073 Göttingen\n", - "Name: plz, dtype: object\n" - ] - } - ], - "source": [ - "# Hier werden ihre Lösungen getestet\n", - "print(uni_plz)\n", - "assert isinstance(uni_plz, pd.Series)\n", - "### BEGIN HIDDEN TESTS\n", - "uni_plz_test = pd.Series([\"38118 Braunschweig\", \"38106 Braunschweig\", \"21614 Buxtehude\", \"38678 Clausthal-Zellerfeld\", \"26723 Emden\", \"37073 Göttingen\", \"37073 Göttingen\",], name=\"plz\")\n", - "for el1, el2 in zip(uni_plz, uni_plz_test):\n", - " assert el1 == el2\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "f308563d-e961-40a8-bf11-79d137b02e88", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-02f5ee7a60d555b6", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Reading Data\n", - "\n", - "Die unweiten des Internets ermöglichen es uns Daten auf die unterschiedlichsten Arten zu Archivieren. \n", - "\n", - "Für die Speicherung und Darstellung von (Roh-)Daten werden Datenbanken ([SQL](https://en.wikipedia.org/wiki/SQL)), Transportformate ([JSON](https://en.wikipedia.org/wiki/JSON), [XML](https://en.wikipedia.org/wiki/XML)), Tabellenformate ([Excel](https://en.wikipedia.org/wiki/Microsoft_Excel), [CSV](https://en.wikipedia.org/wiki/Comma-separated_values)) und noch viele mehr verwendet.\n", - "\n", - "Ein beliebtes Betriebsystemunabhängiges Dateiformat für kleine Datensätze (bis 10GB) werden `.csv` Dateien verwendet. Jedes gängige Tabellenkalkulations- und Umfragentool kann seine Daten als _Comma-seperated values_ kurz _CSV_ Exportieren. Schauen Sie dazu gerne in das Datenset `unis_nd.csv`. Im folgenden wird sich ausschließlich auf das Einlesen von CSV-Dateien bezogen. Welche Funktionen und Dateienformate Pandas unterstüzt entnehmen Sie bitte der Dokumentation zu [input/output](https://pandas.pydata.org/docs/reference/io.html).\n", - "\n", - "Da ich ihnen Die Spannung nicht nehmen möchte lösen Sie für die folgenden Beispiele bitte nächste Aufgabe." - ] - }, - { - "cell_type": "markdown", - "id": "a6d7e62b-df8e-4da5-8f74-6c81be0f00df", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7bf6c58e48e58743", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe - Read CSV\n", - "\n", - "*1 Punkt*\n", - "\n", - "Nutzen sie die Funktion `pd.read_csv` um das Datenset `unis_nd.csv` in die Variable `unis_nd` einzulesen.\n", - "\n", - "Falls Sie hilfe benötigen lesen Sie gerne die Dokumentation im [Getting Started](https://pandas.pydata.org/docs/getting_started/intro_tutorials/02_read_write.html) Guide.\n", - "\n", - "_Hinweis: Die Datei liegt in keinem Ordner, sondern im selben wie dieses Notebook!_" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "b08cc0c4-277b-4e9f-b6e7-a607b912febe", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3cdc8afd6dc12d44", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [], - "source": [ - "unis_nd = None\n", - "\n", - "### BEGIN SOLUTION\n", - "unis_nd = pd.read_csv(\"unis_nd.csv\")\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "e5760557-ddec-4502-b2d3-e8cc2ef70348", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-fd974b3aa3563ca0", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Hier werden ihre Lösungen getestet\n", - "assert isinstance(unis_nd, pd.DataFrame)" - ] - }, - { - "cell_type": "markdown", - "id": "ffcf5c5c-1e35-42f9-b35b-2494ff4c421f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-614d1dc9c4566861", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Ausgabe Operationen\n", - "\n", - "Um den gesamten Data Frame auszugeben nutzen Sie einfach die Syntax aus nächster Zelle.\n", - "\n", - "Achtung die Ausgabe ist vergleichweise Groß! Nehmen Sie sich gerne Zeit und schauen Sie sich das Dataset an." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "c9b8b9a0-0a62-4887-b6e8-c1da6086d7e4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ec7681e1a54af790", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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University nameType of universitySponsorshipRight of promotionFounding yearNumber of studentsAddresslatlonplzpic
0Hochschule für Bildende Künste BraunschweigArtistic universitypublicyes1963976.000Johannes-Selenka-Platz 152.25773810.50231538118 Braunschweighttps://www.hbk-bs.de/fileadmin/_processed_/5/...
1Technische Universität Carolo-Wilhelmina zu Br...Universitypublicyes174517709.000Universitätspl. 252.27355010.53009738106 Braunschweighttps://upload.wikimedia.org/wikipedia/commons...
2Hochschule 21University of Applied Sciencesprivatno20051084.000Harburger Str. 653.4776509.70465021614 Buxtehudehttps://upload.wikimedia.org/wikipedia/commons...
3Technische Universität ClausthalUniversitypublicyes17753446.000Adolph-Roemer-Straße 2A51.80484010.33411038678 Clausthal-Zellerfeldhttps://www.presse.tu-clausthal.de/fileadmin/T...
4Hochschule Emden/LeerUniversity of Applied Sciencespublicno20094481.000Constantiapl. 453.3681607.18141026723 Emdenhttps://sta-hisweb.hs-emden-leer.de/QIS/images...
5PFH – Private Hochschule GöttingenUniversity of Applied Sciencesprivatno19954226.000Weender Landstraße 3-751.5389109.93322037073 Göttingenhttps://goettingen-campus.de/fileadmin/_proces...
6Georg-August-Universität GöttingenUniversitypublicyes173728614.000Wilhelmsplatz 151.5340709.93785037073 Göttingenhttps://upload.wikimedia.org/wikipedia/commons...
7Fachhochschule für die Wirtschaft HannoverUniversity of Applied Sciencesprivatno1996641.000Freundallee 1552.3662009.77247030173 Hannoverhttps://upload.wikimedia.org/wikipedia/commons...
8Hochschule HannoverUniversity of Applied Sciencespublicno19719209.000Ricklinger Stadtweg 12052.3541909.72238030459 Hannoverhttps://upload.wikimedia.org/wikipedia/commons...
9Hochschule für Musik, Theater und Medien HannoverArtistic universitypublicyes18971409.000Neues Haus 152.3773809.75392030175 Hannoverhttps://upload.wikimedia.org/wikipedia/commons...
10Leibniz-FachhochschuleUniversity of Applied Sciencesprivatno1920589.000Expo Plaza 1152.3211509.81868030539 Hannoverhttps://www.visit-hannover.com/var/storage/ima...
11Medizinische Hochschule Hannover (MHH)Universitypublicyes19633778.000Carl-Neuberg-Straße 152.3840509.80603030625 Hannoverhttps://upload.wikimedia.org/wikipedia/commons...
12Stiftung Tierärztliche Hochschule HannoverUniversitypublicyes17782381.000Bünteweg 252.3546809.79773030559 Hannoverhttps://upload.wikimedia.org/wikipedia/de/thum...
13Gottfried Wilhelm Leibniz Universität HannoverUniversitypublicyes183128935.000Welfengarten 152.3822509.71777030167 Hannoverhttps://www.uni-hannover.de/fileadmin/_process...
14Fachhochschule für Interkulturelle Theologie H...University of Applied Sciencesprivatno201291.000Missionsstraße 3-552.70884310.14071029320 Südheidehttps://cdn.max-e5.info/damfiles/logo/fh_herma...
15Universität HildesheimUniversitypublicyes19788378.000Universitätspl. 152.1340109.97469031141 Hildesheimhttps://www.uni-hildesheim.de/media/_processed...
16HAWK Hochschule für angewandte Wissenschaft un...University of Applied Sciencespublicno19716495.000Hohnsen 452.1424609.95798031134 Hildesheimhttps://upload.wikimedia.org/wikipedia/commons...
17HAWK Hochschule für angewandte Wissenschaft un...University of Applied Sciencespublicno19716495.000Haarmannpl. 351.8272609.45069037603 Holzmindenhttps://upload.wikimedia.org/wikipedia/commons...
18HAWK Hochschule für angewandte Wissenschaft un...University of Applied Sciencespublicno19716495.000Von-Ossietzky-Straße 9951.5217509.96967037085 Göttingenhttps://upload.wikimedia.org/wikipedia/commons...
19Leuphana Universität LüneburgUniversitypublicyes19466497.000Universitätsallee 153.22853110.40171021335 Lüneburghttps://upload.wikimedia.org/wikipedia/commons...
20Norddeutsche Hochschule für Rechtspflege – Nie...University of Administrationpublicno20076409.000Godehardspl. 652.1448409.94923031134 Hildesheimhttps://static.studycheck.de/media/images/inst...
21Kommunale Hochschule für Verwaltung in Nieders...University of Administrationpublicno20071570.000Wielandstraße 852.3705009.72239030169 Hannoverhttps://www.nsi-hsvn.de/fileadmin/user_upload/...
22Carl von Ossietzky Universität Oldenburg\\nUniversitypublicyes197315635.000Uhlhornsweg 49-5553.1473408.17902026129 Oldenburghttps://upload.wikimedia.org/wikipedia/commons...
23Hochschule OsnabrückUniversity of Applied Sciencespublicno197113620.000Albrechtstraße 3052.2826808.02501049076 Osnabrückhttps://login.hs-osnabrueck.de/nidp/hsos/image...
24Universität OsnabrückUniversitypublicyes197313640.000Neuer Graben 2952.2713708.04454049074 Osnabrückhttps://www.eh-tabor.de/sites/default/files/st...
25Hochschule Braunschweig/Wolfenbüttel, Ostfalia...University of Applied Sciencespublicno197111577.000Salzdahlumer Str. 46/4852.17683010.54865038302 Wolfenbüttelhttps://www.ostfalia.de/export/system/modules/...
26Hochschule Wolfsburg, Ostfalia Hochschule für ...University of Applied Sciencespublicno197111577.000Robert-Koch-Platz 8A52.42595010.78711038440 Wolfsburghttps://www.ostfalia.de/export/system/modules/...
27Hochschule Suderburg, Ostfalia Hochschule für ...University of Applied Sciencespublicno197111577.000Herbert-Meyer-Straße 752.89761010.44659029556 Suderburghttps://www.ostfalia.de/export/system/modules/...
28Hochschule Salzgitter, Ostfalia Hochschule für...University of Applied Sciencespublicno197111577.000Karl-Scharfenberg-Straße 55/5752.08724010.38055038229 Salzgitterhttps://www.ostfalia.de/export/system/modules/...
29Hochschule für Künste im Sozialen, OttersbergUniversity of Applied Sciencesprivatno1967342.000Große Str. 10753.1066809.16310028870 Ottersberghttps://upload.wikimedia.org/wikipedia/commons...
30Private Hochschule für Wirtschaft und Technik ...University of Applied Sciencesprivatno1998558.000Rombergstraße 4052.7212508.27891049377 Vechtahttps://www.phwt.de/wp-content/uploads/2020/09...
31Private Hochschule für Wirtschaft und Technik ...University of Applied Sciencesprivatno1998558.000Schlesier Str. 13A52.6117108.36334049356 Diepholzhttps://www.phwt.de/wp-content/uploads/2020/09...
32Universität VechtaUniversitypublicyes19954.551Driverstraße 2252.7211708.29380049377 Vechtahttps://upload.wikimedia.org/wikipedia/commons...
33Hochschule WeserberglandUniversity of Applied Sciencesprivatno2010485.000Am Stockhof 252.0987509.35542031785 Hamelnhttps://upload.wikimedia.org/wikipedia/commons...
34Jade Hochschule – WilhelmshavenUniversity of Applied Sciencespublicno20096789.000Friedrich-Paffrath-Straße 10153.5478708.08804026389 Wilhelmshavenhttps://www.jade-hs.de/fileadmin/layout2016/as...
35Jade Hochschule – OldenburgUniversity of Applied Sciencespublicno20096789.000Ofener Str. 16/1953.1417908.20213026121 Oldenburghttps://www.jade-hs.de/fileadmin/layout2016/as...
36Jade Hochschule – ElsflethUniversity of Applied Sciencespublicno20096789.000Weserstraße 5253.2424408.46651026931 Elsflethhttps://www.jade-hs.de/fileadmin/layout2016/as...
37Steuerakademie Niedersachsen RintelnUniversity of Administrationpublicno2006500.000Wilhelm-Busch-Weg 2952.2069609.09112031737 Rintelnhttps://www.steuerakademie.niedersachsen.de/as...
38Steuerakademie Niedersachsen Bad EilsenUniversity of Administrationpublicno2006500.000Bahnhofstraße 552.2398109.10423031707 Bad Eilsenhttps://www.steuerakademie.niedersachsen.de/as...
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"5 1995 4226.000 Weender Landstraße 3-7 \n", - "6 1737 28614.000 Wilhelmsplatz 1 \n", - "7 1996 641.000 Freundallee 15 \n", - "8 1971 9209.000 Ricklinger Stadtweg 120 \n", - "9 1897 1409.000 Neues Haus 1 \n", - "10 1920 589.000 Expo Plaza 11 \n", - "11 1963 3778.000 Carl-Neuberg-Straße 1 \n", - "12 1778 2381.000 Bünteweg 2 \n", - "13 1831 28935.000 Welfengarten 1 \n", - "14 2012 91.000 Missionsstraße 3-5 \n", - "15 1978 8378.000 Universitätspl. 1 \n", - "16 1971 6495.000 Hohnsen 4 \n", - "17 1971 6495.000 Haarmannpl. 3 \n", - "18 1971 6495.000 Von-Ossietzky-Straße 99 \n", - "19 1946 6497.000 Universitätsallee 1 \n", - "20 2007 6409.000 Godehardspl. 6 \n", - "21 2007 1570.000 Wielandstraße 8 \n", - "22 1973 15635.000 Uhlhornsweg 49-55 \n", - "23 1971 13620.000 Albrechtstraße 30 \n", - "24 1973 13640.000 Neuer Graben 29 \n", - "25 1971 11577.000 Salzdahlumer Str. 46/48 \n", - "26 1971 11577.000 Robert-Koch-Platz 8A \n", - "27 1971 11577.000 Herbert-Meyer-Straße 7 \n", - "28 1971 11577.000 Karl-Scharfenberg-Straße 55/57 \n", - 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38Steuerakademie Niedersachsen Bad EilsenUniversity of Administrationpublicno2006500.0Bahnhofstraße 552.239819.1042331707 Bad Eilsenhttps://www.steuerakademie.niedersachsen.de/as...
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" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unis_nd.tail(3)" - ] - }, - { - "cell_type": "markdown", - "id": "7573347e-593f-4dd8-a508-5037eb422628", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-cfdfb8a099225918", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Um zu prüfen welche Typen pandas den einzelnen Spalten gegeben hat könne Sie das Attribut `dtypes` verwenden." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "36d674fe-56ff-4610-8086-adae93bdae62", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-900e418ddc2a0b14", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "University name object\n", - "Type of university object\n", - "Sponsorship object\n", - "Right of promotion object\n", - "Founding year int64\n", - "Number of students float64\n", - "Address object\n", - "lat float64\n", - "lon float64\n", - "plz object\n", - "pic object\n", - "dtype: object" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unis_nd.dtypes" - ] - }, - { - "cell_type": "markdown", - "id": "093b47e6-7d60-4507-b1d4-e17be1409bed", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-502b1bd3f4ce3db1", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Um eine gesamt Übersicht des Dataframes zu bekommen nutzen Sie die Funktion `info`.\n", - "\n", - "Aus dieser können Sie entnehmen in welcher Spalte _#_ wie viele Elemente _Non-Null Count_ vorhanden sind; den Namen der Spalte _Column_ und wie Pandas die Werte der Spalte _dtype_ interpretiert." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "2ea51488-4dcb-4840-aae8-95bf54c226d0", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c47dd110d6a97b52", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 39 entries, 0 to 38\n", - "Data columns (total 11 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 University name 39 non-null object \n", - " 1 Type of university 39 non-null object \n", - " 2 Sponsorship 39 non-null object \n", - " 3 Right of promotion 39 non-null object \n", - " 4 Founding year 39 non-null int64 \n", - " 5 Number of students 39 non-null float64\n", - " 6 Address 39 non-null object \n", - " 7 lat 39 non-null float64\n", - " 8 lon 39 non-null float64\n", - " 9 plz 39 non-null object \n", - " 10 pic 39 non-null object \n", - "dtypes: float64(3), int64(1), object(7)\n", - "memory usage: 3.5+ KB\n" - ] - } - ], - "source": [ - "unis_nd.info()" - ] - }, - { - "cell_type": "markdown", - "id": "46bdc364-7bd2-4b50-bb74-b90d92312465", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d64d555865daff61", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Selecting Subsets\n", - "\n", - "![](https://pandas.pydata.org/docs/_images/03_subset_rows.svg)\n", - "\n", - "Wie Sie bereits aus dem ersten Kapitel Wissen können Sie einzelne Spalten mittels Schlüsselzugriff selektieren. Um mehr als eine Spalte zu Selektieren geben Sie dem Dataframe eine Liste der Schlüssel die sie auswählen möchten mit. Für alle weiterführenden Operationen zum Selektieren von Subsets lesen Sie gerne den [Getting started](https://pandas.pydata.org/docs/getting_started/intro_tutorials/03_subset_data.html) Guide zu Subset Data.\n", - "\n", - "Beispiel Selektion der Spalten _Sponsorship_ & _Founding year_: " - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "4078322c-7354-41ae-a9fa-3af389e37d41", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d98a6a7763f9ebc7", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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SponsorshipFounding year
0public1963
1public1745
2privat2005
3public1775
4public2009
5privat1995
6public1737
7privat1996
8public1971
9public1897
10privat1920
11public1963
12public1778
13public1831
14privat2012
15public1978
16public1971
17public1971
18public1971
19public1946
20public2007
21public2007
22public1973
23public1971
24public1973
25public1971
26public1971
27public1971
28public1971
29privat1967
30privat1998
31privat1998
32public1995
33privat2010
34public2009
35public2009
36public2009
37public2006
38public2006
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" - ], - "text/plain": [ - " Sponsorship Founding year\n", - "0 public 1963\n", - "1 public 1745\n", - "2 privat 2005\n", - "3 public 1775\n", - "4 public 2009\n", - "5 privat 1995\n", - "6 public 1737\n", - "7 privat 1996\n", - "8 public 1971\n", - "9 public 1897\n", - "10 privat 1920\n", - "11 public 1963\n", - "12 public 1778\n", - "13 public 1831\n", - "14 privat 2012\n", - "15 public 1978\n", - "16 public 1971\n", - "17 public 1971\n", - "18 public 1971\n", - "19 public 1946\n", - "20 public 2007\n", - "21 public 2007\n", - "22 public 1973\n", - "23 public 1971\n", - "24 public 1973\n", - "25 public 1971\n", - "26 public 1971\n", - "27 public 1971\n", - "28 public 1971\n", - "29 privat 1967\n", - "30 privat 1998\n", - "31 privat 1998\n", - "32 public 1995\n", - "33 privat 2010\n", - "34 public 2009\n", - "35 public 2009\n", - "36 public 2009\n", - "37 public 2006\n", - "38 public 2006" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unis_nd[[\"Sponsorship\", \"Founding year\"]]" - ] - }, - { - "cell_type": "markdown", - "id": "bef4ece8-00b0-486d-bba2-7f19e885cf6a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-289af023470b6b19", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe\n", - "\n", - "*2 Punkte*\n", - "\n", - "Selektieren Sie die Spalten _University name_, _Founding year_ & _Number of students_, speichern sie ihr Ergebnis in der Variablen `select`.\n", - "\n", - "Geben Sie danach die ersten 5 Werte von Oben der Selektion aus." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "040fa689-6062-44db-a6ea-b58113e41b65", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-2d560e3a83f1c48a", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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University nameFounding yearNumber of students
0Hochschule für Bildende Künste Braunschweig1963976.0
1Technische Universität Carolo-Wilhelmina zu Br...174517709.0
2Hochschule 2120051084.0
3Technische Universität Clausthal17753446.0
4Hochschule Emden/Leer20094481.0
\n", - "
" - ], - "text/plain": [ - " University name Founding year \\\n", - "0 Hochschule für Bildende Künste Braunschweig 1963 \n", - "1 Technische Universität Carolo-Wilhelmina zu Br... 1745 \n", - "2 Hochschule 21 2005 \n", - "3 Technische Universität Clausthal 1775 \n", - "4 Hochschule Emden/Leer 2009 \n", - "\n", - " Number of students \n", - "0 976.0 \n", - "1 17709.0 \n", - "2 1084.0 \n", - "3 3446.0 \n", - "4 4481.0 " - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "select = None\n", - "\n", - "### BEGIN SOLUTION\n", - "select = unis_nd[[\"University name\", \"Founding year\", \"Number of students\"]]\n", - "select.head(5)\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "5bd633ac-afd9-4731-9c00-b494331d2dfb", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-108386a4387dbcc7", - "locked": true, - "points": 2, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Hier werden ihre Lösungen getestet\n", - "assert isinstance(select, pd.DataFrame)\n", - "assert list(select.keys()) == [\"University name\", \"Founding year\", \"Number of students\"]" - ] - }, - { - "cell_type": "markdown", - "id": "e9751208-b181-4764-b436-a57c5313d288", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-4fa720449b4af62e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Statistische Funktionen\n", - "\n", - "Wie in der Dokumentation beschrieben unterstützt Pandas verschiendene Statistische Funktion, welche direkt auf einen Data Frame angewendet werden können.\n", - "\n", - "Als Beispiel wird die Funktion `value_counts` auf die Spalte _Type of university_ gezeigt und im darauffolgenden Schritt als Kuchendiagramm geplottet. Das Ergebnis in der Variablen `count` ist eine `pd.Series`.\n", - "\n", - "Editor Side Note: Für den Plot verwende ich das Stylesheet von dhaitz, eines meiner absoluten Favouriten, dieses und mehrere finden Sie unter [github.com dhaitz](https://github.com/dhaitz/matplotlib-stylesheets). Mittels `plt.style.use` lassen sich externe Stylesheets verwenden." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "14cc37af-13fa-49ce-af37-d327b16bda73", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-daa584941e3d78fe", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Type of university\n", - "University of Applied Sciences 22\n", - "University 11\n", - "University of Administration 4\n", - "Artistic university 2\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "count = unis_nd[\"Type of university\"].value_counts()\n", - "count" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "d8eff47d-e2e7-46c1-94ae-d344b3b62e0f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ea7ef2c3a9ada940", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "format = lambda s: f'Quantity: %d\\n' % s + f'In Percent: %1.1f%%' % s\n", - "\n", - "plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-dark.mplstyle') # Using my favourite Style\n", - "plt.pie(count, labels=count.keys(), autopct=format, startangle=45, explode=[0, 0, 0, 0.2])\n", - "plt.title(\"Types of Universities in lower Saxony (in %)\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "1104848a-c149-4ef0-8835-aee9f4ad9f36", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1c947f45fd0b2759", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe\n", - "\n", - "*2 Punkte*\n", - "\n", - "Nutzen Sie die Funktion `value_counts` und erstellen Sie ein Balkendiagramm, mittels matplotlib, über die Anzahl an Staatlichen und Privaten Hochschulen Niedersachsen.\n", - "\n", - "Die dazugehörige Spalte im Datenset ist _Sponsorship_. Finden Sie eine geeignete Darstellung." - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "id": "1892e3fc-4d89-4966-bb36-b691a3d16ea4", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-fa553fbf09ed469b", - "locked": false, - "points": 2, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "### BEGIN SOLUTION\n", - "c = unis_nd[\"Sponsorship\"].value_counts()\n", - "plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-dark.mplstyle')\n", - "plt.bar(c.keys(), c, color=[\"black\", \"white\"])\n", - "plt.show()\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "1b420d70-9b7c-4e36-9b59-a44316af189a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-cdcf1d5c71a5bfee", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Import Seaborn\n", - "\n", - "Auch hier waren die Entwickler uns einen Schritt vorraus und bevorzugen den Import von Seaborn mit dem Kürzel `sns`." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "b4e4202d-cf3f-4fa0-a66e-44ac08902cf2", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0ab00ccadf1d74a7", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "import seaborn as sns\n", - "import matplotlib.pyplot as plt # Using it alongside sns" - ] - }, - { - "cell_type": "markdown", - "id": "d10ef485-e7c4-4889-9e4f-61c765bf9384", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-25175b946c35845e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Setting a Theme\n", - "\n", - "Auch wenn MatPlotLib es möglichmacht hat seaborn eine simpleren Weg Themes anzuwenden. Schaue dir daher gerne die vorinstallierten Styles unter [Python Graph Gallery](https://python-graph-gallery.com/104-seaborn-themes/) an. Für die Folgenden Beispiele wird das Theme `darkgrid` verwendet.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "1469effb-088e-4721-9a9c-f2fcc1423a1d", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7587373683f9bc52", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "sns.set_style('darkgrid')" - ] - }, - { - "cell_type": "markdown", - "id": "56a7f8ff-8899-4973-be0b-7a680302bfa4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3151571f68fe985d", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Multidimensional Data Plotting\n", - "\n", - "Schauen wir uns im Folgenden an wie sich verschiedene Normalverteilte Größen in einem Plot unterbringen lassen.\n", - "\n", - "Dazu gehen wir davon aus das wir 60 Bienen, 3 verschiedener Arten gefangen haben und diese auf Länge und Anzahl an Parasiten untersuchen. Zunächst lässt sich feststellen das die Messwerkzeuge zum messen der Körperlängen nur auf 1 Dezimalstelle genau sind. \n", - "Die Anzahl der Parasiten ist immer eine ganze Zahl, da wir keine Rückstände halber/toter Parasiten messen wollen. Diese werden zusammengebracht und mittels Seaborn geplottet.\n", - "\n", - "\n", - "Hierzu werden 3 Bienenarten verwendet:\n", - "\n", - "|Biene|$\\mu$|$\\sigma$|Maximale Anzahl Parasiten|\n", - "|-|-|-|-|\n", - "|Fuchsrote Lockensandbiene|13 mm|1 mm|3|\n", - "|Ackerhummel|12 mm|3 mm|4|\n", - "|Platterbsen-Mörtelbiene|14 mm|1 mm|2|\n", - "\n", - "Im folgenden samplen wir von jeder Normalverteilten Körpergrößen der einzelnen Bienenarten 60 Samples:" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "4a63a9b6-4a17-46d0-8708-b3ab35e329b1", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-54d90be534e41353", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "rng = np.random.default_rng(42)\n", - "samples = 60\n", - "\n", - "# Sample lengths\n", - "lockensandbiene_len = rng.normal(13, 1, samples)\n", - "ackerhummel_len = rng.normal(12, 3, samples)\n", - "mörtelbiene_len = rng.normal(14, 1, samples)\n", - "\n", - "# Sample Parasites\n", - "lockensandbiene_par = rng.uniform(0, 3, samples)\n", - "ackerhummel_par = rng.uniform(0, 4, samples)\n", - "mörtelbiene_par = rng.uniform(0, 2, samples)" - ] - }, - { - "cell_type": "markdown", - "id": "e5f1d8d4-0910-4a65-91f3-26c18afdfcbe", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3154b2c952695029", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Aus den Samples lässt sich ein Dataset erstellen die wichtigen Attribute sind `species`, `length` & `parasites`. Nutzen wir dafür eine Dataclass erübrigt sich der Aufwand über listen." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "e5cbfbe6-bf9a-4ab8-8a82-c0f7310907d3", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a38996eee69926d9", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "from dataclasses import dataclass\n", - "\n", - "@dataclass\n", - "class Bee:\n", - " species: str\n", - " length: np.float64\n", - " parasites: np.int64\n", - "\n", - "bees = list()\n", - "\n", - "# For every samples bee append to bees\n", - "for bee_len, bee_par in zip(lockensandbiene_len, lockensandbiene_par):\n", - " b = Bee(species='Fuchsrote Lockensandbiene', length=np.round(bee_len, decimals=1), parasites=np.round(bee_par, decimals=0))\n", - " bees.append(b)\n", - "for bee_len, bee_par in zip(ackerhummel_len, ackerhummel_par):\n", - " b = Bee(species='Ackerhummel', length=np.round(bee_len, decimals=1), parasites=np.round(bee_par, decimals=0))\n", - " bees.append(b)\n", - "for bee_len, bee_par in zip(mörtelbiene_len, mörtelbiene_par):\n", - " b = Bee(species='Platterbsen-Mörtelbiene', length=np.round(bee_len, decimals=1), parasites=np.round(bee_par, decimals=0))\n", - " bees.append(b)" - ] - }, - { - "cell_type": "markdown", - "id": "453420f2-bfa5-448f-b353-7d2c150c86c8", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1cffcdd63ad3fb6c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Da ab diesem Schritt alle gesampleten Bienen geordnet und deklariert sind können wir mittels pandas einfach einen Dataframe erstellen und diesen speichern:" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "e4954edf-ecf9-4097-aab3-de0abf5bc7e5", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b17aafac68bb15b8", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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0Fuchsrote Lockensandbiene13.31.0
1Fuchsrote Lockensandbiene12.02.0
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3Fuchsrote Lockensandbiene13.91.0
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175Platterbsen-Mörtelbiene14.21.0
176Platterbsen-Mörtelbiene15.61.0
177Platterbsen-Mörtelbiene14.22.0
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179Platterbsen-Mörtelbiene14.30.0
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" - ], - "text/plain": [ - " species length parasites\n", - "0 Fuchsrote Lockensandbiene 13.3 1.0\n", - "1 Fuchsrote Lockensandbiene 12.0 2.0\n", - "2 Fuchsrote Lockensandbiene 13.8 2.0\n", - "3 Fuchsrote Lockensandbiene 13.9 1.0\n", - "4 Fuchsrote Lockensandbiene 11.0 0.0\n", - ".. ... ... ...\n", - "175 Platterbsen-Mörtelbiene 14.2 1.0\n", - "176 Platterbsen-Mörtelbiene 15.6 1.0\n", - "177 Platterbsen-Mörtelbiene 14.2 2.0\n", - "178 Platterbsen-Mörtelbiene 13.9 0.0\n", - "179 Platterbsen-Mörtelbiene 14.3 0.0\n", - "\n", - "[180 rows x 3 columns]" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bees_df = pd.DataFrame(bees)\n", - "bees_df" - ] - }, - { - "cell_type": "markdown", - "id": "c5e49fc8-e5f7-403d-9b56-3a560b92f0f4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b1092b46d45a70db", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Es hat Vorteile sich den Dataframe als CSV zu speichern dies versteht sich mittels:" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "fec83133-43d8-40ae-b3d0-43dab722a671", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-735e7a0c1e9c3f9d", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "bees_df.to_csv('Bees.csv', index=False)" - ] - }, - { - "cell_type": "markdown", - "id": "5efba824-f0d2-4134-837d-1be4ce9d2e46", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-55ef638057716b7e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "In dem Notebook Ordner sollte eine Datei `Bees.csv` erstellt worden sein. Schaue sie dir gerne an.\n", - "\n", - "Seaborn macht es dementsprechend einfach. So verlangt `jointplot` ein Pandas DataFrame und für die `x` & `y` Koordinaten einfach den Namen der Spalten. um die Bienen voneinander zu Unterscheiden wird mit dem Parameter `hue` (Farbwert) nach Spezies gefärbt.\n", - "\n", - "Mit einer einzelenen Zeile ensteht dann folgender Plot.\n", - "Dieser Zeigt die Anzahl an Parasiten für jede gesamplete Biene und an den Seiten die Normalverteilungen dieser:" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "9778a0c6-0fb9-4b84-a8c5-e476dd7448a4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3a931b591234c528", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.jointplot(data=bees_df, x=\"length\", y=\"parasites\", hue=\"species\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "46c69a61-fbd6-4c51-a749-a141ed0c8eca", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-38cb7d6f4529d85e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Mit dem `kind` Parameter lösst sich selbiger Plot auch als Histogramm anzeigen:" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "0b17470e-c9a0-4fd9-887e-eccbb3dead1f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d0fb66b692b5eecd", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.jointplot(data=bees_df, x=\"length\", y=\"parasites\", hue=\"species\", kind='hist')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "22c86f08-23e9-4135-a3ef-4a6b1c86554c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d8aa28cae2c7b366", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Aus dem Dataset lassen sich auch Multiplots erstellen:" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "id": "29b3fe01-f7ca-4a2a-822f-cb147e12eece", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-37ffc1027bae59c4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "grid = sns.FacetGrid(bees_df, col='species', hue='species')\n", - "grid.map(sns.histplot, \"length\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "4466fadd-e137-43ab-ba69-e4353cf54aad", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-11da2cf6fff4eafa", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe\n", - "\n", - "*8 Punkte*\n", - "\n", - "Erstelle das Dataset `people_in_germany`.\n", - "\n", - "Folgendes Szenario:\n", - "\n", - "Du bist Part einer Massenhaft angelegten Studie um die bereits bekannten Zahlen des Statistischen Bundesamtes zu überprüfen.\n", - "\n", - "Dazu sind 4 Größen bekannt aus den Angaben des Statistischen Bundesamt für die [männliche Population](https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Gesundheit/Gesundheitszustand-Relevantes-Verhalten/Tabellen/koerpermasse-maenner.html) & die [weibliche Population](https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Gesundheit/Gesundheitszustand-Relevantes-Verhalten/Tabellen/koerpermasse-frauen.html):\n", - "\n", - "||Körpergröße (in cm)|Gewicht (in Kg)|\n", - "|-|-|-|\n", - "|Männlich|178.9|85.8|\n", - "|Weiblich|165.8|69.2|\n", - "\n", - "Gehe dabei wie folgt vor:\n", - "- Treffe annahmen über die Verteilung und finde geeignete Werte für $\\mu$ & $\\sigma$. **Erkläre** deine Annahmen mit einem kurzen Text.\n", - "- Die Samplegröße beträgt 1000. Sample dementsprechend aus den gegebenen Werten.\n", - "- Speichere die gesampleten Personen nach dem Schema: |gender|height|weight| in der File `people_in_germany.csv` als csv.\n", - " -> Nutze für das `gender` Attribut den Datentyp `bool` mit der Kodierung: `True = female` & `False = male`.\n", - "- Stelle dein Ergebnis angemessen dar. Das theme `darkgrid` darf nicht verwendet werden.\n", - "- **Beschreibe** & **Interpretiere** den Plot. Gehe dabei von der Hypothese aus das es **keine** Unterschiede zwischen den Geschlechtern beim samplen geben sollte.\n", - "\n", - "*Tipp: Dataclasses erleichtern die Aufgabe ungemein!*" - ] - }, - { - "cell_type": "code", - "execution_count": 148, - "id": "68702477-b09f-45ee-871d-da9a130f3319", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0c8bc5e7c272cd4c", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "import numpy as np\n", - "import seaborn as sns\n", - "from dataclasses import dataclass\n", - "\n", - "samples = 1000\n", - "\n", - "@dataclass\n", - "class Individual:\n", - " gender: bool\n", - " height: np.float64\n", - " weight: np.float64\n", - " \n", - "\n", - "rng = np.random.default_rng()\n", - "individuals = list()\n", - "\n", - "for i in range(samples):\n", - " if i % 2 == 0:\n", - " gender = False\n", - " height = rng.normal(178.9, 10)\n", - " weight = rng.normal(85.8, 26)\n", - " \n", - " else:\n", - " gender = True\n", - " height = rng.normal(165.8, 11)\n", - " weight = rng.normal(69.2, 19)\n", - "\n", - " height = np.round(height, decimals=1)\n", - " weight = np.round(weight, decimals=1)\n", - " \n", - " individual = Individual(gender=gender,height=height,weight=weight)\n", - " individuals.append(individual)\n", - "\n", - "individuals = pd.DataFrame(individuals)\n", - "individuals.to_csv('people_in_germany.csv', index=False)\n", - "\n", - "sns.set_style('white')\n", - "sns.jointplot(data=individuals, x=\"height\", y=\"weight\", hue=\"gender\")\n", - "plt.show()\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "2fbcac55-ed91-4290-8026-639d215f783c", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-8b5f26594c6567ed", - "locked": false, - "points": 3, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 149, - "id": "9d423664-b098-4fd4-9bad-5f7028424c87", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-3060604f60a0ca6f", - "locked": true, - "points": 8, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Hier werden ihre Lösungen getestet...\n", - "test_individuals = pd.read_csv('people_in_germany.csv')\n", - "assert len(test_individuals) > 999 # Test if enough samples where taken 1 Punkt\n", - "assert list(test_individuals.columns) == ['gender', 'height', 'weight'] # Test if columns are correct 1 Punkt\n", - "\n", - "# Test gender means\n", - "male_height = np.mean(test_individuals[test_individuals['gender'] == False]['height'])\n", - "male_weight = np.mean(test_individuals[test_individuals['gender'] == False]['weight'])\n", - "female_height = np.mean(test_individuals[test_individuals['gender'] == True]['height'])\n", - "female_weight = np.mean(test_individuals[test_individuals['gender'] == True]['weight'])\n", - "\n", - "assert male_height > 176 and male_height < 182 # 1 Punkt\n", - "assert male_weight > 83 and male_weight < 89 # 1 Punkt\n", - "assert female_height > 163 and female_height < 169 # 1 Punkt\n", - "assert female_weight > 66 and female_weight < 72 # 1 Punkt\n", - "\n", - "# test cummultative mean 1 Punkt\n", - "assert test_individuals['height'].mean() > 170 and test_individuals['height'].mean() < 174 \n", - "assert test_individuals['weight'].mean() > 75 and test_individuals['weight'].mean() < 80 " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +version https://git-lfs.github.com/spec/v1 +oid sha256:da1292c34a10e72597bd42322cc99ad13b25804d8e69e6dc191ea7a632e0ff74 +size 468043 diff --git a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/8.Folium.ipynb b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/8.Folium.ipynb index c7c956b..4afcaef 100644 --- a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/8.Folium.ipynb +++ b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/8.Folium.ipynb @@ -1,6064 +1,3 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "11ce8688-2dd2-4a18-aa6c-bce96a801782", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-1720c646ec279d2e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# 8. Programmierübung: Folium\n", - "\n", - "
\n", - "
\n", - " Willkommen zur achten Programmierübung Einführung in Python 3.\n", - "
\n", - " \n", - "
\n", - "\n", - "Wenn Sie Fragen oder Verbesserungsvorschläge zum Inhalt oder Struktur der Notebooks haben, dann können sie eine E-Mail an Phil Keier ([p.keier@hbk-bs.de](mailto:p.keier@hbk-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) oder Martin Le ([martin.le@tu-bs.de](mailto:martin.le@tu-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) schreiben.\n", - "\n", - "Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n", - "\n", - "Der Großteil des Python-Tutorials stammt aus der Veranstaltung _Deep Learning Lab_ und von [www.python-kurs.eu](https://www.python-kurs.eu/python3_kurs.php) und wurde für _Signale und Systeme_, sowie _Einführung in die Programmierung für Nicht Informatiker_ angepasst.\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "ebd295cc-8e6d-435e-a91a-881bf3798b5b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-275178a1d9bade57", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "source": [ - "# Geospatial Data\n", - "\n", - "In the following we will look at how maps can be displayed using Python & the Folium API.\n", - "\n", - "[Folium](https://python-visualization.github.io/folium/) is a Python wrapper that binds the open source library [Leaflet.js](https://leafletjs.com/).\n", - "\n", - "In this way, the developers aim to combine the advantages of data processing with Python, as well as the visualization advantages of the Web.\n", - "\n", - "Objectives of this exercise:\n", - "1. create simple maps with different terrain options\n", - "2. set and adjust markers\n", - " 1. creating a marker\n", - " 2. popup & tooltip\n", - " 3. icons & colors\n", - " 4. circle marker\n", - " 5. setting own markers\n", - "3. layer groups\n", - "4. plugins\n", - "5. handling GeoJSON data\n", - "\n", - "\n", - "__For the entire exercise, all parameters are basically given for illustrative purposes. Unless explicitly requested, you do not have to do this.__" - ] - }, - { - "cell_type": "markdown", - "id": "22adfbb3-e71d-4228-ac02-b8c7135a8943", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d928ae513b1e6ce0", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Import Folium\n", - "\n", - "First we need to Import Folium as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "2725fd60-dbe5-4739-8cbf-3ef9a585032f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a22fce114edbd7bf", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'folium'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Execute this cell everytime you restart the notebook!!!\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mfolium\u001b[39;00m\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'folium'" - ] - } - ], - "source": [ - "# Execute this cell everytime you restart the notebook!!!\n", - "import folium" - ] - }, - { - "cell_type": "markdown", - "id": "4a676854-ebc9-439f-8414-5fe39e65dc13", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d18e7620cb44ddf4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "source": [ - "## Creating maps\n", - "\n", - "The default object of every Folium App is the [Map](https://python-visualization.github.io/folium/modules.html#module-folium.map).\n", - "\n", - "### Location\n", - "To create it, you only need the _location_ parameter which takes a tuple (or list) of two floats.\n", - "The first value is the latitude (lat) and the second the longitude (lon). (For Braunschweig this would be lat: 52.264150 & lon: 10.526420)\n", - "\n", - "### Tiles\n", - "Folium supports the display of different types of maps. These are specified as a string for the tiles parameter when creating the map.\n", - "\n", - "Possible maps are:\n", - "\n", - "1. _OpenStreetMap_ (default)\n", - "2. _Stamen Terrain_\n", - "3. _Stamen Toner_\n", - "\n", - "### Zoom\n", - "The default _zoom_ setting of the map can be adjusted by the following 3 parameters:\n", - "\n", - "- _zoom_start_ (default: 10) creates the map with a zoom setting between _min_zoom_ & _max_zoom_.\n", - "- _min_zoom_ (default: 0) & _max_zoom_ (default: 18) limits the possible zoom radius. Generally not necessary.\n", - "\n", - "### Optimization\n", - "If one of the following cells has excessive computation times, the parameter _prefer_canvas=True_ should be specified for the map object. This will force the web browser to use the web graphics library ([WebGL](https://github.com/KhronosGroup/WebGL)) and will result in a speed bonus, e.g. thousands of markers. By default _prefer_canvas_ is set to _False_ and should only be used if you really want to display lots of data!\n", - "\n", - "The following example illustrates the creation of the map object:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ecd035c9-c1bc-4393-8681-2c058caca527", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1a3da7284c8a4450", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'folium' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m m \u001b[38;5;241m=\u001b[39m \u001b[43mfolium\u001b[49m\u001b[38;5;241m.\u001b[39mMap(\n\u001b[1;32m 2\u001b[0m location\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m52.264150\u001b[39m, \u001b[38;5;241m10.526420\u001b[39m),\n\u001b[1;32m 3\u001b[0m tiles\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOpenStreetMap\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m#iles='Stamen Toner',\u001b[39;00m\n\u001b[1;32m 5\u001b[0m zoom_start\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m13\u001b[39m,\n\u001b[1;32m 6\u001b[0m prefer_canvas\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 7\u001b[0m )\n\u001b[1;32m 9\u001b[0m m\n", - "\u001b[0;31mNameError\u001b[0m: name 'folium' is not defined" - ] - } - ], - "source": [ - "m = folium.Map(\n", - " location=(52.264150, 10.526420),\n", - " tiles='OpenStreetMap',\n", - " #iles='Stamen Toner',\n", - " zoom_start=13,\n", - " prefer_canvas=False\n", - " )\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "id": "94976e7f-1354-428e-ba9d-32ad696e27f4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-bbb8367d84f43da2", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "source": [ - "## Exercise 1: Create a map\n", - "\n", - "Create a map with the coordinates of your hometown, or your favorite city (Braunschweig does not count as a solution). Save your Solution in the variable `my_map`.\n", - "\n", - "Adjust the _zoom_start_ parameter so that your place is visible in the center. To find out which coordinates your place has you can use the online tool [latlong.net](https://www.latlong.net/).\n", - "\n", - "Use _CartoDB Positron_ as tileset.\n", - "\n", - "Set the _prefer_canvas_ parameter to _True_.\n", - "\n", - "Also please add your city as a comment.\n", - "\n", - "In case of problems, please check the Folium API [Map](https://python-visualization.github.io/folium/modules.html#module-folium.map) object.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a05ad147-1990-4d96-983d-c128b60125b5", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-6099ccde55688a94", - "locked": false, - "points": 4, - "schema_version": 3, - "solution": true, - "task": false - }, - "tags": [] - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'folium' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[3], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Your City: \u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m### BEGIN SOLUTION\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Your City: Braunschweig\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m my_map \u001b[38;5;241m=\u001b[39m \u001b[43mfolium\u001b[49m\u001b[38;5;241m.\u001b[39mMap(\n\u001b[1;32m 5\u001b[0m location\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m52.264150\u001b[39m, \u001b[38;5;241m10.526420\u001b[39m),\n\u001b[1;32m 6\u001b[0m tiles\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcartodb positron\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 7\u001b[0m zoom_start\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m13\u001b[39m,\n\u001b[1;32m 8\u001b[0m prefer_canvas\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 9\u001b[0m )\n\u001b[1;32m 10\u001b[0m my_map\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m### END SOLUTION\u001b[39;00m\n", - "\u001b[0;31mNameError\u001b[0m: name 'folium' is not defined" - ] - } - ], - "source": [ - "# Your City: \n", - "### BEGIN SOLUTION\n", - "# Your City: Braunschweig\n", - "my_map = folium.Map(\n", - " location=(52.264150, 10.526420),\n", - " tiles='cartodb positron',\n", - " zoom_start=13,\n", - " prefer_canvas=True\n", - " )\n", - "my_map\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b554a2ec-d32e-4ea6-a91f-a1fe554804cd", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-1b94f6587760b8e8", - "locked": true, - "points": 3, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'my_map' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Your Solutions are tested here...\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[43mmy_map\u001b[49m, folium\u001b[38;5;241m.\u001b[39mMap)\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(my_map\u001b[38;5;241m.\u001b[39mlocation, \u001b[38;5;28mlist\u001b[39m)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(my_map\u001b[38;5;241m.\u001b[39mlocation) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m\n", - "\u001b[0;31mNameError\u001b[0m: name 'my_map' is not defined" - ] - } - ], - "source": [ - "# Your Solutions are tested here...\n", - "assert isinstance(my_map, folium.Map)\n", - "assert isinstance(my_map.location, list)\n", - "assert len(my_map.location) == 2" - ] - }, - { - "cell_type": "markdown", - "id": "d057d2a5-9699-4a88-bea7-46adaa096f54", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ad368f2ba205c714", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "source": [ - "## Marker \n", - "\n", - "The quintessence of any GeoSpatial Data project is the visualization of data.\n", - "\n", - "The [Marker](https://python-visualization.github.io/folium/modules.html#folium.map.Marker) object expects the following parameters:\n", - "- _location_ (Mandatory) Tuple of Floats (Like Map Object) to set a marker.\n", - "- _popup_ (String or folium.Popup object) small info box that can be customized using HTML\n", - "- _tooltip_ (String) hover text with instruction\n", - "- _icon_ (folium.Icon) to customize the marker\n", - "- _dragable_ (bool, default False) allows the user to move the marker\n", - "\n", - "To set a simple marker it is first created with a location.\n", - "Then the marker has to be added to the map with the function _add_to()_. \n", - "\n", - "In the following the HBK BS should be marked on the map." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "59a91300-1dc7-4d27-826c-14a94fa13a45", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3b289b02455e2512", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'folium' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m m \u001b[38;5;241m=\u001b[39m \u001b[43mfolium\u001b[49m\u001b[38;5;241m.\u001b[39mMap(\n\u001b[1;32m 2\u001b[0m location\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m52.264150\u001b[39m, \u001b[38;5;241m10.526420\u001b[39m),\n\u001b[1;32m 3\u001b[0m tiles\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOpenStreetMap\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 4\u001b[0m zoom_start\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m14\u001b[39m,\n\u001b[1;32m 5\u001b[0m prefer_canvas\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 6\u001b[0m )\n\u001b[1;32m 8\u001b[0m my_marker \u001b[38;5;241m=\u001b[39m folium\u001b[38;5;241m.\u001b[39mMarker(\n\u001b[1;32m 9\u001b[0m location\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m52.25802230834961\u001b[39m, \u001b[38;5;241m10.503097534179688\u001b[39m)\n\u001b[1;32m 10\u001b[0m )\n\u001b[1;32m 12\u001b[0m my_marker\u001b[38;5;241m.\u001b[39madd_to(m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'folium' is not defined" - ] - } - ], - "source": [ - "m = folium.Map(\n", - " location=(52.264150, 10.526420),\n", - " tiles='OpenStreetMap',\n", - " zoom_start=14,\n", - " prefer_canvas=False\n", - " )\n", - "\n", - "my_marker = folium.Marker(\n", - " location=(52.25802230834961, 10.503097534179688)\n", - " )\n", - "\n", - "my_marker.add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "id": "179b1901-ac37-4724-aa59-c76345c61805", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d9ba7c4b83368403", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "source": [ - "After executing the code you should see a marker at the address Johannes-Selenka-Platz 1, 38118 Braunschweig.\n", - "\n", - "Since this is relatively boring we will now try to adapt the marker to our needs." - ] - }, - { - "cell_type": "markdown", - "id": "071b4725-8f13-4d38-9ea6-8f62826baf17", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0fd8e8915f7f9613", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "source": [ - "### Popup & Tooltip\n", - "\n", - "The marker accepts strings as _tooltip_ & _popup_ parameters, as the following example demonstrates.\n", - "This is the most primitive form of how a simple marker can be created with information.\n", - "\n", - "To understand what the _tooltip_ parameter does, run the next example and hover over the marker. Clicking on the marker will display the contents of the _popup_ parameter." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "33a5ffa1-7f39-46d4-9a17-35b3c8eb26ef", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8b6f8ca79a9a05c3", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map(\n", - " location=(52.264150, 10.526420),\n", - " tiles='OpenStreetMap',\n", - " zoom_start=16,\n", - " prefer_canvas=False\n", - " )\n", - "\n", - "# Schloss Braunschweig\n", - "castle_popup = \"Ritterbrunnen 1, 38100 Braunschweig\"\n", - "castle_tooltip = \"More about the castle\"\n", - "\n", - "\n", - "castle_marker = folium.Marker(\n", - " location=(52.2643, 10.529),\n", - " popup=castle_popup,\n", - " tooltip=castle_tooltip\n", - " )\n", - "castle_marker.add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "id": "cadd0223-d6d5-445a-ac7b-ead3b010260f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-cfc72d893f2572ff", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "source": [ - "### HTML Popups\n", - "\n", - "To do justice to the inner artist as well, markers can also be designed in Folium using the tools of the modern internet.\n", - "\n", - "The Folium object [Popup](https://python-visualization.github.io/folium/modules.html#folium.map.Popup) can be used to display simple HTML strings. \n", - "\n", - "Before we get to the main features of HTML for Folium, we will first take a look at the other optional parameters of the Popup object.\n", - "\n", - "These include:\n", - "\n", - "- _parse_html_ (bool, default False) not normally needed, forces Folium to interpret the HTML string first. Useful for any customization via JavaScript.\n", - "- _max_width_ (int or str, default '100%') sets the maximum width of the popup. For the str parameter it is important to include the '%' character.\n", - "- _show_ (bool, default False) if this parameter is set to _True_, the popup will load when the map is opened.\n", - "- _sticky_ (bool, default False) if this parameter is set to _True_, the popup will not be closed.\n", - "\n", - "\n", - "Mandatory for creating a popup is the _html_ parameter. This parameter requires a (multi-)string containing HTML code.\n", - "\n", - "As an example we will create a HBK BS popup which renders the following HTML:\n", - "\n", - "---\n", - "

\n", - "\"HBK\n", - "

\n", - "

Johannes-Selenka-Platz 1

\n", - "

38118 Braunschweig

\n", - "

Germany, DE

\n", - "

Visit: hbk-bs.de

\n", - "\n", - "---\n", - "\n", - "\n", - "and the associated HTML:\n", - "\n", - "\n", - "```html\n", - "

\n", - "\"HBK\n", - "

\n", - "

Johannes-Selenka-Platz 1

\n", - "

38118 Braunschweig

\n", - "

Germany, DE

\n", - "

Visit: hbk-bs.de

\n", - "```\n", - "\n", - "\n", - "Do not let this confuse you. The statements in between `<> & ` are HTML tags, for example `

` represents a 'Paragraph' & `` represents a hyperlink. \n", - "For the text semantic elements I recommend the following [reference](https://www.w3.org/html/wiki/Elements).\n", - "\n", - "In Python, no HTML can be displayed directly. For this, the entire HTML must be within a string. To simplify the readability Python offers the multiline string notated with 3 `'''`:\n", - "\n", - "\n", - "\n", - "'''\n", - "hi I am\n", - "\n", - "\n", - "a\n", - "\n", - "multiline string\n", - "'''\n", - "\n", - "\n", - "And as with the already known string, this one supports all string format options. But more about that later in the Factory Patterns part.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "db9725a6-c438-4a23-9e5b-5b401d412832", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b683105305025808", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# HBK Braunschweig\n", - "hbk_popup_html = folium.Popup(\n", - " '''\n", - "

\n", - " \"HBK\n", - "

\n", - "

Johannes-Selenka-Platz 1

\n", - "

38118 Braunschweig

\n", - "

Germany, DE

\n", - "

Visit: hbk-bs.de

\n", - " ''',\n", - " show=True\n", - " )\n", - "\n", - "hbk_tooltip = \"More about the university\"" - ] - }, - { - "cell_type": "markdown", - "id": "2773d557-e6ca-406c-9de1-b9c0675e0604", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-cf6a889f7ebf8a54", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "source": [ - "### Icon\n", - "\n", - "The last step to complete the marker is the [Icon](https://python-visualization.github.io/folium/modules.html#folium.map.Icon) object.\n", - "\n", - "Unlike the other objects discussed, this one has no mandatory parameters.\n", - "\n", - "As usual, here is an explanation of the parameters:\n", - "- _color_ (str, default 'blue') sets the color of the marker.\n", - "\n", - " Possible colors are: \n", - " - red, blue, green,\n", - " - purple, orange, darkred,\n", - " - lightred, beige, darkblue,\n", - " - darkgreen, cadetblue, darkpurple,-\n", - " - white, pink, lightblue,\n", - " - lightgreen, gray, black,\n", - " - lightgray\n", - " \n", - " \n", - " or any hexadecimal value noted with '#XXXXXX'.\n", - "\n", - "- _icon_color_ (str, default 'white') sets the color of the glyphicon. The possible color values are the same as for _color_.\n", - "- _angle_ (int, default 0) sets the rotation of the glyphicon. The possible values are limited to the range 0-359 integer.\n", - "- _prefix_ (str, default 'glyphicon) can take two values 'fa' for the icons of the website [Font Awesome](https://fontawesome.com/icons) (Attention not all icons are free) and 'glyphicon' for the icons of the website [Bootstrap](https://getbootstrap.com/docs/3.3/components/) (All icons behind this link are free). The value in _prefix_ specifies which website is queried. \n", - "- _icon_ (str, default 'info-sign') specifies the name of the icon to be displayed and is therefore dependent on the _prefix_ parameter. The default icon is 'glyphicon glyphicon-info-sign'.\n", - "\n", - "To design a reasonable icon for the HBK marker the following example should be understood:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "8f79bd13-82f0-478c-8409-1e97d79d4b26", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5e1cf782bc0512c1", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "outputs": [], - "source": [ - "hbk_icon = folium.Icon(\n", - " color='black',\n", - " icon_color='#deddda',\n", - " prefix='glyphicon',\n", - " icon='glyphicon-home',\n", - " angle=0\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "4a2ffb76-8045-4cba-a416-5555f521889b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8d4b5a5cb9cc90cc", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "source": [ - "After successfully creating the three variables _hbk_tooltip_, _hbk_html_popup_ & _hbk_icon_, we now display the customized marker:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "852785cc-0dff-4c2c-b6bb-20d08e26349f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-62068ff221befbb2", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map(\n", - " location=(52.258, 10.5),\n", - " tiles='OpenStreetMap',\n", - " zoom_start=16,\n", - " prefer_canvas=False\n", - " )\n", - "\n", - "hbk_marker = folium.Marker(\n", - " location=(52.257770, 10.502490),\n", - " popup=hbk_popup_html,\n", - " tooltip=hbk_tooltip,\n", - " icon=hbk_icon\n", - " )\n", - "\n", - "hbk_marker.add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "id": "6a7a1f4d-720b-4152-a90b-c9a194494cbe", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a6152b205ccf50ed", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "source": [ - "## Exercise 2: Design your own Marker\n", - "\n", - "In the following exercise you will design your own marker.\n", - "\n", - "In case of problems, please check the Folium API:\n", - "- [Marker](https://python-visualization.github.io/folium/modules.html#folium.map.Marker)\n", - "- [Popup](https://python-visualization.github.io/folium/modules.html#folium.map.Popup)\n", - "- [Icon](https://python-visualization.github.io/folium/modules.html#folium.map.Icon)\n", - "- [Tooltip](https://python-visualization.github.io/folium/modules.html#folium.map.Tooltip)" - ] - }, - { - "cell_type": "markdown", - "id": "093f0526-114f-45da-947a-550f2163030a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d5aa18ce07303756", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "source": [ - "### 2.1: Defining a Tooltip\n", - "\n", - "Define a tooltip variable named `tooltip` with the text `More about TU Braunschweig`." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "725abb1e-f8c4-4cca-be6f-326f23126062", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-04f53b3f7eb6c201", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "tags": [] - }, - "outputs": [], - "source": [ - "### BEGIN SOLUTION\n", - "tooltip = \"More about TU Braunschweig\"\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d0c93b34-79de-4a90-b595-dec419b3a930", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-bfcb53d3e2aba304", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Your Solutions are tested here...\n", - "assert tooltip == \"More about TU Braunschweig\"" - ] - }, - { - "cell_type": "markdown", - "id": "97aa8013-defd-408d-8b80-2389ff92904c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-e943331e564dd5c6", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "source": [ - "### 2.2: Defining a Popup\n", - "\n", - "Define a popup object with HTML text named `tu_popup_html` and the [TU BS Logo](https://www.google.com/url?sa=i&url=https%3A%2F%2Fde.m.wikipedia.org%2Fwiki%2FDatei%3ASiegel_TU_Braunschweig_transparent.svg&psig=AOvVaw3PFFLWsIPyXrT81Jo4F6ot&ust=1669222516763000&source=images&cd=vfe&ved=0CA8QjRxqFwoTCIjR-MqgwvsCFQAAAAAdAAAAABAE).\n", - "\n", - "Address: _Universitätspl. 2, 38106 Braunschweig_\n", - "\n", - "Logo URL: https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Siegel_TU_Braunschweig_transparent.svg/1200px-Siegel_TU_Braunschweig_transparent.svg.png\n", - "\n", - "You can also use the Template from the Explanation.\n", - "\n", - "You can find a HTML reference [here](https://www.w3.org/html/wiki/Elements). If you write better in Markdown I recommend the following [converter](https://markdowntohtml.com/) and this [Markdown reference](https://www.markdownguide.org/cheat-sheet/)." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "599b4313-9d53-46d8-be0e-dbf0ab707105", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-1f5f10be1f0c3e95", - "locked": false, - "points": 1, - "schema_version": 3, - "solution": true, - "task": false - }, - "tags": [] - }, - "outputs": [], - "source": [ - "### BEGIN SOLUTION\n", - "tu_popup_html = folium.Popup(\n", - " '''\n", - "

\n", - " \"TU\n", - "

\n", - "

Universitätspl. 2

\n", - "

38106 Braunschweigg

\n", - "

Germany, DE

\n", - "

Visit: tu-bs.de

\n", - " ''',\n", - " show=False\n", - " )\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "541653fa-f882-4f15-90bb-2ad7665d380f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d0ad985cc098c85f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### 2.3 Defining an Icon\n", - "\n", - "Next, a _red_ icon named `tu_icon` should be defined.\n", - "\n", - "The color for the glyphicon should be a gray hexcode that you can choose freely. To make the color selection easier you can use the [Color Picker](https://htmlcolorcodes.com/color-picker/).\n", - "\n", - "As glyph, _glyphicon-education_, should be used." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "7adb36ce-9012-4a9a-b025-2b7a91b4d2ac", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-3ec775c00a9d80a2", - "locked": false, - "points": 4, - "schema_version": 3, - "solution": true, - "task": false - }, - "tags": [] - }, - "outputs": [], - "source": [ - "tu_icon = None\n", - "### BEGIN SOLUTION\n", - "tu_icon = folium.Icon(\n", - " color='red',\n", - " icon_color='#eeeeee',\n", - " prefix='glyphicon',\n", - " icon='glyphicon-education',\n", - " angle=0\n", - " )\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "287924de-5810-4f3c-8b36-e3308798b3ea", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b16e407b56f6a000", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "source": [ - "### 2.4 Defining a Marker\n", - "\n", - "All previously created objects should now be combined into one marker named `tu_bs_marker`.\n", - "\n", - "As the location data use `(52.273460, 10.529231)`." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "c7dd4f94-e7d2-4c11-9426-f911462e122c", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-43ce13ffa68645bc", - "locked": false, - "points": 4, - "schema_version": 3, - "solution": true, - "task": false - }, - "tags": [] - }, - "outputs": [], - "source": [ - "tu_bs_marker = None\n", - "### BEGIN SOLUTION\n", - "tu_bs_marker = folium.Marker(\n", - " location=(52.273460, 10.529231),\n", - " popup=tu_popup_html,\n", - " tooltip=tooltip,\n", - " icon=tu_icon\n", - " )\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "13fa1a0f-f06e-4042-bae6-254fc99640e7", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-365a8eef0b32decd", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Your Solutions are tested and displayed here...\n", - "m = folium.Map(\n", - " location=(52.274, 10.53),\n", - " tiles='OpenStreetMap',\n", - " zoom_start=17,\n", - " prefer_canvas=False\n", - " )\n", - "\n", - "tu_bs_marker.add_to(m)\n", - "m" - ] - }, - { - "cell_type": "markdown", - "id": "6639280d-2c23-421b-9170-418895482fb3", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8cf61a16719f867b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "source": [ - "### Circle & Circle Marker\n", - "\n", - "Another possibility to mark places on maps is the [Circle](https://python-visualization.github.io/folium/modules.html#folium.vector_layers.Circle) and the [CircleMarker](https://python-visualization.github.io/folium/modules.html#folium.vector_layers.CircleMarker). The only difference between the two is the parameter _radius_ which is not fixed for the circle and has a default value of 10 for the circle marker which is specified in pixels, the circle marker scales with the map when zooming. Otherwise the objects _popup_ & _tooltip_ can be set with the marker. For the coloring the parameters _color_ & _fill_color_ are to be assigned. _color_ describes the color of the outer ring of the circle, while _fill_color_ specifies the fill color of the circle, whose default value is copied from the parameter_color_. to use _fill_color_ the parameter _fill_ must also be set to _True_.\n", - "\n", - "Here is an example.\n", - "\n", - "The HBK is marked with a red filled circle marker, which has the radius 100 pixel. To highlight the area inside the HBK, black is used as fill color.\n", - "Also here a tooltip, as well as the usual HBK popup is specified." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "511b9c23-f335-4639-8120-289ee9d4fee2", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-06a4d0cb95f20d93", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Map Setup\n", - "m = folium.Map(\n", - " location=(52.258, 10.5),\n", - " tiles='OpenStreetMap',\n", - " zoom_start=16,\n", - " prefer_canvas=False\n", - " )\n", - "\n", - "# HBK Braunschweig\n", - "hbk_popup_html = folium.Popup(\n", - " '''\n", - "

\n", - " \"HBK\n", - "

\n", - "

Johannes-Selenka-Platz 1

\n", - "

38118 Braunschweig

\n", - "

Germany, DE

\n", - "

Visit: hbk-bs.de

\n", - " ''',\n", - " show=False\n", - " )\n", - "\n", - "# Defining tooltip\n", - "hbk_tooltip = \"More about the university\"\n", - "\n", - "# Create Circle Marker\n", - "hbk_circle_marker = folium.Circle(\n", - " location=(52.2572, 10.501),\n", - " popup=hbk_popup_html,\n", - " tooltip=hbk_tooltip,\n", - " radius=100,\n", - " fill=True,\n", - " fill_color='black',\n", - " color='red'\n", - " )\n", - "\n", - "# Attach Circle Marker to map\n", - "hbk_circle_marker.add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "id": "c8bcbdc6-3b3a-4421-b78f-8c62f7f03d34", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-90296cc00bae2968", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Rectangle \n", - "\n", - "Rectangles can also be defined in the same way. Instead of a location with a radius, the two corner points must be specified. The data structure used for this is a list of tuples (_[tuple, tuple]_).\n", - "\n", - "Consider the following example:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "eee0d9ff-1ada-4f62-9675-39064b68fa32", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-641478b6b1df4ae5", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Map Setup\n", - "m = folium.Map(\n", - " location=(52.258, 10.5),\n", - " tiles='OpenStreetMap',\n", - " zoom_start=16,\n", - " prefer_canvas=False\n", - " )\n", - "\n", - "# HBK Braunschweig\n", - "hbk_popup_html = folium.Popup(\n", - " '''\n", - "

\n", - " \"HBK\n", - "

\n", - "

Johannes-Selenka-Platz 1

\n", - "

38118 Braunschweig

\n", - "

Germany, DE

\n", - "

Visit: hbk-bs.de

\n", - " ''',\n", - " show=False\n", - " )\n", - "\n", - "# Defining tooltip\n", - "hbk_tooltip = \"More about the university\"\n", - "\n", - "# Create Rectangle Marker\n", - "hbk_rectangle_marker = folium.Rectangle(\n", - " bounds=[(52.258077, 10.498424), (52.255896, 10.504092)], # List of tuples defining the Corner Points\n", - " popup=hbk_popup_html,\n", - " tooltip=hbk_tooltip,\n", - " fill=True,\n", - " fill_color='black',\n", - " color='red'\n", - " )\n", - "\n", - "# Attach Rectangle Marker to map\n", - "hbk_rectangle_marker.add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "id": "09574eea-8007-4740-b3f5-e81a40c28cbd", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5d436ff9290eb683", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Builder Pattern\n", - "\n", - "To simplify working with Folium, certain techniques can be used to simplify the creation of objects. \n", - "\n", - "Design patterns are known from the technical literature that lead to a simpler, error-free and clearer way of developing software. \n", - "One of these design patterns, the creation pattern, will be presented below and used for future tasks.\n", - "\n", - "The technical literature states freely quoted:\n", - "\"The creation pattern serves to decouple the construction of objects from their representation!\" - [_Design Patterns: Elements of Reusable Object-Oriented Software\n", - "by Erich Gamma, Ralph Johnson, Richard Helm, Ralph E. Johnson, John Vlissides_](https://books.google.de/books?hl=de&lr=&id=tmNNfSkfTlcC&oi=fnd&pg=PR11&dq=software+design+patterns&ots=e_iImZT2d3&sig=DtkhOov5t0Ot6lf7QubDGNhWzz0#v=onepage&q=software%20design%20patterns&f=false)\n", - "\n", - "A builder is a function (function of an object) that is used to create new objects. In our case, these will be markers and popups." - ] - }, - { - "cell_type": "markdown", - "id": "5f27eaae-3e5b-4f6c-854a-2c37c9fbffac", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-6473594d5d9882b0", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### A simple Popup Factory\n", - "\n", - "Anyone who remembers the HTML popups in the previous chapter will quickly realise that with a larger data set, setting individual HTML tags for images, addresses or other information soon becomes a tedious task.\n", - "\n", - "Assuming we want to plot all the universities in Lower Saxony on a map, a separate popup would have to be created for each marker. To change this, the following function will be introduced:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "ec9a37e4-1dd3-4673-b36f-122adafadeb2", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-069cd8284c5bc36c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "def popup_factory(adr: str, zipc: str, country: str, pic: str):\n", - " html = '''\n", - "

\n", - "

{}

\n", - "

{}

\n", - "

{}

\n", - " '''.format(pic, adr, zipc, country)\n", - " return folium.Popup(html)" - ] - }, - { - "cell_type": "markdown", - "id": "1740ce5e-f7a8-47f9-8211-7a62b19cf08b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-de4c7e1824c4e5a6", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "The _popup_factory_ function just defined takes the four string parameters _adr_ (address), _zipc_ (postcode), _country_ & _pic_ (picture URL) and generates an HTML-compliant string with the given information from the string specified in the _html_ variable. The return value of the function is a Folium popup object.\n", - "\n", - "To get closer to the goal of plotting all universities in Lower Saxony, a few objects are still missing.\n", - "\n", - "The _hbk_icon_ I created above is to be used as the standard icon. It is defined as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "9dddf885-ffa7-4e3b-b948-43ded7f3bd7b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a700169d2ec58d8c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "def icon_factory(is_public=True):\n", - " icon = folium.Icon(\n", - " color='black' if is_public else 'white',\n", - " icon_color = 'white' if is_public else 'black',\n", - " icon='glyphicon-home'\n", - " )\n", - " return icon" - ] - }, - { - "cell_type": "markdown", - "id": "d4f4be32-8d71-41b0-9d8e-5ec2e32416a8", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3fd735bc0f0cba70", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "The only thing missing in the next step is the factory for creating markers.\n", - "\n", - "This is defined as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "07a0df3e-92f9-4e25-89f3-3e27048d2c6b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-393cf1e2b37be7a6", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "def marker_factory(loc, popup, is_public=True):\n", - " std_tooltip = 'Click for more information'\n", - " std_icon = icon_factory(is_public)\n", - " return folium.Marker(loc, popup=popup, icon=std_icon, tooltip=std_tooltip)" - ] - }, - { - "cell_type": "markdown", - "id": "1e7f7f3a-d234-427d-ad06-3026c00312a4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-2b6d551c3e806181", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "With these two functions it is now easy to replicate the map created in the previous chapter:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "6a60ccee-4728-4bf1-93db-1158deae037f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7166c54f214ed13e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Create Map\n", - "m = folium.Map(\n", - " location=(52.258, 10.5),\n", - " tiles='OpenStreetMap',\n", - " zoom_start=16,\n", - " prefer_canvas=False\n", - " )\n", - "\n", - "# Define popup\n", - "pp = popup_factory(\n", - " adr='Johannes-Selenka-Platz 1',\n", - " zipc='38118 Braunschweig',\n", - " country='Germany, DE',\n", - " pic=\"https://www.hbk-bs.de/fileadmin/_processed_/5/1/csm_HBK_Logo_9f3f898a2b.png\",\n", - " )\n", - "\n", - "# Define Marker\n", - "marker = marker_factory(\n", - " loc=(52.257770, 10.502490),\n", - " popup=pp\n", - " )\n", - "\n", - "# Attach Marker to Map\n", - "marker.add_to(m)\n", - "\n", - "# Display Map\n", - "m" - ] - }, - { - "cell_type": "markdown", - "id": "1110a947-36cb-43c8-9348-fe3bafabe185", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a1045f8d87d79df5", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Exercise 3: Mapping the Universties in Lower Saxony\n", - "\n", - "## 3.1: Reading the Dataset\n", - "The data set for this notebook is *unis_nd.csv*.\n", - "\n", - "Read this into the variable `df` using the _pandas_ `read_csv` function.\n", - "\n", - "(I recommend that you take a look at the data set)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "b50d076d-4b5f-4137-93df-846144b119b7", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c55c41f6b9c03dd6", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "16c81c07-5893-42a7-9b1d-a081deea998c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d58de9c13e900a3b", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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University nameType of universitySponsorshipRight of promotionFounding yearNumber of studentsAddresslatlonplzpic
0Hochschule für Bildende Künste BraunschweigArtistic universitypublicyes1963976.000Johannes-Selenka-Platz 152.25773810.50231538118 Braunschweighttps://www.hbk-bs.de/fileadmin/_processed_/5/...
1Technische Universität Carolo-Wilhelmina zu Br...Universitypublicyes174517709.000Universitätspl. 252.27355010.53009738106 Braunschweighttps://upload.wikimedia.org/wikipedia/commons...
2Hochschule 21University of Applied Sciencesprivatno20051084.000Harburger Str. 653.4776509.70465021614 Buxtehudehttps://upload.wikimedia.org/wikipedia/commons...
3Technische Universität ClausthalUniversitypublicyes17753446.000Adolph-Roemer-Straße 2A51.80484010.33411038678 Clausthal-Zellerfeldhttps://www.presse.tu-clausthal.de/fileadmin/T...
4Hochschule Emden/LeerUniversity of Applied Sciencespublicno20094481.000Constantiapl. 453.3681607.18141026723 Emdenhttps://sta-hisweb.hs-emden-leer.de/QIS/images...
5PFH – Private Hochschule GöttingenUniversity of Applied Sciencesprivatno19954226.000Weender Landstraße 3-751.5389109.93322037073 Göttingenhttps://goettingen-campus.de/fileadmin/_proces...
6Georg-August-Universität GöttingenUniversitypublicyes173728614.000Wilhelmsplatz 151.5340709.93785037073 Göttingenhttps://upload.wikimedia.org/wikipedia/commons...
7Fachhochschule für die Wirtschaft HannoverUniversity of Applied Sciencesprivatno1996641.000Freundallee 1552.3662009.77247030173 Hannoverhttps://upload.wikimedia.org/wikipedia/commons...
8Hochschule HannoverUniversity of Applied Sciencespublicno19719209.000Ricklinger Stadtweg 12052.3541909.72238030459 Hannoverhttps://upload.wikimedia.org/wikipedia/commons...
9Hochschule für Musik, Theater und Medien HannoverArtistic universitypublicyes18971409.000Neues Haus 152.3773809.75392030175 Hannoverhttps://upload.wikimedia.org/wikipedia/commons...
10Leibniz-FachhochschuleUniversity of Applied Sciencesprivatno1920589.000Expo Plaza 1152.3211509.81868030539 Hannoverhttps://www.visit-hannover.com/var/storage/ima...
11Medizinische Hochschule Hannover (MHH)Universitypublicyes19633778.000Carl-Neuberg-Straße 152.3840509.80603030625 Hannoverhttps://upload.wikimedia.org/wikipedia/commons...
12Stiftung Tierärztliche Hochschule HannoverUniversitypublicyes17782381.000Bünteweg 252.3546809.79773030559 Hannoverhttps://upload.wikimedia.org/wikipedia/de/thum...
13Gottfried Wilhelm Leibniz Universität HannoverUniversitypublicyes183128935.000Welfengarten 152.3822509.71777030167 Hannoverhttps://www.uni-hannover.de/fileadmin/_process...
14Fachhochschule für Interkulturelle Theologie H...University of Applied Sciencesprivatno201291.000Missionsstraße 3-552.70884310.14071029320 Südheidehttps://cdn.max-e5.info/damfiles/logo/fh_herma...
15Universität HildesheimUniversitypublicyes19788378.000Universitätspl. 152.1340109.97469031141 Hildesheimhttps://www.uni-hildesheim.de/media/_processed...
16HAWK Hochschule für angewandte Wissenschaft un...University of Applied Sciencespublicno19716495.000Hohnsen 452.1424609.95798031134 Hildesheimhttps://upload.wikimedia.org/wikipedia/commons...
17HAWK Hochschule für angewandte Wissenschaft un...University of Applied Sciencespublicno19716495.000Haarmannpl. 351.8272609.45069037603 Holzmindenhttps://upload.wikimedia.org/wikipedia/commons...
18HAWK Hochschule für angewandte Wissenschaft un...University of Applied Sciencespublicno19716495.000Von-Ossietzky-Straße 9951.5217509.96967037085 Göttingenhttps://upload.wikimedia.org/wikipedia/commons...
19Leuphana Universität LüneburgUniversitypublicyes19466497.000Universitätsallee 153.22853110.40171021335 Lüneburghttps://upload.wikimedia.org/wikipedia/commons...
20Norddeutsche Hochschule für Rechtspflege – Nie...University of Administrationpublicno20076409.000Godehardspl. 652.1448409.94923031134 Hildesheimhttps://static.studycheck.de/media/images/inst...
21Kommunale Hochschule für Verwaltung in Nieders...University of Administrationpublicno20071570.000Wielandstraße 852.3705009.72239030169 Hannoverhttps://www.nsi-hsvn.de/fileadmin/user_upload/...
22Carl von Ossietzky Universität Oldenburg\\nUniversitypublicyes197315635.000Uhlhornsweg 49-5553.1473408.17902026129 Oldenburghttps://upload.wikimedia.org/wikipedia/commons...
23Hochschule OsnabrückUniversity of Applied Sciencespublicno197113620.000Albrechtstraße 3052.2826808.02501049076 Osnabrückhttps://login.hs-osnabrueck.de/nidp/hsos/image...
24Universität OsnabrückUniversitypublicyes197313640.000Neuer Graben 2952.2713708.04454049074 Osnabrückhttps://www.eh-tabor.de/sites/default/files/st...
25Hochschule Braunschweig/Wolfenbüttel, Ostfalia...University of Applied Sciencespublicno197111577.000Salzdahlumer Str. 46/4852.17683010.54865038302 Wolfenbüttelhttps://www.ostfalia.de/export/system/modules/...
26Hochschule Wolfsburg, Ostfalia Hochschule für ...University of Applied Sciencespublicno197111577.000Robert-Koch-Platz 8A52.42595010.78711038440 Wolfsburghttps://www.ostfalia.de/export/system/modules/...
27Hochschule Suderburg, Ostfalia Hochschule für ...University of Applied Sciencespublicno197111577.000Herbert-Meyer-Straße 752.89761010.44659029556 Suderburghttps://www.ostfalia.de/export/system/modules/...
28Hochschule Salzgitter, Ostfalia Hochschule für...University of Applied Sciencespublicno197111577.000Karl-Scharfenberg-Straße 55/5752.08724010.38055038229 Salzgitterhttps://www.ostfalia.de/export/system/modules/...
29Hochschule für Künste im Sozialen, OttersbergUniversity of Applied Sciencesprivatno1967342.000Große Str. 10753.1066809.16310028870 Ottersberghttps://upload.wikimedia.org/wikipedia/commons...
30Private Hochschule für Wirtschaft und Technik ...University of Applied Sciencesprivatno1998558.000Rombergstraße 4052.7212508.27891049377 Vechtahttps://www.phwt.de/wp-content/uploads/2020/09...
31Private Hochschule für Wirtschaft und Technik ...University of Applied Sciencesprivatno1998558.000Schlesier Str. 13A52.6117108.36334049356 Diepholzhttps://www.phwt.de/wp-content/uploads/2020/09...
32Universität VechtaUniversitypublicyes19954.551Driverstraße 2252.7211708.29380049377 Vechtahttps://upload.wikimedia.org/wikipedia/commons...
33Hochschule WeserberglandUniversity of Applied Sciencesprivatno2010485.000Am Stockhof 252.0987509.35542031785 Hamelnhttps://upload.wikimedia.org/wikipedia/commons...
34Jade Hochschule – WilhelmshavenUniversity of Applied Sciencespublicno20096789.000Friedrich-Paffrath-Straße 10153.5478708.08804026389 Wilhelmshavenhttps://www.jade-hs.de/fileadmin/layout2016/as...
35Jade Hochschule – OldenburgUniversity of Applied Sciencespublicno20096789.000Ofener Str. 16/1953.1417908.20213026121 Oldenburghttps://www.jade-hs.de/fileadmin/layout2016/as...
36Jade Hochschule – ElsflethUniversity of Applied Sciencespublicno20096789.000Weserstraße 5253.2424408.46651026931 Elsflethhttps://www.jade-hs.de/fileadmin/layout2016/as...
37Steuerakademie Niedersachsen RintelnUniversity of Administrationpublicno2006500.000Wilhelm-Busch-Weg 2952.2069609.09112031737 Rintelnhttps://www.steuerakademie.niedersachsen.de/as...
38Steuerakademie Niedersachsen Bad EilsenUniversity of Administrationpublicno2006500.000Bahnhofstraße 552.2398109.10423031707 Bad Eilsenhttps://www.steuerakademie.niedersachsen.de/as...
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" - ], - "text/plain": [ - " University name \\\n", - "0 Hochschule für Bildende Künste Braunschweig \n", - "1 Technische Universität Carolo-Wilhelmina zu Br... \n", - "2 Hochschule 21 \n", - "3 Technische Universität Clausthal \n", - "4 Hochschule Emden/Leer \n", - "5 PFH – Private Hochschule Göttingen \n", - "6 Georg-August-Universität Göttingen \n", - "7 Fachhochschule für die Wirtschaft Hannover \n", - "8 Hochschule Hannover \n", - "9 Hochschule für Musik, Theater und Medien Hannover \n", - "10 Leibniz-Fachhochschule \n", - "11 Medizinische Hochschule Hannover (MHH) \n", - "12 Stiftung Tierärztliche Hochschule Hannover \n", - "13 Gottfried Wilhelm Leibniz Universität Hannover \n", - "14 Fachhochschule für Interkulturelle Theologie H... \n", - "15 Universität Hildesheim \n", - "16 HAWK Hochschule für angewandte Wissenschaft un... \n", - "17 HAWK Hochschule für angewandte Wissenschaft un... \n", - "18 HAWK Hochschule für angewandte Wissenschaft un... \n", - "19 Leuphana Universität Lüneburg \n", - "20 Norddeutsche Hochschule für Rechtspflege – Nie... \n", - "21 Kommunale Hochschule für Verwaltung in Nieders... \n", - "22 Carl von Ossietzky Universität Oldenburg\\n \n", - "23 Hochschule Osnabrück \n", - "24 Universität Osnabrück \n", - "25 Hochschule Braunschweig/Wolfenbüttel, Ostfalia... \n", - "26 Hochschule Wolfsburg, Ostfalia Hochschule für ... \n", - "27 Hochschule Suderburg, Ostfalia Hochschule für ... \n", - "28 Hochschule Salzgitter, Ostfalia Hochschule für... \n", - "29 Hochschule für Künste im Sozialen, Ottersberg \n", - "30 Private Hochschule für Wirtschaft und Technik ... \n", - "31 Private Hochschule für Wirtschaft und Technik ... \n", - "32 Universität Vechta \n", - "33 Hochschule Weserbergland \n", - "34 Jade Hochschule – Wilhelmshaven \n", - "35 Jade Hochschule – Oldenburg \n", - "36 Jade Hochschule – Elsfleth \n", - "37 Steuerakademie Niedersachsen Rinteln \n", - "38 Steuerakademie Niedersachsen Bad Eilsen \n", - "\n", - " Type of university Sponsorship Right of promotion \\\n", - "0 Artistic university public yes \n", - "1 University public yes \n", - "2 University of Applied Sciences privat no \n", - "3 University public yes \n", - "4 University of Applied Sciences public no \n", - "5 University of Applied Sciences privat no \n", - "6 University public yes \n", - "7 University of Applied Sciences privat no \n", - "8 University of Applied Sciences public no \n", - "9 Artistic university public yes \n", - "10 University of Applied Sciences privat no \n", - "11 University public yes \n", - "12 University public yes \n", - "13 University public yes \n", - "14 University of Applied Sciences privat no \n", - "15 University public yes \n", - "16 University of Applied Sciences public no \n", - "17 University of Applied Sciences public no \n", - "18 University of Applied Sciences public no \n", - "19 University public yes \n", - "20 University of Administration public no \n", - "21 University of Administration public no \n", - "22 University public yes \n", - "23 University of Applied Sciences public no \n", - "24 University public yes \n", - "25 University of Applied Sciences public no \n", - "26 University of Applied Sciences public no \n", - "27 University of Applied Sciences public no \n", - "28 University of Applied Sciences public no \n", - "29 University of Applied Sciences privat no \n", - "30 University of Applied Sciences privat no \n", - "31 University of Applied Sciences privat no \n", - "32 University public yes \n", - "33 University of Applied Sciences privat no \n", - "34 University of Applied Sciences public no \n", - "35 University of Applied Sciences public no \n", - "36 University of Applied Sciences public no \n", - "37 University of Administration public no \n", - "38 University of Administration public no \n", - "\n", - " Founding year Number of students Address \\\n", - "0 1963 976.000 Johannes-Selenka-Platz 1 \n", - "1 1745 17709.000 Universitätspl. 2 \n", - "2 2005 1084.000 Harburger Str. 6 \n", - "3 1775 3446.000 Adolph-Roemer-Straße 2A \n", - "4 2009 4481.000 Constantiapl. 4 \n", - "5 1995 4226.000 Weender Landstraße 3-7 \n", - "6 1737 28614.000 Wilhelmsplatz 1 \n", - "7 1996 641.000 Freundallee 15 \n", - "8 1971 9209.000 Ricklinger Stadtweg 120 \n", - "9 1897 1409.000 Neues Haus 1 \n", - "10 1920 589.000 Expo Plaza 11 \n", - "11 1963 3778.000 Carl-Neuberg-Straße 1 \n", - "12 1778 2381.000 Bünteweg 2 \n", - "13 1831 28935.000 Welfengarten 1 \n", - "14 2012 91.000 Missionsstraße 3-5 \n", - "15 1978 8378.000 Universitätspl. 1 \n", - "16 1971 6495.000 Hohnsen 4 \n", - "17 1971 6495.000 Haarmannpl. 3 \n", - "18 1971 6495.000 Von-Ossietzky-Straße 99 \n", - "19 1946 6497.000 Universitätsallee 1 \n", - "20 2007 6409.000 Godehardspl. 6 \n", - "21 2007 1570.000 Wielandstraße 8 \n", - "22 1973 15635.000 Uhlhornsweg 49-55 \n", - "23 1971 13620.000 Albrechtstraße 30 \n", - "24 1973 13640.000 Neuer Graben 29 \n", - "25 1971 11577.000 Salzdahlumer Str. 46/48 \n", - "26 1971 11577.000 Robert-Koch-Platz 8A \n", - "27 1971 11577.000 Herbert-Meyer-Straße 7 \n", - "28 1971 11577.000 Karl-Scharfenberg-Straße 55/57 \n", - "29 1967 342.000 Große Str. 107 \n", - "30 1998 558.000 Rombergstraße 40 \n", - "31 1998 558.000 Schlesier Str. 13A \n", - "32 1995 4.551 Driverstraße 22 \n", - "33 2010 485.000 Am Stockhof 2 \n", - "34 2009 6789.000 Friedrich-Paffrath-Straße 101 \n", - "35 2009 6789.000 Ofener Str. 16/19 \n", - "36 2009 6789.000 Weserstraße 52 \n", - "37 2006 500.000 Wilhelm-Busch-Weg 29 \n", - "38 2006 500.000 Bahnhofstraße 5 \n", - "\n", - " lat lon plz \\\n", - "0 52.257738 10.502315 38118 Braunschweig \n", - "1 52.273550 10.530097 38106 Braunschweig \n", - "2 53.477650 9.704650 21614 Buxtehude \n", - "3 51.804840 10.334110 38678 Clausthal-Zellerfeld \n", - "4 53.368160 7.181410 26723 Emden \n", - "5 51.538910 9.933220 37073 Göttingen \n", - "6 51.534070 9.937850 37073 Göttingen \n", - "7 52.366200 9.772470 30173 Hannover \n", - "8 52.354190 9.722380 30459 Hannover \n", - "9 52.377380 9.753920 30175 Hannover \n", - "10 52.321150 9.818680 30539 Hannover \n", - "11 52.384050 9.806030 30625 Hannover \n", - "12 52.354680 9.797730 30559 Hannover \n", - "13 52.382250 9.717770 30167 Hannover \n", - "14 52.708843 10.140710 29320 Südheide \n", - "15 52.134010 9.974690 31141 Hildesheim \n", - "16 52.142460 9.957980 31134 Hildesheim \n", - "17 51.827260 9.450690 37603 Holzminden \n", - "18 51.521750 9.969670 37085 Göttingen \n", - "19 53.228531 10.401710 21335 Lüneburg \n", - "20 52.144840 9.949230 31134 Hildesheim \n", - "21 52.370500 9.722390 30169 Hannover \n", - "22 53.147340 8.179020 26129 Oldenburg \n", - "23 52.282680 8.025010 49076 Osnabrück \n", - "24 52.271370 8.044540 49074 Osnabrück \n", - "25 52.176830 10.548650 38302 Wolfenbüttel \n", - "26 52.425950 10.787110 38440 Wolfsburg \n", - "27 52.897610 10.446590 29556 Suderburg \n", - "28 52.087240 10.380550 38229 Salzgitter \n", - "29 53.106680 9.163100 28870 Ottersberg \n", - "30 52.721250 8.278910 49377 Vechta \n", - "31 52.611710 8.363340 49356 Diepholz \n", - "32 52.721170 8.293800 49377 Vechta \n", - "33 52.098750 9.355420 31785 Hameln \n", - "34 53.547870 8.088040 26389 Wilhelmshaven \n", - "35 53.141790 8.202130 26121 Oldenburg \n", - "36 53.242440 8.466510 26931 Elsfleth \n", - "37 52.206960 9.091120 31737 Rinteln \n", - "38 52.239810 9.104230 31707 Bad Eilsen \n", - "\n", - " pic \n", - "0 https://www.hbk-bs.de/fileadmin/_processed_/5/... \n", - "1 https://upload.wikimedia.org/wikipedia/commons... \n", - "2 https://upload.wikimedia.org/wikipedia/commons... \n", - "3 https://www.presse.tu-clausthal.de/fileadmin/T... \n", - "4 https://sta-hisweb.hs-emden-leer.de/QIS/images... \n", - "5 https://goettingen-campus.de/fileadmin/_proces... \n", - "6 https://upload.wikimedia.org/wikipedia/commons... \n", - "7 https://upload.wikimedia.org/wikipedia/commons... \n", - "8 https://upload.wikimedia.org/wikipedia/commons... \n", - "9 https://upload.wikimedia.org/wikipedia/commons... \n", - "10 https://www.visit-hannover.com/var/storage/ima... \n", - "11 https://upload.wikimedia.org/wikipedia/commons... \n", - "12 https://upload.wikimedia.org/wikipedia/de/thum... \n", - "13 https://www.uni-hannover.de/fileadmin/_process... \n", - "14 https://cdn.max-e5.info/damfiles/logo/fh_herma... \n", - "15 https://www.uni-hildesheim.de/media/_processed... \n", - "16 https://upload.wikimedia.org/wikipedia/commons... \n", - "17 https://upload.wikimedia.org/wikipedia/commons... \n", - "18 https://upload.wikimedia.org/wikipedia/commons... \n", - "19 https://upload.wikimedia.org/wikipedia/commons... \n", - "20 https://static.studycheck.de/media/images/inst... \n", - "21 https://www.nsi-hsvn.de/fileadmin/user_upload/... \n", - "22 https://upload.wikimedia.org/wikipedia/commons... \n", - "23 https://login.hs-osnabrueck.de/nidp/hsos/image... \n", - "24 https://www.eh-tabor.de/sites/default/files/st... \n", - "25 https://www.ostfalia.de/export/system/modules/... \n", - "26 https://www.ostfalia.de/export/system/modules/... \n", - "27 https://www.ostfalia.de/export/system/modules/... \n", - "28 https://www.ostfalia.de/export/system/modules/... \n", - "29 https://upload.wikimedia.org/wikipedia/commons... \n", - "30 https://www.phwt.de/wp-content/uploads/2020/09... \n", - "31 https://www.phwt.de/wp-content/uploads/2020/09... \n", - "32 https://upload.wikimedia.org/wikipedia/commons... \n", - "33 https://upload.wikimedia.org/wikipedia/commons... \n", - "34 https://www.jade-hs.de/fileadmin/layout2016/as... \n", - "35 https://www.jade-hs.de/fileadmin/layout2016/as... \n", - "36 https://www.jade-hs.de/fileadmin/layout2016/as... \n", - "37 https://www.steuerakademie.niedersachsen.de/as... \n", - "38 https://www.steuerakademie.niedersachsen.de/as... " - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "### BEGIN SOLUTION\n", - "df = pd.read_csv('unis_nd.csv')\n", - "df\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "a30598a7-c7d4-42cc-a9b8-dc6356d060aa", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-3e80e16a38e85d29", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Your Solutions are tested here..\n", - "assert isinstance(df, pd.DataFrame)" - ] - }, - { - "cell_type": "markdown", - "id": "da44f50f-0c2d-404d-8083-289742c5497a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-237bfc25448676b2", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## 3.2: Defining the Map\n", - "\n", - "Before we plot the dataset, define a map with the name `lower_saxony`. \n", - "\n", - "- The location should be the georaphic centre of Lower Saxony in _Wehrenberg 27318 Hoyerhagen_ `(52.806390, 9.135110)`.\n", - "- Use a suitable tileset from the documentation\n", - "- Use a suitable zoom setting" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "bfe04668-a597-4f93-b6e4-11ef17d18dcd", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0d7c995b3fcc368e", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [], - "source": [ - "### BEGIN SOLUTION\n", - "lower_saxony = folium.Map(\n", - " location=(52.806390, 9.135110), # Georaphical centre Point of Lower Saxony Wehrenberg 27318 Hoyerhagen\n", - " tiles='OpenStreetMap',\n", - " zoom_start=7\n", - " )\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "a2f05ce6-7942-4c71-b1e2-a709c88b0f36", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-77f8257bb3c1308f", - "locked": true, - "points": 4, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Your Solutions are tested here..\n", - "assert isinstance(lower_saxony, folium.Map)\n", - "assert len(lower_saxony.location) == 2" - ] - }, - { - "cell_type": "markdown", - "id": "357c71bb-923a-4de6-bcbd-b9d2741498fc", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d66a439bbfb3778a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## 3.3 Plotting the Dataset\n", - "\n", - "Write a for loop which reads the values from the dataset `df` and add the markers to the map `lower_saxony` using the already defined functions `popup_factory` and `marker_factory`. " - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "b392f48f-f0ae-4158-806f-859e71009c04", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-b744358aaaa7db44", - "locked": false, - "points": 5, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "### BEGIN SOLUTION\n", - "for index, row in df.iterrows():\n", - " pp = popup_factory(\n", - " adr=row['Address'],\n", - " zipc=row['plz'],\n", - " country='Germany, DE',\n", - " pic=row['pic'],\n", - " )\n", - " location = (float(row['lat']), float(row['lon']))\n", - " \n", - " is_public = False\n", - " if row['Sponsorship'] == 'public':\n", - " is_public = True\n", - " \n", - " marker = marker_factory(location, pp, is_public) \n", - " marker.add_to(lower_saxony)\n", - " \n", - "lower_saxony\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "ab37a764-3718-4327-976c-146e5531a048", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-49864685eac331d1", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Your Solutions are tested and displayed here..\n", - "lower_saxony" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - }, - "toc-autonumbering": false - }, - "nbformat": 4, - "nbformat_minor": 5 -} +version https://git-lfs.github.com/spec/v1 +oid sha256:cfb83b650bdd60072cfb492be2d50dae78b475a4067dc36c6515cec73db48ae4 +size 327864 diff --git a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/9.Statistical_Test_Methods.ipynb b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/9.Statistical_Test_Methods.ipynb index 58860da..b49c536 100644 --- a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/9.Statistical_Test_Methods.ipynb +++ b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/9.Statistical_Test_Methods.ipynb @@ -1,1477 +1,3 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c2734eb9-d9b9-43b2-a9fb-c25729a0f3a3", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-5fff5091c9d505d4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# 9. Programmierübung: Statistical Test Methods\n", - "\n", - "
\n", - "
\n", - " Willkommen zur neunten Programmierübung Einführung in Python 3.\n", - "
\n", - " \n", - "
\n", - "\n", - "Wenn Sie Fragen oder Verbesserungsvorschläge zum Inhalt oder Struktur der Notebooks haben, dann können sie eine E-Mail an Phil Keier ([p.keier@hbk-bs.de](mailto:p.keier@hbk-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) oder Martin Le ([martin.le@tu-bs.de](mailto:martin.le@tu-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) schreiben.\n", - "\n", - "Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n", - "\n", - "Der Großteil des Python-Tutorials stammt aus der Veranstaltung _Deep Learning Lab_ und von [www.python-kurs.eu](https://www.python-kurs.eu/python3_kurs.php) und wurde für _Signale und Systeme_, sowie _Einführung in die Programmierung für Nicht Informatiker_ angepasst.\n", - "\n", - "---" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "483f1609-fd3a-4154-a059-7e5d26562bcb", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-2885f7f5d6e48f91", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: seaborn in /home/phil/programming/programmieren_wise_24_25/Material/env/lib64/python3.12/site-packages (0.13.2)\n", - "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /home/phil/programming/programmieren_wise_24_25/Material/env/lib64/python3.12/site-packages (from seaborn) (2.2.1)\n", - "Requirement already satisfied: pandas>=1.2 in /home/phil/programming/programmieren_wise_24_25/Material/env/lib64/python3.12/site-packages (from seaborn) (2.2.3)\n", - "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /home/phil/programming/programmieren_wise_24_25/Material/env/lib64/python3.12/site-packages (from seaborn) (3.10.0)\n", - "Requirement already satisfied: contourpy>=1.0.1 in /home/phil/programming/programmieren_wise_24_25/Material/env/lib64/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.1)\n", - "Requirement already satisfied: cycler>=0.10 in /home/phil/programming/programmieren_wise_24_25/Material/env/lib64/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n", - "Requirement already satisfied: fonttools>=4.22.0 in /home/phil/programming/programmieren_wise_24_25/Material/env/lib64/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.55.3)\n", - "Requirement already satisfied: kiwisolver>=1.3.1 in /home/phil/programming/programmieren_wise_24_25/Material/env/lib64/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.8)\n", - "Requirement already satisfied: packaging>=20.0 in /home/phil/programming/programmieren_wise_24_25/Material/env/lib64/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.2)\n", - "Requirement already satisfied: pillow>=8 in /home/phil/programming/programmieren_wise_24_25/Material/env/lib64/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (11.1.0)\n", - "Requirement already satisfied: pyparsing>=2.3.1 in /home/phil/programming/programmieren_wise_24_25/Material/env/lib64/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.1)\n", - "Requirement already satisfied: python-dateutil>=2.7 in /home/phil/programming/programmieren_wise_24_25/Material/env/lib64/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n", - "Requirement already satisfied: pytz>=2020.1 in /home/phil/programming/programmieren_wise_24_25/Material/env/lib64/python3.12/site-packages (from pandas>=1.2->seaborn) (2024.2)\n", - "Requirement already satisfied: tzdata>=2022.7 in /home/phil/programming/programmieren_wise_24_25/Material/env/lib64/python3.12/site-packages (from pandas>=1.2->seaborn) (2024.2)\n", - "Requirement already satisfied: six>=1.5 in /home/phil/programming/programmieren_wise_24_25/Material/env/lib64/python3.12/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.17.0)\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "pip install seaborn" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "76813bd6-6efe-439f-b48c-30da670b84ab", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-34d4d9909fce30d4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from scipy import stats\n", - "import pandas as pd\n", - "import seaborn as sb" - ] - }, - { - "cell_type": "markdown", - "id": "8af16cfd-827f-4e7d-91a3-970e920ad212", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-fa7bc65ef436bf2f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Was ist ein T-Test?\n", - "\n", - "Der T-Test ist eine Methode um zu bestimmen ob es einen Signifikanten Unterschied zweier distinkter Populationen gibt.\n", - "Hierzu können wir diverse bereits bekannte Python Libraries und deren Funktionen nutzen um Hypothesen zu testen.\n", - "\n", - "Wir beschränken uns im folgenden auf den *One-Sample t-Test*.\n", - "\n", - "Dabei hat der/die Analyst/-in gewissen Erwartung an den T-Test:\n", - "1. Unabhängigkeit der Beobachtung (Independence of Observation)\n", - "2. Normalität (Normality)\n", - "3. Gleiche Varianz (Equal variances)\n", - "\n", - "Ein wenig Notation:\n", - "1. Eine Hypothese ist eine Annahme die über eine Population getroffen wird.\n", - "2. Eine Nullhypothese $H_0$ ist die Grundannahme einer statistischen Untersuchung, welche Beweist oder widerlegt werden soll.\n", - "3. Eine Population ist eine (Daten-)Gruppe, welche über bestimmte Charakteristika verfügt.\n", - "4. Samples sind unter(daten-)gruppen einer bestimmten Population.\n", - "5. Nullverteilung (Null Distribution) ist die Verteilung unter der Annahme das $H_0$ Wahr ist.\n", - "\n", - "## Die Idee\n", - "\n", - "Aus voherigen Übungen ist die Normalverteilung bekannt, diese gibt an wie wahrscheinlich es ist das eine Zufallsvariable aus dieser zu beziehen. Folglich wird der Durschnitt der Samples berechnet und verglichen wie Wahrscheinlich es ist das diese auf die Null Distribution fällt. Unter dieser Annahme ist es nun möglich $H_0$ beweisen. \n", - "\n", - "Dabei sind die benötigten Parameter für jeden Null Hypothesen Test gleich:\n", - "1. Bestimmen der Parameter einer Populationn\n", - "2. Aufstellen der Null Hypothese $H_0$\n", - "3. Sammeln (& bereinigen) der Samples\n", - "4. Aufstellen der Null Distribution\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "358dc549-087d-4353-9fda-eedfbe6bc40b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5d738a69cd7872e6", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Beispiel\n", - "\n", - "### Parameter bestimmung einer Population\n", - "\n", - "Das durchschnittliche Studiendauer für den Bachelor in Deutschland ist auf 6 Semester festgelegt. \n", - "Im folgenden beschränken wir uns auf diese Annahme. Alle Studiengänge die dieses Kriterium nicht erfüllen werden ausgeschlossen.\n", - "\n", - "### Aufstellen der Null Hypothese\n", - "\n", - "Der durchschnittliche Student (im Erststudium) benötigt für sein Bachelorstudium 6 Semester.\n", - "\n", - "Aus folgender [Statista](https://de.statista.com/statistik/daten/studie/189155/umfrage/durchschnittliche-studiendauer-in-deutschland/) Grafik wissen wir das dies nicht stimmt. Dort ist die Durchschnittsdauer für ein Studium mit einem Median von 8.2 Semestern angegeben.\n", - "\n", - "Wir können also aufstellen: $H_0: \\mu=6$\n", - "\n", - "### Sampling\n", - "\n", - "Für das Sampling wird aus der (eigentlich unbekannten) Normalverteilung mit $\\mu = 8.2, \\sigma =$ gesampelt.\n", - "Dabei gehen wir von bekannten Zahlen aus. Stand 2016 hatte die TU Braunschweig 20.000 Studierende nehmen wir als Samplegröße 2% dieser. Somit verändert sich jedoch die Aussage von $H_0$ indem diese nicht ganz Deutschland sondern nur die TU Braunschweig abbildet. \n", - "\n", - "Dies lässt sich wie bereits bekannt mit NumPy folglich erreichen:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "21e52ad5-6eab-4ac5-ac2f-849dd2df07fa", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-92b5ae5f97eb5ca9", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Parameters\n", - "mu: float = 8.2\n", - "sigma: float = 1\n", - "sample_size: int = int(20_000 * 0.02)\n", - "\n", - "# Sampling\n", - "rng = np.random.default_rng(42) # reproduzierbarkeit ist Gewährleistet\n", - "samples = rng.normal(mu, sigma, sample_size)" - ] - }, - { - "cell_type": "markdown", - "id": "7beda001-4ce7-435b-880f-78fe5881b8c1", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-f5613ce17a915c5a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Aufstellen der Null Distribution\n", - "\n", - "Nach dem **Central Limit Theorem** gilt:\n", - "\n", - "$$\\bar X \\sim AN(\\mu, \\frac{\\sigma^2}{n})$$\n", - "\n", - "Das **Central Limit Theorem** geht davon aus das wenn genügend Samples vorhanden sind die Verteilung dieser die Normalverteilung anstrebt. Wir gehen daher davon aus das unsere Samples Normaleverteilt sind.\n", - "\n", - "Das mag vielleicht sehr Obvious klingen, da die erhobenen Samples aus einer Normalverteilung stammen, ist aber eine der wichtigen Annahmen die getroffen werden muss, da wir die Verteilung eigentlich nicht kennen.\n", - "\n", - "Statistiker bevorzugen die normalisierte Variante des **Central Limit Theorems** um immer ein Ergebniss im Interval $[0, 1]$ zu erhalten:\n", - "\n", - "$$\\frac{(\\bar X - \\mu)}{\\frac{\\sigma}{\\sqrt n}} \\sim AN(0, 1)$$\n", - "\n", - "Dies wäre der Standard Z-Test. Das Problem hierbei ist das $\\sigma$ in den meisten Fällen nicht bekannt ist.\n", - "Daher ersetzen wir $\\sigma^2$ die Standardvarianz mit der Samplevarianz $S^2$:\n", - "\n", - "$$S^2 = \\frac{\\sum^n_{i=1} (X_i - \\bar X)^2}{n - 1}$$\n", - "\n", - "Daraus lässt sich die normalisierte Variante des **Central Limit Theorems** transformieren. Dies hat den Vorteil das eine feste unbekannte durch eine zufällig erhobene bekannte Variable ersetzt wird. Diese Verteilung heißt **Student's T Distribution** oder kurz **T Distribution** und wird mit einem kleinen **t** abgekürzt:\n", - "\n", - "$$\\frac{(\\bar X - \\mu)}{\\frac{\\sigma}{\\sqrt n}} \\rightarrow \\frac{(\\bar X - \\mu)}{\\frac{S}{\\sqrt n}}$$\n", - "\n", - "Damit haben wir alles zusammen um den *One-Sample t-test* durchzuführen.\n", - "\n", - "1. Population Parameter: $\\mu$\n", - "2. Null Hypothesis: $H_0: \\mu = 6$\n", - "3. Test Statistic: $t = \\frac{(\\bar X - \\mu)}{\\frac{S}{\\sqrt n}}$\n", - "4. Null Distribution: $t \\sim t_{n-1}$" - ] - }, - { - "cell_type": "markdown", - "id": "a9f7ffee-8d2c-474a-914d-3dcc048c0116", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-63fcdadcfd48afab", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Durchführung\n", - "\n", - "Für die Durchführung müssen folglich erst alle Parameter aufgestellt werden:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "390f312a-0a73-4792-9791-3f9a4aec57b2", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ae1a48d648e99f4b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "mu = 6 # Hypothesized Population mean\n", - "alpha = 0.05 # significance level" - ] - }, - { - "cell_type": "markdown", - "id": "94623047-774a-4b51-b12d-7b792ba35a3c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d9dc4c4bd754b014", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Die samples sind bereits bekannt daher brauchen diese nicht neu berechnet werden.\n", - "\n", - "Der Wert $\\alpha$ gibt an wie Signifikant eine Aussage ist und ist einer der Beiden Rückgabeparameter der SciPy Funktion `stats.ttest_1samp` und gibt den Maximalen Fehler an. Er wird aus der aufkommulierten t Verteilung berechnet und ist der Interessantere der beiden Werte.\n", - "\n", - "Mittels SciPy lässt sich folglich der *One-Sample t-test* durchführen:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "bc65a1d6-612d-4894-b1c0-511e64f25e56", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b64d3a20a71d3205", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "T statistic: 46.09283154236306\n", - "P-value: 6.777927318199721e-162\n", - "Reject the null hypothesis; there is a significant difference between the sample mean and the hypothesized population mean.\n" - ] - } - ], - "source": [ - "t_stat, p_value = stats.ttest_1samp(samples, mu)\n", - "print(\"T statistic:\", t_stat)\n", - "print(\"P-value:\", p_value)\n", - "\n", - "if p_value < alpha:\n", - " print(\"Reject the null hypothesis; there is a significant difference between the sample mean and the hypothesized population mean.\")\n", - "else:\n", - " print(\"Fail to reject the null hypothesis; there is no significant difference between the sample mean and the hypothesized population mean.\")" - ] - }, - { - "cell_type": "markdown", - "id": "fbe8e5fd-5243-4f88-aa4d-56be228e023e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-81d3dc2d85c03c84", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Schlussfolgerung\n", - "\n", - "Aus den gesammelten (herbei gezauberten) Daten lässt sich Schlussfolgern das die Null Hypothese $H_0$, dass Studenten im Erststudium an der TU Braunschweig ihr Erststudium in der Regel nicht nach 6 Semestern abschließen.\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "1e6e76be-08af-42ad-8522-cd86c08d73e7", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-26af8bd4e579ae2c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe T-Test\n", - "\n", - "*6 Punkte - Programmieren*\n", - "\n", - "*6 Punkte - Schriftteil*\n", - "\n", - "Für diese Aufgabe ist extra ein Feld für den Schriftteil reserviert. **Erkläre** dort dein Vorgehen, die Nullhypothese, dein Ergebnis und wie der Nominallohn zu verstehen ist. **Interpretiere** dein Ergebnis und Stelle ein **Fazit** auf. Gebe auch alle verwendeten Quellen (samt link) korrekt an.\n", - "Jupyter unterstütz die Markdown Syntax ein Guide hierfür ist unter [The Ultimate Markdown Guide](https://medium.com/analytics-vidhya/the-ultimate-markdown-guide-for-jupyter-notebook-d5e5abf728fd) zu finden.\n", - "\n", - "**!!Wird der Schriftteil nicht oder nur unzureichend ausgefüllt führt dies zu einer Bewertung von 0 Punkten für die gesamte Aufgabe!!**\n", - "\n", - "Dem Notebook mit beigelegt ist das Datenset `nominallohn_deutschland_2023-2024_nach_geschlecht.csv` (Quelle: [DESTASIS Nominallohnindex: Deutschland, Monate, Geschlecht](https://www-genesis.destatis.de/datenbank/online/statistic/62361/table/62361-0003)).\n", - "\n", - "Analysiere das Datenset, gehen dabei von der Nullhypothese aus das es keinen Signifikanten Unterschied in den Nominallöhnen zwischen den Geschlechtern gibt.\n", - "\n", - "Gehe dabei wie folgt vor:\n", - "1. Stelle die Nullhypothese prüfbar auf.\n", - "2. Importiere das Datenset und filtere angemessen (Kommentare helfen mir später bei der Korrektur diesen Schritt nachzuvollziehen)\n", - "3. Berechne den von der Nullhypothese geforderten Populations Parameter $\\mu$.\n", - "4. Bestimme das Signifikanzlevel $\\alpha$ unter der $H_0$ verworfen wird.\n", - "5. Bestimme mittels t-test den p-wert zur Nullhypothese.\n", - "6. Stelle dein Ergebnis dar." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "10d3b6f1-2c8d-4999-9f02-30496db96297", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-401af51c252cee3b", - "locked": false, - "points": 6, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " Fail to reject the null hypothesis;\n", - " there is no significant difference between the sample mean\n", - " and the hypothesized population mean.\n", - " \n", - "P-Value: 0.95\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "dataset: pd.DataFrame = pd.read_csv('nominallohn_deutschland_2023-2024_nach_geschlecht.csv', delimiter=',')\n", - "male = dataset.loc[dataset[\"Geschlecht\"] == \"männlich\"]\n", - "female = dataset.loc[dataset[\"Geschlecht\"] == \"weiblich\"]\n", - "\n", - "mu = male[\"Nominallohn\"].mean()\n", - "alpha = 0.05\n", - "\n", - "t_stat, p_value = stats.ttest_1samp(female[\"Nominallohn\"], mu)\n", - "\n", - "if p_value < alpha:\n", - " print('''\n", - " Reject the null hypothesis;\n", - " there is a significant difference between the sample mean\n", - " and the hypothesized population mean.\n", - " '''\n", - " )\n", - "else:\n", - " print('''\n", - " Fail to reject the null hypothesis;\n", - " there is no significant difference between the sample mean\n", - " and the hypothesized population mean.\n", - " '''\n", - " )\n", - "\n", - "print(f\"P-Value: {p_value:.2}\")\n", - "\n", - "months = [\"Januar\", \"Februar\", \"März\", \"April\", \"Mai\", \"Juni\", \"Juli\", \"August\", \"September\", \"Oktober\", \"November\", \"Dezember\"]\n", - "\n", - "dw_2023 = {f\"{m} 2023\": female.loc[female[\"Monat\"] == m].loc[female[\"Jahr\"] == 2023][\"Nominallohn\"].values[0] for m in months}\n", - "dw_2024 = {f\"{m} 2024\": female.loc[female[\"Monat\"] == m].loc[female[\"Jahr\"] == 2024][\"Nominallohn\"].values[0] for m in months[:-2]}\n", - "dm_2023 = {f\"{m} 2023\": male.loc[male[\"Monat\"] == m].loc[male[\"Jahr\"] == 2023][\"Nominallohn\"].values[0] for m in months}\n", - "dm_2024 = {f\"{m} 2024\": male.loc[male[\"Monat\"] == m].loc[male[\"Jahr\"] == 2024][\"Nominallohn\"].values[0] for m in months[:-2]}\n", - "\n", - "\n", - "fig, ax = plt.subplots(figsize = (12, 8))\n", - "width = 0.3\n", - "ind_23 = np.arange(len(dw_2023))\n", - "ind_24 = np.arange(len(dw_2023), len(dw_2023) + len(dw_2024))\n", - "labels = list()\n", - "for k in dw_2023.keys():\n", - " labels.append(k)\n", - "for k in dw_2024.keys():\n", - " labels.append(k)\n", - "\n", - "plt.bar(ind_23, dw_2023.values(), width, alpha=0.5, color='red')\n", - "plt.bar(ind_24, dw_2024.values(), width, alpha=0.5, color='red')\n", - "plt.bar(ind_23+width, dm_2023.values(), width, alpha=0.5, color='b')\n", - "plt.bar(ind_24+width, dm_2024.values(), width, alpha=0.5, color='b')\n", - "\n", - "\n", - "plt.xlabel('Monat')\n", - "plt.ylabel('Nominallohn')\n", - "plt.title('Nominallohn nach Geschlechtern')\n", - "plt.xticks(np.arange(len(dw_2023) + len(dw_2024)) + width / 2, labels)\n", - "plt.setp(ax.get_xticklabels(), rotation=30, horizontalalignment='right', fontsize='x-small')\n", - "plt.show()\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "3a3abb0b-81c6-4b84-b298-039b73ba35a2", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-e4292c6997f90bab", - "locked": false, - "points": 6, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# BEGIN SOLUTION\n", - "\n", - "### Nominallohn \n", - ">das tatsächlich in € gezahlte Entgelt für geleistete Arbeit (Gegensatz: Reallohn). Der Nominallohn lässt keine Aussagen über die Kaufkraft zu, da die Preisentwicklung nicht berücksichtigt wird. Werden in einem Tarifvertrag z. B. Lohnsteigerungen von 3 % vereinbart, und die Inflationsrate beträgt gleichzeitig 2 %, so steigen die Löhne nominal um 3 %, tatsächlich (real) aber nur um 1 %.\n", - "\n", - "Quelle: [bpb.de](https://www.bpb.de/kurz-knapp/lexika/lexikon-der-wirtschaft/20175/nominallohn/)\n", - "\n", - "### Nullhypothese\n", - "> Der männliche Nominallohn unterscheidet sich nicht von dem des weiblichen Nominallohns.\n", - "\n", - "### Vorgehen\n", - "Filtern des Datensets in die beiden Kategorien *männlich* & *weiblich*.\n", - "Danach aufstellen der oben genannten Nullhypothese.\n", - "Folglich ist der Populations Parameter zur Nullhypothese $\\mu$ der Durschnittswert der männlichen Nominallöhne.\n", - "Also Signifikanz Parameter $\\alpha$ wird 0.05 verwendet.\n", - "Nach t-test gegen die weiblichen Nominallöhne ergibt sich, das die Null Hypothese nicht verworfen werden kann.\n", - "\n", - "### Fazit\n", - "Die Null Hypothese lässt sich als Bestätigt ansehen. Daher gibt es keinen Unterschied zwischen den Nominallöhnen beider Geschlechter.\n", - "\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "07378cd6-116e-4a6d-8233-f5fc7da17d3e", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-2cb31097d39ef3fa", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "---\n", - "\n", - "# Korrelation\n", - "\n", - "## Was ist eine Korrelation?\n", - "\n", - "Als Korrelation beschreibt man den Zusammenhang zweier Variabeln und ihre Abhängigkeit zueinander. Dabei wird ein Korrelationkoeffizient berechnet. Dieser hat immer einen Wert zwischen -1 & +1. Dabei gilt, dass bei einem Wert von ±1 eine perfekte Assoziation vorliegt. Je weiter der Wert des Korrelationskoeffizienten gegen 0 geht, desto schwächer ist die Beziehung zwischen den beiden Variablen. Die Richtung der Beziehung ist durch das Vorzeichen gegeben; ein positives Vorzeichen (+) zeigt eine positive Beziehung der beiden Variabeln auf, während bei einem negativen Vorzeichen (-) eine negative Beziehung vorliegt.\n", - "\n", - "## Pearson Corellation Coefficient\n", - "\n", - "Nach dem Mathematiker *Karl Pearson* benannt lässt sich mittels pandas auf jedem Dataframe die Funktion *.corr* aufrufen. Diese berechnet den Korrelations koefficienten zwischen allen **numerischen** Variablen in dem DataFrame.\n", - "\n", - "Hierzu ein Beispiel mit dem Datenset `nominallohn_deutschland_2023-2024_nach_geschlecht.csv`.\n", - "Wichtig ist *.corr* die Parameter *method* & *numeric_only* mitzugeben, da es keinen Sinn ergibt einen Koeffizienten zwischen zwei Strings zu berechnen:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "4f7c2990-3f68-47e3-93e9-f4c919f1d93c", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-5294192c404de210", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Jahr Nominallohn\n", - "Jahr 1.000000 0.172602\n", - "Nominallohn 0.172602 1.000000" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset: pd.DataFrame = pd.read_csv('nominallohn_deutschland_2023-2024_nach_geschlecht.csv', delimiter=',')\n", - "pearsoncorr = dataset.corr(method='pearson', numeric_only=True)\n", - "pearsoncorr" - ] - }, - { - "cell_type": "markdown", - "id": "d012e5ff-7e5f-4bb6-9d2d-9993dd4b1947", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-9cbe48bc8062ccae", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Mittels Seaborn lässt sich diese Tabelle auch Farbig darstellen:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "84ad50bb-b8d9-4f69-a2ae-905395a7456a", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-69937404c2eb9a0e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sb.heatmap(pearsoncorr, \n", - " xticklabels=pearsoncorr.columns,\n", - " yticklabels=pearsoncorr.columns,\n", - " cmap='RdBu_r',\n", - " annot=True,\n", - " linewidth=0.5)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "22e6a3f3-b402-4364-a418-70a6e4001d32", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-d81e230add7ed3f2", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Das Datenset enthält nur wenige Numerische Werte, dennoch lässt sich feststellen das die Beziehung zwischen dem erhobenen Jahr (2023/2024) und dem Nominallohn mit 0.17 ist. Damit zeigt sich das diese beiden Variablen keine Abhängigkeit voneinander haben." - ] - }, - { - "cell_type": "markdown", - "id": "b60b2a4d-611d-4da4-9309-9150d500878e", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-94a5452d5be14bae", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Kendall Tau\n", - "\n", - "Eine andere möglichkeit Korrelationen festzustellen ist die Methode nach *David George Kendall*.\n", - "\n", - "Anzuwenden ist sie unter den folgenden Bedingungen:\n", - "1. Die Daten folgend annährend einer Normalverteilung\n", - "2. Die Datenlage ist klein\n", - "\n", - "Das Kendall Tau wird folglich berechnet mit:\n", - "\n", - "$$\\tau = \\frac{C-D}{C+D}$$\n", - "\n", - "Dabei ist $C$ die Anzahl Übereinstimmender Paare und $D$ die Anzahl der nicht übereinstimmenden Paare.\n", - "\n", - "Dazu ein Beispiel:\n", - "\n", - "Wir nehmen an das wir zwei Doktoren hätten, welche 6 Patienten nach ihrem Gesundheitszustand einschätzen. Hierfür ergibt sich folgende Datenlage (Doktor 1 ist die Reference der Datenlage):\n", - "\n", - "|Doktor 1|Doktor 2|\n", - "|-|-|\n", - "|1|3|\n", - "|2|1|\n", - "|3|4|\n", - "|4|2|\n", - "|5|6|\n", - "|6|5|\n", - "\n", - "Nun wird paarweise verglichen und die Vorzeichen beachtet danach aufsummiert. Dies führt dazu das $C=11$ & $D=4$.\n", - "Diese Werte lassen sich wunderbar in die Formel einsetzen um so das Kendallsche Tau zu berechnen:\n", - "\n", - "$$\\tau = \\frac{C-D}{C+D} = \\frac{11-4}{11+4} = \\frac{7}{15} = 0.47$$\n", - "\n", - "Wie bei Pearson ist die Relationseinschätzung die selbe. Für einen Wert im Bereich +0.5 ist eine Moderate Abhängigkeit festzustellen.\n", - "\n", - "Selbiges Beispiel in Python. Diesmal wird in die *.corr* Funktion nur die Methode geändert:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "e4b77f20-493d-4424-a3c2-a7fa140a78e2", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-b8c069ec23d35f28", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Doktor 1Doktor 2
Doktor 11.0000000.466667
Doktor 20.4666671.000000
\n", - "
" - ], - "text/plain": [ - " Doktor 1 Doktor 2\n", - "Doktor 1 1.000000 0.466667\n", - "Doktor 2 0.466667 1.000000" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "kendall = pd.DataFrame({\n", - " \"Doktor 1\": [1, 2, 3, 4, 5, 6],\n", - " \"Doktor 2\": [3, 1, 4, 2, 6, 5]\n", - "})\n", - "kendall_tau = kendall.corr(method='kendall', numeric_only=True)\n", - "kendall_tau" - ] - }, - { - "cell_type": "markdown", - "id": "d2ff93fc-dedb-4dd1-8969-db9fcdc63c66", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-732bc023d7ac4739", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Wie zu sehen kommt der selbe Wert raus." - ] - }, - { - "cell_type": "markdown", - "id": "0cedcad4-0834-43cb-a253-b9d009018a6c", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-818c093a4e3ba72e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Spearman\n", - "\n", - "Die Methode benannt nach dem Psychologen *Charles Spearman* misst auch einen Korrelationskoeffizienten zwischen zwei Datengruppen.\n", - "Im Unterschied zu Pearson ist wird Spearman dann angewandt wenn die Daten keiner Normalverteilung folgen.\n", - "\n", - "Erstellen wir uns für ein Beispiel ein Datenset bei dem die Reaktionsgeschwindigkeit eines Individuums mit seinem Alter verglichen wird:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "2bb83c7d-f3af-42c9-ae93-3655868d12e9", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-ec1bbf166d417dc7", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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AlterReaktionsgeschwindigkeit
01521
12229
22125
31925
41924
52326
61521
72114
81727
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" - ], - "text/plain": [ - " Alter Reaktionsgeschwindigkeit\n", - "0 15 21\n", - "1 22 29\n", - "2 21 25\n", - "3 19 25\n", - "4 19 24\n", - "5 23 26\n", - "6 15 21\n", - "7 21 14\n", - "8 17 27\n", - "9 15 20" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rng = np.random.default_rng(42) # reproduzierbarkeit\n", - "spearman = pd.DataFrame({\n", - " \"Alter\": rng.integers(15, 25, 10),\n", - " \"Reaktionsgeschwindigkeit\": rng.integers(12, 30, 10) \n", - "})\n", - "spearman" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "5c223094-8d54-4cd8-a3cc-4cb56595ca78", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-9a98a7086c0bd404", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Alter Reaktionsgeschwindigkeit\n", - "Alter 1.000000 0.509356\n", - "Reaktionsgeschwindigkeit 0.509356 1.000000" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "spearman_corr = spearman.corr(method='spearman', numeric_only=True)\n", - "spearman_corr" - ] - }, - { - "cell_type": "markdown", - "id": "c52137a3-20dc-46d3-9e30-6b0d49a62964", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-008a676a06ecdd3e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "In diesem Beispiel lässt sich auch wieder selbige Korrelationsmetrik anwenden und es zeigt sich, dass die erzeugten Daten eine moderate Abhängigkeit zueinander haben." - ] - }, - { - "cell_type": "markdown", - "id": "53345351-2494-4142-b550-a047fd7a7849", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-be05fea16294ca00", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Merkzettel\n", - "\n", - "1. Pearson sollte dann angewandt werden, wenn die Daten annährend einer Normaleverteilung folgen bzw. aus einer erhebt wurden.\n", - "2. Kendall (wie Pearson) nur bei kleinerer Datenlage.\n", - "3. Spearman sollte dann angewandt werden, wenn die Daten keiner Normalverteilung folgen.\n", - "\n", - "Korrelationsmetrik:\n", - "\n", - "|Positiver Koeffizient|Negativer Koeffizient|Assoziation|\n", - "|-|-|-|\n", - "|+1.0 - +0.8|-1.0 - -0.8|Perfekte bis sehr Stark|\n", - "|+0.8 - +0.6|-0.8 - -0.6|Stark|\n", - "|+0.6 - +0.4|-0.6 - -0.4|Moderat|\n", - "|+0.4 - +0.2|-0.4 - -0.2|Schwach|\n", - "|+0.2 - +0.0|-0.2 - -0.0|Sehr Schwach bis Garnicht|" - ] - }, - { - "cell_type": "markdown", - "id": "4e82abfb-41d9-4133-9e08-29494b8dab50", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-5fcf92745c0c6744", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe\n", - "\n", - "*6 Punkte - Programmieren*\n", - "\n", - "*6 Punkte - Schriftteil*\n", - "\n", - "Für diese Aufgabe ist extra ein Feld für den Schriftteil reserviert. **Erkläre** dort dein Vorgehen, die Nullhypothese, dein Ergebnis und wie der Bruttoverdienst zu verstehen ist. **Interpretiere** dein Ergebnis und Stelle ein **Fazit** auf. Gebe auch alle verwendeten Quellen (samt link) korrekt an.\n", - "Jupyter unterstütz die Markdown Syntax ein Guide hierfür ist unter [The Ultimate Markdown Guide](https://medium.com/analytics-vidhya/the-ultimate-markdown-guide-for-jupyter-notebook-d5e5abf728fd) zu finden.\n", - "\n", - "**!!Wird der Schriftteil nicht oder nur unzureichend ausgefüllt führt dies zu einer Bewertung von 0 Punkten für die gesamte Aufgabe!!**\n", - "\n", - "Analysiere das Datenset `bruttoverdiensterhebung_deutschland_april_22-23_nach_geschlecht.csv` (Quelle: [Destatis]( https://www-genesis.destatis.de/datenbank/online/statistic/62361/table/62361-0030))\n", - "\n", - "Gehe dabei von der Hypothese aus das einen keinen Bruttoeinkommensunterschied der Geschlechter in Deutschland für den Zeitraum 2022-2023 gibt.\n", - "\n", - "Gehe dabei wie folgt vor:\n", - "1. Lies das Datenset ein und schaue dir die vorhandenen Werte an. Achte auf `Nan` Werte.\n", - "2. Filter aus der Spalte \"Geschlecht\" die benötigten Werte.\n", - "3. Führe einen Korrelationstest zwischen den Geschlechtern & dem Bruttoverdienst durch.\n", - "4. Berechne den Bruttoeinkommensunterschied.\n", - "5. Finde eine geeignete Darstellung für dein Ergebnis." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8bc69bfc-96aa-4fdf-b540-a956ff4c1baf", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-78d2ec73dc266600", - "locked": false, - "points": 6, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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GeschlechtBrancheVerdienstVerdienstart
0weiblichMetalloberflächenbehandlung16.19Mittlere Bruttostundenverdienste ohne Sonderz.
1weiblichMetalloberflächenbehandlung2810.00Durchschn. Bruttomonatsverdienste ohne Sonderz.
2weiblichMetalloberflächenbehandlung17.28Durchschn. Bruttostundenverdienste ohne Sonderz.
3weiblichMetalloberflächenbehandlung2705.00Mittlere Bruttomonatsverdienste ohne Sonderz.
4männlichNicht ärztliche Therapie und Heilkunde18.99Mittlere Bruttostundenverdienste ohne Sonderz.
...............
17767InsgesamtRohstoffgewinn, Glas-, Keramikverarbeitung3181.00Mittlere Bruttomonatsverdienste ohne Sonderz.
17768InsgesamtNichtmed. Gesundheit, Körperpfl., Medizintechnik19.63Mittlere Bruttostundenverdienste ohne Sonderz.
17769InsgesamtNichtmed. Gesundheit, Körperpfl., Medizintechnik3373.00Durchschn. Bruttomonatsverdienste ohne Sonderz.
17770InsgesamtNichtmed. Gesundheit, Körperpfl., Medizintechnik20.74Durchschn. Bruttostundenverdienste ohne Sonderz.
17771InsgesamtNichtmed. Gesundheit, Körperpfl., Medizintechnik3201.00Mittlere Bruttomonatsverdienste ohne Sonderz.
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" - ], - "text/plain": [ - " Geschlecht Branche Verdienst \\\n", - "0 weiblich Metalloberflächenbehandlung 16.19 \n", - "1 weiblich Metalloberflächenbehandlung 2810.00 \n", - "2 weiblich Metalloberflächenbehandlung 17.28 \n", - "3 weiblich Metalloberflächenbehandlung 2705.00 \n", - "4 männlich Nicht ärztliche Therapie und Heilkunde 18.99 \n", - "... ... ... ... \n", - "17767 Insgesamt Rohstoffgewinn, Glas-, Keramikverarbeitung 3181.00 \n", - "17768 Insgesamt Nichtmed. Gesundheit, Körperpfl., Medizintechnik 19.63 \n", - "17769 Insgesamt Nichtmed. Gesundheit, Körperpfl., Medizintechnik 3373.00 \n", - "17770 Insgesamt Nichtmed. Gesundheit, Körperpfl., Medizintechnik 20.74 \n", - "17771 Insgesamt Nichtmed. Gesundheit, Körperpfl., Medizintechnik 3201.00 \n", - "\n", - " Verdienstart \n", - "0 Mittlere Bruttostundenverdienste ohne Sonderz. \n", - "1 Durchschn. Bruttomonatsverdienste ohne Sonderz. \n", - "2 Durchschn. Bruttostundenverdienste ohne Sonderz. \n", - "3 Mittlere Bruttomonatsverdienste ohne Sonderz. \n", - "4 Mittlere Bruttostundenverdienste ohne Sonderz. \n", - "... ... \n", - "17767 Mittlere Bruttomonatsverdienste ohne Sonderz. \n", - "17768 Mittlere Bruttostundenverdienste ohne Sonderz. \n", - "17769 Durchschn. Bruttomonatsverdienste ohne Sonderz. \n", - "17770 Durchschn. Bruttostundenverdienste ohne Sonderz. \n", - "17771 Mittlere Bruttomonatsverdienste ohne Sonderz. \n", - "\n", - "[17772 rows x 4 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "import pandas as pd\n", - "dataset: pd.DataFrame = pd.read_csv('bruttoverdiensterhebung_deutschland_april_22-23_nach_geschlecht.csv')\n", - "dataset\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "d6871b8f-5eda-4cad-a482-7c0bb88dff0d", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-8fb2067332715dfa", - "locked": false, - "points": 6, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "source": [ - "# BEGIN SOLUTION\n", - "# END SOLUTION" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +version https://git-lfs.github.com/spec/v1 +oid sha256:ec6e1f891d23734631c630dd8f6df95fcc672aacd393eee8f019566c94b69380 +size 121271 diff --git a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/meme.png b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/meme.png index 1c5b748..6294990 100644 Binary files a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/meme.png and b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Jupyter/meme.png differ diff --git a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/KC_Deutsch_HS_Anhrung.pdf b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/KC_Deutsch_HS_Anhrung.pdf index dd2c8b3..3dbf2e9 100644 Binary files a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/KC_Deutsch_HS_Anhrung.pdf and b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/KC_Deutsch_HS_Anhrung.pdf differ diff --git a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Timetable.pdf b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Timetable.pdf index a681515..e16d23b 100644 Binary files a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Timetable.pdf and b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/Timetable.pdf differ diff --git a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/archive_filelist.csv b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/archive_filelist.csv index b4442f9..4a030e1 100644 --- a/Documents/Arbeit/IFN/Programmieren WiSe 24 25/archive_filelist.csv +++ b/Documents/Arbeit/IFN/Programmieren WiSe 24 25/archive_filelist.csv @@ -1,9 +1,9 @@ -Name;Größe;Vorname;Nachname;Downloads;Datum;Pfad -3.Extended_Applications_Lösungen.html;285641;Phil;Keier;15;"18.11.2024 11:41";3.Extended_Applications_Lösungen.html -4.NumPy_MatPlotLib_Lösungen.html;495133;Phil;Keier;13;"05.12.2024 12:13";4.NumPy_MatPlotLib_Lösungen.html -6.Lösungen_Monte_Carlo.html;404898;Phil;Keier;11;"13.12.2024 12:46";6.Lösungen_Monte_Carlo.html -7.Lösungen_Pandas_Seaborn.slides.html;464168;Phil;Keier;13;"13.12.2024 12:46";7.Lösungen_Pandas_Seaborn.slides.html -8.Folium_Lösungen.slides.html;450681;Phil;Keier;9;"23.01.2025 10:55";8.Folium_Lösungen.slides.html -9.Lösungen_Statistical_Test_Methods.slides.html;314938;Phil;Keier;17;"23.01.2025 10:55";9.Lösungen_Statistical_Test_Methods.slides.html -Tutorial1_Lösungen.html;311997;Phil;Keier;34;"04.11.2024 17:26";Tutorial1_Lösungen.html -Tutorial2_Lösungen.html;293798;Phil;Keier;25;"08.11.2024 12:01";Tutorial2_Lösungen.html +Name;Größe;Vorname;Nachname;Downloads;Datum;Pfad +3.Extended_Applications_Lösungen.html;285641;Phil;Keier;15;"18.11.2024 11:41";3.Extended_Applications_Lösungen.html +4.NumPy_MatPlotLib_Lösungen.html;495133;Phil;Keier;13;"05.12.2024 12:13";4.NumPy_MatPlotLib_Lösungen.html +6.Lösungen_Monte_Carlo.html;404898;Phil;Keier;11;"13.12.2024 12:46";6.Lösungen_Monte_Carlo.html +7.Lösungen_Pandas_Seaborn.slides.html;464168;Phil;Keier;13;"13.12.2024 12:46";7.Lösungen_Pandas_Seaborn.slides.html +8.Folium_Lösungen.slides.html;450681;Phil;Keier;9;"23.01.2025 10:55";8.Folium_Lösungen.slides.html +9.Lösungen_Statistical_Test_Methods.slides.html;314938;Phil;Keier;17;"23.01.2025 10:55";9.Lösungen_Statistical_Test_Methods.slides.html +Tutorial1_Lösungen.html;311997;Phil;Keier;34;"04.11.2024 17:26";Tutorial1_Lösungen.html +Tutorial2_Lösungen.html;293798;Phil;Keier;25;"08.11.2024 12:01";Tutorial2_Lösungen.html diff --git a/Documents/Arbeit/IFN/Programmieren WiSe 25 26/1. 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"slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# 1. Programmierübung: Python Tutorial\n", - "\n", - "

\n", - " \n", - "Python ist eine universelle Programmiersprache, die aufgrund ihrer Einfachheit sehr leicht zu lernen und zu bedienen ist. Die Funktionalität kann durch den Import von Bibliotheken erweitert werden. Im Folgenden werden wir Ihnen zeigen, wie man hier im Jupyter Notebook Python Code ausführen kann. Die grundlegenden Konzepte und Strukturen in Python werden mit Hilfe von externen Quellen gezeigt. Die Übungsaufgaben dienen zum Testen und der Hands-on-Praxis des gelernten Wissens. \n", - "\n", - "In diesem Jupyter Notebook werden die grundlegende Funktionen und Konzepte in Python vorgestellt. Dazu wird es kleine Programmierübungen um das gelernte Wissen in Beispielen anzuwenden. (Objekt Orientierte Programmierung werden wir in diesem Kurs nicht behandeln!)\n", - "\n", - "Das Jupyter Notebook ist in Zellen unterteilt, die durch Boxen gekennzeichnet sind, die einzeln ausgeführt werden können (entweder über `Shift + Enter` oder den `Run`-Knopf). Sie können auch alle Zellen im Notebook ausführen über `Kernel > Restart & Run All` oder dem \"Vorspulen\"-Zeichen.\n", - "\n", - "Bitte beachten Sie, dass alle Zellen im Notebook ein gemeinsamen Workspace nutzen. Das bedeutet, dass Bibliotheken nur einmal importiert werden müssen und dann innerhalb des Notebooks genutzt werden können. Es können jedoch auch Variablen überschrieben werden, wenn diese nicht richtig gekapselt werden (z.B. über Funktionen).\n", - "\n", - "Viel Spaß und Erfolg!\n", - "\n", - "Es gibt _sehr_ viele weitere Python-Tutorials online, z.B. auf [Youtube](https://youtu.be/kqtD5dpn9C8), mit denen Sie die benötigten Grundlagen für Python lernen können.\n", - "\n", - "Wenn du Fragen oder Verbesserungsvorschläge zum Inhalt oder Struktur der Notebooks haben, dann können Sie eine E-Mail an Phil Keier ([p.keier@hbk-bs.de](mailto:p.keier@hbk-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) schreiben.\n", - "\n", - "Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n", - "\n", - "Der Großteil des Python-Tutorials stammt aus der Veranstaltung _Deep Learning Lab_ und von [www.python-kurs.eu](https://www.python-kurs.eu/python3_kurs.php) und wurden für _Signale und Systeme_, sowie _Einführung in die Programmierung für Nicht Informatiker_ angepasst.\n", - "\n", - "---\n" - ] - }, - { - "cell_type": "markdown", - "id": "6ac5a9e3-7190-4db6-859b-3cb1511fe29f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c8293a61ad8fb19c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Printing\n", - "\n", - "Für viele Anwendungsfälle ist es wichtig, dass der Computer mit uns als Mensch kommunizieren kann. Zu diesem Zweck lernen wir zuerst wie wir eine Ausgabe erzeugen können. Hierzu verwenden wir die Funktion `print()`.\n", - "\n", - "[print()](https://www.w3schools.com/python/ref_func_print.asp) ist eine BuiltIn Funktion, zu diesen später mehr. Es soll aber gesagt sein, dass keinerlei anstrengungen notwendig sind um die Print-Funktion zu verwenden, da Python sie von Haus aus kennt.\n", - "\n", - "## Hello World\n", - "\n", - "Schauen wir uns nun folgend ein einfaches Programm an:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "3733ea3b-12b5-4c1c-9b03-0f1c397babfc", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5f71f94cf9d603e2", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hello World\n" - ] - } - ], - "source": [ - "print(\"Hello World\")" - ] - }, - { - "cell_type": "markdown", - "id": "708d34f1-a479-4277-90e7-14d67a8c688a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d49c16a44b1d3984", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Der Teil in den Klammern ist der Wert den wir ausgeben wollen in diesem Fall eine einfache Zeichenkette (auch dazu später mehr)." - ] - }, - { - "cell_type": "markdown", - "id": "a20546ae-1b5d-4ad8-908e-32f4e860ec63", - "metadata": {}, - "source": [ - "## Aufgabe 1\n", - "\n", - "Gebe den Text `Hallo Python` aus, Nutze dabei die `print`-funktion. *1 Punkt*" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "88b8d4db-6f4c-411c-b8ad-d2d6ca0b427c", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-cd36c0330024bfe5", - "locked": false, - "points": 1, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hallo Python\n" - ] - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "print(\"Hallo Python\")\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "ad8d06df-5d3d-4203-b17c-227a663c8e7b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-95b07b67b0ede718", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Datentypen und Variablen\n", - "\n", - "Python unterstützt verschiedene Datentypen. Zu diesen Zählen :\n", - "1. Integer (Ganze Zahlen) $$\\mathbb{Z} = \\{1,-1,2,-2,3,-3,\\dots\\}$$\n", - "2. Floatings Point Numbers (Fließkommazahlen) $$\\pi = 3.141592653589793$$\n", - "3. Strings (Zeichenketten)\n", - "> \"Ich bin eine Zeichenkette\"\n", - "\n", - "4. Listen\n", - "> [Objekt1, Objekt2, 42]\n", - "\n", - "5. Dictionaries\n", - "> {\"Schlüssel1\": \"Wert1\", \"Schlüssel2\": \"Wert2\",}\n", - "\n", - "6. Sets\n", - "> {\"Wert1\", 7, \"Zeichenkette\"}\n", - "\n", - "7. Tupel\n", - "> (42, 7)" - ] - }, - { - "cell_type": "markdown", - "id": "313d37b0-52ad-4de4-9fc5-0f362bfe7284", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-12a63250c85469f1", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Zahlentypen (Floats, Integers)\n", - "\n", - "### Aufgabe 1-1: Zuweisungen von Variablen" - ] - }, - { - "cell_type": "markdown", - "id": "65dae625-2f0b-46d2-803c-c3d6c54000f7", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-782aef1600674714", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Eine Definition und Zuweisung eines Wertes zu einer Variablen erfolgt über den `=` Operator." - ] - }, - { - "cell_type": "markdown", - "id": "fca50dd5-4a56-451a-9a1a-6cbd8d76519a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7b71098cec169f0f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Aufgabe** *2 Punkte*: \n", - "\n", - "Definieren Sie zunächst die zwei Variablen `a` und `b` und initialisieren diese mit einem Integerwert ungleich `0`:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d7d24e44-8581-4c5c-a723-83b9f7e418ac", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-7be930fd387f1043", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "scrolled": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "a = 1\n", - "b = -2\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8cfd850c-02c7-4b32-b20c-e37f5eb4fe8b", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-d44ec6114b65557c", - "locked": true, - "points": 2, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "assert isinstance(a, int)\n", - "assert isinstance(b, int)\n", - "\n", - "assert a != 0\n", - "assert b != 0" - ] - }, - { - "cell_type": "markdown", - "id": "337fbcc5-c960-4206-83d1-605d05a51d5d", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-2f79b7b52775db8b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Aufgabe** *2 Punkte*:\n", - "\n", - "Definieren Sie zwei Variablen `s` und `t` und initialisieren diese mit einem Floatwert ungleich `0`:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "03b7469a-4f59-437f-aa8f-797d200b41a1", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-7d48f9bed0df944d", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "s = 1.5\n", - "t = -2.7\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "45404bbe-2026-491d-9aef-9d3ee8894adf", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-3b426f39262c1e03", - "locked": true, - "points": 2, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "assert isinstance(s, float)\n", - "assert isinstance(t, float)\n", - "\n", - "assert s != 0\n", - "assert t != 0" - ] - }, - { - "cell_type": "markdown", - "id": "5452589d-5997-4e8f-b8bb-363925c6166a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8690aecc1748ad4a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe 1-2: Operationen auf Zahlen\n", - "\n", - "Aus der Schule sollten die folgenden Grunlegenden Operationen die sich auf Zahlen ausführen lassen bekannt sein:\n", - "\n", - "1. Addition $\\Rightarrow a+b=c$\n", - "2. Subtraktion $\\Rightarrow a-b=c$\n", - "3. Multiplikation $\\Rightarrow a\\cdot b=c$\n", - "4. Division $\\Rightarrow\\frac{a}{b} = c$\n", - "> Teilt man zwei Integer durcheinander werden diese erst in Floats umgewandelt und dann als Float gespeichert: $$10/3=3.3333333333$$\n", - "> Die Integer Division (Ganzzahl Division $\\lfloor\\frac{a}{b}\\rfloor$) (Notiert mit \"//\") zweier Zahlen schneidet den Rest nach dem Komma ab: $$10//3\\equiv 3$$\n", - "\n", - "5. Modulus $\\Rightarrow a\\mod b \\equiv c$\n", - "> \"Teilen mit Rest\" (in Python notiert mit \"%\" hat nichts mit Prozenten zutun) funktioniert genauso wie man die Uhr lesen würde. Ist es 15 Uhr sagt man im Sprachgebrauch 3 Uhr (Mittags). Der Modulus Operator funktioniert genauso. $$15 \\mod 12 \\equiv 3$$\n", - "\n", - "6. Exponentation $\\Rightarrow a^b = c$\n", - "> In Python notiert mit \"a**b\"" - ] - }, - { - "cell_type": "markdown", - "id": "9e408960-4180-4e92-ba88-ebf39441dfa7", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c4551eabf148e18e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Aufgabe** *2 Punkte*:\n", - "\n", - "Addieren Sie die Werte der Variablen `a` und `b` und speichern Sie das Ergebnis in der Variable `c`:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "6aaa0c05-ae16-4c48-b841-79931dae94bd", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-2ff97153b6652687", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "-1" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "c = a + b\n", - "# END SOLUTION\n", - "c" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "2fa4d7f2-235d-411e-956c-4ff335705124", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-3ba3833c7220bbb7", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "assert isinstance(c, int)\n", - "### BEGIN HIDDEN TESTS\n", - "assert a + b == c\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "9fccb90e-1f22-46b9-99a1-14b64274ece5", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-f80a3165c27dc297", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Aufgabe** *5 Punkte*:\n", - "\n", - "Nutzen Sie die Variablen `a` & `b` und Speichern Sie die Ergebnisse für die Multiplikation, Division, Ganzzahldivision, Exponentiation und den Modulo-Operator in den unten angegebenen Variablen:\n", - "\n", - "\\begin{align}\n", - "m &= a\\cdot b\\\\\n", - "d &= \\frac{a}{b}\\\\\n", - "i &= \\lfloor\\frac{a}{b}\\rfloor\\\\\n", - "e &= a^b\\\\\n", - "r &= a\\; \\textrm{mod}\\; b\n", - "\\end{align}\n", - "\n", - "\n", - "Die Ausführung der anderen arithmetischen Operationen in Python erfolgt analog. Eine Übersicht können Sie [hier](https://www.python-kurs.eu/operatoren.php) entnehmen." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "30ce28b3-b97d-4b3f-bac0-6d01356dcc60", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-3f3640eaf7ee2dd3", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "### BEGIN SOLUTION\n", - "m = a * b\n", - "d = a / b\n", - "i = a // b\n", - "e = a ** b\n", - "r = a % b\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "95108d9d-4cba-489b-bdbc-ed5c71316ac8", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-804a957c4a02e824", - "locked": true, - "points": 5, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "### BEGIN HIDDEN TESTS\n", - "assert m == a*b\n", - "assert d == a/b\n", - "assert i == a//b\n", - "assert e == a**b\n", - "assert r == a%b\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "444c4633-9186-497f-88a7-e2ec0013dff4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7ac5c4d8e6463b16", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Sequentielle Datentypen\n", - "\n", - "Sequentielle Datentypen sind ein wichtiger Bestandteil in der Programmierung. Dazu gehören Listen, Tupel und Strings.\n", - "\n", - "Wichtige Eigenschaften dieser Datentypen sind:\n", - "- Die Elemente von Listen, Strings oder Tupeln sind in einer bestimmten Reihenfolge angeordnet (Diese entspricht der Ordnung in der die Elemente eingefügt worden).\n", - "- Der Zugriff (Lesen und Schreiben) dieser Objekte erfolgt über Indizes (Das erste Element eines Sequentiellen Datentypes ist immer `0`).\n", - "- Zugriff auf Elemente kann auch Rückwärts erfolgen das letzte Element wird dann mit `-1` ausgelesen. \n", - "\n", - "Beispiel für eine Liste:\n", - "`some_list = [\"a\", \"b\", \"c\"]`\n", - "\n", - "Beispiel für ein Tupel:\n", - "`some_tuple = (1, 2, 3)`\n", - "\n", - "Beispiel für ein String:\n", - "`some_string = \"Python ist cool!\"`\n", - "\n", - "### Aufgabe 2-1: Strings\n", - "\n", - "Zeichenketten, Text oder Strings lassen sich in Python mit `'Text'`, `\"Text\"` oder der Funktion `str()` definieren.\n", - "\n", - "**Aufgabe** *2 Punkte*:\n", - "\n", - "Ein String-Objekt (Text) können sie mit Hilfe von `'Some Text'` oder `\"Some Text2\"` definieren. Definieren sie die Variable `text` mit einem beliebigen Text." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "f8028cf5-0dc4-4e72-a98e-3e18705c8c98", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-73a9beb04648359b", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "text = \"Hi Mom, I am on TV!\"\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "7a6832ae-e6c7-4230-b3ca-b1f1f90345fb", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-1677fa4f3b4eec12", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "assert isinstance(text, str)" - ] - }, - { - "cell_type": "markdown", - "id": "8e10cd2a-53dc-485f-bbdd-aa2709574660", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-05f0b0cd1211c396", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Python Strings lassen sich mit verschiedenen mitteln formatieren. Dazu zählt die [format-Funktion](https://www.w3schools.com/python/ref_string_format.asp) \n", - "\n", - "**Aufgabe** *1 Punkte*:\n", - "\n", - "Geben Sie die Variablen `a` & `b` aus Aufgabe 1 im format `\"a = 12 und b = 12\"` (Die Werte sollen dann den Werten aus ihrer Definition entsprechen. 12 ist hier nur ein Beispiel) aus." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d3efbadb-3c33-40eb-9260-8f8b11faaf75", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-c94a5b5e9f73479e", - "locked": false, - "points": 1, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a = 1 und b = -2\n" - ] - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "print(\"a = {} und b = {}\".format(a, b))\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "d860edba-749c-41c0-827b-0c68ba6fd00a", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-e4c5420224d04f6a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe 2-2: Listen \n", - "\n", - "Listen lassen sich mit der Funktion `list()` oder `[]` definieren und können eine \"unendliche\" Menge an Elementen unterschiedlichen Datentyps speichern. Die Liste `[420, \"Hallo Jupyter\", 0.222]` ist eine Korrekt definierte Liste. Im Allgemeinen ist es Ratsam listen mit gleichem Datentyp zu füllen, da dies bei der Verarbeitung zu Problemen führen kann." - ] - }, - { - "cell_type": "markdown", - "id": "51416edc-4c96-437c-a1ff-c458f06c9e8a", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-36d12824040df91e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Aufgabe** *1 Punkte*: \n", - "\n", - "Definieren Sie die Variable `l` und weisen Sie dieser Variable eine Liste mit aufsteigenden Integerwerten von `0` bis `4` zu." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "b361ee09-cd48-4c16-89ea-714ee8ab541f", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-89d74b5c210fc331", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "ename": "TypeError", - "evalue": "list expected at most 1 argument, got 5", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m l \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m5\u001b[39m))\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# END SOLUTION\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m l \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mTypeError\u001b[0m: list expected at most 1 argument, got 5" - ] - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "l = list(range(5))\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "65fcfdb4-58ff-47d3-bebd-d3786a971af2", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-589caab43851d55a", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "### BEGIN HIDDEN TESTS\n", - "assert isinstance(l, list)\n", - "assert l == [0, 1, 2, 3, 4]\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "a6dfba7d-4bb5-4530-bb75-689843a718a8", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-5ca56027cd6a5698", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Aufgabe** *1 Punkte*:\n", - "\n", - "Hängen Sie der Liste `l` noch den Wert `42` an.\n", - "\n", - "Hinweis: Nutzen Sie dafür die Methode [.append](https://www.w3schools.com/python/ref_list_append.asp)." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "e39e50dc-3d97-4579-aeb4-04ec2de3dbb3", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-853db222010bee68", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "l.append(42)\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "53000a6c-1187-48b0-b038-129d434cc45a", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-c1aca9603460d1de", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "### BEGIN HIDDEN TESTS\n", - "assert l == [0, 1, 2, 3, 4, 42]\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "4510d2f3-3386-4d33-baa5-1d684ab52370", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c58e5530e380c09a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Zugriff auf Elemente eines Sequentiellen Datentypes lassen sich über `[]` realisieren.\n", - "\n", - "Beispiel - Zugriff auf das erste Element einer Liste:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "259d73e8-eca3-4172-8a0a-1efbbe3b527b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-cb1b7e8055910efc", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "l[0]" - ] - }, - { - "cell_type": "markdown", - "id": "bf6d1243-9650-4c27-b3dc-86a2d48b3abc", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1d8edfe975ed19bf", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Aufgabe** *1 Punkte*:\n", - "\n", - "Geben Sie das dritte Element der Liste `l` aus.\n", - "\n", - "Hinweis: Achten Sie darauf das der erste Index immer `0` ist. " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "358a0b51-3bfa-4e65-9cd4-ee2e7f8bc9d5", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-a386250119dc89fb", - "locked": false, - "points": 1, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2\n" - ] - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "print(l[2])\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "2f25fa6d-9a72-464b-ada6-d2630dd03e92", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0ff369c64d2f8c24", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Aufgabe** *1 Punkte*:\n", - "\n", - "Geben Sie das vorletzte Element der Liste `l` aus.\n", - "\n", - "Hinweis: Achten Sie darauf das der letzte Index mit `-1` ausgegeben wird." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "16ec2e20-e28e-41de-8f85-1091e41bb401", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-2394235b49ebb749", - "locked": false, - "points": 1, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4\n" - ] - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "print(l[-2])\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "291522bc-9c4d-4348-b2cb-99f9653168fa", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c8fe8cb9d2ca1028", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe 2-3: Dictionaries\n", - "\n", - "Das Dictionary ist ein Datentyp, welcher Schlüssel-Werte-Paare speichert. Dabei wird ein Dictionary mit `dict()` oder `{\"Schlüssel1\": \"Wert1\"}` initalisiert. Wichtig ist hierbei das ein Dictionary nicht mit `{}` initialisiert werden kann da dies die Notation für das **Set** Objekt ist.\n", - "\n", - "**Aufgabe** *1 Punkte*:\n", - "\n", - "Initialisieren Sie die Dictionary Variable `my_dict` mit folgendem Mapping:\n", - "\n", - "| Key | Value |\n", - "|:----|:------|\n", - "| `\"apple\"` | `\"Apfel\"` |\n", - "| `\"banana\"` | `\"Banane\"` |\n", - "| `\"cherry\"` | `\"Kirsche\"` |" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "c17338bb-c6df-493c-9d88-0e9ea36a755d", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-86ce3695bf3f6780", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "### BEGIN SOLUTION\n", - "my_dict = {\"apple\": \"Apfel\", \"banana\": \"Banane\", \"cherry\": \"Kirsche\"}\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "a367442e-2c8c-4d32-8acb-5bccf94d64fb", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-969a9415b60857a8", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "assert isinstance(my_dict, dict)\n", - "### BEGIN HIDDEN TESTS\n", - "assert my_dict == {\"apple\": \"Apfel\", \"banana\": \"Banane\", \"cherry\": \"Kirsche\"}\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "8ec10cc1-4b8b-4b53-b1d1-991d6287abda", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0f5df3b99a4774ba", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Aufgabe** *1 Punkte*:\n", - "\n", - "Fügen Sie nun das Key-Value Paar `\"pear\": \"Birne\"` zu `my_dict` hinzu." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "d3aac185-2d6e-4b30-b247-89be8aeeab7d", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-ed3cf3b9d6a8ad58", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "### BEGIN SOLUTION\n", - "my_dict[\"pear\"] = \"Birne\"\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "c377ec37-b382-4f83-a9cc-829a44b7682e", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-9735fc9ff4416c4c", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "### BEGIN HIDDEN TESTS\n", - "assert my_dict == {\"apple\": \"Apfel\", \"banana\": \"Banane\", \"cherry\": \"Kirsche\", \"pear\": \"Birne\"}\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "2b3cfdf2-6864-402c-9ded-9fd0c7c489ee", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-957ca6c50c1cfb70", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Für gewisse Anwendungen reicht es nur die Schlüssel (oder Werte) aus einem Dictionary zu haben. Dazu bietet das Dictionary die Funktionen `.keys()` (für eine Liste der Schlüssel) und `.values()` (für eine Liste der Werte).\n", - "\n", - "**Aufgabe** *1 Punkte*:\n", - "\n", - "Speichere die Werte des Dictionaries `my_dict` in der Variablen `values`." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "03f2c31a-04b4-4dc7-ab00-476cec6922ad", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-f190c63e28ae9e82", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "### BEGIN SOLUTION\n", - "values = my_dict.values()\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "441997b9-3d46-4d2f-bb64-56ebbe4f693c", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-73d4d3b4007a89c6", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Hier werden ihre Lösungen getestet ...\n", - "### BEGIN HIDDEN TESTS\n", - "for v1, v2 in zip(values, my_dict.values()):\n", - " assert v1 == v2\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "6e774e49-895b-4bb2-9436-cddb75a3d46d", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5bd0f8a189d6db1c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Wichtiger für die meisten Probleme ist die Dictionary Funktion `.items()` diese gibt eine Liste an Tupeln mit den Schlüssel Werte Paaren aus.\n", - "\n", - "**Aufgabe** *1 Punkte*:\n", - "\n", - "Speichern Sie die Elemente des Dictionaries `my_dict`, welche mit der Funktion `.items()` ausgegeben werden, in der Variablen `items`. " - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "a399cf66-43eb-4749-8864-18c5e4202f79", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-03afb00cc074d1ef", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "### BEGIN SOLUTION\n", - "items = my_dict.items()\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "ceebb41b-1e38-458a-9afd-e9f898ef4268", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-727afc0671fb1f19", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Hier werden ihre Lösungen getestet...\n", - "### BEGIN HIDDEN TESTS\n", - "for t1, t2 in zip(items, my_dict.items()):\n", - " assert t1 == t2\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "a8d62b7e-ae53-4bd7-a930-84508c3948f9", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-83b1e45bc901dc68", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Funktionen\n", - "\n", - "Mit einigen Funktionen haben wir uns bereits befasst dazu zählen `print()`, `.keys()` und alle weiteren die diesem Schema folgen.\n", - "\n", - "In diesem Kapitel wollen wir uns mit dem Aufbau von Funktionen befassen. Dabei folgt jede Funktion folgendem Aufbaue:\n", - "\n", - "```python\n", - "def some_function_name(param1, param2):\n", - " a = do_something1(param1)\n", - " b = do_something2(a, param2)\n", - " do_something3(b)\n", - " return b\n", - "```\n", - "\n", - "Das `def`-Schlüsselwort leitet die Definition einer Funktion ein, gefolgt von dem Funktionsnamen, den Eingabeparametern der Funktion in runden Klammern und einem Doppelpunkt. Wichtig ist, dass die Anweisungen innerhalb der Funktion eingerückt sein müssen. Das Ergebnis (oder die Ergebnisse) werden mit Hilfe des `return`-Schlüsselworts gekennzeichnet.\n", - "\n", - "**Aufgabe** *1 Punkte*:\n", - "\n", - "Schreibe eine Funktion `successor` die auf jede Eingabe `+1` rechnet." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "1a151db2-617c-48f4-969b-3bafb45b1fd1", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-c6a731a4a13b2bbc", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "def successor(n):\n", - " return n+1\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "615a98b6-139e-485b-874e-d0a70cd22517", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-00693d8d9c92af76", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "assert successor(1) == 2" - ] - }, - { - "cell_type": "markdown", - "id": "d6f47a61-b692-4f46-8ccc-83af10189f93", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-9c358751403a1986", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Aufgabe** *1 Punkte*:\n", - "\n", - "Schreibe eine Funktion `add` mit den Eingabeparametern `a` & `b`, welche die Werte von `a` & `b` miteinander addiert." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "3f101d21-aa1a-4bf3-aadf-bb4e41d8fe12", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-2b72cf583fed9b8c", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "def add(a,b):\n", - " return a+b\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "1d155c26-0875-4a71-8564-a4a0e0e3bb70", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-7a24b5cfd7fc9990", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "assert add(1,2) == 3" - ] - }, - { - "cell_type": "markdown", - "id": "d77ab363-9fe0-4504-b43b-6f7894666525", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0fd1dbfed99faa8a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Kontrollstruckturen\n", - "\n", - "## Aufgabe 3-1: Conditionals - If-Else\n", - "\n", - "Um Entscheidungen treffen zu können nutzt man in Python das Kommando `if ` ist der Ausdruck wahr wird der darauf folgende Code ausgeführt.\n", - "\n", - "Liste von möglichen Ausdrücken:\n", - "\n", - "- `a == b` checkt ob die Werte `a` & `b` gleich sind\n", - "- `a != b` checkt ob die Werte `a` & `b` **nicht** gleich sind\n", - "- `a > b` checkt ob der Wert `a` größer als `b` ist (Analog dazu \"größer gleich\" `a >= b`)\n", - "- `a < b` checkt ob der Wert `a` kleiner als `b` ist (Analog dazu \"kleiner gleich\" `a <= b`)\n", - "- `not ` invertiert das Ergebnis des Ausdruckes, also aus einem wahren Ausdruck wird ein falscher und andersherum.\n", - "\n", - "Zur Verkettung von Ausdrücken:\n", - "\n", - "- ` and ` checkt ob die Ausdrücke `ausdruck1` & `ausdruck2` wahr also erfüllt sind\n", - "- ` or ` checkt ob einer der Ausdrücke `ausdruck1` & `ausdruck2` wahr also erfüllt sind\n", - "\n", - "Beispiel:" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "4363cda5-98c5-4424-8fb3-7c92e1994993", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-020d46673782a358", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "You're the Number One\n" - ] - } - ], - "source": [ - "zahl = 1\n", - "if zahl == 1:\n", - " print(\"You're the Number One\")\n", - "\n", - "if zahl == 2:\n", - " print(\"You Lose\")" - ] - }, - { - "cell_type": "markdown", - "id": "21a23914-d0b7-492d-bcfe-8ecde9c20c85", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-aa2d59d677afd5a3", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Das Kommando `else` funktioniert nur zusammen mit dem `if` Kommando und bietet dem Programm eine Art \"Fall Back\":" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "3eaf3062-ae81-48c0-802d-88a72db587be", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-f4124dd62687158f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "You're not the Number One\n" - ] - } - ], - "source": [ - "zahl = 5\n", - "if zahl == 1:\n", - " print(\"You're the Number One\")\n", - "else:\n", - " print(\"You're not the Number One\")" - ] - }, - { - "cell_type": "markdown", - "id": "7e35f339-3f59-4fd6-a162-e6eb0379c778", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-167fb232c7163fe6", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Um auf mehrere Ausdrücke zu checken kann das `elif` verwendet werden. Es findet seinen Platz zwischen `if` & `else`:" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "e37dd53c-e375-4548-9563-c8c6664dfdd0", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-0c0312666fa7648f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "You're the Number Two\n" - ] - } - ], - "source": [ - "zahl = 2\n", - "if zahl == 1:\n", - " print(\"You're the Number One\")\n", - "elif zahl == 2:\n", - " print(\"You're the Number Two\")\n", - "elif zahl == 2:\n", - " print(\"You're the Number Three\")\n", - "else:\n", - " print(\"You're not the Number One\")" - ] - }, - { - "cell_type": "markdown", - "id": "5248404d-4717-462d-a67f-57431e599945", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-060bd6eb927fa8b1", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Aufgabe** *1 Punkte*:\n", - "\n", - "Schreibe eine Funktion `is_odd` mit einem Eingabeparameter `n` die prüft ob die eingegebene Zahl ungerade ist.\n", - "\n", - "Wenn die Zahl gerade ist gebe den Text `\"Gerade Zahl\"` und bei ungerade `\"Ungerade Zahl\"` zurück." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "d4b1cef3-6222-438d-bbc3-243431fad0cb", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-77afd241bc69d6b1", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "def is_odd(n):\n", - " if n % 2 == 0:\n", - " return \"Gerade Zahl\"\n", - " else:\n", - " return \"Ungerade Zahl\"\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "141d73da-90e6-401a-a651-ad14635de5b7", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-d8541ba8c61147c3", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "assert is_odd(2).lower() == \"Gerade Zahl\".lower()\n", - "assert is_odd(3).lower() == \"Ungerade Zahl\".lower()" - ] - }, - { - "cell_type": "markdown", - "id": "c0086b8c-da02-4fdf-8341-9c0518bb6406", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-69409d8dcfe070e1", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Aufgabe 3-2: Sequentielles - While Loop\n", - "\n", - "*7 Punkte*\n", - "\n", - "Schleifen sind wichtig um eine Aufgabe öfter zu wiederholen. \n", - "\n", - "Schauen wir uns dazu zunächst den `while`-loop an. Die Syntax schaut wie folgt aus:\n", - "\n", - "```python\n", - "while :\n", - " do_something()\n", - "```\n", - "\n", - "Solange der Ausdruck nach dem `while` wahr ist wird die Schleife ausgeführt. **!Vorsichtig!** solange der Ausdruck wahr bleibt und nie falsch wird hört die Schleife nie auf zu laufen.\n", - "\n", - "**Aufgabe**: Schreibe eine Funktion `fubar` mit Eingabeparameter `n`.\n", - "Die Funktion soll wie folgt definiert sein:\n", - "\n", - "- Der Eingabeparameter `n` ist ein Integer, Floats geben `False` zurück\n", - "- Negative zahlen & 0 beenden die Funktion und geben `False` zurück\n", - "- Die Funktion zählt bis einschließlich dem Eingabeparameter\n", - " bsp.: $n=9 \\rightarrow 1, 2, 3, \\dots, 9$\n", - "- Bei jedem Schleifendurchlauf soll die Zahl bei der sich die Schleife gerade befindet mittels `print` ausgegeben werden werden.\n", - "- Ist der zurzeitige Schleifendurchlauf durch `3` teilbar, gebe mittels `print` denn String `Foo` aus.\n", - "- Ist der zurzeitige Schleifendurchlauf durch `5` teilbar, gebe mittels `print` denn String `Bar` aus.\n", - "- Ist der zurzeitge Schleifendurrchlauf durch `3 & 5` teilbar, gebe mittels `print` den String `FooBar` aus.\n", - "\n", - "**Tipp**: Implementiere nicht alles aufeinmal sollte Schritt für Schritt und teste deine Lösung nach jedem Schritt.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "d1f074e2-c036-445c-97f6-618f5aa4cedb", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-0796f3b2cbac6f8e", - "locked": false, - "points": 4, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "def fubar(n: int):\n", - " if isinstance(n, float) or n < 1:\n", - " return False\n", - "\n", - " count = 1\n", - " while count <= n:\n", - " msg = count\n", - " if count % 3 == 0:\n", - " msg = \"Foo\"\n", - " if count % 5 == 0:\n", - " msg = \"Bar\"\n", - " if count % 15 == 0:\n", - " msg = \"FooBar\"\n", - " \n", - " count += 1\n", - " print(msg, end=', ')\n", - " \n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "22784528-9205-4575-84ef-0060732cd053", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-f7774d4246e958a6", - "locked": true, - "points": 3, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fubar to 4\n", - "1, 2, Foo, 4, \n", - "Fubar to 6\n", - "1, 2, Foo, 4, Bar, Foo, \n", - "Fubar to 16\n", - "1, 2, Foo, 4, Bar, Foo, 7, 8, Foo, Bar, 11, Foo, 13, 14, FooBar, 16, \n", - "Fubar to 200\n", - "1, 2, Foo, 4, Bar, Foo, 7, 8, Foo, Bar, 11, Foo, 13, 14, FooBar, 16, 17, Foo, 19, Bar, Foo, 22, 23, Foo, Bar, 26, Foo, 28, 29, FooBar, 31, 32, Foo, 34, Bar, Foo, 37, 38, Foo, Bar, 41, Foo, 43, 44, FooBar, 46, 47, Foo, 49, Bar, " - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "print(\"Fubar to 4\")\n", - "fubar(4)\n", - "print(\"\\nFubar to 6\")\n", - "fubar(6)\n", - "print(\"\\nFubar to 16\")\n", - "fubar(16)\n", - "print(\"\\nFubar to 200\")\n", - "fubar(50)\n", - "### BEGIN HIDDEN TESTS\n", - "assert fubar(-1) == False\n", - "assert fubar(0) == False\n", - "assert fubar(.1) == False\n", - "### END HIDDEN TESTS" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +version https://git-lfs.github.com/spec/v1 +oid sha256:0b7bd8373f2480203df4042f4d7a39e2a6d972f0c606df366b245092fd8ebc92 +size 53076 diff --git a/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/2.Tutorial.ipynb b/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/2.Tutorial.ipynb index 126dd42..b99a96e 100644 --- a/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/2.Tutorial.ipynb +++ b/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/2.Tutorial.ipynb @@ -1,1682 +1,3 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "079afb70-639e-4955-8ca7-1c290cbf08a9", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7057e40105900012", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# 2. Programmierübung: Python Tutorial\n", - "\n", - "
\n", - "
\n", - " Willkommen zur zweiten Programmierübung Einführung in Python 3.\n", - "
\n", - " \n", - "
\n", - "\n", - "Wenn du Fragen oder Verbesserungsvorschläge zum Inhalt oder Struktur der Notebooks hast, dann kannst du mir gerne eine E-Mail an Phil Keier ([p.keier@hbk-bs.de](mailto:p.keier@hbk-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) schreiben.\n", - "\n", - "Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n", - "\n", - "Der Großteil des Python-Tutorials stammt aus der Veranstaltung _Deep Learning Lab_ und von [www.python-kurs.eu](https://www.python-kurs.eu/python3_kurs.php) und wurde für _Signale und Systeme_, sowie _Einführung in die Programmierung für Nicht Informatiker_ angepasst." - ] - }, - { - "cell_type": "markdown", - "id": "85fd88de-a9ee-4149-8bed-1b8ebc0bbad4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-26e0f96baeb79aac", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Kontrollstruckturen\n", - "\n", - "## Sequentielles - For Loop\n", - "\n", - "Python verwendet eine spezielle Form der for-Schleife – genauer gesagt den sogenannten for-each loop.\n", - "\n", - "Inzwischen hat nahezu jede gängige Programmiersprache dieses Konzept auf ihre eigene Weise umgesetzt. Python geht dabei jedoch einen Schritt weiter und nutzt den for-each loop als Standard. Sprachen wie JavaScript oder C/C++ verwenden hingegen standardmäßig eine Zählschleife: Dabei wird meist von 0 bis zu einem definierten Grenzwert n hochgezählt.\n", - "\n", - "Ein anschauliches Beispiel dafür liefert JavaScript:\n", - "\n", - "```js\n", - "for (let i = 0; i < arr.length; i++) {\n", - " // do something\n", - "} \n", - "```\n", - "\n", - "Lesbar ist dieser Code wie folgt:\n", - "„Für ein `i` mit dem Startwert `0 (let i = 0)`, führe die Schleife aus, solange `i` kleiner als die Länge des Arrays `arr` ist `(i < arr.length)`, und erhöhe `i` nach jedem Schleifendurchlauf um `1 (i++)`.“\n", - "\n", - "In Python würde derselbe Code folgendermaßen aussehen:\n", - "\n", - "```python\n", - "for i in range(0,len(arr)):\n", - " # do something\n", - "```\n", - "\n", - "Das lässt sich so lesen:\n", - "„Für jedes `(for each) i` im Bereich von `0 bis arr.length`, führe etwas aus.“\n", - "\n", - "Der zentrale Unterschied besteht darin, dass Python nicht zwingend über Zahlen iterieren muss – es kann über beliebige Mengen oder Sammlungen laufen. Der Clou liegt also darin, dass die Schleife völlig unabhängig davon funktioniert, wie die zugrunde liegende Menge aussieht.\n", - "\n", - "Widmen wir uns nun einer praktischen Aufgabe:" - ] - }, - { - "cell_type": "markdown", - "id": "7215a3e7-a240-43ec-914c-10221d8b28b0", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-80add6da9914f961", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe \n", - "\n", - "*3 Punkte*\n", - "\n", - "Schreibe eine Funktion `sum_up` mit Eingabeparameter `n`, welcher die Zahlen von `1...n` aufsummiert.\n", - "\n", - "Nutze dafür einen `for-loop`.\n", - "\n", - "**Beispiel**:\n", - "\n", - "$$n = 5$$ \n", - "$$sum\\_up(5) \\rightarrow 1 \\rightarrow 1 + 2 = 3 \\rightarrow 3 + 3 = 6 \\rightarrow 6 + 4 = 10 \\rightarrow 10 + 5 = 15$$\n", - "\n", - "Hinweis: die Funktion `range()` zählt standardmässig von `0...n-1`. Schauen Sie sich gerne dazu die offizielle Dokumentation an [PEP 204](https://peps.python.org/pep-0204/#list-ranges)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "5426ddf1-2d2f-4c92-b007-2f6eca61703f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d43ef87a62b03cdf", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "def sum_up(n: int) -> int:\n", - " count = 0\n", - " for i in range(1,n+1):\n", - " count += i\n", - " return count\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "3c38a839-3ab0-466c-98f9-189c35fc5025", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-cff511e86dce0377", - "locked": true, - "points": 3, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "15\n" - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "print(sum_up(5))\n", - "### BEGIN HIDDEN TESTS\n", - "for n in range(3,12):\n", - " assert sum(range(n+1)) == sum_up(n)\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "4e6dfa94-18b9-4fb2-830a-83202d034752", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-02370acddb67290d", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Nachdem wir nun gelernt haben, wie man mithilfe der eingebauten Funktion `range()` zählen kann, schauen wir uns im Folgenden einige Beispiele dafür an, wie in Python eigentlich iteriert werden sollte.\n", - "\n", - "#### Beispiel 1 - Iterieren über eine Liste:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "db89c7c5-6efc-49bb-be92-414a7334ed84", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-dd3ea63dd3b1d927", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n", - "4\n", - "9\n", - "16\n", - "25\n", - "36\n" - ] - } - ], - "source": [ - "square_numbers = [1,4,9,16,25,36]\n", - "for number in square_numbers:\n", - " print(number)" - ] - }, - { - "cell_type": "markdown", - "id": "6413a239-c334-491e-8062-7f78f75182fe", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-9bc7f123a8fb7680", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "#### Beispiel 2 - Iterieren über ein Dictionary:\n", - "\n", - "Erweitern wir Beispiel 1 und arbeiten nun mit einem Dictionary. Dieses besteht – wie Sie noch aus dem ersten Tutorial wissen – stets aus „Key-Value“-Paaren.\n", - "Mithilfe der eingebauten Funktion `.items()` erhalten wir ein Tuple aus diesen Werten zurück, das zunächst entpackt werden muss.\n", - "\n", - "Hier kommt der for-Loop ins Spiel: Durch die gleichzeitige Deklaration von zwei Variablen können die einzelnen Key-Value-Paare direkt verarbeitet werden.\n", - "(Achtung: Mit `.items()` werden die Paare in der Form `(key, value)` zurückgegeben!)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "116ce552-a5c0-4c9c-8d89-fe1f3e40bdba", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-72122af8e519273b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 -> 1\n", - "2 -> 4\n", - "3 -> 9\n", - "4 -> 16\n", - "5 -> 25\n", - "6 -> 36\n" - ] - } - ], - "source": [ - "square_numbers_dict = {1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36}\n", - "\n", - "for key, value in square_numbers_dict.items():\n", - " print(key, \"->\" , value)" - ] - }, - { - "cell_type": "markdown", - "id": "b4e748de-0603-41c9-8282-86e92923e358", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-52b4d0167c7fb9ba", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "#### Beispiel 3 - Iteration mit Zählen:\n", - "\n", - "Die Built-In Funktion `enumerate()` [PEP 279](https://peps.python.org/pep-0279/) ermöglicht das Zählen und gleichzeitige iterieren über eine Liste.\n", - "Dabei wird wieder ein Tuple zurückgegeben welches die Form '(index, value)' annimmt.\n", - "\n", - "Die eingebaute Funktion `enumerate()` [PEP 279](https://peps.python.org/pep-0279/) ermöglicht es, gleichzeitig zu zählen und über eine Liste zu iterieren.\n", - "Dabei wird – ähnlich wie bei `.items()` – ein Tuple zurückgegeben, das die Form `(index, value)` besitzt." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8cbf9142-2cf3-4579-9e19-799ee9b25a54", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-29953c29ed4bcdcf", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 -> a\n", - "1 -> b\n", - "2 -> c\n", - "3 -> d\n" - ] - } - ], - "source": [ - "alphabet = [\"a\", \"b\", \"c\", \"d\"]\n", - "for index, buchstabe in enumerate(alphabet):\n", - " print(index, \"->\", buchstabe)" - ] - }, - { - "cell_type": "markdown", - "id": "4add1ce5-e462-4be3-8bd7-9960d86ae780", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b64ce270167d3025", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Mit den traditionellen Mitteln lässt sich derselbe Output erzeugen.\n", - "Die Verwendung von `enumerate()` ist jedoch deutlich eleganter und übersichtlicher:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d2e8274b-00d4-4042-adbf-937aea8f0e7e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8b2e3cb4e0c977f2", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 -> a\n", - "1 -> b\n", - "2 -> c\n", - "3 -> d\n" - ] - } - ], - "source": [ - "alphabet = [\"a\", \"b\", \"c\", \"d\"]\n", - "for index in range(len(alphabet)):\n", - " print(index, \"->\", alphabet[index])" - ] - }, - { - "cell_type": "markdown", - "id": "7a8dc614-3c49-4455-a12d-a856e1db40af", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-9ffa970f1cdac592", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "#### Beispiel 4 - Iteration ohne Zähler:\n", - "\n", - "Der Unterstrich `_` wird in Python häufig als Platzhalter für ungenutzte Variablen verwendet.\n", - "Bei for-Loops kennzeichnet er Schleifen, deren Zählvariable nicht weiterverwendet wird:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "786461b8-ecf9-4afb-8b93-6186f53f6e97", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8316e1e4eaa0ea0b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Python is Nice!\n", - "Python is Nice!\n", - "Python is Nice!\n" - ] - } - ], - "source": [ - "for _ in range(3):\n", - " print(\"Python is Nice!\")" - ] - }, - { - "cell_type": "markdown", - "id": "b504f072-53ce-4d03-9f72-b4d4ba85ae74", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5e8d9fc47a709ba4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe\n", - "\n", - "*2 Punkte*\n", - "\n", - "Gegeben ist das Dictionary `dict2`.\n", - "Ändere jeden Wert in diesem Dictionary entsprechend der Formel $f(x) = x^3-1$ mittels `for-loop`.\n", - "\n", - "Hinweis: Lass dich dabei nicht von den Schlüsseln verwirren – entscheidend sind ausschließlich die Werte." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "64972ce6-b087-4fb1-b1a8-faa1a3e2690c", - "metadata": {}, - "outputs": [], - "source": [ - "dict2 = {\"a\": 56, 5: 12, \"python\": 9, 3.14: 1.141414}" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "abd323c0-5e1b-4c14-a65a-9d54a06f3a80", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0361320c63b2cb03", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "### BEGIN SOLUTION\n", - "dict2 = {key: value**3-1 for key, value in dict2.items()}\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "7e0f9ac4-c5d4-44b6-a2fc-db98e2b46356", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-82eec3cba623ab8f", - "locked": true, - "points": 2, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'a': 175615, 5: 1727, 'python': 728, 3.14: 0.48706374396146557}\n" - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "print(dict2)\n", - "### BEGIN HIDDEN TESTS\n", - "d = {\"a\": 56, 5: 12, \"python\": 9, 3.14: 1.141414}\n", - "d = {key: value**3-1 for key, value in d.items()}\n", - "assert d == dict2\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "7fd320aa-6bb5-4b9b-9593-2df69cb2ca1e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-cb8f33d1ae55a6a4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## List Comprehension\n", - "\n", - "Seit dem Proposal von [PEP 202](https://peps.python.org/pep-0202/) verfügt Python über die sogenannte List Comprehension.\n", - "\n", - "Für diese Vorlesung ist es nicht erforderlich, dass Sie die Syntax bereits aktiv anwenden können – Sie sollten jedoch zumindest verstehen, wie sie funktioniert.\n", - "\n", - "Angenommen, wir betrachten folgende mathematische Beschreibung einer Menge: $$\\{x^2 \\vert x \\in \\mathbb{N}\\}$$\n", - "\n", - "Dies beschreibt die Funktion $f(x) = x^2$ für alle natürlichen Zahlen.\n", - "\n", - "In Python lässt sich genau diese Menge in einer einzigen Zeile abbilden.\n", - "Dazu wird die List Comprehension verwendet, deren allgemeine Syntax wie folgt aussieht:\n", - "\n", - "```python\n", - "[ for value in ]\n", - "```\n", - "\n", - "Schauen wir uns dazu an wie wir die Quadrat Zahlen von `1...6` also `1...36` erzeugen." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "7bcdc9d9-cc5b-49be-8cfb-ca40eb3b7796", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-4fd6f801463c5ea6", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1, 4, 9, 16, 25, 36]\n" - ] - } - ], - "source": [ - "squared = [n*n for n in range(1,7)]\n", - "print(squared)" - ] - }, - { - "cell_type": "markdown", - "id": "b6dcb24c-e01e-4522-8ac4-074adfe6105a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c6922240434c9d3a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Probieren Sie sich gerne selber aus.\n", - "\n", - "### Zusatzaufgabe \n", - "\n", - "*Keine Punkte*\n", - "\n", - "Erstelle eine Liste mittels `List Comprehension`, welche die Zahlen `1...6` auf deren kubische Zahl `1...216` also der Funktion $f(x) = x^3$ abbildet." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "522f3228-1797-4ca2-9103-44fce48dfd4a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1dc645632c5f653a", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "cubics = []\n", - "### BEGIN SOLUTION\n", - "cubics = [n**3 for n in range(1,7)]\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "7dc19b9f-116b-4741-a798-a66d495d477e", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-4e441b6db861559e", - "locked": true, - "points": 0, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1, 8, 27, 64, 125, 216]\n" - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "print(cubics)\n", - "### BEGIN HIDDEN TESTS\n", - "c = [n**3 for n in range(1,7)]\n", - "assert c == cubics\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "5b1355f0-29ed-4318-92f2-51f151c7946e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7d9eebd920496d59", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# System Interactions\n", - "\n", - "Im folgenden Abschnitt beschäftigen wir uns mit der Eingabe von Daten in ein Programm.\n", - "Dies kann entweder manuell durch den Benutzer erfolgen oder über Dateien geschehen." - ] - }, - { - "cell_type": "markdown", - "id": "976a7ad0-856d-4fb6-bba8-485668df22a2", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-e2df7221e04e8c54", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "In der Praxis möchten wir häufig größere Datenmengen einlesen.\n", - "Dazu verwenden wir die eingebaute Funktion `open` ([Python Docs - Open](https://docs.python.org/3/library/functions.html?highlight=open#open)). Obwohl die Dokumentation zahlreiche Parameter aufführt, werden im Normalfall nur zwei benötigt:\n", - "1. Der Name der Datei\n", - "2. Der Modus, in dem die Datei geöffnet werden soll (als String angegeben)\n", - "\n", - "Zum Bearbeiten von Dateien nutzen wir in Python den Kontext-Manager – also das `with`-Statement.\n", - "Bei externen Daten ist es besonders wichtig, die Datei nach der Bearbeitung wieder zu schließen, um Datenverlust oder unnötige Speichernutzung zu vermeiden.\n", - "\n", - "Der Lebenszyklus einer Datei in einem Programm sieht daher immer wie folgt aus:\n", - "\n", - "`Datei öffnen` -> `Datei Bearbeiten` -> `Datei schließen`\n", - "\n", - "Tritt in einem dieser drei Schritte ein Fehler auf, kann die Datei im Arbeitsspeicher hängen bleiben. In diesem Fall muss der Computer unter Umständen neu gestartet werden, um den belegten Speicher wieder freizugeben.\n", - "\n", - "Daher gibt es Kontext-Manager. Sie garantieren, dass das Schließen der Datei immer erfolgt – selbst bei auftretenden Fehlern.\n", - "\n", - "Die Syntax folgt dabei der folgenden Struktur:\n", - "\n", - "```python\n", - "with as :\n", - " # do something\n", - "```\n", - "\n", - "Dabei ist `` ein Objekt (für uns eine Datei) und `` die Zuweisung zu einer Variablen. Für die Funktion `open` (im Lesemodus) sieht der Kontext wie folgt aus:\n", - "\n", - "```python\n", - "with open(\"filename.txt\", \"r\") as f:\n", - " f.readlines() # do something with f\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "a345cdf5-d1a0-4bd8-9f77-06e6d4562e00", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-58fb9b7e476f3ef2", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe\n", - "\n", - "*2 Punkte*\n", - "\n", - "Erstelle und Öffne eine Datei `testfile.txt` mit der `open` Funktion, nutze dafür das `with`-Statement.\n", - "\n", - "Schreibe in diese Datei 100 mal den String `\"Python\\n\"`. " - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "f11f7a0b-bcca-4db0-a6ca-bf95c70c7303", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0735bb589edcc6a8", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "with open('testfile.txt', 'w') as f:\n", - " for _ in range(100):\n", - " f.write(\"Python\\n\")\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "c91e07bb-fc41-42c1-8b42-0aca56c57c35", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-2592f8b51914455e", - "locked": true, - "points": 2, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "### BEGIN HIDDEN TESTS\n", - "with open('testfile.txt', 'r') as f:\n", - " lines = f.readlines()\n", - " assert len(lines) == 100\n", - " for line in lines:\n", - " assert line == 'Python\\n'\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "4adb5400-8749-4790-ae49-68a8622b4a3d", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-39769ee8cbf2157d", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe\n", - "\n", - "*2 Punkte*\n", - "\n", - "Öffne die zuvor erstellte Datei `testfile.txt` im Lesemodus und weiße den Inhalt der `.readlines()` Funktion der Variabeln `lines` zu. " - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "adf300e9-ee63-4da1-a6f3-c768fa2b5fc9", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0a3b9e01dd66e134", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "lines = None\n", - "# BEGIN SOLUTION\n", - "with open('testfile.txt', 'r') as f:\n", - " lines = f.readlines()\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "229499d6-bb33-492b-af5d-a26ab3b3f5d4", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-aa7c104b5f3f2572", - "locked": true, - "points": 2, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Anzahl der gelesenen Zeilen: 100\n" - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "print(\"Anzahl der gelesenen Zeilen:\", len(lines))\n", - "### BEGIN HIDDEN TESTS\n", - "with open('testfile.txt', 'r') as f:\n", - " assert f.readlines() == lines\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "47d92dde-16a1-4c11-a452-cadd74255db2", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-be695e1423a6ccf4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Import Statement\n", - "\n", - " \n", - "\n", - "Da wir nicht immer das Rad neu erfinden wollen, greifen wir auf Bibliotheken anderer Entwickler zurück.\n", - "\n", - "Dazu verwendet man das Keyword `import`, gefolgt von dem Modul, das man einbinden möchte.\n", - "Ein anschauliches Beispiel ist das Modul `numpy`:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "ced37c65-87d2-48d0-a0ba-4674dcaf104c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7d19506e181bcda9", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import numpy" - ] - }, - { - "cell_type": "markdown", - "id": "f5506975-e1cd-434a-8ef1-05a7a06992df", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b7d981325bea8b84", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Möchte man nun eine Funktion aus dem Modul nutzen folgt die Syntax der Strucktur `.`.\n", - "\n", - "Dazu folgendes Numpy Beispiel:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "69c68b31-d0f7-469b-ae54-eef871ec6ad6", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-25ad372a576a793e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", - " 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n", - " 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n", - " 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n", - " 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n", - " 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "numpy.array(range(100))" - ] - }, - { - "cell_type": "markdown", - "id": "7c5d65db-d1e8-4cad-884e-bb595c57d445", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0176c541098f7c21", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe\n", - "\n", - "*3 Punkte*\n", - "\n", - "Importiere Pythons Built-In Library `random` und rufe zuerst aus dem Modul die Funktion `seed` auf mit dem Eingabewert `42`, und weiße danach der Variable `rand` den Wert des Funktionsaufrufes von `randint(1,100)` zu. " - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "0d80bc9f-6923-4e3f-8a70-909548e693a6", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8ccc5fe1848176c8", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "rand = None\n", - "# BEGIN SOLUTION\n", - "import random\n", - "random.seed(42)\n", - "rand = random.randint(1,100)\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "ada0211b-03bf-463a-b932-2bad621d5559", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-d7817c3dd1ee34f9", - "locked": true, - "points": 3, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "82\n" - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "print(rand)\n", - "### BEGIN HIDDEN TESTS\n", - "assert rand == 82\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "f7b3bdcc-825e-4607-8804-a5ce3d39ace5", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-2cc1d2ed682d1b0e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Das bereits bekannte Keyword `as` kann auch beim Import von Modulen verwendet werden, um diesen einen Alias zu geben.\n", - "Das ist besonders hilfreich, wenn Module lange Namen haben.\n", - "\n", - "So wird zum Beispiel numpy im Internet häufig mit np abgekürzt. Ein Beispiel:" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "f115eae9-500b-448e-a1be-e3b43ffad0fd", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-60f357d6dda4a8d3", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", - " 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n", - " 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n", - " 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n", - " 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n", - " 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "np.array(range(100))" - ] - }, - { - "cell_type": "markdown", - "id": "f7570244-0fea-4683-a19c-069f6ba619dd", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-94fb7e92492a12f7", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe\n", - "\n", - "*1 Punkt*\n", - "\n", - "Importiere die Built-In Library `datetime` als `dt`." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "e7e6c246-6dc4-4555-a202-73887b5f8249", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-59dc2ded4f59471f", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "import datetime as dt\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "b03ee34b-8520-4106-a23f-4419e54dcfcc", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-d77ffdb7f9ba178d", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-11-13 12:40:25.172727\n" - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "print(dt.datetime.now()) # UTC Time also Standard Greenwich Zeit\n", - "### BEGIN HIDDEN TESTS\n", - "assert 'dt' in dir()\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "1cc39ffb-1207-4aa8-a4fe-51df2335fea6", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c55653efe0a77419", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Möchte man nur eine Bestimmte Funktion aus einem Modul haben nutzt man die `import from` Syntax. Beispiel Pretty Print:" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "638e6c20-7bba-4862-9acc-34031bae8514", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-f84e2596969633f5", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{0: 1,\n", - " 1: 2,\n", - " 2: 4,\n", - " 3: 8,\n", - " 4: 16,\n", - " 5: 32,\n", - " 6: 64,\n", - " 7: 128,\n", - " 8: 256,\n", - " 9: 512,\n", - " 10: 1024,\n", - " 11: 2048,\n", - " 12: 4096,\n", - " 13: 8192,\n", - " 14: 16384,\n", - " 15: 32768,\n", - " 16: 65536,\n", - " 17: 131072,\n", - " 18: 262144,\n", - " 19: 524288,\n", - " 20: 1048576,\n", - " 21: 2097152,\n", - " 22: 4194304,\n", - " 23: 8388608,\n", - " 24: 16777216}\n" - ] - } - ], - "source": [ - "from pprint import pprint\n", - "pprint({n: 2**n for n in range(25)})" - ] - }, - { - "cell_type": "markdown", - "id": "c1e077c1-59a5-411b-bf71-32ea66dc731d", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0ac2347c47ff5774", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe\n", - "\n", - "*2 Punkte*\n", - "\n", - "Importiere die Funktion `sqrt` aus dem Built-In Modul `math`.\n", - "Berechne $\\sqrt4$. Speicher das Ergebnis in der variablen `s4`." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "81838f9b-558c-49b3-90ef-647e28380a97", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7c1ea8bca61d5c12", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "from math import sqrt\n", - "s4 = sqrt(4)\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "6d984a74-a93d-4865-b6d1-d2dec8586907", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-83c667de468e9ef8", - "locked": true, - "points": 2, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.0\n" - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "print(s4)\n", - "### BEGIN HIDDEN TESTS\n", - "assert 'sqrt' in dir()\n", - "assert int(s4) == 2\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "6edf5dce-17d2-4a5f-9d96-2c7c3091de88", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-814b3bfa4b6e049a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Es ist auch möglich, in der `from ... import ...`-Syntax das Keyword `as` zu verwenden, um einzelne Elemente eines Moduls umzubenennen.\n", - "\n", - "Beispiel: Aus dem Modul dataclasses wird dataclass als dclass importiert:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "b707bf52-546f-4689-8216-c5ad0b9665a7", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-9961359e9d09d79b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "from dataclasses import dataclass as dclass\n", - "print(dclass)" - ] - }, - { - "cell_type": "markdown", - "id": "842213e0-a55d-4cfc-863e-f6333e1482e6", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0ac2347c47ff5774", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe\n", - "\n", - "*2 Punkte*\n", - "\n", - "Importiere das Objekt `TextCalendar` aus dem Built-In Module `calendar` als `TC`." - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "b71c98a6-2e7b-4b6b-bfde-e6b7e2e944a3", - "metadata": {}, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "from calendar import TextCalendar as TC\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "c3fb8526-3bbd-4440-a366-b671702acfaa", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " November 2025\n", - "Mo Tu We Th Fr Sa Su\n", - " 1 2\n", - " 3 4 5 6 7 8 9\n", - "10 11 12 13 14 15 16\n", - "17 18 19 20 [21] 22 23\n", - "24 25 26 27 28 29 30\n", - "\n", - "21.11.2025 - Nächste Vorlesung\n" - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "year = 2025\n", - "month = 11 # November\n", - "highlight_day = 21\n", - "event_text = \"Nächste Vorlesung\"\n", - "\n", - "# Generate the month as a multi-line string\n", - "month_str = TC().formatmonth(year, month)\n", - "\n", - "# Highlight the day by surrounding it with brackets\n", - "highlighted_month = month_str.replace(\n", - " f\"{highlight_day:2}\",\n", - " f\"[{highlight_day:2}]\"\n", - ")\n", - "\n", - "highlighted_month += f\"\\n{highlight_day:2}.11.{year} - {event_text}\"\n", - "\n", - "print(highlighted_month)\n", - "### BEGIN HIDDEN TESTS\n", - "assert 'TC' in dir()\n", - "### END HIDDEN TESTS" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +version https://git-lfs.github.com/spec/v1 +oid sha256:555998464c462e92080472f1ea0b105088a4ee7fa44548de95ee64c7d894845e +size 41735 diff --git a/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/3.Extended_Applications.ipynb b/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/3.Extended_Applications.ipynb index b93ecde..5aace04 100644 --- a/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/3.Extended_Applications.ipynb +++ b/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/3.Extended_Applications.ipynb @@ -1,1792 +1,3 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "1ddc11f3-0a8a-47ee-b7b7-6dfd22ac40d9", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-8c0ecbebdebcad39", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# 3. Programmierübung: Python Tutorial - Extended Applications\n", - "\n", - "
\n", - "
\n", - " Willkommen zur dritten Programmierübung Einführung in Python 3.\n", - "
\n", - " \n", - "
\n", - "\n", - "In dieser Übung werden erweiterte Konzepte gelernt, welche das Konzipieren von Programmen stark vereinfacht.\n", - "\n", - "Wenn du Fragen oder Verbesserungsvorschläge zum Inhalt oder Struktur der Notebooks hast, dann kannst du mir gerne eine E-Mail an Phil Keier ([p.keier@hbk-bs.de](mailto:p.keier@hbk-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) schreiben.\n", - "\n", - "Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n", - "\n", - "Der Großteil des Python-Tutorials stammt aus der Veranstaltung _Deep Learning Lab_ und von [www.python-kurs.eu](https://www.python-kurs.eu/python3_kurs.php) und wurde für _Signale und Systeme_, sowie _Einführung in die Programmierung für Nicht Informatiker_ angepasst." - ] - }, - { - "cell_type": "markdown", - "id": "f47e9f08-663b-4c2f-85c7-8b6c7f654b45", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-40ca946ad65b66b6", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Python hat seit 2001 einen grundlegenden Styleguide, festgelegt von Guido van Rossum: [PEP 8](https://peps.python.org/pep-0008/).\n", - "\n", - "Hier ein paar wichtige Hinweise dazu:\n", - "\n", - "1. Variablen- und Funktionsnamen werden immer kleingeschrieben und im klassischen *snake_case* notiert. \n", - " Beispiel: `is_alive`\n", - "\n", - "2. Für Einrückungen nutzt man 4 Leerzeichen, üblicherweise automatisch per Tab gesetzt (Jupyter macht das ohnehin richtig).\n", - "\n", - "3. Kommentare starten nach dem `#` immer mit einem Leerzeichen. \n", - " Beispiel: `# Klarer Kommentar`\n", - "\n", - "4. Importe sollten sauber getrennt werden.\n", - "\n", - " **Richtig:**\n", - " ```python\n", - " import os\n", - " import sys\n", - " ```\n", - "\n", - " **Falsch:**\n", - " ```python\n", - " import os, sys\n", - " ```\n", - "\n", - "5. Mehrfachimporte aus einem Modul sind dagegen völlig in Ordnung:\n", - "\n", - "6. Nach `,`, `:` (außer beim Slicing) sowie Operatoren wie `+` oder `=` gehört ein Leerzeichen.\n", - "Beispiel:\n", - "\n", - " ```python\n", - " x = 4 + 2\n", - " arr[5:10]\n", - " \n", - " ```\n", - "\n", - "7. Variablen sollten nicht künstlich ausgerichtet werden.\n", - "\n", - " **So nicht:**\n", - " ```python\n", - " x = 4\n", - " y = 5\n", - " name = \"Lisa\"\n", - " \n", - " ```\n", - "\n", - "8. Die Namen `l`, `O` und `I` solltest du meiden, da sie leicht mit `i`, `0` und `L` verwechselt werden können. In Jupyter ist das meist kein Problem, aber gute Gewohnheiten schaden nie.\n", - "\n", - "Kurz: Halte es klar, lesbar und unkompliziert – der Rest ergibt sich von selbst." - ] - }, - { - "cell_type": "markdown", - "id": "4abb5645-0172-4fd7-8f79-02af5140b234", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ef37f36145723997", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Generatoren\n", - "\n", - "Nachdem wir schon Funktionen gesehen haben, die direkt einen Rückgabewert liefern, schauen wir uns jetzt ein Python-Konzept an, das Daten „on the fly“ erzeugt: Generatoren.\n", - "\n", - "Oft denkt man zunächst, dass Funktionen wie `range` eine fertige Liste zurückgeben. Gedanklich ist das nicht völlig falsch – in der Umsetzung passiert aber etwas anderes.\n", - "\n", - "Mit einem simplen `print` können wir uns das schnell selbst bestätigen:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "f8f4aee6-ce57-41f8-af18-7256b0126515", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1e78bfa1751878c9", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "range(0, 10)\n" - ] - } - ], - "source": [ - "print(range(10))" - ] - }, - { - "cell_type": "markdown", - "id": "dc9f0f52-0a0d-4e5c-8b6f-d7a2b7c5b2e3", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1c1a0f19a370c840", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Statt wie vielleicht zu erwarten eine Liste der Werte `0...9` ausgegeben zu bekommen, zeigt uns Python lediglich `range(0, 10)` an.\n", - "\n", - "Wenn man die Werte jedoch direkt ausgewertet sehen möchte, kann man den `*`-Operator verwenden:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "89986045-bb47-4bca-b561-bab9bb2b988b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-723d591c32fda1a7", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 1 2 3 4 5 6 7 8 9\n" - ] - } - ], - "source": [ - "print(*range(10))" - ] - }, - { - "cell_type": "markdown", - "id": "2248b888-aa12-4367-b1f4-bb4befc43df4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-19e02ebb63f72a88", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "`range(10)` verändert dabei das Verhalten von `print`, indem es alle erzeugten Werte als einzelne Argumente einsetzt — also effektiv `print(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)` aufruft.\n", - "\n", - "Um selbst einen Generator zu bauen, nutzt man das Python-Keyword `yield`. Im Gegensatz zu einem normalen `return` stoppt `yield` die Ausführung nur kurz und setzt sie später an exakt dieser Stelle fort. Der Generator behält also einen veränderbaren Zustand.\n", - "\n", - "Die grundlegende Syntax sieht so aus:\n", - "\n", - "```python\n", - "def ():\n", - " # do something\n", - " yield \n", - "```\n", - "\n", - "Eine einfache Generator-Funktion `count_to` könnte entsprechend so aussehen:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "e4686bab-6686-4464-9015-54d420087e5a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-795ddd785249ca9d", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is a Generator: \n", - "This Generator evaluates to: 0 1 2 3 4 5 6 7 8 9\n" - ] - } - ], - "source": [ - "def count_to(n):\n", - " count = 0\n", - " while count < n:\n", - " yield count\n", - " count += 1\n", - "\n", - "print(\"This is a Generator:\", count_to(10))\n", - "print(\"This Generator evaluates to:\", *count_to(10))" - ] - }, - { - "cell_type": "markdown", - "id": "7adfeae1-bab7-42b8-b9f7-39c91cb5db0b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ca4e8d2864d7cff7", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Dasselbe lässt sich natürlich auch bequem mit einem `for`-Loop formulieren:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a19b17c4-144b-4197-8a71-f0554b467564", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-131bc31d10dd26c9", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This is a Generator: \n", - "This Generator evaluates to: 0 1 2 3 4 5 6 7 8 9\n" - ] - } - ], - "source": [ - "def count_to(n):\n", - " for i in range(n):\n", - " yield i\n", - "\n", - "print(\"This is a Generator:\", count_to(10))\n", - "print(\"This Generator evaluates to:\", *count_to(10))" - ] - }, - { - "cell_type": "markdown", - "id": "7cbdb90b-bedf-4197-97b8-8baf58c92190", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c2c489efaeeb75dd", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Aufgabe\n", - "\n", - "*3 Punkte*\n", - "\n", - "Schreibe einen Generator `square_count` mit dem Eingabeparameter `n`, der die Quadratzahlen von $1 \\dots (n-1)^2$ erzeugt.\n", - "\n", - "Wichtig: Wenn `square_count` **kein** Generator ist, gibt es **0 Punkte**.\n", - "\n", - "Hinweis: Bei einer Eingabe von `5` sollen die Werte `1 4 9 16` ausgegeben werden.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "2df62e85-d5a5-4664-bdf6-c544ed8fb0d1", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-a037e576943e230b", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "def square_count(n): \n", - " for i in range(1, n):\n", - " yield i*i\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "234d782c-88f4-4bc2-a872-15a592ee1248", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-a6c43a5365ad736d", - "locked": true, - "points": 3, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Square Numbers from 0 to 1: 1\n", - "Square Numbers from 0 to 2: 1 4\n", - "Square Numbers from 0 to 3: 1 4 9\n", - "Square Numbers from 0 to 4: 1 4 9 16\n", - "Square Numbers from 0 to 5: 1 4 9 16 25\n" - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "\n", - "# Check if the generator has the right name\n", - "assert \"square_count\" in dir() # 1 Punkt\n", - "\n", - "# Check if square_count is a generator\n", - "import types\n", - "assert isinstance(square_count(1), types.GeneratorType) # 1 Punkt\n", - "\n", - "# Check if the generator generates the right output\n", - "for n in range(10): \n", - " assert [i*i for i in range(1,n)] == [i for i in square_count(n)] # 1 Punkt\n", - "\n", - "# print\n", - "for n in range(2, 7):\n", - " print(f\"Square Numbers from 0 to {n-1}:\", *square_count(n))" - ] - }, - { - "cell_type": "markdown", - "id": "8e1cb8b2-cdaa-422a-9889-c4e0794d7d81", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5f6f32fed19a1933", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Generatoren können auch eine unendliche Menge an Daten erzeugen. Das funktioniert, weil die Werte erst zur Laufzeit berechnet werden – der Generator produziert also immer weiter, solange wir ihn laufen lassen.\n", - "\n", - "Damit eine Berechnung tatsächlich nie endet, muss die Schleifenbedingung dauerhaft `wahr` sein. Klassisch nutzt man dafür `while True:`. Da Python aber seine Eigenheiten hat, ist die kompakte Variante `while 1:` sogar etwas effizienter.\n", - "\n", - "Schauen wir uns nun ein Beispiel für einen unendlichen Generator an, der fortlaufend die nächste Fakultät berechnet und ausgibt:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e7a2e825-b67d-4618-9358-4ee06ef5cc38", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-55717dfe7800e3fb", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "def faktoriel_gen():\n", - " curr = 1\n", - " count = 1\n", - " while 1:\n", - " curr = count * curr\n", - " count += 1\n", - " yield curr" - ] - }, - { - "cell_type": "markdown", - "id": "496d59e9-e3c1-47d1-982d-8c5688cb7893", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7d21c6426cee23f0", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Vorsicht (!!): Wenn man diesen Generator direkt komplett auswertet, würde die Berechnung niemals enden. \n", - "Um stattdessen nur den nächsten Wert zu erhalten, stellt Python die Funktion `next` bereit – sie liefert jeweils den nächsten Zustand des Generators.\n", - "\n", - "In Kombination mit einem `for`-Loop und `next` lassen sich so ganz einfach die Fakultäten der Zahlen bis `5` ausgeben:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "4249c820-f03c-4393-bb8f-5078f9a9a0b8", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-166ede3f392e88e7", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n", - "2\n", - "6\n", - "24\n", - "120\n" - ] - } - ], - "source": [ - "gen = faktoriel_gen()\n", - "\n", - "for _ in range(5):\n", - " print(next(gen))" - ] - }, - { - "cell_type": "markdown", - "id": "8b0d160f-d445-4877-a634-2de69336e70a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-2e5778830ac74399", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Da der Zustand des Generators gespeichert bleibt, kann man ihn in der nächsten Zelle einfach erneut mit `next` aufrufen und erhält dann direkt $6! = 720$:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "32e5eb93-507a-490e-9395-717bc7717973", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-30acb8e9a68a7794", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "720\n" - ] - } - ], - "source": [ - "print(next(gen))" - ] - }, - { - "cell_type": "markdown", - "id": "9901cb74-faf9-47f1-b87e-60d9683778d3", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-3f6a4841c593371f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe\n", - "\n", - "*3 Punkte*\n", - "\n", - "Schreibe einen Generator `naturals`, der bei jedem Aufruf die nächste natürliche Zahl ausgibt, beginnend mit 1.\n", - "\n", - "- Es sind **keine Eingabeparameter** erforderlich. \n", - "- Wenn die Funktion **kein Generator** ist, gibt es **0 Punkte**.\n", - "\n", - "*Hinweis*: Orientiere dich am Beispiel `faktoriel_gen`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a8b2697c-706b-4744-a4b3-00682d6c95c3", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-09dd94e802cad9bc", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "def naturals():\n", - " curr = 1\n", - " while 1:\n", - " yield curr\n", - " curr += 1\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "403c70b9-3ac3-4a85-a302-d9c7d0585cbc", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-e065f7326fb561ad", - "locked": true, - "points": 3, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, " - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "\n", - "# Check if generator is named properly\n", - "assert \"naturals\" in dir() # 1 Punkt\n", - "\n", - "# Check if naturals is a generator\n", - "import types\n", - "assert isinstance(naturals(), types.GeneratorType) # 1 Punkt\n", - "\n", - "# Test if generator works as intended\n", - "import random\n", - "test_n = random.randint(5, 17)\n", - "test_gen = naturals()\n", - "for i in range(1, test_n):\n", - " number = next(test_gen)\n", - " print(number, end=', ')\n", - " assert i == number # 1 Punkt" - ] - }, - { - "cell_type": "markdown", - "id": "41bb0337-7194-4482-a5b9-af1243d4809c", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-bbe0bad0c47d41ae", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Type Hints\n", - "\n", - "Mit [PEP 484](https://peps.python.org/pep-0484/) wurden in Python die sogenannten *Type Hints* eingeführt. Die Idee dahinter: eine standardisierte Möglichkeit, Ein- und Ausgabe von Funktionen klar zu kennzeichnen. Das verbessert unter anderem die Testbarkeit und Lesbarkeit von Python-Programmen deutlich.\n", - "\n", - "*Type Hints* zeigen also den Typ von Variablen und Funktionen an, z. B. `int`, `float`, `dict` usw.\n", - "\n", - "Die allgemeine Syntax sieht so aus:\n", - "\n", - "```python\n", - "def (: , : ) -> :\n", - " # do something\n", - " return \n", - "```\n", - "\n", - "Python ist dynamisch typisiert. Das bedeutet, der Typ einer Variable kann sich während der Laufzeit ändern.\n", - "Beispiel: Eine Ganzzahl `int` kann durch eine einfache Addition zu einer Fließkommazahl `float` werden:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "d2148990-b5cb-4fe5-8751-bac8338fa581", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-560aa9d85c5a4383", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3 \n", - "3.14 \n" - ] - } - ], - "source": [ - "number = 3\n", - "print(number, type(number))\n", - "number += 0.14\n", - "print(number, type(number))" - ] - }, - { - "cell_type": "markdown", - "id": "56c143f5-1c67-4999-8656-dc68d5912fb4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a85903e6bffa1ff1", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Um solche unerwarteten Typwechsel zu vermeiden, kann man *Type Hints* nutzen.\n", - "\n", - "Wichtig: Sie dienen nur als Hinweis und **garantieren nicht**, dass sich der Typ einer Variable tatsächlich ändert!\n", - "\n", - "Ein simples Beispiel: Eine Funktion, die einen Eingabewert als `int` annimmt und ihn in einen `str` umwandelt, könnte so aussehen:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "642dc60b-fa8d-4655-bc47-815f39365506", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-94708ecde4287ff7", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number 42\n" - ] - } - ], - "source": [ - "def useless(number: int) -> str:\n", - " return \"Number {}\".format(number)\n", - "\n", - "print(useless(42))" - ] - }, - { - "cell_type": "markdown", - "id": "ac0a9f33-2e9a-4779-8bf8-1004e6ed7177", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-36dc004fdb6c6682", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Um zu kennzeichnen, dass eine Variable einem bestimmten Datentyp zugeordnet ist, verwendet man die folgende Syntax:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "e25d2f29-5857-4f0a-93d7-609201e8a0da", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-fdc340bdc4a2000e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "name: str = \"Peter Parker\"" - ] - }, - { - "cell_type": "markdown", - "id": "777904ec-3567-41c8-a619-2c38ad6b6c51", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ea3aeed85ff0587c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Dataclasses\n", - "\n", - "Hier gehen wir nicht auf Klassen im Allgemeinen ein, sondern fokussieren uns auf ein spezielles Konzept, das mit [PEP 557](https://peps.python.org/pep-0557/) eingeführt wurde: **Dataclasses**.\n", - "\n", - "Standard-Datenstrukturen wie `dict`, `set`, `list` usw. sind mächtig und flexibel. Will man jedoch feste Datenstrukturen mit klar definierten Feldern verwenden, bietet sich das Modul `dataclasses` an.\n", - "\n", - "Dabei wird eine Klasse mit dem Keyword `class` erstellt und mit dem Decorator `@dataclasses.dataclass` ausgestattet. So lassen sich Datenobjekte mit festgelegter Struktur erzeugen.\n", - "\n", - "Zuerst importieren wir das Modul aus der Standardbibliothek:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "24392aea-8ba8-4f9e-ba95-5bf5c99a127b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-fa6710d8dd259c2d", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "from dataclasses import dataclass" - ] - }, - { - "cell_type": "markdown", - "id": "abb46684-015f-48c7-98b8-f2955e71de2d", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-36842ee26321c3ec", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Anschließend können wir eine Klasse erstellen. Zum Einstieg definieren wir eine Klasse `Person`, die die Werte `vorname` und `nachname` als Strings bereitstellt:\n", - "\n", - "**Wichtig:** Klassennamen beginnen in Python immer mit einem Großbuchstaben – Ausnahmen gibt es vor allem in der Standardbibliothek. \n", - "Ein kleines Beispiel: `range` sieht aus wie eine Funktion, ist intern jedoch eigentlich eine Klasse!\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "b70f39ca-6e57-4b03-a3be-c3b5ac604a9c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8b8b2e316c36725c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "@dataclass\n", - "class Person:\n", - " vorname: str\n", - " nachname: str" - ] - }, - { - "cell_type": "markdown", - "id": "94901847-a849-463b-968a-b498a9f84edc", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-846dcecf70fd8d14", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Wenn wir nun ein Objekt der Klasse `Person` erstellen möchten, geht das so:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "27320011-ee5d-43c9-8e07-fbd3036680f7", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1cf940f3240bf428", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Person(vorname='Eduard', nachname='Jorswieck') \n" - ] - } - ], - "source": [ - "person = Person(\"Eduard\", \"Jorswieck\")\n", - "print(person, type(person))" - ] - }, - { - "cell_type": "markdown", - "id": "be0c2b13-937e-4073-8dfa-a6d305ed9c6f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8338f70364284fc7", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Wie man am Output sieht, ist die Variable `person` ein Objekt vom Typ `Person` und enthält die Werte `vorname='Eduard'` und `nachname='Jorswieck'`.\n", - "\n", - "Auf die einzelnen Felder der Dataclass kann man bequem über den `.`-Operator zugreifen:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "191bb87c-06ab-40a4-b5b3-56e9c533b930", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b6da422f93431723", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vorname: Eduard\n", - "Nachname: Jorswieck\n" - ] - } - ], - "source": [ - "print(\"Vorname:\", person.vorname)\n", - "print(\"Nachname:\", person.nachname)" - ] - }, - { - "cell_type": "markdown", - "id": "3e85dee5-09ea-4c00-9730-05fb006ced2b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d80fa3ef35e81dab", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Ein weiterer Vorteil von Dataclasses:\n", - "\n", - "Die Werte können direkt beim Erstellen des Objekts über die Feldnamen angegeben werden, unabhängig von der Reihenfolge:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "a5ea4ef9-dcba-4411-a2e8-1a1a811e6cac", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-20f0d6f2cb9ef5df", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Person(vorname='Martin', nachname='Le') \n" - ] - } - ], - "source": [ - "person2 = Person(nachname=\"Le\", vorname=\"Martin\")\n", - "print(person2, type(person2))" - ] - }, - { - "cell_type": "markdown", - "id": "68db3f9c-b6ad-4454-87be-71a78a209a40", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-2489e8269aa6414d", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Nicht immer stehen alle Werte beim Erstellen eines Objekts zur Verfügung. Um das abzufangen, können Standardwerte für die Felder definiert werden:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "ce05d4eb-747b-41ad-9fea-5430b8310e8c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-92f8a3cacfda2c15", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "@dataclass\n", - "class Person:\n", - " vorname: str = \"Max\"\n", - " nachname: str = \"Mustermann\"" - ] - }, - { - "cell_type": "markdown", - "id": "e4def98c-60a9-4dcf-a71b-6be49f853b3b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-009056608941f805", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Wenn wir nun eine Dataclass ohne Eingabeparameter erstellen, übernimmt sie automatisch die definierten Standardwerte:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "6d5e0c5d-0f70-4303-83cb-234e9523500c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-dbc29451821056d9", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Aufruf mit print: Person(vorname='Max', nachname='Mustermann')\n" - ] - } - ], - "source": [ - "nameless_person = Person()\n", - "print(\"Aufruf mit print:\", nameless_person) " - ] - }, - { - "cell_type": "markdown", - "id": "ccee5184-2f5e-4b90-99ae-2702405c0ec9", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-1c171ff9c43179be", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe\n", - "\n", - "*8 Punkte*\n", - "\n", - "Erstelle eine Dataclass `Student`.\n", - "\n", - "- Erstelle ein Objekt mit den Werten des Beispielstudenten und weise das Ergebnis von `asdict()` der Variablen `stud` zu.\n", - "\n", - "- Die Dataclass soll die Werte `vorname`, `nachname`, `semester` und `mat_nr` speichern. Wählen Sie für jedes Attribut den **geeigneten Datentyp** (z. B. macht es Sinn, die Semesteranzahl als `int` zu speichern, nicht als `float`).\n", - "\n", - "- Importiere aus dem `dataclasses`-Modul die Funktion `asdict`. \n", - "\n", - "- **Wichtig:** Wenn `Student` keine Dataclass ist, gibt es **0 Punkte**.\n", - "\n", - "- Alle Variablen sollen mit `Type Hints` versehen werden.\n", - "\n", - "- Hat eines der Attribute keinen optimalen Datentyp, gibt es trotzdem Punkte, solange die Mehrheit korrekt ist.\n", - "\n", - "- Die Hinweise sind aktuell nicht in logischer Reihenfolge. \n", - " Struckturier den Code sinnvoll!\n", - " \n", - "Füge Kommentare ein, die sich auf die jeweilige [PEP 8](https://peps.python.org/pep-0008/) Regel beziehen.\n", - "\n", - "Beispielstudent:\n", - "\n", - "|Attribut|Wert|\n", - "|-|-|\n", - "|vorname|Martin|\n", - "|nachname|Le|\n", - "|mat_nr|520420|\n", - "|semester|5|" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "d17b07db-7340-47c0-ab1d-38d75b3194c0", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-ac7d4ba80c4a0341", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "from dataclasses import asdict # Imports\n", - "\n", - "@dataclass\n", - "class Student:\n", - " vorname: str\n", - " nachname: str\n", - " mat_nr: int\n", - " semester: int \n", - "\n", - "stud: dict = asdict(Student(vorname='Martin', nachname='Le', mat_nr=520420, semester=5))\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "ab8dcc00-fc3f-4ae3-a18d-73f97e166ae3", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-10b27d53a9659696", - "locked": true, - "points": 6, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'vorname': 'Martin', 'nachname': 'Le', 'mat_nr': 520420, 'semester': 5}\n" - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "\n", - "# Check if asdict is imported\n", - "assert \"asdict\" in dir() # 1 Punkt\n", - "\n", - "# Check if Student is named properly\n", - "assert \"Student\" in dir() # 1 Punkt\n", - "\n", - "# Check if Student is a Dataclassimport dataclasses\n", - "from dataclasses import is_dataclass\n", - "assert is_dataclass(Student) # 1 Punkt\n", - "\n", - "# Check if stud is properly converted from Dataclass to dict\n", - "# 3 Punkt\n", - "assert stud == {'vorname': 'Martin', 'nachname': 'Le', 'mat_nr': 520420, 'semester': 5}\n", - "print(stud)" - ] - }, - { - "cell_type": "markdown", - "id": "448b2019-405f-4f3d-87d4-afa183363a35", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-b8ee343f30f40e1e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe\n", - "\n", - "*6 Punkte*\n", - "\n", - "Gegeben sind zwei Listen `colorn` und `colorv_hex`, die zueinander indexsortiert sind.\n", - "\n", - "1. Erstelle eine Dataclass `Color` mit den Attributen `name` und `value` und versehe sie mit passenden Type Hints.\n", - "\n", - "2. Erzeuge anschließend eine Liste, die die Werte aus `colorn` und `colorv_hex` in Instanzen der Dataclass `Color` umwandelt, und speichere diese Liste in der Variablen `colors`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "27555b53-51e1-48b8-8962-528c22c85b02", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-8fb05db31c5a091d", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# Diese Zelle vor bearbeiten der Aufgabe einmal ausführen\n", - "colorn = ['RED', 'GREEN', 'BLUE', 'YELLOW', 'PURPLE']\n", - "colorv_hex = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#FF00FF']" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "be9ea7b1-45c4-418d-9a5f-0d101c9fd245", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-dbfa5551c836768a", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Color(name='RED', value='#FF0000'), Color(name='GREEN', value='#00FF00'), Color(name='BLUE', value='#0000FF'), Color(name='YELLOW', value='#FFFF00'), Color(name='PURPLE', value='#FF00FF')]\n" - ] - } - ], - "source": [ - "colors = None\n", - "# BEGIN SOLUTION\n", - "@dataclass\n", - "class Color:\n", - " name: str\n", - " value: str\n", - "\n", - "colors: list = [Color(n, w) for n, w in zip(colorn, colorv_hex)]\n", - " \n", - "print(colors)\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "cbf53cfd-7b06-443c-8aca-55463d77aa57", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-a720d5d5ba2ea35c", - "locked": true, - "points": 6, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Color(name='RED', value='#FF0000'), Color(name='GREEN', value='#00FF00'), Color(name='BLUE', value='#0000FF'), Color(name='YELLOW', value='#FFFF00'), Color(name='PURPLE', value='#FF00FF')]\n" - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "\n", - "# Check if Color is named properly\n", - "assert \"Color\" in dir() \n", - "\n", - "# Check if Color is a Dataclassimport dataclasses\n", - "from dataclasses import is_dataclass\n", - "assert is_dataclass(Color) # 1 Punkt\n", - "\n", - "# Check if colors is a list\n", - "assert isinstance(colors, list) # 1 Punkt\n", - "\n", - "# Check if colors contains only Color Classes \n", - "for c in colors:\n", - " assert is_dataclass(c) # 1 Punkt\n", - "\n", - "### BEGIN HIDDEN TEST\n", - "@dataclass\n", - "class Color:\n", - " name: str\n", - " value: str\n", - "\n", - "c = [Color(n, w) for n, w in zip(colorn, colorv_hex)]\n", - "for r, s in zip(c, colors):\n", - " assert r.name == s.name\n", - " assert r.value == s.value\n", - "### END HIDDEN TEST\n", - "\n", - "print(colors) # 3 Punkte" - ] - }, - { - "cell_type": "markdown", - "id": "7d970394-5982-4e4f-bcea-e72ce0e379c4", - "metadata": {}, - "source": [ - "### Aufgabe\n", - "\n", - "*6 Punkte*\n", - "\n", - "Gegeben ist ein Dictionary `dict_obj`.\n", - "\n", - "1. Erstelle eine Dataclass `Transaction`, die die Originalstruktur des Dictionaries abbildet. \n", - " Achte dabei auf **korrekte Type Hints** für die einzelnen Attribute.\n", - "\n", - "2. Schreibe **unterhalb der Dataclass in einer Markdown-Zeile** deine Begründung, warum du diese Struktur gewählt hast. \n", - " **(!! Nicht als Kommentar !!)**" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "b24c4d57-654c-4092-b8a1-1c942393e06a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{ 'IBAN': 'DE19500211001598716891',\n", - " 'Transaction Number': 2469,\n", - " 'Recipient': { 'First Name': 'Phil',\n", - " 'Last Name': 'Keier'},\n", - " 'Amount': 3.14}\n" - ] - } - ], - "source": [ - "# Diese Zelle vor bearbeiten der Aufgabe einmal ausführen\n", - "import random\n", - "\n", - "dict_obj: dict = {\n", - " \"IBAN\": \"DE19500211001598716891\", # FAKE!\n", - " \"Transaction Number\": random.randint(1, 8000),\n", - " \"Recipient\": {\n", - " \"First Name\": \"Phil\",\n", - " \"Last Name\": \"Keier\",\n", - " },\n", - " \"Amount\": 3.14,\n", - "}\n", - "\n", - "# Just to show dict_obj\n", - "from pprint import pprint\n", - "pprint(dict_obj, depth=3, width=20, indent=2, sort_dicts=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "e26ffb4c-237d-455d-b8b6-c6d652a7abf9", - "metadata": {}, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "@dataclass\n", - "class Transaction:\n", - " iban: str\n", - " trans_nr: int\n", - " recipient: dict\n", - " amount: float\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "9bf7abb7-88ab-405f-bed9-d637fd6046eb", - "metadata": {}, - "source": [ - "# BEGIN SOLUTION\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "920aabb3-81ed-48c2-90ed-6b5648058b18", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Name: iban, Typ: \n", - "Name: trans_nr, Typ: \n", - "Name: recipient, Typ: \n", - "Name: amount, Typ: \n" - ] - } - ], - "source": [ - "# Hier werden die Loesungen getestet...\n", - "\n", - "# Assume Transaction is defined and a Dataclass\n", - "from dataclasses import is_dataclass\n", - "assert 'Transaction' in dir()\n", - "assert is_dataclass(Transaction)\n", - "\n", - "# Show the Transaction Obj Annotations\n", - "for field_name, field_type in Transaction.__annotations__.items():\n", - " print(f\"Name: {field_name}, Typ: {field_type}\")" - ] - }, - { - "cell_type": "markdown", - "id": "afe6543a-b41b-4d3c-9929-525a0463c6af", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-a65f2c871406072c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Walrus Operator – Assignment Expressions\n", - "\n", - "Der sogenannte *Walrus Operator* `:=` wurde mit [PEP 572](https://peps.python.org/pep-0572/) eingeführt. \n", - "Interessanter Fun Fact: Einer der Gründe, warum Guido van Rossum das Python-Projekt verlassen hat, hängt mit der Kontroverse um diesen Operator zusammen. \n", - "\n", - "Aus dem offiziellen Statement:\n", - "\n", - "> \"The straw that broke the camel's back was a very contentious Python enhancement proposal, where after I had accepted it, people went to social media like Twitter and said things that really hurt me personally. And some of the people who said hurtful things were actually core Python developers, so I felt that I didn't quite have the trust of the Python core developer team anymore.\"\n", - "> – Guido van Rossum\n", - "\n", - "Der Operator selbst fügt keine neue Funktionalität hinzu. Er erlaubt lediglich, eine **Zuweisung während einer Auswertung** vorzunehmen.\n", - "\n", - "Ein kurzes Beispiel – bitte nicht nachmachen! Niemand, wirklich niemand, möchte das im echten Code sehen:" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "8055d0b2-eb9f-4a1a-ac74-4bfcd6cbee81", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-860f5501722f78aa", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Liste ist lang genug: 4\n" - ] - } - ], - "source": [ - "# Zuweisung während einer Bedingung\n", - "if (n := len([1, 2, 3, 4])) > 3:\n", - " print(f\"Liste ist lang genug: {n}\")" - ] - }, - { - "cell_type": "markdown", - "id": "af5f2aab-0e6d-41d8-b3e1-96969a5ad264", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-2fb74418e2478ef5", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Berechnung und Zuweisung in einer Zeile:\n", - "\n", - "Der Walrus Operator `:=` wird sinnvoll eingesetzt, wenn man vermeiden möchte, dass eine teure Berechnung **zweimal ausgeführt** wird.\n", - "\n", - "Klassisches Beispiel ohne Walrus: `n + 1` wird zweimal berechnet:" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "b41cae11-20f2-4500-b32a-77775502f6ea", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-85ffa763f1c26ced", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5\n" - ] - } - ], - "source": [ - "n = 4\n", - "if (n + 1) > 3:\n", - " print(n+1)" - ] - }, - { - "cell_type": "markdown", - "id": "5ad0b0b7-5d85-4a1d-9083-30790c494035", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-da3163fec3bf5f09", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Mit dem Walrus Operator lässt sich die Berechnung `n + 1` direkt im `if` einer Variablen `out` zuweisen:" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "cde3f827-477c-46d4-8057-af35b694ebef", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-664ab7388a3cb90c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5\n" - ] - } - ], - "source": [ - "n = 4\n", - "if (out := n + 1) > 3:\n", - " print(out)" - ] - }, - { - "cell_type": "markdown", - "id": "16375c7c-ef2e-44b7-a5f1-2156248f616c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-11c8ad1c8ab2ef15", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Auch ohne Walrus Operator lässt sich verhindern, dass `n + 1` zweimal berechnet wird, indem man die Berechnung vorher einer Variablen zuweist:" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "c1bf6d7e-8ce8-4770-8060-a6d18ab8d85e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-2dbb33acbcf4f8a4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5\n" - ] - } - ], - "source": [ - "n = 4\n", - "out = n + 1\n", - "if out > 3:\n", - " print(out)" - ] - }, - { - "cell_type": "markdown", - "id": "302a6dfd-2e3d-401d-93c9-1335e081e837", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-3254f96c16f5c246", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "**Persönliche Meinung:** Ich rate grundsätzlich davon ab, den Walrus Operator `:=` zu verwenden. \n", - "Meiner Erfahrung nach macht er den Code oft unlesbar und spart bestenfalls nur 2–3 Zeilen. In meinen eigenen Projekten gab es nie einen echten Grund, ihn einzusetzen – er brachte nie einen spürbaren Vorteil bei Berechnungen. \n", - "\n", - "Dennoch wollte ich dir einmal demonstrieren, wie der Walrus Operator funktioniert, damit du seine Anwendung siehst." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +version https://git-lfs.github.com/spec/v1 +oid sha256:f6b474766c6cad945a904f659395064e03caa21f217bf2c49ebdd8f373ef7e35 +size 47507 diff --git a/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/4.NumPy_MatPlotLib.ipynb b/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/4.NumPy_MatPlotLib.ipynb index 5843d08..8841a8a 100644 --- a/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/4.NumPy_MatPlotLib.ipynb +++ b/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/4.NumPy_MatPlotLib.ipynb @@ -1,3136 +1,3 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "40366dea-bec8-4128-b8e9-a7c10536a613", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-44b468fb46efb002", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# 4. Programmierübung: NumPy & MatPlotLib\n", - "\n", - "
\n", - "
\n", - " Willkommen zur vierten Programmierübung Einführung in Python 3.\n", - "
\n", - " \n", - "
\n", - "\n", - "Wenn du Fragen oder Verbesserungsvorschläge zum Inhalt oder Struktur der Notebooks hast, dann kannst du mir gerne eine E-Mail an Phil Keier ([p.keier@hbk-bs.de](mailto:p.keier@hbk-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) schreiben.\n", - "\n", - "Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n", - "\n", - "Der Großteil des Python-Tutorials stammt aus der Veranstaltung _Deep Learning Lab_ und von [www.python-kurs.eu](https://www.python-kurs.eu/python3_kurs.php) und wurde für _Signale und Systeme_, sowie _Einführung in die Programmierung für Nicht Informatiker_ angepasst." - ] - }, - { - "cell_type": "markdown", - "id": "4d4eed4c-36ff-4643-bbc8-6c5440f73065", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5ac025a1f69e3f31", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Was ist NumPy\n", - "\n", - "NumPy steht für Numerical Python, ist OpenSource und wird mittlerweile von nahezu jedem Python Entwickler eingesetzt. Dabei ist das Core Feature von NumPy seine äußerst effiziente Implementierung eines n-dimensionalen Arrays in C, welches nahtlos in Python verwendet werden kann und eine hohe Performance bei großen Datenmengen ermöglicht. Hinzu kommt eine umfassende Hülle an Funktionen wie leistungsstarke Zufallsalgorithmen sowie mathematische Routinen aus den unterschiedlichsten Bereichen der Statistik und numerischen Berechnung, welche konsequent für NumPy Arrays optimiert sind und eine intuitive, performante Datenverarbeitung erlauben. Im Folgenden wollen wir den grundlegenden sowie fortgeschrittenen Umgang mit NumPy Arrays erlernen und deren vielseitige Einsatzmöglichkeiten näher kennenlernen.\n", - "\n", - "__Für dieses Notebook schauen Sie bitte in die [NumPy Docs](https://numpy.org/doc/stable/reference/index.html)!!!__ Dort sind alle Funktionen beschrieben die wir hier bearbeiten und noch mehr!\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "ee26bbc0-d6a6-4721-b371-5cade59879ea", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d2be4b14819d46e9", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Was ist Matplotlib\n", - "\n", - "Matplotlib ist eine leistungsfähige Python Bibliothek zum (interaktiven) Visualisieren von Daten und ermöglicht die Erstellung unterschiedlichster Diagrammtypen, von einfachen Linienplots bis hin zu komplexen wissenschaftlichen Visualisierungen. Die Bibliothek integriert sich dabei nahtlos mit anderen viel benutzten Python Bibliotheken wie NumPy und Pandas, was eine effiziente Weiterverarbeitung der Daten erlaubt. Ein besonderer Vorteil der Kombination mit Jupyter besteht in der direkten, dynamischen Ausgabe eines Plots auf dem Bildschirm, wodurch Visualisierungen unmittelbar analysiert und angepasst werden können.\n", - "\n", - "__Nutzen Sie für diese Aufgaben gerne die [Matplotlib Reference](https://matplotlib.org/stable/users/index.html).__\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "8f6a28a6-9ef0-4f80-9432-80375cf3876b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b8d545ee5d6cabe7", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Das gesamte Internet importiert NumPy mit dem Kürzel `np` und das pyplot Objekt mit `plt`. \n", - "\n", - "Um uns diesem ungeschriebenen Gesetz anzuschließen, importieren wir in der nächsten Zelle NumPy ebenfalls als `np` & MatPlotLib als `plt`, um eine einheitliche und leicht verständliche Schreibweise beizubehalten:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "20dc6e2f-9b96-4f0c-85a8-8763e6722ce8", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3d1fe74859052932", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "id": "57f719db-3269-49e2-acd2-a676911c0470", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-39e898b8ebe45728", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "id": "9e008318-4d2a-4244-ac91-63d7b8806491", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b910cff04746aa1d", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Was ist ein Array?\n", - "\n", - "Ein Array ist eine kontinuierliche Datenstruktur. Dabei werden die Daten zusammenhängend in Reihe im Arbeitsspeicher hinterlegt, vergleichbar mit der Python Liste. \n", - "\n", - "Im Gegensatz zur klassischen Liste besitzen Arrays jedoch eine feste Größe sowie einen einheitlichen Datentyp, was eine deutlich effizientere Speicherverwaltung und schnellere Verarbeitung ermöglicht. Gerade bei numerischen Operationen profitieren Arrays von ihrer strukturierten Anordnung, da Zugriffe und Berechnungen direkt und ohne zusätzlichen Overhead erfolgen können. \n", - "\n", - "Aus diesem Grund bilden Arrays die grundlegende Basis für performante Datenverarbeitung und numerische Berechnungen in Bibliotheken wie NumPy.\n", - "\n", - "\n", - "## Erstellen von Arrays\n", - "\n", - "Alle folgenden Beispiele findest du im [Beginners Guide](https://numpy.org/doc/stable/user/absolute_beginners.html).\n", - "\n", - "Für unser erstes Beispiel erstellen wir aus einer Python Liste ein [NumPy Array](https://numpy.org/doc/stable/reference/arrays.html)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "b44876d4-2376-4651-b489-9708a02f86bd", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1adaa95f01483572", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 2, 3, 4, 5])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "arr = np.array([1,2,3,4,5])\n", - "arr" - ] - }, - { - "cell_type": "markdown", - "id": "a08e52ec-5148-4304-93da-064369cab9f8", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ea5067ebbb1c99bc", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Der Zugriff auf Elemente des Arrays erfolgt analog zu Pythons Liste. Dabei können einzelne Werte über ihren Index adressiert werden, wobei die Zählung – wie in Python üblich – bei `0` beginnt. \n", - "\n", - "Zusätzlich ermöglichen Arrays jedoch auch den Zugriff auf ganze Bereiche mithilfe von Slicing, wodurch mehrere Elemente oder Teilabschnitte effizient und in einem Schritt ausgewählt und verarbeitet werden können." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "68679f58-495f-483e-a394-0cb3016dd488", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-cb73ac88e9fa5d93", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "np.int64(5)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "arr[4]" - ] - }, - { - "cell_type": "markdown", - "id": "97d468b2-2f23-4e8f-9b41-979ae1294754", - "metadata": {}, - "source": [ - "Slicing:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "bd5af69f-9e56-4438-9500-5f8bbf1a50bd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([2, 3, 4])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "arr[1:4]" - ] - }, - { - "cell_type": "markdown", - "id": "3f9f7d47-b9d6-47b2-85e3-bdb015cc72d8", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ec0a814ecfda8547", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "NumPy Arrays sind *n-dimensional*, das heißt Arrays in NumPy können aus geschachtelten Listen bestehen und dadurch mehrere Dimensionen abbilden. Einfache Arrays sind eindimensional, während komplexere Strukturen als zwei-, drei- oder sogar höherdimensionale Arrays vorliegen können. \n", - "\n", - "Ein klassisches Beispiel hierfür ist ein 2-dimensionales Array, das sich als Matrix interpretieren lässt und aus Zeilen und Spalten besteht. Solche Strukturen werden häufig zur Darstellung von Tabellen, Bildern oder mathematischen Matrizen verwendet und bilden die Grundlage für viele Operationen in der Datenanalyse und im Machine Learning." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "04452b0d-ca06-4dd8-9b6a-47aa00b38011", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ff72c8352626ac82", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1, 2, 3, 4, 5],\n", - " [6, 7, 8, 9, 8],\n", - " [7, 6, 5, 4, 3]])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "arr = np.array(\n", - " [\n", - " [1,2,3,4,5],\n", - " [6,7,8,9,8],\n", - " [7,6,5,4,3],\n", - " ])\n", - "arr" - ] - }, - { - "cell_type": "markdown", - "id": "a4d10e8e-6856-4f14-a18c-b4d0d4cfee96", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-413fcd639649e39a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Der Zugriff auf ein Element einer Matrix erfolgt nach dem Prinzip *\"Zeile → Spalte\"*, das heißt, zuerst wird die Zeile und dann die Spalte angegeben. \n", - "\n", - "Beispielsweise greift man in einem 2-dimensionalen Array `arr` auf das Element in der ersten Zeile und ersten Spalte mit `arr[0, 0]` zu. \n", - "\n", - "Die erste Spalte einer Matrix kann durch die Angabe aller Zeilen und der Spalte `0` ausgewählt werden, zum Beispiel mit `arr[:, 0]`, wodurch alle Elemente der ersten Spalte als neues Array zurückgegeben werden." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "9539a428-4cb1-426e-9ff0-bb10f9e5dd75", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c7a59ce293c8e402", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 2, 3, 4, 5])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "arr[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "a7e11cb8-526f-4ded-afef-f2e2b990d3a6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 6, 7])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "arr[:, 0]" - ] - }, - { - "cell_type": "markdown", - "id": "eb7b82f2-c8ba-4336-b3f9-505d89010e3b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-fb0cf79581b45e5e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Der Zugriff auf ein einzelnes Element erfolgt dann Analog:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "c940ad92-43b4-4783-be50-687b2be51541", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-2f107dba2b747fbb", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "np.int64(5)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "arr[0][4]" - ] - }, - { - "cell_type": "markdown", - "id": "37cd7d69-9a6f-4136-95bc-3470ea512f45", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-617d777cf3216789", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Oder mit NumPys eigener Syntax `arr[, ]`" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "630ba433-c2e5-4faf-980a-ae34e57d470a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3e1e7323c57088ad", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "np.int64(5)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "arr[0, 4]" - ] - }, - { - "cell_type": "markdown", - "id": "2b1ad36a-eeec-410f-82f1-768bb134accd", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-62701cdd045c7c4c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Platzreservierung\n", - "\n", - "Falls bereits bekannt ist, wie viele Elemente ein Array im späteren Programmverlauf enthalten soll, bietet NumPy die Möglichkeit, den benötigten Speicherplatz im Voraus zu reservieren. \n", - "\n", - "Hierfür stellt NumPy verschiedene Funktionen bereit, wie zum Beispiel `np.zeros()`, `np.ones()` oder `np.empty()`. Diese Funktionen erzeugen Arrays mit einer vorgegebenen Größe, wobei die Elemente standardmäßig mit Nullen, Einsen oder undefinierten Werten initialisiert werden. Das Vorreservieren des Speichers kann die Effizienz erheblich steigern, da späteres dynamisches Erweitern des Arrays vermieden wird.\n", - "\n", - "### Ones\n", - "\n", - "1-Dimensionales NumPy Array der Größe 10 mit 1 gefüllt:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "1da0eee9-4aa6-4a2e-91ab-7f9110ba5961", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d3aeedf2a30a9b30", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.ones(10)" - ] - }, - { - "cell_type": "markdown", - "id": "dec9de1d-0415-4ce1-8182-cdf5f50cb8cd", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-6e683d4afe40b7ff", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Zeros\n", - "\n", - "Analog dazu mit 0 gefüllt:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "22b81912-0f2b-4ea6-b085-938e8f835daa", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-450f40270416767a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.zeros(10)" - ] - }, - { - "cell_type": "markdown", - "id": "794bfa8c-2c62-4d13-b2cf-65165b9f66d8", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ef1e55c1165e2de5", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Empty\n", - "\n", - "Analog mit zufälligen Werten (bzw. Werte, die bereits an der Speicherstelle waren, meistens 0):" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "5bede573-fda6-4065-bc05-2238cdcd7dc5", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-896e48c096be9062", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.empty(10)" - ] - }, - { - "cell_type": "markdown", - "id": "f8543f2c-37a1-45c7-9e6f-387126d2e0c4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-947ed3289815694d", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Arange\n", - "\n", - "Analog dazu bietet NumPy die Funktion `arange()`, die ähnlich wie die Python `range()` Funktion funktioniert. \n", - "\n", - "Mit `np.arange(start, stop, step)` kann ein Array erzeugt werden, das eine Sequenz von Zahlen von `start` bis `stop` (ausschließlich) in Schritten von `step` enthält. Im Gegensatz zu `range()` liefert `arange()` jedoch direkt ein NumPy Array, das sofort für numerische Berechnungen und Operationen genutzt werden kann." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "57a83964-bf61-4db8-8358-d415d92e49bb", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5329dd48e6129b33", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.arange(10)" - ] - }, - { - "cell_type": "markdown", - "id": "14461c48-a442-41d8-a9dc-bd411beb6b14", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-299417e99c41e05f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Linspace\n", - "\n", - "Um später mit Matplotlib besser arbeiten zu können, bietet NumPy die [`linspace`](https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy-linspace) Funktion. \n", - "\n", - "Mit `np.linspace(start, stop, num=...)` werden Werte gleichmäßig (linear) zwischen einem Start- und einem Endpunkt berechnet. Zusätzlich kann über den Parameter `num` die gewünschte Anzahl der Elemente im Array festgelegt werden. Diese Funktion ist besonders nützlich, wenn man eine feste Anzahl an Punkten für die Darstellung von kontinuierlichen Funktionen oder Graphen benötigt.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "1defeafc-b02a-4e84-a1d3-30878fc69071", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-036bdec449f35dc5", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0. , 2.5, 5. , 7.5, 10. ])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.linspace(0,10, num=5)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "72416872-9e66-4ff8-88ad-9244b6e67827", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-9ac9d3f215fc0237", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 400. , 466.66666667, 533.33333333, 600. ,\n", - " 666.66666667, 733.33333333, 800. , 866.66666667,\n", - " 933.33333333, 1000. ])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.linspace(400, 1000, num=10)" - ] - }, - { - "cell_type": "markdown", - "id": "53cd285e-fe83-4c8b-9ac5-ee65db57107d", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-522faf35a6c76300", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe\n", - "\n", - "*1 Punkt*\n", - "\n", - "Erstelle ein NumPy Array, welches 6 Nullen reserviert und speicher das Array in der Variablen `only_zeros`. " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "8f64d227-64cc-4b26-b076-b9ea3db3d798", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-176f6befb5f45c58", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [], - "source": [ - "only_zeros = None\n", - "# BEGIN SOLUTION\n", - "only_zeros = np.zeros(600)\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "ca435e4c-e686-4e64-b796-6f6884708ce3", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-8ad1e3a41d459d55", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n" - ] - } - ], - "source": [ - "print(only_zeros)\n", - "\n", - "# Check i length is correct\n", - "assert len(only_zeros) == 600\n", - "\n", - "# Check if every element contains a zero\n", - "for el in only_zeros:\n", - " assert el == 0" - ] - }, - { - "cell_type": "markdown", - "id": "07012ea7-35b0-4e24-83d4-5732853761bd", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0108e88f3110e70f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe\n", - "\n", - "*1 Punkt*\n", - "\n", - "Erstelle ein NumPy Array mit 11 Elementen mithilfe der Funktion `linspace`. Der Startwert soll `-6` und der Endwert `16.5` betragen.\n", - "\n", - "Speichere das Ergebnis in der Variablen `x_scale`." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "41f68397-24db-44d8-a2cf-4e7aec431b57", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3231ee937ba8ab7a", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [], - "source": [ - "x_scale = None\n", - "# BEGIN SOLUTION\n", - "x_scale = np.linspace(-6, 16.5, num=11)\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "2439440f-f8f6-4ee1-ae2b-3bca19992675", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-e5d66ef7599f7b91", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-6. -3.75 -1.5 0.75 3. 5.25 7.5 9.75 12. 14.25 16.5 ]\n" - ] - } - ], - "source": [ - "# Hier werden ihre Lösungen getestet\n", - "print(x_scale)\n", - "\n", - "# Check if length is correct\n", - "assert len(x_scale) == 11\n", - "### BEGIN HIDDEN TESTS\n", - "s = np.linspace(-6, 16.5, num=11)\n", - "### END HIDDEN TESTS\n", - "\n", - "# Test if elements are correct\n", - "for el1, el2 in zip(x_scale, s):\n", - " assert el1 == el2" - ] - }, - { - "cell_type": "markdown", - "id": "55b7093a-9fe9-443b-a680-9f5dfc2baf41", - "metadata": {}, - "source": [ - "## Aufgabe\n", - "\n", - "*6 Punkte*\n", - "\n", - "Erstelle ein NumPy Array und bearbeite es anschließend wie folgt:\n", - "\n", - "1. Erzeuge ein eindimensionales NumPy Array `numbers` mit **20 gleichmäßig verteilten Zahlen** zwischen `10` und `50` mithilfe von `np.linspace`. \n", - "2. Berechne ein neues Array `squared_numbers`, das die **Quadrate** der Werte aus `numbers` enthält. \n", - "3. Finde alle Elemente in `numbers`, die **größer als 30** sind, und speichere sie in einem neuen Array `numbers_gt_30`. \n", - "4. Berechne die **Summe** aller Werte in `squared_numbers` und speichere das Ergebnis in `total_squared`. \n", - "5. Sortiere `numbers` absteigend und speichere das Ergebnis in `numbers_sorted_desc`. \n", - "6. Gebe als Kommentar die NumPy Referenz zu den verwendeten Funktionen an. (Keine Kommentare = 0 Punkte)" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "id": "aa10a185-e36c-4121-91d2-7dfa913ffa63", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([False, False, False, False, False, False, False, False, False,\n", - " False, True, True, True, True, True, True, True, True,\n", - " True, True])" - ] - }, - "execution_count": 89, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "# 1. Create the NumPy array\n", - "numbers = np.linspace(10, 50, 20)\n", - "\n", - "# 2. Calculate the squares of the values\n", - "squared_numbers = numbers**2\n", - "squared_numbers = np.square(numbers)\n", - "\n", - "# 3. Find all elements greater than 30\n", - "numbers_gt_30 = numbers[numbers > 30]\n", - "\n", - "# 4. Sum all values in squared_numbers\n", - "total_squared = np.sum(squared_numbers)\n", - "\n", - "# 5. Sort in descending order\n", - "numbers_sorted_desc = np.sort(numbers)[::-1]\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "id": "464b0b7c-8209-4652-98a4-2d8b80137a7d", - "metadata": {}, - "outputs": [ - { - "ename": "AssertionError", - "evalue": "All elements in numbers_gt_30 should be greater than 30", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[88], line 18\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m np\u001b[38;5;241m.\u001b[39mallclose(squared_numbers, expected_squared), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSquared numbers are incorrect\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;66;03m# Test 3: Check numbers_gt_30 filtering\u001b[39;00m\n\u001b[0;32m---> 18\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m np\u001b[38;5;241m.\u001b[39mall(numbers_gt_30 \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m30\u001b[39m), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAll elements in numbers_gt_30 should be greater than 30\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray_equal(numbers_gt_30, numbers[numbers \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m30\u001b[39m]), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumbers_gt_30 values are incorrect\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;66;03m# Test 4: Check total_squared sum\u001b[39;00m\n", - "\u001b[0;31mAssertionError\u001b[0m: All elements in numbers_gt_30 should be greater than 30" - ] - } - ], - "source": [ - "# Hier werden ihre Lösungen getestet ...\n", - "\n", - "### BEGIN HIDDEN TESTS\n", - "expected_squared = numbers**2\n", - "expected_sum = np.sum(numbers**2)\n", - "expected_sorted_desc = np.sort(numbers)[::-1]\n", - "### END HIDDEN TESTS\n", - "\n", - "# Test 1: Check if numbers array has correct length and values\n", - "assert len(numbers) == 20, \"Numbers array should have 20 elements\"\n", - "assert np.isclose(numbers[0], 10), \"First element should be 10\"\n", - "assert np.isclose(numbers[-1], 50), \"Last element should be 50\"\n", - "\n", - "# Test 2: Check squared_numbers calculation\n", - "assert np.allclose(squared_numbers, expected_squared), \"Squared numbers are incorrect\"\n", - "\n", - "# Test 3: Check numbers_gt_30 filtering\n", - "assert np.all(numbers_gt_30 > 30), \"All elements in numbers_gt_30 should be greater than 30\"\n", - "assert np.array_equal(numbers_gt_30, numbers[numbers > 30]), \"numbers_gt_30 values are incorrect\"\n", - "\n", - "# Test 4: Check total_squared sum\n", - "assert np.isclose(total_squared, expected_sum), \"Sum of squared numbers is incorrect\"\n", - "\n", - "# Test 5: Check descending sort\n", - "assert np.array_equal(numbers_sorted_desc, expected_sorted_desc), \"Descending sort is incorrect\"" - ] - }, - { - "cell_type": "markdown", - "id": "dfdb02ea-44b4-4368-9b46-235ca1dca667", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5d8e19cc802e33a4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "id": "9276ba6e-30a0-4803-ace7-14147cd214ee", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-30de8243b097dfdc", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# First plot\n", - "\n", - "Wie dem [Getting Started](https://matplotlib.org/stable/users/getting_started/index.html#getting-started) Beispiel zu entnehmen ist, wollen wir die Sinus-Funktion einmal plotten. \n", - "\n", - "Dazu benötigen wir zwei Attribute: \n", - "1. **x-Skala** – Dies kann die Länge eines Datensets oder ein allgemeiner `linspace` sein. Auf jeden Fall sollte es sich um eine Liste oder ein Array handeln. \n", - "2. **y-Skala** – Im Allgemeinen die Werte des zu plottenden Datensets, ebenfalls als Liste oder Array. \n", - "\n", - "Im Folgenden plotten wir die Sinus-Funktion. Eine der schönen Eigenschaften der Sinus-Funktion ist, dass sie sich im Intervall $[0, 2\\pi]$ wiederholt. Daher enthält die x-Skala einen `linspace` von $0$ bis $2\\pi$. Als Wert für $\\pi$ verwenden wir die NumPy Konstante [np.pi](https://numpy.org/doc/stable/reference/constants.html#numpy.pi). \n", - "\n", - "Auf der y-Skala verwenden wir die zuvor berechneten Werte der Sinus-Funktion. Hierfür wird die Funktion [np.sin](https://numpy.org/doc/stable/reference/generated/numpy.sin.html) genutzt. \n", - "\n", - "Das `plt`-Objekt bietet mehrere Funktionen, die in einer bestimmten Reihenfolge aufgerufen werden müssen. Zuerst können mehrere Plots mit [plt.plot](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html) definiert werden. Zum Schluss erfolgt die Ausgabe des Plots mit [plt.show](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html).\n" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "63f7bd92-7a44-4fe0-b03e-d13b1374be1c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-2eaf77b2d04abff1", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.linspace(0, 2*np.pi, num=200) # Definiere einen linearen Bereich von 0 bis 2pi\n", - "y = np.sin(x) # Berechne den Sinus mit den Werten von x\n", - "\n", - "plt.plot(x, y) # Setze für die X-Achse x und für die Y-Achse y\n", - "plt.show() # Zeige den Plot" - ] - }, - { - "cell_type": "markdown", - "id": "9dc5f266-8956-431d-aa8e-cb33afc8fc34", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1dd3b7172bc39b59", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Zwei Funktionen \n", - "\n", - "Wie bereits zuvor erwähnt, lässt sich `plt.plot` mehrfach aufrufen. Im Folgenden wollen wir zusätzlich die Kosinus-Funktion mit [np.cos](https://numpy.org/doc/stable/reference/generated/numpy.cos.html) plotten. \n", - "\n", - "Dabei werden die Werte aus der bereits definierten x-Skala wiederverwendet. Die Variablen `y1 = np.sin(x)` und `y2 = np.cos(x)` enthalten die jeweiligen y-Werte für Sinus und Kosinus. Auf diese Weise können beide Funktionen in einem gemeinsamen Plot dargestellt werden, was den direkten Vergleich erleichtert.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "7f63b65d-875d-4ee2-998b-fc6f39935ab0", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-2e9cc2ce95f1e20e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y1 = np.sin(x) # Sinus Werte mittels x berechnen\n", - "y2 = np.cos(x) # Kosinus Werte mittels x berechnen\n", - "\n", - "plt.plot(x, y1) # Plotte den Sinus\n", - "plt.plot(x, y2) # Plotte den Kosinus\n", - "\n", - "plt.show() # Zeige das Diagramm" - ] - }, - { - "cell_type": "markdown", - "id": "c1e5749a-8f46-41ae-817a-19e5103a9a7a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d1d50ca1d203ac29", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe - Erster eigener Plot - Squareroot\n", - "\n", - "*3 Punkte*\n", - "\n", - "Analog zur vorherigen Erklärung plotten Sie im Folgenden die Funktion **Square Root**, mathematisch definiert als $f(x) = \\sqrt{x}, \\quad x \\geq 0$. \n", - "\n", - "Gehen Sie dabei wie folgt vor: \n", - "1. Definieren Sie einen **geeigneten** [Linspace](https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy-linspace) für den Zahlenraum von `0` bis `100`. (Tipp: Achten Sie darauf, dass die Quadratwurzel nur für nicht-negative Zahlen definiert ist.) \n", - "2. Berechnen Sie die Werte für die Quadratwurzel mithilfe der Funktion [np.sqrt](https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html#numpy.sqrt). \n", - "3. Plotten Sie das Ergebnis mit `plt.plot` und geben Sie den Plot mit `plt.show()` aus.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "c36ff699-9c30-448c-9d97-0a75e00888fd", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-bae73642cf0a866a", - "locked": false, - "points": 3, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "xs = np.linspace(0, 100, num=200)\n", - "ys = np.sqrt(xs)\n", - "plt.plot(xs, ys)\n", - "plt.show()\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "a98f7855-0bca-4769-aaf5-fa27f0404b23", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-998243908406c7d4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Styling\n", - "\n", - "Da bei mehreren Plots der Überblick schnell verloren geht, beschäftigen wir uns im Folgenden mit dem **Styling**. Dabei werden wir im Schnelldurchlauf die wichtigsten Parameter und Möglichkeiten durchgehen, um Plots anschaulicher und übersichtlicher zu gestalten. \n", - "\n", - "Die Grundlage für alle folgenden Plots wird in der nächsten Zelle gesetzt, sodass wir ein konsistentes Layout und einheitliche Formatierungen verwenden können." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "dd074404-20be-450e-a187-d992c9d804cf", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a86ddac229c0bbbb", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "x = np.linspace(0, 2*np.pi, num=200)\n", - "s = np.sin(x)\n", - "c = np.cos(x)" - ] - }, - { - "cell_type": "markdown", - "id": "7e541a27-5fb0-48a5-98a6-175842a59f31", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-e27c575962048d7b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Farbe ändern\n", - "\n", - "Die Standardfarbe für den ersten Plot ist immer Blau. Um die Farbe zu verändern, kann dem `plt.plot`-Aufruf der Parameter `color` übergeben werden. Dieser erwartet einen String, der die gewünschte Farbe definiert. \n", - "\n", - "Für eine detaillierte Erläuterung siehe die Dokumentation zu [Specifying color](https://matplotlib.org/stable/users/explain/colors/colors.html). \n", - "\n", - "In diesem Notebook werden die Beispiele vorwiegend mit den sogenannten \"Single Character Shorthands\" aus der Dokumentation versehen. \n", - "\n", - "Plotten wir den Sinus nun in Rot:" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "01d03aa6-22c3-4843-8cf0-33155b78740e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1ec79feac73af81f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(x, s, color='r') # Plot mit der Farbe Rot\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "e229b04c-8688-4457-982c-7a3f27c4254c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-6d559d4604922bd9", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Titel für den Plot setzen\n", - "\n", - "Um einem Plot einen Titel zu geben, wird die Funktion [plt.title](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.title.html) verwendet. \n", - "\n", - "Der Titel wird dabei als String übergeben, z. B. `plt.title(\"Mein Plot\")`. Dies hilft, die Darstellung zu beschreiben und Plots leichter interpretierbar zu machen." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "ca46e07c-2f69-40cb-b58b-5278e13a048e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-106786a4fca81b67", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(x, s)\n", - "plt.title(\"Sinus Plot\") # Titel Setzen\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "6d0a0200-ac39-467c-a3e8-66e6a3ba3818", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8d7bed3592e18530", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Legende und Labels\n", - "\n", - "Um eine Legende in einem Plot anzuzeigen, wird die Funktion [plt.legend](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html) **vor** dem Aufruf von `plt.show()` verwendet. \n", - "\n", - "Damit die Legende korrekt dargestellt wird, muss jeder Plot beim Aufruf von `plt.plot` mit dem Parameter `label` versehen werden, der einen beschreibenden String enthält. \n", - "\n", - "Im Folgenden plotten wir den Sinus und Kosinus mit entsprechenden Labels, sodass die Legende die beiden Funktionen eindeutig unterscheidet." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "4d58a327-a11e-408b-a998-ef03c7cfaf87", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b1d037b9a275622c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(x, s, label=\"Sinus\") # Plotte Sinus mit label Sinus\n", - "plt.plot(x, c, label=\"Kosinus\") # Plotte Kosinus mit label Kosinus\n", - "plt.legend() # Füge die Legende ein\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "fb03ce2f-5639-402a-9b32-0b351113b5d6", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-63bbc82ff5e6892a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Linestyle\n", - "\n", - "Eine weitere wichtige Eigenschaft beim Plotten ist das Festlegen des **Linestyles**. \n", - "\n", - "Dazu wird dem Aufruf von `plt.plot` der Parameter `linestyle` als String übergeben. Die verschiedenen verfügbaren Linestyles können der Dokumentation zu [Linestyles](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html) entnommen werden. \n", - "\n", - "Beispiel; Den Sinus als **dashed** Linie darstellen:" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "90be449a-1d18-416f-b22b-782081d1b007", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-402e40ea2ceafc35", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(x, s, linestyle=\"dashed\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "b465a6e5-06ab-4fc0-8238-f4f05593a9d1", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-88e04ff7645c08cd", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe - Multiplot\n", - "\n", - "*8 Punkte*\n", - "\n", - "In der nächsten Aufgabe willst du zwei Funktionen gleichzeitig plotten: $f(x) = \\sqrt{x}, \\; x \\geq 0$ und $g(x) = x^2$. \n", - "\n", - "Gehe dabei wie folgt vor: \n", - "1. Definiere einen geeigneten [Linspace](https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy-linspace) für den Zahlenraum von `0` bis `3`. (Tipp: Achte darauf, dass die Quadratwurzel nur für nicht-negative Zahlen definiert ist.) \n", - "2. Berechne die Werte für die Quadratwurzel mit [np.sqrt](https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html#numpy.sqrt). \n", - "3. Berechne die Werte für die Quadratzahlen mit [np.square](https://numpy.org/doc/stable/reference/generated/numpy.square.html#numpy-square). \n", - "4. Gib den beiden Plots die Farben Grün und Rot. Nutze hierfür die [Color Shorthands](https://matplotlib.org/stable/users/explain/colors/colors.html) aus der Dokumentation. \n", - "5. Plotte die Quadratfunktion mit dem Linestyle `dashdot`, wie in der Dokumentation zu [Linestyles](https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html) beschrieben. \n", - "6. Vergeben angemessene Labels für beide Plots. \n", - "7. Füge die Legende hinzu. \n", - "8. Plotte das Ergebnis mit `plt.show()`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "41c07973-1717-48f1-94a5-c6930b0089ec", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-6bb6ab1d60fffde5", - "locked": false, - "points": 8, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "xt = np.linspace(0, 3, num=200)\n", - "sqrt = np.sqrt(xt)\n", - "square = np.square(xt)\n", - "\n", - "plt.plot(xt, sqrt, color='r', label=\"Square Root\")\n", - "plt.plot(xt, square, color='g', label=\"Square Function\", linestyle=\"dashdot\")\n", - "plt.legend()\n", - "plt.show()\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "d33d372a-ed48-4a31-a1fc-4b325e910d27", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-6d0019ce99d7aacb", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "id": "7dd33f6a-1e5d-4c8f-8778-7820d13fa357", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c7e6f6e837a94d41", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Warum Zufall?\n", - "\n", - "Für statistische Analysen jeglicher Art ist es wichtig, die verwendeten Werkzeuge gut zu verstehen. Da nicht immer direkt ein vollständiges Dataset vorliegt oder dieses sich noch im Erstellungsprozess befindet, bietet es sich an, die mathematischen und programmatischen Methoden zunächst an nachvollziehbaren Zufallsdaten zu testen. \n", - "\n", - "In dieser Übung wollen wir daher lernen, was Zufall bedeutet, wie Zufallsgeneratoren funktionieren und auf welche Weise Zufallszahlen gezielt erzeugt oder manipuliert werden können, um verschiedene Szenarien realistisch zu simulieren.\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "6536feb0-7612-4bad-b797-3960ed3c832c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-f387eeef09033ea3", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Was ist Zufall?\n", - "\n", - "Für Zufall existieren viele unterschiedliche Definitionen. Doch was genau ist Zufall eigentlich? Das Würfeln eines Würfels zum Beispiel ist streng genommen nicht zufällig. Jeder einzelne Würfelwurf kann theoretisch vorhergesagt werden, vorausgesetzt, alle erforderlichen Daten wie Kraft, Winkel, Untergrund und Luftwiderstand sind bekannt. Auch das Wetter ist in diesem Sinne nicht zufällig. Alle Ereignisse lassen sich prinzipiell vorhersagen, wenn genügend Daten vorhanden sind – das eigentliche Problem besteht lediglich darin, dass es in der Praxis niemals ausreichend Daten gibt. \n", - "\n", - "Nahezu jedes weitere Beispiel kann mit derselben Argumentation als „zufällig“ widerlegt werden. Daher werden solche Phänomene als *pseudo-zufällig* bezeichnet, da sie zwar unvorhersehbar erscheinen, aber einem deterministischen System folgen. \n", - "\n", - "Doch das nur am Rande. In der Physik existiert ein Phänomen, das als wirklich unvorhersehbar gilt. Wird ein einzelnes Photon auf einen halbtransparenten Spiegel geschossen, kann __nie__ vorhergesagt werden, auf welchen der beiden Detektoren das Photon treffen wird. Diese Form des echten Zufalls wird von der Firma [ID Quantique](https://www.idquantique.com/random-number-generation/overview/) genutzt, die Geräte entwickelt und vertreibt, welche genau dieses Quantenphänomen zur Erzeugung echter Zufallszahlen verwenden. \n", - "\n", - "Doch genug von echter Koinzidenz – zurück zum Pseudo-Zufall und der Frage, wie Computer mit diesem scheinbaren Zufall umgehen und ihn für Simulationen und statistische Zwecke nutzbar machen.\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "73c86ab1-9f06-4dc5-861b-6558e8315bb3", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-fde25777024719e0", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Pseudo Randomness\n", - "\n", - "Zufallszahlen in Computern werden nicht wirklich zufällig erzeugt, sondern über mathematische Formeln berechnet. Einer der bekanntesten Generatoren ist der _Linear Congruent Generator (LCG)_, dessen mathematische Grundlage leicht verständlich ist:\n", - "\n", - "$$ X_{n+1} = (aX_n + c) \\;\\text{mod}\\; m, \\quad n \\geq 0 $$\n", - "\n", - "Wenn $c = 0$ gilt, spricht man auch vom _Multiplicative Congruent Generator (MCG)_. \n", - "\n", - "Die einzelnen Werte haben folgende Bedeutung: \n", - "\n", - "- $X_n$ ist der Startwert, auch **Seed** genannt. \n", - "- $X_{n+1}$ ist der Folgewert, der im nächsten Schritt als $X_n$ verwendet wird. \n", - "- $a$ ist der Vorfaktor des Startwerts, wird auch als **Skalar** bezeichnet, da er den Wert skaliert. \n", - "- $c$ ist das hinzuaddierte **Offset**. \n", - "- $m$ ist der Restklassenring oder **Modulus**, der die Werte in einem festen Bereich hält.\n" - ] - }, - { - "cell_type": "markdown", - "id": "39b18b10-a831-4fbe-a50b-d8177108bca1", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a1c6ad2b24a97f6b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe\n", - "\n", - "*6 Punkte*\n", - "\n", - "Schreibe einen _Linear Congruent Generator_ mit dem Funktionsnamen `lcg`.\n", - "\n", - "- Verwende die oben gegebene mathematische Definition: \n", - " $$ X_{n+1} = (aX_n + c) \\;\\text{mod}\\; m, \\quad n \\geq 0 $$\n", - "- Prüfe, dass die Eingabewerte gültig sind. \n", - "- `lcg` soll als **unendlicher Generator** implementiert werden, der bei jedem Aufruf den nächsten Wert der Pseudo-Zufallsfolge liefert.\n", - "- Ekläre in der Markdownzeile die Funktionsweise des Generators. (Keine Erklärung = 0 Punkte)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "224134b7-6ab5-4438-ad46-6cb77261680c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d5c7ad13eb813b34", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [], - "source": [ - "# BEGIN SOLUTION\n", - "def lcg(seed: int, scalar: int, modulus: int, offset: int) -> int:\n", - " ''' \n", - " Linear Congruential Generators\n", - "\n", - " X(n+1) = (a X(n) + c) mod m; n >= 0\n", - "\n", - " m > 0; \n", - " 0 <= a < m;\n", - " c > 0; a > 0\n", - "\n", - " '''\n", - " assert modulus > 0, \"Modulus must be greater than 0\"\n", - " assert 0 <= scalar and scalar < modulus, \"Scalar must be in range 0 <= a < m\"\n", - "\n", - " while seed > 1:\n", - " seed = (scalar*seed+offset) % modulus\n", - " assert seed >= 0\n", - " yield seed\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "ef57bf76-127c-4987-be25-126d60453cb9", - "metadata": {}, - "source": [ - "# BEGIN SOLUTION\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "853a70eb-7dd1-43b5-af99-ed1b3126846c", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-35fbf31fbee439df", - "locked": true, - "points": 6, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Lcg using Cocktailshaker Numbers: 3089810780120156248\n", - "Correct should be: 3089810780120156248\n", - "\n", - "Lcg using Cocktailshaker Numbers: 8356396685252565260\n", - "Correct should be: 8356396685252565260\n", - "\n", - "Lcg using Cocktailshaker Numbers: 1921117399837525548\n", - "Correct should be: 1921117399837525548\n", - "\n", - "Lcg using Cocktailshaker Numbers: 14806858147081821235\n", - "Correct should be: 14806858147081821235\n", - "\n", - "Lcg using Cocktailshaker Numbers: 2557599628047639428\n", - "Correct should be: 2557599628047639428\n", - "\n", - "Lcg using Cocktailshaker Numbers: 16453652254840064460\n", - "Correct should be: 16453652254840064460\n", - "\n", - "Lcg using Cocktailshaker Numbers: 15995401842808378843\n", - "Correct should be: 15995401842808378843\n", - "\n", - "Lcg using Cocktailshaker Numbers: 681272290641816305\n", - "Correct should be: 681272290641816305\n", - "\n", - "Lcg using Cocktailshaker Numbers: 10955466795170118648\n", - "Correct should be: 10955466795170118648\n", - "\n", - "Lcg using Cocktailshaker Numbers: 13714992071537968180\n", - "Correct should be: 13714992071537968180\n", - "\n" - ] - } - ], - "source": [ - "# Hier werden ihre Lösungen getestet ...\n", - "\n", - "# Check if lcg is a properly defined\n", - "assert 'lcg' in dir()\n", - "\n", - "# Check if lcg is a generator\n", - "import types\n", - "assert isinstance(lcg(1,1,1,1), types.GeneratorType) # 1 Punkt\n", - "\n", - "# Using Cocktailshaker numbers :)\n", - "s = lcg(3935559000370003845, 3203021881815356449, 2**64-1, 11742185885288659963)\n", - "### BEGIN HIDDEN TESTS\n", - "def lcg_test(seed: int, scalar: int, modulus: int, offset: int) -> int:\n", - " assert modulus > 0, \"Modulus must be greater than 0\"\n", - " assert 0 <= scalar and scalar < modulus, \"Scalar must be in range 0 <= a < m\"\n", - "\n", - " while seed > 1:\n", - " seed = (scalar*seed+offset) % modulus\n", - " assert seed >= 0\n", - " yield seed\n", - "\n", - "t = lcg_test(3935559000370003845, 3203021881815356449, 2**64-1, 11742185885288659963)\n", - "### END HIDDEN TESTS\n", - "\n", - "# Check if Generator works\n", - "for _ in range(10):\n", - " stud = next(s)\n", - " instructor = next(t)\n", - " print(\"Lcg using Cocktailshaker Numbers:\", stud)\n", - " print(\"Correct should be:\", instructor, end='\\n\\n')\n", - " assert stud == instructor\n" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "id": "a8ec682f-79eb-463f-86eb-a063639d040b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1. 3089810780120156248\n", - "2. 8356396685252565260\n", - "3. 1921117399837525548\n", - "4. 14806858147081821235\n", - "5. 2557599628047639428\n" - ] - } - ], - "source": [ - "rand = lcg(\n", - " 3_935_559_000_370_003_845,\n", - " 3_203_021_881_815_356_449,\n", - " 2**64-1,\n", - " 11_742_185_885_288_659_963\n", - " )\n", - "\n", - "for i in range(5):\n", - " print(f\"{i+1}. {next(rand)}\")" - ] - }, - { - "cell_type": "markdown", - "id": "8e516bd4-c203-4b11-9880-31eabba13c2e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-88eadd4386332f26", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "---\n", - "\n", - "# A Family of Better Random Number Generators\n", - "\n", - "
\n", - "
\n", - " \n", - "
\n", - "

\n", - " Melissa E. O’Neill\n", - "

\n", - "
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\n", - "

Linear Congruent Generators zeichnen sich durch ihre Stabilität und Geschwindigkeit als Hervorragende Zufallsgeneratoren. Doch 2014 gelang Melissa E. O'Neil ein neuer Durchbruch in der Konzeption von Pseudozufallsgeneratoren. Das Problem mit existierenden Zufallsgeneratoren ist entweder ihre Stabilität (Wie vorhersehbar die Zufallszahlen sind) oder ihre Geschwindigkeit (Wie lange der Zufallsgenerator braucht um die nächste Zufallszahl zu errechnen).

\n", - "

Ihr Durchbruch gelang, indem Sie die Vorteile eines Linear Congruent Generators mit dem eines XorShift Generators verband. Dadurch erreichte Sie nicht nur eine Normalverteilung in den generierten Zufallszahlen (und eine damit einhergende Stabilität), Sie hatte auch eine Family von schnellen einfachen Algorithmen entwickelt. Diese nennen sich PCG - Permuted Congruential Generator.

\n", - "

Die Implementierungsdetails lassen wir im nächsten Schritt aus, da diese nicht einfach in Python umzusetzen sind. Auf der Webseite pcg-random.org lassen sich Implemtierungen für C & C++ finden. Als weiterführende Literatur ist PCG: A Family of Simple Fast Space-Efficient Statistically Good Algorithms for Random Number Generation - Melissa E. O’Neill angegeben.

\n", - "
\n", - "
\n", - "\n", - "Zum Glück hat _Numpy_ eine Implementierung des _PCG_. Diese findet sich unter [Numpy PCG64](https://numpy.org/doc/stable/reference/random/index.html)." - ] - }, - { - "cell_type": "markdown", - "id": "81959fe2-2f2f-4f57-9360-4e96dc0674ba", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3cfb52b705a8d147", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe\n", - "\n", - "*6 Punkte*\n", - "\n", - "Plotte die Zufallszahlen eines _Permuted Congruent Generators_ (PCG) mithilfe von NumPy und Matplotlib.\n", - "\n", - "- Gegeben ist der Anfangszustand des Generators. \n", - "- Verwende die NumPy-Dokumentation um **20** Zufallszahlen zu erzeugen und speichere sie in der Variablen `pcgs` (Tipp: als NumPy Array). \n", - "- Sortiere anschließend die Werte in `pcgs` und speichere das Ergebnis in `pcgs_sorted`. \n", - "- Plotte sowohl die ursprünglichen als auch die sortierten Zufallszahlen in einem sinnvollen Diagramm (z. B. Linienplot oder Scatterplot) und gestalte den Plot übersichtlich.\n", - "- Erkläre in der gegebenen Markdown Zeile deinen Plot. (Keine Erklärung = 0 Punkte)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "a2fbf6d5-9460-48bc-8183-b2afb9c5c186", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-9e88f0a0a4a77c47", - "locked": false, - "points": 3, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "np.random.seed(42) # Setting a fixed start Value for the Generator\n", - "pcgs: np.array = None\n", - "pcgs_sorted: np.array = None\n", - "\n", - "# BEGIN SOLUTION\n", - "gen = np.random.default_rng()\n", - "pcgs = np.array([gen.random() for _ in range(20)])\n", - "pcgs_sorted = np.sort(pcgs)\n", - "\n", - "# Plot\n", - "plt.scatter(np.arange(len(pcgs_sorted)), pcgs, color='r', label='PCGs')\n", - "plt.plot(np.arange(len(pcgs_sorted)), pcgs_sorted, color='g', label='Sortierte PCGs')\n", - "plt.title(\"PCG Random Numbers\")\n", - "plt.legend()\n", - "plt.show()\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "81f765b9-5a70-4c5f-93bc-b06ee655b3a5", - "metadata": {}, - "source": [ - "# BEGIN SOLUTION\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "91d763ff-71ac-4940-9cf8-54ca7ea6b8ca", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-69197dfdc3015ec5", - "locked": true, - "points": 3, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PCGs [0.39787148 0.93848582 0.15923912 0.11887978 0.28188342 0.0065017\n", - " 0.54349094 0.76825107 0.12562835 0.03294871 0.82149947 0.07167748\n", - " 0.46462778 0.710785 0.06627364 0.13104628 0.77441648 0.69096057\n", - " 0.26849046 0.8829005 ]\n", - "Sorted PCGs [0.0065017 0.03294871 0.06627364 0.07167748 0.11887978 0.12562835\n", - " 0.13104628 0.15923912 0.26849046 0.28188342 0.39787148 0.46462778\n", - " 0.54349094 0.69096057 0.710785 0.76825107 0.77441648 0.82149947\n", - " 0.8829005 0.93848582]\n" - ] - } - ], - "source": [ - "# Hier werden ihre Lösungen getestet ...\n", - "print(\"PCGs\", pcgs)\n", - "print(\"Sorted PCGs\", pcgs_sorted)\n", - "### BEGIN HIDDEN TESTS\n", - "np.random.seed(42)\n", - "test_gen = np.random.default_rng()\n", - "test_pcgs = np.array([gen.random() for _ in range(20)])\n", - "test_pcgs_sorted = np.sort(pcgs)\n", - "### END HIDDEN TESTS\n", - "\n", - "# Check if pcgs are correctly generated\n", - "assert test_pcgs.all() == pcgs.all()\n", - "\n", - "# Check if pcgs are correctly sorted\n", - "assert test_pcgs_sorted.all() == pcgs_sorted.all()\n", - "\n", - "# Plot is manually checked" - ] - }, - { - "cell_type": "markdown", - "id": "9283cd7a-b8a6-4090-84c3-da70e61b2d2e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-486bea96505ad0a6", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "---\n", - "\n", - "# Plot Types\n", - "\n", - "Im folgenden Kapitel beschäftigen wir uns mit verschiedenen **Plot-Typen** und deren Einsatzmöglichkeiten, um Daten auf unterschiedliche Weise anschaulich darzustellen." - ] - }, - { - "cell_type": "markdown", - "id": "8fe0a0f7-a836-4279-bc7c-e993004f97aa", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-79e43d78c9874975", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Line Plots\n", - "\n", - "Line Plots haben wir im vorherigen Kapitel bereits kennengelernt. Sie werden mithilfe von `plt.plot` erstellt und eignen sich besonders gut, um den Verlauf von Daten über eine kontinuierliche Skala darzustellen. \n", - "\n", - "Beispiel: Sinus-Funktion" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "19b25d9b-0ee5-4eff-b290-8513f7948f50", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b48b1eec8fe65537", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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vs2DBAri4uOiPgAB+hNIaZFnGPzccR2WtFn2D3TAuKkh0JGokSRLeGtUdLnbWOJ6rwbJd50VHIiJqU0Y9zWXFihXo0aMH+vXr1+TxsWPH4sEHH0SPHj0watQobNq0CQcOHMCOHTuueZ34+Hio1Wr9kZ2d3Qbpzd8Ph3Kx60wRbKwUeGd0TygU/GjKmHg522L+yG4AgE9+P4tzReWCExERtR2DFhwPDw8olUoUFBQ0ebygoAA+Pj43fG5FRQXWr1+PyZMn3/T7hISEwMPDA+np6df8ukqlgrOzc5ODbk9hWTXe2HQCADArphM6ejoKTkTX8nCv9hjcxRO1Wh1e/ek496oiIoth0IJjY2ODyMhIJCYm6h/T6XRITExEdHT0DZ/73XffoaamBuPHj7/p98nJycGlS5fg6+t725mped7cdBLqqjrc4eeMKYNCRMeh65AkCa8/2B0qKwX2nruEn1PzREciImoTBv+Iavbs2Vi+fDnWrFmDkydPYtq0aaioqMCkSZMAAHFxcYiPj7/qeStWrMCoUaPQrl27Jo+Xl5fjxRdfxL59+5CZmYnExEQ89NBDCA0NRWxsrKFfDgHYm16MjUfyIEnAO4/05IJ+Ri6wnT2eG9IJAPDmryegrqwTnIiIyPAMvgvimDFjUFRUhHnz5iE/Px8RERFISEjQDzzOysqCQtH0DfL06dPYvXs3tmzZctX1lEoljh49ijVr1qC0tBR+fn4YOnQo3njjDahUKkO/HItXW6/Dqz837DU1PioIPfxdBCei5pgyKAQbDucivbAc720+hbce7iE6EhGRQUmyBX4or9Fo4OLiArVazfE4LbRkxzm8m3AK7RxssG3OYLjYW4uORM207/wljF22D5IE/DCtPzdCJSKT05L3b362QM2WW1qFhY2bOP7z/q4sNybmzpB2eKR3e8gy8H8bjqOea+MQkRljwaFme/2XNFTVadEv2B2P9G4vOg7dgn/e3xUudg3bOKxJuiA6DhGRwbDgULNsP12IzWkFUCokvD7qDm7HYKI8HFV4eVgYAODj38+guLxGcCIiIsNgwaGbqqnX4rWNaQCASf2DEebDcUumbEzfAHRv74yy6np82LgDPBGRuWHBoZtatScTFy5VwstJhVn3dRYdh26TUiFh/siGzTjXH8jG8Vy14ERERK2PBYduqKisBp9ua1gh+qVhYXBUGXxlAWoDfYPdMTLcD7IM/OuXNK5wTERmhwWHbujDLadRXlOPnv4ueKQXBxabk/jhYbC1VuBA5mVsOnpRdBwiolbFgkPXlZanxjcHGzYmnfdAN26maWb8XO0w7e5QAMCC/55EVa1WcCIiotbDgkPXJMsyXv/lBGQZeKCnL/oEu4uORAbw97tD0N7VDnnqaizdeU50HCKiVsOCQ9e0Oa0A+zNKoLJSYO7wMNFxyEBsrZX45/1dAQBLd55DXmmV4ERERK2DBYeuUlOvxdv/PQkAmHpXCPzd7AUnIkO6v4cP+gW7o6Zehw+3nBEdh4ioVbDg0FVW78lEVknDtPBn7u4oOg4ZmCRJ+OeIhrs4Px7OwYk8jeBERES3jwWHmiitrMXi7Q3Twl+M7QIHTgu3CBEBrnigpy9kGVjw20nRcYiIbhsLDjXxnx3noKmuR5iPEx7p7S86DrWhl2LDYK2U8MfZYuw6UyQ6DhHRbWHBIb3c0iqs3psJAHh5WBiUnBZuUQLb2ePJO4MBAAt+OwWtjov/EZHpYsEhvQ+3nEZtvQ53hrhjcBdP0XFIgJn3hsLJ1gonL2qw4XCu6DhERLeMBYcAACfy/nxDix/elbuFWyg3BxtMv6dh8b8Pt5xGdR0X/yMi08SCQwCA9zafgiwDI3r6IjzAVXQcEmhi/2C0d7XDRXU1Vu7JEB2HiOiWsOAQ9p4rxo7TRbBSSHhxaBfRcUgwW2sl5gxt2DV+6Y5zUFfWCU5ERNRyLDgWTpZlvPPbKQDAuKhABHs4CE5ExuChiPbo4u0ETXU9lv3BLRyIyPSw4Fi4zWn5OJqjhoONEjOHdBIdh4yEUiHp7+Ks3J2JorIawYmIiFqGBceCaXWyfmn+yQM7wMNRJTgRGZP7unkjPMAVVXVa/eKPRESmggXHgv1yJA9nC8vhbGuFyYNCRMchIyNJf47J+mp/FnK5EScRmRAWHAtVp9Xh37833L35+90d4WJnLTgRGaMBoe0QHdIOtVodFv5+VnQcIqJmY8GxUD+k5ODCpUp4ONpg0oBg0XHISEmShH/ENtzF+f5QDs4VlQtORETUPCw4FqimXouFiQ3/Gp82OBT2NtxQk64vMsgNMV29oNXJ+PfWM6LjEBE1CwuOBfp6fxby1NXwcbbFuKhA0XHIBMxpHIuz6ehFnMjTCE5DRHRzLDgWpqpWi0+3N6xrMnNIKGytlYITkSno6uuMB3r6AgA+SeRdHCIyfiw4FmZtUiaKy2sQ6G6Px/sEiI5DJuT5IZ0gScDmtAKk5alFxyEiuiEWHAtSUVOPz3adBwA8N6QTrJX87afm6+TthAd6+gEAPuGMKiIycnyHsyBf7ruAkopaBLezx6gIP9FxyAQ9PyQUkgRsOVGA47m8i0NExqtNCs7ixYsRHBwMW1tbREVFITk5+brnrl69GpIkNTlsbW2bnCPLMubNmwdfX1/Y2dkhJiYGZ8/yX5Q3Ullbj2WNd29m3NsJVrx7Q7cg1MsJI6/cxUnkf3NEZLwM/i73zTffYPbs2Zg/fz4OHTqE8PBwxMbGorCw8LrPcXZ2xsWLF/XHhQsXmnz9vffew8KFC7F06VLs378fDg4OiI2NRXV1taFfjslaty8LlypqEcS7N3Sbnmsci7OVd3GIyIgZvOB89NFHmDJlCiZNmoRu3bph6dKlsLe3x8qVK6/7HEmS4OPjoz+8vb31X5NlGR9//DFeeeUVPPTQQ+jZsyfWrl2LvLw8/PTTT4Z+OSapqlaLz3Y1zJyafk8o797QbQn1csSD4Q0l+WOOxSEiI2XQd7ra2lqkpKQgJibmz2+oUCAmJgZJSUnXfV55eTmCgoIQEBCAhx56CGlpafqvZWRkID8/v8k1XVxcEBUVdd1r1tTUQKPRNDksybr9F1BcXosAdzs83Ku96DhkBmbe2wkKCfj9JO/iEJFxMmjBKS4uhlarbXIHBgC8vb2Rn59/zed06dIFK1euxM8//4wvv/wSOp0O/fv3R05ODgDon9eSay5YsAAuLi76IyDAcqZHV9VqsXRnw9ibmfdw5hS1Dt7FISJjZ3TvdtHR0YiLi0NERATuvvtu/Pjjj/D09MRnn312y9eMj4+HWq3WH9nZ2a2Y2Lh9lZyF4vKahrs3vXn3hlrPzMaxOL+fLMCpfMu6K0pExs+gBcfDwwNKpRIFBQVNHi8oKICPj0+zrmFtbY1evXohPT0dAPTPa8k1VSoVnJ2dmxyWoLpOi6U7G8feDA7l3RtqVR09HXF/j4bVjRc3ro5NRGQsDPqOZ2Njg8jISCQmJuof0+l0SExMRHR0dLOuodVqcezYMfj6NvxF2qFDB/j4+DS5pkajwf79+5t9TUvx3cFsFJXVoL2rHR7p7S86Dpmh6YNDAQC/Hs1DRnGF4DRERH8y+D/pZ8+ejeXLl2PNmjU4efIkpk2bhoqKCkyaNAkAEBcXh/j4eP35r7/+OrZs2YLz58/j0KFDGD9+PC5cuICnn34aQMMMq1mzZuHNN9/Exo0bcezYMcTFxcHPzw+jRo0y9MsxGXVanX7szTN3h8DGindvqPV183PGkDAv6GRgyY500XGIiPSsDP0NxowZg6KiIsybNw/5+fmIiIhAQkKCfpBwVlYWFIo/33wvX76MKVOmID8/H25uboiMjMTevXvRrVs3/TkvvfQSKioqMHXqVJSWlmLgwIFISEi4akFAS7YxNQ+5pVXwcFThMe45RQY0/d5QJJ4qxI+HcvF8TGe0d7UTHYmICJIsy7LoEG1No9HAxcUFarXaLMfj6HQy7vv3TpwrqsDc4WF45u6OoiORmXti2T4knb+ECdFB+NdD3UXHISIz1ZL3b35uYYa2nMjHuaIKONtaYVxUoOg4ZAFm3NswFmf9gYZxX0REorHgmBlZlvUzWib2D4aTrbXgRGQJ+ndsh4gAV9TU6/D57vOi4xARseCYmz/OFuNYrhp21kpMHNBBdByyEJIkYcY9DXdxvky6gNLKWsGJiMjSseCYmcXbG2ay/C0qEO4ONoLTkCUZ0tULYT5OqKjVYvXeTNFxiMjCseCYkYOZJdifUQJrpYQpg0JExyELI0kSpjfexVm1JxPlNfWCExGRJWPBMSP/2dEw9ubRSH/4uHDKPLW9+3v4IsTDAeqqOny1/4LoOERkwVhwzERanhrbThVCIQF/v4vTwkkMpULCM4Mb/vwt/yMD1XVawYmIyFKx4JiJJY13b0b09EOwh4PgNGTJHu7VHu1d7VBUVoPvDlrOxrZEZFxYcMzA+aJy/HrsIgDg2cG8e0NiWSsV+PvdDWPAlv+RgXqtTnAiIrJELDhm4LOd5yHLwJAwL3T1Nb+Vmcn0PBYZADd7a2SVVCIhLV90HCKyQCw4Ji5fXY0fD+cAAJ69h3dvyDjY2SgxoX8wgCsF3OJ2hCEiwVhwTNyqvRmo08roF+yOyCB30XGI9OKig2FrrcCxXDWSzl8SHYeILAwLjgkrq67DV/uyAABT7+K6N2Rc3B1s8HjjTvaf7eT2DUTUtlhwTNj65GyU1dSjo6cD7g3zEh2H6CpPDwyBQgJ2ninCyYsa0XGIyIKw4JioOq0OK/dkAGi4e6NQSIITEV0tsJ097u/hCwBYtot3cYio7bDgmKhfjuThoroank4qjOrVXnQcouu6svDkL0fykFtaJTgNEVkKFhwTJMuy/l/DE/sHQ2WlFJyI6Pp6+Lugf8d2qNfJWLk7Q3QcIrIQLDgmaNfZYpzKL4O9jRLjo4JExyG6qb/f3XAX5+vkLKgr6wSnISJLwIJjgpbtatiWYWzfQLjYWwtOQ3Rzd3XyQJiPEyprtfiSm3ASURtgwTExx3PV2JN+CUqFhKcGBouOQ9QskiTpt29YtSeTm3ASkcGx4JiYK2NvHujpC383e8FpiJrvgZ5+8HOxRXF5DTYczhUdh4jMHAuOCcm5XKnfVJML+5GpsVYqMHlQ4yacu85Dq+P2DURkOCw4JmTF7gxodTIGhnrgDj8X0XGIWmxs3wA421rhfHEFtp4oEB2HiMwYC46JUFfW4ZsD2QB494ZMl4PKCuPvbJj5xynjRGRILDgm4sv9F1BZq0VXX2cM6uQhOg7RLZvQPxjWSgnJmSU4kl0qOg4RmSkWHBNQU6/F6r2ZAICpd3WAJHFbBjJd3s62GNnTD0DDx65ERIbAgmMCNh25iKKyGvg42+KBxjcGIlP21MAOAIBfj11EHrdvICIDYMExcrIs6/+VG9c/CNZK/paR6eve3gXRIe2g1clY03h3koioNfHd0sjtO1+CExc1sLVW4G/9AkXHIWo1Tw9quIvzVXIWymvqBachInPDgmPkrty9Gd3bH672NoLTELWee7p4IcTDAWXV9fjuYLboOERkZlhwjFhmcQUSTzWsFXJlzAKRuVAoJP2f65V7MrjwHxG1qjYpOIsXL0ZwcDBsbW0RFRWF5OTk6567fPlyDBo0CG5ubnBzc0NMTMxV50+cOBGSJDU5hg0bZuiX0eZW7cmALAP3dPFER09H0XGIWl3DnUlrZJdUYeuJfNFxiMiMGLzgfPPNN5g9ezbmz5+PQ4cOITw8HLGxsSgsLLzm+Tt27MATTzyB7du3IykpCQEBARg6dChyc5vuXTNs2DBcvHhRf3z99deGfiltSl1Vh+9ScgAAkwdyYT8yT3Y2SoyPalj47/M/OGWciFqPwQvORx99hClTpmDSpEno1q0bli5dCnt7e6xcufKa569btw7PPvssIiIiEBYWhs8//xw6nQ6JiYlNzlOpVPDx8dEfbm5uhn4pbWp9chYqa7UI83HCgNB2ouMQGUxcdBCslRIOXriMw1mXRcchIjNh0IJTW1uLlJQUxMTE/PkNFQrExMQgKSmpWdeorKxEXV0d3N3dmzy+Y8cOeHl5oUuXLpg2bRouXbp03WvU1NRAo9E0OYxZvVannzr71AAu7EfmzcvZFiPDufAfEbUugxac4uJiaLVaeHt7N3nc29sb+fnN+7z95Zdfhp+fX5OSNGzYMKxduxaJiYl49913sXPnTgwfPhxarfaa11iwYAFcXFz0R0BAwK2/qDbw2/F85Kmr4eFogwcjuLAfmb/JjYONfzuej5zLlYLTEJE5MOpZVO+88w7Wr1+PDRs2wNbWVv/42LFj8eCDD6JHjx4YNWoUNm3ahAMHDmDHjh3XvE58fDzUarX+yM427impV/4VOy4qCLbWSsFpiAzvDj8X9O/Ihf+IqPUYtOB4eHhAqVSioKCgyeMFBQXw8fG54XM/+OADvPPOO9iyZQt69ux5w3NDQkLg4eGB9PT0a35dpVLB2dm5yWGsUi5cRmp2KWyUCv2uy0SW4MrCf+uTs1FWXSc4DRGZOoMWHBsbG0RGRjYZIHxlwHB0dPR1n/fee+/hjTfeQEJCAvr06XPT75OTk4NLly7B19e3VXKLtLLx7s1DEX7wdFIJTkPUdgZ39kKIpwPKaurxfeMMQiKiW2Xwj6hmz56N5cuXY82aNTh58iSmTZuGiooKTJo0CQAQFxeH+Ph4/fnvvvsuXn31VaxcuRLBwcHIz89Hfn4+ysvLAQDl5eV48cUXsW/fPmRmZiIxMREPPfQQQkNDERsba+iXY1A5lyvx2/GLAIDJg7iwH1kWhULCpP7BAIA1ezOh48J/RHQbDF5wxowZgw8++ADz5s1DREQEUlNTkZCQoB94nJWVhYsXL+rPX7JkCWpra/Hoo4/C19dXf3zwwQcAAKVSiaNHj+LBBx9E586dMXnyZERGRuKPP/6ASmXadzzW7M2ETgYGhLZDmI/xfoxGZCiP9PaHk60VMi9VYvvpa6+VRUTUHJIsyxb3zySNRgMXFxeo1WqjGY9TXlOP6LcTUVZTj5UT++DeMO+bP4nIDL316wks/yMDA0M98OXTUaLjEJERacn7t1HPorIk3x3MRllNPUI8HTC4s5foOETCxEUHQyEBu9OLcaagTHQcIjJRLDhGQKuTsWpPJgBg0oAOUCi4sB9ZrgB3e9zXreEO5pX/LoiIWooFxwj8frIAWSWVcLGzxuje7UXHIRJu0oCGQfYbDuegtLJWcBoiMkUsOEbgysJ+f4sKhL2NleA0ROJFdXBHN19nVNfp8HWycS/MSUTGiQVHsLQ8NZIzSqBUSIiL5sJ+RAAgSRImDQgGAHyRlIl6rU5sICIyOSw4gl1Zln54dx/4utiJDUNkREaG+6Gdgw3y1NXYnFZw8ycQEf0FC45AJRW1+Ck1DwD0/1ologa21kqMiwoEAKzcw13GiahlWHAE+jo5C7X1OvRo74LegW6i4xAZnfF3BsFaKSHlwmUczSkVHYeITAgLjiB1Wh2+3HcBADCxfzAkiVPDif6Xl7MtRvRo2GOOU8aJqCVYcATZklaAi+pqeDja4IFw098klMhQrkwZ33Q0D4WaasFpiMhUsOAIsnpv49TwfoFQWSkFpyEyXuEBrugd6Io6rYwv92eJjkNEJoIFR4DjuWocyLwMK4WEcXdyajjRzTw1sOEuzlf7L6CmXis4DRGZAhYcAVY3Tg2/v4cvvJ1txYYhMgGxd/jA18UWxeW1+OXIRdFxiMgEsOC0seLyGmxsnBo+kVPDiZrFWqnAk40LYa7akwFZlgUnIiJjx4LTxtYnZ6FWq0O4vwt6BbiKjkNkMp7oGwhbawXS8jQ4kHlZdBwiMnIsOG2oTqvDF1emhg/g1HCilnBzsMHDvRo2o13Fhf+I6CZYcNpQwvF8FGhq4OGowv09ODWcqKUm9m8YbLw5LR85lysFpyEiY8aC04auDC4eF8Wp4US3oouPEwaEtoNOhv5uKBHRtbDgtJGjOaVIuXAZ1kpJv78OEbXcpMa7OOuTs1FVyynjRHRtLDht5MrdmxE9fOHFqeFEt+yeMC8EuttDXVWHn1JzRcchIiPFgtMGispqsKlx7Y6JjcvOE9GtUSokxDVOGV+9J5NTxonomlhw2sDXjVPDIwJcEcGp4US37bE+AbC3UeJ0QRmSzl8SHYeIjBALjoHV1v+5a/gkLuxH1Cpc7Kwxurc/AO4yTkTXxoJjYL8dv4jCshp4OqkwvDunhhO1lgn9Gz6m+v1kAbJLOGWciJpiwTGwK4OLx0cFwcaKP26i1hLq5YRBnTwgy8DapEzRcYjIyPAd14BSs0txOKsU1koJf+PUcKJWd+Vj3/UHslFRUy82DBEZFRYcA1rTePdmZE8/eDqpxIYhMkODO3shuJ09yqrrseEwp4wT0Z9YcAyksKwam4427Bo+oX+w2DBEZkqhkPT/fa3eyynjRPQnFhwD+Wp/Fuq0MnoHuiKcU8OJDObRSH842CiRXliOPemcMk5EDVhwDKBhangWAC7sR2RoTrbWeKxPAABg9V7uMk5EDVhwDOC/xy6iuLwG3s4qDO/uIzoOkdm7srJx4qlCXLhUITgNERmDNik4ixcvRnBwMGxtbREVFYXk5OQbnv/dd98hLCwMtra26NGjB/773/82+bosy5g3bx58fX1hZ2eHmJgYnD171pAvoUVW/WVquLWSHZLI0EI8HTG4iydkGVizl7uME1EbFJxvvvkGs2fPxvz583Ho0CGEh4cjNjYWhYWF1zx/7969eOKJJzB58mQcPnwYo0aNwqhRo3D8+HH9Oe+99x4WLlyIpUuXYv/+/XBwcEBsbCyqq6sN/XJu6nDWZRzJLoWNUoEnODWcqM1MbBxs/N3BbJRzyjiRxZNkA087iIqKQt++ffHpp58CAHQ6HQICAjBz5kzMnTv3qvPHjBmDiooKbNq0Sf/YnXfeiYiICCxduhSyLMPPzw9z5szBP/7xDwCAWq2Gt7c3Vq9ejbFjx940k0ajgYuLC9RqNZydnVvplTZ4fv1h/Jyah9G9/fHh4+Gtem0iuj6dTkbMRztxvrgCrz90B+Kig0VHIqJW1pL3b4PewamtrUVKSgpiYmL+/IYKBWJiYpCUlHTN5yQlJTU5HwBiY2P152dkZCA/P7/JOS4uLoiKirruNWtqaqDRaJochlCgqcavRxt3DefUcKI2pVBImNi48N/qvZnQ6ThlnEiEMwVl+PsXB7FP8Ea4Bi04xcXF0Gq18Pb2bvK4t7c38vPzr/mc/Pz8G55/5X9bcs0FCxbAxcVFfwQEBNzS67mZdfuzUK+T0SfIDT38XQzyPYjo+h7p7Q8nlRXOF1Xgj/Ri0XGILNLqvZnYnFagX+xWFIsYARsfHw+1Wq0/srOzDfJ9Hu3tj6cHdsDUu0IMcn0iujFHldWfU8b3cMo4UVtTV9Zhw6GGVcVFL3Jr0ILj4eEBpVKJgoKCJo8XFBTAx+fa06d9fHxueP6V/23JNVUqFZydnZschhDYzh6vPNANQ+/g1HAiUeKigyBJwPbTRThfVC46DpFF+fZgNqrqtAjzcUJUB3ehWQxacGxsbBAZGYnExET9YzqdDomJiYiOjr7mc6Kjo5ucDwBbt27Vn9+hQwf4+Pg0OUej0WD//v3XvSYRWY5gDwfc28ULALA2iVPGidqKVidjTVImgIaNcCVJEprH4B9RzZ49G8uXL8eaNWtw8uRJTJs2DRUVFZg0aRIAIC4uDvHx8frzn3/+eSQkJODDDz/EqVOn8Nprr+HgwYOYMWMGAECSJMyaNQtvvvkmNm7ciGPHjiEuLg5+fn4YNWqUoV8OEZmAK4ONvzuYjbLqOrFhiCxE4skC5Fyugqu9NR6KaC86DqwM/Q3GjBmDoqIizJs3D/n5+YiIiEBCQoJ+kHBWVhYUij97Vv/+/fHVV1/hlVdewT//+U906tQJP/30E7p3764/56WXXkJFRQWmTp2K0tJSDBw4EAkJCbC1tTX0yyEiEzAw1AOhXo5ILyzH9yk5mMQtU4gM7srdm7F9A2FrrRQbBm2wDo4xMuQ6OERkHL7YdwGv/nQcwe3ssW3OYCgUYm+XE5mzMwVlGPrvXVBIwB8v34v2rnYG+T5Gsw4OEZEoo3u3h5OtFTIvVWLnmSLRcYjM2urGKeFDu/kYrNy0FAsOEZklexsrjO3bMGV8leD1OIjM2V+nhl8Z/2YMWHCIyGzFRQdDkoBdZ4qQXsgp40SGYExTw/+KBYeIzFaAuz1iujZMaBC9qiqROfrr1PCJ/cVPDf8rFhwiMmuTGldT/eFQDtRVnDJO1Jq2nSo0qqnhf8WCQ0RmLbpjO3T2dkRlrRbfHTTMNi1Elmr13oYtUcb0DYCdjfip4X/FgkNEZk2SJEzs37AOztqkC9Byl3GiVnGmoAx70i9BIQFP3hkkOs5VWHCIyOyN6uUHFztrZJVUYvupQtFxiMzCmr9MDfd3sxcb5hpYcIjI7P11yvhqDjYmum3qyjr8aCS7hl8PCw4RWYQno4OgkIDd6cU4W1AmOg6RSfsu5c+p4XeGGM/U8L9iwSEii+DvZo+h3XwAcOE/otvx16nhE4xsavhfseAQkcW4ssrqj4dyoK7klHGiW7HtVCGyS6rgYmeNUUY2NfyvWHCIyGJEdXBHmI8Tqut0+OZglug4RCbpyuDisf2Mb2r4X7HgEJHFkCQJkxrv4qzZyynjRC11tqAMu9OLjXZq+F+x4BCRRXkooj1c7a2RW1qF308WiI5DZFKuzEK8r5u3UU4N/ysWHCKyKLbWSjzRLxAAsHpPptgwRCaktLIWPxzKAQD94pnGjAWHiCzOk3cGQamQkHT+Ek7la0THITIJ6w9ko7pOh66+zkY7NfyvWHCIyOL4udph2B0NU8Z5F4fo5uq1Oqxt/Hhq0gDjnRr+Vyw4RGSRrkwZ33A4F5crasWGITJym9MKkKeuRjsHGzwY7ic6TrOw4BCRReoT5IY7/JxRU6/D+gPcZZzoRlbtadg1fFxUIGytjXdq+F+x4BCRRWrYZTwYAPBFUibqtTqxgYiM1NGcUhy8cBnWSgnjjXxq+F+x4BCRxRoZ7gd3Bxvkqaux9QSnjBNdy6rGcWoP9PSDl7Ot2DAtwIJDRBbL1lqJvzVOGef+VERXK9RUY9PRPADQL5JpKlhwiMiijW+cMp6cUYK0PLXoOERG5ct9F1CnlREZ5Iae/q6i47QICw4RWTQfF1sM794wZXwN7+IQ6VXXabFuf8OebU8NMP6F/f4XCw4RWbxJjX95/5Sah0vlNYLTEBmHjUfycKmiFn4utoi9w1t0nBZjwSEii9c70BU9/V1QyynjRAAAWZb1g4vj+gfDSml6dcH0EhMRtbKmU8YvoI5TxsnC7c8owcmLGthaKzC2b4DoOLeEBYeICMCInr7wcLRBvqYam9PyRcchEmrl7oaF/R7p7Q9XexvBaW4NCw4REQCVlRJ/i2pYxIz7U5Ely7pUia0nG9aFmtR4Z9MUseAQETUaHxUIK4WEgxcu41gOp4yTZVqTlAlZBgZ18kAnbyfRcW6ZQQtOSUkJxo0bB2dnZ7i6umLy5MkoLy+/4fkzZ85Ely5dYGdnh8DAQDz33HNQq5v+RSNJ0lXH+vXrDflSiMgCeDnbYkRPXwDAak4ZJwtUXlOPbxsH2j810PSmhv+VQQvOuHHjkJaWhq1bt2LTpk3YtWsXpk6det3z8/LykJeXhw8++ADHjx/H6tWrkZCQgMmTJ1917qpVq3Dx4kX9MWrUKAO+EiKyFFcGG/9yJA9FZZwyTpbl+4PZKKupR4iHA+7u5Ck6zm2xMtSFT548iYSEBBw4cAB9+vQBACxatAj3338/PvjgA/j5Xb3devfu3fHDDz/of92xY0e89dZbGD9+POrr62Fl9WdcV1dX+Pj4GCo+EVmoXoFuiAhwRWp2Kb7YdwGz7+ssOhJRm9DpZKxJugAAmDggGAqFJDjR7THYHZykpCS4urrqyw0AxMTEQKFQYP/+/c2+jlqthrOzc5NyAwDTp0+Hh4cH+vXrh5UrV0KW5eteo6amBhqNpslBRHQ9Tw9quDX/5b4LqK7TCk5D1DZ2nClERnEFnGytMLq3v+g4t81gBSc/Px9eXl5NHrOysoK7uzvy85s3BbO4uBhvvPHGVR9rvf766/j222+xdetWjB49Gs8++ywWLVp03essWLAALi4u+iMgwDTn9BNR2xh2hw/au9qhpKIWGw7nio5D1CZWNE4NH9s3AA4qg33A02ZaXHDmzp17zUG+fz1OnTp128E0Gg1GjBiBbt264bXXXmvytVdffRUDBgxAr1698PLLL+Oll17C+++/f91rxcfHQ61W64/sbK5USkTXZ6VU6HdOXrE7Azrd9e8QE5mDtDw19qRfglIhYYIJTw3/qxZXtDlz5mDixIk3PCckJAQ+Pj4oLCxs8nh9fT1KSkpuOnamrKwMw4YNg5OTEzZs2ABra+sbnh8VFYU33ngDNTU1UKlUV31dpVJd83Eiout5vG8APv79LNILy7HzbBHu6eJ18ycRmagrd2+Gd/eBv5u94DSto8UFx9PTE56eNx9ZHR0djdLSUqSkpCAyMhIAsG3bNuh0OkRFRV33eRqNBrGxsVCpVNi4cSNsbW1v+r1SU1Ph5ubGEkNErcbZ1hpj+gZgxe4MrNydwYJDZqtAU41fjuQBAJ4eFCI4Tesx2Bicrl27YtiwYZgyZQqSk5OxZ88ezJgxA2PHjtXPoMrNzUVYWBiSk5MBNJSboUOHoqKiAitWrIBGo0F+fj7y8/Oh1TYM9Pvll1/w+eef4/jx40hPT8eSJUvw9ttvY+bMmYZ6KURkoSb2D4ZCAv44W4xT+ZycQOZpzd5M1Gll9A1umEFoLgw6imjdunWYMWMGhgwZAoVCgdGjR2PhwoX6r9fV1eH06dOorKwEABw6dEg/wyo0NLTJtTIyMhAcHAxra2ssXrwYL7zwAmRZRmhoKD766CNMmTLFkC+FiCxQgLs9hnf3xa/HLmLFHxl4/7Fw0ZGIWlVlbT3W7c8CYF53bwBAkm80v9pMaTQauLi46KegExFdz6Gsy3jkP3tho1Rg99x74OV084/NiUzF2qRMzPs5DUHt7LFtzmAojXztm5a8f3MvKiKiG+gd6Ibega6o1erwZeMiaETmQKuT9YOLJw/sYPTlpqVYcIiIbuLKrfsvuPAfmZHfTxbgwqVKuNhZ49FI01/Y73+x4BAR3cTQbt7wd7PD5co6/HiIC/+Refj8j/MAgHFRgbC3Mf2F/f4XCw4R0U00LPzXsH3Dit3nufAfmbzU7FIcyLwMa6X5LOz3v1hwiIia4fE+/nBSWeFcUQV2nikSHYfotly5ezMy3A/ezuY5cJ4Fh4ioGZxsrTG2X8M+dssb3xyITFHO5Ur8drxhT8inB5rX1PC/YsEhImqmiQMaZprsPXcJx3PVouMQ3ZLVezKh1ckYENoO3fzMd6kUFhwiomZq72qHB3r6AgA+28W7OGR6yqrrsP5Aw4bT5raw3/9iwSEiaoGpdzW8Kfx6NA/ZJZWC0xC1zPrkbJTX1CPUyxF3d7r5vpKmjAWHiKgF7vBzwaBOHtDJf+7ATGQKaut1+j+zUwZ1gMLMFvb7Xyw4REQt9MzdHQEA6w9koaSiVnAaoub5OTUX+ZpqeDmpMKpXe9FxDI4Fh4iohfp3bIc7/JxRXafDF9y+gUyATidjWeO4sacGdoDKSik4keGx4BARtZAkSfh7412cNUmZ3L6BjN62U4U4W1gOJ5UV/hYVKDpOm2DBISK6Bfd394G/mx1KKmrxXUqO6DhEN/TZrnMAgL/dGQhnW2vBadoGCw4R0S2wUirw9MCG7RuW7zoPLbdvICOVcqEEBzIvw0apwFONW45YAhYcIqJb9HjfALjaWyOrpBIJjSvDEhmbpTsbxt483Ku92W7LcC0sOEREt8jexgpx0cEAgGW7zkGWeReHjEt6YRm2niiAJAFT7zbvhf3+FwsOEdFtmBAdBJWVAkdy1Nh3vkR0HKImrsycuq+rNzp6OgpO07ZYcIiIbkM7RxUe6+MP4M+BnETGIF9djQ2HcwEAzwzuKDhN22PBISK6TU8PDIFCAnacLsKpfI3oOEQAgFV7MlCnldEv2B29A91Ex2lzLDhERLcp2MMBw7r7AACW7eQmnCSeuqoO6/ZnAQCeGWxZY2+uYMEhImoFV7Zv+PkIN+Ek8dbtv4Dymnp08XbCPV28RMcRggWHiKgV9PR3xaBOHtDqZCzdybE4JE51nRar9mQCAKbeFQJJMu9NNa+HBYeIqJVMvycUAPDdwRwUaKoFpyFL9X1KDorKauDrYosHI/xExxGGBYeIqJVEdXBHnyA31Gp1WL6LY3Go7dVpdViyo+EO4t/vCoG10nLf5i33lRMRtTJJkjD93oa7OOv2Z6GkolZwIrI0P6fmIbe0Ch6ONhjbzzI21bweFhwiolY0uLMnurd3RlWdFqv2ZIiOQxZEq5Pxn+3pAIApg0Jga60UnEgsFhwiolYkSRKmD264i7N6byY01XWCE5Gl+O+xizhfXAFXe2uMuzNIdBzhWHCIiFpZ7B0+CPVyRFl1Pb5IuiA6DlkAnU7Gp9sa7t48NaADHFVWghOJx4JDRNTKFAoJzzYujb9ydwaqarWCE5G5+/1kAU4XlMFJZYUJ/YNFxzEKLDhERAbwYLgfAtztcKmiFl8nZ4mOQ2ZMlmV82jj2Jq5/EFzsrAUnMg4GLTglJSUYN24cnJ2d4erqismTJ6O8vPyGzxk8eDAkSWpyPPPMM03OycrKwogRI2Bvbw8vLy+8+OKLqK+vN+RLISJqESulQr+68bJd51FbrxOciMzVrrPFOJqjhp21Ek8N6CA6jtEwaMEZN24c0tLSsHXrVmzatAm7du3C1KlTb/q8KVOm4OLFi/rjvffe039Nq9VixIgRqK2txd69e7FmzRqsXr0a8+bNM+RLISJqsUcj/eHtrEK+pho/HsoRHYfMkCzLWJR4FgDwt6hAtHNUCU5kPAxWcE6ePImEhAR8/vnniIqKwsCBA7Fo0SKsX78eeXl5N3yuvb09fHx89Iezs7P+a1u2bMGJEyfw5ZdfIiIiAsOHD8cbb7yBxYsXo7aWa04QkfFQWSkxZVDDRodLdp5DvZZ3cah17c8owcELl2GjVGDqXZa5qeb1GKzgJCUlwdXVFX369NE/FhMTA4VCgf3799/wuevWrYOHhwe6d++O+Ph4VFb+uXFdUlISevToAW9vb/1jsbGx0Gg0SEtLu+b1ampqoNFomhxERG3hb1GBcHewwYVLldh09KLoOGRmrsyceryvP7ydbQWnMS4GKzj5+fnw8mq6g6mVlRXc3d2Rn59/3ef97W9/w5dffont27cjPj4eX3zxBcaPH9/kun8tNwD0v77edRcsWAAXFxf9ERAQcKsvi4ioRextrDB5YMO4iEXbzkKrkwUnInNxKOsydqcXw0oh4e93dRQdx+i0uODMnTv3qkHA/3ucOnXqlgNNnToVsbGx6NGjB8aNG4e1a9diw4YNOHfu1nfnjY+Ph1qt1h/Z2dm3fC0iopaKiw6Cq701zhVVYNPRG39ET9Rcixvv3jzcqz0C3O0FpzE+LV4JaM6cOZg4ceINzwkJCYGPjw8KCwubPF5fX4+SkhL4+Pg0+/tFRUUBANLT09GxY0f4+PggOTm5yTkFBQUAcN3rqlQqqFQceEVEYjjZWmPKoBC8v/k0Pkk8iwd6+kGpkETHIhN2LEeNxFOFUEjAtMG8e3MtLS44np6e8PT0vOl50dHRKC0tRUpKCiIjIwEA27Ztg06n05eW5khNTQUA+Pr66q/71ltvobCwUP8R2NatW+Hs7Ixu3bq18NUQEbWNCf2DsfyP8zhfVIFfjuRhVK/2oiORCfv372cAAKMi2iPE01FwGuNksDE4Xbt2xbBhwzBlyhQkJydjz549mDFjBsaOHQs/Pz8AQG5uLsLCwvR3ZM6dO4c33ngDKSkpyMzMxMaNGxEXF4e77roLPXv2BAAMHToU3bp1w5NPPokjR45g8+bNeOWVVzB9+nTepSEio+WostLPqFqYyLE4dOsOZ13GtlOFUCokzBzSSXQco2XQdXDWrVuHsLAwDBkyBPfffz8GDhyIZcuW6b9eV1eH06dP62dJ2djY4Pfff8fQoUMRFhaGOXPmYPTo0fjll1/0z1Eqldi0aROUSiWio6Mxfvx4xMXF4fXXXzfkSyEium0T+gfDzd4a54srsPFIrug4ZKL+/XvDujcP92qPDh4OgtMYL0mWZYv7Z4RGo4GLiwvUanWTNXaIiAztPzvS8V7CaYR4OGDLC3fBSskdc6j5Ui6UYPSSJCgVErbPGYzAdpY1uLgl79/8L4uIqA3FRf/1Lg5nVFHL/Htrw92bxyL9La7ctBQLDhFRG3JUWWFq45oli7alc3Vjarb95y/p172Zfk+o6DhGjwWHiKiNxUUHwd3BBhnFFfjxEMfiUPNcmTn1eN8ArnvTDCw4RERtzEFlhWcb1y75+PczqKnXCk5Exm7vuWLsO18CG6WCd2+aiQWHiEiA8XcGwdfFFnnqany1P0t0HDJisizjg82nAQBj+gagvaud4ESmgQWHiEgAW2slnmtcw2Tx9nRU1NQLTkTG6veThTiUVQpbawVm3su7N83FgkNEJMijkf4IbmeP4vJarN6bKToOGSGt7s+7N5MGdIAXdwxvNhYcIiJBrJUKvHBfZwDA0p3noK6sE5yIjM3GI7k4XVAGZ1srPMMdw1uEBYeISKCRPf0Q5uOEsup6fLbrnOg4ZERq63X4aGvDzKlnBneEi7214ESmhQWHiEgghULCnKFdAACr9mSisKxacCIyFusPZCG7pApeTipM6t9BdByTw4JDRCRYTFcvRAS4oqpOi0WJ6aLjkBGorK3HwsY/CzOHdIKdjVJwItPDgkNEJJgkSXh5WBgA4OvkLJwvKheciERbtScTxeU1CGpnj7F9A0THMUksOERERiC6YzvcG+aFep2M9xJOi45DAl2uqMXSnQ3jsWbf1xnW3JD1lvCnRkRkJOYOD4NCAhLS8nEws0R0HBLkk8SzKKuuR1dfZ4zs6Sc6jsliwSEiMhKdvZ3weJ+GjyPe/u9JyLIsOBG1tYziCny57wIA4JURXaFQSIITmS4WHCIiI/LCfZ1hZ63EoaxSJBzPFx2H2tg7v51EvU7GPV08MSDUQ3Qck8aCQ0RkRLydbTFlUMOU4HcTTqFOqxOciNpKckYJNqcVQCEB/7y/q+g4Jo8Fh4jIyEy9uyM8HG2QeamSG3FaCJ1Oxlu/ngAAjO0XiE7eToITmT4WHCIiI+OossLzMQ1bOHySeBaaam7hYO5+OZqHIzlqONgo8ULj7z3dHhYcIiIjNLZvADp6OqCkohaLEs+KjkMGVF2n1S8NMG1wR3g6qQQnMg8sOERERshaqcCrD3QD0LDo2zku/me2Vu3JRG5pFXycbTF5YIjoOGaDBYeIyEgN7uKlX/zvzU0nRMchAyjQVOPTbQ136F6M7cItGVoRCw4RkRF7ZURXWCslbD9dhO2nCkXHoVb2zm+nUFGrRa9AVzzcq73oOGaFBYeIyIiFeDpi0oCGaeNvbDqB2npOGzcXBzNLsOFwLiQJeG3kHVzUr5Wx4BARGbmZ94bCw9EG54srsDYpU3QcagVanYz5G9MAAI9HBiA8wFVsIDPEgkNEZOScbK3xUmzDbuOf/H4WxeU1ghPR7frmQDbS8jRwsrXCi8O6iI5jllhwiIhMwKOR/ujR3gVlNfV4L+GU6Dh0G9SVdXh/c8Pv4QsxneHhyGnhhsCCQ0RkAhQKCa89eAcA4NuDOTjA3cZN1kdbT+NyZR06ezviyegg0XHMFgsOEZGJiAxywxP9GnYb/78Nx7hPlQlKy1Pji8bdwl8beQeslXwbNhT+ZImITMjLw8Lg7mCDMwXlWLE7Q3QcagGtTkb8j8egk4ERPX3Rn7uFGxQLDhGRCXG1t9HvNP3J72eRc7lScCJqri+SMnE0Rw0nWyvMb1ylmgzHoAWnpKQE48aNg7OzM1xdXTF58mSUl19/ufHMzExIknTN47vvvtOfd62vr1+/3pAvhYjIaIzu3R5RHdxRVafFaxu5wrEpuKiuwvubG/abenlYGLycbQUnMn8GLTjjxo1DWloatm7dik2bNmHXrl2YOnXqdc8PCAjAxYsXmxz/+te/4OjoiOHDhzc5d9WqVU3OGzVqlCFfChGR0ZAkCW893B3WSgm/nyzAlrR80ZHoJl7bmIaKWi16B7rib/0CRcexCFaGuvDJkyeRkJCAAwcOoE+fPgCARYsW4f7778cHH3wAPz+/q56jVCrh4+PT5LENGzbg8ccfh6OjY5PHXV1drzqXiMhShHo5YcqgEPxnxzm8tjENA0I94KAy2F/pdBu2pOVjc1oBrBQSFjzSkysWtxGD3cFJSkqCq6urvtwAQExMDBQKBfbv39+sa6SkpCA1NRWTJ0++6mvTp0+Hh4cH+vXrh5UrV0KW5etep6amBhqNpslBRGTqZt7bCQHudshTV+Ndro1jlMpr6vUrFk+9KwRdfJwEJ7IcBis4+fn58PLyavKYlZUV3N3dkZ/fvNupK1asQNeuXdG/f/8mj7/++uv49ttvsXXrVowePRrPPvssFi1adN3rLFiwAC4uLvojICCg5S+IiMjI2Nko8c4jPQEAa5MuIOncJcGJ6H99sPk0LqqrEehuj+eGdBIdx6K0uODMnTv3ugOBrxynTt3+vySqqqrw1VdfXfPuzauvvooBAwagV69eePnll/HSSy/h/fffv+614uPjoVar9Ud2dvZt5yMiMgYDQj3wROOYjpd/OIrK2nrBieiKpHOXsHpvJgDgrYe7w9ZaKTaQhWnxB7Zz5szBxIkTb3hOSEgIfHx8UFhY2OTx+vp6lJSUNGvszPfff4/KykrExcXd9NyoqCi88cYbqKmpgUp19ZLXKpXqmo8TEZmDf94fhp2nC5FVUon3N5/G/JF3iI5k8Spq6vHi90cAAE/0C8SgTp6CE1meFhccT09PeHre/DcqOjoapaWlSElJQWRkJABg27Zt0Ol0iIqKuunzV6xYgQcffLBZ3ys1NRVubm4sMURkkZxsrbFgdE9MWJmM1XszcX8PX/QNdhcdy6K9/d+TyLlchfaudvi/EV1Fx7FIBhuD07VrVwwbNgxTpkxBcnIy9uzZgxkzZmDs2LH6GVS5ubkICwtDcnJyk+emp6dj165dePrpp6+67i+//ILPP/8cx48fR3p6OpYsWYK3334bM2fONNRLISIyend39sTjffwhy8BL3x9FVa1WdCSL9cfZIqzbnwUAeP/RnnDk7DYhDLoOzrp16xAWFoYhQ4bg/vvvx8CBA7Fs2TL91+vq6nD69GlUVjZdiXPlypXw9/fH0KFDr7qmtbU1Fi9ejOjoaEREROCzzz7DRx99hPnz5xvypRARGb3/G9EN3s4qZBRX4IMtp0XHsUia6jq8/P1RAEBcdBC3YxBIkm80v9pMaTQauLi4QK1Ww9nZWXQcIqJWs/1UISatPgAA+GJyP479aGMvfX8E3x7MQaC7PRJmDYK9De/etKaWvH9zLyoiIjNyT5gXnrwzCAAw+9sjuFReIziR5diSlo9vD+ZAkoAPHgtnuRGMBYeIyMz834iu6OTliKKyGrz8w9EbLoRKrSOvtAovNn409fTADujXgYO8RWPBISIyM7bWSix8ohdslAr8frIQX+67IDqSWavX6vD8+sNQV9Whp78LXowNEx2JwIJDRGSWuvo64+XhDW+0b/56EmcKygQnMl8Lt6XjQOZlOKqssOiJXrCx4lurMeDvAhGRmZrUPxh3d/ZETb0Oz319GNV1nDre2vadv4RPt50F0LBacVA7B8GJ6AoWHCIiM6VQSPjgsXC0c7DBqfwyzPv5OMfjtKLLFbWYtT4VOhl4NNIfD0W0Fx2J/oIFh4jIjHk6qfDJ2F5QSMC3B3Ow/gD34msNWp2M2d+mIl9TjRBPB/zrQW6PYWxYcIiIzNzATh6YM7QLAGD+z2lIzS4VG8gM/HvrGWw/XQSVlQKLnugFB65WbHRYcIiILMCzgztiaDdv1Gp1ePbLFK6Pcxv+e+wiPt2eDgB4d3RP3OHnIjgRXQsLDhGRBZAkCR88Ho4QDwfkqasx8+vDqNfqRMcyOafyNfjHdw27hD89sANG9eK4G2PFgkNEZCGcba2x9MlI2NsosffcJbybcEp0JJNSWlmLqWtTUFmrxYDQdpg7nOvdGDMWHCIiC9LZ2wnvPdoTALD8jwys289FAJujXqvDzK8PI6ukEv5udvj0id6wUvIt1Jjxd4eIyMI80NMPs2I6AQBe/ek4tp8qFJzIuMmyjFd/TsMfZ4thZ63Esif7wM3BRnQsugkWHCIiC/T8kE54NNIfOhmY/tUhHM9Vi45ktBYmpuPr5CxIEvDvMRHo5nfjXazJOLDgEBFZIEmS8PbDPTAgtB0qa7WYtPoAckurRMcyOuuTs/Dv388AAF5/qDuGdfcRnIiaiwWHiMhC2VgpsGR8JLp4O6GorAaTViVDXVknOpbR+P1EAf654RgAYMY9oXjyziDBiaglWHCIiCyYs601Vk7qCy8nFc4UlCNu5X5oqllyUi5cxoyvD0EnA49F+mPO0M6iI1ELseAQEVm49q52WDu5H9zsrXEkR40JK5NRZsEl53DWZUxclYzqOh3u6eKJtx/pAUmSRMeiFmLBISIihPk448uno+BiZ43DWaWYtOoAKmrqRcdqcykXSvDkimSUVdejX7A7Fo/rDWtOBzdJ/F0jIiIAwB1+Llj3dBScba1w8MJlTFp9AJW1llNyDmSWIG5FMspr6nFniDtWP9UX9jbcY8pUseAQEZFe9/Yu+GJyFJxUVkjOKMHEVQcsYkzOvvOXMGFlMioaVyleNbEfy42JY8EhIqImwgNcsWZyP33JGfvZPhSVme/mnNtPFWLSqgOorNViUCcPrJjQF3Y2StGx6Dax4BAR0VV6B7rh66l3wsPRBicuavDo0r04V1QuOlarW5+chafXHkRVnRZ3d/bE8rg+sLVmuTEHLDhERHRN3du74Ptn+iPA3Q4XLlXikf/sxd5zxaJjtQqtTsa7Cacw98dj0OpkPNK7PcuNmWHBISKi6wr2cMCGZwegV6Ar1FV1iFuRjLVJmZBlWXS0W6aprsOUtQexZMc5AA2L+H34WDhsrPiWaE74u0lERDfk4ajC11PuxMhwP9TrZMz7OQ2zvz1ikjOs0vLUGPXpHmw7VQiVlQIfj4nAP2K7cJ0bM8SCQ0REN2VrrcTCsRF4ZURXKBUSNhzOxchFu01mk05ZlvFFUiYe/s9enC+ugK+LLb57JhqjerUXHY0MRJJN+T7jLdJoNHBxcYFarYazM3eFJSJqiX3nL+G5rw+jsKwG1koJs2I6Y+pdIUa7IN5FdRVe/uEYdp0pAgAMCfPCB4+Fw83BRnAyaqmWvH+z4LDgEBG12OWKWsz98Sg2pxUAALr6OuPd0T3Q099VbLC/0OpkfLX/At7bfBpl1fWwsVLgpdgumDywAz+SMlEsODfBgkNEdPtkWcYPh3LxxqYTUFfVQZKAxyMDMCe2M7ycbIVmS84oweub0nA8VwOgYW2fDx8LR6iXo9BcdHtYcG6CBYeIqPUUl9fgjU0n8HNqHgDAwUaJiQOC8fTAkDb/GOh4rhofbjmN7acbPo5ysrXCi7FdMC4qCEoF79qYupa8fxvsA9O33noL/fv3h729PVxdXZv1HFmWMW/ePPj6+sLOzg4xMTE4e/Zsk3NKSkowbtw4ODs7w9XVFZMnT0Z5ufktPkVEZCo8HFX4ZGwv/DAtGuEBrqio1WLx9nMY+O42vP7LCWQUVxj0++t0MnadKcKTK/bjgUW7sf10EZQKCU/0C8S2OYMRFx3McmOBDHYHZ/78+XB1dUVOTg5WrFiB0tLSmz7n3XffxYIFC7BmzRp06NABr776Ko4dO4YTJ07A1rbhdufw4cNx8eJFfPbZZ6irq8OkSZPQt29ffPXVV83Oxjs4RESGodPJ2HqyAAsTzyItT6N/fGCoBx6M8MOw7j5wtrVule+VXliOX49exHcp2ci5XAUAUEjAAz39MCumE0I8+XGUuTGqj6hWr16NWbNm3bTgyLIMPz8/zJkzB//4xz8AAGq1Gt7e3li9ejXGjh2LkydPolu3bjhw4AD69OkDAEhISMD999+PnJwc+Pn5NSsTCw4RkWHJsoydZ4qwZm8mdpwpwpV3GmulhN6Bbrirsyd6Bbqie3uXZhUeWZaRp65GalYpDmSWYNfZIpwv+vPOkJPKCqMj/TF5YAcEuNsb6mWRYC15/zaarVIzMjKQn5+PmJgY/WMuLi6IiopCUlISxo4di6SkJLi6uurLDQDExMRAoVBg//79ePjhh6957ZqaGtTU/LlRnEajueZ5RETUOiRJwuAuXhjcxQtZlyqx8UgufkrNQ3phOfZnlGB/Ron+XG9nFQLc7OHlrIKTyhq21grU62TU1OtQUlGLwrJqZBRVoKJW2+R7WCslRHf0wKgIPwzv7ssNMqkJoyk4+fn5AABvb+8mj3t7e+u/lp+fDy8vryZft7Kygru7u/6ca1mwYAH+9a9/tXJiIiJqjsB29phxbyfMuLcTMosr8MfZIiSdv4SjOWrkXK5CgaYGBZqb71ZupZDQydsJ/YLd0K9DO9zV2QNOrfRxF5mfFhWcuXPn4t13373hOSdPnkRYWNhthWpt8fHxmD17tv7XGo0GAQEBAhMREVmmYA8HBHs44MnoYABAaWUtLlyqRFZJJUoqalFeU4/qOi2UCgnWSgXaOdjAw1GFoHb2CPZwMNrFBMn4tKjgzJkzBxMnTrzhOSEhIbcUxMfHBwBQUFAAX19f/eMFBQWIiIjQn1NYWNjkefX19SgpKdE//1pUKhVUKtUt5SIiIsNxtbeBq70NwgNcRUchM9OiguPp6QlPT0+DBOnQoQN8fHyQmJioLzQajQb79+/HtGnTAADR0dEoLS1FSkoKIiMjAQDbtm2DTqdDVFSUQXIRERGR6THYvb6srCykpqYiKysLWq0WqampSE1NbbJmTVhYGDZs2ACgYUDarFmz8Oabb2Ljxo04duwY4uLi4Ofnh1GjRgEAunbtimHDhmHKlClITk7Gnj17MGPGDIwdO7bZM6iIiIjI/BlskPG8efOwZs0a/a979eoFANi+fTsGDx4MADh9+jTU6j93on3ppZdQUVGBqVOnorS0FAMHDkRCQoJ+DRwAWLduHWbMmIEhQ4ZAoVBg9OjRWLhwoaFeBhEREZkgbtXAdXCIiIhMglFs1UBEREQkCgsOERERmR0WHCIiIjI7LDhERERkdlhwiIiIyOyw4BAREZHZYcEhIiIis8OCQ0RERGaHBYeIiIjMjsG2ajBmVxZv1mg0gpMQERFRc115327OJgwWWXDKysoAAAEBAYKTEBERUUuVlZXBxcXlhudY5F5UOp0OeXl5cHJygiRJrXptjUaDgIAAZGdnc5+r/8GfzY3x53Nj/PncGH8+18efzY2Z0s9HlmWUlZXBz88PCsWNR9lY5B0chUIBf39/g34PZ2dno/+DIgp/NjfGn8+N8edzY/z5XB9/NjdmKj+fm925uYKDjImIiMjssOAQERGR2WHBaWUqlQrz58+HSqUSHcXo8GdzY/z53Bh/PjfGn8/18WdzY+b687HIQcZERERk3ngHh4iIiMwOCw4RERGZHRYcIiIiMjssOERERGR2WHBa0eLFixEcHAxbW1tERUUhOTlZdCSjsWvXLowcORJ+fn6QJAk//fST6EhGY8GCBejbty+cnJzg5eWFUaNG4fTp06JjGY0lS5agZ8+e+kXIoqOj8dtvv4mOZZTeeecdSJKEWbNmiY5iFF577TVIktTkCAsLEx3LqOTm5mL8+PFo164d7Ozs0KNHDxw8eFB0rFbBgtNKvvnmG8yePRvz58/HoUOHEB4ejtjYWBQWFoqOZhQqKioQHh6OxYsXi45idHbu3Inp06dj37592Lp1K+rq6jB06FBUVFSIjmYU/P398c477yAlJQUHDx7Evffei4ceeghpaWmioxmVAwcO4LPPPkPPnj1FRzEqd9xxBy5evKg/du/eLTqS0bh8+TIGDBgAa2tr/Pbbbzhx4gQ+/PBDuLm5iY7WOmRqFf369ZOnT5+u/7VWq5X9/PzkBQsWCExlnADIGzZsEB3DaBUWFsoA5J07d4qOYrTc3Nzkzz//XHQMo1FWViZ36tRJ3rp1q3z33XfLzz//vOhIRmH+/PlyeHi46BhG6+WXX5YHDhwoOobB8A5OK6itrUVKSgpiYmL0jykUCsTExCApKUlgMjJFarUaAODu7i44ifHRarVYv349KioqEB0dLTqO0Zg+fTpGjBjR5O8ganD27Fn4+fkhJCQE48aNQ1ZWluhIRmPjxo3o06cPHnvsMXh5eaFXr15Yvny56FithgWnFRQXF0Or1cLb27vJ497e3sjPzxeUikyRTqfDrFmzMGDAAHTv3l10HKNx7NgxODo6QqVS4ZlnnsGGDRvQrVs30bGMwvr163Ho0CEsWLBAdBSjExUVhdWrVyMhIQFLlixBRkYGBg0ahLKyMtHRjML58+exZMkSdOrUCZs3b8a0adPw3HPPYc2aNaKjtQqL3E2cyFhNnz4dx48f5ziB/9GlSxekpqZCrVbj+++/x4QJE7Bz506LLznZ2dl4/vnnsXXrVtja2oqOY3SGDx+u//89e/ZEVFQUgoKC8O2332Ly5MkCkxkHnU6HPn364O233wYA9OrVC8ePH8fSpUsxYcIEweluH+/gtAIPDw8olUoUFBQ0ebygoAA+Pj6CUpGpmTFjBjZt2oTt27fD399fdByjYmNjg9DQUERGRmLBggUIDw/HJ598IjqWcCkpKSgsLETv3r1hZWUFKysr7Ny5EwsXLoSVlRW0Wq3oiEbF1dUVnTt3Rnp6uugoRsHX1/eqfyR07drVbD7GY8FpBTY2NoiMjERiYqL+MZ1Oh8TERI4ToJuSZRkzZszAhg0bsG3bNnTo0EF0JKOn0+lQU1MjOoZwQ4YMwbFjx5Camqo/+vTpg3HjxiE1NRVKpVJ0RKNSXl6Oc+fOwdfXV3QUozBgwICrlqQ4c+YMgoKCBCVqXfyIqpXMnj0bEyZMQJ8+fdCvXz98/PHHqKiowKRJk0RHMwrl5eVN/tWUkZGB1NRUuLu7IzAwUGAy8aZPn46vvvoKP//8M5ycnPTjtlxcXGBnZyc4nXjx8fEYPnw4AgMDUVZWhq+++go7duzA5s2bRUcTzsnJ6aqxWg4ODmjXrh3HcAH4xz/+gZEjRyIoKAh5eXmYP38+lEolnnjiCdHRjMILL7yA/v374+2338bjjz+O5ORkLFu2DMuWLRMdrXWInsZlThYtWiQHBgbKNjY2cr9+/eR9+/aJjmQ0tm/fLgO46pgwYYLoaMJd6+cCQF61apXoaEbhqaeekoOCgmQbGxvZ09NTHjJkiLxlyxbRsYwWp4n/acyYMbKvr69sY2Mjt2/fXh4zZoycnp4uOpZR+eWXX+Tu3bvLKpVKDgsLk5ctWyY6UquRZFmWBXUrIiIiIoPgGBwiIiIyOyw4REREZHZYcIiIiMjssOAQERGR2WHBISIiIrPDgkNERERmhwWHiIiIzA4LDhEREZkdFhwiIiIyOyw4REREZHZYcIiIiMjssOAQERGR2fl/dYvO+rD7BaEAAAAASUVORK5CYII=", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.linspace(0, 2*np.pi, num=200)\n", - "y = np.sin(x) \n", - "\n", - "plt.plot(x, y) \n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "10d13d03-03f5-44c4-a62b-b68bf8dcaaf9", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d81b654168ec4bc8", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Bar Charts\n", - "\n", - "Beliebt sind auch **Bar Charts** (Balkendiagramme). Bei dieser Art von Plot werden mehrere Parameter benötigt, da einfache mathematische Funktionen hier keinen direkten Sinn ergeben. \n", - "\n", - "Betrachten wir folgendes Beispiel: \n", - "\n", - "Wir wollen herausfinden, wie viele Kinder an einer Grundschule in jeder Klassenstufe sind. \n", - "\n", - "Dazu benötigen wir zwei Listen: \n", - "1. Die **Klassenstufen** \n", - "2. Die **Anzahl der Kinder** in jeder Klassenstufe \n", - "\n", - "Mit diesen Daten kann anschließend ein übersichtliches Balkendiagramm erstellt werden, das die Verteilung der Kinder über die Klassenstufen visualisiert.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "06b54439-c88c-438a-9870-896280151491", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3767e976a92e292a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "classes = [\"1. Klasse\", \"2. Klasse\", \"3. Klasse\", \"4. Klasse\"]\n", - "kids = [42, 30, 26, 45]" - ] - }, - { - "cell_type": "markdown", - "id": "55022635-12f7-4666-991b-e802ce67c9a5", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a702a6b994c5809e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Plotten wir die Werte nun als Bar Chart mit der Funktion `plt.bar`:" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "d73faeb6-2b83-495f-b246-2e4b4c6ff784", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8c604c68ae96c752", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.bar(classes, kids)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "a641132d-cf1a-4e92-bdc3-55f5425b9388", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c2444cb0f1af6626", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Bessere Datenrepräsentation\n", - "\n", - "Da die Daten aus `classes` und `kids` miteinander verknüpft sind, ist die Verwendung eines **Dictionaries** die sinnvollere Wahl, um Fehler beim Plotten zu vermeiden. \n", - "\n", - "Mit den Methoden `.keys()` und `.values()` des Dictionaries lassen sich die Daten gezielt extrahieren und anschließend plotten. \n", - "\n", - "Betrachten wir dazu folgendes Beispiel:" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "d21c9c59-2b84-4e87-a2f2-1388070b2419", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1777b8fcd5bd30c4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Definiere ein Dictionary welches die Anzahl der Schüler ihrer Klasse zuweist \n", - "school = { \n", - " \"1. Klasse\": 42,\n", - " \"2. Klasse\": 30,\n", - " \"3. Klasse\": 26,\n", - " \"4. Klasse\": 45,\n", - "}\n", - "\n", - "plt.bar(school.keys(), school.values()) # Plotte mit den Werten des Dictionarys\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "a921963b-94df-4c6a-81f7-c3a3fb09b7c0", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d2c659803a58f15e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Styling\n", - "\n", - "Auch **Bar Plots** können gestylt werden, um die Darstellung anschaulicher zu machen. \n", - "\n", - "Hierzu wird dem Parameter `color` eine Liste mit Farbwerten übergeben, sodass jeder Balken individuell eingefärbt werden kann. Dies erleichtert die Unterscheidung der Kategorien und macht den Plot visuell ansprechender." - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "00e9f1c6-5ccd-4ea3-bcec-b2265602917f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a3604899d50585d2", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "school = { \n", - " \"1. Klasse\": 42,\n", - " \"2. Klasse\": 30,\n", - " \"3. Klasse\": 26,\n", - " \"4. Klasse\": 45,\n", - "}\n", - "\n", - "bar_colors = [\"red\", \"blue\", \"green\", \"yellow\"] # Farben definieren\n", - "\n", - "plt.bar(school.keys(), school.values(), color=bar_colors) # Farben übergeben\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "a74afec9-34fd-4cfb-bcd3-db6be64ef35f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-964f579ce46a2882", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Y-Label\n", - "\n", - "Die Beschriftung der y-Achse erfolgt mit `plt.ylabel`. \n", - "\n", - "Hierbei wird der Achsentitel als String übergeben, z. B. `plt.ylabel(\"Anzahl Kinder\")`, um die dargestellten Werte klar und verständlich zu kennzeichnen." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "e9f4c9a3-e39c-4eb8-95fd-77a3b40fa249", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-21cee3bf50f011e1", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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zfv16vf766xo5cmQg5gEAAPCZ43NkevbsqYSEhEDMAgAA4IjjkHnwwQc1Z84cffHFF4GYBwAAwGc+vbR0xRVXeJ0Ls3//fiUnJ+t73/ueunXr5nXfbdu2+XdCAACAs/ApZPLz8wM8BgAAgHM+hczcuXMDPQcAAIBjXBAPAABYy/Hbr1taWvTYY4/p5Zdf1sGDB3Xq1Cmv/ceOHfPbcAAAAB1x/IzMvHnztHDhQt10002qr69XUVGRJk6cqLCwMP3ud78LwIgAAADtcxwyL7zwgpYsWaJ7771XERERuuWWW/Tcc89pzpw52rJlSyBmBAAAaJfjkKmtrdXll18uSYqNjVV9fb0kady4cXr99df9Ox0AAEAHHIdMWlqaDh8+LEn6/ve/r7/+9a+SpKqqKkVFRfl3OgAAgA44DpkbbrhBZWVlkqTZs2frt7/9rTIyMjRlyhRNnz7d7wMCAACcjeN3LZWWlnr++6abblLfvn31zjvvKCMjQ+PHj/frcAAAAB1xHDLfNmLECI0YMcIfswAAADji+KWl8PBwZWdnt7leTF1dncLDwx0dq6SkRMOGDVNcXJySkpKUn5+vvXv3et3n5MmTKiwsVGJiomJjY1VQUKC6ujqnYwMAgC7IccgYY9Tc3KyhQ4fqww8/bLPPifLychUWFmrLli3auHGjTp8+rWuvvVZNTU2e+9xzzz167bXXtHr1apWXl+vQoUOaOHGi07EBAEAX5DIO6yM8PFyfffaZSktLtXTpUq1YsUITJkxQXV2dUlNT1dLS0ulhjh49qqSkJJWXl+vHP/6x6uvr1atXL61cuVKTJk2SJO3Zs0eXXXaZKisrfXpJq6GhQW63W/X19YqPj+/0bO36xieC4zxzGM1OsbTBE+CllWseixssZm4gF5d1DZ7ArKuvf3936hmZ8PBwPf7443rkkUd00003af78+Y6fjWnP19ekSUhIkCRVV1fr9OnTysvL89ynf//+Sk9PV2VlZbvHaG5uVkNDg9cNAAB0Ted0su/MmTOVkZGhG2+8UZs3bz6nQVpbW3X33Xdr5MiRGjhwoKSvLr4XGRmpHj16eN03OTlZtbW17R6npKRE8+bNO6dZAACAHRw/I9O3b1+vk3qzs7O1ZcsW1dTUnNMghYWF2rVrl1atWnVOxykuLlZ9fb3ndq5zAQCA0OX4GZkDBw602XbxxRfr/fff7/S7iWbNmqW//OUv2rx5s9LS0jzbU1JSdOrUKR0/ftzrWZm6ujqlpKS0e6yoqCiuMAwAwL+ITr+0dOrUKR05ckStra2ebS6HZ0gaYzR79mytXbtWmzZtUr9+/bz2Z2Zmqlu3biorK1NBQYEkae/evTp48KCysrI6OzoAAOgiHIfMxx9/rBkzZuidd97x2m6MkcvlcvSupcLCQq1cuVKvvvqq4uLiPOe9uN1ude/eXW63WzNmzFBRUZESEhIUHx+v2bNnKysri4vwAQAA5yFz2223KSIiQn/5y1/Uu3dvx8/CfNPTTz8tSfr3f/93r+1Lly7VtGnTJEmPPfaYwsLCVFBQoObmZo0ePVpPPfVUp39PAADQdTi+jkxMTIyqq6vVv3//QM3kV1xHpoviOjJdFteR6bq4jkxXZdl1ZAYMGKB//vOf5zQcAACAPzgOmYcffli//vWvtWnTJn3++edcfA4AAASN43Nkvr7Kbm5urtf2zpzsCwAAcC4ch8xbb70ViDkAAAAccxwyo0aNOuu+Xbt2ndMwAAAATjg+R+bbGhsb9eyzz2r48OEaPHiwP2YCAADwSadDZvPmzZo6dap69+6tRx55RDk5OdqyZYs/ZwMAAOiQo5eWamtrtWzZMj3//PNqaGjQT37yEzU3N2vdunUaMGBAoGYEAABol8/PyIwfP16XXnqpduzYoUWLFunQoUP64x//GMjZAAAAOuTzMzLr16/XnXfeqTvuuEMZGRmBnAkAAMAnPj8jU1FRocbGRmVmZurKK6/U4sWLucIvAAAIKp9DZsSIEVqyZIkOHz6s22+/XatWrVJqaqpaW1u1ceNGNTY2BnJOAACANhy/aykmJkbTp09XRUWFdu7cqXvvvVelpaVKSkrS9ddfH4gZAQAA2nVO15G59NJL9fvf/16fffaZXnzxRX/NBAAA4JNzviCeJIWHhys/P1//8z//44/DAQAA+MQvIQMAABAMhAwAALAWIQMAAKxFyAAAAGsRMgAAwFqEDAAAsBYhAwAArEXIAAAAaxEyAADAWoQMAACwFiEDAACsRcgAAABrETIAAMBahAwAALAWIQMAAKxFyAAAAGsRMgAAwFqEDAAAsBYhAwAArEXIAAAAaxEyAADAWoQMAACwFiEDAACsRcgAAABrETIAAMBahAwAALAWIQMAAKxFyAAAAGsRMgAAwFqEDAAAsBYhAwAArEXIAAAAaxEyAADAWoQMAACwFiEDAACsRcgAAABrETIAAMBahAwAALAWIQMAAKxFyAAAAGsRMgAAwFqEDAAAsBYhAwAArEXIAAAAaxEyAADAWoQMAACwFiEDAACsRcgAAABrETIAAMBahAwAALAWIQMAAKxFyAAAAGsFNWQ2b96s8ePHKzU1VS6XS+vWrfPab4zRnDlz1Lt3b3Xv3l15eXnat29fcIYFAAAhJ6gh09TUpMGDB+vJJ59sd//vf/97PfHEE/rTn/6kd999VzExMRo9erROnjx5nicFAAChKCKYv/mYMWM0ZsyYdvcZY7Ro0SL95je/0YQJEyRJy5cvV3JystatW6ebb775fI4KAABCUMieI3PgwAHV1tYqLy/Ps83tduvKK69UZWXlWR/X3NyshoYGrxsAAOiaQjZkamtrJUnJycle25OTkz372lNSUiK32+259enTJ6BzAgCA4AnZkOms4uJi1dfXe241NTXBHgkAAARIyIZMSkqKJKmurs5re11dnWdfe6KiohQfH+91AwAAXVPIhky/fv2UkpKisrIyz7aGhga9++67ysrKCuJkAAAgVAT1XUsnTpzQ/v37Pb8+cOCAtm/froSEBKWnp+vuu+/W/PnzlZGRoX79+um3v/2tUlNTlZ+fH7yhAQBAyAhqyGzdulXZ2dmeXxcVFUmSpk6dqmXLlunXv/61mpqaNHPmTB0/flxXX3213nzzTUVHRwdrZAAAEEJcxhgT7CECqaGhQW63W/X19f4/X8bl8u/x4LsA/2/L0gZPoH8iueaxuMFi5gZycVnX4AnMuvr693fIniMDAADwXQgZAABgLUIGAABYi5ABAADWImQAAIC1CBkAAGAtQgYAAFiLkAEAANYiZAAAgLUIGQAAYC1CBgAAWIuQAQAA1iJkAACAtQgZAABgLUIGAABYi5ABAADWImQAAIC1CBkAAGAtQgYAAFiLkAEAANYiZAAAgLUIGQAAYC1CBgAAWIuQAQAA1iJkAACAtQgZAABgLUIGAABYi5ABAADWImQAAIC1CBkAAGAtQgYAAFiLkAEAANYiZAAAgLUIGQAAYC1CBgAAWIuQAQAA1iJkAACAtQgZAABgLUIGAABYi5ABAADWImQAAIC1CBkAAGAtQgYAAFiLkAEAANYiZAAAgLUIGQAAYC1CBgAAWIuQAQAA1iJkAACAtQgZAABgLUIGAABYi5ABAADWImQAAIC1CBkAAGAtQgYAAFiLkAEAANYiZAAAgLUIGQAAYC1CBgAAWIuQAQAA1iJkAACAtQgZAABgLUIGAABYi5ABAADWImQAAIC1CBkAAGAtQgYAAFjLipB58skn9b3vfU/R0dG68sor9d577wV7JAAAEAJCPmReeuklFRUVae7cudq2bZsGDx6s0aNH68iRI8EeDQAABFnIh8zChQv185//XLfddpsGDBigP/3pT7rgggv05z//OdijAQCAIIsI9gAdOXXqlKqrq1VcXOzZFhYWpry8PFVWVrb7mObmZjU3N3t+XV9fL0lqaGgI7LA4v1jPLivgS3sywMfHWfFzuKsKzLp+/f+LMabD+4V0yPzzn/9US0uLkpOTvbYnJydrz5497T6mpKRE8+bNa7O9T58+AZkRQeJ2B3sCBAhL23W5S1ncrimw69rY2Ch3Bz8YQjpkOqO4uFhFRUWeX7e2turYsWNKTEyUy+UK4mShpaGhQX369FFNTY3i4+ODPQ78iLXtmljXrou1bZ8xRo2NjUpNTe3wfiEdMhdeeKHCw8NVV1fntb2urk4pKSntPiYqKkpRUVFe23r06BGoEa0XHx/PH5wuirXtmljXrou1baujZ2K+FtIn+0ZGRiozM1NlZWWeba2trSorK1NWVlYQJwMAAKEgpJ+RkaSioiJNnTpVQ4cO1fDhw7Vo0SI1NTXptttuC/ZoAAAgyEI+ZG666SYdPXpUc+bMUW1trX74wx/qzTffbHMCMJyJiorS3Llz27wMB/uxtl0T69p1sbbnxmW+631NAAAAISqkz5EBAADoCCEDAACsRcgAAABrETL/Qj799FO5XC5t37492KPAzzZt2iSXy6Xjx48HexT4EevadbG2/kPIhIDNmzdr/PjxSk1Nlcvl0rp16zp1nG8/9vTp07rlllt00UUXadeuXf4ZFo6UlJRo2LBhiouLU1JSkvLz87V3715Hx2gvQBsbG5Wdna0BAwbos88+8/PU+C5PP/20Bg0a5LmAWVZWltavX+/oGKxr6CstLZXL5dLdd9/t6HGs7flFyISApqYmDR48WE8++aTfjvnFF1/o+uuvV1VVlSoqKjRw4EC/HRu+Ky8vV2FhobZs2aKNGzfq9OnTuvbaa9XU1NTpYx49elTZ2dlqamrS22+/rbS0ND9ODF+kpaWptLRU1dXV2rp1q3JycjRhwgR9+OGHnT4m6xpaqqqq9Mwzz2jQoEHnfCzWNrAImRAwZswYzZ8/XzfccINfjnf8+HFdc801OnTokCoqKtSvX79279fS0qIZM2aoX79+6t69uy699FI9/vjjXvfZtGmThg8frpiYGPXo0UMjR47U3//+d0nSBx98oOzsbMXFxSk+Pl6ZmZnaunWr57EVFRX60Y9+pO7du6tPnz668847z+kvcBu9+eabmjZtmn7wgx9o8ODBWrZsmQ4ePKjq6upOHa+mpkY/+tGP5Ha79be//U2JiYnt3u/zzz/3PBt3wQUX6PLLL9eLL77odZ81a9bo8ssvV/fu3ZWYmKi8vDzP+nS07pL06quvasiQIYqOjta//du/ad68eTpz5kynviYbjR8/Xtddd50yMjJ0ySWXaMGCBYqNjdWWLVs6dTzWNbScOHFCkydP1pIlS9SzZ89zOhZrG3iETBdTW1urUaNGSfrq2YCzfSaV9NXHPaSlpWn16tXavXu35syZo//6r//Syy+/LEk6c+aM8vPzNWrUKO3YsUOVlZWaOXOm58M3J0+erLS0NFVVVam6ulr333+/unXrJkn63//9X/3Hf/yHCgoKtGPHDr300kuqqKjQrFmzAvwdCG319fWSpISEBMeP3bt3r0aOHKkBAwbojTfeUGxs7Fnve/LkSWVmZur111/Xrl27NHPmTN1666167733JEmHDx/WLbfcounTp+ujjz7Spk2bNHHiRBljvnPd3377bU2ZMkV33XWXdu/erWeeeUbLli3TggULOvEdsV9LS4tWrVqlpqamTn10CusaegoLCzV27Fjl5eWd03FY2/PEIKRIMmvXru30YyMjI03//v1NU1NTm/0HDhwwksz7779/1mMUFhaagoICY4wxn3/+uZFkNm3a1O594+LizLJly9rdN2PGDDNz5kyvbW+//bYJCwszX375pY9fUdfS0tJixo4da0aOHOnocV+vW2RkpMnOzjZnzpxpc5+33nrLSDL/93//d9bjjB071tx7773GGGOqq6uNJPPpp5+2ud93rXtubq556KGHvLatWLHC9O7d28FXZb8dO3aYmJgYEx4ebtxut3n99dcdPZ51DU0vvviiGThwoOfn1KhRo8xdd93l6Bis7flFyISYcw2ZiRMnmrCwMLNw4cI2+9sLmcWLF5shQ4aYCy+80MTExJhu3bqZYcOGefZPmzbNREVFmXHjxplFixaZQ4cOefbNnTvXREREmNzcXFNSUmL279/v2Td06FATGRlpYmJiPLcLLrjASDK7d+/u1Ndnu1/84hemb9++pqamxtHjvl63SZMmmYiICPPyyy+3uc+3fyieOXPGPPDAA2bgwIGmZ8+eJiYmxkRERJgbb7zRsz83N9fExcWZSZMmmWeffdYcO3bMc7yO1v3CCy800dHRXmsbHR1tJLUb0F1Vc3Oz2bdvn9m6dau5//77zYUXXmg+/PBDnx/PuoaegwcPmqSkJPPBBx94tp1LyLC25wchE2LONWTWrl1rli1bZsLCwsyjjz7qtf/bIfPiiy+a6Oho8+STT5pt27aZffv2mZkzZ5rBgwd7PW7btm3moYceMllZWSY2NtZUVlZ69u3du9csXLjQXHPNNSYyMtK88sorxhhj+vfvb2bPnm327dvX5tbc3Nypr89mhYWFJi0tzXzyySeOH/vNdXvggQdMRESEeemll7zu8+0fiiUlJSYxMdGsWLHCbN++3ezbt8+MHTvWTJgwwfOY1tZWU1FRYebMmWMuv/xy06tXL6/5zrbu0dHR5uGHH253bVtaWpx/c7qI3NzcNs9CdoR1DT1r1641kkx4eLjnJsm4XC4THh7e7jMr7WFtzy9CJsT4I2SMMWb58uUmPDzc/OEPf/Ds/3bIzJo1y+Tk5HgdIzc3t03IfNOIESPM7Nmz29138803m/HjxxtjjPnpT39qcnNzO/V1dCWtra2msLDQpKammo8//rhTx/j2us2fP99ERESYVatWee7z7R+K48aNM9OnT/fsb2lpMRkZGV4/FL/pzJkz5qKLLmoTv1/75rpfddVVXsfGV7Kzs83UqVN9vj/rGnoaGhrMzp07vW5Dhw41//mf/2l27tzp83FY2/Mr5D/9+l/BiRMntH//fs+vDxw4oO3btyshIUHp6emSpOLiYv3jH//Q8uXLfTrmrbfeqrCwME2dOlXGGP3qV79qc5+MjAwtX75cGzZsUL9+/bRixQpVVVV53uV04MABPfvss7r++uuVmpqqvXv3at++fZoyZYq+/PJL/epXv9KkSZPUr18/ffbZZ6qqqlJBQYEk6b777tOIESM0a9Ys/exnP1NMTIx2796tjRs3avHixef6LbNGYWGhVq5cqVdffVVxcXGqra2VJLndbnXv3l2SNGXKFF100UUqKSnx6Zj//d//rfDwcE2ePFmtra265ZZb2twnIyNDa9as0TvvvKOePXtq4cKFqqur04ABAyRJ7777rsrKynTttdcqKSlJ7777ro4eParLLrusw3WXpDlz5mjcuHFKT0/XpEmTFBYWpg8++EC7du3S/Pnz/fFtC3nFxcUaM2aM0tPT1djYqJUrV2rTpk3asGGD5z6sq33i4uLaXKoiJiZGiYmJXttZ2xAT7JLC/y/zb9+++a+7qVOnmlGjRnV4HLXzbM7KlStNeHi4KS0tbfOvhJMnT5pp06YZt9ttevToYe644w5z//33e56Rqa2tNfn5+aZ3794mMjLS9O3b18yZM8e0tLSY5uZmc/PNN5s+ffqYyMhIk5qaambNmuV1Iu97771nrrnmGhMbG2tiYmLMoEGDzIIFC/zwHbNHe+sqySxdutRzn1GjRnX4L/mznaT98MMPm/DwcPPCCy+0+dfd559/biZMmGBiY2NNUlKS+c1vfmOmTJni+dfd7t27zejRo02vXr1MVFSUueSSS8wf//hHY0zH6/61N99801x11VWme/fuJj4+3gwfPtw8++yz/viWWWH69Ommb9++JjIy0vTq1cvk5uaav/71r173YV27hvbOkWFtQ4vLGGPOczsBAAD4BdeRAQAA1iJkAACAtQgZAABgLUIGAABYi5ABAADWImQAAIC1CBkAAGAtQgYAAFiLkAEAANYiZAAAgLUIGQAAYC1CBgAAWOv/AVLrMfQ/TEAjAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "school = { \n", - " \"1. Klasse\": 42,\n", - " \"2. Klasse\": 30,\n", - " \"3. Klasse\": 26,\n", - " \"4. Klasse\": 45,\n", - "}\n", - "\n", - "bar_colors = [\"red\", \"blue\", \"green\", \"yellow\"]\n", - "\n", - "plt.bar(school.keys(), school.values(), color=bar_colors)\n", - "\n", - "plt.ylabel(\"Anzahl Kinder\") # Beschriften der Y-Achse\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "9d90f3b3-adc9-498e-9dbe-13e0c577b886", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-32dabb34444f6190", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### X-Label\n", - "\n", - "Analog dazu die Beschriftung der X-Achse mit `plt.xlabel`." - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "2644cc38-238e-4cec-b951-3ff88b80011e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3737280b071f9d91", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "school = { \n", - " \"1. Klasse\": 42,\n", - " \"2. Klasse\": 30,\n", - " \"3. Klasse\": 26,\n", - " \"4. Klasse\": 45,\n", - "}\n", - "\n", - "bar_colors = [\"red\", \"blue\", \"green\", \"yellow\"]\n", - "\n", - "plt.bar(school.keys(), school.values(), color=bar_colors)\n", - "\n", - "plt.ylabel(\"Anzahl Kinder\") # Beschriften der Y-Achse\n", - "plt.xlabel(\"Klassenstufen\") # Beschriften der X-Achse\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "16e05c9d-80ef-47b8-a17f-df7a96398ab5", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-230328a26793cddb", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Aufgabe\n", - "\n", - "*5 Punkte*\n", - "\n", - "Dir liegt ein Datenset `sec_school` einer Hauptschule vor, das die Klassenstufen von 5 bis 9 auf die Anzahl ihrer Schüler im jeweiligen Jahrgang abbildet. \n", - "\n", - "Erstelle einen **Barplot** und gehe dabei wie folgt vor: \n", - "1. Wähle ein passendes **Farbschema**, um die einzelnen Klassenstufen anschaulich darzustellen. \n", - "2. Extrahiere die **Schlüssel** (Klassenstufen) und **Werte** (Anzahl der Schüler) aus dem Datenset und übergebe diese zusammen mit den Farbwerten an `plt.bar()`. \n", - "3. Setze geeignete Beschriftungen für die **X- und Y-Achse**, um die Darstellung verständlich zu machen. \n", - "4. Vergiss nicht, einen passenden **Titel** für den Plot zu setzen. \n", - "5. Gib den Plot mit `plt.show()` aus, um die Werte zu visualisieren." - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "0608b1ea-6275-47c7-82a8-fed86e5e5973", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b954e989a8bbc2fa", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "sec_school = {\n", - " '5. Klasse': 29,\n", - " '6. Klasse': 35,\n", - " '7. Klasse': 25,\n", - " '8. Klasse': 28,\n", - " '9. Klasse': 31\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "ae63d6f9-cd8c-4603-9a15-7ee336d905e9", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-8caba57a6ad34b87", - "locked": false, - "points": 5, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "colors = [\"red\", \"blue\", \"green\", \"yellow\", \"pink\"]\n", - "plt.bar(sec_school.keys(), sec_school.values(), color=colors)\n", - "plt.xlabel(\"Klassenstufe\")\n", - "plt.ylabel(\"Anzahl Kinder\")\n", - "plt.title(\"Verteilung Kinder einer Hauptschule pro Klassenstufe\")\n", - "plt.show()\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "bf9e917c-6949-4bb1-8184-270747e8cb4e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-42bf44a09515d0fd", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Horizontales Bar Chart\n", - "\n", - "Analog zum klassischen Bar Chart erzeugt `plt.barh` ein **horizontales Balkendiagramm**. \n", - "\n", - "Dieses Format eignet sich besonders, wenn die Kategorienamen länger sind oder die horizontale Darstellung die Lesbarkeit verbessert. \n", - "\n", - "Beispiel:" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "146615f7-1315-41a3-a690-73c98ee465e3", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-54166820b406e29e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "school = { \n", - " \"1. Klasse\": 42,\n", - " \"2. Klasse\": 30,\n", - " \"3. Klasse\": 26,\n", - " \"4. Klasse\": 45,\n", - "}\n", - "\n", - "plt.barh(list(school.keys()), list(school.values()), color=\"maroon\") # barh statt bar\n", - "\n", - "plt.xlabel(\"Anzahl Kinder\") \n", - "plt.ylabel(\"Klassenstufen\") \n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "df9117c3-0ca8-4e0d-8779-9b8826044a3a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3adde3f53176bcb0", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Pie Chart\n", - "\n", - "Kommen wir als Nächstes zu einer beliebten und anschaulichen Darstellung von Daten: dem **Kuchendiagramm** (Pie Chart).\n", - "\n", - "![](https://flowingdata.com/wp-content/uploads/2014/12/Pie-Pyramid-620x311.png)\n", - "\n", - "Kuchendiagramme lassen sich in Matplotlib mit `plt.pie` erstellen. \n", - "\n", - "Wir nutzen dazu wieder das Beispiel aus dem vorherigen Kapitel. Für ein Pie Chart werden in erster Linie nur die **Werte** benötigt, z. B. `school.values` aus dem Datenset." - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "bb311d1b-978d-41f8-9648-874b4a862bc5", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-46cde6d166912ad0", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "school = { \n", - " \"1. Klasse\": 42,\n", - " \"2. Klasse\": 30,\n", - " \"3. Klasse\": 26,\n", - " \"4. Klasse\": 45,\n", - "}\n", - "\n", - "plt.pie(school.values()) # Pie Chart\n", - "\n", - "plt.title(\"Klassenverteilung einer Grundschule\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "76e19a82-7aa7-416d-beae-667b77497cf3", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-aec08dc408437049", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Styling\n", - "\n", - "Alle üblichen Parameter wie `title`, `color`, `xlabel`, `ylabel` usw. lassen sich auch bei einem Pie Chart setzen. \n", - "\n", - "Die **Beschriftung der einzelnen Stücke** funktioniert jedoch etwas anders. \n", - "\n", - "Dazu wird der Parameter `labels` mit den entsprechenden Kategorienamen versehen, sodass jedes Segment des Kuchendiagramms eindeutig gekennzeichnet ist." - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "09124a3e-1f76-4f32-8638-19e7efc234b0", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c742155fd484b71b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "school = { \n", - " \"1. Klasse\": 42,\n", - " \"2. Klasse\": 30,\n", - " \"3. Klasse\": 26,\n", - " \"4. Klasse\": 45,\n", - "}\n", - "\n", - "plt.pie(school.values(), labels=school.keys()) # Setzen der Labels\n", - "\n", - "plt.title(\"Klassenverteilung einer Grundschule\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "d3e68330-6319-4a87-acda-91abd4f98900", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5336af155ef45527", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Zum Anzeigen von Prozentwerten im Pie Chart wird der Parameter `autopct` verwendet. \n", - "\n", - "Dieser akzeptiert entweder einen **Format-String** oder eine Funktion, mit der die Prozentwerte der einzelnen Segmente definiert werden können. \n", - "\n", - "Weitere Informationen findest du in der [Matplotlib-Dokumentation zu autopct](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.pie.html#matplotlib-pyplot-pie) sowie in einer Übersicht zu [Format-Strings](https://www.geeksforgeeks.org/format-specifiers-in-c/). \n", - "\n", - "Beispiel für die Darstellung von Prozentwerten:" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "974a71d0-552c-45d4-bb01-88edd6be608c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-183044afa87a0492", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "school = { \n", - " \"1. Klasse\": 42,\n", - " \"2. Klasse\": 30,\n", - " \"3. Klasse\": 26,\n", - " \"4. Klasse\": 45,\n", - "}\n", - "\n", - "plt.pie(school.values(), labels=school.keys(), autopct='%1.1f%%') # Setzen von Prozentwerten\n", - "\n", - "plt.title(\"Klassenverteilung einer Grundschule\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "a0a13fc0-3ae1-4ab5-a488-8c5247cf74c5", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-430a11a7f58f4fa5", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Pie Charts besitzen außerdem den Parameter `shadow`. \n", - "\n", - "Standardmäßig ist dieser auf `False` gesetzt. Wird der Wert auf `True` geändert, erscheint das Pie Chart mit einem **Schatten**, was die Darstellung plastischer wirken lässt." - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "62971d68-8cdd-48c4-8f60-209bffe64e84", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-408e63a1464d64ea", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "school = { \n", - " \"1. Klasse\": 42,\n", - " \"2. Klasse\": 30,\n", - " \"3. Klasse\": 26,\n", - " \"4. Klasse\": 45,\n", - "}\n", - "\n", - "plt.pie(school.values(), labels=school.keys(), autopct='%1.1f%%', shadow=True) # Zeige einen Schatten\n", - "\n", - "plt.title(\"Klassenverteilung einer Grundschule\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "59e8be26-c070-4205-8260-f4597074ec6f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-376fc818f2a3d818", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Zum Herausheben einzelner Kuchenstücke gibt es den Parameter `explode`. \n", - "\n", - "Dieser erwartet eine Liste mit Fließkommazahlen zwischen `0.0` (keine Verschiebung) und `1.0` (maximale Verschiebung), die angibt, wie stark jedes Segment vom Mittelpunkt des Pie Charts herausgezogen wird. \n", - "\n", - "Beispiel: Das Segment der Klasse 3 wird um 20 % vom Ursprung entfernt:" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "bf1ea228-1547-4c1d-975e-d7f49111486c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-e3921561732c3895", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "school = { \n", - " \"1. Klasse\": 42,\n", - " \"2. Klasse\": 30,\n", - " \"3. Klasse\": 26,\n", - " \"4. Klasse\": 45,\n", - "}\n", - "\n", - "plt.pie(school.values(), labels=school.keys(), autopct='%1.1f%%', explode=[0, 0, 0.2, 0]) # Zeige einen Schatten\n", - "\n", - "plt.title(\"Klassenverteilung einer Grundschule\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "cbe9aba9-529d-4d4c-87fa-ff46e3a24e5b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ed3d080835960776", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe\n", - "\n", - "*5 Punkte*\n", - "\n", - "Dir liegt ein Datenset `sec_school` einer Hauptschule vor, das die Klassenstufen von 5 bis 9 auf die Anzahl der Schüler im jeweiligen Jahrgang abbildet. \n", - "\n", - "Erstelle einen **Pieplot** und gehe dabei wie folgt vor: \n", - "1. Wähle ein passendes **Farbschema**, um die einzelnen Klassenstufen anschaulich darzustellen. \n", - "2. Extrahiere die **Schlüssel** (Klassenstufen) und **Werte** (Anzahl der Schüler) aus dem Datenset und übergebe diese zusammen mit den Farbwerten an `plt.pie()`. Nutze `autopct='%1.1f%%'`, um die Prozentwerte der Segmente anzuzeigen. \n", - "3. Lasse die 6. Klasse um 25 % und die 9. Klasse um 40 % aus dem Kuchen hervortreten, indem du den Parameter `explode` entsprechend setzt. \n", - "4. Setze einen geeigneten **Titel** für den Plot. \n", - "5. Gib den Plot mit `plt.show()` aus, um die Werte zu visualisieren." - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "id": "5a2e4c2b-051d-4e9d-ae93-0e6fe4b25003", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-bf48088c515caf5c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "sec_school = {\n", - " '5. Klasse': 29,\n", - " '6. Klasse': 35,\n", - " '7. Klasse': 25,\n", - " '8. Klasse': 28,\n", - " '9. Klasse': 31\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "id": "fdebbc40-17eb-48e5-89fa-f8d12ec65688", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-d201bd3e919fcf1c", - "locked": false, - "points": 5, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "plt.pie(\n", - " sec_school.values(),\n", - " labels=sec_school.keys(),\n", - " autopct='%1.1f%%',\n", - " explode=[0, 0.25, 0, 0, 0.4]\n", - ")\n", - "\n", - "plt.title(\"Klassenverteilung einer Grundschule\")\n", - "\n", - "plt.show()\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "71899ae9-85bb-49ef-a077-1d5caaa4913f", - "metadata": {}, - "source": [ - "# Bonus\n", - "\n", - "Programmatisch lässt sich das Meme, wie folgt, generieren:" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "c3883c67-06ab-4713-ad96-5706522a7bc3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib.patches import Patch\n", - "\n", - "colors = ['#0095D9','#F7EC59','#CBBE4A'] # Color Picker helps\n", - "\n", - "plt.pie(\n", - " [0.8, 0.15, 0.05], # Try and Error ratios\n", - " colors=colors,\n", - " startangle=-55\n", - ")\n", - "\n", - "plt.legend(\n", - " labels=[\"Sky\", \"Sunny side of pyramid\", \"Shady side of pyramid\"],\n", - " # Set right color and size for Patches\n", - " handles=[Patch(facecolor=color) for color in colors],\n", - " handlelength=1,\n", - " handleheight=1,\n", - " # Move Legend to the right\n", - " loc=\"center left\",\n", - " bbox_to_anchor=(1, 0.5),\n", - " fontsize=16,\n", - " # Disable background and edge\n", - " facecolor='none',\n", - " edgecolor='none'\n", - ")\n", - "\n", - "plt.tight_layout() # Layout shouldnt Overflow\n", - "\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +version https://git-lfs.github.com/spec/v1 +oid sha256:2ad3c79a233a8b881a98e5167591a22f90aaac0e4d82ae42965a24c2a785d7ff +size 739884 diff --git a/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/5.SciPy.ipynb b/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/5.SciPy.ipynb index 9e5f094..eaea1ef 100644 --- a/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/5.SciPy.ipynb +++ b/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/5.SciPy.ipynb @@ -1,1233 +1,3 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fd2f3bf7-b314-4449-95dc-f07d1a13f1c6", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-04300aa5f61f7f12", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# 5. Programmierübung: SciPy\n", - "\n", - "
\n", - "
\n", - " Willkommen zur fünften Programmierübung Einführung in Python 3.\n", - "
\n", - " \n", - "
\n", - "\n", - "Wenn Sie Fragen oder Verbesserungsvorschläge zum Inhalt oder Struktur der Notebooks haben, dann können sie eine E-Mail an Phil Keier ([p.keier@hbk-bs.de](mailto:p.keier@hbk-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) oder Martin Le ([martin.le@tu-bs.de](mailto:martin.le@tu-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) schreiben.\n", - "\n", - "Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n", - "\n", - "Der Großteil des Python-Tutorials stammt aus der Veranstaltung _Deep Learning Lab_ und von [www.python-kurs.eu](https://www.python-kurs.eu/python3_kurs.php) und wurde für _Signale und Systeme_, sowie _Einführung in die Programmierung für Nicht Informatiker_ angepasst.\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "bb677cea-65c2-486d-9442-6495b5063bd2", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-110034393ecbe731", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Was ist SciPy\n", - "\n", - "SciPy steht für Scientific Python und ist eine Open-Source-Bibliothek, die auf der bewährten Architektur von NumPy aufbaut. Sie bietet eine Vielzahl von Funktionen, die speziell für ingenieurtechnische und wissenschaftliche Anwendungen entwickelt wurden. In diesem Zusammenhang möchten wir uns insbesondere mit Teilen des Statistikmoduls von SciPy vertraut machen.\n", - "\n", - "__Für dieses Notebook schauen Sie bitte in die [SciPy Docs](https://docs.scipy.org/doc/scipy/tutorial/index.html)!!!__ Dort sind alle Funktionen beschrieben die wir hier bearbeiten und noch mehr!\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "3dde4289-bc10-49fc-875d-ebc44fbf807a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-82f61f58224c5db9", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "SciPys wird meist als `sp` importiert da für diese Aufgabe nur das Statistik modul nötig ist wird einfach dieses importiert. Aufgrund des kurzen schlüssigen namens findet keine umbenennung statt:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "e058b67a-ab34-4685-b023-084bfa80f171", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-fe3ab9d39498717f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "from scipy import stats" - ] - }, - { - "cell_type": "markdown", - "id": "57ada7f0-5eea-45cc-b8d0-85f52c7f6b55", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-eec41b75718f0e4e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "id": "5983dc91-91f3-4b6b-b6c2-9873b9ea8db9", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-65e8f06a2b373c8c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Lineare Regression\n", - "\n", - "## Mathematische Definition\n", - "\n", - "Ausgehend von dem stochastischen Zusammenhang: $$Y = f(X) + \\epsilon$$\n", - "Hierfür gilt: \n", - "- $Y$ ist die abhängige Variable, auch `Outcome`\n", - "- $X$ die unabhängige Variable, auch `Covariant`\n", - "- $\\epsilon$ der Fehler, auch `random Noise`\n", - "\n", - "Für $\\epsilon$ wird angenommen, dass der erwartete Durschnitt gleich 0 ist $E(\\epsilon)=0$. Das bedeutet, dass die Verteilung des Fehlers zum erwarteten Wert bei genügend Datenpunkten sich gegenseitig aufheben: $$\\lim_{\\epsilon\\rightarrow\\infty} f(x_\\epsilon) = 0;\\quad x \\in X$$\n", - "Auch wird angenommen, dass die Varianz der Fehler $\\epsilon$ konstant ist. $$Var(\\epsilon) = \\sigma^2$$\n", - "## Motivation\n", - "\n", - "Die **lineare Regression** ist eine grundlegende Methode zur Modellierung von Beziehungen zwischen Variablen. Sie hilft, Zusammenhänge zu verstehen und ermöglicht Vorhersagen auf Basis vorhandener Daten. Durch die Bestimmung einer linearen Beziehung zwischen einer abhängigen und einer oder mehreren unabhängigen Variablen können wir Trends identifizieren und zukünftige Werte schätzen. Ihre Einfachheit, Effizienz und die Möglichkeit, auch bei großen Datensätzen präzise Ergebnisse zu erzielen, machen sie zu einem wertvollen Werkzeug in der Datenanalyse und ein Fundament für komplexere Modelle.\n", - "\n", - "Dafür geht man davon aus, dass es zwei unabhängige Variablen $x_1$ & $x_2$ gibt, für die eine Zukünftige Aussage getätigt werden soll. Hierfür wird eine fehlerminimierte Gerade $g(x)$ zwischen die Datenpunkte gelegt. Daraus resultierenden lässt sich mit $g(x)$ eine zukünftige Vorhersage in einem bestimmten Fehlerbereich tätigen.\n", - "\n", - "Aus der Schule sollte die Geraden Gleichung $g(x) = m\\cdot x+b$ bekannt sein. Dabei beschreibt $m$ die Steigung (Slope) & $b$ den Schnittpunkt mit der y-Achse (interception). Die **lineare regression** bietet daher die Möglichkeit diese beiden Parameter herauszufinden.\n", - "\n", - "Für das folgende Beispiel werden zwei (pseudo-)zufällig erzeugte Datensets generiert, auf welche dann die lineare regression angewandt wird. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "60712a1d-7a55-44f4-89e9-cae5ceab952a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-fbb9bfd35036d55e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "rand = np.random.default_rng(42) # RNG with fixed seed\n", - "\n", - "# Generate, Scale, round and sort random numbers for an increasing slope\n", - "scalar: int = 10\n", - "x: np.array = np.round(np.sort(rand.random(20) * scalar), decimals=2)\n", - "y: np.array = np.round(np.sort(rand.random(20) * scalar), decimals=2)\n", - "\n", - "# Plot these values\n", - "plt.title(\"Scattered Random Values\")\n", - "plt.grid()\n", - "plt.xlabel(\"X\")\n", - "plt.ylabel(\"Y\")\n", - "plt.scatter(x,y, color='g')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "083ba4ea-8e6f-4286-8049-1c8e3f22c004", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-caa85fe6a76214bf", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Aus den erzeugten Daten ist klar ersichtlich, dass diese einem Trend folgen. Mittels SciPy wollen wir diesen Trend darstellen.\n", - "\n", - "Dazu wird die Funktion `linregress` verwendet. Diese verlangt, wie aus der [Dokumentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html#scipy.stats.linregress) zu entnehmen, zwei arrays mit Daten. \n", - "\n", - "Die Ausgabe ist aufgespalten in 5 Parameter:\n", - "- slope: die Steigung `m` der Geraden\n", - "- intercept: der Punkt an dem die Gerade die y-Achse trifft oder das `b`\n", - "- rvalue: der Pearson Korrelations Koefficient, welcher voerst ignoriert wird\n", - "- pvalue: der p-Wert das die Nullhypothese stimmt, wird auch ignoriert\n", - "- stderr: der Standard Error der Steigung, unter der Annahme einer Normalverteilung der Daten *(PCGs sind Normalverteilt)*" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "9c1959d8-5bac-45fa-9aa2-47fa79943d72", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-553a583b633273d5", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Slope: 0.00011705479307062861\n", - "Intercept: 0.12499266634828204\n", - "Std Err: 0.002148109946128207\n" - ] - } - ], - "source": [ - "slope, intercept, _, _, stderr = stats.linregress(x,y)\n", - "print(f\"Slope: {slope}\\nIntercept: {intercept}\\nStd Err: {stderr}\")" - ] - }, - { - "cell_type": "markdown", - "id": "2c2bd060-bb4c-440f-9a04-72382c9a33eb", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-111845c39238eeee", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Aus `slope` & `intercept` lässt sich folglich eine Gerade definieren:" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "1e30868c-f11f-43bd-a60b-200ec00e903f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-49d79f2b8dcf7315", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "def line(x: float) -> float:\n", - " '''\n", - " Evaluates the rounded line from the previous\n", - " evaluated linear regression model\n", - "\n", - " Note: Output rounded to 2 decimal places\n", - " '''\n", - " res: float = slope * x + intercept\n", - " rounded: np.float64 = np.round(res, decimals=2)\n", - " return float(rounded)" - ] - }, - { - "cell_type": "markdown", - "id": "8fdd9724-5c61-471d-a121-787f2d56f132", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a63e440ba0b57186", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Diese kann über den gesamten bereich dargestellt werden. Dazu werden die bereits bekannten x-Werte verwendet:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "0a59d146-4ae3-429a-a67e-7c158d5ebac7", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5239bfcad5788ae3", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Calculate the Line using vectorization\n", - "regline: np.array = np.vectorize(line)(x)\n", - "\n", - "# Plot tvalues\n", - "plt.title(\"Scattered Random Values\")\n", - "plt.grid()\n", - "\n", - "plt.xlabel(\"X\")\n", - "plt.ylabel(\"Y\")\n", - "\n", - "plt.scatter(x,y, color='g', label=\"Original Data\")\n", - "plt.plot(x, regline, color='m', label=\"Fitted line\")\n", - "\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "802bf60a-e402-4912-96bf-baa6cd398c6e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-e0da661ffad2086e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Mit diesem Model lässt sich dementsprechend die \"Zukunft\" vorhersagen. Hierfür können wir im folgenden einfach die Werte für `-1` & `11` berechnen und diese dem Plot hinzufügen:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "e62f6248-5813-4df0-9715-a72d3a5467f8", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-92a5bbb041e28a56", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Future Values\n", - "fut: np.array = np.array([line(-1), line(11)])\n", - "\n", - "# Calculate extended Line using vectorization\n", - "ext_range: np.array = np.arange(-1,12)\n", - "regline: np.array = np.vectorize(line)(ext_range)\n", - "\n", - "# Plot values\n", - "plt.title(\"Scattered Random Values\")\n", - "plt.grid()\n", - "\n", - "plt.xlabel(\"X\")\n", - "plt.ylabel(\"Y\")\n", - "\n", - "plt.scatter(x,y, color='g', label=\"Original Data\")\n", - "plt.plot(ext_range, regline, color='m', label=\"Fitted Line\")\n", - "plt.scatter((-1, 11), fut, color='r', label=\"Predicted Data\")\n", - "\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "04f002a5-4339-4225-9515-848513347697", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-6c8576c776d11325", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Wie zu erwarten liegen beide Werte auf der Geraden.\n", - "\n", - "Um deren Werte zu ermitteln lassen sich diese mittels print einfach ausgeben, dies sollte immer mit Angabe des Standard Errors erfolgen. Sonst ist unklar wie genau die Daten sind:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "563d452b-350c-4628-a97d-5d9ce268f4c4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7677ec5dda3e8e13", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Prediction of f(11) = 10.16, with standard deviation 0.07\n" - ] - } - ], - "source": [ - "print(\"Prediction of f(11) = {}, with standard deviation {}\".format(line(11), np.round(stderr, decimals=2)))" - ] - }, - { - "cell_type": "markdown", - "id": "5e81b1a9-4155-4bfd-8613-2530dbe61f03", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-facd8e8ef9f4aed1", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Aufgabe\n", - "\n", - "*7 Punkte*\n", - "\n", - "Bestimme mittels Linearer Regression die *best fit* Funktion für die beiden gegebenen Datensets `x_data` & `y_data`, unter beachtung folgender Punkte:\n", - "\n", - "- Plotte das Ergebnis angemessen\n", - "- Nutze SciPys `linregress` Funktion, speichere den Output vor dem entpacken in der Variablen `l`\n", - "- Definiere die Funktion `reg_line` mit einem Eingabeparameter\n", - "- Bestimme die Werte für `-0.3` & `3.4` speichere diese als liste in variablen `future`\n", - "\n", - "Markdown Zeile (0 Punkte bei nicht beantworten):\n", - "- Erkläre die berechneten Werte `slope`, `intercept` & `stderr` in eigenen Worten und setze diese in den Kontext stochastischer Analytik.\n", - "- Interpretiere den resultierenden Plot nutze hierfür gerne Reale Beispiele." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "f5b801bc-450e-417b-aeea-9b69d638fb64", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-cb5b277089c75c40", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "random = np.random.default_rng(420)\n", - "\n", - "# two scuffed up One-Liners :)\n", - "x_data: np.array = np.sort(np.round(random.random(40)*np.pi, decimals=2))\n", - "y_data: np.array = np.flip(np.sort(np.round(random.random(40)*np.sqrt(2), decimals=2)))" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "2645541f-c30f-487e-be65-3ec5ac22d427", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-12b5b631856ffe41", - "locked": false, - "points": 1, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "slope, intercept, _, _, stderr = stats.linregress(x_data,y_data)\n", - "\n", - "def reg_line(x: float) -> float:\n", - " return float(np.round(slope*x+intercept,decimals=2))\n", - "\n", - "ext: tuple = (-0.3, 3.4)\n", - "rl: np.array = np.vectorize(reg_line)(ext)\n", - "\n", - "future: list = [reg_line(x) for x in ext]\n", - "# Plot values\n", - "plt.title(\"Scattered Random Values\")\n", - "plt.grid()\n", - "\n", - "plt.xlabel(\"X\")\n", - "plt.ylabel(\"Y\")\n", - "\n", - "plt.scatter(x_data,y_data, color='g', label=\"Original Data\")\n", - "plt.plot(ext, rl, color='m', label=\"Fitted Line\")\n", - "plt.scatter(ext, future, color='r', label=\"Predicted Data\")\n", - "\n", - "plt.legend()\n", - "\n", - "plt.show()\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "de8c0d91-1da1-49ed-9987-7fd2f23db0c8", - "metadata": {}, - "source": [ - "# BEGIN SOLUTION\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "aa34ed84-f200-4863-be0b-40ff5492c654", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-8f20c3645d0a9b46", - "locked": true, - "points": 6, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Hier werden ihre Lösungen getestet\n", - "\n", - "# Check if reg_line is defined\n", - "assert 'reg_line' in dir()\n", - "\n", - "# Check if reg_line gives right output\n", - "assert reg_line(10) == -3.04\n", - "\n", - "# Check if future values are calculated right\n", - "assert future == [1.51, -0.12]\n", - "\n", - "### BEGIN HIDDEN TESTS\n", - "sl, ip, _, _, se = stats.linregress(x_data,y_data)\n", - "slope, intercept, _, _, stderr = l\n", - "\n", - "assert slope == sl\n", - "assert intercept == ip\n", - "assert stderr == se\n", - "\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "cbc252e7-a49b-4a30-ae51-ce3a95a3f932", - "metadata": {}, - "source": [ - "### Aufgabe\n", - "\n", - "*15 Punkte*\n", - "\n", - "Gegeben ist die Geburtenzahl insgesamt der Stadt Braunschweig `geburten_bs`. (Quelle: [Landesamt für Statistik Niedersachsen (LSN)](https://www.braunschweig.de/politik_verwaltung/statistik/jahrbuch/jahrbuch/02_38_export.pdf))\n", - "\n", - "Bestimme mittels Linearer Regression die Geburtenzahl für das Jahr 2030. Nutze dir die dafür gegebenen Mittel. Achte darauf auch eine angemessene Visualisierung zu finden.\n", - "\n", - "Bestimme auch die Geburtenrate für das Jahr 2028 mittels:\n", - "$$\\text{Birth Rate} = \\frac{B}{P} * 1000$$\n", - "\n", - "mit:\n", - "- $B$ = Zahl der Lebengeborenen\n", - "- $P$ = Bevölkerung zur Jahresmitte\n", - "\n", - "Nutze für $P$ den aktuellen Wert aus dem Jahr 2025 $254.867$. (Quelle: [G 2.01 Entwicklung der Einwohnerzahl seit 1988](https://www.braunschweig.de/politik_verwaltung/statistik/jahrbuch/jahrbuch/02_01_export_Grafik.pdf))\n", - "\n", - "Erkläre & Interpretiere in der Markdownzeile (Keine Antwort 0 Punkte) deine Berechnung, übe auch Kritik an der verwendeten Methodik aus. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "78db2b92-00ea-43a2-a4cc-ffc6125cfd64", - "metadata": {}, - "outputs": [], - "source": [ - "geburten_bs = {\n", - " 2000: 2091,\n", - " 2001: 1989,\n", - " 2002: 2016,\n", - " 2003: 1946,\n", - " 2004: 1987,\n", - " 2005: 1994,\n", - " 2006: 1986,\n", - " 2007: 2126,\n", - " 2008: 2048,\n", - " 2009: 2042,\n", - " 2010: 2193,\n", - " 2011: 2154,\n", - " 2012: 2208,\n", - " 2013: 2199,\n", - " 2014: 2300,\n", - " 2015: 2410,\n", - " 2016: 2567,\n", - " 2017: 2371,\n", - " 2018: 2474,\n", - " 2019: 2434,\n", - " 2020: 2282,\n", - " 2021: 2471,\n", - " 2022: 2328,\n", - " 2023: 2066,\n", - " 2024: 2041,\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "702ab8d8-1bff-485e-9b3a-2bc56480d199", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2481.41\n", - "9.608580161417525\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "years = list(geburten_bs.keys())\n", - "births = list(geburten_bs.values())\n", - "slope, intercept, _, _, stderr = stats.linregress(years, births)\n", - "\n", - "def reg_line(x: float) -> float:\n", - " return float(np.round(slope*x+intercept, decimals=2))\n", - "\n", - "births_2028 = reg_line(2028)\n", - "births_2030 = reg_line(2030)\n", - "\n", - "p_2025 = 254867\n", - "\n", - "birth_rate_2028 = births_2028 / p_2025 * 1000\n", - "\n", - "print(births_2030)\n", - "print(birth_rate_2028)\n", - "\n", - "# Plot\n", - "xs = np.linspace(min(years), 2030, 31)\n", - "ys = np.array(\n", - " [reg_line(x) for x in range(2000,2031)]\n", - ")\n", - "\n", - "plt.bar(\n", - " years, births,\n", - " label=\"Geburten pro Jahre\",\n", - " color=\"g\"\n", - ")\n", - "\n", - "plt.plot(\n", - " xs, ys,\n", - " label=\"Geschätze Geburten pro Jahr\",\n", - " color=\"r\"\n", - ")\n", - "\n", - "plt.title(\"Geburten Braunschweig\")\n", - "plt.xlabel(\"Jahr\")\n", - "plt.ylabel(\"Geburtenzahl\")\n", - "plt.legend()\n", - "\n", - "plt.show()\n", - "\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "5ce09493-4265-4b43-a615-d377863144b0", - "metadata": {}, - "source": [ - "# BEGIN SOLUTION\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "8369e788-5862-488b-a003-d3cd31ebaa99", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-59872a1e95590378", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "id": "02656b99-d250-4a21-bc2d-46007806d81d", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-455ed6a04d0d701f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Verteilungen\n", - "\n", - "## Probability Density Function - Normal Verteilung\n", - "\n", - "### Motivation\n", - "\n", - "Es wurden Daten über die Körperlänge eines Bienenvolkes erhoben, dementsprechend liegen diskrete Werte über die deren Körperlängen vor. Logischerweise repräsentiert dies nur das erhobene Bienenvolk, dieses arbeitet aber kontinuierlich weiter und erzeugt nachkommen. Die Population verändert sich. \n", - "\n", - "Daher ist anzunehmen, dass auch Werte zwischen den bereits Erhobenen auftreten können. Um dies zu Modellieren wird eine Normalverteilung benötigt.\n", - "\n", - "Schauen wir uns dazu erst die Gaussche Normalverteilung an, diese hat ihren Mittelpunkt bei $x=0$, bezeichnet als Erwartungswert $\\mu$, und eine Standardabweichung $\\sigma = 1$. Mathematisch ist sie definiert als $$p(x|\\mu,\\sigma)=\\frac{1}{\\sqrt(2\\pi\\sigma^2)}e^{-\\frac{(x-\\mu)^2}{2\\sigma^2}}$$\n", - "\n", - "SciPy bietet hierfür das `norm` objekt aus dem `stats` Modul an, dieses verlangt die beiden Parameter $\\mu$ & $\\sigma$, das auf dem `norm` Objekt kann dann die Funktion `pdf` mit einem Paramter als Stepsize oder einem Array aufgerufen werden. Nach Plotten ergibt sich folglich die Normalverteilung (von -4 bis 4):" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "2803dcac-bb3b-4aff-a3c9-c0085148c376", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-54174b5f7d8309db", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.arange(-4, 4, 0.01) # Plot between -4 and 4 with stepsize 0.01\n", - "y = stats.norm(0,1).pdf(x) # Calculate pdf with mu=0, sigma=1\n", - "\n", - "# Plot\n", - "plt.title(\"Gaussche Normalverteilung\")\n", - "plt.plot(x, y)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "ca2f768f-16ff-47ff-b898-fe0b8cf3511f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-259b8d6cb9d76981", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Um herauszufinden wie viel Prozent einer Population innerhalb dieser Normalverteilung fallen wird die Funktion `ppf` (Percent Point Function) verwendet.\n", - "\n", - "Am Beispiel 90% der Population:" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "0e5e7709-90a2-4a30-b49f-cbb243aa4e4b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5a9ed9588ce2ea27", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "90% of the Population fall into the range 1.282.\n" - ] - } - ], - "source": [ - "percentile = stats.norm(0,1).ppf(0.9)\n", - "print(f\"90% of the Population fall into the range {percentile:0.3f}.\")" - ] - }, - { - "cell_type": "markdown", - "id": "96afa424-d697-4c60-aa9b-4655491f4f13", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5102a53fda328278", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Um dies zu veranschaulichen:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "4f5d7f54-856f-4e12-8251-d0cdc5c0f002", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5d84b05b2808a672", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(9,6))\n", - "ax.plot(x,y, color='r')\n", - "\n", - "# filling under the curve\n", - "x_fill = np.arange(-4, percentile, 0.01)\n", - "y_fill = stats.norm(0,1).pdf(x_fill)\n", - "ax.fill_between(x_fill, y_fill, 0, color='g')\n", - "plt.title(\"Filled Percentile under Normal Curve\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "1f7a26f6-36d8-4561-ad68-db6c19c7720e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-c469fe12a69725b1", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Machen wir dies alles am Beispiel der Fuchsrote Lockensandbiene fest. Diese ist laut [Bundesministerium für Ernährung und Landwirtschaft](https://www.bmel.de/DE/themen/landwirtschaft/artenvielfalt/bienen-fuettern/wildbienen-honigbienen-und-co.html) 12-14 mm groß. Daher lässt sich annehmen das die meisten Bienen $\\mu=13mm$, mit einer Standardabweichung von $\\sigma=1mm$ haben. Wir wollen nun wissen wie groß 95% der Bienen sind, rechnen wir dies mittels `ppf` aus:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "6d57bdf0-8e75-4760-8159-d42effd9ea62", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-360e11812d2e9932", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mu = 13 # Mean\n", - "sigma = 1 # std deviation in mm\n", - "\n", - "x_norm = np.linspace(10, 16, 100) # Normaldistribution range from 10-16mm \n", - "y_norm = stats.norm(mu, sigma).pdf(x_norm) # Calculate normal\n", - "\n", - "# Height of 95th percentile of bees\n", - "percentile = stats.norm(mu,sigma).ppf(0.95)\n", - "\n", - "x_percentile = np.arange(x_norm[0], percentile, 0.01)\n", - "y_percentile = stats.norm(mu,sigma).pdf(x_percentile)\n", - "\n", - "# Plot\n", - "fig, ax = plt.subplots(figsize=(9,6))\n", - "ax.plot(x_norm, y_norm, color='r')\n", - "\n", - "# filling under the curve\n", - "ax.fill_between(x_percentile, y_percentile, 0, alpha=0.2, color='#FE0000')\n", - "\n", - "\n", - "# Set text\n", - "ax.text(0.5,0.25,\n", - " f\"95th percentile of bees fall under {percentile:.2f}mm\",\n", - " ha='center', va='center', transform=ax.transAxes,\n", - " bbox={'facecolor':'#fafafa','alpha':1,'edgecolor':'none','pad':1},\n", - " color='#de2e0b'\n", - " )\n", - "\n", - "# Show\n", - "plt.title(\"Filled Percentile under Normal Curve\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "e9c59eb8-bfb1-46f2-bf1f-af1ffe59e190", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7718d285f36c2fad", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Aufgabe \n", - "\n", - "*20 Punkte*\n", - "\n", - "Gegeben sind die nach Altersgruppe aufgeschlüsselten Durschnittskörpergrößen (in cm) von Frauen in Deutschland. (Zu finden beim [Statistischen Bundesamt](https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Gesundheit/Gesundheitszustand-Relevantes-Verhalten/Tabellen/koerpermasse-frauen.html))\n", - "\n", - "Gehe wie folgt vor *(8 Punkte)*:\n", - "\n", - "- Berechne das arithmetische Mittel nutze dafür NumPy. und speichere das Ergebnis mit einer Genauigkeit von 1 Dezimalstelle nach dem Komma in der Variablen `avg_height`.\n", - "- Gegeben ist auch die Standardabweichung von 15cm, stelle die Normalverteilung mittels `norm.pdf` auf. Speichere den Wert in `norm_height` und finde einen geeigneten linespace zum plotten.\n", - "- Berechne folgend die Körpergröße unter die 80% aller Frauen (nach Datenset) fallen. Speichere den Wert in der Variablen `avg_percentile`.\n", - "- Plotte das Ergebnis. Orientiere dich gerne an dem Bienenbeispiel. Finde eine geeignete Darstellung. *Tipp: Da die Y-Achse in diesem Beispiel keinen Sinn ergibt kannst du sie einfach austellen mit `plt.yticks([])`*\n", - "\n", - "Markdown Zeile (0 Punkte bei nicht beantworten) *(12 Punkte)*:\n", - "- Beschreibe die gegebenen Daten, nutze dazu die verlinkte und weitere Quellen. (Quellenangaben nicht vergessen)\n", - "- Beschreibe den resultierenden Plot und setze diesen in Kontext zum Datenset.\n", - "- Stelle die Annahmen der Aufgabe in Frage und setze diese in Kontext.\n", - "- Beurteile die Aussage aus resultierender Berechnung: \"Frauen sind im durchschnitt ungeeignet Basketball zu spielen!\"" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "3983d224-e885-43bd-aadd-4e276a56a370", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-048d8330d555432a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Given\n", - "avg_height_per_woman = {\n", - " \"18 - 20\": 167.6,\n", - " \"20 - 25\": 167.7,\n", - " \"25 - 30\": 167.3,\n", - " \"30 - 35\": 167.2,\n", - " \"35 - 40\": 167.3,\n", - " \"40 - 45\": 167.5,\n", - " \"45 - 50\": 167.1,\n", - " \"50 - 55\": 167.1,\n", - " \"55 - 60\": 166.9,\n", - " \"60 - 65\": 165.4,\n", - " \"65 - 70\": 164.5,\n", - " \"70 - 75\": 163.9,\n", - " \"75+\": 162.8\n", - "}\n", - "\n", - "avg_height = None\n", - "norm_height = None\n", - "avg_percentile = None" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "2c2e2cf6-c743-4cb0-a329-2240205fc7da", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-a59f2b3230aad3d6", - "locked": false, - "points": 3, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "avg_height = np.round(np.mean(list(avg_height_per_woman.values())), decimals=1)\n", - "\n", - "\n", - "std_sigma = 15\n", - "norm_height = stats.norm(avg_height, std_sigma).pdf(norm_x)\n", - "norm_x = np.linspace(120, 220, 1000)\n", - "# Height of 80th percentile of woman heights\n", - "avg_percentile = stats.norm(avg_height, std_sigma).ppf(0.8)\n", - "\n", - "x_percentile = np.arange(norm_x[0], avg_percentile, 0.01)\n", - "y_percentile = stats.norm(avg_height, std_sigma).pdf(x_percentile)\n", - "\n", - "# Plot\n", - "fig, ax = plt.subplots(figsize=(9,6))\n", - "ax.plot(norm_x, norm_height, color='r')\n", - "\n", - "# filling under the curve\n", - "ax.fill_between(x_percentile, y_percentile, 0, alpha=.5, color='#fa0000')\n", - "\n", - "\n", - "# Set text\n", - "ax.text(0.4,0.18,\n", - " f\"80th percentile of Womens heigth\\n fall under {avg_percentile:.1f}cm\",\n", - " ha='center', va='center', transform=ax.transAxes,\n", - " bbox={'facecolor':'#fafafa','alpha':1,'edgecolor':'none','pad':1},\n", - " color='#de2e0b'\n", - " )\n", - "\n", - "# Show\n", - "plt.title(\"Woman Height Normal Distribution\")\n", - "plt.xlabel(\"Height\")\n", - "plt.yticks([]) # hide y\n", - "plt.show()\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "9ed860df-a2c4-4d99-a020-2312212419dc", - "metadata": {}, - "source": [ - "# BEGIN SOLUTION\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "d9f8aeae-6b51-4645-b578-19693a07ece3", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-26f141bc8b12d7fe", - "locked": true, - "points": 3, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Hier werden ihre Lösungen getestet...\n", - "import math \n", - "\n", - "# Check if average height is close to real value\n", - "assert math.isclose(avg_height, 166.3, rel_tol=.2) # 1 Punkt\n", - "\n", - "# Check if norm height is close to real value\n", - "assert math.isclose(np.round(np.sum(norm_height), decimals=1), 10, rel_tol=.2) # 1 Punkt\n", - "\n", - "# Check if percentile is close to real value\n", - "assert math.isclose(avg_percentile, 179, rel_tol=.2) # 1 Punkt" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +version https://git-lfs.github.com/spec/v1 +oid sha256:c6e6f7fb8efce4ed055939a17b8ce2607b50a56a7199e012c2e86bead2831f3d +size 371217 diff --git a/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/6.Monte_Carlo.ipynb b/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/6.Monte_Carlo.ipynb index b809155..b89f2d7 100644 --- a/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/6.Monte_Carlo.ipynb +++ b/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/6.Monte_Carlo.ipynb @@ -1,1119 +1,3 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "839f9f95-d3ea-477f-96b6-b23eea60be3c", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-94f52a4a45c90a6a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# 6. Programmierübung: Monte Carlo Simulationen\n", - "\n", - "
\n", - "
\n", - " Willkommen zur sechsten Programmierübung Einführung in Python 3.\n", - "
\n", - " \n", - "
\n", - "\n", - "Wenn du Fragen oder Verbesserungsvorschläge zum Inhalt oder Struktur der Notebooks hast, dann kannst du mir gerne eine E-Mail an Phil Keier ([p.keier@hbk-bs.de](mailto:p.keier@hbk-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) schreiben.\n", - "\n", - "Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n", - "\n", - "Der Großteil des Python-Tutorials stammt aus der Veranstaltung _Deep Learning Lab_ und von [www.python-kurs.eu](https://www.python-kurs.eu/python3_kurs.php) und wurde für _Signale und Systeme_, sowie _Einführung in die Programmierung für Nicht Informatiker_ angepasst.\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "53c75485-1cd4-4ac7-9092-e14b3c400980", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-107d356336d8d991", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## (Continues) Uniform Distribution\n", - "\n", - "Die gleichmäßige Verteilung zeigt sich durch ihre charakteristische Form eines Rechtecks mit dem Flächeninhalt 1 = 100%. \n", - "Technisch gesehen ist diese Art der Verteilung keineswegs exotisch, da PCGs ihr üblicherweise folgen.\n", - "\n", - "Eine Uniform Distribution setzt sich aus denselben zwei Parametern zusammen wie die Normalverteilung: \n", - "$\\mu$ als Mittelwert und $\\sigma$ als Standardabweichung vom Mittelwert. \n", - "\n", - "Allerdings interpretieren die Libraries NumPy & SciPy diese Werte etwas anders: \n", - "$\\mu$ entspricht hier dem kleinsten Wert, während $\\sigma$ den größten Wert markiert.\n", - "\n", - "Die folgende Grafik verdeutlicht die Verteilung mit $\\mu = 0$ & $\\sigma = 1$:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "52d292d3-66ee-4d00-b5eb-e65497839fba", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-654d073e308d12e3", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "from scipy.stats import uniform # Import scipys uniform\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Define the distribution parameters to be plotted\n", - "sigma = 1\n", - "mu = 0\n", - "x = np.linspace(-0.5, 1.5, 1000)\n", - "\n", - "\n", - "# plot the distributions\n", - "fig, ax = plt.subplots(figsize=(8, 5))\n", - "\n", - "dist = uniform(mu, sigma)\n", - "\n", - "plt.plot(x, dist.pdf(x), c='black', label=r'$\\mu=%i,\\ \\sigma=%i$' % (mu, sigma))\n", - "\n", - "plt.xlim(-0.5, 1.5)\n", - "plt.ylim(0, 1.2)\n", - "\n", - "plt.xlabel('$x$')\n", - "plt.ylabel(r'$p(x|\\mu, \\sigma)$')\n", - "plt.title('Uniform Distribution')\n", - "\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "55a561f8-46dc-4a72-a4e3-c7711aede96b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0ccc61b247199a19", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Random Sampling\n", - "\n", - "Wie man aus Verteilungen sampelt, ist bereits aus dem Kapitel zu den PCGs bekannt – schließlich sind diese uniform verteilt.\n", - "\n", - "Das folgende Beispiel zieht drei zufällige Zahlen aus einer PCG-uniformen Verteilung mittels NumPy:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a312483e-058b-4efc-a92a-bc9e370a3559", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-c4c29720ffba7a48", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Parameters\n", - "mu = 0\n", - "sigma = 1\n", - "\n", - "# Get a 3 random samples\n", - "rand = np.random.default_rng(5000)\n", - "uniform_samples = rand.uniform(mu, sigma, 3)\n", - "\n", - "# Linespace\n", - "x = np.linspace(-0.5, 2, 1000)\n", - "\n", - "# Get uniform from linespace\n", - "dist = uniform(mu, sigma)\n", - "\n", - "# Plot Uniform\n", - "fig, ax = plt.subplots(figsize=(8, 5))\n", - "plt.plot(x, dist.pdf(x), c='black', label=r'$\\mu=%i,\\ \\sigma=%i$' % (mu, sigma))\n", - "\n", - "# Plot samples\n", - "step = 0.1\n", - "for u in uniform_samples:\n", - " u = np.round(u, decimals=2)\n", - " plt.axvline(u, color='r')\n", - " plt.text(u, .5+step, u, color='g')\n", - " step += 0.1\n", - "\n", - "# Cosmetics\n", - "plt.xlim(-0.5, 1.5)\n", - "plt.ylim(0, 1.2)\n", - "\n", - "plt.xlabel('$x$')\n", - "plt.ylabel(r'$p(x|\\mu, \\sigma)$')\n", - "plt.title('3 Samples from a Uniform Distribution')\n", - "\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "a87b1a90-adff-4eb4-b91b-2de5284f64d3", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-42065ce8f33baff7", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Random Sampling funktioniert selbstverständlich auch auf Normalverteilungen. Beispiel:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f1981ddf-87e1-4010-a39c-8a89d172d771", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-d87cc7a18f64858f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Import scipys norm\n", - "from scipy.stats import norm \n", - "\n", - "#Parameters\n", - "mu = 0\n", - "sigma = 1\n", - "\n", - "# Get a 3 random samples\n", - "rand = np.random.default_rng(5000)\n", - "uniform_samples = rand.normal(mu, sigma, 3)\n", - "\n", - "# Linespace\n", - "x = np.linspace(-4, 4, 1000)\n", - "\n", - "# Get uniform from linespace\n", - "dist = norm(mu, sigma)\n", - "\n", - "# Plot Uniform\n", - "fig, ax = plt.subplots(figsize=(8, 5))\n", - "plt.plot(x, dist.pdf(x), c='black', label=r'$\\mu=%i,\\ \\sigma=%i$' % (mu, sigma))\n", - "\n", - "# Plot samples\n", - "step = 0.1\n", - "for u in uniform_samples:\n", - " u = np.round(u, decimals=2)\n", - " plt.axvline(u, color='r')\n", - " plt.text(u, .1+step, u, color='g')\n", - " step += 0.1\n", - "\n", - "# Cosmetics\n", - "plt.xlim(-4, 4)\n", - "plt.ylim(0, .5)\n", - "\n", - "plt.xlabel('$x$')\n", - "plt.ylabel(r'$p(x|\\mu, \\sigma)$')\n", - "plt.title('3 Samples from a Normal Distribution')\n", - "\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "541dd4a5-8849-43fe-8e8c-305b3b99a350", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-de3ef99af1cebfe1", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Monte-Carlo-Simulationen\n", - "\n", - "Monte-Carlo-Simulationen werden in vielen wissenschaftlichen und finanziellen (Forschungs-)Feldern angewandt, um stochastisch relevante Probleme auf vergleichsweise elegante Weise zu lösen. Insbesondere lassen sich damit Zufallsereignisse unterschiedlichster Verteilungen miteinander kombinieren. So kann man beispielsweise die Zahl $\\pi$ approximieren.\n", - "\n", - "Dazu betrachte folgende Darstellung:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "cd6000b8-cb79-4513-8cae-dc9752e47f93", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-3b11e71ec9f993b5", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "circle = plt.Circle((0, 0), 1, color='black', fill=False)\n", - "fig, ax = plt.subplots(figsize=(5,5))\n", - "ax.add_patch(circle)\n", - "ax.plot(np.linspace(0,1), np.zeros(50), color='black')\n", - "ax.text(0.5, 0.05, \"$r = 1$\")\n", - "\n", - "plt.xlim(-1, 1)\n", - "plt.ylim(-1, 1)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "ad96fd0a-3e95-4670-966b-6defe5dbade4", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-c67a16c39ab32478", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Vorliegend ist der Einheitskreis mit Radius = 1, dessen Flächeninhalt sich zu \n", - "$$A_{circle} = \\pi$$ ergibt.\n", - "\n", - "Das umgebende Rechteck besitzt die Seitenlänge \n", - "$a = r \\cdot 2 = 2$, woraus ein Flächeninhalt von \n", - "$A_{square} = 2 \\cdot 2 = 4$ folgt.\n", - "\n", - "Damit ergibt sich das Verhältnis\n", - "$$\n", - "\\frac{A_{circle}}{A_{square}} = \\frac{\\pi}{4}\n", - "$$\n", - "\n", - "Jeder Punkt $(x, y)$ in diesem Diagramm ist gleichwahrscheinlich, also uniform verteilt. \n", - "Mittels Monte-Carlo-Methodik können wir nun den Wert von $\\pi\\$ approximieren. \n", - "Nach einfachem Umstellen erhalten wir:\n", - "\n", - "$$\n", - "\\pi = 4 \\cdot \\frac{A_{circle}}{A_{square}}\n", - "$$\n", - "\n", - "Nacheinander werden dafür zufällig gleichverteilte $x$- und $y$-Werte gezogen.\n", - "\n", - "Für das folgende Beispiel nutzen wir bewusst nur wenige Werte, um die Darstellung nicht zu überladen:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "082ce292-4b01-4b93-abaa-c810232f0d0a", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-1c33d4a20d47764c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Get Uniform x, y samples\n", - "rand = np.random.default_rng(42) # Set fixed Value\n", - "x_rand = rand.uniform(-1, 1, 100)\n", - "y_rand = rand.uniform(-1, 1, 100)\n", - "\n", - "# Plot \n", - "circle = plt.Circle((0, 0), 1, color='black', fill=False)\n", - "fig, ax = plt.subplots(figsize=(5,5))\n", - "ax.add_patch(circle)\n", - "ax.plot(np.linspace(0,1), np.zeros(50), color='black')\n", - "ax.text(0.5, 0.05, \"$r = 1$\")\n", - "plt.xlim(-1, 1)\n", - "plt.ylim(-1, 1)\n", - "\n", - "# Plot samples as small circles\n", - "for x, y in zip(x_rand, y_rand):\n", - " c = plt.Circle((x,y), 0.01, color='green')\n", - " ax.add_patch(c)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "162d35ab-bd1d-4304-8302-cf96b493740d", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-102b588af1bbdfe3", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Um zu bestimmen, ob einer der Werte im Kreis liegt, nutzen wir die parametrisierte Kreisgleichung:\n", - "\n", - "$$\n", - "x^2 + y^2 = r,\\quad r = 1\n", - "$$\n", - "\n", - "Mittels dieser lassen sich die Werte elegant in zwei Kategorien einordnen: \n", - "„im Kreis (`circle`)\" und „im Rechteck (`square`)\".\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "400c3fb5-6e2b-48c1-9b0b-9b4aa4504da2", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-856462e1221faa4c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "# reserve two arrays\n", - "circle_coords = []\n", - "square_coords = []\n", - "\n", - "for x,y in zip(x_rand, y_rand):\n", - " # calculate distance from origin\n", - " dist_from_origin = x**2 + y**2\n", - "\n", - " # if distance smaller or equal than 1, the point is in the circle\n", - " if dist_from_origin <= 1:\n", - " circle_coords.append((x,y))\n", - "\n", - " # by definition is every point in the square\n", - " square_coords.append((x,y))" - ] - }, - { - "cell_type": "markdown", - "id": "fcdc0830-f68f-4b28-b5d8-3c15271900ad", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-19f6113a9887beef", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Mittels der Länge der beiden Listen lässt sich dementsprechend $\\pi$ approximieren:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "86d96bbb-9877-4fcc-af03-8b5b1898dda5", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-f8c1f8a2e8c65aae", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pi = 3.12\n", - "Difference to real Value 0.02159265358979301\n" - ] - } - ], - "source": [ - "pi = 4 * len(circle_coords) / len(square_coords)\n", - "print(f\"Pi = {pi}\")\n", - "print(f\"Difference to real Value {np.pi - pi}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9f83c18b-189b-4090-9080-c3bb74207418", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-204384254c42936a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Plotten wir im Folgenden die 100 zufällig gleichverteilten Werte:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "18862b4a-cdb5-4799-9c32-68b411da3d58", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-cb4b3ecde6b0ab70", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot \n", - "circle = plt.Circle((0, 0), 1, color='black', fill=False)\n", - "fig, ax = plt.subplots(figsize=(5,5))\n", - "ax.add_patch(circle)\n", - "ax.plot(np.linspace(0,1), np.zeros(50), color='black')\n", - "ax.text(0.5, 0.05, \"$r = 1$\")\n", - "plt.xlim(-1, 1)\n", - "plt.ylim(-1, 1)\n", - "\n", - "# Plot samples not in the circle\n", - "for coord in square_coords:\n", - " c = plt.Circle(coord, 0.01, color='blue')\n", - " ax.add_patch(c)\n", - "\n", - "# Plot samples in the circle\n", - "for coord in circle_coords:\n", - " c = plt.Circle(coord, 0.01, color='red')\n", - " ax.add_patch(c)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "704b68d6-4210-4f90-a1ae-9a0078fc8796", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b0626976cd84b809", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Um einen genaueren Wert für $\\pi$ zu erhalten, muss lediglich die Anzahl der Samples erhöht werden (hier: 1 Million).\n", - "\n", - "Da im Folgenden auf eine grafische Darstellung verzichtet wird, lässt sich die Berechnung darauf vereinfachen, lediglich zu zählen, wie viele Punkte im Kreis liegen – die konkreten Koordinaten sind dabei nicht mehr von praktischem Nutzen:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "38ca2a0d-3198-48c2-8af0-26516a01c9d9", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-0512fe6559228e5c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pi = 3.14178\n", - "Difference to real Value -0.00018734641020667908\n" - ] - } - ], - "source": [ - "# Get Uniform x, y samples\n", - "sample_size = 1_000_000\n", - "rand = np.random.default_rng(42) # Set fixed Value\n", - "x_rand = rand.uniform(-1, 1, sample_size)\n", - "y_rand = rand.uniform(-1, 1, sample_size)\n", - "\n", - "# reserve two arrays\n", - "in_circle = 0\n", - "in_square = 0\n", - "\n", - "for x,y in zip(x_rand, y_rand):\n", - " # calculate distance from origin\n", - " dist_from_origin = x**2 + y**2\n", - "\n", - " # if distance smaller or equal than 1, the point is in the circle\n", - " if dist_from_origin <= 1:\n", - " in_circle += 1\n", - "\n", - " # by definition is every point in the square\n", - " in_square += 1\n", - "\n", - "pi = 4 * in_circle / in_square\n", - "print(f\"Pi = {pi}\")\n", - "print(f\"Difference to real Value {np.pi - pi}\")" - ] - }, - { - "cell_type": "markdown", - "id": "167e5e63-254b-4158-9e14-f3c9ddf0259f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-95ca10cecf8fe36e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Aufgabe – Arbeitszeit abschätzen\n", - "\n", - "*15 Punkte*\n", - "\n", - "Du befindest dich erneut in der Situation, dass dein Chef dich beauftragt, zwei wichtige Aufgaben bis zum Ende des Arbeitstages zu bearbeiten. Gleichzeitig bist du abends auf einem Grillfest mit deinen Freunden verabredet, das um 18 Uhr beginnt. \n", - "\n", - "Da du nun im Zwiespalt stehst, sowohl deinen beruflichen Aufgaben nachzukommen als auch das Grillfest pünktlich zu erreichen, fragst du dich, wie hoch die Wahrscheinlichkeit ist, dass du es in den nächsten 9 Stunden schaffst, beides unter einen Hut zu bringen.\n", - "\n", - "Nach einigen Überlegungen stellst du Folgendes fest:\n", - "\n", - "- Für die erste Aufgabe benötigst du zwischen 1–5 Stunden. \n", - "- Für die zweite Aufgabe benötigst du zwischen 2–6 Stunden. \n", - "- Egal wie schnell du eine Aufgabe erledigst, es gibt keine Auswirkungen auf die andere Aufgabe (die Aufgaben sind unabhängig voneinander).\n", - "\n", - "Nun sollst du die Wahrscheinlichkeit berechnen, dass du pünktlich beim Grillfest ankommst.\n", - "\n", - "Gehe dazu wie folgt vor:\n", - "\n", - "1. Nimm an, dass beide Aufgaben gleichmäßig verteilt sind, und speichere die Verteilungen in den Variablen `exc1` & `exc2`.\n", - "2. Verwende eine geeignete Anzahl an Samples und speichere diese in der Variablen `sims`.\n", - "3. Speichere die Wahrscheinlichkeit, dass du pünktlich zum Grillfest kommst, in der Variablen `chance` mit einer Genauigkeit von zwei Nachkommastellen.\n", - "4. Plotte die Verteilungen der Aufgabenzeiten und beziehe dabei die Variable `chance` ein, z.B. als Annotation oder Beschriftung.\n", - "\n", - "In der Markdownzelle (Keine Antwort = 0 Punkte):\n", - "- Stelle dein Ergebnis begründet dar.\n", - "- Interpretiere deinen Plot.\n", - "- Erkläre in eigenen Worten, warum dein Ergebnis aussagekräftig ist." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "cb2bda83-a3f7-4c46-a2be-f809ec55558c", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-65cc1054d6bb7253", - "locked": false, - "points": 2, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "rand = np.random.default_rng(42) # Use this rng!\n", - "# BEGIN SOLUTION\n", - "import matplotlib.pyplot as plt\n", - "sims = 1_000 # 1 Punkt\n", - "\n", - "# 2 Punkte\n", - "exc1 = rand.uniform(1,5,sims) \n", - "exc2 = rand.uniform(2,6,sims)\n", - "\n", - "duration = exc1 + exc2 # 1 Punkt\n", - "chance = float(np.round((duration > 9).sum()/sims, decimals=2)) # 1 Punkt\n", - "\n", - "fig, ax = plt.subplots(figsize=(9,6))\n", - "ax.hist(duration, density=True) # 1 Punkt\n", - "ax.axvline(9, color='r') # 1 Punkt\n", - "ax.text(0.5,0.25,\n", - " f\"Chance to attend the Party {(1-chance)*100:.0f}%\",\n", - " ha='center', va='center', transform=ax.transAxes,\n", - " bbox={'facecolor':'#fafafa','alpha':1,'edgecolor':'none','pad':1},\n", - " color='#de2e0b'\n", - " )\n", - "plt.show()\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "a12f9c24-2068-4ea9-8a73-83738cf654aa", - "metadata": {}, - "source": [ - "# BEGIN SOLUTION\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "0910695e-4c60-4608-9152-519db84687d9", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-9fd6737ce69879f1", - "locked": true, - "points": 5, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Hier werden ihre Lösungen getestet...\n", - "import math\n", - "assert math.isclose(chance, 0.13, rel_tol=0.1)\n", - "\n", - "### BEGIN HIDDEN TESTS\n", - "assert sims > 999\n", - "assert sims < 10_000_000\n", - "\n", - "trand = np.random.default_rng(42)\n", - "\n", - "texc1 = trand.uniform(1,5,sims) \n", - "texc2 = trand.uniform(2,6,sims)\n", - "\n", - "assert np.array_equal(exc1, texc1)\n", - "assert np.array_equal(exc2, texc2)\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "aaaf624d-63ed-4645-a793-0ef2aebcb44d", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8eb45ec252910d85", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Aufgabe – Simulation einer Körpergewichtszunahme\n", - "\n", - "*25 Punkte*\n", - "\n", - "Gegeben sind die nach Altersgruppen aufgeschlüsselten Durchschnittskörpergewichte (in kg) von Männern in Deutschland. \n", - "Quelle: [Statistisches Bundesamt](https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Gesundheit/Gesundheitszustand-Relevantes-Verhalten/Tabellen/koerpermasse-maenner.html)\n", - "\n", - "Ziel ist es, mittels Monte-Carlo-Simulation den mittleren wöchentlichen Gewichtszuwachs zu bestimmen und die Wahrscheinlichkeit zu finden, dass ein Durchschnittsmann innerhalb einer Woche 3 kg abnimmt.\n", - "\n", - "**Vorgehensweise:**\n", - "\n", - "1. Bestimme das arithmetische Mittel der Durchschnittsgewichte über die Altersgruppen und speichere diesen Wert in `avg_weight` mit einer Genauigkeit von 1 Dezimalstelle.\n", - "2. Wähle eine geeignete Anzahl an Samples (`sims`) für die Simulation.\n", - "3. Nimm an, dass das Durchschnittsgewicht normalverteilt ist, mit einer Standardabweichung von 5% des Durchschnittsgewichts, und speichere die simulierten Werte in `men_normal`.\n", - "4. Nimm an, dass die tägliche Gewichtsschwankung eines Mannes gleichverteilt zwischen ±2.5 kg ist.\n", - "5. Simuliere die Gewichtszunahmen/-abnahmen für eine Woche (7 Tage) und speichere die Ergebnisse in `gain_week`.\n", - "6. Berechne die durchschnittliche wöchentliche Gewichtszunahme/-abnahme und speichere das Ergebnis in `duration`.\n", - "7. Bestimme die Wahrscheinlichkeit, dass ein Mann 3 kg oder mehr abnimmt, und speichere sie in `gain_percent`.\n", - "8. Plotte die Verteilung der simulierten Werte und markiere die relevanten Kennzahlen (Durchschnitt, Schwankungsbereich, Wahrscheinlichkeit).\n", - "\n", - "In der Markdownzelle (Keine Antwort = 0 Punkte):\n", - "- Bewerte & Beurteile die gegebenen Daten, sowie Quelle.\n", - "- Erkläre & Interpretiere deinen Plot.\n", - "- Bewerte die angewandte Methodik im Zusammenhang mit der Aufgabe, beziehe dein Ergebnis anschaulich mit ein.\n", - "- Beurteile anhand deiner Simulation den [Bild Artikel](https://www.bild.de/leben-wissen/psychologie-liebe/gewichtszunahme-in-der-ehe-das-stimmt-vor-allem-fuer-maenner-67d2e348aae7056ac7dd201d) - *\"Ehe macht dick – vor allem die Männer\"*" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "b8b21830-be8e-43f2-ae68-7e1867aa46e4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a9d02e6e26d7f2eb", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Given\n", - "avg_weight_per_men = {\n", - " \"18 - 20\": 77.9,\n", - " \"20 - 25\": 80.5,\n", - " \"25 - 30\": 83.3,\n", - " \"30 - 35\": 85.6,\n", - " \"35 - 40\": 86.7,\n", - " \"40 - 45\": 88.1,\n", - " \"45 - 50\": 89.8,\n", - " \"50 - 55\": 89.0,\n", - " \"55 - 60\": 88.8,\n", - " \"60 - 65\": 87.9,\n", - " \"65 - 70\": 86.7,\n", - " \"70 - 75\": 85.3,\n", - " \"75+\": 81.0\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "id": "926897fc-25a8-470c-b06b-946f7370a048", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-ef59ae554f2f4540", - "locked": false, - "points": 2, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.31\n" - ] - } - ], - "source": [ - "rand = np.random.default_rng(420) # Use this rng!\n", - "\n", - "# BEGIN SOLUTION\n", - "avg_weight = np.round(np.mean(list(avg_weight_per_men.values())), decimals=1) # 1 Punkt\n", - "\n", - "sims = 1_000 # 1 Punkt\n", - "\n", - "# 3 Punkte\n", - "sigma = np.round(avg_weight * 0.05, decimals=1)\n", - "men_normal = rand.normal(avg_weight, sigma, sims)\n", - "gain_week = [\n", - " rand.uniform(-2.5, 2.5, sims)\n", - " for _ in range(7)\n", - "]\n", - "\n", - "# Sum up 2 Punkte\n", - "duration = np.zeros(sims)\n", - "for gain in gain_week:\n", - " duration += gain\n", - "duration += men\n", - "\n", - "gain_percent = float(np.round((duration < avg_weight-3).sum()/sims, decimals=2)) # 1 Punkt\n", - "\n", - "plt.figure(figsize=(10,5))\n", - "plt.hist(duration, density=True) # 1 Punkt\n", - "plt.axvline(avg_weight-3, color='r') # 1 Punkt\n", - "plt.show()\n", - "print(gain_percent)\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "01d0d2ee-af6a-46b4-8cdf-652024bb468d", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-089ff54f3c3db43b", - "locked": false, - "points": 3, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "source": [ - "# BEGIN SOLUTION\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "dfe81dfc-5096-4c5b-8e84-81267e212cfa", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-87149b330de46548", - "locked": true, - "points": 8, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Hier werden ihre Lösungen getestet...\n", - "import math\n", - "assert math.isclose(float(gain_percent), 0.3, rel_tol=0.1)\n", - "\n", - "### BEGIN HIDDEN TESTS\n", - "assert sims > 499\n", - "assert sims < 2_001\n", - "\n", - "trand = np.random.default_rng(420)\n", - "\n", - "t_sigma = np.round(avg_weight * 0.05, decimals=1)\n", - "t_men = trand.normal(avg_weight, sigma, sims)\n", - "t_gain_week = [\n", - " trand.uniform(-2.5, 2.5, sims)\n", - " for _ in range(7)\n", - "]\n", - "\n", - "assert math.isclose(sigma, t_sigma, rel_tol=0.1)\n", - "\n", - "for el1, el2 in zip(gain_week, t_gain_week):\n", - " assert np.array_equal(el1, el2)\n", - "\n", - "assert np.array_equal(t_men, men)\n", - "\n", - "t_duration = np.zeros(sims)\n", - "for gain in t_gain_week:\n", - " t_duration += gain\n", - "t_duration += t_men\n", - "\n", - "assert np.array_equal(t_duration, duration)\n", - "### END HIDDEN TESTS" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +version https://git-lfs.github.com/spec/v1 +oid sha256:ec12c7dac074acf43db5c3497c733aea638ecfc7d3cb7ae48b1a6aa0b20a6fce +size 298060 diff --git a/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/7.Pandas_Seaborn.ipynb b/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/7.Pandas_Seaborn.ipynb index 3755f45..2bdf409 100644 --- a/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/7.Pandas_Seaborn.ipynb +++ b/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/7.Pandas_Seaborn.ipynb @@ -1,3717 +1,3 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c2734eb9-d9b9-43b2-a9fb-c25729a0f3a3", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-5fff5091c9d505d4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# 7. Programmierübung: Pandas & Seaborn\n", - "\n", - "
\n", - "
\n", - " Willkommen zur siebten Programmierübung Einführung in Python 3.\n", - "
\n", - " \n", - "
\n", - "\n", - "Wenn Sie Fragen oder Verbesserungsvorschläge zum Inhalt oder Struktur der Notebooks haben, dann können sie eine E-Mail an Phil Keier ([p.keier@hbk-bs.de](mailto:p.keier@hbk-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) oder Martin Le ([martin.le@tu-bs.de](mailto:martin.le@tu-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) schreiben.\n", - "\n", - "Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n", - "\n", - "Der Großteil des Python-Tutorials stammt aus der Veranstaltung _Deep Learning Lab_ und von [www.python-kurs.eu](https://www.python-kurs.eu/python3_kurs.php) und wurde für _Signale und Systeme_, sowie _Einführung in die Programmierung für Nicht Informatiker_ angepasst.\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "8af16cfd-827f-4e7d-91a3-970e920ad212", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-fa7bc65ef436bf2f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Was ist Pandas?\n", - "\n", - ">_pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis/manipulation tool available in any language. It is already well on its way toward this goal._\n", - "\n", - "Pandas Keyfeature sind die beiden Datenstruckturen [Series](https://pandas.pydata.org/docs/reference/series.html) und [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame).\n", - "\n", - "Diesem Notebook liegt zu Lernzwecken das Datenset `unis_nd.csv` bei, mit dem die Grundlegenden Funktionen Pandas gezeigt werden.\n", - "\n", - "__Für dieses Notebook schauen Sie bitte in die [Pandas Docs](https://pandas.pydata.org/docs/)!!!__ Dort sind alle Funktionen beschrieben die wir hier bearbeiten und noch mehr!\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "496ec5e1-2fdb-4075-853b-c91a6144ac82", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-7fc3ec9d80a10c43", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Was ist Seaborn?\n", - "\n", - ">_Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics._\n", - "\n", - "Seaborn bietet dabei keine neue Funktionalität gegenüber dem bekannten MatPlotLib, jedoch abstrahiert es Darstellungen die Aufwendig mit MatPlotLib darzustelln wären. Schaue dir daher gerne die [Gallerie](https://seaborn.pydata.org/examples/index.html) an.\n", - "\n", - "__Für dieses Notebook schauen Sie bitte in die [Searborn Docs](https://seaborn.pydata.org/api.html)!!!__ Dort sind alle Funktionen beschrieben die wir hier bearbeiten und noch mehr!\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "62286603-158e-4448-98e5-e8f4aa60448a", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-d7a24465d2b7b67c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Achtung\n", - "\n", - "Falls es zu Problemen kommen sollte das Seaborn nicht bekannt sei führe bitte vor **jedem** bearbeiten folgende Zeile aus:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "7198387d-3fcc-4501-9460-d07e84384a75", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-a2a83048f4c8f0b8", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: seaborn in /opt/conda/lib/python3.11/site-packages (0.13.2)\n", - "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /opt/conda/lib/python3.11/site-packages (from seaborn) (2.3.5)\n", - "Requirement already satisfied: pandas>=1.2 in /opt/conda/lib/python3.11/site-packages (from seaborn) (2.3.3)\n", - "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /opt/conda/lib/python3.11/site-packages (from seaborn) (3.10.8)\n", - "Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.3)\n", - "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n", - "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.60.1)\n", - "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.9)\n", - "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.2)\n", - "Requirement already satisfied: pillow>=8 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (12.0.0)\n", - "Requirement already satisfied: pyparsing>=3 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.5)\n", - "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n", - "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2023.3.post1)\n", - "Requirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2025.3)\n", - "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "pip install seaborn" - ] - }, - { - "cell_type": "markdown", - "id": "8c6fa552-7e9c-4561-b7c3-e72268db9afb", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-430939b1ccd3736f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Import Pandas\n", - "\n", - "Pandas wird vom Internet mit der Abkürzung `pd` importiert.\n", - "\n", - "Führen Sie die nächste Zelle beim neustart des Notebooks bitte immer aus." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "64315664-4ab4-46db-9600-b0dd58f95174", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-44f74bf2f8803769", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "id": "2ea5a1da-05f7-41a1-96cc-aaba1c74cf0d", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-53884904925c1212", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Data Frame\n", - "\n", - "Ein Pandas Data Frame `pd.DataFrame` ist eine 2-Dimensionale Datenstrucktur, vergleichbar mit einer Excel Tabelle.\n", - "\n", - "![](https://pandas.pydata.org/docs/_images/01_table_dataframe.svg)\n", - "\n", - "Um aus einem Dictionary einen Pandas DataFrame zu erstellen wird das Objekt `pd.DataFrame` verwendet. Dabei ist es wichtig das das Dicitionary einer Ordnung folgt, bei dem die Schlüssel die Namen der Spalten sind. Die Reihe im Data Frame wird dann einem Schlüssel als Liste mit Werten zugeordnet.\n", - "\n", - "Schauen Sie sich dazu Folgendes Dictionary an, welches ein Subset aus dem beiliegenden Datenset `unis_nd.csv`:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "30d2f79a-3f54-4397-a1f3-626b58fdf67e", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-e30f1b0549eb4481", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "unis_data = {\n", - " \t\"University name\": [ \n", - " \"Hochschule für Bildende Künste Braunschweig\",\n", - " \"Technische Universität Carolo-Wilhelmina zu Braunschweig\",\n", - " \"Hochschule 21\",\n", - " \"Technische Universität Clausthal\",\n", - " \"Hochschule Emden/Leer\",\n", - " \"PFH – Private Hochschule Göttingen\",\n", - " \"Georg-August-Universität Göttingen\"\n", - " ],\n", - " \n", - " \"Type of university\": [\n", - " \"Artistic university\",\n", - " \"University\",\n", - " \"University of Applied Sciences\",\n", - " \"University\",\n", - " \"University of Applied Sciences\",\n", - " \"University of Applied Sciences\",\n", - " \"University\"\n", - " ]\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "ba23542f-9030-45a3-a7bc-9c3841ae843e", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-08a43ccd460568f5", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Um aus dem Dictionary ein DataFrame zu erstellen, wird es einfach als Input für `pd.DataFrame` verwendet.\n", - "\n", - "Anschließend lassen wir es ausgeben. Nehmen Sie sich gerne die Zeit uns inspizieren Sie die Strucktur des beispielhaften DataFrames:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "074d682c-e936-40c3-97e1-2208b4e86a79", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-b519f770ad46c82b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " University name \\\n", - "0 Hochschule für Bildende Künste Braunschweig \n", - "1 Technische Universität Carolo-Wilhelmina zu Br... \n", - "2 Hochschule 21 \n", - "3 Technische Universität Clausthal \n", - "4 Hochschule Emden/Leer \n", - "5 PFH – Private Hochschule Göttingen \n", - "6 Georg-August-Universität Göttingen \n", - "\n", - " Type of university \n", - "0 Artistic university \n", - "1 University \n", - "2 University of Applied Sciences \n", - "3 University \n", - "4 University of Applied Sciences \n", - "5 University of Applied Sciences \n", - "6 University " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unis = pd.DataFrame(unis_data)\n", - "unis" - ] - }, - { - "cell_type": "markdown", - "id": "e732d3ff-463e-4411-b023-47f2fe02e4c5", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-6ed4bfb85a3f95bb", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Zugriffs Operationen\n", - "\n", - "Wie bereits vom Dicitonary bekannt lässt sich mittels Schlüssel (im Beispiel `University name`) auf eine Spalte zugreifen. Dabei ist wichtig zu erwähnen das jede Spalte in einem `DataFrame` eine `Series` ist. Eine `Series` ist dabei das 1-Dimensionale äquivalent zum 2-Dimensionales `DataFrame`.\n", - "\n", - "Im Folgenden Beispiel Selektieren wir die Series `University name` aus dem DataFrame `unis_nd`:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "cfa48421-7e56-4c10-a4fc-3d907f429309", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-ef292cd51db1030b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 Hochschule für Bildende Künste Braunschweig\n", - "1 Technische Universität Carolo-Wilhelmina zu Br...\n", - "2 Hochschule 21\n", - "3 Technische Universität Clausthal\n", - "4 Hochschule Emden/Leer\n", - "5 PFH – Private Hochschule Göttingen\n", - "6 Georg-August-Universität Göttingen\n", - "Name: University name, dtype: object" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unis[\"University name\"]" - ] - }, - { - "cell_type": "markdown", - "id": "62a92dad-506a-434f-b343-c020f89b19a1", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-dfdff5bf7e9be2ad", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Um eine `Series` manuel zu definieren wird `pd.Series` verwendet. Dabei kann mittels Parameter `name` ein label für die Series gesetzt werden:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "7d54c159-119d-4350-9cf9-081ecc8f5712", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-2a1d8c078f0249d8", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 Hochschule für Bildende Künste Braunschweig\n", - "1 Technische Universität Carolo-Wilhelmina zu Br...\n", - "2 Hochschule 21\n", - "3 Technische Universität Clausthal\n", - "4 Hochschule Emden/Leer\n", - "5 PFH – Private Hochschule Göttingen\n", - "6 Georg-August-Universität Göttingen\n", - "Name: University name, dtype: object" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "uni_names = pd.Series([ \n", - " \"Hochschule für Bildende Künste Braunschweig\",\n", - " \"Technische Universität Carolo-Wilhelmina zu Braunschweig\",\n", - " \"Hochschule 21\",\n", - " \"Technische Universität Clausthal\",\n", - " \"Hochschule Emden/Leer\",\n", - " \"PFH – Private Hochschule Göttingen\",\n", - " \"Georg-August-Universität Göttingen\"\n", - " ], name=\"University name\")\n", - "\n", - "uni_names" - ] - }, - { - "cell_type": "markdown", - "id": "97930782-3e33-4036-8be2-f40c10f18794", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-e989fac8445abfe4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Wie Sie sehen ist der Output identisch.\n", - "\n", - "Um auf einzelne Elemente in der `Series` zuzugreifen werden, wie bereits bekannt von Listen, Index zugriffe verwendet.\n", - "\n", - "Beispiel; Selektion des 2 Elementes:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "3947baa1-1a3d-41fa-866d-a8acbd07a89a", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-07bcadf8f88e6603", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'Technische Universität Carolo-Wilhelmina zu Braunschweig'" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "uni_names[1]" - ] - }, - { - "cell_type": "markdown", - "id": "d8541372-36fa-4f59-b131-9c737b1e3457", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-0662b5716d2ffbf6", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Analog dazu für den Data Frame. Bei dem zuerst die Spalte und dann Reihe selektiert wird:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "69e57b94-06ea-445b-a8ae-63952dd95358", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-d17cf7e2161c24e3", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'Technische Universität Carolo-Wilhelmina zu Braunschweig'" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unis[\"University name\"][1]" - ] - }, - { - "cell_type": "markdown", - "id": "720262aa-1d99-469b-896e-1bf5da4b712b", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-a5f5d2b918c6704b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Wie beim Dictionary lassen sich auch die bekannten Funktionen `.values`, `.keys` & `.items` ausgeben.\n", - "\n", - "Beispiel `.keys`:\n", - "\n", - "Achtung die Ausgabe ist keine Liste auch wenn es den Anschein erwirkt!" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "84809ffa-64a2-4c9d-b3b1-3ff792ebda86", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-0a4876a0ab9f0672", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Schlüssel:\n", - "Index(['University name', 'Type of university'], dtype='object')\n", - "\n", - "Rückgabetype:\n", - "\n" - ] - } - ], - "source": [ - "print(\"Schlüssel:\", unis.keys(), \"\\nRückgabetype:\", type(unis.keys()), sep='\\n')" - ] - }, - { - "cell_type": "markdown", - "id": "88957f18-bbc0-4ce3-981d-9b6735ea437b", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-7708becc41b11601", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Aufgabe - Erstellen eines Dataframes\n", - "\n", - "*2 Punkte*\n", - "\n", - "Erstelle einen Pandas Data Frame mit dem namen `uni_addr`, nachdem Schema folgender Tabelle:\n", - "\n", - "| Address | plz |\n", - "|--------------------------|----------------------------|\n", - "| Johannes-Selenka-Platz 1 | 38118 Braunschweig |\n", - "| Universitätspl. 2 | 38106 Braunschweig |\n", - "| Harburger Str. 6 | 21614 Buxtehude |\n", - "| Adolph-Roemer-Straße 2A | 38678 Clausthal-Zellerfeld |\n", - "| Constantiapl. 4 | 26723 Emden |\n", - "| Weender Landstraße 3-7 | 37073 Göttingen |\n", - "| Wilhelmsplatz 1 | 37073 Göttingen |" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "2a6555ac-97ce-4d78-8d19-359b1eeb5a1a", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-6ea306cdf2a57ea3", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "uni_addr = None\n", - "\n", - "### BEGIN SOLUTION\n", - "uni_addr = pd.DataFrame({\n", - " \"Address\": [\"Johannes-Selenka-Platz 1\", \"Universitätspl. 2\", \"Harburger Str. 6\", \"Adolph-Roemer-Straße 2A\", \"Constantiapl. 4\", \"Weender Landstraße 3-7\", \"Wilhelmsplatz 1\"],\n", - " \"plz\": [\"38118 Braunschweig\", \"38106 Braunschweig\", \"21614 Buxtehude\", \"38678 Clausthal-Zellerfeld\", \"26723 Emden\", \"37073 Göttingen\", \"37073 Göttingen\",]\n", - "})\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "1936705b-949b-47ad-95e1-67d987662efc", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-daa317b892c6c606", - "locked": true, - "points": 5, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Hier werden ihre Lösung gestestet ...\n", - "assert isinstance(uni_addr, pd.DataFrame)\n", - "assert len(uni_addr[\"Address\"]) == 7\n", - "assert len(uni_addr[\"plz\"]) == 7\n", - "### BEGIN HIDDEN TESTS\n", - "uni_addr_test = pd.DataFrame({\n", - " \"Address\": [\"Johannes-Selenka-Platz 1\", \"Universitätspl. 2\", \"Harburger Str. 6\", \"Adolph-Roemer-Straße 2A\", \"Constantiapl. 4\", \"Weender Landstraße 3-7\", \"Wilhelmsplatz 1\"],\n", - " \"plz\": [\"38118 Braunschweig\", \"38106 Braunschweig\", \"21614 Buxtehude\", \"38678 Clausthal-Zellerfeld\", \"26723 Emden\", \"37073 Göttingen\", \"37073 Göttingen\",]\n", - "})\n", - "\n", - "for el1, el2 in zip(uni_addr[\"Address\"], uni_addr_test[\"Address\"]):\n", - " assert el1 == el2\n", - "\n", - "for el1, el2 in zip(uni_addr[\"plz\"], uni_addr_test[\"plz\"]):\n", - " assert el1 == el2\n", - "\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "8f25a6fb-3f9b-497a-a966-3e53c2f86b9c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-8871c67f03f141dd", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe - Extrahieren einer Series\n", - "\n", - "*1 Punkte*\n", - "\n", - "Exthahiere die Series `plz` aus dem zuvor erstelltem Data Frame `uni_addr` und speicher dein Ergebnis in `uni_plz` mit dem richtigen Type Hint.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "2de2487d-158e-4dec-a5fa-ccd7478c161c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-240ce62f624ce706", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [], - "source": [ - "uni_plz = None\n", - "\n", - "### BEGIN SOLUTION\n", - "uni_plz = uni_addr[\"plz\"]\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "613f9082-d963-4129-a70c-acfab56759e0", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-dbd86892a80c1f08", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 38118 Braunschweig\n", - "1 38106 Braunschweig\n", - "2 21614 Buxtehude\n", - "3 38678 Clausthal-Zellerfeld\n", - "4 26723 Emden\n", - "5 37073 Göttingen\n", - "6 37073 Göttingen\n", - "Name: plz, dtype: object\n" - ] - } - ], - "source": [ - "# Hier werden ihre Lösungen getestet\n", - "print(uni_plz)\n", - "assert isinstance(uni_plz, pd.Series)\n", - "### BEGIN HIDDEN TESTS\n", - "uni_plz_test = pd.Series([\"38118 Braunschweig\", \"38106 Braunschweig\", \"21614 Buxtehude\", \"38678 Clausthal-Zellerfeld\", \"26723 Emden\", \"37073 Göttingen\", \"37073 Göttingen\",], name=\"plz\")\n", - "for el1, el2 in zip(uni_plz, uni_plz_test):\n", - " assert el1 == el2\n", - "### END HIDDEN TESTS" - ] - }, - { - "cell_type": "markdown", - "id": "f308563d-e961-40a8-bf11-79d137b02e88", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-02f5ee7a60d555b6", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Reading Data\n", - "\n", - "Die unweiten des Internets ermöglichen es uns Daten auf die unterschiedlichsten Arten zu Archivieren. \n", - "\n", - "Für die Speicherung und Darstellung von (Roh-)Daten werden Datenbanken ([SQL](https://en.wikipedia.org/wiki/SQL)), Transportformate ([JSON](https://en.wikipedia.org/wiki/JSON), [XML](https://en.wikipedia.org/wiki/XML)), Tabellenformate ([Excel](https://en.wikipedia.org/wiki/Microsoft_Excel), [CSV](https://en.wikipedia.org/wiki/Comma-separated_values)) und noch viele mehr verwendet.\n", - "\n", - "Ein beliebtes Betriebsystemunabhängiges Dateiformat für kleine Datensätze (bis 10GB) werden `.csv` Dateien verwendet. Jedes gängige Tabellenkalkulations- und Umfragentool kann seine Daten als _Comma-seperated values_ kurz _CSV_ Exportieren. Schauen Sie dazu gerne in das Datenset `unis_nd.csv`. Im folgenden wird sich ausschließlich auf das Einlesen von CSV-Dateien bezogen. Welche Funktionen und Dateienformate Pandas unterstüzt entnehmen Sie bitte der Dokumentation zu [input/output](https://pandas.pydata.org/docs/reference/io.html).\n", - "\n", - "Da ich ihnen Die Spannung nicht nehmen möchte lösen Sie für die folgenden Beispiele bitte nächste Aufgabe." - ] - }, - { - "cell_type": "markdown", - "id": "a6d7e62b-df8e-4da5-8f74-6c81be0f00df", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7bf6c58e48e58743", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe - Read CSV\n", - "\n", - "*1 Punkt*\n", - "\n", - "Lies das Datenset `unis_nd.csv` aus und speicher den resultierenden DataFrame in der Variabeln `unis_nd`.\n", - "\n", - "Falls Sie hilfe benötigen lesen Sie gerne die Dokumentation im [Getting Started](https://pandas.pydata.org/docs/getting_started/intro_tutorials/02_read_write.html) Guide.\n", - "\n", - "_Hinweis: Die Datei liegt in keinem Ordner, sondern im selben wie dieses Notebook!_" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "b08cc0c4-277b-4e9f-b6e7-a607b912febe", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3cdc8afd6dc12d44", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [], - "source": [ - "unis_nd = None\n", - "\n", - "### BEGIN SOLUTION\n", - "unis_nd = pd.read_csv(\"unis_nd.csv\")\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "e5760557-ddec-4502-b2d3-e8cc2ef70348", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-fd974b3aa3563ca0", - "locked": true, - "points": 1, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Hier werden ihre Lösungen getestet\n", - "assert isinstance(unis_nd, pd.DataFrame)" - ] - }, - { - "cell_type": "markdown", - "id": "ffcf5c5c-1e35-42f9-b35b-2494ff4c421f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-614d1dc9c4566861", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Ausgabe Operationen\n", - "\n", - "Um den gesamten Data Frame auszugeben nutzen Sie einfach die Syntax aus nächster Zelle.\n", - "\n", - "Achtung die Ausgabe ist vergleichweise Groß! Nehmen Sie sich gerne Zeit und schauen Sie sich das Dataset an." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "c9b8b9a0-0a62-4887-b6e8-c1da6086d7e4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ec7681e1a54af790", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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University nameType of universitySponsorshipRight of promotionFounding yearNumber of studentsAddresslatlonplzpic
0Hochschule für Bildende Künste BraunschweigArtistic universitypublicyes1963976.000Johannes-Selenka-Platz 152.25773810.50231538118 Braunschweighttps://www.hbk-bs.de/fileadmin/_processed_/5/...
1Technische Universität Carolo-Wilhelmina zu Br...Universitypublicyes174517709.000Universitätspl. 252.27355010.53009738106 Braunschweighttps://upload.wikimedia.org/wikipedia/commons...
2Hochschule 21University of Applied Sciencesprivatno20051084.000Harburger Str. 653.4776509.70465021614 Buxtehudehttps://upload.wikimedia.org/wikipedia/commons...
3Technische Universität ClausthalUniversitypublicyes17753446.000Adolph-Roemer-Straße 2A51.80484010.33411038678 Clausthal-Zellerfeldhttps://www.presse.tu-clausthal.de/fileadmin/T...
4Hochschule Emden/LeerUniversity of Applied Sciencespublicno20094481.000Constantiapl. 453.3681607.18141026723 Emdenhttps://sta-hisweb.hs-emden-leer.de/QIS/images...
5PFH – Private Hochschule GöttingenUniversity of Applied Sciencesprivatno19954226.000Weender Landstraße 3-751.5389109.93322037073 Göttingenhttps://goettingen-campus.de/fileadmin/_proces...
6Georg-August-Universität GöttingenUniversitypublicyes173728614.000Wilhelmsplatz 151.5340709.93785037073 Göttingenhttps://upload.wikimedia.org/wikipedia/commons...
7Fachhochschule für die Wirtschaft HannoverUniversity of Applied Sciencesprivatno1996641.000Freundallee 1552.3662009.77247030173 Hannoverhttps://upload.wikimedia.org/wikipedia/commons...
8Hochschule HannoverUniversity of Applied Sciencespublicno19719209.000Ricklinger Stadtweg 12052.3541909.72238030459 Hannoverhttps://upload.wikimedia.org/wikipedia/commons...
9Hochschule für Musik, Theater und Medien HannoverArtistic universitypublicyes18971409.000Neues Haus 152.3773809.75392030175 Hannoverhttps://upload.wikimedia.org/wikipedia/commons...
10Leibniz-FachhochschuleUniversity of Applied Sciencesprivatno1920589.000Expo Plaza 1152.3211509.81868030539 Hannoverhttps://www.visit-hannover.com/var/storage/ima...
11Medizinische Hochschule Hannover (MHH)Universitypublicyes19633778.000Carl-Neuberg-Straße 152.3840509.80603030625 Hannoverhttps://upload.wikimedia.org/wikipedia/commons...
12Stiftung Tierärztliche Hochschule HannoverUniversitypublicyes17782381.000Bünteweg 252.3546809.79773030559 Hannoverhttps://upload.wikimedia.org/wikipedia/de/thum...
13Gottfried Wilhelm Leibniz Universität HannoverUniversitypublicyes183128935.000Welfengarten 152.3822509.71777030167 Hannoverhttps://www.uni-hannover.de/fileadmin/_process...
14Fachhochschule für Interkulturelle Theologie H...University of Applied Sciencesprivatno201291.000Missionsstraße 3-552.70884310.14071029320 Südheidehttps://cdn.max-e5.info/damfiles/logo/fh_herma...
15Universität HildesheimUniversitypublicyes19788378.000Universitätspl. 152.1340109.97469031141 Hildesheimhttps://www.uni-hildesheim.de/media/_processed...
16HAWK Hochschule für angewandte Wissenschaft un...University of Applied Sciencespublicno19716495.000Hohnsen 452.1424609.95798031134 Hildesheimhttps://upload.wikimedia.org/wikipedia/commons...
17HAWK Hochschule für angewandte Wissenschaft un...University of Applied Sciencespublicno19716495.000Haarmannpl. 351.8272609.45069037603 Holzmindenhttps://upload.wikimedia.org/wikipedia/commons...
18HAWK Hochschule für angewandte Wissenschaft un...University of Applied Sciencespublicno19716495.000Von-Ossietzky-Straße 9951.5217509.96967037085 Göttingenhttps://upload.wikimedia.org/wikipedia/commons...
19Leuphana Universität LüneburgUniversitypublicyes19466497.000Universitätsallee 153.22853110.40171021335 Lüneburghttps://upload.wikimedia.org/wikipedia/commons...
20Norddeutsche Hochschule für Rechtspflege – Nie...University of Administrationpublicno20076409.000Godehardspl. 652.1448409.94923031134 Hildesheimhttps://static.studycheck.de/media/images/inst...
21Kommunale Hochschule für Verwaltung in Nieders...University of Administrationpublicno20071570.000Wielandstraße 852.3705009.72239030169 Hannoverhttps://www.nsi-hsvn.de/fileadmin/user_upload/...
22Carl von Ossietzky Universität Oldenburg\\nUniversitypublicyes197315635.000Uhlhornsweg 49-5553.1473408.17902026129 Oldenburghttps://upload.wikimedia.org/wikipedia/commons...
23Hochschule OsnabrückUniversity of Applied Sciencespublicno197113620.000Albrechtstraße 3052.2826808.02501049076 Osnabrückhttps://login.hs-osnabrueck.de/nidp/hsos/image...
24Universität OsnabrückUniversitypublicyes197313640.000Neuer Graben 2952.2713708.04454049074 Osnabrückhttps://www.eh-tabor.de/sites/default/files/st...
25Hochschule Braunschweig/Wolfenbüttel, Ostfalia...University of Applied Sciencespublicno197111577.000Salzdahlumer Str. 46/4852.17683010.54865038302 Wolfenbüttelhttps://www.ostfalia.de/export/system/modules/...
26Hochschule Wolfsburg, Ostfalia Hochschule für ...University of Applied Sciencespublicno197111577.000Robert-Koch-Platz 8A52.42595010.78711038440 Wolfsburghttps://www.ostfalia.de/export/system/modules/...
27Hochschule Suderburg, Ostfalia Hochschule für ...University of Applied Sciencespublicno197111577.000Herbert-Meyer-Straße 752.89761010.44659029556 Suderburghttps://www.ostfalia.de/export/system/modules/...
28Hochschule Salzgitter, Ostfalia Hochschule für...University of Applied Sciencespublicno197111577.000Karl-Scharfenberg-Straße 55/5752.08724010.38055038229 Salzgitterhttps://www.ostfalia.de/export/system/modules/...
29Hochschule für Künste im Sozialen, OttersbergUniversity of Applied Sciencesprivatno1967342.000Große Str. 10753.1066809.16310028870 Ottersberghttps://upload.wikimedia.org/wikipedia/commons...
30Private Hochschule für Wirtschaft und Technik ...University of Applied Sciencesprivatno1998558.000Rombergstraße 4052.7212508.27891049377 Vechtahttps://www.phwt.de/wp-content/uploads/2020/09...
31Private Hochschule für Wirtschaft und Technik ...University of Applied Sciencesprivatno1998558.000Schlesier Str. 13A52.6117108.36334049356 Diepholzhttps://www.phwt.de/wp-content/uploads/2020/09...
32Universität VechtaUniversitypublicyes19954.551Driverstraße 2252.7211708.29380049377 Vechtahttps://upload.wikimedia.org/wikipedia/commons...
33Hochschule WeserberglandUniversity of Applied Sciencesprivatno2010485.000Am Stockhof 252.0987509.35542031785 Hamelnhttps://upload.wikimedia.org/wikipedia/commons...
34Jade Hochschule – WilhelmshavenUniversity of Applied Sciencespublicno20096789.000Friedrich-Paffrath-Straße 10153.5478708.08804026389 Wilhelmshavenhttps://www.jade-hs.de/fileadmin/layout2016/as...
35Jade Hochschule – OldenburgUniversity of Applied Sciencespublicno20096789.000Ofener Str. 16/1953.1417908.20213026121 Oldenburghttps://www.jade-hs.de/fileadmin/layout2016/as...
36Jade Hochschule – ElsflethUniversity of Applied Sciencespublicno20096789.000Weserstraße 5253.2424408.46651026931 Elsflethhttps://www.jade-hs.de/fileadmin/layout2016/as...
37Steuerakademie Niedersachsen RintelnUniversity of Administrationpublicno2006500.000Wilhelm-Busch-Weg 2952.2069609.09112031737 Rintelnhttps://www.steuerakademie.niedersachsen.de/as...
38Steuerakademie Niedersachsen Bad EilsenUniversity of Administrationpublicno2006500.000Bahnhofstraße 552.2398109.10423031707 Bad Eilsenhttps://www.steuerakademie.niedersachsen.de/as...
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" - ], - "text/plain": [ - " University name \\\n", - "0 Hochschule für Bildende Künste Braunschweig \n", - "1 Technische Universität Carolo-Wilhelmina zu Br... \n", - "2 Hochschule 21 \n", - "3 Technische Universität Clausthal \n", - "4 Hochschule Emden/Leer \n", - "5 PFH – Private Hochschule Göttingen \n", - "6 Georg-August-Universität Göttingen \n", - "7 Fachhochschule für die Wirtschaft Hannover \n", - "8 Hochschule Hannover \n", - "9 Hochschule für Musik, Theater und Medien Hannover \n", - "10 Leibniz-Fachhochschule \n", - "11 Medizinische Hochschule Hannover (MHH) \n", - "12 Stiftung Tierärztliche Hochschule Hannover \n", - "13 Gottfried Wilhelm Leibniz Universität Hannover \n", - "14 Fachhochschule für Interkulturelle Theologie H... \n", - "15 Universität Hildesheim \n", - "16 HAWK Hochschule für angewandte Wissenschaft un... \n", - "17 HAWK Hochschule für angewandte Wissenschaft un... \n", - "18 HAWK Hochschule für angewandte Wissenschaft un... \n", - "19 Leuphana Universität Lüneburg \n", - "20 Norddeutsche Hochschule für Rechtspflege – Nie... \n", - "21 Kommunale Hochschule für Verwaltung in Nieders... \n", - "22 Carl von Ossietzky Universität Oldenburg\\n \n", - "23 Hochschule Osnabrück \n", - "24 Universität Osnabrück \n", - "25 Hochschule Braunschweig/Wolfenbüttel, Ostfalia... \n", - "26 Hochschule Wolfsburg, Ostfalia Hochschule für ... \n", - "27 Hochschule Suderburg, Ostfalia Hochschule für ... \n", - "28 Hochschule Salzgitter, Ostfalia Hochschule für... \n", - "29 Hochschule für Künste im Sozialen, Ottersberg \n", - "30 Private Hochschule für Wirtschaft und Technik ... \n", - "31 Private Hochschule für Wirtschaft und Technik ... \n", - "32 Universität Vechta \n", - "33 Hochschule Weserbergland \n", - "34 Jade Hochschule – Wilhelmshaven \n", - "35 Jade Hochschule – Oldenburg \n", - "36 Jade Hochschule – Elsfleth \n", - "37 Steuerakademie Niedersachsen Rinteln \n", - "38 Steuerakademie Niedersachsen Bad Eilsen \n", - "\n", - " Type of university Sponsorship Right of promotion \\\n", - "0 Artistic university public yes \n", - "1 University public yes \n", - "2 University of Applied Sciences privat no \n", - "3 University public yes \n", - "4 University of Applied Sciences public no \n", - "5 University of Applied Sciences privat no \n", - "6 University public yes \n", - "7 University of Applied Sciences privat no \n", - "8 University of Applied Sciences public no \n", - "9 Artistic university public yes \n", - "10 University of Applied Sciences privat no \n", - "11 University public yes \n", - "12 University public yes \n", - "13 University public yes \n", - "14 University of Applied Sciences privat no \n", - "15 University public yes \n", - "16 University of Applied Sciences public no \n", - "17 University of Applied Sciences public no \n", - "18 University of Applied Sciences public no \n", - "19 University public yes \n", - "20 University of Administration public no \n", - "21 University of Administration public no \n", - "22 University public yes \n", - "23 University of Applied Sciences public no \n", - "24 University public yes \n", - "25 University of Applied Sciences public no \n", - "26 University of Applied Sciences public no \n", - "27 University of Applied Sciences public no \n", - "28 University of Applied Sciences public no \n", - "29 University of Applied Sciences privat no \n", - "30 University of Applied Sciences privat no \n", - "31 University of Applied Sciences privat no \n", - "32 University public yes \n", - "33 University of Applied Sciences privat no \n", - "34 University of Applied Sciences public no \n", - "35 University of Applied Sciences public no \n", - "36 University of Applied Sciences public no \n", - "37 University of Administration public no \n", - "38 University of Administration public no \n", - "\n", - " Founding year Number of students Address \\\n", - "0 1963 976.000 Johannes-Selenka-Platz 1 \n", - "1 1745 17709.000 Universitätspl. 2 \n", - "2 2005 1084.000 Harburger Str. 6 \n", - "3 1775 3446.000 Adolph-Roemer-Straße 2A \n", - "4 2009 4481.000 Constantiapl. 4 \n", - "5 1995 4226.000 Weender Landstraße 3-7 \n", - "6 1737 28614.000 Wilhelmsplatz 1 \n", - "7 1996 641.000 Freundallee 15 \n", - "8 1971 9209.000 Ricklinger Stadtweg 120 \n", - "9 1897 1409.000 Neues Haus 1 \n", - "10 1920 589.000 Expo Plaza 11 \n", - "11 1963 3778.000 Carl-Neuberg-Straße 1 \n", - "12 1778 2381.000 Bünteweg 2 \n", - "13 1831 28935.000 Welfengarten 1 \n", - "14 2012 91.000 Missionsstraße 3-5 \n", - "15 1978 8378.000 Universitätspl. 1 \n", - "16 1971 6495.000 Hohnsen 4 \n", - "17 1971 6495.000 Haarmannpl. 3 \n", - "18 1971 6495.000 Von-Ossietzky-Straße 99 \n", - "19 1946 6497.000 Universitätsallee 1 \n", - "20 2007 6409.000 Godehardspl. 6 \n", - "21 2007 1570.000 Wielandstraße 8 \n", - "22 1973 15635.000 Uhlhornsweg 49-55 \n", - "23 1971 13620.000 Albrechtstraße 30 \n", - "24 1973 13640.000 Neuer Graben 29 \n", - "25 1971 11577.000 Salzdahlumer Str. 46/48 \n", - "26 1971 11577.000 Robert-Koch-Platz 8A \n", - "27 1971 11577.000 Herbert-Meyer-Straße 7 \n", - "28 1971 11577.000 Karl-Scharfenberg-Straße 55/57 \n", - "29 1967 342.000 Große Str. 107 \n", - "30 1998 558.000 Rombergstraße 40 \n", - "31 1998 558.000 Schlesier Str. 13A \n", - "32 1995 4.551 Driverstraße 22 \n", - "33 2010 485.000 Am Stockhof 2 \n", - "34 2009 6789.000 Friedrich-Paffrath-Straße 101 \n", - "35 2009 6789.000 Ofener Str. 16/19 \n", - "36 2009 6789.000 Weserstraße 52 \n", - "37 2006 500.000 Wilhelm-Busch-Weg 29 \n", - "38 2006 500.000 Bahnhofstraße 5 \n", - "\n", - " lat lon plz \\\n", - "0 52.257738 10.502315 38118 Braunschweig \n", - "1 52.273550 10.530097 38106 Braunschweig \n", - "2 53.477650 9.704650 21614 Buxtehude \n", - "3 51.804840 10.334110 38678 Clausthal-Zellerfeld \n", - "4 53.368160 7.181410 26723 Emden \n", - "5 51.538910 9.933220 37073 Göttingen \n", - "6 51.534070 9.937850 37073 Göttingen \n", - "7 52.366200 9.772470 30173 Hannover \n", - "8 52.354190 9.722380 30459 Hannover \n", - "9 52.377380 9.753920 30175 Hannover \n", - "10 52.321150 9.818680 30539 Hannover \n", - "11 52.384050 9.806030 30625 Hannover \n", - 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3public1775Technische Universität Clausthal
4public2009Hochschule Emden/Leer
5privat1995PFH – Private Hochschule Göttingen
6public1737Georg-August-Universität Göttingen
7privat1996Fachhochschule für die Wirtschaft Hannover
8public1971Hochschule Hannover
9public1897Hochschule für Musik, Theater und Medien Hannover
10privat1920Leibniz-Fachhochschule
11public1963Medizinische Hochschule Hannover (MHH)
12public1778Stiftung Tierärztliche Hochschule Hannover
13public1831Gottfried Wilhelm Leibniz Universität Hannover
14privat2012Fachhochschule für Interkulturelle Theologie H...
15public1978Universität Hildesheim
16public1971HAWK Hochschule für angewandte Wissenschaft un...
17public1971HAWK Hochschule für angewandte Wissenschaft un...
18public1971HAWK Hochschule für angewandte Wissenschaft un...
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23public1971Hochschule Osnabrück
24public1973Universität Osnabrück
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26public1971Hochschule Wolfsburg, Ostfalia Hochschule für ...
27public1971Hochschule Suderburg, Ostfalia Hochschule für ...
28public1971Hochschule Salzgitter, Ostfalia Hochschule für...
29privat1967Hochschule für Künste im Sozialen, Ottersberg
30privat1998Private Hochschule für Wirtschaft und Technik ...
31privat1998Private Hochschule für Wirtschaft und Technik ...
32public1995Universität Vechta
33privat2010Hochschule Weserbergland
34public2009Jade Hochschule – Wilhelmshaven
35public2009Jade Hochschule – Oldenburg
36public2009Jade Hochschule – Elsfleth
37public2006Steuerakademie Niedersachsen Rinteln
38public2006Steuerakademie Niedersachsen Bad Eilsen
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" - ], - "text/plain": [ - " Sponsorship Founding year \\\n", - "0 public 1963 \n", - "1 public 1745 \n", - "2 privat 2005 \n", - "3 public 1775 \n", - "4 public 2009 \n", - "5 privat 1995 \n", - "6 public 1737 \n", - "7 privat 1996 \n", - "8 public 1971 \n", - "9 public 1897 \n", - "10 privat 1920 \n", - "11 public 1963 \n", - "12 public 1778 \n", - "13 public 1831 \n", - "14 privat 2012 \n", - "15 public 1978 \n", - "16 public 1971 \n", - "17 public 1971 \n", - "18 public 1971 \n", - "19 public 1946 \n", - "20 public 2007 \n", - "21 public 2007 \n", - "22 public 1973 \n", - "23 public 1971 \n", - "24 public 1973 \n", - "25 public 1971 \n", - "26 public 1971 \n", - "27 public 1971 \n", - "28 public 1971 \n", - "29 privat 1967 \n", - "30 privat 1998 \n", - "31 privat 1998 \n", - "32 public 1995 \n", - "33 privat 2010 \n", - "34 public 2009 \n", - "35 public 2009 \n", - "36 public 2009 \n", - "37 public 2006 \n", - "38 public 2006 \n", - "\n", - " University name \n", - "0 Hochschule für Bildende Künste Braunschweig \n", - "1 Technische Universität Carolo-Wilhelmina zu Br... \n", - "2 Hochschule 21 \n", - "3 Technische Universität Clausthal \n", - "4 Hochschule Emden/Leer \n", - "5 PFH – Private Hochschule Göttingen \n", - "6 Georg-August-Universität Göttingen \n", - "7 Fachhochschule für die Wirtschaft Hannover \n", - "8 Hochschule Hannover \n", - "9 Hochschule für Musik, Theater und Medien Hannover \n", - "10 Leibniz-Fachhochschule \n", - "11 Medizinische Hochschule Hannover (MHH) \n", - "12 Stiftung Tierärztliche Hochschule Hannover \n", - "13 Gottfried Wilhelm Leibniz Universität Hannover \n", - "14 Fachhochschule für Interkulturelle Theologie H... \n", - "15 Universität Hildesheim \n", - "16 HAWK Hochschule für angewandte Wissenschaft un... \n", - "17 HAWK Hochschule für angewandte Wissenschaft un... \n", - "18 HAWK Hochschule für angewandte Wissenschaft un... \n", - "19 Leuphana Universität Lüneburg \n", - "20 Norddeutsche Hochschule für Rechtspflege – Nie... \n", - "21 Kommunale Hochschule für Verwaltung in Nieders... \n", - "22 Carl von Ossietzky Universität Oldenburg\\n \n", - "23 Hochschule Osnabrück \n", - "24 Universität Osnabrück \n", - "25 Hochschule Braunschweig/Wolfenbüttel, Ostfalia... \n", - "26 Hochschule Wolfsburg, Ostfalia Hochschule für ... \n", - "27 Hochschule Suderburg, Ostfalia Hochschule für ... \n", - "28 Hochschule Salzgitter, Ostfalia Hochschule für... \n", - "29 Hochschule für Künste im Sozialen, Ottersberg \n", - "30 Private Hochschule für Wirtschaft und Technik ... \n", - "31 Private Hochschule für Wirtschaft und Technik ... \n", - "32 Universität Vechta \n", - "33 Hochschule Weserbergland \n", - "34 Jade Hochschule – Wilhelmshaven \n", - "35 Jade Hochschule – Oldenburg \n", - "36 Jade Hochschule – Elsfleth \n", - "37 Steuerakademie Niedersachsen Rinteln \n", - "38 Steuerakademie Niedersachsen Bad Eilsen " - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unis_nd[[\"Sponsorship\", \"Founding year\", \"University name\"]]" - ] - }, - { - "cell_type": "markdown", - "id": "bef4ece8-00b0-486d-bba2-7f19e885cf6a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-289af023470b6b19", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe\n", - "\n", - "*2 Punkte*\n", - "\n", - "Selektiere die Spalten _University name_, _Founding year_ & _Number of students_, speichern sie ihr Ergebnis in der Variablen `select`.\n", - "\n", - "Gebe danach die ersten 5 Werte von Oben der Selektion aus." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "040fa689-6062-44db-a6ea-b58113e41b65", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-2d560e3a83f1c48a", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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University nameFounding yearNumber of students
0Hochschule für Bildende Künste Braunschweig1963976.0
1Technische Universität Carolo-Wilhelmina zu Br...174517709.0
2Hochschule 2120051084.0
3Technische Universität Clausthal17753446.0
4Hochschule Emden/Leer20094481.0
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" - ], - "text/plain": [ - " University name Founding year \\\n", - "0 Hochschule für Bildende Künste Braunschweig 1963 \n", - "1 Technische Universität Carolo-Wilhelmina zu Br... 1745 \n", - "2 Hochschule 21 2005 \n", - "3 Technische Universität Clausthal 1775 \n", - "4 Hochschule Emden/Leer 2009 \n", - "\n", - " Number of students \n", - "0 976.0 \n", - "1 17709.0 \n", - "2 1084.0 \n", - "3 3446.0 \n", - "4 4481.0 " - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "select = None\n", - "\n", - "### BEGIN SOLUTION\n", - "select = unis_nd[[\"University name\", \"Founding year\", \"Number of students\"]]\n", - "select.head(5)\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "5bd633ac-afd9-4731-9c00-b494331d2dfb", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-108386a4387dbcc7", - "locked": true, - "points": 2, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Hier werden ihre Lösungen getestet\n", - "assert isinstance(select, pd.DataFrame)\n", - "assert list(select.keys()) == [\"University name\", \"Founding year\", \"Number of students\"]" - ] - }, - { - "cell_type": "markdown", - "id": "e9751208-b181-4764-b436-a57c5313d288", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-4fa720449b4af62e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Statistische Funktionen\n", - "\n", - "Wie in der Dokumentation beschrieben unterstützt Pandas verschiendene Statistische Funktion, welche direkt auf einen Data Frame angewendet werden können.\n", - "\n", - "Als Beispiel wird die Funktion `value_counts` auf die Spalte _Type of university_ gezeigt und im darauffolgenden Schritt als Kuchendiagramm geplottet. Das Ergebnis in der Variablen `count` ist eine `pd.Series`.\n", - "\n", - "Editor Side Note: Für den Plot verwende ich das Stylesheet von dhaitz, eines meiner absoluten Favouriten, dieses und mehrere finden Sie unter [github.com dhaitz](https://github.com/dhaitz/matplotlib-stylesheets). Mittels `plt.style.use` lassen sich externe Stylesheets verwenden." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "14cc37af-13fa-49ce-af37-d327b16bda73", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-daa584941e3d78fe", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Type of university\n", - "University of Applied Sciences 22\n", - "University 11\n", - "University of Administration 4\n", - "Artistic university 2\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "count = unis_nd[\"Type of university\"].value_counts()\n", - "count" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "d8eff47d-e2e7-46c1-94ae-d344b3b62e0f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ea7ef2c3a9ada940", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "format = lambda s: f'Quantity: %d\\n' % s + f'In Percent: %1.1f%%' % s\n", - "\n", - "plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-dark.mplstyle') # Using my favourite Style\n", - "plt.pie(count, labels=count.keys(), autopct=format, startangle=45, explode=[0, 0, 0, 0.2])\n", - "plt.title(\"Types of Universities in lower Saxony (in %)\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "1104848a-c149-4ef0-8835-aee9f4ad9f36", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1c947f45fd0b2759", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe\n", - "\n", - "*6 Punkte*\n", - "\n", - "Erstelle eine visualisierung, welche die Unterschiede zwischen staatlichen & privaten Hochschulen in Niedersachsen zeigt.\n", - "\n", - "Die dazugehörige Spalte im Datenset ist _Sponsorship_. Finde eine geeignete Darstellung.\n", - "\n", - "Erkläre anschließend deine Visualisierung und warum du dich für diese Entschieden hast. (0 Punkte bei keiner Erklärung)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "1892e3fc-4d89-4966-bb36-b691a3d16ea4", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-fa553fbf09ed469b", - "locked": false, - "points": 2, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "### BEGIN SOLUTION\n", - "c = unis_nd[\"Sponsorship\"].value_counts()\n", - "plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-dark.mplstyle')\n", - "plt.bar(c.keys(), c, color=[\"black\", \"white\"])\n", - "plt.show()\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "e7b7d860-7068-43a8-a7b6-8dfd708c3597", - "metadata": {}, - "source": [ - "### BEGIN SOLUTION\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "1b420d70-9b7c-4e36-9b59-a44316af189a", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-cdcf1d5c71a5bfee", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "# Import Seaborn\n", - "\n", - "Auch hier waren die Entwickler uns einen Schritt vorraus und bevorzugen den Import von Seaborn mit dem Kürzel `sns`." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "b4e4202d-cf3f-4fa0-a66e-44ac08902cf2", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0ab00ccadf1d74a7", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "import seaborn as sns\n", - "import matplotlib.pyplot as plt # Using it alongside sns" - ] - }, - { - "cell_type": "markdown", - "id": "d10ef485-e7c4-4889-9e4f-61c765bf9384", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-25175b946c35845e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Setting a Theme\n", - "\n", - "Auch wenn MatPlotLib es möglichmacht hat seaborn eine simpleren Weg Themes anzuwenden. Schaue dir daher gerne die vorinstallierten Styles unter [Python Graph Gallery](https://python-graph-gallery.com/104-seaborn-themes/) an. Für die Folgenden Beispiele wird das Theme `darkgrid` verwendet.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "1469effb-088e-4721-9a9c-f2fcc1423a1d", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-7587373683f9bc52", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "sns.set_style('darkgrid')" - ] - }, - { - "cell_type": "markdown", - "id": "56a7f8ff-8899-4973-be0b-7a680302bfa4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3151571f68fe985d", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Multidimensional Data Plotting\n", - "\n", - "Schauen wir uns im Folgenden an, wie sich verschiedene Normalverteilte Größen in einem Plot unterbringen lassen.\n", - "\n", - "Dazu gehen wir davon aus das wir 60 Bienen, 3 verschiedener Arten gefangen haben und diese auf Länge und Anzahl an Parasiten untersuchen. Zunächst lässt sich feststellen das die Messwerkzeuge zum messen der Körperlängen nur auf 1 Dezimalstelle genau sind. \n", - "Die Anzahl der Parasiten ist immer eine ganze Zahl, da wir keine Rückstände halber/toter Parasiten messen wollen. Diese werden zusammengebracht und mittels Seaborn geplottet.\n", - "\n", - "\n", - "Hierzu werden 3 Bienenarten verwendet:\n", - "\n", - "|Biene|$\\mu$|$\\sigma$|Maximale Anzahl Parasiten|\n", - "|-|-|-|-|\n", - "|Fuchsrote Lockensandbiene|13 mm|1 mm|3|\n", - "|Ackerhummel|12 mm|3 mm|4|\n", - "|Platterbsen-Mörtelbiene|14 mm|1 mm|2|\n", - "\n", - "Im folgenden samplen wir von jeder Normalverteilten Körpergrößen der einzelnen Bienenarten 60 Samples:" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "4a63a9b6-4a17-46d0-8708-b3ab35e329b1", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-54d90be534e41353", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "rng = np.random.default_rng(42)\n", - "samples = 60\n", - "\n", - "# Sample lengths\n", - "lockensandbiene_len = rng.normal(13, 1, samples)\n", - "ackerhummel_len = rng.normal(12, 3, samples)\n", - "mörtelbiene_len = rng.normal(14, 1, samples)\n", - "\n", - "# Sample Parasites\n", - "lockensandbiene_par = rng.uniform(0, 3, samples)\n", - "ackerhummel_par = rng.uniform(0, 4, samples)\n", - "mörtelbiene_par = rng.uniform(0, 2, samples)" - ] - }, - { - "cell_type": "markdown", - "id": "e5f1d8d4-0910-4a65-91f3-26c18afdfcbe", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3154b2c952695029", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Aus den Samples lässt sich ein Dataset erstellen die wichtigen Attribute sind `species`, `length` & `parasites`. Nutzen wir dafür eine Dataclass erübrigt sich der Aufwand über listen." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "e5cbfbe6-bf9a-4ab8-8a82-c0f7310907d3", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-a38996eee69926d9", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "from dataclasses import dataclass\n", - "\n", - "@dataclass\n", - "class Bee:\n", - " species: str\n", - " length: np.float64\n", - " parasites: np.int64\n", - "\n", - "bees = list()\n", - "\n", - "# For every samples bee append to bees\n", - "for bee_len, bee_par in zip(lockensandbiene_len, lockensandbiene_par):\n", - " b = Bee(species='Fuchsrote Lockensandbiene', length=np.round(bee_len, decimals=1), parasites=np.round(bee_par, decimals=0))\n", - " bees.append(b)\n", - "for bee_len, bee_par in zip(ackerhummel_len, ackerhummel_par):\n", - " b = Bee(species='Ackerhummel', length=np.round(bee_len, decimals=1), parasites=np.round(bee_par, decimals=0))\n", - " bees.append(b)\n", - "for bee_len, bee_par in zip(mörtelbiene_len, mörtelbiene_par):\n", - " b = Bee(species='Platterbsen-Mörtelbiene', length=np.round(bee_len, decimals=1), parasites=np.round(bee_par, decimals=0))\n", - " bees.append(b)" - ] - }, - { - "cell_type": "markdown", - "id": "453420f2-bfa5-448f-b353-7d2c150c86c8", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-1cffcdd63ad3fb6c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Da ab diesem Schritt alle gesampleten Bienen geordnet und deklariert sind können wir mittels pandas einfach einen Dataframe erstellen und diesen speichern:" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "e4954edf-ecf9-4097-aab3-de0abf5bc7e5", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b17aafac68bb15b8", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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0Fuchsrote Lockensandbiene13.31.0
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" - ], - "text/plain": [ - " species length parasites\n", - "0 Fuchsrote Lockensandbiene 13.3 1.0\n", - "1 Fuchsrote Lockensandbiene 12.0 2.0\n", - "2 Fuchsrote Lockensandbiene 13.8 2.0\n", - "3 Fuchsrote Lockensandbiene 13.9 1.0\n", - "4 Fuchsrote Lockensandbiene 11.0 0.0\n", - ".. ... ... ...\n", - "175 Platterbsen-Mörtelbiene 14.2 1.0\n", - "176 Platterbsen-Mörtelbiene 15.6 1.0\n", - "177 Platterbsen-Mörtelbiene 14.2 2.0\n", - "178 Platterbsen-Mörtelbiene 13.9 0.0\n", - "179 Platterbsen-Mörtelbiene 14.3 0.0\n", - "\n", - "[180 rows x 3 columns]" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bees_df = pd.DataFrame(bees)\n", - "bees_df" - ] - }, - { - "cell_type": "markdown", - "id": "c5e49fc8-e5f7-403d-9b56-3a560b92f0f4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b1092b46d45a70db", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Es hat Vorteile sich den Dataframe als CSV zu speichern dies versteht sich mittels:" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "fec83133-43d8-40ae-b3d0-43dab722a671", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-735e7a0c1e9c3f9d", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "bees_df.to_csv('Bees.csv', index=False)" - ] - }, - { - "cell_type": "markdown", - "id": "5efba824-f0d2-4134-837d-1be4ce9d2e46", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-55ef638057716b7e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "In dem Notebook Ordner sollte eine Datei `Bees.csv` erstellt worden sein. Schaue sie dir gerne an.\n", - "\n", - "Seaborn macht es dementsprechend einfach. So verlangt `jointplot` ein Pandas DataFrame und für die `x` & `y` Koordinaten einfach den Namen der Spalten. um die Bienen voneinander zu Unterscheiden wird mit dem Parameter `hue` (Farbwert) nach Spezies gefärbt.\n", - "\n", - "Mit einer einzelenen Zeile ensteht dann folgender Plot.\n", - "Dieser Zeigt die Anzahl an Parasiten für jede gesamplete Biene und an den Seiten die Normalverteilungen dieser:" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "9778a0c6-0fb9-4b84-a8c5-e476dd7448a4", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-3a931b591234c528", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.jointplot(data=bees_df, x=\"length\", y=\"parasites\", hue=\"species\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "46c69a61-fbd6-4c51-a749-a141ed0c8eca", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-38cb7d6f4529d85e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Mit dem `kind` Parameter lösst sich selbiger Plot auch als Histogramm anzeigen:" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "0b17470e-c9a0-4fd9-887e-eccbb3dead1f", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d0fb66b692b5eecd", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.jointplot(data=bees_df, x=\"length\", y=\"parasites\", hue=\"species\", kind='hist')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "22c86f08-23e9-4135-a3ef-4a6b1c86554c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d8aa28cae2c7b366", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Aus dem Dataset lassen sich auch Multiplots erstellen:" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "29b3fe01-f7ca-4a2a-822f-cb147e12eece", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-37ffc1027bae59c4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "grid = sns.FacetGrid(bees_df, col='species', hue='species')\n", - "grid.map(sns.histplot, \"length\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "4466fadd-e137-43ab-ba69-e4353cf54aad", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-11da2cf6fff4eafa", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Aufgabe\n", - "\n", - "*8 Punkte*\n", - "\n", - "Erstelle das Dataset `people_in_germany`.\n", - "\n", - "Folgendes Szenario:\n", - "\n", - "Du bist Part einer Massenhaft angelegten Studie um die bereits bekannten Zahlen des Statistischen Bundesamtes zu überprüfen.\n", - "\n", - "Dazu sind 4 Größen bekannt aus den Angaben des Statistischen Bundesamt für die [männliche Population](https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Gesundheit/Gesundheitszustand-Relevantes-Verhalten/Tabellen/koerpermasse-maenner.html) & die [weibliche Population](https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Gesundheit/Gesundheitszustand-Relevantes-Verhalten/Tabellen/koerpermasse-frauen.html):\n", - "\n", - "||Körpergröße (in cm)|Gewicht (in Kg)|\n", - "|-|-|-|\n", - "|Männlich|178.9|85.8|\n", - "|Weiblich|165.8|69.2|\n", - "\n", - "Gehe dabei wie folgt vor:\n", - "- Treffe annahmen über die Verteilung und finde geeignete Werte für $\\mu$ & $\\sigma$. **Erkläre** deine Annahmen mit einem kurzen Text. Quellen sind gerne gesehen.\n", - "- Die Samplegröße beträgt 1000. Sample dementsprechend aus den gegebenen Werten.\n", - "- Speichere die gesampleten Personen nach dem Schema: `|sex|height|weight|` in der File `people_in_germany.csv` als csv.\n", - " -> Nutze für das `sex` Attribut den Datentyp `bool` mit der Kodierung: `True = female` & `False = male`.\n", - "- Stelle dein Ergebnis angemessen dar. Das theme `darkgrid` darf nicht verwendet werden.\n", - "- **Beschreibe** & **Interpretiere** den Plot. Gehe dabei von der Hypothese aus das es **keine** Unterschiede zwischen den Geschlechtern beim samplen geben sollte. (Auch hier 0 Punkte bei keiner Antwort)\n", - "\n", - "*Tipp: Dataclasses erleichtern die Aufgabe ungemein!*" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "68702477-b09f-45ee-871d-da9a130f3319", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-0c8bc5e7c272cd4c", - "locked": false, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "import numpy as np\n", - "import seaborn as sns\n", - "from dataclasses import dataclass\n", - "\n", - "samples = 1000\n", - "\n", - "@dataclass\n", - "class Individual:\n", - " sex: bool\n", - " height: np.float64\n", - " weight: np.float64\n", - " \n", - "\n", - "rng = np.random.default_rng()\n", - "individuals = list()\n", - "\n", - "for i in range(samples):\n", - " if i % 2 == 0:\n", - " sex = False\n", - " height = rng.normal(178.9, 10)\n", - " weight = rng.normal(85.8, 26)\n", - " \n", - " else:\n", - " sex = True\n", - " height = rng.normal(165.8, 11)\n", - " weight = rng.normal(69.2, 19)\n", - "\n", - " height = np.round(height, decimals=1)\n", - " weight = np.round(weight, decimals=1)\n", - " \n", - " individual = Individual(sex=sex,height=height,weight=weight)\n", - " individuals.append(individual)\n", - "\n", - "individuals = pd.DataFrame(individuals)\n", - "individuals.to_csv('people_in_germany.csv', index=False)\n", - "\n", - "sns.set_style('white')\n", - "sns.jointplot(data=individuals, x=\"height\", y=\"weight\", hue=\"sex\")\n", - "plt.show()\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "2fbcac55-ed91-4290-8026-639d215f783c", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-8b5f26594c6567ed", - "locked": false, - "points": 3, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 149, - "id": "9d423664-b098-4fd4-9bad-5f7028424c87", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-3060604f60a0ca6f", - "locked": true, - "points": 8, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "# Hier werden ihre Lösungen getestet...\n", - "test_individuals = pd.read_csv('people_in_germany.csv')\n", - "assert len(test_individuals) > 999 # Test if enough samples where taken 1 Punkt\n", - "assert list(test_individuals.columns) == ['gender', 'height', 'weight'] # Test if columns are correct 1 Punkt\n", - "\n", - "# Test gender means\n", - "male_height = np.mean(test_individuals[test_individuals['gender'] == False]['height'])\n", - "male_weight = np.mean(test_individuals[test_individuals['gender'] == False]['weight'])\n", - "female_height = np.mean(test_individuals[test_individuals['gender'] == True]['height'])\n", - "female_weight = np.mean(test_individuals[test_individuals['gender'] == True]['weight'])\n", - "\n", - "assert male_height > 176 and male_height < 182 # 1 Punkt\n", - "assert male_weight > 83 and male_weight < 89 # 1 Punkt\n", - "assert female_height > 163 and female_height < 169 # 1 Punkt\n", - "assert female_weight > 66 and female_weight < 72 # 1 Punkt\n", - "\n", - "# test cummultative mean 1 Punkt\n", - "assert test_individuals['height'].mean() > 170 and test_individuals['height'].mean() < 174 \n", - "assert test_individuals['weight'].mean() > 75 and test_individuals['weight'].mean() < 80 " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +version https://git-lfs.github.com/spec/v1 +oid sha256:04ea841f6e1efbb8fe34a505c02b177f8a2e967691f0c2413362aa17cdb1fb47 +size 474296 diff --git a/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/8.Statistical_Test_Methods.ipynb b/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/8.Statistical_Test_Methods.ipynb index c4d8a4c..3304f00 100644 --- a/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/8.Statistical_Test_Methods.ipynb +++ b/Documents/Arbeit/IFN/Programmieren WiSe 25 26/Jupyter/8.Statistical_Test_Methods.ipynb @@ -1,1566 +1,3 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c2734eb9-d9b9-43b2-a9fb-c25729a0f3a3", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-5fff5091c9d505d4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# 8. Programmierübung: Statistische Test Methoden\n", - "\n", - "
\n", - "
\n", - " Willkommen zur achten Programmierübung Einführung in Python 3.\n", - "
\n", - " \n", - "
\n", - "\n", - "Wenn Sie Fragen oder Verbesserungsvorschläge zum Inhalt oder Struktur der Notebooks haben, dann können sie eine E-Mail an Phil Keier ([p.keier@hbk-bs.de](mailto:p.keier@hbk-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) schreiben.\n", - "\n", - "Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n", - "\n", - "Der Großteil des Python-Tutorials stammt aus der Veranstaltung _Deep Learning Lab_ und von [www.python-kurs.eu](https://www.python-kurs.eu/python3_kurs.php) und wurde für _Signale und Systeme_, sowie _Einführung in die Programmierung für Nicht Informatiker_ angepasst.\n", - "\n", - "---" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "483f1609-fd3a-4154-a059-7e5d26562bcb", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-2885f7f5d6e48f91", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - 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"id": "76813bd6-6efe-439f-b48c-30da670b84ab", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-34d4d9909fce30d4", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'numpy'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m stats\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'numpy'" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from scipy import stats\n", - "import pandas as pd\n", - "import seaborn as sb" - ] - }, - { - "cell_type": "markdown", - "id": "8af16cfd-827f-4e7d-91a3-970e920ad212", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-fa7bc65ef436bf2f", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Was ist ein T-Test?\n", - "\n", - "Der T-Test ist eine Methode um zu bestimmen ob es einen Signifikanten Unterschied zweier distinkter Populationen gibt.\n", - "Hierzu können wir diverse bereits bekannte Python Libraries und deren Funktionen nutzen um Hypothesen zu testen.\n", - "\n", - "Wir beschränken uns im folgenden auf den *One-Sample t-Test*.\n", - "\n", - "Dabei hat der/die Analyst/-in gewissen Erwartung an den T-Test:\n", - "1. Unabhängigkeit der Beobachtung (Independence of Observation)\n", - "2. Normalität (Normality)\n", - "3. Gleiche Varianz (Equal variances)\n", - "\n", - "Ein wenig Notation:\n", - "1. Eine Hypothese ist eine Annahme die über eine Population getroffen wird.\n", - "2. Eine Nullhypothese $H_0$ ist die Grundannahme einer statistischen Untersuchung, welche Beweist oder widerlegt werden soll.\n", - "3. Eine Population ist eine (Daten-)Gruppe, welche über bestimmte Charakteristika verfügt.\n", - "4. Samples sind unter(daten-)gruppen einer bestimmten Population.\n", - "5. Nullverteilung (Null Distribution) ist die Verteilung unter der Annahme das $H_0$ Wahr ist.\n", - "\n", - "## Die Idee\n", - "\n", - "Aus voherigen Übungen ist die Normalverteilung bekannt, diese gibt an wie wahrscheinlich es ist das eine Zufallsvariable aus dieser zu beziehen. Folglich wird der Durschnitt der Samples berechnet und verglichen wie Wahrscheinlich es ist das diese auf die Null Distribution fällt. Unter dieser Annahme ist es nun möglich $H_0$ beweisen. \n", - "\n", - "Dabei sind die benötigten Parameter für jeden Null Hypothesen Test gleich:\n", - "1. Bestimmen der Parameter einer Populationn\n", - "2. Aufstellen der Null Hypothese $H_0$\n", - "3. Sammeln (& bereinigen) der Samples\n", - "4. Aufstellen der Null Distribution\n", - "\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "358dc549-087d-4353-9fda-eedfbe6bc40b", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-5d738a69cd7872e6", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Beispiel\n", - "\n", - "### Parameter bestimmung einer Population\n", - "\n", - "Das durchschnittliche Studiendauer für den Bachelor in Deutschland ist auf 6 Semester festgelegt. \n", - "Im folgenden beschränken wir uns auf diese Annahme. Alle Studiengänge die dieses Kriterium nicht erfüllen werden ausgeschlossen.\n", - "\n", - "### Aufstellen der Null Hypothese\n", - "\n", - "Der durchschnittliche Student (im Erststudium) benötigt für sein Bachelorstudium 6 Semester.\n", - "\n", - "Aus folgender [Statista](https://de.statista.com/statistik/daten/studie/189155/umfrage/durchschnittliche-studiendauer-in-deutschland/) Grafik wissen wir das dies nicht stimmt. Dort ist die Durchschnittsdauer für ein Studium mit einem Median von 9 Semestern (2024) angegeben.\n", - "\n", - "![](./Semester_Deutschland.png)\n", - "\n", - "Wir können also aufstellen: $H_0: \\mu=6$\n", - "\n", - "### Sampling\n", - "\n", - "Für das Sampling wird aus der (eigentlich unbekannten) Normalverteilung mit $\\mu = 9, \\sigma = 1.5$ gesampelt.\n", - "Dabei gehen wir von bekannten Zahlen aus. Stand 2016 hatte die TU Braunschweig 20.000 Studierende nehmen wir als Samplegröße 2% dieser. Somit verändert sich jedoch die Aussage von $H_0$ indem diese nicht ganz Deutschland sondern nur die TU Braunschweig abbildet. \n", - "\n", - "Dies lässt sich wie bereits bekannt mit NumPy folglich erreichen:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "21e52ad5-6eab-4ac5-ac2f-849dd2df07fa", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-92b5ae5f97eb5ca9", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Parameters\n", - "mu: float = 9\n", - "sigma: float = 1.5\n", - "sample_size: int = int(20_000 * 0.02)\n", - "\n", - "# Sampling\n", - "rng = np.random.default_rng(42) # reproduzierbarkeit ist Gewährleistet\n", - "samples = rng.normal(mu, sigma, sample_size)\n", - "\n", - "# Plot\n", - "min_semester = int(np.floor(samples.min()))\n", - "max_semester = int(np.floor(samples.max()))\n", - "bins = np.arange(min_semester - 0.5, max_semester + 1.5, 1)\n", - "\n", - "plt.hist(samples, bins=bins, edgecolor=\"black\")\n", - "plt.title(\"Bachelor Abschluss nach Semester (TU BS) Sample\")\n", - "plt.xlabel(\"Semester\")\n", - "plt.ylabel(\"Samples\")\n", - "plt.xticks(range(min_semester, max_semester+1))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "7beda001-4ce7-435b-880f-78fe5881b8c1", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-f5613ce17a915c5a", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "### Aufstellen der Null Distribution\n", - "\n", - "Nach dem **Central Limit Theorem** gilt:\n", - "\n", - "$$\\bar X \\sim AN(\\mu, \\frac{\\sigma^2}{n})$$\n", - "\n", - "Das **Central Limit Theorem** geht davon aus das wenn genügend Samples vorhanden sind, die Verteilung dieser, die Normalverteilung anstrebt. Wir gehen daher davon aus das unsere Samples Normaleverteilt sind.\n", - "\n", - "Das mag vielleicht sehr Obvious klingen, da die erhobenen Samples aus einer Normalverteilung stammen, ist aber eine der wichtigen Annahmen die getroffen werden muss, da wir die Verteilung eigentlich nicht kennen.\n", - "\n", - "Statistiker bevorzugen die normalisierte Variante des **Central Limit Theorems** um immer ein Ergebniss im Interval $[0, 1]$ zu erhalten:\n", - "\n", - "$$\\frac{(\\bar X - \\mu)}{\\frac{\\sigma}{\\sqrt n}} \\sim AN(0, 1)$$\n", - "\n", - "Dies wäre der Standard Z-Test. Das Problem hierbei ist das $\\sigma$ in den meisten Fällen nicht bekannt ist.\n", - "Daher ersetzen wir $\\sigma^2$, die Standardvarianz, mit der Samplevarianz $S^2$:\n", - "\n", - "$$S^2 = \\frac{\\sum^n_{i=1} (X_i - \\bar X)^2}{n - 1}$$\n", - "\n", - "Daraus lässt sich die normalisierte Variante des **Central Limit Theorems** transformieren. Dies hat den Vorteil das eine feste unbekannte durch eine zufällig erhobene bekannte Variable ersetzt wird. Diese Verteilung heißt **Student's T Distribution** oder kurz **T Distribution** und wird mit einem kleinen **t** abgekürzt:\n", - "\n", - "$$\\frac{(\\bar X - \\mu)}{\\frac{\\sigma}{\\sqrt n}} \\rightarrow \\frac{(\\bar X - \\mu)}{\\frac{S}{\\sqrt n}}$$\n", - "\n", - "Damit haben wir alles zusammen um den *One-Sample t-test* durchzuführen.\n", - "\n", - "1. Population Parameter: $\\mu$\n", - "2. Null Hypothesis: $H_0: \\mu = 6$\n", - "3. Test Statistic: $t = \\frac{(\\bar X - \\mu)}{\\frac{S}{\\sqrt n}}$\n", - "4. Null Distribution: $t \\sim t_{n-1}$" - ] - }, - { - "cell_type": "markdown", - "id": "a9f7ffee-8d2c-474a-914d-3dcc048c0116", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-63fcdadcfd48afab", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Durchführung\n", - "\n", - "Für die Durchführung müssen folglich erst alle Parameter aufgestellt werden:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "390f312a-0a73-4792-9791-3f9a4aec57b2", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-ae1a48d648e99f4b", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [], - "source": [ - "mu = 6 # Hypothesized Population mean\n", - "alpha = 0.05 # significance level" - ] - }, - { - "cell_type": "markdown", - "id": "94623047-774a-4b51-b12d-7b792ba35a3c", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-d9dc4c4bd754b014", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "Die samples sind bereits bekannt daher brauchen diese nicht neu berechnet werden.\n", - "\n", - "Der Wert $\\alpha$ gibt an, wie signifikant eine Aussage ist, und ist einer der beiden Rückgabeparameter der SciPy-Funktion `stats.ttest_1samp`. Er gibt den maximalen Fehler an, wird aus der aufkumulierten t-Verteilung berechnet und ist der interessantere der beiden Werte.\n", - "\n", - "Mittels SciPy lässt sich folglich der *One-Sample t-test* durchführen:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "bc65a1d6-612d-4894-b1c0-511e64f25e56", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-b64d3a20a71d3205", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "T statistic: 41.89267214862724\n", - "P-value: 3.6070939577287835e-148\n", - "Reject the null hypothesis; there is a significant difference between the sample mean and the hypothesized population mean.\n" - ] - } - ], - "source": [ - "t_stat, p_value = stats.ttest_1samp(samples, mu)\n", - "print(\"T statistic:\", t_stat)\n", - "print(\"P-value:\", p_value)\n", - "\n", - "if p_value < alpha:\n", - " print(\"Reject the null hypothesis; there is a significant difference between the sample mean and the hypothesized population mean.\")\n", - "else:\n", - " print(\"Fail to reject the null hypothesis; there is no significant difference between the sample mean and the hypothesized population mean.\")" - ] - }, - { - "cell_type": "markdown", - "id": "fbe8e5fd-5243-4f88-aa4d-56be228e023e", - "metadata": { - "nbgrader": { - "grade": false, - "grade_id": "cell-81d3dc2d85c03c84", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - } - }, - "source": [ - "## Schlussfolgerung\n", - "\n", - "Aus den gesammelten (herbei gezauberten) Daten lässt sich Schlussfolgern das die Null Hypothese $H_0$, dass Studenten im Erststudium an der TU Braunschweig ihr Erststudium in der Regel nicht nach 6 Semestern abschließen.\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "1e6e76be-08af-42ad-8522-cd86c08d73e7", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-26af8bd4e579ae2c", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe T-Test\n", - "\n", - "*6 Punkte - Programmieren*\n", - "\n", - "*6 Punkte - Schriftteil*\n", - "\n", - "Für diese Aufgabe ist extra ein Feld für den Schriftteil reserviert. **Erkläre** dort dein Vorgehen, die Nullhypothese, dein Ergebnis und wie der Nominallohn zu verstehen ist. **Interpretiere** dein Ergebnis und Stelle ein **Fazit** auf. Gebe auch alle verwendeten Quellen (samt link) korrekt an.\n", - "Jupyter unterstütz die Markdown Syntax ein Guide hierfür ist unter [The Ultimate Markdown Guide](https://medium.com/analytics-vidhya/the-ultimate-markdown-guide-for-jupyter-notebook-d5e5abf728fd) zu finden.\n", - "\n", - "**!!Wird der Schriftteil nicht oder nur unzureichend ausgefüllt führt dies zu einer Bewertung von 0 Punkten für die gesamte Aufgabe!!**\n", - "\n", - "Dem Notebook mit beigelegt ist das Datenset `nominallohn_deutschland_2023-2024_nach_geschlecht.csv` (Quelle: [DESTASIS Nominallohnindex: Deutschland, Monate, Geschlecht](https://www-genesis.destatis.de/datenbank/online/statistic/62361/table/62361-0003)).\n", - "\n", - "Analysiere das Datenset, gehen dabei von der Nullhypothese aus das es keinen Signifikanten Unterschied in den Nominallöhnen zwischen den Geschlechtern gibt.\n", - "\n", - "Gehe dabei wie folgt vor:\n", - "1. Stelle die Nullhypothese prüfbar auf.\n", - "2. Importiere das Datenset und filtere angemessen (Kommentare helfen mir später bei der Korrektur diesen Schritt nachzuvollziehen)\n", - "3. Berechne den von der Nullhypothese geforderten Populations Parameter $\\mu$.\n", - "4. Bestimme das Signifikanzlevel $\\alpha$ unter der $H_0$ verworfen wird.\n", - "5. Bestimme mittels t-test den p-wert zur Nullhypothese.\n", - "6. Stelle dein Ergebnis dar." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "10d3b6f1-2c8d-4999-9f02-30496db96297", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-401af51c252cee3b", - "locked": false, - "points": 6, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " Fail to reject the null hypothesis;\n", - " there is no significant difference between the sample mean\n", - " and the hypothesized population mean.\n", - " \n", - "P-Value: 0.95\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "# Load dataset from CSV file\n", - "dataset: pd.DataFrame = pd.read_csv('nominallohn_deutschland_2023-2024_nach_geschlecht.csv', delimiter=',')\n", - "\n", - "# Split dataset by gender\n", - "male = dataset.loc[dataset[\"Geschlecht\"] == \"männlich\"]\n", - "female = dataset.loc[dataset[\"Geschlecht\"] == \"weiblich\"]\n", - "\n", - "# Compute population mean (male wages) and define significance level\n", - "mu = male[\"Nominallohn\"].mean()\n", - "alpha = 0.05\n", - "\n", - "# One-sample t-test: female wages against male population mean\n", - "t_stat, p_value = stats.ttest_1samp(female[\"Nominallohn\"], mu)\n", - "\n", - "# Hypothesis test decision based on p-value\n", - "if p_value < alpha:\n", - " print('''\n", - " Reject the null hypothesis;\n", - " there is a significant difference between the sample mean\n", - " and the hypothesized population mean.\n", - " '''\n", - " )\n", - "else:\n", - " print('''\n", - " Fail to reject the null hypothesis;\n", - " there is no significant difference between the sample mean\n", - " and the hypothesized population mean.\n", - " '''\n", - " )\n", - "\n", - "print(f\"P-Value: {p_value:.2}\")\n", - "\n", - "\n", - "# Define month labels\n", - "months = [\"Januar\", \"Februar\", \"März\", \"April\", \"Mai\", \"Juni\", \"Juli\", \"August\", \"September\", \"Oktober\", \"November\", \"Dezember\"]\n", - "\n", - "# Extract monthly nominal wages for each gender and year\n", - "dw_2023 = {f\"{m} 2023\": female.loc[female[\"Monat\"] == m].loc[female[\"Jahr\"] == 2023][\"Nominallohn\"].values[0] for m in months}\n", - "dw_2024 = {f\"{m} 2024\": female.loc[female[\"Monat\"] == m].loc[female[\"Jahr\"] == 2024][\"Nominallohn\"].values[0] for m in months[:-2]}\n", - "dm_2023 = {f\"{m} 2023\": male.loc[male[\"Monat\"] == m].loc[male[\"Jahr\"] == 2023][\"Nominallohn\"].values[0] for m in months}\n", - "dm_2024 = {f\"{m} 2024\": male.loc[male[\"Monat\"] == m].loc[male[\"Jahr\"] == 2024][\"Nominallohn\"].values[0] for m in months[:-2]}\n", - "\n", - "# Plot\n", - "\n", - "# Prepare bar plot layout\n", - "fig, ax = plt.subplots(figsize = (12, 8))\n", - "width = 0.3\n", - "ind_23 = np.arange(len(dw_2023))\n", - "ind_24 = np.arange(len(dw_2023), len(dw_2023) + len(dw_2024))\n", - "\n", - "# Build x-axis labels\n", - "labels = list(dw_2023.keys()) + list(dw_2024.keys())\n", - "\n", - "# Plot nominal wages by gender and year\n", - "plt.bar(ind_23, dw_2023.values(), width, alpha=0.5, color='red')\n", - "plt.bar(ind_24, dw_2024.values(), width, alpha=0.5, color='red')\n", - "plt.bar(ind_23+width, dm_2023.values(), width, alpha=0.5, color='b')\n", - "plt.bar(ind_24+width, dm_2024.values(), width, alpha=0.5, color='b')\n", - "\n", - "# Configure axes and display plot\n", - "plt.xlabel('Monat')\n", - "plt.ylabel('Nominallohn')\n", - "plt.title('Nominallohn nach Geschlechtern')\n", - "plt.xticks(np.arange(len(dw_2023) + len(dw_2024)) + width / 2, labels)\n", - "plt.setp(ax.get_xticklabels(), rotation=30, horizontalalignment='right', fontsize='x-small')\n", - "plt.show()\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "3a3abb0b-81c6-4b84-b298-039b73ba35a2", - "metadata": { - "editable": true, - "nbgrader": { - "grade": true, - "grade_id": "cell-e4292c6997f90bab", - "locked": false, - "points": 6, - "schema_version": 3, - "solution": true, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# BEGIN SOLUTION\n", - "\n", - "### Nominallohn \n", - ">das tatsächlich in € gezahlte Entgelt für geleistete Arbeit (Gegensatz: Reallohn). Der Nominallohn lässt keine Aussagen über die Kaufkraft zu, da die Preisentwicklung nicht berücksichtigt wird. Werden in einem Tarifvertrag z. B. Lohnsteigerungen von 3 % vereinbart, und die Inflationsrate beträgt gleichzeitig 2 %, so steigen die Löhne nominal um 3 %, tatsächlich (real) aber nur um 1 %.\n", - "\n", - "Quelle: [bpb.de](https://www.bpb.de/kurz-knapp/lexika/lexikon-der-wirtschaft/20175/nominallohn/)\n", - "\n", - "### Nullhypothese\n", - "> Der männliche Nominallohn unterscheidet sich nicht von dem des weiblichen Nominallohns.\n", - "\n", - "### Vorgehen\n", - "Filtern des Datensets in die beiden Kategorien *männlich* & *weiblich*.\n", - "Danach aufstellen der oben genannten Nullhypothese.\n", - "Folglich ist der Populations Parameter zur Nullhypothese $\\mu$ der Durschnittswert der männlichen Nominallöhne.\n", - "Also Signifikanz Parameter $\\alpha$ wird 0.05 verwendet.\n", - "Nach t-test gegen die weiblichen Nominallöhne ergibt sich, das die Null Hypothese nicht verworfen werden kann.\n", - "\n", - "### Fazit\n", - "Die Null Hypothese lässt sich als Bestätigt ansehen. Daher gibt es keinen Unterschied zwischen den Nominallöhnen beider Geschlechter.\n", - "\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "07378cd6-116e-4a6d-8233-f5fc7da17d3e", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-2cb31097d39ef3fa", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "---\n", - "\n", - "# Korrelation\n", - "\n", - "## Was ist eine Korrelation?\n", - "\n", - "Als Korrelation beschreibt man den Zusammenhang zweier Variabeln und ihre Abhängigkeit zueinander. Dabei wird ein Korrelationkoeffizient berechnet. Dieser hat immer einen Wert zwischen -1 & +1. Dabei gilt, dass bei einem Wert von ±1 eine perfekte Assoziation vorliegt. Je weiter der Wert des Korrelationskoeffizienten gegen 0 geht, desto schwächer ist die Beziehung zwischen den beiden Variablen. Die Richtung der Beziehung ist durch das Vorzeichen gegeben; ein positives Vorzeichen (+) zeigt eine positive Beziehung der beiden Variabeln auf, während bei einem negativen Vorzeichen (-) eine negative Beziehung vorliegt.\n", - "\n", - "## Pearson Corellation Coefficient\n", - "\n", - "Nach dem Mathematiker *Karl Pearson* benannt lässt sich mittels pandas auf jedem Dataframe die Funktion *.corr* aufrufen. Diese berechnet den Korrelations koefficienten zwischen allen **numerischen** Variablen in dem DataFrame.\n", - "\n", - "Hierzu ein Beispiel mit dem Datenset `nominallohn_deutschland_2023-2024_nach_geschlecht.csv`.\n", - "Wichtig ist *.corr* die Parameter *method* & *numeric_only* mitzugeben, da es keinen Sinn ergibt einen Koeffizienten zwischen zwei Strings zu berechnen:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "4f7c2990-3f68-47e3-93e9-f4c919f1d93c", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-5294192c404de210", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Jahr Nominallohn\n", - "Jahr 1.000000 0.172602\n", - "Nominallohn 0.172602 1.000000" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset: pd.DataFrame = pd.read_csv('nominallohn_deutschland_2023-2024_nach_geschlecht.csv', delimiter=',')\n", - "pearsoncorr = dataset.corr(method='pearson', numeric_only=True)\n", - "pearsoncorr" - ] - }, - { - "cell_type": "markdown", - "id": "d012e5ff-7e5f-4bb6-9d2d-9993dd4b1947", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-9cbe48bc8062ccae", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Mittels Seaborn lässt sich diese Tabelle auch Farbig darstellen:" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "84ad50bb-b8d9-4f69-a2ae-905395a7456a", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-69937404c2eb9a0e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sb.heatmap(pearsoncorr, \n", - " xticklabels=pearsoncorr.columns,\n", - " yticklabels=pearsoncorr.columns,\n", - " cmap='RdBu_r',\n", - " annot=True,\n", - " linewidth=0.5)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "22e6a3f3-b402-4364-a418-70a6e4001d32", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-d81e230add7ed3f2", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Das Datenset enthält nur wenige Numerische Werte, dennoch lässt sich feststellen das die Beziehung zwischen dem erhobenen Jahr (2023/2024) und dem Nominallohn mit 0.17 ist. Damit zeigt sich das diese beiden Variablen keine Abhängigkeit voneinander haben." - ] - }, - { - "cell_type": "markdown", - "id": "b60b2a4d-611d-4da4-9309-9150d500878e", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-94a5452d5be14bae", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Kendall Tau\n", - "\n", - "Eine andere möglichkeit Korrelationen festzustellen ist die Methode nach *David George Kendall*.\n", - "\n", - "Anzuwenden ist sie unter den folgenden Bedingungen:\n", - "1. Die Daten folgend annährend einer Normalverteilung\n", - "2. Die Datenlage ist klein\n", - "\n", - "Das Kendall Tau wird folglich berechnet mit:\n", - "\n", - "$$\\tau = \\frac{C-D}{C+D}$$\n", - "\n", - "Dabei ist $C$ die Anzahl Übereinstimmender Paare und $D$ die Anzahl der nicht übereinstimmenden Paare.\n", - "\n", - "Dazu ein Beispiel:\n", - "\n", - "Wir nehmen an das wir zwei Doktoren hätten, welche 6 Patienten nach ihrem Gesundheitszustand einschätzen. Hierfür ergibt sich folgende Datenlage (Doktor 1 ist die Reference der Datenlage):\n", - "\n", - "|Doktor 1|Doktor 2|\n", - "|-|-|\n", - "|1|3|\n", - "|2|1|\n", - "|3|4|\n", - "|4|2|\n", - "|5|6|\n", - "|6|5|\n", - "\n", - "Nun wird paarweise verglichen und die Vorzeichen beachtet danach aufsummiert. Dies führt dazu das $C=11$ & $D=4$.\n", - "Diese Werte lassen sich wunderbar in die Formel einsetzen um so das Kendallsche Tau zu berechnen:\n", - "\n", - "$$\\tau = \\frac{C-D}{C+D} = \\frac{11-4}{11+4} = \\frac{7}{15} = 0.47$$\n", - "\n", - "Wie bei Pearson ist die Relationseinschätzung die selbe. Für einen Wert im Bereich +0.5 ist eine Moderate Abhängigkeit festzustellen.\n", - "\n", - "Selbiges Beispiel in Python. Diesmal wird in die *.corr* Funktion nur die Methode geändert:" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "e4b77f20-493d-4424-a3c2-a7fa140a78e2", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-b8c069ec23d35f28", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Doktor 1Doktor 2
Doktor 11.0000000.466667
Doktor 20.4666671.000000
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" - ], - "text/plain": [ - " Doktor 1 Doktor 2\n", - "Doktor 1 1.000000 0.466667\n", - "Doktor 2 0.466667 1.000000" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "kendall = pd.DataFrame({\n", - " \"Doktor 1\": [1, 2, 3, 4, 5, 6],\n", - " \"Doktor 2\": [3, 1, 4, 2, 6, 5]\n", - "})\n", - "kendall_tau = kendall.corr(method='kendall', numeric_only=True)\n", - "kendall_tau" - ] - }, - { - "cell_type": "markdown", - "id": "d2ff93fc-dedb-4dd1-8969-db9fcdc63c66", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-732bc023d7ac4739", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Wie zu sehen kommt der selbe Wert raus." - ] - }, - { - "cell_type": "markdown", - "id": "0cedcad4-0834-43cb-a253-b9d009018a6c", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-818c093a4e3ba72e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Spearman\n", - "\n", - "Die Methode benannt nach dem Psychologen *Charles Spearman* misst auch einen Korrelationskoeffizienten zwischen zwei Datengruppen.\n", - "Im Unterschied zu Pearson ist wird Spearman dann angewandt wenn die Daten keiner Normalverteilung folgen.\n", - "\n", - "Erstellen wir uns für ein Beispiel ein Datenset bei dem die Reaktionsgeschwindigkeit eines Individuums mit seinem Alter verglichen wird:" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "2bb83c7d-f3af-42c9-ae93-3655868d12e9", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-ec1bbf166d417dc7", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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AlterReaktionsgeschwindigkeit
01521
12229
22125
31925
41924
52326
61521
72114
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" - ], - "text/plain": [ - " Alter Reaktionsgeschwindigkeit\n", - "0 15 21\n", - "1 22 29\n", - "2 21 25\n", - "3 19 25\n", - "4 19 24\n", - "5 23 26\n", - "6 15 21\n", - "7 21 14\n", - "8 17 27\n", - "9 15 20" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rng = np.random.default_rng(42) # reproduzierbarkeit\n", - "spearman = pd.DataFrame({\n", - " \"Alter\": rng.integers(15, 25, 10),\n", - " \"Reaktionsgeschwindigkeit\": rng.integers(12, 30, 10) \n", - "})\n", - "spearman" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "5c223094-8d54-4cd8-a3cc-4cb56595ca78", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-9a98a7086c0bd404", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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AlterReaktionsgeschwindigkeit
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" - ], - "text/plain": [ - " Alter Reaktionsgeschwindigkeit\n", - "Alter 1.000000 0.509356\n", - "Reaktionsgeschwindigkeit 0.509356 1.000000" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "spearman_corr = spearman.corr(method='spearman', numeric_only=True)\n", - "spearman_corr" - ] - }, - { - "cell_type": "markdown", - "id": "c52137a3-20dc-46d3-9e30-6b0d49a62964", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-008a676a06ecdd3e", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "In diesem Beispiel lässt sich auch wieder selbige Korrelationsmetrik anwenden und es zeigt sich, dass die erzeugten Daten eine moderate Abhängigkeit zueinander haben." - ] - }, - { - "cell_type": "markdown", - "id": "53345351-2494-4142-b550-a047fd7a7849", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-be05fea16294ca00", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Merkzettel\n", - "\n", - "1. Pearson sollte dann angewandt werden, wenn die Daten annährend einer Normaleverteilung folgen bzw. aus einer erhebt wurden.\n", - "2. Kendall (wie Pearson) nur bei kleinerer Datenlage.\n", - "3. Spearman sollte dann angewandt werden, wenn die Daten keiner Normalverteilung folgen.\n", - "\n", - "Korrelationsmetrik:\n", - "\n", - "|Positiver Koeffizient|Negativer Koeffizient|Assoziation|\n", - "|-|-|-|\n", - "|+1.0 - +0.8|-1.0 - -0.8|Perfekte bis sehr Stark|\n", - "|+0.8 - +0.6|-0.8 - -0.6|Stark|\n", - "|+0.6 - +0.4|-0.6 - -0.4|Moderat|\n", - "|+0.4 - +0.2|-0.4 - -0.2|Schwach|\n", - "|+0.2 - +0.0|-0.2 - -0.0|Sehr Schwach bis Garnicht|" - ] - }, - { - "cell_type": "markdown", - "id": "4e82abfb-41d9-4133-9e08-29494b8dab50", - "metadata": { - "editable": true, - "nbgrader": { - "grade": false, - "grade_id": "cell-5fcf92745c0c6744", - "locked": true, - "schema_version": 3, - "solution": false, - "task": false - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Aufgabe\n", - "\n", - "*8 Punkte - Programmieren*\n", - "\n", - "*8 Punkte - Schriftteil*\n", - "\n", - "Für diese Aufgabe ist extra ein Feld für den Schriftteil reserviert. **Erkläre** dort dein Vorgehen, die Nullhypothese, dein Ergebnis und wie der Bruttoverdienst zu verstehen ist. **Analysiere** den Gender Pay Gap zum Erhebungszeitraum der Daten und **Vergleiche** diesen mit deiner Berechnung. **Interpretiere** dein Ergebnis und Stelle ein **Fazit** auf. Gebe auch alle verwendeten Quellen (samt link) korrekt an. \n", - "Jupyter unterstütz die Markdown Syntax ein Guide hierfür ist unter [The Ultimate Markdown Guide](https://medium.com/analytics-vidhya/the-ultimate-markdown-guide-for-jupyter-notebook-d5e5abf728fd) zu finden.\n", - "\n", - "**!!Wird der Schriftteil nicht oder nur unzureichend ausgefüllt führt dies zu einer Bewertung von 0 Punkten für die gesamte Aufgabe!!**\n", - "\n", - "Analysiere das Datenset `bruttoverdiensterhebung_deutschland_april_22-23_nach_geschlecht.csv` (Quelle: [Destatis]( https://www-genesis.destatis.de/datenbank/online/statistic/62361/table/62361-0030))\n", - "\n", - "Gehe dabei von der Hypothese aus das einen keinen Bruttoeinkommensunterschied der Geschlechter in Deutschland für den Zeitraum 2022-2023 gibt.\n", - "\n", - "Gehe dabei wie folgt vor:\n", - "1. Lies das Datenset ein und schaue dir die vorhandenen Werte an. Achte auf `Nan` Werte.\n", - "2. Filter aus der Spalte \"Geschlecht\" die benötigten Werte.\n", - "3. Führe einen Korrelationstest zwischen den Geschlechtern & dem Bruttoverdienst durch.\n", - "4. Berechne den Bruttoeinkommensunterschied.\n", - "5. Stelle den unbereinigten Gender Pay Gap auf.\n", - "6. Finde eine geeignete Darstellung für dein Ergebnis.\n", - "\n", - "*PS: Da es sich hier um ein Kontroverses, in der Gesellschaft viel disskutiertes, Thema handelt, bitte ich die Contenance zu bewahren. Es gibt bei einer komplexen Erhebung wie dieser kein richtig oder falsch. Es kommt ganz auf die Methodik an mit der Berechnet wird, begründe daher alles.*" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "8bc69bfc-96aa-4fdf-b540-a956ff4c1baf", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-78d2ec73dc266600", - "locked": false, - "points": 6, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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GeschlechtBrancheVerdienstVerdienstart
0weiblichMetalloberflächenbehandlung16.19Mittlere Bruttostundenverdienste ohne Sonderz.
1weiblichMetalloberflächenbehandlung2810.00Durchschn. Bruttomonatsverdienste ohne Sonderz.
2weiblichMetalloberflächenbehandlung17.28Durchschn. Bruttostundenverdienste ohne Sonderz.
3weiblichMetalloberflächenbehandlung2705.00Mittlere Bruttomonatsverdienste ohne Sonderz.
4männlichNicht ärztliche Therapie und Heilkunde18.99Mittlere Bruttostundenverdienste ohne Sonderz.
...............
17767InsgesamtRohstoffgewinn, Glas-, Keramikverarbeitung3181.00Mittlere Bruttomonatsverdienste ohne Sonderz.
17768InsgesamtNichtmed. Gesundheit, Körperpfl., Medizintechnik19.63Mittlere Bruttostundenverdienste ohne Sonderz.
17769InsgesamtNichtmed. Gesundheit, Körperpfl., Medizintechnik3373.00Durchschn. Bruttomonatsverdienste ohne Sonderz.
17770InsgesamtNichtmed. Gesundheit, Körperpfl., Medizintechnik20.74Durchschn. Bruttostundenverdienste ohne Sonderz.
17771InsgesamtNichtmed. Gesundheit, Körperpfl., Medizintechnik3201.00Mittlere Bruttomonatsverdienste ohne Sonderz.
\n", - "

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" - ], - "text/plain": [ - " Geschlecht Branche Verdienst \\\n", - "0 weiblich Metalloberflächenbehandlung 16.19 \n", - "1 weiblich Metalloberflächenbehandlung 2810.00 \n", - "2 weiblich Metalloberflächenbehandlung 17.28 \n", - "3 weiblich Metalloberflächenbehandlung 2705.00 \n", - "4 männlich Nicht ärztliche Therapie und Heilkunde 18.99 \n", - "... ... ... ... \n", - "17767 Insgesamt Rohstoffgewinn, Glas-, Keramikverarbeitung 3181.00 \n", - "17768 Insgesamt Nichtmed. Gesundheit, Körperpfl., Medizintechnik 19.63 \n", - "17769 Insgesamt Nichtmed. Gesundheit, Körperpfl., Medizintechnik 3373.00 \n", - "17770 Insgesamt Nichtmed. Gesundheit, Körperpfl., Medizintechnik 20.74 \n", - "17771 Insgesamt Nichtmed. Gesundheit, Körperpfl., Medizintechnik 3201.00 \n", - "\n", - " Verdienstart \n", - "0 Mittlere Bruttostundenverdienste ohne Sonderz. \n", - "1 Durchschn. Bruttomonatsverdienste ohne Sonderz. \n", - "2 Durchschn. Bruttostundenverdienste ohne Sonderz. \n", - "3 Mittlere Bruttomonatsverdienste ohne Sonderz. \n", - "4 Mittlere Bruttostundenverdienste ohne Sonderz. \n", - "... ... \n", - "17767 Mittlere Bruttomonatsverdienste ohne Sonderz. \n", - "17768 Mittlere Bruttostundenverdienste ohne Sonderz. \n", - "17769 Durchschn. Bruttomonatsverdienste ohne Sonderz. \n", - "17770 Durchschn. Bruttostundenverdienste ohne Sonderz. \n", - "17771 Mittlere Bruttomonatsverdienste ohne Sonderz. \n", - "\n", - "[17772 rows x 4 columns]" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# BEGIN SOLUTION\n", - "import pandas as pd\n", - "dataset: pd.DataFrame = pd.read_csv('bruttoverdiensterhebung_deutschland_april_22-23_nach_geschlecht.csv')\n", - "dataset\n", - "# END SOLUTION" - ] - }, - { - "cell_type": "markdown", - "id": "d6871b8f-5eda-4cad-a482-7c0bb88dff0d", - "metadata": { - "nbgrader": { - "grade": true, - "grade_id": "cell-8fb2067332715dfa", - "locked": false, - "points": 6, - "schema_version": 3, - "solution": true, - "task": false - } - }, - "source": [ - "# BEGIN SOLUTION\n", - "# END SOLUTION" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.linspace(0, 2*np.pi, num=100) # Definiere einen linearen Bereich von 0 bis 2pi\n", - "y = np.sin(x) # Berechne den Sinus mit den Werten von x\n", - "\n", - "plt.plot(x, y+1) # Setze für die X-Achse x und für die Y-Achse y\n", - "plt.show() # Zeige den Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "29242c2a-e5df-4273-8362-2f06d226cb3b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0. , 0.6981317 , 1.3962634 , 2.0943951 , 2.7925268 ,\n", - " 3.4906585 , 4.1887902 , 4.88692191, 5.58505361, 6.28318531])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "28020b1f-7cf6-4c99-9263-71a536035943", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.00000000e+00, 6.42787610e-01, 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y1 = np.sin(x) # Sinus Werte mittels x berechnen\n", - "y2 = np.cos(x) # Kosinus Werte mittels x berechnen\n", - "\n", - "plt.plot(x, y1) # Plotte den Sinus\n", - "plt.plot(x, y2) # Plotte den Kosinus\n", - "\n", - "plt.show() # Zeige das Diagramm" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "9f62662e-6dfe-4523-8eae-8625f9b8a9db", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "xt = np.linspace(0, 3, num=50)\n", - "sqrt = np.sqrt(xt)\n", - "square = np.square(xt)\n", - "\n", - "plt.plot(xt, sqrt, color='r', label=\"Square Root\")\n", - "plt.plot(xt, square, color='g', label=\"Square Function\", linestyle=\"dashdot\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "20b2826a-eca2-4aa8-baaf-23c1b8a764a9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "============================================================\n", - "AUFGABENSTELLUNG\n", - "============================================================\n", - "Kreis mit Radius r = 3 cm\n", - "AB ist ein Durchmesser\n", - "Winkel BAC = 85°\n", - "\n", - "============================================================\n", - "THEORETISCHE GRUNDLAGE\n", - "============================================================\n", - "Satz des Thales:\n", - "Da AB ein Durchmesser ist und C auf dem Kreis liegt,\n", - "gilt: Winkel ACB = 90°\n", - "\n", - "============================================================\n", - "WINKELBERECHNUNG\n", - "============================================================\n", - "Winkelsumme im Dreieck = 180°\n", - "Winkel BAC = 85°\n", - "Winkel ACB = 90° (Thales)\n", - "Winkel ABC = 180° - 85° - 90° = 5°\n", - "\n", - "============================================================\n", - "SEITENLÄNGEN-BERECHNUNG\n", - "============================================================\n", - "AC liegt gegenüber von Winkel ABC = 5°\n", - "AC = AB × sin(ABC) = 6 × sin(5°)\n", - "AC = 6*sin(pi/36)\n", - "AC ≈ 0.52 cm\n", - "\n", - "BC liegt gegenüber von Winkel BAC = 85°\n", - "BC = AB × sin(BAC) = 6 × sin(85°)\n", - "BC = 6*sin(17*pi/36)\n", - "BC ≈ 5.98 cm\n", - "\n", - "============================================================\n", - "KONTROLLE (Satz des Pythagoras)\n", - "============================================================\n", - "AC² + BC² = AB²\n", - "0.5229² + 5.9772² = 6²\n", - "0.2735 + 35.7265 = 36\n", - "36.0000 = 36\n", - "√(AC² + BC²) = 6.0000 ≈ 6 ✓\n", - "\n", - "============================================================\n", - "ERGEBNIS\n", - "============================================================\n", - "AC = 0.52 cm\n", - "BC = 5.98 cm\n", - "============================================================\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Die Visualisierung zeigt das Dreieck ABC auf dem Kreis.\n", - "Der rechte Winkel bei C ist durch ein kleines Quadrat markiert.\n" - ] - } - ], - "source": [ - "import sympy as sp\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.patches import Circle\n", - "\n", - "# Gegeben\n", - "r = 3 # Radius in cm\n", - "winkel_BAC = 85 # in Grad\n", - "\n", - "print(\"=\" * 60)\n", - "print(\"AUFGABENSTELLUNG\")\n", - "print(\"=\" * 60)\n", - "print(f\"Kreis mit Radius r = {r} cm\")\n", - "print(f\"AB ist ein Durchmesser\")\n", - "print(f\"Winkel BAC = {winkel_BAC}°\")\n", - "print()\n", - "\n", - "# WICHTIGE EIGENSCHAFT: Thaleskreis\n", - "# Wenn AB ein Durchmesser ist, dann liegt C auf dem Kreis,\n", - "# und nach dem Satz des Thales gilt: Winkel ACB = 90°\n", - "print(\"=\" * 60)\n", - "print(\"THEORETISCHE GRUNDLAGE\")\n", - "print(\"=\" * 60)\n", - "print(\"Satz des Thales:\")\n", - "print(\"Da AB ein Durchmesser ist und C auf dem Kreis liegt,\")\n", - "print(\"gilt: Winkel ACB = 90°\")\n", - "print()\n", - "\n", - "# Im Dreieck ABC gilt:\n", - "# - AB = 2r (Durchmesser)\n", - "# - Winkel ACB = 90° (Thaleskreis)\n", - "# - Winkel BAC = 85°\n", - "# - Daraus folgt: Winkel ABC = 180° - 90° - 85° = 5°\n", - "\n", - "AB = 2 * r\n", - "winkel_ACB = 90\n", - "winkel_ABC = 180 - winkel_ACB - winkel_BAC\n", - "\n", - "print(\"=\" * 60)\n", - "print(\"WINKELBERECHNUNG\")\n", - "print(\"=\" * 60)\n", - "print(f\"Winkelsumme im Dreieck = 180°\")\n", - "print(f\"Winkel BAC = {winkel_BAC}°\")\n", - "print(f\"Winkel ACB = {winkel_ACB}° (Thales)\")\n", - "print(f\"Winkel ABC = 180° - {winkel_BAC}° - {winkel_ACB}° = {winkel_ABC}°\")\n", - "print()\n", - "\n", - "# Berechnung mit SymPy\n", - "print(\"=\" * 60)\n", - "print(\"SEITENLÄNGEN-BERECHNUNG\")\n", - "print(\"=\" * 60)\n", - "\n", - "# Umrechnung in Radiant für SymPy\n", - "alpha_rad = sp.rad(winkel_BAC) # Winkel bei A\n", - "beta_rad = sp.rad(winkel_ABC) # Winkel bei B\n", - "\n", - "# Sinussatz oder direkte Berechnung im rechtwinkligen Dreieck:\n", - "# Im rechtwinkligen Dreieck mit Hypotenuse AB:\n", - "# AC / AB = sin(Winkel ABC) = sin(5°)\n", - "# BC / AB = sin(Winkel BAC) = sin(85°)\n", - "\n", - "# AC liegt gegenüber von Winkel ABC\n", - "AC_sympy = AB * sp.sin(beta_rad)\n", - "AC_exact = AC_sympy\n", - "AC_numerical = float(AC_sympy.evalf())\n", - "\n", - "print(f\"AC liegt gegenüber von Winkel ABC = {winkel_ABC}°\")\n", - "print(f\"AC = AB × sin(ABC) = {AB} × sin({winkel_ABC}°)\")\n", - "print(f\"AC = {AC_exact}\")\n", - "print(f\"AC ≈ {AC_numerical:.2f} cm\")\n", - "print()\n", - "\n", - "# BC liegt gegenüber von Winkel BAC\n", - "BC_sympy = AB * sp.sin(alpha_rad)\n", - "BC_exact = BC_sympy\n", - "BC_numerical = float(BC_sympy.evalf())\n", - "\n", - "print(f\"BC liegt gegenüber von Winkel BAC = {winkel_BAC}°\")\n", - "print(f\"BC = AB × sin(BAC) = {AB} × sin({winkel_BAC}°)\")\n", - "print(f\"BC = {BC_exact}\")\n", - "print(f\"BC ≈ {BC_numerical:.2f} cm\")\n", - "print()\n", - "\n", - "# Kontrolle mit Pythagoras\n", - "pythagoras_check = sp.sqrt(AC_sympy**2 + BC_sympy**2)\n", - "pythagoras_numerical = float(pythagoras_check.evalf())\n", - "\n", - "print(\"=\" * 60)\n", - "print(\"KONTROLLE (Satz des Pythagoras)\")\n", - "print(\"=\" * 60)\n", - "print(f\"AC² + BC² = AB²\")\n", - "print(f\"{AC_numerical:.4f}² + {BC_numerical:.4f}² = {AB}²\")\n", - "print(f\"{AC_numerical**2:.4f} + {BC_numerical**2:.4f} = {AB**2}\")\n", - "print(f\"{AC_numerical**2 + BC_numerical**2:.4f} = {AB**2}\")\n", - "print(f\"√(AC² + BC²) = {pythagoras_numerical:.4f} ≈ {AB} ✓\")\n", - "print()\n", - "\n", - "# VISUALISIERUNG\n", - "print(\"=\" * 60)\n", - "print(\"ERGEBNIS\")\n", - "print(\"=\" * 60)\n", - "print(f\"AC = {AC_numerical:.2f} cm\")\n", - "print(f\"BC = {BC_numerical:.2f} cm\")\n", - "print(\"=\" * 60)\n", - "\n", - "# Plotten\n", - "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n", - "\n", - "# Kreis zeichnen\n", - "circle = Circle((0, 0), r, fill=False, edgecolor='blue', linewidth=2, label='Kreis')\n", - "ax.add_patch(circle)\n", - "\n", - "# Punkte festlegen\n", - "# A auf der linken Seite, B auf der rechten Seite (Durchmesser horizontal)\n", - "A = np.array([-r, 0])\n", - "B = np.array([r, 0])\n", - "\n", - "# C auf dem Kreis mit Winkel BAC = 85°\n", - "# Wir berechnen die Position von C\n", - "# C liegt so, dass Winkel ACB = 90° (Thaleskreis)\n", - "# und Winkel BAC = 85°\n", - "\n", - "# Verwende die berechneten Koordinaten:\n", - "# Setze A = (-3, 0), B = (3, 0)\n", - "# C liegt auf dem Kreis, also C = (x, y) mit x² + y² = 9\n", - "# Winkel BAC = 85° bedeutet: Vector AC mit Vector AB einschließt 85°\n", - "\n", - "# Einfachere Methode: C liegt auf dem Kreis\n", - "# Winkel CAB = 85° von der positiven x-Achse (AB-Richtung)\n", - "# Von A aus gesehen ist der Winkel zur positiven x-Achse 0°\n", - "# C muss so liegen, dass der Winkel 85° ist\n", - "\n", - "# Parametrisierung: C auf Kreis mit Winkel theta zur x-Achse\n", - "# Wir finden theta so, dass Winkel BAC = 85°\n", - "\n", - "# Mittelpunkt des Kreises ist (0,0)\n", - "# Peripheriewinkel = halber Zentriwinkel\n", - "# Der Zentriwinkel für Bogen BC von B zu C\n", - "\n", - "# Berechnung über den Thaleskreis:\n", - "# C liegt im oberen Halbkreis (y > 0)\n", - "# Winkel ABC = 5°, also liegt C nahe bei B\n", - "\n", - "# Berechne Position von C direkt aus den Seitenlängen:\n", - "# Von A aus: C ist AC_numerical entfernt in Richtung 85° zur AB-Richtung\n", - "angle_to_C = np.radians(85) # Winkel von A zu C relativ zur AB-Linie\n", - "C_x = A[0] + AC_numerical * np.cos(angle_to_C)\n", - "C_y = A[1] + AC_numerical * np.sin(angle_to_C)\n", - "C = np.array([C_x, C_y])\n", - "\n", - "# Punkte plotten\n", - "ax.plot(*A, 'ro', markersize=10, label='A')\n", - "ax.plot(*B, 'go', markersize=10, label='B')\n", - "ax.plot(*C, 'mo', markersize=10, label='C')\n", - "\n", - "# Dreieck zeichnen\n", - "triangle = plt.Polygon([A, B, C], fill=False, edgecolor='red', linewidth=2)\n", - "ax.add_patch(triangle)\n", - "\n", - "# Beschriftungen\n", - "offset = 0.4\n", - "ax.text(A[0]-offset, A[1]-offset, 'A', fontsize=14, fontweight='bold')\n", - "ax.text(B[0]+offset, B[1]-offset, 'B', fontsize=14, fontweight='bold')\n", - "ax.text(C[0]+offset, C[1]+offset, 'C', fontsize=14, fontweight='bold')\n", - "\n", - "# Seitenlängen einzeichnen\n", - "mid_AB = (A + B) / 2\n", - "mid_AC = (A + C) / 2\n", - "mid_BC = (B + C) / 2\n", - "\n", - "ax.text(mid_AB[0], mid_AB[1]-0.5, f'AB = {AB:.2f} cm', fontsize=11, ha='center', \n", - " bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.8))\n", - "ax.text(mid_AC[0]-0.7, mid_AC[1], f'AC = {AC_numerical:.2f} cm', fontsize=11, ha='center',\n", - " bbox=dict(boxstyle='round', facecolor='lightblue', alpha=0.8))\n", - "ax.text(mid_BC[0]+0.5, mid_BC[1], f'BC = {BC_numerical:.2f} cm', fontsize=11, ha='center',\n", - " bbox=dict(boxstyle='round', facecolor='lightgreen', alpha=0.8))\n", - "\n", - "# Rechter Winkel bei C markieren\n", - "from matplotlib.patches import Rectangle\n", - "angle_size = 0.5\n", - "angle_vec1 = (A - C) / np.linalg.norm(A - C) * angle_size\n", - "angle_vec2 = (B - C) / np.linalg.norm(B - C) * angle_size\n", - "corner = C + angle_vec1 + angle_vec2\n", - "square = plt.Polygon([C + angle_vec1, corner, C + angle_vec2], \n", - " fill=False, edgecolor='purple', linewidth=1.5)\n", - "ax.add_patch(square)\n", - "\n", - "# Achseneinstellungen\n", - "ax.set_xlim(-4.5, 4.5)\n", - "ax.set_ylim(-4.5, 4.5)\n", - "ax.set_aspect('equal')\n", - "ax.grid(True, alpha=0.3)\n", - "ax.axhline(y=0, color='k', linewidth=0.5)\n", - "ax.axvline(x=0, color='k', linewidth=0.5)\n", - "ax.set_xlabel('x (cm)', fontsize=12)\n", - "ax.set_ylabel('y (cm)', fontsize=12)\n", - "ax.set_title(f'Dreieck ABC auf Kreis (r = {r} cm)\\nAB = Durchmesser, ∠BAC = {winkel_BAC}°, ∠ACB = {winkel_ACB}° (Thales)', \n", - " fontsize=14, fontweight='bold')\n", - "ax.legend(loc='upper left', fontsize=10)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "print(\"\\nDie Visualisierung zeigt das Dreieck ABC auf dem Kreis.\")\n", - "print(\"Der rechte Winkel bei C ist durch ein kleines Quadrat markiert.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ca572a79-3150-4403-b56a-16147b231a8c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dice Population (1-6) Mean: 3.500, Std: 1.708\n", - "Population Distribution: Discrete Uniform (dice faces)\n", - "\n", - "============================================================\n", - "\n", - "Sample Size n = 1 consecutive dice throws\n", - " Theoretical: μ = 3.500, σ = 1.708\n", - " Actual: μ = 3.505, σ = 1.709\n", - "\n", - "Sample Size n = 2 consecutive dice throws\n", - " Theoretical: μ = 3.500, σ = 1.208\n", - " Actual: μ = 3.466, σ = 1.220\n", - "\n", - "Sample Size n = 5 consecutive dice throws\n", - " Theoretical: μ = 3.500, σ = 0.764\n", - " Actual: μ = 3.501, σ = 0.763\n", - "\n", - "Sample Size n = 10 consecutive dice throws\n", - " Theoretical: μ = 3.500, σ = 0.540\n", - " Actual: μ = 3.489, σ = 0.542\n", - "\n", - "Sample Size n = 20 consecutive dice throws\n", - " Theoretical: μ = 3.500, σ = 0.382\n", - " Actual: μ = 3.500, σ = 0.380\n", - "\n", - "Sample Size n = 50 consecutive dice throws\n", - " Theoretical: μ = 3.500, σ = 0.242\n", - " Actual: μ = 3.502, σ = 0.238\n", - "\n", - "Sample Size n = 100 consecutive dice throws\n", - " Theoretical: μ = 3.500, σ = 0.171\n", - " Actual: μ = 3.499, σ = 0.171\n", - "\n", - "Sample Size n = 1000 consecutive dice throws\n", - " Theoretical: μ = 3.500, σ = 0.054\n", - " Actual: μ = 3.500, σ = 0.055\n", - "\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from scipy import stats\n", - "\n", - "# ============================================================================\n", - "# SETUP AND CONFIGURATION\n", - "# ============================================================================\n", - "\n", - "# Set random seed for reproducibility using PCG64 generator\n", - "rng = np.random.Generator(np.random.PCG64(420))\n", - "\n", - "# Parameters for the simulation\n", - "dice_faces = 6 # Six-sided dice\n", - "sample_sizes = [1, 2, 5, 10, 20, 50, 100, 1000] # Number of consecutive dice throws\n", - "n_samples = 5000 # Number of samples to draw for each sample size\n", - "\n", - "# ============================================================================\n", - "# POPULATION GENERATION\n", - "# ============================================================================\n", - "\n", - "# Define the dice as a discrete uniform population\n", - "population = np.arange(1, dice_faces + 1)\n", - "\n", - "# Calculate population parameters\n", - "pop_mean = np.mean(population)\n", - "pop_std = np.std(population)\n", - "\n", - "print(f\"Dice Population (1-{dice_faces}) Mean: {pop_mean:.3f}, Std: {pop_std:.3f}\")\n", - "print(\"Population Distribution: Discrete Uniform (dice faces)\")\n", - "print(\"\\n\" + \"=\"*60 + \"\\n\")\n", - "\n", - "# ============================================================================\n", - "# SAMPLE MEANS FUNCTION\n", - "# ============================================================================\n", - "\n", - "def generate_sample_means(pop, n, num_samples, rng):\n", - " \"\"\"\n", - " Generate sample means by repeatedly sampling consecutive dice throws.\n", - " \"\"\"\n", - " sample_means = []\n", - " for _ in range(num_samples):\n", - " # Sample n dice throws with replacement\n", - " sample = rng.choice(pop, size=n, replace=True)\n", - " sample_means.append(np.mean(sample))\n", - " return np.array(sample_means)\n", - "\n", - "# ============================================================================\n", - "# SIMULATION: GENERATE SAMPLE MEANS\n", - "# ============================================================================\n", - "\n", - "results = {}\n", - "for n in sample_sizes:\n", - " sample_means = generate_sample_means(population, n, n_samples, rng)\n", - " results[n] = sample_means\n", - " \n", - " # Theoretical CLT values\n", - " theoretical_mean = pop_mean\n", - " theoretical_std = pop_std / np.sqrt(n)\n", - " \n", - " # Actual values from simulation\n", - " actual_mean = np.mean(sample_means)\n", - " actual_std = np.std(sample_means)\n", - " \n", - " print(f\"Sample Size n = {n} consecutive dice throws\")\n", - " print(f\" Theoretical: μ = {theoretical_mean:.3f}, σ = {theoretical_std:.3f}\")\n", - " print(f\" Actual: μ = {actual_mean:.3f}, σ = {actual_std:.3f}\\n\")\n", - "\n", - "# ============================================================================\n", - "# VISUALIZATION: INDIVIDUAL HISTOGRAMS FOR EACH SAMPLE SIZE\n", - "# ============================================================================\n", - "\n", - "num_plots = len(sample_sizes)\n", - "ncols = 2\n", - "nrows = int(np.ceil(num_plots / ncols))\n", - "\n", - "fig, axes = plt.subplots(nrows, ncols, figsize=(14, 5*nrows))\n", - "axes = axes.flatten()\n", - "\n", - "for idx, n in enumerate(sample_sizes):\n", - " ax = axes[idx]\n", - " sample_means = results[n]\n", - " \n", - " # Histogram\n", - " ax.hist(sample_means, bins=50, density=True, alpha=0.7, \n", - " color='steelblue', edgecolor='black', linewidth=0.5)\n", - " \n", - " # Overlay theoretical normal distribution\n", - " x = np.linspace(sample_means.min(), sample_means.max(), 100)\n", - " ax.plot(x, stats.norm.pdf(x, pop_mean, pop_std/np.sqrt(n)), \n", - " 'r-', linewidth=2, label='Theoretical Normal')\n", - " \n", - " ax.set_title(f'n = {n} dice throws', fontsize=12, fontweight='bold')\n", - " ax.set_xlabel('Sample Mean')\n", - " ax.set_ylabel('Density')\n", - " ax.legend()\n", - " ax.grid(True, alpha=0.3)\n", - "\n", - "for idx in range(num_plots, len(axes)):\n", - " axes[idx].set_visible(False)\n", - "\n", - "plt.suptitle('Central Limit Theorem: Consecutive Dice Throws', fontsize=14, fontweight='bold', y=0.995)\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# ============================================================================\n", - "# VISUALIZATION: COMBINED HISTOGRAMS\n", - "# ============================================================================\n", - "\n", - "fig, ax = plt.subplots(figsize=(14, 8))\n", - "colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#F7DC6F', '#BB8FCE']\n", - "\n", - "for idx, n in enumerate(sample_sizes):\n", - " ax.hist(results[n], bins=50, density=True, alpha=0.5, \n", - " color=colors[idx % len(colors)], label=f'n = {n}', edgecolor='black', linewidth=0.5)\n", - "\n", - "ax.set_title('CLT Progression: Sample Means of Consecutive Dice Throws', fontsize=14, fontweight='bold')\n", - "ax.set_xlabel('Sample Mean')\n", - "ax.set_ylabel('Density')\n", - "ax.legend(fontsize=11)\n", - "ax.grid(True, alpha=0.3)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "56f53747-51f2-44e4-a502-92025b068acd", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "### BEGIN SOLUTION\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from pydantic import BaseModel, Field, ValidationError\n", - "from typing import List\n", - "\n", - "samples = 10000\n", - "\n", - "class Individual(BaseModel):\n", - " sex: bool\n", - " height: int = Field(..., ge=120, le=230)\n", - " weight: int = Field(..., ge=40, le=200)\n", - "\n", - "\n", - "rng = np.random.default_rng()\n", - "\n", - "# Preallocate arrays\n", - "sex_array = np.zeros(samples, dtype=bool)\n", - "sex_array[1::2] = True # Alternate sexes\n", - "\n", - "height_array = np.zeros(samples, dtype=int)\n", - "weight_array = np.zeros(samples, dtype=int)\n", - "\n", - "# Function to generate height and weight arrays for a given sex\n", - "def generate_batch(sex_flag, size):\n", - " if sex_flag:\n", - " heights = rng.normal(165.8, 11, size)\n", - " weights = rng.normal(69.2, 19, size)\n", - " else:\n", - " heights = rng.normal(178.9, 10, size)\n", - " weights = rng.normal(85.8, 26, size)\n", - " # Convert to integers\n", - " return np.round(heights).astype(int), np.round(weights).astype(int)\n", - "\n", - "# Initial generation\n", - "height_array[sex_array == False], weight_array[sex_array == False] = generate_batch(False, np.sum(sex_array == False))\n", - "height_array[sex_array == True], weight_array[sex_array == True] = generate_batch(True, np.sum(sex_array == True))\n", - "\n", - "# Validation and recalculation\n", - "valid_count = 0\n", - "while valid_count < samples:\n", - " records: List[dict] = []\n", - " invalid_indices = []\n", - "\n", - " # Validate each individual\n", - " for i in range(samples):\n", - " try:\n", - " individual = Individual(\n", - " sex=sex_array[i],\n", - " height=int(height_array[i]),\n", - " weight=int(weight_array[i])\n", - " )\n", - " records.append(individual.model_dump())\n", - " except ValidationError:\n", - " invalid_indices.append(i)\n", - "\n", - " valid_count = samples - len(invalid_indices)\n", - "\n", - " # Recalculate only invalid samples\n", - " if invalid_indices:\n", - " for i in invalid_indices:\n", - " h, w = generate_batch(sex_array[i], 1)\n", - " height_array[i] = h[0] \n", - " weight_array[i] = w[0] \n", - "\n", - "# Convert to DataFrame\n", - "individuals = pd.DataFrame(records)\n", - "individuals = individuals.astype({\"sex\": \"bool\", \"height\": \"int\", \"weight\": \"int\"})\n", - "\n", - "# Save CSV\n", - "#individuals.to_csv(\"people_in_germany.csv\", index=False)\n", - "\n", - "# Plot\n", - "sns.set_style(\"darkgrid\")\n", - "sns.jointplot(data=individuals, x=\"height\", y=\"weight\", hue=\"sex\")\n", - "plt.show()\n", - "### END SOLUTION" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "b7a4582c-a040-46e2-a40a-e7d6508e3fca", - "metadata": {}, - "outputs": [], - "source": [ - "# --------------------------------------------------------------\n", - "# Imports\n", - "# --------------------------------------------------------------\n", - "import sympy as sp # symbolic maths\n", - "import mpmath as mp # high‑precision floating point\n", - "import matplotlib.pyplot as plt # plotting\n", - "import numpy as np # numeric arrays\n", - "from sympy import Rational, continued_fraction, init_printing, N, pi, log, exp\n", - "from IPython.display import display, Math, HTML, clear_output\n", - "import ipywidgets as widgets # interactive slider\n", - "\n", - "# Make SymPy render LaTeX automatically in the notebook\n", - "init_printing(use_latex='mathjax')" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "20c4b21c-3970-4ebd-a49b-a72c9e53cc65", - "metadata": {}, - "outputs": [], - "source": [ - "# --------------------------------------------------------------\n", - "# Revised core computation routine\n", - "# --------------------------------------------------------------\n", - "import sympy as sp\n", - "from sympy import pi, log, N, Rational, nsimplify, continued_fraction\n", - "import mpmath as mp\n", - "\n", - "def _cf_from_mpmath(x: mp.mpf, max_terms: int = 30):\n", - " \"\"\"\n", - " Return an *approximate* continued‑fraction list for a high‑precision\n", - " mp.mpf value. This is not the exact SymPy routine (which only works\n", - " for rationals/quadratics) but is often what people need when they\n", - " start from a decimal expansion.\n", - " \"\"\"\n", - " cf = []\n", - " a = x\n", - " for _ in range(max_terms):\n", - " ai = mp.floor(a)\n", - " cf.append(int(ai))\n", - " frac = a - ai\n", - " if mp.almosteq(frac, 0):\n", - " break\n", - " a = 1/frac\n", - " return cf\n", - "\n", - "\n", - "def compute_x(prec: int = 50, rat_den_limit: int = 1_000_000,\n", - " cf_max_terms: int = 30):\n", - " \"\"\"\n", - " Compute everything needed to express e**π as 2**x.\n", - "\n", - " Parameters\n", - " ----------\n", - " prec : int\n", - " Number of **decimal digits** for the numeric approximation.\n", - " rat_den_limit : int\n", - " Upper bound for the denominator when searching for a rational\n", - " approximation.\n", - " cf_max_terms : int, optional\n", - " Maximum number of terms to generate when we compute a\n", - " *floating‑point* continued fraction via mpmath. Ignored when the\n", - " rational expansion already terminates.\n", - "\n", - " Returns\n", - " -------\n", - " dict with keys\n", - " - ``x_sym`` – symbolic expression π/ln(2)\n", - " - ``x_num`` – high‑precision Decimal (SymPy Float)\n", - " - ``x_rat`` – rational approximation (SymPy Rational)\n", - " - ``cf_rational`` – continued‑fraction list of the rational\n", - " approximation (exact, finite)\n", - " - ``cf_float`` – continued‑fraction list derived from the float\n", - " (approximate, may be longer)\n", - " - ``true_val`` – e**π (mpmath high‑precision float)\n", - " - ``approx_val`` – 2**x (mpmath high‑precision float)\n", - " - ``rel_err`` – relative error |2**x‑e**π| / e**π\n", - " - ``prec`` , ``rat_den_limit`` , ``cf_max_terms`` – echo of inputs\n", - " \"\"\"\n", - " # ------------------------------------------------------------\n", - " # 1. exact symbolic exponent\n", - " # ------------------------------------------------------------\n", - " x_sym = pi / log(2) # sympy.log is natural log\n", - "\n", - " # ------------------------------------------------------------\n", - " # 2. high‑precision decimal value (SymPy Float)\n", - " # ------------------------------------------------------------\n", - " x_num = N(x_sym, prec) # `prec` decimal digits\n", - "\n", - " # ------------------------------------------------------------\n", - " # 3. rational approximation (bounded denominator)\n", - " # ------------------------------------------------------------\n", - " # Try a “nice” rational first; if that fails, force a denominator bound.\n", - " rat_candidate = nsimplify(x_num, rational=True)\n", - " if isinstance(rat_candidate, Rational):\n", - " x_rat = rat_candidate\n", - " else:\n", - " x_rat = Rational(x_num).limit_denominator(rat_den_limit)\n", - "\n", - " # ------------------------------------------------------------\n", - " # 4. continued‑fraction expansions\n", - " # ------------------------------------------------------------\n", - " # • exact finite CF of the rational we just built\n", - " cf_rational = continued_fraction(x_rat) # always works\n", - "\n", - " # • optional *approximate* CF of the floating point value\n", - " # (useful when you want many terms beyond the rational's period)\n", - " cf_float = _cf_from_mpmath(mp.mpf(str(x_num)), max_terms=cf_max_terms)\n", - "\n", - " # ------------------------------------------------------------\n", - " # 5. verification with mpmath (high‑precision floats)\n", - " # ------------------------------------------------------------\n", - " mp.mp.dps = prec + 5 # guard digits\n", - " true_val = mp.e ** mp.pi\n", - " approx_val = mp.power(2, mp.mpf(str(x_num)))\n", - " rel_err = abs((approx_val - true_val) / true_val)\n", - "\n", - " return {\n", - " \"x_sym\": x_sym,\n", - " \"x_num\": x_num,\n", - " \"x_rat\": x_rat,\n", - " \"cf_rational\": cf_rational,\n", - " \"cf_float\": cf_float,\n", - " \"true_val\": true_val,\n", - " \"approx_val\": approx_val,\n", - " \"rel_err\": rel_err,\n", - " \"prec\": prec,\n", - " \"rat_den_limit\": rat_den_limit,\n", - " \"cf_max_terms\": cf_max_terms,\n", - " }" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "392bb9fa-ef02-4f8e-9065-bfbe020fc28d", - "metadata": {}, - "outputs": [], - "source": [ - "# --------------------------------------------------------------\n", - "# Compute once with the default precision (50 decimal digits)\n", - "# --------------------------------------------------------------\n", - "res = compute_x(prec=50) # change the integer if you want a different default" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "a9053005-e4c0-478a-ac04-7185c0427912", - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle x = \\frac{\\pi}{\\ln 2}$" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✅ Exact symbolic expression:\n" - ] - }, - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{\\pi}{\\log{\\left(2 \\right)}}$" - ], - "text/plain": [ - " π \n", - "──────\n", - "log(2)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "🔢 Decimal approximation (50 digits):\n" - ] - }, - { - "data": { - "text/latex": [ - "$\\displaystyle 4.5323601418271938096276829457166668101718614677238$" - ], - "text/plain": [ - "4.5323601418271938096276829457166668101718614677238" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "🧮 Rational approximation (denominator ≤ 1,000,000):\n" - ] - }, - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{22661800709135969048138414728583334050859307338619}{5000000000000000000000000000000000000000000000000}$" - ], - "text/plain": [ - "22661800709135969048138414728583334050859307338619\n", - "──────────────────────────────────────────────────\n", - "5000000000000000000000000000000000000000000000000 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "🔎 Verification\n" - ] - }, - { - "data": { - "text/latex": [ - "$\\displaystyle 2^{x} = \\mathtt{\\text{23.14069263277926900572908636794854738026610624260028282}}$" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/latex": [ - "$\\displaystyle e^{\\pi} = \\mathtt{\\text{23.14069263277926900572908636794854738026610624260021199}}$" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# --------------------------------------------------------------\n", - "# Display the results in a readable way\n", - "# --------------------------------------------------------------\n", - "display(Math(r\"x = \\frac{\\pi}{\\ln 2}\"))\n", - "\n", - "print(\"✅ Exact symbolic expression:\")\n", - "display(res[\"x_sym\"])\n", - "\n", - "print(\"\\n🔢 Decimal approximation ({} digits):\".format(res[\"prec\"]))\n", - "display(res[\"x_num\"])\n", - "\n", - "print(\"\\n🧮 Rational approximation (denominator ≤ {:,}):\".format(res[\"rat_den_limit\"]))\n", - "display(res[\"x_rat\"])\n", - "\n", - "print(\"\\n🔎 Verification\")\n", - "display(Math(r\"2^{x} = \" + sp.latex(res[\"approx_val\"])))\n", - "display(Math(r\"e^{\\pi} = \" + sp.latex(res[\"true_val\"])))" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "482980d9-b747-407e-81f2-c5569da133a2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# --------------------------------------------------------------\n", - "# Visualise the equality 2**x = e**π\n", - "# --------------------------------------------------------------\n", - "%matplotlib inline\n", - "\n", - "# A narrow window around the exponent we just computed\n", - "x_vals = np.linspace(res[\"x_num\"] - 1, res[\"x_num\"] + 1, 400)\n", - "\n", - "# Evaluate 2**x with mpmath (high precision)\n", - "mp.mp.dps = res[\"prec\"] + 5\n", - "y2 = [mp.power(2, mp.mpf(str(v))) for v in x_vals] # 2**x\n", - "y_e = [mp.e**mp.pi] * len(x_vals) # constant e**π\n", - "\n", - "plt.figure(figsize=(8, 4.5))\n", - "plt.plot(x_vals, y2, label=r\"$2^{x}$\", color=\"#1f77b4\")\n", - "plt.axhline(mp.e**mp.pi, color=\"#ff7f0e\", linestyle=\"--\", label=r\"$e^{\\pi}$\")\n", - "plt.scatter([float(res[\"x_num\"])], [float(res[\"approx_val\"])],\n", - " color=\"red\", zorder=5, label=\"intersection\")\n", - 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Was ist Erkenntnis - Note +short_desc: +tags: + - Note + - Uni +timestamp: 30.04.2026 - 15:12 +path: +public: true +update: true +editor: markdown +uuid: "1777554750018" +--- +![[Baumgarten_-_Theoretische_Ästhetik_Auszug.pdf]] + +- Geschichte der Ästhetik ist kurz +- es ging schon früh um Hässlichkeit +- Philosophiertes Verständnis vom schönen, weitem, gutem +- Die Sinne stören +- Natürlich Ästhetik = angeborenes Verständnis +- "Wir sind alle natürliche Ästhetiker" +- klar/dunkel = unterscheidbar/unentscheidbar +- Zusammenarbeit Geist & Gemüt §50 Vergiftung 7 Totsünden +- Regelmäßigkeit der Ästhetik +- Schönheit des Denkens entsteht durch wiederholung & Übung +- Ästhetik ist die Theorie; Übung ist die +- Roh & ungebildet; gebildet +- Hierachie wird angezweifelt aber nicht aufgegeben +- Emotion führen zu Elend & Leid +- Kunst & Wissenschaft sind keine Gegensätze + +--- diff --git a/Semester/14. 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Nietzsche - Exzerpte.md @@ -0,0 +1,33 @@ +--- +title: 04. Nietzsche - Exzerpte - Note +short_desc: +tags: + - Note + - Uni +timestamp: 13.05.2026 - 14:12 +path: +public: true +update: true +editor: markdown +uuid: "1778674333761" +--- +# Nietzsche >Über Wahrheit & Lüge< + +*Frage: Was sind Metaphern?* +- **Wahrheit** ist etwas erzeugtes, durch die lange Rezeption in der Gesellschaft wurde aus einer Idee / Relation eine Wahrheit (Akzeptiertes Wissen). +- Umschrieben mit dem Beispiel der Münze, welche ohne Prägung ihren Wert (Wahrheit) verliert. + +> [!info] Zitat Vgl. 880-881 +> Ein bewegliches Heer von Metaphern, Metonymien, Anthropomorphismen kurz ein e Summe von menschlichen Relationen, die, poetisch und rhetorisch gesteigert, übertragen, geschmückt wurden, und die nach langem Gebrauche einem Volke fest, canonisch und verbindlich dünken: die Wahrheiten sind Illusionen, von denen man vergessen hat, dass sie welche sind, die abgenutzt und sinnlich kraftlos geworden sind, Münzen, die ihr Bild verloren haben und nun als Metall, nicht mehr als Münzen in Betracht kommen. + +- Als **Metapher** sind alle Bezeichner & ihre Relationen beschrieben +- **Metaphern** können erstarren -> Vgl. **Wahrheit** + +> [!info] Vgl. 883 +> Nur durch das Vergessen jener primitiven Metaphernwelt, nur durch das Hart- und Starr-Werden einer ursprünglichen in hitziger Flüssigkeit aus dem Urvermögen menschlicher Phantasie hervorströmenden Bildermasse, [...] + +> [!info] Vgl. 879 +> Ein Nervenreiz zuerst übertragen in ein Bild! erste Metapher. Das Bild wieder nachgeformt in einem Laut! Zweite Metapher. + + +--- diff --git a/Semester/14. SoSe 26/Grund & Unbegreiflichkeit/Übung/04. Sitzung - Nietzsche/Sina_Nachwort_Nietzsche-Über_Wahrheit_und_Lüge_im_außermoralischen_Sinne.pdf b/Semester/14. SoSe 26/Grund & Unbegreiflichkeit/Übung/04. Sitzung - Nietzsche/Sina_Nachwort_Nietzsche-Über_Wahrheit_und_Lüge_im_außermoralischen_Sinne.pdf new file mode 100644 index 0000000..c00ac34 --- /dev/null +++ b/Semester/14. SoSe 26/Grund & Unbegreiflichkeit/Übung/04. Sitzung - Nietzsche/Sina_Nachwort_Nietzsche-Über_Wahrheit_und_Lüge_im_außermoralischen_Sinne.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:82bc57aeb6e47573e78857e7482beb7d8f3d15c26ade00b77b1932f9f2d55281 +size 7867051 diff --git a/Semester/14. SoSe 26/Grund & Unbegreiflichkeit/Übung/Seminarplan_UE/Unbegriffe_Seminarplan_Übung.pdf b/Semester/14. 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Öffentlicher Raum - Note +short_desc: +tags: + - Note + - Uni +timestamp: 29.04.2026 - 14:22 +path: +public: true +update: true +editor: markdown +uuid: "1777465356616" +--- +Hannah Arendt +- Auflösung des privaten +- Polis = Sklaven Gesellschaft +- Bürger hatten teilhabe in der Öffentlichkeit +- Grundbesitz/Eigentum -> Über den Tod hinauswachsens +- Öffentlicher Raum ist spiritistischen zions +- Sklaven hatten kein schlechtes leben nur kein gehör +- Proletarier = besitzloser Kindererzeuger + + + +--- diff --git a/Semester/14. SoSe 26/Stadtideale der Moderne/Seminar/Impressionismus.pdf b/Semester/14. SoSe 26/Stadtideale der Moderne/Seminar/Impressionismus.pdf new file mode 100644 index 0000000..4fdbde9 --- /dev/null +++ b/Semester/14. SoSe 26/Stadtideale der Moderne/Seminar/Impressionismus.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0da8136d7444cab679f4b50a3ce3e50d5e01e1f7a4b6d74cb82375573bfb1e33 +size 1692300 diff --git a/Semester/14. 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Einführung - Note +short_desc: +tags: + - Note + - Uni +timestamp: 29.04.2026 - 13:18 +path: +public: true +update: true +editor: markdown +uuid: "1777461535053" +--- +**Prüfungsleistung**: +- Öffentlich zugänglicher Text + + +- Kunstkritik anhand eigener Texte +- Geschichte Kunstkritik = Geschichte Gesellschaft + - loslösen feudaler Struckturen + - Pressewesen + - Buchdruck +- Publikation, Akt der Veröffentlichung von Schriftstücken +- Kritik schafft Publikum; Autor steht hinter der Kritik +- Ist Kritik gerechtfertigt; Kritikparameter +- Kriterien der eigenen Kritik rechtfertigen +- + +# Tasks +- Ausstellungen recherchieren +- Rezensionsbücher recherchieren (max 2 Jahre alt) + - Bereich Kunstästhetik + - Kunstwissenschaftlich +- +# Ablauf +- [[29.05.2026]] Begriff der Öffentlichkeit & Kritik +- [[12.06.2026]] Wie schreibe ich eine Buch- & Ausstellungskritik +- [[26.06.2026]] Diskussion der Ausstellungsbesuche +- [[03.07.2026]] Besprechung eigener Texte +- [[10.07.2026]] Öffentliche Veranstaltung zu Buch- & Ausstellungskritik im Rahmen des Rundgangs +- [[12.07.2026]] Möglicher Zusatztermin + + +*Linktree*: +- [Ehemaliger Blog des Instituts für Kunstwissenschaft](https://sendungenvois.wordpress.com/) +- [Webjournal des Lehrgebiets >>Kunstvermitllung als künstlerische Praxis<< (Prof. Martin Krenn)](www.appropriate-journal.art) +- [Forschungsforum Kunst & Schule](https://fokus-hbk-bs.de/) + +--- diff --git a/Semester/14. 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Intersektionalität und Ausstellungsgeschichte - - - - - Dieses Werk ist unter der Creative Commons-Lizenz 4.0 (CC BY-SA 4.0) veröffentlicht. Die Umschlaggestaltung unterliegt der Creative Commons-Lizenz CC BY-ND 4.0. Text © 2025. Maria Bremer und Isabelle Lindermann - - - - - DOI: https://doi.org/10.11588/kb.2025.3 Abstract: Das Heft knüpft an die Konjunkturen des Intersektionalitätsbegriffs an und fragt, inwiefern dieser als sozialhistorisch fundiertes Konzept nicht nur gegenwartsbezogen verstanden, sondern auch rückblickend auf Kunstausstellungen angewendet werden kann – als ein produktives Werkzeug der Ausstellungsgeschichte. Intersektionalität bezeichnet im Ausstellungsmodus sowohl das Zusammenwirken verschiedener Diskriminierungsformen als auch Strategien zu deren Sichtbarmachung und Überwindung. 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Maria Bremer und Isabelle Lindermann - - - - - DOI: https://doi.org/10.11588/kb.2025.3 Abstract: Das Heft knüpft an die Konjunkturen des Intersektionalitätsbegriffs an und fragt, inwiefern dieser als sozialhistorisch fundiertes Konzept nicht nur gegenwartsbezogen verstanden, sondern auch rückblickend auf Kunstausstellungen angewendet werden kann – als ein produktives Werkzeug der Ausstellungsgeschichte. Intersektionalität bezeichnet im Ausstellungsmodus sowohl das Zusammenwirken verschiedener Diskriminierungsformen als auch Strategien zu deren Sichtbarmachung und Überwindung. Kann deren Berücksichtigung in der Analyse etablierte Ausstellungsgeschichten infrage stellen und zugleich der Tendenz zur Partikularisierung dieses kunsthistorischen Arbeitsbereichs entlang singulärer Identifikationskategorien entgegenwirken? - - - - - Intersektionalität - Ausstellungsgeschichte - Sozialgeschichte - Identität - Historisierung - - - 2025-09-05T17:32:36+02:00 - 2025-09-15T11:11:15+02:00 - Adobe InDesign 20.4 (Windows) - 2025-09-15T11:11:15+02:00 - uuid:d2065a3b-be6b-44e9-b28f-1cc17d353865 - xmp.did:0e78bfd6-1fec-bf44-8b89-aee02f3fea8a - xmp.id:9e8358c8-5d6c-9b4e-9813-bff57ef9a234 - proof:pdf - - xmp.iid:f43ce4d8-5c13-cc4f-8b83-be62f1d06dc0 - xmp.did:7461cac2-2f58-b344-89d4-561aeec0d746 - xmp.did:0e78bfd6-1fec-bf44-8b89-aee02f3fea8a - default - - - - - converted - from application/x-indesign to application/pdf - Adobe InDesign 20.4 (Windows) - / - 2025-09-05T17:32:36+02:00 - - - - http://creativecommons.org/licenses/by-sa/4.0/ - True - Adobe PDF Library 17.0 - False - 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