From 0c1b11db6f4f0205beeb82f2d790b3572225c6f9 Mon Sep 17 00:00:00 2001 From: Liam Connors Date: Fri, 31 Oct 2025 14:28:46 -0400 Subject: [PATCH] remove chart studio content --- _config.yml | 2 +- _data/display_as.yml | 3 - _data/display_as_py_r_js.yml | 3 - _includes/layouts/chart_studio_plug.html | 53 - _includes/layouts/side-bar.html | 2 - _includes/posts/documentation_eg.html | 22 - .../2021-08-04-chart-studio-index.html | 27 - .../2021-08-04-chart-studio-index.html | 27 - .../chart-studio/2021-08-04-filenames.Rmd | 62 - .../chart-studio/2021-08-04-get-requests.Rmd | 56 - ...8-04-getting-started-with-chart-studio.Rmd | 132 - .../chart-studio/2021-08-04-privacy.Rmd | 68 - .../2021-08-04-chart-studio-index.html | 27 - .../2021-08-04-chart-studio-index.html | 27 - .../2021-08-04-chart-studio-index.html | 31 - .../2019-06-17-python-chart-studio-index.html | 28 - .../data-api/2015-06-30-grid-api.html | 5943 ----- .../chart-studio/data-api/grid-api.ipynb | 673 - 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| Bin 259850 -> 0 bytes .../chart-studio/offline/temp-plot.html | 7 - .../2015-06-30-presentations-api.html | 1142 - .../2018-03-06-presentations.html | 5 - .../presentations/presentations-api.ipynb | 1109 - .../privacy/2015-06-30-privacy.html | 539 - .../chart-studio/privacy/privacy.ipynb | 502 - .../proxies/2015-07-27-proxy_index.md | 47 - .../sending-data/2015-04-09-add-traces.html | 21 - .../sending-data/2015-04-09-extend.html | 21 - .../sending-data/2015-04-09-overwrite.html | 19 - .../2015-04-09-sending-data_index.html | 13 - .../python/2019-07-03-chart-studio-index.html | 29 - .../chart-studio/2019-07-03-data-api.html | 699 - .../chart-studio/2019-07-03-delete-plots.html | 418 - .../2019-07-03-embedding-charts.html | 65 - .../chart-studio/2019-07-03-get-requests.html | 165 - ...-03-getting-started-with-chart-studio.html | 996 - .../2019-07-03-ipython-notebook-tutorial.html | 958 - .../2019-07-03-presentations-tool.html | 655 - .../chart-studio/2019-07-03-privacy.html | 499 - 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_posts/python/chart-studio/regen.sh | 7 - _posts/r/2019-07-03-is-plotly-free-r.md | 2 +- _posts/r/README.md | 13 +- .../2015-04-09-static-image_r_index.Rmd | 75 - .../2015-04-09-static-image_r_index.md | 73 - .../2015-07-29-dashboard-index.html | 5 - .../r/chart-studio/2015-07-30-filenames.Rmd | 52 - _posts/r/chart-studio/2015-07-30-filenames.md | 57 - .../chart-studio/2015-07-30-get-requests.Rmd | 56 - .../r/chart-studio/2015-07-30-get-requests.md | 64 - _posts/r/chart-studio/2015-07-30-privacy.Rmd | 59 - _posts/r/chart-studio/2015-07-30-privacy.md | 64 - _posts/r/chart-studio/2015-08-10-knitr.Rmd | 80 - _posts/r/chart-studio/2015-08-10-knitr.md | 103 - .../2015-08-10-plotly-offline.html | 5 - .../2016-02-20-jupyter-notebook-r.html | 242 - .../2017-07-17-configuration-options.Rmd | 42 - .../2017-07-17-configuration-options.md | 42 - .../2019-12-18-chart-studio-index.html | 26 - ...1-17-getting-started-with-chart-studio.Rmd | 131 - ...01-17-getting-started-with-chart-studio.md | 129 - 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_posts/r/chart-studio/sending-data/2015-04-09-overwrite.html delete mode 100644 _posts/r/chart-studio/sending-data/2015-04-09-sending-data_index.html delete mode 100644 all_static/images/chart-studio-banner.png delete mode 100644 all_static/images/icon-chart-studio.png delete mode 100644 all_static/images/thumbnails/matlab/getting-started-with-chart-studio.png diff --git a/_config.yml b/_config.yml index 180475a24..1516b0420 100755 --- a/_config.yml +++ b/_config.yml @@ -14,7 +14,7 @@ imgurl: https://images.plot.ly/plotly-documentation/ # Excludes # --- #exclude: ['*.Rmd','_posts/2015-09-09-matlab-reference.html','_posts/2015-09-06-r-reference.html','_posts/2015-09-06-python-reference.html','_posts/2015-08-19-plotly_js-reference.html','_posts/2015-04-05-ggplot2-index.html','_posts/2015-04-05-julia-index.html,'_posts/2015-04-05-node_js-index.html','_posts/2015-04-05-plotly_js-index.html','_posts/2015-04-05-plotlyjs-function-reference.md','_posts/2015-07-13-eula_index.html','_posts/2015-07-26-index.html','_posts/2015-07-30-r-index.Rmd','_posts/2015-07-30-r-index.md','_posts/2015-08-20-research-box-index.html','_posts/ggplot2','_posts/julia','_posts/nodejs','_posts/plotly_js','_posts/r','_posts/tutorials','_posts/user_guide_python'] -exclude: [_posts/temp, '*.Rmd', vendor, node_modules] #'_posts/python/chart-studio/*.md'] +exclude: [_posts/temp, '*.Rmd', vendor, node_modules] # --- # Markdown / Syntax diff --git a/_data/display_as.yml b/_data/display_as.yml index 74d89ff02..f4eebc173 100644 --- a/_data/display_as.yml +++ b/_data/display_as.yml @@ -70,9 +70,6 @@ layout_opt: legacy_charts: reference: '#legacy-charts' text: Legacy Charts -chart_studio: - reference: 'chart-studio' - text: Chart Studio advanced_opt: reference: '#advanced-options' text: Advanced diff --git a/_data/display_as_py_r_js.yml b/_data/display_as_py_r_js.yml index d01a415a9..e0ead1e5f 100644 --- a/_data/display_as_py_r_js.yml +++ b/_data/display_as_py_r_js.yml @@ -172,6 +172,3 @@ chart_events: ipython_notebooks_gallery: reference: '#language' text: By Data Science Language -chart_studio: - reference: 'chart-studio' - text: Chart Studio diff --git a/_includes/layouts/chart_studio_plug.html b/_includes/layouts/chart_studio_plug.html deleted file mode 100644 index d1c435762..000000000 --- a/_includes/layouts/chart_studio_plug.html +++ /dev/null @@ -1,53 +0,0 @@ -

Share you Plotly chart with a link for free

- -

To save your chart online for free, please {% if page.language == "python" %}create a Chart Studio account -{% elsif page.language == "plotly_js %} -create a Chart Studio account -{% elsif page.language == "r" %} -create a Chart Studio account -{% elsif page.language == "ggplot2" %} -create a Chart Studio account -{% elsif page.language == "julia" %} -create a Chart Studio account -{% elsif page.language == "fsharp" %} -create a Chart Studio account -{% elsif page.language == "csharp" %} -create a Chart Studio account -{% elsif page.language == "matlab" %} -create a Chart Studio account -{% endif %} to retrieve your free API key.

- -

To save private charts (not discoverable or viewable by anyone but you and trusted collaborators), there is a $14/month hosting option (billed annually). Revenue from private chart hosting on Chart Studio also supports the open-source development team maintaining the Plotly Python library.

- -

Please click "Upgrade" in the Chart Creator if you wish to support our work!

- - -{% if page.language == "python" %} -

Example usage - upload a plot to Chart Studio

-
import plotly.express as px
-import chart_studio.plotly as py
-
-fig = px.scatter(x=[0, 1, 2, 3, 4], y=[0, 1, 4, 9, 16])
-
-py.plot(fig, filename = 'basic-line', auto_open=True)
-{% elsif page.language == "plotly_js %} - -{% elsif page.language == "r" %} - -{% elsif page.language == "ggplot2" %} - -{% elsif page.language == "julia" %} -

Example usage - upload a plot to Chart Studio

-
using Plotly
-p = plot(scatter(x=0:4, y=(0:4).^2));
-rp = post(p, filename="basic-line")
-{% elsif page.language == "fsharp" %} - -{% elsif page.language == "csharp" %} - -{% elsif page.language == "matlab" %} -

Example usage - upload a plot to Chart Studio

-
scatter([0, 1, 2, 3, 4], [0, 1, 4, 9, 16]);
-
-fig2plotly(gcf,'offline',false, filename = 'basic-line');
-{% endif %} diff --git a/_includes/layouts/side-bar.html b/_includes/layouts/side-bar.html index 823f3ebdc..998924c15 100644 --- a/_includes/layouts/side-bar.html +++ b/_includes/layouts/side-bar.html @@ -67,8 +67,6 @@ {% assign report_generation = true %} {% elsif page.display_as == "databases" %} {% assign databases = true %} -{% elsif page.display_as == "chart_studio" %} -{% assign chart_studio = true %} {% elsif page.display_as == "advanced_opt" %} {% assign advanced_opt = true %} diff --git a/_includes/posts/documentation_eg.html b/_includes/posts/documentation_eg.html index 7a831ae1e..128549555 100644 --- a/_includes/posts/documentation_eg.html +++ b/_includes/posts/documentation_eg.html @@ -32,8 +32,6 @@ {% assign chart_events = true %} {% elsif page.display_as == "financial_analysis" %} {% assign financial_analysis = true %} -{% elsif page.display_as == "chart_studio" %} -{% assign chart_studio = true %} {% elsif page.display_as == "advanced_opt" %} {% assign advanced_opt = true %} {% elsif page.display_as == "ai_ml" %} @@ -418,26 +416,6 @@ {% endif %} -{% if chart_studio %} -
-
Chart Studio Integration -
-
-
    - {%- for page in languagelist -%} - {% if page.display_as == "chart_studio" %} - - - {% include layouts/grid-item.html %} - - - {% endif %} - {%- endfor -%} -
-
-
-{% endif %} - {% if financial_analysis %}
Financial Analysis diff --git a/_posts/csharp/indexes/2021-08-04-chart-studio-index.html b/_posts/csharp/indexes/2021-08-04-chart-studio-index.html deleted file mode 100644 index 23148b24e..000000000 --- a/_posts/csharp/indexes/2021-08-04-chart-studio-index.html +++ /dev/null @@ -1,27 +0,0 @@ ---- -permalink: csharp/chart-studio/ -description: Plotly's C# graphing library makes interactive, publication-quality graphs online. Tutorials and tips about fundamental features of Plotly's Python API. -name: Chart Studio Docs -layout: langindex -language: csharp -display_as: chart_studio -thumbnail: thumbnail/mixed.jpg -page_type: example_index ---- - -
-
- -
- -
-

Plotly C# Chart Studio Integration

-

{{page.description}}


- {% include layouts/page-another-language.html %} -
-
-
-
- -{% assign languagelist = site.posts | where:"language","csharp" |where:"display_as","chart_studio" | where: "layout","base" | sort: "order" %} -{% include posts/documentation_eg.html %} diff --git a/_posts/fsharp/indexes/2021-08-04-chart-studio-index.html b/_posts/fsharp/indexes/2021-08-04-chart-studio-index.html deleted file mode 100644 index c47ab9d6c..000000000 --- a/_posts/fsharp/indexes/2021-08-04-chart-studio-index.html +++ /dev/null @@ -1,27 +0,0 @@ ---- -permalink: fsharp/chart-studio/ -description: Plotly's F# graphing library makes interactive, publication-quality graphs online. Tutorials and tips about fundamental features of Plotly's Python API. -name: Chart Studio Docs -layout: langindex -language: fsharp -display_as: chart_studio -thumbnail: thumbnail/mixed.jpg -page_type: example_index ---- - -
-
- -
- -
-

Plotly F# Chart Studio Integration

-

{{page.description}}


- {% include layouts/page-another-language.html %} -
-
-
-
- -{% assign languagelist = site.posts | where:"language","fsharp" |where:"display_as","chart_studio" | where: "layout","base" | sort: "order" %} -{% include posts/documentation_eg.html %} diff --git a/_posts/ggplot2/chart-studio/2021-08-04-filenames.Rmd b/_posts/ggplot2/chart-studio/2021-08-04-filenames.Rmd deleted file mode 100644 index 603dc6e07..000000000 --- a/_posts/ggplot2/chart-studio/2021-08-04-filenames.Rmd +++ /dev/null @@ -1,62 +0,0 @@ ---- -description: How to update graphs stored in Chart Studio with ggplot2. -display_as: chart_studio -language: ggplot2 -layout: base -name: Updating Graphs Stored In Chart Studio -order: 10 -output: - html_document: - keep_md: true -permalink: ggplot2/file-options/ -thumbnail: thumbnail/horizontal-bar.jpg ---- - -```{r, echo = FALSE, message=FALSE} -knitr::opts_chunk$set(message = FALSE, warning=FALSE) -Sys.setenv("plotly_username"="RPlotBot") -Sys.setenv("plotly_api_key"="q0lz6r5efr") -``` -### Save ggplot2 Plot To Chart Studio - -Using the `plotly` ggplot2 package, you can create a Chart Studio figure based on your ggplot2 chart. Simply pass your chart as a parameter to the `api_create()` function: - -```{r} -library(plotly) - -p <- qplot(mpg, wt, data = mtcars, size = cyl) - -api_create(p) -``` - -### How To Overwrite An Existing Plot - -By default, when you call `api_create()`, a new plot is created in your Chart Studio account with its own unique URL. - -If you would like to overwrite an existing plot in your Chart Studio account and keep the same URL, then supply a `filename` as an extra parameter to the `api_create()` function. This will keep the same URL for the plot. - -```{r} -library(plotly) - -p <- qplot(mpg, wt, data = mtcars, size = cyl) - -api_create(p, filename = "name-of-my-plotly-file") -``` - -### Saving Plots In Folders - -If the `filename` parameter contains the character "/", then the `api_create()` function will save that plot in a folder in your Chart Studio account. - -This option is only available for [Chart Studio Enterprise subscribers](https://plotly.com/online-chart-maker/) - -```{r} -library(plotly) - -p <- qplot(mpg, wt, data = mtcars, size = cyl) - -api_create(p, filename="r-docs/name-of-my-chart-studio-file") -``` - -### Viewing Saved Plots - -View the ggplot2 graphs you have saved in your Chart Studio account at [https://plotly.com/organize](https://plotly.com/organize). \ No newline at end of file diff --git a/_posts/ggplot2/chart-studio/2021-08-04-get-requests.Rmd b/_posts/ggplot2/chart-studio/2021-08-04-get-requests.Rmd deleted file mode 100644 index 04a40b278..000000000 --- a/_posts/ggplot2/chart-studio/2021-08-04-get-requests.Rmd +++ /dev/null @@ -1,56 +0,0 @@ ---- -description: How to download Chart Studio users' public graphs and data into an ggplot2 session. -display_as: chart_studio -language: ggplot2 -layout: base -name: Working With Chart Studio Graphs -order: 5 -output: - html_document: - keep_md: true -permalink: ggplot2/working-with-chart-studio-graphs/ -thumbnail: thumbnail/hover.jpg ---- - -```{r, echo = FALSE, message=FALSE} -knitr::opts_chunk$set(message = FALSE, warning=FALSE) -Sys.setenv("plotly_username"="RPlotBot") -Sys.setenv("plotly_api_key"="q0lz6r5efr") -``` -### Download Chart Studio Graphs Into ggplot2 Sessions - -Download Chart Studio figures directly into your ggplot2 session with the `api_download_plot()` function. This takes the `plot_id` of the Chart Studio plot and the `username` of the plot's creator as arguments. - -For example, to download [https://plotly.com/~cpsievert/559](https://plotly.com/~cpsievert/559) into ggplot2, call: - -```{r} -library(plotly) - -fig <- api_download_plot("559", "cpsievert") - -fig -``` - -### Update The Layout on A Downloaded Graph - -Once the figure is downloaded from Chart Studio into your ggplot2 session, you can update its layout just like you would any other figure you create with the `plotly` ggplot2 package. - -**Note:** If you were to re-upload this figure to Chart Studio, a new figure would be created unless you specify the same `filename` as the figure that you downloaded. In that case, the existing figure will be overwritten. - -```{r} -p <- layout(fig, title = paste("Modified on ", Sys.time())) -p -``` - -### Adding a Trace to a Subplot Figure - -```{r} -fig <- api_download_plot("6343", "chelsea_lyn") - -p <- add_lines(fig, x = c(1, 2), y = c(1, 2), xaxis = "x2", yaxis = "y2") -p -``` - -### Reference - -See the documentation for [getting started with Chart Studio in ggplot2](https://plotly.com/r/getting-started-with-chart-studio). \ No newline at end of file diff --git a/_posts/ggplot2/chart-studio/2021-08-04-getting-started-with-chart-studio.Rmd b/_posts/ggplot2/chart-studio/2021-08-04-getting-started-with-chart-studio.Rmd deleted file mode 100644 index 54ddd969f..000000000 --- a/_posts/ggplot2/chart-studio/2021-08-04-getting-started-with-chart-studio.Rmd +++ /dev/null @@ -1,132 +0,0 @@ ---- -name: Getting Started with Chart Studio -permalink: ggplot2/getting-started-with-chart-studio/ -description: Get started with Chart Studio and Plotly's ggplot2 graphing library. -page_type: example_index -display_as: chart_studio -layout: base -language: ggplot2 -thumbnail: thumbnail/bubble.jpg -order: 1 -output: - html_document: - keep_md: true ---- - - - -# Getting Started with Chart Studio and the `plotly` ggplot2 Package - -`plotly` is an ggplot2 package for creating interactive web-based graphs via the open source JavaScript graphing library [plotly.js](http://plot.ly/javascript). - -As of version 2.0 (November 17, 2015), ggplot2 graphs created with the `plotly` ggplot2 package are, by default, rendered *locally* through the [htmlwidgets](http://www.htmlwidgets.org/) framework. - -## Initialization for Online Plotting - -You can choose to publish charts you create with the `plotly` ggplot2 package to the web using [Chart Studio](https://plotly.com/online-chart-maker). In order to do so, follow these steps: - -1 - [Create a free Chart Studio account](https://plotly.com/api_signup):
-A Chart Studio account is required to publish ggplot2 charts to the web using Chart Studio. It's free to get started, and you control the privacy of your charts. - -2 - Store your Chart Studio authentication credentials as environment variables in your ggplot2 session
-Your Chart Studio authentication credentials consist of your Chart Studio username and your Chart Studio API key, which can be found [in your online settings](https://plotly.com/settings/api). - -Use the [`Sys.setenv()`](https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/Sys.setenv) function to set these credentials as environment variables in your ggplot2 session. - -```r -Sys.setenv("plotly_username"="your_plotly_username") -Sys.setenv("plotly_api_key"="your_api_key") -``` - -Save these commands in your [.Rprofile](http://www.statmethods.net/interface/customizing.html) file if you want them to be run every time you start a new ggplot2 session. - -3 - Use the `api_create()` function to publish ggplot2 charts to Chart Studio: - -Use the `filename` attribute to set the title of the file that will be generated in your Chart Studio account. - -```r -library(plotly) -p <- ggplot(faithful, aes(x = eruptions, y = waiting)) + - stat_density_2d(aes(fill = ..level..), geom = "polygon") + - xlim(1, 6) + ylim(40, 100) - -api_create(p, filename = "ggplot2-example-plot") -``` - -4 (optional) - Suppress auto open behavior: - -When following the instructions above, executing `api_create(p)` will auto open the created Chart Studio URL in the browser. To suppress this behavior, set the `browser` option to `false` in your ggplot2 session. - -```r -options(browser = 'false') -api_create(p, filename = "ggplot2-example-plot") -``` - -## Special Instructions for Chart Studio Enterprise Users - -### Where To Find Your API Key - -Your API key for your free Chart Studio account will be different than the API key for your [Chart Studio Enterprise](https://plotly.com/product/enterprise/) account. - -Visit to find your Chart Studio Enterprise account API key. - -Remember to replace "your-company.com" with the URL of your company's Chart Studio Enterprise server. - -### Set the `plotly_domain` environment variable - -The URL that the `plotly` package uses to communicate with Chart Studio will be different if your company has a Chart Studio Enterprise server. In order to make your ggplot2 session aware of the new URL, set the `plotly_domain` environment variable equal to the URL of your Chart Studio Enterprise server using the `Sys.setenv()` function. - -Save the following command in your [.Rprofile](http://www.statmethods.net/interface/customizing.html) so that it runs every time you start a new ggplot2 session: - -```r -Sys.setenv("plotly_domain"="https://plotly.your-company.com") -``` - -Remember to replace "your-company" with the URL of your company's Chart Studio Enterprise server. - -## Chart Studio Plot Privacy Modes - -Chart Studio plots can be set to three different type of privacy modes: `public`, `private`, or `secret`. - -* **public:** - - Anyone can view this graph. - It will appear in your Chart Studio profile and can be indexed by search engines. - Being logged in to a Chart Studio account is not required to view this chart. - -* **private:** - - Only you can view this plot. - It will not appear in the public Chart Studio feed, your Chart Studio profile, or be indexed by search engines. - Being logged into your Chart Studio account is required to view this graph. - You can privately share this graph with other Chart Studio users. They will also need to be logged in to their Chart Studio account to view this plot. - This option is only available to Chart Studio Enterprise subscribers. - -* **secret:** - - Anyone with this secret link can view this chart. - It will not appear in the public Chart Studio feed, your Chart Studio profile, or be indexed by search engines. - If it is embedded inside a webpage or an IPython notebook, anybody who is viewing that page will be able to view the graph. - You do not need to be logged in to your Chart Studio account view this plot. - This option is only available to Chart Studio Enterprise subscribers. - -By default all Chart Studio plots you create with the `plotly` ggplot2 package are set to `public`. Users with free Chart Studio accounts are limited to creating `public` plots. - -### Appending Static Image File Types to Chart Studio Plot URLs - -You can also view the static image version of any public Chart Studio graph by appending `.png` or `.jpeg` to the end of the URL for the graph. - -For example, view the static image of at . - -[Chart Studio Enterprise](https://plotly.com/online_chart_maker) users can also use this method to get static images in the `.pdf`, `.svg`, and `.eps` file formats. - -## Private Charts In Chart Studio - -If you have private storage needs, please learn more about [Chart Studio Enterprise](https://plotly.com/online-chart-maker/). - -If you're a [Chart Studio Enterprise subscriber](https://plotly.com/settings/subscription/?modal=true&utm_source=api-docs&utm_medium=support-oss) and would like the setting for your plots to be private, you can specify sharing as private: - -```r -api_create(filename = "private-graph", sharing = "private") -``` -For more information regarding the privacy of plots published to Chart Studio using the `plotly` ggplot2 package, please visit [our Chart Studio privacy documentation](https://plotly.com/ggplot2/privacy/) \ No newline at end of file diff --git a/_posts/ggplot2/chart-studio/2021-08-04-privacy.Rmd b/_posts/ggplot2/chart-studio/2021-08-04-privacy.Rmd deleted file mode 100644 index 70637f85d..000000000 --- a/_posts/ggplot2/chart-studio/2021-08-04-privacy.Rmd +++ /dev/null @@ -1,68 +0,0 @@ ---- -description: How to set the privacy settings of Chart Studio graphs in ggplot2. -display_as: chart_studio -language: ggplot2 -layout: base -name: Privacy Settings For Chart Studio Graphs -order: 3 -output: - html_document: - keep_md: true -permalink: ggplot2/privacy/ -thumbnail: thumbnail/privacy.jpg ---- - -```{r, echo = FALSE, message=FALSE} -knitr::opts_chunk$set(message = FALSE, warning = FALSE) -Sys.setenv("plotly_username"="RPlotBot") -Sys.setenv("plotly_api_key"="q0lz6r5efr") -``` -#### Default Privacy -The `plotly` ggplot2 package renders plots entirely **locally** by default. - -However, you can also choose to publish plots on the web using Chart Studio via the `api_create()` function. - -By default, the `api_create()` function creates public graphs. With a [Chart Studio Enterprise subscription](https://plotly.com/online-chart-maker/), you can easily make graphs private by using the `sharing` argument of the `api_create()` function. - -### Public Graph - -Please note, this is the default privacy option. - -```{r} -library(plotly) -library(ggplot2) - -p <- qplot(mpg, wt, data = mtcars, size = cyl) - -chart_link = api_create(p, filename = "public-graph-file") -chart_link -``` - -Below is the URL of this public plot. Anyone can view public plots even if they are not logged into Plotly.
[Try it out](https://chart-studio.plotly.com/~danton267/1535/#/) - -### Private Graph -```{r} -library(plotly) -library(ggplot2) - -p <- qplot(mpg, wt, data = mtcars, size = cyl) - -chart_link = api_create(p, filename = "private-graph-file", sharing = "private") -chart_link -``` - -Below is the URL of the private plot above. Only the owner can view the private plot. You won't be able to view this plot.
[Try it out](https://chart-studio.plotly.com/~danton267/1533/#/) - -### Secret Graph -```{r} -library(plotly) -library(ggplot2) - -p <- qplot(mpg, wt, data = mtcars, size = cyl) - -chart_link = api_create(p, filename = "secret-graph-file", sharing = "secret") -chart_link -``` - -Below is the URL of this secret plot. Anyone with the secret link can view this chart. However, it will not appear in the Plotly feed, your profile, or search engines.
Try it out: -[https://chart-studio.plotly.com/~danton267/1531/?share_key=UrA8XN1nhtLMmtTbckbkUK#/](https://chart-studio.plotly.com/~danton267/1531/?share_key=UrA8XN1nhtLMmtTbckbkUK#/) \ No newline at end of file diff --git a/_posts/ggplot2/indexes/2021-08-04-chart-studio-index.html b/_posts/ggplot2/indexes/2021-08-04-chart-studio-index.html deleted file mode 100644 index 9418d7193..000000000 --- a/_posts/ggplot2/indexes/2021-08-04-chart-studio-index.html +++ /dev/null @@ -1,27 +0,0 @@ ---- -permalink: ggplot2/chart-studio/ -description: Plotly's ggplot2 graphing library makes interactive, publication-quality graphs online. Tutorials and tips about fundamental features of Plotly's Python API. -name: Chart Studio Docs -layout: langindex -language: ggplot2 -display_as: chart_studio -thumbnail: thumbnail/mixed.jpg -page_type: example_index ---- - -
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Plotly ggplot2 Chart Studio Integration

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Plotly Julia Chart Studio Integration

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{{page.description}}


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Plotly MATLAB® Chart Studio Integration

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{{page.description}}

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- -{% assign languagelist = site.posts | where:"language","matlab" |where:"display_as","chart_studio" | where: "layout","base" | sort: "order" %} -{% include posts/documentation_eg.html %} - -

- MATLAB® is a registered trademark of The MathWorks, Inc. -

diff --git a/_posts/python-v3/chart-studio/2019-06-17-python-chart-studio-index.html b/_posts/python-v3/chart-studio/2019-06-17-python-chart-studio-index.html deleted file mode 100644 index 50cfc896b..000000000 --- a/_posts/python-v3/chart-studio/2019-06-17-python-chart-studio-index.html +++ /dev/null @@ -1,28 +0,0 @@ ---- -permalink: python/v3/chart-studio/ -description: Plotly's Python graphing library makes interactive, publication-quality graphs online. Tutorials and tips about fundamental features of Plotly's Python API. -name: More Chart Studio Docs -layout: langindex -language: python/v3 -display_as: chart_studio -thumbnail: thumbnail/mixed.jpg -page_type: example_index -order: 20 ---- - - -
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Plotly Python Chart Studio Integration

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New to Plotly?¶

Plotly's Python library is free and open source! Get started by downloading the client and reading the primer. -
You can set up Plotly to work in online or offline mode, or in jupyter notebooks. -
We also have a quick-reference cheatsheet (new!) to help you get started!

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Creating a Plotly Grid¶

You can instantiate a grid with data by either uploading tabular data to Plotly or by creating a Plotly grid using the API. To upload the grid we will use plotly.plotly.grid_ops.upload(). It takes the following arguments:

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-import plotly.plotly as py
-import plotly.tools as tls
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-from datetime import datetime as dt
-import numpy as np
-from IPython.display import Image
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-column_1 = Column(['a', 'b', 'c'], 'column 1')
-column_2 = Column([1, 2, 3], 'column 2') # Tabular data can be numbers, strings, or dates
-grid = Grid([column_1, column_2])
-url = py.grid_ops.upload(grid,
-                         filename='grid_ex_'+str(dt.now()),
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-print(url)
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View and Share your Grid¶

You can view your newly created grid at the url:

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You are also able to view the grid in your list of files inside your organize folder.

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Upload Dataframes to Plotly¶

Along with uploading a grid, you can upload a Dataframe as well as convert it to raw data as a grid:

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-df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_apple_stock.csv')
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grid = Grid([Column(df[column_name], column_name) for column_name in df.columns])
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Making Graphs from Grids¶

Plotly graphs are usually described with data embedded in them. For example, here we place x and y data directly into our Histogram2dContour object:

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We can also create graphs based off of references to columns of grids. Here, we'll upload several columns to our Plotly account:

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column_1 = Column(np.random.randn(1000), 'column 1')
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Make Graph from Raw Data¶

Instead of placing data into x and y, we'll place our Grid columns into xsrc and ysrc:

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So, when you view the data, you'll see your original grid, not just the columns that compose this graph:

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Attaching Meta Data to Grids¶

In Chart Studio Enterprise, you can upload and assign free-form JSON metadata to any grid object. This means that you can keep all of your raw data in one place, under one grid.

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If you update the original data source, in the workspace or with our API, all of the graphs that are sourced from it will be updated as well. You can make multiple graphs from a single Grid and you can make a graph from multiple grids. You can also add rows and columns to existing grids programatically.

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meta = {
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-    "Experiment ID": "d3kbd",
-    "Operator": "James Murphy",
-    "Initial Conditions": {
-          "Voltage": 5.5
-    }
-}
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-grid_url = py.grid_ops.upload(grid, filename='grid_with_metadata_'+str(dt.now()), meta=meta)
-print(url)
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https://plotly.com/~chelsea_lyn/17400/
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Image('metadata_view.png')
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Reference¶

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In [11]:
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help(py.grid_ops)
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Help on class grid_ops in module plotly.plotly.plotly:
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-class grid_ops
- |  Interface to Plotly's Grid API.
- |  Plotly Grids are Plotly's tabular data object, rendered
- |  in an online spreadsheet. Plotly graphs can be made from
- |  references of columns of Plotly grid objects. Free-form
- |  JSON Metadata can be saved with Plotly grids.
- |
- |  To create a Plotly grid in your Plotly account from Python,
- |  see `grid_ops.upload`.
- |
- |  To add rows or columns to an existing Plotly grid, see
- |  `grid_ops.append_rows` and `grid_ops.append_columns`
- |  respectively.
- |
- |  To delete one of your grid objects, see `grid_ops.delete`.
- |
- |  Class methods defined here:
- |
- |  append_columns(cls, columns, grid=None, grid_url=None) from __builtin__.classobj
- |      Append columns to a Plotly grid.
- |
- |      `columns` is an iterable of plotly.grid_objs.Column objects
- |      and only one of `grid` and `grid_url` needs to specified.
- |
- |      `grid` is a ploty.grid_objs.Grid object that has already been
- |      uploaded to plotly with the grid_ops.upload method.
- |
- |      `grid_url` is a unique URL of a `grid` in your plotly account.
- |
- |      Usage example 1: Upload a grid to Plotly, and then append a column
- |      ```
- |      from plotly.grid_objs import Grid, Column
- |      import plotly.plotly as py
- |      column_1 = Column([1, 2, 3], 'time')
- |      grid = Grid([column_1])
- |      py.grid_ops.upload(grid, 'time vs voltage')
- |
- |      # append a column to the grid
- |      column_2 = Column([4, 2, 5], 'voltage')
- |      py.grid_ops.append_columns([column_2], grid=grid)
- |      ```
- |
- |      Usage example 2: Append a column to a grid that already exists on
- |                       Plotly
- |      ```
- |      from plotly.grid_objs import Grid, Column
- |      import plotly.plotly as py
- |
- |      grid_url = 'https://plotly.com/~chris/3143'
- |      column_1 = Column([1, 2, 3], 'time')
- |      py.grid_ops.append_columns([column_1], grid_url=grid_url)
- |      ```
- |
- |  append_rows(cls, rows, grid=None, grid_url=None) from __builtin__.classobj
- |      Append rows to a Plotly grid.
- |
- |      `rows` is an iterable of rows, where each row is a
- |      list of numbers, strings, or dates. The number of items
- |      in each row must be equal to the number of columns
- |      in the grid. If appending rows to a grid with columns of
- |      unequal length, Plotly will fill the columns with shorter
- |      length with empty strings.
- |
- |      Only one of `grid` and `grid_url` needs to specified.
- |
- |      `grid` is a ploty.grid_objs.Grid object that has already been
- |      uploaded to plotly with the grid_ops.upload method.
- |
- |      `grid_url` is a unique URL of a `grid` in your plotly account.
- |
- |      Usage example 1: Upload a grid to Plotly, and then append rows
- |      ```
- |      from plotly.grid_objs import Grid, Column
- |      import plotly.plotly as py
- |      column_1 = Column([1, 2, 3], 'time')
- |      column_2 = Column([5, 2, 7], 'voltage')
- |      grid = Grid([column_1, column_2])
- |      py.grid_ops.upload(grid, 'time vs voltage')
- |
- |      # append a row to the grid
- |      row = [1, 5]
- |      py.grid_ops.append_rows([row], grid=grid)
- |      ```
- |
- |      Usage example 2: Append a row to a grid that already exists on Plotly
- |      ```
- |      from plotly.grid_objs import Grid
- |      import plotly.plotly as py
- |
- |      grid_url = 'https://plotly.com/~chris/3143'
- |
- |      row = [1, 5]
- |      py.grid_ops.append_rows([row], grid=grid_url)
- |      ```
- |
- |  delete(cls, grid=None, grid_url=None) from __builtin__.classobj
- |      Delete a grid from your Plotly account.
- |
- |      Only one of `grid` or `grid_url` needs to be specified.
- |
- |      `grid` is a plotly.grid_objs.Grid object that has already
- |             been uploaded to Plotly.
- |
- |      `grid_url` is the URL of the Plotly grid to delete
- |
- |      Usage example 1: Upload a grid to plotly, then delete it
- |      ```
- |      from plotly.grid_objs import Grid, Column
- |      import plotly.plotly as py
- |      column_1 = Column([1, 2, 3], 'time')
- |      column_2 = Column([4, 2, 5], 'voltage')
- |      grid = Grid([column_1, column_2])
- |      py.grid_ops.upload(grid, 'time vs voltage')
- |
- |      # now delete it, and free up that filename
- |      py.grid_ops.delete(grid)
- |      ```
- |
- |      Usage example 2: Delete a plotly grid by url
- |      ```
- |      import plotly.plotly as py
- |
- |      grid_url = 'https://plotly.com/~chris/3'
- |      py.grid_ops.delete(grid_url=grid_url)
- |      ```
- |
- |  upload(cls, grid, filename, world_readable=True, auto_open=True, meta=None) from __builtin__.classobj
- |      Upload a grid to your Plotly account with the specified filename.
- |
- |      Positional arguments:
- |          - grid: A plotly.grid_objs.Grid object,
- |                  call `help(plotly.grid_ops.Grid)` for more info.
- |          - filename: Name of the grid to be saved in your Plotly account.
- |                      To save a grid in a folder in your Plotly account,
- |                      separate specify a filename with folders and filename
- |                      separated by backslashes (`/`).
- |                      If a grid, plot, or folder already exists with the same
- |                      filename, a `plotly.exceptions.RequestError` will be
- |                      thrown with status_code 409
- |
- |      Optional keyword arguments:
- |          - world_readable (default=True): make this grid publically (True)
- |                                           or privately (False) viewable.
- |          - auto_open (default=True): Automatically open this grid in
- |                                      the browser (True)
- |          - meta (default=None): Optional Metadata to associate with
- |                                 this grid.
- |                                 Metadata is any arbitrary
- |                                 JSON-encodable object, for example:
- |                                 `{"experiment name": "GaAs"}`
- |
- |      Filenames must be unique. To overwrite a grid with the same filename,
- |      you'll first have to delete the grid with the blocking name. See
- |      `plotly.plotly.grid_ops.delete`.
- |
- |      Usage example 1: Upload a plotly grid
- |      ```
- |      from plotly.grid_objs import Grid, Column
- |      import plotly.plotly as py
- |      column_1 = Column([1, 2, 3], 'time')
- |      column_2 = Column([4, 2, 5], 'voltage')
- |      grid = Grid([column_1, column_2])
- |      py.grid_ops.upload(grid, 'time vs voltage')
- |      ```
- |
- |      Usage example 2: Make a graph based with data that is sourced
- |                       from a newly uploaded Plotly grid
- |      ```
- |      import plotly.plotly as py
- |      from plotly.grid_objs import Grid, Column
- |      from plotly.graph_objs import Scatter
- |      # Upload a grid
- |      column_1 = Column([1, 2, 3], 'time')
- |      column_2 = Column([4, 2, 5], 'voltage')
- |      grid = Grid([column_1, column_2])
- |      py.grid_ops.upload(grid, 'time vs voltage')
- |
- |      # Build a Plotly graph object sourced from the
- |      # grid's columns
- |      trace = Scatter(xsrc=grid[0], ysrc=grid[1])
- |      py.plot([trace], filename='graph from grid')
- |      ```
- |
- |  ----------------------------------------------------------------------
- |  Static methods defined here:
- |
- |  ensure_uploaded(fid)
-
-
-
-
- -
-
- -
- - -{% endraw %} diff --git a/_posts/python-v3/chart-studio/data-api/grid-api.ipynb b/_posts/python-v3/chart-studio/data-api/grid-api.ipynb deleted file mode 100644 index d172b345b..000000000 --- a/_posts/python-v3/chart-studio/data-api/grid-api.ipynb +++ /dev/null @@ -1,673 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### New to Plotly?\n", - "Plotly's Python library is free and open source! [Get started](https://plotly.com/python/getting-started/) by downloading the client and [reading the primer](https://plotly.com/python/getting-started/).\n", - "
You can set up Plotly to work in [online](https://plotly.com/python/getting-started/#initialization-for-online-plotting) or [offline](https://plotly.com/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plotly.com/python/getting-started/#start-plotting-online).\n", - "
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started!\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Creating a Plotly Grid\n", - "You can instantiate a grid with data by either uploading tabular data to Plotly or by creating a Plotly `grid` using the API. To upload the grid we will use `plotly.plotly.grid_ops.upload()`. It takes the following arguments:\n", - "- `grid` (Grid Object): the actual grid object that you are uploading.\n", - "- `filename` (str): name of the grid in your plotly account,\n", - "- `world_readable` (bool): if `True`, the grid is `public` and can be viewed by anyone in your files. If `False`, it is private and can only be viewed by you. \n", - "- `auto_open` (bool): if determines if the grid is opened in the browser or not.\n", - "\n", - "You can run `help(py.grid_ops.upload)` for a more detailed description of these and all the arguments." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "https://plotly.com/~chelsea_lyn/17398/\n" - ] - } - ], - "source": [ - "import plotly\n", - "import plotly.plotly as py\n", - "import plotly.tools as tls\n", - "import plotly.graph_objs as go\n", - "from plotly.grid_objs import Column, Grid\n", - "\n", - "from datetime import datetime as dt\n", - "import numpy as np\n", - "from IPython.display import Image\n", - "\n", - "column_1 = Column(['a', 'b', 'c'], 'column 1')\n", - "column_2 = Column([1, 2, 3], 'column 2') # Tabular data can be numbers, strings, or dates\n", - "grid = Grid([column_1, column_2])\n", - "url = py.grid_ops.upload(grid, \n", - " filename='grid_ex_'+str(dt.now()), \n", - " world_readable=True, \n", - " auto_open=False)\n", - "print(url)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### View and Share your Grid\n", - "You can view your newly created grid at the `url`:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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8oSlqlNJNLcwK/3FDLdMyNbVQ61ZmlqlXK83M8pZm5qPMq2Vp2sOim3U1b6blM9+Kr3yh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\ng+gVc4sIofmkR7+G9Og1CK23az4/ywa0U0m3e56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fwbftIK0pK0ht3X5+4r+p7DAt22oc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\n4MdAi1NI9m6ncqHRGDO4B+pIgylI2YcZ8/VzGJC/d3rLTl2jqXDu6c2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cAMHtnelETxO0ouh9ZG2uZVq9u5v/M5gbZTo/afE2jXvjFnQAABBBBAAAEEEEAAAQQQQAABBBCo\nOQFfIBAIhsMSCIXKKTlSc65Jj5ROoK0H1FIg2hM60FykzIw9uPY3szCc9FSS19rss3mk9IiG2UW/\nm13MgI3JJg2om/Uz5zOPZStMD/A/RLzCbPs42qZm/c0+rUR0IEjtnV1RbK9N/XFDA+0ZcxeK3+eX\nXht39jxpsLhUChYvE7/fL21bN5dWLZqb5453Cjz39F5R2yVH1gVLZMXqtbJmbdAarLFNq+bSvJlL\nDRrvJrqu8Qq0B2/ZRw7ay3S9d5maN20sPbp1kkDA77I2sijdHtoLzH35ZcpMa5DK3XcYtMEDU+pg\nnXMLlkin9m02+FieF8sKBBBAAAEEEEAAAQQQQAABBBBAAIGMCRBoZ4x6/YnSDbTXHyF3n6UbaGuQ\n/cjz78lfsxZYOO3btpKrzjrKCrZDFRUy6fs/5MMvfpb5i5cmlNho2qSR7DJ0Sxmx62DZpHuntHBr\nI9CeOmOeTPphinzz81TXOtddO7WTfXbeRoab4LdFc0f3+GpcgVegXdUhtDb4sQfuLiP3GOq6aTqB\n9ryCQrni1idE33DQKT8vIBefepgMHeRSz8b1rLELv508Te57+i0pWhd5Z2UvU0bl1KNHSJ45LhMC\nCCCAAAIIIIAAAggggAACCCCAQP0UINCug/tGoO2Nnm6gfc2dz8pv02bHHFh75R601/by6ntfugbC\nMRtXzmy31eZywqF7iIbF1ZlqMtDWQPeJVz6Ujyb9nFITNFw+ZO8d5OC9h0l+vqO+TAp7pxto66F9\nplP7/TecI507mDo1cVM6gfbosU/LlOmxtfy1J/q9155Z7d7ocxYskUvGPCr6ZoZzOmzEjnLcwbs7\nF/EcAQQQQAABBBBAAAEEEEAAAQQQQKAeCRBo18HNItD2Rk8n0F5XXCInXDRWKiqqqL/ifdqYNQFT\niuTQfXeUo0fuaoW2MSs9Zmoq0J41b5Hc/MDLUrjcFC6v5tTB9Eq/9PTDpW/PbinvuSGBtp7kMnO+\n7bc29WnipuoG2stWrJZTr7wn7iiR2eMPHi6HjtjBdZ3XwtseekW+Nj3b4ycN3x+48Zz4xcwjgAAC\nCCCAAAIIIIAAAgjUQwH9tPZVtz4pTZs0li4d28qYS08U/Zu+LidT1lduefAl+XP6PNHyoVeceaRo\nWU+mWIFsvHexLWQumwUItOvg7hBoe6OnE2hrb9wLb3jY+6BprhnYv5dcccYR0qRxoyqPUBOB9idf\n/yr3PztBQqHYXsVVntyxgf7gPvWYEbK3KUWSyrShgfblxkd7tcdP1Q20y8rK5djzb03oUa3H1fIx\nD//3XPPmQmp1zjUcP+2qeyWsv0XETZ07tDWB9tlxS5lFAAEEEEAAAQQQQAABBBCojwI//zFDbrj3\neavp+gnfR246P+l4T5m4Rv1T9MIbHjLjORVapzv/3wfJrtttmYlT16tzZOO9q1eADbyxBNp18AIg\n0PZGz6ZAW1s5fNggOefEA7wbXLlmQwNtrf19pakf7ZLBVnlutw2uPu9Y2WrApm6rYpZtSKCtA2lq\nb+eO7czIo3FTdQNt3V1/CdEfaG6T9jwf5tIT3G3bZ1+fKOPf+8ptlRX0n3Hcfq7rWIgAAggggAAC\nCCCAAAIIIFC/BH6dOkuuves5q9FWoH2zCbSzoIf2RTc+LNr5TicCbYsh4Z9svHcJjWRB1goQaNfB\nrSHQ9kbPtkBbW3qlGVxyyMC+3o02azYk0Naa2edf96AsNT2La2pq1bKZ3HfdWdKiWZOkh0w30Na6\n3ccdNFz2Hz7E9fjpBNo//jZdxox7wfV4A/r2kBsvPsF1nXNhWXlITr78LllbFHQujj6/59ozZKMu\nHaLzPEEAAQQQQAABBBBAAAEEEKi/AtkYimpHNQLtql9T2Xjvqm41W2SLQGWgHQ6EQqH8bGlUrrcj\n3UC7UReRZqa6Q6C5SNlSkbW/ilQUJ9ca0tknx/fzSfcWIgVFIq9OD8tn8xNLMTiPkm/yvmb9RfJa\nmvOsECn6XSS0xrlF4vMB7Xxy0hY+6dlKZInJEt+aGZZ3Zyc/T+JRRGoz0NaKFUMHbS7Ddxhk1dda\naupUzzQ1qyf/MVPmLzKgHpO+y/vQmHOTDri4IYH24y9/IBM+/s7j7CItmjeVPXfcSrbZordVkmNe\nwVLRWlPf//qXVY/La8e9dtpazjx+f6/V1nKvQFvrex201zDXfZs3bSI9unVM+jGudAJt/aF/1n/G\nyeKl5kXnMqUSRn/05WS5/5kJLnuLbLl5T7nuwuNd17EQAQQQQAABBBBAAAEEEECg/glkYyhKoJ3a\n6ygb711qLWerbBAg0K6Du5BOoN14I5G2e8Q2ttyMG7jMZHfh8tjl9twOXX3y9D6JgyEc/W6F/LDY\nPWzO72hqFu9rjuAoV1xhAuplb5tQ2wTibpOG2S/t75cmgdi1Z06skA/nup8ndsv1c7UVaOuAiddc\ncJx079x+/ckqn4UqKuSVdybJS29/7lny45gDd5Mj9tspYV97QbqB9vKVa+T0Ufd61s3WOlsaSjfK\nz7NPFX3Ufe98/HX546850WXOJ1oSZNz1Z4sOhOg1eQXae5ow/KwqwnCvY+rydAJt3W/CxO/k8Zc+\n0KcJk4b6Z/1zZMJy54Lzrn3Q880JHYhj6KDNnJvzHAEEEEAAAQQQQAABBBBAoB4LZGMoSqCd2gsq\nG+9dai1nq2wQINCug7uQTqDdbh8R7aEdP635MdKDOn65zj++l1926e5Ipis3enJKWG78zn3gwTa7\nijTpWbmh46HoD5E1PzgWOJ6O3dkvB/dOPI/20r7wc/fzOHaPeVobgXazpo1l7FWnWCMex5wsbubj\nrybLuKfde/fqMZ66/WLPWlzpBtrJemdv848+cpUpd6LBtNdUURG2ynR41Z6uqpd2tgXa64pL5OTL\n7pKS0rKES87PC8hjt1xg9VhPWGkWTPl7roy+/Wm3VdUeWNL1ICxEAAEEEEAAAQQQQAABBBDIKoGE\nUDRLBoWk5EjVL5OEe5cF9c+rbjVbZIuAr8dx3UtKCoK+RROXUXIkQ3clnUC701EifpdyyMG/TW9Y\n9/Hv5NPDA7JRi8SLmjArLBd85h40dzjQlBppm7hPyTyRFRMTl+uSV0zvbA2i46evFoblhPfdzxO/\nrT1fG4H2BScdLLsM/Yd9iqSPOpiEflN1m/576YnSr/fGbqvSrqF97jUPyILFyxKO2aRxIzM683nS\nvIoa2LpjoSmdoj2T3UJg7Zn+sDmO15Rtgba285EX3pN3P3V/9+T4g4fLoSN2cL2c2x56Rb7+earr\nun8dvpccuOd2rutYiAACCCCAAAIIIIAAAgggkB0COsbUc298Ip98/Yvoc3vScZx6dOsk25rxrfbd\nddvo38rxoeijN18g8xYWyqvvfim/TZttfXpYj6ElSLt1ai8HmL8L99ppG2vePrbX4/TZBfLU+I+t\nzlNh7XZdOfXcqLPop7i9xtpKtYe2fur61fe+lK9/miorV6+1D2+VO+3UrrUMGrCp7GY+td17k26e\n7dX9nnzlI/nul7+kuGS9V7s2LeXAPbaTkeYrWSc5PemKVWvli+9+l29/mSYLFi2T1WvXWW1Rs/Zt\nWsnuwwbKkfvvkrTsaLTx1XgSf+8eMYG23+eXh/73jiwqXCFl5eWy325DZMdtByQ9qno/8sK7UrB4\nubXPnjtubbVZOwDe/uh4a4ytzTfdSI49aDdR8zc//lY+//b3GPOWptTrTkO2kGMO2NWzE13SRrAy\n4wK+AWP6hIMLSmTmuLkZP3lDPWE6gXa7vU0P7a6JYtprWntPu03pBNptdjE9tHslHq3oN9ND+6fE\n5bokmwPtjuaHwINjzjHf/BMDd7er0W/e5177gNsqOWL/na1vbm4r0+mhvcwMAnnqlfe4HU4O3nuY\nnHBoXI0Z1y0jC//3xqfyyruTXLdIVns6GwNtraF95uhxrtfSXgP6/56bcD/V8rSr7jUlY9b/kmEf\noHGjfHns1gukWZPG9iIeEUAAAQQQQAABBBBAAAEEskzgk69/lfuefsv17zpnU7UUpZak1MkZinbt\n2E4GD+yTdIwq3UfD3lsu/7f1SV6dj5/0z8qHn39X3v/cfCQ+ybTVgN4y6pyjEj7JnUqgraG9hu6p\nTOf/+yDRcqTx00QT+t/31Fvxi2PmW7dsbj6xfrLrtWo7733qTfn0GzNAWxVTIOCX20edao2nVcWm\nKa923jsdu8wOtK++85loadUOmunceE7SUD6+lOu/j9hLDjBB/uo160zmcrcJuUOinf1OPmofueXB\nl5O2T7OjS087TLbful/S7VJZuXTFGvOGQIuE/KKqfTXXWLZyrWlzy6o2bdDrff2u61NRsrjER6Cd\nuddBOoF2o27mm+5esW0sWx6pbS0enaDTCbTzTYnpdiPMu5eOks0h80ah1tD2GoAymwPt/YcPkZOP\nNPVaqjGdMeo+WbJsZcIeQwZuJleedWTCcl2QTqCtgzredP9Lrse72fxw3axXd9d1bgv/mrVArrjl\nCbdVctEph8hO227hui4bA21t6PX3/E8mT5np2uZLTz9chsX9cHn29Yky/j33jyqM2HWwnHaMFoZn\nQgABBBBAAAEEEEAAAQQQyEaBl97+Ql5467OYpmm42KVjW6sHsd37OOA3PXhNJycNpXWyQ1EtUanB\npXNq1aKZ9OjeSYrWFcuseYucq6zxte6+5oyEoFRD3psfeEn073V70v5x2ju83By/YMnymMB9E3N8\nDXqdvaCrCrS15/g1dz5rH9567NqpnRW6am9p/RS33VlLDe699kzp1rldzPZu40/pNj7zv+Wr1sT0\nbtcw+hFTikVD4/jpxvtekJ9+nx5dnG/G79Kxx/RT49Nmzo+2QzfQcPmBG89OCPCjO1fziX3vdDc7\n0Nb761yu9uqrveK9ptc/+FqeNj3pddJrffK2i6we/OuCJfLvy+6UsrLEgef0OnuZY+p9mzZzQcx1\nJvPyakP88t/+mi+Tp86VTbq1l50Hb5ZyqK33/Ysf/5I5Bctkq349ZMvNNoo/NPOVAj5/IBBs1Kd1\nIDiVkiOZelWkE2hr23TAxmZmTLuA+R5UtlRkrek1HU4sNRy9jHQCbd05z4wh2Ky/eTQ/H8pWRGp0\n68CQXlM2B9qnHTNCRpiPI1Vn0pDZ+cPL3rdPz25y6xUn2bMxj+kE2h988ZM8+Nw7McexZ56+4xJp\nkUK5EXv7UKhCjjj7v/ZszKP97mTMwsqZbA20f/xtulUb3K3NA/r2kBsvPiG6Sn9pOfnyu6yPEUUX\nOp7oD9zOHVzq6Di24SkCCCCAAAIIIIAAAggggEDdCMxZsEQuvOHh6Mk1xD392H1jSoNoL9xJP0yx\nSohoKUoNOnVyhp+RJSIaZF9x5pGmZOj6MFD3HzPuxZhg+9rzj5OB/XvZu1mP2ktcey3b01am7Mdl\nplOVBrw66d+fdz3+uikT8qe9iTh7jOvCZIG2rnP2QNZA/D/nHhMN6O2Dzpq3WN755HvrOvX4zkk7\n4Omnmu3QW8P9a8y1bNy1Q3Sz197/Sp55bWJ0fuigzSyT6ILKJ1NnzJNRY5+SfXbZVkYOHxoTnOu1\n3v3E6/LVj+uv9aqzj5Jtt+wbf5i05p33zhloa6mQU664O1oSZPiwQXLOiQe4nkMN9FP2Wm5EJ+d1\nugXabq8tvc7HXnxfNKOxJ7U46ci97dlqP378zRTz5keko2TP7h1kp236Vhlq67VM+ulvmb3ABH5m\n6tapjeyxffJyK9VuWA7tYAXa5r+CQCgUooZ2hm5suoF2dZuXbqBd3fNkc6B9oamfvXOK9bPt69aB\nIXWAyPgpWT3qdAJtt3eg7XO++sDo6A9oe1lVj0efe7OUurzzeIgpX/JPj/Il2Rpo6w/5s/4zTrT8\niNvkLKPy0ZeT5f5nJrhtJvrLx9XnHeu6joUIIIAAAggggAACCCCAAAJ1L6BlIL6dPM1qiPbQvX10\n6qUtnKGoHkBLbGiZSu2BGz/p38v/uuR2U2s60jMwvgOcBopaFlTDb50G9d/UBMWJf0/q36ujb39K\n/pxuBhszk2YFD5iyGNqzVydd7zUopK678IaHZG5BofU3/y2XnyTaea46kwbqn5ua1zo1bdJIHr/1\nQtFSm/HTy6bX+/OVvd69enrH7xM/r+HyicZMe7nrVJ0xyuKPFT/vvHfOQFu305KqWlpVpzzT+/4Z\n0+nP7Rq1bOx51z0YDfedb1LEB9pqcOuVJ0nvHon1fPW+XHnrE6KfftdJy8lcfd4x1vN0/tHe/B9/\n86csWb7a2r2qUDs+zO7UrpUJs/tb157O+RvCPgTadXCXCbS90Wt6UMgLTz5EdjaF/aszaTiqIWn8\npD8Yn7jtwvjF1nw6gfZzr5uaWWYACLdp/IOj3RYnXXbMebe4DgyZ7J3FbA209UInfPydPP7yB67X\nrPXS7HepdUDM+Ysi72DGbzzq7KNl8JZ94hczjwACCCCAAAIIIIAAAgggkAUC64pNWYhL15eFSDZ2\nlVtznaGorj/l6H2sgQTdttVlj7zwnrz76Q/W6h0G95dLTj0suumMOQvl0pses+Y1/Lz/Bv20r/kI\nu8ukPZuvuu0p121TDbR15/12HyKnmNrOqU7xofw5Jxwgw3cY5Lq72p5iPs1sB/hetbhdd3YsdJol\n+wS4Y5eUnjrvXXygrW8qnDn6vmgZGa9s56lXP5I3PvzGOp91DFNaxX5jIT7QTlZGVg/w8x8z5IZ7\nn3c9lrWwmv/Eh9q9NuooO26tPbVjD6Rh9pc/T5dZ8wutFYTZsT5ecz6/P2CKSeSZHtrFiW/neO3F\n8g0SIND25iPQjtgQaIvoD9+TL7vLNaTX+miP3XKB9a726Nufdn1BaZkR/QUk/oeF68YsRAABBBBA\nAAEEEEAAAQQQyLiAM9TU3tn3m5KRHU2t5lQn5/5VhdB6TGcAuuPgAXLxqYdGT+UcqFFLgdz5n9Oi\n6+KfhCoq5Njzb7XqM+t5dZBJu6d1skBbjxPfiU4/WXzq0SNE62hXNTnrb2tw+8wdl5pyKN5xno61\nZfc6TjeMfv7NT+XldyZZTYt/E6Cq9iZb77x38YG27ues76222pvd+fd9fGmSg82n009wfDo9PtCu\nKtB3tsfqdT/G9Lo3r8kNmeJD7U1NqL2DI9TW18pXP/8tMwmzq83s6zSye0njTv78uY/NjXuPoNrH\nYocUBQi0vaEItCM2BNoRh4eff0/e+yzy7nn8q0brps2YUyBf/zw1fpU1r4OB6qCgTAgggAACCCCA\nAAIIIIAAAtkpsKEhonP/Zk0aW5+qdis3Yl99skDbuU6336xXd9NDOHFAQTEDL65cvdYarNI+rrOn\ndFWBtvY+Puea+6M9p+1j9DWh7UF7DZPtt+4XM8ikvV4fnder8xq8Owek1GXOSWtx21OyMFo/9fzD\nr3+bv7EXypp1QSkxZVlKy8xXabksM+21B+WMfxPAPnY6j85rcQu0nevd3qyI7yUfP3imM9DW/e8w\npWzUy2tyns+tPV77VbU8PtTuvXEnGbZV5JPkX0+eLjPmLbEOQc/sqiRj1/sGjOkTDi4okZnj5sau\nYa7WBAi0vWkJtCM2BNoRB62hrYNduE36A2bVmnXRWlnObbS21pNjL3KtseXcjucIIIAAAggggAAC\nCCCAAAJ1J/DT79OtnrjagnRCRGcImUqvWmdoHR/OOktrVFfE2fu3qkBbj71i1VqrvMXs+esDZ/uc\nGr4esOd2cvTIXaKDUdrrnGUx7GWpPsZfr+6n/nrdi5dGBjCs6lhux6hqH6/1znvndu/je2DHl6O5\n7+m3ZOJXv1iH16D6jtGnxfTgjg+0HzQ9rpP1/q+qPV7XkcryhFC7RyRYnzGXMDsVP7dtCLTdVGp5\nGYG2N3CNB9ppDAoZ//Efu7XtzUAPj9x0nj0b85hODe0XJ3wu+uU2pTMopFcN7fiP3TjPl801tO12\nXnf3/+SXP2fasyk9as9s7aHNhAACCCCAAAIIIIAAAgggkL0Cr3/wtTw9/mOrgW6hZlUtr24I6RVo\nawit9ZMnT5kRPWXL5k1NQFp1MQMdMPHKs46Ubf4R6XWbSqBtn+TXP2fJ/0xJD7ssiL1cH/Xcl51+\nuGy31ebRxU4vXai90Zs2bhRd7/WkpLTM1BbfVv7pKMnh9Ynobp3bWeVP/Ob8rVu1kEnf/x7tTZ7J\nQFuvxTk4pPX6qKyRXaYDfF56hwSLS61LdvaQtw2yKdDWNsWH2nY76ZltS1Tv0ddqmw7Bpj3z8xe9\nWhCo3q5sna5AuoF29xYiJ2/hlx4tRaatEHnp7wqZExkw1bUpnx4ekI3MPvHThFlhueCzivjF0fn2\nTURO39Ivm5qyVTNXibw6PWzOZ767e0yv7O8XDaLjp68WhuWE973PE7+9ztd0oJ1O2QnnCMvONvbe\npKvcduXJzkXR5+kE2u9//qM89L93o8dwPnnytoukVctmzkVJn4dCFXLE2f913eZfh+8lB5p3d90m\nHcF52YrEF9GO25paYqesryXmtm+yZavWFFkDe7htc9KRe4sOVJnq9MNvf8t/x72Y6ubWO7KRwTva\nprwPGyKAAAIIIIAAAggggAACCGRe4Pe/5sjVdzxjnTiVHtbxLaypQFuP66yhvdv2A+W8fx0Yf7qU\n5qsTaNsHXL12nXzwxU9Wpzf9+96eNE/X2tF2fe4YL1Nr/MEbz0lacsQ+Tvzjd7/8JTc/8FJ0casW\nzeS0Y/aVIQP7WiF5dIV54gyVMx1orzafyj71yrujg0Nec/6xMqj/pjEDOGotcc1QmjczYZZjyrZA\nW5sWH2oTZjtuWDWfVg4KGTaDQoa8q8hX86BsnlwgnUB7s7Y+eXk/vzR33KXSkMge40OysMj9fOkE\n2l2bi7xxQEDaOb4PaJQ98o0Kz1A7mwPtPXfcSs7650h3II+lF1z/kDXYYPzqwVv2kVFnHx2/2JpP\nJ9D+dvI00fDcbbr2guNkYL9ebqtcl80tKBRtt9t0gemlvsvQf7itkkv++6jMnLsoYd1GXTrIPdee\nkbA81QU1GWjrLwOnXeUevLu1R98VH32O+31y255lCCCAAAIIIIAAAggggAACdSPgLKGRlxcwgxxe\nUq3SkTUZaDtLjmgJi9tHnZpWWJxOoG3rh83Oz742UV4zPdftaasBveXq846xZp1eGuQ+ceuF0sL0\nJK/u5LzW1i2bi5bj0NKd8ZNey9V3PiN/mDcedMp0oK3ndA4OOXTQZnLFmUdaYbyG8jp5tSkbA21t\nr4baE7/9U5/K8O36i77umaovYAJtf9DsRqBdfbu090gn0H50T7/stlFiL+ixP1bIg7+5955OJ9C+\neUe/HN438TxPTAnLmO/Wv0vovPhsDrTbtWkpD405V/QbfSrTwiXL5eyr73fdNL5ek3OjdALtpctX\nmaD2Xudhos+1B7P2ZE51euntL+SFtz5z3VyDaQ2o3SbnDwbnev1o04v3XpH2N9aaDLS1XW99/K08\n8fKHziZ6Pr/6vGNFR4lmQgABBBBAAAEEEEAAAQQQyG6BBYuXyXnXPhgdGylZhyy3K6nJQPvLH6bI\n7Y+Ot06jfxPfc80Z0r1Le7fTJl22IYG2fWBnaZGundpZHc4Cfr8sWbbSyizsXtynHTNCRuy6rb1b\nSo/avgtvWN+Rzw6J3XbWgF0/2a0DWerkFR677VvVslTvnbNXug78qZ/IPmP0vVYZFO3BfuPFJ0r/\nPhsnnC5bA+2EhrIgL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X+zBYX1DIaHx5DNsE1VLa5bfSOa2xbit9tf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\nCIiACIjAHCAwZ4tCnuk5YYvWRmQ2DmcjatuRIxSFzbZigUVbJKZwTI81u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cGC9jfxZZP1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\nI8MMjCBg1Ih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MkQ++u+2I1NXVSvWbDfLV/+M+KUtIk9RY87ys26rHa+w1VubfM0/T8xTG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\n2L/62hS9pVgyj03R612J9QNf+AJgL2i8des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UK+0trbq8lb9xrEybcoW0fPqx\nHG7p0nrmypXXXC2Fo8NF5WzHcWlqt7qN+2XcFeNlTOdx+fCTL6S9q0dyAiPk6imTU+x3ZNmB/P+s\n9tb+jdVbW3to3649tOm1NBBNlkVgcAW4vxlcb7aGwMAFuMe5sPc4rbLtzf3S7M+X22dfZ9zT6P1e\ntd7v6Y1l7tgJsnDm5IE3JWtAAIELLsB9jjtiAtrunAhou3SimLcEuBB6qz2oDQK9CXQ2H5I39x2X\nU0bOw8Sy4zQ1yG1GapDOo7rMe70sk5snpTdfJ9fm9/bwVR8B7bONUvV6k3RGK5E/QRbP0j+CknJU\n5sh1ZTNk6uh4b6joIunf/VL6zZvl2pFaj9f0D63oI7G6kF8fkT1rfLcC3dfO+IqUFvS2L+m31Nvc\n9sPvyWv7TxHQ7g2I6Qh4WID7Gw83DlVDIEGAe5w4yGDd48S32CpvVu+XFiugXXCFLJxRGJ/FJwQQ\n8KwA9znumoaAtjsnAtounSjmLQEuhN5qD2qDQCqBs80H5NU9X8QHawz4ZdTIgJzp6JIzRmDXTA3S\n2fShVL3XFlvdyItHScHogJxqOykngtGouAabZ2mwOeXTpX0EtKVNdu88IIdbI3m2Y716zmqOyt9r\nHu7ukJwJjziUI1M1oH2d9rY+9G6t7D0a6aEdrliOX0bmnJVOTWuS6jXqikKZ/5Wxsnfnh3Ko1U7s\nbRf0XzRCRgW6pT3cQ1wnXjRW/vTWKZGe4KlWdg7TCGifAxqLIOARAe5vPNIQVAOBPgS4xxmae5xo\ns8TudXTCFdeXyte/lBedxTsCCHhYgPscd41DQNudEwFtl04U85YAF0JvtQe1QSBZQAPLv9UeyuF4\nbo6ML/qyzC4ebxcLSu2uD+SjlkhQOR7QPi5vVB+SE+G4tV+u/sr1Mu2K+B8oJw5/KDX72yIB8jwN\nBM9OFQjuK6AdqcKh3btl73FNEpL0mGq0R3Y8oG0tEUvj4Rsht9x+oxQaPb1HXa4B7BuvkA937pL3\nW3sc6zz76Xua/kN7S+trfNHVanBp+PPR/bXy+8NWkNwv07RH99Ujw5PPy/9if+SRcuS8eLISBAZT\ngPubwdRmWwicqwD3OJbcUNzjhFus5ZBs3nVcwreYOl7IbTpeyLhzbUqWQwCBQRXgPscdNwFtd04E\ntF06UcxbAlwIvdUe1AaBJAFN4bH5rWPhPzZGXXmVzL8hGsyOloz/MRhLOdJ2SH6z43g4YB0PckfL\nR94P79sjuz+z/oTxaw/qm8M9qJ0l3AW0P9q1W2o1oJ4c0I4u7wxox4LEsYEWo+W0Z9AN2jPoyjw5\n+t4++X1Tl2Od0eX8F18qf/r1q+NV7WxQn6PqE01tEp810E/RbZJDe6CSLI/A4AtwfzP45mwRgX4L\ncI8TJovebwzmPY50NsqWt5ok0lXAr+nhbtb0cP1uQRZAAIEhEuA+xx08AW13TgS0XTpRzFsCXAi9\n1R7UBoFEgbOaOuQ34dQhGhj+pqbuSNUD+WybNDR3yXgdMNGa3X5Icz8ftP5E0WC19lpOuUws6B3N\nVZ245XigubeguLXEOQe0Y72eo9uJ71+n5q6u0tzVZpA8+sdeboEOWDRDc3VHX7E/hp2B8+jsgbxH\nt0lAeyCKLIvA0AhwfzM07mwVgf4IcI8TuZ+J3m8M2j2OFczepsFsO23dFddfp6lGUuaf609zUhYB\nBAZRgPscd9gEtN05EdB26UQxbwlwIfRWe1AbBJIEYoFn9z2QOw9/oAHhk7qqeJA4ab3N++XlPa2R\nMnaOa2eZaKBZZNAC2nY9Yn/YGWlMUk0L15eAtrPZ+IYAAmEB7m84EBDIAAHuccKNNKj3OG2HZcuO\nY3bPbE3jpgOKzzYGFM+Ao4YqIoCACnCf4+4wsAPaE/2tJw/lulskO0txQGVnu2f6XnPcZnoLUv9h\nLxAL2PY++vyJTw9JraboGF9cLNcV6D/VxjLRvNSJTu/v2CUftllJtnNl2pzpcrU/sYQZ0L5Obr06\ndc+daL5rf/54+dNZVxkrOSpbtjboH0zOntPRP9okKeVIvFy0TMoe2kaQO7yx2L7GlzcqMaCP0XrE\n6zqg1bEwAggMogD3N4OIzaYQOFeB2L/h3OO8lvBkWpg05nN+7nHONh+Sqj12zmy9Pyu8Yarcoqne\nrFdnm3ZyGD1WRibdD4Zn8z8EEPCYAPc57hpEA9plwe8s/PZFv3jh4Rx3i2RnKQ6o7Gz3TN9rjttM\nb0HqP/wFzsje3++TQyfDIzzKqPHjpWz6VTJGd7yz46jUvtckDa2RQSHHFE2R24vH6pw22fHmh9J0\nOqIz7sorpfyGSZqAxHq1yfu7D8mHx8NDAEliIPrsWXu6v03eeE0HltTHUQuKrpZbi/PlbHgzueI3\n/thp1kEZt4UHZcyRyTdcKzdfqYFvrde2txukObz9HLl2xo1SagXa9XXiQK28Ua+DOObkyey5pTJe\n67PtDR30slvLzZohpbp4NJCcO3a8LJx5lWM5c1p4hvbuelnzhVu90aPLh6cP5H9qcNafKy3RfbPr\nWqDT/TqdFwIIeF+A+xvvtxE1REB0BAzuceL3Rhf2Hifa0SBy3I370pVy06SR0tkt0vX5Edmt93J+\n7TTwpzONtG4coggg4FkB7nPcNY3vrx945WxO60c5D1U86G6JLC3FAZWlDZ/hu81xm+ENSPWzQ6BT\n/wjZpr2d7VyHqXc6V0pnTZdrox2pOw5L1fZj0hkt7NMPurxf389G16OB2lu+VSqFdoC64d198vbR\nrugSvb6Pn3KdzJ5sb6hFA8q7rICy/bK3E/0afc8tuEKu8zVL7fFI8D0y3a+9w4vls5oP5aj+QWW9\nxl8/Xb4u++U3H0SGKbJ6R1/mOyWfR+LskUJ2r/LJRzS/+Adt9rTIW2890h2F0nyJD5bZS6HAxXL7\nbdeHf1DopQSTEUDAAwLc33igEagCAm4EuMcZlHucQ7t3y17HPVhy4xDQTjZhCgJeFeA+x13L+P75\n0c2hQ0f3CwHt9GAcUOl9mOtNAY5bb7YLtUIgSeDscXn77cPS0GYGhK1SOTLm0rEybVqxjDd6ToeX\n72yWHXsbpClpGZFRBZfKrBlXyzhjQ64D2jp40Gxj8KCjBz6QnfUnxazZqAINeLe1ySk7UD3yUg1o\n5zTL3mazlBXQvl6at9XGepNbAe3ZAQ2Sv2sHqnPtgLbd2zxcXd8Iufn2G+VLn36gAW0rV3j8NerK\nQpl/wxXxCf38dPSDffL7T9ME9XPz5fZvXUdAu5+uFEdgsAW4vxlscbaHwAAEuMeJ412ge5z4E3Xx\nTSV+GqP3ULcP4B4qcX18RwCBCyfAfY4723BA++ChF3v+8p8qSTmSxowDKg0OszwrwHHr2aahYgik\nFuhslaMngvqQruabvChPxheMtVOJpC4enmosY+XMHjf+UhmTGPxOs7i7WUFpbtbgsj9HRo69EOt3\nVwtKIYAAApYA9zccBwhkoIBxv8I9Tga2H1VGAIFBE+A+xx213UP7fe2h/ZfulsjSUhxQWdrwGb7b\nHLcZ3oBUHwEEEEAAAQSSBLi/SSJhAgIIIIAAAggMEwHuc9w1pO9ZTTnSQMqRPrU4oPokooAHBThu\nPdgoVAkBBBBAAAEEBiTA/c2A+FgYAQQQQAABBDwswH2Ou8b5/wEAAP//DpLxqQAAQABJREFU7N1t\nkF3lfSD459zT10gCvVhYsnizGBBSkEiwwYbYSwowcm2xy9px1e4msSdfdr9s1SiRZ0aVxJOt1Hi2\nVFvJqubL+MOUJvFMKow8sT2eAcshOHY8BGIGY2RoZGS1pA5CSDQIYbXU0r3XbenuUXdLNHq5ffqe\nc+8999yfPsDtc57znOf5/Z/q+ve/Tz8n2vjwv2k8cO3k+zZv3xT8u7LAf/mb56dOfvZTH7tyI2cI\nFEzAui1YQAyHAAECBAgQyCwgv8lMqAMCBAgQIECgoALynHSBiSpxXNv44B9W//Pj/3ec7pLBbGVB\nDWbc+33W1m2/R9D4CRAgQIAAgYsF5DcXi/iaAAECBAgQKIuAPCddJKcK2qHZjMcn6tV0lwxmKwtq\nMOPe77O2bvs9gsZPgAABAgQIXCwgv7lYxNcECBAgQIBAWQTkOekiqaCdzilYUCmhNCuUgHVbqHAY\nDAECBAgQIJCDgPwmB0RdECBAgAABAoUUkOekC4uCdjonBe2UTpoVS8A3wmLFw2gIECBAgACB7ALy\nm+yGeiBAgAABAgSKKSDPSReXqFKJayHYcmQuLgtqLiHniyhg3RYxKsZEgAABAgQIZBGQ32TRcy0B\nAgQIECBQZAF5TrroKGinc/KEdkonzYol4BthseJhNAQIECBAgEB2AflNdkM9ECBAgAABAsUUkOek\ni4uCdjonBe2UTpoVS8A3wmLFw2gIECBAgACB7ALym+yGeiBAgAABAgSKKSDPSRcXBe10TgraKZ00\nK5aAb4TFiofRECBAgAABAtkF5DfZDfVAgAABAgQIFFNAnpMuLgra6ZwuFLRTNteMAAECBAgQIECA\nAAECBAgQIECAAAEC8xb47Kc+Nu9rBukCBe2U0T7/G5KUzTUjQIAAAQIECBAgQIAAAQIECBAgQIDA\nvAUUtFuTKWi39rlw9nxB24K6QOJDHwhYt30QJEMkQIAAAQIE5iUgv5kXl8YECBAgQIBAHwnIc9IF\nS0E7ndOFLUcUtFOCaVYIAd8ICxEGgyBAgAABAgRyFJDf5IipKwIECBAgQKBQAvKcdOFQ0E7npKCd\n0kmzYgn4RliseBgNAQIECBAgkF1AfpPdUA8ECBAgQIBAMQXkOenikhS0K7WkaTw+Ua+mu2QwW1lQ\ngxn3fp+1ddvvETR+AgQIECBA4GIB+c3FIr4mQIAAAQIEyiIgz0kXSU9op3PyhHZKJ82KJeAbYbHi\nYTQECBAgQIBAdgH5TXZDPRAgQIAAAQLFFJDnpIuLgnY6JwXtlE6aFUvAN8JixcNoCBAgQIAAgewC\n8pvshnogQIAAAQIEiikgz0kXFwXtdE4K2imdNCuWgG+ExYqH0RAgQIAAAQLZBeQ32Q31QIAAAQIE\nCBRTQJ6TLi4K2umcFLRTOmlWLAHfCIsVD6MhQIAAAQIEsgvIb7Ib6oEAAQIECBAopoA8J11cog2r\n76zVGrX4pdHdXgrZwsyCaoHjVGEFrNvChsbACBAgQIAAgTYF5DdtwrmMAAECBAgQKLyAPCddiKId\nW3Y2R8dGwubtm9JdMaCtLKgBDXyfT9u67fMAGj4BAgQIECBwiYD85hISBwgQIECAAIGSCMhz0gVS\nQTudky1HUjppViwB3wiLFQ+jIUCAAAECBLILyG+yG+qBAAECBAgQKKaAPCddXKL/69cerv/Xn7xe\n2ff6C7YcaWFmQbXAyXDqTP1EOH0m6SBeEhYvyNCRSy8rYN1elsVBAgQIECBAoI8F5Dd9HDxDJ0CA\nAAECBFoKyHNa8lw46aWQFyhaf7CgWvvM/+yJ8KNn94dDE+eq2dP/Fl27Imy86+YQnz/g/5kFrNvM\nhDogQIAAAQIECiaQR35Tf2csjL4xHo787FQ4WUvy0fctCQ/fvy54vqJgwTYcAgQIECAwYAJ55DmD\nQBZ9bO0naqfqJ+PnfuoJ7VYBt6Ba6cz3XC386O93h0OnL72uunxleOTu1ZeecKQtAeu2LTYXESBA\ngAABAgUWyJrf1F/fE57YM/HeGVYWhQcf2hCWvfeorwgQIECAAAECXRXImud0dbA9vJk9tFPiW1Ap\noVI0O3nwJ+G7I9PV7MXX3xQ2brg27N31cnjl2PTT2qvv/li4a3mKjjSZU8C6nZNIAwIECBAgQKDP\nBLLmN/Wx/eH7e8ZDZcGCcHWlEY6eSHLQoUVh44MbwuI+szBcAgQIECBAoFwCWfOccmlceTbRo//s\nsbOjY/vCP/3T342u3MwZCyq/NfDSsy+E0YmzyZ92Lg2fvn/tzBYj4+Gp742Ed5LD1WuvD4/cdUN+\nNxzgnnJZt/XxcPDN4+H4RCNMJvEJoRKWrlgRblu1dIBlTZ0AAQIECBDolUAu+c3M4M8kT2s/fu5p\n7eQJ7Y3JE9oK2r2KqvsSIECAAAEC5wTyzHPKLBpV4rgWms14fKLupZAtIm1BtcCZ16l3C9eLV98S\nNq699sLVo7t2hZfOPaXtCZkLJlk/ZF23YyO7w7MHa5cdRnXJtWHjvbfYa/KyOg4SIECAAAECnRLI\nmt/MHteFvxxU0J7N4jMBAgQIECDQI4E885weTaErt43ipKDdVNCeE9uCmpMoXYNTB8POH7wVJpOn\nfG+5++5w56ytRfxAkY5wPq2yrtu9zz0fXjkxfcfqwoVh2TVxOPWziXD6F9PHVqxdF+5bvWQ+Q9KW\nAAECBAgQIJBJIGt+M/vm8s/ZGj4TIECAAAECvRbIM8/p9Vw6eX8F7ZS6FlRKqLma1ZOC9tPTBe31\nn7g7rLv63Qv8QPGuRV6fsq7b+tHDYXT8bLj+xpvCsgXnRzUZfvTMcDhUOxvipSvDp+/xEs/zMv5P\ngAABAgQIdF4ga34ze4Tyz9kaPhMgQIAAAQK9Fsgzz+n1XDp5/+gPH/ps4//9/mORLUdaM1tQrX1S\nn1XQTk2VR8Ps63Y87P3JkfDq0dPJU9nJBtpRJVSHojD58+kXeFaTgvYjCtp5hEofBAgQIECAQEqB\n7PnNuzdS0H7XwicCBAgQIECg9wJ55jm9n03nRhDt2LKzOXrklebmP/2Cl0K2cLagWuDM59SsLUfW\nJU9or5/1hHb94J7wxEjyUh57aM9HtGXbTOv2QqyufAsF7SvbOEOAAAECBAh0RiBTfnPRkBS0LwLx\nJQECBAgQINBTgTzznJ5OpMM3ny5oj42Ezds3dfhW/d29BZVX/I6G73/31XC8GcLyW9eG+29ZeqHj\nQy+/GH40Npm8ZX5hePChO8KyC2d8aFcgy7p95bkXwt4TyVPZyX7nK25cGe740AfCsquHwpn66fDS\nj/eHgxNng4J2u5FxHQECBAgQINCuQJb85uJ7KmhfLOJrAgQIECBAoJcCeeY5vZxHp+8dbfzEb9fj\nxnjlG09/o9rpm/Vz/xZUXtE7t//yi8n+yyHES1aET99780zHtfDcU7vDkZ8nx5cmx+85fzyv+w5m\nP+2v2/HwzPdGwtGknr1s9drw4Np3f/FwTnLX3z8fDp4OCtqDuazMmgABAgQI9FSg/fzm0mFfKGj7\nC8FLcRwhQIAAAQIEui6QZ57T9cF38YYzL4UM8fhETUG7BbwF1QJnnqeOjgyHZw42pq66fsMd4d7r\nF4aj+3eHZ/4hqXIn/1Ylxz6eHPMvu0D76/ZEeOb7e8PRXyRjWLgkfPyj68Kq5KWQJ5OXRO7e/2YY\nmzi/h/aKZA/tm7MPVA8ECBAgQIAAgZQC7ec3s25wZjKciavhnZEkBz2Y5KDJXwjel/yF4PLkeJwc\n948AAQIECBAg0AuBXPKcXgy8y/dU0E4JbkGlhErV7Fj4/vdGw/Fzu1kk21ksWhSF06enC6ThfUvC\nw/evC0nt1L8cBLKs24PDL4ZdbyZbwKT4t2LtunDf6iUpWmpCgAABAgQIEMgmkCW/OXfnOXOcoWvC\nxgdvD4uzDdPVBAgQIECAAIF5C2TNc+Z9wz69QEE7ZeAsqJRQaZudOhSe/O9j4fRUUXvmouqi8PEH\nNoRVafvQbk6BbOv2RHjph/vD6PjMLxsuhGlJuO26EPYdOBHOl7tX3b4ufPxGBe05A6IBAQIECBAg\nkFkgW34Twtie4fDs69N/LXjZwVSXhI0PrFPQviyOgwQIECBAgEAnBbLmOZ0cW5H6VtBOGQ0LKiXU\nvJrVwqHX3wlnKyH8/Oz7wi03rgjxvK7XeC6BPNbtmVPHwpGT50rXcVi2bFlYvMCf4c7l7jwBAgQI\nECDQOYE88pvOjU7PBAgQIECAAIH2BeQ56ewUtNM5BQsqJZRmhRKwbgsVDoMhQIAAAQIEchCQ3+SA\nqAsCBAgQIECgkALynHRhUdBO56SgndJJs2IJ+EZYrHgYDQECBAgQIJBdQH6T3VAPBAgQIECAQDEF\n5Dnp4qKgnc5JQTulk2bFEvCNsFjxMBoCBAgQIEAgu4D8JruhHggQIECAAIFiCshz0sVFQTudk4J2\nSifNiiXgG2Gx4mE0BAgQIECAQHYB+U12Qz0QIECAAAECxRSQ56SLi4J2OicF7ZROmhVLwDfCYsXD\naAgQIECAAIHsAvKb7IZ6IECAAAECBIopIM9JF5fodz7zxcabxw5Ef/adR6vpLhnMVhbUYMa932dt\n3fZ7BI2fAAECBAgQuFhAfnOxiK8JECBAgACBsgjIc9JFMtqxZWdzdGwkbN6+Kd0VA9rKghrQwPf5\ntK3bPg+g4RMgQIAAAQKXCMhvLiFxgAABAgQIECiJgDwnXSCjr/zuY2ePvHMgUtBuDWZBtfZxtpgC\n1m0x42JUBAgQIECAQPsC8pv27VxJgAABAgQIFFtAnpMuPlN7aK9b/1vxc899xZYjLcwsqBY4ThVW\nwLotbGgMjAABAgQIEGhTQH7TJpzLCBAgQIAAgcILyHPShWjmpZDNeHyirqDdwsyCaoHjVGEFrNvC\nhsbACBAgQIAAgTYF5DdtwrmMAAECBAgQKLyAPCddiBS00zkFCyollGaFErBuCxUOgyFAgAABAgRy\nEJDf5ICoCwIECBAgQKCQAvKcdGGJKnFcC01PaM/FdX5BzdXOeQIECBAgQIAAAQIECBAgQIAAAQIE\nCLQr8NlPfazdSwfiOgXtlGFW0E4JpRkBAgQIECBAgAABAgQIECBAgAABAm0LKGi3ppspaN+T7KH9\nlD20W1idL2hbUC2QnCqcgHVbuJAYEAECBAgQIJBRQH6TEdDlBAgQIECAQGEF5DnpQjNV0P7o//B7\n8ff++ksK2i3MLKgWOE4VVsC6LWxoDIwAAQIECBBoU0B+0yacywgQIECAAIHCC8hz0oUoqty2qfbF\ne29Z8AfbN6W7YkBbWVADGvg+n7Z12+cBNHwCBAgQIEDgEgH5zSUkDhAgQIAAAQIlEZDnpAtk9Mdf\n+NbZybf3RZsVtFuKWVAteZwsqIB1W9DAGBYBAgQIECDQtoD8pm06FxIgQIAAAQIFF5DnpAtQtGPL\nzubo2Cth8/YvpLtiQFtZUAMa+D6ftnXb5wE0fAIECBAgQOASAfnNJSQOECBAgAABAiURkOekC+RU\nQfvpp//l5J98/wf20G5hZkG1wHGqsALWbWFDY2AECBAgQIBAmwLymzbhXEaAAAECBAgUXkCeky5E\n0b/+zK83tnzr8Wh8oq6g3cLMgmqB41RhBazbwobGwAgQIECAAIE2BeQ3bcK5jAABAgQIECi8gDwn\nXYiih+98uH7qzGTl2z/8KwXtFmYWVAscpworYN0WNjQGRoAAAQIECLQpIL9pE85lBAgQIECAQOEF\n5DnpQjSzh/ZIsof2pnRXDGgrC2pAA9/n07Zu+zyAhk+AAAECBAhcIiC/uYTEAQIECBAgQKAkAvKc\ndIFU0E7nFCyolFCaFUrAui1UOAyGAAECBAgQyEFAfpMDoi4IECBAgACBQgrIc9KFJapU4loIzdge\n2q3BLKjWPs4WU8C6LWZcjIoAAQIECBBoX0B+076dKwkQIECAAIFiC8hz0sUnKWhXkoJ2UNCew8uC\nmgPI6UIKtL1uz5wIh948Hc4ms6pUF4abViwt5PwMigABAgQIEBg8gbbzm8tQnanXwukzk6G6YElY\nEF+mgUMECBAgQIAAgS4K5JnndHHYXb+VJ7RTkltQKaFSNqu/MxZG3xgPR352KpysnQnhfUvCw/ev\nCwtSXq9ZOoF21+2+F14Iu985V85O/kULw4Mb7wjLpr/yXwIECBAgQIBATwXazW/eHXQt7B0+EPYd\nrYXJmXTn3LlFS98f7r1njZznXSifCBAgQIAAgS4LZM9zujzgHt1OQTslvAWVEipFs/rre8ITeybe\n27KyKDz40AY/QLxXJfNX7a7b4wf3hmdHJ0L9F8lPeUOLwsYHN4TFmUejAwIECBAgQIBAdoF285vp\nO58Izz21Nxz5+axxRMnn5szX1WvCxgdul/fM4vGRAAECBAgQ6J5Atjyne+Ps9Z0UtFNGwIJKCZWi\nWX1sf/j+nvFQWbAgXF1phKMnkie0FU1TyM2/SaZ1e+ZQ2Pm3Y2FSbOYP7woCBAgQIECgYwKZ8ptT\nr4bHf3A0JNlnqC5Jnsj+6JqwIp4M+17eE3aPNabGvGLtunDf6iUdG7+OCRAgQIAAAQJXEsiU51yp\n0xIeV9BOGVQLKiXUPJudSZ7Wfvzc09rJE9obkye0PQU8T8A5mmdat6cOhp0/eGuqoP1w8oT2gmRf\n7aPvNEL9zJmwYNHSsGLJwjnu7jQBAgQIECBAIH+BTPlNGA/PPDUSjsZLwsb71s3KPU+EZ763NxxN\n/jitunRleOSe1fkPXI8ECBAgQIAAgTkEsuU5c3ReotMK2imDaUGlhJpns5MHfxK+O3JaQXuebmmb\nZ1q39aSg/XRS0K5cFVa9P4SxY9NPLV24d/WqsP5Xbg/rllcvHPKBAAECBAgQINBpgUz5zRUHNx6e\n+t5IOPcKkeryVeGRu2+6YksnCBAgQIAAAQKdEuhMntOp0fauXwXtlPYWVEqoeTZT0J4n2DybZ1q3\n5wvas+9ZqYQ4nA1nLrxAqRJW//KvhLtWKWrPZvKZAAECBAgQ6JxApvzmCsO6kJMm51fdfkf4+I3+\nEu0KVA4TIECAAAECHRToRJ7TweH2rGsF7ZT0FlRKqHk2u/DDgy1H5imXrnmmdfuegnYlXL/25nDv\n6munbnw0eWnkcyMnwuS5r7zQM10wtCJAgAABAgRyEciU31xuBO+Mhp0vHJvOa5J3hzyYbLW27HLt\nHCNAgAABAgQIdFgg9zynw+PtVfcK2inlLaiUUPNspqA9T7B5Ns+0bmcVtFfcmrwc6Zb3vhzp5Giy\nXcyBZLuYUAm33H13uHP5PAenOQECBAgQIECgDYFM+c3F96sfDk8+fSScy2hC8ndo6z5xV1h/9cWN\nfE2AAAECBAgQ6I5ArnlOd4bck7soaKdkt6BSQs2zmYL2PMHm2TzTur1Q0L4qfPRTvxIu2UnyzFh4\n8m8PTf0AuPzWteH+W5bOc3SaEyBAgAABAgTmL5Apv5l9u3PF7GeSYnZz+uCq29clW4289xf4s5v7\nTIAAAQIECBDotEBueU6nB9rj/qNfW3tnbd/+l+N9E3Wb4LYIhgXVAifDKQXtDHgpLs20bi8UtONw\n5yfvCrfEF93wzOHwxN8eCfXksIL2RTa+JECAAAECBDomkCm/OT+qEwfDk8+9NfNkdggrkl/O3+eX\n8+d1/J8AAQIECBDokUAueU6Pxt7N20Y7tnyz+Y2v/a+Tf/5TBe1W8BZUK532z10oaCf7FW5M9itc\n3H5XrryMQKZ1e6GgHcLi628OGzeseM8dxvYMh2dfbyTHKuG2e+8Od3ig6T0+viBAgAABAgQ6I5Ap\nv0mGdOboaHjixZk9s5M85qYN68NHr59+CWT9xHgIVy8NCy7+RX5npqJXAgQIECBAgMB7BLLmOe/p\nrMRfJAXtrzf/8mu/MfmognbLMFtQLXnmf/LMZDgTV8M7I7vDMwdrSU10YbjvoTvC8uR4nBz3Lx+B\nTOv2xKth53NHp1+QlAxn8QdXho+uXRkWhJ+Hg/sOhVfGkrid+1ddEjY+sM4vI6Y1/JcAAQIECBDo\nsECm/CYkW6b9zfSWaeeGuezG68NHblgQ6r8IofH2G2FXkpfGS1eGT9+zusOz0D0BAgQIECBA4FKB\nbHnOpf2V9chUQfvRr/zG5F++7gntVkG2oFrpzO/cweEXw643J6980dA1ydPatyuQXlko9Zl21+2h\nl4fDj8bOPX0917/k5Un3Ji9P8nT2XFDOEyBAgAABAjkJtJvfnLv96K5d4aVjZ1qOREG7JY+TBAgQ\nIECAQAcFsuQ5HRxW4bpOCto7myMHdzX/+Z//QVS40RVoQBZUfsF4d6uKK/Tpid8rwMz/cLvrdmzP\n7mQ7kZknsJM/xV1+7VXh5LHahae1z42kuuia8Msfvj2svnr+43IFAQIECBAgQKBdgXbzm3P3O3r+\nrwNb3Hzx9TclW62tatHCKQIECBAgQIBAZwSy5DmdGVExe50qaI+OjYTN2zcVc4QFGZUFVZBAGMa8\nBPJdt7Vw/FSY2nKkHi8KyxbYGmZewdCYAAECBAgQyEUg3/wmlyHphAABAgQIECCQi4A8Jx1jUtB+\nrPnUf/+Pk9u+86jqVAszC6oFjlOFFbBuCxsaAyNAgAABAgTaFJDftAnnMgIECBAgQKDwAvKcdCGa\n2kP7Lx9NXgo5ag/tVmQWVCsd54oqYN0WNTLGRYAAAQIECLQrIL9pV851BAgQIECAQNEF5DnpIjRd\n0P5aUtD+qYJ2KzILqpWOc0UVsG6LGhnjIkCAAAECBNoVkN+0K+c6AgQIECBAoOgC8px0EVLQTucU\nLKiUUJoVSsC6LVQ4DIYAAQIECBDIQUB+kwOiLggQIECAAIFCCshz0oVlpqD9m8kT2jV7aLcws6Ba\n4DhVWAHrtrChMTACBAgQIECgTQH5TZtwLiNAgAABAgQKLyDPSRciBe10Tp7QTumkWbEEfCMsVjyM\nhgABAgQIEMguIL/JbqgHAgQIECBAoJgC8px0cZkqaH/ja785+eee0G4pZkG15HGyoALWbUEDY1gE\nCBAgQIBA2wLym7bpXEiAAAECBAgUXECeky5ASUF7Z/Ppp/7F5J889SNbjrQws6Ba4DhVWAHrtrCh\nMTACBAgQIECgTQH5TZtwLiNAgAABAgQKLyDPSReiqYL2K/seP/v7X/1yJd0lg9nKghrMuPf7rK3b\nfo+g8RMgQIAAAQIXC8hvLhbxNQECBAgQIFAWAXlOukhOFbRHx0bC5u2b0l0xoK0sqAENfJ9P27rt\n8wAaPgECBAgQIHCJgPzmEhIHCBAgQIAAgZIIyHPSBTL6t8mWI28raM+pZUHNSaRBAQWs2wIGxZAI\nECBAgACBTALym0x8LiZAgAABAgQKLCDPSRecKI7vrP3qL18X//UPHreHdguz8wuqRROnCBAgQIAA\nAQIECBAgQIAAAQIECBAgkEngs5/6WKbry35xUtCOa81miMcnagraLaKtoN0CxykCBAgQIECAAAEC\nBAgQIECAAAECBHIRUNBuzaig3drnwtnzBW0L6gKJD30gYN32QZAMkQABAgQIEJiXgPxmXlwaEyBA\ngAABAn0kIM9JF6yZgnYzeUK77gntFmYWVAscpworYN0WNjQGRoAAAQIECLQpIL9pE85lBAgQIECA\nQOEF5DnpQqSgnc4pWFApoTQrlIB1W6hwGAwBAgQIECCQg4D8JgdEXRAgQIAAAQKFFJDnpAuLgnY6\nJwXtlE6aFUvAN8JixcNoCBAgQIAAgewC8pvshnogQIAAAQIEiikgz0kXFwXtdE4K2imdNCuWgG+E\nxYqH0RAgQIAAAQLZBeQ32Q31QIAAAQIECBRTQJ6TLi4K2umcFLRTOmlWLAHfCIsVD6MhQIAAAQIE\nsgvIb7Ib6oEAAQIECBAopoA8J11cokoc10LTSyHn4rKg5hJyvogC1m0Ro2JMBAgQIECAQBYB+U0W\nPdcSIECAAAECRRaQ56SLjoJ2OidPaKd00qxYAr4RFiseRkOAAAECBAhkF5DfZDfUAwECBAgQIFBM\nAXlOurhEv/OZLzbePLY/+rPv/MdquksGs5UFNZhx7/dZW7f9HkHjJ0CAAAECBC4WkN9cLOJrAgQI\nECBAoCwC8px0kYx2bNnZHB0bCZu3b0p3xYC2sqAGNPB9Pm3rts8DaPgECBAgQIDAJQLym0tIHCBA\ngAABAgRKIiDPSRfI6Cu/+9jZI+8ciBS0W4NZUK19nC2mgHVbzLgYFQECBAgQINC+gPymfTtXEiBA\ngAABAsUWkOeki09UqcS1X1r/W/FzP/yKLUdamFlQLXAynDpTr4XTZyZDdcGSsCDO0JFLLytg3V6W\nxUECBAgQIECgjwXyzG/kon28EAydAAECBAiUUCDPPKeEPBemNFXQDqEZj0/UFbQvsFz6wYK61KT9\nI7Wwd/hA2He0FibPvtvLoqXvD/fesyYse/eQTxkFrNuMgC4nQIAAAQIECieQR35zcM+e8MqRiVCf\nlYtWr1kSPnrPurDKQxaFi7kBESBAgACBQRHII88ZBCsF7ZRRtqBSQs3Z7ER47qm94cjPZzWMks/N\nma+r14SND9weFs867WP7AtZt+3auJECAAAECBIopkDW/OTi8K+x688zlJ1dZFO57aENYcfmzjhIg\nQIAAAQIEOiqQNc/p6OAK1LmCdspgWFApoeZqdurV8PgPjoZzP0JUlyRPZH90TVgRT4Z9L+8Ju8ca\nU1evWLsu3Ld6yVw9OZ9CwLpNgaQJAQIECBAg0FcCWfOb0V27wkvHzoRFy98f7tywOqxaEN6Tiy6/\ndV24/xa5aF8tCoMlQIAAAQIlEcia55SEYc5pKGjPSTTdwIJKCTVns/HwzFMj4Wi8JGy8b92sJ7FP\nhGe+tzccTf7ss7p0ZXjkntVz9qTB3AL5rNvJMPb64XDkZz8P5/4qt3rVVeGDq1Ymv4iYCKNvnwkr\nblwVlvnT3LmDoQUBAgQIECCQi0Ae+c25hyvem76Mh6e+NxLeSZKd5beuTQraS3MZq04IECBAgAAB\nAvMRyCPPmc/9+rVtUtCu1EK4N9lD+yl7aLeIogXVAieXU+/+EFFdvio8cvdNufQ66J1kXbf1o6Ph\nqeFj4fSs/SUvNl2W/ND3oB/6LmbxNQECBAgQINAhgaz5zeWHNRae/JtD4XRyUkH78kKOEiBAgAAB\nAp0X6Eye0/lxd/sOUwXtu+/7/fhv//pLCtot9C2oFjg5nDp58CfhuyPnfoQIYdXtd4SP37gwh151\nkWXdnjm6P3z7xZ9NbQ8zJTkUh0ULhsLkqUaYPL/neXLCD33WGQECBAgQINBNgSz5zcXjPHl0LPkr\ntBPh1UPjM7/Ar4Y7fu3D4bZkGxL/CBAgQIAAAQLdFsgzz+n22Lt5v6hy2z+pffHeWxf8wfZN3bxv\n393LgupgyN4ZDTtfOBYmz91iaFF48MENYVkHbzdIXbe/bpMtYP5bsgXMVFAqYcU/+lC4b8351yPV\nwu4X9oR970y/TElBe5BWlLkSIECAAIHeC7Sf31w09vqhsPPpsekcdObU4mT/7I32z74IypcECBAg\nQIBAtwRyy3O6NeAe3Sf64y986+zk2/uizQraLUNgQbXkaf9k/XB48ukjU3/eeW4nw3WfuCusv7r9\n7lz5XoG21239YPID3ltTP+Atuv7m8D9uOF/MPt//uwVvW46cN/F/AgQIECBAoBsCbec3lwzuRHjp\nhVfD2MQvwumfT/+i/lyT629fF+690UshL+FygAABAgQIEOi4QH55TseH2tMbRDu27GyOjr0SNm//\nQk8HUvSbW1AdiNC5YvYzSTF7ZvuKVckPDx/3w0Ou0O2u2zNH9obHf3IiGUslrP+1u8O6y/3Z7ZkT\n4dDRRlixakW43OlcJ6IzAgQIECBAgMCMQLv5TUvAEwfDkz98azovveba8NmP39KyuZMECBAgQIAA\ngU4IdCTP6cRAe9znVEH76af/5eSffP8H9tBuEQwLqgVOO6fO/dDwXPJDw8y1K5IXC97nxYLtSLa8\npu11e2I0PP7csWT/7Ery1PzdnppvqewkAQIECBAg0E2BtvObOQY59vJweHaskfw+f2F48KE7bIE3\nh5fTBAgQIECAQP4Cncpz8h9pb3uM/vVnfr2x5VuPR+MTdQXtFrGwoFrgzPPUmaOj4YkXZ/bMTgqm\nN21YHz56/fRLIOsnxkO4emlYEM+zU80vK9D2up215Uh1+arwyN03XdL/8ddHw+4jyRPaa9aEdct9\n+7gEyAECBAgQIECgIwJt5zdzjObg8Ith15vJC0QUtOeQcpoAAQIECBDolECn8pxOjbdX/UYP3/lw\n/dSZycq3f/hXKlItomBBtcCZ16mx8OTfHLrwZPayG68PH7lhQaj/IoTG22+EXQdrIV66Mnz6ntXz\n6lXjywu0v24nw0vPDofRibNTHS9asSJ84sM3h8XJV/VTY2H3T46EQ+PTe00u/kdrw8Y1Sy8/AEcJ\nECBAgAABAjkLtJ/fhHD+wYqwaFG47dYbw7pV0znM0YN7w7MjJ5K/Tkv+2XIk54jpjgABAgQIEEgr\nkCXPSXuPMrSb2UN7JNlDe1MZ5tOxOVhQ+dCO7toVXjr27kt3LtergvblVNo7lmnd1pNfPjyT/PJh\nZo/zy4+gGu6498PhNu9NujyPowQIECBAgEDuAlnym/rre8ITeyZajKkSbrn7V8Kd/vqshZFTBAgQ\nIECAQKcEsuQ5nRpTEftV0E4ZFQsqJdQczY6O7A7PJE9ht/q3+PqbwsYNq1o1cS6lQOZ1e+ZY+NGP\nDoZDJy7+JUQlLL52abjzzjVhhe1hUkZDMwIECBAgQCAPgWz5zWQY/clIsm3a6emnsWcPqHJVuG3D\nbeGOVdNb4c0+5TMBAgQIECBAoBsC2fKcboywGPeIKpU4qS42Y3totw6IBdXax9liCuS2buvjYex4\nLSS7Sobq+xaGFcuXBnXsYsbcqAgQIECAQNkF8slvJsPRsWPheONcdhPC+xYtCatX2EKt7GvH/AgQ\nIECAQNEF8slzij7L7OOLKnFS0G4qaM9FaUHNJeR8EQWs2yJGxZgIECBAgACBLALymyx6riVAgAAB\nAgSKLCDPSRcdBe10TsGCSgmlWaEErNtChcNgCBAgQIAAgRwE5Dc5IOqCAAECBAgQKKSAPCddWKI4\neUK76QntObUsqDmJNCiggHVbwKAYEgECBAgQIJBJQH6Tic/FBAgQIECAQIEF5DnpgjNT0A7JHtq1\narpLBrOVBTWYce/3WVu3/R5B4ydAgAABAgQuFpDfXCziawIECBAgQKAsAvKcdJFU0E7nZMuRlE6a\nFUvAN8JixcNoCBAgQIAAgewC8pvshnogQIAAAQIEiikgz0kXFwXtdE4K2imdNCuWgG+ExYqH0RAg\nQIAAAQLZBeQ32Q31QIAAAQIECBRTQJ6TLi5JQXuo1gzNePykLUdakVlQrXScK6qAdVvUyBgXAQIE\nCBAg0K6A/KZdOdcRIECAAAECRReQ56SLkIJ2OidPaKd00qxYAr4RFiseRkOAAAECBAhkF5DfZDfU\nAwECBAgQIFBMAXlOurgoaKdzUtBO6aRZsQR8IyxWPIyGAAECBAgQyC4gv8luqAcCBAgQIECgmALy\nnHRxiT70+Rsajbfq0f7H3qimu2QwW1lQgxn3fp+1ddvvETR+AgQIECBA4GIB+c3FIr4mQIAAAQIE\nyiIgz0kXyWj91jXN2uFGGN52IN0VA9rKghrQwPf5tK3bPg+g4RMgQIAAAQKXCMhvLiFxgAABAgQI\nECiJgDwnXSCj2/9ozdn6241IQbs12PkF1bqVswQIECBAgAABAgQIECBAgAABAgQIEGhf4LOf+lj7\nFw/AlVE8NFRb+OFl8Rt/d8SWIy0CrqDdAscpAgQIECBAgAABAgQIECBAgAABAgRyEVDQbs04VdAO\nzWZ8/GRNQbu1lbMECBAgQIAAAQIECBAgQIAAAQIECBAg0EMBBe0e4rs1AQIECBAgQIAAAQIECBAg\nQIAAAQIECKQXUNBOb6UlAQIECBAgQIAAAQIECBAgQIAAAQIECPRQQEG7h/huTYAAAQIECBAgQIAA\nAQIECBAgQIAAAQLpBWYK2ouSPbTftod2ejctCRAgQIAAAQIECBAgQIAAAQIECBAgQKDLAlEcD9Wu\n+dQH49e/+Q8K2l3GdzsCBAgQIECAAAECBAgQIECAAAECBAgQSC8QxRuuq914/9CC3dsOpL9KSwIE\nCBAgQIAAAQIECBAgQIAAAQIECBAg0GWB6JZ/ddvZ5lg9GlbQ7jK92xEgQIAAAQIECBAgQIAAAQIE\nCBAgQIDAfASi9VvXNGuH62F42+h8rtOWAAECBAgQIECAAAECBAgQIECAAAECBAh0VWCqoH3yO2OT\nrzzxjj20u0rvZgQIECBAgAABAgQIECBAgAABAgQIECAwH4Fo7eeXN/Z/dTwan6gpaM9HTlsCBAgQ\nIECAAAECBAgQIECAAAECBAgQ6KpAtOJXl9V/MdmsvPb0UQXtrtK7GQECBAgQIECAAAECBAgQIECA\nAAECBAjMR2BmD+1Gsof2gflcpy0BAgQIECBAgAABAgQIECBAgAABAgQIEOiqgIJ2V7ndjAABAgQI\nECBAgAABAgQIECBAgAABAgTaFYgqlbgWQjMen6jbcqRdRdcRIECAAAECBAgQIECAAAECBAgQIECA\nQMcFFLQ7TuwGBAgQIECAAAECBAgQIECAAAECBAgQIJCHgIJ2Hor6IECAAAECBAgQIECAAAECBAgQ\nIECAAIGOCyhod5zYDQgQIECAAAECBAgQIECAAAECBAgQIEAgD4GkoF1J9tAO9tDOQ1MfBAgQIECA\nAAECBAgQIECAAAECBAgQINAxAQXtjtHqmAABAgQIECBAgAABAgQIECBAgAABAgTyFFDQzlNTXwQI\nECBAgAABAgQIECBAgAABAgQIECDQMQEF7Y7R6pgAAQIECBAgQIAAAQIECBAgQIAAAQIE8hRQ0M5T\nU18ECBAgQIAAAQIECBAgQIAAAQIECBAg0DEBBe2O0eqYAAECBAgQIECAAAECBAgQIECAAAECBPIU\niG763A2Nxlu16MDjY9U8O9YXAQIECBAgQIAAAQIECBAgQIAAAQIECBDIUyBav3VNs3a4EYa3Hciz\nX30RIECAAAECBAgQIECAAAECBAgQIECAAIFcBaLb/2jN2frbjUhBO1dXnREgQIAAAQIECBAgQIAA\nAQIECBAgQIBAzgJTe2gv/Mj747Gn37DlSM64uiNAgAABAgQIECBAgAABAgQIECBAgACB/AS8FDI/\nSz0RIECAAAECBAgQIECAAAECBAgQIECAQAcFFLQ7iKtrAgQIECBAgAABAgQIECBAgAABAgQIEMhP\nQEE7P0s9ESBAgAABAgQIECBAgAABAgQIECBAgEAHBRS0O4irawIECBAgQIAAAQIECBAgQIAAAQIE\nCBDITyApaMe1EBbF4xPHvBQyP1c9ESBAgAABAgQIECBAgAABAgQIECBAgEDOAlMF7cWf+mD8+n95\nVUE7Z1zdESBAgAABAgQIECBAgAABAgQIECBAgEB+AlFl/XW1mx4YWrB724H8etUTAQIECBAgQIAA\nAQIECBAgQIAAAQIECBDIWSC65UtrzjbfbETDCto50+qOAAECBAgQIECAAAECBAgQIECAAAECBPIU\niNZvXdOsHa6H4W2jefarLwIECBAgQIAAAQIECBAgQIAAAQIECBAgkKvAVEH7xHfGJvc88Y49tHOl\n1RkBAgQIECBAgAABAgQIECBAgAABAgQI5CkQrf388sb+r45H4xM1Be08ZfVFgAABAgQIECBAgAAB\nAgQIECBAgAABArkKRCt/dVl9cjJUXnv6LQXtXGl1RoAAAQIECBAgQIAAAQIECBAgQIAAAQJ5Cszs\nod1I9tA+kGe/+iJAgAABAgQIECBAgAABAgQIECBAgAABArkKKGjnyqkzAgQIECBAgAABAgQIECBA\ngAABAgQIEOiUQBTHQ7VmaMbjJ+2h3Slk/RIgQIAAAQIECBAgQIAAAQIECBAgQIBAdoEoHhqqhWYz\nPq6gnV1TDwQIECBAgAABAgQIECBAgAABAgQIECDQMQEF7Y7R6pgAAQIECBAgQIAAAQIECBAgQIAA\nAQIE8hRQ0M5TU18ECBAgQIAAAQIECBAgQIAAAQIECBAg0DGBaCjZcqRpy5GOAeuYAAECBAgQIECA\nAAECBAgQIECAAAECBPIRUNDOx1EvBAgQIECAAAECBAgQIECAAAECBAgQINBhAQXtDgPrngABAgQI\nECBAgAABAgQIECBAgAABAgTyEVDQzsdRLwQIECBAgAABAgQIECBAgAABAgQIECDQYQEF7Q4D654A\nAQIECBAgQIAAAQIECBAgQIAAAQIE8hFQ0M7HUS8ECBAgQIAAAQIECBAgQIAAAQIECBAg0GEBBe0O\nA+ueAAECBAgQIECAAAECBAgQIECAAAECBPIRiOKhoVpoNuPjJ2vVfLrUCwECBAgQIECAAAECBAgQ\nIECAAAECBAgQyF9AQTt/Uz0SIECAAAECBAgQIECAAAECBAgQIECAQAcEFLQ7gKpLAgQIECBAgAAB\nAgQIECBAgAABAgQIEMhfIIrjoVozNONxW47kr6tHAgQIECBAgAABAgQIECBAgAABAgQIEMhNIFr7\n29c29u84Hilo52aqIwIECBAgQIAAAQIECBAgQIAAAQIECBDogEC0fuua5snvjE2+8sQ7XgrZAWBd\nEiBAgAABAgQIECBAgAABAgQIECBAgEA+AlMF7drhehjeNppPj3ohQIAAAQIECBAgQIAAAQIECBAg\nQIAAAQIdEJgpaDeSgvaBDnSvSwIECBAgQIAAAQIECBAgQIAAAQIECBAgkI+AgnY+jnohQIAAAQIE\nCBAgQIAAAQIECBAgQIAAgQ4LKGh3GFj3BAgQIECAAAECBAgQIECAAAECBAgQIJCPwExBu5ZsOfIP\n+fSoFwIECBAgQIAAAQIECBAgQIAAAQIECBAg0AGBpKB9a/PY116f3P/syWoH+tclAQIECBAgQIAA\nAQIECBAgQIAAAQIECBDIRSC65eHF9VefPFUZn6graOdCqhMCBAgQIECAAAECBAgQIECAAAECBAgQ\n6IRAVKnEtRCasYJ2J3j1SYAAAQIECBAgQIAAAQIECBAgQIAAAQJ5CSQF7UpS0A4K2nmJ6ocAAQIE\nCBAgQIAAAQIECBAgQIAAAQIEOiKgoN0RVp0SIECAAAECBAgQIECAAAECBAgQIECAQN4CCtp5i+qP\nAAECBAgQIECAAAECBAgQIECAAAECBDoioKDdEVadEiBAgAABAgQIECBAgAABAgQIECBAgEDeAgra\neYvqjwABAgQIECBAgAABAgQIECBAgAABAgQ6IqCg3RFWnRIgQIAAAQIECBAgQIAAAQIECBAgQIBA\n3gIK2nmL6o8AAQIECBAgQIAAAQIECBAgQIAAAQIEOiKgoN0RVp0SIECAAAECBAgQIECAAAECBAgQ\nIECAQN4CCtp5i+qPAAECBAgQIECAAAECBAgQIECAAAECBDoioKDdEVadEiBAgAABAgQIECBAgAAB\nAgQIECBAgEDeAgraeYvqjwABAgQIECBAgAABAgQIECBAgAABAgQ6IpAUtONaCM14fKJe7cgddEqA\nAAECBAgQIECAAAECBAgQIECAAAECBHIQUNDOAVEXBAgQIECAAAECBAgQIECAAAECBAgQINB5AQXt\nzhu7AwECBAgQIECAAAECBAgQIECAAAECBAjkIBBV4mTLkaYtR3Kw1AUBAgQIECBAgAABAgQIECBA\ngAABAgQIdFAgipOCdlNBu4PEuiZAgAABAgQIECBAgAABAgQIECBAgACBPAQUtPNQ1AcBAgQIECBA\ngAABAgQIECBAgAABAgQIdFxgpqAd4vGJWrXjd3MDAgQIECBAgAABAgQIECBAgAABAgQIECDQpkC0\n9revbezfcTwaP6mg3aahywgQIECAAAECBAgQIECAAAECBAgQIECgCwLR+q1rmie/8+bkK08c84R2\nF8DdggABAgQIECBAgAABAgQIECBAgAABAgTaE5gqaNcO18PwttH2enAVAQIECBAgQIAAAQIECBAg\nQIAAAQIECBDogsBMQbuRFLQPdOF2bkGAAAECBAgQIECAAAECBAgQIECAAAECBNoTUNBuz81VBAgQ\nIECAAAECBAgQIECAAAECBAgQINBlAQXtLoO7HQECBAgQIECAAAECBAgQIECAAAECBAi0JzBT0K4l\nW478Q3s9uIoAAQIECBAgQIAAAQIECBAgQIAAAQIECHRBIClo39o8/vXDk3t/cKLahfu5BQECBAgQ\nIECAAAECBAgQIECAAAECBAgQaEsguvV/Xlp/9a9PVY6fPK2g3RahiwgQIECAAAECBAgQIECAAAEC\nBAgQIECgGwLR0FC11gzN+PgJBe1ugLsHAQIECBAgQIAAAQIECBAgQIAAAQIECLQnoKDdnpurCBAg\nQIAAAQIECBAgQIAAAQIECBAgQKDLAgraXQZ3OwIECBAgQIAAAQIECBAgQIAAAQIECBBoTyAaqlZr\nodmMf2bLkfYEXUWAAAECBAgQIECAAAECBAgQIECAAAECXRGYKWiHpKB9ykshu0LuJgQIECBAgAAB\nAgQIECBAgAABAgQIECDQjoCCdjtqriFAgAABAgQIECBAgAABAgQIECBAgACBrgsoaHed3A0JECBA\ngAABAgQIECBAgAABAgQIECBAoB0BBe121FxDgAABAgQIECBAgAABAgQIECBAgAABAl0XmCloeylk\n1+XdkAABAgQIECBAgAABAgQIECBAgAABAgTmJRANDVVrzdCMj5847aWQ86LTmAABAgQIECBAgAAB\nAgQIECBAgAABAgS6KaCg3U1t9yJAgAABAgQIECBAgAABAgQIECBAgACBtgUUtNumcyEBAgQIECBA\ngAABAgQIECBAgAABAgQIdFNguqDdTLYcOWnLkW7CuxcBAgQIECBAgAABAgQIECBAgAABAgQIzE8g\nKWgP1ZrNoKA9PzetCRAgQIAAAQIECBAgQIAAAQIECBAgQKDLAtHqz9/YaIzVon3fesNLIbuM73YE\nCBAgQIAAAQIECBAgQIAAAQIECBAgkF4gWr91TbN2uBGGtx1If5WWBAgQIECAAAECBAgQIECAAAEC\nBAgQIECgywLR7f9qzdn6WCNS0O6yvNsRIECAAAECBAgQIECAAAECBAgQIECAwLwEpvbQHlqzND66\ny5Yj85LTmAABAgQIECBAgAABAgQIECBAgAABAgS6KhDFyUshQ7OZvBSyZg/trtK7GQECBAgQIECA\nAAECBAgQIECAAAECBAjMRyCK46FaMzTjcQXt+bhpS4AAAQIECBAgQIAAAQIECBAgQIAAAQJdFpgp\naA8lBe0TntDuMr7bESBAgAABAgQIECBAgAABAgQIECBAgEB6gWjl/3JD46oVleorXz4Qpb9MSwIE\nCBAgQIAAAQIECBAgQIAAAQIECBAg0F2BaP3WNc3a4UYY3nagu3d2NwIECBAgQIAAAQIECBAgQIAA\nAQIECBAgMA8BBe15YGlKgAABAgQIECBAgAABAgQIECBAgAABAr0TiJbc/YHawtXvq+5/9NW4d8Nw\nZwIECBAgQIAAAQIECBAgQIAAAQIECBAg0FogqlTiWgjNeHyi7qWQra2cJUCAAAECBAgQIECAAAEC\nBAgQIECAAIEeCiho9xDfrQkQIECAAAECBAgQIECAAAECBAgQIEAgvYCCdnorLQkQIECAAAECBAgQ\nIECAAAECBAgQIECghwIK2j3Ed2sCBAgQIECAAAECBAgQIECAAAECBAgQSC+QFLSvri375DXxwccP\n2UM7vZuWBAgQIECAAAECBAgQIECAAAECBAgQINBlgWj91jXN2uFGGN52oMu3djsCBAgQIECAAAEC\nBAgQIECAAAECBAgQIJBeQEE7vZWWBAgQIECAAAECBAgQIECAAAECBAgQINBDgei6h1fW66+dqrz2\n/M9sOdLDQLg1AQIECBAgQIAAAQIECBAgQIAAAQIECLQW8FLI1j7OEiBAgAABAgQIECBAgAABAgQI\nECBAgEBBBBS0CxIIwyBAgAABAgQIECBAgAABAgQIECBAgACB1gJRJY5rodmMxyfqthxpbeUsAQIE\nCBAgQIAAAQIECBAgQIAAAQIECPRQIIqTgnZTQbuHIXBrAgQIECBAgAABAgQIECBAgAABAgQIEEgj\noKCdRkkbAgQIECBAgAABAgQIECBAgAABAgQIEOi5wExBOyRbjtRsOdLzcBgAAQIECBAgQIAAAQIE\nCBAgQIAAAQIECFxJICloD9WaIdlD+6SC9pWQHCdAgAABAgQIECBAgAABAgQIECBAgACB3gsoaPc+\nBkZAgAABAgQIECBAgAABAgQIECBAgAABAikEonhoqBaSl0Ie94R2Ci5NCBAgQIAAAQIECBAgQIAA\nAQIECBAgQKBXAtFQUtBuKmj3yt99CRAgQIAAAQIECBAgQIAAAQIECBAgQCClQHT/urtq+/a/FO/1\nhHZKMs0IECBAgAABAgQIECBAgAABAgQIECBAoBcC0Y4t32z+56//b5P/YY+XQvYiAO5JgAABAgQI\nECBAgAABAgQIECBAgAABAukEkoL215tf+/pvTv6FgnY6Ma0IECBAgAABAgQIECBAgAABAgQIECBA\noCcCUwXtr/7735rcceh0tScjcFMCBAgQIECAAAECBAgQIECAAAECBAgQIJBCIClo72yOvPbj5j//\nD78fpWivCQECBAgQIECAAAECBAgQIECAAAECBAgQ6InAVEF7dGwkbN6+qScDcFMCBAgQIECAAAEC\nBAgQIECAAAECBAgQIJBGICloP9b8u+d2TP5/T/6FLUfSiGlDgAABAgQIECBAgAABAgQIECBAgAAB\nAj0RmNpD+2tf/a3Jv9hnD+2eRMBNCRAgQIAAAQIECBAgQIAAAQIECBAgQCCVwHRB+xtJQfsVBe1U\nYhoRIECAAAECBAgQIECAAAECBAgQIECAQE8EZgran0sK2qdsOdKTELgpAQIECBAgQIAAAQIECBAg\nQIAAAQIECKQRUNBOo6QNAQIECBAgQIAAAQIECBAgQIAAAQIECPRcQEG75yEwAAIECBAgQIAAAQIE\nCBAgQIAAAQIECBBIIzBV0P7mNz43+e9tOZLGSxsCBAgQIECAAAECBAgQIECAAAECBAgQ6JFAUtDe\n2Xzm7/5w8o//2/P20O5RENyWAAECBAgQIECAAAECBAgQIECAAAECBOYWmCpo79n/rbO/t+PfVOZu\nrgUBAgQIECBAgAABAgQIECBAgAABAgQIEOiNwFRBe3RsJGzevqk3I3BXAgQIECBAgAABAgQIECBA\ngAABAgQIECCQQiD6t8mWI28raKeg0oQAAQIECBAgQIAAAQIECBAgQIAAAQIEeikQDQ3dXfvVX14Z\nP/H3j9lDu5eRcG8CBAgQIECAAAECBAgQIECAAAECBAgQaCmQFLSHas1miI+fPK2g3ZLKSQIECBAg\nQIAAAQIECBAgQIAAAQIECBDopcBMQbuZFLRrCtq9jIR7EyBAgAABAgQIECBAgAABAgQIECBAgEBL\nAQXtljxOEiBAgAABAgQIECBAgAABAgQIECBAgEBRBBS0ixIJ4yBAgAABAgQIECBAgAABAgQIECBA\ngACBlgIK2i15nCRAgAABAgQIECBAgAABAgQIECBAgACBoghEcfJSyNC0h3ZRAmIcBAgQIECAAAEC\nBAgQIECAAAECBAgQIHB5gSiOh2rN0IzHvRTy8kKOEiBAgAABAgQIECBAgAABAgQIECBAgEAhBBS0\nCxEGgyBAgAABAgQIECBAgAABAgQIECBAgACBuQSSgnZcazZDPD5Rq87V2HkCBAgQIECAAAECBAgQ\nIECAAAECBAgQINArgehDn7+h0ThSi73jw8wAADvwSURBVPZ/e0xBu1dRcF8CBAgQIECAAAECBAgQ\nIECAAAECBAgQmFMgWr91TbN2uBGGtx2Ys7EGBAgQIECAAAECBAgQIECAAAECBAgQIECgVwLRL31p\nzdnGm41IQbtXIXBfAgQIECBAgAABAgQIECBAgAABAgQIEEgjEFUqca1629L47R/bciQNmDYECBAg\nQIAAAQIECBAgQIAAAQIECBAg0BuBqYJ2CM3kpZB1e2j3JgbuSoAAAQIECBAgQIAAAQIECBAgQIAA\nAQIpBBS0UyBpQoAAAQIECBAgQIAAAQIECBAgQIAAAQK9F0gK2pVaCNXkCe2TntDufTyMgAABAgQI\nECBAgAABAgQIECBAgAABAgSuIBCtfOSGxlUrK9VXvnwgukIbhwkQIECAAAECBAgQIECAAAECBAgQ\nIECAQM8FovVb1zRrhxtheNuBng/GAAgQIECAAAECBAgQIECAAAECBAgQIECAwJUEFLSvJOM4AQIE\nCBAgQIAAAQIECBAgQIAAAQIECBRKIFpy1wdqC2+uVvc/ejAu1MgMhgABAgQIECBAgAABAgQIECBA\ngAABAgQIzBKYeSlkSF4KWfdSyFkwPhIgQIAAAQIECBAgQIAAAQIECBAgQIBAsQQUtIsVD6MhQIAA\nAQIECBAgQIAAAQIECBAgQIAAgSsIKGhfAcZhAgQIECBAgAABAgQIECBAgAABAgQIECiWgIJ2seJh\nNAQIECBAgAABAgQIECBAgAABAgQIECBwBYGkoH11beknr4lfe/yQPbSvgOQwAQIECBAgQIAAAQIE\nCBAgQIAAAQIECPReIFq/dU2zdrgRhrcd6P1ojIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFxBQEH7\nCjAOEyBAgAABAgQIECBAgAABAgQIECBAgECxBKLrHl5Zr782UXnt+eO2HClWbIyGAAECBAgQIECA\nAAECBAgQIECAAAECBGYJeCnkLAwfCRAgQIAAAQIECBAgQIAAAQIECBAgQKC4AgraxY2NkREgQIAA\nAQIECBAgQIAAAQIECBAgQIDALAEF7VkYPhIgQIAAAQIECBAgQIAAAQIECBAgQIBAcQUUtIsbGyMj\nQIAAAQIECBAgQIAAAQIECBAgQIAAgVkCCtqzMHwkQIAAAQIECBAgQIAAAQIECBAgQIAAgeIKKGgX\nNzZGRoAAAQIECBAgQIAAAQIECBAgQIAAAQKzBBS0Z2H4SIAAAQIECBAgQIAAAQIECBAgQIAAAQLF\nFVDQLm5sjIwAAQIECBAgQIAAAQIECBAgQIAAAQIEZgkoaM/C8JEAAQIECBAgQIAAAQIECBAgQIAA\nAQIEiisQfehzNzQab5yO9n/7zWpxh2lkBAgQIECAAAECBAgQIECAAAECBAgQIDDoAtH6rWuatcON\nMLztwKBbmD8BAgQIECBAgAABAgQIECBAgAABAgQIFFgg+qUvrTnbeLMRKWgXOEqGRoAAAQIECBAg\nQIAAAQIECBAgQIAAAQJhag/t6m3L4rd/PGbLEQuCAAECBAgQIECAAAECBAgQIECAAAECBAor4KWQ\nhQ2NgREgQIAAAQIECBAgQIAAAQIECBAgQIDAbAEF7dkaPhMgQIAAAQIECBAgQIAAAQIECBAgQIBA\nYQVmCtrVeHzipC1HChsmAyNAgAABAgQIECBAgAABAgQIECBAgACBaOUjNzSuWlmpvvLlAxEOAgQI\nECBAgAABAgQIECBAgAABAgQIECBQVIFo/dY1zdrhRhjedqCoYzQuAgQIECBAgAABAgQIECBAgAAB\nAgQIECAQFLQtAgIECBAgQIAAAQIECBAgQIAAAQIECBDoC4FoyV0fqC28uVrd/+jBuC9GbJAECBAg\nQIAAAQIECBAgQIAAAQIECBAgMJACMy+FDMlLIeteCjmQS8CkCRAgQIAAAQIECBAgQIAAAQIECBAg\n0B8CCtr9ESejJECAAAECBAgQIECAAAECBAgQIECAwMALKGgP/BIAQIAAAQIECBAgQIAAAQIECBAg\nQIAAgf4QUNDujzgZJQECBAgQIECAAAECBAgQIECAAAECBAZeICloX11b+slr4tceP2QP7YFfDgAI\nECBAgAABAgQIECBAgAABAgQIECBQXIFo/dY1zdrhRhjedqC4ozQyAgQIECBAgAABAgQIECBAgAAB\nAgQIEBh4AQXtgV8CAAgQIECAAAECBAgQIECAAAECBAgQINAfAtF1D6+s11+bqLz2/HFbjvRHzIyS\nAAECBAgQIECAAAECBAgQIECAAAECAyngpZADGXaTJkCAAAECBAgQIECAAAECBAgQIECAQP8JKGj3\nX8yMmAABAgQIECBAgAABAgQIECBAgAABAgMpoKA9kGE3aQIECBAgQIAAAQIECBAgQIAAAQIECPSf\ngIJ2/8XMiAkQIECAAAECBAgQIECAAAECBAgQIDCQAgraAxl2kyZAgAABAgQIECBAgAABAgQIECBA\ngED/CSho91/MjJgAAQIECBAgQIAAAQIECBAgQIAAAQIDKaCgPZBhN2kCBAgQIECAAAECBAgQIECA\nAAECBAj0n4CCdv/FzIgJECBAgAABAgQIECBAgAABAgQIECAwkAIK2gMZdpMmQIAAAQIECBAgQIAA\nAQIECBAgQIBA/wkoaPdfzIyYAAECBAgQIECAAAECBAgQIECAAAECAymgoD2QYTdpAgQIECBAgAAB\nAgQIECBAgAABAgQI9J+Agnb/xcyICRAgQIAAAQIECBAgQIAAAQIECBAgMJACCtoDGXaTJkCAAAEC\nBAgQIECAAAECBAgQIECAQP8JRBtW31mrnXg5fulwvdp/wzdiAgQIECBAgAABAgQIECBAgAABAgQI\nEBgUgWjHlp3N0bGRsHn7pkGZs3kSIECAAAECBAgQIECAAAECBAgQIECAQB8KKGj3YdAMmQABAgQI\nECBAgAABAgQIECBAgAABAoMoEG1cfUttNF4bv7T7cVuODOIKMGcCBAgQIECAAAECBAgQIECAAAEC\nBAj0iYCXQvZJoAyTAAECBAgQIECAAAECBAgQIECAAAECgy6goD3oK8D8CRAgQIAAAQIECBAgQIAA\nAQIECBAg0CcCCtp9EijDJECAAAECBAgQIECAAAECBAgQIECAwKALKGgP+gowfwIECBAgQIAAAQIE\nCBAgQIAAAQIECPSJgIJ2nwTKMAkQIECAAAECBAgQIECAAAECBAgQIDDoAgrag74CzJ8AAQIECBAg\nQIAAAQIECBAgQIAAAQJ9IqCg3SeBMkwCBAgQIECAAAECBAgQIECAAAECBAgMusB0QfvGT8bjP/2r\n6qBjmD8BAgQIECBAgAABAgQIECBAgAABAgQIFFcg+uo/e+zsgbcORJu3byruKI2MAAECBAgQIECA\nAAECBAgQIECAAAECBAZeINqxZWdzdGwkKGgP/FoAQIAAAQIECBAgQIAAAQIECBAgQIAAgUILRJ97\n4B/XXz/yUuXbL75gy5FCh8rgCBAgQIAAAQIECBAgQIAAAQIECBAgMNgCXgo52PE3ewIECBAgQIAA\nAQIECBAgQIAAAQIECPSNgIJ234TKQAkQIECAAAECBAgQIECAAAECBAgQIDDYAgragx1/sydAgAAB\nAgQIECBAgAABAgQIECBAgEDfCCho902oDJQAAQIECBAgQIAAAQIECBAgQIAAAQKDLaCgPdjxN3sC\nBAgQIECAAAECBAgQIECAAAECBAj0jYCCdt+EykAJECBAgAABAgQIECBAgAABAgQIECAw2AIK2oMd\nf7MnQIAAAQIECBAgQIAAAQIECBAgQIBA3wgoaPdNqAyUAAECBAgQIECAAAECBAgQIECAAAECgy2g\noD3Y8Td7AgQIECBAgAABAgQIECBAgAABAgQI9I1A9KHP3dBovHE62v/tN6t9M2oDJUCAAAECBAgQ\nIECAAAECBAgQIECAAIGBE4jWb13TrB1uhOFtBwZu8iZMgAABAgQIECBAgAABAgQIECBAgAABAv0j\nEP3Sl9acbbzZiBS0+ydoRkqAAAECBAgQIECAAAECBAgQIECAAIFBFJjaQ7t627L47R+P2XJkEFeA\nORMgQIAAAQIECBAgQIAAAQIECBAgQKBPBLwUsk8CZZgECBAgQIAAAQIECBAgQIAAAQIECBAYdAEF\n7UFfAeZPgAABAgQIECBAgAABAgQIECBAgACBPhGYKWhX4/GJk7Yc6ZOgGSYBAgQIECBAgAABAgQI\nECBAgAABAgQGUSBa+cgNjatWVqqvfPlANIgA5kyAAAECBAgQIECAAAECBAgQIECAAAEC/SEQrd+6\nplk73AjD2w70x4iNkgABAgQIECBAgAABAgQIECBAgAABAgQGUkBBeyDDbtIECBAgQIAAAQIECBAg\nQIAAAQIECBDoP4FoyV0fqC28uVrd/+jBuP+Gb8QECBAgQIAAAQIECBAgQIAAAQIECBAgMCgCyUsh\n41oIzeSlkHUvhRyUqJsnAQIECBAgQIAAAQIECBAgQIAAAQIE+lBAQbsPg2bIBAgQIECAAAECBAgQ\nIECAAAECBAgQGEQBBe1BjLo5EyBAgAABAgQIECBAgAABAgQIECBAoA8FFLT7MGiGTIAAAQIECBAg\nQIAAAQIECBAgQIAAgUEUiCrx1bVlDy6ODz7+mj20B3EFmDMBAgQIECBAgAABAgQIECBAgAABAgT6\nRCBav3VNs3a4EYa3HeiTIRsmAQIECBAgQIAAAQIECBAgQIAAAQIECAyigIL2IEbdnAkQIECAAAEC\nBAgQIECAAAECBAgQINCHAtF1D6+s1187VXnt+Z/ZcqQPA2jIBAgQIECAAAECBAgQIECAAAECBAgQ\nGBSBKI7jWrMZ4vGJmoL2oETdPAkQIECAAAECBAgQIECAAAECBAgQINCHAgrafRg0QyZAgAABAgQI\nECBAgAABAgQIECBAgMAgCihoD2LUzZkAAQIECBAgQIAAAQIECBAgQIAAAQJ9KKCg3YdBM2QCBAgQ\nIECAAAECBAgQIECAAAECBAgMooCC9iBG3ZwJECBAgAABAgQIECBAgAABAgQIECDQhwJJQXsoeSlk\n00sh+zB4hkyAAAECBAgQIECAAAECBAgQIECAAIFBElDQHqRomysBAgQIECBAgAABAgQIECBAgAAB\nAgT6WEBBu4+DZ+gECBAgQIAAAQIECBAgQIAAAQIECBAYJAEF7UGKtrkSIECAAAECBAgQIECAAAEC\nBAgQIECgjwWmC9oh2UP7ZK3ax/MwdAIECBAgQIAAAQIECBAgQIAAAQIECBAouYCCdskDbHoECBAg\nQIAAAQIECBAgQIAAAQIECBAoi4CCdlkiaR4ECBAgQIAAAQIECBAgQIAAAQIECBAouYCCdskDbHoE\nCBAgQIAAAQIECBAgQIAAAQIECBAoi0B086qP1OLKqfjF/bvtoV2WqJoHAQIECBAgQIAAAQIECBAg\nQIAAAQIESigQ7diyszk6NhI2b99UwumZEgECBAgQIECAAAECBAgQIECAAAECBAiURUBBuyyRNA8C\nBAgQIECAAAECBAgQIECAAAECBAiUXCD63z/8UP2ZQ3srew+N2nKk5ME2PQIECBAgQIAAAQIECBAg\nQIAAAQIECPSzgJdC9nP0jJ0AAQIECBAgQIAAAQIECBAgQIAAAQIDJBB99o6P1B7bMxyPn6x5QnuA\nAm+qBAgQIECAAAECBAgQIECAAAECBAgQ6DeBqT209x8ebv7TP9sS9dvgjZcAAQIECBAgQIAAAQIE\nCBAgQIAAAQIEBkfASyEHJ9ZmSoAAAQIECBAgQIAAAQIECBAgQIAAgb4WiD73P/1RY2l1aOhPvvoH\nlb6eicETIECAAAECBAgQIECAAAECBAgQIECAQKkFvBSy1OE1OQIECBAgQIAAAQIECBAgQIAAAQIE\nCJRHQEG7PLE0EwIECBAgQIAAAQIECBAgQIAAAQIECJRaQEG71OE1OQIECBAgQIAAAQIECBAgQIAA\nAQIECJRHIPp3v/PVM68dG4t+70//SVSeaZkJAQIECBAgQIAAAQIECBAgQIAAAQIECJRNINqxZWdz\ndGwkbN6+qWxzMx8CBAgQIECAAAECBAgQIECAAAECBAgQKJFA9I8f/D/rR08cqXzj6ceqJZqXqRAg\nQIAAAQIECBAgQIAAAQIECBAgQIBAyQTsoV2ygJoOAQIECBAgQIAAAQIECBAgQIAAAQIEyiqgoF3W\nyJoXAQIECBAgQIAAAQIECBAgQIAAAQIESiagoF2ygJoOAQIECBAgQIAAAQIECBAgQIAAAQIEyiqg\noF3WyJoXAQIECBAgQIAAAQIECBAgQIAAAQIESiagoF2ygJoOAQIECBAgQIAAAQIECBAgQIAAAQIE\nyiqgoF3WyJoXAQIECBAgQIAAAQIECBAgQIAAAQIESiYwXdBuNuPxiVq1ZHMzHQIECBAgQIAAAQIE\nCBAgQIAAAQIECBAokYCCdomCaSoECBAgQIAAAQIECBAgQIAAAQIECBAos4CCdpmja24ECBAgQIAA\nAQIECBAgQIAAAQIECBAokUD0hc/8i8bhoz+NvvLd/2TLkRIF1lQIECBAgAABAgQIECBAgAABAgQI\nECBQNoFox5adzdGxkbB5+6ayzc18CBAgQIAAAQIECBAgQIAAAQIECBAgQKBEAtFXvvDY2SNvH4gU\ntEsUVVMhQIAAAQIECBAgQIAAAQIECBAgQIBACQWSPbTj2gdv/ky892VbjpQwvqZEgAABAgQIECBA\ngAABAgQIECBAgACB0ghMFbSbzRCPT9TsoV2asJoIAQIECBAgQIAAAQIECBAgQIAAAQIEyicwU9Bu\nJgXtuoJ2+eJrRgQIECBAgAABAgQIECBAgAABAgQIECiNQFRJthwJzeuSgvaognZpwmoiBAgQIECA\nAAECBAgQIECAAAECBAgQKJ9A9OsPfbFx8/Jrq//Pn/9uVL7pmREBAgQIECBAgAABAgQIECBAgAAB\nAgQIlEUg2rFlZ3N0bCRs3r6pLHMyDwIECBAgQIAAAQIECBAgQIAAAQIECBAooYCCdgmDakoECBAg\nQIAAAQIECBAgQIAAAQIECBAoo0D0sQ2/Ubv1ptuq/+6bfxSXcYLmRIAAAQIECBAgQIAAAQIECBAg\nQIAAAQLlEIgqleSlkKGZvBSy7qWQ5YipWRAgQIAAAQIECBAgQIAAAQIECBAgQKCUAgrapQyrSREg\nQIAAAQIECBAgQIAAAQIECBAgQKB8Agra5YupGREgQIAAAQIECBAgQIAAAQIECBAgQKCUAgrapQyr\nSREgQIAAAQIECBAgQIAAAQIECBAgQKB8AklB+87ag5+4P/6v39lmD+3yxdeMCBAgQIAAAQIECBAg\nQIAAAQIECBAgUBqBaMeWnc3RsZGwefum0kzKRAgQIECAAAECBAgQIECAAAECBAgQIECgfAIK2uWL\nqRkRIECAAAECBAgQIECAAAECBAgQIECglALR5x74P+qvH3m+8u0XX7DlSClDbFIECBAgQIAAAQIE\nCBAgQIAAAQIECBAoh0Cyh3allkwlHp+oK2iXI6ZmQYAAAQIECBAgQIAAAQIECBAgQIAAgVIKKGiX\nMqwmRYAAAQIECBAgQIAAAQIECBAgQIAAgfIJKGiXL6ZmRIAAAQIECBAgQIAAAQIECBAgQIAAgVIK\nKGiXMqwmRYAAAQIECBAgQIAAAQIECBAgQIAAgfIJKGiXL6ZmRIAAAQIECBAgQIAAAQIECBAgQIAA\ngVIKKGiXMqwmRYAAAQIECBAgQIAAAQIECBAgQIAAgfIJKGiXL6ZmRIAAAQIECBAgQIAAAQIECBAg\nQIAAgVIKKGiXMqwmRYAAAQIECBAgQIAAAQIECBAgQIAAgfIJKGiXL6ZmRIAAAQIECBAgQIAAAQIE\nCBAgQIAAgVIKKGiXMqwmRYAAAQIECBAgQIAAAQIECBAgQIAAgfIJKGiXL6ZmRIAAAQIECBAgQIAA\nAQIECBAgQIAAgVIKKGiXMqwmRYAAAQIECBAgQIAAAQIECBAgQIAAgfIJKGiXL6ZmRIAAAQIECBAg\nQIAAAQIECBAgQIAAgVIKKGiXMqwmRYAAAQIECBAgQIAAAQIECBAgQIAAgfIJKGiXL6ZmRIAAAQIE\nCBAgQIAAAQIECBAgQIAAgVIKKGiXMqwmRYAAAQIECBAgQIAAAQIECBAgQIAAgfIJKGiXL6ZmRIAA\nAQIECBAgQIAAAQIECBAgQIAAgVIKKGiXMqwmRYAAAQIECBAgQIAAAQIECBAgQIAAgfIJKGiXL6Zm\nRIAAAQIE/v/27gQ4yvu84/izEkIgAToBCYQkhBCHuO/DxDa2Y2M7dmo7YZpJp0knx3TihqZx4vRI\nppl2Jk3STiY9ZhzSHJ7YjmMnjl2DHeMLc9rcIA4hIaETdF9I6AT1/7zyLkLaU6tr3/3+O7VW777v\nu/v/vCt78nufff4IIIAAAggggAACCCCAAAIIIGBLAQJtW15WJoUAAggggAACCCCAAAIIIIAAAggg\ngAACCNhPgEDbfteUGSGAAAIIIIAAAggggAACCCCAAAIIIIAAArYUINC25WVlUggggAACCCCAAAII\nIIAAAggggAACCCCAgP0ECLTtd02ZEQIIIIAAAggggAACCCCAAAIIIIAAAgggYEsBAm1bXlYmhQAC\nCCCAAAIIIIAAAggggAACCCCAAAII2E+AQNt+15QZIYAAAggggAACCCCAAAIIIIAAAggggAACthQg\n0LblZWVSCCCAAAIIIIAAAggggAACCCCAAAIIIICA/QQItO13TZkRAggggAACCCCAAAIIIIAAAggg\ngAACCCBgSwECbVteViaFAAIIIIAAAggggAACCCCAAAIIIIAAAgjYT4BA237XlBkhgAACCCCAAAII\nIIAAAggggAACCCCAAAK2FCDQtuVlZVIIIIAAAggggAACCCCAAAIIIIAAAggggID9BAi07XdNmREC\nCCCAAAIIIIAAAggggAACCCCAAAIIIGBLAQJtW15WJoUAAggggAACCCCAAAIIIIAAAggggAACCNhP\ngEDbfteUGSGAAAIIIIAAAggggAACCCCAAAIIIIAAArYUINC25WVlUggggAACCCCAAAIIIIAAAggg\ngAACCCCAgP0ECLTtd02ZEQIIIIAAAggggAACCCCAAAIIIIAAAgggYEsBAm1bXlYmhQACCCCAAAII\nIIAAAggggAACCCCAAAII2E+AQNt+15QZIYAAAggggAACCCCAAAIIIIAAAggggAACthQg0LblZWVS\nCCCAAAIIIIAAAggggAACCCCAAAIIIICA/QQItO13TZkRAggggAACCCCAAAIIIIAAAggggAACCCBg\nSwECbVteViaFAAIIIIAAAggggAACCCCAAAIIIIAAAgjYT4BA237XlBkhgAACCCCAAAIIIIAAAggg\ngAACCCCAAAK2FLAC7Rkz5kUWFp+LsuUMmRQCCCCAAAIIIIAAAggggAACCCCAAAIIIICALQQcz/zt\nazfr6oocO3Y+aYsJMQkEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABewo4XnhqV29xVYEQaNvzAjMr\nBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDALgKO7237bOdzHx50nK68TMsRu1xV5oEAAggggAACCCCA\nAAIIIIAAAggggAACCNhQgEUhbXhRmRICCCCAAAIIIIAAAggggAACCCCAAAIIIGBHAQJtO15V5oQA\nAggggAACCCCAAAIIIIAAAggggAACCNhQgEDbhheVKSGAAAIIIIAAAggggAACCCCAAAIIIIAAAnYU\nINC241VlTggggAACCCCAAAIIIIAAAggggAACCCCAgA0FCLRteFGZEgIIIIAAAggggAACCCCAAAII\nIIAAAgggYEcBAm07XlXmhAACCCCAAAIIIIAAAggggAACCCCAAAII2FCAQNuGF5UpIYAAAggggAAC\nCCCAAAIIIIAAAggggAACdhRwfHPL1o6fHNwb0dzaEWXHCTInBBBAAAEEEEAAAQQQQAABBBBAAAEE\nEEAAAXsIOF54aldv8ZX83h3/+3WHPabELBBAAAEEEEAAAQQQQAABBBBAINwErtQ0StmVOmlubZeO\nzi6RXpGIyAiJjIiQ6IkTZHL0RJkSM0kyZidL3NSYcONhvgiMqEBd4zWprG6Q5mvXpb2jS6bETpK4\nKTHm7226TDWPGQgMp0BfoF1VIDt2Pjmc5+VcbgQ6Ojqlrr5J0mbPdPMsmxBAAAEEEEAAAQQQQAAB\nBBBAIFCB3t5eOZVfKiUVtX4dumF5tqTOSPBrX3ZCAAHvAt09NySvoFxKK93//UVEOGTB3FmSk5kq\n+piBwHAIODLnb2ufFzMh8tXDf6DlyHCIejnHqTMXpbikQh575B4ve/EUAggggAACCCCAAAIIIIAA\nAgj4I3DzZq98eKpQquub/dnd2ueTdyyT2MnRfu8fCju2Xu8QrZDV6tjOrm4xGb/HETUhUpYtzJAJ\npnrd07je3mmFlMkJU2VeOkV5npzCfbt+1vYfuyjX2trF4XBIpqnGTkqYYn0bQj+TVbVNctX8v46Z\nSXGyYcV8Qu1w/9AM0/xZFHKYIP05zfO/2y0XL5XK957+ikyYMMGfQ9gHAQQQQAABBBBAAAEEEEAA\nAQQ8CFRWN8qRM5c8PDt4c6QJcR/ZunrwEyG6RdusFFy+Ko0tbSZniJReE/DfuHlz0GwS46bInNQk\nq/VDgnnsLczWg5tMMP7+h+ckPTVZVi+ZO+h84bKhobnVulHQ2NwmLaaVjbbRSDJ+yYlTRU3dje7u\nHomKsn/m0z/M1lY+65bPs1qMDDS5aj6jx84WS8+Nm+bzlGQ+T1kDd+F3BAIWINAOmGxoB/SYr2B8\n/9+ekU7Tx+tzn3lQVixbMLQTcRQCCCCAAAIIIIAAAggggAACCFgCh08WSlVdXwWoPyQJcbFy17rF\n/uw6rvfRcPDI6UtWZfqk6ChZsShTUqfHW5XZFVX1cu5ShdXH2DmJ6IlRsjp3rsxMjnNu8vozVANt\nrRTWXuka7gcztPI/r6BMistrPJ4ma84MWZqT7qo4bmq5LsUVNaJV7Rrc2nkMDLO3rFko+jn0NOqb\nWuXQiYtWqL3etPyZRcsfT1Rs91OAQNtPqGB3yy8okV/+5o/WaXKyM+RLf/lYsKfkeAQQQAABBBBA\nAAEEEEAAAQTCVkBD3V3vnzAhruf+GhNNpawuANnV1SNtZl2rtJmJsnJxZkibaQX2/mP5olXDOjau\nzJGUAUF12/VO+eDoedN+pMc1V61Ov3fjEonxo91KqAbaWq3f0dktm1ctEJ3vUIYWJO4zvtq+xdfQ\nz1aKuZFQWdUg2mJjauxkuWdjrtV+w9exofp8oGG2c556o+VoXrG1QOsnNy8L+qaD87z8DE8BAu1R\nuu5/eO0d+ehYnvVq2lfoH576ksRNc//1lFF6S7wMAggggAACCCCAAAIIIIAAAiEroH2e3zpwxu37\njzD/u1v79Q6sSNYF7LSHdCiPgpKrcq6wwppCwjRTcb7efcV5VV2zHD5ZcNtUF2bNkkXzZt+2zd0v\noRhoa9C/670TctPc4JiRNE02rshxVU+7m6OnbWfyy6SovNrT0x63642CTebmwlTTlkRDX62Kt9sY\napjtdHB+o2Lzqhxzjfz7toDz2FD62WXazpwtLP+4Wj85lN56yLxXAu1RuFR6s/hff7RTrrX23T3V\nl3zg3s2y9c51o/DqvAQCCCCAAAIIIIAAAggggAAC9hNwhq7uZqb9je9ct8jdUyG9TUPbNz84JRrM\n65ibNsO0G8nwOKd3D5+1ej87d9CAX0NXX8NpG0o9tJtMH/H3PzrvmppWTq81/ZoDaT+iVdl6Dm9V\n/64X6PdA221kp6dIc+t1qW1okXVL55nFEaf22yP0HwYbZqvApdIqa7HR3Ow0yZmbGvooHmZQUlkr\nJ8+XWM8+eOdKqyrdw65sHqIAgfYQ4QI5rKz8qvz3zhdvO2R6coJ8a8cXbtvGLwgggAACCCCAAAII\nIIAAAggg4J9AfeM1qzWEu71nmurPTaYK1G5DA9P3Dp9zTWuxqbZeYKquPY3CkiqrUtT5/MCgv81U\nuReVDa5G1hYt5aZFhLbQ0GrngUMXAdQe0uNpaBj9nlnIsv+ImTTRajHjbzXw2YJyKTShazAjJzNV\ncuenBXOKcXmsBv1600Cvva+e2Z4m4Gw7Mi99pixbkO5pt5Df/pHpb68Lturfz72bloT8fMbjBAi0\nR+GqvLHngOzdf3TQK33ty9slI93zf3gGHcAGBBBAAAEEEEAAAQQQQAABBBCwBOpMoK29pN0Nuwba\nldUNcuRMkWvKmbOne+0JrqGahmvOEW9alNzdr0WJt5sCzmPc/dSFDzXUHE9Dq9a1p7q7oU5Lc+b4\nrNY+ePyi1JgK66GMyIgIyc1Jk3lzZg7l8HF/zNG8Iqs/+VpTfe5tAUhvE9FWOdoyZ5VZoDRjVui1\n4tC+/RN89GbXlje7954U7cU+PyNFlpjPnXP4c7xzX356FyDQ9u4zLM/++Ke/ltq6xkHnWrd6iTzx\n6fsGbWcDAggggAACCCCAAAIIIIAAAgh4FwjHQLv8ar0cO1vsgkmMN61V1npurdLQ1GoWh7zg2l/b\ncGw0vcWdQwM2Xcxw4LjW1i7HzAJ+Kcnxsih7cM9tDfW0Une8DQ20ne1YBr43XSA0LSVRMky4HW8W\nc3Q33jDtXLS1xlCG9jLXnuYM9wLaLkdb4OiCpfeYxUmnTZnsfsdxurWhuVUOniiQOSlJsnxhhln4\n0/0b1ZYzB8yNER13rF4g0xOnmRY2IqfzS61vPWj/cP2mBCM4AQLt4Px8Hl1T2yD//p/Put1vUvRE\n+e7TX5Uo8y9VBgIIIIAAAggggAACCCCAAAIIeBdo7+gSkw1Zo7G5zVQr36o+7n+kVhCvNv2TBw4N\nYjXYDNWhodoHR24F1DoPb+Fg2dU6OX72smu6/vYuDsUe2jrJs6YCuNBUAPsacSbQnpOaJHFTYiQ2\nJlq0NYnDJJRv7jtlVSH7Ot7d8w/dtTKkP1vu5qTbNIxt7+yyqrJ1sdWhjjMXzWKbpr3NrBkJsn55\n9lBPMybHOcNsrbrWcd/mpR5v6Djb1mjv9odM/+yICId10+jtg3nWsbqdUNuiCOofBNpB8fk++P19\nR+XNtw943PHPn9gmK5ePr6/peHyzPIEAAggggAACCCCAAAIIIIDAGAq89u4xuXnTGWkH/ka0unLN\n0sFBd+BnGpsjurt75A0TuvY3mD0zUdYtm+f2DenCdLpAnQ4N8rWfb/TEKLf79t8YqoF2l/F568AZ\nq91D//n4eqxhtobaHZ3dopXEQxkP371KokxYaZehPdSLy2qshS5vmEp+DWa1J/QcU+WebVppqJm/\nQ/uSa9AbyGfQ33OP9H4Dw2ytzvbWP965EGuqCe439Avui8trrCptfb+E2sFfNQLt4A29nkEXg9RF\nIT2N7Kx0+coXH/f0NNsRQAABBBBAAAEEEEAAAQQQQOBjgWADbTssRqetCzQc6z/cVV63tLZbiyT2\nmhJbDSPvWLVAkkzluj8jFANtrSTWjLWiqsFqy6LzHs3xqa2rffZXHs33M9TX6jQLgp48f1mu1jZZ\np1DTmMnR0t19Q/SGgY74aTGyOjfLr7YhzjBbe4xvXDnfasFhnSQE/hFomH3dfIPkrf2nrZmtXJQp\nmWnTb5slofZtHEH9QqDtJ1+rWUn4ldfflaama34e0bdb5dVq6+sZng7SO1qzUwNbGXh6coI88uBd\nEmvujDEQQAABBBBAAAEEEEAAAQQQCBeBYAPtRfNmy8KsWSHNpT2e95nFMFvbbu99ra0ccjJTrepP\n7Z197lKF1Q9ag0StStfn/R2hGGi3tXdaC2DqgqAaRGqP9dEa2kr2YdNyJNSHVqjvP9732dJq6iXz\n50haaqLoZ0jHdWOsnyu9aaDV6JtNj2hvfcPDKcxWn8sVtXLqQok+lAe2LJfJpup/4CDUHigytN8J\ntANwa2/vkN+9skfO599aUTiAw4PeVe+KbVy3Qh66fwt9t4PW5AQIIIAAAggggAACCCCAAAKhJhBs\noL1iUYbMTQusqGw8GmmofepCqVypafT69oba2iAUA22F2L33pKuK2CvMMD+pIfoms9hfKA/tT69h\nti7aqAt+3rFmgUw2a7+5G86gelJ0lBXcums/4txHw/ANZiHSGUnT3J1qXG4LtDLbOYkPTxVale26\n4KX2tvc0CLU9yfi/3ZGbsby9viUvsrCyw3cTJf/Pa+s99x86IW/s2S/aQ2i0Rty0KfLZx+6X+fPS\nR+sleR0EEEAAAQQQQAABBBBAAAEExpVAsIG29prWntN2Ga3XO0SrsTWA1nYHzS1t1k/n/LTCdn5m\nivNXv392m8XvtMJZA01tLxEq42hekVU9PNrvN9Qr/7Xyev/xi1YFtobZW9YstBaB9OaYX3zF6jmu\nN4kGjtvD7GwTZscN3GXc/j7UMFv72u/ee0J6TFY433xTYsn8NK9zJNT2yuPzSccLT+3qLa4qkB07\nn/S5MzvcEiivqJLnfrdbGptabm0coUerli+SRx++23xVIXqEXoHTIoAAAggggAACCCCAAAIIIDD+\nBarrmqXX/J8O7RF9rrDC7ZvWEFZDxoEjwRSLRU+cMHCzbX4/YXofl1bWueajLUhyfQRrrp1t8EBD\n/XcO5Y1qAaKybTb9yUOpArn/pdZWLQdMCxu18zfM7n/8wMfOMFv7tmtltlavh8oYapit86ttaJED\n5qaADr0hkOxHv3pCbYtrSP8g0B4SW99B7R2d8vIf98jZ85eCOIvnQ2NjJstjj9wjS3Pne96JZxBA\nAAEEEEAAAQQQQAABBBAIQwGtIN5vgjh3ww4tINzNy9e2MxfLpKis2rWbtoJYa/pn26kq3TU5Dw/6\nh4QedhnWzd7abgzrC43AybTC/8Cxi9LeOQJh9nITZieHTpjd2NwmB05clB7z7QQdyxdmSNYc/9sT\n5RWUy6XSKqu3+EOmn7q7NizuLmH/z6u2CNLFWxPiYt3tyrZ+Ao7MiIj2mMV/EfnRkZ/TcqQfTCAP\nDxw+Kbvf0hYkfR/6QI71tO/ihVnyxKP3yZQpofPVHk9zYTsCCCCAAAIIIIAAAggggAACwy1AoD1Y\ntKu7R97/6LzVOqL/s4uz02TB3NT+m2z9uOxqvZw01eraBmKkR6i2G7nW1m6F2R2mH/twV2avX54t\nKcnxI00/rOfXMFpDaR26IOZd6xdL7GT/OyW8c+isqKkuvqrz93dohfxe8zerf7s6lubMkeyMwNsE\n+ft6dtmPRSGH6UpWVFZbLUgaGpuDOmO06U/1qW13yrrVnpvHB/UCHIwAAggggAACCCCAAAIIIICA\nDQQItPsuYm9vr+iCftoyQntfl12pc7tY5Lz0mbJsQfisy9Xcet1Uq9dIZVW91dd4JD7y2lbjgS3L\nTRub0KoR1XY92h5DFxf1FWbfNJ+vqtommZ44zao+dudYWFIlZwvLRT3WLzNh9vTQCrOdc8oz33C4\n9PE3HCZPmmi1DvEn1NYe5G8dOGOdZtXiuZIxO9l5Sq8/NczWb5no36+ObPM3ujSM/ka94vh4kkDb\nB1AgT3doC5JX35a8c4WBHObaNyszTbY/fr8kxIfOyq+uN88DBBBAAAEEEEAAAQQQQAABBEZRIJwD\nba3mLDdVyFdN0NjY3OoKbDVQ9FaVrAvV6YJ1dh+XK2qluq7J6rbeZtpqXGvrGJEpZ8yeLqsWZ47I\nuUfqpM1mAVENs/Uz5CvM1vegYfbhU4VWaL9swRxTgZxoBdf6nLYs0apm3SfCtLdZZyqTU0M0zNb5\n6BhKqH25okZOXSi1jt/2iRU+F9TUHQmzLa4h/+PjQDs3srn1eGjdThrylEf2wNa26/IvP/yZmBtY\nAY+v//XnJG3WzICP4wAEEEAAAQQQQAABBBBAAAEEwk0gHANtrcYuuHxV8i9fcQXXCdNiJTNtuiTG\nTZFpUyaLVtR2dfVIY0ubXC6vkZoGs5Bmv4xCF+oL9dDR12e924S1WjGrFesjNbQq+95NS6z2FCP1\nGsN9Xq1a3296ZquPP2G2vv6NGzdFe7OXVNZab0d7Q2vVslZ3O321mnl17lyrinu43/NYnC/QUFsD\nfw3146bGyNYNuT7fMmG2TyKfOzj+5yu/7Cm/1hTxj7940uFzb3bwKXDs5Dl56ZU9Pvdzt8MdG1fK\nIw/e5e4ptiGAAAIIIIAAAggggAACCCCAQD+BcAu0NZQ+dLJAaur7Wp1GRkbIctOeIH3WdLMAXT+Y\nAQ9rGlrkQxO4aTCpw9/QbcBpQu7XCtNq5NjZ4tvC/OGchPZJ1n7JoTL0Zoj2V9cKbX/D7P5za2hq\nlYvmZkqD+UaAs9/zlNhJMsO0ItEe7VFmQUM7DX9Dbf1GxK69J6y/L+1TrxbeBmG2Nx3/n3O88NSu\n3uKqAtmx80n/j2JPjwK/eXHXkFuOTImNkX/69pfNVzciPJ6fJxBAAAEEEEAAAQQQQAABBBBAQCTc\nAm2tzD53qcJ16dcuzZK0lCTX794e1NS3yMETF127aN9nraq1+9Cq4lMXSoY91E5LSZS1S+eFFJ/z\n86PB89aNSyQmiOuv/donmvNMsFmIPfCC+hNq9//b+sTaRZIUP2XgaVy/E2a7KIJ+4Hj64ac6C+tL\nHc+++zwtR4LkvHHjhvzzD56Rzs6+Zu5DOd0XP/+oLFqQNZRDOQYBBBBAAAEEEEAAAQQQQACBsBEI\nt0D7jQ9OWW0e9ALronsbTeuQQMbbB/Osnsd6zJY1CyU5YWogh4fsvroA4lnT57n648r2YCeSEBcr\nd6xaEHJh7juH8qxe4mvMjZA5ft4ICdbKDsf7CrWdz0dFTZCH7lxhvi3h/usShNnD+2lgUchh9LxU\nXCY7f/WHoM64bEmOfH77Q0Gdg4MRQAABBBBAAAEEEEAAAQQQsLtAOAXa7aYi9k/7T7suaa5pa5Bj\n2hsEMo7mFUlFVYN1yD2mQlf7bYfT0J7PuoimBpA9H7dfCXT+GmZvNmF2qLXX0Pnuev+41RHgU3ev\n8hi6+vIoKquW0so62bQqx6+FD32dL1Sed4bW+n71mw3aJ3uiCbB1vG1uFLSaRUdnz0yUdcvcV+1r\ni5b3Pjwn+nesIzt9piw17YIYQxdwPPd3r90sqSly0HJk6IjOI19/8wPZf+iE89dBP9esypXE+Gny\n7gdHTG8d9wsT6Nc1vvv0V80fSPSg49mAAAIIIIAAAggggAACCCCAAAJ9AuEUaGvfYw3EnGNpzhzJ\nzkhx/urXz3cPnxWtVtYK0ke2rjbhpvtKUr9OFmI79ZjFIS+WXJWi0mq5cbOvl3igU9DFNzevDr0w\nW+fZZBYI1f7ZunDonesWBTp1a/8C43eusEIiTZtcXQwzxiwMGU7DGWrrvO9ev9gKtLXqeo9ZfFSH\nLoqZPivZLYkG2up/3exPmO2WKOCN9NAOmMzzAT/8ya+kvqFp0A4xkyfJ44/eK0tz+74OVF1TLy/9\ncY+UV1QN2lc3PPbIPbJh7TK3z7ERAQQQQAABBBBAAAEEEEAAAQTCq4e2hrCvv3fc1Qs60GBSF/Lb\ndzTfHN8r8dNiTCCXGxYfobbrnXKlplEKS6tc7VqGMvGM2cmyLCc95NqMOOeq1enaskb7Zt9v+qcH\nOnQxyPOmf7uG2RtXzpfpZiHIcBxanT4zOc5VnV5cXiOn80stigdNu5HoiZ67OXd0dkt1XbPoZ4kR\nvIDj6Se+31leVxzxszd+3lcrH/w5w/IMtXWN8uOf/nrQ3LOz0mX74/dL3LTbm8LrKqj7Dx2Xt949\nLD09Pbcdl5E+S7725e23beMXBBBAAAEEEEAAAQQQQAABBBC4JRBOFdo6671Hzktjc5sLIHe+aTuS\n6bvtiLY50GM1UNOh7SJmJsW5zmOHBxrUt7S1W3PU+WoluoaHrdc7gpqeVuOuXJwpM2wQ4O7ee1K0\nUnjbJ1a4All/cPKLr8iFokorzN5g+rbPSArPMNud1eGTBVJlPmfxpnpfq7YZoydAD+1hst538Ljs\n+tM+19kiIyNl232bZcum1ebrPK7Ngx5oEP7yq29LSWnlbc89/Y0vSlJi/G3b+AUBBBBAAAEEEEAA\nAQQQQAABBPoEwi3Q1jBbg+n+I3P2dJmfmSJTYib132w91kK6orIqyTfVtdpyQ0daSqKsXeq+z6+1\nQwj/Q8Nrq8fzlTrT5nVobUWc09cF/jJNJe2irNkSGRnh3BzSP09eKJGSilpJT02S1Uuy/JqLM8zW\n9jQaZtvtRohfCB520m9N7H7/pNXCZmHWLFk0b7aHPdk8EgIE2sOk+rNfvixFlyuss82ckSSf+8w2\nSU2Z7tfZ9U7ioY9Oy5tvH5Au8zUQHffevUE+uXWjX8ezE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- "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image('view_grid_url.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You are also able to view the grid in your list of files inside your [organize folder](https://plotly.com/organize)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Upload Dataframes to Plotly\n", - "Along with uploading a grid, you can upload a Dataframe as well as convert it to raw data as a grid:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import plotly.plotly as py\n", - "import plotly.figure_factory as ff\n", - "\n", - "import pandas as pd\n", - "\n", - "df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_apple_stock.csv')\n", - "df_head = df.head()\n", - "table = ff.create_table(df_head)\n", - "py.iplot(table, filename='dataframe_ex_preview')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "https://plotly.com/~chelsea_lyn/17399/\n" - ] - } - ], - "source": [ - "grid = Grid([Column(df[column_name], column_name) for column_name in df.columns])\n", - "url = py.grid_ops.upload(grid, filename='dataframe_ex_'+str(dt.now()), world_readable=True, auto_open=True)\n", - "print(url)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Making Graphs from Grids\n", - "Plotly graphs are usually described with data embedded in them. For example, here we place `x` and `y` data directly into our `Histogram2dContour` object:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x = np.random.randn(1000)\n", - "y = np.random.randn(1000) + 1\n", - "\n", - "data = [\n", - " go.Histogram2dContour(\n", - " x=x,\n", - " y=y\n", - " )\n", - "]\n", - "\n", - "py.iplot(data, filename='Example 2D Histogram Contour')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also create graphs based off of references to columns of grids. Here, we'll upload several `column`s to our Plotly account:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "https://plotly.com/~chelsea_lyn/17400/\n" - ] - } - ], - "source": [ - "column_1 = Column(np.random.randn(1000), 'column 1')\n", - "column_2 = Column(np.random.randn(1000)+1, 'column 2')\n", - "column_3 = Column(np.random.randn(1000)+2, 'column 3')\n", - "column_4 = Column(np.random.randn(1000)+3, 'column 4')\n", - "\n", - "grid = Grid([column_1, column_2, column_3, column_4])\n", - "url = py.grid_ops.upload(grid, filename='randn_int_offset_'+str(dt.now()))\n", - "print(url)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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LozGwpFcWiwWLxdKnadvbNf6ViIiIiIiIiHxx1AJLehUXF0dcXGe+abVasdls\nWK1WHA5Hx+sWSxw2WyLKQiXmmAF8rSb2BCeGjnQiIiLyBfrUY/LPz9VhhtoxrBZ+c88Qhrl0ASLn\n1px/P9jt7zU/HXFOpxeJdTqqSq9GjBjBN77xDVpbWwFwOp0MHDgQq9XKjBkzGD9+PBaLhbi4OIYP\nHw5Yzu0KhAPUHGjF4w9DXBxDhzkZknLq3bWm0kNdQgKT3I4zztpXVceCl00e+9Fwch1fROkFqPzI\nh3NkP9JTrB2vBo97Kd9rMmRsP9Id1jPOY8UTR1mfk8yKm/vhO1TPC38Ps/C2QaTGnW76JFbc3B/8\nXla83kLODQPJ7W9l73t1bGiyc8/1/b8S+6/nozX8sagq+peLW++9C/dX/mjnY83Tz1M9cib3zsnW\nQU5E5CustraWHTt20NDQQDAYJCkpifT0dMaNG4fdblcBfQbeljBmKNIrwQy1420JM0zF8pmtfq+Z\n9/e0MHdyMpMvS1CBdHE2IVTXaU8Os0S+jBRgSa+SkpJOamkVCauCwSDhcLhbF0OLJQyEAes5WHIr\nRas9PFl+8rhaTdhc8fxi0RBGnRw6BRt58hU/ewnywk8dDLnQhef389DrfmbNd3DPuGiZHPdw/6/9\n1GCw7Gt9LCcbYEYuhDx7AqzfEebaGyHVcbrpo/9uCFCwuw3blHZy+5vs/Hsba0wLi6+PTHZRM6tZ\nU1QFrmxunTka33ErI3Ski7B22UdEROQrp729nY0bN7Jjxw6cTidOp5OWlhaOHj3Kvn37eO+995g3\nbx6DBw8+47wCtVVs/9SHEb2sMez9cI924zyLc27lqmdYdyiTJT+YjbPXhVTwzLINuG9YwpyxTqqK\n11DWmsHcmdm6ibmI1RwzKd3byv+a6+LRvxxjYqYdI85C0Gxn9XvNeP1hbpuahCvpzNfUwWNeyg+G\niO9yAdwWhCGXpZyiQrl7pfCZK5EvDnXVx6nxtxPf5bU24hg7rl/vv81uhXychT9vZtYdg1iYaady\n9SEeqIqn4EeDz/zZHveBx2kYk8JtWQ79EKRXOvZLrywWC1Zrz4O61+tl8+bNHDp0KJKZ2OKZMmUS\nkydPPgdLbaXgqWOs8FpY+I0UbpuQ0hG2+A438Ivnfdz/VB0vPDikW0hV93ELeyPJBeurAizMPEXN\nYRiI46wurD7br8rCECD+xEnO38jDv/ZTYxgs+8lQ0j/DyS/9msEUTAan49TJRLdgariLgvtNnNFW\na3OWDmIGxsUfXkX3Iy+QOXUG7jQD0vR7PnG41wFfROSr60R49emnn7Jo0SKSk5NpaWmhqKiIQ4cO\nYZomra2trFy5kvnz558xxKp6bx3F1SfVirwFWdcvZvb4lL7fiYRMQqeZxAqEQiEgQM2OKmpMg5AC\nrItavBWONYUoqWol0NaONS5SaV70sZ/6phDuwfH8ucjLD+eknnFeO99t4pHynuP1zpmf2FnRfLIu\nlcInVyL7qo6xptHOgrzkC15OXVtU/cO0ZO6a0Q+APxY28t+bmzreO3OLrQBrXm7mtR6VnBYec/fr\nU4+VJCAYrXjHAIKfYYzkcBuvlZvsNQMKsOS0pw2RPjPbTOqPHePwp7W0WyAhwY7P5zsn864p8rDC\na+GR+4czKSVE2XtH+eBgGJszjpysZB75SRwP/7yJR9Z5WTY7peOAu77IxOa2cbM3SMGmph4BVs1H\ndTz0ehsNAA6Du7O7H1B91fU8tbaVLZ7I66nD43n8W0NIdwDBRh75tY/cKw3Wvx2kBsAweGxpCr53\nG3m8NNT5me8MIT2aEDVHp4Mmnn6qiTLiePL+zvCqpugw91dYWfGDQR01E8Hqo3xrhckvfpLGqJOS\npuCBBu55OcTjPxoaWUawiRV/8lJwKLLOkybasQW7fqCZXzzbzKS7hjBnuMHeTR4eORDPC4sHYiPE\n3veOsXxzG2X+yOSTchz8eK4LJxCsPsLda9tZmA3PFbcRBHBYeehbLq4afmG6FZjHq1i3dhNVR73R\nKxsXeTfPJ9/dvV7HrH2fl17dgglUrX2GX5HCzO8tJjsJKjetZF1pTcfnr7plAZPT7VS+uZx1teks\nWTwzsi2Ol7H8j4UYo2dz141ZAHg/WsPyoiPM/t5ispK6nu9r2LBqHRW10d9AnMHg0fn8w425GIB3\nZzEFG0rwhSOH28ypc5kzJf3E2lK5aS0bS/d3NIpKm3grC65xA16KV/2Vkurev2/Vm3+k0J+Omyoq\naiLLtrsyuXHBHNz2znVbU7CGKk8AAFdmNkao66qXsOqtrdQ2R5ce7yTr6jnMnnBy4mdSVbyOTeVV\neNsir7jcVzH/1snRfdekctPqzrKNM7DbrQRaAmTfvJSZo60nfU+DjLw5zM1364AqInIevfPOO9TV\n1fHNb36zo5V9a2srbW1tuN1uJk2axPbt2ykpKWHlypXceeed9OvX79TBkgHEZbD4vrmkYOKtqeDN\nvxVSuXEVWVln13X/dFcXoS5T5X93CZOxE0udHIs+9tPoD3Oksfvdf3GFn10Hg/RzxDH98r4McXGE\nBS8Hu72WmpPEirnnb/gH37EmcCXjvMAtjYamGkwencDTaxr4f/9hQMdAJU0tnT00vC19ewq63QCM\neAoeHIIz3OVCKO5Urbe6VwqfXIm8rbCVFS6DBXkXpmx6C6PGX2rvCK8A7prRjx01QbZ/EjjjZzsy\nOxvYxjl5dW4qdJSTtc/DHDefiy8Xl8wv7o+HFHUZFQVYcs60Q3v0/1F9fVrhGU6ZvFYc5qqbXExK\naWXFU8co8MKkywwatgUo2Bam4KdDuGeqj6UVAYKzo13hDjdR4IcF+S7mHT/Ga68HKPPTUVPgqzrC\n0tfbsA2N57GZCdSV+Xh6W5iuY3Z5qtvYaTN4YL6d1KYAj65v4/7XPby6wAW0U+cP88LbQeZc5+Ae\nZ4jlbwZ4+NceII57vpHE0OMBHilu46E3j3e7yLCZfl571st6M45HfjiMrhUJvsYQQW/3cmszwwQJ\n02zSo59fmxmmwQzhMSHdFqBgWSMFXrhtVhJXJ5usWNnKB0C60bmd9prtDG0yAYO2QJiGuhBtgI12\n9laY4LbzSHY8nr1+ni7188KIBO7Lc9BmttPgaePpYgsLb3KSZTVZ/nqAx5c3sOynQ0m/ELvd8Rqq\n6yE7L59hyX7e21RCyao1ZP/LAlzdzr6DyRqbwZby/TjdeUxMTyEtAWo2/ZF1pR5S3LnkZTqpfGcL\nW1Yux/m9paQ4DfBWUd08k+wkqPm4Am8Y2FOB58YsXEDlriqIy2BYUvfVqnp7DRW1IbKmziDdbvLp\n3go8dicGYNYUsvytMkjJ4KrJbhortlCxdSVrnIuZMz6F6rdfYl25B/ugLKZmuThaVYXV5QICFC5f\nTpkX0nPyyR4couzdLZSseh7mLyU/3Y4Z8uGrKaMiPo2rrs/HeqSC4vIqVhUUc+9d+Rj4WPdfK6lq\ngYy8mYxJ9rClqATPiQs5Amx6o5haczB503NJCXmoqtiD3ZnS661D7f5qGJpN/phh+Kvfo6RqC2ve\ny2TBFBe+netYV1rD4OwZzJjoonLjGspqA2TkzWbKJXaq3/4j68o9ONNzmTFuMJ+Wb6KiZBUvMZ87\n89N1SBUROR/BhM9HeXk5/fv37/aQHpfLxdy5c/F6vSQkJJCcnIzdbicQCFBaWsqMGTPOMGdrdAAJ\ng5T0XK7JqaCgpBFfK1S//xKr9g1iyfdmdlTWBfZsYNnaWuYsvYtMO2BawRGisOB5KqOVQSnpedw+\nP5/ezkjVm1ayrjaDJXflR0Ks45WsfnUj+73R8CgxjVu/u6CzMuc8eGVLE58caevx+ur3Irf0lw6O\n71OAFbm2NnhyaQqpZjtBwiQ5Es/jXhJgxTIvk36SzKQYaLLvsEf2U2dC5/56y5Qk7v1tHRvLfPzu\nB0M/Q0DSS2h1hkrhzkrkgewtqOPJw8DhZubtboaBdv6yZNAF7eFw+aV2Pv4kwEubvNx5TeRX89Im\nL9s/CTD+UnuPEOt0nCfuj7qVk8n65XW8MyyJx2Z3hmR7N37Kj/fHf6bvX/PRUZ58M8De6M821WVw\n/4JBTBposragnp2jU3houo3Xnj/KC8eit0U2C07aGZWbwiPXpwB+1q88ztM7IkHmyY0JRAGWSGSH\niTdI6ZeMJe7EuFiQmHgOTq7+AEVY+NnlDqiuo8ALD/1wBFf1h73rDnH/h5HJhritsDUcDWKg7N0A\nEM88t4ETJ7mvN/Lf7zeROz0ZMCksDALxvLBkCKkA7n4ktR7i8d2di06fPpQV0zv//tnhQzy8ow0f\ndFxwTbquH/dcHWkq/MDxT1laHOaBHw5jejSvemDfQZ6sCnR8xgUUvBJpPWMbbmdS/3P4yzvkZYUX\nZn3Dxd0TIhdEjw3zMO/X/h7jG8Wf4mc/a/FwZp34MyuRmvKjrK0OcF9e5wXWPUuGMmdo5AT2eEId\nC14x8fiJtEw73/udewb33td5EZ1peFi2sYaaZnB1CZWMAW4mX9NKSfl+0sZMJm+sHahhXakH0maw\n+NZcAHJHO3lm2Qa27/KwIDsDSkuoqvaRPd6gcteRyMzCNVTUQn5aLftrwXBn9riYDgRCgAP3hFyy\n7JA9Ia/jZL+1sAzi0lm8eG7kc+Mz8T/9PFUVeyBrAJvKPeDKY8m38yO7Q16kG65ZU0yZFwbnLWB+\nfqQ1VNb4wfzxV6soe7+S/PTcE7cd3PqDBbgjU5DgfYYNBzz4AUf1+1S2QFr+XczNi0R8Wel2frVi\nS3QfCeAPAI5BTJyQjRPIzcs/Renbyb/rXjreHe/G8/Tz7D9YA1NcVO+qBgYze2YuLiBt/gwqnlkH\nA9NJsVazqtwDg/JYMj8yh+yxY0hY/gwlH76PJz+9ewApIiJfiH379hEOh/F4PL1WPKakpFBeXk5x\ncXG0ux5UVVX1IcA66bzYZgIhTKDV6wH/SS2lzQDgxddGpNmVYUDzfipDaVw1fTKBo5WUVJSwvMDO\nvyzoOTyFGfBiHvcQAOxmNS/91zqOYCcrbyqu9qNU1VpxnefmWf/n2wN5cMVRDh7rOcjkiIEG/+fb\nA/s2IxMwLIwc6OgWCPgq61jwSpgnf5RGlgOCh45y9/IgS384lCEfHOHp2jiGeNrY4gUMKw8tGsRV\nQ41oUHCEh14PRnshWHnk24OYNNSI9DBY5mfSpDgK3m6jwWGwONVkDbDmlwfBjOPJnwwjK8bCAGuc\nhZZgO2NH2Ps0/lVXzeEQ8WY7EKINK06bAZy5UrizEtnCqDw7udWtlLnieejqeEiIv6Dh1Z3XpnDn\nNSm8tMnLS+94O15/6R1vx3t/LGw8iwvu3holhKira2fngO49WNoC7QSPdd6T9VnYy5OvB6hx23js\nChttjW2sKQ+RmhIZqLXZ284HjSHAYFK+gyE+sCVaqCnx8UJ15N+RUM3D04dgTr6DqckhnnsjwNJl\nRyi4/2zH3RIFWHJRS0pK4orJkxg0eCBxcZFxstxu9+eer+9AG0HicNmgpjoERjy5/U8cIIGR8ZHu\nbScdAF/b0Q6Y/GVdHbYQHAAatvppmJ5MKiEavMBl8XTtIZ+ba8DuzouM4LFG1r7bwvqqEHXB9sgF\nRJeQphmYNKTz0DxkhBVoJ7XL0XroEAvUdf59otvdbe52XtvRwuPvNfHQlHPTV97XFKlp6PbExf4J\n3Gz4KerjPOqqPKzZ2krRkXZ8wXaCJqR3O2lZSHdZY+dIEajl/U3vs31fDf6AGc3p7L2vVihECAiF\nApGr42YvjQC1hTzzbHHks22ROdRW10BeNmmUUFNVA1khdjVD5vQZ+IoKqdxZQ35iLbVA5pie+7l7\nQi5GdQnrlv2K4kEZTJycT97oSCuqxpZICLb82WcgBIbVxAwDddV4Q058QMbEiT2+Q6DRAxhkf61r\nVz43uelQeLgGH7lACOJdDOp6UWfAiZrwQHPkIibD3SUeGpBJRtyWSDdYUsidkMb+0gqe/1UFg925\nTJ6eT2b/3jd07c73eb90OzXH/ZghE8JgjywQ1wAXVHuoqg0wOc2Ot7IKk+gjHUKt+ID03Nxup53c\niRmUFPUMIEVE5IvR2Nh5E7tr1y5ycnK6vb9//37efvvt7tcbPh+maWIYp7oIsELYR3VNDc5WLzW7\nSiipilTOZCdBVfS8dPobEBNwces/naiQyWWQ+TzrdpVQ0TyZ7Pjel2sFaku2cATIm7+E/GjaMPkC\nlG1qkpUnFg7qEWKNGGjwxMJBpPY1aDEAM0R5tZckM3I9mnpJEkOy+rMw5SgP/PkYa5Y4eG55AHKc\nXNXfSmUgzN7qEDmzklkxJo6iVxt5/PljvPDToaRWH2Xp60HmXJfMwsvjeHeDl0eeP8JTPxnGKNqp\n84Z47u0w930zhbHJBq42H8tXBLjvjn7kJli7XefGAq8/zO83HMfTFOLWKWdz8WABs427H6vt9upj\nPxpBbkPfK4XBSvq4/uS8eZgD7gQmTeh3wcvkRIurO69JgXY6Qqw7r0npeK9r18Iz7X8NlT6ebm3p\n/O5JNu652Qk2zl0oZIYJAulpdnKzIus2aUr3SZwnyjvLFe350cT6lcBQOw9cnQzHjkXCq/kDuWdc\npLvhU8465r0SZKcXJqXomK8AS77U2tvbqa+vJxQK0d7ejsViISkpicTERJqbm2lpaenyugOHI5G2\nNpOmJh/BYBCLxYLNZqNfv34kJCRw2WVjGDlyVLQGrx3DiKdrl7zPsaaRG+KhVjBDeMLgjPNTtKMd\nhkTe+2BrG7jsOIGGcj8fAKkp8EGFSbMBSSkWGrxtrK82WRDNG2wJ3dctvutef6yeby1rIeiwsvDK\nRLKGWNm3qYkXGk4+2HbWOrT18dvcdvMg7s4C1+HDvLC+kaJRDqYP7HIRc9KFQfxZ/WotOG3dT6pJ\nNujLaGR17x3m7vUmqcPjWTjdxiX92vnLy/7oQPhdvzMx8sjCGl5atpIjGKRn55KXNgiz9n2KK85u\n7DV7WhZ5bheBYCTYshPAnp4BpJCVDrV11VSW+TBxMn5CLo27CincU0ml0wM4yRrZ89TtdOdz79Js\nSt7dQml5FcVr91M2ZiaLb3RHyi8xjbwJGRDsbL5tT3bjwBe5TjUDnOqS4OSDs2E96ZXTjnhrBQy6\nPwndjt3aeU3ivmYBS7Or2LL1fcqqyljzX2W9DrxbW/wSBSVHMFzp5E7KY1CKyftvFXOini9t6lWk\nfbiKLQXL2HJiXdPymDnWCYFQ9Lt0v3j3BVoBq05AIiIXQGFhISkpKbjdbnbt2kVtbS11dXWf8Tai\nlg0rV3a8Mjgzjzlz8s/i+N6zQiYjMw12VZ/xk55aD8RlMjH9wp9NTg6xzjq86hDmkRWdLWkWfDOB\nhVl2FixxUvRLH/OeaCXoiGfF3C5Vsyl27p4SCQJu+06IV3/eTFG1SVZZEIYncs/VkfdmzY9jy44G\nNuxrZdTI6LXqHQOZdWLs2GCQVIIMGZbMkBgKrz7aF2DNtmbe391KW6id63Od3Hbl2VQKR7pmPrbY\niautPVoZbiHdAb4DZ1spHIp83my/OA8OwXbqPOFu++PnTKv4YF09WxojQ5jQz8bi2U5uc/t4emsT\nc7b5uG1qAvOucZ3mCY8h1i9vZAtxPPbtSHdF37HI9eWa1cd4983IfV6DP3qveLCVSeM0hpYCLPly\nH4uCQd55551uQVVmZiaXX345u3btYu/evbS1tWGxWBg1KoPx48fR1NRMaWlZR1Nzl8tFXl4eQ4cO\nJS7Ois128gn58wVYzmHx2Ghj73FIHxZPOm0sfewQqbTTYACHWpj3xEGCJty3JNLCZf07JqTYeeH+\nrn2vfTz+7w2sKPaywJ1CUgIEq4LdugPu29V557/3owBBLDz5/6SRdWKA9beaPneZN3ckQAnctiSZ\nD37exJPPH2Xsg0M7n6DoD+M5xXqdTrwRCQ4/OBAgN+vERUcLRf6+1I6YbPnABEc8LyweEi23pj6H\nchdEfS1HgIwbljB3bOT7Vtdu6fvnEyKDvfqNdCZPye51kowxGRTWVLLxXcCVhxvwZWdSuLGCje8B\nKbmnHk/D7iLvujnkXWdS/MdnKNlThffGTJx2wJ/AxCmTe26XQFUkHNpbAxO6d6KzD3AB+6ms8pI9\n4USYVMv2ahMG9a1ZdKS23KS62kfu+OgnAtXsb+t+0LcPyGTGnExmUMNLv1pJ5a79zB7ftbVUgO27\njkB8JkvumhMdNLeaLW+d9HXCkJI9k1vHuyB5EK6k6FLsKfQDanZVQZf5flpdC6Sp9ZWIyHnSdTD2\n9vZ21qxZw4IFC9ixYwf19fW9PpDH6XSepvVV9DonLoMF/3wzrlAIq72XltHWPtx4hHrOti+s0TEd\nT10V9MV5bt1xPtrX2u21myYl8cTCQfz5HS/fvjaFdytbWPN+96GtJ4xM4J7ZpxuUPZ6Cnw7p+X0c\nqTxwRQv3bwuz4Jv9O3oWBEywubukTUYcLqCh1cROO91HY7czyuis7GzGwqRh9l43ayw9trpgcyTQ\nG3+JnRsmOpl+uePs7zoMC5nDk3uUa9vnrBS+0E6Me/XSJm/k39dGx8B6xwsWOt470RrrDLcIpOYk\n8djNJ7fYCnyudfQ0hth5LBpgBUO0YWfWwuFcUe1hTXErBcV+Xitu5bH7h5Hby2rWlRzl6UNw23xX\n59MQoweSWVckMik52vPFsIAJo9zxiAIsuQgCrPLy8h4XJ6NHj+bQoUNs376d1tZWoJ1QqI1L3Zdw\nvOE4u3fv5tNPPwVg6NChXHrJpQwdOpRz09rqJCkObjb8PPnqUaYvHsSyH8Wz5cMAwYR4rsrrB4eP\ns2VviKFj+pE10ArHjkeb/DpPOsc6mXdFI1u2tVDmdzH9aoMXXm/j8dX13HN1Ar5PmnmgtHMQ96HD\nrEAb725rJHVUHDvfa+I5D3Aum57a+vGzhUHmrQiwtCAyOHz6KANK21ix0cPdkxNo2NnUbb0647he\nZudOYo7h4bVX6hl1RzJjk8KsX9lMDfRhgHWDkUMtsCPElqqmyGdfb6KsT5+9QKIXxEd2vU9N/0x8\nB0pYV+GFvj6DyMjkqjFO1u3awPI1Hq7LdWM2e6jaVUvmjbPJtEOKOxMn+/GFIT0nEnI5s7JI2ViF\nNwyuzMxeD5bVm1ZTZo4gd+ww7KYHT0vkwseKnclXZ1L2VhXPL1/DzOtyceKjZucuyLye/NGZTHYb\nFFYX8tIGyB/vwltVhSc5i/wJuWQlllBZ9EcKmUPWEJPtb6+jFsiamN3tiv9Ukad99GTS4yrZv7GA\nYutMMpN8lLy1gUBHqfkoXrUOMz2bLPdgzCPV+OillRd2BqcaVNTU8v7Omsh8Nqyje+lHugmGaisp\nwUkoYIJjMLmTJ5OWlE5uppMNVYUUvA3X5A7mSGkhxbXgHJNLmg7PIiLnxciRIyksLCQcjrSoME2T\n1atXM3r0aDIyMigqKurxmczMzDPMNQRYSTEM7L0GXVZo83SrrNu/t/akVMsKYS/eLtMcOerhVF0P\nu53e7XYI116Q7uiF5X58ge6tU3731nFqjrWRn+3g5U1NvFHS87lsDc3+0wdYp7oz8zbw5LYwNgMK\nXjnOaGODUwAAIABJREFUnAcjY7vaDQhWdx1goz1S3oaFgAkEu7YUCtJggu1UjWrM9pgMbS6/1M7S\nG/tzyaDPF0z09unPXClsWGKibF56x8vH1QE+/iTQMeZVr+9d08cbm1O0LLMZ0HDE7BZqlVW19yHo\nNJi1YFjnuLtdpLpdLHTDwsPHmPN8K1sPtpI77qRy9Xp44A2T1Bwnd3dpVRWfGAlmbSOSuCrLjijA\nkotMb4N19vZaO0CcBYsFOOl9CxBnsWBph/Yv5JidwMI77Ly2IsCcp+p4bEE/rro6GQjhO97Ezk/C\n5F49oKPGae8HAcDKbTk9RxTPujIB2zY/W3f7yZ0wiMcOH+HhbS0sLW8BLMwZZ2XNjnYwwJnp4DZX\nI6+tb+I1AIfBwomworJzfkmArZcTlf3kX4/N0lFarh6h0yCemlrL/Vv9rKhysjCrH/ddVs/TW/1s\n2eoHLMy6LI71u7v/KofYu49LFVmmg3uWBji8zMeTL0fGtEgdHs8kRxt1pzuhOiLvjb06kdzd/m6f\nXTC8jS1Gb8uKAUlZTHaXsKW6hJXVJYBBRnYG+yuOnPIjVsBq7bz4zbrxLnyhAoqrSlhVVRJ91UXH\npXmSm/QkqGxOIXtsdOsZbkYPMig5ChmjTxG1mI3sL9/P/vLo33Ep5N0yI3LRM3YO8/1rWF1cxYZV\nVR0fyY4uNPfWu/CuLKCkopCVFZHXnGPSyJ+Qxuzv3krgz6soK1pFWfRQnTl1PrPHOju/od3e/fLe\nau1yve9i7rdn8NKfCyl5ayUlgOFKxxVfg8+IdC80vTWUFddQVnyiDDK49fqeLdSyJk+m5NCWLvPJ\nJNNVRY0RWVj11rJI3VzTEapaIGQGMNuqqCyvYcG/zCd7zp14VhVQUl5IQbScXJn5LLgxSwdnEZHz\nxOl0kpOTw0cffdTxWnNzMx9++CEpKSmMHTuWK664gv3797N161YMw2DixImfa5np7mFQtZ/iDSXM\nuCKDxo+LWVflA+xdGlkZwBFW/nkDc67Lg7oS1pR6ICWPMUmcovFHpAIn84rJGBWFFP6hAPPmqxjc\n5qWqykPWzHzSLtA1zJslPt4s6YyBJmTYaWoJ0+gPMzbdTmlV6xkDhMPH/ZExsACbw06qI0TBiz5q\nhiayZkkiT/+7h/tWN3R2I/QEWV/lZ1amjQ9e99FAHNNH2nEF42GlnzVVCczJtFNZ5GU9Fh4bkQD0\nsh4Ogxzaee3jJnKvcPT+xL7zyB8NCGdPdH7u8AozRNFHHpJO1PyZkD62H+lnXSlsJSkBGipbKJsQ\nh8seT/rAC9td7eNPAlx+qb1bSHXnNSkd4dXnZyc308KK0gAFH3mZ47aw7Z0mVnj5bBX+QS8vvNZC\nbp6D9IFxHP4kslHi4zrvQyK/oFZWvOinAbgv16DmUFPkbtXpIH14CgtTjrLilaM4ZyUx/VIrPk+Q\nd/fDt24eoEHcL/Zso7W1tV3FcPEyXmzA5/Pxm9/8hubm5hMxFTk5uVx33XVs2fIuH35YSiAQOcCN\nv3w8M2ZcS4PnOBs3vk1t7adYLBaGDBnCrJk3cNllY07bAMtclPq51jd42MOTBf7Ik1S6+ZxPQwkG\n8JkQn2DH1ksf66DfBANstvN8xRNdL6fjs9QehPD5I8NlOx0nrXfwOEt/3kzuNwdyT1ZCr58N+tvB\nsPTSHTQ2mYFApK7Xbv/sybsZwNdqQryB024/VysWmScGzqTe5mniaw4ABvaknutuBnxE3nVy8iqZ\ngQCBNhMjyclnW9vOZfe2bmbAR6ANiLfjtJ++m0igOQTxVuxdpwtU8syydbjy5nNnfuelnm/nGp5/\nq5oZ37uX3KSTy94Z6V4pIiLnVXt7Oxs3bqSioqLHe9OmTWPSpElUVFSwZcsW5s6dy+DBg087v6o3\nl7FmTzpL7ptzihtGL8UFf6akNtARVqWlOaitDTFz6RKy7VC5ZhkbPYNxtdRwpCU6WVIGty6cG+m6\nH6hg2bINpN+whDljnVSuWca6Qxks+afZOAHvnmIK1pZ0aTmUwuyli/miG2V86xef0tx65vGBTg6w\nPtzbyl9+PKzXaX1VdSx4ufugDqk5STw+sIWlb7d3XAcHDx1l3vIAdy8eytiP6niglMiQG5FbO+6+\nYwC3ZUau/co2HubhrSfiQgt3fzOV27IcEDzOwp/7eOBHwzu7ZRGibG0dD0d7BDzW7b3z63CDyb//\ndz1zJyexcmsTz/3z0M/c/2Pvuk+5f1vPbXXbNwdyd1YCHG/gkWU+PogWU+rweEY1tFGXlcyym/tF\nt0uoo/wbdhzh7pXBaLe1eAoeHHJeA5M5/36QNT8d0fHvjtCqSwusk59K2HX6E//uLkDBU0dZ405i\nxdxeWgh6j/P0i82sPzFLRxyzUsKs99siT/2L3ndcdccgFmbaqVx7iAeq4nt/ImDQyyM/9/JBl5dy\ncxJ5aO4AnARY8cujbMlOZtk17dz/y+YeY/Taxjl5dX4qBJtY8ScvBYc6owzbcDt/WTwImw73CrDk\nyx1gmabJli1b8Pv9RJp7t3PppSPJyMigqmoPn3xSTShkYrFYyMjIYNSoUTQ3N7Njxw4aGo5jsVhI\nTU3l8ssnkJp6+oDq8wZYHSdxrw9PUxisYEuwMaS/7nj7LAx15XXc/XobD/1wBFf1V5HIF6S5gmd+\nvwFH9mwWz8yK5lQetq56iZJaK7OXLkUtu0VEYj/EysrKor6+nqamJubNm3fG8OpsnKgssSc5T1sB\nFQj4MNtOVRl02iXgaw5gRCuKzoeVW5so23/2rVtyM+zMn5p8ztajbPUhHiWJV+f2Ixg0wWb0vHkP\nm/haQ32vLA0GCGJc0ArOmmMmv19/nPu+kcqjfznGf949GCPui+y2d5pK4V6vtc1IBbRhQNz5LZtT\nh1Bf7GcBgv4AbX0toz7N69SNC/o+o2gjBcP40lTKy+fMN1QEX4GNbBhMmTKFUMjkxEiZNlsiVqtB\ndnY248f37DaUmJjINddcG+1JaIn+d/6O0M4UJ049AvXs+RuY90tftFbIIF1lKF+kpDFMdW+huGId\nv6pY1+2tzPz5Cq9ERGKMxWJh5syZjB8/PlpR2UAwGCQYDDJu3DjGjRuH3X5uD96G3YnRh1na7T1b\nIvf1dsaZdH5vaeZPTT6nQdTnYoaj1/anKIM44+wCB5v9grdgSR9oMHFUAv+52sN3ru33BYdXEAmu\nziL8iDNOGvj9q8HmOHf7xjmbl83+ldwWX+lsQ0Vw8QuHw3z88cfRLoQmcXFxXHrpSNLShmGzxZ/m\nIqfbXyrILwNHMk8ttOIzIX1kSveHz4h8AaeQvFuXMP54DfsPegkB9iQX6e40lF2JiMSutLQ00tL0\nKI0vuyS7hXTj4rzYmzsliblT9Lji3nTtOnimFlVdpxW5GKgL4cV+exkdA+s//uM/8PmagUgtzde+\nNonZs2czYMCAc7q8c9WFUERERERERETkBLXA+krp7AbY3h5WcYiIiIiIiIjIl4ICrK+A9vZ24uLi\nsFo7+3bHxWmQOxERERERERH5clCA9RUQFxfHiBEjaG1tBSKB1uDBg4mPj1fhiIiIiIiIiEjM0xhY\nFznjxYbzujyNgSUiIiIiIiIi55qeUSYiIiIiIiIiIjFNLbBERERERERERCSmqQWWiIiIiIiIiIjE\nNAVYIiIiIiIiIiIS0xRgiYiIiIiIiIhITFOAJSIiIiIiIiIiMU0BloiIiIiIiIiIxDQFWCIiIiIi\nIiIiEtMUYImIiIiIiIiISExTgCUiIiIiIiIiIjFNAZaIiIiIiIiIiMQ0BVgiIiIiIiIiIhLTFGCJ\niIiIiIiIiEhMU4AlIiIiIiIiIiIxTQGWiIiIiIiIiIjENAVYIiIiIiIiIiIS0xRgiYiIiIiIiIhI\nTFOAJSIiIiIiIiIiMU0BloiIiIiIiIiIxDQFWCIiIiIiIiIiEtMUYImIiIiIiIiISExTgCUiIiIi\nIiIiIjFNAZaIiIiIiIiIiMQ0BVgiIiIiIiIiIhLTFGCJiIiIiIiIiEhMU4AlIiIiIiIiIiIxTQGW\niIiIiIiIiIjENAVYIiIiIiIiIiIS0xRgiYiIiIiIiIhITFOAJSIiIiIiIiIiMU0BloiIiIiIiIiI\nxDQFWCIiIiIiIiIiEtMUYImIiIiIiIiISExTgCUiIiIiIiIiIjHNUBFc3Ox2uwohRgUCARWCiIiI\niIiISB+oBZaIiIiIiIiIiMQ0BVgiIiIiIiIiIhLTFGCJiIiIiIiIiEhMU4AlIiIiIiIiIiIxTQGW\niIiIiIiIiIjENAVYIiIiIiIiIiIS0xRgiYiIiIiIiIhITFOAJSIiIiIiIiIiMU0BloiIiIiIiIiI\nxDQFWCIiIiIiIiIiEtMUYImIiIiIiIiISExTgCUiIiIiIiIiIjFNAZaIiIiIiIiIiMQ0BVgiIiIi\nIiIiIhLTFGCJiIiIiIiIiEhMM1QEcr4d3neMA/52bACEAYOhI/sz1GFV4VxwfnaWlLL7aAAMO0OG\npZGdOYpkm0rmohOspfzjWkgcTM64ET3fD3sp/7AKf6KLK8e5z9tqtez+Gw/9oZZFD/8TOQ5tJhER\nERERiVCAJedZC6v/2MhfzZNfP86ECUn87PYhJPVxTs27a/nb8UTunNxfxXouBPfym8fWsuekbWOM\n/zq/uCNb5XOx/RKrtvHiq9WAlUUP/6BHWFS3dS0vvnEUDDfZj7pJ7sM8m3ZvZn2Dm/lTRnyONbMC\nZp+nPjfLFBERERGRWKcAS847mw1s41NYe/sgAILe46z7m4dff9TMglZY+50hfZrP+xv8vDggnjsn\nq0zPhepNW9ljwvR//A5zL3MBIZpqq6l3uFU4F+XR3x79R4jCLdXkXN91O/vZ/N7RyD+d9j6fKErW\nl7J5QOpnC5PCQBwknuVZ6XMtU0REREREvjy3MCoCuRCSsHT825bSn1u+05+hf9nHw9ubebM2lRvT\nbOD18OJfG/nrvjBBAIeVBxcO4bp0K28vP8gTtUBtIzc/0giDEnntB8MI7jvML1f72VzfDoAr3c4v\nFo7gEnVFOqOWVh/gYtJlrugrVpLTRnVredO0ezO/faWUQ/7I3+6vTeT7t08jETBrinjwuWoWP/xd\nxp4o7+Aunnp0I+67FzF3pANz31v87zfgW1PhL6/uogV7tKtYiJ3vrGXF+mpaoh8dN+sWllzrhsZd\nvPjCW5TXR5d5xTR+cOvEyMEreJDVBesoqoyukGFl9BX5/POcHG3QM4q0chrQD6qLy2m6vksrq6Ol\nbK6HRAe0tHVtDeVhw8t/443tXgAS00fx/cU347YFKHrh96yuBWr/hx//7H9gUA5P3JvP/nfXsbpo\nb5d9Zirfv/0KEqNzPFSylmdf3RvZ7o5BTM8NdT81Ne6l4K/FfLDPG1ljh4u5d81jerr1FMucTsu+\nzaxYVcGe+kDnet51M24dB0REREREvrQ0iLvEjMk3JeMCDjREb5gbW1l3ABbdlMQvvuVgQjDEE789\nigcrmZMTmQAwwMbPbkniZzMc2ADP/iA7bPE8+K1+/OImG801AZa+WqfC7YPRl40APDz7wv9wqDHU\nc4KaIv73H0o5lDiCBf94A/Pz06j+sJSHXtgMQFuLFxMfbSeFJHWE8LZGtmmbadJSu4sXX93FiKk5\n3DQjmwEGVG98md+tr8YYOYYF86YyLWsQgwe5IFzNU//xFuX1KcycNz2yzG2b+bdVFQDsWb+WosoA\n026ZzqI7pjE9qx/xCU5tzD5zMP3rY8Cs5u2aQMer5e+UgcPNrGwHkfQYIMCG5/7EG9u95MyYyqJb\nsjFq9vLUr96iCQP3lFyGAwxwM/+Wqcy/LhMDk+qPq2FkNgu+/XVuuiKF6g+38qf3PAC07H6LJ1/d\nS1vaKBbdfQM3jQlQtNXTfRW9NZQdgGk3TWPRHRMZHvSw+rm11J9ymVC/r4aDtkHMvePrLLrJTVvN\nXp5dWarNLSIiIiLyJaYWWBJDe2OYZuDw8SDggPRhFDzS+fa4uBpufqmNA34rE8YP4GsOPwcyHEye\nOKBjmku+fgkFX+/8zGOf7uPH24M0Q5/H1vrKFv9ls1mU/9+8WFzBk/9RQeKANG669QamjUwBQmxY\nWw6k8a//ax5DAC4bQ4bxJ54sLOPvjdPIpe+D8E+847ssHJ8S+SNczVOFHkifyL/dPS3yWt4VANS9\nu5ZqYMHD3+VKB0AOKc3P8uK2XdTfmk1Lqwk4Gfe1HMbaIGf8RG3IsxJiwNgcctjF5qIK5i6cCOFq\nCj8M4b5pIsOb1nZOenQbb9RAzh3fZVF0241L9vHjP++iovEGrhx/BRmOUjwjx3Bl3piOj8285wfM\n7PgRZ3D4w99Tvu8gTHFSuH4XMIJ/vfdmBgCMHENiy7OsrOyyiunTefzRrscBDz/+cw2H/FZyTrFM\n9/V38Pj1J/7KZmHts7z48UGamNinsbxERERERCQG71lVBBI7e6OVoYArIbpbBr1sLmxkdVkbB/zt\nNAN0dD0MEQAw27vNIni0nr+94+PN3SaHg+2RXlIOi8q2T6zkzL6D/3/qQTZvfZ83ig+y8oX/onzW\nLfzztYPx1IPxtXF0HaFs+BU5JBYWseeQl9w+H03s5F6W0vmnGaAemHhNz/Cp7mikq9rKnz/L3wBs\nIVr8AAfZ44fcKbkkfljK7x59mgEj3Vw/YxpXjnRpU/aZCQlpzJjqoHxrKXuYyIgdpVRjZdHUEUQK\nPaLlaCMA5a/8Fw+tjoSVLf5IS72dNV6u7GdEuviZ3Qdgr9u9jfXFFew87KMtGMI0ITneCoRoagSy\n0hnQZfrcvHRWVtZ0+VHXsvntbfy9vAaPLxRt4dc5fldvyzSP7mLtO6WU7PLQEgxFjwNWnfBERERE\nRL7MkYGKQGJFsDrAAWByQhxwnEf/rZ7NWLglP5Fvp9sI1jTxcHH41DM4epjbnvIRdFhZdLWTcWkG\nVYXH+W29yvas9BvBtNkjmDa7loL/fIW/b66g5drBEIT4k6eNt/Y8pBjd/+55kDEjgYKt8xUbYIZb\nge6DFMVHw4orZ01kCAFMwDDAJJVsByQ6pvH4z8bx901b2Vi4l4J91Wye+nUemKOnJvZVmwljr87F\n2LqVzdurydh8ENImkhMHO7s+DTC6IcdNncjY5OgIWoYdTHCPdAKBHvOue/dl/r83jpKYPoIZ109k\nRKrJ+j9spmunXiPR3u0ziUbXfeogv3n0VfZgJSc/l1mXDMJ/YBsFxb7THAc28+CvSjEdLqZNm8zo\ntBT2v/0WRToOiIiIiIh8qSnAkgvkpFZR3nqeeKkVsHJjlgMamngfuP2uEfzTZZGk40CNt5c9uHM+\nVaUtBLHw1L+6GRcd3e3A2uMq6s8sjbGXWPn7xyFM7Ay5BFo+3kvT7dkd3bDqP95BC5A+yAkNAAEO\n1ofISYuEEGZ1FU19PAzt+fggjO/eeioxwQ74GH3FFeTYTvFxm4srr7+ZK68PUfTcs6zetpemOdnq\nKtZXJpCaw7QBWyl6+W+UAxO/3XMQ/PjEhMjWumQi08adZjR048RpJcDf3zsKjlH82z03R7dyNZ2d\nEq0kJIK5q4YWcjoGdd+zo7bz1NRQy35g4j9+j4WXRYKu6gNbT7NM2FNSiYmd7z/4HcZGjwMH17yl\n7SwiIiIi8iWnAEsuCE99Kx/tOAbhEFWVLfz2o0hXpEV3DuaSOMAeRxLwUbmHA6kOmvd7ub84TGfw\nZSU5ETw7fHyUZ8WVYGPocAMIUvz3elyjrex4t4Ff1wMpKu++2LPxVTY0u5h2WRopyQYHK0pZ+WEI\nRrpIxsqU/DGs/sMunlhRxPdnj6Gtbgcv/q0W+o1hyiAriU43iexlw8p1jF4wFUfDDl7+w94zL9g2\nilu+ZuXFD4v4zTqYm+eifncVdf3GMPO6KxhQ/D+8+NifmLtgKqOTrdQf2MVOM5MF146i+p2/Udw2\ngvzcNBJND5GMU13Fzp6dadeOoOjVg8AgrhvX80djpE9lWr8KNv/59xTc9HWmjXTirT/IB1Vw+63T\nSIwGUi0V5ZTnORmQkEJGmpWi7bVs3n2Qsck+ila+xSGIhot2pl2bRtGre3n2r5u549pRePdt43fb\n/HR0EbTbiQc+Kd/GodRRePeV8rtib+f7vSxz6AgXcJAPtu7CdZmTne9uZEM90A9MbWgRERERkS8t\n3efJeZecCNS08uOXWjtem5CVyD/dMoTMlGj3IUc/fjihmUc/8nH3Rz7Awu35Nv5WfOIW1MZ1MxN4\n8S+t/Pi39WDYeO2hZG4fUM9f3zjOXwEc8SyaBC/uUJn3RTwB9mwrZ8+28o7Xho+fyKI7IgOrJ152\nA/ffFODZN8p5qjIyjZE2ige+f0Ok9Ywjm0UzdvCbwr385leR4Gp41iASK49iM4xuYUn8SUeenNsX\nclPbf/NGcRFPFkdeGzA1jZnjs/nXewM8u3wzq//c2XbHPSMz8o82L6WFmyktPHFES2Hu92Z0tOaR\n02xvw+h2ChjwtVzcrx6kaerEyJP9AKPbdnMw/1/mYS5fzd/f+B/+fmKa9BzuiG7XabPGsPnlXbz4\n3KtguPm3eyYzvHIrq//wKquBxPRRXJm+l4rofAfkzWNR7cu8uLWUJz8sBazkjHdRvr01smq2Mdz0\ntVJWftj5/sR8N+XFRzr2pZOX+fjDE5k44CClb7xF6RuAYxDTrkhhc4VOeCIiIiIiX2aW1tbWdhXD\nxctut3+5v0AwSBCw2U7RfywcpNmEJMMG0e5CQX8QjNN8JkYEAoHYW6lwgJbWUCRgMhwYcb1NFKLF\nHwCsJDrsp55Hwqk+f7rt7acFiMeBcdLmM/1+2qDnfKPLO+X6yBfwu/TTYkK8YcewWXtufxMSDXv0\nNxnC9JtgGD2nPXl+p9pngtHxz2z2U++33ZYJpj8QGZLNpn1CRERERORioADrIvelD7AuYjEZYImI\niIiIiIjEoDgVgYiIiIiIiIiIxDIFWCIiIiIiIiIiEtMUYImIiIiIiIiISExTgCUiIiIiIiIiIjFN\nAZaIiIiIiIiIiMQ0BVgiIiIiIiIiIhLTFGCJiIiIiIiIiEhMU4AlIiIiIiIiIiIxTQGWiIiIiIiI\niIjENAVYIiIiIiIiIiIS0xRgiYiIiIiIiIhITFOAJSIiIiIiIiIiMU0BloiIiIiIiIiIxDQFWCIi\nIiIiIiIiEtMMFYHIhWG321UIIiIiIiIi50ggEFAhXMTUAktERERERERERGKaAiwREREREREREYlp\nCrBERERERERERCSmKcASEREREREREZGYpgBLRERERERERERimgIsERERERERERGJaQqwRERERERE\nREQkpinAEhERERERERGRmKYAS0REREREREREYpoCLBERERERERERiWkKsEREREREREREJKYpwBIR\nERERERERkZimAEtERERERERERGKaAiwREREREREREYlpCrBERERERERERCSmKcASkf/L3v1GRXXl\nCb//jpz2wBRYYkVRiVYCSckUbdHBJ6R1Wka84iNpa2ye4MQ8g71MLvGhvaxpslx3+WZe3Le8cI0z\n401z00z0duyRjGZILo44khYD0zpWAg4YiKkWOqUhiqbEAqrl2Af6vqii+CPsKjHG0v591nItoTi7\ndu2zf3v/zq7zRwghhBBCCCGEiGuygCWEEEIIIYQQQggh4posYAkhhBBCCCGEEEKIuCYLWEIIIYQQ\nQgghhBAirskClhBCCCGEEEIIIYSIa7KAJYQQQgghhBBCCCHimixgCSGEEEIIIYQQQoi4JgtYQggh\nhBBCCCGEECKuyQKWEEIIIYQQQgghhIhrsoAlhBBCCCGEEEIIIeKaLGAJIYQQQgghhBBCiLgmC1hC\nCCGEEEIIIYQQIq7JApYQQgghhBBCCCGEiGuygCWEEEIIIYQQQggh4posYAkhhBBCCCGEEEKIuCYL\nWEIIIYQQQgghhBAirmnSBOJBqqqqkkYQQgghhBBCiD8Ce/bskUYQD4wsYIkHrrKy8pGu/759+x75\nzyCExJwQQuJOCIkRIR50nAjxIMklhEIIIYQQQgghhBAirskClhBCCCGEEEIIIYSIa7KAJYQQQggh\nhBBCCCHimixgCSGEEEIIIYQQQoi4JjdxF0LEP9NgIBDEBPTkeVh0GbqEeNCMoQGChgkJOvPmW+In\nYTANjJEEdBkHhMxh9x/nhkGCrssBgZA5RwjxSJCxQQgR966ceZf32m6GflhWSOVL2Q+9TsErrRxr\n9HB1wODpDa+x5bvzZEeJx+iA+wverXmfm+FUoaCsgpzkh12nq5z45/e4eNMM1WphNv/z5UIWSCYj\nZA679znsN00c+Ld2zPDPWeteZtP3lsjOEjLnTFO3X+x/n4GnCqn4UbbsKyEeMrmEUAgR95bl/5jK\nykoKl8XPkerI8O+wLFmGLrtHPI60p/hxZSWVOwri5puu4KfNXLyZwJofvcbrL60l4UYnje03ZV8J\nmcNm4ea1ARZmr+W1n7zO2qd0LjZ7CMquEjLn3KXzRAMy0wgRR8OFNIEQ4tsxQMt779J6JZQi68ka\nhrGEl/6Pl1gG3Pysifca2wmOhv56wYq1vFy0atICUUICMDLpkJYTNT/n4u8WsHnXj9Gbf8F7HTfR\nFuTw6v9cxgdvHuNmApi/B+boWHSD4G1YkL2JH68e4ec1jbBgCfrgVW7+HpiXxY9f28SCGD7NvGfX\nsvlZk2O/2Y8hO1fEa9R91sK7ja2huJqjo2Ow4IWXefmFJcBNWt57LxKTfGcRa//yJVYtmxB1SXen\nCcZvGvnZv3WyIPtH/LgQfvEP73NzVCP7h6/y7JUPeP/TmzBqhuPcgjEUBBZQuOPHBP/9Z5zp01iS\nlsDVqwMAZK37MZu+Fz3qLN/dzGvP6MxL1oBnWUQLV69eh5giVgiZwyZatnYLL4f//+xTi2j5wpxc\nNSH+yOccAPwtNF7SWbN6Ke1fmrKDhYgDcgaWEOJb4Xn3bVqvmGSv28zLPypgwYgJv7/K9SEwr7Qd\nqrHfAAAgAElEQVTwi39vJyFjDS+Xvc5LG3IY+LyFn7/fPqmMu5NrC2v+PAdGBwgOw7K8zRRkz8O8\nNQDaU+T+2QLM3+us2rCWRRgEk3IoyF3Ezc5OBpKfYa1rEcGbV7G4NvHyD1ehD1yk5dN7+Q7akIRf\nxC+/h7f/vRUzLYfNL71EwXcXYIzC1S+vAyYtv/gFrVcSWPPDl3l9x0vkzL9Jy3tv035LXaz+9Cpy\nFsDNgSDwFJv/soB5mAwYIzyZm8uCURP9qVUUuBZhDAXJXlfAIm7y6RcD5GwoYBFBrt6ysenl0IHL\nxeaW2M780CzhxSu4ea6BK0De6izZz0LmsNnOYcYl/vn/3sfbp6+gZ69CLoQXMudMzvEaj7ZiyS0i\ndwEYsn4lRFyQBSwhxINnXuLTq7Bk3V9T+L1nWPJUDi//9SaWLVnB0mS41N4OPM3Lm/NYkmxh2XcL\neCl3AeYXXzAQpeh5GYvGTyVNXkDWkzYYBdB4+kkrzFnG2u/mMA9Y9vxacpbPAzQS0Hl6iQWSstmy\nNoslz+axbA7Id9DicXHJ0wos469fLuCZZcvIWf8ym1zLyFqxDIxLfHoTlm14mbxnl2CZv4yCv97C\nAgwufREl6rQFLJk3/i35gqeyWBTOJrT5T7NgDizNXk1OxjzgaQq+l828OaBpoNuWMQ/I+u8/JGvJ\nMvJcy8Lxeg/DyZUWfnH2KvOyN5Nnk/0sZA6b9RymLyH3+6tYNg+MzjN8IQfoQuaciOCnJ+i8baHg\nhSUMDAyDOcCArGIJ8dDJJYRCiAdvJPTNc6KWMP675Cxeejl09sRNk7uW06d7wliCajD7zgyj2ujI\npITe/P2E/4+Y6E8sDW9ixvAuTFsn8x63EeJbOeYeGYE5+qTembX+JbIAjOvTbDHzU58i8TUe0uHr\noe6OBgDTnHggPTIptkYSdJYtCUedeW8Lxjc/PcEvPryIZUUhrxU+IztZyBx2X3OYhaxVa8nKWcj+\n/ScIDAPJsruFzDkAX1y6Apgc+9m+8G+u8vaxeVS+lCM7W4iHSM7AEkI8ePpTZM2D3576gM6rAwRv\nXaXl2Nvs2/c2l0x4JvsZGP0tJ1ouYZgmwSutvH/2OixcwjzAHBpg4NbN0Ddfwze5OTTAzVvGhBTG\n5NJ/fUFw6ArNZ38Lc0xumnB9IAgMc8MM/a3xu4HwKeDD3DTgZr+BEbwZvofVAAOjYARjvFWnEWTg\n1tXQNv1XGbg1gHwvJ+LJMznZMHqJD051MmAEufpZC2/v28fPGy+BvoxnkuDKr09wyW9gmgO0NrzP\ndWBZ2p8CJgP+AW5ev4mJyc2rNxnw34xcQpGgJUDv53xxK8iV/2ri0iiYwZtg3iQ4CsbAjUisDYQj\nzOgPvT48MsLA9dAFHAPhGL0Zw43kjN808osPL0JyFoV5S2hv/Gf++dQl2dFC5rB7ncPMqzS++x4t\nn13BMA0uNrdhot+1aCDEH/Ock735VV7b8RqvbX+ZguwFMC+bH2+WxSshHraEv/3bv/2/pBkeX5r2\ncE+y+/Wvf833v//9R7oN//M///OR/wwP3xzszsX0fdZK6/nztP1XJ1dvjvDM6hf5/nIrmu0ZnrjT\nyydtn/CJx0PbZ5cZWZBN6Sv5JM2BX9f+jONn27kyBPzuKu1t52nvvMXKPAdztQX8obeTC50XaGv7\njBsGMDrAb64NcKnzC0YY4oubSSyec4Wezy5hzXqGq7/5jM96r9Lp/QpuX+Wz3z2B7be/orXPIPjl\nZ9xe/BxPz1fHzqXG/5d3f9VJEAhe9XL+vy5g+W4ei+fK3paYi5Oosz5N+p/08UlrK+c/aaOz+yoj\nC55h8w+/j1WbS2bWE/Re+IRPWj/B4znPZf8I2RtKyc+0YF75iP+n9gTtn10F4OrFds53tOOft5Ks\nRXOZn/IH2jsu0PlfbXz2xdcADH3Zw8B1L7+9NULwyy9IXLaQK1/0cKl/Po75V/ms8zN6vZ303h7l\n6meXeCIrhV8dO4/BEJ95f8/3n7MrP88XbR/hvWHAna+52NHOFzeCMN/BqmflJu4SdzKH3dMcNucP\nXG77iPOffsYnnk+41Bdk2eof8Rd2q+xqiRGZcyIfaC56os5vTx3iw8+HwLjBpcH5rHr2CdnZUeLk\nBz/4wUOtw8iI3A7kcfYnw8PDf5BmeHzpuv5Q37+qqorKyspHug337dv3yH+GeGIMBTEJPSlmmmfN\nELxlQNKfYtHvbfHVNIKMJFjQ5cLoR57E3DfMNAgOm/AdCxZ9+pg0TPjT+ZZ7vK+ASXBoBEuyLm0s\ncSdz2CM4h5lDoXv66MkLph0bhMSIzDliNnGyZ8+ehztOG/J88MeZHOoJIb5VerKFmVMPHcv82SUm\nmm6RAU2I6YNDmfCrY1KdQliSJeqEzGGP6hymJc9jgdzzSsicI4R4hMg9sIQQQgghhBBCCCFEXJMF\nLCGEEEIIIYQQQggR12QBSwghhBBCCCGEEELENVnAEkIIIYQQQgghhBBxTZ5C+JiLh6cQCiGEEEII\nIYR4/MlTCMWDJI9yEA/co/7I4Xh4HKwQf0yqqqok5oSQuBNCYkSIRzBOhHiQ5BJCIYQQQgghhBBC\nCBHXZAFLCCGEEEIIIYQQQsQ1WcASQgghhBBCCCGEEHFNFrCEEEIIIYQQQgghRFyTBSzxSDMNA8P8\nI66naWDMVLBpKJ/CYRgG5mzKfWCf1VTWN173tWlM31ZG1Pqa4b8xJZBnSdmH47DcWcfyA4sNc/ZP\n6rmPMeJ+yjUMAwmZxzPu4q++9xl3pjlDfzUf7Nhl3nt9Z5rHHtV9LnPTN1xulL78zcdI9PrOdvqZ\nbYwIIeKHPIVQPLIuNv6CE503AXh67ctsWbUkLuvZdbyG+gt+ADLXlVLyQnpM2wUve6g7fobegEHm\npnJKcqwTX6XxQDVt18MzrdVJaZmb9HBE+87UUdviDf2Q4qB0VzFj7xq82Ej1B22RZMW5oRT3qvCr\nZi/1B2vp8ode1Ra52LG9CJsGGF4O/kMdfaMTqmF1UlHuxgJg+Kg7UIs3EHpJX57H668UhF7DpOPk\nUU61+zA0B+VvFDPx03SdPEj9+b7QD3NsFO7YQe5CLWobGj2N7D/SNinxsjrdlLudAHSfOsjRj/vG\nB7yl+fx0+2o0oPvkQY6e75vU5s7NFbizLVHLBTBvtHHoUCN9dwCsFO8ux6GF2+GfavEOhv7OsaGU\n4lWT97m/vYGDJzpC5c91UvFGuA1FjAI01FTT4QewkL+9nNVLY5nOgjTWVNPmH9+z6WtLKV0T2j+9\nrfXUftgV3u8auZvLKMwO91Szm0P/eJTeO+PTZ972n1KwVItSrkFjzf5JrzHHSlF5Oa6U8KfpbKD6\nWEcobjIKqNiaF5mclbGhijllfaOX2/AvjXR95Scho4jKra4JGb5ijJig+2Q1R88PU7SrMvI5jYsN\n7PugY/yP5topfWNbZGxStYNx2cOBd5sIhMcf+/PFbFvvkFD4FinjY9Zxp44P5VgcbU5SjRNR4oPB\nLmrerMcPkJRJ6a6SyPxqfNXG4XfHxn5IW1lE6YuuCQm1ScfxQzRcCMWX7nBTWRyaO3zn6qg9HZ6b\nsZC35VUKsizR4y5SdDc1e48SyChi94TXlfGhnJsNPEcO0NQTetHiKKS8ODfyWZT5gri7p99He/nO\nNdBwrovA7QQKd1WSm3KPsTdD31CVG72+M/dlVYyo+5yi3FjyzBnyq2h5mypG1DGtzl+FEN8+OQNL\nPJLMK02c6LxJ9n//MS+tXsJvW96jfSgO63m5kfoLflyby9i2Np3u07W0Dca47e+CWNLt6NO+eI0A\n6RRsLadiewF6oIuWzkAkiWlo8WJdWUTlrhLshpdDR9oim359LUDaygLKKysoyNDpOnWW4Fgy095E\nlz+B/K3lVLxSQML1Dhpa/eFXhzH1dAq3FFO8tZi8pcBoYqR+/tYmvAEb7l27qdxeAJc9NHSOlTzC\nsGnBvlgDc+rKuUGfH1zrSqjctQ2H7qfxw66Y2tC8PYy+KJfiLcUUby0iHTD18RYL9t3C4sjHvdlN\n0aZCiv5i/CDDMEzSVhaGtt2cF0oLk7SYymWwg/1vN9I3P5fSXZXs3l0WWrwCuj+qxztopei1CkrW\n2vF+OHmfB9vrqDnRge25IioqK9kti1f3zHfqMB1+K+6d5eQvDdL8y3oCsS59DYJjbRHuzUUUbShi\n7cq0yKJYy4ddkJEfig1HAm3Hwsk5wO0AX9+xkr/ZjXtTEYWb3OQu0mIo12TY1HFtcFO8pYSiF9Jh\nFCxJkU/DkWMdWFe6KX8lD6OniffbAzHFhjLmlPVVl8udIGZyGmlzYWTqAZpyjBiPj/rzgfCnnzBs\nGUFIcVC02U3RpiKKNq4mjVjaAc6cbCKQkkv5nj2UrsvE9/FxOgyJhW9z0VgZH7OOO3V8qMdi9Zyk\nHCeU8WHS9Mt6/FYXZbtKSDe6qa0bX3jt/FUjfWRSsrOSbesc9F1ooK1//LN21O2n4UIfuZtLqazc\nPX7ATy9Np73YnnOze08lBRkGnvqTkflXFXeRsuunb3NVfKjGCbPnFE09AZybyijfmofhbZwUd6p8\nQdztftrLuA1py2zAyJQcKbbYm6lvzFxu9PrO3JfVMaLOB1XlqmNalV9Fy9tUMaKOaVX+KoR4GCQO\nxSPpi08vQlI2+X+2AJ3NPH3251z6IkjOd+NrKaCnvROSXKzPtqFTTGbLfrw9QXJzotfTmlVAcZZJ\n3cW93HWcpmVS8mpm+IcVLKZp/JTo2wECaLg3utA1cD0Jvi96CJCLFbCvK8E+tmXGYpp6zMhBpiWn\nmHKHjjVFi5Tb+1UfYAPdRdnfuCLJS/cxSPtv49/U+v23YO4KHCkaWko2i2lieHisZJ28F92YF+vY\n+8HU1Fyn4JUdkZ/sC8E74fxuVRtast1UZI/9pZeTwKrnMieVnpy6iPRUjVvfycS+cDyZcbrLiKRi\nPd1AOnkZodejldt2ohEjyUn5q4VYTNAmjKRBfxAy3LgWWiDVCS0+vD0BcnOsQICGk150RxGlG10y\nCM9KkI5PA+jOYpypVvirQpr3NdE9yKRvlmdYFgYSmP9kOlZziMRn7NgiXSJ0oKunWtGA7OWLafJe\no89g/G/mJLNoaRpa/zCZGekTFpdV5Vpwl1dE/rK7pwEWrSJz7GyOix34sVC80YlVc1K03ENDlw9y\nXFFjQx1zqvqqyyXFibvYibfOS93w5BZUjhFhZ99tAGc+jiutUy5LMdGS0rEvtfL1YCKZy8e3UbdD\nEH8/WHIcWAGrMwNOdzN8B6Zf4RffvBjiY1Zxp44P5VisnJNiGCdmig/DS3sAnFvWY0vRKd5oZ/8J\nL0FcWADXj8pZkWTFogHOdDjtpferIKRaoL+NRq+BY9PYGTITRvhBP32AK8uBhka2I52mngmXbini\nDoAbTTR4E8lf+yStPnPSmKiKD9U4YQwGYI6DjTk2dNZgn+Oh2zsWd+p8QdztftrLsa4Ih9nF3r0N\nU7aJIfZm7BuqcqPUV9WXo8SIcm5SlRslz1TlV+q8TR0jyphW5q9CCFnAEiJGhjECaUsjCWfCd+D6\n9RsQZ+eyDA+PwJLxxDhhLlzruwZkxvpJUU2XvtOHqD3XC1jGLzGcm4iOyZlTXpwb0/AFAC1hcrAb\nXg6+WUffHdBXloyfDq1ZsIaTe/+ZenxA/g+c0yTSH9NxW6No5fgBqOMHa9C6mvj76j6so334sVHs\nmHyitTEy86eZePq3a2v2Pbeh/8wZgnMcuBZOHOFG6Dt3lOpz4dxoeT4/eWX1Xce8H3/UARlFEw7B\nVeUG8X1pwp0uqqtCZ63YXyhh27pQfRKtFrhwBq/hJO1S7+SBdtDHl6NgeBvYW9UAWMh75XUKlstR\n+L3FPyxenh6ZxjRM+m4YkBKtHTUwDTyHa/CEfzPxklRL6lh/9VN/yge2PBwTihwZ7eXoWzWRRaD8\n7T9h9VI9arkTehNnLwTJ3DR+eYdpGDBnceTSCy1Rg6t9GBPWZmaKjWgxN3N91eVGYm+6I68oY4TR\nU0+z30ppWS6evzs3JcnQMK83U/1W+MekTLbtKsGuRWsHC4Xr7VR/WEt1bxrm9T6w5pKdIrHwbYoW\nH7ONO1V8RB3jFXNStHFi5vgwGUHD/nT4w2ka8DW9Jjg00FKskX599v0mwEZudij3CH7lwwS8J2qo\nOgEk2Sl+dRuOFCDFReHyRhoP/z19S630feXH+tzdlyNNG3cYNBz2YHm+lOcXnOVM96Q9o4wP1Tih\n6RYY7aL5YpDCtG5ujU5zYDBTviBmmKDuo71GzFnEnqpvqMtV1VfZl6PEiKrPqctVx7Qyv1KOFeoY\nUcV0LPmrEOLbJZcQiseG+TDuvGj6qDtQw8EDB8f/1dRw9HS3op7f3CT4REYueSvtQJCm5vB76k6K\nX0jHf76Oqqqx+39MvpQHPZ28NXnYrWBcaKF7StOZl5uoaenFurKY1dMcLHg/PgcpLpwT76XQH8AE\ndEsyyXN1YJhbw7Hvk4SFDta+4MIKdJw6g6Hc1yN3JfeeT/qw5LgmLWHaXyii+LVK9uzZQ9kGB8bl\nM3ROvYTT9NJ6HVyrVkx70HB3uRqJOjDXTvHOCtzPpeE7Vx85Fd2xdiPpc/3U7auK3M8nYm64hKV5\nlFeWkWsL4nnvuFyS8U2E4p1Y+prO8y+6Kavcw549uyl06HSfbply2UWQxgM1+EZtFG8vGE+OkzIp\n2lRC5Z497NlThiPJoPmjznsoF4yeM/Ri5bnsKAvtU+4wO1NsKGNOWd97j7m7qjjtGBHk+Htd6CvX\nk84QBhC44Y+MPfry53FvKWPPnj3sfq0Q/XY3Z9sDMbVDnz90jXhycjKJScCdIYal2z8EM8THfcdd\ntPiYfoxXzUnKcSKG+JiyJeaUacd3+hDNX0Hu1u2Rs1jG555iKne6Sbvtoz5yeW4Qf9AEdJKTk9GB\n4UBs9z4IttfTcdvCxj9PJxAYBjNAwIgtPlTjhJ61nrxFGm0f7KfqrYbpLwmNki+Ib6u9po+9aH3j\nfus7fV9Wx0gs+WC0cqeLaWV+FWWsiGUOUcW0EEIWsIS4v/xA1+Hr6+OLMiOwaNlDuIn7SJDArQC3\nBm9N+Bdg6E5oFk9M1OFG33g9zYnfCId/1e+j7VwHvYPTJxwJ4UWT6ViWOyl4cRvuDA1/3/iBoH1d\nKbt3VVC5u5xcG5CUPCXpt+B8oYBtZW40+gjcHn/F317P3sMeLM4iyl+c7ibJfjwXDNK+55xUq+5z\nbWDLp2J7CdvKfoJjTpC2C72TtlQd7GgpdvLWFVG2ORP8fZHEIpY2pL+djtt3Xz5oXe7EEb5s0LYq\nGx2TnsuTl4sCrR6CpOPKmOZUgmnLHWY4CDzhwJFqwblxDRojmGM3A05xUPrGbsp3VlC+JTdUj5TE\n0Gt3Qvc2SXPmYdVtFKx3wJ0RuSTjHiVo8HXvtUk9Kz3dMimR9nW20Xaxd0rbatizneHLLjRcOZlA\nL77B8b5dX72ftutWinaVTT67RLPizMkMnxVlw/W0Dtd84cXHaOWGtP+6CxblRC6PCm2aAKNfc80c\n798sTJ90luBMsaGMOWV91eVGi9cZxwijG98oGBfqqKqqwXfHwHOkho5wO2ipdpxZoW/TtYUuVsyF\n3su+GNohQMd5P2nryindWkLZLjfabS9tlyVyvl2K+LivuFPER5QxXjUnKceJKPGRgElvrzkhDhZj\n18c/Z0d9NbXnenFuKqdw4twRPoJ3rHagpzpZ69Awh8ORNfg5bX7I31lBSfE2KjZnYvS03bVgMN3H\n7/b6gCB1+6qoOd2Led1D9b+OHcCr40M9N1soeHU3FTsrqNzpxgZYrFOXCGfOF8S0mZmivWaKkSnm\nxh576r4RrVxFfVV9OUqMKPtclHJVMa3Mr5RjRbQ5RBHTMeSvQghZwBIiqmXPLIOhds5eMRj4tIlL\no/DUkw/h8kHdyY43dlP5N5Xj/97YzY6NoYM6+7N2GGyj5bJBoP0k3lHIWD6xnkHqD9bSeLqBQ1O/\nTTKCBPp7CYzCcH8vgfA3WgD+1gZq65rwDRoYN7rw9JhoiYlTFoQ0Pj95hDY/5L4Y/rbO7KXhnVqa\nOn0YpkHXKQ8mOlo4qTEuNlBzogtSnBR9P5224wc5eNI7uVo9HnrRyF05eREp0WqBfh8+A+jvpm8U\nkq3zxz/pYJAvbwwBw1zrDxAIL9iZN9qofaeejq8CmIafprPdMFePJAvR2xC6z3pg6qUlph/P6bN0\n3whimgYdJ5sx0HE+M3FbA09rL2S4mO45QdOWi5XMpzT4ugefCb3nQk8TnHqpVKLZzZEP2sCaR8FY\nMpRiJ2Mu9Hk7MDBoa+2BOZIY3evBgWuFheCFFnymScfxU5ikY594NmBnPbXHGmn8oDayeBJaLPbS\ndKYD/6AR6munumBuBitSQrHYWF1DVwCcGwpJ72/j0JuH8IZPSwpc9HC2tZugaWLc6KCly0B7agWW\nqOWOvbkXz1eQmTv58ijLimwsBGj5Dx8MdnCyxyQ9Iz2m2FDFnLK+UcoFg+Cgn74hE0w//v4AQTOG\nMUJ38vrOcsp3llG6tZC0ORquTWWRew55z52lrcePYZr4O5vouAMZjsyo7QCJWJKgz9sVOqursxsT\nwvfhEt8OdXzMPu7U8aEei1VzknqcUMUH+gpWJEHHRy2YBDj5q25YujzyJZD3eA0NXQEszkLWLP2a\nhgPVNF4MLX1ZMhxogLfVB2Yv57+YcJPEuaGbUXd/Flq07eq5BiTzhBY97lzF5ZTvLKf8tVIKV9rA\n6qLsf+TGFB/R5mYAy5/e4tR79fixUbg+fOAfJV8QU9dvo7fXTDECYA4G8ff6MTEJ9PoJ9BsxxZ66\nbyjKjVJfZV+OEiOqPqcsN0qeqcyvlGOFOkZUMa3KX4UQD4dkf+KRpD+7gbVP/ZaW935GK7DAtZlV\n8+OwnllFFFzopunwPjyA7bli8lInh+D8+RpcN7Fakydt6z15gLqu8AR6ro7qcxqFu3aTmwKJieDz\nevB5w3cUSbKzLfyoYEwv1Xvrwk9b0sjdXE7h8rEbyySjGT48x3x4joUXiNZuwxWe/3094bMhBrs4\n+nbolG7LlFtgfd7aAUm5d12q4Vi7nrTP66ndVxVerHFQNHZfLqOLA2/WR77dPvpWNVr4Uc9aYiJD\n17poeKeLhlCrkffKi5FEKHobBjl/IYhl5dRLS4bwftxM77nmyG8y123DOTHXMT6nbRBcm1ZMe8A2\nfbngKi7B+2YttXtDn9XqLIy0h/d4NXUXwq2/KJfyVydeZmPF/XI++99pZl9VqF7ODQXyFMJ7lPli\nMZmXDlG7dy8AuVvck+5fpqfMjxzEJk482Lrdi6fFg6elIfJ6/vZwXzO6+Tx8EmPXh0fpCu+vsTT1\n1jUvzed6af6QSP+OxJyq3MiBSwdBLHdfHqU5KF6XyaHTtVSdA2y5FL8QPkspSmyoYk5V32jlGhdP\nsv+DsUs6PNS85SFzUwUlOZYoY4QWuleL0cWBtxoxgL4Th7FnVeDUoberGc/1ZhrHWimjgBfH2kPR\nDqCTvzGXrg/G48aSUUBOqsTCtyZKfMw67qLFR5SxWDUnqcYJZTyjU1hSwOfvNLG3ygOkUfy/8iJ1\n+fxSqCGCXY3UhMMkEgIpLrat7eJQSy1VLQA2Ctc5Iwu87ufbOBp5DTLXuSP3HFLFHZoFayp01R2g\n0WsAfg6ftFPhdkaND9U4YVxsYN8H4S/P5qbj3lk6fgZclHxBTD2iit5eM8YIQRoP7KcjfPaT50gN\nnrkOKt4oxhIt9pR9Q1FutPqq+rIyRqLkg8py1TGtzq9UY4UqRqLEtCJ/FUI8HH8yPDz8B2mGx3ih\nR3+4mUZVVRWVlZUPrPzgrZuMYGHe/Af3Offt28eePXvur579fkySsaZ+k/U0CdwIYGqJ2FInXz4V\n7A8wbGokL7RO+5AuczBAYNgkcZ4Ny71WyTQw0NFnWP4O3PBPU6fY2khVZ1UbmoYBuj7tirzRH2DI\nNElMtYWeMDOlDQ1jZMY4UZUbaf/EZGwTbh5uGkECA8NoiclYZ7qpuGng7x+aXfv/Eaiqqooh5kLt\nzzwbVv0e46Y/gGlqWBda7+1bHNMg0D80Q/+OVq5JqDvN0JsGAwSGwbbQes+xMWPMKesbvdwHwTQC\nBAZMtETrtGdQqdoBDPw3hmbcVnwbcXcf89X9xIdqLFbOSYpxIkp8YAbx95uzio+xfp680Hb3tkYA\n/8BMc9J9rTIq42PacSJaG9xvvvBHGCOPWntFq6+yL0eJEVU+qC53+piOJb9S520yhzz6c0mMo6Fh\nyI54jEn0ikeaZf6CR6OeqbYHEr7WhbZpf29JtSnP6tFSrNhm+wQvTVcm89PXKbY2ssyyDTXFQq2e\nqjr40GY8YIpW7kztr+kWbAstUdvQtlBWrh5M/49hu9nGo6ZjnXG/RStXQ9WdVDEZLTZmbAdlfaOX\n+0D2mm7FtnC2Y5PEzSMdr/cTH+oXlWP8bOMDzaLsq7Pu51FiYPbU8TFtO0Rrg/vNF/4Ye/oj1l7R\n6qvsy1FiRDVHq8udPqZjya/UeZvMIUI8DuQeWEIIIYQQQgghhBAirskClhBCCCGEEEIIIYSIa7KA\nJYQQQgghhBBCCCHimixgCSGEEEIIIYQQQoi4Jk8hfMzFw1MIhRBCCCGEEEI8/uQphOJBkqcQigeu\nsrLyka7/vn37HvpALMQfk3h4BLMQEndCCIkRIe49ToR4kOQSQiGEEEIIIYQQQggR12QBSwghhBBC\nCCGEEELENVnAEkIIIYQQQgghhBBxTRawhBBCCCGEEEIIIURckwUsIb4hhmFgfsvlmoaBYaje1cQw\nDAzTvPv3g4r6mtHKjVrp6ev9oMp9UPvmfusr4jjmZoqNWDYNbXuvmyrr+6D6mrJcU/mkHtMwmG2V\nVO1jqMqVmHuMqfvbg4yP6HPlg/i4sxsnHtRcZzygeBbfYvwMPpg8c/Z9efZzyIOKyWjlRhvoywwA\nACAASURBVI2rWcaeEOLbI08hFI/okesVGt9v4vOrN0l4qpCf/Cj7IVYmQENNNR1+AAv528tZvTSW\n0ArSWFNNm398mkxfW0rpmvTQqxcbqf6gLTKJOjeU4l6VPtYAeI4coKknAIDFUUh5ce6kgPa3N3Dw\nREdo+7lOKt5wYwGMy2c5cLiZ0JZWCl4rI2+hNl5u3QGavKFXSbJT/Oo2HCnR6xvKHHppeOcIHddD\nCY1jcwXF2ZbQtgeqabse3tbqpLTMTfqkZjJorN5H220HFW8UY5mUkcxQrtnNoX88Su+d8SEtb/tP\nKYi0v2LfGD4a/qWRrq/8JGQUUbnVNakuM7cDdJ86yNGP+8YH0qX5/HT7ahlQ4z7m1LERvOyh7vgZ\negMGmZvKKcmx3pWsdxw/RMOF0L7XHW4qi50x9GF1fQOdDVQf6wiVmVFAxda8SF/ynWug4VwXgdsJ\nFO6qJDdl4tup+nCUmBvsoubNevwASZmU7iqZ9NrBmnr6wnFlcxaywz15fMHspmbvUQIZReye8L7G\nZQ8H3m0iMBr62f58MdvWOyL1rfunWryDoR8dG0opHhvTzF7qD9bSFR5ftEUudmwvwiZBFTeix4eC\nor/1ttZT+2FXeK7TyN1cRmG29R76xcxzh3mjjUOHGsN92Urx7nIcWvR+rIo742ID+z7oGP/FXDul\nb2wjPYZxQhnPyjlUXd9oc92McXc/8Sxm1H2ymqPnhynaVYkrJZac1svBf6ijb3TC76xOKspjydtM\nOk4e5VS7D0NzUP5GMdYYy41eX8Wcp5pDgK7jNdRf8AOQua6Ukhdiz19niunukwc5er5v0l86N1fg\nzrZELVedU88ce0ZPI/uPtE1a0LI63ZS7ndLRhXhI5Aws8Wj6fZARyyIWfgdGHnJVfKcO0+G34t5Z\nTv7SIM2/rA8nGTEchg+CY20R7s1FFG0oYu3KtMhrX18LkLaygPLKCgoydLpOnSU4Ns/2nKKpJ4Bz\nUxnlW/MwvI283z7+rsH2OmpOdGB7roiKykp2vzGWrBg0H2smYHVRVllO/vIgTQffH6/vV2do8gbI\n3VLOnsptZI74qDvREVN9IUDdPx6i4+v5FG2voHL37vHE27xGgHQKtpZTsb0APdBFS+fkVgq2H6ct\nMP1ixYzl3g7w9R0r+ZvduDcVUbjJTe4iLbZ9cyeImZxG2txp+lCUdgj23cLiyMe92U3RpkKK/sIl\ni1ePSMzNHBtg/i6IJd2OPsO2HXX7abjQR+7mUiord09ZvJq5D6vr6+PIsQ6sK92Uv5KH0dM0KZaN\n25C2zAaM3N3HVH1YGXMmTb+sx291UbarhHSjm9q68f5t3OiDJ1yU7Kxk27pM/F1NdAxOaYv68IHL\nFGdONhFIyaV8zx5K12Xi+/g4HeEv6Ls/qsc7aKXotQpK1trxflhL2+BY2zXR5U8gf2s5Fa8UkHC9\ng4ZWv3T2OBItPhRbKvpbgJYPuyAjPzTXORJoOzbet2LpFzPOHYMd7H+7kb75uZTuqmT37rLJi1eK\nfqyKO9MIQoqDos1uijYVUbRxNWkxjhPKeFbNdVHqqxpjVHF3P/EsZlqs7aD+fCDc82M1jKmnU7il\nmOKtxeQtBUYTw7EWJW9jhGHTgn2xBubUMxNU5Uav78x9WT2HmJcbqb/gx7W5jG1r0+k+Pd7nouWv\nqpg2DJO0lYUUbymmeHNeaFE7SYupXFVOrYo98/Yw+qLc0HtuLSIdMHVd+rkQsoAlxD1KzmLT5k2s\nsmuYD3UFK0jHpwF053qcqVZW/1UhjPbQPRhbUg8JzH8yHWuSlfTvurCnjKce9nUllL6Yh1W3sCJj\nMYyakeTCGAzAHAcbc2xYM9ZgnwPdXl9kIm446UV3FFG60YWu6xMSGpPAIGSuXY9Nt5LrssOoL1Lf\n4A0/YCUjywq6HcdiYNiMqb6B1ka8dywUlZXiWmRB1yakUVomJa9uIy/DimXpChbDlFPRezlywotj\nXR7WhJFJSZSyXIA5ySxamoYlxUZ2jgOrFuO+SXHiLnaT95R212nx6nYId8HURaSnWrAuzca53CIx\n+UjEnCo2wJpVQLHbjX3ONAce/W00eg0cm8oozE5D16cefs7Uh9X1NS524MfC+o1OrMsLKFoO3V2+\nyNaOdUUUu0NnZN1VJ0UfVsac4aU9AM5167GlZFK80Y7Z440k83pGATu2F5GZqmN3LAfMye99o4kG\nbyL5azPRJ71xEH8/WDIcWIF0ZwZgMBw+kyvoD0JGPq6FFjLznICJd+zb8pxiyndVsDrDimV5qL59\nX/VJd48jyvhQUfa30KLLT7euxqpbyF6+GPiavvAiSfR+MfPc0XaiESPJSfmrhaQl6WhT544Z+3GU\nuMNES1qMfakVS2o6rmz7+DgSZZxQlRt1rlPEnWqMUcXd/cSzmN7ZdxvAmY8jRYs9TnQXZX9TSm6W\nA0dGBsP9kPa9sbOH1Hkb6OS96Mb9fAaMjtxDuVHqq+rLUeaQnvZOSHKxPtuGfU0xmZh4e4Ix5K/q\nmHa6y9jxYi6OLAcZScNAOnkZekzlqnJqVexZst1UvFqII8uBIyORW8Cq5zKlowvxEMlJA+KRFg+3\nSjEMWLw8PRJSGiZ9NwxI0aOHn2ngOVyDJ/ybyadZh5KEg2/W0XcH9JUlkdPCNd0Co100XwxSmNbN\nrdEJwTzo48tRMLwN7K1qACzkvfI6Bct1QMOSBB1nmwlmF+Dz3QISIttacgrIPFnD0b+rJj1lmF4/\n5G5ZEVN9ey9/CRg0vLWXBkBfnsfrrxRMOk3dd/oQted6Acuky0+8x+voS8ljx6o09jX7Jw1M0cod\nGe3l6Fs1kUQuf/tPWL1Uj3nfDE/Th9TtAGgj9J07SvW58Lsuz+cnr6xGvpOL85hTxkak9GnP6gx+\n5cMEvCdqqDrBXZeVqvqwqr6mYcCcxZFLL7REDa72YcB4fxpRD3TDipenjzmTETTsT4ffQdOAr+k1\nmXCGikHTgf14rpuQlDvhUieDhsMeLM+X8vyCs5zpnhQ5FK63U/1hLdW9aZjX+8CaS3Z420SrBS6c\nwWs4SbvUOzkJ0SxYw3/nP1OPD8j/gVyiEYfRN4uzntX9zZI61i/91J/ygS0Phx5bv5g57oL4vjTh\nThfVVV2hA9gXSti2LjOGfhwt7jTM681UvxX+MSmTbbtKsGvRxwlVueq5Tl1f1RijjLv7iGcxTXT0\n1NPst1Jalovn787N7iDrxsd03NYoWmmL7E9V3hZ575GReyxXXV91X1bH9PDwCCxJj8xhCXPhWt81\nIFOdv0aZSyf6+KMOyCjCFkteHCWnjiV/DY1BZwjOceBaKH1diIdJzsAS4gEw78Sysqbz/Ituyir3\nsGfPbgodOt2nWyafxq+nk7cmD7sVjAstdIeL1bPWk7dIo+2D/VS91TB5m7nhyXxpHuWVZeTagnje\nOx7+Zkwnf2Memr+N/VV7w/cnmJD0GH5ujQJ6MhZLMgBDweHY64tO3pZyyrfkYlz2cLIzOOnVJzJy\nyVtpB4I0NYcz5cE26i8Eca1fA4MBYJjefuOutpq23KRMijaVULlnD3v2lOFIMmj+qPP+942yHcD+\nQhHFr1WyZ88eyjY4MC6foXNQ+n3cx5wyNmJjf6GYyp1u0m77qP+w6x768D3U9xu8W/K0MTfDIsPk\ns1kTyMgrwLXcCrfbaOkJfZ5gez0dty1s/PN0AoFhMAMEJnyT0OcfAiA5OZnEJODOEGOR41i7kfS5\nfur2VUXu+XVXLS43UdPSi3VlMavlIOFxjtgp/S1I44EafKM2ircX3H3Z3nT9Qhl3Gok6MNdO8c4K\n3M+l4TtXH7n8LVo/Vs7cy5/HvaWMPXv2sPu1QvTb3ZydcgnUtONEbKVPO9fNpr5jY4wq7u4nnsVU\nQY6/14W+cj3pDGEAgRv+e74ZuPfjc5Diwpky3ieUedusy42tvrH3ZVN5RYRpjETPX2OdS00vrdfB\ntWr8i0VluVFy6ljyVzDwfNKHJceFnHMvhCxgCTFrCfFQBw2+7r024Tca6emTbiOLr7ONtou9UxID\nDXu2E5se+r8rJxPoxTdpIcSC84UCtpW50egjcHv89wWv7qZiZwWVO93YAIs1/J53QqlNmjMPq26j\nYL0D7oyfhm3JKmD37grKd1VS/JwNsGJJCqczF9vwk0bZrlKKXynDnaHh/bgjpvqa5ghgZ02WFWtW\nAc453PWEN8tyJwUvbsOdoeHvCyX8wZ4eTKDjg31UvdWMMdpL3Vt1kfs7KMvVrDhzMsPf8tlwPa3D\nNV9kQSL6vpn+NFR1O4B1uRPHwvC7rspGx6TnclACMt5jLkpsTB5XpvSMcGbuWO1AT3Wy1qFhDg/H\n1IeV9dUSYPRrrk28Undh+vRn882dvj1U3/JPF3Ohz2jS22tO2H4xdn3q+JRL0StlOOZAb3/os4Yu\nyQhSt6+KmtO9mNc9VP/r+P2MOs77SVtXTunWEsp2udFue2m7HP5wKQ5K39hN+c4KyrfkhmIpJTHy\njv72evYe9mBxFlH+otwoOr7nXW3aA9jp57po/c1PffV+2q5bKdpVNn72VZR+oY67YYaDwBMOHKkW\nnBvXoDGCGb78Td2P1XGnpdpxZoXO+9AWulgxF3ov+6KOE9HKVc110eqrHGMUcXdf8SymrG904xsF\n40IdVVU1+O4YeI7UTLl/4MwxMhYLngsGad9zToowVd4WyzwwbbnR6hulL6tiOjFRhxt945/RnHiG\n4Mz5a7S5dEyg1UOQdFwZ+qR8eca8OEpOHUv+Sn87Hbfl8kEh4oFcQige1UyB4FCQG0ETtJvcvDWA\nnjwPy7feoy24VljwXmjBt9FO4PgpTNKxTzjFPthZT+0xbyjc0ndHLsUx+720fDaMa+UKrHOHaDrV\nBXMdrEgh9DSUwy0k5q5mzYrFdJ/yYKKjTUl4LX96i4Z36vFjo2R9eFJNsZMxF3q8HRircmlv7YE5\n9snBrlkwexqoO+/H9vw2MscuX0qxAL10XA5SsNzE5zfBYo1eXyDTYYeeHtq+MniedrpH4cnwu/pb\nG2i8nMjqDWtYPNyNp8dEc4QSaEuOm4rlw5imyddfeXj/ZC+FO8afoqMqN3DRQ1fQhivHjtbfRUuX\ngeZYEf52LNq+MQgODtE3ZILmx98fIDHFikVTtwOmH89/eLFlu7CnanSdasZAx/mMfCcX7zEXNTaM\nIIHf9YaeuNXfS6A/DUuqFY3QfWA0uvG2+sjN0zj/hQlPajH0YXV9LSuysRzz0vIfPjJXBTjZY5K+\nbsLzzAaD4W/FTQK9fgKpyVhT9ah9WBVz6CtYkdRAx0ctFGbkcvJX3bB0/HKJtvpafCku1q92YF5q\nwjsKjsTQZ3UVl2MfNMEcovvjBhovp1P2P3LDWyZiSYJubxfGC6sZ7uzGBKwp2qS0I9Hs4p0P2sCa\nR8HY/UsuNlBzogtSnBR9P5224wfp0NawY6MsZMXPtDtzfCjjTtnfgjRW19AVAOeGQtL72zj0Thd5\n/3spDl3dL9RxZyXzKQ3vlz34zFy01tBTR8fqqu7H6rjznjvL0EIH2cutDH3eRMcdcDhC869qnIhW\nrmquU9c3+pg4U9zdfzyLCN3J6zvtmJgM9ftofK+JtI07Jj1pcsYYGQuxHg+9aBStTJ/miG36vA0g\nOBjk2o0hQONaf4AnNMuk/TRtuVHqq+zLUeYQ+7N26Gqj5XI+uf0n8Y5CwZR7hU6Xv0bLB8fmPU9r\nL2QUkT5dhjBdXhwlp1bF3pjusx6QyweFkAUsIWadR/+miZ//28XwT6384mArT294nS3f/fYXETJf\nLCbz0iFq9+4FIHeLG9vEnCZlfiTJTJy4AHW7F0+LB09LQ+T1/O0vhhIALRnN8OE55sNzLJwQrN2G\nK3zcOukx3nPTce8snZDMWHG/nM/+d5rZV9UMgHPDhPtovLkv8g1b2nNudqy3j9c1I5/cRV48h/eH\n73NlIf8vs6PXN5x45Hf9jOZ39tEMYHVSkBN6NTERfF4PPm/47llJdrZFHkGsY0nVMTrrOHrCC0DD\n4ZNk/k3o6XCqcm9d89J8rpfmD8cWKBwTylXvG+PiSfZ/MHY6vIeatzxkbqqgJMeibgeG8H7cTO+5\n5vH3WbcNp9wAK/5jThkb4D15gLqu8Jl05+qoPqdRuCt8kJHiYtvaLg611FLVAmCjcF1sfVhZX81B\n8bpMDp2upeocYMul+IWxV4M0HthPR/hbYs+RGjxzxx8rrurD0WKusKSAz99pYm+VB0ij+H/lRVop\nMWEI77l6vBPu8fbi2NPQNAvWVOiqO0Cj1wD8HD5pp8LtJHSpSy5dH4y3ryWjgJzUcPser6buQui7\ndG1RLuWvjl8q5usJn8Ey2MXRt0OfySK3wIoryvhQxp2ivxndfB4+vaLrw6N0hePUjKlfqOPOVVyC\n981aavdWhUp1Fo5fPqXsx+q46+1qxnO9mcaxT5tRMB4fynFCXa5qrlPXVz3GqOLufuJZ3H1IZUm1\ngtHFgbcaMYC+E4exZ1VE8oOZYyTk89YOSMqdcJlf9LwNo4sDb9ZHzjw/+lY1WkYRu7e6opQbpb5R\n5jzVHKJnFVFwoZumw/vwALbnislLjSV/Vcd0qIDPaRsE16YVk48LVOVGyamVsReO3fMXglhWyuWD\nQsSDPxkeHv6DNMPjS3/Ij3qtqqqisrLykW7Dffv2sWfPnih/ZRK4EYB5Nqz31OQmgf4ApqlhXWi9\n+74fgwECwyaJ82xYJpZrGgT6hzC1RGypM0ynpoG/f+iubY3BAEPTlTkxEej3MzRtndT1Hd82EdtC\ny7RtpKyzatFypnKjtsVs942qHcDoDzBkmiSm2h7CmX+Pt6qqqgcYczPHRmybBggMmCQvtN3jTfvV\n9Q3FOtgWWr/BlowSc2YQf79J8kLr3Z/FDOLvH0ZLTMaacq8NbOC/MYSWaJ10FoBpBAkMzLZMER9x\ndz/dUdHfHtybhmIgMRnbN9jnxsaBqX38/scJ1Rw6uzHm/uNu+niWGHkQ3dXAQGfqQ25jydtmU+59\nz3lRYjrY78dk4lnDMeavUfq5YYzcfXwTQ7kz5tQxxJ5pGDDlycXiIcVJLKOWYciOeIxJHArxDYWS\ndaFtdtulzrydlmLFNt0TfzQd68IoWYymY5vmb/QUK3qUpwjpqTMl3er6Rt12Vm0UpdyobTH79535\ns4CeapWnDj6SMTdzbMS2qRXbwm++vjPG+oNsI80y82fRLLM4gI5Ex7Ttq+n3U6Z49EPWMsvYeUjj\nxH2MA7MfJ9Tzzmw+6/3H3ezHS3Hvc9N0LR1L3jabcu+7r0eJact0+WIs+WuUfq5PtxIXQ7nR5llV\n7Gm6xIAQ8UJu4i6EEEIIIYQQQggh4posYAkhhBBCCCGEEEKIuCYLWEIIIYQQQgghhBAirskClhBC\nCCGEEEIIIYSIa/IUwsdcPDyFUAghhBBCCCHE40+eQigeJHkKoXjgKisrH+n679u376EPxEL8MYmH\nRzALIXEnhJAYEeLe40SIB0kuIRRCCCGEEEIIIYQQcU0WsIQQQgghhBBCCCFEXJMFLCGEEEIIIYQQ\nQggR12QBSwghhBBCCCGEEELENVnAEo800zAwTGmHGVoHwzAwZ9uus2zYqPvEDNfL/EY7wqzra6jq\nG6Xc2bav+GY8qPa/v/5phvuUeW/lmg8o5u6j3G9kfJ7mvVX1vZ+xR4hvc+6I2znJfLTGCZmbvsFy\nZ5lfPahyw4VPX/799rkHUO79HlPI/CXEt0OeQigezeTgajvv/WsT138f+nlRdiF/VZgdfx3a8NHw\nL410feUnIaOIyq2uey6i+2Q1R88PU7SrEldKuNieRvYfaZs0cVudbsrdTgB85+qoPe0Nv2Ihb8ur\nFGRZAAhe9lB3/Ay9AYPMTeWU5FgnVhjPkQM09QRCWzoKKS/ODbWr4eXgP9TRNzrpTakod2MJf9a6\nA7V4Q5uiL8/j9VcKQq+FD+w7jh+i4UJf6HWHm8piZ/Ryx7buaWTvkTYyN1VQkjP2SpDGA9W0XTcj\n25WWuUkPVZjGmv20+Se00hwrReXloXY0fNT9Uy3ewdBLjg2lFK9KH2+HugM0jX2YJDvFr27DEW7/\n3tZ6aj/sCre/Ru7mMgqzrRKY35oADTXVdPhD/Tt/ezmrl8YQ/VH6mnHZw4F3mwiEX7c/X8y29Y7x\n/nvyKKfafRiag/I3ipm6x/3tDRw80RHqF3OdVLwRQ7lmL/UHa+kK91NtkYsd24uwaeH+XVM9qQ+n\nry2ldE2onxpftXH43Ub67oReS1tZROmLrsg4GOhsoPpYRyjeMgqo2JoXUyx3nzzI0fN9kz6bc3MF\n7uwJEWl2U7P3KIGMInZPGdfMG20cOjRWLyvFu8txaNHqqxh7RFxQzx2zj7uYyp2uv0Xrx6cOcvTj\n8X6sLc3np9tXh/uUau5Qx516XleUG8scOuOcpIjnKGOMOu5U7aDOQ8Q0MXKxkeoPxnMz54ZS3BP2\n4WxjJPqcN0N+BfjONdBwrovA7QQKd1WSm3Iv9Z253LFcqbF6H223HVS8UTwpZ8PspeGdI3RcN0L9\neXMFxeE5RNWXZ12uci6N0pcV+Wu0fFs15wkhZAFLiIiLzU1c52l+tGMTCd2NvNfSSPvz2ayaH2cV\nvRPETE4jba6fvtlsP9hB/fkAoE+aPM3bw+iLcnGvtsPcYTxHGril6+FXe2k67cX2nJsdGzNpO7Kf\npvqT5GWFkgDzd0Es6Xb0gPfuY4SeUzT1BHBuKiM/pYOaI428354ZPqAYxtTTKdyYR/Jc6P11HZ7B\nRMbe1d/ahDdgw71rB5mDbfzsnSYaOvMoCScsHXX7afAa5G4uJf+ZNHR9bPhRlzu2YFF/rC2SUI1X\n+BoB0inYWkR24uf8/J0mWjrz2ZZjBUyGTR3XhiIyLTrD187ScG4IS1I4ifmoHu+glaLXtmP5TT1H\nP6ylzbE7lNx9dYYmb4DcLeUUPn2Lo2/WUneigz1bXUCAlg+7ICOf8r908fnxn9N0rJ7c7FJsEprf\nCt+pw3T4rbh3vkLgWDXNv6zH+X/evaB0N3VfO3OyiUBKLuXlhQydO8qh08fp+HMHLh1ghGHTgn2x\nhvfa3ZNnsL2OmhNe0p4rYutfrEDX9cjfqMoNtjfR5U8gf2sZLu1zfn64iYbWPEpfCPWmwCA41hax\nwgrmMFgdaZH37PxVI31kUrLTjeY9Tu3pBtpWu8hLBfBx5FgH1pVuXvluH9WHm3i/fUVMsWwYJmkr\nC1mTkQwjvdQd86AlTf7EHfX1+KdLIgY72P92I8aiXEpL8klLSkDTotdXPfaIeKCaO+4n7mIpd/r+\npi432HcLiyOf9Q4rpjmMlrpifHvl3KGOO+W8rixXXV/lnKSMZ/UYoxwnorSDKg8Rd/v6WoC0lQW4\n/7dsPv//fk7TqbOsX1UyeQFmFjESbc6bOb8C4zakLbMR8PrvGq+j1VdVbmjeO05bAJg79fMEqPvH\nQ3jNNIq2b2XFIh19bCKI0pdnW260uVTVl1X5qzrfVs95QghZwBIiIvuHr/FM4jwsGrBiCbRc4urV\nIMy3xFdFU5y4i51467zUDd/75mffbQBnPo4rrZMmW0u2m4rssZ+8nARWPZcZnkxDSbUry4GGRrYj\nnaae8dOsrVkFFGeZ1F3cizHl/YzBAMxxsDHHhs4a7HM8dHt9kOMC3UXZ37gii0jdxyDtv42fIeH3\n34K5K3CkaGgp2SymieHh8Lv2t9HoNXBsGjtTacLQE6VcgN7TR/DiJC+jl76JDaFlUvJq+HOzgsU0\nTTjF3YK7vCLyp909DbBoFZnhgoP+IGS4cS20QKoTWnx4ewLk5lgJ3vADVjKyrIAVx2LoHo60IO6d\n5eipVjQge/limrzX6DPApktsPnhBOj4NoDuLcaZa4a8Kad7XRPcgk75ZnpayrwXx94Mlx4EVsDoz\n4HQ3w3cgdBShk/eiG/NiHXs/GLkrqW446UV3FFG60TVlclWXa8kpptyhY03RIn2496s+wBZerE1g\n/pPpWM0hEp+xT+pjrh+VsyLJGhoHnelw2kvvV0FItWBc7MCPheKNTqyak6LlHhq6Yotlp7sMZ2RR\nuxtIJy9jwhvfaKLBm0j+2idp9U0+pG070YiR5KT81UIsJpMSeWV9VWOPiAuquWP2cRdDuTP1txjm\njuTURaSnatz6Tib2hXqMc4c67pTzuqrcKPVVzUnKeI4yxqjiTt0O6jxE3M2+rgT7WGtmLKapx4yt\nzaLMTco5T5VfAY51RTjMLvbubbirLsr6RikXejlywotjXR595/yTyg60NuK9Y6FoZymuFG3Spuq+\nPPty1XOpui+r8ldlvh1lzhNCyAKWEOMdN3lepPN6/q0FWEDOn1nitr7Ds8j6jJ56mv1WSsty8fzd\nuRmD1X/mDME5DlwLx5JrF4XLG2k8/Pf0LbXS95Uf63NTz04xGJmuXXULjHbRfDFIYVo3t0ZnGCRu\nfEzHbY2ileOJgeMHa9C6mv5/9t73OYosvff8GNKkcEqTrakG9aAZqkeaW+DiUrirbwuDjYzYFot6\nKTNlCw/tFTfoXg23hlV41EHc4G/QC8KyTbQVPdqBvc0sGsNYzYpFLGojRvJAqNpUL4UpQ92Wdgp3\nMQioEdmiBiWb4H1RpdLvzJKEGtF+PhFEIJXyqZPnnO/zPOfkOSf5q5ZB9KeDpHAR9GS+NX07gQXE\nz7XSdI4pW/Ls7GLFae9L4d+7j9JfHCE5TXESF4/T1pcEtBlWbKS4fC1N+Y6xwXCBrsG1S8RNLyWf\nJSc4RG1DFeXnWzn1ly2UFo2QTIF/15qxRKlYz9ntuJAAVwUembz60jBNeGV1aS6MKVgM3jOhaBaN\nMKWvaVRvc9PycRstyRKsu4Og+1k3qY+aT6ZRznCCz5+CGe/kcFMnoFHx9vepWq0621U09KJRLXeQ\nACr/0DsWoi2T8IlWwtnflG+to3Zj5t6VIj3XZy9/1A248GdXPFqmCUteyW0FUgoU1dR++AAAIABJ\nREFU+NUg5uh8nJ3mxvHJz6NQVjNuCGDSeSKM9kYdb3z9Mpf6J04uJj634HGMlqZYZoC0sZY9W8sd\ny5u37xGet/qmjR1z152TXbv+5mBXecJg3yla+rJzBKsr+cHbmyb0/+ljh73u8onrjjFpmvLaxSR7\nPdv7GDvdOZU33zxEGN9l4xx7v53Bx6Cur0V/Bhqxi3l55VdPrFmX18lu/Gw7g0UV7Hu9hOaeiau7\nkrc+z2j3g8N0MnFLnlNsmqtd+1hq35ft8lfbfNsh5gmCsDDIIe7CC82/9v6US7+CDd/9Ht96HgWw\nErQfbeXY0WNj/1pbOXWxf56G05z9WQx1/TZKeYgJGPdS0zzJMwn/0yDaBt+4JeppUmkLUCksLEQF\nRoyHeX2runYbFSsVIqeP0PRBJ6kZ/i7+SR8U+fCOP0thyMACVK2QwmUqMMKDSRm+e2OQxv0BSh4l\n6Pg4lpfdaHsH6eU+KlcpPBgByxiccsjmy2V+Kta7gTTdPf3TTAZeIonOa+OSds+W7ZQuS9He3JQ7\ni2HsghQPngJqIZpWCMDD9MiUNuo62kriqYvg3ipJ7J8z1uPZzRJP19cGUxmdFBYWUrAcePyQvBZO\nZrc5KKsqCDXW43elCf/sLOlZ2LVuddPam0RfH2RTLjlWeeOtAPWNhzh06CDVHpX+i71TdJm4eJye\n2+DfvTf3NH36SrLyqoexv49z5S74Xh+bvE1f7SD6SGP7H5RiGCNgGRg5QSoUqMAyN8H9DQReKyHR\n10HUdC5vvr5HeLGx7W/TRULb/mZv172xhuC7jRw6dIj6Nz2Yty5xfTif2JGf7uxwiknTldc2Jjno\nOR8fY+cnpi9vvnmIMDGRKqVicwVuHcxrvfRbC6ORyTHPKb+aa3mntTscoeNaGt+2zTBsACMkhyav\nn1Sp2BUitMuPeSvM+etp5778DOxOH0vt+3I++ev0+XZ+MU8QBJnAEgTA4nrnj/nZlV+x9s13qXr1\nOS1/eZLGeGDwYPjBuH8GDx9PfJZsN7lhDSWI9EVJDo8LlmY/iadgXmunqamVxGOT8MlWopOSb4au\nEn00cTkzwzeJpKByfwO1wT007CzHHIhMSUqWTlsyjap3DtKwv4HG/QFcgKZPXtmWInzNpOT3vBOu\n7u+LgKuShr217Kn/AZ4laSLXsuulrEx9eDZ5UIu9bPEoWCMjedg1iP/SgkdRmpua6L5tMvhJO+1X\njYmlXu2l6q09BMoUUoPGlDq++osYrNyQ2z4IQJGHuvcOEtrfQGiXHwC9qCCTzNyIkKKE+gN1BN+u\nJ1CmEP8kOqGsHS1HiNzVqTlQL6uvvmSWKnA/eWeCwkpLJxz5T+J6hMiN5AyDren7WvTTFCVbQ9Tt\nrqX+QADlUZzILctZy4/hCVDirUBXXVRt88DjJ9nvdrabutrB4RNhNG8Nobc8E77Nvc6b3b6k4NtQ\nDiRJDI/dZ7Sjhba+JN4dIarHb/NTlsLT+9yxcn8KK0onnS03vZZzNXIlTJpSfOPs9scTQJr25iZa\nLyax7oZp+ftRbYwwkgZe9uAp1vBu34zCE6zHeZQ3L98jLAr9zaiEuejO3q59f7O3q6/24sluG3S9\nvg4Vi4Fb6Txih5PunOO6fUyaoR5sYpK9np18jJ3ubMqbbx4iTMmjvBur2FMfQGEQ49H8NWIb8/LK\nr7Ism0V5beymBwawgOjpZpo+6MF8mqT9g3aM3HzUE8DN5rU6+toqvEsYezOvTV+el127WOrQl23z\nV7t82zHmCYKwEMiiAeGF5LOu/0bXzS/Q1lRR8Y1f0/WTUygV36PqP3zJAx7Vy773vDZ/YJIefsjg\nQwuUFKkhg4Ki7FkUmTSAjmNtxB8DAxaH3vbn7H5/vxsLi4dDCbp+1k3J9n1TzvjpvxyGCcuZgWWZ\ngz/7/yXBps1uYgN3gJd5Ofe6ojTGb5KZtxUNJTGGStCK9QnOQPudB3R+2EEKF7XbJi6FNgfCJFGo\nWT9xO0WBrsHnCRLmJty/6WfwKRTqmVP1tTIPCv3EryTwVyh8+ksLvqnkYVfnu6EG0lhYw/cJn/2I\nxKpqgq9nlnanrnTSdauATW9u5pWRfsIDFoqnYNKYKk74NhO2D453gQVWjA9PR0CvoCqb2CtFGpAk\neitN1WqLRMoCLbewnq6WVmIGeN+spnQowvEPY1T8L3UykfUlDQ58azTi13pJbHdjnL2ARSnu8asB\nr3fQdiaeCXGlB6foZvq+VoC2HPrjMcyNmxi53o8F2fM0snaH09y59xBQuDNk8LKiZT4vclO2DAbi\nUczX/Vy9MgBL3FlN2ds1b3TSei4GRV5qfr+UyNljRJXN7NvuwRqK0/svI/jWr0Ff9pDuCzFY5mFN\nbgtHK50xA81bzeZV9+k8egJl016q12poa9ahnYnT+48Jyl83OD9gUbq1NC8tj/qu8JUklNUw/lNf\nMIR72ALrIf2fdNJ1q5T6P/Hn9Fr+qkL88wESlh/lSuaNjKM1aFfefHyP8JxxiB1z0529Xfv+ZmPX\nShH+xziudT7cxQqxCz2YqHi/oznGDifd2cX1fGKSve6mj0n2erb3MXa6sy1vnnmIMNrnknSe6KXA\nv4nNa16h/0IYCxVl2Xxjk33Mc8qvrOF0drWRhZFMYRQXoherjuW1s6ttCNCwegTLsrh/O8xH55NU\n7xs7qqLc44aBASK3Td7gKv1P4ZtZT2HXl+dj1y6WOvVlu/zVNt92iHmCIMgEliDkJhA+G/gi87+b\n3fy3m5nfrl2M+f6N8xw5PbqUO0zrB2HKdzRQu0HLSfCllxS4a6HrhROkqRXrYMY4+kEXJjB47gTu\ntQ141bF6+PRaGm29b+IbblQvgTcinOpto6k3G/S3BnIJQPz8Udpj2afQfe209ClUH8gkUuaNTppP\nZ59uLyslsL9u4qol4OaVKCz3T1ne7tmyjZKbHbQ1N2V+UeShZvQsjSIfe7bEOJ4rk4vqrd687CpF\nGjppOo6eIvYISHVy3l1OYJ1GQQEk4mES8exJJcvd7AlMtJu+HiWNNmH7YCapb6H9Wua5nrLST+id\nsW2Aalkl/pVxwieOZM9A0aj84+wpnmY/N7OPA2MfnyKWTWJkW8WXR/lbQco/O07b4cMA+HcFJrwB\nUi16KZf4F0zzxHn6vqZSud1P7HQPzU092QS+ig3Fo2KOcfT9jty2wFMftKCU1XBwtw/QCXyvkiMf\njl3rfTN7LoeD3cRAIvOf4RinfpzpTdpoF36UJNwbJtzbmbufyr1vZe2muflZpiOmY120Zt1Mrvcr\nHoJbyzl+sY2mPsDlJ7jRlZfmMvd7k8gw+HasmSRIDb0YYu1H6YqbQIoT5900ZHXnC9YSf7+NtsMZ\nP6B7q7P27cubj+8Rni92sWPuunOw69DfZrb7kPgnPST7esb8xtY9ufhpGztsdWcf1/OJSTPWg01M\nsteznY+x1519efPJQ4Qx31iIYiYIn0kQPpP5lXvLnuxbbOenEduYZ5tfpek6eoRodlVV+GQr4WUe\nGt4LojmV19auilasYl5v59S5zNtDO0+cp/wvAmjZiajK2N/S82EzPZlAQNVo3uvQl+dq1zaWOvRl\n2/zVLt+2jXmCICwUvzUyMvJvUg1fXVT1+WYZTU1NNDY2vtB12NzczKFDhxZl2SzTBFWdfibaNEh9\nYVFQ7Bq34svRIMbQQyylAFexNuPfmKioM9g07qVmvN4yDYwvLApXuFBnadem0Bj3DJsyW2SqSZn0\ndWmML0ZQCgrRZzj82xxK8dBS0FfoMtv/JfsNZ81l2p2vudDVWQvHpq+ZpO49RCnQJ6y+ytduaugh\nBV9zoanPyq6FMWRgzbEfWsMGxgi4VuizrAcL03wyxxiS1WRBIa58D9bPx/cIi0B38wpYc/Txc7dr\nDhk8tGaKg3axYz66c4hJM5Q3n5hkq+f5+BjbGCrMRiOZNrJmiAPz0Yh9zLPNr+ZR3rnaHcuhCnCt\n0GbZl+dudz7Y5682+fZcYp7EkgXFNOUgsq8yMiYThBdZwHaDS1XHtWLWBtFXqI5/Y/cX+gqXzaU2\nZXKwa+fG7L4TFKarJkXVHJMftdiFpCKLN3zZt/tc+7CKa4U6Z7szXztXuwp6sWvutVSk4yqaSz0o\nUyZ9F7Rt8vE9wosesBbGn9rYVYt12z4+cz+dj+4c+v8M5c0nJtnqeT4+ZoVL+uez6o62bTQfjShz\nz6/mUd652nXKoeZTTwuVm9nXryoaEoRFghziLgiCIAiCIAiCIAiCICxqZAJLEARBEARBEARBEARB\nWNTIBJYgCIIgCIIgCIIgCIKwqJEJLEEQBEEQBEEQBEEQBGFRI28h/IqzGN5CKAiCIAiCIAiCIHz1\nkbcQCguJvIVQWHAaGxtf6PI3Nzc/d0csCP+eWAyvYBYE0Z0gCKIRQZi9TgRhIZEthIIgCIIgCIIg\nCIIgCMKiRiawBEEQBEEQBEEQBEEQhEWNTGAJgiAIgiAIgiAIgiAIixqZwBIEQRAEQRAEQRAEQRAW\nNTKBJbzQWKaJaS2OspimibWQ9zndjVoz/D7zoe1bOBayvLO+jzzK+++hDwmLo11ttWGnOcucex+2\n1fI87nWh7D6XtrEwh2duG+d6kLcSPf9Yl/HzpmXNqm0zf5K5dsql84l189DH8/Bd9jFU+AoErgX0\nUzNp7znF4Tzy1zmXdEZfMQ8dWaI9QVgsyFsIhReThzf4P/73c9z9/zI/fn1NFX9es+E5dWiDztYW\noikAjcq9ITatyqckabpaW4ikxgJi6ZY66jaXjsXLexGOH+9i8DGATvBgCI8CYBJuP0p33Mj84XI3\nwXf24CnKXjgco/X9DlIAy8upO1BLabZIySsdtH0cyyYGCv6d9VSv0ycF6n5aD5/CKKvh4G5ffuU1\nE3T+XRex2ymWltXQmLsuW96TR+keyJRX81QTCvqz7WUS6ThBV2ww86fLSgnsq8NbjKNdc6CLIycj\nE5Ic3RsgFPBmSnwrTPvZSyQNk/IdIWo3jN2neTvCiZ+O1i2UrK+h7i2fOMWvALGzrXRcSwFQvrWO\n2o2lz0DLabqOthC5a412NOrqAzldJS6109Ybz/xQ5KHuQJDRb7Xrh7Z2zTjH/rqdwacTOjgNoQCa\n470ulF2L6PlTXLiawFQ8hN4LMv5u+i8c49Qng2NJxqpKfrh3E4qj5uztmrcuc/REDxkPolP1bj0V\nK5S82tyubYT8ySt22JC62smxc9HM9cu8NLyX6W9ObQsW0bPH6byW6VeqJ0Bj0OsYO9I3umg5PRYf\nvG/WEXg92/JWko5jbcSy8UxZ6WPf3hpcSn6xGcAa6OLwyQjlOxqo3aA5xyQn3Q3HONbakdOHy1vN\nvsBYnJw5htr7mP7zxzj16eCEsnt3NhBYp4HVz/G/OUXy8diwoGLvD6laJZFwLtj7envmHEPyaMPp\ntWfS1XpkQj9niU5NKISvyMkn22vEKb8yrnfSciaa0XNZFQ27K3KfJfraabuYrQc0Kna9Q9XarL5u\ndNJ8OjpW3mVu6t7bM86fz+Qr7HNqe7sO+bYgCF86sgJLeCEx79+Dl9fx3X0/4E+3fJtf3+zl+sPn\nU5bEhRNEUzqB/SEqV6Xp+UlHNhHPY7g8DJ4tNQR21lDzZg1b1peMfTgc5ciPuxh8yU/dgUYOHqzP\nTl4Bty/RHTfw7wpxqHEP5U8StJ+L5gJ49086SOk+6g/UUmr209YezQ3Qez+OQVklocYGqjxLiZzJ\nTnSNI9ox9XeO5X2cxiosoWQZPJl0nTVwge4BA++OekK7KzDjXXx0NVtLw9fpig3i3lJL44E9eJYm\n6fjocn52H42grvQT3BUkuLuGUsBS1bHPf5NGK3WjTnMv1/+hi0HKqd3fyJ6tHgavdRIZEm296Fi3\nuui4lsK3s549W0rpv9hGZPgZaNm6g0EpVbtDNOytQjVi9F43cgOIzt44+voaGg/U4jbjHD8Zyasf\n2tplBEstpXpXkODuIBWrgKcFOTu297pQdnnCiKXhfkUBa+pTsPTgAzRPJYGdAWp2VFPzR2ODFnvN\n2dk16TnTg6H7qG8MUbk6Tfexj3JtY18P9m0j5Et+sWPGgf3VdlrPRXG9VkNDYyMH3xudLLVvW4Bo\n+xE6rw3i31lHY+PBsQGpQ+y4f8egZH1VprxlKrELl0nnytNNLLWUyt0hGt6uYundKJ1XUvnFumx9\ndJyJ5GJufjHJXnfmvUF42ZfVRzmpWDfR4TxiqIOPMU2LkvXVmTLtrMhMcSzPKuyRwf3HOpU7AwR2\n1FC9I4B/pUxezTn+2Pl62wvnEUMc2nBm7VmMWCq+NwMEd9VSs7EUnoK2PD9fb6cRe1+f4OSZKPr6\nAKG3KzAHusf15STdF+O4Xgtw8FAjVWUm4Y7zOd1aZhqKPNTsDFCzo4aa7ZsoycdXOOTUtnZt821B\nEJ4HEqWEFxL11S38+avZH8q/Cb3/L89nYW+a6D8bqN4g3mId/qyanuZu+ofB7/h0xgKW8tI3S9Gt\nhxR8x41rXHYSOdeFudxL6J1qNAuUcWpN30sBOmVrdUDH8wr0j2RrwIxz1QDvrm24ilSC290cORcn\njQ+NzOBcLdZRgHWrX6E7fodBk7HvvtdNZ7yAyi3f5ErCyru8FHkJBL3E2+O0j0xKoIcNWOJh+wYX\nKptxLwnTH0/ABh8U+Qi9uwZ9RSalKl8B8Tv3SUMmybKxq60L0LBu9Kc454HXXyvPfa6vrSK41qL9\nxmEmL8r3fTfEmuU6mgJ4S+FinOTtNBRrIrAXmIGr12G5j23rXKgEKe89QnwgjX+DNj8tK+XUvjPa\nt9bwCt1j2xMeGRgoBLb7UBXwfRMSvxzAwI/u0A9t7ao+6v/Cl9Nf/xko+U9jqy5s73Wh7KJS8VYA\n60Y7h08/mbYmC4tXUlqs8OC3y3GvUPPUnJ1dC2MYynduw6Wq+H1uem4lcm1jW16HthHyJY/YYTP5\n1Xk+juqpoW67b1Lyad+2DEXoipt4doyu9hoXCB1ih3trLe5RBZS9QveAlcsTtA1BQh4VvUjJ6SN5\nexBwOcc6IHnxJHG8VJQlGbTyjEkOulPLqthXlv3Bsxou9ufKaxtDHXyMN1DP6DDeGugHSqkoG3dD\nSwpZuaoEZWiE8rLS2U++CHnlHLbMJ4bYtqGd9jQCoYbcT/0DnbDydcpzf2Dvk+00YufrzRtRUmgE\nt3vRFS81q8N0xrJ9eTjFIOBb60FBYZ2nlO6B8VsJLZTlpbhX6dwfLqB8tWvsS+18hUNObWfXNt8W\nBEEmsARhdpj0/uRHXLlnwfINbCh8TqUw4ZXVpTlJKVgM3jOhSHWWn2USPtFKeHQuLrf1JU3icwse\nx2hpigHg3ljLnq3l2eS7ivLzrZz6yxZKi0ZIpsC/a00uED9Bwf3t7PcrCnCfpAUeBbTi0WFbio4L\nCXBV4FHH6rTzRBjtjTre+PplLvXnW94xpovriqrB0xg9N9JUl/Tz4Ol456Ogj24XGQrTeQtcb/iY\nPN3glC+kLl0ivcSDb8XUfjLdMFsp0nNluPxRN+DCv04mr150RkaewDfGEvily+DO4B2g/JloOXHx\nOG19SUAb28axrAAVi0sX4ni3l5AwAGXplFVET2y+e1q747n3CdFHCjXrXbO614Wyaz6Z4W6UJwz2\nnaKlLzsEWl3JD97ehJqn5qa3q6Ath+jlHtLrqkgkHgBj9Wtb3rzaRsgH+9hhw3CCz5+CGe/kcFMn\noFHx9vepWq06tm36dgILiJ9rpekck7bv5BE7zDjH3m9n8DGo62vHJi0VDb1oNHZ0kAAq/9CbX6yz\n4rT3pfDv3UfpL46QnHVMml53o36i++gRwnctWO7PPQizj6H5+RiAT34ehbIaxn/rk6dJTn3Qmpu0\nqNz7Azatkmms+eSmT2Z7yTxjyIxtaKu9Cb2Vy9fSlO/wTb2bGXyynUbsfL1lmrDkldz2e6VAgV8N\nYgJqkY/q1V10nfgrBlfpDN5Oob82fuuignW3h5YPsj8uL2fPgVrcipOvsM+p7eza59uCIMgEliDM\niqW86t/CSOwK1//1Kpd/uZktry6OpMt6bIHjc0yVN94KUPUdLy7VItJ+hK6LvaQ27sGFQoEK4Ca4\nL4D1yUk6+jqIbmrEpwJmigdPAbUQTVMgZfIwPQLMNAFjYT0Zr/g0XUdbSTx1EdxbNTZYuNpB9JFG\n8A9KMf6fEbAMDNNCVxWH8jrc6dptVFyOEz59hMhMzmc4zrEPukH3s3ebe9YJY/ifBtE2bGG2U1CJ\ni8fpuQ3+3Xtxi6gWP1aC9g+7MMb3oCcWhd+ponbr9JNUlvnkmWn55TI/Fb9RCF9L0N3TT/m2clC9\nBDdGaOtrp+nT0QEJs1oVOq3dccQ/6YMiH16HlZ2T73Wh7M6Ee2MNwa3leFaopK600/rxJa4Pb5qw\nInX2mlOp3F5B7HSYI02RvNKXXHmfQdsI45k+dtgPzrMttqqC+j/zEf6wlfDPzlLxXhAtz7Z1bwwS\n3GBx4oMOOj6OcXDC1iCb2KGWUrG5guinYRLXeunfXj5uhQlYt7pp7U2irw+yaUU+sRmi7R2kl/uo\nXKVwdQQsYxDT0lGV/GPSzLpbSllFFSPRMNFbEXoHKqkqU/OLoY7OLM6Vu+D7o3GD7+Xl1OzQKd9Q\njkqK9r9upefn19n0tl+6+pfJfPyUXRvaam9cbx24RBKd2rwf4uWXD+bt63PLg9Ok0hagUVhYyANS\njBhj54Ooq98gsKsK71oX1r0IR37cxeWrBu7XdQdfYZ9T29qddb4tCMJCI2dgCS8wCt/63Q1U/+l/\n5jtL4PaD5/OGqaUK3E/emVCu0lJtwuRR4nqEyI3kpGREwb3Om112reDbUA4kSQwDjDCSBl724CnW\n8G7fjMITrOyBmOkbEVKUUH+gjuDb9QTKFOKfRMelwBbJpDUuyX0Fd24MnqKj5QiRuzo1B+onPEHv\njyeANO3NTbReTGLdDdPy99E8yjv+rqZDo+qdgzTsb6BxfwAXoOnj6uhehJb32xks8hIKVU879Web\nrA9dJfpo4vbBiUOC6SxYRDtaaOtL4t0RorpMnji/EDxJYzwweDD8YNw/g4ePMxMWBQUq3Bsc05o1\nflVV9ldDCSJ9UZLD1iy1DNpqL1Vv7SFQppAaHDuDxr21joMHGmg8GMLvApYXTklvl9opZAa7o5oN\nXzMp+T3vhKvzudeFsjuTHvXVXjzZbYOu19ehYjFwK5235mayq62t4uDBBkIHGgm+5gL03FktTuXN\np22EfJg5dtjGuseZ8wtLvBXoqouqbR54/GRsO59N22aevIBnkwe12MsWj4I1MjKL2KHh3VjFnvoA\nCoMYj8bdzdUODp8Io3lrCL3lyTM2G8R/acGjKM1NTXTfNhn8pJ32q8YsYtL0uhv7bj81b9fjWQLJ\noZH8YmgePsa4EiZNKb7xulN0vBvKs/XmwvdtFe4kcmcOCXPMC2dsh5nywXnEELs2dNDeKFd/EYOV\nGyZM7tr7ZKd80MbXK0vh6X3uWGP+mhXZFbTDN4mkoHJ/A7XBPTTsLMcciNCf/Vul2I13bWaKTFnh\nY80ySN5K5OEr7HNqO7tO+bYgCDKBJQh5cbXzZ5zpvcEXpsWv/6WXz56Cpi59DiXR8K3RSF/rJWFZ\nRM9ewKIU97inqunrHbSd6aLrdFvuQNbMIDpO96UoqWETy0zRfSEGy8pYUwSgU/6qAvcHSFiQ7Mu8\nPWY0kVCKNCBF9FYaMEikLNCyT6DUNaxZDtGf92JhcP4f+mHV6mwilKarpZWYAd43qykdinD8/ePE\ns3N/vmCI0P4QoXfrqF7vAt1H/Z/48ygvgEl6OMXgQwusFKkhg/SkLEn7nQdc+FkHKVxUj64EMWO0\n/LgLA52qnZu5f6WTlqNd4855cLbbfzkM023VMNMYQ0mMpzAylMQYMnKJW/xsK50xA81bzeZV9+k8\n2kLXDUnbFz2ql33vHaTxLxrH/r13kH3bMwNQ939ww3CE3lsmxtXzxJ9C2erxw4A0Hcfa6LrYyfEz\n0by1nLrSSVt7N4lhE/NejPCAhVJQMDGlL1K4ef4kkRT43xq3OsWmH+Zj1xwIk0TBv37iJJLdvS6U\nXYD0cJrP7z0ERrgzZGCMTgRaKcIXL9N/L41lmUTP92Ci4v2OlpfmZrSbq2ANa+AC7Z+mcL1RnRts\nObe5TdsIeWIfO+xiHUVuypbBYDyKiUnkygAsmTQwnqFttTIPChC/kgAryae/HHd4jV3ssJJ0fthG\n9/UEpmUSuxDGQkXJrkgxb3TSei4GRV5qfr+UyNljHDsfzys2fzfUQOhAiPq9tfhcCvr6GoKv6/nF\nJBvdRTraaL8YwzAtUte7iT8FrUBxjqEOPmY0joavJKHMN+ENnMaNMJev9JO2LMx7UXpjJsqra2SC\nd644tMOMGplHDLFtw3y0Z8UJ34Zy/9TtgzP5ZKd80M7Xa2vWoWHQ+48JGI5yfsCitCzbK5dlXmrQ\n/y+ZyaPYwB2gkJezBY73XSYykMK0MhqJPoYyT7mzr3DIqe3s2ubbgiA8FySHE17MMayS5rMr5/js\nSvbnb22m+nefT8pV/laQ8s+O03b4MAD+XYEJS6jVopdyA+SCZeM+eJQk3Bsm3NuZ+7xy71u5xNEX\nrCX+fhtth5sy4ddbndtuoJZV4l8ZJ3ziSPb8AY3KPx49OValuraKmx92c7gpDJQQ/C8V2SSon5vZ\nB8Wxj08RywZ2a9wgQi+GWPtRuuImkOLEeTcNAa9jec0b5zlyOpb9KUzrB+Hc68UnvKJ4WSmB/XW5\nAUr6s5vZN04ZdJ/InuFQ5GN045ad3dGB1afX0mjrp56bFT9/lPZYdoDc105Ln0L1gYP4i9Lc/Czz\nrelYF61Z816R1ovvG9bWUHWtn+4TzYQB12tBKoonhr2XXlLgroWuF+at5YICSMTDJOLZEz+Wu9kT\n8OYGAC2H27P9WMG/M0T1aiWPfuhgN8vNK1FY7p+y3cjuXhfKLmaMo+935FbcZMNbAAAgAElEQVRo\nnPqgBaWshoO7fcBD4p/0kOzrGavTrXvwqhmd2mrO1q5J5/vNuQFfyWsB9o3bKmZbXoe2EfIdmDvE\nDrtYh07ge5Uc+bCH5qZM3/C+WZV7C6Fd21LkY8+WGMd722jqBXBRvdXrHDuUQhQzQfhMgvCZ7ETn\nlj2ZLfhAYiC7amM4xqkfZ+5G8+YXm5UiDZ00HUdPEXsEpDo57y4nsM45JtnprmDpQ+J9HcTHnR/3\nVtamXQx18jEZAzeJDINvx8Szex7cidPTl6Tn49H69kzxE0L+OLXDjBqZRwyxb0M77Y1OqkVJo/Ha\n5O2Ddj7ZViMOvl7xENxazvGLbTT1AS4/wY3ZSKt6CbwR4VRO71C+NZA7AysZ6yF8t4eu0W8tq8pp\nxM5XOOXUdnbt821BEJ4HvzUyMvJvUg1f4cGc+ny3RTU1NdHY2Lgwxq00vzZMFFXja4ULd5/Nzc0c\nOnTIqTAY9wz4mgt9VkWxMk/SLAV9hT7NjHLGrlVQiGuaQ+HNoRQPZ7rWSpMasihcoT/Dtwo5lXem\ny0yMoYdYSgGuBXjLn2WaoKoyI/8VoampKQ/N2ZMeSmFRiF6szr6Pz6jlrB6n9GOL9JDBiKXMUW8z\n2R3Tj4k66YydfO51oew6zHUMGTy0LAqKXZm3UD2L+ZNhg4cjFgVfc6GpsynvfNtGdPcMHTWpoYdT\n2jCftrVMA+MLi8IVrlm1oTVsYDjYfqaxLp+YZKc7K01qaASloBB9fMyfdwy1MM0n0+eECxyfRSP5\ntc+8/JRTG86gvbG+Aao6614+P40MGxgj4FoxzWom0yD1xfQxZNQXKAV69i2is/EVM+fUTnZt823h\ny40l+eQMpikN8RVGNCi8wL1X4+subdFISV/hmtt1xa4521WLbZJ5RcO1YgHus3gO96mo6CsWbuio\nqDIsFSaizaWfOmpups8UtGLXPLbdOPgPRbUd0Mx8rwtl1x61+NlPFKlFOmrRXNp8vm0jPENHjWua\nOJBP2yqqPqd4phTpuIq+xFiXT0yy052i4VqhLUAMVWaeoFjg+Czk1z7z8lNObTiD9sb6xnPQiJ02\nbfTu5AvsP585JjrZtc23BUH4UpEzsARBEARBEARBEARBEIRFjUxgCYIgCIIgCIIgCIIgCIsamcAS\nBEEQBEEQBEEQBEEQFjUygSUIgiAIgiAIgiAIgiAsauQthF9xFsNbCAVBEARBEARBEISvPvIWQmEh\nkbcQCgtOY2PjC13+5ubm5+6IBeHfE4vhFcyCILoTBEE0Igiz14kgLCSyhVAQBEEQBEEQBEEQBEFY\n1MgEliAIgiAIgiAIgiAIgrCokQksQRAEQRAEQRAEQRAEYVEjE1iCIAiCIAiCIAiCIAjCokYmsATh\nGWGaJtaXbdcyMc2ZPrWey1s4TNPEtKsIK1Muy1ocbWPZlde2foWvquZscewTVlYD1tTfD8+xvJb5\nXLRsOWn5OWGZosuvrJ4XIHY8Fz9h34Nt9fw8dGevqXn4LolNL0SfcyzvfPI205zW/nz8+ELlbZIP\nCsKLgbyFUHihsX7ZzZGPrvLtN7/Prv+oPZcyJK900PZxLBucFfw766lep+d1baKvk86+GMajpVQf\naMRflL9d43onLWeiAKhlVTTsrhgT9HCM1vc7SAEsL6fuQC2lCoBJV+sRIqlxQXiJTk0ohK9ofMX2\n03r4FEZZDQd3+yZnI3S1NBN55KHhvSC5WjcTtP9vbcSHMz963qwj+HrphAQqevY4ndcGM2X2BGgM\nesGMc+yv2xl8Ou5PdS8NoUDGtpmg8++6iN1OsbSshsbx5bH6Of43p0g+HnNpFXt/SNWq0Zow6Gxt\nIZoC0KjcG2JT9jPzdoQTP+1iMHttyfoa6t7yZerQStJxrI1Ytp6UlT727a3BNZs6FF44zaVvhWk/\ne4mkYVK+I0TtBn1Cvw+3H6U7bmR+XO4m+M4ePOOuT13t5Ni5aKZcy7w0vJfpw+atyxw90UPmSp2q\nd+upWJHthwNdHDkZmZDc694AoYA3U95L7bT1xjMfFHmoOxBkVFX9549x6tPBCffn3dlAYJ2W0dv5\nU1y4msBUPITeC6LnWQ+YCdqPtjF6q+rqCr7/dhUTPOy0PsJBGzZadqoHAOtehOPHRzWrEzwYwiNZ\nzJeGvT7mrjvzVpijP+3GyMYA9xtB9mzzOMcO0nS1tkzob6Vb6qjbXOrsJ2xjh73d+ehu5tic+exY\na0cuJrm81ewL+LMxyT7W2bWNeaOT5tPRsTIsc1P33p6sHzEJnzxK90BG7JqnmlDQn8sl7HyXMI1G\nbnTRcnrMj3nfrCMwIQ+aaZLHPg/qv3CMU5+M9TllVSU/3LsJJR/fadPnnMs7k/YcYkg2j+r88CTR\nu5mJM8/OBoLrNIc+Z68927yNNF1HW4jctXL1V1cfGNNXLm62cOrTEWoONI7lbLYxzzn2z2hXEASZ\nwBKEiXzBuf/7avb/T55TGQx6P45BWSWhP/Zx8+yP6D7TgX9dHa48rjYfQcm3XBjx1CQxOtlNcPJM\nFH19gLf/4yAtJ7r56OqabNJq0f2TDlK6j/r/2UNnyyna2qPZQabFiKXie7OGck1l5M5lOvseoi2f\nWK5oRybZmc5BpK+eJWIAyyYF7593EB/WqXl3L9p/7+DUx21EPAdzSU20/QidcRP/zjoqv1OCqo5a\nH8FSS6neXkHhMkj+op3wcAHqqOHHaazCEkqWpRicXJhHBvcf61TurES3LEYooHzlWKkTF04QTekE\n9r+NcaaFnp904P2vmQHF9X/oYpByavcHUOJnabvYSWSTj4piSF/tJpZaSuXuenzKTX50opvOKxXU\nbXTlXYfCi6Y5sH6TRit1oxrxqRfevkR33MC/K0T1tx9w6v022s9FOZSdhElfbaf1XJyS12rY/Udr\nUFU1a9+k50wPhu6j/p3NxP++le5jH7Hmv9aiA9ajEdSVfgKb3LBshPDJTh6oam6Q3dkbR19fwztb\nNNpbT3H8ZIRDu/0Zy6ZFyfpqNpcVwpMk7WfCKMuVnE8csTTcryjE70zVsl09pK50EzdcBA7so3w4\nwt9+2E3n9Qpq12kOPsJBGzZatq0HgOEoR37chbnST11tJSXLl6JIBvOlYquPeeju0vlujCI/oVA1\nD/tOcfziWaJ/4MGnOsUOMIbBs6WGNTpYI6B7SvLzEw6xY2a789GdXWwG894gvOyjduc2lHgHbRe7\niW71Z2KoQ3nt2sYy01DkoeaP1oBlgaIzejfWwAW6Bwy8O+qpLIrSerKLj66WZ3MJe98lTOX+HYOS\n9VUE/od13Pw/f0T3hctse70W58er9nlQevABmqeSbR4dyxpBKV6T61v2vtO+zzmV1057dpoGg/a/\nOU7cKqFm727WrFRRsw7bvs/Za88ub8O6g0EpVbtrWFdwkx992E3v9Ur2jJ9oH47S8akBqBMm/Gxj\nnkPst7MrCIJMYAnCBH7V+xGfsZbXX73N3ecWMXQC+0OoxToKsG71K3TH7zBogkt1vtqztQaPFePw\n4c5JQc/ernkjSgqN4HYvuuKlZnWYzlgCNvjAjHPVAO+ubbiKVILb3Rw5FyeNDw2NQKhhbNJpoBNW\nvk75eE9wr5vOeAGVW77JlcTkik1y8lwcz9YKBvtSE8qcTqWhLIBvhQbFXuhNEB8w8G/QYShCV9zE\ns2P0Cfi4L1R91P+FL5ds9Z+Bkv809gSYIi+BoJd4e5z2kWkqcUkhK1eVoAyNUF5WOjbxRZroPxuo\n3iDeYh3+rJqe5m76h8FfBL7vhlizXEdTAG8pXIyTvJ2GYg1tQ5CQR0UvUoA1vEI3yduDgAvyqUPh\nBdQc6GurCK61aL9xmMmbLdL3UoBO2Vod0PG8Av0jVi5Z7zwfR/XUULfdNym4WhjDUL5zGy5Vxe9z\n03MrkeuH2roADetG/zbOeeD118pzE7QGCoHtPlQFfN+ExC8HMPCjA95APd7cQLQfKKWibLQSVCre\nCmDdaOfw6SezqodU6gEsW4OnSEEpWscrdDMyYuXhIxy0YaNl23oAIue6MJd7Cb1TjWYhk1fPQ3k2\n+pi77tKkhkDb4EEHdG8ZXOxn5DGgYh87sIClvPTNUnTrIQXfcY/zAXn4iRljh53deejONjZnVlLv\nKxutsNVwsX9iXc1YXqe2sVCWl+JepXN/uIDy1WNT/eawAUs8bN/gQmUz7iVh+uPZXMLBdwlTcW+t\nxZ39/5qyV+gesPKb0HDKg4DC4pWUFis8+O1y3CvU/HynQ5+zLa+t9uxjiHGli/hjjZr9dfiKlAmX\nOvU5O+3Z5W0o5dS+MxozMnnb5C2Pl3/aCd5KPP96ZeIElk3Ms4/99nYFQZAJLEEYl499xpkrv2bD\n9/6cb/T9iNvPsSha8ejTnRQdFxLgqsCjzsLAE2vWdi3ThCWv5JZGKwUK/GoQk8zznycouL+d/WNF\nAe6TtJi03SbF5WtpyneM3yJo0nkijPZGHW98/TKX+ieWKX62ncGiCva9XkJzz8SnbgW6BtcuETe9\nlHyWnOBg0rcTWED8XCtN55hxCTb3PiH6SKFm/dS1NCMzZAVPniY59UFrbvBQufcHbFqVuXfThFdW\nl+bcnYLF4D0TilSUIj1XvssfdQMu/KMrTBQNPVu21KUOEkDlH3qn+fbp6lB4UTU3qoHp1nNqG6oo\nP9/Kqb9sobRohGQK/LvWZD4cTvD5UzDjnRxu6gQ0Kt7+PlWrVUBBWw7Ryz2k11WRSDwAlk4bfFOX\nLpFe4sG3IvuLZQWoWFy6EMe7vYSEASjTX/vJz6NQVjNlFZr55Mms68Hzh5tRYt38Vcsg+tNBUrgI\nevS8fEQ+2hhxyPCn1ANpEp9b8DhGS1MsM1DcWMuereUihi8dc+7rnaftbxrV29y0fNxGS7IE6+4g\n6H7WFeUTOxSwTMInWglnrZVvraN2Y2lefmLm2GFvd+66yyc2m3QfPUL4rgXL/RMmiuxinX3bKFh3\ne2j5IPvj8nL2HKjFrYCiavA0Rs+NNNUl/Tx4On5gkL/vEiZOVB57v53Bx6Cun8NqtenyIOUJg32n\naOnLtv7qSn7w9iZUR9+ZR5+bobx55W0zxJDkrc8zseKDw3QycUueU5+z055t3pYlcfE4bX1JQJu4\nlXagg56UTl29n/Bf9k3ox3Yxzzb2O9gVBGFhkEPchReS62fOkV6+js3fUDBMePLF3ed82HCarqOt\nJJ66CO6teoYBbBZ27U+/xZr8MHjgEkl0XhsX/NNXO4g+0tj+B6UYxghYBsZoxQ5H6LiWxrdtMwwb\nwAjJobFnvZ4t2yldlqK9uSl3Ntdk3BuDNO4PUPIoQcfHsSmfxz/pgyIf3nyf7i4vp2ZHLY2HDnHo\nUD2e5SY9P79uP/f52JqS7PTcBv/uvbknkbm/vdVNa28SfX2QTSumyVOnqUPhRdfcTIOSFA+eAmoh\nmlYIwMN0dhlRdjutsqqCUGM9flea8M/Okh4daG6vQElFONJ0mI5rKabf8mwS/qdBtA2+se0mqpfg\nxlJSn7bT1DR6lhtTn/Baca7cBd/ra55NzQ4ZWICqFVK4TAVGeDD6NNrORzwTbUxTDygUqMAyN8H9\nDQReKyHR10HUFBV8FRhMPQSgsLCQguXA44eM5BU7VN54K0B94yEOHTpItUel/2IvqXz8hG3syMfu\ns9Ld5Ni8lLKKKnyrdXgUoXfAnHOsy7mR1W8Q2FXPoUOHOPhuNeqjfi5fzZzno67dRsVKhcjpIzR9\n0DnpHvP1XcLEaiulYnMFbh3Ma730zzI3nS4Pcm+sIfhuI4cOHaL+TQ/mrUtcH87Hd+bR5xzK65S3\n2VQEFbtChHb5MW+FOX89nVefy0d7dnnby2V+Kta7gTTdPf05P3D2ZzHU9dso5SEmYNwb20VgF/Ns\nY7+DXUEQZAJLELJ8wWe3LHh0nb9tbqb3VyZ3I2c4889fPKfypOhoOULkrk7NgfppVoJYJK5HiNxI\n2ge1ZbOwqyyFp/e5YzE2ol0xtqVgKRbJ5OjRtQCv4J5Urqu/iMHKDRO2vvXHE0Ca9uYmWi8mse6G\nafn7zGRUemAAC4iebqbpgx7Mp0naP2jPHu5K5oDp9w4S2t9AaFfmjB69qCBbvky25NnkQS32ssWj\nYI2MTLnf8DWTkt/zTjsZMe0EhaLj3VCevW8Xvm+rcCeRnTiApQrcT96ZYKW0VMtVWrSjhba+JN4d\nIarLJlZQ6moHh0+E0bw1hN7yTNtk09Wh8CJrblQ/U3tc+kaEFCXUH6gj+HY9gTKF+CfZidrHmWFd\nibcCXXVRtc0Dj5/kvltbW8XBgw2EDjQSfM0F6FPPTBu6SvTRxG1zAO6tdRw80EDjwRB+F7C8cMrg\nxLgSJk0pvjI1P9041EN/XwRclTTsrWVP/Q/wLEkTuZZ09BH5asO2TNPWwwgjaeBlD55iDe/2zSg8\nwXosSngeLJ2xFeeiO4PopylKtoao211L/YEAyqM4kVtWHrFDwb3Om91ipODbUA4kSQznE0PtYoeT\n3bnrzjk2K7jX+al5ux7PEkgOjeQV6+zaRil2412bWc2jrPCxZhkkbyVGvRNV7xykYX8DjfsDmU3y\n+piHyct3CZPQ8G6sYk99AIVBjEez0cj0eZC+2osnu23Q9fo6VCwGbqXziiHOfW6G8uaVt00fQyzr\nCeBm81odfW0V3iWMezOvXZ9z0p593gagrfZS9dYeAmUKqcFshmr2k3gK5rV2mppaSTw2CZ9sJTrs\nHPNsY7+DXUEQZAJLELJ8jf/p3e/zbv27/OfvfZd1X1f42rpqdv7e155DWdJ0tbQSM8D7ZjWlQxGO\nv3+c+LiVAenrHbSd6aLrdNuUoGYNp0klU1hYGMkURm5Fk71dbc06NAx6/zEBw1HOD1iUlmW3N6hr\nWLMcoj/vxcLg/D/0w6rVk94gFid8G8r9E7f3+IIhQvtDhN6to3q9C3Qf9X+SmYzSNgRo2B8i9G49\ntTt8KEtc1Lw76Q1LKBRY/Zw8HQG9gqpscqGVeVCA+JUEWEk+/eXUQ2zMgTBJFPzrJ2/TMEkPpxh8\naIGVIjVkkM7mQcaNMJev9JO2LMx7UXpjJsqra7L3quFbo5G+1kvCsoievYBFKe7sU8342VY6Ywaa\nt5rNq+7TebSFrhuZhNC80UnruRgUean5/VIiZ49x7Pykw3FnqEPhRdUcYKYxhpIYT2FkKImRfSoL\noBRpQIrorTRgkEhZoGV7f5GbsmUwGI9iYhK5MgBLJg0jFQ1r4ALtn6ZwvVE9ZWKn/3IYJmz9GHdp\nkcLN8yeJpMD/1uTVZibhK0ko8zFZOenhNJ/fewiMcGfIwBi28qqHAl2DoQQJExjqZ/ApFOovOfoI\nZ23MrGX7etApf1WB+wMkLEj2Zd70KPPGXzI2+pi77grQlsNgPJZZvXA9c+5T5vxB+9hhDcXpvhQl\nNWximSm6L8RgWRlripz9hF3ssLc7D905xOZIRxvtF2MYpkXqejfxp6AVKHnEOvu2ifddJjKQwrQy\ndqOPocwzcZJD+50HXPhZBylcVG+btDXXwXcJo508SeeHbXRfT2BaJrELYSxUlGX5aWTGPMhKEb54\nmf57aSzLJHq+BxMV73c0Z99p1+ccyuuUt9nFkHKPGxggctvEuh2h/2nmEAenPuekPbu8LXWlk7b2\nbhLDJua9GOEBC6Ug+xBV9fL9/SFC++up211NyRIF34763BZdu5hnG/sd7AqCsDBIGBJezI5bqPE1\n0pz7yUfceAT8uovub77Kjt/9krdymf3czD7giX18ilh2sDU+qVeLXhoN1xQsmzQQP3qEaPZpV/hk\nK+FlHhreC6I52VU8BLeWc/xiG019gMtPcOPoeQkq1bVV3Pywm8NNYaCE4H+pmJhgX4+SRpu6vUfR\n0Ish1n6UrrgJpDhx3k1DwAuoaMUq5vV2Tp3LTOZ0njhP+V9kXvMcP9tC+7VMoZWVfkLvjBtoF/nY\nsyXG8d42mnoBXFRvnXim1M0rUVjun7J90LxxniOnR5eth2n9IEz5jgZqN2g8uBOnpy9Jz8ej3+Nh\nT2DMbvlbQco/O07b4cMA+HcFsmeVpLn5Waas6VgXrVnzo1cmBrJPp4djnPpx5kNt0hFYM9ah8GJq\nDoifP0p7LPtUu6+dlj6F6gOZN2mqZZX4V8YJnziSPZtDo/KP1+UmWALfq+TIhz00N/Vk+tKbY6/g\n7ny/OTdYKXktwL5t7imTcp9eS6Ot902ZaG45PLrKUcG/M0T16klh27xJZBh8OyZtYzJjHH2/I7dC\n49QHLShlNdm3T9nXg2fLNkpudtDW3JTTVc3oWSK2PsJeG3Zatq0HwBesJf5+G22HM2XSvdX5bzUW\nngl2+pi77lQqt/uJnR7TjlZWxYbiPGLHoyTh3jDh3s7c91bufSvTdxz8hG3ssLM7L93Zx+aCpQ+J\n93UQH3fO0VtZDTnFOru2ScZ6CN/toWv0bsqqcnbNG500n86uJllWSmB/3bgJqnx8lzAuMUUxE4TP\nJAifyfzKvWVP7m2a9hqxy4MeEv+kh2Rfz1hus3UPXjWPGGLX55zKa5u32ccQbUOAytjf0vNhMz0Z\nh03Vhjz6nK327PO2ggJIxMMk4tnTs5a7x2lEyZyJZ8Y4+kEXJjB47gTutQ14VfuYZx/77e0KgrAw\n/NbIyMi/STV8dVHV5+tBm5qaaGxsfKHrsLm5mUOHDi3KslnDBsYIuFZMc0yolSY1ZFG4QmdqL7Aw\nTSa8EnneZTHTGF+MoBQUohepM/yNgfGFReEK19QyWSYmKrMukmViDD3EUgpwFU83mWRh3DPgay70\nZyqHZ1+HwpjfWKyaM4dSPLQU9BX61CdAlklq6CEFX3Ohjetr5rDBwxFryu8nXmqCqk6yaZEeMhix\nlBl0PNoPnyyIrzfupWx0tTDamL4eJmrZKijEVSSjg6+W7kxS9x6iFOi51Vf5xQ4rs9poJk3OOXY4\n2Z2H7uxis5UmNTRDHHWMdXa3mqm/KfXrYDMf3yUamS4vm2Od2eRB5pDBQ8uioNiVeQvfbHynTZ9z\nKq9t3pZXvCzAtUKbXc42V02Pxog5xS37mGcb+4VFl8OZphyS+VVGNCgIL7KAi3RcM61CUDRcK2aW\n/rMe7yqqNjFJmfZv9JnLpKioc/ti9BWqrZvTV7gWxH2qktD/u0MttkniFRXXNH1RLdJRi5y6sTpt\nH9OKXQ4H8ioLNok6d93MXRuK+jy0LCwCZU2rHefYoaAXu+ba2Wxih5PdeejOLjYrNnHUMdbNIfY6\n2MzHdwmzyMvy6JPqjLFHt82RbH2nTZ9zKq9t3jaXeJlPzlY899gznxhhd61t7BcE4UtFzsASBEEQ\nBEEQBEEQBEEQFjUygSUIgiAIgiAIgiAIgiAsamQCSxAEQRAEQRAEQRAEQVjUyASWIAiCIAiCIAiC\nIAiCsKiRtxB+xVkMbyEUBEEQBEEQBEEQvvrIWwiFhUTeQigsOI2NjS90+Zubm5+7IxaEf08shlcw\nC4LoThAE0YggzF4ngrCQyBZCQRAEQRAEQRAEQRAEYVEjE1iCIAiCIAiCIAiCIAjCokYmsARBEARB\nEARBEARBEIRFjUxgCYIgCIIgCIIgCIIgCIsamcAShC8ByzQxrS/9WzFNk5m+1u4zLBNzjgW2vdfn\nZNc0TSy7rzXN6esjH7vSvV9UVWKaJuaUjmFhDj+HdrXta9bc36gzX7szaQOny2a+xk7Lljl3HyG8\nGJr7su3a+v+vYqxbVHmIsDg0Mnd/PZ8Y4tjnLGsGfc7DrrVAMcSS2CQIiwV5C6HwQmL+9y7+9v+6\nPvaL3/4W3/tf/5RvLMKyxs620nEtBUD51jpqN5bmdV36Vpj2s5dIGiblO0LUbtCnCaj9tB4+hVFW\nw8HdvtyvE33ttF2MZ3/SqNj1DlVrtYzdG120nI7kkhXvm3UEXs+WyUrScayNWCrzqbLSx769NbgU\ngDRdrS1EUmMBvHRLHXWbS/O41zRdR1uI3M1eq3upqw9QqoyWt5POvhjGo6VUH2jEXzS+sRO0H20j\nbmR+VFdX8P23q8jcjUm4/Sjdox8udxN8Zw+e7PXmrTBHf9qN8TTzs/uNIHu2ecbVX5LOD08SvZtJ\nlDw7Gwiu0xzqwaEOzQSdf9dF7HaKpWU1NI5rF8w4x/66ncGn4+5P99IQCmTvR3hm2rAhdbWTY+ei\nmfZb5qXhvUz9m7cuc/RED5nepFP1bj0VKzKNbg50ceRkZEKSr3sDhALe8Z2VrpZmIo88NLwXnNKm\n/edbOPXpCDUHGvEVjV1j14dj54/R8elg5oclLqr37cOfLZNtXwOM6520nIlmdFNWRcPuimzQN4l0\nnKArlrW7rJTAvjq8xeShjX6O/80pko/H0oiKvT+katVoOmHQ2dpCNJXxPZV7Q2xapeThI0zCJ4/S\nPZCpB81TTSjolyRlEdF/4RinPhkcSyBXVfLDvZvyaKM8+tsM+rDXnb1de/9vH5Ns9Twc41hrB4NZ\nDbi81ewLjPbVhYp1dnqG5JUO2j6OZetJwb+znup1Gb9o3o5w4qddufKWrK+h7i1f5lpHPQuzyEzp\naj0yIUdiiU5NKDTO3z97jTjlg3Z92TaXsY1NzuW1z30tomeP03ktc73qCdAYdNb0fDSST95gDXRx\n+GSE8h0N1G7Q8siLBUF4HsgKLOGFxDJ/A4Xfofp/3EH1m9VUb6tgxWIs560uOq6l8O2sZ8+WUvov\nthEZzvPa36TRSt2oNn8T7eggNeW3SbovxnG9FuDgoUaqykzCHedJZz+9f8egZH0VocYGqspUYhcu\n5z5LX+0mllpK5e4QDW9XsfRulM4rY99gDINnSw2BnTXUvFnDlvUl+d2rdQeDUqp2h2jYW4VqxOi9\nboylVo+g5Fsu4MmUwVDqSjdxw0XgwEEa91bBrTCd17Mlvn2J7riBf1eIQ417KH+SoP1cNHftpfPd\nGEV+QocOUbe1nMQnZ4nmHuoZtP/NcaL3X6JmbwONBw9mBuh51INdHSryPyAAACAASURBVPI4jVVY\nQskyeDKlbUaw1FKqdwUJ7g5SsQp4WmDbxsLctDHj5NfVdlrPRXG9VkNDYyMH3wvkJkN7zvRg6D7q\nG0NUrk7TfewjRnup9WgEdaWf4K4gwd01lAKWqk6yfZaIMcMXD0fp+NTIpu7jsO3DJoMp8G2tpfHA\nHjxqiq6PY2PX2va1BCfPRNHXBwi9XYE50M1HV7OFG75OV2wQ95as3aVJOj66PH7qa0Zt8Mjg/mOd\nyp0BAjtqqN4RwL9yTLWJCyeIpnQC+0NUrkrT85OOsTq08RHWwAW6Bwy8O+oJ7a7AjHeNlVdYHBPH\ngw/QPJUEdgao2VFNzR/58ptgdOxvM+vDVncOdm39v0NMstOzeW8QXvZRu7+RPVvLScW6iS50rLPT\nMwa9H8egrDITkzxLiZwZyw2u/0MXg5Rny+th8FonkSHy0rMwq8jEiKXiezNAcFctNRtL4SloyxdW\nI/b5oH1fts1l7GKTQ3mdct9o+xE6rw3i31lHY+PB7OSVs925aySfvMGg40wk15bkmRcLgvDlI1FK\neGETBWX5N/jWN3R+Pazy6re+vihLOXD1Oiz3sW2dC5Ug5b1HiA+k8W9wXm+jr60iuNbi/2fvjZ+b\nytL87k/ghmsiq297NGAWzVgzdo9g5SC2RdoEdnEwaRPci7ZXiXsDKbNF93o6Hsq14y5+8N/geota\nb0J1/FLOQFWzgQlM3F2mMIU7mDG7UFYHdzCLltbbdkbsiMFNK0Zta/AlF/L+IFmyjXWubGMwvc+n\niips+R6dc+7zfZ7nnHvOuV23jzDnQur7ffREi6je8T2ux6alM+MJRgH/Ri8aGpVeN30jueXhnp31\neDL/31C+jr4RK/uZY3OIJq+O4dSADayjj/jdUcCVCegrefV7bgxrgqLXPLj0AtuqVVD/bsXUt7KO\nvhlLxr076/BaEY4c6XkqMUskHsCqDXidGpqzknX0MTmZ/qvU/QRgUL7RAAy862B4cqqEFIkxcGz2\nYgCGrxwuDzP5CNAheb2X6CMHde834HdqM7yhuh/UfYjTRzDkI9oVpWtyVmN0P41/7s/a8PA5KP1n\nsspkvthqQ5Gg9lyMonvraNjtnxUELZLjULF3Fy5dJ+D30H8nxvA4BJzgqAzSXDn1t1EuAlter5gx\ncXzmQhTvzipGBxJP2fG1n/eArxrv31+f8ZnahnVq9h/M/q1nDUSnC0dha+btIRI4CO32YWg+6srC\n9ERisNkPTj9N723AWJP2QxVrIHrva1KAA7U2AFhRzNr1pWhjk1SUu6cNCFIM/W0S3RfCV2LAn9TS\n396X7UOVjzDHk7DCy+7NLnS241kRZjiaqa+wbCguWYu7ROPBP67As6bAKWQbe1PpQ6k7Zblq/28X\nk1R61strOFg+FbzK4PJw7vMlinVKPZOeMNZLDDSgsmwdfdF7jJrg0sH/x01sWG3g0ACfGy5Hid9N\nQYnDRs/C/HAQbGrO/jQ80gNrt1ChLa1GlPmgjS2rchllbLKprzIfHBukN2ri3TO1SlAruB8WrhH7\nvCF++QxRfFSVxxm1Cs8HBUGQCSxBKNh0rftX+dmJzI+rf8i/+bO3+f4ys+jJycfwO7mEcOUquDd6\nD6gosARzjpUV6d/3nArjeKOBN75zjavDMxOh2rJeek/9JaPrDUbvJjBeDzFjsbQZ5cSHXYw+An1T\nfe4zzYGRWZaeuNpNDKj+A1/OXVgm4VOdhDO/mb4svJC2xi6f5PRAHHA8vXz78dxnC3j/YDtapI+/\n7BjFeDJKAhchr5FJLGqouNjJ2b/owO2cJJ6AwNsbsslk7S4PHZ+epiNeivXVKBgBKjPti9/5dbof\njx2hh1lL0ZX9YNOHU/fe7qiE+58x9FCjbpMkQQsjnzYUjMf49RMwoz0caesBHFTt/zE1ZTqg4VgN\nQ9f6SVXWEIs9AFbOGSQTV6+SWuHFP23ZZ/R8F6POKg5uKaW9PzHjOnOkm/6EQUNjgPBfDMz4TG3D\nU9fntlH536l82s/MYWuWacKKddmtS1qRBr8ZxQR0NIypbYhjYXrugOsNf3agpNQG8PhJnLPHOrOT\nbNUHfsK29WnlmyasK3NnfYaGxeh9E5y60kdougOeROi/naK2dJgHTyRBWX5h9zGjA2fpGMjc+bJq\nfrJ/WwETHmp7U+lDrTtVuWr/bxeTVHqe8j99x48S/sqC1YGZWwGXINap9QyOkqnvSNB9KQauKryZ\nG6M5jWz9r33cB7gIVOYeoqn0LCyUBNdupqjYU+gE/GI0osgHC7HlPLmMOjap66vy9am7MSwgeqGT\ntgvM2pqoLncxGlHmDVaUroEEgQMHcf/NUeIzbk0B+aAgCM8V2UIovJTo3wuw5w//lJaWlvQy/Yf/\ni/DffvP8K2LF6DreyYnjJ3L/Ojs5e3k4/yXm40V/bepGN0MPHez+fTfJ5CRYSZLmtJVHKQvQKS4u\nRgcmkxOzOtBN1fYqPAaYN68wPCuftu700XkljrEpxLbsYEHnjbeCNLa00tp6mFqvzvDlK6gWUs9u\n63fLA1Rt8gAp+vqHC2vrWBIL0B3FFK/SgUkeTI3YzQQPngB6MQ5HMQATqdxSlNFEut3FxcUUrQYe\nTTBzoYpO1dtNNL0dwLwT5mJ2KbqqHwrrQzuinw2A04/PKXp+bqzK5KPrq2hqaSTgShH+xfnMlgmd\n6t1VaIlBjrYdyZzd8XjOibPw/xjFsdk/7UycQbpvpvDv2g7jSWCS+JiZ1eP5X0TQN+3CzQQmkLw/\n7Sm4jQ0DrFzjZcdWPwYwdOkqCz7id/ZJueNRThzrAyPAgV2e2QY+tzZWV1C3p56W1lZaWxvxrjbp\n/+Ut9dc+smx9hL5xF1VrNQY/OUrbsR5kg8byw7O1jtB7LbS2ttL4phfzzlVujc+jgDntzUYfKt3Z\n2LG9/88Tk5R6zqqS8qoa/GUGPBzkyoi5tLGuED2Tovd4J7EnLkIHap6aqIhdPkn/XQi8cyC76mYh\nehbsMUeuEsfg9cp5nm65AI0o88FCbDlfLlNAbFLHEHU+6NkaouX9IKUPY3RP3xqvKHfxGpmboa5u\nUqv9VK/XeDAJVnL0qUPilfmgIAgygSUIdmivfp+NP0pvG9Rclbz2j+E3v/7751+RxymSD5I8GH8w\n7V+SiUfpQF1UpMP90Vwybk1fnZD51ViMwYEh4uNzB9qV6VbO+N1wNAak6Gpvo/NyHOurMB3/beps\ngi8YTED1+83Uh/bRvLcCc2Rw1gSLA9/WGvY1BtEYJfkw90niRjdHToVx+Opoess7vdfxVPoy2wY1\n/JsrgDixzACmkLY6ynzUvLWPYLlGYjSpnGTItnVgEFzVNB+oZ1/jT/CuSDF4M/18LHV7kASlNB5q\nILS/kWC5RvSzqfODkgx9nqB0ZxMN79TTeCiI9jDK4B0rk9c8Bjxs32hgbKzBt4IZb6TL3w/2ffj0\nHZtNgvBNk9Lf88kqk0WwMm9PW8RuDTJ4Oz5zIPwoPSVV6qvC0F3U7PLCo8e5LbQbazh8uJmmQy2E\nXncBxtPnl4zdYOjhzO2DqZERLGDok3bajvVjPonTdawrffaTOUzsCZg3u2hr6yT2yCR8pjN7Zo7a\nhjO25PRQtbOOxr0VkBh9ahA+pw1pK+HJ19yzcnpkzbTtQfcH6fiwi1Gnj6am2hmraJTa0Ax8mysy\nf+/C/0Md7sWy56as1ODr+L0ZtXO7HQX4CAc17x6m+f1mWt4P4gIchrzaYDlhlPnwZrYNurZUomMx\ncidlrzuVvdnoQ6U7tR2r/b8qJin1PCMeBqjb34h3BcTHJpc01tnqmQTdHUcZ/Mqg7lBjdvXV1B8P\ndXdweiCOb08TteXTPrTRs7AwbvxNBNZunmP74LPXiCofLMyW585lbGOTIoYofb2Vzo+927zoJT52\neDWsycmCyl2cRvLlDUmiv7Lg4RDtbW303TUZ/ayLrmnnZ9nng4IgPNd5AOkC4WXky+thUq7X2Pi9\nV0j9f1e49X/gtYofPP+K6D4OfpB/KbHnRx6IDHLlTjWBsYtEn0BN2fRBWYruE6eJPgJGLFr3B3If\nmSmSv42n36A0Fic5Voojc8aFP9SEZ9wCa4Lhz3roveOm8V9nrl2VPhR8+O9ibNvuITJyD/gu39VI\nv1ns1BWKAtvYvmEdw5fCWOhomUTavN1D54UIOH3U/XM3g+dPMKRt5+BuL9ZYlCt/N4l/0waMVRP0\nXYrAKi8bnPZtTVzvofdOEdve3M66yWHCIxaatyiX0o2nMk8ULZLxBMmSYoySdNpRZDjg1zFi5jY8\nvx1m9AkUG69mBvYOIM7QnRQ1ZRaxhAWOqQXwRThWw3A0grl1G5O30ueUpM8xgAqvB0ZGGLxr8gY3\nGH4C35t6R5uiH+z6EExS4xOMTligJUiMJSlyZs4fmfqLkTBxNOo2uUXMC0GhDYDUrW5On4umQ5z7\ncG57j9ND+SoYiQ5hbglw4/oIrPDMDISaA2ukh67PE7je2PfUAGT4WhhmbR90bA7SXDaJZVl8fTfM\nxxfj1B7MbNvVffz4fQ8WFhNjMXp/0Ufp7oPZOqls2Lo/yNkLcXz/shqfy6Lv2jCs8k6rb35bc2yo\nxHEuypW/jlGxJcnFEQv3zqk3ZUbo+FkvSQxq9m7n6+s9nBrSePfd9GBBpY3k7TCRlAv/Zg/aWIQr\nERPNuyG7bcu/wUH05hViuz0kz1/Cwo3HWag/BMc/eUDPR90kcFG/q0JsfblgJQj/dRRXpR9PiUbk\nUj8mOr7Xcvcvr+5U9majD5Xu1Has9v+qmKTUMzDYfZqY08+ubV6sL/uIPgFvkbaksU6pZ1L0dnQS\nSYLvzVrcY4Oc/ChC1Z814NUher6TnkgSh6+W7eu/puf4KbRtB6jd6LDRs7AwrUQJ32XO7YNLoRFV\nPqi0ZZtcRplf2cQQla93lHvRGCZ6PUagSuPzX1nwPa2gcheuEVXeYPDHTc2ksLDGvyZ8/mNi62sJ\nbTHs80FBEGQCSxAK5Te3r3L9/lX6pgY9P9hB7e8uv5RL31hHzc1h+k61EwZcr4eoKpkpwVdf1eAr\nC8MonnFt9OJxuiKZ56ADXXQMaNQeyiQ8mgOjBCJdx+mNmkCCUxc9NAd9oPsIvjHI2SunabuSvrxi\nZzCdsGjFaGaM8LkY4XOZQeWOffgzj6hiI7H0f8YjnP1Zekm3Y2p+7mGc8JUw4Ss9U71O9YG3somu\nqq1FRRCLholFM6dnrfawL+jLJd/HjzKUeeoXPtNJeFXuVc/eHbso/aKb0+1tmUkIL3WZM0X08moC\na6OETx3NnMvloPqPps4I0qneHSDyST/tbf2ZxKmGzSW5SYfqyH+i/6N2+gEMHzWZw/WV/WDTh+bt\nixz9ZGo5fJjOY+GZr2QGvrg+BKsDsn1wgSi1AejOV7M2WjRjlYNB8N9Wc/SjnE343qzJvoWw58P2\n7FPt0teDHHxqS0SKz2+mcGyavY1Jx1GiY97q4uyFKAA9py5S8edBHGjp82nMCMeP9WICoxdO4dnY\njE9X27BWVMTEvQg9H0XoyXxP1f6c5pS2pnkJ7azg5OXTtA0ArgChrenz1lJffpF5Ap+k71Tm7Bun\nP701w0YbD+5F6R+I0/8pWT3mtAwVb4Wo+PIkp48cASDwdjB71K3KR5i3e2j/JPN0f5Wb4PsNhR1+\nLDwnJoh+1k98oD93r3fuwzdteUM+3antTa0Ple7U5ar9vzomqfQMRSsniA50E512FthblY4ljXUq\nPWMO80VmsUjk07NEMr4u86oTvvgys7Is0ktnxl1M1chOz8L8Sd0aIoVjzu2DS6IRVT6osmWbXEYV\nm+xiiDL3dfrZtyPCyWyO6qJ2p6+gchesEZu8QXM6MEjRffwskYdAooeLngqClQ51PigIwgvhH01O\nTv5f6YZvL7r+Yg/ibGtro6WlZUnKtsxv+GbCQtNf4ZXipRvptLe309rauriEZiyBRe5J63PBTJL4\nxqKoxDVj9Q+ANZ4kOWlR9IoLx7yqZJEcS2JZGsYaY84Z8PxttUjeT2JpRbhK5j/ZmLyfyHutOZZg\nIm+dTBL3J9CKjOzT96evLcK1Zn51WngfApaJiY4uA/S8fmOxmrPr/8TYxFP3zhxPMmFzTy3TBF1/\n5k9/VDacGkswaWkUrzHm/YawtJ2Ca42xwDrNoQ3LJDk2odByWuu84sLQC/QRtmUKy0F35liSCWvu\nuLKULFx3Kv+/iJhkpUiMTaIVFWM4n1+sW4yeVf5QtPcsY5NF2lxfjgBvl8uo86uF576WmST5jUXx\nGte849pz14iwvHK4Qjy/acqN+BYjwyfh5TVe/RW+85K8KMdR8gLeNKcbuPIcNKk5DVwLWv2jYdi0\nJX9bNYw1C+8H1bV6iSoB0nEpXveuvlbREwvuQ0DT5VXlL9Z5zGkTutNAd9pdujR3TmWHjhLXgrf0\nLMZO89ZJ0zHW6Go/odDrnD7CtkxhOaCXGC/Edy1cdyr/v4iYpDkUDz2WLtYtKu4o/KFo79kOrfSX\nqDvtbGqhOZJd7qspctRlpxFBEJYVcoi7IAiCIAiCIAiCIAiCsKyRCSxBEARBEARBEARBEARhWSMT\nWIIgCIIgCIIgCIIgCMKyRiawBEEQBEEQBEEQBEEQhGWNvIXwW85yeAuhIAiCIAiCIAiC8O1H3kIo\nLCXyFkJhyWlpaXmp69/e3v7CHbEg/ENiObyCWRBEd4IgiEYEYf46EYSlRLYQCoIgCIIgCIIgCIIg\nCMsamcASBEEQBEEQBEEQBEEQljUygSUIgiAIgiAIgiAIgiAsa2QCSxAEQRAEQRAEQRAEQVjWyASW\n8PJjmpimifWtbZ6ibZaJaVqKz5biLRzWC+lvyzTJ31RFP2Bhjn977UNYuA0vgZEuqeYWog2lj1iE\n5pZKy4upr7AMYtIy1POCY+hLZ8eLiHWiu2WikaWy5YWXa5tfLTQ2LVUMeUH+69s8DhGE5Ya8hVB4\nicegv6H35x9z6346eL72r37M3t91LLMsJkbPf+0lcjfByvI6Wt7xz+PiJD2dHQwlABxUH2hi2/op\nyZqEu47TF02mf1ztIfTuPrzO9I+xq12cvhJN/+D00nAohDtzXW/nUQYT08LsCoO6pib8TjBHejl6\nZnBGEDZ8QZqCvnS5A12cvpwpFwdVb79LzUaHfVutYU7+x7PEH+VcT9WBn1KzfroLMuntaGfwoZfm\nD0Jk7+R4hBOd3YxmrnX5ajkYDGScl0n4zHH6RtL94PDW0hQKZB2beecax0/1k/7UoOa9RqrWzHR7\n1kgvR84MUrGnmfrN6W+164fhSyc4+9lozpGur+anB7alv3dR91wohNSdMF3nrxJPmlTsaaJ+s1H4\nxeMROj/sJgGwuoKGQ/W4NftybbWRV3M5hi92cPbzSeoOteB35gYAQxfPculGDFPz0vRBiGnfymD3\nKXojGVtb5SZ4sAFfSSHaSNF7vIPBr6ypytLQGMy2NTbQQ89AhOTDldQeaiHgnNlH+cvNabrzyFmS\n5XUcnmbjyntjp2WFT1P3ofA8iF/v5vSnkYwGNAJ7G6mtLEB7ZpQT/6GL0SczxENzUxCHnT9V2pta\nH6nbvXR8ktOs780Ggltyqkze6qHj3BAAenkNze9UZXy4ur6YMbqOn2bKVPWyKn68vyYTsxS6synX\nvN1D+ydDuc9WeWj4YB/uAvyPMtYpY5JKd+p8QZgjNtnYnEIkal+vygetON0nThPJ3CdtrZ+DB+pw\naYvViDq/ilw8QffnmXJXuKg9eJDAlM0pfb0i5tlpD4ic76T7ZgKAip0N1G+d1b95YpPKf6m0p47R\ni/CLgiAsGFmBJbykfMO5//fn3EoY1P7bH/OT5ublN3kF8CiFVVxK6Sp4PM9LY5dOMZQwCL7fRPX6\nFP1/1Z1JToG7V+mLJgm83URryz4qHsfoujCUDd49V6IYm+poOVSPx4xy8sxgdrA8aen43wwSerue\nuq1ueAKO1ZlPH06irw0QejtE6J063ICl61Nhmr7LUVyvBznc2kJNuUm4+yKpQtr6MMnXjwyq9wYJ\n7qmjdk+QwNqZE0mpG+cZTM6R1t0fhe/6qX+/hX07K0hE+hgan5p8ukTfSBLfnkaa3qnCjPby8Y1k\nNvnqP9dP0vDT2NJEdVmKvhMfk5w1Sdh9Ltc32fxH2Q+QGn2Aw1tNcG+Quj211P0Lf26wtYh7LhQ4\nd/3bFA63B33+V9L3V90kDD+Nh+pxm8Oc7hoqqFylTSg1NzV5M0T358lZlgbwmEnLgWedBtasp0rj\nt+iNjOLZUU/LoX14V8bp/vhaQdrAukcSNzXvNNF8oAY9GeHKrZz1mw+h9Psu4PFTT7KU5WYY6s5M\nAs7j3ijLVfk02z4Ulp4kVz6NQHk1TS3N1HhXMnhubht4mkks3U3t2yFC74SoWg88KcraiNKfquzN\nRh9f30tSuqkmXd9yncila7l4RYwz54YwNgVp2l+FOdI3LXao65u43kc06SJ46DAtB2rgTpieW6kC\ndKcu1zJT4PRStzdI3Z466nZvo7SgmGQT61QxSak7db4gPI3a5hTY2LIqH0zd6COSWEn1O000769h\n5VdD9FxPLFojdvnVaAL8OzPl6gl6P40UGEMUMc9OI3d66b6ZwL+3kX073AxfPs1gQbFJ7b9U2lPX\ndzF+URCEhSIrsISXc/rqf/bx5f9xUHvwT6gs1pavJTt9BEM+ol1Ruibnc2GKob9NovtC+EoM+JNa\n+tv7GB6HgBNS9xOAQflGAzDwroPhSSs7WZREI7jbj66B/3sQ+9UISQIYOAg2NWe/ZXikB9ZuoSLT\nf47KIM2VU59GuQhseb0ikwglGAX8G71oaFR63fSNTFsybdfWFcWsXV+KNjZJRbl71gA3zpkLUbw7\nqxgdSMwYnOrlNRwsz/zgLYPLw9nPzfEkrPCye7MLne14VoQZjsZgsx+wSI5Dxd5duHSdgN9D/51Y\ntg8B4pfPEMVHVXmc0WlfquyHDMUla3GXaDz4xxV41ujP4J4LhWJsrCG00aLr9hHmtWHPjHIjCb63\nd+Fy6oR2ezh6IUoKPw6bcpU2odRcmms/7wFfNd6/vz5r8kWn6q0g1u0ujnwya3jp9NP03gaMNenJ\n+Yo1EL33NSnAYaMNtArq352y2Q2sow9r2hd7d9bhtSIcOdLz1GSQslyA+330RIuo3vE9rsesgu+N\nqlylT7PtQ+E5qI7g+03oJQYaUFm2jr7oPUZNcNnNJOt+Gv/cn50UGT4Hpf9s5oq+vP5UZW82+vDs\nrMczpYDydfSNWLnYcXuIBA5Cu30Ymo+6sjA9kUzssKlvIvEAVm3A69TQnJWso4/JKVtV6c62Hyy0\n1W486w2+Hi+iosxVYEyyiXWKmKTWnTpfEJ5GZXPqXFFly+p80LE5RJNXx3BqWZuL3x0FXIvTiDK/\n0qnZfzDX7jUQnRZg1DFEEfNsNDJy4xas9rOr0oVOiIorR4mOpAhkVs/nj012/iu/9pT1XYxfFARB\nJrCEf1jc/fVdwKT3xFF6Af37W/jTf7MDxzKt7+QCRlumCevK3FmpaliM3jfBqePYXEPFxU7O/kUH\nbuck8QQE3t6Q/tNVRehYXL0Uxbe7lFgS0FbOIfYE126mqNgz9xa3xNWrpFZ48a/JJVi1Zb30nvpL\nRtcbjN5NYLw+cym1qq2Pn8Q5e6wzmxBUH/gJ29anI3z0fBejzioObimlvT8xR11N+o4fJfyVBasD\n2QkoTXfAkwj9t1PUlg7z4Ml0p6bhWA1D1/pJVdYQiz0ApvWDFaVrIEHgwEHcf3OUeJ778FQ/AGiP\nGR04S8dApjVl1fxk/7YZk3KTMsJeYswFrHCzeIyG54eZO6VpwNfELfBqhZf7lE3YaM4c6aY/YdDQ\nGCD8FwNzBl7z8eM5Q7QxtSVjLEzPHXC94Z/l5+bWxhSxyyc5PRAHHE9vtXxsKft37nJNek6FcbzR\nwBvfucbV4fnem7nLVfq0AvtQWFocJUY2dnRfioGrCu98B2n3P2PooUbdJleB/lRlbwXow4xy4sMu\nRh+Bvqk+G68s04QV67JbarUiDX4zigkzH67MUV/vH2xHi/Txlx2jGE9GSeAi5DUK112+fkDD+qqf\njmOZH1dXsO9QPR7NLibZxDpFTLLTXaH5gmBvc3bDMZUtq/JBNAeGc8o2uokB1X/gW7RG1PnVlF/O\nbW31v1NZYAxRxTy1RiYnH8Pv5B6ArlwF90bvARW2sUntv+y1l6++z8QvCoIwL2QLofASo7PlD9/j\nvT/cjPn31+n7u9Tzr4IVo+t4JyeOn8j96+zk7OXhpfm6R5kM1Ezw4AmgF+NwFAMwkco8WtV9hLa6\nSXzeRVvb1JkJT2+5MUeuEsfg9UrHnIPM8P8YxbF5eqKTIpGyAJ3i4mJ0YDI5UVjFV1dQt6eeltZW\nWlsb8a426f/lrfRn44N030zh37UdxpPAJPGx2Ws3VlJeVYO/zICHg1wZSX+ub9xF1VqNwU+O0nas\nZ9aybZ3q3VVoiUGOth3JnJmQS0CGurpJrfZTvV7jwSRYydE5DgWdqx/As7WO0HsttLa20vimF/PO\nVW6NiyJfTiysec2EzWETSs2lOP+LCPqmXbiZwASS9xPzW0E0HuXEsT4wAhzY5SlIG1N8tzxA1SYP\nkKKvfz5+ae5yUze6GXroYPfvu0kmJ8FKkpzXabp56qvyac+iD4VnRIre453EnrgIHaiZ90Ri9LMB\ncPrxOQvzpwXZm0ofupuq7VV4DDBvXmFYZTSWVVB9U2NJLEB3FFO8SgcmeTBrdshOd3OVq5e9QfDt\nRlpbWzn8Xi36w2Gu3UgWEJPUsU7tzlS6KzRfEBZsc/Py9Xnywamf7/TReSWOsSnEtjUsWiPq/Crj\nzdd42bHVjwEMXbo6a9WtOjYtxFfM2Q/m43nEprn9V2HaWzq/KAiCTGAJ/wB4bD0Gvk/Vj17hlR/t\nYOOKAp7mLElFUiQfJHkw/mDavyQTj2bWRRXMrLEYgwNDxMdnWqbz5gAAIABJREFUBtqVGnwdvzej\nFLc7c0zs7UESlNJ4qIHQ/kaC5RrRz3LnxXh2NnD4UDMth5sIuIDVxU+tTrvxNxFYu3nu7QBjNxh6\nOGvb3PgXDCag+v1m6kP7aN5bgTky+FRyNmdbNQPf5orMUzMX/h/qcC9GCkiNjGABQ5+003asH/NJ\nnK5jXbPOqtLwVAao29+IdwXEx6YSbAc17x6m+f1mWt4P4gIcRq6ljo01HD7cTNOhFkKvuwAjc35H\nkuivLHg4RHtbG313TUY/66JrdsIyVz8ARpkPb2abi2tLJToWI3dSBd9z4dmwMm9PW8RuDTJ4O/7U\nJMdKLOJxa9qV6/DohZab3ybyas4cJvYEzJtdtLV1EntkEj7T+dSZUnnt5f4gHR92Mer00dRUO8fZ\nUvm0kdFAmY+at/YRLNdIjOZJyFfNKdo5yx2OxoAUXe1tdF6OY30VpuO/Dc3j3sxdrtKnFdiHwlKT\noLvjKINfGdQdapxjlUF+3U1dH75pUvp7vhmWofKntvZmqw8Hvq017GsMojFK8uGUGa6EJ19zz8pW\nHdbM3to+d32HBwbBVU3zgXr2Nf4E74oUgzfj89Dd3OVqJR58G9OrTbQ1fjasgvidWEH+J3+sU/sY\nu1yioHxBKMzm7DSisGVVPgiQuNHNkVNhHL46mt7yzjOG5KuvOr8C0JweqnbW0bi3AhKjTM4jNmk2\nvmYujRQV6XB/NNd3Vm5lmn1syu+/CtGetmC/KAjCs0ZCkfBS8oPXvg+/+hU3fmMS4G/5X09gfWbI\n9FzRfRz8wKf4A5PU+ASjExZoCRJjSYqcBo6s8lJ0nzhN9BEwYtG6P5BNHPwbHERvXiG220Py/CUs\n3Himts45HUCcoTspasosYgkLHMasxEIjcv4UgwkI7J/1RMiKEr5L3u0Aw9fCMHvb3Kr0QZrDfxdj\n23YPkZF7wHf5rmbf1uTtMJGUC/9mD9pYhCsRE827IT3A3xykuWwSy7L4+m6Yjy/GqT2Y25o42H2a\nmNPPrm1erC/7iD4Bb9FM1+X4Jw/o+aibBC7qd1XM8nIOrJEeuj5P4HpjXyYBN/jjpmZSWFjjXxM+\n/zGx9bWEthj2/WAlCP91FFelH0+JRuRSPyY6vtccBd5zYdGYKZK/jZN8AozFSY6V4sicQQGQutXN\n6XPRdIhzH85tXdA3sGF1D0O/vEJteYCL/30Y1tfkJndtys1rEyrN6T5+/L4HC4uJsRi9v+ijdPfB\nGdspUuMp7t2fADTujSX5ruZIn2diRuj4WS9JDGr2bufr6z2cGtJ49930IESljcT1HnrvFLHtze2s\nmxwmPGKheYtyZjyeyqxiskjGEyRLijFKdFvN+UNNeMYtsCYY/qyH3jtuGv91oKB7oypX6dMK6ENh\nqUnR29FJJAm+N2txjw1y8qMIVX/WkB2w5dXdlGmMhImjUbfJXbA/VdqbSh9WnJ5TVygKbGP7hnUM\nXwpjoaNlJmsdGypxnIty5a9jVGxJcnHEwr3TbV9foMhwwK9jxMxteH47zOgTKDZeLUh3qnKjA9eY\nWOOlssxg4os+hh6B11thH5OUsU4dkwrJJezyBWGqn9Q2p9SI0ter80Hzdg+dFyLg9FH3z90Mnj/B\nkLadg7u9i9KIKr+y7g9y9kIc37+sxuey6Ls2DKu82Vhpl7fljXk2GvH8yAORQa7cqSYwdpHoE6gp\nK8BX2PgvO+3lr6+9XxQEQSawBCEdUP/pHrZ/8TOu/vw/cRXglY3s+KfLb2m7efsiRz+ZejNLmM5j\nYSr2NFM/deAkGq++qsFXFoZRPOPairdCVHx5ktNHjgAQeDv9BAxAL68msDZK+NRRwpkJr+o/qswm\nmx1HplYwaQT2NlFbNuuNf7eGSOHIsx0gxec3Uzg2zTonQfcRfGOQs1dO03YlU8edwexEk6qtD+5F\n6R+I0/9p5mOnl33BqYk/HUeJjnmri7MXogD0nLpIxZ+nX5tctHKC6EA30Wnno7yVqfeMVx+vchN8\nv2FG0t7zYXt2lUbp60EOTls6rzkdGKToPn6WyEMg0cNFTwXBbJ/k6QcmiH7WT3ygP3evdu7Dpxd6\nz4XFEr14nK5IZsXbQBcdAxq1h3KDAd35anYiuGhGMq5TW1/DFx/1caQtDJQS+vdVBZeb1yaUmtPS\nZ2SYEY4f68UERi+cwrOxOW0zZoTjH3Zn3/x09lgHWub136kvv8iUmaTvVOb8OKc/vX3JRhtFRRCL\nholF0x6C1Z5pmkvRe/woQ5kn7eEznYRXeWn+IGSrOTQHRglEuo7TGzWBBKcuemjOlK3qQ1W5Sp9m\n14fCcwhmw3yRWUgU+fQsaQ9nzHzhRl7dpfni+hCsDszaEqT2pyp7U+pDK0YzY4TPxQifywx+d+zD\nny3XS2hnBScvn6ZtAHAFCG11FVBf8O7YRekX3Zxub8vGs7rMOVdq3anLjUf6CX/VT+9UL5bX5HSn\njEnqWKeKSWrdFZIvCLmkwsbmFBqx8/WqfDA2klkpNB7h7M/S99nhK6Bcm/qq8iutqIiJexF6PorQ\nk4mtVfvfytqlMoYoYp6dRvSNddTcHKbvVDthwPV6iKoSe19h57+U2lPVtwC/KAjCs+cfTU5O/l/p\nhm8vuv5is/u2tjZaWlqWLqd+8L9JPdb5jmvpEqv29nZaW1tfUA9aJO8n4RUXxhy30hxLMGFpGGuM\nGW8ySo0lmbQ0itcY6HnKNU3Q9bnnsK30h3PPcJtJEt9YFJW45reqyDJJjk1gaUW4SuZ5v6wUibFJ\ntKJiDKdecJnmeJKJSYuiV1w4FiAFVT+YY0kmrAX0g1CQ31hSzVkpEmOWQh/ztYlCNLeUbZlDG9P8\nxzPV3JLWN59PE74dujMx0Zkr7CyVP7XGkyQVMSD9ObjWGPOqL2TOYZtTWza6U5RrmUmS31hoRcaM\nFSl2MWmxsU6tO3W+IBqZn80tVT74zDVSQM6WGkvkj3uLiSE22kuNJbDIrRp+Nu5JrT1hGcWSQnya\nacqN+BYjChVeavRXv4P+LZeoscaVv/0lrjnPxHGUuGzeyKihmtvUVB/qBq41C2mKjrFmgXdLc+Ba\n45h3mbrTQF/ENiNVP+glBrL442WVlWNhNpzXJgrR3FK2xbEg/7Hwcpeqvvl8mvDt0J2e994ulT/V\nnAYu5wI/V9QXUGjLRneKcjWb+JovJi021ql1p84XhPnZ3FLlg8+8vgXkbMq4t5gYYqM9R8kS9MNC\nc1tBEJ47coi7IAiCIAiCIAiCIAiCsKyRCSxBEARBEARBEARBEARhWSMTWIIgCIIgCIIgCIIgCMKy\nRiawBEEQBEEQBEEQBEEQhGWNvIXwW85yeAuhIAiCIAiCIAiC8O1H3kIoLCXyFkJhyWlpaXmp69/e\n3v7CHbEg/ENiObyCWRBEd4IgiEYEYf46EYSlRLYQCoIgCIIgCIIgCIIgCMsamcASBEEQBEEQBEEQ\nBEEQljUygSUIgiAIgiAIgiAIgiAsa2QCSxAEQRAEQRAEQRAEQVjWyASWIDwHLNPEtJaqcAvTNLGs\np7504W/hsEzMvBW2XsjbPZR9qKzvIr9TUe6c/f487rmwpJimibVAm1iEsS1Yc4uztXTZpvUs22Rh\njtv0obUAn2ZzD0Rzyz4QvpDYobKlQuzt2ceORcZQRX1tfdcCY6iqXOFZ9pfaNpYqNtnWN6/N2dfX\ntBaukedty6bEEEF4KZC3EAovJb+69F/4eOirGb/b+K9+zJ7fdSy7ukbOd9J9MwFAxc4G6re6C7ou\ndSdM1/mrxJMmFXuaqN9sPBXgh86fpOfmKAC6N0hLyAdA7GoXp69E03/m9NJwKMTUtw5fOsHZz0Zz\nTmB9NT89sC3jDFL0Hu9g8KtMBDd8NDQGcWsAJoPdp+iNZK5d5SZ4sAFfSabciyc4+/nojBr69jYT\nrHSANczJ/3iW+KOc66k68FNq1mtgRjnxH7oYfTLtQsNHc1MQB8B4hBOd3YxmrnX5ajkYDKTra8Xp\nPnGaSCJdX22tn4MH6nDN8mzDFzs4+/kkdYda8DunMpUYPf+1l8jdBCvL62h5xz89jSF85jh9I0kA\nHN5amkKBrMM074Q5/vM+kpk6e94IsW+XN/2Dqr7Tyu/taGfwoZfmD0I4RNLPcOSguq9q4te7Of1p\nJJPcagT2NlJbaRRkE6r7qtaySnNpe+r8sJsEwOoKGg7Vz/jM1tasYTqPnCVZXsfhWX2RuNHDiQtD\n6fau8tH8QVpz5u0e2j8Zyv3hKg8NH+zDDZgjvRw9MzhjAGD4gjQFfRltXOP4qX7SvWRQ814jVWu0\nAv1hfp8GYN0f5OTJ3kx7DUKHm/BqhWpOWCz2MSk/+WOSSW/nUQYT0yxqhUFdUxN+p729qXSn9NN2\nMXSgh56BCMmHK6k91ELAOe3bFPqw9RMKPStjqK0+kvR0djCUAHBQfaCJbesL0Z1JuOs4fdF0fVnt\nIfTuPrzOQnyiML8YYpMrXjxB99T9X+Gi9uBBAhnfuWSx6XYvHZ/k9OV7s4HglgJ9sio2mTG6/vNp\nouPpH71vNhDa4i6ovrGBLk5fzvgKHFS9/S41Gx2LtmVlW1X1VeWvgiC8EGQFlvBSMmlarK2sYe8f\n7mXvv9oCwMrVyy+YWHd66b6ZwL+3kX073AxfPs3geIHX/jaFw+1Bz/P5UNdRem6OEtjbQEvL4VxS\nYQ3TcyWKsamOlkP1eMwoJ88M5gYhow9weKsJ7g1St6eWun/hzyU61j2SuKl5p4nmAzXoyQhXbmWS\ngfFb9EZG8eyop+XQPrwr43R/fC2XHpkWpZtqCb0dIrS3Kh3mp+7JwyRfPzKo3hskuKeO2j1BAmun\nvnUSS3dT+3aI0DshqtYDT4qy7Tbvj8J3/dS/38K+nRUkIn0MZfowdaOPSGIl1e800by/hpVfDdFz\nPTGzo8aH6P48mUnFpvEohVVcSukqeDy770cu0TeSxLenkaZ3qjCjvXx8I5n9/OrFPpLOAE2trTTs\nrCD22XmGTPv6Zu/BjfMMJkXHS4LivqpJcuXTCJRX09TSTI13JYPnMsl5ATahuq9KLas0h0XfX3WT\nMPw0HqrHbQ5zuis3cC7E1oa6c22YWdcuOi8M4Xq9juaWFg5nJq8ALDMFTi91e4PU7amjbvc2Sqc+\neziJvjaQ1vk7dbgBS8+qlf5z/SQNP40tTVSXpeg78THJAv1hXp+W0fHRn/Uy+mqAhkMtHD7cmJ68\nKrAfhGcQz2xiUv4LVTHJYtLS8b8ZJPR2PXVb3fAEHKsLsTe17lR+2s7ezIdQ+n0X8PipiVClPpR+\nwkbPqhhqU9/YpVMMJQyC7zdRvT5F/191F6a7u1fpiyYJvN1Ea8s+Kh7H6LowVJBPFOYXQ9SYjCbA\nvzOTX+kJej+NLHls+vpektJNNelyy3Uil66RKiTPtLHl4V92Ex03qHuvmfodHqKf5mxOXd84fZej\nuF4Pcri1hZpyk3D3xWydFm7L6raq6qvOXwVBkAksQSiQjXV/yr+r3cxrP3qNH6yeBH6HLT/Ql109\nR27cgtV+dlW68GwPUYFFdCRV0LXGxhpCwSCeFTy93HlskN6oiXdPI7WVpej6tGD6MEkSjerdfnRn\nBf7vAb8aYXruUlyyFneJA2N9Jb6yaet/tArq391HVbmBY/0G1kFuybjTT9N7zezbXoHu9FCxBnjw\ndTYB8AUbOfhWAO9GL+WrJwE3VeXT7smKYtauL8XhdFG52YsxVWXdT+OfNxDY6MVbXs7kGJT+Xu5p\nnF5ew8EDdVSU6Hi8ZYCV7Q/H5hBNh5rZVm7gKEvXd/TuzCfY137eA75qvE5tZj86fQRDQap+oD21\nLN4cT8IKL7s3uzDKt+NZAcPR2FQqSGIMHOVeDMDtKwdMJh/Z13cqQTtzIYp3ZxXGyseyLeNZo7iv\nNooj+H4TP31nG4buoLJsHfA1o2YhNqG+r0otqzRnRrmRBN/OXbicFYR2e7BGolnN2dra/T56okVU\n76hAn9EZSXouRtG9dTTs9qPr+qxBuoW2eh2e9QaOEjf+Sk/2c0dlkOZ3a/Fu9OItL+IBsOX1iux1\nyXGo2LELl24Q8HvgSYzh8QL8ocqnAYMXejFX+2h6t5bS1TqalvvcXnPCs0BpxyqUMclBsKmZui0+\nvBsrcPz2AazdTIVWiL2pdKf203b25t1ZRyhYhcZcbc2vD6WfsNGzMoYq65ti6G+T6L5d+EoMtv1J\nLTwZKUh3qfsJwKB8owG6B+86YNIqyCcK84shanRq9h+kbms6v/KsmR4Ili42eXbW0/BWFYbuYEP5\nOngyzXeqbM7GllOJFJRX41/joKLKB1hEMyuulPUdTzAKuDd60dCp9LrhSW7L38JtWd1WVX2V+asg\nCC8EkaDw0jP4N7fgB7V8ZxnWbXLyMfyOO/vEeuUquDd6D6gosARzzlUkqbsxLCB6oZO2C8xcKr2q\nCB2Lq5ei+HaXEksC2sqc2LXHjA6cpWMgkzaVVfOT/dtmPFWPXT7J6YE44Ji2TUTDmNoKNBam5w64\n3vDPuf3ts18OQXkdrmm/e/wkztljndlkrfrAT9i2ftak4/3PGHqoUbfJ9VQ/9B0/SvgrC1YHcts5\nNAdG5v+Jq93EgOo/mPYUfaSb/oRBQ2OA8F8MzOnwJucYiWm6A55E6L+dorZ0mAdPpjtLB7W7PHR8\nepqOeCnWV6NgBKh0FlBfIHq+i1FnFQe3lNLenxAnvFTaW8AMhqNkytYTdF+KgasKr16ITRRyX03l\nirC5NWfxGA3PD6cqoQFfE7fIrj7Kb2smPafCON5o4I3vXOPq8LQvG4/x6ydgRns40tYDOKja/2Nq\nyvSs1q2v+uk4lvlxdQX7DtXjmdWoxNWrpFZ48a/JpRSO1TB0rZ9UZQ2x2AMg53tU/lDp00gR+7UF\njyJ0tKVXJXi21rNvZ0VBmhOeJeY8VzYWEJNyFsW1mykq9sy97fdpe1PpTu2n1fY2FbSsvKlzPn2o\n/UQhep47htrV1zRhXZk7Wz8Ni9H7Jjh1pe4cm2uouNjJ2b/owO2cJJ6AwNsbCvKJwvxiSEHqmrZl\n1v9O5fOJTWaUEx92MfoI9E31GAXZnNqWiwwH3LxK1PRR+mV8xoBTWV+nn9qyXnpP/SWj6w1G7yYw\nXg9l67QYW1a1VVXfgvNXQRCeG7ICS3i5sb7kxn2o/L3XXtD3x+g63smJ4ydy/zo7OXt5OP8l5uNn\n9vWerSFa3g9S+jBG99Ryc91HaKubxOddtLVNnYmRe4rs2VpH6L0WWltbaXzTi3nnKrdmbbf5bnmA\nqk0eIEVf/6y2jEc5cawPjAAHdnnmaGCU61+Bf8u0xGF1BXV76mlpbaW1tRHvapP+X9566tLoZwPg\n9ON7avC5kvKqGvxlBjwc5MrIzEea1p0+Oq/EMTaF2JYd3KQ4/4sI+qZduJnABJL3EwWtHNA37qJq\nrcbgJ0dpO9bz1BaA0cQEAMXFxRStBh5NMFlIfccH6b6Zwr9rO4wngUniY/I4e6k1Nz9S9B7vJPbE\nRehATW4loMomnsF9VWpuZgdgPba3tdSNboYeOtj9+26SyUmwkiSnTqddlRlIrK+iqaWRgCtF+Bfn\ncyu7yt4g+HYjra2tHH6vFv3hMNduJJ+ayAj/j1Ecm6dPYutU765CSwxytO1I5pwStb+b7Q/n9Glo\nFOnAKg+h95sJvl5KbKB7xnYwOx8hvEBsYlJu8H6VOAavVzrmnDh7yt5sdGfvp/PZm01zFPqwix32\nes4TQxdQX+uRZa87M8GDJ4BejMNRDMBEarIgnyjML4YUwso1XnZs9WMAQ5euYj6P2KS7qdpehccA\n8+YVhq2F2lzOlr07duNelaCrvY2Oc0PzyK9SJFIWoFNcXIwOTCYnCoshhdhynraq6lto/ioIwvND\n4pDwUvPNjeuk+B3+6YvaPvg4RfJBkgcrV0773WN4lA6oRUU6/P0o1pTYrOlPSTPBdyzGUDRJqc+H\n2/m0JFcC1mypZrIE7zYvug47vD2cncwFas/OBg5vSfF4tUX/iQ4GreJs0m+U+bJPnVxbKtE/jTJy\nJ0Vg2qDBUeajpsxHaeoI3aPTBq73B+n4WS9Jp4+mpto5z0JJXg+Two1/+vZBzcCXXVXiwv9DneiX\nMVIEpg1+E4RvmpTu8M3hmDQ8lQE8lX4m/58jxMcmIfPtiRvddF6I4PDV0fTWtAN6zWFiT8C82UXb\nzfSvYmc6MQ61zlidMbcTdFDz7mGqxlJoxPjoWDeWMVXTJEOfJyjd2UTDVgOsCEeOdDN4x6K2TFPW\nNzUyggUMfdLOVIrUdayLptZ9yLG4z0ZzhQS3/JpL0N3RSSRpUHeocdaT8/w2Ueh9nVPLNppbiUU8\nbuEv1zJXrsOj22sjvSXDoqu9LfN3cTr+m0Hr/gA8Sk8rlfrS2ylqdnkZPJPbWqKVeLIvZ9DW+Nmw\nqpfInRhsmbYyZuwGQw+h+vWZq0kdG2s4/FoVyYcao9c+ouvz3HlGSn+o9GmTTKaAdV68JQ7YvZ2e\nz7uxHmXdgNJHCM+W/HZsEbs1RGJlKf6N7hl/oYpJU9z4mwisrc5uH5zBHPam1p2Nn7aJoTNYNSsa\nKfWhih2F6DlPDLWp70oN7sXvweaKrB7cboet7lK3B0lQSuOhBlxA5MwRuj8bgi01BfhE4Wns+iu/\nRgA0p4eqnR4Ca1IcOTfKtExnCWOTA9/WGnxbSjlypJvkQ8BZgM2pbNnppeGDwyTHTBi9SscngxjO\nIvv8avwLBhNQ/X4z20rAunWWI+cGGbYCVGiLtWVFW1X1LSh/FQRBJrAEoSBMrn/+G/hBLb/zoqqg\n+zj4gS/vx54feSAyyJU71QTGLhJ9AjXTz5wiRfeJ00QfASNWenCZbV6K5G/j6TcojcVJjpXiKDHQ\nSJ/roTFM9HqMQJXG57+y4HvarERII3L+FIMJCOzPPK2zEoT/Ooqr0o+nRCNyqR8THd9r6TolrvfQ\ne6eIbW9uZ93kMOERC82bCeJmJD15hUHN3u18fb2HU0Ma7747fSLLJHw9DuV1TJ+mS94OE0m58G/2\noI1FuBIx0bwbZgR/cyRMHI26TTMn+Aa7TxNz+tm1zYv1ZR/RJ+AtSrfVvN1D54UIOH3U/XM3g+dP\nMKRt5+BuL+g+fvy+BwuLibEYvb/oo3T3wRlbrFLjE4xOWKAlSIwlKXIaOKZ1o+OfPKDno24SuKjf\nNTUwKMKxGoajEcyt25i8NYwFGJmJEFV9HZuDNJdNYlkWX98N8/HFOLUHQzJ59Qw1Z39f82kuRW9H\nJ5Ek+N6sxT02yMmPIlT9WcOMwcJcNmF7XxVaVmpO38CG1T0M/fIKteUBLv73YVhfk9WNytb8oSY8\n4xZYEwx/1kPvHTeN/zrTVqeH8lUwEh3C3BLgxvURWJE7xyc6cI2JNV4qywwmvuhj6BF4vTMnqoav\nhWHWdq6c83FgjfTQ9XkC1xv7shMSKn+o9mkGFT/QiP56hJgVQLs+lBvA2PSD8CxDbn47Bkjd6ub0\nuWj6zrgPP7WNc86YlB3XRwnfJe/2wbnsTa07tZ+2i6HWeCqzYtciGU+QLCnGKNEL1secscNGz6oY\nqq6vA/8GB9GbV4jt9pA8fwkLNx6nve40pwOIM3QnRU2ZRSxhgcOYl08UKLi/8mnEuj/I2QtxfP+y\nGp/Lou/aMKzy5t4OvRSxyYrTc+oKRYFtbN+wjuFLYSx0tFUF2FxBtqxRZEX46JNBMKqoKdftNbIq\n/QKf4b+LsW27h8jIPeC7fFdbpC3btFVV30LyV0EQZAJLEApMpr/kxgRUvvnasq2ivrGOmpvD9J1q\nJwy4Xg9RVTJTgq++qsFXFoZRPOPa6MXjdEUym3oGuugY0Kg9lEl4nH727Yhw8spp2q4AuKjd6csO\nBDqOdGUOyNUI7G2atjJoguhn/cQH+rPfU7FzH75MXlFUBLFomFg0nP7Fag/7Mq8rT335RabMJH2n\nMmcBOP2kF3tP3ZMvGBwH/56ZWx8e3IvSPxCn/9PML5zebLlTfHF9CFYHnto+WLRyguhAN9FpZ3a9\nlVktFhuZOvgzwtmfpZe2O3y5vnWUGGBGOH6sFxMYvXAKz8ZmfDqYty9y9JOp5fBhOo+FqdjTTP1m\nx8zXpK9yE3y/YdqqAJ3q3QEin/TT3tafSfRq2FxiX1/QcZTomLe6OHsh/ZronlMXqfjzoCRDz8ot\nKO6rUnPmMF9kFj5FPj1LJDNxkj1P3cYmVPdVpWWV5kCntr6GLz7q40hbGCgl9O+rCtIGmgOjBCJd\nx+mNmkCCUxc9NAd9gEHw31Zz9KOcDfvezA0+4pF+wl/105sdPNdMs+H0gOrzmykcm/xPDb57PmzP\nvgGw9PUgB6dtM1b6Q5VPA/yheqIfnub0kfSKMsNXm/UVas0JzwplTAJ056vZCZWiVTMnp/LHpKmB\n/RApHHm2D+azN5Xu1H5abW8peo8fZehhxouc6SS8ykvzByEcNvqw8xMqPatiqJ0+Kt4KUfHlSU4f\nOQJA4O1g9vwsle708moCa6OETx0lnLl31X9UWZBPFGYHH/v+yqcRraiIiXsRej6K0JOxlar9b6Xt\nfalik1aMZsYIn4sRPpeZINqxD79eiM2pbTl6voOumxnFrw3Q9G5uwlpZX91H8I1Bzma/Eyp2BrMP\ngxZsyzZtVdW3kPxVEITnyz+anJz8v9IN3150/cU+Jmtra6OlpWWJSrcwzcdL3sb29nZaW1sXVUZq\nLIFF7gnuM+sBM0nyG4viNa5pq6AsUmNJJi2N4jXGnJtozLEkE5ZFUYlrxoqjqeuT95NYWhGuEsez\nuyeWSXJsIn+5lomJjj7nzpQUibFJtKJiDOdzsGm7umYG64n7E2hFRvap/gur77eMtra2RWvuxdjE\nwn2ZUnNWisSYNbeeF2NrlklibIKiV1w49Ll9y5z2DVimCU+9vTD9hqmJSWvOMgvxh3P7tFn9VFSM\na3ZbRXPLWHf2MSkdO3jqbYB29lbArEJ+P21rb/ax96nVFwnTAAAgAElEQVRyC/ETKj3b5DWF6INX\nXBj6/HRnjiWYsDSMNYY82X6BsSk1lrDRybOPTdZ4kqTCZyttLo8tW2aK5Dd5/HEh9TWTJL7Jl6Mu\n3JbztVVZ3yWP/6KTpcA05RzMbzMSp4SX2nzzJbvLDUeJa2l6QDdwrXm6XxwlLuWKHr1ElRxpGGtc\nz/6eaDrGGl3VmPx10hy41jzHpMGurulexJXvb553fYVlYhML92VKzWmOOXT+DGxNy2/Dc/uW6Z/P\nfZ3uNNCdC/eH6u9V9JNoblnHaruYlI4d87c3exR+ugA7n/d1hfgJlZ5t8poF68NGd3qJS06MWya5\nomP+xrio2KQ5DVxOFmZzeWxZ0xX+uJD62uhyobacr63K+i55/BcEYb7IWwgFQRAEQRAEQRAEQRCE\nZY1MYAmCIAiCIAiCIAiCIAjLGpnAEgRBEARBEARBEARBEJY1MoElCIIgCIIgCIIgCIIgLGvkLYTf\ncpbDWwgFQRAEQRAEQRCEbz/yFkJhKZG3EApLTktLy0td//b29hfuiAXhHxLL4RXMgiC6EwRBNCII\n89eJICwlsoVQEARBEARBEARBEARBWNbIBJYgCIIgCIIgCIIgCIKwrJEJLEEQBEEQBEEQBEEQBGFZ\nIxNYgiAIgiAIgiAIgiAIwrJGJrCElxrLNDGtb3cbTdPEWlAbLdu3cFimiZmvAy0rz3ery7W9J3nL\nfYH32jTTdXr6S/P3j9jm8tAGC9fGwmS1cJuwL/vZa0Op8QL6d6FNVbVDtPEPNmIv+M1QajteonIt\nc4li3SJj6FLEOmGZxKacfZh5HGg+my1EI1a+z8YV9V2qmLeUsVQQhG818hZC4SXNDv6ec3/1C778\nJv2j/v0t/Om/2YFj2dUzRs9/7SVyN8HK8jpa3vEXfumdMMd/3kfySfpnzxsh9u3yzkoAhuk8cpZk\neR2Hp5UduXiC7s9H0z+scFF78CCBNTm5W/cHOXmyl9FHAAahw014tVwyM3T+JD0309fr3iAtIV9B\n5UbOd9J9MwFAxc4G6re6ZyRJ+crNtJjejnYGH3pp/iA0417mLdca5uR/PEv8Uc6lVR34KTXr03Ua\nvnSCs5+N5hze+mp+emBbzvFZcXo+OsPQV+nBhHdvM6HK9Dcnb/XQcW4oXdfyGprfqcpepyzXjNF1\n/DTRZMY2y6r48f6a5WebLzVJejo7GEoAOKg+0MS29YWFs9hAF6cvRzM/Oah6+11qNjoKspf8NpGi\nt7ODwUQuGXfvaKBhu3vGdw9f7ODs55PUHWrB76QAbdiXm19zJuEzx+kbSRuiw1tLUyhQWH3NGF3/\n+TTR8fSP3jcbCG1x5+p68SyXbsQwNS9NH4QwCvVbKm3YaNm+D4WlJnUnTNf5q8STJhV7mqjfbBQa\nzRjsPkVvJKOtVW6CBxvwlRTgT5V2DIxH6PywmwTA6goaDtXj1gqpr7rc2NUuTl/J+Amnl4ZDIbLK\nujvIqZ9PxU8o3VRHw1v+aQm1ItYp6rvwWGfS23l0hp5ZYVDX1JTTSJ5YZ470cvTM4IzJC8MXpCno\nKyyGCjM1cruXjk9y/el7s4HgFnfB1ydu9HDiwlD6+lU+mj8IZnOH/HmbjS0rYp555xrHT/WTvtKg\n5r1GqrI5XYre4x0MfmVNGQYNjcGMvRYW86yRXo6cGaRiTzP1mx1ZW+w+cZpI5lptrZ+DB+pwaVP1\n7aFnIELy4UpqD7UQcBaYUy8mHywkV8+TbwuCIBNYglAQ//t/XuHLb77DnsZ/xw/Hb/Czn1/h078L\n8PbvLrNpgkcprOJSSlclGJ3npVcv9pF0BmhqqmVi4CwnL59n6Pe9+PXc3wx1pxNhbdZgYfT/Z+/9\nn5vK8jvvV+CGa3Ktvu1RGzN4gmasXkHJg5x2P5hAghezbQr3ou0oj8lAxTwFvR7iYb2Ju6gUf4N/\noEISqsfFeBtqYQrPQsfNYx7MgzuYMRkoq4N6EUFptG3viBkxGNCYi63Bl+faeX6QLMvGOlfYfHH3\nnFcVVe2+0kfnnvt5fz6fc+459ybAt6mezV6Fc0c76Pk0QuXOdMIdCXP4ox7MZZU01FdTsnQxSpaB\ncOdhuqMmldsaqH6zBFVV8rJr3e6h60YC37ZGvEY3HZc6CHn3Z4qP3HbTpdL1c4QMYMmMmkFk97HB\ngyc61duq0S2LMQpwL5uymxx6iOapZrNHx7LGUIpWZfWVQeffnyBqlVC3azurlqmomY6IcepsGH2N\nn53fHaLtZC+fXF+VGQCJ7Cau9RI1nPj37cY9EuKHx3vpvllFfbmcwnpexC6eJJzQ8e/diXG2jb4f\nd+H96+kTKbMTp/dSFOdbfnZvcRM6dZjergtUrU5NmIr9RewTxgh4NtaxSgdrDHRPyfSfHgnT9bkB\nqE/d6RZpQ2RXpA1r8CK9gwberY1UO8K0n+rhk+vuvNo78NMuoiM6de/vQvtfXZz+tIOQZ1LL44xZ\nGq7lCtG7TxcRorgl1IaNlu36UPLisX6TRCt1oRrRZ/viyE16IkO4NtYTWJPKHV2fXMW7Z71tPBX7\nsUXvj7tI6D4a/9xDd9tpOjrDmcGlqL1Cu9YA3Zej6Gvq2LNRo7P9NCdOhTiwvRKAm//YwxBu6vf6\nUaLn6LjUTWi9j6oiOz3btHfOuc5izFLxvVOHW1MZu3uV7v5RtKX2uc56PIa6rBL/ehcsGSN4qpuH\nqppnDpXM5MFdg5I1Nfj/Qzm3/u8f0XvxKpvfrs/rBlbyeift56OUvFXH9n+/ClVVsyZqc9dtYo2I\ncp5J39k+DN1H454NRP+hnd5jn7Dqr+tTudS6i0EpNdvrKC+4xY+O93L5ZjU78s15GHSdDWV8dOo8\ne4kkFlO9vRGfcosfneyl+1oVDeucqUrzMZT8vhMjmnja10Q19XzqwTxq9dnrbYlE8iqQWwglX80J\nrF8b8Lvf5M1CBfWbq1kGjJnjC6+hDi/+gJ+qbyvPuC0oSWIYtDIPOlDqLQNMxp5kfeR+L93RAqo3\nulGnGVep2bmbunVuVIcLVzFk/3jofA/mUi9Ne2opWaqiZM9eDYfoiZp4tjZSWz5zkklsd/D6TVjq\nY3O5E9eGAG4sooPJPOymJhZOnY/i2VSFvnh82uBUaBdgUSHLVpSgOZyUV3jQZ5guLFpGaZGGvqIc\n78qpMtK41kP0iUZdYwO+ZVrW5BWYX4RJoLF5ixd9ZQ11K2EgEsvLbiLxEJaU4nEoqCvKWQ6Mjcnh\n9vMjSfhfDFTvZrxFOuv/rBYmBhkYyWcgnSpOS1d7UFAp95TCxPStE7muq9gnLGAxr3+rFH2pTul3\nfbgc0x3x6k+6wVuNx6FMn3wRakNsV6QNc8SARR62VDjRyzbgWgQD0fzam0wkoawaX7GGu8oLWETT\nd/dBpepdP/61ZTAx/kxxy1YbNlrO2YeSl4K+uoaA349rEc/W/w4fTe83s2NDKne4i4GHD0jmozuR\nH5tRrhvg3bQZp8NNYIsLazCasStqr9DuYwMDheotPlSHG9+3gJ8PMqkA35800fxf63EXqbi8qRUn\n8Tt55Dqb9s4912n4m5qpe9uLZ7Ub7TcPYVkFbsU+12nlfpr31OJZ7cFTVsBD4O233HnlOsnTuDbV\n0/BuFbqqsapsOUxYeWrFoPtCFNVTR8MW3/TJK5u6TejLwpxnYYyAe+NmnKpOpc8FE7GpXKq4qd+z\ng6oyHW3FKpZPK/nsc1780imieKkq06fVv1pFgKZ9zawv09FWpuwO3ZmaNvJsqiPgT61utp61pp5j\nPWhrN2e9LZFIXgVyIlnyleTN9VUoty7T9tF9Xpu4x6/5BtvefG3BtvfZ5y80aje7aPu0g7Z4Cda9\nIdArKc8spTbpPhlEW9vA2m9c5crA0xaytwb4tpdnBpixX1rwJEJbayRVcK2rZ8emVMGavBPDAqLn\n22k9Dyx1EdizA4/Dzi6MjY3DN0uZvHe7eAncHboLuG3tRs91MuSoYvfbJRzqm37XTWQXYHwizukj\n7ZnBdfWuH7B+RfrTyjhD/adp608fXVnND3auRwXit3+Z6scjB+lm+nYmyzRh0fLM1g6lQIFfDWFC\nqh0Cu54/3oAS6eVv24bQJ4ZI4CTg0aVonyOmCctXlmbSmILF0H0THKrtQLp2ZQ89J/+WoRU6Q3cS\n6G9lrdwSXFexTyhgmQRPthNMm8re/mMOdtGX0GlorCT4N/3T/FusDbFdkTYUVYOJCH1fJKktGeDh\nRHbCF9st0DW4cYWo6aXky/isxYI5Pv7McctOGyIti/pQ8lLVx7PfKlLQJ7clDQfpvg3Otb6pVSkC\n3Yn92GIcBdd3JuO9AjwgbpG1JX729grtLilAxeLKxSjeLSXEDEBZnDmuOPTMf1/9pBdwUpleYSvW\ns7i988p1GRJcvZHEvXVqi5Mo10375pUrJBd58BVnd1TuayPJJZEoxz7sZOgJqGvqySv7j8T45QSY\n0W4OtnYDGlU7v0/NStW2bhP6sjDnKWhLIXy1j2R5DbHYQ2DxU7E1dukEHf1xQMvahivOIVhROvsT\nVO7aTenPDhOfJj4N3THpc13EgOo/9k7/0XFrTjX1XOtBsV37elsikcgJLInEluTDR1iApmlopsqv\nMTFMCwpfsktbMTqP92BkS2ncovDNGuo3uedleigxCkBhYSFjI0Mkn4wyBqmtTte7CD/WCPxRKcb/\nHAPLwDAt9Ky7vYuLPWxcZxHqDxO+eIXNZTWoKBSoAC4Cu/1Yn52iq7+L8PqWaVsTXesCBCosTh7p\nouvTCPuznlU1u90c3TNjVdysdkdCdN1I4ntvA4yEgDHiwyaeotwlcsbuUjd1W3XcFW5UEnT+XTt9\nP73J+p2V6d+rI7DJjadYJXGtk/ZPr3BzZH3WMxVUqt7bQyVB2s4EuXCzKvMMrKd/1Mo6j9x2k8NG\n2jcLKTQfkmCMh2MWOGS4faFSfGJNTi+KIgeJpAVoFBYW8pAEY8ZoXtdV7BMqa9/1U/OmF6dqEeo8\nTM+lyyTW7cBJknMfR1DXBChlFBMw7iewHM5pCXh2zYnsirWhrt5M1dUowTOHCT2V8MV2PRu3UHqr\nk85Drc83bom0IdRyfn0oWeCMRDl2pBf0SnZtduWlO7Efz6oArHH76lZoV/USWBeio7+T1s8nJ7We\nXg0Su3SCvjtQuX0XrhnHRDn0Wdqbb67LDLUHrxBHn2XLul2uMwn+8xBaxfRniT5zTJSAWkrVhirC\nnweJ3bjMwBZ3ZjVcTtKPTlBWVNH4Zz6Cx9sJfnyOqg8CaDZ1m1gjopynUr2lisiZIIdbc6vrjbJK\nqn6jELwRo7dvAPdmt20OCXd2kVzqo3qFwvUxsIwhTEsne0GidbuX9stx9DUB1hc/h36fdz2Yo2rI\no96WSCQvF7mFUPKV5OfXrsM3NvD9773H//l/vc+bi5Jcv/mrl9+Q8STGQ4OHIw+z/hmMPpk+cSNK\nc9ZwjFB/mPhIdnlsEP48QcmmJhq219O4z4/yOEroduozqeXhSToPtdJ+KY51L0jbP4Sn/6bDRdWm\nOhq3uSExxBgAY4wlgTc8eIo0vFs2oDCONbk10Uq127Peg1rkZaNHwRoby8MuFBSocH+I7BXmmVUy\nArvJwUEsIHzmEK1H+jAn4nQe6cxs1xDaVXS8Fe70tIUT33dUuBub2kKy0ounOH307XJULAZvJ9Nz\nD+OAiw2rdfTVNXgXMfXWH2UxTDzgrjX1mxRP3RkX2R3oD4GzmuZd9exo/AGeRUlCN+JStM+RxQo8\niN+dprDSUm3aoDB2M0Toi/j0QefILUIJqN7bTH1gB83b3JiDIQYs++sq9gkFV7kXZ2p5Hr4KNxAn\nNgKYA8QmwLzRSWtrO7EnJsFT7YRH8tGcwK6dNtCo2bOf5r3NtOz14wQ0XcvLLg4PDR/sp2lvM03v\npYp/3VGQR0yziVsibYi0bNeHkperv5wekEN3APdDtH3YyZDDS1NT7bSpZqHuhH4Mi7GIx62sFi3H\npebTXrFd16YG9u9rpmV/E5VOYGlh1sSORbirjY7+ON6tTdSWZf2gTQ4VtXc+uW6S6z+LTNs+aJvr\nJhm+Tvjx09sHxddGMjsa3nU17Gj0ozCE8TgPjTyBcaDEW4WuOqnZ7IEnk49TsKnbRL5sk/O01TXs\n399M074WAm85AT3r2Wnpz6z0UvPuDvxlCokhI48cYhD9uQWPwxxqbaX3jsnQZ510XjcyNhPXuzh4\nMojmraPpXY/txN5MZs0/86gHRXbzqbclEomcwJJIbFFf0+DhL/iFCTz839ybAO21V7BNS/Wy+4P9\ntPxly9S/D/aze8tkQjZJjiQYGrXASpAYNkhOq1qSdB3roOdSNyfOZifEArSlMBSNpFYb3BzAAvT0\nKh5foImmvU00vd9A7Ron6D4a/zQ10LTuh+g43kX4joFlJui9OgBLJp+noOP+tgIPBolZEO9Pve1m\nMmlrZR4UIHo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j39uEWqSjAOUrl9MbvcuQCU5VbFcr99NcPtmCKBeAt99yZ9qkr64hsNqi\n84uDmFKOLxnxdRXr0UfT+6vQi1P3ct3FEL37gCRk7u5e/Uk3eKvx/OLadB9X3NTvmfSBlOamb6eI\nc+p8FM+mKob6E0/pI6ddkeZUH41/6csMqgfOQsn/kX1H2UJZWoprhc6DkQLcK7MK6uEQPVETz9bJ\nO+rZk0ypIt232oOCQrmnlN7B7K0Yc7RrE19EMcIu9ojioeQlKW/OcW/uOUmcO0Q5KUliGLQKDzqg\ne8vg0gBjTwDVxo/NKNcN8L63GadDJbDFxeHzUZL40nFijvqwiT9z14eFMQLubZtxqiqVPhd9t2MM\njEClA4xrPUSfaNTtbcDnUKZLVqjnecRamZuesb9EGkkS/hcD1RvAW6TDn9XSd6g3c33t6pXcGrFp\nr8g3bHJTMpGEMj++Yg2KvHA5RnTQoLJCB1Sq3vVjfdHJwTPjMwpJsV2RL9vFCmEeFtTq4npbIpHI\nCSyJJE/U1zS4GeRLczXLBu8seGcem8u2eWWcof7TtPWnz3llNT/YuT51J0rR0NN3ZRNXuogB1X/s\nnZK1ZRI82U4w/X/cmxqoX1cKJIn90oInEdpaIwC41tWzY9NksWPSfTKItraBtd+4ypWBWdo1LjoZ\nk96jhwnes2Dp1J1jRdVgIkLfF0lqSwZ4OJF9vUTtnc5nPw1DWV2mbLBMExYtz2ztUAoU+NUQZnqM\nohVN3nlL0HUxBs4qPLMUkjPtZpO4coXkIg++4qfPdVxK8ZWQ73WdLeXpk9tqhoN03wbnWl9m8soc\n7KIvodPQWEnwb/pnjSmxSyfo6I8D2rQ7u9FznQw5qtj9dgmH+qbf6RbbzUNzAPc/I/xYoW6Nc9r5\nWPf6aDuS/nOpmx376nEpkLwTwwKi59tpPQ8sdRHYswOPIzWQrl3ZQ8/Jv2Vohc7QnQT6W1nbOOZq\n1za+5I4Rtv0gioeSl8hc4t7cc5I4d4hykkbtZhdtn3bQFi/BujcEeiXlaX8T+7HFOAqu76S9S1GA\nB8Qt0tsa56oPcfyZuz4UtKUQvtpHsryGWOwhsHhqK/3tX6a+f+Qg3YC6sorv76xBs+2H+cRamZue\nNTeJ6iDThOUrSzOfVbAYum+CQ82vXslRt4naa+cbotxUoGtw4wpR00vJl/FZa3Rz3CaSzGJX5Mt2\nsSKf/D5rrS6styUSyatAPsRd8pXkzfU1fPN3f83ZHx7io//35qtriBWj82g7x44em/rX3s7pSwPz\nNu1aV0fg/RYOHDhA4zsezNtXuDky4+dv99J+OY6+JsD6TMGisvZdP40tBzhwYD+1HpWBS5dTz/JA\noUAFlrgI7G3G/1YJsf4uwunb6cnrXYQfa2z5o1IMYwwsA+OZHlq5mLKqGnwrdXgc4vJgyrC6ejNV\nyxRCZw7TeqQ73ZZ82pt9slGu3QPf26tsrsnM9ibpOdpObMJJYFfN00WL0K5J8J+H0Cp8Tz3PSPKq\nsbmuIkaiHDvSC3oluza7MvbOfRxBXbOZUkYxAeP+0yup3iirpGqNC0jS25fW+UiIrhtJfJs3wIgB\njBEfNvOym6/mop/1g8OHN2vwoK5ci/+9Rg4cOMD+92tRHw9w9boxI44EaNnrp+RxjK5PI5k2JZIW\noFJYWIgKjBmjz8GuOL6IYoRdP+QTDyULlbnnJHHuEDOUSPl0YWEhBUuBJ6OMPZVnZ/PjWRML1vh8\n9SGKP/PRh0r1liqURIjDrQfpupGAp6YZVarea6LpvUrM20Eu3Ew+Qz/MI9bK3JRnf+VZB2V75BPr\nOdUr4vbaaWS23OTZuIXSJQk6D7VmnlH6rMxmV+TL4liRX34Xlpaz1tsSiUROYEkk+VL4Jt/7L828\nv/v7vP8fKwB4rfAV3BYcT2I8NHg48jDrn8Hok+nFo6iAsYZjhPrDxEdmLO5e6cWT3ibjfLscFYvB\n21NFZ+J6FwdPBtG8dTS965n2a65yb3r5uYKvwg3EiY0AjDGWBN7w4CnS8G7ZgMI4VvqhsQPRGJCk\n81Ar7ZfiWPeCtP1DjuJjtuf8oOAqr6RuZyOeRRAfnhwuaNTs2U/z3mZa9vpxApqu5dHeKYxrQZKU\n4ivLus7KYph4wF0rM8aA4tKsVRkJutoOE7qnU7evcdY7obPanWT4OuHH05fjTx9q2F1dyYvB7rpa\nxG6GCH0Rn2W7Xoi2DzsZcnhpaqqd8hVzgNgEmDc6aW1tJ/bEJHiqnfAMP9RWeql5dwf+MoXEUGrQ\nmhwcxALCZw7ReqQPcyJO55HO1AOVbezmp7kEwRsmJX/gneZtSpEL7+rU3Wml2MeqJRC/HUt3QSoG\nedZ7UIu8bPQoWGNpPY7cIpSA6r3N1Ad20LzNjTkYYsCap12b+CKKEXb9YBcPJS+P3HEvl+7mnpPE\nuUOUkwzCnyco2dREw/Z6Gvf5UR5HCd228vBjWIxFPG5lnelyXOp89SGIP/PUh7a6hv37m2na10Lg\nLSegoy1NN8kaB1xsWK2jr67BuwhMK79+yCeHSp5HbhLXQYsVeBC/O+3zpaXaM9Urs9dtgvba+kbu\n3ITDQ8MH+2na20zTe5WpGO4oyLsuzmVX6MuiWJFnfs/Vptz1tkQieRXIkZfkK+2+6vgtfvL/XIfX\n3mbjt19BZaV62f2BaCmxSXJklKFRC5QEiWGDAoeOllFekq5jHUSfAIMWB3ZWprN0guA/RXGW+3AV\nKUQu9mGi4n1z6i1h7ecj4PBS94elhM4dI6xsYPcWD9ZwlMv/OoZvzSr0JaP0XozAEg+rHAA67m8r\nRH85SMyqRLkWxsoKBL5AE64RC6xRBj7rpud2KY1/WjlVPIwk03etLIx4AqOoEL0o1e+hrg5iDh+b\n13uwvuwlOgGegukhRvu9h3Qf7yKBk/rN7vQEnqi9U/0YvBaHsjqyNxZqq8rRzka5/E8x3G8bXBi0\nKN1UmunbnrZ2IgZ436mldDjEieMRqv5zQ1aRNrvdSQauBmG25fhmEuM38dTbrYbjGMMlaOnnSEhe\nNPbXNXmzi46z0ZRnl+6f2oZjRmj7qAcDnZptG3hwrZuTYYU9e2pRVS/f3+vCwmJ0OEbPx72UbNmd\n+W7iWjc9twtY/84Glo8NEBy0UDypglyr8NO8cgzLsnhwJ8gnF+LU7k5vybOxa6c5AHMwSByFujXT\nvTTaf5XRYg/lK3VGb/USfgIeT0pXWpkHhQGi12JUVil8/nMLvpX20CWph+IO/GuM9RtcRAbvAm/w\nhjJPuzbxRRQjhP1gEw8lLwmbuJdLd/PJSaLcIc5JBWhLYSAawVy3nrGbqedJpZ5jY+PH6ipWLe0m\n/NPL1JZVcuEfB2BFTWZVy5z1IYo/89FHpiTSsAa76fw8gXPtDtzpn3V7XDA4SOiOyVquMzAB3yKP\nfsgrh0qeR24Sa0TDt0ojeuMysS0ujHMXsSjF5civXsmtEXF7xb4hzk2TNXqBFeH4mRDoVdRk3SRM\njiS5e38UULg7bPCGomW0KbIr8mVhrLDJw6JaXVRvSyQSOYElkeTNlz0fcfbmo5QTF1fw/p9vXJDO\nbH5xgcNnJpdcB2k/EsS9tZn6iqnVR6+/rsA9C13PfoXiKNHP+oj3900l7k078Kbzf2wwfbd3JMLp\nj1L2tcl5tMdxgpeDBC93T6Zzqne9mym+fYF6oh920HGwNTXk9NZOLdFWNPQiiHQepSdqAglOXnDR\n7Pemip2jhwk/Tp/NqXaCSzw0f5B6c1nB4lGi/V1Es55R82751IRb5rXjS0rx723IFNd27U0ZuEVo\nBHxbZ2zzUzwENrk5camD1n7AWUlg8s0w5gC30rs6Ip+eJpIeYFv52E0Xo5/fSKKteXo5fvTCUToj\n6dUf/Z209SvU7ts/7W1RkhclKvvrqjpez/hSQdYd5+SXt9KvmTfoPdme+p8OH6nNdErqeSBmhKNH\nejCBofMnca1uxqtCQQHEokFi0fQTSpa62OGfFJ2KVqRi3uzk9PnUG9q6T17A/Zd+NBu7Ys2luHUt\nDEsrn9pKEY/0EbzXR8/k2ZbVZDSHw8eOjRFOXO6g9TKAk9pN3kwx718b4nTmGLg3+TPPwJqzXZv4\nIooR4n4Qx0PJy8Eu7uXS3XxykjB3CHOSSvWWSiJn+jjU2pfx44oi8vBjldr6Gm4d7+VgaxAoIfAX\nVfPWnTj+zEcfJt0fHsqsJil5y8/urK2JWoWf6sgP6Tt+iL5UB1NTkYee88mhkueSm+w04n43gPvL\nE3QcPAhA5Xv+Gc/szFWvCDRi116bWC/KTdFzbXTeSBlXllXStCdra6IZ4eiHXUyunz19pA2lrC7z\nlk+RXZEvi2OFOA+LanVhvS2RSF4JvzM2NvZvshu+vqjqq63wW1tbaWlpee52LTPJo1ETRdVe+NbB\nQ4cOceDAgVdTDw0bjFoWBUXOrFVbefUQxrCBZSnoxbOtDLIw7htYBYU4Hc+x/6wkieExlIJC9Gy7\nlokxPIqlFOAs0ubUXtMcz+nP1oiBMQbOYv1ZGyy2a5qgqnKm/xXEjVelOVtd3TcEfvyiftbEREVV\nZjtkYDyyUAr0aXewZx4vLHY+vV3JNEg8mj2+zMuuKL7kihEvNB5KXr3u5piTbHOHrdeQuD86Nz+2\nkiSGLQqL9aeOzU8fc8ihdmc5YjA6ZlHwmhNNzaWfBKNWAc5i7fm1V2rkpWuE15zo6surV8QamT03\nWWYS49Hc4rxdzsvpy/OOFZKvUw1nmvId4V9nZAko+Wo6rqrxDfXrn6DUIn2OxaSCXuQUHy9+Aa8A\nVrRZi2MUFV342nv79qpq7nClOHScjrn1k9CuKkt5yUvQjX3AyxkHFFXHWSz6quC44Ni87Ir6KVeM\neKHxULIgtDOXnGSbO2y9Bqfg+0I/VrQXpI855FC7s3ToqA47/TjnHEckC1gjL7heEWtk9tykqHOP\n83Y5L6cvzztWSCSSrwryIe4SiUQikUgkEolEIpFIJJIFjZzAkkgkEolEIpFIJBKJRCKRLGjkBJZE\nIpFIJBKJRCKRSCQSiWRBIyewJBKJRCKRSCQSiUQikUgkCxr5FsKvOQvhLYQSiUQikUgkEolEIvn6\nI99CKHmRyLcQSl44LS0tX+n2Hzp06JUHYonkt4mF8ApmiUTqTiKRSI1IJM+uE4nkRSK3EEokEolE\nIpFIJBKJRCKRSBY0cgJLIpFIJBKJRCKRSCQSiUSyoJETWBKJRCKRSCQSiUQikUgkkgWNnMCSSCQS\niUQikUgkEolEIpEsaOQEluRrhWmaWAuwXZZpYr70hlm2/WGZJmauhlnp71tPfUn4do+5n6sltGuK\n7FqC81iQ19ySb0hZwJoT6maevpZTc/Owa81LG1ZaW89XP7ax2DRn/YwwJi3IOCvz7DNcoDnHvbn7\nhYU58mLqAvNF+lsOfdjpedac/Zz6dz7alBp5zrntma+DfX01x6gr/O6Lyk3mC8rvModIJF8N5FsI\nJV8ZfnGth55/vsWjx4upafwBFYXZRx/R898/4uavATQ2fG8PVd9cGO4dOddO140EAO5NDdSvK83r\ne8nbQTrPXSFumLi3NlFfoU8l4MEeDp8KTUvCutdPk98LQKy/k45L0fQRjar39lCzWptK0vdDnDjR\nw9ATAJ3A/iY8ylTRED53gu4bQwCoHj8tgbTdK510XE7bdXho2BcgczYjEY61d6VtgtNby25/ZTrI\nWIQvnObi9Rim4qHpgwB69smORGj/sIsEwFI3DfvqKZ1sjxmj8791EB1J/el5p4HA21N9aNzspu1s\nONXWshqat1elf9Okp/0woURWLy3SqWtqwucA84tuDp0JTx1b4qLhgx3p8xG3d+DiMU5/NjQVSFdU\n81e71mcCquiaRy4co+vz9HcXOandvZvKYhmKF4LmwKC7vY1wIqWb6l1NrF+RcUSCnUfpjRqpP5e6\nCOzZgcdBWnPddPdHMB4vpnZfC5WOGYVxTs0l6TnaRuieNSlkGhr9Kf83oxz7u06GJqYJneYmP5rd\nuVpxuo51EEn7v7LMx+5ddTizXC1xvZtj58OpOLLES/MHU3YBrMEeDp4K4d7aTH1F+og1wIm/P038\nyVQZUbXrr6hJ91Pyix7azkzFJu87Dfiz9IoVp/v4KcL3UoMpz7ZmAuVaqn9PHaV3MNW/mqeWpkBl\nRlMizZl3Qpz8SU8m9pSsqaPhXZ8scJ4z8WtddHwaSV9bhcptjdSW63l9N3fuEMdpoV/Y6MO8fZWj\nJ/tIfVOn5v1GqjKxVhDj7XRnk5MmddJ+8DRGWR37t/um+sEmTgj1IYg/5u0gR3/Si5Fus2ttgB2b\nPaljwlwn1p19vSCZVrfZxb+csyr2sT7ndRDGZJNQ10l6IunYuaQU/+4GvEX5tFdcB4nqTGFMnk9u\nEmnPJjeJ8ru4vXZ20znqQhunPx+jbl9LOnZJJJIXhVyBJfnKYP4GlpV+Axh/amDyi77T3Pz1a2zd\n/T4bvpnkyqnzPFoAbbZu99B1I4FvWyM7NpYycKmD0Eie3/1NEq3UhTrbscdjqMsqCbwXILC9jlLA\nUic/Gaf3UhTnW372H2ihpswk2HWBZGayKMzhj3oYer2Shn0t7N/fOK0YDXcepvvGEJXbGmhp2Z+Z\nvMIaoPtyFH1NHS376nGZUU6cCk1dn/tD8IaP+r0t7NjkJhHpJZw513HGLA3XcgWsmTPnFr0/7iKh\n+2jcV0+pOUBH51SxPfDTLqIjOnXvN1O/0UX00+w+jHHqbBh9jZ+mnVWYg718ct3I2B2zVHzv+Am8\nV0/dulKYAG1p+qiZBIeHum1+6rbWUbdlPSXk015IDj1E81Tj3+anbmstdf9+arAsvuYmQwnwbaqn\nZd8OPGqCnk8jUtwLRHOxiycJJ3T8e5uoXpGk78ddTHoTd67QGzWofK+JAy07cI/H6Dw/5afmYyj5\nfees8UmoOesuBqXUbG+ieVcNqhHh8s3JXx3DUkupfS9AYHuAqhXAREEmJojONXm9l0hiMdXbm2je\nWcPie2G6ryWmfPh6J+3nwzjfqqO5pYX9MyavwKDrbCijpQyPDR480ane5se/tY7arX4ql02d8YO7\nBiVramhqaaamTCVy8epU7MGg8+9PEH7wOnW7mmnZvz89OAdr8CK9gwberY00ba/CjPZkaVmsuZv/\n2MMQ7nTs8TB0o5vQsNTC88Xg8qcRKKtOXVvPYkJn0zcdbEUpyh02cVroFyJ9mPSd7cPQfTS2NFG9\nMknvsU+m9CyM8WLdiXNSOo92zd43wjgh0Idd/LlyoRfDUUnTgQM0bHIT++wcYdM+19npzq5ekExH\nHP9EiH1OeB1EMXnkJj2RIVwb0zXH4jhdn1zNs70ijYjrTFFMnk9uEmrPJjeJ8rswh9jYnbw+XZ8b\nM7OlRCKRE1iS33be3FjLtq2pO4PTE0SSm//6CHVVNatff42qP6mBiZ/zv0dffZsHr9+EpT42lztx\nbQjgxiI6mF85o6+uIeD341r0dELUyv0076nFs9qDp6yAh8Dbb7nTiTTBEFC62oOCSrmnFCamlkyH\nzvdgLvXStKeWkqUqipKViIdD9ERNPFsbqS0vQVWVaQNXA4XqLT5Uhxvft4CfD2YKALWsht276nAX\nqbg8KwErq90qVe/68a8tg4nxGRV9lOsGeDdtxulwE9jiwhqMZgqhZCIJZdX4ijXcVV7AIpq+W2x+\nESaBxuYtXvSVNdSthIFIbLKX8Dc1U/e2F89qN9pvHsKyCtxZK82UpctxrdDRikrxlbuyCjRBe9MU\nFi2jtEhDX1GOd6WW5zVXqdm5m7p1blSHC1cxYMlyZ2FoLkn4XwxU72a8RTrr/6wWJgYZmJwQup8A\ndMpW66C68CwHxqaunWdTHQF/1SzxyUZzipv6PTuoKtPRVqxiebZLqD4a/7KBytUePGVljA1DyR9M\nrY4QnatWEaBpXzPry3S0lSm7Q3eGMgPl7gtRVE8dDVt8qKr61GA6fukUUbxUlelPu+iiQpatKEFz\nOCmv8KBnfdm1qZ6Gd6vQVY1VZcthYioOGNd6iD7RqGtswLdMQ83qB3PEgEUetlQ40cs24FoEA9FY\nXprz/UkTzf+1PhV7vKm78fE7SSmG50pq4PdX29ejqxrlK5cDDxjKZ0egMHeI47TQL4T6sDBGwL1x\nM05Vp9LngolYRs/CGG+jO1FOAuB+L93RAqo3ulFniEcUJ0T6EMefJIlh0Mo86ECptwwwGXtin+vs\ndCeMXZKnEMU/ITY+Z3sdcsVkh4+m95vZsSFVc7iLgYcPMvWVuL0CjdjUmaKYPJ/cZKu9nLlJnN9t\nc4gg5wFc/Uk3eKvxOBQ5gSWRvARkJpJ8tRiffTLBNGHZ738z49YKFvcemFCovtLmjo2NwzdLM3fR\nFi+Bu0N3AXeeFkzGbT6RuHKF5CIPvuKpgqV2ZQ89J/+WoRU6Q3cS6G9NLv1OEvulBU8itLWmVv64\n1tWzY1OqPck7MSwger6d1vNM36awpAAViysXo3i3lBAzAGXxjCBi0nv0MMF7FiytfGp7hDnr9bMY\nR8H1nXQvKQrwgLgFHgUKdA1uXCFqein5Mj4tcFmmCYuWZ7YbKgUK/GoIE2asXEtw9UYS91bftPBn\n3euj7Uj6z6Vuduyrx6XYtRdQxhnqP01bf7rMW1nND3auR83zmmdvAfVtL5e6XiCaM01YvrJ0WhwZ\num+CQ0WrqMF9oZ3Tf9NGqWOMeAIq31s1Iz5Zs06MiTQ3SezSCTr644A2bbvw1KD4M8KPFerWOPM7\nV0VDd0zGiC5iQPUfp1dTjsT45QSY0W4OtnYDGlU7v0/NyrQlK0pnf4LKXbsp/dlh4jPD8ESc00fa\nM4Oc6l0/YP0Kddqk9LEPOxl6Auqa+sy2k/jtXwIm3UcO0g2oK6v4/s4aNEBRNZiI0PdFktqSAR5O\nzChQBJpTHHrms1c/6QWcVJZrUgzPGa1Iz8TTrosxcFbhySfF5pU7Zo/Ttn6RUx8K2lIIX+0jWV5D\nLPYQePo3c8Z4ge5EOQlMuk8G0dY2sPYbV7kyMFsdM/sQV6QPcfzRqN3sou3TDtriJVj3hkCvpNxh\nn+vE/Ztf7JLMdKrZ41/ePOVz9tchd0xW0Ce3zQ4H6b4NzrW+6attbdo7q0aEdaZNTJ5HbhJrT5yb\nRPndLocI7Q520ZfQaWisJPg3/XJgLZHICSyJZB5zXf+f9dQ0xnPHitF5vAcjW0rjFoVv1lCfo8iz\nzPHn2ACT4D8PoVVszCpIkiSSFqBRWFjIQxKMGaMZyReoAC4Cu/1Yn52iq7+L8PoWfFld5VoXIFBh\ncfJIF12fRtgf8ILqJbAuREd/J62fTw5MZt5FXkxZVQ1j4SDh2yEuD1ZTUzaXa2BhjacilGfjFkpv\nddJ5qDXPr87ywM/BK8TRqc8qSNSVa/G/V4N3tRPrfojDH/Vw9bqB6237ctO1ro7AJjeeYpXEtU7a\nP73CzZH1Tz/PJMc1X1zsYeM6i1B/mPDFK2wuq0GVkl2QmrOepOOImeDhBKAWomkKJExGk2OA3URJ\nfpp7o6ySqt8oBG/E6O0bwL15+rlEP+sHhw+vzbM1Zp6rdbuX9stx9DUB1hdP6RZAWVFF45/5CB5v\nJ/jxOao+CKAB4c4ukkt9VK9QuD4GljGEaemoSmrwW7dVx13hRiVB59+10/fTm6zfWTn1o2opVRuq\nCH8eJHbjMgNb3FkrH1Wq3ttDJUHazgS5cLOKQLmGunozVVejBM8cJjRLgZKP5mKXTtB3Byq378Il\nlfKCSNJztJ3YhJPArpr8isi8ckeOOG3jF7n1oVK9pYrImSCHW0NzLnln050oJyWvdxF+rBH4o1KM\n/zkGloFhWuhqvr89uz7s4s9QIpXjCwsLGRsZIvlklMmjolwn7t/8Ypdk5iUUxb+5+JzNdcgnJo9E\nOXakF/RKdm12PYf2iurM/GLyXHKTsB7Mpx9y5XdRe4V2k5z7OIK6JkApo5iAcT+B5XDKAbZE8gKR\nWwglX01+d/qfixfDr391f9qg8ZvffAl34MeTGA8NHo48zPpnMPokNYgsKFDh/tBUoW5l3wFK/6/h\nGKH+MPGR2e/KLhYV3sPXCT/O2j4IMHKLUAKq9zZTH9hB8zY35mCIAQtgjLEk8IYHT5GGd8sGFMax\nJrcaWKl2e9Z7UIu8bPQoWGNjU4PITQ3s39dMy/4mKp3A0sIZw3cFV3kldTsb8SyC+PDYjKPkOEeL\neNzK+sxyXJM1hcNDwwf7adrbTNN7qUJEdxSkDS6GiQfctab6l+LSpyaDrv8sMmP7IChFLryrU3c4\nlWIfq5ZA/HYsr/bqK714ilO/4ny7HBWLwdvJvK+54nBRtamOxm1uSAwxJhW9IDS3WIEH8bvTPKC0\nNOXhyS9CJCihcV8DgZ2N+MsUop+FZ2/jkuw/bDSXRlvppebdHfjLFBJDxgyDCYI3TEr+wDvNJ+3O\nNXG9i4Mng2jeOpre9Ux98QmMAyXeKnTVSc1mDzwZT9sxiP7cgsdhDrW20nvHZOizTjonn4uj6Hgr\n3GmNOfF9R4W7sRnPedHwrqthR6MfhSGMx5Nzy+OAiw2rdfTVNXgXkfWGKY2aPftp3ttMy14/TkDT\ntbw0BxbhrjY6+uN4tzZRWyZH1y+GBF1tmd4yTgAAIABJREFUhwnd06nb1zjL6iuL2M0QoS/iT01O\n2eeO2eO0nV+I9KGtrmH//maa9rUQeMsJ6Jlna9nFeJFdUU5Kbb9L0nmolfZLcax7Qdr+IZ84IdaH\nOP4YhD9PULKpiYbt9TTu86M8jhK6beWR60T9m1/sksxk9vhnp5HcPmdzHexi8v0QbR92MuTw0tRU\nO8vNMlF7c2hEWGfax+S55Sa7elDcD6L8LmyvyK45QGwCzBudtLa2E3tiEjzVnvX8V4lE8iKQE8SS\nrwzWaJJHD36NhcWjX/2aR69rvPa6Cmh816Px5c0r/KLmWzzq6cPim/x+4UtolOpl9wfenIdd/84F\nkRCXb1dTOXyB6ATUrMwuvpN0Hesg+gQYtDiQfafITGL8Jp56q9BwHGO4BK1InybagatByN4+COnt\nGjDwrzHWb3ARGbwLvMEbCoCO+9sK0V8OErMqUa6l3vIyaVMr86AwQPRajMoqhc9/bsG3lBmTLwqR\ncycJJaBy59Qd+FBXBzGHj83rPVhf9hKdAE9B1maEkSR3748CCneHDd5QNHSHAuoqVi3tJvzTy9SW\nVXLhHwdgRc1TE2MFVoTjZ0KgV2VWdWmrytHORrn8TzHcbxtcGLQo3TTzbVBRgneYsX0Qov1XGS32\nUL5SZ/RWL+En4PG47dtrJQj+UxRnuQ9XkULkYh8mKt43Ndtrbt0Pcfp8HO9/qMbrtOi9OgBLPDIQ\nLwjNafhWaURvXCa2xYVx7iIWpbgck36vAXHCt5PUrLSIJSzQplbrWSPJ1J1XLIx4AqOoEL1ItdVc\n4lo3PbcLWP/OBpaPDRActFA8BdPOyRwMEkehbk1p3udqftFN+/kIOLzU/WEpoXPHCCsb2L3FAw4X\nZUtgMBrGfLuS69cGYdHkc3F0/qSpmSQW1sgDguc+IbailkB6ZaLxRZBI0omvwoUyHOFyxETxrErp\n1YrTffIyBZXr2bBqOQMXg1ioKOmButvjgsFBQndM1nKdgQn41gzv137vId3Hu0jgpH5yFZqN5qLn\n2umOGGjeWjaseED30ZMo63dRu1puI3x+JOlpaydigPedWkqHQ5w4HqHqPzdkJrKSN7voOBtNeXfp\n/qdWpObKHaI4LfQLG32kflTDGuym8/MEzrU7pk2O5Yzx+djNkZN8gSZcIxZYowx81k3P7VIa/7Qy\njzgh1oc4/hSgLYWBaARz3XrGbg5gQeZc7HJd7v4Vxy7JzCJVHP/y0cjsPie+DsKYbEZo+6gHA52a\nbRt4cK2bk2GFPXtqUfNpby6NCOtMcUyee24Sa0/YDzb5XdReoV3Vy/f3urCwGB2O0fNxLyVbdudc\njS+RSOQEluS3rHju/fGPuJm+M3Ttk//Otd99k+//l21owLdrt/GdwZ/w8eHDAFT8x618YyGMtVfX\nUXNjgN6ThwgCzrcCVBVNl+Drrytwz0LXp8+4RS8cpTOSvnfU30lbv0LtvuyCJ8nnN5Joa2Y8z0D1\n4l8b4vTlDlovpweOm/yZZxP4AvVEP+yg42BqCbburZ1aqu7wsWNjhBOZ7zqp3eTNDDDaDnamH7yr\nULmtidqVUyGkYPEo0f4uolnPqHl3ciuIGeHoh12ZO2Gnj7ShZF4vrlJbX8Ot470cbA0CJQT+omqq\nH8610Xkj/avLKmnakzXwUTwENrk5camD1n7AWUlgnXO659wMk0TjrRnPxIlH+gje66MnM3lXk2d7\nR4l+1ke8vy9jy71pB17V/porBQWM3o3QfTxCd+rTVO18FznMXhiac78bwP3lCToOHgSg8r3UigQA\ntayaymVRgicPE0wXxNX/qXxqcH/0MOF0fAqeaie4xENzetuDSHMFBRCLBolFU1ZZ6mKHf/oE3a1r\nYVha+dT2QdG5xgbTKyxGIpz+KPXcFM07NSDyf6+aw8f7ONSa8mPvO1OTxopDQydJ19HTRB4DiW4u\nuNz4yzUe3o3S1x+n79PJmOGZaq9SiGLGCJ6NETybnmTbuCOz3Uir8FMd+SF9xw/Rl+oIaiqmJtwO\nnUmvKFlSin9vQ9Zkg0hzSW59mYoPyUgP7emXenqlFJ4v5gC30ovwIp+eJpL2o+xVJKrj9cxkcMGS\n6ZNTotwhitNivxDpw6T7w0OZlRAlb/nZnb11ShjjxboT5yQNvQginUfpiZpAgpMXXDT7vbZxQqQP\ncfxRqd5SSeTMlJ61shoqiuxznV3/CusFyYwRlTj+CTVi43Oi6yCKyckvb6V1Z9B7sj1T51mAatde\nkUaEdaY4Js8nN4m0J8xNwvwubq/YrpJ6NqAZ4eiRHkxg6PxJXKubMzWhRCJ5/vzO2NjYv8lu+BoP\n5tRXG0FbW1tpaWl5Sb9m8SjxCAq/wWvP8bQPHTrEgQMH5jf9NpzAYupO63M7Y9OEWd4glio+DBKP\nLAqKnGjK031l3DewCgpxOtRZ7BoYjywKi51Zy80tksMGY5ZCYbE++zObrCSJ4TGUgkJ0xzOeq5Uk\nMWw9ZdsykxiPxDatEQNjDJzF+qx+keomJed5KgX6tDvveY3nhg1GrVz9K77myeGEuB9/y2ltbX2F\nmktpg9ec6Ops1z3BqKWgF+vPeAdIpLn0MaUAZ5E2m9AxUcn1GJ05n6tlkhgepeA1J5r6bN8zhkdz\ntjelRyun3VQfFuAs1vK2mY/mJK9ed7l83zZ35IrTefhFLn2YIwajAj/Mx89ns5tPTprXXOFs+sgr\n/pj/P3vv+9xUlt77fg7sYZvI7t0eDbiDJmhidwRXDiJtbptAgoM5bS7uQun4XJPQdU2K7uuhNBzf\njLuoU/wNfkGFnFAdV8dnoNLMwXNg4u5rLuLiDmZMAoU6iIMICq0zdo2YFoMAtRFGgzd343NfSJZ/\nYK0t2/xwd55PFV1tbe1Ha6/1fNfzrLXX2pvU3YczxrOCsa6Y+rXJF0Qjs+v/5uJztu1QVDs+h/Iq\n88x5JbczxiZb7dnWgzq+zzXmCS8qlszCNU1TGuIbjKSAwjfKnV9xfntBlsxR7nw+V6yaoNQNnMsK\n15WxzKmwO9O5Go5yp3q1kOaYMeku7mIcM5ZX0+1tamUGzrLC11qomjRlHanRy9WTT6o2t61H4SVq\nTq0Nvdw5x0lHlV31b6Lpc/Y1mw4E5zJ9TucZivPUeixQhzY2i9GcsHBjs32fV6CfLsIvCulDLzPQ\n57NaqIDdYmLSfFD1Mer+p7CeC8a6YurXrn8SZtX/zcXnbNuhqHZ8DuWdRw41l9hkqz3bepijL8+j\nfgVBePbIQ9wFQRAEQRAEQRAEQRCEBY1MYAmCIAiCIAiCIAiCIAgLGpnAEgRBEARBEARBEARBEBY0\nMoElCIIgCIIgCIIgCIIgLGjkLYTfcBbCWwgFQRAEQRAEQRCEbz7yFkLheSJvIRSeO+3t7V/r8h88\nePCld8SC8G+JhfAKZkEQ3QmCIBoRhNnrRBCeJ7KFUBAEQRAEQRAEQRAEQVjQyASWIAiCIAiCIAiC\nIAiCsKCRCSxBEARBEARBEARBEARhQSMTWIIgCIIgCIIgCIIgCMKCRiawBOEZYZom1nOybZkmpjlb\n65ayTKZpYs2pwNbcr9Wyu46sbbNQwUxz5t9W2rUwR2zKW8CuXb2r6sEyTUxLdPGymE/9K/3bztcU\nb76x1ZxlFfiOZf9GnblowzLnbreYOrRerDZEcwtelPN7M1QhfRRht2BfPg89zznW2ZRX6cfziHWm\nwu7ccgzh2eeD1r+tt6cVjHnqfHDuGplP7isIwkJB3kIofG345eU++v75Cx48Wkx96w9YWzpxLPPL\ny5zsC/GrBya//db7vPO7r7zAkqUJdnUSSQE4qNsVYMOK4qQVvxQkeClK+tFiGva2U1M2LQ7fDXP0\naB/JxwAGTfsCeLRsYI+cOcHZq3FMzUPggyaMKXZ76D4Xy/3loPad96hf7cgG75shDv+kn/RY9qj7\nzSZ2bvHkE4a52s2XeaiPA8fDVG1ro3nt+LEMfYc7Cd/JZQ2Gl5ZWP65J1ZS6GuTI6Ug26Vvipe0D\nP3nLVoLgx8eJ3Mkmdp7tbTRVO8BK0Hukm2gqa1db7mP3rkacObvmzYscPjZAOvuj1L/fSu2yST9a\nyC4moeOH6R/KnunwNBBoqsl3mJkbfXR+Gs4nqN63WvCvc2V/81aYYz8ZbzOoWNNIy9s+6WxfINFT\nXfReSwFQtbmF5vWuos5Ttavah03Cvcfoiyazx5a48O9uwVtOEZrL6e7UUYLXsufrHj/tTd7stZw5\nQu+VnN1FThp276amWB/uOUx/LOvDLHXT9N5OPLk+Jn6hh+7zOS2XeWjZ28SUWipod/z4IF0HTpCu\nbGTfDt/E4MDmWgu3jbrvydwM0XPqAom0SdW2AM1rjSLbPENfVyfh1MSIxbWphZaNLhHKLLFrA2Ws\nU/jb4NkjnPg8OZGYrqjjh7s2TOozC+vDzo8LxlCb2KHS3eCZI5y4kpxyfd7tbfjnqTtl7JhPrDPj\n9PyXbmIj2T89b7XQlO/X1LFOmKVGlDHEJm7Z9fXA4JlOTlwZpXFvO76yib746F+fIPF4YmhXu+uH\n1E/JQ036Og8SfuSh7YOmfG6l0p451Meh4+EpE3GG10/A77W3q9SIWtN2+WDhvl6tPfs4XCh/Vccm\nQRBePLICS/jaYP4alru+DTx5Krl6MvprHL/5W+gvoVzxs8eIpAz8ewLUrcgw8OPeXBJZxDU9gorf\ncs54TYxEOPSjPpKv1tCyt519+1pzk1cATxi1HLhf08CaPhOdoP9cDOcbfvbtb6e+0iTUe4ZM7uiF\nM/2ky2oI7N9Py+Yq4p+fImLO3+74ZF7vyXA+6E9kBbdJ46J+R4C2XfXo6Sjnr0/UUuZqD12nIzjf\naKStvZ19kyevSNPz10eJ3HuVxl1ttO/blx9IZ672E00tpm5HgLZ361l8J0LwciqfzAycHCBt+Ght\nD1C3MkP/kU8mtU1hu9bQWfqH0ni3tRLYUYsZ6+OTqxNn3rudpmJNPYH2NuordaJnL+br4fo/9JGk\niuY97ezc7CF5LUh4WPT7orBu9tF7LYVveys7N7kYPNdNeKS4c1XtqvThkev0RZO4NzXTvncnnsUJ\nej+5mLer1hxEeg4RvJakZnsL7e37JiXyJskU+Dbn7Oop+j6LTtFbIR/m1gX6Y2lq3gmwv30nVU/i\n9JyO5Ac8wfMxjDWNtO9txm3GOHo8XJzd8TL39pKaoQ5V16puG1XfA9avMzhc7hn7eLs2T4+AZ1Mj\n/u2NNL7VyKY1FSKUuWhL0QbqE9X+lknex+Gpw7/dT+O2Bhr/aOqEf0F92PmxIobaxQ6V7kzTomJN\nA03vNNG0vTY78F+qzVt3qtgxn1g3+LNeYiMGje+30bzJTeyzCX3YxTphdihjiDobtOnrs/7ceyU9\nPbuCR2nuPTao2+7Hv62Rhm1+apZP7UEzV08RnqFZVdqzHo2iL6/J+vmORlyApetF2VVqRBnz1Pmg\nsq9Xaa+IOFwwf7WJTYIgyASWIBTk9U0NbN+WvTM4ffXvK7+zie2N2/itRfBiVwZniPxLGt27BW+5\nwYY/bYCxIQaLHDB7NjfS5K+d8ZrCp/swl3oJvNdAxVIdTZscNnVq3/bjf7MSxp5MS3JSJAHXag8a\nOtUeF4yNL2fPkBoGR6UHA3B5KwGT0cfztZub4jp3nBheaiuNqUu0tSqa39tJbaWBY8UqXoNJx9ME\nz8TQPY20bPWh6/qUBCF9uY/YYweNrS34ljvQJ9WDY20Tgb1tbKg0cKzM2k3eSuYTkPQIVG3aglM3\nqPG5YSyebxuVXXMkDYs8bF3rxKjciHsRDMbi+ePuzc20vF2LoTtYVfkajFn5evD9SYC2/6uZqnId\ntzd7ZzBxKyMCfkEMXb0OS31sqXbi3thEFRaxoeLqX9WuSh8u8xF4v42dG6vQy9xULQPu38sNXGw0\nNxymL2bi2dZKQ3UFuj5V5/Xv7qZxfdaue9kU4Sh9OHM3BRhUrjZAd+N5DRi18gOeNBp1W33oZVX4\nvgv8Yig/4FXZBeBuP8FYCXWbqtCnCF19req2UfQ9gLG6nia/H/cMfbzargUs5tXvujCWGrh+14e7\nTIYgc0HVBkps/A2gtHw5rnIHxopqvCsnTZaq9GFjVxVD1bFDrTuvv5Xdb9fgWe2hcuko4KK2Up+3\n7lSxYz6xLpPKQGUdvmUOqmq9gEUst+LKLtYJs0MZQ5SofQ7g4k+C4K3DU6Y9bXNRKctXVOAoc1K9\n1oMxpYtLcPx0DM/mWozFT546t5D2HNV+2t5rwLPag6eyhPvAujeqirKr0og65qnzQVVfr9Sebe6r\nyF9tYpMgCC8eyeKErxdPVMHD5GWEFtOE11a68pLSsEjeNaGsyPvUT6wZJ8biX1rwOEpnR/YunHt9\nMzs3V0397Znqo8xHw8o++o79FckVBslbKYw3xpc8O2jY4qbzs246ExVYd5Jg1FA9bevi7O0CVoye\nSylqdu3G9U+HSMxwVfFzR+m+lAAcE9tPRuJ8OQZmLMiBjiDgoPbd71O/Mlt/iZtfAibBjw4QBPSV\ntXz/3frsHTnNgZEre+pCL3Gg7g+9+bZwLIXIxQEy1fXE4/eBxflOT2VX0x0wFmXgRoaGikHuj83Q\nWZoxjnzYQ/Ix6Gua8/WglRn57178pB9wUjNt9Yrw/BgdfQK/6cqvElm8BG4nbwNVRQp65nZV+jAa\nxvhWj+EQwZvgfNOXu2us1lzmVhwLiJ3uouM0T217gKlbOXw7qicSboUPO9bWU3WmixN/2YmrbJRE\nCmreWZU9cUkJOhYXzsbwbq0gnga04rQBJsFjIRxvtvDmty9yYXBy7aivtZi2MefQx6vtamCZhI51\nEcodn822UuEZxFkbf0N7QvLSCTov5YaLK+v4wbsb0O30obRrE0OVsUOtu8l8/rMIVDbizE+MzV13\nytgxj1hXYjjg2gVippeKnyemJP9FxTphlhJRxxDlqQV8zhzqZSBl0NJaQ+gvLz29C2EswYmPuvIT\nLnW7fsCGFdkeMXaqh2RZLbvXVXBwIDX1XIX2JpO6cIHMIg++ZROfKe0qNKLUtE0+qOrrldqzy32L\nyF/VsUkQhBeJrMAShOeA9Xi+68A0SnRgiZumPW3436ggfql32nLnQmRIZSxAp7S0FB0YTT/MH02m\nsv9fWlpKyVLg8UNGmb/dSE8vmaU+6lZo3B8FK5186iGb36msoXaNG8jQP5Ab+S7JXfGKWgLtrdQ4\nM4R+emrasnud2ncCBN6pwbwZ4sz1qUetm/10nU9grGliw7KJc+q21qKlwhzqOJB7ZsL0BGRmu/rq\nLdQu1wh/eoiOj4IzbpVCd1G7sRa3Aea18wxaT090DNyCmh27cIsknqG44vQc7uLI4SMT/7q6OHFu\nsPAp5iwST5t2ndGHxxmJceSjfjBq2LXFPSvNudc30b7HT8WjOL3Tto4sXuZh03ofBhA5ewGzGG2Y\nKe6PAXopDkf2gYEPM7lf1b00rXeRutJDR8f48/umr6qZ2W7mai+RRw62/oGLdHoUrDTpSUKfbf8y\nq7aZjZvk7eq8+baf1vb97N+/jwaPzuC58zNrWng+2Pibe30jTe+3s3//flrf8mDevMD1kSL0obRb\nXAydOXYUo7vsoPfyHfCtWzVplDtf3aljx1xinWfTVlxLUvQc7KDzZGRq0xQT64RZ+rs6hqiY2ecy\nnPppFH3NFlw8xATSd1MTfrO0isZtzbTv38/+/a14lpoM/Ox6LiaF6b2WwbdlI4ykgVESw+YkXdlr\nD0xC/5zEsdY38WgHG7tKjag0XVQ+WKCvV2nPJjYVk78KgiATWIIwP75VIPhP+u+LYrEG9xK3p0w+\nuVyTV9xYxK+HCd9IqJeSL5n8xyijGeA7HjzlDrxbN6LxBOvx9GmuGRj5gnAK6va00dy0k7btVZhD\n4VwSlSZyJUXF5gAtO5pp3etHexQjfNOat93YLyx4FOFgRwf9t0ySn/fQM+1ZGo6VXurf3om/UiOV\nzB17nE21K7y1GLqT+i0eeDyxHN2yngBuNq42MFbX413ElLfSpK72cuBYCIe3kcDbUx/I6Vhdz759\nbQT2ttP0hhMwcCwtxq6D+vf20banjfY9fpyAw5i+isqBd309O1v9aCRJP5po70hvJ92XEni3BWio\n1EWvz5InGdL309wfuT/pX5qHj7NJbEmJDneTE1qzJq+QzH00HCd8KUJiZCZFFmpXhQ8D3A3T+WEP\nyTIvgUDDpDvYNpqzsuX2bPCgl3vZ5NGwRqdO+Whlbmo3N9K6vQpSyXzSrfLhzI0wKSpo3dtC07ut\n+Cs1Yp9PDF7dm1vYt7eN9n0BapzA0tKJ54wo7Ga3F2XoOdhB17kE1p0QnX8fKepai2kbu9Ufi2f4\nltquhrvai1PP/r9vbRWQID4iUppzzCvYUoVjncrfjJVePMuyinGuq0bHYuhmpih9FLZrH0NVsUOl\nu7yyL4fI4MI3qY+fj+7sYsdcYx1lHlo+2EdgTxuBd2qydV5WMotYJ8wOVQxR54Mz+pw5SHwMzGs9\ndHR0EX9sEjreRWS8D9MMvGurcjHHie+3dbgdJwNkhoawgMinB+n4aABzLEHPRz35batK7Y0zfJXI\no6nbB+3sqjSi1LRNPqjq69XaU8Wm4vJXWZkoCDKBJQizxnqY4atffYWFxYNffcWD+5Pu9pgZHtz/\nFQ/GwBz+FQ/uP3hBz8Jy4FvlIHPtPHHLInLqLBYu3JO2/2Su99J9so++T7snEo7xaxrJkEqksLBI\nJ1Kk83ewDKq+p8G9IeIWJC5l38YyOYBmRjJ8efchMMrt4TTp8cH4khJ0YPBfs8+xiA7dBkr5jgZQ\ngmMpJGPR7F2864NYgDHpmTBzs2vwJ4E2AnsDtO5qxufUMNY00rQuu3g+dTlId08/8RET826U0JCF\nVpJLoMvcVC6BZCyCiUn48hAsmrjWKo8bGCJ8y8S6FWZwLLtRE8C8EaTrdBTKvDT+vovwqSMcOROb\nlhE6sIbO0nMlhfPNBqo0e7v51v2N+5z9aS8pnDRsySVvVoLgx930X49jWibRsyEsdLTcBGTsVBfB\naBqHt4GNK+4RPNxJ3w15BtYzQ/ey+4N9tP9F+8S/D/axe2t2QOf+HTeMhDl/0yR99QyxMaic/Ewd\nMvQe6abvXJCjk1cj2LSr0ofNKJ0/6iONQf32jdy7HKTzcF/u7rlac45KDxoQuxwHK8GVX1iQe1aP\ndTdM98e9RG6lscwU/RcHYYlelDa0MgeQInIzA6SJpyxwGNMGSxpfnDlOOAU1b9cXZdfXFCCwJ0Dg\n/RYa1jjB8NH6H2rGhxfKa7Vrm4J9T66PTw8nSI/B6HCC9HB6YgWPwq41HKP/QoTUiJmtw7NRWFLJ\nqjKR0qxRtIFdrCvob1aK0LmLDN7NYFkmkTMDmOh4X3fY6kPtx+oYqooddrrLWSB0OQGVvilvPZyP\n7lSxYz6xbnz4XWINcvzTMBi11E+bHJsx1gmzTFLVMUSlEaXP6V6+vydAYE8rLTsaqFik4dvWmn9r\ndfpGiIuXB8lYFubdCOejJtr3VuW2kvtp2xMg8H4rzdt8aIucNL6fe/SDjfbGGbwYgmnbB5V2bTSi\n1LRNPqjq69XaU8Umdf5qG5sEQXjhyISy8DUhQ/+P/5bruTtZlz/5Oy5/63W+/x+34wB+fva/cvKL\n3CTB5ZP86LJGfWsba0uff8mq3m6i6udH6T5wAICad/z5vf4Aetmr+cmukiVTr6nv8CEiuWsKHe8i\ntGTiVcS+pmZiH3bTfaAjm457G/COD7rMKIc/7M0vqz7xUSfa+OvsdS/+N8OcON9Nx/lcGTf7c4mF\nTt3WGqKfDnCwYyCXTNSztny+drPJg0GG3sMniD4CUkHOuKvwVzsoKYF4LEQ8lnsKzVI3O/OvYjbw\n/1kdhz6eKJP3rfr8XWnHWj910b9h4OODDGQrgvrc643jQ7mHzY5EOfGj7BJ0h3cieQp+eDCfJFa8\n4Wf3pG1dKrvmjSAHP81Nbixx4d/TMjEY0ErRzDihk3FCJ3NJ1aad+PRsm37x8+xdu0y0j67cqniv\nCPjFzW+tbqT+2iD9xw4SApxvNFFbPjXsvfqqBncsDGNSB6FsV5Q+nPn5F7k7z2n6j+WeQ1LmI7vh\n1kZzZT52bopyNK8rJw2bs3a1khIe3o4S/DhKMKff2nffLkobemUdNctjhI4dyj37yUHdH+eeqWLF\n6Dwwfrdco2Z7gIaVWlHaQHNglEO05zB9MRNIceyMmza/17Z/UbaNqu8BYmcO0xPNHb3UQ+cljYa9\n+6gps7H7KEHofIjQ+WC+H67b9TayxmT2qNpAGeuU/vaQ2OcDJC4NTMTUzTvx6vb6sPNjVQxVxQ47\n3WX99QvCI+DbNnVr1Nx1p44d84l1sVOd9FzL/eryGgLv1U+ZyCsY64RZjqjUMUSlEbXPaTjKjWwf\n+VH2xkjy9DHcq9vw6nD/doyBSwkGPhvXjGdSfqXjKNcxr/dw4nR2wjN47AxVf+HHYae9nF9euZbB\nscY3rc9U2VVrRKlpm3xQ1dcrtWcTm1T5q11sEgThxfPvRkdH/6dUwzd4MKe/3O1LHR0dtLe3f63r\n8ODBg+zfv9/mWxbpu2l4xYnxTKs8a9cqKcVZNkvDZprUA4uScieOp59ATuruQ7QSY8rqq/nbLeJa\ntBKc5TMMHy2T1PBDSl5x4pjhUs3hFA+tEpzLih96miNpHo5aBW0WtGuZpIcfFi4rYI2kSdvYFube\nb9hrzmbKeziFRSlG+ewaR92uNj6s9kal5iwzTfqBReky51MP0M0Mpxi1NEqXGejMThvZYxrGMmPS\nHSuLzHBaaXOumivmWufaNnNvcyu7WuipehCete4K9f12/mYOp3loFY4rM+ujGD+eewxV687CNJ8U\nzLHmo7u5oIp1lpkh/WAUraQUo+ypg7axTpidRuaTG9j19YV/dO7taK89E3R9Dv2mWiOqmGeXD6pi\nyMzaewa5r7AAYsks/No0pSG+wYgqw8diAAAgAElEQVR6BeEZSclY5lxYdnUD57KCB3Eu05+D3Xlc\ni6Yuk17unHXCr5cZ6DZbhWa0q+kYNvWjlRk4ZRvSgsVRPjfdqNt1PjpX+7em0JWj3KlcMaTSxszH\nNFubc9VcMdc617aZe5trGM/pN4Xi+n57H1YP2GfWRzF+PHfNqm1r6Lo2S+0Up7s5KU4R6zTdUXgS\nuohYJzzLGDIfn1MKZM7taK89fc66V2lEFfPs8kFVDFHHrXnkvoIgLBjkGViCIAiCIAiCIAiCIAjC\ngkYmsARBEARBEARBEARBEIQFjUxgCYIgCIIgCIIgCIIgCAsamcASBEEQBEEQBEEQBEEQFjTyFsJv\nOAvhLYSCIAiCIAiCIAjCNx95C6HwPJG3EArPnfb29q91+Q8ePPjSO2JB+LfEQngFsyCI7gRBEI0I\nwux1IgjPE9lCKAiCIAiCIAiCIAiCICxoZAJLEARBEARBEARBEARBWNDIBJYgCIIgCIIgCIIgCIKw\noJEJLEEQBEEQBEEQBEEQBGFBIxNYwjcK0zSxvlFXZGGOzPWaLNv6sEwT07RmOjDz50Xa5TmVV3Eh\nivKKv4nen6Wb2mtjTlqes92cjq25a47noDnRhujuxdrN+rJpWTPbtZ5HVzBX3c3D7jxi3fOyKzxL\nX7aez9vTLPO59fVFVMaM9VEw/5xvzCuivOrfFgRhoSNvIRS+Nvzych99//wFDx4tpr71B6wtnTj2\nq/9+mp+eu5ELkBpr/7c/p/5/eeWFlCtzM0TPqQsk0iZV2wI0rzVmG93p6zxI+JGHtg+acIx/evMi\nh48NkAbAoP79VmqXTZOsNUjXgROkKxvZt8OX/zh+qYfuc7HcXw5q33mP+tWOidPuhjl6tI/k46zt\npn0BPBpAhr7DnYTv5AK74aWl1Y9Ls7dr3ghy8NPIRNmWuGn5YCcuwBzq49Dx8JQExvD6Cfi9RZUX\nwBrq48DxMFXb2mheO35MXd5xBs90cuLKKI172/GVUYRddf1mbvTR+enE9XjfasG/ziUifWGkCXZ1\nEkll/aVuV4ANK4oJZxn6ujoJpyY80bWphZaN2bYbPHuEE58nJwLkijp+uGtDLlCahHoO0x/LKpKl\nbpre24mnLHss3HuMvmju3CUu/Ltb8JYXo2W1D0fPHKH3Ss7uIicNu3dTkzvXvBXm2E/GdQwVaxpp\neduXLa81yNG/PkHi8US4r931Q+pXaLblxYwT/G99RG+lWFzZSPsk37crbzHamIvmhJdP4nIv3Z9F\n83G2ZnsrDdWziXczxzqlnpV+nCV1NciR05FsuZZ4afvAjwMwb4Y4/JN+0mPZ77nfbGLnFk+RMTRI\n8FKU9KPFNOxtp2Zy3DDj9BzuZrwr0FfW8v1367O/qYp1Zowj/7mH5NiUg7QFsuVV2VX3PzZ5iNJu\ncTFUKDIfnEduoOrri8ozC/nyhR66z+fyqzIPLXubGC+ROuZB+nqQzpPZvE6vrKdtR23+mFIjAFaC\n4MfHidzJTkR5trfRVO3I+vLxw/QPZR3S4Wkg0FSTt6uqB6UvWwl6j3QTzcV3bbmP3bsacU7y5UK5\n7+CZI5y4kpxSfO/2NvzVDnFqQViAyAos4WuD+WtY7vo28GTazOsDLpy7Ad/byPs/+D6bXl/M1f83\nyFcvqFzWrzM4XG70uSY8V08RTj+d6A+cHCBt+GhtD1C3MkP/kU+Y/rVIby+pp4cZ9J+L4XzDz779\n7dRXmoR6z5AZPzwS4dCP+ki+WkPL3nb27WvNTV4B1m3SuKjfEaBtVz16Osr56+mi7FpmBso8NG73\n07itkcatG6gYP/ZoFH15DU3vNNG0oxEXYOl6ceXNDXB6T4bHrU2qfFV5J66390p6+plqu8r6hXu3\n01SsqSfQ3kZ9pU707MVp5RWeJ/Gzx4ikDPx7AtStyDDw496ntFFw6msEPJsa8W9vpPGtRjatqZjQ\nYvI+Dk8d/u1+Grc10PhHvom+5tYF+mNpat4JsL99J1VP4vSczk3YjlynL5rEvamZ9r078SxO0PvJ\nxeK0rPRhk2QKfJtzdvUUfZ9F8+W9/g99JKmieU87Ozd7SF4LEh7OHXyU5t5jg7rtfvzbGmnY5qdm\nuVZEeYHHGazSCiqWwJOnOjy15uy1MTfNCS9/0vj8Z1GorMu2rWcx4ZOza6uZY52NnlV+DGSu9tB1\nOoLzjUba2tvZl5u8Arhwpp90WQ2B/ftp2VxF/PNTRMzi/M18BBW/5Zwh34DU5X5iaSf+vfto31UP\nN0MEr2eKiHWjWLqLhneaaNrRRO0KYKwknzuo7Cr7H5s8RGm3mBgqFM3ccwN1X19MnjmjL1uDBM/H\nMNY00r63GbcZ4+jxcHExjzjHT0Yw1vgJvFuLOdTPJ1fTRWkE0vT89VEi916lcVcb7fv25SavwBo6\nS/9QGu+2VgI7ajFjfZPsqutB5cuZq/1EU4up2xGg7d16Ft+JELycmpILFsp9TdOiYk1DVrfba7MT\nYEtlFlcQFiqiTuFrw+ubGnjdusGhQ59NG/a8wrbd76O/+goasPq7yzn/8zvcMeHb+vMvl7G6nqbV\nFj03DjD7BdoJjp+O4dlcS/JSatJ1WaRHoGr7Fpy6To3PzcDNOIMjTNzluttPMFZC3abvcjk+qUZG\nUiQB32oPGhrVHhf9QxPLt8On+zCXegm814DDAm1yL6BV0fxeVe6PVbxG/8TWCxu7YKEtdeFeYXBv\npISqlc78EUe1n7bq8b9inAHWvVFVpF1InDtODC+1lQmSVpHlzXHxJ0Hw1uH55eWnhssF7arqF3Bv\nbsY9/quVr9E/ZMl2qRdGhsi/pNG9TXjLDfjTBgYO9k/VRuHpZmAxr37XhWE9pOR1N85pfURp+XJc\n5Rr3v1WFe9nEwczdFGBQudoADDyvweBortXLfATeX4WxLJugVy2D2O17ZACHnZaVPqxT/+7uCb9b\nBrFJDu77kwCrlho4NMDrgnMxErcyUJ4bwi8qZfmKCrThUaoqXRODH2V5gTIv/iYvsZ4YPaPTswa1\n5uy0MVfNCS+b7ASTXm6gAdUrX6M/dpukyVMaml2sK0LPhfyYNMEzMXRPIy1bfdOS2gypYXCs9WAA\nhrcSzg0y+hjyBhT+5tnciMeKcuBA8Km+PZW6D0tW4SnT0MqqeY1+RnN9gTLW6T5a/8KX74sGT0LF\n/zqx8kRlV9n/2OQhKrvFxFCheOaeG6j7ets8s5AvP0qTRsO/1Yeuge+7EP/FEGlqMGxinnkjQgoH\nTVu9GJqXxpUhgtE4rPXZaiR9uY/YYweNe1rwlWlTRpvmSBoWedi61onORtyLQgzGxu2q60GpvbVN\nBDw6RpmW9+XErSTgtM19vf5WvOPKHBoEXNRW6uLQgiATWILwDHjyZMaPHa+Obxf8itMDv4Rvr+P1\nFxp7TJ7M4azYqR6SZbXsXlfBwYHUJEFqOJZC5OIAmep64vH7wOJJx02Cx0I43mzhzW9f5MLgJKNl\nPhpW9tF37K9IrjBI3kphvNGUS1YyxL+04HGUzo7sXS33+mZ2bq6aUq74uaN0X0oAjoml6kq72TJb\ndwbo/Cj359Iqdu5txj2tl0lduEBmkQffMoqza8XouZSiZtduXP90iMQM9ThjeQFzqJeBlEFLaw2h\nv7w0tcNT2lXUb/4rMY582EPyMehrmjFEnS9ObSa8ttKV9zsNi+RdE8rsRK+BZRI61kUo90nV5haa\n1+dsaU9IXjpB56XckGJlHT94dwM64FhbT9WZLk78ZSeuslESKah5Z1XerjG+xWE4RPAmON/05VaC\n2GlZ7cNZP57YluTbUT1xNWVG3s7FT/oBJzWTtjw8GUtw4qOu/ACpbtcP2LBCtynvBKOKkZeqvAW1\nMV/NCS8VR/l4S6boPRsHZy2eIuNs4Vhnr+eCfjwS58sxMGNBDnQEAQe1736f+pU64KBhi5vOz7rp\nTFRg3UmCUUN12Sz87cnMAvD84Ua0aD9/1ZnEGEuSwkmT5+kI8FSsmzLh8DmRRxqNa5xF2VX3P+o8\npJjyKvUszDJAzT03KNTXq/NMhS8vKUHH4sLZGN6tFcTTgDYp/ihinmWasOi1/HZSrUSDXyUxJ80B\nF9JI4uaX2XJ9dIAgU7f6aboDxqIM3MjQUDHI/bGnB6OF6kHpy5oDo2xce73Egbo/HJ+WKi73Bfj8\nZxGobMQpniwICxbZQih8g8jQ/+O/45dj32b7n21a+LOzI2F6r2XwbdkII2lglMSwOZGkb61FS4U5\n1HGA3mspJm/myVztJfLIwdY/cJFOj4KVJp1/IGWGVMYCdEpLS9GB0fTD/OCgRAeWuGna04b/jQri\nl3qf2lbxncoaate4s3U6MFiEXdBXvon/nVb279/Pvvcb0B8NcvFq+qlEK/TPSRxrJw+W1XYjPb1k\nlvqoW6FxfxSsdPKph3cWKu+pn0bR12zBxUNMIH134s6/yq66fscv2EXtxlrcBpjXzjMod61fKtbj\nYhpA5823/bS272f//n00eHQGz53Pb7twr2+k6f129u/fT+tbHsybF7g+Mu66Ke6PAXopDkf2AXwP\nM9OWJ43EOPJRPxg17NriLkrLah/OsniZh03rfRhA5OyFp+7Ax88dZeAW1OzYlb/7z9IqGrc1075/\nP/v3t+JZajLws+tFlLc4VOUtpI15a05YEHG273AX8TEnTbvqi4uzylhno2eVHy/JRbUVtQTaW6lx\nZgj99FR+y1YylY0jpaWllCwFHj9klPn7W2Y4jQXojlJKl+jAKPefmu2dKdZNmtD7/BKU+fCWFWm3\nmP5nHuVV6lmYHfPIDez6+hnbV+XLupem9S5SV3ro6Bh/ztzE5m1lzJtRmLPpk3Vq3wkQeKcG82aI\nM7mtfvrqLdQu1wh/eoiOj4IzbuEtVA/F+LJ1s5+u8wmMNU1syE8eF5f7YsW4fAd861aJHwuCTGAJ\nwjPmW9M/+IrTP/pbrt59hYbWP3/Bq69yATcXJGeI+MSvhwnfSExZZp0ZGsICIp8epOOjAcyxBD0f\n9eSf/eFYXc++fW0E9rbT9IYTMHAszR4bjMWBDD0HO+g6l8C6E6Lz78efx/MF4RTU7WmjuWknbdur\nMIfCuSRqlNEM8B0PnnIH3q0b0XiC9XhqiR0rvdS/vRN/pUYqmS7CLmjlbryrs/estGU+Vi2BxM34\nVMPDV4k8mrSlwtZumtgvLHgU4WBHB/23TJKf99AzbWJsxvKag8THwLzWQ0dHF/HHJqHjXURG7O0q\n63fiV/Gur2dnqx+NJOlHIssXpjUN7iVuT/pEw+Vy2GoONNzV3tyWJw3f2iogQTyXsBsrvXhyWyic\n66rRsRi6mXu+xo0wKSpo3dtC07ut+Cs1Yp9P8om7YTo/7CFZ5iUQaJjyrBKVlpU+PF7qMje1mxtp\n3V4FqSSjk64z0ttJ96UE3m0BGiZvedAMvGurcuVw4vttHW7HJ57HoijvRG0VRlXembXxLDQnvFxS\n9HYeInzHoHFv6wyrr+YW65R6Vvnx4+xUcIW3FkN3Ur/FA4+f5H47TeRKiorNAVp2NNO614/2KEb4\npjV7f1sy9c/BS2Fw1tG2q5mdrT/AsyhD+FrCPtZNqsfQNZOK3/NO0ZjKrm3/o8hDiimvWs/C7FDl\nBoVik11fr2hfG192b25h39422vcFqHECS0vzk6qqmIe2GMbucdtiYtZrmWvm53AtmT7P9QRws3G1\ngbG6Hu8iJr0h1EH9e/to29NG+x4/TsBhOIqqBztfTl3t5cCxEA5vI4G3J7+wobjcN305RAYXPtk+\nKAgLGtlCKHxtsB5meHDvKywsHvzqKx686uCVV3UgQ/+P/o4bD2D15i385vBVftJ9g3W7/uzFTGSZ\nGdK/TmTfdDScID1cgaN8YmtP5nov3SdjWbm59uWf6+FY66dt5SiWZXHvVohPziRo2N00dbm55sAa\nCtJzJYXzzZ1U5Yz6mgK4RyywHjL4eZC+my5a/0NNLpHIPhR28F/jbNjoJjp0G/gO39EADKq+pxH7\ncoi4VYN2OfvmpvwzOC4H6btZwoa3NvLa6CChIQvNU1KEXYhdusjDZR6qVxo8/KKfyGPweKYm74MX\nQzB9S4VNef8k0EYGC2vkHqFTnxBf0UDTOsO+vLqX7+9xY2HxcDhO30/7qdi6O1f/arvK+rUSBI+d\np6RmAxtXvcbg2RAWOtoS0eiLGhz4VjmIXTtPfKub9KmzWLhwT17JUEBz1nCM8/86im/NKowlD+k/\nG4UlHlaVAVaK0D/GcFb7cJdrRM8OYKLjfT23EbDMASSI3MxQv9IinrLAkVOrGaXzR32kMajfvpF7\nl4Mci2i8996kiaECWlb5sHU3zInTCbz/vg6v06L/4iAs8eT1GjvVRTCaxuFtYOOKewQPH0PbsIuG\n1Q7SN0JEM058a91ow1HOR000z6rc287symuSGXlI8qEFWorUcJqSsuyztpSaU2pjHpoTFgAZ+jq7\niKbB+1YDruEwRz+OUvt/tuQnsuYW69R6VvpxmZvKJTAUi2Cuq+Hq5SFY5M7powTHUhiMRTHXb2D0\n+iAW5J6PY+9v1kgmt2LXIp1IkS4vxSjPXmiJ4YAv48TNDbh/PUhyDEqNV+1j3XjKMBQigUbjmqlv\np1PZVfY/NnmIyq5Sz8Isk1T73KBgbLLp61XtW0zfqZVpRE8dI5yCmndzKydtYp5jVTWOkzHO/2Oc\nqnVpzgxZuDa7itJIlccNQ0OEb5m8yVUGx+C704acjt+4T/DjXlI4ad5SVVQ9qHzZvBGk63QUyrw0\n/r6L8KkjRLSN7N7qsc19c5VM6HICKhuRd0oLgkxgCcIzSZ77f/y3XM/dybr8yd9x+Vuv8/3/uB2H\n+Qt+/iD7+Y1zn3ADgFde2EO1Y2cO0xPN3bG61EPnJY2GvROJiV72an7gXTJlkkPHUa5jXu/hxOns\nK46Dx85Q9Rd+HJgEPzyYWy0EFW/42T15i4/mwCiHaM9h+mImkOLYGTdtfi/oXvxvhjlxvpuO89mv\nV2325yfGfE3NxD7spvtABwCGtyG/jaGkBOKxEPFY7glBS93s9HvzE0Iqu4noAKE7A/SNX21lPW9P\neQVxhivXMjjWTNtSYWNXK3NgkKH38Amij4BUkDPuKvzVDnV50bLPbDGjHP6oDxNInj6Ge3UbXl1t\nV1m/WimaGSd0Mk7oZPaX3Jt24pMbdi+MqrebqPr5UboPHACg5h3/lOdVFNTcowSh8yFC54P543W7\n3s7540Ninw+QuDQw8Tubd+LNtateWUfN8hihY4dyz89yUPfH2WdzZH7+RW41SZr+Y7ln9ZT5slsd\nbLSs8mGtpISHt6MEP44SzPUZte+OlzfDFz/P/mom2kdX7kVN495//3aMgUsJBj7LfVDmydtVlxfM\nG2c49On4m59CdH0UompbG81rbTRno405a054+ZiDfJFbmBP97ARZ7zCmxNm5xTq1nlV+DAb+P6vj\n0McDHOzI6tb7Vn1OHzp1W2uIfjpxzFFZz9ryImIoGfoOHyKSyzdCx7sILfHQ9kETDsCzaQsVX/TS\nfbAjX6bGKc+NKhDrcnxxOQJLa6ZsH8TGrqr/sctDVHbVMVSY3YjKPjcopBF1X2+TZ6p82YrReWB8\ntaNGzfYADSvHh37qmIfmoWlzFUfPddNxCXDW0LTeOTGhrdCIY62fuujfMPDxQQayiSb1ax35iaaD\nn+ZWiC1x4d/Tkr+hY1cPKl+OD+VW/I9EOfGjbA/lmOTKqtw3W7AvCI+Ab5tsHxSEhc6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P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- "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image('rand_int_histogram_view.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Make Graph from Raw Data\n", - "Instead of placing data into `x` and `y`, we'll place our Grid columns into `xsrc` and `ysrc`:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data = [\n", - " go.Histogram2dContour(\n", - " xsrc=grid[0],\n", - " ysrc=grid[1]\n", - " )\n", - "]\n", - "\n", - "py.iplot(data, filename='2D Contour from Grid Data')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So, when you view the data, you'll see your original grid, not just the columns that compose this graph:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Attaching Meta Data to Grids\n", - "In [Chart Studio Enterprise](https://plotly.com/product/enterprise/), you can upload and assign free-form JSON `metadata` to any grid object. This means that you can keep all of your raw data in one place, under one grid.\n", - "\n", - "If you update the original data source, in the workspace or with our API, all of the graphs that are sourced from it will be updated as well. You can make multiple graphs from a single Grid and you can make a graph from multiple grids. You can also add rows and columns to existing grids programatically." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "https://plotly.com/~chelsea_lyn/17400/\n" - ] - } - ], - "source": [ - "meta = {\n", - " \"Month\": \"November\",\n", - " \"Experiment ID\": \"d3kbd\",\n", - " \"Operator\": \"James Murphy\",\n", - " \"Initial Conditions\": {\n", - " \"Voltage\": 5.5\n", - " }\n", - "}\n", - "\n", - "grid_url = py.grid_ops.upload(grid, filename='grid_with_metadata_'+str(dt.now()), meta=meta)\n", - "print(url)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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IiIgCloiIiIgCloiIiIgoYImIiIgoYImIiIgoYImIiIiIApaIiIiIApaIiIiI\nApaIiIiIKGCJiIiIKGCJiIiIKGCJiIiIiAKWiIiIiAKWiIiIiAKWiIiIiAKWiIiIiChgiYiIiChg\niYiIiChgiYiIiIgCloiIiIgCloiIiMjXi1VFICLy2Ww2mwpB2uT1elUIch7VYImIiIgoYImIiIgo\nYImIiIgoYImIiIiIApaIiIiIApaIiIiIApaIiIiIKGCJiIiIKGCJiIiIKGCJiIiIiAKWiIiIiAKW\niIiIiAKWiIiIiAKWiIiIiChgiYiIiHxlWVUEcjmOHvgXZbUNhAHgA9rTpXcfuoRZVDhfulo++L+d\n7P3EC1YbXaK7cUufm7AbKpmvHfMkxf+sg3Y2BvbvdP77jbUUF3sw29m4o633Pyc1eyu4b0U9Gb/u\nzcAwnSZRwBK56Bv43/74Djt9576+ge6D7uKRcUMIvcg1efYW8Ldjvbj/zhtUrFflhrufzEVv8uE5\n58Z66z1k/OQWlc/XTM2+E6S+bgKnyPh1p/PCzJH3qkh9qx6sJuv+qxPhFxWODpN3PJRJd1xJIAu6\nxEB2NbYpooAlXwOGAdYBd5MxLg4A34mP+Vvem7y96x88WQcZU4dc1Hr+7287KYi8TgHrKjm45R98\n6IO7fzqFpL4RQAOewwdxh/VS4Xwtr+Bne3mcYe0/jjPwnpYBpY5N2+v9fwy7+N4g779dS3ZkCJPu\nuIz9aQSCIfwS7yxXtE0RBSz5ugmhuTnQ2vEGxk59mIj/fYk1//wHbx+O4/vdbHBiP2te28qOAyfx\nAYRFkDTtx9zdw8K7r7xM7mHg8DukPvUOXB/HM/9xN3UHCnj1L//iQ7cXgNAeN/HzaT+kl5oaPlPd\n6VNABIP7RgResWDvdhP2Fst49hbw0tqdHKr1/73XoNv5+bhhhAK+ind5YvlBUub+jG+dLW/TydL5\nG+n1QDJJvcPwHdjAk2/Bv98Ff3rdSR02kuc+TFxYAx/8/U1e/dtB6gIf7f+DRB76Xi844ST7lQ04\n3IFtDhnGIz+63X8BMj8md00+75YGdshq4eYhw5l5b5xO6Gc6A0CfDvD+Vg8197SopfrkONluiAiD\nat+ZFp+pYf2fPmHxPxsBiOhhIyP5BnoaJpuyPuaZw8DhE/zwv07A9aGse6QL5dsO89K7XooDp+iO\ngeE8Ma5L07bKd35M6uteqgHCQng4rrH1bp6sJvu1E7x2oBETIMzCE1O7MLKH5QLbjMY8cIT/zq2l\nwH2meT+n3kBPXQfkGqFO7nJVfWfsXYQCR455AxfWCnaXw7Cxw0j+ye10N6vJXf4mbqz0uvM2ugNE\n9uL+xLu4f2QfrID7QAUfG9eT9JN7SB7bi/qK/bz4550q3Itwc98bgGpefOUdDp1oOH+Bind58o87\nORR6AxN/Opr7h3fj4K6d/PqVAgDq607i4xT1rT7k4ygNnDztb3es9/moO+wk+3UnN9wVx9gRtxBp\nhYMb/5ff/+0g1t6xTPzxXQzrdz2dr4+AxoMsTd+Aw92B7//4bv82iwp4+i//AuDDv73Ju6VehiXe\nTfJPhnF3v46EtGuvk3kJl/HJo9qBzyS/wmx6tfjvdRBm8HD/YGh62WT9S0dZ/M9GEkeEk5EYSniF\nlweWHKIGC33uCGUgQKTBU4nhPDUiDIMGPtxjQu9QFk3qyGODg3m/uIbfv1/jj2t7D/HA615qutnI\nSOnEY30beOm9c757J06TXw7JY8PJ+PcwBpoNPPPSJ1RfcJtQXWZSYoTwxL93JGOsQU2FlxmvH9Xp\nFtVgyTe1WstHPeA5dhLoAD3u5jfzm9/uH1xN6qoKDtVaiLt1CDFhO6nuHct3vh3btEyvUT/hN6PO\n/u0Wph5+kew9H+Ph9lY1MdLGD7pvAsnDc8je+i9+l/4vQiO7MfZHoxnWuwPQwNtvOoBu/OqxH9MF\noG8sMdaV/G7zbt47MYzbuPiHFG7/yc+YemsH/18aD7J0czX0uJ2nHxjmf+3b/mbio4VvchCYOPdn\nfCcMII4ONS+SXeTE/aNbqDvtf0ii/6A4vmVA3K2360ReYi1WRD87iZwm++/HGDelCzQeZ23xGe4Y\na6enp7p50U/cLK6AxH/vxi9u9VcFLQuv4IerT1Ny0sIdt0YyKKyW8pgw7rg9suljYx7uzZimH3F7\nyotd5B2o47E7DDa9fRowePWRG4gABvaOxH76APNLW+xij2jW/FfL60AFP1xdT3mthYEX2GbPe3qy\n5p7mzyxyHSD1nyY1cFF9yUQUsORr9o2yEQqEtgvU45uHKdhUxHuOCqpPNQRqRmyBhRv8zYa+1j2y\nfZ84efPvO/k/ZzV1ZoP/IcUwi76sF8VCXMJPeO6ujyn4x/u8tfVj/vzK/+D4QSIzv9eZajdYB/X3\nh6uA7kPiCN38Lh8eOsltF13INm7r26HFSfPiBm7/7vnh6OgnJwH487MvkgdgNFBXC/AxH9bCbXfe\nRuiunfx+/vNE9u7FqBHD+E7vCJ3KS9GuA0nfcZP3Xi37gK4lHt4niIzvdIK85oBV84m/ZinvtcMU\n5Pk7olfX+pvgtpfXcsetQXgBWjUpwpG9R8ktqGXTkTPUmGcwfdDTGgQ04D4B9DNoecYG3h4CpS3q\nQc2TFGw+Qe7uesprz+Cv+wpqug60tU3zEzd5fz/F+r0+jphnAteBIJ1rUcCSbybfwY/xANZQK/Ax\nmfNf50MsxA2/jR/0vJ7a8iLWbD114RV8UsATS3biC4tg2LA7uLlbB8o2beBdt8r2knS8gWEJNzAs\n4TBrFq/lvYJ/Ufe9zmBCyLnLhljOvyxYW//9/AuFz3/DazH0gwH4Gk8DrTvJhAQC2Xd+cDtd8OID\nrFbwcR23hEFo2DB+81R/3tvyDzZu3s+aAwcpuOse/vNePfV4sUwf9IwPw3ivhvX/PM4thSZ0C2Ng\nMJS0ccUfc1d77rAHWg6tQeCDPr1ttGhLbA5X28qZ+lY9ET0MfjaqHTd2amTVihr2tVjGaNe6t4lh\nbRmEjjP/aTcFBJE4PJTJPQzMCg9ztzZ+ynXgCPctPYUZZiE5vj39u1nZt/k4L+k6IApY8o38+pxw\n8vtVTiCC4f06wDEnZcDtP32QqX39tVYHy//Rxmqa1/Ph/5Xiw8bPn5jCtwLX7I//ukFFfdm68a2e\nFt7b04APG116Qt2e/XjG3dLU3OreU0Id0OP69nAMwMvH7gbiulkCoXkfnov8Lny452O4tXXtU2g7\nG3CKm4cMIe5CY3EZEXxn1A/5zqgG3l3+IrlF+/Hce4uahC/6XzbAddeRHFnDS39ykweMm9Tx/GIO\n9f+oQnp0ZFj/TxlMpSkgmRRsr4cwG68+fEMgTx/HDNRBgQV7KJh7va2a7vaVtKi9OublfWDctBt4\nuK9/DeUVJz9lm7BvZx0mQSz9VS/6B64D5W8e13kWBSz5ZqirOoijxAeNXspKS3h3l78pYtjkRHoF\nAzYbIcBHjiIOXXcTJw/s5PdbT9LcRGihXSjU/cuB49vtiWzXga43RAAfs+MfTiL6tueDwo287QY6\n+u8h8uk+3Pg6b9dEMKxvNzrYrXz8r538eVcD9I7AjoU7h8eS+0cnz7z6Lj9PiKX+aAnZeYehYyx3\nXm8htH0vQtnP23/O5+aJdxF2rIT//eP+z96wcROJgyxk73qXzHxI+nYE7r37ONoxlu+PHELk1nfI\nXrSSpIl3cbPdgrvcyQe+Pkz83k0c/HseW+tvYPht3Qj1VeO/96pJ+NIZjPyewUuvm0AIY/qf/7id\n0eM6kjvUkb3aRfjYjtwTY6XGfZqt+2Dyj7oSHghM1SWnKP62hYh2Bn26BcE/6ynYe5z+9gbWv36c\nYqBn0zZDeOl1L/NfO8IvvhdGTdkJZu1opKkJ0BZMOFDsqKb8ujBqyk4ya2uL99vYZtfuVsBk63tu\nIm62UFJ4jBfcQAedZVHAkq+5dqFAhZPsVc6m17r3u4WkxLu5uWOgySkslrGDdvLnXTv53a6dgIXb\nh/fCsbUy8Akbw34QS8H/Osle/jpYe/Gbubdze+TH7HxrAzvfAsKuZ9iQDhT8S1/WixGClw+LHHxY\n5Gg+L7feTvJP/B3PQ/uOZtZYLy++5WBpqX8Za7eb+M+fj/YPDht2C8kjSsjcvJ/MJfsD5/V6Qks/\nwbC2PAM2Qs45IXHjpjK2Poe3tr7L77b6X4u8qxvfv/UWfvUfXl7MKiB31ZtNy/ca0cf/h/qT7Nxc\nwM7NZ69KHUh6cMRFD1b7jY5U1tZ9kiIGtueO102qv9M+EIDAaHWeQpk0OxJvdjWr3zrB6qbgFUry\n2cD0/XZk/+k0qS+5wWqQ83B7BpbW8MwKf/tcRA8bk3p4KQhsO+L27mS4Kkh97xQPFJ8Cgki81ULe\nP8/4f7RGR34xsIb5xc3vjxtukLfV1xwMz9nmul/bGRfp5rW3jvMaQFgIyYMhu0TnXK4dQadPnz6j\nYpDPlRnod2PY2n6/0UudD0KttqaBQ3y1Xn9XoAt9Ri6s0Uvd6QZ/ALKGNY9F2UoDdbVewEJomO3C\n62h3oc9/2vmupQ4IIQzrOU2Cvtpa6uH89Qa2d8H9+Qqw2b5m30WzjhofGFYDwzinH16jSY0Pwq1G\n4DfZgFnbAFbL+cueu752oRhtfWdMExMwjAu0E5+3TTBrTbB+yme+Irxer647ooAlIqKAJQpY8nnT\nQKMiIiIiClgiIiIiClgiIiIiClgiIiIiooAlIiIiooAlIiIiooAlIiIiIgpYIiIiIgpYIiIiIgpY\nIiIiIqKAJSIiIqKAJSIiIqKAJSIiIqKAJSIiIiJXj1VF8NVns9lUCCIiukbLNUQ1WCIiIiIKWCIi\nIiIKWCIiIiIKWCIiIiKigCUiIiKigCUiIiKigCUiIiIiClgiIiIiClgiIiIiClgiIiIiooAlIiIi\nooAlIiIiooAlIiIiooAlIiIiIgpYIiIiIgpYIiIiIgpYIiIiIqKAJSIiIqKAJSIiIqKAJSIiIiIK\nWCIiIiIKWCIiIiIKWCIiIiKigCUiIiKigCUiIiKigCUiIiKigCUiIiIiClgiIiIiClgiIiIiClgi\nIiIiooAlIiIiooAlIiIiooAlIiIiIgpYIiIiIgpYIiIiIgpYIiIiIqKAJSIiIqKAJSIiIqKAJSIi\nIqKAJSIiIiIKWCIiIiIKWCIiIiIKWCIiIiKigCUiIiKigCUiIiKigCUiIiIiClgiIiIiClgiIiIi\nClgiIiIiooAlIiIiooAlIiIiooAlIiIiooAlcolM3C4XnlqVhIiIiAKWXLkGF5ljhzI6MZERq50q\nDxEREcCqIpArYR4sIKsS5izP40eDolUgIiIiqAZLroo4bouLxrCoJERERBSw5Mp5TcCL16eiEBER\nUcCSK2YeL2Pl4iXAbUTZVB4iIiJnqQ+WXJ5aB0NHpfj/PD2JaDUPioiINFENllyesH7kZaeT1BfY\nVIhHJSIiIqKAJVfKIHrASB5fMBv2vkOZxsASERFRwJKrF7QA1AVLREREAUuuFq8JVHHSVFGIiIgo\nYMlVYfSIJRY3M0YNJu2tMhWIiIgIEHT69OkzKoavNpvtq90AZ7ocbNpeRuSgkQzpZdcJExERBSwF\nLAUsERERubrURCgiIiKigCUiIiKigCXSNtNF1mMprNzuwrU9i5SMTehhxE9xvIgFD6ZR5PJQ9PIs\n0jdc/YcKGk68z0fJqVRWVlP5+xmU5+9TuX9efNW4PyjG7aq+wvWUUzH7ASp2lHNixwuUZaynTqUr\nooAl32ReCrY4KPOC11WAY7ML73kZzEHOiwtIeyyNBS/mUnb8q3kknj25rNzw+T5Fabpd5Bbn48HE\ntamQd45e/TjaWLmPhtxiGjHxbdyHr7LmvGXqXMUc/mseh/PzOPzuev//8wuouVYm/PaVczSwz54W\n+3xi1yYO5+dR+WH1F7Ib9WW5nIhP5cRP1nJl4/TW4FtRTmMdNHxUzJm/lqOOtSIKWPJNZnQnoTPE\n9IzCFmZvI7TkMDQxhfQ3vfQb2Juq7AWMHzWa/INfvXquqpJMlswt/Fxr4Iwe/YgllpgudmxdAdN7\n1bcREtOfICIIiYggKBo4ff4ydY5c6jJWcTprFXU/XkzdMy9R98x6vNfI167hVDm1k16gbtJ8qnJL\n/C+eeh/3Pc9QN+kF6vZ/MQEr5OZkOiyJgC52Qq5kRdaeGAMguHtXLO0NXVdEviI02bN8qWz9YrFh\nEN1/JHHxdppillnGs8npRE5M543UkRjAlGnjyXlwBGnjXmbItjG88rM0SsP6MWHyEBxv5LD7CPRL\nmMDMyQlEnp18usFNfvZzpC3P9/+9bwIL0x4nob9/S64N6SQv3U1U9D0s/F0Czj+kkbbGAcSRvm45\nI3sYuPfk8vQzmRTudQMQN3EeGY8l+bdhunE6DrDbAVBEwY447HiBKG4bHMPZ251rTz6Zz6SRvxcg\nkoTpv+SXyYH9NMtI/9l0dhNF/EML+VGUk7TH03BUQlzyUpY/Eh9Yj0EUMWAxiBuewLBOUecXqOli\n07p8XEYsYxLjm8vh4mMcQfQkyGoQNnwgZmTEeUtEJDxFRAKAyaHv/ZDGl9fR4+bmG3vVXxfjyVjP\nmT3+9Vn/sIju9w+EyvWUj/8f6A1ncqthQE+s93al4Zn3OTNgIOGvZ9A50r/eqvyX8EzKC9TERBCy\n+gmiEwZiCdTYVK75b2pmFPg3OKonQUePYHt5DdE3h3/mEVo6DqPjij4cn7aPMw+9Q+39/Wkoyg2s\nK5nohK4cXvAAdRshNHsZ3XpXU5E8C98pCL7/CW6cOBCAyt/PoGZjNcHDf0bkpG4c+8+5+HJNGDWG\nyBWJnJg2l4b2fWj300HU/2U9DS4IvjeJiEmJ2ANX3iBbOGxcxeEFW/E9tw8GDCTs5afoenM4nCrm\no+RnaIyOAFdXOq5+CkvBYqoz/wXtawi+fxE33tvHv55BEVgw6Bg3nOpR12lmBRHVYMk3vAqLpMWr\nSOoF9Egia+7I5pywr4h84IEfD6P51m3nR4/NBrIo9UQRd0sVjuJc0lLTKI0czD2D7OQ+n8bof8vE\n5a8DY+XU0aQtL2J2xjLWrlnGzK5FpE0bQe5+f12TvU88M/89nqriTMaPSiRtczSzH00hlkOYDf6t\nHtiSQ2HXJNKzV5G1eB7eNQtIzQs0B3pKeXT6DNI3uIFC5kxPYcb0GcyYvoSyQHWWa0s6iclpFHWd\nybIVq1g6N4n85WmMnrrSP0m2JYr4KTOJ7+okK3U8iclp2H84m5TRsRxytZhG24hh6Y6FxADR9y1k\n3ojo80rUVfgKczIyWbJoFrkllzEFt7U/vaoziAA63p/BjXf3/JSFTRoBfC3r7aqpW7Ee7p1E+Pal\ntF82EN9DqRw9UAPtb8S4N5wzudVYlkzC0qUc3zPvY/nDJIL3FHPqzeJAcJnKyUl5BC15jI47lhL6\n2wjqJ6VS8Wd/bVP9gTxqZhQQkptB5D8zaDcEzuwxaTh98YfZWG0S9MQ4gsnjROURav5YTPD8gYAH\nH+EYdw8jaE85Pq8JhGP7aRKW9tU0ZG5vas6z3D6GkOERND61mE9uTsUXfQ8hvx0IR934rBGEDKjh\nTO771P34JRoib8E6xKBh9gt8cs9iTrT4DYCJ78DNhOU+TPCeYmofXOvvQ2WLwBgSzpkV+zhz8y20\ns4LRdyBsLOdMbjhG7NnwG070kjVE9waiJxEzbwwWXVxEVIMl0nbVFkAst3U7p8nD5v97lcdG0uSZ\npK1bQMriPGZ+1x82xv8wlxHTFvCXHVN58Lr1LNkLCYuWMyG+O14vTJm7kIItM8h8fTdJqUOw3xRP\nUq9oNj2fRel9C3ltbgJ2YMq0mU2bHPLQ82S9V0RZyW5Mu3/XvJ5AqIiMZ8OOHZStmcz4P45h81tT\naN3Y6SH/tzkweiFvLErwh8X+sWzu04ERyUvILZnAlP524scmEe3dRNaWUhaueC1QwzaFmZdYbPao\n7k1/jgz7MuoxIuj6h2VU/6OY+p3FBFsj/RWJXrC070/X+4fx0TMmN0xLpsZWgPvoMLrfn8wx92uc\nPA34ijn1q2qY8Qs6TxoOXujw0yc4svEB6h/Kx3N/f6xef3C0hBpg60qnnz/FyZ7vYOkZfgn7WQM9\n7yTst5uo+Y9ZsPEOwh/rSc1TNTQAkcOTOT1gtT9AEk7nuydR7X6H488bBJ0t38GJdIqo4aOn9mFZ\nkcGN9/prtvh5oPJ08mQ+ei4ba+4r9BzuD6q1967mSHw2J3b9jI6DIgATGMb12Y9hB+oK3RyOL+CU\nL5lQa0+6pT7FR888QEPXroQBdI4gCAjOXUS3myN0nRBRwBK5RC3nOAxrO3+ZDV4gjnu+01yTY48d\nQgJQWFzGgyP8r+XPHU/+uSs47MYM1B/QYFJFJBmpCZzXE8xTRMqIGTiA+NFJRFHFIaD7BWrkzos0\nDR4OVUJkTAwto6I9Np44lgDes3sBnioipz/f1Hx5OewDUtixZTwe7NjDru4pqT+wmo+fN7hhybim\nPkPBEAghfrUfZHMkfjVgEPzLgeA+0rrOq8EfTBsBHzVNfw5qF97U3ysIOLPsBY4se+Hck4bpA/vN\nSdhm5HH6B7NadBHrSej28dDx4kPWGW84141KoOZXq+GJJOyRJdSQT533F4TbalodF8CZNjrxmw0e\nYBydz4arVu95gZ6E3dVcCxh28zCCyaahqBwGRfiD3oCezRdiqwHUcKbh7NW5J51evwP3j+dTmfI2\n1r8+QyPDiBzeVdcIEQUskUtnRMcQiZuVm8sYcl9M0+ulhblAPLHRBlQAOCitMIm96WxIOUQRMKRH\nVCCkwbzXtpHUy8A0TQzDANPEtBjNgcdiAFFt9lsxK5w4iCVryyriAoFlU91gnqWNzsSd2liBxUYk\n4D52svXrR504gGENV7ngPE6ynn+VA0YcDzwygZirGLJ8Xg9nVpj4lhAIWNXU76FVc1RtUT6MmkTX\nnORALi7n4IoHAsEhcMEZ0KJvQpcWf24HNMAZIHjFMnrd24d6n0lwoBnNxCDUCvWV5XDXU3R9sj/1\nJ6rBV83JuanU/SaPuuxJhF7EsfhroUxCek8mfIWd4BEDMU74myCDLM3hseFUc/NnQ+UR6NL6omn9\nzAtpOWZZDZztG3aqnEYguKe/9inIGt56PRZ/jVlwi0LtePfDHOd9av5jBuRWY8lNpuPlfDVK8lmy\nrgi6xPP4gyNRd3iRz5f6YMlXU6d4FiZHUrhoPFlbyvB4PDg2pJPyvJPYRx8gpsXdYcHEf2PlhkKK\ntuQya9QM3MSSNDQao1c88cCCXy+haL8Lr6eKos05pAwdytAn/XVa7oNlOHY4qKKK0l1OnHscOA+6\nz9kZJzu2O3AddJCTMZk5WyCqwkFZRXMfJ5s9CvYWUlBSRllJESsXTWbw4FmUmZGMSY2DNTNYsKYQ\n13EPrpJ8Zt2XBiQxItYOtW7KSpw4KqpwHy3Fud+JY48T92U8u+/a9iqZ6/LJX5PO5v2ez+HE5FG1\ndR/1PpPqrbn+sHBuutj4Pic/2Ef1nvWUT3iARqDR8T4nqo9yzFEOe/ZR7a7hDOGw8X2qTwG2cNhZ\ngsfSKQs77AAAIABJREFUB1sSNE6bi2tHCd5T1Rzfk8ehh37I4c5zOeEDn3sX3mlzqXp7H5aOXTHa\nG5w5BURfd1Ghof5UOd6PqsFZTLXbpPO944iy1eDeuBWowfzgSFP486W/QKWrnE/yX6DmKRM2vs8x\ntwnUUP1Bsf94KOfYB8VU7Smm2n3+sBan75xIRf4mKreu5mDMfCCCsOE9wXeE01vLYU8JxwLDYRzf\nVQKUc3pPeYs19KTT68Mgdx8wjE7De17WmXO8+Ry563LJPWZTuBL5AmguwmvAN3cuQg+bXpzFnGxH\n0yvxjy4jY9oQf53G/hyGTkwnKXkCpdk5OAH6JrH02ceJ72EEaqCKePrhGeRXNq817r7ZPD59ArGR\nkDNpKOl7z9ls59nNfalMF1kzE8ksDrw3MImUmDKy1jlg4Dy2vZzkv1mZLjJnJpJ1drnO8cxOncmU\nEbGASdGaZ5mRkdu8jYEpZD07k7jI5uM418wVm0m5xOZCc38uQycuACJZuO4NEnpcxVvpqWLK587F\nt6K5Vido/mN0/cWYplqj+spNHOr3TFPzWtAvxxB84B0ack2CnvoBZ+b/zf/GtGTCEz6kZlIBwX9Y\nSof2qzg+6X1CCtfR4+ZqDj01A++yFp3nk4YRNudhun6rK3UfvsThO187Z+f6EPbPDLpGf3YTofvP\nMzjxUGAA1VGT6JaTjPXDl6g4u85R4+ia8zCNe7Kp/N7qppATNKCcM3sgaEkGMZOgrHPqeeNNBf02\ng5if+5sL6z7M5vCdq7HMH0bjUwX+ZUfdQfslT9AlOrz1ccx4ghvnd6W886zAOocRWfkUHc+G1xPr\nORCzuFV/rkuM3qSPTSSnMpKlGzYQH6nrqogClnzjJ3s2PR68DYBxTr+ig7kMHpfL2h1ZxFzMOiw2\n7GHG5e8DNuz2T/+8WWuC1cBoazHTxOP1whXsx1dBg68G7ymTRms44RcYd6n2VA1B1nBCr+CrW++t\nod4HIbZwQs6pJavz4W8u9Jr4MDFs4Z/Pk3M+kzog1Hrp56vhwGo+GvwOnapf4Uq7o3/y+4l4Vg2j\n899/QfjlrMC1idGJc3Dfl86OFk/risjnR32w5CvPsNvbaNLwkPP8AgCWZGQSGxbFmOQL9zlqex1X\nug9tLPdpwckwsBvXfuOMxRpO2Gd0AgprH37F2wmxhRNygYAWaj27jEHI59ngZTUuqk/X+ao58nw2\nACczFlPT7jra/zyZyEsJnL4jHM5aTb3Xje+paqCYU64awqMvvWzNOhduYGGywpWIApbIp1YPeDAj\nk0iaaAPThfNwDcMu8MShyBfOV0Nj5B1YnjDg9GEaD3loPA2XNgKoie+jwzS2M7A8MQyOmzSeAKIv\n4x8IN01h2+YJGHadGpEvipoIrwHf9CZCERGRa42eIhQRERFRwBIRERFRwBJpm+ki67EUVm534dqe\nRUrGJsxr+XAqNpEydhabKsxv9nk9XsSCB9MocnkoenkW6RvKrvomGk68z0fJqVRWVlP5+xmU5++7\n8MK+aqo/KKbqg33UnjMae4O3Bk9lOZ4Trc9Z/Ylyqj4oxn3gyAXX6Q68f7Fjxda49lH1QTHVlS3H\nyjI5+vtUDq54n9rKTRxMXky1T5cGka8DdXKXL5GXgi0OYn4EXncBjs0G3tTAxDGeMjZtdWKG+kde\nb8neYwjx/b+CA/l4XDgqCyk97mVkjy/hacEGN/nL1xObPOXyRnD3lLFpm9Mfco3mcm8q7+Nl5G9z\nYlgIBGGDyD63MeSm1ufCdLvILc4nnl/i2VTIOwkzmXOVD7Wxch8NucU0PmvSuHEfvntrLrhsfXk+\nx+P9T/TV/X0dYQP8T+F9siYVz4zipuWqfvsEN/x8JCGAZ+N8Tj5UDgPGEfr3h897dqKubC0n4l+7\n4PttxCtOzJ6BdyMwYBztmj5j0vCPYhp7j6Gx5iMac7fTsFxXZhEFLJErYXQnoTOYPaOw1bV+vMk8\n7uSVJ9MCg4fGEosNWxh4ix04Ww4E+lU6nNgk1q6IJ6rPl7VnJ3k1ewljEiYQc9OlBzzT7eSVuc9B\n3yice51ALLF9q7DFLyS+fyTmcSdpT6YBkcR9tzuHtjhwA5GjF/LaouZ5HI0e/YgllpgudpxdAdN7\n1Y80JKY/QUQQEhGBGQ0tJiU8f9nek+hd2Z+yzqktrng1+MohZHUG143oQ91f5+N56Bnc94+kayRE\n3P8K7fq/wJHpba8z9OaH6bBkEydfMZrmZfx04XTPeRv3iomceKV5wmgIxzYigtP0JLRjja4JIgpY\nIleHrV8sNgyi+48kLt7e4iadwKrtsUy+M42Fq1cRg4nLDVGeHIaOMwGT/EXTebXMjq0WHvjdQoy3\nnmXZex68riqmZmYTW7KEtJWl9Bs5gSGGg5z83dC1HxMenElCixowd0k+zy1MIz8wonvC9HQeTx6J\n3QK4i5g1NY2qTv144DdPElXyCqlP5uAGUjLymDkiGvCQv2g6z/3LRr+uMUz5z8cZEu0POGVvpTN9\nZSnd8XJorxMGJnBPjIecdYVEfncOqzMmEGkJ1D5lP0fa8sC01H0TWJj2eGDi57PHCv0GDabqzSwK\nKyE+OZ2F0/376d7v4EB5KVVAUWEBccfseBsgqsdtxERfXNgyeiWwakcCYJIzdig1L2ST0iKoGb0S\nWJv6KtO9C8maFtNUdpOmpTErLoasibFnlySKGLAYxA1PYFinqDbSnItN6/JxGbGMSYz3l8GlxVmC\n6EmQ1SBs+EDMyHOG8vRVc2T1C9T9tQSi+9Muoes5o66H0y01o/lvo5LwUExDdQ1E+mu4gqwG7PHP\nu3j0hRnU7jThQA3tsl+hW+9wgmzhsGcfVX99Ce+fN3GGCKwTH6ZrwsCm0FXz4XqqX3yNBlcNwfeO\nx/pRNXRpvatB7SMIBiyR/Qke1R/DouuCyNeB+mDJl1nnQ9LiVST1AnokkXXuCNMNJlCFc78L51up\nJI7OwdNpCPMWxWPHIHboSCguxLG3EDrZiSIfR3EhtvgkYjoZRMXGUbXXQe7yNNKeL2XwyHuwl+aS\nNm00mdv98w16dmQxeloaRTGzWbZmLcsWzaRo+RxGpOb6m8EsUYx5aCpRewuZM240KU++Q9Kjs0no\nC666szUzNmKGTmVmUj8Kt+TiPN5cYxMVG889YQ4ce21MTZ1J9+J8ctbBnLkpuLeks97pATysnDqa\ntOVFzM5Yxto1y5jZtYi0aSPI3W8CBrGDYnAWO8jNzqL7Q+ksfDSBwuw5rC3xACZFWSnMSE3HDRQ+\nP4eU6TOY8cgMlmw9dFlnxgTwnl/zZHpp1WQb2T+BjOmROP5YRNPMh0YMS3csJAaIvm8h80acP3CT\nq/AV5mRksmTRLHJLLmPORGt/elVnEAF0vD+DG+9uOX3MESomTaR2dgFBo0Zi7X6EukmvtX3B8x3h\nyJoXKIuZDwPG0fHmFoN4+oABdvDto/apfZzJrcHywHhsXcKbvr/wPnXTXiN4RALW3tXUT0ql4oUC\nGoCaXS9QeedifK4IrPeP5Mzsl/A+B7RvvQsR9y+j1/19gD70ymkxPY6IqAZL5PMShZu0iYmBWp14\n7J1iSRrt/2vMiCmsyoshJXEWTz86i+7FEDd9GVkPDvEv0GkkvxwIaa4U8t6YSbQFeHAK+XOHkjY/\nl6lvTWH94kwgieW/mkB3vNBjCgsfLWDG85kUuZOIj4wh4b4YvG8uodCVxKo184i1A9OmtAqKsSMS\niG2IJTcjp9X+22+KZ/z3Y3nnO48zZWIs9txMvAmzmXBfd2oWZWEC5v71LNkLCYuWMyG+O14vTJm7\nkIItM8h8fTdJqUOIGT2FuCdzicnIY86IaGAYJ/+UT+4uFykDYklYtIOEuU4mf3cy91zGHIZXwm7v\nDhiXNIamPap7c0gLu7rjvNXuWUv9Rgjdvo5ugcDkGfECn/wgr2mOxCbeI5zOzPPXbu054g+QZ3fH\nasCebCo6Z8OoRCJX/+Kc8GMCEYR/uIbOkQDJVA+Zy/FJz+BOWYN3dh7MeJhui8b5R4OfmETFhKnU\nn9LvWkQBS+RLVkUsq7atIrYih8HzbHhND4eOQkyPQICIjmf58gkMnZ6DmwTyzoargJO1EPvTMf5w\nFQhDcfEJsOEdymqn0CEG2JvL+BG552/bY0KkAZh4XTDzd4/7w9WFNJi01duouQvS2ff9NUDhnaFl\nr5v8uePJP/fDh92YgBFYd0zPFs1tnc7/NdsC9SpfLG/TsV3stu0DUtixZTwezplf8iqoP3QEGIgt\nprk2yj7wHqrIO3/h9gPp9fe3aagsoLzffE6+vY+Ie/u0sVw4lvOuljUwKgF7iz7+HW8fxnHex1u6\nB98eCE4d2GKqna60v78nxzP1uxZRwBL5Mlla3K5vmsCO1WDuz2L8RNi8I8XfX8vj4OnpOUR+N4Hu\nW/JJfCyOzYsnNHeADwPn2w7MiTFNN/9DZUXAMKJsXkrLgO8uZNviBAzTxLQYGJiY3jbmFbR99v7a\nAOOckfcNoNXszzajOWbZbOD1B655r20jqZeBaZoYgaf4/PvTvO7zdsfWYr0+f9QxrsLI/wZgtrGe\n8wOUi7UZThj9wKU9dOBxkvX8qxww4njgkQmX99TjBZlAOT5v8xWuobyEM0DwBa54ls4DsQL1rpoW\nF0cTSCTywzupvnkulXPtWM7WRgFB1gjY+D61vuSmuRG9R/3DRVijO9EANJZVt9ov7z/KoYvt0i+8\nHidZz+dwiO5MSU0hxtDlQeSrTn2w5Kup1o1zh4Mqqijd5cRZ4sRZUsZu5yHo7K9Vcu0vJG1ECvnE\nkb14IcuzU2BLOiMeW0mZp0UIKV7Av81dSeGOInJfnMWMbDex05OIttgZMj4etqSxZF0RrlovVQeL\nyFk8naHfHUp+hYnpLsNZshtnJRxw7KasxImjpKzVeF2mqwzHHifOHQ4OAYU7iigrceA86Mb0uHBU\nVOEuduCq9WALBD4PYHSCA45S6BVPPLDg10so2u/C66miaHMOKUOHMvRJf52Wu6QIB1C6Y7d/28dL\nKdoLzu1FuMzm4BYF5G8uoKyijKK3VjJ58GBS1pRdfJnvcZxzvA7KXIHCNN04nU7cR8soqyjDsSWf\nWWMTySGSpY9d2iTCrm2vkrkun/w16Wze77mqX52OdyUB1dSMTeXInhKqdqymfPBL/tqtnSXUA3Uf\nruZAxAMcereYE+5yjq6YTz1gvb2rf7kT5Rxz7IMBBkbkHdywfRIse4kjGaupclWD7wint+4D9nFi\n0lyO7CqmMv8ljnwvD0ZNomO3bxE234Cn5nJwxXrcB4pxZUylbgWwcSvH3Jc2Vpp7Vy6Z63LJXVfV\nKlOLyFeX5iK8BnwT5yI0969k6MQlbb4XOXohbzwVw/Shk3EEXluYt4247akkLioEIPbRVayaFkPu\ng0NZUJvEhJgCcjb4O7YnPbqMx6cN4Ww9UtGKp5nxfIvGuc7xzE6dyYQRsRxak8L4DMc5exBL1pZV\nxAVqXTYtGsycdW3saOfZ/M9PN/GzwOfj5y5jTEkaaevczHstD9sfEknb4F9XP3cRTz88g/zK5o/H\n3Tebx6dPIDYSciYNJT3wlOO8dduI2za9ab/in17L0rH+p/pcmzNJTM1qWkf8fbOZ+cgEYjsZF1Hm\nOQydmH7+G6MXsm1RApSsZOi01uck9rsTmP3L2Qy5xHG/zP25DJ24AIhk4bo3SLjK44bV7FnNJ9/L\nbnpyMPiXA2l8rhiIwH54DddTQvnUWfg2tvjX5pKn6D5tGCGAe80DnJhRDtOS6bZkEtYDr1ERCGmM\nmkTkInDfuRqIIHhaDY0rAoFpxjg6PfkwETbAdwTXM7M4/VygFmtUH4Lbl9OYaxK0JIOYaQMv+ngK\nM0Yza42bCYs3MOe7kbooiihgiQLWlxrTyJ00lJzxeay6L/ozFjXxeL3YbPZWrXlf+B57PHgtNuxh\nV7ATDRdo4vym8ZnUek2CbOFNTXjnqjtVje+0iSW8K1fS177eW4MPg9A2qpfqvTXU+8DWPpzLG4HB\nRfrgRHKYQN6OOUTrhy1yTVAfLPnacu/IYcFe4A/PkumOJeqmMUwYEdP2woaB3fjyA4lht195J3WL\ngRGm84/VIMz66aUZ2j7ivGETLkeILfyCA46G2MIJuZJ/I5kedgMJi6YoXIkoYIl8+by1kHBfEh0A\nV5mTmsh7VChy7TFiyd6yGcLsKguRa4iaCK8BaiIUERG5tugpQhEREREFLBEREREFLJG2mS6yHkth\n5XYXru1ZpGRswvycNuXanMnkBzObx4y69J2l8MVZpLx4efvoPliGc38ZzpIy3LXXxunxVJThLHE2\nj4P1FSrPq+J4EQseTKPI5aHo5Vmkbyj7fL/ux9243B797kW+IdTJXb5EXgq2OIj5EXjdBTg2G3hT\nm0cL9xwsorDEDRYwDP8I5zRAzJ0jiY28tGftvJWFOIudHPLNJPrcjx53kPVaFeMfHPmpo5FXFRbi\n2Hsb3kcucToa00nquOYxu2ZmbyZlwOV0WDZxbPgLuVsc0Kk3Q+KiOVlrZ8x98Vz97s8m6x8f7x97\nq/NsNr81pdU2vtTyvFpH6HaRW5xPPL/Es6mQdxJmMufzCvhb0kl8LAeIJGvLhqYx1EREAUvk6jO6\nk9AZzJ5R2OrOvxVXleST9v9Kie1UhXOvG/rGEnu8invC44j97qU9sB4zNoNVg2lzShbT7SBz+avE\nTxtJ7AXv9AYjn11LHFGXHmaMWLJ27AA8rBw7AvNyBkMyXWT+KJGsSkiaPpvux4pIm5sJxNJvdPzn\ncMM2mLB6B0NWTGb8n86fpudLLc+rdYQ9+hFLLDFd7Di70nLSyKseVguW58DoeeT9agzRClciClgi\nnzdbv1hsGET3H0lcvL3VzTZm7Dx2jAUayph8ZxpP/s+qFjdsk/xF03m1DPoNGkzVm1kUVkJ8cjoL\np4/EfjbEuIuYNTUNT3Q/7F3iefzpCS0mfjYp27ObspJSwE1hYRFeO3gboPuAIU03Qvf2lUyav57u\n/aKIGTiFedNaTyhNg5vcxU+TuaYQ/1jxcczLziBpwLkjbtv8c/xdRjkVLU0mqzKerI1LiesEMIWE\nuDQS53qwW89msCJefnEJWRuc/r0YPZPZj00hLtIAs4z0n6VRGhZFv0Hd2Z2dg5NIUhYtZ+bo5rHB\nPPs3sezFlew+4qVfwlRiK5ytJ5X+IsoTE8dbL/Psk1k4ATrHMXPWbKaMjsO4yPNuVhSxZH4aOcVu\nIJK4gVEc6jCVNxYntKgtM4giBiwGccMTGNYpqs1gu2ldPi4jljGJ8URaLv+7HhsXR7Rd89yIfFOo\nD5Z8mVVYJC1eRVIvoEcSWXMvMJ9dgz+SeH2tPxs7KAZnsYPc7Cy6P5TOwkcTKMyew9qSFv1c7N1J\neuiXJAzyULghl6qWlRQNh3gleQZzMvzT5GSmziBl+gxmPDKDgsPNMcjeI45fPjSBwScLyf2Tk/N6\n0RwvJWdNKffMTWfVmizmTfSyIPlpysy26jIupwKkjJVr3MQ++kAgXPlFD3+AeY8mEWUArk38W+IM\nsjbEsHB5FlmL58GGTFJGT6fIAxhRxN1ShaO4kJzsUiZkpDN7NGTNTcMR6BPm2ZHFiIlzyDkSzZik\nMVQ9n0b6OqBljcsXUJ6FGf9GypNZRE1fyKoVWcz7IWTOTSF5heMiz7vJ+vkzyKmdyNq8PFa9OBOK\nnbhLq2hVR2XEsHTHQmKA6PsWMm/E+bWirsJXmJORyZJFs8gtuYL+U8ehymvqJy+iGiyRr76Y0VOI\nezKXmIw85oyIBoZx8k/55O5ykTIgNnATjWbkfdFw8CTp2bmtm7osMSzcsYMn9+cwdGJuq/kFW92H\no+NIuC+OMnLJ+sP5zWVExrP8tQwKS8rY7TADgcSD6ePqdC6ygB24bdA5o9CHxZA0zf+a461XcJPA\n2m0LiTH8tWhZm2NIGZHCknVOVk2LJWHyTNLWLSA9L4uR0UB8d9ZvmIzjoIe4/pD7ZGbTvIMGMGXi\nGLImjSaztlVhfL7l6XGQucZNwqK1LAzUrMX2zyLGnkLK80twjssi9iLOu9cF9AsHICp2JAtX2Nl0\nsPslN0fao7o3n+bLmUunwaSscCXplZDUQwOFiqgGS+Ra0GDiBWJ6tmja6XSBSqCGz17dZ94+L9BH\nx7EihRHjUkj7f5twlhRRuKvKf5O/Wsfp9eACCkqqLrCAh9JNThg4hO4tA529H0l9oaqpDLxAHN2j\n2jjm2jLWV0J8fGyLTBjJiKQ4OP7FladZ4cAJ9OvRurmu33cTmkv0M8+7wT1PzYYt6YxPTGT0qBEk\nTpvDpn+Vcal1UPYBKezYspnNW3aQdNOlp2Vn9r8x/rFMACbEa6IbEQUska8Si//GZrOe/7qtrZt6\nGxPu+mfZtWG0WWdrfsp756y90/mfLf2Tg8jpWex4aynz5s5j6dO/BLzn7y+BCq1LHZk/LIaRfcHx\nx7W4znnLXeHCxE73QUDxodYBouEQRXsh6myQsZwT+wLlathsYLMTA5SWVbU6Nud2B3SyfWHlaUTH\nEgmcOzLEoRJHc2j9zPNucuAgLFu3mc0b8sjbkMeqjBQca9JYv/8Sm+k8TrKee5ZnX8yh7DKG14gZ\nl036o0kAvLPLrd+yiAKWyJfPdJfh2OPEuctBFVWU7nLi3ONsuvm6S4pwAKU7dvv7Nh0vpWgvOLcX\nNY3P5NrvxFFSxu5dTsBB4XsOnHsclLlb3GiNKMDB5kIHroNO8lcsYPDgweTsN6HBQ9keB879Thz7\nDsHeQopKnDj2OJvHs+oE7pIdOA66cGzJYXJiGgClu5x4TMB049zjwFmyG2clHHDspqzEgaPEdZF9\nsuwkPTEbKnNIfDCTov0uykqKyHpsMKPvS2R3LQz58Rwgi0lzcyhzuXFXOMhKHU8+MHV0P38Q3F4I\nONntcAXKz4EDKNyxG9MSQ1JyJO7sGaSt2ETZQSe5GcmkbQH2bmJ3oLw+9/Ls1I+ZAyHnkXHkbCnD\nfdyNc3MW45/Mh9FT6Rd2cee9LHcJMx5fRmmtnSh7VFOtXFTopdVCuba9Sua6fPLXpLN5/6X3wTI6\nRTNy2uPM6Qy5ew7pRy3yDaK5CK8B39S5CMvWpDA+w3He6wmL8lg4OoqcSUP94zQB89ZtI27b9Kbl\n459ey9LRNhbcmUhuG+uOfXQVq6bFNtV4FL44nVnZZ7cVSdKjjzN78kjsR/MZHAhM55q5YjMp/e24\ntmSRGGgGAkiankLZ61k4KmHOmm38iL8wdGJ6G2uIY9W2rE8ZyuCcm/2OXFKnL/A/WQfQOZ6FTz9O\nwmB/05Nrew6pj6Q3v08cC7MzSBgQCaaTlKFnx+KKZ+32DBxTh7LgbPm9to2kHh5yf/soC9YF1tA3\nnoSwUvKL3f7ymmz/QsoT00VORirp61ocycSFZDyWQKTF/OzzPrZ7q2XOip++lIwH4y+pW5y5P5eh\nExcAkSxc9wYJPS6nU51/n3MTWpaRiChgiQLWN6bKzMTEP6jpJWsw8dR6sYXZMSyf607iOe4Fw4Y9\nzGj7fY8XsGG/zCEBzFoP3obL//xVKU+AWg+eBrDZ7FzyKkwTDMO/Dz7AamB8aSMk+APWK/HL2PDI\nEP3ORBSwRAFLRK4Gx8uTSVnuhFZPeoqIApYoYInIFTBxbtlEaV0kI0cNaR4IV0QUsEQBS0RERC6O\nniIUERERUcD6/+3df3SU5YHo8W8SmEHMIJI0QiyU+CvAXVLohbol4J6gLdHeJUsrsAuUvaAWwq7y\nozcoS9RDxUVhq1hvobYab6lwFnSvDa0SdiG0SLCY3BqDGwiig1BGTAkGJkAyJOH+kYCJRQWNC+j3\nc47nkPnxzsyTZPzmeZ95X0mSJANL+uzUlvLA7fmURqKUPjmTxevDHf4Q0UiYqsow4cqq9sd7utDE\nIhTMmcoz2yJEthUwdUkxnv1Oks4Pz0Woi1qsJkJheRGZzCZ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- "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image('metadata_view.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Reference" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on class grid_ops in module plotly.plotly.plotly:\n", - "\n", - "class grid_ops\n", - " | Interface to Plotly's Grid API.\n", - " | Plotly Grids are Plotly's tabular data object, rendered\n", - " | in an online spreadsheet. Plotly graphs can be made from\n", - " | references of columns of Plotly grid objects. Free-form\n", - " | JSON Metadata can be saved with Plotly grids.\n", - " | \n", - " | To create a Plotly grid in your Plotly account from Python,\n", - " | see `grid_ops.upload`.\n", - " | \n", - " | To add rows or columns to an existing Plotly grid, see\n", - " | `grid_ops.append_rows` and `grid_ops.append_columns`\n", - " | respectively.\n", - " | \n", - " | To delete one of your grid objects, see `grid_ops.delete`.\n", - " | \n", - " | Class methods defined here:\n", - " | \n", - " | append_columns(cls, columns, grid=None, grid_url=None) from __builtin__.classobj\n", - " | Append columns to a Plotly grid.\n", - " | \n", - " | `columns` is an iterable of plotly.grid_objs.Column objects\n", - " | and only one of `grid` and `grid_url` needs to specified.\n", - " | \n", - " | `grid` is a ploty.grid_objs.Grid object that has already been\n", - " | uploaded to plotly with the grid_ops.upload method.\n", - " | \n", - " | `grid_url` is a unique URL of a `grid` in your plotly account.\n", - " | \n", - " | Usage example 1: Upload a grid to Plotly, and then append a column\n", - " | ```\n", - " | from plotly.grid_objs import Grid, Column\n", - " | import plotly.plotly as py\n", - " | column_1 = Column([1, 2, 3], 'time')\n", - " | grid = Grid([column_1])\n", - " | py.grid_ops.upload(grid, 'time vs voltage')\n", - " | \n", - " | # append a column to the grid\n", - " | column_2 = Column([4, 2, 5], 'voltage')\n", - " | py.grid_ops.append_columns([column_2], grid=grid)\n", - " | ```\n", - " | \n", - " | Usage example 2: Append a column to a grid that already exists on\n", - " | Plotly\n", - " | ```\n", - " | from plotly.grid_objs import Grid, Column\n", - " | import plotly.plotly as py\n", - " | \n", - " | grid_url = 'https://plotly.com/~chris/3143'\n", - " | column_1 = Column([1, 2, 3], 'time')\n", - " | py.grid_ops.append_columns([column_1], grid_url=grid_url)\n", - " | ```\n", - " | \n", - " | append_rows(cls, rows, grid=None, grid_url=None) from __builtin__.classobj\n", - " | Append rows to a Plotly grid.\n", - " | \n", - " | `rows` is an iterable of rows, where each row is a\n", - " | list of numbers, strings, or dates. The number of items\n", - " | in each row must be equal to the number of columns\n", - " | in the grid. If appending rows to a grid with columns of\n", - " | unequal length, Plotly will fill the columns with shorter\n", - " | length with empty strings.\n", - " | \n", - " | Only one of `grid` and `grid_url` needs to specified.\n", - " | \n", - " | `grid` is a ploty.grid_objs.Grid object that has already been\n", - " | uploaded to plotly with the grid_ops.upload method.\n", - " | \n", - " | `grid_url` is a unique URL of a `grid` in your plotly account.\n", - " | \n", - " | Usage example 1: Upload a grid to Plotly, and then append rows\n", - " | ```\n", - " | from plotly.grid_objs import Grid, Column\n", - " | import plotly.plotly as py\n", - " | column_1 = Column([1, 2, 3], 'time')\n", - " | column_2 = Column([5, 2, 7], 'voltage')\n", - " | grid = Grid([column_1, column_2])\n", - " | py.grid_ops.upload(grid, 'time vs voltage')\n", - " | \n", - " | # append a row to the grid\n", - " | row = [1, 5]\n", - " | py.grid_ops.append_rows([row], grid=grid)\n", - " | ```\n", - " | \n", - " | Usage example 2: Append a row to a grid that already exists on Plotly\n", - " | ```\n", - " | from plotly.grid_objs import Grid\n", - " | import plotly.plotly as py\n", - " | \n", - " | grid_url = 'https://plotly.com/~chris/3143'\n", - " | \n", - " | row = [1, 5]\n", - " | py.grid_ops.append_rows([row], grid=grid_url)\n", - " | ```\n", - " | \n", - " | delete(cls, grid=None, grid_url=None) from __builtin__.classobj\n", - " | Delete a grid from your Plotly account.\n", - " | \n", - " | Only one of `grid` or `grid_url` needs to be specified.\n", - " | \n", - " | `grid` is a plotly.grid_objs.Grid object that has already\n", - " | been uploaded to Plotly.\n", - " | \n", - " | `grid_url` is the URL of the Plotly grid to delete\n", - " | \n", - " | Usage example 1: Upload a grid to plotly, then delete it\n", - " | ```\n", - " | from plotly.grid_objs import Grid, Column\n", - " | import plotly.plotly as py\n", - " | column_1 = Column([1, 2, 3], 'time')\n", - " | column_2 = Column([4, 2, 5], 'voltage')\n", - " | grid = Grid([column_1, column_2])\n", - " | py.grid_ops.upload(grid, 'time vs voltage')\n", - " | \n", - " | # now delete it, and free up that filename\n", - " | py.grid_ops.delete(grid)\n", - " | ```\n", - " | \n", - " | Usage example 2: Delete a plotly grid by url\n", - " | ```\n", - " | import plotly.plotly as py\n", - " | \n", - " | grid_url = 'https://plotly.com/~chris/3'\n", - " | py.grid_ops.delete(grid_url=grid_url)\n", - " | ```\n", - " | \n", - " | upload(cls, grid, filename, world_readable=True, auto_open=True, meta=None) from __builtin__.classobj\n", - " | Upload a grid to your Plotly account with the specified filename.\n", - " | \n", - " | Positional arguments:\n", - " | - grid: A plotly.grid_objs.Grid object,\n", - " | call `help(plotly.grid_ops.Grid)` for more info.\n", - " | - filename: Name of the grid to be saved in your Plotly account.\n", - " | To save a grid in a folder in your Plotly account,\n", - " | separate specify a filename with folders and filename\n", - " | separated by backslashes (`/`).\n", - " | If a grid, plot, or folder already exists with the same\n", - " | filename, a `plotly.exceptions.RequestError` will be\n", - " | thrown with status_code 409\n", - " | \n", - " | Optional keyword arguments:\n", - " | - world_readable (default=True): make this grid publically (True)\n", - " | or privately (False) viewable.\n", - " | - auto_open (default=True): Automatically open this grid in\n", - " | the browser (True)\n", - " | - meta (default=None): Optional Metadata to associate with\n", - " | this grid.\n", - " | Metadata is any arbitrary\n", - " | JSON-encodable object, for example:\n", - " | `{\"experiment name\": \"GaAs\"}`\n", - " | \n", - " | Filenames must be unique. To overwrite a grid with the same filename,\n", - " | you'll first have to delete the grid with the blocking name. See\n", - " | `plotly.plotly.grid_ops.delete`.\n", - " | \n", - " | Usage example 1: Upload a plotly grid\n", - " | ```\n", - " | from plotly.grid_objs import Grid, Column\n", - " | import plotly.plotly as py\n", - " | column_1 = Column([1, 2, 3], 'time')\n", - " | column_2 = Column([4, 2, 5], 'voltage')\n", - " | grid = Grid([column_1, column_2])\n", - " | py.grid_ops.upload(grid, 'time vs voltage')\n", - " | ```\n", - " | \n", - " | Usage example 2: Make a graph based with data that is sourced\n", - " | from a newly uploaded Plotly grid\n", - " | ```\n", - " | import plotly.plotly as py\n", - " | from plotly.grid_objs import Grid, Column\n", - " | from plotly.graph_objs import Scatter\n", - " | # Upload a grid\n", - " | column_1 = Column([1, 2, 3], 'time')\n", - " | column_2 = Column([4, 2, 5], 'voltage')\n", - " | grid = Grid([column_1, column_2])\n", - " | py.grid_ops.upload(grid, 'time vs voltage')\n", - " | \n", - " | # Build a Plotly graph object sourced from the\n", - " | # grid's columns\n", - " | trace = Scatter(xsrc=grid[0], ysrc=grid[1])\n", - " | py.plot([trace], filename='graph from grid')\n", - " | ```\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Static methods defined here:\n", - " | \n", - " | ensure_uploaded(fid)\n", - "\n" - ] - } - ], - "source": [ - "help(py.grid_ops)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting git+https://github.com/plotly/publisher.git\n", - " Cloning https://github.com/plotly/publisher.git to /private/var/folders/k_/zf24qrfn2kg710j9pdrxzrz40000gn/T/pip-9_JTe2-build\n", - "Installing collected packages: publisher\n", - " Found existing installation: publisher 0.11\n", - " Uninstalling publisher-0.11:\n", - " Successfully uninstalled publisher-0.11\n", - " Running setup.py install for publisher ... \u001b[?25ldone\n", - "\u001b[?25hSuccessfully installed publisher-0.11\n", - "\u001b[33mYou are using pip version 9.0.3, however version 10.0.1 is available.\n", - "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/chelsea/venv/venv2/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning:\n", - "\n", - "The `IPython.nbconvert` package has been deprecated since IPython 4.0. You should import from nbconvert instead.\n", - "\n", - "/Users/chelsea/venv/venv2/lib/python2.7/site-packages/publisher/publisher.py:53: UserWarning:\n", - "\n", - "Did you \"Save\" this notebook before running this command? Remember to save, always save.\n", - "\n" - ] - } - ], - "source": [ - "from IPython.display import display, HTML\n", - "\n", - "display(HTML(''))\n", - "display(HTML(''))\n", - "\n", - "! pip install git+https://github.com/plotly/publisher.git --upgrade\n", - "import publisher\n", - "publisher.publish(\n", - " 'grid-api.ipynb', 'python/data-api/', 'Upload Data to Plotly from Python',\n", - " 'How to upload data to Plotly from Python with the Plotly Grid API.',\n", - " title = 'Plotly Data API', name = 'Plots from Grids', order = 5,\n", - " language='python', has_thumbnail='true', thumbnail='thumbnail/table.jpg', display_as='chart_studio'\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, 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New to Plotly?

Plotly's Python library is free and open source! Get started by downloading the client and reading the primer. -
You can set up Plotly to work in online or offline mode, or in jupyter notebooks. -
We also have a quick-reference cheatsheet (new!) to help you get started!

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Imports and Credentials

In additional to importing python's requests and json packages, this tutorial also uses Plotly's REST API

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First define YOUR username and api key and create auth and headers to use with requests

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In [1]:
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import plotly
-import plotly.plotly as py
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-import json
-import requests
-from requests.auth import HTTPBasicAuth
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-username = 'private_plotly' # Replace with YOUR USERNAME
-api_key = 'k0yy0ztssk' # Replace with YOUR API KEY
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-auth = HTTPBasicAuth(username, api_key)
-headers = {'Plotly-Client-Platform': 'python'}
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-plotly.tools.set_credentials_file(username=username, api_key=api_key)
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Trash and Restore

Create a plot and return the url to see the file id which will be used to delete the plot.

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In [32]:
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url = py.plot({"data": [{"x": [1, 2, 3],
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-               "layout": {"title": "Let's Trash This Plot<br>(then restore it)"}},
-              filename='trash example')
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-url
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Out[32]:
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u'https://plotly.com/~private_plotly/52'
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Include the file id in your request.
The file id is your username:plot_id#

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In [33]:
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fid = username+':18'
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Out[33]:
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'private_plotly:18'
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The following request moves the plot from the organize folder into the trash.
Note: a successful trash request will return a Response [200].

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In [34]:
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requests.post('https://api.plot.ly/v2/files/'+fid+'/trash', auth=auth, headers=headers)
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Out[34]:
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<Response [200]>
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Now if you visit the url, the plot won't be there.
However, at this point, there is the option to restore the plot (i.e. move it out of trash and back to the organize folder) with the following request:

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PERMANENT Delete

This request CANNOT!!!!!!! be restored. -Only use permanent_delete when absolutely sure the plot is no longer needed.

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In [35]:
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url = py.plot({"data": [{"x": [1, 2, 3],
-                         "y": [3, 2, 1]}],
-               "layout": {"title": "Let's Delete This Plot<br><b>FOREVER!!!!</b>"}},
-              filename='PERMANENT delete ex')
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Out[35]:
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u'https://plotly.com/~private_plotly/79'
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In [36]:
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fid_permanent_delete = username+':79'
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Out[36]:
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'private_plotly:79'
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To PERMANENTLY delete a plot, first move the plot to the trash (as seen above):

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In [37]:
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requests.post('https://api.plot.ly/v2/files/'+fid_permanent_delete+'/trash', auth=auth, headers=headers)
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<Response [200]>
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Then permanent delete.
-Note: a successful permanent delete request will return a Response [204] (No Content).

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In [38]:
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requests.delete('https://api.plot.ly/v2/files/'+fid_permanent_delete+'/permanent_delete', auth=auth, headers=headers)
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<Response [204]>
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Delete All Plots and Grids PERMANENTLY!

In order to delete all plots and grids permanently, you need to delete all of your plots first, then delete all the associated grids.

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In [ ]:
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def get_pages(username, page_size):
-    url = 'https://api.plot.ly/v2/folders/all?user='+username+'&page_size='+str(page_size)
-    response = requests.get(url, auth=auth, headers=headers)
-    if response.status_code != 200:
-        return
-    page = json.loads(response.content)
-    yield page
-    while True:
-        resource = page['children']['next']
-        if not resource:
-            break
-        response = requests.get(resource, auth=auth, headers=headers)
-        if response.status_code != 200:
-            break
-        page = json.loads(response.content)
-        yield page
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-def permanently_delete_files(username, page_size=500, filetype_to_delete='plot'):
-    for page in get_pages(username, page_size):
-        for x in range(0, len(page['children']['results'])):
-            fid = page['children']['results'][x]['fid']
-            res = requests.get('https://api.plot.ly/v2/files/' + fid, auth=auth, headers=headers)
-            res.raise_for_status()
-            if res.status_code == 200:
-                json_res = json.loads(res.content)
-                if json_res['filetype'] == filetype_to_delete:
-                    # move to trash
-                    requests.post('https://api.plot.ly/v2/files/'+fid+'/trash', auth=auth, headers=headers)
-                    # permanently delete
-                    requests.delete('https://api.plot.ly/v2/files/'+fid+'/permanent_delete', auth=auth, headers=headers)
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-permanently_delete_files(username, filetype_to_delete='plot')
-permanently_delete_files(username, filetype_to_delete='grid')
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{% endraw %} diff --git a/_posts/python-v3/chart-studio/delete/delete.ipynb b/_posts/python-v3/chart-studio/delete/delete.ipynb deleted file mode 100644 index 6a120f57d..000000000 --- a/_posts/python-v3/chart-studio/delete/delete.ipynb +++ /dev/null @@ -1,398 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### New to Plotly?\n", - "Plotly's Python library is free and open source! [Get started](https://plotly.com/python/getting-started/) by downloading the client and [reading the primer](https://plotly.com/python/getting-started/).\n", - "
You can set up Plotly to work in [online](https://plotly.com/python/getting-started/#initialization-for-online-plotting) or [offline](https://plotly.com/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plotly.com/python/getting-started/#start-plotting-online).\n", - "
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Imports and Credentials\n", - "In additional to importing python's `requests` and `json` packages, this tutorial also uses [Plotly's REST API](https://api.plot.ly/v2/)\n", - "\n", - "First define YOUR [username and api key](https://plotly.com/settings/api) and create `auth` and `headers` to use with `requests`" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import plotly\n", - "import plotly.plotly as py\n", - "\n", - "import json\n", - "import requests\n", - "from requests.auth import HTTPBasicAuth\n", - "\n", - "username = 'private_plotly' # Replace with YOUR USERNAME\n", - "api_key = 'k0yy0ztssk' # Replace with YOUR API KEY\n", - "\n", - "auth = HTTPBasicAuth(username, api_key)\n", - "headers = {'Plotly-Client-Platform': 'python'}\n", - "\n", - "plotly.tools.set_credentials_file(username=username, api_key=api_key)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### [Trash](https://api.plot.ly/v2/files/#trash) and [Restore](https://api.plot.ly/v2/files/#restore)\n", - "Create a plot and return the url to see the file id which will be used to delete the plot. " - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "u'https://plotly.com/~private_plotly/52'" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "url = py.plot({\"data\": [{\"x\": [1, 2, 3],\n", - " \"y\": [4, 2, 4]}],\n", - " \"layout\": {\"title\": \"Let's Trash This Plot
(then restore it)\"}},\n", - " filename='trash example') \n", - "\n", - "url" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Include the file id in your request.
The file id is your `username:plot_id#`" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'private_plotly:18'" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fid = username+':18'\n", - "fid" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following request moves the plot from the [organize folder](https://plotly.com/organize/home) into the trash.
Note: a successful trash request will return a `Response [200]`." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "requests.post('https://api.plot.ly/v2/files/'+fid+'/trash', auth=auth, headers=headers)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now if you visit the url, the plot won't be there.
However, at this point, there is the option to restore the plot (i.e. move it out of trash and back to the organize folder) with the following request:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### [PERMANENT Delete](https://api.plot.ly/v2/files/#permanent_delete)\n", - "\n", - "This request CANNOT!!!!!!! be restored. \n", - "Only use `permanent_delete` when absolutely sure the plot is no longer needed.
" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "u'https://plotly.com/~private_plotly/79'" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "url = py.plot({\"data\": [{\"x\": [1, 2, 3],\n", - " \"y\": [3, 2, 1]}],\n", - " \"layout\": {\"title\": \"Let's Delete This Plot
FOREVER!!!!\"}},\n", - " filename='PERMANENT delete ex') \n", - "url" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'private_plotly:79'" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fid_permanent_delete = username+':79'\n", - "fid_permanent_delete" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To PERMANENTLY delete a plot, first move the plot to the trash (as seen above):" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "requests.post('https://api.plot.ly/v2/files/'+fid_permanent_delete+'/trash', auth=auth, headers=headers)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then [permanent delete](https://api.plot.ly/v2/files/#permanent_delete).
\n", - "Note: a successful permanent delete request will return a `Response [204]` (No Content)." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "requests.delete('https://api.plot.ly/v2/files/'+fid_permanent_delete+'/permanent_delete', auth=auth, headers=headers)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Delete All Plots and Grids PERMANENTLY!\n", - "In order to delete all plots and grids permanently, you need to delete all of your plots first, then delete all the associated grids." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def get_pages(username, page_size):\n", - " url = 'https://api.plot.ly/v2/folders/all?user='+username+'&page_size='+str(page_size)\n", - " response = requests.get(url, auth=auth, headers=headers)\n", - " if response.status_code != 200:\n", - " return\n", - " page = json.loads(response.content)\n", - " yield page\n", - " while True:\n", - " resource = page['children']['next'] \n", - " if not resource:\n", - " break\n", - " response = requests.get(resource, auth=auth, headers=headers)\n", - " if response.status_code != 200:\n", - " break\n", - " page = json.loads(response.content)\n", - " yield page\n", - " \n", - "def permanently_delete_files(username, page_size=500, filetype_to_delete='plot'):\n", - " for page in get_pages(username, page_size):\n", - " for x in range(0, len(page['children']['results'])):\n", - " fid = page['children']['results'][x]['fid']\n", - " res = requests.get('https://api.plot.ly/v2/files/' + fid, auth=auth, headers=headers)\n", - " res.raise_for_status()\n", - " if res.status_code == 200:\n", - " json_res = json.loads(res.content)\n", - " if json_res['filetype'] == filetype_to_delete:\n", - " # move to trash\n", - " requests.post('https://api.plot.ly/v2/files/'+fid+'/trash', auth=auth, headers=headers)\n", - " # permanently delete\n", - " requests.delete('https://api.plot.ly/v2/files/'+fid+'/permanent_delete', auth=auth, headers=headers)\n", - "\n", - "permanently_delete_files(username, filetype_to_delete='plot')\n", - "permanently_delete_files(username, filetype_to_delete='grid')" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated since IPython 4.0. You should import from nbconvert instead.\n", - " \"You should import from nbconvert instead.\", ShimWarning)\n", - "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/publisher/publisher.py:53: UserWarning: Did you \"Save\" this notebook before running this command? Remember to save, always save.\n", - " warnings.warn('Did you \"Save\" this notebook before running this command? '\n" - ] - } - ], - "source": [ - "from IPython.display import display, HTML\n", - "\n", - "display(HTML(''))\n", - "display(HTML(''))\n", - "\n", - "#!pip install git+https://github.com/plotly/publisher.git --upgrade\n", - "import publisher\n", - "publisher.publish(\n", - " 'delete.ipynb', 'python/delete-plots/', 'Deleting Plots with the Python API',\n", - " 'How to delete plotly graphs in python.',\n", - " name = 'Deleting Plots', language='python', \n", - " has_thumbnail='true', thumbnail= 'thumbnail/delete.jpg',\n", - " display_as='chart_studio', order=9)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/_posts/python-v3/chart-studio/fileopt/2015-06-30-fileopts.html b/_posts/python-v3/chart-studio/fileopt/2015-06-30-fileopts.html deleted file mode 100644 index 5d8f94dd2..000000000 --- a/_posts/python-v3/chart-studio/fileopt/2015-06-30-fileopts.html +++ /dev/null @@ -1,284 +0,0 @@ ---- -permalink: python/v3/file-options/ -redirect_from: python/file-options/ -description: How to update your graphs in Python with the fileopt parameter. -name: Updating Plotly Graphs -thumbnail: thumbnail/horizontal-bar.jpg -layout: base -language: python/v3 -display_as: chart_studio -page_type: example_index -order: 3 ---- -{% raw %} -
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New to Plotly?

Plotly's Python library is free and open source! Get started by downloading the client and reading the primer. -
You can set up Plotly to work in online or offline mode, or in jupyter notebooks. -
We also have a quick-reference cheatsheet (new!) to help you get started!

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Version Check

Plotly's python package is updated frequently. Run pip install plotly --upgrade to use the latest version.

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Overwriting existing graphs and updating a graph at its unique URL

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By default, Plotly will overwrite files made with the same filename. For example, if a graph named 'my plot' already exists in your account, then it will be overwritten with this new version and the URL of the graph will persist.

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In [2]:
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import plotly.plotly as py
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-data = [
-    go.Scatter(
-        x=[1, 2],
-        y=[3, 4]
-    )
-]
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Saving to a folder

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Filenames that contain "/" be treated as a Plotly directory and will be saved to your Plotly account in a folder tree. For example, to save your graphs to the folder my-graphs use the filename = "my-graphs/my plot" (if it doesn't already exist it will be created)

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In [3]:
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import plotly.plotly as py
-import plotly.graph_objs as go
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-data = [
-    go.Scatter(
-        x=[1, 2],
-        y=[3, 4]
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-]
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-plot_url = py.plot(data, filename='my-graphs/my plot')
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Creating new files

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With fileopt='new', Plotly will always create a new file. If a file with the same name already exists, then Plotly will append a '(1)' to the end of the filename, e.g. new plot (1) and create a unique URL.

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In [4]:
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import plotly.plotly as py
-import plotly.graph_objs as go
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-data = [
-    go.Scatter(
-        x=[1, 2],
-        y=[3, 4]
-    )
-]
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Extending traces in an existing graph

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To extend existing traces with your new data, use fileopt='extend'.

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In [5]:
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import plotly.plotly as py
-import plotly.graph_objs as go
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-trace0 = go.Scatter(
-    x=[1, 2],
-    y=[1, 2]
-)
-
-trace1 = go.Scatter(
-    x=[1, 2],
-    y=[2, 3]
-)
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-trace2 = go.Scatter(
-    x=[1, 2],
-    y=[3, 4]
-)
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-data = [trace0, trace1, trace2]
-
-# Take 1: if there is no data in the plot, 'extend' will create new traces.
-plot_url = py.plot(data, filename='extend plot', fileopt='extend')
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Then, extend the traces with more data.

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In [6]:
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import plotly.plotly as py
-import plotly.graph_objs as go
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-trace0 = go.Scatter(
-    x=[3, 4],
-    y=[2, 1]
-)
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-trace1 = go.Scatter(
-    x=[3, 4],
-    y=[3, 2]
-)
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-trace2 = go.Scatter(
-    x=[3, 4],
-    y=[4, 3]
-)
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-data = [trace0, trace1, trace2]
-
-# Take 2: extend the traces on the plot with the data in the order supplied.
-plot_url = py.plot(data, filename='extend plot', fileopt='extend')
-
- -
-
-
- -
- - -{% endraw %} diff --git a/_posts/python-v3/chart-studio/fileopt/fileopts.ipynb b/_posts/python-v3/chart-studio/fileopt/fileopts.ipynb deleted file mode 100644 index 09d210fff..000000000 --- a/_posts/python-v3/chart-studio/fileopt/fileopts.ipynb +++ /dev/null @@ -1,347 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### New to Plotly?\n", - "Plotly's Python library is free and open source! [Get started](https://plotly.com/python/getting-started/) by downloading the client and [reading the primer](https://plotly.com/python/getting-started/).\n", - "
You can set up Plotly to work in [online](https://plotly.com/python/getting-started/#initialization-for-online-plotting) or [offline](https://plotly.com/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plotly.com/python/getting-started/#start-plotting-online).\n", - "
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Version Check\n", - "Plotly's python package is updated frequently. Run `pip install plotly --upgrade` to use the latest version." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'3.0.0rc5'" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import plotly\n", - "plotly.__version__" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Overwriting existing graphs and updating a graph at its unique URL" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By default, Plotly will overwrite files made with the same filename. For example, if a graph named 'my plot' already exists in your account, then it will be overwritten with this new version and the URL of the graph will persist." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import plotly.plotly as py\n", - "import plotly.graph_objs as go\n", - "\n", - "data = [\n", - " go.Scatter(\n", - " x=[1, 2],\n", - " y=[3, 4]\n", - " )\n", - "]\n", - "\n", - "plot_url = py.plot(data, filename='my plot')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Saving to a folder" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Filenames that contain `\"/\"` be treated as a Plotly directory and will be saved to your Plotly account in a folder tree. For example, to save your graphs to the folder `my-graphs` use the `filename = \"my-graphs/my plot\"` (if it doesn't already exist it will be created)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import plotly.plotly as py\n", - "import plotly.graph_objs as go\n", - "\n", - "data = [\n", - " go.Scatter(\n", - " x=[1, 2],\n", - " y=[3, 4]\n", - " )\n", - "]\n", - "\n", - "plot_url = py.plot(data, filename='my-graphs/my plot')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Creating new files" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With `fileopt='new'`, Plotly will always create a new file. If a file with the same name already exists, then Plotly will append a '(1)' to the end of the filename, e.g. `new plot (1)` and create a unique URL." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import plotly.plotly as py\n", - "import plotly.graph_objs as go\n", - "\n", - "data = [\n", - " go.Scatter(\n", - " x=[1, 2],\n", - " y=[3, 4]\n", - " )\n", - "]\n", - "\n", - "plot_url = py.plot(data, filename='new plot', fileopt='new')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Extending traces in an existing graph" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To extend existing traces with your new data, use `fileopt='extend'`." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "import plotly.plotly as py\n", - "import plotly.graph_objs as go\n", - "\n", - "trace0 = go.Scatter(\n", - " x=[1, 2],\n", - " y=[1, 2]\n", - ")\n", - "\n", - "trace1 = go.Scatter(\n", - " x=[1, 2],\n", - " y=[2, 3]\n", - ")\n", - "\n", - "trace2 = go.Scatter(\n", - " x=[1, 2],\n", - " y=[3, 4]\n", - ")\n", - "\n", - "data = [trace0, trace1, trace2]\n", - "\n", - "# Take 1: if there is no data in the plot, 'extend' will create new traces.\n", - "plot_url = py.plot(data, filename='extend plot', fileopt='extend')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then, extend the traces with more data. " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import plotly.plotly as py\n", - "import plotly.graph_objs as go\n", - "\n", - "trace0 = go.Scatter(\n", - " x=[3, 4],\n", - " y=[2, 1]\n", - ")\n", - "\n", - "trace1 = go.Scatter(\n", - " x=[3, 4],\n", - " y=[3, 2]\n", - ")\n", - "\n", - "trace2 = go.Scatter(\n", - " x=[3, 4],\n", - " y=[4, 3]\n", - ")\n", - "\n", - "data = [trace0, trace1, trace2]\n", - "\n", - "# Take 2: extend the traces on the plot with the data in the order supplied.\n", - "py.iplot(data, filename='extend plot', fileopt='extend')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting git+https://github.com/plotly/publisher.git\n", - " Cloning https://github.com/plotly/publisher.git to c:\\users\\brand\\appdata\\local\\temp\\pip-req-build-fwemv9ri\n", - "Installing collected packages: publisher\n", - " Found existing installation: publisher 0.11\n", - " Uninstalling publisher-0.11:\n", - " Successfully uninstalled publisher-0.11\n", - " Running setup.py install for publisher: started\n", - " Running setup.py install for publisher: finished with status 'done'\n", - "Successfully installed publisher-0.11\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Python27\\lib\\site-packages\\IPython\\nbconvert.py:13: ShimWarning:\n", - "\n", - "The `IPython.nbconvert` package has been deprecated since IPython 4.0. You should import from nbconvert instead.\n", - "\n", - "C:\\Python27\\lib\\site-packages\\publisher\\publisher.py:53: UserWarning:\n", - "\n", - "Did you \"Save\" this notebook before running this command? Remember to save, always save.\n", - "\n" - ] - } - ], - "source": [ - "from IPython.display import display, HTML\n", - "\n", - "display(HTML(''))\n", - "display(HTML(''))\n", - "\n", - "! pip install git+https://github.com/plotly/publisher.git --upgrade\n", - "import publisher\n", - "publisher.publish(\n", - " 'fileopts.ipynb', 'python/file-options/', 'Updating Plotly Graphs',\n", - " 'How to update your graphs in Python with the fileopt parameter.',\n", - " title = 'Python Filenames Options | Plotly',\n", - " has_thumbnail='true', \n", - " thumbnail='thumbnail/horizontal-bar.jpg', \n", - " language='python', \n", - " page_type='example_index',\n", - " display_as='chart_studio', \n", - " order=3, \n", - " #ipynb='~notebook_demo/1'\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/_posts/python-v3/chart-studio/get-requests/2015-04-09-get-requests_python_index.html b/_posts/python-v3/chart-studio/get-requests/2015-04-09-get-requests_python_index.html deleted file mode 100755 index 967fc5bbe..000000000 --- a/_posts/python-v3/chart-studio/get-requests/2015-04-09-get-requests_python_index.html +++ /dev/null @@ -1,15 +0,0 @@ ---- -name: Working With Chart Studio Graphs -permalink: python/v3/working-with-chart-studio-graphs/ -redirect_from: -- python/v3/get-requests/ -description: How to download Chart Studio users' public graphs and data with Python. -layout: base -thumbnail: thumbnail/get-requests.jpg -language: python/v3 -thumbnail: thumbnail/spectral.jpg -display_as: chart_studio -order: 8 ---- -{% assign examples = site.posts | where:"language","python/v3" | where:"suite","get-requests" | sort: "order" %} -{% include posts/auto_examples.html examples=examples %} diff --git a/_posts/python-v3/chart-studio/get-requests/2015-04-09-python-change_plot.html b/_posts/python-v3/chart-studio/get-requests/2015-04-09-python-change_plot.html deleted file mode 100755 index 2e68554fe..000000000 --- a/_posts/python-v3/chart-studio/get-requests/2015-04-09-python-change_plot.html +++ /dev/null @@ -1,18 +0,0 @@ ---- -name: Get and Change a Public Figure -plot_url: https://plotly.com/~PlotBot/128 -arrangement: horizontal -language: python/v3 -suite: get-requests -order: 0 -sitemap: false ---- -import plotly.plotly as py -# Learn about API authentication here: https://plotly.com/python/getting-started -# Find your api_key here: https://plotly.com/settings/api - -fig = py.get_figure("https://plotly.com/~PlotBot/5") - -fig['layout']['title'] = "Never forget that title!" - -plot_url = py.plot(fig, filename="python-change_plot") diff --git a/_posts/python-v3/chart-studio/get-requests/2015-04-09-python-get-data.html b/_posts/python-v3/chart-studio/get-requests/2015-04-09-python-get-data.html deleted file mode 100755 index 2cbff2617..000000000 --- a/_posts/python-v3/chart-studio/get-requests/2015-04-09-python-get-data.html +++ /dev/null @@ -1,24 +0,0 @@ ---- -name: Get Data and Change Plot -plot_url: https://plotly.com/~PlotBot/130 -arrangement: horizontal -language: python/v3 -suite: get-requests -order: 1 -sitemap: false ---- -import plotly.plotly as py -import plotly.graph_objs as go -# Learn about API authentication here: https://plotly.com/python/getting-started -# Find your api_key here: https://plotly.com/settings/api - -data = py.get_figure("https://plotly.com/~AlexHP/68").get_data() -distance = [d['y'][0] for d in data] # check out the data for yourself! - -fig = go.Figure() -fig['data'] += [go.Histogram(y=distance, name="flyby distance", histnorm='probability')] -xaxis = dict(title="Probability for Flyby at this Distance") -yaxis = dict(title="Distance from Earth (Earth Radii)") -fig['layout'].update(title="data source: https://plotly.com/~AlexHP/68", xaxis=xaxis, yaxis=yaxis) - -plot_url = py.plot(fig, filename="python-get-data") diff --git a/_posts/python-v3/chart-studio/get-requests/2015-04-09-python-replot1.html b/_posts/python-v3/chart-studio/get-requests/2015-04-09-python-replot1.html deleted file mode 100755 index 1e6449a09..000000000 --- a/_posts/python-v3/chart-studio/get-requests/2015-04-09-python-replot1.html +++ /dev/null @@ -1,16 +0,0 @@ ---- -name: Get and Replot a Public Figure with URL -plot_url: https://plotly.com/~PlotBot/127 -arrangement: horizontal -language: python/v3 -suite: get-requests -order: 2 -sitemap: false ---- -import plotly.plotly as py -# Learn about API authentication here: https://plotly.com/python/getting-started -# Find your api_key here: https://plotly.com/settings/api - -fig = py.get_figure("https://plotly.com/~PlotBot/5") - -plot_url = py.plot(fig, filename="python-replot1") diff --git a/_posts/python-v3/chart-studio/get-requests/2015-04-09-python-replot2.html b/_posts/python-v3/chart-studio/get-requests/2015-04-09-python-replot2.html deleted file mode 100755 index 93e7e9f1e..000000000 --- a/_posts/python-v3/chart-studio/get-requests/2015-04-09-python-replot2.html +++ /dev/null @@ -1,16 +0,0 @@ ---- -name: Get and Replot a Public Figure with ID -plot_url: https://plotly.com/~PlotBot/129 -arrangement: horizontal -language: python/v3 -suite: get-requests -order: 3 -sitemap: false ---- -import plotly.plotly as py -# Learn about API authentication here: https://plotly.com/python/getting-started -# Find your api_key here: https://plotly.com/settings/api - -fig = py.get_figure("PlotBot", 5) - -plot_url = py.plot(fig, filename="python-replot2") diff --git a/_posts/python-v3/chart-studio/getting-started/2015-06-30-getting-started.html b/_posts/python-v3/chart-studio/getting-started/2015-06-30-getting-started.html deleted file mode 100644 index a717fc6c4..000000000 --- a/_posts/python-v3/chart-studio/getting-started/2015-06-30-getting-started.html +++ /dev/null @@ -1,14 +0,0 @@ ---- -permalink: python/v3/getting-started/ -redirect_to: /python/getting-started/ -sitemap: false -description: Installation and Initialization Steps for Using Plotly in Python. -display_as: chart_studio -thumbnail: thumbnail/bubble.jpg -name: Getting Started with Chart Studio -language: python/v3 -layout: base -order: 0.1 -ipynb: ~notebook_demo/123/installation -page_type: example_index ---- diff --git a/_posts/python-v3/chart-studio/getting-started/getting-started.ipynb b/_posts/python-v3/chart-studio/getting-started/getting-started.ipynb deleted file mode 100644 index 40e0da13e..000000000 --- a/_posts/python-v3/chart-studio/getting-started/getting-started.ipynb +++ /dev/null @@ -1,973 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Installation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To install Plotly's python package, use the package manager **pip** inside your terminal.
\n", - "If you don't have **pip** installed on your machine, [click here](https://pip.pypa.io/en/latest/installing.html) for pip's installation instructions.\n", - "
\n", - "
\n", - "`$ pip install plotly`\n", - "
or\n", - "
`$ sudo pip install plotly`\n", - "
\n", - "
\n", - "Plotly's Python package is [updated frequently](https://github.com/plotly/plotly.py/blob/master/CHANGELOG.md)! To upgrade, run:\n", - "
\n", - "
\n", - "`$ pip install plotly --upgrade`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialization for Online Plotting\n", - "Plotly provides a web-service for hosting graphs! Create a [free account](https://plotly.com/api_signup) to get started. Graphs are saved inside your online Plotly account and you control the privacy. Public hosting is free, for private hosting, check out our [paid plans](https://plotly.com/products/cloud/).\n", - "
\n", - "
\n", - "After installing the Plotly package, you're ready to fire up python:\n", - "
\n", - "
\n", - "`$ python`\n", - "
\n", - "
\n", - "and set your credentials:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import plotly \n", - "plotly.tools.set_credentials_file(username='DemoAccount', api_key='lr1c37zw81')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You'll need to replace **'DemoAccount'** and **'lr1c37zw81'** with *your* Plotly username and [API key](https://plotly.com/settings/api).
\n", - "Find your API key [here](https://plotly.com/settings/api).\n", - "
\n", - "
\n", - "The initialization step places a special **.plotly/.credentials** file in your home directory. Your **~/.plotly/.credentials** file should look something like this:\n", - "
\n", - "```\n", - "{\n", - " \"username\": \"DemoAccount\",\n", - " \"stream_ids\": [\"ylosqsyet5\", \"h2ct8btk1s\", \"oxz4fm883b\"],\n", - " \"api_key\": \"lr1c37zw81\"\n", - "}\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Online Plot Privacy\n", - "\n", - "Plot can be set to three different type of privacies: public, private or secret.\n", - "- **public**: Anyone can view this graph. It will appear in your profile and can appear in search engines. You do not need to be logged in to Plotly to view this chart.\n", - "- **private**: Only you can view this plot. It will not appear in the Plotly feed, your profile, or search engines. You must be logged in to Plotly to view this graph. You can privately share this graph with other Plotly users in your online Plotly account and they will need to be logged in to view this plot.\n", - "- **secret**: Anyone with this secret link can view this chart. It will not appear in the Plotly feed, your profile, or search engines. If it is embedded inside a webpage or an IPython notebook, anybody who is viewing that page will be able to view the graph. You do not need to be logged in to view this plot.\n", - "\n", - "By default all plots are set to **public**. Users with free account have the permission to keep one private plot. If you need to save private plots, [upgrade to a pro account](https://plotly.com/plans). If you're a [Personal or Professional user](https://plotly.com/settings/subscription/?modal=true&utm_source=api-docs&utm_medium=support-oss) and would like the default setting for your plots to be private, you can edit your Plotly configuration:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import plotly \n", - "plotly.tools.set_config_file(world_readable=False,\n", - " sharing='private')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For more examples on privacy settings please visit [Python privacy documentation](https://plotly.com/python/privacy/)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Special Instructions for [Chart Studio Enterprise](https://plotly.com/product/enterprise/) Users " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Your API key for account on the public cloud will be different than the API key in Chart Studio Enterprise. Visit https://plotly.your-company.com/settings/api/ to find your Chart Studio Enterprise API key. Remember to replace \"your-company.com\" with the URL of your Chart Studio Enterprise server.\n", - "If your company has a Chart Studio Enterprise server, change the Python API endpoint so that it points to your company's Plotly server instead of Plotly's cloud.\n", - "
\n", - "
\n", - "In python, enter:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import plotly \n", - "plotly.tools.set_config_file(plotly_domain='https://plotly.your-company.com',\n", - " plotly_streaming_domain='https://stream-plotly.your-company.com')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Make sure to replace **\"your-company.com\"** with the URL of *your* Chart Studio Enterprise server." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Additionally, you can set your configuration so that you generate **private plots by default**. For more information on privacy settings see: https://plotly.com/python/privacy/
\n", - "
\n", - "In python, enter:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import plotly \n", - "plotly.tools.set_config_file(plotly_domain='https://plotly.your-company.com',\n", - " plotly_streaming_domain='https://stream-plotly.your-company.com', \n", - " world_readable=False,\n", - " sharing='private')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotly Using virtualenv\n", - "Python's `virtualenv` allows us create multiple working Python environments which can each use different versions of packages. We can use `virtualenv` from the command line to create an environment using plotly.py version 3.3.0 and a separate one using plotly.py version 2.7.0. See [the virtualenv documentation](https://virtualenv.pypa.io/en/stable) for more info.\n", - "\n", - "**Install virtualenv globally**\n", - "
`$ sudo pip install virtualenv`\n", - " \n", - "**Create your virtualenvs**\n", - "
`$ mkdir ~/.virtualenvs`\n", - "
`$ cd ~/.virtualenvs`\n", - "
`$ python -m venv plotly2.7`\n", - "
`$ python -m venv plotly3.3`\n", - "\n", - "**Activate the virtualenv.**\n", - "You will see the name of your virtualenv in parenthesis next to the input promt.\n", - "
`$ source ~/.virtualenvs/plotly2.7/bin/activate`\n", - "
`(plotly2.7) $`\n", - "\n", - "**Install plotly locally to virtualenv** (note that we don't use sudo).\n", - "
`(plotly2.7) $ pip install plotly==2.7`\n", - "\n", - "**Deactivate to exit**\n", - "
\n", - "`(plotly2.7) $ deactivate`\n", - "
`$`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Jupyter Setup\n", - "**Install Jupyter into a virtualenv**\n", - "
`$ source ~/.virtualenvs/plotly3.3/bin/activate`\n", - "
`(plotly3.3) $ pip install notebook`\n", - "\n", - "**Start the Jupyter kernel from a virtualenv**\n", - "
`(plotly3.3) $ jupyter notebook`\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Start Plotting Online\n", - "When plotting online, the plot and data will be saved to your cloud account. There are two methods for plotting online: `py.plot()` and `py.iplot()`. Both options create a unique url for the plot and save it in your Plotly account.\n", - "- Use `py.plot()` to return the unique url and optionally open the url.\n", - "- Use `py.iplot()` when working in a Jupyter Notebook to display the plot in the notebook.\n", - "\n", - "Copy and paste one of the following examples to create your first hosted Plotly graph using the Plotly Python library:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'https://plotly.com/~tobin/22'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import plotly.plotly as py\n", - "import plotly.graph_objs as go\n", - "\n", - "trace0 = go.Scatter(\n", - " x=[1, 2, 3, 4],\n", - " y=[10, 15, 13, 17]\n", - ")\n", - "trace1 = go.Scatter(\n", - " x=[1, 2, 3, 4],\n", - " y=[16, 5, 11, 9]\n", - ")\n", - "data = [trace0, trace1]\n", - "\n", - "py.plot(data, filename = 'basic-line', auto_open=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Checkout the docstrings for more information:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function plot in module plotly.plotly.plotly:\n", - "\n", - "plot(figure_or_data, validate=True, **plot_options)\n", - " Create a unique url for this plot in Plotly and optionally open url.\n", - " \n", - " plot_options keyword arguments:\n", - " filename (string) -- the name that will be associated with this figure\n", - " fileopt ('new' | 'overwrite' | 'extend' | 'append') -- 'new' creates a\n", - " 'new': create a new, unique url for this plot\n", - " 'overwrite': overwrite the file associated with `filename` with this\n", - " 'extend': add additional numbers (data) to existing traces\n", - " 'append': add additional traces to existing data lists\n", - " auto_open (default=True) -- Toggle browser options\n", - " True: open this plot in a new browser tab\n", - " False: do not open plot in the browser, but do return the unique url\n", - " sharing ('public' | 'private' | 'secret') -- Toggle who can view this\n", - " graph\n", - " - 'public': Anyone can view this graph. It will appear in your profile\n", - " and can appear in search engines. You do not need to be\n", - " logged in to Plotly to view this chart.\n", - " - 'private': Only you can view this plot. It will not appear in the\n", - " Plotly feed, your profile, or search engines. You must be\n", - " logged in to Plotly to view this graph. You can privately\n", - " share this graph with other Plotly users in your online\n", - " Plotly account and they will need to be logged in to\n", - " view this plot.\n", - " - 'secret': Anyone with this secret link can view this chart. It will\n", - " not appear in the Plotly feed, your profile, or search\n", - " engines. If it is embedded inside a webpage or an IPython\n", - " notebook, anybody who is viewing that page will be able to\n", - " view the graph. You do not need to be logged in to view\n", - " this plot.\n", - " world_readable (default=True) -- Deprecated: use \"sharing\".\n", - " Make this figure private/public\n", - "\n" - ] - } - ], - "source": [ - "import plotly.plotly as py\n", - "help(py.plot)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import plotly.plotly as py\n", - "import plotly.graph_objs as go\n", - "\n", - "trace0 = go.Scatter(\n", - " x=[1, 2, 3, 4],\n", - " y=[10, 15, 13, 17]\n", - ")\n", - "trace1 = go.Scatter(\n", - " x=[1, 2, 3, 4],\n", - " y=[16, 5, 11, 9]\n", - ")\n", - "data = [trace0, trace1]\n", - "\n", - "py.iplot(data, filename = 'basic-line')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "See more examples in our [IPython notebook documentation](https://plotly.com/ipython-notebooks/) or check out the `py.iplot()` docstring for more information." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function iplot in module chart_studio.plotly.plotly:\n", - "\n", - "iplot(figure_or_data, **plot_options)\n", - " Create a unique url for this plot in Plotly and open in IPython.\n", - " \n", - " plot_options keyword arguments:\n", - " filename (string) -- the name that will be associated with this figure\n", - " fileopt ('new' | 'overwrite' | 'extend' | 'append')\n", - " - 'new': create a new, unique url for this plot\n", - " - 'overwrite': overwrite the file associated with `filename` with this\n", - " - 'extend': add additional numbers (data) to existing traces\n", - " - 'append': add additional traces to existing data lists\n", - " sharing ('public' | 'private' | 'secret') -- Toggle who can view this graph\n", - " - 'public': Anyone can view this graph. It will appear in your profile\n", - " and can appear in search engines. You do not need to be\n", - " logged in to Plotly to view this chart.\n", - " - 'private': Only you can view this plot. It will not appear in the\n", - " Plotly feed, your profile, or search engines. You must be\n", - " logged in to Plotly to view this graph. You can privately\n", - " share this graph with other Plotly users in your online\n", - " Plotly account and they will need to be logged in to\n", - " view this plot.\n", - " - 'secret': Anyone with this secret link can view this chart. It will\n", - " not appear in the Plotly feed, your profile, or search\n", - " engines. If it is embedded inside a webpage or an IPython\n", - " notebook, anybody who is viewing that page will be able to\n", - " view the graph. You do not need to be logged in to view\n", - " this plot.\n", - " world_readable (default=True) -- Deprecated: use \"sharing\".\n", - " Make this figure private/public\n", - "\n" - ] - } - ], - "source": [ - "import plotly.plotly as py\n", - "help(py.iplot)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also create plotly graphs with **matplotlib** syntax. Learn more in our [matplotlib documentation](https://plotly.com/matplotlib/)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialization for Offline Plotting\n", - "Plotly Offline allows you to create graphs offline and save them locally. There are also two methods for plotting offline: `plotly.offline.plot()` and `plotly.offline.iplot()`. \n", - "- Use `plotly.offline.plot()` to create and standalone HTML that is saved locally and opened inside your web browser.\n", - "- Use `plotly.offline.iplot()` when working offline in a Jupyter Notebook to display the plot in the notebook.\n", - "\n", - "Check your Plotly version, version 1.9.4+ is needed for offline plotting:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'3.9.0'" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import plotly\n", - "plotly.__version__" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copy and paste one of the following examples to create your first offline Plotly graph using the Plotly Python library:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'temp-plot.html'" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import plotly\n", - "import plotly.graph_objs as go\n", - "\n", - "plotly.offline.plot({\n", - " \"data\": [go.Scatter(x=[1, 2, 3, 4], y=[4, 3, 2, 1])],\n", - " \"layout\": go.Layout(title=\"hello world\")\n", - "}, auto_open=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Learn more by calling `help()`:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function plot in module plotly.offline.offline:\n", - "\n", - "plot(figure_or_data, show_link=True, link_text='Export to plot.ly', validate=True, output_type='file', include_plotlyjs=True, filename='temp-plot.html', auto_open=True, image=None, image_filename='plot_image', image_width=800, image_height=600, config=None, include_mathjax=False)\n", - " Create a plotly graph locally as an HTML document or string.\n", - " \n", - " Example:\n", - " ```\n", - " from plotly.offline import plot\n", - " import plotly.graph_objs as go\n", - " \n", - " plot([go.Scatter(x=[1, 2, 3], y=[3, 2, 6])], filename='my-graph.html')\n", - " # We can also download an image of the plot by setting the image parameter\n", - " # to the image format we want\n", - " plot([go.Scatter(x=[1, 2, 3], y=[3, 2, 6])], filename='my-graph.html',\n", - " image='jpeg')\n", - " ```\n", - " More examples below.\n", - " \n", - " figure_or_data -- a plotly.graph_objs.Figure or plotly.graph_objs.Data or\n", - " dict or list that describes a Plotly graph.\n", - " See https://plotly.com/python/ for examples of\n", - " graph descriptions.\n", - " \n", - " Keyword arguments:\n", - " show_link (default=True) -- display a link in the bottom-right corner of\n", - " of the chart that will export the chart to Chart Studio Cloud or\n", - " Chart Studio Enterprise\n", - " link_text (default='Export to plot.ly') -- the text of export link\n", - " validate (default=True) -- validate that all of the keys in the figure\n", - " are valid? omit if your version of plotly.js has become outdated\n", - " with your version of graph_reference.json or if you need to include\n", - " extra, unnecessary keys in your figure.\n", - " output_type ('file' | 'div' - default 'file') -- if 'file', then\n", - " the graph is saved as a standalone HTML file and `plot`\n", - " returns None.\n", - " If 'div', then `plot` returns a string that just contains the\n", - " HTML
that contains the graph and the script to generate the\n", - " graph.\n", - " Use 'file' if you want to save and view a single graph at a time\n", - " in a standalone HTML file.\n", - " Use 'div' if you are embedding these graphs in an HTML file with\n", - " other graphs or HTML markup, like a HTML report or an website.\n", - " include_plotlyjs (True | False | 'cdn' | 'directory' | path - default=True)\n", - " Specifies how the plotly.js library is included in the output html\n", - " file or div string.\n", - " \n", - " If True, a script tag containing the plotly.js source code (~3MB)\n", - " is included in the output. HTML files generated with this option are\n", - " fully self-contained and can be used offline.\n", - " \n", - " If 'cdn', a script tag that references the plotly.js CDN is included\n", - " in the output. HTML files generated with this option are about 3MB\n", - " smaller than those generated with include_plotlyjs=True, but they\n", - " require an active internet connection in order to load the plotly.js\n", - " library.\n", - " \n", - " If 'directory', a script tag is included that references an external\n", - " plotly.min.js bundle that is assumed to reside in the same\n", - " directory as the HTML file. If output_type='file' then the\n", - " plotly.min.js bundle is copied into the directory of the resulting\n", - " HTML file. If a file named plotly.min.js already exists in the output\n", - " directory then this file is left unmodified and no copy is performed.\n", - " HTML files generated with this option can be used offline, but they\n", - " require a copy of the plotly.min.js bundle in the same directory.\n", - " This option is useful when many figures will be saved as HTML files in\n", - " the same directory because the plotly.js source code will be included\n", - " only once per output directory, rather than once per output file.\n", - " \n", - " If a string that ends in '.js', a script tag is included that\n", - " references the specified path. This approach can be used to point\n", - " the resulting HTML file to an alternative CDN.\n", - " \n", - " If False, no script tag referencing plotly.js is included. This is\n", - " useful when output_type='div' and the resulting div string will be\n", - " placed inside an HTML document that already loads plotly.js. This\n", - " option is not advised when output_type='file' as it will result in\n", - " a non-functional html file.\n", - " filename (default='temp-plot.html') -- The local filename to save the\n", - " outputted chart to. If the filename already exists, it will be\n", - " overwritten. This argument only applies if `output_type` is 'file'.\n", - " auto_open (default=True) -- If True, open the saved file in a\n", - " web browser after saving.\n", - " This argument only applies if `output_type` is 'file'.\n", - " image (default=None |'png' |'jpeg' |'svg' |'webp') -- This parameter sets\n", - " the format of the image to be downloaded, if we choose to download an\n", - " image. This parameter has a default value of None indicating that no\n", - " image should be downloaded. Please note: for higher resolution images\n", - " and more export options, consider making requests to our image servers.\n", - " Type: `help(py.image)` for more details.\n", - " image_filename (default='plot_image') -- Sets the name of the file your\n", - " image will be saved to. The extension should not be included.\n", - " image_height (default=600) -- Specifies the height of the image in `px`.\n", - " image_width (default=800) -- Specifies the width of the image in `px`.\n", - " config (default=None) -- Plot view options dictionary. Keyword arguments\n", - " `show_link` and `link_text` set the associated options in this\n", - " dictionary if it doesn't contain them already.\n", - " include_mathjax (False | 'cdn' | path - default=False) --\n", - " Specifies how the MathJax.js library is included in the output html\n", - " file or div string. MathJax is required in order to display labels\n", - " with LaTeX typesetting.\n", - " \n", - " If False, no script tag referencing MathJax.js will be included in the\n", - " output. HTML files generated with this option will not be able to\n", - " display LaTeX typesetting.\n", - " \n", - " If 'cdn', a script tag that references a MathJax CDN location will be\n", - " included in the output. HTML files generated with this option will be\n", - " able to display LaTeX typesetting as long as they have internet access.\n", - " \n", - " If a string that ends in '.js', a script tag is included that\n", - " references the specified path. This approach can be used to point the\n", - " resulting HTML file to an alternative CDN.\n", - "\n" - ] - } - ], - "source": [ - "import plotly\n", - "help(plotly.offline.plot)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When using `plotly.offline.iplot` to plot offline in Jupyter Notebooks, there is an additional initialization step of running: `plotly.offline.init_notebook_mode()` at the start of each notebook session.
See the example below:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "linkText": "Export to plot.ly", - "plotlyServerURL": "https://plotly.com", - "responsive": true, - "showLink": false - }, - "data": [ - { - "type": "scatter", - "uid": "0cd26e74-0eaa-4502-b27a-49c1f93a966d", - "x": [ - 1, - 2, - 3, - 4 - ], - "y": [ - 4, - 3, - 2, - 1 - ] - } - ], - "layout": { - "title": { - "text": "hello world" - } - } - }, - "text/html": [ - "
\n", - " \n", - " \n", - "
\n", - " \n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import plotly\n", - "import plotly.graph_objs as go\n", - "\n", - "plotly.offline.init_notebook_mode(connected=True)\n", - "\n", - "plotly.offline.iplot({\n", - " \"data\": [go.Scatter(x=[1, 2, 3, 4], y=[4, 3, 2, 1])],\n", - " \"layout\": go.Layout(title=\"hello world\")\n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function iplot in module plotly.offline.offline:\n", - "\n", - "iplot(figure_or_data, show_link=False, link_text='Export to plot.ly', validate=True, image=None, filename='plot_image', image_width=800, image_height=600, config=None, auto_play=True, animation_opts=None)\n", - " Draw plotly graphs inside an IPython or Jupyter notebook\n", - " \n", - " figure_or_data -- a plotly.graph_objs.Figure or plotly.graph_objs.Data or\n", - " dict or list that describes a Plotly graph.\n", - " See https://plotly.com/python/ for examples of\n", - " graph descriptions.\n", - " \n", - " Keyword arguments:\n", - " show_link (default=False) -- display a link in the bottom-right corner of\n", - " of the chart that will export the chart to\n", - " Plotly Cloud or Plotly Enterprise\n", - " link_text (default='Export to plot.ly') -- the text of export link\n", - " validate (default=True) -- validate that all of the keys in the figure\n", - " are valid? omit if your version of plotly.js\n", - " has become outdated with your version of\n", - " graph_reference.json or if you need to include\n", - " extra, unnecessary keys in your figure.\n", - " image (default=None |'png' |'jpeg' |'svg' |'webp') -- This parameter sets\n", - " the format of the image to be downloaded, if we choose to download an\n", - " image. This parameter has a default value of None indicating that no\n", - " image should be downloaded. Please note: for higher resolution images\n", - " and more export options, consider using plotly.io.write_image. See\n", - " https://plotly.com/python/static-image-export/ for more details.\n", - " filename (default='plot') -- Sets the name of the file your image\n", - " will be saved to. The extension should not be included.\n", - " image_height (default=600) -- Specifies the height of the image in `px`.\n", - " image_width (default=800) -- Specifies the width of the image in `px`.\n", - " config (default=None) -- Plot view options dictionary. Keyword arguments\n", - " `show_link` and `link_text` set the associated options in this\n", - " dictionary if it doesn't contain them already.\n", - " auto_play (default=True) -- Whether to automatically start the animation\n", - " sequence if the figure contains frames. Has no effect if the figure\n", - " does not contain frames.\n", - " animation_opts (default=None) -- dict of custom animation parameters to be\n", - " passed to the function Plotly.animate in Plotly.js. See\n", - " https://github.com/plotly/plotly.js/blob/master/src/plots/animation_attributes.js\n", - " for available options. Has no effect if the figure\n", - " does not contain frames, or auto_play is False.\n", - " \n", - " Example:\n", - " ```\n", - " from plotly.offline import init_notebook_mode, iplot\n", - " init_notebook_mode()\n", - " iplot([{'x': [1, 2, 3], 'y': [5, 2, 7]}])\n", - " # We can also download an image of the plot by setting the image to the\n", - " format you want. e.g. `image='png'`\n", - " iplot([{'x': [1, 2, 3], 'y': [5, 2, 7]}], image='png')\n", - " ```\n", - " \n", - " animation_opts Example:\n", - " ```\n", - " from plotly.offline import iplot\n", - " figure = {'data': [{'x': [0, 1], 'y': [0, 1]}],\n", - " 'layout': {'xaxis': {'range': [0, 5], 'autorange': False},\n", - " 'yaxis': {'range': [0, 5], 'autorange': False},\n", - " 'title': 'Start Title'},\n", - " 'frames': [{'data': [{'x': [1, 2], 'y': [1, 2]}]},\n", - " {'data': [{'x': [1, 4], 'y': [1, 4]}]},\n", - " {'data': [{'x': [3, 4], 'y': [3, 4]}],\n", - " 'layout': {'title': 'End Title'}}]}\n", - " iplot(figure,animation_opts=\"{frame: {duration: 1}}\")\n", - " ```\n", - "\n" - ] - } - ], - "source": [ - "import plotly\n", - "help(plotly.offline.iplot)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For more examples on plotting offline with Plotly in python please visit our [offline documentation](https://plotly.com/python/offline/)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using Plotly with Pandas\n", - "\n", - "To use Plotly with Pandas first `$ pip install pandas` and then import pandas in your code like in the example below." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import plotly.plotly as py\n", - "import plotly.graph_objs as go\n", - "import pandas as pd\n", - "\n", - "df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminder2007.csv')\n", - "\n", - "fig = {\n", - " 'data': [\n", - " {\n", - " 'x': df.gdpPercap, \n", - " 'y': df.lifeExp, \n", - " 'text': df.country, \n", - " 'mode': 'markers', \n", - " 'name': '2007'},\n", - " ],\n", - " 'layout': {\n", - " 'xaxis': {'title': 'GDP per Capita', 'type': 'log'},\n", - " 'yaxis': {'title': \"Life Expectancy\"}\n", - " }\n", - "}\n", - "\n", - "py.iplot(fig, filename='pandas-multiple-scatter')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### [MORE EXAMPLES](https://plotly.com/python/)\n", - "Check out more examples and tutorials for using Plotly in python [here](https://plotly.com/python)!" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import display, HTML\n", - "\n", - "display(HTML(''))\n", - "display(HTML(''))\n", - "\n", - "#!pip install git+https://github.com/plotly/publisher.git --upgrade\n", - "import publisher\n", - "publisher.publish(\n", - " 'getting-started.ipynb', 'python/getting-started/', 'Getting Started Plotly for Python',\n", - " 'Installation and Initialization Steps for Using Plotly in Python.',\n", - " title = 'Getting Started with Plotly for Python | plotly',\n", - " name = 'Getting Started with Plotly for Python', display_as='chart_studio'\n", - " language='python', layout='user-guide', has_thumbnail='true', thumbnail='thumbnail/bubble.jpg',\n", - " ipynb= '~notebook_demo/123/installation', uses_plotly_offline=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/_posts/python-v3/chart-studio/iframes/2015-05-26-iframes_python_index.html b/_posts/python-v3/chart-studio/iframes/2015-05-26-iframes_python_index.html deleted file mode 100755 index 0689a214a..000000000 --- a/_posts/python-v3/chart-studio/iframes/2015-05-26-iframes_python_index.html +++ /dev/null @@ -1,25 +0,0 @@ ---- -name: Embedding Graphs in HTML -permalink: python/v3/embedding-plotly-graphs-in-HTML/ -description: How to embed plotly graphs with an iframe in HTML. -layout: base -language: python/v3 -thumbnail: thumbnail/embed.jpg -display_as: chart_studio -order: 6 ---- - -
-

Plotly graphs can be embedded in any HTML page. This includes IPython notebooks, -Wordpress sites, dashboards, blogs, and more.


- -

For more on embedding Plotly graphs in HTML documents, see our tutorial.


- -

From Python, you can generate the HTML code to embed Plotly graphs with the plotly.tools.get_embed function.


- -

-import plotly.tools as tls
-
-tls.get_embed('https://plotly.com/~chris/1638')
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diff --git a/_posts/python-v3/chart-studio/ipython-notebooks/2015-06-30-jupyter_tutorial.html b/_posts/python-v3/chart-studio/ipython-notebooks/2015-06-30-jupyter_tutorial.html deleted file mode 100644 index 7dbab534e..000000000 --- a/_posts/python-v3/chart-studio/ipython-notebooks/2015-06-30-jupyter_tutorial.html +++ /dev/null @@ -1,911 +0,0 @@ ---- -permalink: python/v3/ipython-notebook-tutorial/ -description: Jupyter notebook tutorial on how to install, run, and use Jupyter for interactive matplotlib plotting, data analysis, and publishing code -name: Jupyter Notebook Tutorial -thumbnail: thumbnail/ipythonnb.jpg -layout: base -name: Jupyter Notebook Tutorial -language: python/v3 -display_as: chart_studio -page_type: example_index -order: 11 -ipynb: ~chelsea_lyn/14070 ---- -{% raw %} -
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Introduction

Jupyter has a beautiful notebook that lets you write and execute code, analyze data, embed content, and share reproducible work. Jupyter Notebook (previously referred to as IPython Notebook) allows you to easily share your code, data, plots, and explanation in a sinle notebook. Publishing is flexible: PDF, HTML, ipynb, dashboards, slides, and more. Code cells are based on an input and output format. For example:

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print "hello world"
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hello world
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Installation

There are a few ways to use a Jupyter Notebook:

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  • Install with pip. Open a terminal and type: $ pip install jupyter.
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  • Windows users can install with setuptools.
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  • Anaconda and Enthought allow you to download a desktop version of Jupyter Notebook.
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  • nteract allows users to work in a notebook enviornment via a desktop application.
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  • Microsoft Azure provides hosted access to Jupyter Notebooks.
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  • Domino Data Lab offers web-based Notebooks.
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  • tmpnb launches a temporary online Notebook for individual users.
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Getting Started

Once you've installed the Notebook, you start from your terminal by calling $ jupyter notebook. This will open a browser on a localhost to the URL of your Notebooks, by default http://127.0.0.1:8888. Windows users need to open up their Command Prompt. You'll see a dashboard with all your Notebooks. You can launch your Notebooks from there. The Notebook has the advantage of looking the same when you're coding and publishing. You just have all the options to move code, run cells, change kernels, and use Markdown when you're running a NB.

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Helpful Commands

- Tab Completion: Jupyter supports tab completion! You can type object_name.<TAB> to view an object’s attributes. For tips on cell magics, running Notebooks, and exploring objects, check out the Jupyter docs. -
- Help: provides an introduction and overview of features.

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help
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Type help() for interactive help, or help(object) for help about object.
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- Quick Reference: open quick reference by running:

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In [12]:
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quickref
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- Keyboard Shortcuts: Shift-Enter will run a cell, Ctrl-Enter will run a cell in-place, Alt-Enter will run a cell and insert another below. See more shortcuts here.

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Languages

The bulk of this tutorial discusses executing python code in Jupyter notebooks. You can also use Jupyter notebooks to execute R code. Skip down to the [R section] for more information on using IRkernel with Jupyter notebooks and graphing examples.

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Package Management

When installing packages in Jupyter, you either need to install the package in your actual shell, or run the ! prefix, e.g.:

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!pip install packagename
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You may want to reload submodules if you've edited the code in one. IPython comes with automatic reloading magic. You can reload all changed modules before executing a new line.

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Some useful packages that we'll use in this tutorial include:

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  • Pandas: import data via a url and create a dataframe to easily handle data for analysis and graphing. See examples of using Pandas here: https://plotly.com/pandas/.
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  • NumPy: a package for scientific computing with tools for algebra, random number generation, integrating with databases, and managing data. See examples of using NumPy here: https://plotly.com/numpy/.
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  • SciPy: a Python-based ecosystem of packages for math, science, and engineering.
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  • Plotly: a graphing library for making interactive, publication-quality graphs. See examples of statistic, scientific, 3D charts, and more here: https://plotly.com/python.
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import pandas as pd
-import numpy as np
-import scipy as sp
-import plotly.plotly as py
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Import Data

You can use pandas read_csv() function to import data. In the example below, we import a csv hosted on github and display it in a table using Plotly:

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import plotly.plotly as py
-import plotly.figure_factory as ff
-import pandas as pd
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-df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv")
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-table = ff.create_table(df)
-py.iplot(table, filename='jupyter-table1')
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Use dataframe.column_title to index the dataframe:

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schools = df.School
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'MIT'
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Most pandas functions also work on an entire dataframe. For example, calling std() calculates the standard deviation for each column.

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df.std()
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Women    12.813683
-Men      25.705289
-Gap      14.137084
-dtype: float64
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Plotting Inline

You can use Plotly's python API to plot inside your Jupyter Notebook by calling plotly.plotly.iplot() or plotly.offline.iplot() if working offline. Plotting in the notebook gives you the advantage of keeping your data analysis and plots in one place. Now we can do a bit of interactive plotting. Head to the Plotly getting started page to learn how to set your credentials. Calling the plot with iplot automaticallly generates an interactive version of the plot inside the Notebook in an iframe. See below:

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import plotly.plotly as py
-import plotly.graph_objs as go
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-data = [go.Bar(x=df.School,
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-py.iplot(data, filename='jupyter-basic_bar')
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Plotting multiple traces and styling the chart with custom colors and titles is simple with Plotly syntax. Additionally, you can control the privacy with sharing set to public, private, or secret.

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import plotly.plotly as py
-import plotly.graph_objs as go
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-trace_women = go.Bar(x=df.School,
-                  y=df.Women,
-                  name='Women',
-                  marker=dict(color='#ffcdd2'))
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-trace_men = go.Bar(x=df.School,
-                y=df.Men,
-                name='Men',
-                marker=dict(color='#A2D5F2'))
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-trace_gap = go.Bar(x=df.School,
-                y=df.Gap,
-                name='Gap',
-                marker=dict(color='#59606D'))
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-data = [trace_women, trace_men, trace_gap]
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-layout = go.Layout(title="Average Earnings for Graduates",
-                xaxis=dict(title='School'),
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-fig = go.Figure(data=data, layout=layout)
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-py.iplot(fig, sharing='private', filename='jupyter-styled_bar')
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Now we have interactive charts displayed in our notebook. Hover on the chart to see the values for each bar, click and drag to zoom into a specific section or click on the legend to hide/show a trace.

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Plotting Interactive Maps

Plotly is now integrated with Mapbox. In this example we'll plot lattitude and longitude data of nuclear waste sites. To plot on Mapbox maps with Plotly you'll need a Mapbox account and a Mapbox Access Token which you can add to your Plotly settings.

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import plotly.plotly as py
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-# mapbox_access_token = 'ADD YOUR TOKEN HERE'
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-df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/Nuclear%20Waste%20Sites%20on%20American%20Campuses.csv')
-site_lat = df.lat
-site_lon = df.lon
-locations_name = df.text
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-data = [
-    go.Scattermapbox(
-        lat=site_lat,
-        lon=site_lon,
-        mode='markers',
-        marker=dict(
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-        text=locations_name,
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-        mode='markers',
-        marker=dict(
-            size=8,
-            color='rgb(242, 177, 172)',
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-        hoverinfo='none'
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-layout = go.Layout(
-    title='Nuclear Waste Sites on Campus',
-    autosize=True,
-    hovermode='closest',
-    showlegend=False,
-    mapbox=dict(
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-        bearing=0,
-        center=dict(
-            lat=38,
-            lon=-94
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-        pitch=0,
-        zoom=3,
-        style='light'
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-fig = dict(data=data, layout=layout)
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-py.iplot(fig, filename='jupyter-Nuclear Waste Sites on American Campuses')
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3D Plotting

Using Numpy and Plotly, we can make interactive 3D plots in the Notebook as well.

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import plotly.plotly as py
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-s = np.linspace(0, 2 * np.pi, 240)
-t = np.linspace(0, np.pi, 240)
-tGrid, sGrid = np.meshgrid(s, t)
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-r = 2 + np.sin(7 * sGrid + 5 * tGrid)  # r = 2 + sin(7s+5t)
-x = r * np.cos(sGrid) * np.sin(tGrid)  # x = r*cos(s)*sin(t)
-y = r * np.sin(sGrid) * np.sin(tGrid)  # y = r*sin(s)*sin(t)
-z = r * np.cos(tGrid)                  # z = r*cos(t)
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-data = [surface]
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-layout = go.Layout(
-    title='Parametric Plot',
-    scene=dict(
-        xaxis=dict(
-            gridcolor='rgb(255, 255, 255)',
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-            showbackground=True,
-            backgroundcolor='rgb(230, 230,230)'
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-        yaxis=dict(
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-py.iplot(fig, filename='jupyter-parametric_plot')
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Animated Plots

Checkout Plotly's animation documentation to see how to create animated plots inline in Jupyter notebooks like the Gapminder plot displayed below: -https://plotly.com/~PythonPlotBot/231/

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Plot Controls & IPython widgets

Add sliders, buttons, and dropdowns to your inline chart:

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import plotly.plotly as py
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-data = [dict(
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-        line=dict(color='00CED1', width=6),
-        name = '𝜈 = '+str(step),
-        x = np.arange(0,10,0.01),
-        y = np.sin(step*np.arange(0,10,0.01))) for step in np.arange(0,5,0.1)]
-data[10]['visible'] = True
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-steps = []
-for i in range(len(data)):
-    step = dict(
-        method = 'restyle',
-        args = ['visible', [False] * len(data)],
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-    step['args'][1][i] = True # Toggle i'th trace to "visible"
-    steps.append(step)
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-    active = 10,
-    currentvalue = {"prefix": "Frequency: "},
-    pad = {"t": 50},
-    steps = steps
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Additionally, IPython widgets allow you to add sliders, widgets, search boxes, and more to your Notebook. See the widget docs for more information. For others to be able to access your work, they'll need IPython. Or, you can use a cloud-based NB option so others can run your work. -
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Executing R Code

IRkernel, an R kernel for Jupyter, allows you to write and execute R code in a Jupyter notebook. Checkout the IRkernel documentation for some simple installation instructions. Once IRkernel is installed, open a Jupyter Notebook by calling $ jupyter notebook and use the New dropdown to select an R notebook.

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See a full R example Jupyter Notebook here: https://plotly.com/~chelsea_lyn/14069

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Additional Embed Features

We've seen how to embed Plotly tables and charts as iframes in the notebook, with IPython.display we can embed additional features, such a videos. For example, from YouTube:

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from IPython.display import YouTubeVideo
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LaTeX

We can embed LaTeX inside a Notebook by putting a $$ around our math, then run the cell as a Markdown cell. For example, the cell below is $$c = \sqrt{a^2 + b^2}$$, but the Notebook renders the expression.

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$$c = \sqrt{a^2 + b^2}$$

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Or, you can display output from Python, as seen here.

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from IPython.display import display, Math, Latex
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Exporting & Publishing Notebooks

We can export the Notebook as an HTML, PDF, .py, .ipynb, Markdown, and reST file. You can also turn your NB into a slideshow. You can publish Jupyter Notebooks on Plotly. Simply visit plot.ly and select the + Create button in the upper right hand corner. Select Notebook and upload your Jupyter notebook (.ipynb) file! -The notebooks that you upload will be stored in your Plotly organize folder and hosted at a unique link to make sharing quick and easy. -See some example notebooks:

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Publishing Dashboards

Users publishing interactive graphs can also use Plotly's dashboarding tool to arrange plots with a drag and drop interface. These dashboards can be published, embedded, and shared.

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Publishing Dash Apps

For users looking to ship and productionize Python apps, dash is an assemblage of Flask, Socketio, Jinja, Plotly and boiler plate CSS and JS for easily creating data visualization web-apps with your Python data analysis backend. -
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For more Jupyter tutorials, checkout Plotly's python documentation: all documentation is written in jupyter notebooks that you can download and run yourself or checkout these user submitted examples!

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IPython Notebook Gallery

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- - -{% endraw %} diff --git a/_posts/python-v3/chart-studio/ipython-notebooks/jupyter_tutorial.ipynb b/_posts/python-v3/chart-studio/ipython-notebooks/jupyter_tutorial.ipynb deleted file mode 100644 index 0a4f40db1..000000000 --- a/_posts/python-v3/chart-studio/ipython-notebooks/jupyter_tutorial.ipynb +++ /dev/null @@ -1,830 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Introduction \n", - "[Jupyter](http://jupyter.org/) has a beautiful notebook that lets you write and execute code, analyze data, embed content, and share reproducible work. Jupyter Notebook (previously referred to as IPython Notebook) allows you to easily share your code, data, plots, and explanation in a sinle notebook. Publishing is flexible: PDF, HTML, ipynb, dashboards, slides, and more. Code cells are based on an input and output format. For example:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "hello world\n" - ] - } - ], - "source": [ - "print \"hello world\" " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Installation \n", - "There are a few ways to use a Jupyter Notebook:\n", - "\n", - "* Install with [```pip```](https://pypi.python.org/pypi/pip). Open a terminal and type: ```$ pip install jupyter```.\n", - "* Windows users can install with [```setuptools```](http://ipython.org/ipython-doc/2/install/install.html#windows). \n", - "* [Anaconda](https://store.continuum.io/cshop/anaconda/) and [Enthought](https://store.enthought.com/downloads/#default) allow you to download a desktop version of Jupyter Notebook.\n", - "* [nteract](https://nteract.io/) allows users to work in a notebook enviornment via a desktop application. \n", - "* [Microsoft Azure](https://notebooks.azure.com/) provides hosted access to Jupyter Notebooks. \n", - "* [Domino Data Lab](http://support.dominodatalab.com/hc/en-us/articles/204856585-Jupyter-Notebooks) offers web-based Notebooks.\n", - "* [tmpnb](https://github.com/jupyter/tmpnb) launches a temporary online Notebook for individual users. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Getting Started\n", - "Once you've installed the Notebook, you start from your terminal by calling ```$ jupyter notebook```. This will open a browser on a [localhost](https://en.wikipedia.org/wiki/Localhost) to the URL of your Notebooks, by default http://127.0.0.1:8888. Windows users need to open up their Command Prompt. You'll see a dashboard with all your Notebooks. You can launch your Notebooks from there. The Notebook has the advantage of looking the same when you're coding and publishing. You just have all the options to move code, run cells, change kernels, and [use Markdown](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet) when you're running a NB." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Helpful Commands\n", - "**- Tab Completion:** Jupyter supports tab completion! You can type ```object_name.``` to view an object’s attributes. For tips on cell magics, running Notebooks, and exploring objects, check out the [Jupyter docs](https://ipython.org/ipython-doc/dev/interactive/tutorial.html#introducing-ipython).\n", - "
**- Help:** provides an introduction and overview of features." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Type help() for interactive help, or help(object) for help about object." - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "help" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**- Quick Reference:** open quick reference by running:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "quickref" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**- Keyboard Shortcuts:** ```Shift-Enter``` will run a cell, ```Ctrl-Enter``` will run a cell in-place, ```Alt-Enter``` will run a cell and insert another below. See more shortcuts [here](https://ipython.org/ipython-doc/1/interactive/notebook.html#keyboard-shortcuts)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Languages \n", - "The bulk of this tutorial discusses executing python code in Jupyter notebooks. You can also use Jupyter notebooks to execute R code. Skip down to the [R section] for more information on using IRkernel with Jupyter notebooks and graphing examples.\n", - "#### Package Management\n", - "When installing packages in Jupyter, you either need to install the package in your actual shell, or run the ```!``` prefix, e.g.:\n", - "\n", - " !pip install packagename\n", - " \n", - "You may want to [reload submodules](http://stackoverflow.com/questions/5364050/reloading-submodules-in-ipython) if you've edited the code in one. IPython comes with automatic reloading magic. You can reload all changed modules before executing a new line. \n", - "\n", - " %load_ext autoreload\n", - " %autoreload 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Some useful packages that we'll use in this tutorial include:\n", - "* [Pandas](https://plotly.com/pandas/): import data via a url and create a dataframe to easily handle data for analysis and graphing. See examples of using Pandas here: https://plotly.com/pandas/.\n", - "* [NumPy](https://plotly.com/numpy/): a package for scientific computing with tools for algebra, random number generation, integrating with databases, and managing data. See examples of using NumPy here: https://plotly.com/numpy/.\n", - "* [SciPy](http://www.scipy.org/): a Python-based ecosystem of packages for math, science, and engineering.\n", - "* [Plotly](https://plotly.com/python/getting-started): a graphing library for making interactive, publication-quality graphs. See examples of statistic, scientific, 3D charts, and more here: https://plotly.com/python." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import scipy as sp\n", - "import plotly.plotly as py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Import Data\n", - "You can use pandas `read_csv()` function to import data. In the example below, we import a csv [hosted on github](https://github.com/plotly/datasets/) and display it in a [table using Plotly](https://plotly.com/python/table/): " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import plotly.plotly as py\n", - "import plotly.figure_factory as ff\n", - "import pandas as pd\n", - "\n", - "df = pd.read_csv(\"https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv\")\n", - "\n", - "table = ff.create_table(df)\n", - "py.iplot(table, filename='jupyter-table1')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use `dataframe.column_title` to index the dataframe:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'MIT'" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "schools = df.School\n", - "schools[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Most pandas functions also work on an entire dataframe. For example, calling ```std()``` calculates the standard deviation for each column." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Women 12.813683\n", - "Men 25.705289\n", - "Gap 14.137084\n", - "dtype: float64" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.std()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plotting Inline\n", - "You can use [Plotly's python API](https://plotly.com/python) to plot inside your Jupyter Notebook by calling ```plotly.plotly.iplot()``` or ```plotly.offline.iplot()``` if working offline. Plotting in the notebook gives you the advantage of keeping your data analysis and plots in one place. Now we can do a bit of interactive plotting. Head to the [Plotly getting started](https://plotly.com/python/) page to learn how to set your credentials. Calling the plot with ```iplot``` automaticallly generates an interactive version of the plot inside the Notebook in an iframe. See below:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import plotly.plotly as py\n", - "import plotly.graph_objs as go\n", - "\n", - "data = [go.Bar(x=df.School,\n", - " y=df.Gap)]\n", - "\n", - "py.iplot(data, filename='jupyter-basic_bar')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotting multiple traces and styling the chart with custom colors and titles is simple with Plotly syntax. Additionally, you can control the privacy with [```sharing```](https://plotly.com/python/privacy/) set to ```public```, ```private```, or ```secret```." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import plotly.plotly as py\n", - "import plotly.graph_objs as go\n", - "\n", - "trace_women = go.Bar(x=df.School,\n", - " y=df.Women,\n", - " name='Women',\n", - " marker=dict(color='#ffcdd2'))\n", - "\n", - "trace_men = go.Bar(x=df.School,\n", - " y=df.Men,\n", - " name='Men',\n", - " marker=dict(color='#A2D5F2'))\n", - "\n", - "trace_gap = go.Bar(x=df.School,\n", - " y=df.Gap,\n", - " name='Gap',\n", - " marker=dict(color='#59606D'))\n", - "\n", - "data = [trace_women, trace_men, trace_gap]\n", - "\n", - "layout = go.Layout(title=\"Average Earnings for Graduates\",\n", - " xaxis=dict(title='School'),\n", - " yaxis=dict(title='Salary (in thousands)'))\n", - "\n", - "fig = go.Figure(data=data, layout=layout)\n", - "\n", - "py.iplot(fig, sharing='private', filename='jupyter-styled_bar')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we have interactive charts displayed in our notebook. Hover on the chart to see the values for each bar, click and drag to zoom into a specific section or click on the legend to hide/show a trace. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plotting Interactive Maps\n", - "Plotly is now integrated with [Mapbox](https://www.mapbox.com/). In this example we'll plot lattitude and longitude data of nuclear waste sites. To plot on Mapbox maps with Plotly you'll need a Mapbox account and a [Mapbox Access Token](https://www.mapbox.com/studio/signin/) which you can add to your [Plotly settings]()." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import plotly.plotly as py\n", - "import plotly.graph_objs as go\n", - "\n", - "import pandas as pd\n", - "\n", - "# mapbox_access_token = 'ADD YOUR TOKEN HERE'\n", - "\n", - "df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/Nuclear%20Waste%20Sites%20on%20American%20Campuses.csv')\n", - "site_lat = df.lat\n", - "site_lon = df.lon\n", - "locations_name = df.text\n", - "\n", - "data = [\n", - " go.Scattermapbox(\n", - " lat=site_lat,\n", - " lon=site_lon,\n", - " mode='markers',\n", - " marker=dict(\n", - " size=17,\n", - " color='rgb(255, 0, 0)',\n", - " opacity=0.7\n", - " ),\n", - " text=locations_name,\n", - " hoverinfo='text'\n", - " ),\n", - " go.Scattermapbox(\n", - " lat=site_lat,\n", - " lon=site_lon,\n", - " mode='markers',\n", - " marker=dict(\n", - " size=8,\n", - " color='rgb(242, 177, 172)',\n", - " opacity=0.7\n", - " ),\n", - " hoverinfo='none'\n", - " )]\n", - "\n", - " \n", - "layout = go.Layout(\n", - " title='Nuclear Waste Sites on Campus',\n", - " autosize=True,\n", - " hovermode='closest',\n", - " showlegend=False,\n", - " mapbox=dict(\n", - " accesstoken=mapbox_access_token,\n", - " bearing=0,\n", - " center=dict(\n", - " lat=38,\n", - " lon=-94\n", - " ),\n", - " pitch=0,\n", - " zoom=3,\n", - " style='light'\n", - " ),\n", - ")\n", - "\n", - "fig = dict(data=data, layout=layout)\n", - "\n", - "py.iplot(fig, filename='jupyter-Nuclear Waste Sites on American Campuses')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 3D Plotting\n", - "Using Numpy and Plotly, we can make interactive [3D plots](https://plotly.com/python/#3d) in the Notebook as well." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import plotly.plotly as py\n", - "import plotly.graph_objs as go\n", - "\n", - "import numpy as np\n", - "\n", - "s = np.linspace(0, 2 * np.pi, 240)\n", - "t = np.linspace(0, np.pi, 240)\n", - "tGrid, sGrid = np.meshgrid(s, t)\n", - "\n", - "r = 2 + np.sin(7 * sGrid + 5 * tGrid) # r = 2 + sin(7s+5t)\n", - "x = r * np.cos(sGrid) * np.sin(tGrid) # x = r*cos(s)*sin(t)\n", - "y = r * np.sin(sGrid) * np.sin(tGrid) # y = r*sin(s)*sin(t)\n", - "z = r * np.cos(tGrid) # z = r*cos(t)\n", - "\n", - "surface = go.Surface(x=x, y=y, z=z)\n", - "data = [surface]\n", - "\n", - "layout = go.Layout(\n", - " title='Parametric Plot',\n", - " scene=dict(\n", - " xaxis=dict(\n", - " gridcolor='rgb(255, 255, 255)',\n", - " zerolinecolor='rgb(255, 255, 255)',\n", - " showbackground=True,\n", - " backgroundcolor='rgb(230, 230,230)'\n", - " ),\n", - " yaxis=dict(\n", - " gridcolor='rgb(255, 255, 255)',\n", - " zerolinecolor='rgb(255, 255, 255)',\n", - " showbackground=True,\n", - " backgroundcolor='rgb(230, 230,230)'\n", - " ),\n", - " zaxis=dict(\n", - " gridcolor='rgb(255, 255, 255)',\n", - " zerolinecolor='rgb(255, 255, 255)',\n", - " showbackground=True,\n", - " backgroundcolor='rgb(230, 230,230)'\n", - " )\n", - " )\n", - ")\n", - "\n", - "fig = go.Figure(data=data, layout=layout)\n", - "py.iplot(fig, filename='jupyter-parametric_plot')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Animated Plots\n", - "Checkout Plotly's [animation documentation](https://plotly.com/python/#animations) to see how to create animated plots inline in Jupyter notebooks like the Gapminder plot displayed below:\n", - "![https://plotly.com/~PythonPlotBot/231/](https://raw.githubusercontent.com/cldougl/plot_images/add_r_img/anim.gif)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plot Controls & IPython widgets\n", - "Add sliders, buttons, and dropdowns to your inline chart: " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import plotly.plotly as py\n", - "import numpy as np\n", - "\n", - "data = [dict(\n", - " visible = False,\n", - " line=dict(color='00CED1', width=6),\n", - " name = '𝜈 = '+str(step),\n", - " x = np.arange(0,10,0.01),\n", - " y = np.sin(step*np.arange(0,10,0.01))) for step in np.arange(0,5,0.1)]\n", - "data[10]['visible'] = True\n", - "\n", - "steps = []\n", - "for i in range(len(data)):\n", - " step = dict(\n", - " method = 'restyle',\n", - " args = ['visible', [False] * len(data)],\n", - " )\n", - " step['args'][1][i] = True # Toggle i'th trace to \"visible\"\n", - " steps.append(step)\n", - "\n", - "sliders = [dict(\n", - " active = 10,\n", - " currentvalue = {\"prefix\": \"Frequency: \"},\n", - " pad = {\"t\": 50},\n", - " steps = steps\n", - ")]\n", - "\n", - "layout = dict(sliders=sliders)\n", - "fig = dict(data=data, layout=layout)\n", - "\n", - "py.iplot(fig, filename='Sine Wave Slider')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Additionally, [IPython widgets](http://moderndata.plot.ly/widgets-in-ipython-notebook-and-plotly/) allow you to add sliders, widgets, search boxes, and more to your Notebook. See the [widget docs](https://ipython.org/ipython-doc/3/api/generated/IPython.html.widgets.interaction.html) for more information. For others to be able to access your work, they'll need IPython. Or, you can use a cloud-based NB option so others can run your work.\n", - "
\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Executing R Code\n", - "IRkernel, an R kernel for Jupyter, allows you to write and execute R code in a Jupyter notebook. Checkout the [IRkernel documentation](https://irkernel.github.io/installation/) for some simple installation instructions. Once IRkernel is installed, open a Jupyter Notebook by calling `$ jupyter notebook` and use the New dropdown to select an R notebook.\n", - "\n", - "![](https://raw.githubusercontent.com/cldougl/plot_images/add_r_img/rkernel.png)\n", - "\n", - "See a full R example Jupyter Notebook here: https://plotly.com/~chelsea_lyn/14069 " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Additional Embed Features\n", - "We've seen how to embed Plotly tables and charts as iframes in the notebook, with `IPython.display` we can embed additional features, such a videos. For example, from YouTube:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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- "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import YouTubeVideo\n", - "YouTubeVideo(\"wupToqz1e2g\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### LaTeX\n", - "We can embed LaTeX inside a Notebook by putting a ```$$``` around our math, then run the cell as a Markdown cell. For example, the cell below is ```$$c = \\sqrt{a^2 + b^2}$$```, but the Notebook renders the expression." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$c = \\sqrt{a^2 + b^2}$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Or, you can display output from Python, as seen [here](http://stackoverflow.com/questions/13208286/how-to-write-latex-in-ipython-notebook)." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$$F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx$$" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import display, Math, Latex\n", - "\n", - "display(Math(r'F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx'))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "#### Exporting & Publishing Notebooks\n", - "We can export the Notebook as an HTML, PDF, .py, .ipynb, Markdown, and reST file. You can also turn your NB [into a slideshow](http://ipython.org/ipython-doc/2/notebook/nbconvert.html). You can publish Jupyter Notebooks on Plotly. Simply visit [plot.ly](https://plotly.com/organize/home?create=notebook) and select the `+ Create` button in the upper right hand corner. Select Notebook and upload your Jupyter notebook (.ipynb) file!\n", - "The notebooks that you upload will be stored in your [Plotly organize folder](https://plotly.com/organize) and hosted at a unique link to make sharing quick and easy.\n", - "See some example notebooks:\n", - "- https://plotly.com/~chelsea_lyn/14066\n", - "- https://plotly.com/~notebook_demo/35\n", - "- https://plotly.com/~notebook_demo/85\n", - "- https://plotly.com/~notebook_demo/128" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Publishing Dashboards\n", - "Users publishing interactive graphs can also use [Plotly's dashboarding tool](https://plotly.com/dashboard/create) to arrange plots with a drag and drop interface. These dashboards can be published, embedded, and shared. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Publishing Dash Apps\n", - "For users looking to ship and productionize Python apps, [dash](https://github.com/plotly/dash) is an assemblage of Flask, Socketio, Jinja, Plotly and boiler plate CSS and JS for easily creating data visualization web-apps with your Python data analysis backend.\n", - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Jupyter Gallery\n", - "For more Jupyter tutorials, checkout [Plotly's python documentation](https://plotly.com/python/): all documentation is written in jupyter notebooks that you can download and run yourself or checkout these [user submitted examples](https://plotly.com/ipython-notebooks/)! \n", - "\n", - "[![IPython Notebook Gallery](http://i.imgur.com/AdElJQx.png)](https://plotly.com/ipython-notebooks/)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting git+https://github.com/plotly/publisher.git\n", - " Cloning https://github.com/plotly/publisher.git to c:\\users\\brand\\appdata\\local\\temp\\pip-req-build-f94f3d84\n", - "Installing collected packages: publisher\n", - " Found existing installation: publisher 0.11\n", - " Uninstalling publisher-0.11:\n", - " Successfully uninstalled publisher-0.11\n", - " Running setup.py install for publisher: started\n", - " Running setup.py install for publisher: finished with status 'done'\n", - "Successfully installed publisher-0.11\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Python27\\lib\\site-packages\\IPython\\nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated since IPython 4.0. You should import from nbconvert instead.\n", - " \"You should import from nbconvert instead.\", ShimWarning)\n", - "C:\\Python27\\lib\\site-packages\\publisher\\publisher.py:53: UserWarning: Did you \"Save\" this notebook before running this command? Remember to save, always save.\n", - " warnings.warn('Did you \"Save\" this notebook before running this command? '\n" - ] - } - ], - "source": [ - "from IPython.display import display, HTML\n", - "\n", - "display(HTML(''))\n", - "display(HTML(''))\n", - "\n", - "!pip install git+https://github.com/plotly/publisher.git --upgrade\n", - "import publisher\n", - "publisher.publish(\n", - " 'jupyter_tutorial.ipynb', 'python/ipython-notebook-tutorial/', 'Jupyter Notebook Tutorial',\n", - " 'Jupyter notebook tutorial on how to install, run, and use Jupyter for interactive matplotlib plotting, data analysis, and publishing code',\n", - " title = 'Jupyter Notebook Tutorial | plotly',\n", - " name = 'Jupyter Notebook Tutorial',\n", - " thumbnail='thumbnail/ipythonnb.jpg', language='python',\n", - " page_type='example_index', has_thumbnail='true', display_as='chart_studio', order=11,\n", - " ipynb='~chelsea_lyn/14070') " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.14" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/_posts/python-v3/chart-studio/offline/2015-06-30-plotly_offline.html b/_posts/python-v3/chart-studio/offline/2015-06-30-plotly_offline.html deleted file mode 100644 index 7dcea1e2c..000000000 --- a/_posts/python-v3/chart-studio/offline/2015-06-30-plotly_offline.html +++ /dev/null @@ -1,5 +0,0 @@ ---- -permalink: python/v3/offline/ -redirect_to: /python/getting-started/ -sitemap: false ---- diff --git a/_posts/python-v3/chart-studio/offline/plotly_offline.ipynb b/_posts/python-v3/chart-studio/offline/plotly_offline.ipynb deleted file mode 100644 index 06b71b8ba..000000000 --- a/_posts/python-v3/chart-studio/offline/plotly_offline.ipynb +++ /dev/null @@ -1,17952 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### New to Plotly?\n", - "Plotly's Python library is free and open source! [Get started](https://plotly.com/python/getting-started/) by downloading the client and [reading the primer](https://plotly.com/python/getting-started/).\n", - "
You can set up Plotly to work in [online](https://plotly.com/python/getting-started/#initialization-for-online-plotting) or [offline](https://plotly.com/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plotly.com/python/getting-started/#start-plotting-online).\n", - "
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Version Check\n", - "Plotly's python package is updated frequently. Run `pip install plotly --upgrade` to use the latest version." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'3.6.1'" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import plotly\n", - "plotly.__version__" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plotly Offline from Command Line\n", - "You can plot your graphs from a python script from command line. On executing the script, it will open a web browser with your Plotly Graph drawn." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'file:///Users/Chelsea/Repos/documentation/_posts/python/offline/temp-plot.html'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import plotly.graph_objs as go\n", - "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n", - "\n", - "plot([go.Scatter(x=[1, 2, 3], y=[3, 1, 6])])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Generating Offline Graphs within Jupyter Notebook\n", - "You can also plot your graphs offline inside a Jupyter Notebook Environment. First you need to initiate the Plotly Notebook mode as below:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/vnd.plotly.v1+html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "init_notebook_mode(connected=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Run at the start of every ipython notebook to use plotly.offline. This injects the plotly.js source files into the notebook." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "linkText": "Export to plot.ly", - "plotlyServerURL": "https://plotly.com", - "showLink": false - }, - "data": [ - { - "type": "scatter", - "uid": "61d3007d-ba34-409d-b7a2-6d9b58ad3f0d", - "x": [ - 1, - 2, - 3 - ], - "y": [ - 3, - 1, - 6 - ] - } - ], - "layout": {} - }, - "text/html": [ - "
" - ], - "text/vnd.plotly.v1+html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "iplot([{\"x\": [1, 2, 3], \"y\": [3, 1, 6]}])" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "linkText": "Export to plot.ly", - "plotlyServerURL": "https://plotly.com", - "showLink": false - }, - "data": [ - { - "contours": { - "coloring": "heatmap" - }, - "type": "histogram2dcontour", - "uid": "3354fe72-7d85-458b-87a9-dbed50a998ae", - "x": [ - 1.1958560930174873, - 0.9931700190798765, - -0.8517531635903051, - -0.09275902345703635, - 0.2910447280914965, - 2.4020700675562034, - -0.6791748918298753, - 1.0054793904358998, - 0.1614104971097231, - 0.6108349224596314, - 0.9983417951521442, - 2.049798974935194, - -0.25863450683144756, - 1.4885177577391138, - -0.226945717464598, - 1.2749892344885172, - 1.2730515375621663, - -0.1432718322995126, - -0.02385383951490074, - -0.5366507112986306, - 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" - ], - "text/vnd.plotly.v1+html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import cufflinks as cf\n", - "\n", - "iplot(cf.datagen.lines().iplot(asFigure=True,\n", - " kind='scatter',xTitle='Dates',yTitle='Returns',title='Returns'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Use with the Cloud\n", - "All methods in plotly.plotly will communicate with a Chart Studio Cloud or Chart Studio Enterprise.
\n", - "`get_figure` downloads a figure from plot.ly or Chart Studio Enterprise.
\n", - "You need to provide credentials to download figures: https://plotly.com/python/getting-started/" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "linkText": "Export to plot.ly", - "plotlyServerURL": "https://plotly.com", - "showLink": false - }, - "data": [ - { - "geo": "geo", - "lat": [ - 36.342234999999995 - ], - "lon": [ - -94.07141 - ], - "marker": { - "color": "rgb(0, 0, 255)", - "opacity": 0.5 - }, - "name": "1962", - "showlegend": false, - "type": "scattergeo", - "uid": "02603eab-eb08-4467-b080-51754b1a3b5c" - }, - { - "geo": "geo", - "lat": [ - 47 - ], - "lon": [ - -78 - ], - "mode": "text", - "showlegend": false, - "text": [ - "1962" - ], - "type": "scattergeo", - "uid": "b40ab91a-cb02-4eaa-9b25-167aeb127d24" - }, - { - "geo": "geo2", - "lat": [ - 36.236984 - ], - "lon": [ - -93.09345 - ], - "marker": { - "color": "rgb(0, 0, 255)", - "opacity": 0.5 - }, - 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Source: University of Minnesota" - }, - "width": 1000 - } - }, - "text/html": [ - "
" - ], - "text/vnd.plotly.v1+html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import plotly.plotly as py \n", - "\n", - "fig = py.get_figure('https://plotly.com/~jackp/8715', raw=True)\n", - "iplot(fig)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Static Image Export" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `plotly.io.to_image` function can then be used to convert a plotly figure to a static image bytes string." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "import plotly.io as pio\n", - "\n", - "static_image_bytes = pio.to_image(fig, format='png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use `IPython.display.Image` to display the image bytes as image in the notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "\n", - "Image(static_image_bytes)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use `plotly.io.write_image` to convert a figure to a static image and write it to a file or writeable object.\n", - "\n", - "Make sure to add a file extension or specify the file type using the format parameter." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "pio.write_image(fig, file='plotly_static_image.png', format='png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reference\n", - "For more information, run `help(plotly.offline.iplot)` or `help(plotly.offline.plot)` or `help(plotly.io.to_image)` or `help(plotly.io.write_image)`" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting git+https://github.com/plotly/publisher.git\n", - " Cloning https://github.com/plotly/publisher.git to c:\\users\\priyat~1\\appdata\\local\\temp\\pip-req-build-r4149umc\n", - "Building wheels for collected packages: publisher\n", - " Running setup.py bdist_wheel for publisher: started\n", - " Running setup.py bdist_wheel for publisher: finished with status 'done'\n", - " Stored in directory: C:\\Users\\PRIYAT~1\\AppData\\Local\\Temp\\pip-ephem-wheel-cache-9ur3vl6p\\wheels\\99\\3e\\a0\\fbd22ba24cca72bdbaba53dbc23c1768755fb17b3af0f33966\n", - "Successfully built publisher\n", - "Installing collected packages: publisher\n", - " Found existing installation: publisher 0.13\n", - " Uninstalling publisher-0.13:\n", - " Successfully uninstalled publisher-0.13\n", - "Successfully installed publisher-0.13\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Anaconda\\Anaconda3\\lib\\site-packages\\IPython\\nbconvert.py:13: ShimWarning:\n", - "\n", - "The `IPython.nbconvert` package has been deprecated since IPython 4.0. You should import from nbconvert instead.\n", - "\n", - "C:\\Anaconda\\Anaconda3\\lib\\site-packages\\publisher\\publisher.py:53: UserWarning:\n", - "\n", - "Did you \"Save\" this notebook before running this command? Remember to save, always save.\n", - "\n" - ] - } - ], - "source": [ - "from IPython.display import display, HTML\n", - "\n", - "display(HTML(''))\n", - "display(HTML(''))\n", - "\n", - "! pip install git+https://github.com/plotly/publisher.git --upgrade\n", - " \n", - "import publisher\n", - "publisher.publish(\n", - " 'plotly_offline.ipynb', 'python/offline/', 'Plotly Offline for IPython Notebooks',\n", - " 'How to use Plotly offline inside IPython notebooks',\n", - " title= 'Plotly Offline for IPython Notebooks',\n", - " name = 'Offline Plots in Plotly',\n", - " has_thumbnail='true',thumbnail='thumbnail/offline.png' \n", - " language='python', page_type='example_index', layout='user-guide', display_as='chart_studio'\n", - " ipynb= '~notebook_demo/267',\n", - " uses_plotly_offline=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - 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\ No newline at end of file diff --git a/_posts/python-v3/chart-studio/presentations/2015-06-30-presentations-api.html b/_posts/python-v3/chart-studio/presentations/2015-06-30-presentations-api.html deleted file mode 100644 index b960f2a42..000000000 --- a/_posts/python-v3/chart-studio/presentations/2015-06-30-presentations-api.html +++ /dev/null @@ -1,1142 +0,0 @@ ---- -permalink: python/v3/presentations-tool/ -description: How to create and publish a spectacle-presentation with the Python API. -name: Presentations Tool | plotly -thumbnail: thumbnail/pres_api.jpg -layout: base -name: Presentations Tool -language: python/v3 -display_as: chart_studio -page_type: u-guide -order: 0.6 ---- -{% raw %} -
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New to Plotly?¶

Plotly's Python library is free and open source! Get started by downloading the client and reading the primer. -
You can set up Plotly to work in online or offline mode, or in jupyter notebooks. -
We also have a quick-reference cheatsheet (new!) to help you get started!

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Version Check¶

Note: The presentations API is available in version 2.2.1.+
-Run pip install plotly --upgrade to update your Plotly version.

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-plotly.__version__
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Plotly Presentations¶

To use Plotly's Presentations API you will write your presentation code in a string of markdown and then pass that through the Presentations API function pres.Presentation(). This creates a JSON version of your presentation. To upload the presentation online pass it through py.presentation_ops.upload().

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In your string, use --- on a single line to seperate two slides. To put a title in your slide, put a line that starts with any number of #s. Only your first title will be appear in your slide. A title looks like:

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# slide title

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Anything that comes after the title will be put as text in your slide. Check out the example below to see this in action.

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Current Limitations¶

Boldface, italics and hypertext are not supported features of the Presentation API.

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Display in Jupyter¶

The function below generates HTML code to display the presentation in an iframe directly in Jupyter.

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In [3]:
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def url_to_iframe(url, text=True):
-    html = ''
-    # style
-    html += '''<head>
-    <style>
-    div.textbox {
-        margin: 30px;
-        font-weight: bold;   
-    }
-    </style>
-    </head>'
-    '''
-    # iframe
-    html += '<iframe src=' + url + '.embed#{} width=750 height=400 frameBorder="0"></iframe>'
-    if text:
-        html += '''<body>
-        <div class="textbox">
-            <p>Click on the presentation above and use left/right arrow keys to flip through the slides.</p>
-        </div>
-        </body>
-        '''
-    return html
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Simple Example¶

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import plotly.plotly as py
-import plotly.presentation_objs as pres
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-filename = 'simple-pres'
-markdown_string = """
-# slide 1
-There is only one slide.
-
----
-# slide 2
-Again, another slide on this page.
-
-"""
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-my_pres = pres.Presentation(markdown_string)
-pres_url_0 = py.presentation_ops.upload(my_pres, filename)
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https://plotly.com/~AdamKulidjian/3700/simple-pres/

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In [3]:
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import IPython
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-iframe_0 = url_to_iframe(pres_url_0, True)
-IPython.display.HTML(iframe_0)
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Click on the presentation above and use left/right arrow keys to flip through the slides.

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Insert Plotly Chart¶

If you want to insert a Plotly chart into your presentation, all you need to do is write a line in your presentation that takes the form:

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Plotly(url)

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where url is a Plotly url. For example:

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Plotly(https://plotly.com/~AdamKulidjian/3564)

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The Plotly url lines should be written on a separate line after your title line. You can put as many images in your slide as you want, as the API will arrange them on the slide automatically, but it is highly encouraged that you use 4 OR FEWER IMAGES PER SLIDE. This will produce the cleanest look.

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Useful Tip:
-For Plotly charts it is HIGHLY ADVISED that you use a chart that has layout['autosize'] set to True. If it is False the image may be cropped or only partially visible when it appears in the presentation slide.

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In [4]:
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import plotly.plotly as py
-import plotly.presentation_objs as pres
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-filename = 'pres-with-plotly-chart'
-markdown_string = """
-# 3D scatterplots
-3D Scatterplot are just a collection of balls in a 3D cartesian space each of which have assigned properties like color, size, and more.
-
----
-# simple 3d scatterplot
-
-Plotly(https://plotly.com/~AdamKulidjian/3698)
----
-# different colorscales
-
-There are various colorscales and colorschemes to try in Plotly. Check out plotly.colors to find a list of valid and available colorscales.
-
-Plotly(https://plotly.com/~AdamKulidjian/3582)
-Plotly(https://plotly.com/~AdamKulidjian/3698)
-"""
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-my_pres = pres.Presentation(markdown_string)
-pres_url_1 = py.presentation_ops.upload(my_pres, filename)
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https://plotly.com/~AdamKulidjian/3710/pres-with-plotly-chart/

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import IPython
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-iframe_1 = url_to_iframe(pres_url_1, True)
-IPython.display.HTML(iframe_1)
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Click on the presentation above and use left/right arrow keys to flip through the slides.

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Insert Web Images¶

To insert an image from the web, insert the a Image(url) where url is the image url.

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import plotly.plotly as py
-import plotly.presentation_objs as pres
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-filename = 'pres-with-images'
-markdown_string = """
-# Animals of the Wild
----
-# The Lion
-
-Panthera leo is one of the big cats in the Felidae family and a member of genus Panthera. It has been listed as Vulnerable on the IUCN Red List since 1996, as populations in African range countries declined by about 43% since the early 1990s. Lion populations are untenable outside designated protected areas. Although the cause of the decline is not fully understood, habitat loss and conflicts with humans are the greatest causes of concern. The West African lion population is listed as Critically Endangered since 2016. The only lion population in Asia survives in and around India's Gir Forest National Park and is listed as Endangered since 1986.
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-Image(https://i.pinimg.com/736x/da/af/73/daaf73960eb5a21d6bca748195f12052--lion-photography-lion-kings.jpg)
----
-# The Giraffe
-
-The giraffe is a genus of African even-toed ungulate mammals, the tallest living terrestrial animals and the largest ruminants. The genus currently consists of one species, Giraffa camelopardalis, the type species. Seven other species are extinct, prehistoric species known from fossils. Taxonomic classifications of one to eight extant giraffe species have been described, based upon research into the mitochondrial and nuclear DNA, as well as morphological measurements of Giraffa, but the IUCN currently recognizes only one species with nine subspecies.
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-Image(https://img.purch.com/w/192/aHR0cDovL3d3dy5saXZlc2NpZW5jZS5jb20vaW1hZ2VzL2kvMDAwLzA2OC8wOTQvaTMwMC9naXJhZmZlLmpwZz8xNDA1MDA4NDQy)
-Image(https://upload.wikimedia.org/wikipedia/commons/9/9f/Giraffe_standing.jpg)
-
-"""
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-my_pres = pres.Presentation(markdown_string)
-pres_url_2 = py.presentation_ops.upload(my_pres, filename)
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https://plotly.com/~AdamKulidjian/3702/pres-with-images/

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-iframe_2 = url_to_iframe(pres_url_2, True)
-IPython.display.HTML(iframe_2)
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Click on the presentation above and use left/right arrow keys to flip through the slides.

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Image Stretch¶

If you want to ensure that your image maintains its original width:height ratio, include the parameter imgStretch=False in your pres.Presentation() function call.

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import plotly.plotly as py
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-filename = 'pres-with-no-imgstretch'
-markdown_string = """
-# images in native aspect ratio
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-Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png)
-Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png)
-Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png)
-Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png)
-Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png)
-"""
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-my_pres = pres.Presentation(markdown_string, imgStretch=False)
-pres_url_3 = py.presentation_ops.upload(my_pres, filename)
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https://plotly.com/~AdamKulidjian/3703/pres-with-no-imgstretch/

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Insert Code Blocks¶

The Presentations API also supports the insertion of blocks of code with various available langauges to choose from.

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To instantiate a "code environment" in your string, place a ``` at the beginning of a line, followed by the name of the programming language you want your code block to be styled in. Then the next lines will be considered "code lines ". To close the "code environment" put another ``` at the end of the line For example:

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```python
-# code goes here
-```
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The valid languages to choose from are: arecpp, cs, css, fsharp, go, haskell, java, javascript, jsx, julia, xml, matlab, php, python, r, ruby, scala, sql and yaml.

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import plotly.plotly as py
-import plotly.presentation_objs as pres
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-filename = 'pres-with-code'
-markdown_string = """
-# Getting Started Using Code
-A beginner's introduction to computer science.
-
----
-# Python Functions
-Functions are one of the most useful tools in Python. Intuitively, you select an input and get an output.
-
-In order to set up a function use the key word "def" then the name of the function with open parentheses afterwards. Inside the parentheses, write variable names your function will use. These variables can then go into the body of your function and when you give a value to the variable in the call signature, it will pass through the guts of the function until it returns a value.
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-```python
-def somePrintFunction():
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-    print("boo")
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-somePrintFunction()
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->>>print(new_z)
-10
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-def someAddFunction(a, b):
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-    print(a+b)
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->>>someAddFunction(12,451)
-463
-```
----
-# Use scala
-You can write functions in other languages as well. For example, check out this scala code and notice how the print functions look different:
-
-We write 'println()' as opposed to 'print()' as we do in Python.
-
-```scala
-/** Basic command line parsing. */
-object Main {
-  var verbose = false
-
-  def main(args: Array[String]) {
-    for (a <- args) a match {
-      case "-h" | "-help"    =>
-        println("Usage: scala Main [-help|-verbose]")
-      case "-v" | "-verbose" =>
-        verbose = true
-      case x =>
-        println("Unknown option: '" + x + "'")
-    }
-    if (verbose)
-      println("How are you today?")
-  }
-}
-```
----
-# Under the Hood
-
-There are many things to find when you look under the Plotly Hood. Of many things, one expected thing is the compliance and adherance to alphebetized and PEP-8'ed imports at the top of any module.
-
-This is what the PEP-8 guide has more to say about Imports:
-
-Wildcard imports (from <module> import *) should be avoided, as they make it unclear which names are present in the namespace, confusing both readers and many automated tools. There is one defensible use case for a wildcard import, which is to republish an internal interface as part of a public API (for example, overwriting a pure Python implementation of an interface with the definitions from an optional accelerator module and exactly which definitions will be overwritten isn't known in advance).
-
-Image(https://help.plot.ly/images/dashboard-carousel.jpg)
-
-```python
-from __future__ import absolute_import
-
-import copy
-import json
-import os
-import time
-import warnings
-import webbrowser
-
-import six
-import six.moves
-from requests.compat import json as _json
-
-from plotly import exceptions, files, session, tools, utils
-from plotly.api import v1, v2
-from plotly.plotly import chunked_requests
-from plotly.grid_objs import Grid, Column
-from plotly.dashboard_objs import dashboard_objs as dashboard
-```
-"""
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-my_pres = pres.Presentation(markdown_string)
-pres_url_4 = py.presentation_ops.upload(my_pres, filename)
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https://plotly.com/~AdamKulidjian/3704/pres-with-code/

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In [11]:
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-iframe_4 = url_to_iframe(pres_url_4, True)
-IPython.display.HTML(iframe_4)
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Style Your Presentation¶

The Presentations API currently has two styles to choose from: Martik and Moods. These themes are inspired by already existing PowerPoint Templates. Let's use the same markdown_string in the previous example but this time try the Martik style.

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In [12]:
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import plotly.plotly as py
-import plotly.presentation_objs as pres
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-filename = 'martik-style'
-markdown_string = """
-# Getting Started Using Code
-A beginner's introduction to computer science.
-
----
-# Python Functions
-Functions are one of the most useful tools in Python. Intuitively, you select an input and get an output.
-
-In order to set up a function use the key word "def" then the name of the function with open parentheses afterwards. Inside the parentheses, write variable names your function will use. These variables can then go into the body of your function and when you give a value to the variable in the call signature, it will pass through the guts of the function until it returns a value.
-
-```python
-def somePrintFunction():
-
-    print("boo")
-
-somePrintFunction()
-
-
-
-
->>>print(new_z)
-10
-
-def someAddFunction(a, b):
-
-    print(a+b)
-
-
-
-
->>>someAddFunction(12,451)
-463
-```
----
-# Use scala
-You can write functions in other languages as well. For example, check out this scala code and notice how the print functions look different:
-
-We write 'println()' as opposed to 'print()' as we do in Python.
-
-```scala
-/** Basic command line parsing. */
-object Main {
-  var verbose = false
-
-  def main(args: Array[String]) {
-    for (a <- args) a match {
-      case "-h" | "-help"    =>
-        println("Usage: scala Main [-help|-verbose]")
-      case "-v" | "-verbose" =>
-        verbose = true
-      case x =>
-        println("Unknown option: '" + x + "'")
-    }
-    if (verbose)
-      println("How are you today?")
-  }
-}
-```
----
-# Under the Hood
-
-There are many things to find when you look under the Plotly Hood. Of many things, one expected thing is the compliance and adherance to alphebetized and PEP-8'ed imports at the top of any module.
-
-This is what the PEP-8 guide has more to say about Imports:
-
-Wildcard imports (from <module> import *) should be avoided, as they make it unclear which names are present in the namespace, confusing both readers and many automated tools. There is one defensible use case for a wildcard import, which is to republish an internal interface as part of a public API (for example, overwriting a pure Python implementation of an interface with the definitions from an optional accelerator module and exactly which definitions will be overwritten isn't known in advance).
-
-Image(https://help.plot.ly/images/dashboard-carousel.jpg)
-
-```python
-from __future__ import absolute_import
-
-import copy
-import json
-import os
-import time
-import warnings
-import webbrowser
-
-import six
-import six.moves
-from requests.compat import json as _json
-
-from plotly import exceptions, files, session, tools, utils
-from plotly.api import v1, v2
-from plotly.plotly import chunked_requests
-from plotly.grid_objs import Grid, Column
-from plotly.dashboard_objs import dashboard_objs as dashboard
-```
-"""
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-my_pres = pres.Presentation(markdown_string, style='martik')
-pres_url_5 = py.presentation_ops.upload(my_pres, 'martik-style')
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Transitions¶

You can specify how your want your slides to transition to one another. Just like in the Plotly Presentation Application, there are 4 types of transitions: slide, zoom, fade and spin.

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-filename = 'pres-with-transitions'
-markdown_string = """
-transition: slide
-# slide
----
-transition: zoom
-# zoom
----
-transition: fade
-# fade
----
-transition: spin
-# spin
----
-transition: spin and slide
-# spin, slide
----
-transition: fade zoom
-# fade, zoom
----
-transition: slide, zoom, fade, spin, spin, spin, zoom, fade
-# slide, zoom, fade, spin
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-"""
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Add Thin Border Around Text Boxes¶

Every slide has children, and each of these children have a style attribute. This style property is derived from the CSS element of the same name. Since you have the power of CSS to work with, you could customize text borders in your presentation if you want.

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-filename = 'pres-with-custom-css'
-markdown_string = """
-# custom css
----
-transition: zoom, slide, spin, fade
-# fun with css
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-Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
-```python
-x = 4
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-if x < 2:
-    x = 2 * x
-    if x >= 2:
-        break
-```
-
-"""
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-my_pres = pres.Presentation(markdown_string)
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-# change text border style
-my_pres['presentation']['slides'][1]['children'][0]['props']['style']['border'] = 'solid red'
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https://plotly.com/~AdamKulidjian/3709/pres-with-custom-css/

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Reference¶

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help(py.presentation_ops)
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Help on class presentation_ops in module plotly.plotly.plotly:
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-class presentation_ops
- |  Interface to Plotly's Spectacle-Presentations API.
- |
- |  Class methods defined here:
- |
- |  upload(cls, presentation, filename, sharing='public', auto_open=True) from __builtin__.classobj
- |      Function for uploading presentations to Plotly.
- |
- |      :param (dict) presentation: the JSON presentation to be uploaded. Use
- |          plotly.presentation_objs.Presentation to create presentations
- |          from a Markdown-like string.
- |      :param (str) filename: the name of the presentation to be saved in
- |          your Plotly account. Will overwrite a presentation of the same
- |          name if it already exists in your files.
- |      :param (str) sharing: can be set to either 'public', 'private'
- |          or 'secret'. If 'public', your presentation will be viewable by
- |          all other users. If 'private' only you can see your presentation.
- |          If it is set to 'secret', the url will be returned with a string
- |          of random characters appended to the url which is called a
- |          sharekey. The point of a sharekey is that it makes the url very
- |          hard to guess, but anyone with the url can view the presentation.
- |      :param (bool) auto_open: automatically opens the presentation in the
- |          browser.
- |
- |      See the documentation online for examples.
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- - -{% endraw %} diff --git a/_posts/python-v3/chart-studio/presentations/2018-03-06-presentations.html b/_posts/python-v3/chart-studio/presentations/2018-03-06-presentations.html deleted file mode 100644 index d6b1fe3db..000000000 --- a/_posts/python-v3/chart-studio/presentations/2018-03-06-presentations.html +++ /dev/null @@ -1,5 +0,0 @@ ---- -permalink: python/v3/presentations-api/ -redirect_to: python/presentations-tool/ -sitemap: false ---- diff --git a/_posts/python-v3/chart-studio/presentations/presentations-api.ipynb b/_posts/python-v3/chart-studio/presentations/presentations-api.ipynb deleted file mode 100644 index a21ae1e64..000000000 --- a/_posts/python-v3/chart-studio/presentations/presentations-api.ipynb +++ /dev/null @@ -1,1109 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### New to Plotly?\n", - "Plotly's Python library is free and open source! [Get started](https://plotly.com/python/getting-started/) by downloading the client and [reading the primer](https://plotly.com/python/getting-started/).\n", - "
You can set up Plotly to work in [online](https://plotly.com/python/getting-started/#initialization-for-online-plotting) or [offline](https://plotly.com/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plotly.com/python/getting-started/#start-plotting-online).\n", - "
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started!\n", - "#### Version Check\n", - "Note: The presentations API is available in version 2.2.1.+
\n", - "Run `pip install plotly --upgrade` to update your Plotly version." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'2.4.1'" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import plotly\n", - "plotly.__version__" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plotly Presentations\n", - "To use Plotly's Presentations API you will write your presentation code in a string of markdown and then pass that through the Presentations API function `pres.Presentation()`. This creates a JSON version of your presentation. To upload the presentation online pass it through `py.presentation_ops.upload()`.\n", - "\n", - "In your string, use `---` on a single line to seperate two slides. To put a title in your slide, put a line that starts with any number of `#`s. Only your first title will be appear in your slide. A title looks like:\n", - "\n", - "`# slide title`\n", - "\n", - "Anything that comes after the title will be put as text in your slide. Check out the example below to see this in action." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Current Limitations\n", - "`Boldface`, _italics_ and [hypertext](https://www.w3.org/WhatIs.html) are not supported features of the Presentation API." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Display in Jupyter\n", - "The function below generates HTML code to display the presentation in an iframe directly in Jupyter." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def url_to_iframe(url, text=True):\n", - " html = ''\n", - " # style\n", - " html += '''\n", - " \n", - " '\n", - " '''\n", - " # iframe\n", - " html += ''\n", - " if text:\n", - " html += '''\n", - "
\n", - "

Click on the presentation above and use left/right arrow keys to flip through the slides.

\n", - "
\n", - " \n", - " '''\n", - " return html" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Simple Example" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import plotly.plotly as py\n", - "import plotly.presentation_objs as pres\n", - "\n", - "filename = 'simple-pres'\n", - "markdown_string = \"\"\"\n", - "# slide 1\n", - "There is only one slide.\n", - "\n", - "---\n", - "# slide 2\n", - "Again, another slide on this page.\n", - "\n", - "\"\"\"\n", - "\n", - "my_pres = pres.Presentation(markdown_string)\n", - "pres_url_0 = py.presentation_ops.upload(my_pres, filename)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "https://plotly.com/~AdamKulidjian/3700/simple-pres/" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " '\n", - " \n", - "
\n", - "

Click on the presentation above and use left/right arrow keys to flip through the slides.

\n", - "
\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import IPython\n", - "\n", - "iframe_0 = url_to_iframe(pres_url_0, True)\n", - "IPython.display.HTML(iframe_0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Insert Plotly Chart\n", - "If you want to insert a Plotly chart into your presentation, all you need to do is write a line in your presentation that takes the form:\n", - "\n", - "`Plotly(url)`\n", - "\n", - "where url is a Plotly url. For example:\n", - "\n", - "`Plotly(https://plotly.com/~AdamKulidjian/3564)`\n", - "\n", - "The Plotly url lines should be written on a separate line after your title line. You can put as many images in your slide as you want, as the API will arrange them on the slide automatically, but it is _highly_ encouraged that you use `4 OR FEWER IMAGES PER SLIDE`. This will produce the cleanest look.\n", - "\n", - "`Useful Tip`:
\n", - "For Plotly charts it is HIGHLY ADVISED that you use a chart that has `layout['autosize']` set to `True`. If it is `False` the image may be cropped or only partially visible when it appears in the presentation slide." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import plotly.plotly as py\n", - "import plotly.presentation_objs as pres\n", - "\n", - "filename = 'pres-with-plotly-chart'\n", - "markdown_string = \"\"\"\n", - "# 3D scatterplots\n", - "3D Scatterplot are just a collection of balls in a 3D cartesian space each of which have assigned properties like color, size, and more.\n", - "\n", - "---\n", - "# simple 3d scatterplot\n", - "\n", - "Plotly(https://plotly.com/~AdamKulidjian/3698)\n", - "---\n", - "# different colorscales\n", - "\n", - "There are various colorscales and colorschemes to try in Plotly. Check out plotly.colors to find a list of valid and available colorscales.\n", - "\n", - "Plotly(https://plotly.com/~AdamKulidjian/3582)\n", - "Plotly(https://plotly.com/~AdamKulidjian/3698)\n", - "\"\"\"\n", - "\n", - "my_pres = pres.Presentation(markdown_string)\n", - "pres_url_1 = py.presentation_ops.upload(my_pres, filename)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "https://plotly.com/~AdamKulidjian/3710/pres-with-plotly-chart/" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " '\n", - " \n", - "
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Click on the presentation above and use left/right arrow keys to flip through the slides.

\n", - "
\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import IPython\n", - "\n", - "iframe_1 = url_to_iframe(pres_url_1, True)\n", - "IPython.display.HTML(iframe_1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Insert Web Images\n", - "To insert an image from the web, insert the a `Image(url)` where url is the image url." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import plotly.plotly as py\n", - "import plotly.presentation_objs as pres\n", - "\n", - "filename = 'pres-with-images'\n", - "markdown_string = \"\"\"\n", - "# Animals of the Wild\n", - "---\n", - "# The Lion\n", - "\n", - "Panthera leo is one of the big cats in the Felidae family and a member of genus Panthera. It has been listed as Vulnerable on the IUCN Red List since 1996, as populations in African range countries declined by about 43% since the early 1990s. Lion populations are untenable outside designated protected areas. Although the cause of the decline is not fully understood, habitat loss and conflicts with humans are the greatest causes of concern. The West African lion population is listed as Critically Endangered since 2016. The only lion population in Asia survives in and around India's Gir Forest National Park and is listed as Endangered since 1986.\n", - "\n", - "Image(https://i.pinimg.com/736x/da/af/73/daaf73960eb5a21d6bca748195f12052--lion-photography-lion-kings.jpg)\n", - "---\n", - "# The Giraffe\n", - "\n", - "The giraffe is a genus of African even-toed ungulate mammals, the tallest living terrestrial animals and the largest ruminants. The genus currently consists of one species, Giraffa camelopardalis, the type species. Seven other species are extinct, prehistoric species known from fossils. Taxonomic classifications of one to eight extant giraffe species have been described, based upon research into the mitochondrial and nuclear DNA, as well as morphological measurements of Giraffa, but the IUCN currently recognizes only one species with nine subspecies.\n", - "\n", - "Image(https://img.purch.com/w/192/aHR0cDovL3d3dy5saXZlc2NpZW5jZS5jb20vaW1hZ2VzL2kvMDAwLzA2OC8wOTQvaTMwMC9naXJhZmZlLmpwZz8xNDA1MDA4NDQy)\n", - "Image(https://upload.wikimedia.org/wikipedia/commons/9/9f/Giraffe_standing.jpg)\n", - "\n", - "\"\"\"\n", - "\n", - "my_pres = pres.Presentation(markdown_string)\n", - "pres_url_2 = py.presentation_ops.upload(my_pres, filename)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "https://plotly.com/~AdamKulidjian/3702/pres-with-images/" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " '\n", - " \n", - "
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\n", - "
\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import IPython\n", - "\n", - "iframe_2 = url_to_iframe(pres_url_2, True)\n", - "IPython.display.HTML(iframe_2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Image Stretch\n", - "If you want to ensure that your image maintains its original width:height ratio, include the parameter `imgStretch=False` in your `pres.Presentation()` function call." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import plotly.plotly as py\n", - "import plotly.presentation_objs as pres\n", - "\n", - "filename = 'pres-with-no-imgstretch'\n", - "markdown_string = \"\"\"\n", - "# images in native aspect ratio\n", - "\n", - "Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png)\n", - "Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png)\n", - "Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png)\n", - "Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png)\n", - "Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png)\n", - "\"\"\"\n", - "\n", - "my_pres = pres.Presentation(markdown_string, imgStretch=False)\n", - "pres_url_3 = py.presentation_ops.upload(my_pres, filename)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "https://plotly.com/~AdamKulidjian/3703/pres-with-no-imgstretch/" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " '\n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import IPython\n", - "\n", - "iframe_3 = url_to_iframe(pres_url_3, False)\n", - "IPython.display.HTML(iframe_3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Insert Code Blocks\n", - "The Presentations API also supports the insertion of blocks of code with various available langauges to choose from.\n", - "\n", - "To instantiate a \"code environment\" in your string, place a \\`\\`\\` at the beginning of a line, followed by the name of the programming language you want your code block to be styled in. Then the next lines will be considered \"code lines \". To close the \"code environment\" put another \\`\\`\\` at the end of the line For example:\n", - "\n", - "```\n", - "```python\n", - "# code goes here\n", - "``` ```\n", - "\n", - "The valid languages to choose from are: arecpp, cs, css, fsharp, go, haskell, java, javascript, jsx, julia, xml, matlab, php, python, r, ruby, scala, sql and yaml." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import plotly.plotly as py\n", - "import plotly.presentation_objs as pres\n", - "\n", - "filename = 'pres-with-code'\n", - "markdown_string = \"\"\"\n", - "# Getting Started Using Code\n", - "A beginner's introduction to computer science.\n", - "\n", - "---\n", - "# Python Functions\n", - "Functions are one of the most useful tools in Python. Intuitively, you select an input and get an output.\n", - "\n", - "In order to set up a function use the key word \"def\" then the name of the function with open parentheses afterwards. Inside the parentheses, write variable names your function will use. These variables can then go into the body of your function and when you give a value to the variable in the call signature, it will pass through the guts of the function until it returns a value.\n", - "\n", - "```python\n", - "def somePrintFunction():\n", - "\n", - " print(\"boo\")\n", - "\n", - "somePrintFunction()\n", - "\n", - "\n", - "\n", - "\n", - ">>>print(new_z)\n", - "10\n", - "\n", - "def someAddFunction(a, b):\n", - "\n", - " print(a+b)\n", - "\n", - "\n", - "\n", - "\n", - ">>>someAddFunction(12,451)\n", - "463\n", - "```\n", - "---\n", - "# Use scala\n", - "You can write functions in other languages as well. For example, check out this scala code and notice how the print functions look different:\n", - "\n", - "We write 'println()' as opposed to 'print()' as we do in Python.\n", - "\n", - "```scala\n", - "/** Basic command line parsing. */\n", - "object Main {\n", - " var verbose = false\n", - "\n", - " def main(args: Array[String]) {\n", - " for (a <- args) a match {\n", - " case \"-h\" | \"-help\" =>\n", - " println(\"Usage: scala Main [-help|-verbose]\")\n", - " case \"-v\" | \"-verbose\" =>\n", - " verbose = true\n", - " case x =>\n", - " println(\"Unknown option: '\" + x + \"'\")\n", - " }\n", - " if (verbose)\n", - " println(\"How are you today?\")\n", - " }\n", - "}\n", - "```\n", - "---\n", - "# Under the Hood\n", - "\n", - "There are many things to find when you look under the Plotly Hood. Of many things, one expected thing is the compliance and adherance to alphebetized and PEP-8'ed imports at the top of any module.\n", - "\n", - "This is what the PEP-8 guide has more to say about Imports:\n", - "\n", - "Wildcard imports (from import *) should be avoided, as they make it unclear which names are present in the namespace, confusing both readers and many automated tools. There is one defensible use case for a wildcard import, which is to republish an internal interface as part of a public API (for example, overwriting a pure Python implementation of an interface with the definitions from an optional accelerator module and exactly which definitions will be overwritten isn't known in advance).\n", - "\n", - "Image(https://help.plot.ly/images/dashboard-carousel.jpg)\n", - "\n", - "```python\n", - "from __future__ import absolute_import\n", - "\n", - "import copy\n", - "import json\n", - "import os\n", - "import time\n", - "import warnings\n", - "import webbrowser\n", - "\n", - "import six\n", - "import six.moves\n", - "from requests.compat import json as _json\n", - "\n", - "from plotly import exceptions, files, session, tools, utils\n", - "from plotly.api import v1, v2\n", - "from plotly.plotly import chunked_requests\n", - "from plotly.grid_objs import Grid, Column\n", - "from plotly.dashboard_objs import dashboard_objs as dashboard\n", - "```\n", - "\"\"\"\n", - "\n", - "my_pres = pres.Presentation(markdown_string)\n", - "pres_url_4 = py.presentation_ops.upload(my_pres, filename)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "https://plotly.com/~AdamKulidjian/3704/pres-with-code/" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " '\n", - " \n", - "
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Click on the presentation above and use left/right arrow keys to flip through the slides.

\n", - "
\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import IPython\n", - "\n", - "iframe_4 = url_to_iframe(pres_url_4, True)\n", - "IPython.display.HTML(iframe_4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Style Your Presentation\n", - "The Presentations API currently has two styles to choose from: [_Martik_](https://www.pinterest.ca/pin/822540319412564330/) and [_Moods_](https://www.pinterest.ca/pin/822540319412564320/). These themes are inspired by already existing PowerPoint Templates. Let's use the same `markdown_string` in the previous example but this time try the `Martik` style." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import plotly.plotly as py\n", - "import plotly.presentation_objs as pres\n", - "\n", - "filename = 'martik-style'\n", - "markdown_string = \"\"\"\n", - "# Getting Started Using Code\n", - "A beginner's introduction to computer science.\n", - "\n", - "---\n", - "# Python Functions\n", - "Functions are one of the most useful tools in Python. Intuitively, you select an input and get an output.\n", - "\n", - "In order to set up a function use the key word \"def\" then the name of the function with open parentheses afterwards. Inside the parentheses, write variable names your function will use. These variables can then go into the body of your function and when you give a value to the variable in the call signature, it will pass through the guts of the function until it returns a value.\n", - "\n", - "```python\n", - "def somePrintFunction():\n", - "\n", - " print(\"boo\")\n", - "\n", - "somePrintFunction()\n", - "\n", - "\n", - "\n", - "\n", - ">>>print(new_z)\n", - "10\n", - "\n", - "def someAddFunction(a, b):\n", - "\n", - " print(a+b)\n", - "\n", - "\n", - "\n", - "\n", - ">>>someAddFunction(12,451)\n", - "463\n", - "```\n", - "---\n", - "# Use scala\n", - "You can write functions in other languages as well. For example, check out this scala code and notice how the print functions look different:\n", - "\n", - "We write 'println()' as opposed to 'print()' as we do in Python.\n", - "\n", - "```scala\n", - "/** Basic command line parsing. */\n", - "object Main {\n", - " var verbose = false\n", - "\n", - " def main(args: Array[String]) {\n", - " for (a <- args) a match {\n", - " case \"-h\" | \"-help\" =>\n", - " println(\"Usage: scala Main [-help|-verbose]\")\n", - " case \"-v\" | \"-verbose\" =>\n", - " verbose = true\n", - " case x =>\n", - " println(\"Unknown option: '\" + x + \"'\")\n", - " }\n", - " if (verbose)\n", - " println(\"How are you today?\")\n", - " }\n", - "}\n", - "```\n", - "---\n", - "# Under the Hood\n", - "\n", - "There are many things to find when you look under the Plotly Hood. Of many things, one expected thing is the compliance and adherance to alphebetized and PEP-8'ed imports at the top of any module.\n", - "\n", - "This is what the PEP-8 guide has more to say about Imports:\n", - "\n", - "Wildcard imports (from import *) should be avoided, as they make it unclear which names are present in the namespace, confusing both readers and many automated tools. There is one defensible use case for a wildcard import, which is to republish an internal interface as part of a public API (for example, overwriting a pure Python implementation of an interface with the definitions from an optional accelerator module and exactly which definitions will be overwritten isn't known in advance).\n", - "\n", - "Image(https://help.plot.ly/images/dashboard-carousel.jpg)\n", - "\n", - "```python\n", - "from __future__ import absolute_import\n", - "\n", - "import copy\n", - "import json\n", - "import os\n", - "import time\n", - "import warnings\n", - "import webbrowser\n", - "\n", - "import six\n", - "import six.moves\n", - "from requests.compat import json as _json\n", - "\n", - "from plotly import exceptions, files, session, tools, utils\n", - "from plotly.api import v1, v2\n", - "from plotly.plotly import chunked_requests\n", - "from plotly.grid_objs import Grid, Column\n", - "from plotly.dashboard_objs import dashboard_objs as dashboard\n", - "```\n", - "\"\"\"\n", - "\n", - "my_pres = pres.Presentation(markdown_string, style='martik')\n", - "pres_url_5 = py.presentation_ops.upload(my_pres, 'martik-style')" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " '\n", - " \n", - "
\n", - "

Click on the presentation above and use left/right arrow keys to flip through the slides.

\n", - "
\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import IPython\n", - "\n", - "iframe_5 = url_to_iframe(pres_url_5, True)\n", - "IPython.display.HTML(iframe_5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Transitions\n", - "You can specify how your want your slides to transition to one another. Just like in the Plotly Presentation Application, there are 4 types of transitions: `slide`, `zoom`, `fade` and `spin`.\n", - "\n", - "To apply any combination of these transition to a slide, just insert transitions at the top of the slide as follows:\n", - "\n", - "`transition: slide, zoom`\n", - "\n", - "Make sure that this line comes before any heading that you define in the slide, i.e. like this:\n", - "\n", - "```\n", - "transition: slide, zoom\n", - "# slide title\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import plotly.plotly as py\n", - "import plotly.presentation_objs as pres\n", - "\n", - "filename = 'pres-with-transitions'\n", - "markdown_string = \"\"\"\n", - "transition: slide\n", - "# slide\n", - "---\n", - "transition: zoom\n", - "# zoom\n", - "---\n", - "transition: fade\n", - "# fade\n", - "---\n", - "transition: spin\n", - "# spin\n", - "---\n", - "transition: spin and slide\n", - "# spin, slide\n", - "---\n", - "transition: fade zoom\n", - "# fade, zoom\n", - "---\n", - "transition: slide, zoom, fade, spin, spin, spin, zoom, fade\n", - "# slide, zoom, fade, spin\n", - "\n", - "\"\"\"\n", - "\n", - "my_pres = pres.Presentation(markdown_string, style='moods')\n", - "pres_url_6 = py.presentation_ops.upload(my_pres, filename)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " '\n", - " \n", - "
\n", - "

Click on the presentation above and use left/right arrow keys to flip through the slides.

\n", - "
\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import IPython\n", - "\n", - "iframe_6 = url_to_iframe(pres_url_6, True)\n", - "IPython.display.HTML(iframe_6)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Add Thin Border Around Text Boxes\n", - "Every `slide` has `children`, and each of these `children` have a `style` attribute. This `style` property is derived from the CSS element of the same name. Since you have the power of CSS to work with, you could customize text borders in your presentation if you want." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import plotly.plotly as py\n", - "import plotly.presentation_objs as pres\n", - "\n", - "filename = 'pres-with-custom-css'\n", - "markdown_string = \"\"\"\n", - "# custom css\n", - "---\n", - "transition: zoom, slide, spin, fade\n", - "# fun with css\n", - "\n", - "Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.\n", - "```python\n", - "x = 4\n", - "\n", - "if x < 2:\n", - " x = 2 * x\n", - " if x >= 2:\n", - " break\n", - "```\n", - "\n", - "\"\"\"\n", - "\n", - "my_pres = pres.Presentation(markdown_string)\n", - "\n", - "# change text border style\n", - "my_pres['presentation']['slides'][1]['children'][0]['props']['style']['border'] = 'solid red'\n", - "\n", - "pres_url_7 = py.presentation_ops.upload(my_pres, filename)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "https://plotly.com/~AdamKulidjian/3709/pres-with-custom-css/" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " '\n", - " \n", - "
\n", - "

Click on the presentation above and use left/right arrow keys to flip through the slides.

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\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import IPython\n", - "\n", - "iframe_7 = url_to_iframe(pres_url_7, True)\n", - "IPython.display.HTML(iframe_7)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Reference" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on class presentation_ops in module plotly.plotly.plotly:\n", - "\n", - "class presentation_ops\n", - " | Interface to Plotly's Spectacle-Presentations API.\n", - " | \n", - " | Class methods defined here:\n", - " | \n", - " | upload(cls, presentation, filename, sharing='public', auto_open=True) from __builtin__.classobj\n", - " | Function for uploading presentations to Plotly.\n", - " | \n", - " | :param (dict) presentation: the JSON presentation to be uploaded. Use\n", - " | plotly.presentation_objs.Presentation to create presentations\n", - " | from a Markdown-like string.\n", - " | :param (str) filename: the name of the presentation to be saved in\n", - " | your Plotly account. Will overwrite a presentation of the same\n", - " | name if it already exists in your files.\n", - " | :param (str) sharing: can be set to either 'public', 'private'\n", - " | or 'secret'. If 'public', your presentation will be viewable by\n", - " | all other users. If 'private' only you can see your presentation.\n", - " | If it is set to 'secret', the url will be returned with a string\n", - " | of random characters appended to the url which is called a\n", - " | sharekey. The point of a sharekey is that it makes the url very\n", - " | hard to guess, but anyone with the url can view the presentation.\n", - " | :param (bool) auto_open: automatically opens the presentation in the\n", - " | browser.\n", - " | \n", - " | See the documentation online for examples.\n", - "\n" - ] - } - ], - "source": [ - "help(py.presentation_ops)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting git+https://github.com/plotly/publisher.git\n", - " Cloning https://github.com/plotly/publisher.git to /private/var/folders/tc/bs9g6vrd36q74m5t8h9cgphh0000gn/T/pip-qxJ5r5-build\n", - "Installing collected packages: publisher\n", - " Found existing installation: publisher 0.11\n", - " Uninstalling publisher-0.11:\n", - " Successfully uninstalled publisher-0.11\n", - " Running setup.py install for publisher ... \u001b[?25ldone\n", - "\u001b[?25hSuccessfully installed publisher-0.11\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated since IPython 4.0. You should import from nbconvert instead.\n", - " \"You should import from nbconvert instead.\", ShimWarning)\n", - "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/publisher/publisher.py:53: UserWarning: Did you \"Save\" this notebook before running this command? Remember to save, always save.\n", - " warnings.warn('Did you \"Save\" this notebook before running this command? '\n" - ] - } - ], - "source": [ - "from IPython.display import display, HTML\n", - "\n", - "display(HTML(''))\n", - "display(HTML(''))\n", - "\n", - "!pip install git+https://github.com/plotly/publisher.git --upgrade\n", - "import publisher\n", - "publisher.publish(\n", - " 'presentations-api.ipynb', 'python/presentations-tool/', 'Presentations Tool | plotly',\n", - " 'How to create and publish a spectacle-presentation with the Python API.',\n", - " title = 'Presentations Tool | plotly',\n", - " name = 'Presentations Tool',\n", - " thumbnail='thumbnail/pres_api.jpg', language='python',\n", - " has_thumbnail='true', display_as='chart_studio', order=0.6)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/_posts/python-v3/chart-studio/privacy/2015-06-30-privacy.html b/_posts/python-v3/chart-studio/privacy/2015-06-30-privacy.html deleted file mode 100644 index 972e30e0d..000000000 --- a/_posts/python-v3/chart-studio/privacy/2015-06-30-privacy.html +++ /dev/null @@ -1,539 +0,0 @@ ---- -permalink: python/v3/privacy/ -description: How to set the privacy settings of plotly graphs in python. Three examples of different privacy options: public, private and secret. -name: Privacy -thumbnail: thumbnail/privacy.jpg -layout: base -name: Privacy -language: python/v3 -display_as: chart_studio -order: 2 -ipynb: ~notebook_demo/97 ---- -{% raw %} -
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New to Plotly?¶

Plotly's Python library is free and open source! Get started by downloading the client and reading the primer. -
You can set up Plotly to work in online or offline mode, or in jupyter notebooks. -
We also have a quick-reference cheatsheet (new!) to help you get started!

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Version Check¶

Plotly's python package is updated frequently. Run pip install plotly --upgrade to use the latest version.

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Default Privacy¶

By default, plotly.iplot() and plotly.plot() create public graphs (which are free to create). With a plotly subscription you can easily make charts private or secret via the sharing argument.

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Public Graphs¶

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Below is the URL of this public plot. Anyone can view public plots even if they are not logged into Plotly. Go ahead and try it out:

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py.plot(data, filename='privacy-public', sharing='public')
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'https://plotly.com/~jordanpeterson/1083'
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Private Graphs¶

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py.iplot(data, filename='privacy-private', sharing='private')
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Below is the URL of the private plot above. Only the owner can view the private plot. You won't be able to view this plot, try it out:

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py.plot(data, filename='privacy-private', sharing='private')
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Secret Graphs¶

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py.iplot(data, filename='privacy-secret', sharing='secret')
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Below is the URL of this secret plot. Anyone with the secret link can view this chart. However, it will not appear in the Plotly feed, your profile, or search engines. Go ahead and try it out:

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py.plot(data, filename='privacy-secret', sharing='secret')
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'https://plotly.com/~jordanpeterson/1087?share_key=mId9Rao9B6Pyh7UrNdPotP'
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Make All Future Plots Private¶

To make all future plots private, you can update your configuration file to create private plots by default:

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Make All Existing Plots Private¶

This example uses Plotly's REST API

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Define variables, including YOUR USERNAME and API KEY

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username = 'private_plotly' # Replace with YOUR USERNAME
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Collect filenames of ALL of your plots and
update world_readable of each plot with a PATCH request

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def get_pages(username, page_size):
-    url = 'https://api.plot.ly/v2/folders/all?user='+username+'&filetype=plot&page_size='+str(page_size)
-    response = requests.get(url, auth=auth, headers=headers)
-    if response.status_code != 200:
-        return
-    page = json.loads(response.content.decode('utf-8'))
-    yield page
-    while True:
-        resource = page['children']['next']
-        if not resource:
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-        response = requests.get(resource, auth=auth, headers=headers)
-        if response.status_code != 200:
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-        page = json.loads(response.content.decode('utf-8'))
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-def make_all_plots_private(username, page_size=500):
-    for page in get_pages(username, page_size):
-        for x in range(0, len(page['children']['results'])):
-            fid = page['children']['results'][x]['fid']
-            requests.patch('https://api.plot.ly/v2/files/'+fid, {"world_readable": False}, auth=auth, headers=headers)
-    print('ALL of your plots are now private - visit: https://plotly.com/organize/home to view your private plots!')
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ALL of your plots are now private - visit: https://plotly.com/organize/home to view your private plots!
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Reference¶

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help(py.plot)
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Help on function plot in module plotly.plotly.plotly:
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-plot(figure_or_data, validate=True, **plot_options)
-    Create a unique url for this plot in Plotly and optionally open url.
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-    plot_options keyword arguments:
-    filename (string) -- the name that will be associated with this figure
-    fileopt ('new' | 'overwrite' | 'extend' | 'append') -- 'new' creates a
-        'new': create a new, unique url for this plot
-        'overwrite': overwrite the file associated with `filename` with this
-        'extend': add additional numbers (data) to existing traces
-        'append': add additional traces to existing data lists
-    auto_open (default=True) -- Toggle browser options
-        True: open this plot in a new browser tab
-        False: do not open plot in the browser, but do return the unique url
-    sharing ('public' | 'private' | 'secret') -- Toggle who can view this
-                                                  graph
-        - 'public': Anyone can view this graph. It will appear in your profile
-                    and can appear in search engines. You do not need to be
-                    logged in to Plotly to view this chart.
-        - 'private': Only you can view this plot. It will not appear in the
-                     Plotly feed, your profile, or search engines. You must be
-                     logged in to Plotly to view this graph. You can privately
-                     share this graph with other Plotly users in your online
-                     Plotly account and they will need to be logged in to
-                     view this plot.
-        - 'secret': Anyone with this secret link can view this chart. It will
-                    not appear in the Plotly feed, your profile, or search
-                    engines. If it is embedded inside a webpage or an IPython
-                    notebook, anybody who is viewing that page will be able to
-                    view the graph. You do not need to be logged in to view
-                    this plot.
-    world_readable (default=True) -- Deprecated: use "sharing".
-                                     Make this figure private/public
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- -
- - -{% endraw %} diff --git a/_posts/python-v3/chart-studio/privacy/privacy.ipynb b/_posts/python-v3/chart-studio/privacy/privacy.ipynb deleted file mode 100644 index c23562135..000000000 --- a/_posts/python-v3/chart-studio/privacy/privacy.ipynb +++ /dev/null @@ -1,502 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### New to Plotly?\n", - "Plotly's Python library is free and open source! [Get started](https://plotly.com/python/getting-started/) by downloading the client and [reading the primer](https://plotly.com/python/getting-started/).\n", - "
You can set up Plotly to work in [online](https://plotly.com/python/getting-started/#initialization-for-online-plotting) or [offline](https://plotly.com/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plotly.com/python/getting-started/#start-plotting-online).\n", - "
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started!\n", - "#### Version Check\n", - "Plotly's python package is updated frequently. Run `pip install plotly --upgrade` to use the latest version." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'3.1.0'" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import plotly \n", - "plotly.__version__" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Default Privacy\n", - "By default, `plotly.iplot()` and `plotly.plot()` create public graphs (which are free to create). With a [plotly subscription](https://plotly.com/plans) you can easily make charts private or secret via the sharing argument." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Public Graphs" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import plotly.plotly as py\n", - "import plotly.graph_objs as go\n", - "\n", - "data = [\n", - " go.Scatter(\n", - " x=[1, 2, 3],\n", - " y=[1, 3, 1]\n", - " )\n", - "]\n", - "\n", - "py.iplot(data, filename='privacy-public', sharing='public')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below is the URL of this public plot. Anyone can view public plots even if they are not logged into Plotly. Go ahead and try it out:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'https://plotly.com/~jordanpeterson/1083'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "py.plot(data, filename='privacy-public', sharing='public')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Private Graphs" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "py.iplot(data, filename='privacy-private', sharing='private')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below is the URL of the private plot above. Only the owner can view the private plot. You won't be able to view this plot, try it out:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'https://plotly.com/~jordanpeterson/1085'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "py.plot(data, filename='privacy-private', sharing='private')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Secret Graphs " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "py.iplot(data, filename='privacy-secret', sharing='secret')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below is the URL of this secret plot. Anyone with the secret link can view this chart. However, it will not appear in the Plotly feed, your profile, or search engines. Go ahead and try it out:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'https://plotly.com/~jordanpeterson/1087?share_key=mId9Rao9B6Pyh7UrNdPotP'" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "py.plot(data, filename='privacy-secret', sharing='secret')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Make All Future Plots Private\n", - "To make all future plots private, you can update your configuration file to create private plots by default:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "import plotly \n", - "plotly.tools.set_config_file(world_readable=False, sharing='private')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Make All Existing Plots Private\n", - "This example uses [Plotly's REST API](https://api.plot.ly/v2/)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import requests\n", - "from requests.auth import HTTPBasicAuth" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define variables, including YOUR [USERNAME and API KEY](https://plotly.com/settings/api)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "username = 'private_plotly' # Replace with YOUR USERNAME\n", - "api_key = 'k0yy0ztssk' # Replace with YOUR API KEY\n", - "\n", - "auth = HTTPBasicAuth(username, api_key)\n", - "headers = {'Plotly-Client-Platform': 'python'}\n", - "\n", - "page_size = 500" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Collect filenames of ALL of your plots and
update `world_readable` of each plot with a PATCH request" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ALL of your plots are now private - visit: https://plotly.com/organize/home to view your private plots!\n" - ] - } - ], - "source": [ - "def get_pages(username, page_size):\n", - " url = 'https://api.plot.ly/v2/folders/all?user='+username+'&filetype=plot&page_size='+str(page_size)\n", - " response = requests.get(url, auth=auth, headers=headers)\n", - " if response.status_code != 200:\n", - " return\n", - " page = json.loads(response.content.decode('utf-8'))\n", - " yield page\n", - " while True:\n", - " resource = page['children']['next']\n", - " if not resource:\n", - " break\n", - " response = requests.get(resource, auth=auth, headers=headers)\n", - " if response.status_code != 200:\n", - " break\n", - " page = json.loads(response.content.decode('utf-8'))\n", - " yield page\n", - "\n", - "def make_all_plots_private(username, page_size=500):\n", - " for page in get_pages(username, page_size):\n", - " for x in range(0, len(page['children']['results'])):\n", - " fid = page['children']['results'][x]['fid']\n", - " requests.patch('https://api.plot.ly/v2/files/'+fid, {\"world_readable\": False}, auth=auth, headers=headers)\n", - " print('ALL of your plots are now private - visit: https://plotly.com/organize/home to view your private plots!')\n", - "\n", - "make_all_plots_private(username)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reference" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function plot in module plotly.plotly.plotly:\n", - "\n", - "plot(figure_or_data, validate=True, **plot_options)\n", - " Create a unique url for this plot in Plotly and optionally open url.\n", - " \n", - " plot_options keyword arguments:\n", - " filename (string) -- the name that will be associated with this figure\n", - " fileopt ('new' | 'overwrite' | 'extend' | 'append') -- 'new' creates a\n", - " 'new': create a new, unique url for this plot\n", - " 'overwrite': overwrite the file associated with `filename` with this\n", - " 'extend': add additional numbers (data) to existing traces\n", - " 'append': add additional traces to existing data lists\n", - " auto_open (default=True) -- Toggle browser options\n", - " True: open this plot in a new browser tab\n", - " False: do not open plot in the browser, but do return the unique url\n", - " sharing ('public' | 'private' | 'secret') -- Toggle who can view this\n", - " graph\n", - " - 'public': Anyone can view this graph. It will appear in your profile\n", - " and can appear in search engines. You do not need to be\n", - " logged in to Plotly to view this chart.\n", - " - 'private': Only you can view this plot. It will not appear in the\n", - " Plotly feed, your profile, or search engines. You must be\n", - " logged in to Plotly to view this graph. You can privately\n", - " share this graph with other Plotly users in your online\n", - " Plotly account and they will need to be logged in to\n", - " view this plot.\n", - " - 'secret': Anyone with this secret link can view this chart. It will\n", - " not appear in the Plotly feed, your profile, or search\n", - " engines. If it is embedded inside a webpage or an IPython\n", - " notebook, anybody who is viewing that page will be able to\n", - " view the graph. You do not need to be logged in to view\n", - " this plot.\n", - " world_readable (default=True) -- Deprecated: use \"sharing\".\n", - " Make this figure private/public\n", - "\n" - ] - } - ], - "source": [ - "help(py.plot)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting git+https://github.com/plotly/publisher.git\n", - " Cloning https://github.com/plotly/publisher.git to /private/var/folders/tc/bs9g6vrd36q74m5t8h9cgphh0000gn/T/pip-req-build-oEmPsN\n", - "Building wheels for collected packages: publisher\n", - " Running setup.py bdist_wheel for publisher ... \u001b[?25ldone\n", - "\u001b[?25h Stored in directory: /private/var/folders/tc/bs9g6vrd36q74m5t8h9cgphh0000gn/T/pip-ephem-wheel-cache-NzZ88C/wheels/99/3e/a0/fbd22ba24cca72bdbaba53dbc23c1768755fb17b3af0f33966\n", - "Successfully built publisher\n", - "Installing collected packages: publisher\n", - " Found existing installation: publisher 0.11\n", - " Uninstalling publisher-0.11:\n", - " Successfully uninstalled publisher-0.11\n", - "Successfully installed publisher-0.11\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning:\n", - "\n", - "The `IPython.nbconvert` package has been deprecated since IPython 4.0. You should import from nbconvert instead.\n", - "\n", - "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/publisher/publisher.py:53: UserWarning:\n", - "\n", - "Did you \"Save\" this notebook before running this command? Remember to save, always save.\n", - "\n" - ] - } - ], - "source": [ - "from IPython.display import display, HTML\n", - "\n", - "display(HTML(''))\n", - "display(HTML(''))\n", - "\n", - "! pip install git+https://github.com/plotly/publisher.git --upgrade\n", - "import publisher\n", - "publisher.publish(\n", - " 'privacy.ipynb', 'python/privacy/', 'Privacy',\n", - " 'How to set the privacy settings of plotly graphs in python. Three examples of different privacy options: public, private and secret.',\n", - " title = 'Privacy | plotly',\n", - " name = 'Privacy', language='python',\n", - " has_thumbnail= True, thumbnail= 'thumbnail/privacy.jpg',\n", - " display_as='chart_studio', order=2,\n", - " ipynb= '~notebook_demo/97')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/_posts/python-v3/chart-studio/proxies/2015-07-27-proxy_index.md b/_posts/python-v3/chart-studio/proxies/2015-07-27-proxy_index.md deleted file mode 100755 index f890ca7a7..000000000 --- a/_posts/python-v3/chart-studio/proxies/2015-07-27-proxy_index.md +++ /dev/null @@ -1,47 +0,0 @@ ---- -name: Requests Behind Corporate Proxies -permalink: python/v3/proxy-configuration/ -description: How to configure Plotly's Python API to work with corporate proxies -layout: base -language: python/v3 -thumbnail: thumbnail/net.jpg -display_as: chart_studio -order: 10 ---- - -### Using Plotly's Python API Behind a Corporate Proxy - -If you are behind a corporate firewall, you may see the error message: - -
requests.exceptions.ConnectionError: ('Connection aborted.', TimeoutError(10060, ...)
- -Plotly uses the `requests` module to communicate with the Plotly server. You can configure proxies by setting the environment variables `HTTP_PROXY` and `HTTPS_PROXY`. - - -
$ export HTTP_PROXY="http://10.10.1.10:3128"
-$ export HTTPS_PROXY="http://10.10.1.10:1080"
-
- -To use HTTP Basic Auth with your proxy, use the http://user:password@host/ syntax: - -
$ export HTTP_PROXY="http://user:pass@10.10.1.10:3128/"
- -Note that proxy URLs must include the scheme. - -You may also see this error if your proxy variable is set but you are no longer behind the -corporate proxy. Check if a proxy variable is set with: - -
$ echo $HTTP_PROXY
-$ echo $HTTPS_PROXY
-
- - -**Still not working?** - -- [Log an issue](https://github.com/plotly/python-api) -- Contact -- Get in touch with your IT department, and ask them about corporate proxies -- [Requests documentation on configuring proxies](http://docs.python-requests.org/en/latest/user/advanced/#proxies) -the requests documentation. -- Plotly for IPython Notebooks is also [available for offline use](https://plotly.com/python/offline/) -- [Chart Studio Enterprise](https://plotly.com/product/enterprise) is available for behind-the-firewall corporate installations diff --git a/_posts/python-v3/chart-studio/sending-data/2015-04-09-add-traces.html b/_posts/python-v3/chart-studio/sending-data/2015-04-09-add-traces.html deleted file mode 100755 index 9173450a9..000000000 --- a/_posts/python-v3/chart-studio/sending-data/2015-04-09-add-traces.html +++ /dev/null @@ -1,21 +0,0 @@ ---- -name: Add new traces to a chart -description: NOT RECOMMENDED
When updating a chart's data remotely, we recommend overwriting all of the chart's data instead of adding new traces. -plot_url: http://i.imgur.com/RzrURdn.gif -arrangement: horizontal -language: python/v3 -suite: sending-data -order: 2 -sitemap: false ---- -# Learn about API authentication here: https://plotly.com/python/getting-started -# Find your api_key here: https://plotly.com/settings/api - -import plotly.plotly as py -from plotly.graph_objs import * - -new_trace = Scatter( x=[3, 4], y=[3, 2] ) - -data = Data( [ new_trace1 ] ) - -plot_url = py.plot(data, filename='append plot', fileopt='append') diff --git a/_posts/python-v3/chart-studio/sending-data/2015-04-09-extend.html b/_posts/python-v3/chart-studio/sending-data/2015-04-09-extend.html deleted file mode 100755 index 732ca0795..000000000 --- a/_posts/python-v3/chart-studio/sending-data/2015-04-09-extend.html +++ /dev/null @@ -1,21 +0,0 @@ ---- -name: Add data to an existing trace -description: Add data to an existing trace by setting fileopt='extend'.
This method is used for embedded systems that may not have the memory for a full overwrite of the chart data in one API call. -plot_url: http://i.imgur.com/2LhVSX6.gif -arrangement: horizontal -language: python/v3 -suite: sending-data -order: 1 -sitemap: false ---- -# Learn about API authentication here: https://plotly.com/python/getting-started -# Find your api_key here: https://plotly.com/settings/api - -import plotly.plotly as py -from plotly.graph_objs import * - -new_data = Scatter(x=[3, 4], y=[3, 2] ) - -data = Data( [ new_data ] ) - -plot_url = py.plot(data, filename='extend plot', fileopt='extend') diff --git a/_posts/python-v3/chart-studio/sending-data/2015-04-09-overwrite.html b/_posts/python-v3/chart-studio/sending-data/2015-04-09-overwrite.html deleted file mode 100755 index 199a5330a..000000000 --- a/_posts/python-v3/chart-studio/sending-data/2015-04-09-overwrite.html +++ /dev/null @@ -1,19 +0,0 @@ ---- -name: Overwrite chart data with new data -description: The simplest and recommended way to update a chart remotely.
You can overwrite a chart's data with new data remotely, simply by including its file name in the filename kwarg.
Note that setting a filename overwrites the entire chart (i.e., style & layout settings are not preserved). -plot_url: http://i.imgur.com/VuobuN3.gif -arrangement: horizontal -language: python/v3 -suite: sending-data -order: 0 -sitemap: false ---- -# Learn about API authentication here: https://plotly.com/python/getting-started -# Find your api_key here: https://plotly.com/settings/api - -import plotly.plotly as py -from plotly.graph_objs import * - -data = Data([ Scatter(x=[1, 2], y=[3, 4]) ]) - -plot_url = py.plot(data, filename='my plot') diff --git a/_posts/python-v3/chart-studio/sending-data/2015-04-09-sending-data_index.html b/_posts/python-v3/chart-studio/sending-data/2015-04-09-sending-data_index.html deleted file mode 100644 index 3dda533e6..000000000 --- a/_posts/python-v3/chart-studio/sending-data/2015-04-09-sending-data_index.html +++ /dev/null @@ -1,13 +0,0 @@ ---- -name: Sending Data to Charts -permalink: python/v3/sending-data-to-charts/ -redirect_from: python/sending-data-to-charts/ -description: How to send data to charts in Python. Examples of overwriting charts with new data, extending traces, and adding new traces. -layout: base -language: python/v3 -thumbnail: thumbnail/ff-subplots.jpg -display_as: chart_studio -order: 4 ---- -{% assign examples = site.posts | where:"language","python/v3" | where:"suite","sending-data" | sort: "order" %} -{% include posts/auto_examples.html examples=examples %} diff --git a/_posts/python/2019-07-03-chart-studio-index.html b/_posts/python/2019-07-03-chart-studio-index.html deleted file mode 100644 index d5f1e7801..000000000 --- a/_posts/python/2019-07-03-chart-studio-index.html +++ /dev/null @@ -1,29 +0,0 @@ ---- -permalink: python/chart-studio/ -redirect_from: python/next/chart-studio/ -description: Plotly's Python graphing library makes interactive, publication-quality graphs online. Tutorials and tips about fundamental features of Plotly's Python API. -name: Chart Studio Docs -layout: langindex -language: python -display_as: chart_studio -thumbnail: thumbnail/mixed.jpg -page_type: example_index ---- - - -
-
- -
- -
-

Plotly Python Chart Studio Integration1

-

{{page.description}}


- {% include layouts/page-another-language.html %} -
-
-
-
- -{% assign languagelist = site.posts | where:"language","python" |where:"display_as","chart_studio" | where: "layout","base" | sort: "order" %} -{% include posts/documentation_eg.html %} diff --git a/_posts/python/chart-studio/2019-07-03-data-api.html b/_posts/python/chart-studio/2019-07-03-data-api.html deleted file mode 100644 index 9e28b6de5..000000000 --- a/_posts/python/chart-studio/2019-07-03-data-api.html +++ /dev/null @@ -1,699 +0,0 @@ ---- -description: How to upload data to Plotly from Python with the Plotly Grid API. -display_as: chart_studio -language: python -layout: base -name: Plots from Grids -order: 4 -page_type: example_index -permalink: python/data-api/ -thumbnail: thumbnail/table.jpg -v4upgrade: True ---- - -{% raw %} - -
-
-
-
-

Creating a Plotly Grid

You can instantiate a grid with data by either uploading tabular data to Plotly or by creating a Plotly grid using the API. To upload the grid we will use plotly.plotly.grid_ops.upload(). It takes the following arguments:

-
    -
  • grid (Grid Object): the actual grid object that you are uploading.
    • -
  • filename (str): name of the grid in your plotly account,
    • -
  • world_readable (bool): if True, the grid is public and can be viewed by anyone in your files. If False, it is private and can only be viewed by you.
    • -
  • auto_open (bool): if determines if the grid is opened in the browser or not.
    • -
-

You can run help(py.grid_ops.upload) for a more detailed description of these and all the arguments.

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In [1]:
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-
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import chart_studio
-import chart_studio.plotly as py
-import chart_studio.tools as tls
-import plotly.graph_objects as go
-from chart_studio.grid_objs import Column, Grid
-
-from datetime import datetime as dt
-import numpy as np
-from IPython.display import IFrame
-
-column_1 = Column(['a', 'b', 'c'], 'column 1')
-column_2 = Column([1, 2, 3], 'column 2') # Tabular data can be numbers, strings, or dates
-grid = Grid([column_1, column_2])
-url = py.grid_ops.upload(grid,
-                         filename='grid_ex_'+str(dt.now()),
-                         world_readable=True,
-                         auto_open=False)
-print(url)
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https://plotly.com/~PythonPlotBot/3534/
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View and Share your Grid

You can view your newly created grid at the url:

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In [2]:
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IFrame(src= url.rstrip('/') + ".embed", width="100%",height="200px", frameBorder="0")
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Out[2]:
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You are also able to view the grid in your list of files inside your organize folder.

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Upload Dataframes to Plotly

Along with uploading a grid, you can upload a Dataframe as well as convert it to raw data as a grid:

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In [3]:
-
-
-
import chart_studio.plotly as py
-import plotly.figure_factory as ff
-
-import pandas as pd
-
-df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_apple_stock.csv')
-df_head = df.head()
-table = ff.create_table(df_head)
-py.iplot(table, filename='dataframe_ex_preview')
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Out[3]:
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Making Graphs from Grids

Plotly graphs are usually described with data embedded in them. For example, here we place x and y data directly into our Histogram2dContour object:

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In [4]:
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-
x = np.random.randn(1000)
-y = np.random.randn(1000) + 1
-
-data = [
-    go.Histogram2dContour(
-        x=x,
-        y=y
-    )
-]
-
-py.iplot(data, filename='Example 2D Histogram Contour')
-
- -
-
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- -
Out[4]:
- - - -
- - - -
- -
- -
-
- -
-
-
-
-
-

We can also create graphs based off of references to columns of grids. Here, we'll upload several columns to our Plotly account:

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In [5]:
-
-
-
column_1 = Column(np.random.randn(1000), 'column 1')
-column_2 = Column(np.random.randn(1000)+1, 'column 2')
-column_3 = Column(np.random.randn(1000)+2, 'column 3')
-column_4 = Column(np.random.randn(1000)+3, 'column 4')
-
-grid = Grid([column_1, column_2, column_3, column_4])
-url = py.grid_ops.upload(grid, filename='randn_int_offset_'+str(dt.now()))
-print(url)
-
- -
-
-
- -
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- - -
- -
- - -
-
https://plotly.com/~PythonPlotBot/3537/
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In [6]:
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IFrame(src= url.rstrip('/') + ".embed", width="100%",height="200px", frameBorder="0")
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Out[6]:
- - - -
- - - -
- -
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- -
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Make Graph from Raw Data

Instead of placing data into x and y, we'll place our Grid columns into xsrc and ysrc:

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In [7]:
-
-
-
data = [
-    go.Histogram2dContour(
-        xsrc=grid[0],
-        ysrc=grid[1]
-    )
-]
-
-py.iplot(data, filename='2D Contour from Grid Data')
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- -
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- -
Out[7]:
- - - -
- - - -
- -
- -
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- -
-
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-
-

So, when you view the data, you'll see your original grid, not just the columns that compose this graph:

- -
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-
-

Attaching Meta Data to Grids

In Chart Studio Enterprise, you can upload and assign free-form JSON metadata to any grid object. This means that you can keep all of your raw data in one place, under one grid.

-

If you update the original data source, in the workspace or with our API, all of the graphs that are sourced from it will be updated as well. You can make multiple graphs from a single Grid and you can make a graph from multiple grids. You can also add rows and columns to existing grids programatically.

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In [8]:
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meta = {
-    "Month": "November",
-    "Experiment ID": "d3kbd",
-    "Operator": "James Murphy",
-    "Initial Conditions": {
-          "Voltage": 5.5
-    }
-}
-
-grid_url = py.grid_ops.upload(grid, filename='grid_with_metadata_'+str(dt.now()), meta=meta)
-print(url)
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https://plotly.com/~PythonPlotBot/3537/
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Reference

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In [9]:
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help(py.grid_ops)
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Help on class grid_ops in module chart_studio.plotly.plotly:
-
-class grid_ops(builtins.object)
- |  Interface to Plotly's Grid API.
- |  Plotly Grids are Plotly's tabular data object, rendered
- |  in an online spreadsheet. Plotly graphs can be made from
- |  references of columns of Plotly grid objects. Free-form
- |  JSON Metadata can be saved with Plotly grids.
- |  
- |  To create a Plotly grid in your Plotly account from Python,
- |  see `grid_ops.upload`.
- |  
- |  To add rows or columns to an existing Plotly grid, see
- |  `grid_ops.append_rows` and `grid_ops.append_columns`
- |  respectively.
- |  
- |  To delete one of your grid objects, see `grid_ops.delete`.
- |  
- |  Class methods defined here:
- |  
- |  append_columns(columns, grid=None, grid_url=None) from builtins.type
- |      Append columns to a Plotly grid.
- |      
- |      `columns` is an iterable of plotly.grid_objs.Column objects
- |      and only one of `grid` and `grid_url` needs to specified.
- |      
- |      `grid` is a ploty.grid_objs.Grid object that has already been
- |      uploaded to plotly with the grid_ops.upload method.
- |      
- |      `grid_url` is a unique URL of a `grid` in your plotly account.
- |      
- |      Usage example 1: Upload a grid to Plotly, and then append a column
- |      ```
- |      from plotly.grid_objs import Grid, Column
- |      import plotly.plotly as py
- |      column_1 = Column([1, 2, 3], 'time')
- |      grid = Grid([column_1])
- |      py.grid_ops.upload(grid, 'time vs voltage')
- |      
- |      # append a column to the grid
- |      column_2 = Column([4, 2, 5], 'voltage')
- |      py.grid_ops.append_columns([column_2], grid=grid)
- |      ```
- |      
- |      Usage example 2: Append a column to a grid that already exists on
- |                       Plotly
- |      ```
- |      from plotly.grid_objs import Grid, Column
- |      import plotly.plotly as py
- |      
- |      grid_url = 'https://plotly.com/~chris/3143'
- |      column_1 = Column([1, 2, 3], 'time')
- |      py.grid_ops.append_columns([column_1], grid_url=grid_url)
- |      ```
- |  
- |  append_rows(rows, grid=None, grid_url=None) from builtins.type
- |      Append rows to a Plotly grid.
- |      
- |      `rows` is an iterable of rows, where each row is a
- |      list of numbers, strings, or dates. The number of items
- |      in each row must be equal to the number of columns
- |      in the grid. If appending rows to a grid with columns of
- |      unequal length, Plotly will fill the columns with shorter
- |      length with empty strings.
- |      
- |      Only one of `grid` and `grid_url` needs to specified.
- |      
- |      `grid` is a ploty.grid_objs.Grid object that has already been
- |      uploaded to plotly with the grid_ops.upload method.
- |      
- |      `grid_url` is a unique URL of a `grid` in your plotly account.
- |      
- |      Usage example 1: Upload a grid to Plotly, and then append rows
- |      ```
- |      from plotly.grid_objs import Grid, Column
- |      import plotly.plotly as py
- |      column_1 = Column([1, 2, 3], 'time')
- |      column_2 = Column([5, 2, 7], 'voltage')
- |      grid = Grid([column_1, column_2])
- |      py.grid_ops.upload(grid, 'time vs voltage')
- |      
- |      # append a row to the grid
- |      row = [1, 5]
- |      py.grid_ops.append_rows([row], grid=grid)
- |      ```
- |      
- |      Usage example 2: Append a row to a grid that already exists on Plotly
- |      ```
- |      from plotly.grid_objs import Grid
- |      import plotly.plotly as py
- |      
- |      grid_url = 'https://plotly.com/~chris/3143'
- |      
- |      row = [1, 5]
- |      py.grid_ops.append_rows([row], grid=grid_url)
- |      ```
- |  
- |  delete(grid=None, grid_url=None) from builtins.type
- |      Delete a grid from your Plotly account.
- |      
- |      Only one of `grid` or `grid_url` needs to be specified.
- |      
- |      `grid` is a plotly.grid_objs.Grid object that has already
- |             been uploaded to Plotly.
- |      
- |      `grid_url` is the URL of the Plotly grid to delete
- |      
- |      Usage example 1: Upload a grid to plotly, then delete it
- |      ```
- |      from plotly.grid_objs import Grid, Column
- |      import plotly.plotly as py
- |      column_1 = Column([1, 2, 3], 'time')
- |      column_2 = Column([4, 2, 5], 'voltage')
- |      grid = Grid([column_1, column_2])
- |      py.grid_ops.upload(grid, 'time vs voltage')
- |      
- |      # now delete it, and free up that filename
- |      py.grid_ops.delete(grid)
- |      ```
- |      
- |      Usage example 2: Delete a plotly grid by url
- |      ```
- |      import plotly.plotly as py
- |      
- |      grid_url = 'https://plotly.com/~chris/3'
- |      py.grid_ops.delete(grid_url=grid_url)
- |      ```
- |  
- |  upload(grid, filename=None, world_readable=True, auto_open=True, meta=None) from builtins.type
- |      Upload a grid to your Plotly account with the specified filename.
- |      
- |      Positional arguments:
- |          - grid: A plotly.grid_objs.Grid object,
- |                  call `help(plotly.grid_ops.Grid)` for more info.
- |          - filename: Name of the grid to be saved in your Plotly account.
- |                      To save a grid in a folder in your Plotly account,
- |                      separate specify a filename with folders and filename
- |                      separated by backslashes (`/`).
- |                      If a grid, plot, or folder already exists with the same
- |                      filename, a `plotly.exceptions.RequestError` will be
- |                      thrown with status_code 409.  If filename is None,
- |                      and randomly generated filename will be used.
- |      
- |      Optional keyword arguments:
- |          - world_readable (default=True): make this grid publically (True)
- |                                           or privately (False) viewable.
- |          - auto_open (default=True): Automatically open this grid in
- |                                      the browser (True)
- |          - meta (default=None): Optional Metadata to associate with
- |                                 this grid.
- |                                 Metadata is any arbitrary
- |                                 JSON-encodable object, for example:
- |                                 `{"experiment name": "GaAs"}`
- |      
- |      Filenames must be unique. To overwrite a grid with the same filename,
- |      you'll first have to delete the grid with the blocking name. See
- |      `plotly.plotly.grid_ops.delete`.
- |      
- |      Usage example 1: Upload a plotly grid
- |      ```
- |      from plotly.grid_objs import Grid, Column
- |      import plotly.plotly as py
- |      column_1 = Column([1, 2, 3], 'time')
- |      column_2 = Column([4, 2, 5], 'voltage')
- |      grid = Grid([column_1, column_2])
- |      py.grid_ops.upload(grid, 'time vs voltage')
- |      ```
- |      
- |      Usage example 2: Make a graph based with data that is sourced
- |                       from a newly uploaded Plotly grid
- |      ```
- |      import plotly.plotly as py
- |      from plotly.grid_objs import Grid, Column
- |      from plotly.graph_objs import Scatter
- |      # Upload a grid
- |      column_1 = Column([1, 2, 3], 'time')
- |      column_2 = Column([4, 2, 5], 'voltage')
- |      grid = Grid([column_1, column_2])
- |      py.grid_ops.upload(grid, 'time vs voltage')
- |      
- |      # Build a Plotly graph object sourced from the
- |      # grid's columns
- |      trace = Scatter(xsrc=grid[0], ysrc=grid[1])
- |      py.plot([trace], filename='graph from grid')
- |      ```
- |  
- |  ----------------------------------------------------------------------
- |  Static methods defined here:
- |  
- |  ensure_uploaded(fid)
- |  
- |  ----------------------------------------------------------------------
- |  Data descriptors defined here:
- |  
- |  __dict__
- |      dictionary for instance variables (if defined)
- |  
- |  __weakref__
- |      list of weak references to the object (if defined)
-
-
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- -
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- -
- - - - -{% endraw %} diff --git a/_posts/python/chart-studio/2019-07-03-delete-plots.html b/_posts/python/chart-studio/2019-07-03-delete-plots.html deleted file mode 100644 index c5d6303b7..000000000 --- a/_posts/python/chart-studio/2019-07-03-delete-plots.html +++ /dev/null @@ -1,418 +0,0 @@ ---- -description: How to delete plotly graphs in python. -display_as: chart_studio -ipynb: ~notebook_demo/98 -language: python -layout: base -name: Deleting Plots -order: 7 -page_type: u-guide -permalink: python/delete-plots/ -thumbnail: thumbnail/delete.jpg -v4upgrade: True ---- - -{% raw %} - -
-
-
-
-

Imports and Credentials

In additional to importing python's requests and json packages, this tutorial also uses Plotly's REST API

-

First define YOUR username and api key and create auth and headers to use with requests

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-
In [1]:
-
-
-
import chart_studio
-import chart_studio.plotly as py
-
-import json
-import requests
-from requests.auth import HTTPBasicAuth
-
-username = 'private_plotly' # Replace with YOUR USERNAME
-api_key = 'k0yy0ztssk' # Replace with YOUR API KEY
-
-auth = HTTPBasicAuth(username, api_key)
-headers = {'Plotly-Client-Platform': 'python'}
-
-chart_studio.tools.set_credentials_file(username=username, api_key=api_key)
-
- -
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-
- -
-
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-
-

Trash and Restore

Create a plot and return the url to see the file id which will be used to delete the plot.

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In [2]:
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-
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url = py.plot({"data": [{"x": [1, 2, 3],
-                         "y": [4, 2, 4]}],
-               "layout": {"title": "Let's Trash This Plot<br>(then restore it)"}},
-              filename='trash example')
-
-url
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Out[2]:
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'https://plotly.com/~private_plotly/658/'
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Include the file id in your request.
The file id is your username:plot_id#

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In [3]:
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fid = username+':658'
-fid
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Out[3]:
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'private_plotly:658'
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The following request moves the plot from the organize folder into the trash.
Note: a successful trash request will return a Response [200].

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In [4]:
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requests.post('https://api.plot.ly/v2/files/'+fid+'/trash', auth=auth, headers=headers)
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Out[4]:
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<Response [200]>
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-

Now if you visit the url, the plot won't be there.
However, at this point, there is the option to restore the plot (i.e. move it out of trash and back to the organize folder) with the following request:

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PERMANENT Delete

This request CANNOT!!!!!!! be restored. -Only use permanent_delete when absolutely sure the plot is no longer needed.

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In [5]:
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-
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url = py.plot({"data": [{"x": [1, 2, 3],
-                         "y": [3, 2, 1]}],
-               "layout": {"title": "Let's Delete This Plot<br><b>FOREVER!!!!</b>"}},
-              filename='PERMANENT delete ex')
-url
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Out[5]:
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'https://plotly.com/~private_plotly/661/'
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In [6]:
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fid_permanent_delete = username+':661'
-fid_permanent_delete
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Out[6]:
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'private_plotly:661'
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To PERMANENTLY delete a plot, first move the plot to the trash (as seen above):

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In [7]:
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requests.post('https://api.plot.ly/v2/files/'+fid_permanent_delete+'/trash', auth=auth, headers=headers)
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Out[7]:
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<Response [200]>
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Then permanent delete.
-Note: a successful permanent delete request will return a Response [204] (No Content).

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In [8]:
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requests.delete('https://api.plot.ly/v2/files/'+fid_permanent_delete+'/permanent_delete', auth=auth, headers=headers)
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Out[8]:
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<Response [204]>
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Delete All Plots and Grids PERMANENTLY!

In order to delete all plots and grids permanently, you need to delete all of your plots first, then delete all the associated grids.

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In [ ]:
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def get_pages(username, page_size):
-    url = 'https://api.plot.ly/v2/folders/all?user='+username+'&page_size='+str(page_size)
-    response = requests.get(url, auth=auth, headers=headers)
-    if response.status_code != 200:
-        return
-    page = json.loads(response.content)
-    yield page
-    while True:
-        resource = page['children']['next']
-        if not resource:
-            break
-        response = requests.get(resource, auth=auth, headers=headers)
-        if response.status_code != 200:
-            break
-        page = json.loads(response.content)
-        yield page
-
-def permanently_delete_files(username, page_size=500, filetype_to_delete='plot'):
-    for page in get_pages(username, page_size):
-        for x in range(0, len(page['children']['results'])):
-            fid = page['children']['results'][x]['fid']
-            res = requests.get('https://api.plot.ly/v2/files/' + fid, auth=auth, headers=headers)
-            res.raise_for_status()
-            if res.status_code == 200:
-                json_res = json.loads(res.content)
-                if json_res['filetype'] == filetype_to_delete:
-                    # move to trash
-                    requests.post('https://api.plot.ly/v2/files/'+fid+'/trash', auth=auth, headers=headers)
-                    # permanently delete
-                    requests.delete('https://api.plot.ly/v2/files/'+fid+'/permanent_delete', auth=auth, headers=headers)
-
-permanently_delete_files(username, filetype_to_delete='plot')
-permanently_delete_files(username, filetype_to_delete='grid')
-
- -
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- -
- - - - -{% endraw %} diff --git a/_posts/python/chart-studio/2019-07-03-embedding-charts.html b/_posts/python/chart-studio/2019-07-03-embedding-charts.html deleted file mode 100644 index a1231ec5d..000000000 --- a/_posts/python/chart-studio/2019-07-03-embedding-charts.html +++ /dev/null @@ -1,65 +0,0 @@ ---- -description: How to embed plotly graphs with an iframe in HTML. -display_as: chart_studio -language: python -layout: base -name: Embedding Graphs in HTML -order: 5 -permalink: python/embedding-plotly-graphs-in-HTML/ -thumbnail: thumbnail/embed.jpg -v4upgrade: True ---- - -{% raw %} - -
-
-
-
-

Plotly graphs can be embedded in any HTML page. This includes IPython notebooks, Wordpress sites, dashboards, blogs, and more.

-

For more on embedding Plotly graphs in HTML documents, see our tutorial.

-

From Python, you can generate the HTML code to embed Plotly graphs with the plotly.tools.get_embed function.

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In [1]:
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import chart_studio.tools as tls
-
-tls.get_embed('https://plotly.com/~chris/1638')
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Out[1]:
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'<iframe id="igraph" scrolling="no" style="border:none;" seamless="seamless" src="https://plotly.com/~chris/1638.embed" height="525" width="100%"></iframe>'
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- - - - -{% endraw %} diff --git a/_posts/python/chart-studio/2019-07-03-get-requests.html b/_posts/python/chart-studio/2019-07-03-get-requests.html deleted file mode 100644 index 429da0b6e..000000000 --- a/_posts/python/chart-studio/2019-07-03-get-requests.html +++ /dev/null @@ -1,165 +0,0 @@ ---- -description: How to download Chart Studio users' public graphs and data into Python. -display_as: chart_studio -language: python -layout: base -name: Working With Chart Studio Graphs -order: 6 -permalink: python/working-with-chart-studio-graphs/ -redirect_from: python/get-requests -thumbnail: thumbnail/spectral.jpg -v4upgrade: True ---- - -{% raw %} - -
-
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-

Get and Change a Public Figure

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In [1]:
-
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import chart_studio.plotly as py
-# Learn about API authentication here: https://plotly.com/python/getting-started
-# Find your api_key here: https://plotly.com/settings/api
-
-fig = py.get_figure("https://plotly.com/~PlotBot/5")
-
-fig['layout']['title'] = "Never forget that title!"
-
-py.iplot(fig, filename="python-change_plot")
-
- -
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- -
Out[1]:
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Get Data and Change Plot

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In [2]:
-
-
-
import chart_studio.plotly as py
-import plotly.graph_objects as go
-# Learn about API authentication here: https://plotly.com/python/getting-started
-# Find your api_key here: https://plotly.com/settings/api
-
-data = py.get_figure("https://plotly.com/~PythonPlotBot/3483").data
-distance = [d['y'][0] for d in data]  # check out the data for yourself!
-
-fig = go.Figure()
-fig.add_histogram(y=distance, name="flyby distance", histnorm='probability')
-xaxis = dict(title="Probability for Flyby at this Distance")
-yaxis = dict(title="Distance from Earth (Earth Radii)")
-fig.update_layout(title="data source: https://plotly.com/~AlexHP/68", xaxis=xaxis, yaxis=yaxis)
-
-plot_url = py.plot(fig, filename="python-get-data")
-
- -
-
-
- -
-
-
-
-
-

Get and Replot a Public Figure with URL

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In [3]:
-
-
-
import chart_studio.plotly as py
-# Learn about API authentication here: https://plotly.com/python/getting-started
-# Find your api_key here: https://plotly.com/settings/api
-
-fig = py.get_figure("https://plotly.com/~PlotBot/5")
-
-plot_url = py.plot(fig, filename="python-replot1")
-
- -
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-
- -
-
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-
-
-

Get and Replot a Public Figure with ID

-
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In [4]:
-
-
-
import chart_studio.plotly as py
-# Learn about API authentication here: https://plotly.com/python/getting-started
-# Find your api_key here: https://plotly.com/settings/api
-
-fig = py.get_figure("PlotBot", 5)
-
-plot_url = py.plot(fig, filename="python-replot2")
-
- -
-
-
- -
- - - - -{% endraw %} diff --git a/_posts/python/chart-studio/2019-07-03-getting-started-with-chart-studio.html b/_posts/python/chart-studio/2019-07-03-getting-started-with-chart-studio.html deleted file mode 100644 index 491c73993..000000000 --- a/_posts/python/chart-studio/2019-07-03-getting-started-with-chart-studio.html +++ /dev/null @@ -1,996 +0,0 @@ ---- -description: Installation and Initialization Steps for Using Chart Studio in Python. -display_as: chart_studio -ipynb: ~notebook_demo/123/installation -language: python -layout: base -name: Getting Started with Chart Studio -order: 1 -page_type: example_index -permalink: python/getting-started-with-chart-studio/ -thumbnail: thumbnail/bubble.jpg -v4upgrade: True ---- - -{% raw %} - -
-
-
-

Installation

-
-
-
-
-
-
-

To install Chart Studio's python package, use the package manager pip inside your terminal.
-If you don't have pip installed on your machine, click here for pip's installation instructions. -
-
-$ pip install chart_studio -
or -
$ sudo pip install chart_studio -
-
-Plotly's Python package is installed alongside the Chart Studio package and it is updated frequently! To upgrade, run: -
-
-$ pip install plotly --upgrade

- -
-
-
-
-
-
-

Initialization for Online Plotting

Chart Studio provides a web-service for hosting graphs! Create a free account to get started. Graphs are saved inside your online Chart Studio account and you control the privacy. Public hosting is free, for private hosting, check out our paid plans. -
-
-After installing the Chart Studio package, you're ready to fire up python: -
-
-$ python -
-
-and set your credentials:

- -
-
-
-
-
-
In [1]:
-
- 
import chart_studio
-chart_studio.tools.set_credentials_file(username='DemoAccount', api_key='lr1c37zw')
-
-
- -
-
-
-
-

You'll need to replace 'DemoAccount' and 'lr1c37zw81' with your Plotly username and API key.
-Find your API key here. -
-
-The initialization step places a special .plotly/.credentials file in your home directory. Your ~/.plotly/.credentials file should look something like this: -

- -
{
-"username": "DemoAccount",
-"stream_ids": ["ylosqsyet5", "h2ct8btk1s", "oxz4fm883b"],
-"api_key": "lr1c37zw81"
-}
- -
-
-
-
-
-
-

Online Plot Privacy

Plot can be set to three different type of privacies: public, private or secret.

-
    -
  • public: Anyone can view this graph. It will appear in your profile and can appear in search engines. You do not need to be logged in to Chart Studio to view this chart.
    • -
  • private: Only you can view this plot. It will not appear in the Plotly feed, your profile, or search engines. You must be logged in to Plotly to view this graph. You can privately share this graph with other Chart Studio users in your online Chart Studio account and they will need to be logged in to view this plot.
    • -
  • secret: Anyone with this secret link can view this chart. It will not appear in the Chart Studio feed, your profile, or search engines. If it is embedded inside a webpage or an IPython notebook, anybody who is viewing that page will be able to view the graph. You do not need to be logged in to view this plot.
    • -
-

By default all plots are set to public. Users with free account have the permission to keep one private plot. If you need to save private plots, upgrade to a pro account. If you're a Personal or Professional user and would like the default setting for your plots to be private, you can edit your Chart Studio configuration:

- -
-
-
-
-
-
In [2]:
-
-
-
-
import chart_studio
-chart_studio.tools.set_config_file(world_readable=False, sharing='private')
- -
-
-
- -
-
-
-
-

For more examples on privacy settings please visit Python privacy documentation

- -
-
-
-
-
-
-

Special Instructions for Chart Studio Enterprise Users

-
-
-
-
-
-
-

Your API key for account on the public cloud will be different than the API key in Chart Studio Enterprise. Visit https://plotly.your-company.com/settings/api/ to find your Chart Studio Enterprise API key. Remember to replace "your-company.com" with the URL of your Chart Studio Enterprise server. -If your company has a Chart Studio Enterprise server, change the Python API endpoint so that it points to your company's Plotly server instead of Plotly's cloud. -
-
-In python, enter:

- -
-
-
-
-
-
In [3]:
-
-
- 
import chart_studio
-chart_studio.tools.set_config_file(
-    plotly_domain='https://plotly.your-company.com',
-    plotly_api_domain='https://plotly.your-company.com',
-    plotly_streaming_domain='https://stream-plotly.your-company.com'
-)
-
-
- -
-
-
-
-

Make sure to replace "your-company.com" with the URL of your Chart Studio Enterprise server.

- -
-
-
-
-
-
-

Additionally, you can set your configuration so that you generate private plots by default. For more information on privacy settings see: https://plotly.com/python/privacy/
-
-In python, enter:

- -
-
-
-
-
-
In [4]:
-
-
-
import chart_studio
-chart_studio.tools.set_config_file(
-plotly_domain='https://plotly.your-company.com',
-plotly_api_domain='https://plotly.your-company.com',
-plotly_streaming_domain='https://stream-plotly.your-company.com',
-world_readable=False,
-sharing='private'
-)
-
-
- -
-
-
-
-

Plotly Using virtualenv

Python's virtualenv allows us create multiple working Python environments which can each use different versions of packages. We can use virtualenv from the command line to create an environment using plotly.py version 3.3.0 and a separate one using plotly.py version 2.7.0. See the virtualenv documentation for more info.

-

Install virtualenv globally -
$ sudo pip install virtualenv

-

Create your virtualenvs -
$ mkdir ~/.virtualenvs -
$ cd ~/.virtualenvs -
$ python -m venv plotly2.7 -
$ python -m venv plotly3.3

-

Activate the virtualenv. -You will see the name of your virtualenv in parenthesis next to the input promt. -
$ source ~/.virtualenvs/plotly2.7/bin/activate -
(plotly2.7) $

-

Install plotly locally to virtualenv (note that we don't use sudo). -
(plotly2.7) $ pip install plotly==2.7

-

Deactivate to exit -
-(plotly2.7) $ deactivate -
$

- -
-
-
-
-
-
-

Jupyter Setup

Install Jupyter into a virtualenv -
$ source ~/.virtualenvs/plotly3.3/bin/activate -
(plotly3.3) $ pip install notebook

-

Start the Jupyter kernel from a virtualenv -
(plotly3.3) $ jupyter notebook

- -
-
-
-
-
-
-

Start Plotting Online

When plotting online, the plot and data will be saved to your cloud account. There are two methods for plotting online: py.plot() and py.iplot(). Both options create a unique url for the plot and save it in your Plotly account.

-
    -
  • Use py.plot() to return the unique url and optionally open the url.
    • -
  • Use py.iplot() when working in a Jupyter Notebook to display the plot in the notebook.
    • -
-

Copy and paste one of the following examples to create your first hosted Plotly graph using the Plotly Python library:

- -
-
-
-
-
-
In [5]:
-
-
- 
import chart_studio.plotly as py
-import plotly.graph_objects as go
-
-trace0 = go.Scatter(
-    x=[1, 2, 3, 4],
-    y=[10, 15, 13, 17]
-)
-trace1 = go.Scatter(
-    x=[1, 2, 3, 4],
-    y=[16, 5, 11, 9]
-)
-data = [trace0, trace1]
-
-py.plot(data, filename = 'basic-line', auto_open=True)
-
-
- -
-
- - -
- -
Out[5]:
- -
'https://plotly.com/~PythonPlotBot/27/'
- -
-
- -
-
-
-
-

Checkout the docstrings for more information:

- -
-
-
-
-
-
In [6]:
-
-
- 
import chart_studio.plotly as py
-(py.plot)
-
-
- -
-
- - -
- -
- - -
- 
Help on function plot in module chart_studio.plotly.plotly:
-
-plot(figure_or_data, validate=True, **plot_options)
-    Create a unique url for this plot in Plotly and optionally open url.
-
-    plot_options keyword arguments:
-    filename (string) -- the name that will be associated with this figure
-    auto_open (default=True) -- Toggle browser options
-        True: open this plot in a new browser tab
-        False: do not open plot in the browser, but do return the unique url
-    sharing ('public' | 'private' | 'secret') -- Toggle who can view this
-                                                    graph
-        - 'public': Anyone can view this graph. It will appear in your profile
-                    and can appear in search engines. You do not need to be
-                    logged in to Plotly to view this chart.
-        - 'private': Only you can view this plot. It will not appear in the
-                        Plotly feed, your profile, or search engines. You must be
-                        logged in to Plotly to view this graph. You can privately
-                        share this graph with other Plotly users in your online
-                        Plotly account and they will need to be logged in to
-                        view this plot.
-        - 'secret': Anyone with this secret link can view this chart. It will
-                    not appear in the Plotly feed, your profile, or search
-                    engines. If it is embedded inside a webpage or an IPython
-                    notebook, anybody who is viewing that page will be able to
-                    view the graph. You do not need to be logged in to view
-                    this plot.
-    world_readable (default=True) -- Deprecated: use "sharing".
-                                        Make this figure private/public
-
- -
-
- -
-
-
-
In [7]:
-
-
-
import chart_studio.plotly as py
-import plotly.graph_objects as go
-
-trace0 = go.Scatter(
-    x=[1, 2, 3, 4],
-    y=[10, 15, 13, 17]
-)
-trace1 = go.Scatter(
-    x=[1, 2, 3, 4],
-    y=[16, 5, 11, 9]
-)
-data = [trace0, trace1]
-
-py.iplot(data, filename = 'basic-line')
-
-
- -
-
- - -
- -
Out[7]:
- - - -
- - - -
- -
- -
-
- -
-
-
-
-

See more examples in our IPython notebook documentation or check out the py.iplot() docstring for more information.

- -
-
-
-
-
-
In [8]:
-
-
- 
import chart_studio.plotly as py
-help(py.iplot)
-
-
- -
-
- - -
- -
- - -
-
Help on function iplot in module chart_studio.plotly.plotly:
-
-iplot(figure_or_data, **plot_options)
-Create a unique url for this plot in Plotly and open in IPython.
-
-plot_options keyword arguments:
-filename (string) -- the name that will be associated with this figure
-sharing ('public' | 'private' | 'secret') -- Toggle who can view this graph
-- 'public': Anyone can view this graph. It will appear in your profile
-            and can appear in search engines. You do not need to be
-            logged in to Plotly to view this chart.
-- 'private': Only you can view this plot. It will not appear in the
-                Plotly feed, your profile, or search engines. You must be
-                logged in to Plotly to view this graph. You can privately
-                share this graph with other Plotly users in your online
-                Plotly account and they will need to be logged in to
-                view this plot.
-- 'secret': Anyone with this secret link can view this chart. It will
-            not appear in the Plotly feed, your profile, or search
-            engines. If it is embedded inside a webpage or an IPython
-            notebook, anybody who is viewing that page will be able to
-            view the graph. You do not need to be logged in to view
-            this plot.
-world_readable (default=True) -- Deprecated: use "sharing".
-                                Make this figure private/public
-
-
Help on function iplot in module chart_studio.plotly.plotly:
-
-iplot(figure_or_data, **plot_options)
-    Create a unique url for this plot in Plotly and open in IPython.
-
-    plot_options keyword arguments:
-    filename (string) -- the name that will be associated with this figure
-    sharing ('public' | 'private' | 'secret') -- Toggle who can view this graph
-        - 'public': Anyone can view this graph. It will appear in your profile
-                    and can appear in search engines. You do not need to be
-                    logged in to Plotly to view this chart.
-        - 'private': Only you can view this plot. It will not appear in the
-                     Plotly feed, your profile, or search engines. You must be
-                     logged in to Plotly to view this graph. You can privately
-                     share this graph with other Plotly users in your online
-                     Plotly account and they will need to be logged in to
-                     view this plot.
-        - 'secret': Anyone with this secret link can view this chart. It will
-                    not appear in the Plotly feed, your profile, or search
-                    engines. If it is embedded inside a webpage or an IPython
-                    notebook, anybody who is viewing that page will be able to
-                    view the graph. You do not need to be logged in to view
-                    this plot.
-    world_readable (default=True) -- Deprecated: use "sharing".
-                                     Make this figure private/public
-
- -
-
- -
-
-
-
-

You can also create plotly graphs with matplotlib syntax. Learn more in our matplotlib documentation.

- -
-
-
-
-
-
-

Initialization for Offline Plotting

Plotly allows you to create graphs offline and save them locally. There are also two methods for interactive plotting offline: plotly.io.write_html() and plotly.io.show().

-
    -
  • Use plotly.io.write_html() to create and standalone HTML that is saved locally and opened inside your web browser.
    • -
  • Use plotly.io.show() when working offline in a Jupyter Notebook to display the plot in the notebook.
    • -
-

For information on all of the ways that plotly figures can be displayed, see Displaying plotly figures with plotly for Python.

- -
-
-
-
-
-
-

Copy and paste one of the following examples to create your first offline Plotly graph using the Plotly Python library:

- -
-
-
-
-
-
In [9]:
-
-
- -

-import plotly.graph_objects as go
-import plotly.io as pio
-fig = go.Figure(go.Scatter(x=[1, 2, 3, 4], y=[4, 3, 2, 1]))
-fig.update_layout(title_text='hello world')
-pio.write_html(fig, file='hello_world.html', auto_open=True)
-
-
-
-
-
- - -
-
-
-
-

Learn more by calling help():

- -
-
-
-
-
-
In [10]:
-
-
- 
impoimport plotly
-help(plotly.io.write_html)
-
-
- -
-
- - -
- -
- - -
- 
Help on function write_html in module plotly.io._html:
-
-write_html(fig, file, config=None, auto_play=True, include_plotlyjs=True, include_mathjax=False, post_script=None, full_html=True, animation_opts=None, validate=True, default_width='100%', default_height='100%', auto_open=False)
-Write a figure to an HTML file representation
-
-Parameters
-----------
-fig:
-    Figure object or dict representing a figure
-file: str or writeable
-    A string representing a local file path or a writeable object
-    (e.g. an open file descriptor)
-config: dict or None (default None)
-    Plotly.js figure config options
-auto_play: bool (default=True)
-    Whether to automatically start the animation sequence on page load
-    if the figure contains frames. Has no effect if the figure does not
-    contain frames.
-include_plotlyjs: bool or string (default True)
-    Specifies how the plotly.js library is included/loaded in the output
-    div string.
-
-    If True, a script tag containing the plotly.js source code (~3MB)
-    is included in the output.  HTML files generated with this option are
-    fully self-contained and can be used offline.
-
-    If 'cdn', a script tag that references the plotly.js CDN is included
-    in the output. HTML files generated with this option are about 3MB
-    smaller than those generated with include_plotlyjs=True, but they
-    require an active internet connection in order to load the plotly.js
-    library.
-
-    If 'directory', a script tag is included that references an external
-    plotly.min.js bundle that is assumed to reside in the same
-    directory as the HTML file. If `file` is a string to a local file path
-    and `full_html` is True then
-
-    If 'directory', a script tag is included that references an external
-    plotly.min.js bundle that is assumed to reside in the same
-    directory as the HTML file.  If `file` is a string to a local file
-    path and `full_html` is True, then the plotly.min.js bundle is copied
-    into the directory of the resulting HTML file. If a file named
-    plotly.min.js already exists in the output directory then this file
-    is left unmodified and no copy is performed. HTML files generated
-    with this option can be used offline, but they require a copy of
-    the plotly.min.js bundle in the same directory. This option is
-    useful when many figures will be saved as HTML files in the same
-    directory because the plotly.js source code will be included only
-    once per output directory, rather than once per output file.
-
-    If 'require', Plotly.js is loaded using require.js.  This option
-    assumes that require.js is globally available and that it has been
-    globally configured to know how to find Plotly.js as 'plotly'.
-    This option is not advised when full_html=True as it will result
-    in a non-functional html file.
-
-    If a string that ends in '.js', a script tag is included that
-    references the specified path. This approach can be used to point
-    the resulting HTML file to an alternative CDN or local bundle.
-
-    If False, no script tag referencing plotly.js is included. This is
-    useful when the resulting div string will be placed inside an HTML
-    document that already loads plotly.js.  This option is not advised
-    when full_html=True as it will result in a non-functional html file.
-
-include_mathjax: bool or string (default False)
-    Specifies how the MathJax.js library is included in the output html
-    div string.  MathJax is required in order to display labels
-    with LaTeX typesetting.
-
-    If False, no script tag referencing MathJax.js will be included in the
-    output.
-
-    If 'cdn', a script tag that references a MathJax CDN location will be
-    included in the output.  HTML div strings generated with this option
-    will be able to display LaTeX typesetting as long as internet access
-    is available.
-
-    If a string that ends in '.js', a script tag is included that
-    references the specified path. This approach can be used to point the
-    resulting HTML div string to an alternative CDN.
-post_script: str or list or None (default None)
-    JavaScript snippet(s) to be included in the resulting div just after
-    plot creation.  The string(s) may include '{plot_id}' placeholders
-    that will then be replaced by the `id` of the div element that the
-    plotly.js figure is associated with.  One application for this script
-    is to install custom plotly.js event handlers.
-full_html: bool (default True)
-    If True, produce a string containing a complete HTML document
-    starting with an  tag.  If False, produce a string containing
-    a single 
 element.
-animation_opts: dict or None (default None)
-    dict of custom animation parameters to be passed to the function
-    Plotly.animate in Plotly.js. See
-    https://github.com/plotly/plotly.js/blob/master/src/plots/animation_attributes.js
-    for available options. Has no effect if the figure does not contain
-    frames, or auto_play is False.
-default_width, default_height: number or str (default '100%')
-    The default figure width/height to use if the provided figure does not
-    specify its own layout.width/layout.height property.  May be
-    specified in pixels as an integer (e.g. 500), or as a css width style
-    string (e.g. '500px', '100%').
-validate: bool (default True)
-    True if the figure should be validated before being converted to
-    JSON, False otherwise.
-auto_open: bool (default True
-    If True, open the saved file in a web browser after saving.
-    This argument only applies if `full_html` is True.
-Returns
--------
-str
-    Representation of figure as an HTML div string

-
- -
-
- -
-
-
-
In [11]:
-
-
-
import plotly.graph_objects as go
-import plotly.io as pio
-
-fig = go.Figure(go.Scatter(x=[1, 2, 3, 4], y=[4, 3, 2, 1]))
-fig.update_layout(title_text='hello world')
-pio.show(fig)
-
-
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-
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- - - -
- - -
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You can also call plotly.io.show directly from the go.Figure object.

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In [12]:
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fig.show()
-
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In [13]:
-
-
- 
import plotly
-help(plotly.io.show)
-
-
- -
-
- - -
- -
- - -
- 
Help on function show in module plotly.io._renderers:
-
-show(fig, renderer=None, validate=True, **kwargs)
-    Show a figure using either the default renderer(s) or the renderer(s)
-    specified by the renderer argument
-
-    Parameters
-    ----------
-    fig: dict of Figure
-        The Figure object or figure dict to display
-
-    renderer: str or None (default None)
-        A string containing the names of one or more registered renderers
-        (separated by '+' characters) or None.  If None, then the default
-        renderers specified in plotly.io.renderers.default are used.
-
-    validate: bool (default True)
-        True if the figure should be validated before being shown,
-        False otherwise.
-
-    Returns
-    -------
-    None
-
- -
-
- -
-
-
-
-

For more examples on plotting offline with Plotly in python please visit our offline documentation.

- -
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-
-
-
-
-

Using Plotly with Pandas

To use Plotly with Pandas first $ pip install pandas and then import pandas in your code like in the example below.

- -
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-
-
-
-
In [14]:
-
-
-
import chart_studio.plotly as py
-import plotly.graph_objects as go
-import pandas as pd
-
-df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminder2007.csv')
-
-fig = go.Figure(go.Scatter(x=df.gdpPercap, y=df.lifeExp, text=df.country, mode='markers', name='2007'))
-fig.update_xaxes(title_text='GDP per Capita', type='log')
-fig.update_yaxes(title_text='Life Expectancy')
-py.iplot(fig, filename='pandas-multiple-scatter')
-
-
- -
-
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- -
Out[14]:
- - - -
- - - -
- -
- -
-
- -
-
-
-
-

MORE EXAMPLES

Check out more examples and tutorials for using Plotly in python here!

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In [ ]:
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-
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- -
- - - - -{% endraw %} diff --git a/_posts/python/chart-studio/2019-07-03-ipython-notebook-tutorial.html b/_posts/python/chart-studio/2019-07-03-ipython-notebook-tutorial.html deleted file mode 100644 index 5f97258a6..000000000 --- a/_posts/python/chart-studio/2019-07-03-ipython-notebook-tutorial.html +++ /dev/null @@ -1,958 +0,0 @@ ---- -description: Jupyter notebook tutorial on how to install, run, and use Jupyter for interactive matplotlib plotting, data analysis, and publishing code -display_as: chart_studio -ipynb: ~chelsea_lyn/14070 -language: python -layout: base -name: Jupyter Notebook Tutorial -order: 9 -permalink: python/ipython-notebook-tutorial/ -redirect_from: ipython-notebooks/ipython-notebook-tutorial/ -thumbnail: thumbnail/ipythonnb.jpg -v4upgrade: True ---- - -{% raw %} - -
-
-
-
-

Introduction

Jupyter has a beautiful notebook that lets you write and execute code, analyze data, embed content, and share reproducible work. Jupyter Notebook (previously referred to as IPython Notebook) allows you to easily share your code, data, plots, and explanation in a sinle notebook. Publishing is flexible: PDF, HTML, ipynb, dashboards, slides, and more. Code cells are based on an input and output format. For example:

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In [1]:
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print("hello world")
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hello world
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Installation

There are a few ways to use a Jupyter Notebook:

-
    -
  • Install with pip. Open a terminal and type: $ pip install jupyter.
    • -
  • Windows users can install with setuptools.
    • -
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  • nteract allows users to work in a notebook enviornment via a desktop application.
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  • Microsoft Azure provides hosted access to Jupyter Notebooks.
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Getting Started

Once you've installed the Notebook, you start from your terminal by calling $ jupyter notebook. This will open a browser on a localhost to the URL of your Notebooks, by default http://127.0.0.1:8888. Windows users need to open up their Command Prompt. You'll see a dashboard with all your Notebooks. You can launch your Notebooks from there. The Notebook has the advantage of looking the same when you're coding and publishing. You just have all the options to move code, run cells, change kernels, and use Markdown when you're running a NB.

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Helpful Commands

- Tab Completion: Jupyter supports tab completion! You can type object_name.<TAB> to view an object’s attributes. For tips on cell magics, running Notebooks, and exploring objects, check out the Jupyter docs. -
- Help: provides an introduction and overview of features.

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help
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Type help() for interactive help, or help(object) for help about object.
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- Quick Reference: open quick reference by running:

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- Keyboard Shortcuts: Shift-Enter will run a cell, Ctrl-Enter will run a cell in-place, Alt-Enter will run a cell and insert another below. See more shortcuts here.

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Languages

The bulk of this tutorial discusses executing python code in Jupyter notebooks. You can also use Jupyter notebooks to execute R code. Skip down to the [R section] for more information on using IRkernel with Jupyter notebooks and graphing examples.

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Package Management

When installing packages in Jupyter, you either need to install the package in your actual shell, or run the ! prefix, e.g.:

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!pip install packagename
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You may want to reload submodules if you've edited the code in one. IPython comes with automatic reloading magic. You can reload all changed modules before executing a new line.

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%load_ext autoreload
-%autoreload 2
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Some useful packages that we'll use in this tutorial include:

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  • Pandas: import data via a url and create a dataframe to easily handle data for analysis and graphing. See examples of using Pandas here: https://plotly.com/pandas/.
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  • NumPy: a package for scientific computing with tools for algebra, random number generation, integrating with databases, and managing data. See examples of using NumPy here: https://plotly.com/numpy/.
    • -
  • SciPy: a Python-based ecosystem of packages for math, science, and engineering.
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  • Plotly: a graphing library for making interactive, publication-quality graphs. See examples of statistic, scientific, 3D charts, and more here: https://plotly.com/python.
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In [4]:
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import pandas as pd
-import numpy as np
-import scipy as sp
-import chart_studio.plotly as py
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Import Data

You can use pandas read_csv() function to import data. In the example below, we import a csv hosted on github and display it in a table using Plotly:

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In [5]:
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import chart_studio.plotly as py
-import plotly.figure_factory as ff
-import pandas as pd
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-df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv")
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-table = ff.create_table(df)
-py.iplot(table, filename='jupyter-table1')
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Use dataframe.column_title to index the dataframe:

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schools = df.School
-schools[0]
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'MIT'
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Most pandas functions also work on an entire dataframe. For example, calling std() calculates the standard deviation for each column.

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df.std()
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Women    12.813683
-Men      25.705289
-Gap      14.137084
-dtype: float64
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Plotting Inline

You can use Plotly's python API to plot inside your Jupyter Notebook by calling plotly.plotly.iplot() or plotly.offline.iplot() if working offline. Plotting in the notebook gives you the advantage of keeping your data analysis and plots in one place. Now we can do a bit of interactive plotting. Head to the Plotly getting started page to learn how to set your credentials. Calling the plot with iplot automaticallly generates an interactive version of the plot inside the Notebook in an iframe. See below:

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In [8]:
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import chart_studio.plotly as py
-import plotly.graph_objects as go
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-data = [go.Bar(x=df.School,
-            y=df.Gap)]
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-py.iplot(data, filename='jupyter-basic_bar')
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Plotting multiple traces and styling the chart with custom colors and titles is simple with Plotly syntax. Additionally, you can control the privacy with sharing set to public, private, or secret.

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In [9]:
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import chart_studio.plotly as py
-import plotly.graph_objects as go
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-trace_women = go.Bar(x=df.School,
-                  y=df.Women,
-                  name='Women',
-                  marker=dict(color='#ffcdd2'))
-
-trace_men = go.Bar(x=df.School,
-                y=df.Men,
-                name='Men',
-                marker=dict(color='#A2D5F2'))
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-trace_gap = go.Bar(x=df.School,
-                y=df.Gap,
-                name='Gap',
-                marker=dict(color='#59606D'))
-
-data = [trace_women, trace_men, trace_gap]
-
-layout = go.Layout(title="Average Earnings for Graduates",
-                xaxis=dict(title='School'),
-                yaxis=dict(title='Salary (in thousands)'))
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-fig = go.Figure(data=data, layout=layout)
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-py.iplot(fig, sharing='private', filename='jupyter-styled_bar')
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Now we have interactive charts displayed in our notebook. Hover on the chart to see the values for each bar, click and drag to zoom into a specific section or click on the legend to hide/show a trace.

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Plotting Interactive Maps

Plotly is now integrated with Mapbox. In this example we'll plot lattitude and longitude data of nuclear waste sites. To plot on Mapbox maps with Plotly you'll need a Mapbox account and a Mapbox Access Token which you can add to your Plotly settings.

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In [10]:
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import chart_studio.plotly as py
-import plotly.graph_objects as go
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-import pandas as pd
-
-# mapbox_access_token = 'ADD YOUR TOKEN HERE'
-
-df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/Nuclear%20Waste%20Sites%20on%20American%20Campuses.csv')
-site_lat = df.lat
-site_lon = df.lon
-locations_name = df.text
-
-data = [
-    go.Scattermapbox(
-        lat=site_lat,
-        lon=site_lon,
-        mode='markers',
-        marker=dict(
-            size=17,
-            color='rgb(255, 0, 0)',
-            opacity=0.7
-        ),
-        text=locations_name,
-        hoverinfo='text'
-    ),
-    go.Scattermapbox(
-        lat=site_lat,
-        lon=site_lon,
-        mode='markers',
-        marker=dict(
-            size=8,
-            color='rgb(242, 177, 172)',
-            opacity=0.7
-        ),
-        hoverinfo='none'
-    )]
-
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-layout = go.Layout(
-    title='Nuclear Waste Sites on Campus',
-    autosize=True,
-    hovermode='closest',
-    showlegend=False,
-    mapbox=dict(
-        accesstoken=mapbox_access_token,
-        bearing=0,
-        center=dict(
-            lat=38,
-            lon=-94
-        ),
-        pitch=0,
-        zoom=3,
-        style='light'
-    ),
-)
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-fig = dict(data=data, layout=layout)
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-py.iplot(fig, filename='jupyter-Nuclear Waste Sites on American Campuses')
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3D Plotting

Using Numpy and Plotly, we can make interactive 3D plots in the Notebook as well.

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In [11]:
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import chart_studio.plotly as py
-import plotly.graph_objects as go
-
-import numpy as np
-
-s = np.linspace(0, 2 * np.pi, 240)
-t = np.linspace(0, np.pi, 240)
-tGrid, sGrid = np.meshgrid(s, t)
-
-r = 2 + np.sin(7 * sGrid + 5 * tGrid)  # r = 2 + sin(7s+5t)
-x = r * np.cos(sGrid) * np.sin(tGrid)  # x = r*cos(s)*sin(t)
-y = r * np.sin(sGrid) * np.sin(tGrid)  # y = r*sin(s)*sin(t)
-z = r * np.cos(tGrid)                  # z = r*cos(t)
-
-surface = go.Surface(x=x, y=y, z=z)
-data = [surface]
-
-layout = go.Layout(
-    title='Parametric Plot',
-    scene=dict(
-        xaxis=dict(
-            gridcolor='rgb(255, 255, 255)',
-            zerolinecolor='rgb(255, 255, 255)',
-            showbackground=True,
-            backgroundcolor='rgb(230, 230,230)'
-        ),
-        yaxis=dict(
-            gridcolor='rgb(255, 255, 255)',
-            zerolinecolor='rgb(255, 255, 255)',
-            showbackground=True,
-            backgroundcolor='rgb(230, 230,230)'
-        ),
-        zaxis=dict(
-            gridcolor='rgb(255, 255, 255)',
-            zerolinecolor='rgb(255, 255, 255)',
-            showbackground=True,
-            backgroundcolor='rgb(230, 230,230)'
-        )
-    )
-)
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-fig = go.Figure(data=data, layout=layout)
-py.iplot(fig, filename='jupyter-parametric_plot')
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Animated Plots

Checkout Plotly's animation documentation to see how to create animated plots inline in Jupyter notebooks like the Gapminder plot displayed below: -https://plotly.com/~PythonPlotBot/231/

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Plot Controls & IPython widgets

Add sliders, buttons, and dropdowns to your inline chart:

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In [12]:
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import chart_studio.plotly as py
-import numpy as np
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-data = [dict(
-        visible = False,
-        line=dict(color='#00CED1', width=6),
-        name = '𝜈 = '+str(step),
-        x = np.arange(0,10,0.01),
-        y = np.sin(step*np.arange(0,10,0.01))) for step in np.arange(0,5,0.1)]
-data[10]['visible'] = True
-
-steps = []
-for i in range(len(data)):
-    step = dict(
-        method = 'restyle',
-        args = ['visible', [False] * len(data)],
-    )
-    step['args'][1][i] = True # Toggle i'th trace to "visible"
-    steps.append(step)
-
-sliders = [dict(
-    active = 10,
-    currentvalue = {"prefix": "Frequency: "},
-    pad = {"t": 50},
-    steps = steps
-)]
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-layout = dict(sliders=sliders)
-fig = dict(data=data, layout=layout)
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Additionally, IPython widgets allow you to add sliders, widgets, search boxes, and more to your Notebook. See the widget docs for more information. For others to be able to access your work, they'll need IPython. Or, you can use a cloud-based NB option so others can run your work. -
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Executing R Code

IRkernel, an R kernel for Jupyter, allows you to write and execute R code in a Jupyter notebook. Checkout the IRkernel documentation for some simple installation instructions. Once IRkernel is installed, open a Jupyter Notebook by calling $ jupyter notebook and use the New dropdown to select an R notebook.

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See a full R example Jupyter Notebook here: https://plotly.com/~chelsea_lyn/14069

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Additional Embed Features

We've seen how to embed Plotly tables and charts as iframes in the notebook, with IPython.display we can embed additional features, such a videos. For example, from YouTube:

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LaTeX

We can embed LaTeX inside a Notebook by putting a $$ around our math, then run the cell as a Markdown cell. For example, the cell below is $$c = \sqrt{a^2 + b^2}$$, but the Notebook renders the expression.

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$$c = \sqrt{a^2 + b^2}$$

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Or, you can display output from Python, as seen here.

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from IPython.display import display, Math, Latex
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-display(Math(r'F(k) = \int_{-\infty}^{\infty} f(x) e^{2\pi i k} dx'))
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Exporting & Publishing Notebooks

We can export the Notebook as an HTML, PDF, .py, .ipynb, Markdown, and reST file. You can also turn your NB into a slideshow. You can publish Jupyter Notebooks on Plotly. Simply visit plot.ly and select the + Create button in the upper right hand corner. Select Notebook and upload your Jupyter notebook (.ipynb) file! -The notebooks that you upload will be stored in your Plotly organize folder and hosted at a unique link to make sharing quick and easy. -See some example notebooks:

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Publishing Dashboards

Users publishing interactive graphs can also use Plotly's dashboarding tool to arrange plots with a drag and drop interface. These dashboards can be published, embedded, and shared.

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Publishing Dash Apps

For users looking to ship and productionize Python apps, dash is an assemblage of Flask, Socketio, Jinja, Plotly and boiler plate CSS and JS for easily creating data visualization web-apps with your Python data analysis backend. -
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For more Jupyter tutorials, checkout Plotly's python documentation: all documentation is written in jupyter notebooks that you can download and run yourself or checkout these user submitted examples!

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IPython Notebook Gallery

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- - - - -{% endraw %} diff --git a/_posts/python/chart-studio/2019-07-03-presentations-tool.html b/_posts/python/chart-studio/2019-07-03-presentations-tool.html deleted file mode 100644 index 57dc3927f..000000000 --- a/_posts/python/chart-studio/2019-07-03-presentations-tool.html +++ /dev/null @@ -1,655 +0,0 @@ ---- -description: How to create and publish a spectacle-presentation with the Python API. -display_as: chart_studio -language: python -layout: base -name: Presentations Tool -order: 2 -page_type: example_index -permalink: python/presentations-tool/ -thumbnail: thumbnail/pres_api.jpg -v4upgrade: True ---- - -{% raw %} - -
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Plotly Presentations

To use Plotly's Presentations API you will write your presentation code in a string of markdown and then pass that through the Presentations API function pres.Presentation(). This creates a JSON version of your presentation. To upload the presentation online pass it through py.presentation_ops.upload().

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In your string, use --- on a single line to seperate two slides. To put a title in your slide, put a line that starts with any number of #s. Only your first title will be appear in your slide. A title looks like:

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# slide title

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Anything that comes after the title will be put as text in your slide. Check out the example below to see this in action.

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Current Limitations

Boldface, italics and hypertext are not supported features of the Presentation API.

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Display in Jupyter

The function below generates HTML code to display the presentation in an iframe directly in Jupyter.

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def url_to_iframe(url, text=True):
-    html = ''
-    # style
-    html += '''<head>
-    <style>
-    div.textbox {
-        margin: 30px;
-        font-weight: bold;
-    }
-    </style>
-    </head>'
-    '''
-    # iframe
-    html += '<iframe src=' + url + '.embed#{} width=750 height=400 frameBorder="0"></iframe>'
-    if text:
-        html += '''<body>
-        <div class="textbox">
-            <p>Click on the presentation above and use left/right arrow keys to flip through the slides.</p>
-        </div>
-        </body>
-        '''
-    return html
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Simple Example

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In [13]:
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import chart_studio.plotly as py
-import chart_studio.presentation_objs as pres
-
-filename = 'simple-pres'
-markdown_string = """
-# slide 1
-There is only one slide.
-
----
-# slide 2
-Again, another slide on this page.
-
-"""
-
-my_pres = pres.Presentation(markdown_string)
-pres_url_0 = py.presentation_ops.upload(my_pres, filename)
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https://plotly.com/~AdamKulidjian/3700/simple-pres/

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import IPython
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-iframe_0 = url_to_iframe(pres_url_0, True)
-IPython.display.HTML(iframe_0)
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Click on the presentation above and use left/right arrow keys to flip through the slides.

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Insert Plotly Chart

If you want to insert a Plotly chart into your presentation, all you need to do is write a line in your presentation that takes the form:

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Plotly(url)

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where url is a Plotly url. For example:

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Plotly(https://plotly.com/~AdamKulidjian/3564)

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The Plotly url lines should be written on a separate line after your title line. You can put as many images in your slide as you want, as the API will arrange them on the slide automatically, but it is highly encouraged that you use 4 OR FEWER IMAGES PER SLIDE. This will produce the cleanest look.

-

Useful Tip:
-For Plotly charts it is HIGHLY ADVISED that you use a chart that has layout['autosize'] set to True. If it is False the image may be cropped or only partially visible when it appears in the presentation slide.

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In [15]:
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import chart_studio.plotly as py
-import chart_studio.presentation_objs as pres
-
-filename = 'pres-with-plotly-chart'
-markdown_string = """
-# 3D scatterplots
-3D Scatterplot are just a collection of balls in a 3D cartesian space each of which have assigned properties like color, size, and more.
-
----
-# simple 3d scatterplot
-
-Plotly(https://plotly.com/~AdamKulidjian/3698)
----
-# different colorscales
-
-There are various colorscales and colorschemes to try in Plotly. Check out plotly.colors to find a list of valid and available colorscales.
-
-Plotly(https://plotly.com/~AdamKulidjian/3582)
-Plotly(https://plotly.com/~AdamKulidjian/3698)
-"""
-
-my_pres = pres.Presentation(markdown_string)
-pres_url_1 = py.presentation_ops.upload(my_pres, filename)
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https://plotly.com/~AdamKulidjian/3710/pres-with-plotly-chart/

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In [16]:
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import IPython
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-iframe_1 = url_to_iframe(pres_url_1, True)
-IPython.display.HTML(iframe_1)
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Click on the presentation above and use left/right arrow keys to flip through the slides.

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Insert Web Images

To insert an image from the web, insert the a Image(url) where url is the image url.

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In [17]:
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import chart_studio.plotly as py
-import chart_studio.presentation_objs as pres
-
-filename = 'pres-with-images'
-markdown_string = """
-# Animals of the Wild
----
-# The Lion
-
-Panthera leo is one of the big cats in the Felidae family and a member of genus Panthera. It has been listed as Vulnerable on the IUCN Red List since 1996, as populations in African range countries declined by about 43% since the early 1990s. Lion populations are untenable outside designated protected areas. Although the cause of the decline is not fully understood, habitat loss and conflicts with humans are the greatest causes of concern. The West African lion population is listed as Critically Endangered since 2016. The only lion population in Asia survives in and around India's Gir Forest National Park and is listed as Endangered since 1986.
-
-Image(https://i.pinimg.com/736x/da/af/73/daaf73960eb5a21d6bca748195f12052--lion-photography-lion-kings.jpg)
----
-# The Giraffe
-
-The giraffe is a genus of African even-toed ungulate mammals, the tallest living terrestrial animals and the largest ruminants. The genus currently consists of one species, Giraffa camelopardalis, the type species. Seven other species are extinct, prehistoric species known from fossils. Taxonomic classifications of one to eight extant giraffe species have been described, based upon research into the mitochondrial and nuclear DNA, as well as morphological measurements of Giraffa, but the IUCN currently recognizes only one species with nine subspecies.
-
-Image(https://img.purch.com/w/192/aHR0cDovL3d3dy5saXZlc2NpZW5jZS5jb20vaW1hZ2VzL2kvMDAwLzA2OC8wOTQvaTMwMC9naXJhZmZlLmpwZz8xNDA1MDA4NDQy)
-Image(https://upload.wikimedia.org/wikipedia/commons/9/9f/Giraffe_standing.jpg)
-
-"""
-
-my_pres = pres.Presentation(markdown_string)
-pres_url_2 = py.presentation_ops.upload(my_pres, filename)
-
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https://plotly.com/~AdamKulidjian/3702/pres-with-images/

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import IPython
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-iframe_2 = url_to_iframe(pres_url_2, True)
-IPython.display.HTML(iframe_2)
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Click on the presentation above and use left/right arrow keys to flip through the slides.

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Image Stretch

If you want to ensure that your image maintains its original width:height ratio, include the parameter imgStretch=False in your pres.Presentation() function call.

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import chart_studio.plotly as py
-import chart_studio.presentation_objs as pres
-
-filename = 'pres-with-no-imgstretch'
-markdown_string = """
-# images in native aspect ratio
-
-Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png)
-Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png)
-Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png)
-Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png)
-Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png)
-"""
-
-my_pres = pres.Presentation(markdown_string, imgStretch=False)
-pres_url_3 = py.presentation_ops.upload(my_pres, filename)
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https://plotly.com/~AdamKulidjian/3703/pres-with-no-imgstretch/

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Transitions

You can specify how your want your slides to transition to one another. Just like in the Plotly Presentation Application, there are 4 types of transitions: slide, zoom, fade and spin.

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To apply any combination of these transition to a slide, just insert transitions at the top of the slide as follows:

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transition: slide, zoom

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transition: slide, zoom
-# slide title
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-import chart_studio.presentation_objs as pres
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-filename = 'pres-with-transitions'
-markdown_string = """
-transition: slide
-# slide
----
-transition: zoom
-# zoom
----
-transition: fade
-# fade
----
-transition: spin
-# spin
----
-transition: spin and slide
-# spin, slide
----
-transition: fade zoom
-# fade, zoom
----
-transition: slide, zoom, fade, spin, spin, spin, zoom, fade
-# slide, zoom, fade, spin
-
-"""
-
-my_pres = pres.Presentation(markdown_string, style='moods')
-pres_url_6 = py.presentation_ops.upload(my_pres, filename)
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help(py.presentation_ops)
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Help on class presentation_ops in module chart_studio.plotly.plotly:
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-class presentation_ops(builtins.object)
- |  Interface to Plotly's Spectacle-Presentations API.
- |  
- |  Class methods defined here:
- |  
- |  upload(presentation, filename, sharing='public', auto_open=True) from builtins.type
- |      Function for uploading presentations to Plotly.
- |      
- |      :param (dict) presentation: the JSON presentation to be uploaded. Use
- |          plotly.presentation_objs.Presentation to create presentations
- |          from a Markdown-like string.
- |      :param (str) filename: the name of the presentation to be saved in
- |          your Plotly account. Will overwrite a presentation of the same
- |          name if it already exists in your files.
- |      :param (str) sharing: can be set to either 'public', 'private'
- |          or 'secret'. If 'public', your presentation will be viewable by
- |          all other users. If 'private' only you can see your presentation.
- |          If it is set to 'secret', the url will be returned with a string
- |          of random characters appended to the url which is called a
- |          sharekey. The point of a sharekey is that it makes the url very
- |          hard to guess, but anyone with the url can view the presentation.
- |      :param (bool) auto_open: automatically opens the presentation in the
- |          browser.
- |      
- |      See the documentation online for examples.
- |  
- |  ----------------------------------------------------------------------
- |  Data descriptors defined here:
- |  
- |  __dict__
- |      dictionary for instance variables (if defined)
- |  
- |  __weakref__
- |      list of weak references to the object (if defined)
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- - - - -{% endraw %} diff --git a/_posts/python/chart-studio/2019-07-03-privacy.html b/_posts/python/chart-studio/2019-07-03-privacy.html deleted file mode 100644 index 4f6b670ee..000000000 --- a/_posts/python/chart-studio/2019-07-03-privacy.html +++ /dev/null @@ -1,499 +0,0 @@ ---- -description: How to set the privacy settings of plotly graphs in python. Three examples of different privacy options: public, private and secret. -display_as: chart_studio -ipynb: ~notebook_demo/97 -language: python -layout: base -name: Privacy -order: 3 -page_type: example_index -permalink: python/privacy/ -thumbnail: thumbnail/privacy.jpg -v4upgrade: True ---- - -{% raw %} - -
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Default Privacy

By default, plotly.iplot() and plotly.plot() create public graphs (which are free to create). With a plotly subscription you can easily make charts private or secret via the sharing argument.

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Public Graphs

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import chart_studio.plotly as py
-import plotly.graph_objects as go
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-data = [
-    go.Scatter(
-        x=[1, 2, 3],
-        y=[1, 3, 1]
-    )
-]
-
-py.iplot(data, filename='privacy-public', sharing='public')
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Below is the URL of this public plot. Anyone can view public plots even if they are not logged into Plotly. Go ahead and try it out:

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'https://plotly.com/~PythonPlotBot/2677/'
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Private Graphs

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py.iplot(data, filename='privacy-private', sharing='private')
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Below is the URL of the private plot above. Only the owner can view the private plot. You won't be able to view this plot, try it out:

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py.plot(data, filename='privacy-private', sharing='private')
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Secret Graphs

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Below is the URL of this secret plot. Anyone with the secret link can view this chart. However, it will not appear in the Plotly feed, your profile, or search engines. Go ahead and try it out:

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'https://plotly.com/~PythonPlotBot/475?share_key=UaGz0FTFLklnEd7XTKaqy8'
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Make All Future Plots Private

To make all future plots private, you can update your configuration file to create private plots by default:

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import chart_studio
-chart_studio.tools.set_config_file(world_readable=False, sharing='private')
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Make All Existing Plots Private

This example uses Plotly's REST API

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import json
-import requests
-from requests.auth import HTTPBasicAuth
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Define variables, including YOUR USERNAME and API KEY

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In [9]:
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username = 'private_plotly' # Replace with YOUR USERNAME
-api_key = 'k0yy0ztssk' # Replace with YOUR API KEY
-
-auth = HTTPBasicAuth(username, api_key)
-headers = {'Plotly-Client-Platform': 'python'}
-
-page_size = 500
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Collect filenames of ALL of your plots and
update world_readable of each plot with a PATCH request

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In [ ]:
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def get_pages(username, page_size):
-    url = 'https://api.plot.ly/v2/folders/all?user='+username+'&filetype=plot&page_size='+str(page_size)
-    response = requests.get(url, auth=auth, headers=headers)
-    if response.status_code != 200:
-        return
-    page = json.loads(response.content.decode('utf-8'))
-    yield page
-    while True:
-        resource = page['children']['next']
-        if not resource:
-            break
-        response = requests.get(resource, auth=auth, headers=headers)
-        if response.status_code != 200:
-            break
-        page = json.loads(response.content.decode('utf-8'))
-        yield page
-
-def make_all_plots_private(username, page_size=500):
-    for page in get_pages(username, page_size):
-        for x in range(0, len(page['children']['results'])):
-            fid = page['children']['results'][x]['fid']
-            requests.patch('https://api.plot.ly/v2/files/'+fid, {"world_readable": False}, auth=auth, headers=headers)
-    print('ALL of your plots are now private - visit: https://plotly.com/organize/home to view your private plots!')
-
-make_all_plots_private(username)
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Reference

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help(py.plot)
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Help on function plot in module chart_studio.plotly.plotly:
-
-plot(figure_or_data, validate=True, **plot_options)
-    Create a unique url for this plot in Plotly and optionally open url.
-    
-    plot_options keyword arguments:
-    filename (string) -- the name that will be associated with this figure
-    auto_open (default=True) -- Toggle browser options
-        True: open this plot in a new browser tab
-        False: do not open plot in the browser, but do return the unique url
-    sharing ('public' | 'private' | 'secret') -- Toggle who can view this
-                                                  graph
-        - 'public': Anyone can view this graph. It will appear in your profile
-                    and can appear in search engines. You do not need to be
-                    logged in to Plotly to view this chart.
-        - 'private': Only you can view this plot. It will not appear in the
-                     Plotly feed, your profile, or search engines. You must be
-                     logged in to Plotly to view this graph. You can privately
-                     share this graph with other Plotly users in your online
-                     Plotly account and they will need to be logged in to
-                     view this plot.
-        - 'secret': Anyone with this secret link can view this chart. It will
-                    not appear in the Plotly feed, your profile, or search
-                    engines. If it is embedded inside a webpage or an IPython
-                    notebook, anybody who is viewing that page will be able to
-                    view the graph. You do not need to be logged in to view
-                    this plot.
-    world_readable (default=True) -- Deprecated: use "sharing".
-                                     Make this figure private/public
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- - - - -{% endraw %} diff --git a/_posts/python/chart-studio/2019-07-03-proxy-configuration.html b/_posts/python/chart-studio/2019-07-03-proxy-configuration.html deleted file mode 100644 index 07d7aaf81..000000000 --- a/_posts/python/chart-studio/2019-07-03-proxy-configuration.html +++ /dev/null @@ -1,49 +0,0 @@ ---- -description: How to configure Plotly's Python API to work with corporate proxies -display_as: chart_studio -language: python -layout: base -name: Requests Behind Corporate Proxies -order: 8 -permalink: python/proxy-configuration/ -thumbnail: thumbnail/net.jpg -v4upgrade: True ---- - -{% raw %} - -
-
-
-
-

If you are behind a corporate firewall, you may see the error message:

- -
requests.exceptions.ConnectionError: ('Connection aborted.', TimeoutError(10060, ...)
-

Plotly uses the requests module to communicate with the Plotly server. You can configure proxies by setting the environment variables HTTP_PROXY and HTTPS_PROXY.

- -
$ export HTTP_PROXY="http://10.10.1.10:3128"
-$ export HTTPS_PROXY="http://10.10.1.10:1080"
-

To use HTTP Basic Auth with your proxy, use the http://user:password@host/ syntax:

- -
$ export HTTP_PROXY="http://user:pass@10.10.1.10:3128/"
-

Note that proxy URLs must include the scheme.

-

You may also see this error if your proxy variable is set but you are no longer behind the corporate proxy. Check if a proxy variable is set with:

- -
$ echo $HTTP_PROXY
-$ echo $HTTPS_PROXY
-

Still not working?

-

Log an issue

-

Contact support@plot.ly

-

Get in touch with your IT department, and ask them about corporate proxies.

-

Requests documentation on configuring proxies the requests documentation.

-

Plotly for IPython Notebooks is also available for offline use.

-

Chart Studio Enterprise is available for behind-the-firewall corporate installations.

- -
-
-
- - - - -{% endraw %} diff --git a/_posts/python/chart-studio/data-api.ipynb b/_posts/python/chart-studio/data-api.ipynb deleted file mode 100644 index b60364586..000000000 --- a/_posts/python/chart-studio/data-api.ipynb +++ /dev/null @@ -1,620 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Creating a Plotly Grid\n", - "You can instantiate a grid with data by either uploading tabular data to Plotly or by creating a Plotly `grid` using the API. To upload the grid we will use `plotly.plotly.grid_ops.upload()`. It takes the following arguments:\n", - "- `grid` (Grid Object): the actual grid object that you are uploading.\n", - "- `filename` (str): name of the grid in your plotly account,\n", - "- `world_readable` (bool): if `True`, the grid is `public` and can be viewed by anyone in your files. If `False`, it is private and can only be viewed by you.\n", - "- `auto_open` (bool): if determines if the grid is opened in the browser or not.\n", - "\n", - "You can run `help(py.grid_ops.upload)` for a more detailed description of these and all the arguments." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "https://plotly.com/~PythonPlotBot/3534/\n" - ] - } - ], - "source": [ - "import chart_studio\n", - "import chart_studio.plotly as py\n", - "import chart_studio.tools as tls\n", - "import plotly.graph_objects as go\n", - "from chart_studio.grid_objs import Column, Grid\n", - "\n", - "from datetime import datetime as dt\n", - "import numpy as np\n", - "from IPython.display import IFrame\n", - "\n", - "column_1 = Column(['a', 'b', 'c'], 'column 1')\n", - "column_2 = Column([1, 2, 3], 'column 2') # Tabular data can be numbers, strings, or dates\n", - "grid = Grid([column_1, column_2])\n", - "url = py.grid_ops.upload(grid,\n", - " filename='grid_ex_'+str(dt.now()),\n", - " world_readable=True,\n", - " auto_open=False)\n", - "print(url)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### View and Share your Grid\n", - "You can view your newly created grid at the `url`:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "IFrame(src= url.rstrip('/') + \".embed\", width=\"100%\",height=\"200px\", frameBorder=\"0\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You are also able to view the grid in your list of files inside your [organize folder](https://plotly.com/organize)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Upload Dataframes to Plotly\n", - "Along with uploading a grid, you can upload a Dataframe as well as convert it to raw data as a grid:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import chart_studio.plotly as py\n", - "import plotly.figure_factory as ff\n", - "\n", - "import pandas as pd\n", - "\n", - "df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_apple_stock.csv')\n", - "df_head = df.head()\n", - "table = ff.create_table(df_head)\n", - "py.iplot(table, filename='dataframe_ex_preview')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Making Graphs from Grids\n", - "Plotly graphs are usually described with data embedded in them. For example, here we place `x` and `y` data directly into our `Histogram2dContour` object:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x = np.random.randn(1000)\n", - "y = np.random.randn(1000) + 1\n", - "\n", - "data = [\n", - " go.Histogram2dContour(\n", - " x=x,\n", - " y=y\n", - " )\n", - "]\n", - "\n", - "py.iplot(data, filename='Example 2D Histogram Contour')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also create graphs based off of references to columns of grids. Here, we'll upload several `column`s to our Plotly account:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "https://plotly.com/~PythonPlotBot/3537/\n" - ] - } - ], - "source": [ - "column_1 = Column(np.random.randn(1000), 'column 1')\n", - "column_2 = Column(np.random.randn(1000)+1, 'column 2')\n", - "column_3 = Column(np.random.randn(1000)+2, 'column 3')\n", - "column_4 = Column(np.random.randn(1000)+3, 'column 4')\n", - "\n", - "grid = Grid([column_1, column_2, column_3, column_4])\n", - "url = py.grid_ops.upload(grid, filename='randn_int_offset_'+str(dt.now()))\n", - "print(url)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "IFrame(src= url.rstrip('/') + \".embed\", width=\"100%\",height=\"200px\", frameBorder=\"0\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Make Graph from Raw Data\n", - "Instead of placing data into `x` and `y`, we'll place our Grid columns into `xsrc` and `ysrc`:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data = [\n", - " go.Histogram2dContour(\n", - " xsrc=grid[0],\n", - " ysrc=grid[1]\n", - " )\n", - "]\n", - "\n", - "py.iplot(data, filename='2D Contour from Grid Data')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So, when you view the data, you'll see your original grid, not just the columns that compose this graph:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Attaching Meta Data to Grids\n", - "In [Chart Studio Enterprise](https://plotly.com/product/enterprise/), you can upload and assign free-form JSON `metadata` to any grid object. This means that you can keep all of your raw data in one place, under one grid.\n", - "\n", - "If you update the original data source, in the workspace or with our API, all of the graphs that are sourced from it will be updated as well. You can make multiple graphs from a single Grid and you can make a graph from multiple grids. You can also add rows and columns to existing grids programatically." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "https://plotly.com/~PythonPlotBot/3537/\n" - ] - } - ], - "source": [ - "meta = {\n", - " \"Month\": \"November\",\n", - " \"Experiment ID\": \"d3kbd\",\n", - " \"Operator\": \"James Murphy\",\n", - " \"Initial Conditions\": {\n", - " \"Voltage\": 5.5\n", - " }\n", - "}\n", - "\n", - "grid_url = py.grid_ops.upload(grid, filename='grid_with_metadata_'+str(dt.now()), meta=meta)\n", - "print(url)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Reference" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on class grid_ops in module chart_studio.plotly.plotly:\n", - "\n", - "class grid_ops(builtins.object)\n", - " | Interface to Plotly's Grid API.\n", - " | Plotly Grids are Plotly's tabular data object, rendered\n", - " | in an online spreadsheet. Plotly graphs can be made from\n", - " | references of columns of Plotly grid objects. Free-form\n", - " | JSON Metadata can be saved with Plotly grids.\n", - " | \n", - " | To create a Plotly grid in your Plotly account from Python,\n", - " | see `grid_ops.upload`.\n", - " | \n", - " | To add rows or columns to an existing Plotly grid, see\n", - " | `grid_ops.append_rows` and `grid_ops.append_columns`\n", - " | respectively.\n", - " | \n", - " | To delete one of your grid objects, see `grid_ops.delete`.\n", - " | \n", - " | Class methods defined here:\n", - " | \n", - " | append_columns(columns, grid=None, grid_url=None) from builtins.type\n", - " | Append columns to a Plotly grid.\n", - " | \n", - " | `columns` is an iterable of plotly.grid_objs.Column objects\n", - " | and only one of `grid` and `grid_url` needs to specified.\n", - " | \n", - " | `grid` is a ploty.grid_objs.Grid object that has already been\n", - " | uploaded to plotly with the grid_ops.upload method.\n", - " | \n", - " | `grid_url` is a unique URL of a `grid` in your plotly account.\n", - " | \n", - " | Usage example 1: Upload a grid to Plotly, and then append a column\n", - " | ```\n", - " | from plotly.grid_objs import Grid, Column\n", - " | import plotly.plotly as py\n", - " | column_1 = Column([1, 2, 3], 'time')\n", - " | grid = Grid([column_1])\n", - " | py.grid_ops.upload(grid, 'time vs voltage')\n", - " | \n", - " | # append a column to the grid\n", - " | column_2 = Column([4, 2, 5], 'voltage')\n", - " | py.grid_ops.append_columns([column_2], grid=grid)\n", - " | ```\n", - " | \n", - " | Usage example 2: Append a column to a grid that already exists on\n", - " | Plotly\n", - " | ```\n", - " | from plotly.grid_objs import Grid, Column\n", - " | import plotly.plotly as py\n", - " | \n", - " | grid_url = 'https://plotly.com/~chris/3143'\n", - " | column_1 = Column([1, 2, 3], 'time')\n", - " | py.grid_ops.append_columns([column_1], grid_url=grid_url)\n", - " | ```\n", - " | \n", - " | append_rows(rows, grid=None, grid_url=None) from builtins.type\n", - " | Append rows to a Plotly grid.\n", - " | \n", - " | `rows` is an iterable of rows, where each row is a\n", - " | list of numbers, strings, or dates. The number of items\n", - " | in each row must be equal to the number of columns\n", - " | in the grid. If appending rows to a grid with columns of\n", - " | unequal length, Plotly will fill the columns with shorter\n", - " | length with empty strings.\n", - " | \n", - " | Only one of `grid` and `grid_url` needs to specified.\n", - " | \n", - " | `grid` is a ploty.grid_objs.Grid object that has already been\n", - " | uploaded to plotly with the grid_ops.upload method.\n", - " | \n", - " | `grid_url` is a unique URL of a `grid` in your plotly account.\n", - " | \n", - " | Usage example 1: Upload a grid to Plotly, and then append rows\n", - " | ```\n", - " | from plotly.grid_objs import Grid, Column\n", - " | import plotly.plotly as py\n", - " | column_1 = Column([1, 2, 3], 'time')\n", - " | column_2 = Column([5, 2, 7], 'voltage')\n", - " | grid = Grid([column_1, column_2])\n", - " | py.grid_ops.upload(grid, 'time vs voltage')\n", - " | \n", - " | # append a row to the grid\n", - " | row = [1, 5]\n", - " | py.grid_ops.append_rows([row], grid=grid)\n", - " | ```\n", - " | \n", - " | Usage example 2: Append a row to a grid that already exists on Plotly\n", - " | ```\n", - " | from plotly.grid_objs import Grid\n", - " | import plotly.plotly as py\n", - " | \n", - " | grid_url = 'https://plotly.com/~chris/3143'\n", - " | \n", - " | row = [1, 5]\n", - " | py.grid_ops.append_rows([row], grid=grid_url)\n", - " | ```\n", - " | \n", - " | delete(grid=None, grid_url=None) from builtins.type\n", - " | Delete a grid from your Plotly account.\n", - " | \n", - " | Only one of `grid` or `grid_url` needs to be specified.\n", - " | \n", - " | `grid` is a plotly.grid_objs.Grid object that has already\n", - " | been uploaded to Plotly.\n", - " | \n", - " | `grid_url` is the URL of the Plotly grid to delete\n", - " | \n", - " | Usage example 1: Upload a grid to plotly, then delete it\n", - " | ```\n", - " | from plotly.grid_objs import Grid, Column\n", - " | import plotly.plotly as py\n", - " | column_1 = Column([1, 2, 3], 'time')\n", - " | column_2 = Column([4, 2, 5], 'voltage')\n", - " | grid = Grid([column_1, column_2])\n", - " | py.grid_ops.upload(grid, 'time vs voltage')\n", - " | \n", - " | # now delete it, and free up that filename\n", - " | py.grid_ops.delete(grid)\n", - " | ```\n", - " | \n", - " | Usage example 2: Delete a plotly grid by url\n", - " | ```\n", - " | import plotly.plotly as py\n", - " | \n", - " | grid_url = 'https://plotly.com/~chris/3'\n", - " | py.grid_ops.delete(grid_url=grid_url)\n", - " | ```\n", - " | \n", - " | upload(grid, filename=None, world_readable=True, auto_open=True, meta=None) from builtins.type\n", - " | Upload a grid to your Plotly account with the specified filename.\n", - " | \n", - " | Positional arguments:\n", - " | - grid: A plotly.grid_objs.Grid object,\n", - " | call `help(plotly.grid_ops.Grid)` for more info.\n", - " | - filename: Name of the grid to be saved in your Plotly account.\n", - " | To save a grid in a folder in your Plotly account,\n", - " | separate specify a filename with folders and filename\n", - " | separated by backslashes (`/`).\n", - " | If a grid, plot, or folder already exists with the same\n", - " | filename, a `plotly.exceptions.RequestError` will be\n", - " | thrown with status_code 409. If filename is None,\n", - " | and randomly generated filename will be used.\n", - " | \n", - " | Optional keyword arguments:\n", - " | - world_readable (default=True): make this grid publically (True)\n", - " | or privately (False) viewable.\n", - " | - auto_open (default=True): Automatically open this grid in\n", - " | the browser (True)\n", - " | - meta (default=None): Optional Metadata to associate with\n", - " | this grid.\n", - " | Metadata is any arbitrary\n", - " | JSON-encodable object, for example:\n", - " | `{\"experiment name\": \"GaAs\"}`\n", - " | \n", - " | Filenames must be unique. To overwrite a grid with the same filename,\n", - " | you'll first have to delete the grid with the blocking name. See\n", - " | `plotly.plotly.grid_ops.delete`.\n", - " | \n", - " | Usage example 1: Upload a plotly grid\n", - " | ```\n", - " | from plotly.grid_objs import Grid, Column\n", - " | import plotly.plotly as py\n", - " | column_1 = Column([1, 2, 3], 'time')\n", - " | column_2 = Column([4, 2, 5], 'voltage')\n", - " | grid = Grid([column_1, column_2])\n", - " | py.grid_ops.upload(grid, 'time vs voltage')\n", - " | ```\n", - " | \n", - " | Usage example 2: Make a graph based with data that is sourced\n", - " | from a newly uploaded Plotly grid\n", - " | ```\n", - " | import plotly.plotly as py\n", - " | from plotly.grid_objs import Grid, Column\n", - " | from plotly.graph_objs import Scatter\n", - " | # Upload a grid\n", - " | column_1 = Column([1, 2, 3], 'time')\n", - " | column_2 = Column([4, 2, 5], 'voltage')\n", - " | grid = Grid([column_1, column_2])\n", - " | py.grid_ops.upload(grid, 'time vs voltage')\n", - " | \n", - " | # Build a Plotly graph object sourced from the\n", - " | # grid's columns\n", - " | trace = Scatter(xsrc=grid[0], ysrc=grid[1])\n", - " | py.plot([trace], filename='graph from grid')\n", - " | ```\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Static methods defined here:\n", - " | \n", - " | ensure_uploaded(fid)\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Data descriptors defined here:\n", - " | \n", - " | __dict__\n", - " | dictionary for instance variables (if defined)\n", - " | \n", - " | __weakref__\n", - " | list of weak references to the object (if defined)\n", - "\n" - ] - } - ], - "source": [ - "help(py.grid_ops)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "notebook_metadata_filter": "all", - "text_representation": { - "extension": ".md", - "format_name": "markdown", - "format_version": "1.1", - "jupytext_version": "1.1.7" - } - }, - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.5" - }, - "plotly": { - "description": "How to upload data to Plotly from Python with the Plotly Grid API.", - "display_as": "chart_studio", - "has_thumbnail": true, - "language": "python", - "layout": "base", - "name": "Plots from Grids", - "order": 5, - "page_type": "u-guide", - "permalink": "python/data-api/", - "thumbnail": "thumbnail/table.jpg", - "title": "Plotly Data API", - "v4upgrade": true - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/_posts/python/chart-studio/data-api.md b/_posts/python/chart-studio/data-api.md deleted file mode 100644 index 9072abeaf..000000000 --- a/_posts/python/chart-studio/data-api.md +++ /dev/null @@ -1,170 +0,0 @@ ---- -jupyter: - jupytext: - notebook_metadata_filter: all - text_representation: - extension: .md - format_name: markdown - format_version: '1.1' - jupytext_version: 1.1.7 - kernelspec: - display_name: Python 3 - 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: 3.6.5 - plotly: - description: How to upload data to Plotly from Python with the Plotly Grid API. - display_as: chart_studio - language: python - layout: base - name: Plots from Grids - order: 5 - page_type: u-guide - permalink: python/data-api/ - thumbnail: thumbnail/table.jpg - v4upgrade: true ---- - -#### Creating a Plotly Grid1 -You can instantiate a grid with data by either uploading tabular data to Plotly or by creating a Plotly `grid` using the API. To upload the grid we will use `plotly.plotly.grid_ops.upload()`. It takes the following arguments: -- `grid` (Grid Object): the actual grid object that you are uploading. -- `filename` (str): name of the grid in your plotly account, -- `world_readable` (bool): if `True`, the grid is `public` and can be viewed by anyone in your files. If `False`, it is private and can only be viewed by you. -- `auto_open` (bool): if determines if the grid is opened in the browser or not. - -You can run `help(py.grid_ops.upload)` for a more detailed description of these and all the arguments. - -```python -import chart_studio -import chart_studio.plotly as py -import chart_studio.tools as tls -import plotly.graph_objects as go -from chart_studio.grid_objs import Column, Grid - -from datetime import datetime as dt -import numpy as np -from IPython.display import IFrame - -column_1 = Column(['a', 'b', 'c'], 'column 1') -column_2 = Column([1, 2, 3], 'column 2') # Tabular data can be numbers, strings, or dates -grid = Grid([column_1, column_2]) -url = py.grid_ops.upload(grid, - filename='grid_ex_'+str(dt.now()), - world_readable=True, - auto_open=False) -print(url) -``` - -#### View and Share your Grid -You can view your newly created grid at the `url`: - -```python -IFrame(src= url.rstrip('/') + ".embed", width="100%",height="200px", frameBorder="0") -``` - -You are also able to view the grid in your list of files inside your [organize folder](https://plotly.com/organize). - - -#### Upload Dataframes to Plotly -Along with uploading a grid, you can upload a Dataframe as well as convert it to raw data as a grid: - -```python -import chart_studio.plotly as py -import plotly.figure_factory as ff - -import pandas as pd - -df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_apple_stock.csv') -df_head = df.head() -table = ff.create_table(df_head) -py.iplot(table, filename='dataframe_ex_preview') -``` - -#### Making Graphs from Grids -Plotly graphs are usually described with data embedded in them. For example, here we place `x` and `y` data directly into our `Histogram2dContour` object: - -```python -x = np.random.randn(1000) -y = np.random.randn(1000) + 1 - -data = [ - go.Histogram2dContour( - x=x, - y=y - ) -] - -py.iplot(data, filename='Example 2D Histogram Contour') -``` - -We can also create graphs based off of references to columns of grids. Here, we'll upload several `column`s to our Plotly account: - -```python -column_1 = Column(np.random.randn(1000), 'column 1') -column_2 = Column(np.random.randn(1000)+1, 'column 2') -column_3 = Column(np.random.randn(1000)+2, 'column 3') -column_4 = Column(np.random.randn(1000)+3, 'column 4') - -grid = Grid([column_1, column_2, column_3, column_4]) -url = py.grid_ops.upload(grid, filename='randn_int_offset_'+str(dt.now())) -print(url) -``` - -```python -IFrame(src= url.rstrip('/') + ".embed", width="100%",height="200px", frameBorder="0") -``` - -#### Make Graph from Raw Data -Instead of placing data into `x` and `y`, we'll place our Grid columns into `xsrc` and `ysrc`: - -```python -data = [ - go.Histogram2dContour( - xsrc=grid[0], - ysrc=grid[1] - ) -] - -py.iplot(data, filename='2D Contour from Grid Data') -``` - -So, when you view the data, you'll see your original grid, not just the columns that compose this graph: - - -#### Attaching Meta Data to Grids -In [Chart Studio Enterprise](https://plotly.com/product/enterprise/), you can upload and assign free-form JSON `metadata` to any grid object. This means that you can keep all of your raw data in one place, under one grid. - -If you update the original data source, in the workspace or with our API, all of the graphs that are sourced from it will be updated as well. You can make multiple graphs from a single Grid and you can make a graph from multiple grids. You can also add rows and columns to existing grids programatically. - -```python -meta = { - "Month": "November", - "Experiment ID": "d3kbd", - "Operator": "James Murphy", - "Initial Conditions": { - "Voltage": 5.5 - } -} - -grid_url = py.grid_ops.upload(grid, filename='grid_with_metadata_'+str(dt.now()), meta=meta) -print(url) -``` - -#### Reference - -```python -help(py.grid_ops) -``` - -```python - -``` diff --git a/_posts/python/chart-studio/delete-plots.ipynb b/_posts/python/chart-studio/delete-plots.ipynb deleted file mode 100644 index 46f3a4bb5..000000000 --- a/_posts/python/chart-studio/delete-plots.ipynb +++ /dev/null @@ -1,343 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Imports and Credentials\n", - "In additional to importing python's `requests` and `json` packages, this tutorial also uses [Plotly's REST API](https://api.plot.ly/v2/)\n", - "\n", - "First define YOUR [username and api key](https://plotly.com/settings/api) and create `auth` and `headers` to use with `requests`" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import chart_studio\n", - "import chart_studio.plotly as py\n", - "\n", - "import json\n", - "import requests\n", - "from requests.auth import HTTPBasicAuth\n", - "\n", - "username = 'private_plotly' # Replace with YOUR USERNAME\n", - "api_key = 'k0yy0ztssk' # Replace with YOUR API KEY\n", - "\n", - "auth = HTTPBasicAuth(username, api_key)\n", - "headers = {'Plotly-Client-Platform': 'python'}\n", - "\n", - "chart_studio.tools.set_credentials_file(username=username, api_key=api_key)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### [Trash](https://api.plot.ly/v2/files/#trash) and [Restore](https://api.plot.ly/v2/files/#restore)\n", - "Create a plot and return the url to see the file id which will be used to delete the plot." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'https://plotly.com/~private_plotly/658/'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "url = py.plot({\"data\": [{\"x\": [1, 2, 3],\n", - " \"y\": [4, 2, 4]}],\n", - " \"layout\": {\"title\": \"Let's Trash This Plot
(then restore it)\"}},\n", - " filename='trash example')\n", - "\n", - "url" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Include the file id in your request.
The file id is your `username:plot_id#`" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'private_plotly:658'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fid = username+':658'\n", - "fid" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following request moves the plot from the [organize folder](https://plotly.com/organize/home) into the trash.
Note: a successful trash request will return a `Response [200]`." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "requests.post('https://api.plot.ly/v2/files/'+fid+'/trash', auth=auth, headers=headers)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now if you visit the url, the plot won't be there.
However, at this point, there is the option to restore the plot (i.e. move it out of trash and back to the organize folder) with the following request:\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### [PERMANENT Delete](https://api.plot.ly/v2/files/#permanent_delete)\n", - "\n", - "This request CANNOT!!!!!!! be restored.\n", - "Only use `permanent_delete` when absolutely sure the plot is no longer needed.
" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'https://plotly.com/~private_plotly/661/'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "url = py.plot({\"data\": [{\"x\": [1, 2, 3],\n", - " \"y\": [3, 2, 1]}],\n", - " \"layout\": {\"title\": \"Let's Delete This Plot
FOREVER!!!!\"}},\n", - " filename='PERMANENT delete ex')\n", - "url" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'private_plotly:661'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fid_permanent_delete = username+':661'\n", - "fid_permanent_delete" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To PERMANENTLY delete a plot, first move the plot to the trash (as seen above):" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "requests.post('https://api.plot.ly/v2/files/'+fid_permanent_delete+'/trash', auth=auth, headers=headers)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then [permanent delete](https://api.plot.ly/v2/files/#permanent_delete).
\n", - "Note: a successful permanent delete request will return a `Response [204]` (No Content)." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "requests.delete('https://api.plot.ly/v2/files/'+fid_permanent_delete+'/permanent_delete', auth=auth, headers=headers)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Delete All Plots and Grids PERMANENTLY!\n", - "In order to delete all plots and grids permanently, you need to delete all of your plots first, then delete all the associated grids." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def get_pages(username, page_size):\n", - " url = 'https://api.plot.ly/v2/folders/all?user='+username+'&page_size='+str(page_size)\n", - " response = requests.get(url, auth=auth, headers=headers)\n", - " if response.status_code != 200:\n", - " return\n", - " page = json.loads(response.content)\n", - " yield page\n", - " while True:\n", - " resource = page['children']['next']\n", - " if not resource:\n", - " break\n", - " response = requests.get(resource, auth=auth, headers=headers)\n", - " if response.status_code != 200:\n", - " break\n", - " page = json.loads(response.content)\n", - " yield page\n", - "\n", - "def permanently_delete_files(username, page_size=500, filetype_to_delete='plot'):\n", - " for page in get_pages(username, page_size):\n", - " for x in range(0, len(page['children']['results'])):\n", - " fid = page['children']['results'][x]['fid']\n", - " res = requests.get('https://api.plot.ly/v2/files/' + fid, auth=auth, headers=headers)\n", - " res.raise_for_status()\n", - " if res.status_code == 200:\n", - " json_res = json.loads(res.content)\n", - " if json_res['filetype'] == filetype_to_delete:\n", - " # move to trash\n", - " requests.post('https://api.plot.ly/v2/files/'+fid+'/trash', auth=auth, headers=headers)\n", - " # permanently delete\n", - " requests.delete('https://api.plot.ly/v2/files/'+fid+'/permanent_delete', auth=auth, headers=headers)\n", - "\n", - "permanently_delete_files(username, filetype_to_delete='plot')\n", - "permanently_delete_files(username, filetype_to_delete='grid')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "notebook_metadata_filter": "all", - "text_representation": { - "extension": ".md", - "format_name": "markdown", - "format_version": "1.1", - "jupytext_version": "1.1.7" - } - }, - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.5" - }, - "plotly": { - "description": "How to delete plotly graphs in python.", - "display_as": "chart_studio", - "has_thumbnail": true, - "ipynb": "~notebook_demo/98", - "language": "python", - "layout": "base", - "name": "Deleting Plots", - "order": 9, - "page_type": "u-guide", - "permalink": "python/delete-plots/", - "thumbnail": "thumbnail/delete.jpg", - "v4upgrade": true - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/_posts/python/chart-studio/delete-plots.md b/_posts/python/chart-studio/delete-plots.md deleted file mode 100644 index 568974e9a..000000000 --- a/_posts/python/chart-studio/delete-plots.md +++ /dev/null @@ -1,163 +0,0 @@ ---- -jupyter: - jupytext: - notebook_metadata_filter: all - text_representation: - extension: .md - format_name: markdown - format_version: '1.1' - jupytext_version: 1.1.7 - kernelspec: - display_name: Python 3 - 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: 3.6.5 - plotly: - description: How to delete plotly graphs in python. - display_as: chart_studio - ipynb: ~notebook_demo/98 - language: python - layout: base - name: Deleting Plots - order: 9 - page_type: u-guide - permalink: python/delete-plots/ - thumbnail: thumbnail/delete.jpg - v4upgrade: true ---- - -#### Imports and Credentials -In additional to importing python's `requests` and `json` packages, this tutorial also uses [Plotly's REST API](https://api.plot.ly/v2/) - -First define YOUR [username and api key](https://plotly.com/settings/api) and create `auth` and `headers` to use with `requests` - -```python -import chart_studio -import chart_studio.plotly as py - -import json -import requests -from requests.auth import HTTPBasicAuth - -username = 'private_plotly' # Replace with YOUR USERNAME -api_key = 'k0yy0ztssk' # Replace with YOUR API KEY - -auth = HTTPBasicAuth(username, api_key) -headers = {'Plotly-Client-Platform': 'python'} - -chart_studio.tools.set_credentials_file(username=username, api_key=api_key) -``` - -#### [Trash](https://api.plot.ly/v2/files/#trash) and [Restore](https://api.plot.ly/v2/files/#restore) -Create a plot and return the url to see the file id which will be used to delete the plot. - -```python -url = py.plot({"data": [{"x": [1, 2, 3], - "y": [4, 2, 4]}], - "layout": {"title": "Let's Trash This Plot
(then restore it)"}}, - filename='trash example') - -url -``` - -Include the file id in your request.
The file id is your `username:plot_id#` - -```python -fid = username+':658' -fid -``` - -The following request moves the plot from the [organize folder](https://plotly.com/organize/home) into the trash.
Note: a successful trash request will return a `Response [200]`. - -```python -requests.post('https://api.plot.ly/v2/files/'+fid+'/trash', auth=auth, headers=headers) -``` - -Now if you visit the url, the plot won't be there.
However, at this point, there is the option to restore the plot (i.e. move it out of trash and back to the organize folder) with the following request: - - - - - -#### [PERMANENT Delete](https://api.plot.ly/v2/files/#permanent_delete) - -This request CANNOT!!!!!!! be restored. -Only use `permanent_delete` when absolutely sure the plot is no longer needed.
- -```python -url = py.plot({"data": [{"x": [1, 2, 3], - "y": [3, 2, 1]}], - "layout": {"title": "Let's Delete This Plot
FOREVER!!!!"}}, - filename='PERMANENT delete ex') -url -``` - -```python -fid_permanent_delete = username+':661' -fid_permanent_delete -``` - -To PERMANENTLY delete a plot, first move the plot to the trash (as seen above): - -```python -requests.post('https://api.plot.ly/v2/files/'+fid_permanent_delete+'/trash', auth=auth, headers=headers) -``` - -Then [permanent delete](https://api.plot.ly/v2/files/#permanent_delete).
-Note: a successful permanent delete request will return a `Response [204]` (No Content). - -```python -requests.delete('https://api.plot.ly/v2/files/'+fid_permanent_delete+'/permanent_delete', auth=auth, headers=headers) -``` - -#### Delete All Plots and Grids PERMANENTLY! -In order to delete all plots and grids permanently, you need to delete all of your plots first, then delete all the associated grids. - -```python -def get_pages(username, page_size): - url = 'https://api.plot.ly/v2/folders/all?user='+username+'&page_size='+str(page_size) - response = requests.get(url, auth=auth, headers=headers) - if response.status_code != 200: - return - page = json.loads(response.content) - yield page - while True: - resource = page['children']['next'] - if not resource: - break - response = requests.get(resource, auth=auth, headers=headers) - if response.status_code != 200: - break - page = json.loads(response.content) - yield page - -def permanently_delete_files(username, page_size=500, filetype_to_delete='plot'): - for page in get_pages(username, page_size): - for x in range(0, len(page['children']['results'])): - fid = page['children']['results'][x]['fid'] - res = requests.get('https://api.plot.ly/v2/files/' + fid, auth=auth, headers=headers) - res.raise_for_status() - if res.status_code == 200: - json_res = json.loads(res.content) - if json_res['filetype'] == filetype_to_delete: - # move to trash - requests.post('https://api.plot.ly/v2/files/'+fid+'/trash', auth=auth, headers=headers) - # permanently delete - requests.delete('https://api.plot.ly/v2/files/'+fid+'/permanent_delete', auth=auth, headers=headers) - -permanently_delete_files(username, filetype_to_delete='plot') -permanently_delete_files(username, filetype_to_delete='grid') -``` - -```python - -``` diff --git a/_posts/python/chart-studio/embedding-charts.ipynb b/_posts/python/chart-studio/embedding-charts.ipynb deleted file mode 100644 index 8057ab8be..000000000 --- a/_posts/python/chart-studio/embedding-charts.ipynb +++ /dev/null @@ -1,87 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotly graphs can be embedded in any HTML page. This includes [IPython notebooks](https://plotly.com/ipython-notebooks/), [Wordpress sites](https://wordpress.org/plugins/wp-plotly), dashboards, blogs, and more.\n", - "\n", - "For more on embedding Plotly graphs in HTML documents, [see our tutorial](https://plotly.com/how-to-embed-plotly-graphs-in-websites).\n", - "\n", - "From Python, you can generate the HTML code to embed Plotly graphs with the `plotly.tools.get_embed` function.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "''" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import chart_studio.tools as tls\n", - "\n", - "tls.get_embed('https://plotly.com/~chris/1638')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "notebook_metadata_filter": "all", - "text_representation": { - "extension": ".md", - "format_name": "markdown", - "format_version": "1.1", - "jupytext_version": "1.1.7" - } - }, - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.5" - }, - "plotly": { - "description": "How to embed plotly graphs with an iframe in HTML.", - "display_as": "chart_studio", - "has_thumbnail": true, - "language": "python", - "layout": "base", - "name": "Embedding Graphs in HTML", - "order": 6, - "permalink": "python/embedding-plotly-graphs-in-HTML/", - "thumbnail": "thumbnail/embed.jpg", - "title": "Python Embedding Graphs in HTML | Examples | Plotly", - "v4upgrade": true - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/_posts/python/chart-studio/embedding-charts.md b/_posts/python/chart-studio/embedding-charts.md deleted file mode 100644 index 2015537a5..000000000 --- a/_posts/python/chart-studio/embedding-charts.md +++ /dev/null @@ -1,51 +0,0 @@ ---- -jupyter: - jupytext: - notebook_metadata_filter: all - text_representation: - extension: .md - format_name: markdown - format_version: '1.1' - jupytext_version: 1.1.7 - kernelspec: - display_name: Python 3 - 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: 3.6.5 - plotly: - description: How to embed plotly graphs with an iframe in HTML. - display_as: chart_studio - language: python - layout: base - name: Embedding Graphs in HTML - order: 6 - permalink: python/embedding-plotly-graphs-in-HTML/ - thumbnail: thumbnail/embed.jpg - v4upgrade: true ---- - -Plotly graphs can be embedded in any HTML page. This includes [IPython notebooks](https://plotly.com/ipython-notebooks/), [Wordpress sites](https://wordpress.org/plugins/wp-plotly), dashboards, blogs, and more. - -For more on embedding Plotly graphs in HTML documents, [see our tutorial](https://plotly.com/how-to-embed-plotly-graphs-in-websites). - -From Python, you can generate the HTML code to embed Plotly graphs with the `plotly.tools.get_embed` function. - - -```python -import chart_studio.tools as tls - -tls.get_embed('https://plotly.com/~chris/1638') -``` - -```python - -``` diff --git a/_posts/python/chart-studio/get-requests.ipynb b/_posts/python/chart-studio/get-requests.ipynb deleted file mode 100644 index d1f0ac469..000000000 --- a/_posts/python/chart-studio/get-requests.ipynb +++ /dev/null @@ -1,174 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Get and Change a Public Figure" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import chart_studio.plotly as py\n", - "# Learn about API authentication here: https://plotly.com/python/getting-started\n", - "# Find your api_key here: https://plotly.com/settings/api\n", - "\n", - "fig = py.get_figure(\"https://plotly.com/~PlotBot/5\")\n", - "\n", - "fig['layout']['title'] = \"Never forget that title!\"\n", - "\n", - "py.iplot(fig, filename=\"python-change_plot\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Get Data and Change Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import chart_studio.plotly as py\n", - "import plotly.graph_objects as go\n", - "# Learn about API authentication here: https://plotly.com/python/getting-started\n", - "# Find your api_key here: https://plotly.com/settings/api\n", - "\n", - "data = py.get_figure(\"https://plotly.com/~PythonPlotBot/3483\").data\n", - "distance = [d['y'][0] for d in data] # check out the data for yourself!\n", - "\n", - "fig = go.Figure()\n", - "fig.add_histogram(y=distance, name=\"flyby distance\", histnorm='probability')\n", - "xaxis = dict(title=\"Probability for Flyby at this Distance\")\n", - "yaxis = dict(title=\"Distance from Earth (Earth Radii)\")\n", - "fig.update_layout(title=\"data source: https://plotly.com/~AlexHP/68\", xaxis=xaxis, yaxis=yaxis)\n", - "\n", - "plot_url = py.plot(fig, filename=\"python-get-data\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Get and Replot a Public Figure with URL" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import chart_studio.plotly as py\n", - "# Learn about API authentication here: https://plotly.com/python/getting-started\n", - "# Find your api_key here: https://plotly.com/settings/api\n", - "\n", - "fig = py.get_figure(\"https://plotly.com/~PlotBot/5\")\n", - "\n", - "plot_url = py.plot(fig, filename=\"python-replot1\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Get and Replot a Public Figure with ID" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import chart_studio.plotly as py\n", - "# Learn about API authentication here: https://plotly.com/python/getting-started\n", - "# Find your api_key here: https://plotly.com/settings/api\n", - "\n", - "fig = py.get_figure(\"PlotBot\", 5)\n", - "\n", - "plot_url = py.plot(fig, filename=\"python-replot2\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "notebook_metadata_filter": "all", - "text_representation": { - "extension": ".md", - "format_name": "markdown", - "format_version": "1.1", - "jupytext_version": "1.1.7" - } - }, - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.5" - }, - "plotly": { - "description": "How to download plotly users's public graphs and data with python.", - "display_as": "chart_studio", - "has_thumbnail": true, - "language": "python", - "layout": "base", - "name": "Get Requests", - "order": 8, - "permalink": "python/get-requests/", - "thumbnail": "thumbnail/spectral.jpg", - "title": "Python Get Requests | Examples | Plotly", - "v4upgrade": true - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/_posts/python/chart-studio/get-requests.md b/_posts/python/chart-studio/get-requests.md deleted file mode 100644 index 58c13261f..000000000 --- a/_posts/python/chart-studio/get-requests.md +++ /dev/null @@ -1,96 +0,0 @@ ---- -jupyter: - jupytext: - notebook_metadata_filter: all - text_representation: - extension: .md - format_name: markdown - format_version: '1.1' - jupytext_version: 1.1.7 - kernelspec: - display_name: Python 3 - 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: 3.6.5 - plotly: - description: How to download plotly users's public graphs and data with python. - display_as: chart_studio - language: python - layout: base - name: Get Requests - order: 8 - permalink: python/get-requests/ - thumbnail: thumbnail/spectral.jpg - v4upgrade: true ---- - -#### Get and Change a Public Figure1 - -```python -import chart_studio.plotly as py -# Learn about API authentication here: https://plotly.com/python/getting-started -# Find your api_key here: https://plotly.com/settings/api - -fig = py.get_figure("https://plotly.com/~PlotBot/5") - -fig['layout']['title'] = "Never forget that title!" - -py.iplot(fig, filename="python-change_plot") -``` - -#### Get Data and Change Plot - -```python -import chart_studio.plotly as py -import plotly.graph_objects as go -# Learn about API authentication here: https://plotly.com/python/getting-started -# Find your api_key here: https://plotly.com/settings/api - -data = py.get_figure("https://plotly.com/~PythonPlotBot/3483").data -distance = [d['y'][0] for d in data] # check out the data for yourself! - -fig = go.Figure() -fig.add_histogram(y=distance, name="flyby distance", histnorm='probability') -xaxis = dict(title="Probability for Flyby at this Distance") -yaxis = dict(title="Distance from Earth (Earth Radii)") -fig.update_layout(title="data source: https://plotly.com/~AlexHP/68", xaxis=xaxis, yaxis=yaxis) - -plot_url = py.plot(fig, filename="python-get-data") -``` - -#### Get and Replot a Public Figure with URL - -```python -import chart_studio.plotly as py -# Learn about API authentication here: https://plotly.com/python/getting-started -# Find your api_key here: https://plotly.com/settings/api - -fig = py.get_figure("https://plotly.com/~PlotBot/5") - -plot_url = py.plot(fig, filename="python-replot1") -``` - -#### Get and Replot a Public Figure with ID - -```python -import chart_studio.plotly as py -# Learn about API authentication here: https://plotly.com/python/getting-started -# Find your api_key here: https://plotly.com/settings/api - -fig = py.get_figure("PlotBot", 5) - -plot_url = py.plot(fig, filename="python-replot2") -``` - -```python - -``` diff --git a/_posts/python/chart-studio/getting-started-with-chart-studio.ipynb b/_posts/python/chart-studio/getting-started-with-chart-studio.ipynb deleted file mode 100644 index a78c9ae69..000000000 --- a/_posts/python/chart-studio/getting-started-with-chart-studio.ipynb +++ /dev/null @@ -1,2394 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Installation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To install Chart Studio's python package, use the package manager **pip** inside your terminal.
\n", - "If you don't have **pip** installed on your machine, [click here](https://pip.pypa.io/en/latest/installing.html) for pip's installation instructions.\n", - "
\n", - "
\n", - "`$ pip install chart_studio`\n", - "
or\n", - "
`$ sudo pip install chart_studio`\n", - "
\n", - "
\n", - "Plotly's Python package is installed alongside the Chart Studio package and it is [updated frequently](https://github.com/plotly/plotly.py/blob/master/CHANGELOG.md)! To upgrade, run:\n", - "
\n", - "
\n", - "`$ pip install plotly --upgrade`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialization for Online Plotting\n", - "Chart Studio provides a web-service for hosting graphs! Create a [free account](https://plotly.com/api_signup) to get started. Graphs are saved inside your online Chart Studio account and you control the privacy. Public hosting is free, for private hosting, check out our [paid plans](https://plotly.com/products/cloud/).\n", - "
\n", - "
\n", - "After installing the Chart Studio package, you're ready to fire up python:\n", - "
\n", - "
\n", - "`$ python`\n", - "
\n", - "
\n", - "and set your credentials:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import chart_studio\n", - "chart_studio.tools.set_credentials_file(username='DemoAccount', api_key='lr1c37zw81')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You'll need to replace **'DemoAccount'** and **'lr1c37zw81'** with *your* Plotly username and [API key](https://plotly.com/settings/api).
\n", - "Find your API key [here](https://plotly.com/settings/api).\n", - "
\n", - "
\n", - "The initialization step places a special **.plotly/.credentials** file in your home directory. Your **~/.plotly/.credentials** file should look something like this:\n", - "
\n", - "```\n", - "{\n", - " \"username\": \"DemoAccount\",\n", - " \"stream_ids\": [\"ylosqsyet5\", \"h2ct8btk1s\", \"oxz4fm883b\"],\n", - " \"api_key\": \"lr1c37zw81\"\n", - "}\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Online Plot Privacy\n", - "\n", - "Plot can be set to three different type of privacies: public, private or secret.\n", - "- **public**: Anyone can view this graph. It will appear in your profile and can appear in search engines. You do not need to be logged in to Chart Studio to view this chart.\n", - "- **private**: Only you can view this plot. It will not appear in the Plotly feed, your profile, or search engines. You must be logged in to Plotly to view this graph. You can privately share this graph with other Chart Studio users in your online Chart Studio account and they will need to be logged in to view this plot.\n", - "- **secret**: Anyone with this secret link can view this chart. It will not appear in the Chart Studio feed, your profile, or search engines. If it is embedded inside a webpage or an IPython notebook, anybody who is viewing that page will be able to view the graph. You do not need to be logged in to view this plot.\n", - "\n", - "By default all plots are set to **public**. Users with free account have the permission to keep one private plot. If you need to save private plots, [upgrade to a pro account](https://plotly.com/plans). If you're a [Personal or Professional user](https://plotly.com/settings/subscription/?modal=true&utm_source=api-docs&utm_medium=support-oss) and would like the default setting for your plots to be private, you can edit your Chart Studio configuration:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import chart_studio\n", - "chart_studio.tools.set_config_file(world_readable=False,\n", - " sharing='private')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For more examples on privacy settings please visit [Python privacy documentation](https://plotly.com/python/privacy/)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Special Instructions for [Chart Studio Enterprise](https://plotly.com/product/enterprise/) Users" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Your API key for account on the public cloud will be different than the API key in Chart Studio Enterprise. Visit https://plotly.your-company.com/settings/api/ to find your Chart Studio Enterprise API key. Remember to replace \"your-company.com\" with the URL of your Chart Studio Enterprise server.\n", - "If your company has a Chart Studio Enterprise server, change the Python API endpoint so that it points to your company's Plotly server instead of Plotly's cloud.\n", - "
\n", - "
\n", - "In python, enter:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import chart_studio\n", - "chart_studio.tools.set_config_file(\n", - " plotly_domain='https://plotly.your-company.com',\n", - " plotly_api_domain='https://plotly.your-company.com',\n", - " plotly_streaming_domain='https://stream-plotly.your-company.com'\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Make sure to replace **\"your-company.com\"** with the URL of *your* Chart Studio Enterprise server." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Additionally, you can set your configuration so that you generate **private plots by default**. For more information on privacy settings see: https://plotly.com/python/privacy/
\n", - "
\n", - "In python, enter:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import chart_studio\n", - "chart_studio.tools.set_config_file(\n", - " plotly_domain='https://plotly.your-company.com',\n", - " plotly_api_domain='https://plotly.your-company.com',\n", - " plotly_streaming_domain='https://stream-plotly.your-company.com',\n", - " world_readable=False,\n", - " sharing='private'\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotly Using virtualenv\n", - "Python's `virtualenv` allows us create multiple working Python environments which can each use different versions of packages. We can use `virtualenv` from the command line to create an environment using plotly.py version 3.3.0 and a separate one using plotly.py version 2.7.0. See [the virtualenv documentation](https://virtualenv.pypa.io/en/stable) for more info.\n", - "\n", - "**Install virtualenv globally**\n", - "
`$ sudo pip install virtualenv`\n", - "\n", - "**Create your virtualenvs**\n", - "
`$ mkdir ~/.virtualenvs`\n", - "
`$ cd ~/.virtualenvs`\n", - "
`$ python -m venv plotly2.7`\n", - "
`$ python -m venv plotly3.3`\n", - "\n", - "**Activate the virtualenv.**\n", - "You will see the name of your virtualenv in parenthesis next to the input promt.\n", - "
`$ source ~/.virtualenvs/plotly2.7/bin/activate`\n", - "
`(plotly2.7) $`\n", - "\n", - "**Install plotly locally to virtualenv** (note that we don't use sudo).\n", - "
`(plotly2.7) $ pip install plotly==2.7`\n", - "\n", - "**Deactivate to exit**\n", - "
\n", - "`(plotly2.7) $ deactivate`\n", - "
`$`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Jupyter Setup\n", - "**Install Jupyter into a virtualenv**\n", - "
`$ source ~/.virtualenvs/plotly3.3/bin/activate`\n", - "
`(plotly3.3) $ pip install notebook`\n", - "\n", - "**Start the Jupyter kernel from a virtualenv**\n", - "
`(plotly3.3) $ jupyter notebook`\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Start Plotting Online\n", - "When plotting online, the plot and data will be saved to your cloud account. There are two methods for plotting online: `py.plot()` and `py.iplot()`. Both options create a unique url for the plot and save it in your Plotly account.\n", - "- Use `py.plot()` to return the unique url and optionally open the url.\n", - "- Use `py.iplot()` when working in a Jupyter Notebook to display the plot in the notebook.\n", - "\n", - "Copy and paste one of the following examples to create your first hosted Plotly graph using the Plotly Python library:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'https://plotly.com/~PythonPlotBot/27/'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import chart_studio.plotly as py\n", - "import plotly.graph_objects as go\n", - "\n", - "trace0 = go.Scatter(\n", - " x=[1, 2, 3, 4],\n", - " y=[10, 15, 13, 17]\n", - ")\n", - "trace1 = go.Scatter(\n", - " x=[1, 2, 3, 4],\n", - " y=[16, 5, 11, 9]\n", - ")\n", - "data = [trace0, trace1]\n", - "\n", - "py.plot(data, filename = 'basic-line', auto_open=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Checkout the docstrings for more information:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function plot in module chart_studio.plotly.plotly:\n", - "\n", - "plot(figure_or_data, validate=True, **plot_options)\n", - " Create a unique url for this plot in Plotly and optionally open url.\n", - " \n", - " plot_options keyword arguments:\n", - " filename (string) -- the name that will be associated with this figure\n", - " auto_open (default=True) -- Toggle browser options\n", - " True: open this plot in a new browser tab\n", - " False: do not open plot in the browser, but do return the unique url\n", - " sharing ('public' | 'private' | 'secret') -- Toggle who can view this\n", - " graph\n", - " - 'public': Anyone can view this graph. It will appear in your profile\n", - " and can appear in search engines. You do not need to be\n", - " logged in to Plotly to view this chart.\n", - " - 'private': Only you can view this plot. It will not appear in the\n", - " Plotly feed, your profile, or search engines. You must be\n", - " logged in to Plotly to view this graph. You can privately\n", - " share this graph with other Plotly users in your online\n", - " Plotly account and they will need to be logged in to\n", - " view this plot.\n", - " - 'secret': Anyone with this secret link can view this chart. It will\n", - " not appear in the Plotly feed, your profile, or search\n", - " engines. If it is embedded inside a webpage or an IPython\n", - " notebook, anybody who is viewing that page will be able to\n", - " view the graph. You do not need to be logged in to view\n", - " this plot.\n", - " world_readable (default=True) -- Deprecated: use \"sharing\".\n", - " Make this figure private/public\n", - "\n" - ] - } - ], - "source": [ - "import chart_studio.plotly as py\n", - "help(py.plot)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import chart_studio.plotly as py\n", - "import plotly.graph_objects as go\n", - "\n", - "trace0 = go.Scatter(\n", - " x=[1, 2, 3, 4],\n", - " y=[10, 15, 13, 17]\n", - ")\n", - "trace1 = go.Scatter(\n", - " x=[1, 2, 3, 4],\n", - " y=[16, 5, 11, 9]\n", - ")\n", - "data = [trace0, trace1]\n", - "\n", - "py.iplot(data, filename = 'basic-line')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "See more examples in our [IPython notebook documentation](https://plotly.com/ipython-notebooks/) or check out the `py.iplot()` docstring for more information." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function iplot in module chart_studio.plotly.plotly:\n", - "\n", - "iplot(figure_or_data, **plot_options)\n", - " Create a unique url for this plot in Plotly and open in IPython.\n", - " \n", - " plot_options keyword arguments:\n", - " filename (string) -- the name that will be associated with this figure\n", - " sharing ('public' | 'private' | 'secret') -- Toggle who can view this graph\n", - " - 'public': Anyone can view this graph. It will appear in your profile\n", - " and can appear in search engines. You do not need to be\n", - " logged in to Plotly to view this chart.\n", - " - 'private': Only you can view this plot. It will not appear in the\n", - " Plotly feed, your profile, or search engines. You must be\n", - " logged in to Plotly to view this graph. You can privately\n", - " share this graph with other Plotly users in your online\n", - " Plotly account and they will need to be logged in to\n", - " view this plot.\n", - " - 'secret': Anyone with this secret link can view this chart. It will\n", - " not appear in the Plotly feed, your profile, or search\n", - " engines. If it is embedded inside a webpage or an IPython\n", - " notebook, anybody who is viewing that page will be able to\n", - " view the graph. You do not need to be logged in to view\n", - " this plot.\n", - " world_readable (default=True) -- Deprecated: use \"sharing\".\n", - " Make this figure private/public\n", - "\n" - ] - } - ], - "source": [ - "import chart_studio.plotly as py\n", - "help(py.iplot)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also create plotly graphs with **matplotlib** syntax. Learn more in our [matplotlib documentation](https://plotly.com/matplotlib/)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialization for Offline Plotting\n", - "Plotly allows you to create graphs offline and save them locally. There are also two methods for interactive plotting offline: `plotly.io.write_html()` and `plotly.io.show()`.\n", - "- Use `plotly.io.write_html()` to create and standalone HTML that is saved locally and opened inside your web browser.\n", - "- Use `plotly.io.show()` when working offline in a Jupyter Notebook to display the plot in the notebook.\n", - "\n", - "For information on all of the ways that plotly figures can be displayed, see [*Displaying plotly figures with plotly for Python*](https://plotly.com/python/renderers/)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Copy and paste one of the following examples to create your first offline Plotly graph using the Plotly Python library:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "import plotly.graph_objects as go\n", - "import plotly.io as pio\n", - "\n", - "fig = go.Figure(go.Scatter(x=[1, 2, 3, 4], y=[4, 3, 2, 1]))\n", - "fig.update_layout(title_text='hello world')\n", - "pio.write_html(fig, file='hello_world.html', auto_open=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Learn more by calling `help()`:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function write_html in module plotly.io._html:\n", - "\n", - "write_html(fig, file, config=None, auto_play=True, include_plotlyjs=True, include_mathjax=False, post_script=None, full_html=True, animation_opts=None, validate=True, default_width='100%', default_height='100%', auto_open=False)\n", - " Write a figure to an HTML file representation\n", - " \n", - " Parameters\n", - " ----------\n", - " fig:\n", - " Figure object or dict representing a figure\n", - " file: str or writeable\n", - " A string representing a local file path or a writeable object\n", - " (e.g. an open file descriptor)\n", - " config: dict or None (default None)\n", - " Plotly.js figure config options\n", - " auto_play: bool (default=True)\n", - " Whether to automatically start the animation sequence on page load\n", - " if the figure contains frames. Has no effect if the figure does not\n", - " contain frames.\n", - " include_plotlyjs: bool or string (default True)\n", - " Specifies how the plotly.js library is included/loaded in the output\n", - " div string.\n", - " \n", - " If True, a script tag containing the plotly.js source code (~3MB)\n", - " is included in the output. HTML files generated with this option are\n", - " fully self-contained and can be used offline.\n", - " \n", - " If 'cdn', a script tag that references the plotly.js CDN is included\n", - " in the output. HTML files generated with this option are about 3MB\n", - " smaller than those generated with include_plotlyjs=True, but they\n", - " require an active internet connection in order to load the plotly.js\n", - " library.\n", - " \n", - " If 'directory', a script tag is included that references an external\n", - " plotly.min.js bundle that is assumed to reside in the same\n", - " directory as the HTML file. If `file` is a string to a local file path\n", - " and `full_html` is True then\n", - " \n", - " If 'directory', a script tag is included that references an external\n", - " plotly.min.js bundle that is assumed to reside in the same\n", - " directory as the HTML file. If `file` is a string to a local file\n", - " path and `full_html` is True, then the plotly.min.js bundle is copied\n", - " into the directory of the resulting HTML file. If a file named\n", - " plotly.min.js already exists in the output directory then this file\n", - " is left unmodified and no copy is performed. HTML files generated\n", - " with this option can be used offline, but they require a copy of\n", - " the plotly.min.js bundle in the same directory. This option is\n", - " useful when many figures will be saved as HTML files in the same\n", - " directory because the plotly.js source code will be included only\n", - " once per output directory, rather than once per output file.\n", - " \n", - " If 'require', Plotly.js is loaded using require.js. This option\n", - " assumes that require.js is globally available and that it has been\n", - " globally configured to know how to find Plotly.js as 'plotly'.\n", - " This option is not advised when full_html=True as it will result\n", - " in a non-functional html file.\n", - " \n", - " If a string that ends in '.js', a script tag is included that\n", - " references the specified path. This approach can be used to point\n", - " the resulting HTML file to an alternative CDN or local bundle.\n", - " \n", - " If False, no script tag referencing plotly.js is included. This is\n", - " useful when the resulting div string will be placed inside an HTML\n", - " document that already loads plotly.js. This option is not advised\n", - " when full_html=True as it will result in a non-functional html file.\n", - " \n", - " include_mathjax: bool or string (default False)\n", - " Specifies how the MathJax.js library is included in the output html\n", - " div string. MathJax is required in order to display labels\n", - " with LaTeX typesetting.\n", - " \n", - " If False, no script tag referencing MathJax.js will be included in the\n", - " output.\n", - " \n", - " If 'cdn', a script tag that references a MathJax CDN location will be\n", - " included in the output. HTML div strings generated with this option\n", - " will be able to display LaTeX typesetting as long as internet access\n", - " is available.\n", - " \n", - " If a string that ends in '.js', a script tag is included that\n", - " references the specified path. This approach can be used to point the\n", - " resulting HTML div string to an alternative CDN.\n", - " post_script: str or list or None (default None)\n", - " JavaScript snippet(s) to be included in the resulting div just after\n", - " plot creation. The string(s) may include '{plot_id}' placeholders\n", - " that will then be replaced by the `id` of the div element that the\n", - " plotly.js figure is associated with. One application for this script\n", - " is to install custom plotly.js event handlers.\n", - " full_html: bool (default True)\n", - " If True, produce a string containing a complete HTML document\n", - " starting with an tag. If False, produce a string containing\n", - " a single
element.\n", - " animation_opts: dict or None (default None)\n", - " dict of custom animation parameters to be passed to the function\n", - " Plotly.animate in Plotly.js. See\n", - " https://github.com/plotly/plotly.js/blob/master/src/plots/animation_attributes.js\n", - " for available options. Has no effect if the figure does not contain\n", - " frames, or auto_play is False.\n", - " default_width, default_height: number or str (default '100%')\n", - " The default figure width/height to use if the provided figure does not\n", - " specify its own layout.width/layout.height property. May be\n", - " specified in pixels as an integer (e.g. 500), or as a css width style\n", - " string (e.g. '500px', '100%').\n", - " validate: bool (default True)\n", - " True if the figure should be validated before being converted to\n", - " JSON, False otherwise.\n", - " auto_open: bool (default True\n", - " If True, open the saved file in a web browser after saving.\n", - " This argument only applies if `full_html` is True.\n", - " Returns\n", - " -------\n", - " str\n", - " Representation of figure as an HTML div string\n", - "\n" - ] - } - ], - "source": [ - "import plotly\n", - "help(plotly.io.write_html)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plotly.com" - }, - "data": [ - { - "type": "scatter", - "x": [ - 1, - 2, - 3, - 4 - ], - "y": [ - 4, - 3, - 2, - 1 - ] - } - ], - "layout": { - "autosize": true, - "template": { - "data": { - "bar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - } - }, - "type": "barpolar" - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "choropleth": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "type": "choropleth" - } - ], - "contour": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, 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\n", - " \n", - " \n", - "
\n", - " \n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import plotly.graph_objects as go\n", - "import plotly.io as pio\n", - "\n", - "fig = go.Figure(go.Scatter(x=[1, 2, 3, 4], y=[4, 3, 2, 1]))\n", - "fig.update_layout(title_text='hello world')\n", - "pio.show(fig)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also call plotly.io.show directly from the go.Figure object." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plotly.com" - }, - "data": [ - { - "type": "scatter", - "x": [ - 1, - 2, - 3, - 4 - ], - "y": [ - 4, - 3, - 2, - 1 - ] - } - ], - "layout": { - "autosize": true, - "template": { - "data": { - "bar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - } - }, - "type": "bar" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": 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\n", - " \n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function show in module plotly.io._renderers:\n", - "\n", - "show(fig, renderer=None, validate=True, **kwargs)\n", - " Show a figure using either the default renderer(s) or the renderer(s)\n", - " specified by the renderer argument\n", - " \n", - " Parameters\n", - " ----------\n", - " fig: dict of Figure\n", - " The Figure object or figure dict to display\n", - " \n", - " renderer: str or None (default None)\n", - " A string containing the names of one or more registered renderers\n", - " (separated by '+' characters) or None. If None, then the default\n", - " renderers specified in plotly.io.renderers.default are used.\n", - " \n", - " validate: bool (default True)\n", - " True if the figure should be validated before being shown,\n", - " False otherwise.\n", - " \n", - " Returns\n", - " -------\n", - " None\n", - "\n" - ] - } - ], - "source": [ - "import plotly\n", - "help(plotly.io.show)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For more examples on plotting offline with Plotly in python please visit our [offline documentation](https://plotly.com/python/offline/)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using Plotly with Pandas\n", - "\n", - "To use Plotly with Pandas first `$ pip install pandas` and then import pandas in your code like in the example below." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import chart_studio.plotly as py\n", - "import plotly.graph_objects as go\n", - "import pandas as pd\n", - "\n", - "df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminder2007.csv')\n", - "\n", - "fig = go.Figure(go.Scatter(x=df.gdpPercap, y=df.lifeExp, text=df.country, mode='markers', name='2007'))\n", - "fig.update_xaxes(title_text='GDP per Capita', type='log')\n", - "fig.update_yaxes(title_text='Life Expectancy')\n", - "\n", - "py.iplot(fig, filename='pandas-multiple-scatter')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### [MORE EXAMPLES](https://plotly.com/python/)\n", - "Check out more examples and tutorials for using Plotly in python [here](https://plotly.com/python)!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "notebook_metadata_filter": "all", - "text_representation": { - "extension": ".md", - "format_name": "markdown", - "format_version": "1.1", - "jupytext_version": "1.1.7" - } - }, - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.5" - }, - "plotly": { - "description": "Installation and Initialization Steps for Using Chart Studio in Python.", - "display_as": "chart_studio", - "has_thumbnail": true, - "ipynb": "~notebook_demo/123/installation", - "language": "python", - "layout": "base", - "name": "Getting Started with Plotly for Python", - "order": 0.1, - "page_type": "example_index", - "permalink": "python/getting-started-with-chart-studio/", - "thumbnail": "thumbnail/bubble.jpg", - "title": "Getting Started with Chart Studio for Python | plotly", - "v4upgrade": true - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/_posts/python/chart-studio/getting-started-with-chart-studio.md b/_posts/python/chart-studio/getting-started-with-chart-studio.md deleted file mode 100644 index e78b045e1..000000000 --- a/_posts/python/chart-studio/getting-started-with-chart-studio.md +++ /dev/null @@ -1,300 +0,0 @@ ---- -jupyter: - jupytext: - notebook_metadata_filter: all - text_representation: - extension: .md - format_name: markdown - format_version: "1.1" - jupytext_version: 1.1.7 - kernelspec: - display_name: Python 3 - 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: 3.6.5 - plotly: - description: Installation and Initialization Steps for Using Chart Studio in Python. - display_as: chart_studio - ipynb: ~notebook_demo/123/installation - language: python - layout: base - name: Getting Started with Plotly - order: 0.1 - page_type: example_index - permalink: python/getting-started-with-chart-studio/ - thumbnail: thumbnail/bubble.jpg - v4upgrade: true ---- - -### Installation - -To install Chart Studio's python package, use the package manager **pip** inside your terminal.
-If you don't have **pip** installed on your machine, [click here](https://pip.pypa.io/en/latest/installing.html) for pip's installation instructions. -
-
-`$ pip install chart_studio` -
or -
`$ sudo pip install chart_studio` -
-
-Plotly's Python package is installed alongside the Chart Studio package and it is [updated frequently](https://github.com/plotly/plotly.py/blob/master/CHANGELOG.md)! To upgrade, run: -
-
-`$ pip install plotly --upgrade` - -### Initialization for Online Plotting - -Chart Studio provides a web-service for hosting graphs! Create a [free account](https://plotly.com/api_signup) to get started. Graphs are saved inside your online Chart Studio account and you control the privacy. Public hosting is free, for private hosting, check out our [paid plans](https://plotly.com/products/cloud/). -
-
-After installing the Chart Studio package, you're ready to fire up python: -
-
-`$ python` -
-
-and set your credentials: - -```python -import chart_studio -chart_studio.tools.set_credentials_file(username='DemoAccount', api_key='lr1c37zw81') -``` - - - -You'll need to replace **'DemoAccount'** and **'lr1c37zw81'** with _your_ Plotly username and [API key](https://plotly.com/settings/api).
-Find your API key [here](https://plotly.com/settings/api). -
-
-The initialization step places a special **.plotly/.credentials** file in your home directory. Your **~/.plotly/.credentials** file should look something like this: -
- -``` -{ - "username": "DemoAccount", - "stream_ids": ["ylosqsyet5", "h2ct8btk1s", "oxz4fm883b"], - "api_key": "lr1c37zw81" -} -``` - - - -### Online Plot Privacy - -Plot can be set to three different type of privacies: public, private or secret. - -- **public**: Anyone can view this graph. It will appear in your profile and can appear in search engines. You do not need to be logged in to Chart Studio to view this chart. -- **private**: Only you can view this plot. It will not appear in the Plotly feed, your profile, or search engines. You must be logged in to Plotly to view this graph. You can privately share this graph with other Chart Studio users in your online Chart Studio account and they will need to be logged in to view this plot. -- **secret**: Anyone with this secret link can view this chart. It will not appear in the Chart Studio feed, your profile, or search engines. If it is embedded inside a webpage or an IPython notebook, anybody who is viewing that page will be able to view the graph. You do not need to be logged in to view this plot. - -By default all plots are set to **public**. Users with free account have the permission to keep one private plot. If you need to save private plots, [upgrade to a pro account](https://plotly.com/plans). If you're a [Personal or Professional user](https://plotly.com/settings/subscription/?modal=true&utm_source=api-docs&utm_medium=support-oss) and would like the default setting for your plots to be private, you can edit your Chart Studio configuration: - -```python -import chart_studio -chart_studio.tools.set_config_file(world_readable=False, - sharing='private') -``` - -For more examples on privacy settings please visit [Python privacy documentation](https://plotly.com/python/privacy/) - -### Special Instructions for [Chart Studio Enterprise](https://plotly.com/product/enterprise/) Users - -Your API key for account on the public cloud will be different than the API key in Chart Studio Enterprise. Visit https://plotly.your-company.com/settings/api/ to find your Chart Studio Enterprise API key. Remember to replace "your-company.com" with the URL of your Chart Studio Enterprise server. -If your company has a Chart Studio Enterprise server, change the Python API endpoint so that it points to your company's Plotly server instead of Plotly's cloud. -
-
-In python, enter: - -```python -import chart_studio -chart_studio.tools.set_config_file(plotly_domain='https://plotly.your-company.com', - plotly_streaming_domain='https://stream-plotly.your-company.com') -``` - -Make sure to replace **"your-company.com"** with the URL of _your_ Chart Studio Enterprise server. - -Additionally, you can set your configuration so that you generate **private plots by default**. For more information on privacy settings see: https://plotly.com/python/privacy/
-
-In python, enter: - -```python -import chart_studio -chart_studio.tools.set_config_file(plotly_domain='https://plotly.your-company.com', - plotly_streaming_domain='https://stream-plotly.your-company.com', - world_readable=False, - sharing='private') -``` - -### Plotly Using virtualenv - -Python's `virtualenv` allows us create multiple working Python environments which can each use different versions of packages. We can use `virtualenv` from the command line to create an environment using plotly.py version 3.3.0 and a separate one using plotly.py version 2.7.0. See [the virtualenv documentation](https://virtualenv.pypa.io/en/stable) for more info. - -**Install virtualenv globally** -
`$ sudo pip install virtualenv` - -**Create your virtualenvs** -
`$ mkdir ~/.virtualenvs` -
`$ cd ~/.virtualenvs` -
`$ python -m venv plotly2.7` -
`$ python -m venv plotly3.3` - -**Activate the virtualenv.** -You will see the name of your virtualenv in parenthesis next to the input promt. -
`$ source ~/.virtualenvs/plotly2.7/bin/activate` -
`(plotly2.7) $` - -**Install plotly locally to virtualenv** (note that we don't use sudo). -
`(plotly2.7) $ pip install plotly==2.7` - -**Deactivate to exit** -
-`(plotly2.7) $ deactivate` -
`$` - -### Jupyter Setup - -**Install Jupyter into a virtualenv** -
`$ source ~/.virtualenvs/plotly3.3/bin/activate` -
`(plotly3.3) $ pip install notebook` - -**Start the Jupyter kernel from a virtualenv** -
`(plotly3.3) $ jupyter notebook` - -### Start Plotting Online - -When plotting online, the plot and data will be saved to your cloud account. There are two methods for plotting online: `py.plot()` and `py.iplot()`. Both options create a unique url for the plot and save it in your Plotly account. - -- Use `py.plot()` to return the unique url and optionally open the url. -- Use `py.iplot()` when working in a Jupyter Notebook to display the plot in the notebook. - -Copy and paste one of the following examples to create your first hosted Plotly graph using the Plotly Python library: - -```python -import chart_studio.plotly as py -import plotly.graph_objects as go - -trace0 = go.Scatter( - x=[1, 2, 3, 4], - y=[10, 15, 13, 17] -) -trace1 = go.Scatter( - x=[1, 2, 3, 4], - y=[16, 5, 11, 9] -) -data = [trace0, trace1] - -py.plot(data, filename = 'basic-line', auto_open=True) -``` - -Checkout the docstrings for more information: - -```python -import chart_studio.plotly as py -help(py.plot) -``` - -```python -import chart_studio.plotly as py -import plotly.graph_objects as go - -trace0 = go.Scatter( - x=[1, 2, 3, 4], - y=[10, 15, 13, 17] -) -trace1 = go.Scatter( - x=[1, 2, 3, 4], - y=[16, 5, 11, 9] -) -data = [trace0, trace1] - -py.iplot(data, filename = 'basic-line') -``` - -See more examples in our [IPython notebook documentation](https://plotly.com/ipython-notebooks/) or check out the `py.iplot()` docstring for more information. - -```python -import chart_studio.plotly as py -help(py.iplot) -``` - -You can also create plotly graphs with **matplotlib** syntax. Learn more in our [matplotlib documentation](https://plotly.com/matplotlib/). - -### Initialization for Offline Plotting - -Plotly allows you to create graphs offline and save them locally. There are also two methods for interactive plotting offline: `plotly.io.write_html()` and `plotly.io.show()`. - -- Use `plotly.io.write_html()` to create and standalone HTML that is saved locally and opened inside your web browser. -- Use `plotly.io.show()` when working offline in a Jupyter Notebook to display the plot in the notebook. - -For information on all of the ways that plotly figures can be displayed, see [_Displaying plotly figures with plotly for Python_](https://plotly.com/python/renderers/). - -Copy and paste one of the following examples to create your first offline Plotly graph using the Plotly Python library: - -```python -import plotly.graph_objects as go -import plotly.io as pio - -fig = go.Figure(go.Scatter(x=[1, 2, 3, 4], y=[4, 3, 2, 1])) -fig.update_layout(title_text='hello world') -pio.write_html(fig, file='hello_world.html', auto_open=True) -``` - -Learn more by calling `help()`: - -```python -import plotly -help(plotly.io.write_html) -``` - -```python -import plotly.graph_objects as go -import plotly.io as pio - -fig = go.Figure(go.Scatter(x=[1, 2, 3, 4], y=[4, 3, 2, 1])) -fig.update_layout(title_text='hello world') -pio.show(fig) -``` - -You can also call plotly.io.show directly from the go.Figure object. - -```python -fig.show() -``` - -```python -import plotly -help(plotly.io.show) -``` - -For more examples on plotting offline with Plotly in python please visit our [offline documentation](https://plotly.com/python/offline/). - -### Using Plotly with Pandas - -To use Plotly with Pandas first `$ pip install pandas` and then import pandas in your code like in the example below. - -```python -import chart_studio.plotly as py -import plotly.graph_objects as go -import pandas as pd - -df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminder2007.csv') - -fig = go.Figure(go.Scatter(x=df.gdpPercap, y=df.lifeExp, text=df.country, mode='markers', name='2007')) -fig.update_xaxes(title_text='GDP per Capita', type='log') -fig.update_yaxes(title_text='Life Expectancy') - -py.iplot(fig, filename='pandas-multiple-scatter') -``` - -### [MORE EXAMPLES](https://plotly.com/python/) - -Check out more examples and tutorials for using Plotly in python [here](https://plotly.com/python)! diff --git a/_posts/python/chart-studio/ipython-notebook-tutorial.ipynb b/_posts/python/chart-studio/ipython-notebook-tutorial.ipynb deleted file mode 100644 index 4096dc8b0..000000000 --- a/_posts/python/chart-studio/ipython-notebook-tutorial.ipynb +++ /dev/null @@ -1,820 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Introduction\n", - "[Jupyter](http://jupyter.org/) has a beautiful notebook that lets you write and execute code, analyze data, embed content, and share reproducible work. Jupyter Notebook (previously referred to as IPython Notebook) allows you to easily share your code, data, plots, and explanation in a sinle notebook. Publishing is flexible: PDF, HTML, ipynb, dashboards, slides, and more. Code cells are based on an input and output format. For example:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "hello world\n" - ] - } - ], - "source": [ - "print(\"hello world\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Installation\n", - "There are a few ways to use a Jupyter Notebook:\n", - "\n", - "* Install with [```pip```](https://pypi.python.org/pypi/pip). Open a terminal and type: ```$ pip install jupyter```.\n", - "* Windows users can install with [```setuptools```](http://ipython.org/ipython-doc/2/install/install.html#windows).\n", - "* [Anaconda](https://store.continuum.io/cshop/anaconda/) and [Enthought](https://store.enthought.com/downloads/#default) allow you to download a desktop version of Jupyter Notebook.\n", - "* [nteract](https://nteract.io/) allows users to work in a notebook enviornment via a desktop application.\n", - "* [Microsoft Azure](https://notebooks.azure.com/) provides hosted access to Jupyter Notebooks.\n", - "* [Domino Data Lab](http://support.dominodatalab.com/hc/en-us/articles/204856585-Jupyter-Notebooks) offers web-based Notebooks.\n", - "* [tmpnb](https://github.com/jupyter/tmpnb) launches a temporary online Notebook for individual users." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Getting Started\n", - "Once you've installed the Notebook, you start from your terminal by calling ```$ jupyter notebook```. This will open a browser on a [localhost](https://en.wikipedia.org/wiki/Localhost) to the URL of your Notebooks, by default http://127.0.0.1:8888. Windows users need to open up their Command Prompt. You'll see a dashboard with all your Notebooks. You can launch your Notebooks from there. The Notebook has the advantage of looking the same when you're coding and publishing. You just have all the options to move code, run cells, change kernels, and [use Markdown](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet) when you're running a NB." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Helpful Commands\n", - "**- Tab Completion:** Jupyter supports tab completion! You can type ```object_name.``` to view an object’s attributes. For tips on cell magics, running Notebooks, and exploring objects, check out the [Jupyter docs](https://ipython.org/ipython-doc/dev/interactive/tutorial.html#introducing-ipython).\n", - "
**- Help:** provides an introduction and overview of features." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Type help() for interactive help, or help(object) for help about object." - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "help" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**- Quick Reference:** open quick reference by running:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "quickref" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**- Keyboard Shortcuts:** ```Shift-Enter``` will run a cell, ```Ctrl-Enter``` will run a cell in-place, ```Alt-Enter``` will run a cell and insert another below. See more shortcuts [here](https://ipython.org/ipython-doc/1/interactive/notebook.html#keyboard-shortcuts)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Languages\n", - "The bulk of this tutorial discusses executing python code in Jupyter notebooks. You can also use Jupyter notebooks to execute R code. Skip down to the [R section] for more information on using IRkernel with Jupyter notebooks and graphing examples.\n", - "#### Package Management\n", - "When installing packages in Jupyter, you either need to install the package in your actual shell, or run the ```!``` prefix, e.g.:\n", - "\n", - " !pip install packagename\n", - "\n", - "You may want to [reload submodules](http://stackoverflow.com/questions/5364050/reloading-submodules-in-ipython) if you've edited the code in one. IPython comes with automatic reloading magic. You can reload all changed modules before executing a new line.\n", - "\n", - " %load_ext autoreload\n", - " %autoreload 2\n", - "\n", - "\n", - "Some useful packages that we'll use in this tutorial include:\n", - "* [Pandas](https://plotly.com/pandas/): import data via a url and create a dataframe to easily handle data for analysis and graphing. See examples of using Pandas here: https://plotly.com/pandas/.\n", - "* [NumPy](https://plotly.com/numpy/): a package for scientific computing with tools for algebra, random number generation, integrating with databases, and managing data. See examples of using NumPy here: https://plotly.com/numpy/.\n", - "* [SciPy](http://www.scipy.org/): a Python-based ecosystem of packages for math, science, and engineering.\n", - "* [Plotly](https://plotly.com/python/getting-started): a graphing library for making interactive, publication-quality graphs. See examples of statistic, scientific, 3D charts, and more here: https://plotly.com/python." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import scipy as sp\n", - "import chart_studio.plotly as py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Import Data\n", - "You can use pandas `read_csv()` function to import data. In the example below, we import a csv [hosted on github](https://github.com/plotly/datasets/) and display it in a [table using Plotly](https://plotly.com/python/table/):" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import chart_studio.plotly as py\n", - "import plotly.figure_factory as ff\n", - "import pandas as pd\n", - "\n", - "df = pd.read_csv(\"https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv\")\n", - "\n", - "table = ff.create_table(df)\n", - "py.iplot(table, filename='jupyter-table1')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use `dataframe.column_title` to index the dataframe:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'MIT'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "schools = df.School\n", - "schools[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Most pandas functions also work on an entire dataframe. For example, calling ```std()``` calculates the standard deviation for each column." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Women 12.813683\n", - "Men 25.705289\n", - "Gap 14.137084\n", - "dtype: float64" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.std()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plotting Inline\n", - "You can use [Plotly's python API](https://plotly.com/python) to plot inside your Jupyter Notebook by calling ```plotly.plotly.iplot()``` or ```plotly.offline.iplot()``` if working offline. Plotting in the notebook gives you the advantage of keeping your data analysis and plots in one place. Now we can do a bit of interactive plotting. Head to the [Plotly getting started](https://plotly.com/python/) page to learn how to set your credentials. Calling the plot with ```iplot``` automaticallly generates an interactive version of the plot inside the Notebook in an iframe. See below:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import chart_studio.plotly as py\n", - "import plotly.graph_objects as go\n", - "\n", - "data = [go.Bar(x=df.School,\n", - " y=df.Gap)]\n", - "\n", - "py.iplot(data, filename='jupyter-basic_bar')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotting multiple traces and styling the chart with custom colors and titles is simple with Plotly syntax. Additionally, you can control the privacy with [```sharing```](https://plotly.com/python/privacy/) set to ```public```, ```private```, or ```secret```." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import chart_studio.plotly as py\n", - "import plotly.graph_objects as go\n", - "\n", - "trace_women = go.Bar(x=df.School,\n", - " y=df.Women,\n", - " name='Women',\n", - " marker=dict(color='#ffcdd2'))\n", - "\n", - "trace_men = go.Bar(x=df.School,\n", - " y=df.Men,\n", - " name='Men',\n", - " marker=dict(color='#A2D5F2'))\n", - "\n", - "trace_gap = go.Bar(x=df.School,\n", - " y=df.Gap,\n", - " name='Gap',\n", - " marker=dict(color='#59606D'))\n", - "\n", - "data = [trace_women, trace_men, trace_gap]\n", - "\n", - "layout = go.Layout(title=\"Average Earnings for Graduates\",\n", - " xaxis=dict(title='School'),\n", - " yaxis=dict(title='Salary (in thousands)'))\n", - "\n", - "fig = go.Figure(data=data, layout=layout)\n", - "\n", - "py.iplot(fig, sharing='private', filename='jupyter-styled_bar')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we have interactive charts displayed in our notebook. Hover on the chart to see the values for each bar, click and drag to zoom into a specific section or click on the legend to hide/show a trace." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plotting Interactive Maps\n", - "Plotly is now integrated with [Mapbox](https://www.mapbox.com/). In this example we'll plot lattitude and longitude data of nuclear waste sites. To plot on Mapbox maps with Plotly you'll need a Mapbox account and a [Mapbox Access Token](https://www.mapbox.com/studio/signin/) which you can add to your [Plotly settings]()." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import chart_studio.plotly as py\n", - "import plotly.graph_objects as go\n", - "\n", - "import pandas as pd\n", - "\n", - "# mapbox_access_token = 'ADD YOUR TOKEN HERE'\n", - "\n", - "df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/Nuclear%20Waste%20Sites%20on%20American%20Campuses.csv')\n", - "site_lat = df.lat\n", - "site_lon = df.lon\n", - "locations_name = df.text\n", - "\n", - "data = [\n", - " go.Scattermapbox(\n", - " lat=site_lat,\n", - " lon=site_lon,\n", - " mode='markers',\n", - " marker=dict(\n", - " size=17,\n", - " color='rgb(255, 0, 0)',\n", - " opacity=0.7\n", - " ),\n", - " text=locations_name,\n", - " hoverinfo='text'\n", - " ),\n", - " go.Scattermapbox(\n", - " lat=site_lat,\n", - " lon=site_lon,\n", - " mode='markers',\n", - " marker=dict(\n", - " size=8,\n", - " color='rgb(242, 177, 172)',\n", - " opacity=0.7\n", - " ),\n", - " hoverinfo='none'\n", - " )]\n", - "\n", - "\n", - "layout = go.Layout(\n", - " title='Nuclear Waste Sites on Campus',\n", - " autosize=True,\n", - " hovermode='closest',\n", - " showlegend=False,\n", - " mapbox=dict(\n", - " accesstoken=mapbox_access_token,\n", - " bearing=0,\n", - " center=dict(\n", - " lat=38,\n", - " lon=-94\n", - " ),\n", - " pitch=0,\n", - " zoom=3,\n", - " style='light'\n", - " ),\n", - ")\n", - "\n", - "fig = dict(data=data, layout=layout)\n", - "\n", - "py.iplot(fig, filename='jupyter-Nuclear Waste Sites on American Campuses')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 3D Plotting\n", - "Using Numpy and Plotly, we can make interactive [3D plots](https://plotly.com/python/#3d) in the Notebook as well." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import chart_studio.plotly as py\n", - "import plotly.graph_objects as go\n", - "\n", - "import numpy as np\n", - "\n", - "s = np.linspace(0, 2 * np.pi, 240)\n", - "t = np.linspace(0, np.pi, 240)\n", - "tGrid, sGrid = np.meshgrid(s, t)\n", - "\n", - "r = 2 + np.sin(7 * sGrid + 5 * tGrid) # r = 2 + sin(7s+5t)\n", - "x = r * np.cos(sGrid) * np.sin(tGrid) # x = r*cos(s)*sin(t)\n", - "y = r * np.sin(sGrid) * np.sin(tGrid) # y = r*sin(s)*sin(t)\n", - "z = r * np.cos(tGrid) # z = r*cos(t)\n", - "\n", - "surface = go.Surface(x=x, y=y, z=z)\n", - "data = [surface]\n", - "\n", - "layout = go.Layout(\n", - " title='Parametric Plot',\n", - " scene=dict(\n", - " xaxis=dict(\n", - " gridcolor='rgb(255, 255, 255)',\n", - " zerolinecolor='rgb(255, 255, 255)',\n", - " showbackground=True,\n", - " backgroundcolor='rgb(230, 230,230)'\n", - " ),\n", - " yaxis=dict(\n", - " gridcolor='rgb(255, 255, 255)',\n", - " zerolinecolor='rgb(255, 255, 255)',\n", - " showbackground=True,\n", - " backgroundcolor='rgb(230, 230,230)'\n", - " ),\n", - " zaxis=dict(\n", - " gridcolor='rgb(255, 255, 255)',\n", - " zerolinecolor='rgb(255, 255, 255)',\n", - " showbackground=True,\n", - " backgroundcolor='rgb(230, 230,230)'\n", - " )\n", - " )\n", - ")\n", - "\n", - "fig = go.Figure(data=data, layout=layout)\n", - "py.iplot(fig, filename='jupyter-parametric_plot')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Animated Plots\n", - "Checkout Plotly's [animation documentation](https://plotly.com/python/#animations) to see how to create animated plots inline in Jupyter notebooks like the Gapminder plot displayed below:\n", - "![https://plotly.com/~PythonPlotBot/231/](https://raw.githubusercontent.com/cldougl/plot_images/add_r_img/anim.gif)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plot Controls & IPython widgets\n", - "Add sliders, buttons, and dropdowns to your inline chart:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import chart_studio.plotly as py\n", - "import numpy as np\n", - "\n", - "data = [dict(\n", - " visible = False,\n", - " line=dict(color='#00CED1', width=6),\n", - " name = '𝜈 = '+str(step),\n", - " x = np.arange(0,10,0.01),\n", - " y = np.sin(step*np.arange(0,10,0.01))) for step in np.arange(0,5,0.1)]\n", - "data[10]['visible'] = True\n", - "\n", - "steps = []\n", - "for i in range(len(data)):\n", - " step = dict(\n", - " method = 'restyle',\n", - " args = ['visible', [False] * len(data)],\n", - " )\n", - " step['args'][1][i] = True # Toggle i'th trace to \"visible\"\n", - " steps.append(step)\n", - "\n", - "sliders = [dict(\n", - " active = 10,\n", - " currentvalue = {\"prefix\": \"Frequency: \"},\n", - " pad = {\"t\": 50},\n", - " steps = steps\n", - ")]\n", - "\n", - "layout = dict(sliders=sliders)\n", - "fig = dict(data=data, layout=layout)\n", - "\n", - "py.iplot(fig, filename='Sine Wave Slider')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Additionally, [IPython widgets](http://moderndata.plot.ly/widgets-in-ipython-notebook-and-plotly/) allow you to add sliders, widgets, search boxes, and more to your Notebook. See the [widget docs](https://ipython.org/ipython-doc/3/api/generated/IPython.html.widgets.interaction.html) for more information. For others to be able to access your work, they'll need IPython. Or, you can use a cloud-based NB option so others can run your work.\n", - "
\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Executing R Code\n", - "IRkernel, an R kernel for Jupyter, allows you to write and execute R code in a Jupyter notebook. Checkout the [IRkernel documentation](https://irkernel.github.io/installation/) for some simple installation instructions. Once IRkernel is installed, open a Jupyter Notebook by calling `$ jupyter notebook` and use the New dropdown to select an R notebook.\n", - "\n", - "![](https://raw.githubusercontent.com/cldougl/plot_images/add_r_img/rkernel.png)\n", - "\n", - "See a full R example Jupyter Notebook here: https://plotly.com/~chelsea_lyn/14069" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Additional Embed Features\n", - "We've seen how to embed Plotly tables and charts as iframes in the notebook, with `IPython.display` we can embed additional features, such a videos. For example, from YouTube:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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1qcyyreP3DyeZNg03qnqHvIHhRue2PPUefm1LkOjrvR4nHvyA9xZRoSTqHe2Uxfgyl5My1Il7tzWZA0I+0ipKlZjwJ08O78OO5D2v7GUsc9tLHxJcdhkXJXrfyZBh77VLWse5vmUiWtbbckmWcl3uPaE7V+Jvj4pRlZv24kEmLhBwGO9iTHDsvyAg8Gdjp1FKVlrhpjwAZinGsxcwO/5fn9oNSJ0ZVxAd7IH1TAsStVqrHKywclSx7HJP2MDGSwGzYFh8nwIxi1XUqimy8l7D8yD6XtOiFB/ynZXpi37VgefDa8TRughASrkn9JdNUGx1/ClUPeZzc3pNb0+ou0YDkanrsXpq0VnfJPzF/Twcd6zvTeCJNNfNHq1sd29TZez0wCCDPS9W6BVjUK9at3EfHvKVXTqXx9WP/N9gBsw3K49RK8c8/Etd5CrUD9LTt4v8J5DgPsAH2Imt38O3UJtlZyp/pBMiagwqm0VKBu0cakvWOnNd220VlK+0Bv13/wCpNRXlU7VcdwP8YUz0GIGyKjVk1/QfkSpa8R+Gsx+0sp7SP7iToqWHjYGvjxPU9SwqxjdqAHtBHI8TjdJp9PqYrsAZHGtESrK5tq/gsxG3pTztZ6TE6nj5VfYr9roNbPG5x+rvXQ7VLWmweAROLWz2Xdqkj3IBkP16PIxamfvDhSW22veei6NetiarLkDx3ThdH6VbfWHcfR9/eepw8MYtfap/yk6vxE7qHUgynQwqtKsZJlZAxQXbeveczNv9O1MhOUs8f9/aYkZp/EGPbZjF6T2t4P8AeeGycS71ACpJJ0NT6XTZXlV6Ojv5mE6Xih+81qSDsbXxNe2NRV6BjmjoVFbjRAbz/wDYzNh78ezmdB1Hb2rwB8SkavoKtzuXlKgwbCq6Y7+JvkMtmxrmV7F9FiUIAPnnxN6t2kMxG/tN4y4vUcUtcGC/T7yjlYP4nHapB7bE9XZSGP1j6fuJUOPQtm0Yb+Jq/Ziy4+a3VNTayMOVOpHPSfxR00VOMiteGP1aH2nnCJ4e+cr083YxERMNEREBERAREQEREBERAREQECJlRtgB5JgdToWL6+UWKgqo3/pO3m4wrrJ39R95v0TD/D4qrrVjcsf3/wDU26kXsykrXjXvPZ4ucjh31txBjEnHbuQ8gKNzqZli4/SwRoMeOPbzIL17UqRjyf8AiUeu2NTjIg4BP+06ubldU1+I2PccyrjIHyFBGxvmLbTYdt5ljptgpsZz51oTOuk+J8y2v0mCVa9t/tOco+0tXo9gZrGAAPiaLUV0+/pBEmiUr20DuXW/A+ZaxcSyvttA7WPtJa0e0rY412DiXsclrGs57mGuIFCvGNz9qeZ2uj9Ia23utAKoQZd6V01lcF10pnfrRKVCoJm3GdczP6ar0j0wFInC9DIssZ3Y9i8T2O+6cjIxLbrDpiF+BHNZ1xgfSsXtXj7+87WNSrorldA+RIhhOSo1sr7/ABOnRQSg9Tz9pbYiP1lQeNt8CVzdc9oCOAPg+ZdyKQB3oOQJx29Yv3KSrkzM+o6iM2vrbmb1ud8nunPIHeo3v537yUZArIU8fIlsGvUEF9Lix/TA2ATObgYmPjX+qchHGtENI+qX2Z3UqqETurB0QfGtzTIs6V0+/RHdZrRQDgf5Q3HqsWxLU/ln6fkScIvOxvc87h/xH0yioVkldf4VOv8ASdbE6riZZApt2T7FSP8AWc6q21NZX8omPSRR4Em177mrDiTaY5Werdp7Rvc5mDju+cbfTAVR5nes7QSWnn+t53pY/o47BQx5M6y/GXJ/iXPoa5qvSAsH9QlD+HsNMrOHqN2p8/MofhzdkKi7csdcT3nQOjpiUh7Qe7jQ+I1ufI7OFiJRWAnj4lhjrcrW51VB7S2z8Cc/qHWVpr3SO4/E55azauZfZcCje8jGMluF6LDx4+0p9PybMq3+ahBPIIE6qVsP0mvxHFw2srs3vRXys69OYH+luJWzcYLct438NKmSnZdWf6W8GaydDtHRG1MhesMvaW8yonf28nWpiu87A7lMTnBMMKseef3ktdNdQ+lZH6pOu4aHzJO4Mm1O4uop9QvCg8cTzz52riPEs9XztXej/nK1eNj2Dfds+4nXn4YtWdnUMJ6m5JHvPB5VDY97VP5We+qorRPoPieb/iXFItGQBwRozl5udmuvjv8AHn4gxPG7kREBERAREQEREBERAREQEudLp9bNrHsrA+PvKc738PU6D2kckjX+c3zNqX8elxBuzge0qM288hlBB8ToYqjvPtxOVWxd2dm2J7cx5v2pbLi+XUnZ/UB/nKf8VMB6dYAGv/E1x2ts6kmjtVs/3mn8TOrX639XH+kL/XAHJEsoDV2+5J8Squ97l6ld1lwf5gGxMulXH6d6xVUuXv1s7lbGxb/XANdjIp2R2nUxjre+QGV9N53PTYV1i1k/To/m2BIxa5tlrdx+kqP8Opc6StluR+UhR9prk5GN6++3e+OJNi5S1W6r4HvBvx6XFYqBsED7yw1y78jXzuefHVTdYKqd6+dCTjJZ9Ip9/rkscnYNyjkuoX53DWKBtV3OJ1Cq2/HCUP2EHnc69Tp2ACMEisO0nUhNwVgQ5CjzJa2Bdl1KfUMynFBDp3SKlOR6wPbYPp9gZTVgWZnYMx8facTGyMh8svU3bWfzAzpihrl7lPapM1EZfLBvWqod7H8z/E3yVNlPb3hNHZO5OuIioPTUdw95zepg1vXjoT3n6nO/+/EEUszIuDehirpn49QeT+8io6Q9j/ztk+58zrYuJ3WCxxoKOJeprWtmLDYI2JMb3Hk2wscZjV+tpQ2gdf8AM9Z0vpqVgHauugVbXP8AeedzsUZOT20r2fffvO/0evOxaEF7hk0O0fb+0lWu8vA1vgfMw76EpZWetNfc7doPE5mZ1lHxG9BiXPiZnKat5+Wtan6wOD7zymU79QsNNNZdyeCPaXsfpnUuokPZYBWeefj+07S4uJ0bFNpADAcnzub/AD4SKnReh4/TaRfl9htPOnH5f7yTq3VUroZaLF2eO5T4nnMjrtmfk+j6jemTxwBLQxi5+pdJ8H3iRc1B0+m3KyGf17n587M9PidMx1bvZfUf3Lczl9OsrxO5QRsn29p6GhgEDe0VKsV1qijtRV/QTcFfGxKy51BtFPd9beBNLiUtDKDOeWosZADoyjR2JzuMiiylgA6DSky2bv5qqJXvpNd/qr4bzNz4ipVYRU2PY2rQPJ95wctMldPjWOGH3Inp7cMZBDDhh4MiyKKxR25Kfqwm9WPMN1/NorNWRS3cRoNsj/aWul/iM6nu/HWVnZ4DE/7y9m9JryMUdhB7RwZzKOmZtNf4jBcKwJBU/wDqVXRu6arBnZ/UcD4nI9F29mpcHnjzOz0l8uwMcgqwPjX/AKm9/pFyO36vmaiOdguy3FXYkER1PHF9FlbfGx7yxfiitVsU8zW/6tOp4Il6mxZceBsUq5U8EHU1l7q9BpzXJ8Oxb/OUp8/qZXpn4xERMqREQEREBERAREQEREDI5M9d0lOzGq/+oJ/tPK0L33INb5nrcIqtJ5HA1O/in1z7X/V0D2+ZyfUJUEa7j5lhbd2HniUz3CpiF+v20J6XJb6O7jLsGh274nK68e7PcfGv9J1+hkEhm4cnkTldfA/+Ss7ffX+gg/rl46NZcoA3zOw9CtbUEXtGvrPx8zndP0Ld9wUy9u1DYV7idnXvM1ur6rTX9NW2Uc7MxkXIlRavZdv6TK/T8jRK26LfMv5GPRvvNtSsRvt2JlHEQ2O7F10w54l3FyNEqOWMx3Y/pv2kd/vzOfiXhMpt/PzIr1GPUMfuYeSPM55zil7BD9ZJEjv6k7V+krDZ99yx0/AN+7XrI0B7eTNaxjo+kowUsuY6PnX7y7Q9JpW2tyQPtLGLg124iLeO4ewPtKuZUmBQVpQhN7/SNYdBLl9IO51obnncimzrHUGUH6N6DD2kGZ1Swr6db8Nxuei6JirVio+wSeSdc+YWTG1XSsbEqATZ17mYurCL9O/vL7qP6iP7zn5+VTWmu4M3wDzES6xkZiY1IO9n4nm2zTbnCxtAE7Mzn5ug3dx8bM5IcuAfbclrfHL11GfVkNpzpBwPvLaUllchuDwJ402uhXsJ/vL1fVb66O0WbJ+8nsXl0cmmuuzRcgkeR7SGjKyEV0a9RUPG/M4Gf1G7vP8AMYn9ZQa+5xtrG/cmTV9XrKfwllhfIyePjX/ElHWOlYvNbGxvb6T/AOJ4nvb5P95OOxPfZl09Xqr/AOLsk1/yaUUEELsH/wAzn49OV17MP4uz0kA7iw/aQ4FBy0Pauwo3Om9L1P2U2n02X+ga5+NiIYxgdPFdrriDvq/xt7zq1rXj1/zX7be0kJ8Sh62bUFqq7a1POwutTa5LDU2RYQ7/AJe5jxNpbqi2Zt39JNuSfaXcLrDIPRs0CPtMnHr9Lvq9Nn9wutyFOn2vZ3+myb+U8QZHRxMQZXUVzKsgA162uvPP/E6t6d9hRtgedj5nna/WxLBtXRt/nAIBnfxssXUr3Ed36yYzVeyt68pdHYnQtQ21prjia6WzxrcnqXXn2ktRrj1mrg8yS6oWr2t4knBmez7zFqucenpUzOrHmZxqEVm17y86jt15kPphAe3yZqdCuy11fyx7zKVgIR8yNhp+9lZz8CYW2z1wChVDNxEWTQO3kcTl3Iqgz0FgBGpxuoVdg2o3NyjyfXKu9PU3yhM4M9Jnr3U3b+DxPOEaM8vnmXXo4vxiIicHQiIgIiICIiAiIgIiIFvpw/8A6QT7A/6ToU5DrYyA8Eyp05dKz637CWHArsU+55nq8U+OXdX0s0RMXluw1jz8iV6nLOd+NSfPfjafladXNb6QhDEOfAkH8QUhO2xBsk+Yx8kVVp3D82hubdUza3Rax8/7QPPVhu/S+ZYtybe3s7iNccQj+jbb2637AyXpuEMyxwxI0CZmuiolro2weZtlZt2S4Z21rjjiMig02svOt8SD35mfwiZO8EEOCW9hNLEdH2RqKnAfc7Q6eMvCW03BSPb5kHPobZUt7Ge86Gvr4y8aUATwlNDPkrR7me36CyUUeip2RrcrHTudvaNL4E5ebf31Mjr545nQtuCJsnUoIFyF7j4Msjk4y9I9QPZx2g7HM9R01AMVAPAE1ppVkK+0xk5lWEgBkrW6pdWqvaxRS+t/JlGzp7VIGs+okckEmQZPXgLSaj3f/aQP/EIu+i5dA/4Y2RrK5vWayCdHgTm4dvevZ7ie2fBxupUL2v5HtOBm/wAP3YNrPXtlEzW+aoerz44B0ZtejVkFf6p0ejYfq5Sd6bHcN/3lv+IvRxclWCg/ToiJF368xfTadsUJlVrD4+J6Kuth0O07ADNv/SedZCCZFjTe5t38amGGl3JMMg5ad2tfeIuO30HqBotrrUA95UHc9R1Kqs4yszKiedjieEyPXryCQhVe4lW9vMt47W5lgW+89i+dTcrn1HdudsrsNDn018mR5FrXhcak7XuHPzzLOLmU41qY6uLAw0BOjmPidNo/F2IA/kD7zTOubl2f/GsiKNkrzI16xk9gCAMf8PGxOT+Jt6rnWOdBSeF8T0FldOBiG6tSXGtk/rLBYxWynXuywn1Dhf8Aomz43hq9r9pJh5NXUMJXIK/ebhAH0GI/WVmrOE4ar+Z9LeJOGAbWzIq6hrx5k3pfSJiolQ7k0iTgCSTnWow/iR63N2mPaBgqNeBI3QEeJKZq3iaiK1niU8lFZDxLtnmVbF8ideUeT6nSQ7L7NueVvXsuZfgz2XVyRZo+xM8n1Aaym++v9Jz8/wBmu3jVYiJ5HYiIgIiICIiAiIgIiIHU6aN1aH+KTZwAddeZp0ofyifvNcl+61vsTPX4/wAce/0qcl/2l2yxLKBsjg+Jzqm7XJ+0eoShHzOlZXUUmu0d3CLtf85QyQ/0uxPMvYQ9ZPSO9nUn6phdmIHVdBT/ALSDmZqqPTtrbZZdn9Z0Ok5FW1AYJYfJPG5z8un066tHYZdyfp+Op7bLPy79pK1/HQ6g+KzlewMQPzD2M4T1Mu+DxPRejSe4nSAjwZSLdmLaqIr/AHmVcT3k65VqqFDHtHtuRFTuZCggkkQrrdHXud7bNkgcEz0HSX7fUsB239I35PM4FF3oVduv1M6XRsn1rCoGgD5llY6jq35jWqFsPaSeVJlnp1hFNpf8iHyJVo6Zbl5LMT21n3Mx1K49PwnoQEFvBltc5EGX/Eq4rAY5LbPPPtOX1DrFubYG39Pxuc1gWcl4C2OpavQT5M4221254kWzdV6DItYLH+o+ZWA95LRh5NtffVpvtJlwMw6DUEfvOd5rrLGlGZfitum5l37AmdSnruS9Rrs7bO7jmc2/p+VUO56jqVQWRhriJbGeuZfx7ChBiY/rJ2l2XY+043W7WygodF9QeSJBXkXkDbsBrxLFfT829+5F7t++52345+uX6ovafwdOPve9bEp5eMabD3Tqp0bKbNCaH0H5kfXKPSuVSwLAEETLUcTtDeRxIXUI/wBJl3t0JEMZW3oxK2kuzzbiV0tz2jRO5pjXkI1YBAPuJWtratuRxO//AApg/iMgvYo9MDnc3GLGcPpmRWi5QJBTkE+8pZV+ZnuxvsscIfB3PSdQyHvyDTigKqaB3IFy0xMv0cmpT3Dhh7zbnrgYpejISzW9+FnoGyhlUn1LVWrwV3z/AGkVPTRl57Wp9KKfHxLVVdKdQcVKHHP5v0lhUnTrGXVdDfyg3G+PJnWfIqTXfqUOiVC05B4IR+P7mS3Yy2Fe5tfVwPmViuymyQN8SVfiaV170dakoGhOVqMgTcTAEzqZajVxxMAw/iYAgZJmjHibGaP4liILDzKzvrmWLJQyX5M7co4vWFLsXH3nlOqL/MVvmewzB3o37zyvWF7eyTyz/V18f65cRE8LuREQEREBERAREQEREDqdPfsxyfvI7T3OT8maYrapI+82bmezx/jj1+ozJ6qTapCn6h7SEyxjFq39QeR7TbK30r+VaWs47SNzr9UetsDR2QeROfY9Rw/WUAMy7bXzLODaMvCNLgeoOQDA4y7yKfT1yviKrzjHtAGt6aWc/DbHvX0yVLfHEr39NyO3vHPuZFjq4GMc5zx3LJsvol2G/dSdo39PHErfwxl215P4fW+48nfierurL41qtyf6fmTNZ6uV4J+mZHqttRwfYgyG9OzSmoqw8nU6GW92DlhkO+eVMxldTSyk91KF248eJmtxWwsZ8oHuBCf4vaej/hTHRntVxtVI0f7zjDqaJ05aKkAIOyR+k06f1W7Gs7l4G963rchdfRMhhRUOzQAniuu9QbMsUofpAkt3XHzqypb0z9m3OFm2LWfTpbuH+KP4zzPqPKvOu0cn3kmBTZkWKjKTX8TXp+H+LZtj8vkzv49S1VqEADD7STlu1ZexcNVssYdw/IvxKzdaZlPe366nG6tkW+oQxOvbmcz1HPuf7zaTnXYs/iLLR2FNmkP2nLfMtdy7HkyCJizW5MX8fqdlI2NbHid3oX8SGqxhkkaI86nlFHcQB5MnOHeqhvTbR+0FmvbZP8R4ShvwrH1WHnRnmb7GutexudnZlB6LsdQ1ikA+xk+NaxBHcdHjWou1mTGS45mE2nJnoMLpGFdjixztj50ZBZ0NbW1Tbr7Mv/Mx61r3ciysXCdvpuScfAamvSkiVbOl5GLvuTuA9xJMbGtySBWjTU2JbrmXWZlF5dN6PjUhe/ItuU2k7U+48T0f4I0W6sBPG+RKrVVXXFO0HZ44nRmvYdNprbBRlI7nXkyqmKq2sQg8cmb9PxrsXHQHfZqSC1e6zuGhrzqWOVQdManFd0/Kz8n/AL+8rdYtalKSOdW7/bRm1uOtjUsvcQSdn+02zafVpQfmKnxKj0gP0jUD7Srg3+pX2t+YS3rU434pM73MampkVkzUzAPJmCZqIbmrGDNTNREVx0s5mQ3M6OQfonJyG1udOVinkN9JnmOt89v6z0OQ21M891g7C/rM+X8deP1yIiJ4nYiIgIiICIiAiIgIiIFrGP0EfeSNK9DaMnJnr8d+OPc+sHzOoagum1pdaM5i7BE7DENidy8zbKpSybKvsrvxOjVi2V3C1COzU5dbNZYO3gg8id7KPZ01Qo2RINRdXYDXboueB9pzuoXnE/krsjXJ+ZFcag6Wrwo5IlPIvGRd3n3PH6RqrXR7hVmi3kT1wtbv7msHYRwJwOn1VGlbrNBd6J+J0LfxBuAqIao+DLGenO/iClarkbfkEzhMQzaI4nsc7HR61e8bIEgxPSJ0UAHtJYsrk4mEhoa0rtV9tyln5CWuq1r2qvE9V1BavQLs/aB7zx2W/fe3b43M41PrNR+sAGdjA6T+JG2A1+s26FhV2gWP4I/3nadyvCL4lkS1zclK+noK6Rpj5MnxHVccWWckyrk025doCryvmQXu2PqpvzCNP1Q6l35F7EL9K+8qmtaqwT+cy9Y9tbAgDtbzKOUxNrM52TI3Fc8mdHCfDRGF1RtJHHOtH+85pk+GypeGY6AhpgbS/ajw29T1HTeroaRXbT3MBxwJx0xjalmQoBGydmVri618fSN+BGsX66edlHNy1CoAmuRJaBTRci+ntmOpz+nABDZ8GdBQtqH6SWPiEdOpVstFZ+gHz2mZzcoDKC1kKuvMrOgpwUdT9Z4I+PMroncO9VJ141NM10TmNWB6o7gfy/eXOmZJstArpCgyph0X3Mr2DSjxLvpGu3YPJ95cZrpPg1XbZhsn7zXG6PiVW+t2bb25M2xdqBsyz3k8CTE0t5XtH5Z5+7JbHWyiwlmBGuPbYnfOt8zjdUqBurc8bPmWJ+p0dK6ayCACJqvffey9ugBsMPea5qg9MPz28frIsTJPahQgHWmEq4v3r+FRshAe9R4Blvp2W+RjhrBptDcVMDXs+8iq7cZLSp45IB+ZixHQ74Mo4Wd+KZgF4XyZc3MYrVjMbhjIiZqREhaab8zBMjsbQE1INMhvpnLyNFCZZyLJz7bNgzcWKN54M891ZtsBO9knyZ5vqTBrZz8l+O3KlERPG6kREBERAREQEREBERA2U6MsKd6lUSZD4nbx1jqL+Ooc9pnUKqEKAa4nKobt+qWvxTK228e87OaPGtrxsphYARvzOocpbKWK67deNzjXolwaxTojkgyW21acMKrbYymKOQ7FiO4kfG5CCQZPSa2U+oNmQtrv48b4ht6Xo6Ld0yygn62OwfOpax8h6EZXBPb4PtKnSLRRQdjZMznW6TS7hzqY9TTItFLgfVx58Tm5t1uOzemSNGUEZ2yFYH6t8ToZxFtQYDn3ktXGK8w5uI+PdYqc/mYzm5GI9Vmu7uX2YSBid8To4K2W4dq/m8a+3mG8db+HbU9E1toMu/J+86bhx9Q5HxPHrk3Yl3B7W8Geoxco52E3YdPLHPqL+NUrIzaCsw+J5nrGHkpkszFim+Doy9RfnLa9auD2nn/upV6ndk93azbHvxFSSuc1pVO0kt+8oW93qHu95aCrY47T/eRZSE2kzDrEHbxuT4tHq9xLBQvuZqayKe723qYV2VCo95VT1ZJVhV3sK988+ZNZvJ/l0qWI53r2lXGx7L3Ir0SPO52cTEtosAayvbL/AE/G/wBJEqPFARlQgdrDex4nbwUqOLc7BSUHx+soGkV/nAAXxo8SzgVOtJVuUtOyR8f9MsZqPIP4grXX9IPOp18equmsKND38TmJWjZpWg/l+Z0eoGzHxmtUAgCbZrR+s0U5deOqh2ZwvB8c6l206bZ43954/pG83rK2N5Vg3+c9bnuE0TzEZsWFuVFH/mWaLQRsnzOMbQqEtLOHeXXu3xDLpuwJ+Jy8/fpsG9iCD+8msvJOjK+W3q45HvEJ+q2bnIuJXjEbe4aU78eP/Mr02vXZ2en+Xy2pVyN3Cpvesn/b/wASfCudlYjTOPzAiHTHexriy9u96+DMZzlaj2gtsHxKXTrgGZyGHPidB2SxCV/zlc1Dot9y5j1BSazyT8eJ6EPOb03HWkO2uWMvEgCYVl24kRa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- "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import YouTubeVideo\n", - "YouTubeVideo(\"wupToqz1e2g\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### LaTeX\n", - "We can embed LaTeX inside a Notebook by putting a ```$$``` around our math, then run the cell as a Markdown cell. For example, the cell below is ```$$c = \\sqrt{a^2 + b^2}$$```, but the Notebook renders the expression." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$c = \\sqrt{a^2 + b^2}$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Or, you can display output from Python, as seen [here](http://stackoverflow.com/questions/13208286/how-to-write-latex-in-ipython-notebook)." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx$" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import display, Math, Latex\n", - "\n", - "display(Math(r'F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Exporting & Publishing Notebooks\n", - "We can export the Notebook as an HTML, PDF, .py, .ipynb, Markdown, and reST file. You can also turn your NB [into a slideshow](http://ipython.org/ipython-doc/2/notebook/nbconvert.html). You can publish Jupyter Notebooks on Plotly. Simply visit [plot.ly](https://plotly.com/organize/home?create=notebook) and select the `+ Create` button in the upper right hand corner. Select Notebook and upload your Jupyter notebook (.ipynb) file!\n", - "The notebooks that you upload will be stored in your [Plotly organize folder](https://plotly.com/organize) and hosted at a unique link to make sharing quick and easy.\n", - "See some example notebooks:\n", - "- https://plotly.com/~chelsea_lyn/14066\n", - "- https://plotly.com/~notebook_demo/35\n", - "- https://plotly.com/~notebook_demo/85\n", - "- https://plotly.com/~notebook_demo/128" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Publishing Dashboards\n", - "Users publishing interactive graphs can also use [Plotly's dashboarding tool](https://plotly.com/dashboard/create) to arrange plots with a drag and drop interface. These dashboards can be published, embedded, and shared. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Publishing Dash Apps\n", - "For users looking to ship and productionize Python apps, [dash](https://github.com/plotly/dash) is an assemblage of Flask, Socketio, Jinja, Plotly and boiler plate CSS and JS for easily creating data visualization web-apps with your Python data analysis backend.\n", - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Jupyter Gallery\n", - "For more Jupyter tutorials, checkout [Plotly's python documentation](https://plotly.com/python/): all documentation is written in jupyter notebooks that you can download and run yourself or checkout these [user submitted examples](https://plotly.com/ipython-notebooks/)!\n", - "\n", - "[![IPython Notebook Gallery](http://i.imgur.com/AdElJQx.png)](https://plotly.com/ipython-notebooks/)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "notebook_metadata_filter": "all", - "text_representation": { - "extension": ".md", - "format_name": "markdown", - "format_version": "1.1", - "jupytext_version": "1.1.7" - } - }, - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.5" - }, - "plotly": { - "description": "Jupyter notebook tutorial on how to install, run, and use Jupyter for interactive matplotlib plotting, data analysis, and publishing code", - "display_as": "chart_studio", - "has_thumbnail": true, - "ipynb": "~chelsea_lyn/14070", - "language": "python", - "layout": "base", - "name": "Jupyter Notebook Tutorial", - "order": 11, - "page_type": "example_index", - "permalink": "python/ipython-notebook-tutorial/", - "thumbnail": "thumbnail/ipythonnb.jpg", - "title": "Jupyter Notebook Tutorial | plotly", - "v4upgrade": true - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/_posts/python/chart-studio/ipython-notebook-tutorial.md b/_posts/python/chart-studio/ipython-notebook-tutorial.md deleted file mode 100644 index 457f023cc..000000000 --- a/_posts/python/chart-studio/ipython-notebook-tutorial.md +++ /dev/null @@ -1,400 +0,0 @@ ---- -jupyter: - jupytext: - notebook_metadata_filter: all - text_representation: - extension: .md - format_name: markdown - format_version: '1.1' - jupytext_version: 1.1.7 - kernelspec: - display_name: Python 3 - 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: 3.6.5 - plotly: - permalink: python/ipython-notebook-tutorial/ - redirect_from: ipython-notebooks/ipython-notebook-tutorial/ - description: Jupyter notebook tutorial on how to install, run, and use Jupyter for interactive matplotlib plotting, data analysis, and publishing code - name: Jupyter Notebook Tutorial - thumbnail: thumbnail/ipythonnb.jpg - layout: base - name: Jupyter Notebook Tutorial - language: python - display_as: chart_studio - order: 11 - ipynb: ~chelsea_lyn/14070 - v4upgrade: true ---- - -#### Introduction -[Jupyter](http://jupyter.org/) has a beautiful notebook that lets you write and execute code, analyze data, embed content, and share reproducible work. Jupyter Notebook (previously referred to as IPython Notebook) allows you to easily share your code, data, plots, and explanation in a sinle notebook. Publishing is flexible: PDF, HTML, ipynb, dashboards, slides, and more. Code cells are based on an input and output format. For example: - -```python -print("hello world") -``` - -#### Installation -There are a few ways to use a Jupyter Notebook: - -* Install with [```pip```](https://pypi.python.org/pypi/pip). Open a terminal and type: ```$ pip install jupyter```. -* Windows users can install with [```setuptools```](http://ipython.org/ipython-doc/2/install/install.html#windows). -* [Anaconda](https://store.continuum.io/cshop/anaconda/) and [Enthought](https://store.enthought.com/downloads/#default) allow you to download a desktop version of Jupyter Notebook. -* [nteract](https://nteract.io/) allows users to work in a notebook enviornment via a desktop application. -* [Microsoft Azure](https://notebooks.azure.com/) provides hosted access to Jupyter Notebooks. -* [Domino Data Lab](http://support.dominodatalab.com/hc/en-us/articles/204856585-Jupyter-Notebooks) offers web-based Notebooks. -* [tmpnb](https://github.com/jupyter/tmpnb) launches a temporary online Notebook for individual users. - - -#### Getting Started -Once you've installed the Notebook, you start from your terminal by calling ```$ jupyter notebook```. This will open a browser on a [localhost](https://en.wikipedia.org/wiki/Localhost) to the URL of your Notebooks, by default http://127.0.0.1:8888. Windows users need to open up their Command Prompt. You'll see a dashboard with all your Notebooks. You can launch your Notebooks from there. The Notebook has the advantage of looking the same when you're coding and publishing. You just have all the options to move code, run cells, change kernels, and [use Markdown](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet) when you're running a NB. - - -#### Helpful Commands -**- Tab Completion:** Jupyter supports tab completion! You can type ```object_name.``` to view an object’s attributes. For tips on cell magics, running Notebooks, and exploring objects, check out the [Jupyter docs](https://ipython.org/ipython-doc/dev/interactive/tutorial.html#introducing-ipython). -
**- Help:** provides an introduction and overview of features. - -```python -help -``` - -**- Quick Reference:** open quick reference by running: - -```python -quickref -``` - -**- Keyboard Shortcuts:** ```Shift-Enter``` will run a cell, ```Ctrl-Enter``` will run a cell in-place, ```Alt-Enter``` will run a cell and insert another below. See more shortcuts [here](https://ipython.org/ipython-doc/1/interactive/notebook.html#keyboard-shortcuts). - - -#### Languages -The bulk of this tutorial discusses executing python code in Jupyter notebooks. You can also use Jupyter notebooks to execute R code. Skip down to the [R section] for more information on using IRkernel with Jupyter notebooks and graphing examples. -#### Package Management -When installing packages in Jupyter, you either need to install the package in your actual shell, or run the ```!``` prefix, e.g.: - - !pip install packagename - -You may want to [reload submodules](http://stackoverflow.com/questions/5364050/reloading-submodules-in-ipython) if you've edited the code in one. IPython comes with automatic reloading magic. You can reload all changed modules before executing a new line. - - %load_ext autoreload - %autoreload 2 - - -Some useful packages that we'll use in this tutorial include: -* [Pandas](https://plotly.com/pandas/): import data via a url and create a dataframe to easily handle data for analysis and graphing. See examples of using Pandas here: https://plotly.com/pandas/. -* [NumPy](https://plotly.com/numpy/): a package for scientific computing with tools for algebra, random number generation, integrating with databases, and managing data. See examples of using NumPy here: https://plotly.com/numpy/. -* [SciPy](http://www.scipy.org/): a Python-based ecosystem of packages for math, science, and engineering. -* [Plotly](https://plotly.com/python/getting-started): a graphing library for making interactive, publication-quality graphs. See examples of statistic, scientific, 3D charts, and more here: https://plotly.com/python. - -```python -import pandas as pd -import numpy as np -import scipy as sp -import chart_studio.plotly as py -``` - -#### Import Data -You can use pandas `read_csv()` function to import data. In the example below, we import a csv [hosted on github](https://github.com/plotly/datasets/) and display it in a [table using Plotly](https://plotly.com/python/table/): - -```python -import chart_studio.plotly as py -import plotly.figure_factory as ff -import pandas as pd - -df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv") - -table = ff.create_table(df) -py.iplot(table, filename='jupyter-table1') -``` - -Use `dataframe.column_title` to index the dataframe: - -```python -schools = df.School -schools[0] -``` - -Most pandas functions also work on an entire dataframe. For example, calling ```std()``` calculates the standard deviation for each column. - -```python -df.std() -``` - -#### Plotting Inline -You can use [Plotly's python API](https://plotly.com/python) to plot inside your Jupyter Notebook by calling ```plotly.plotly.iplot()``` or ```plotly.offline.iplot()``` if working offline. Plotting in the notebook gives you the advantage of keeping your data analysis and plots in one place. Now we can do a bit of interactive plotting. Head to the [Plotly getting started](https://plotly.com/python/) page to learn how to set your credentials. Calling the plot with ```iplot``` automaticallly generates an interactive version of the plot inside the Notebook in an iframe. See below: - -```python -import chart_studio.plotly as py -import plotly.graph_objects as go - -data = [go.Bar(x=df.School, - y=df.Gap)] - -py.iplot(data, filename='jupyter-basic_bar') -``` - -Plotting multiple traces and styling the chart with custom colors and titles is simple with Plotly syntax. Additionally, you can control the privacy with [```sharing```](https://plotly.com/python/privacy/) set to ```public```, ```private```, or ```secret```. - -```python -import chart_studio.plotly as py -import plotly.graph_objects as go - -trace_women = go.Bar(x=df.School, - y=df.Women, - name='Women', - marker=dict(color='#ffcdd2')) - -trace_men = go.Bar(x=df.School, - y=df.Men, - name='Men', - marker=dict(color='#A2D5F2')) - -trace_gap = go.Bar(x=df.School, - y=df.Gap, - name='Gap', - marker=dict(color='#59606D')) - -data = [trace_women, trace_men, trace_gap] - -layout = go.Layout(title="Average Earnings for Graduates", - xaxis=dict(title='School'), - yaxis=dict(title='Salary (in thousands)')) - -fig = go.Figure(data=data, layout=layout) - -py.iplot(fig, sharing='private', filename='jupyter-styled_bar') -``` - -Now we have interactive charts displayed in our notebook. Hover on the chart to see the values for each bar, click and drag to zoom into a specific section or click on the legend to hide/show a trace. - - -#### Plotting Interactive Maps -Plotly is now integrated with [Mapbox](https://www.mapbox.com/). In this example we'll plot lattitude and longitude data of nuclear waste sites. To plot on Mapbox maps with Plotly you'll need a Mapbox account and a [Mapbox Access Token](https://www.mapbox.com/studio/signin/) which you can add to your [Plotly settings](). - -```python -import chart_studio.plotly as py -import plotly.graph_objects as go - -import pandas as pd - -# mapbox_access_token = 'ADD YOUR TOKEN HERE' - -df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/Nuclear%20Waste%20Sites%20on%20American%20Campuses.csv') -site_lat = df.lat -site_lon = df.lon -locations_name = df.text - -data = [ - go.Scattermapbox( - lat=site_lat, - lon=site_lon, - mode='markers', - marker=dict( - size=17, - color='rgb(255, 0, 0)', - opacity=0.7 - ), - text=locations_name, - hoverinfo='text' - ), - go.Scattermapbox( - lat=site_lat, - lon=site_lon, - mode='markers', - marker=dict( - size=8, - color='rgb(242, 177, 172)', - opacity=0.7 - ), - hoverinfo='none' - )] - - -layout = go.Layout( - title='Nuclear Waste Sites on Campus', - autosize=True, - hovermode='closest', - showlegend=False, - mapbox=dict( - accesstoken=mapbox_access_token, - bearing=0, - center=dict( - lat=38, - lon=-94 - ), - pitch=0, - zoom=3, - style='light' - ), -) - -fig = dict(data=data, layout=layout) - -py.iplot(fig, filename='jupyter-Nuclear Waste Sites on American Campuses') -``` - -#### 3D Plotting -Using Numpy and Plotly, we can make interactive [3D plots](https://plotly.com/python/#3d) in the Notebook as well. - -```python -import chart_studio.plotly as py -import plotly.graph_objects as go - -import numpy as np - -s = np.linspace(0, 2 * np.pi, 240) -t = np.linspace(0, np.pi, 240) -tGrid, sGrid = np.meshgrid(s, t) - -r = 2 + np.sin(7 * sGrid + 5 * tGrid) # r = 2 + sin(7s+5t) -x = r * np.cos(sGrid) * np.sin(tGrid) # x = r*cos(s)*sin(t) -y = r * np.sin(sGrid) * np.sin(tGrid) # y = r*sin(s)*sin(t) -z = r * np.cos(tGrid) # z = r*cos(t) - -surface = go.Surface(x=x, y=y, z=z) -data = [surface] - -layout = go.Layout( - title='Parametric Plot', - scene=dict( - xaxis=dict( - gridcolor='rgb(255, 255, 255)', - zerolinecolor='rgb(255, 255, 255)', - showbackground=True, - backgroundcolor='rgb(230, 230,230)' - ), - yaxis=dict( - gridcolor='rgb(255, 255, 255)', - zerolinecolor='rgb(255, 255, 255)', - showbackground=True, - backgroundcolor='rgb(230, 230,230)' - ), - zaxis=dict( - gridcolor='rgb(255, 255, 255)', - zerolinecolor='rgb(255, 255, 255)', - showbackground=True, - backgroundcolor='rgb(230, 230,230)' - ) - ) -) - -fig = go.Figure(data=data, layout=layout) -py.iplot(fig, filename='jupyter-parametric_plot') -``` - -#### Animated Plots -Checkout Plotly's [animation documentation](https://plotly.com/python/#animations) to see how to create animated plots inline in Jupyter notebooks like the Gapminder plot displayed below: -![https://plotly.com/~PythonPlotBot/231/](https://raw.githubusercontent.com/cldougl/plot_images/add_r_img/anim.gif) - - -#### Plot Controls & IPython widgets -Add sliders, buttons, and dropdowns to your inline chart: - -```python -import chart_studio.plotly as py -import numpy as np - -data = [dict( - visible = False, - line=dict(color='#00CED1', width=6), - name = '𝜈 = '+str(step), - x = np.arange(0,10,0.01), - y = np.sin(step*np.arange(0,10,0.01))) for step in np.arange(0,5,0.1)] -data[10]['visible'] = True - -steps = [] -for i in range(len(data)): - step = dict( - method = 'restyle', - args = ['visible', [False] * len(data)], - ) - step['args'][1][i] = True # Toggle i'th trace to "visible" - steps.append(step) - -sliders = [dict( - active = 10, - currentvalue = {"prefix": "Frequency: "}, - pad = {"t": 50}, - steps = steps -)] - -layout = dict(sliders=sliders) -fig = dict(data=data, layout=layout) - -py.iplot(fig, filename='Sine Wave Slider') -``` - -Additionally, [IPython widgets](http://moderndata.plot.ly/widgets-in-ipython-notebook-and-plotly/) allow you to add sliders, widgets, search boxes, and more to your Notebook. See the [widget docs](https://ipython.org/ipython-doc/3/api/generated/IPython.html.widgets.interaction.html) for more information. For others to be able to access your work, they'll need IPython. Or, you can use a cloud-based NB option so others can run your work. -
- - - -#### Executing R Code -IRkernel, an R kernel for Jupyter, allows you to write and execute R code in a Jupyter notebook. Checkout the [IRkernel documentation](https://irkernel.github.io/installation/) for some simple installation instructions. Once IRkernel is installed, open a Jupyter Notebook by calling `$ jupyter notebook` and use the New dropdown to select an R notebook. - -![](https://raw.githubusercontent.com/cldougl/plot_images/add_r_img/rkernel.png) - -See a full R example Jupyter Notebook here: https://plotly.com/~chelsea_lyn/14069 - - -#### Additional Embed Features -We've seen how to embed Plotly tables and charts as iframes in the notebook, with `IPython.display` we can embed additional features, such a videos. For example, from YouTube: - -```python -from IPython.display import YouTubeVideo -YouTubeVideo("wupToqz1e2g") -``` - -#### LaTeX -We can embed LaTeX inside a Notebook by putting a ```$$``` around our math, then run the cell as a Markdown cell. For example, the cell below is ```$$c = \sqrt{a^2 + b^2}$$```, but the Notebook renders the expression. - - -$$c = \sqrt{a^2 + b^2}$$ - - -Or, you can display output from Python, as seen [here](http://stackoverflow.com/questions/13208286/how-to-write-latex-in-ipython-notebook). - -```python -from IPython.display import display, Math, Latex - -display(Math(r'F(k) = \int_{-\infty}^{\infty} f(x) e^{2\pi i k} dx')) -``` - -#### Exporting & Publishing Notebooks -We can export the Notebook as an HTML, PDF, .py, .ipynb, Markdown, and reST file. You can also turn your NB [into a slideshow](http://ipython.org/ipython-doc/2/notebook/nbconvert.html). You can publish Jupyter Notebooks on Plotly. Simply visit [plot.ly](https://plotly.com/organize/home?create=notebook) and select the `+ Create` button in the upper right hand corner. Select Notebook and upload your Jupyter notebook (.ipynb) file! -The notebooks that you upload will be stored in your [Plotly organize folder](https://plotly.com/organize) and hosted at a unique link to make sharing quick and easy. -See some example notebooks: -- https://plotly.com/~chelsea_lyn/14066 -- https://plotly.com/~notebook_demo/35 -- https://plotly.com/~notebook_demo/85 -- https://plotly.com/~notebook_demo/128 - - -#### Publishing Dashboards -Users publishing interactive graphs can also use [Plotly's dashboarding tool](https://plotly.com/dashboard/create) to arrange plots with a drag and drop interface. These dashboards can be published, embedded, and shared. - - -### Publishing Dash Apps -For users looking to ship and productionize Python apps, [dash](https://github.com/plotly/dash) is an assemblage of Flask, Socketio, Jinja, Plotly and boiler plate CSS and JS for easily creating data visualization web-apps with your Python data analysis backend. -
- -
- - -### Jupyter Gallery -For more Jupyter tutorials, checkout [Plotly's python documentation](https://plotly.com/python/): all documentation is written in jupyter notebooks that you can download and run yourself or checkout these [user submitted examples](https://plotly.com/ipython-notebooks/)! - -[![IPython Notebook Gallery](http://i.imgur.com/AdElJQx.png)](https://plotly.com/ipython-notebooks/) - -```python - -``` diff --git a/_posts/python/chart-studio/nb.tpl b/_posts/python/chart-studio/nb.tpl deleted file mode 100644 index 85f4fa8a5..000000000 --- a/_posts/python/chart-studio/nb.tpl +++ /dev/null @@ -1,18 +0,0 @@ -{%- extends 'basic.tpl' -%} -{%- block header -%} ---- -{% for k in nb.metadata.get("plotly") -%} -{{ k }}: {{ nb.metadata.get("plotly")[k] }} -{% endfor -%} ---- -{{ super() }} -{{ '{% raw %}' }} - - -{%- endblock header-%} - - -{%- block footer %} -{{ super() }} -{{ '{% endraw %}' }} -{%- endblock footer-%} diff --git a/_posts/python/chart-studio/presentations-tool.ipynb b/_posts/python/chart-studio/presentations-tool.ipynb deleted file mode 100644 index d356e50f0..000000000 --- a/_posts/python/chart-studio/presentations-tool.ipynb +++ /dev/null @@ -1,603 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Plotly Presentations\n", - "To use Plotly's Presentations API you will write your presentation code in a string of markdown and then pass that through the Presentations API function `pres.Presentation()`. This creates a JSON version of your presentation. To upload the presentation online pass it through `py.presentation_ops.upload()`.\n", - "\n", - "In your string, use `---` on a single line to seperate two slides. To put a title in your slide, put a line that starts with any number of `#`s. Only your first title will be appear in your slide. A title looks like:\n", - "\n", - "`# slide title`\n", - "\n", - "Anything that comes after the title will be put as text in your slide. Check out the example below to see this in action." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Current Limitations\n", - "`Boldface`, _italics_ and [hypertext](https://www.w3.org/WhatIs.html) are not supported features of the Presentation API." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Display in Jupyter\n", - "The function below generates HTML code to display the presentation in an iframe directly in Jupyter." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def url_to_iframe(url, text=True):\n", - " html = ''\n", - " # style\n", - " html += '''\n", - " \n", - " '\n", - " '''\n", - " # iframe\n", - " html += ''\n", - " if text:\n", - " html += '''\n", - "
\n", - "

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\n", - "
\n", - " \n", - " '''\n", - " return html" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Simple Example" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "import chart_studio.plotly as py\n", - "import chart_studio.presentation_objs as pres\n", - "\n", - "filename = 'simple-pres'\n", - "markdown_string = \"\"\"\n", - "# slide 1\n", - "There is only one slide.\n", - "\n", - "---\n", - "# slide 2\n", - "Again, another slide on this page.\n", - "\n", - "\"\"\"\n", - "\n", - "my_pres = pres.Presentation(markdown_string)\n", - "pres_url_0 = py.presentation_ops.upload(my_pres, filename)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "https://plotly.com/~AdamKulidjian/3700/simple-pres/" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " '\n", - " \n", - "
\n", - "

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\n", - "
\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import IPython\n", - "\n", - "iframe_0 = url_to_iframe(pres_url_0, True)\n", - "IPython.display.HTML(iframe_0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Insert Plotly Chart\n", - "If you want to insert a Plotly chart into your presentation, all you need to do is write a line in your presentation that takes the form:\n", - "\n", - "`Plotly(url)`\n", - "\n", - "where url is a Plotly url. For example:\n", - "\n", - "`Plotly(https://plotly.com/~AdamKulidjian/3564)`\n", - "\n", - "The Plotly url lines should be written on a separate line after your title line. You can put as many images in your slide as you want, as the API will arrange them on the slide automatically, but it is _highly_ encouraged that you use `4 OR FEWER IMAGES PER SLIDE`. This will produce the cleanest look.\n", - "\n", - "`Useful Tip`:
\n", - "For Plotly charts it is HIGHLY ADVISED that you use a chart that has `layout['autosize']` set to `True`. If it is `False` the image may be cropped or only partially visible when it appears in the presentation slide." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "import chart_studio.plotly as py\n", - "import chart_studio.presentation_objs as pres\n", - "\n", - "filename = 'pres-with-plotly-chart'\n", - "markdown_string = \"\"\"\n", - "# 3D scatterplots\n", - "3D Scatterplot are just a collection of balls in a 3D cartesian space each of which have assigned properties like color, size, and more.\n", - "\n", - "---\n", - "# simple 3d scatterplot\n", - "\n", - "Plotly(https://plotly.com/~AdamKulidjian/3698)\n", - "---\n", - "# different colorscales\n", - "\n", - "There are various colorscales and colorschemes to try in Plotly. Check out plotly.colors to find a list of valid and available colorscales.\n", - "\n", - "Plotly(https://plotly.com/~AdamKulidjian/3582)\n", - "Plotly(https://plotly.com/~AdamKulidjian/3698)\n", - "\"\"\"\n", - "\n", - "my_pres = pres.Presentation(markdown_string)\n", - "pres_url_1 = py.presentation_ops.upload(my_pres, filename)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "https://plotly.com/~AdamKulidjian/3710/pres-with-plotly-chart/" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " '\n", - " \n", - "
\n", - "

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\n", - "
\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import IPython\n", - "\n", - "iframe_1 = url_to_iframe(pres_url_1, True)\n", - "IPython.display.HTML(iframe_1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Insert Web Images\n", - "To insert an image from the web, insert the a `Image(url)` where url is the image url." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "import chart_studio.plotly as py\n", - "import chart_studio.presentation_objs as pres\n", - "\n", - "filename = 'pres-with-images'\n", - "markdown_string = \"\"\"\n", - "# Animals of the Wild\n", - "---\n", - "# The Lion\n", - "\n", - "Panthera leo is one of the big cats in the Felidae family and a member of genus Panthera. It has been listed as Vulnerable on the IUCN Red List since 1996, as populations in African range countries declined by about 43% since the early 1990s. Lion populations are untenable outside designated protected areas. Although the cause of the decline is not fully understood, habitat loss and conflicts with humans are the greatest causes of concern. The West African lion population is listed as Critically Endangered since 2016. The only lion population in Asia survives in and around India's Gir Forest National Park and is listed as Endangered since 1986.\n", - "\n", - "Image(https://i.pinimg.com/736x/da/af/73/daaf73960eb5a21d6bca748195f12052--lion-photography-lion-kings.jpg)\n", - "---\n", - "# The Giraffe\n", - "\n", - "The giraffe is a genus of African even-toed ungulate mammals, the tallest living terrestrial animals and the largest ruminants. The genus currently consists of one species, Giraffa camelopardalis, the type species. Seven other species are extinct, prehistoric species known from fossils. Taxonomic classifications of one to eight extant giraffe species have been described, based upon research into the mitochondrial and nuclear DNA, as well as morphological measurements of Giraffa, but the IUCN currently recognizes only one species with nine subspecies.\n", - "\n", - "Image(https://img.purch.com/w/192/aHR0cDovL3d3dy5saXZlc2NpZW5jZS5jb20vaW1hZ2VzL2kvMDAwLzA2OC8wOTQvaTMwMC9naXJhZmZlLmpwZz8xNDA1MDA4NDQy)\n", - "Image(https://upload.wikimedia.org/wikipedia/commons/9/9f/Giraffe_standing.jpg)\n", - "\n", - "\"\"\"\n", - "\n", - "my_pres = pres.Presentation(markdown_string)\n", - "pres_url_2 = py.presentation_ops.upload(my_pres, filename)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "https://plotly.com/~AdamKulidjian/3702/pres-with-images/" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " '\n", - " \n", - "
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\n", - "
\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import IPython\n", - "\n", - "iframe_2 = url_to_iframe(pres_url_2, True)\n", - "IPython.display.HTML(iframe_2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Image Stretch\n", - "If you want to ensure that your image maintains its original width:height ratio, include the parameter `imgStretch=False` in your `pres.Presentation()` function call." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "import chart_studio.plotly as py\n", - "import chart_studio.presentation_objs as pres\n", - "\n", - "filename = 'pres-with-no-imgstretch'\n", - "markdown_string = \"\"\"\n", - "# images in native aspect ratio\n", - "\n", - "Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png)\n", - "Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png)\n", - "Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png)\n", - "Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png)\n", - "Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png)\n", - "\"\"\"\n", - "\n", - "my_pres = pres.Presentation(markdown_string, imgStretch=False)\n", - "pres_url_3 = py.presentation_ops.upload(my_pres, filename)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "https://plotly.com/~AdamKulidjian/3703/pres-with-no-imgstretch/" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " '\n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import IPython\n", - "\n", - "iframe_3 = url_to_iframe(pres_url_3, False)\n", - "IPython.display.HTML(iframe_3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Transitions\n", - "You can specify how your want your slides to transition to one another. Just like in the Plotly Presentation Application, there are 4 types of transitions: `slide`, `zoom`, `fade` and `spin`.\n", - "\n", - "To apply any combination of these transition to a slide, just insert transitions at the top of the slide as follows:\n", - "\n", - "`transition: slide, zoom`\n", - "\n", - "Make sure that this line comes before any heading that you define in the slide, i.e. like this:\n", - "\n", - "```\n", - "transition: slide, zoom\n", - "# slide title\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "import chart_studio.plotly as py\n", - "import chart_studio.presentation_objs as pres\n", - "\n", - "filename = 'pres-with-transitions'\n", - "markdown_string = \"\"\"\n", - "transition: slide\n", - "# slide\n", - "---\n", - "transition: zoom\n", - "# zoom\n", - "---\n", - "transition: fade\n", - "# fade\n", - "---\n", - "transition: spin\n", - "# spin\n", - "---\n", - "transition: spin and slide\n", - "# spin, slide\n", - "---\n", - "transition: fade zoom\n", - "# fade, zoom\n", - "---\n", - "transition: slide, zoom, fade, spin, spin, spin, zoom, fade\n", - "# slide, zoom, fade, spin\n", - "\n", - "\"\"\"\n", - "\n", - "my_pres = pres.Presentation(markdown_string, style='moods')\n", - "pres_url_6 = py.presentation_ops.upload(my_pres, filename)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " '\n", - " \n", - "
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\n", - "
\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import IPython\n", - "\n", - "iframe_6 = url_to_iframe(pres_url_6, True)\n", - "IPython.display.HTML(iframe_6)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Reference" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on class presentation_ops in module chart_studio.plotly.plotly:\n", - "\n", - "class presentation_ops(builtins.object)\n", - " | Interface to Plotly's Spectacle-Presentations API.\n", - " | \n", - " | Class methods defined here:\n", - " | \n", - " | upload(presentation, filename, sharing='public', auto_open=True) from builtins.type\n", - " | Function for uploading presentations to Plotly.\n", - " | \n", - " | :param (dict) presentation: the JSON presentation to be uploaded. Use\n", - " | plotly.presentation_objs.Presentation to create presentations\n", - " | from a Markdown-like string.\n", - " | :param (str) filename: the name of the presentation to be saved in\n", - " | your Plotly account. Will overwrite a presentation of the same\n", - " | name if it already exists in your files.\n", - " | :param (str) sharing: can be set to either 'public', 'private'\n", - " | or 'secret'. If 'public', your presentation will be viewable by\n", - " | all other users. If 'private' only you can see your presentation.\n", - " | If it is set to 'secret', the url will be returned with a string\n", - " | of random characters appended to the url which is called a\n", - " | sharekey. The point of a sharekey is that it makes the url very\n", - " | hard to guess, but anyone with the url can view the presentation.\n", - " | :param (bool) auto_open: automatically opens the presentation in the\n", - " | browser.\n", - " | \n", - " | See the documentation online for examples.\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Data descriptors defined here:\n", - " | \n", - " | __dict__\n", - " | dictionary for instance variables (if defined)\n", - " | \n", - " | __weakref__\n", - " | list of weak references to the object (if defined)\n", - "\n" - ] - } - ], - "source": [ - "help(py.presentation_ops)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "notebook_metadata_filter": "all", - "text_representation": { - "extension": ".md", - "format_name": "markdown", - "format_version": "1.1", - "jupytext_version": "1.1.7" - } - }, - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.5" - }, - "plotly": { - "description": "How to create and publish a spectacle-presentation with the Python API.", - "display_as": "chart_studio", - "has_thumbnail": true, - "language": "python", - "layout": "base", - "name": "Presentations Tool", - "order": 0.6, - "page_type": "u-guide", - "permalink": "python/presentations-tool/", - "thumbnail": "thumbnail/pres_api.jpg", - "title": "Presentations Tool | plotly", - "v4upgrade": true - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/_posts/python/chart-studio/presentations-tool.md b/_posts/python/chart-studio/presentations-tool.md deleted file mode 100644 index 8fa2200d8..000000000 --- a/_posts/python/chart-studio/presentations-tool.md +++ /dev/null @@ -1,289 +0,0 @@ ---- -jupyter: - jupytext: - notebook_metadata_filter: all - text_representation: - extension: .md - format_name: markdown - format_version: '1.1' - jupytext_version: 1.1.7 - kernelspec: - display_name: Python 3 - 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: 3.6.5 - plotly: - v4upgrade: true - permalink: python/presentations-tool/ - description: How to create and publish a spectacle-presentation with the Python API. - name: Presentations Tool | plotly - thumbnail: thumbnail/pres_api.jpg - layout: base - name: Presentations Tool - language: python - display_as: chart_studio - page_type: u-guide - order: 0.6 ---- - -#### Plotly Presentations123222 -To use Plotly's Presentations API you will write your presentation code in a string of markdown and then pass that through the Presentations API function `pres.Presentation()`. This creates a JSON version of your presentation. To upload the presentation online pass it through `py.presentation_ops.upload()`. - -In your string, use `---` on a single line to seperate two slides. To put a title in your slide, put a line that starts with any number of `#`s. Only your first title will be appear in your slide. A title looks like: - -`# slide title` - -Anything that comes after the title will be put as text in your slide. Check out the example below to see this in action. - - -#### Current Limitations -`Boldface`, _italics_ and [hypertext](https://www.w3.org/WhatIs.html) are not supported features of the Presentation API. - - -#### Display in Jupyter -The function below generates HTML code to display the presentation in an iframe directly in Jupyter. - -```python -def url_to_iframe(url, text=True): - html = '' - # style - html += ''' - - ' - ''' - # iframe - html += '' - if text: - html += ''' -
-

Click on the presentation above and use left/right arrow keys to flip through the slides.

-
- - ''' - return html -``` - -#### Simple Example - -```python -import chart_studio.plotly as py -import chart_studio.presentation_objs as pres - -filename = 'simple-pres' -markdown_string = """ -# slide 1 -There is only one slide. - ---- -# slide 2 -Again, another slide on this page. - -""" - -my_pres = pres.Presentation(markdown_string) -pres_url_0 = py.presentation_ops.upload(my_pres, filename) -``` - -https://plotly.com/~AdamKulidjian/3700/simple-pres/ - -```python -import IPython - -iframe_0 = url_to_iframe(pres_url_0, True) -IPython.display.HTML(iframe_0) -``` - -#### Insert Plotly Chart -If you want to insert a Plotly chart into your presentation, all you need to do is write a line in your presentation that takes the form: - -`Plotly(url)` - -where url is a Plotly url. For example: - -`Plotly(https://plotly.com/~AdamKulidjian/3564)` - -The Plotly url lines should be written on a separate line after your title line. You can put as many images in your slide as you want, as the API will arrange them on the slide automatically, but it is _highly_ encouraged that you use `4 OR FEWER IMAGES PER SLIDE`. This will produce the cleanest look. - -`Useful Tip`:
-For Plotly charts it is HIGHLY ADVISED that you use a chart that has `layout['autosize']` set to `True`. If it is `False` the image may be cropped or only partially visible when it appears in the presentation slide. - -```python -import chart_studio.plotly as py -import chart_studio.presentation_objs as pres - -filename = 'pres-with-plotly-chart' -markdown_string = """ -# 3D scatterplots -3D Scatterplot are just a collection of balls in a 3D cartesian space each of which have assigned properties like color, size, and more. - ---- -# simple 3d scatterplot - -Plotly(https://plotly.com/~AdamKulidjian/3698) ---- -# different colorscales - -There are various colorscales and colorschemes to try in Plotly. Check out plotly.colors to find a list of valid and available colorscales. - -Plotly(https://plotly.com/~AdamKulidjian/3582) -Plotly(https://plotly.com/~AdamKulidjian/3698) -""" - -my_pres = pres.Presentation(markdown_string) -pres_url_1 = py.presentation_ops.upload(my_pres, filename) -``` - -https://plotly.com/~AdamKulidjian/3710/pres-with-plotly-chart/ - -```python -import IPython - -iframe_1 = url_to_iframe(pres_url_1, True) -IPython.display.HTML(iframe_1) -``` - -#### Insert Web Images -To insert an image from the web, insert the a `Image(url)` where url is the image url. - -```python -import chart_studio.plotly as py -import chart_studio.presentation_objs as pres - -filename = 'pres-with-images' -markdown_string = """ -# Animals of the Wild ---- -# The Lion - -Panthera leo is one of the big cats in the Felidae family and a member of genus Panthera. It has been listed as Vulnerable on the IUCN Red List since 1996, as populations in African range countries declined by about 43% since the early 1990s. Lion populations are untenable outside designated protected areas. Although the cause of the decline is not fully understood, habitat loss and conflicts with humans are the greatest causes of concern. The West African lion population is listed as Critically Endangered since 2016. The only lion population in Asia survives in and around India's Gir Forest National Park and is listed as Endangered since 1986. - -Image(https://i.pinimg.com/736x/da/af/73/daaf73960eb5a21d6bca748195f12052--lion-photography-lion-kings.jpg) ---- -# The Giraffe - -The giraffe is a genus of African even-toed ungulate mammals, the tallest living terrestrial animals and the largest ruminants. The genus currently consists of one species, Giraffa camelopardalis, the type species. Seven other species are extinct, prehistoric species known from fossils. Taxonomic classifications of one to eight extant giraffe species have been described, based upon research into the mitochondrial and nuclear DNA, as well as morphological measurements of Giraffa, but the IUCN currently recognizes only one species with nine subspecies. - -Image(https://img.purch.com/w/192/aHR0cDovL3d3dy5saXZlc2NpZW5jZS5jb20vaW1hZ2VzL2kvMDAwLzA2OC8wOTQvaTMwMC9naXJhZmZlLmpwZz8xNDA1MDA4NDQy) -Image(https://upload.wikimedia.org/wikipedia/commons/9/9f/Giraffe_standing.jpg) - -""" - -my_pres = pres.Presentation(markdown_string) -pres_url_2 = py.presentation_ops.upload(my_pres, filename) -``` - -https://plotly.com/~AdamKulidjian/3702/pres-with-images/ - -```python -import IPython - -iframe_2 = url_to_iframe(pres_url_2, True) -IPython.display.HTML(iframe_2) -``` - -#### Image Stretch -If you want to ensure that your image maintains its original width:height ratio, include the parameter `imgStretch=False` in your `pres.Presentation()` function call. - -```python -import chart_studio.plotly as py -import chart_studio.presentation_objs as pres - -filename = 'pres-with-no-imgstretch' -markdown_string = """ -# images in native aspect ratio - -Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png) -Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png) -Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png) -Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png) -Image(https://raw.githubusercontent.com/jackparmer/gradient-backgrounds/master/moods1.png) -""" - -my_pres = pres.Presentation(markdown_string, imgStretch=False) -pres_url_3 = py.presentation_ops.upload(my_pres, filename) -``` - -https://plotly.com/~AdamKulidjian/3703/pres-with-no-imgstretch/ - -```python -import IPython - -iframe_3 = url_to_iframe(pres_url_3, False) -IPython.display.HTML(iframe_3) -``` - - -#### Transitions -You can specify how your want your slides to transition to one another. Just like in the Plotly Presentation Application, there are 4 types of transitions: `slide`, `zoom`, `fade` and `spin`. - -To apply any combination of these transition to a slide, just insert transitions at the top of the slide as follows: - -`transition: slide, zoom` - -Make sure that this line comes before any heading that you define in the slide, i.e. like this: - -``` -transition: slide, zoom -# slide title -``` - - -```python -import chart_studio.plotly as py -import chart_studio.presentation_objs as pres - -filename = 'pres-with-transitions' -markdown_string = """ -transition: slide -# slide ---- -transition: zoom -# zoom ---- -transition: fade -# fade ---- -transition: spin -# spin ---- -transition: spin and slide -# spin, slide ---- -transition: fade zoom -# fade, zoom ---- -transition: slide, zoom, fade, spin, spin, spin, zoom, fade -# slide, zoom, fade, spin - -""" - -my_pres = pres.Presentation(markdown_string, style='moods') -pres_url_6 = py.presentation_ops.upload(my_pres, filename) -``` - -```python -import IPython - -iframe_6 = url_to_iframe(pres_url_6, True) -IPython.display.HTML(iframe_6) -``` - -#### Reference - -```python -help(py.presentation_ops) -``` diff --git a/_posts/python/chart-studio/privacy.ipynb b/_posts/python/chart-studio/privacy.ipynb deleted file mode 100644 index a541764d9..000000000 --- a/_posts/python/chart-studio/privacy.ipynb +++ /dev/null @@ -1,420 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Default Privacy1\n", - "By default, `plotly.iplot()` and `plotly.plot()` create public graphs (which are free to create). With a [plotly subscription](https://plotly.com/plans) you can easily make charts private or secret via the sharing argument." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Public Graphs" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import chart_studio.plotly as py\n", - "import plotly.graph_objects as go\n", - "\n", - "data = [\n", - " go.Scatter(\n", - " x=[1, 2, 3],\n", - " y=[1, 3, 1]\n", - " )\n", - "]\n", - "\n", - "py.iplot(data, filename='privacy-public', sharing='public')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below is the URL of this public plot. Anyone can view public plots even if they are not logged into Plotly. Go ahead and try it out:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'https://plotly.com/~PythonPlotBot/2677/'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "py.plot(data, filename='privacy-public', sharing='public')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Private Graphs" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "py.iplot(data, filename='privacy-private', sharing='private')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below is the URL of the private plot above. Only the owner can view the private plot. You won't be able to view this plot, try it out:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'https://plotly.com/~PythonPlotBot/2679/'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "py.plot(data, filename='privacy-private', sharing='private')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Secret Graphs" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "py.iplot(data, filename='privacy-secret', sharing='secret')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below is the URL of this secret plot. Anyone with the secret link can view this chart. However, it will not appear in the Plotly feed, your profile, or search engines. Go ahead and try it out:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'https://plotly.com/~PythonPlotBot/475?share_key=UaGz0FTFLklnEd7XTKaqy8'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "py.plot(data, filename='privacy-secret', sharing='secret')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Make All Future Plots Private\n", - "To make all future plots private, you can update your configuration file to create private plots by default:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "import chart_studio\n", - "chart_studio.tools.set_config_file(world_readable=False, sharing='private')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Make All Existing Plots Private\n", - "This example uses [Plotly's REST API](https://api.plot.ly/v2/)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import requests\n", - "from requests.auth import HTTPBasicAuth" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define variables, including YOUR [USERNAME and API KEY](https://plotly.com/settings/api)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "username = 'private_plotly' # Replace with YOUR USERNAME\n", - "api_key = 'k0yy0ztssk' # Replace with YOUR API KEY\n", - "\n", - "auth = HTTPBasicAuth(username, api_key)\n", - "headers = {'Plotly-Client-Platform': 'python'}\n", - "\n", - "page_size = 500" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Collect filenames of ALL of your plots and
update `world_readable` of each plot with a PATCH request" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def get_pages(username, page_size):\n", - " url = 'https://api.plot.ly/v2/folders/all?user='+username+'&filetype=plot&page_size='+str(page_size)\n", - " response = requests.get(url, auth=auth, headers=headers)\n", - " if response.status_code != 200:\n", - " return\n", - " page = json.loads(response.content.decode('utf-8'))\n", - " yield page\n", - " while True:\n", - " resource = page['children']['next']\n", - " if not resource:\n", - " break\n", - " response = requests.get(resource, auth=auth, headers=headers)\n", - " if response.status_code != 200:\n", - " break\n", - " page = json.loads(response.content.decode('utf-8'))\n", - " yield page\n", - "\n", - "def make_all_plots_private(username, page_size=500):\n", - " for page in get_pages(username, page_size):\n", - " for x in range(0, len(page['children']['results'])):\n", - " fid = page['children']['results'][x]['fid']\n", - " requests.patch('https://api.plot.ly/v2/files/'+fid, {\"world_readable\": False}, auth=auth, headers=headers)\n", - " print('ALL of your plots are now private - visit: https://plotly.com/organize/home to view your private plots!')\n", - "\n", - "make_all_plots_private(username)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reference" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function plot in module chart_studio.plotly.plotly:\n", - "\n", - "plot(figure_or_data, validate=True, **plot_options)\n", - " Create a unique url for this plot in Plotly and optionally open url.\n", - " \n", - " plot_options keyword arguments:\n", - " filename (string) -- the name that will be associated with this figure\n", - " auto_open (default=True) -- Toggle browser options\n", - " True: open this plot in a new browser tab\n", - " False: do not open plot in the browser, but do return the unique url\n", - " sharing ('public' | 'private' | 'secret') -- Toggle who can view this\n", - " graph\n", - " - 'public': Anyone can view this graph. It will appear in your profile\n", - " and can appear in search engines. You do not need to be\n", - " logged in to Plotly to view this chart.\n", - " - 'private': Only you can view this plot. It will not appear in the\n", - " Plotly feed, your profile, or search engines. You must be\n", - " logged in to Plotly to view this graph. You can privately\n", - " share this graph with other Plotly users in your online\n", - " Plotly account and they will need to be logged in to\n", - " view this plot.\n", - " - 'secret': Anyone with this secret link can view this chart. It will\n", - " not appear in the Plotly feed, your profile, or search\n", - " engines. If it is embedded inside a webpage or an IPython\n", - " notebook, anybody who is viewing that page will be able to\n", - " view the graph. You do not need to be logged in to view\n", - " this plot.\n", - " world_readable (default=True) -- Deprecated: use \"sharing\".\n", - " Make this figure private/public\n", - "\n" - ] - } - ], - "source": [ - "help(py.plot)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "notebook_metadata_filter": "all", - "text_representation": { - "extension": ".md", - "format_name": "markdown", - "format_version": "1.1", - "jupytext_version": "1.1.7" - } - }, - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.5" - }, - "plotly": { - "description": "How to set the privacy settings of plotly graphs in python. Three examples of different privacy options: public, private and secret.", - "display_as": "chart_studio", - "has_thumbnail": true, - "ipynb": "~notebook_demo/97", - "language": "python", - "layout": "base", - "name": "Privacy", - "order": 2, - "permalink": "python/privacy/", - "thumbnail": "thumbnail/privacy.jpg", - "title": "Privacy | plotly", - "v4upgrade": true - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/_posts/python/chart-studio/privacy.md b/_posts/python/chart-studio/privacy.md deleted file mode 100644 index 5291f651e..000000000 --- a/_posts/python/chart-studio/privacy.md +++ /dev/null @@ -1,155 +0,0 @@ ---- -jupyter: - jupytext: - notebook_metadata_filter: all - text_representation: - extension: .md - format_name: markdown - format_version: '1.1' - jupytext_version: 1.1.7 - kernelspec: - display_name: Python 3 - 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: 3.6.5 - plotly: - description: How to set the privacy settings of plotly graphs in python. Three - examples of different privacy options: public, private and secret. - display_as: chart_studio - ipynb: ~notebook_demo/97 - language: python - layout: base - name: Privacy - order: 2 - permalink: python/privacy/ - thumbnail: thumbnail/privacy.jpg - v4upgrade: true ---- - -#### Default Privacy -By default, `plotly.iplot()` and `plotly.plot()` create public graphs (which are free to create). With a [plotly subscription](https://plotly.com/plans) you can easily make charts private or secret via the sharing argument. - - -#### Public Graphs - -```python -import chart_studio.plotly as py -import plotly.graph_objects as go - -data = [ - go.Scatter( - x=[1, 2, 3], - y=[1, 3, 1] - ) -] - -py.iplot(data, filename='privacy-public', sharing='public') -``` - -Below is the URL of this public plot. Anyone can view public plots even if they are not logged into Plotly. Go ahead and try it out: - -```python -py.plot(data, filename='privacy-public', sharing='public') -``` - -### Private Graphs - -```python -py.iplot(data, filename='privacy-private', sharing='private') -``` - -Below is the URL of the private plot above. Only the owner can view the private plot. You won't be able to view this plot, try it out: - -```python -py.plot(data, filename='privacy-private', sharing='private') -``` - -### Secret Graphs - -```python -py.iplot(data, filename='privacy-secret', sharing='secret') -``` - -Below is the URL of this secret plot. Anyone with the secret link can view this chart. However, it will not appear in the Plotly feed, your profile, or search engines. Go ahead and try it out: - -```python -py.plot(data, filename='privacy-secret', sharing='secret') -``` - -### Make All Future Plots Private -To make all future plots private, you can update your configuration file to create private plots by default: - -```python -import chart_studio -chart_studio.tools.set_config_file(world_readable=False, sharing='private') -``` - -### Make All Existing Plots Private -This example uses [Plotly's REST API](https://api.plot.ly/v2/) - -```python -import json -import requests -from requests.auth import HTTPBasicAuth -``` - -Define variables, including YOUR [USERNAME and API KEY](https://plotly.com/settings/api) - -```python -username = 'private_plotly' # Replace with YOUR USERNAME -api_key = 'k0yy0ztssk' # Replace with YOUR API KEY - -auth = HTTPBasicAuth(username, api_key) -headers = {'Plotly-Client-Platform': 'python'} - -page_size = 500 -``` - -Collect filenames of ALL of your plots and
update `world_readable` of each plot with a PATCH request - -```python -def get_pages(username, page_size): - url = 'https://api.plot.ly/v2/folders/all?user='+username+'&filetype=plot&page_size='+str(page_size) - response = requests.get(url, auth=auth, headers=headers) - if response.status_code != 200: - return - page = json.loads(response.content.decode('utf-8')) - yield page - while True: - resource = page['children']['next'] - if not resource: - break - response = requests.get(resource, auth=auth, headers=headers) - if response.status_code != 200: - break - page = json.loads(response.content.decode('utf-8')) - yield page - -def make_all_plots_private(username, page_size=500): - for page in get_pages(username, page_size): - for x in range(0, len(page['children']['results'])): - fid = page['children']['results'][x]['fid'] - requests.patch('https://api.plot.ly/v2/files/'+fid, {"world_readable": False}, auth=auth, headers=headers) - print('ALL of your plots are now private - visit: https://plotly.com/organize/home to view your private plots!') - -make_all_plots_private(username) -``` - -### Reference - -```python -help(py.plot) -``` - -```python - -``` diff --git a/_posts/python/chart-studio/proxy-configuration.ipynb b/_posts/python/chart-studio/proxy-configuration.ipynb deleted file mode 100644 index f0a5f2e10..000000000 --- a/_posts/python/chart-studio/proxy-configuration.ipynb +++ /dev/null @@ -1,96 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you are behind a corporate firewall, you may see the error message:\n", - "```\n", - "requests.exceptions.ConnectionError: ('Connection aborted.', TimeoutError(10060, ...)\n", - "```\n", - "Plotly uses the requests module to communicate with the Plotly server. You can configure proxies by setting the environment variables HTTP_PROXY and HTTPS_PROXY.\n", - "```\n", - "$ export HTTP_PROXY=\"http://10.10.1.10:3128\"\n", - "$ export HTTPS_PROXY=\"http://10.10.1.10:1080\"\n", - "```\n", - "To use HTTP Basic Auth with your proxy, use the http://user:password@host/ syntax:\n", - "\n", - "```\n", - "$ export HTTP_PROXY=\"http://user:pass@10.10.1.10:3128/\"\n", - "```\n", - "\n", - "Note that proxy URLs must include the scheme.\n", - "\n", - "You may also see this error if your proxy variable is set but you are no longer behind the corporate proxy. Check if a proxy variable is set with:\n", - "\n", - "```\n", - "$ echo $HTTP_PROXY\n", - "$ echo $HTTPS_PROXY\n", - "```\n", - "**Still not working?**\n", - "\n", - "[Log an issue](https://github.com/plotly/plotly.py)\n", - "\n", - "Contact [support@plot.ly]()\n", - "\n", - "Get in touch with your IT department, and ask them about corporate proxies.\n", - "\n", - "[Requests documentation on configuring proxies](http://docs.python-requests.org/en/latest/user/advanced/#proxies) the requests documentation.\n", - "\n", - "Plotly for IPython Notebooks is also available for [offline use](https://plotly.com/python/offline/).\n", - "\n", - "[Chart Studio Enterprise](https://plotly.com/product/enterprise) is available for behind-the-firewall corporate installations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jupytext": { - "notebook_metadata_filter": "all", - "text_representation": { - "extension": ".md", - "format_name": "markdown", - "format_version": "1.1", - "jupytext_version": "1.1.7" - } - }, - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.5" - }, - "plotly": { - "description": "How to configure Plotly's Python API to work with corporate proxies", - "display_as": "chart_studio", - "has_thumbnail": true, - "language": "python", - "layout": "base", - "name": "Requests Behind Corporate Proxies", - "order": 10, - "permalink": "python/proxy-configuration/", - "thumbnail": "thumbnail/net.jpg", - "title": "requests.exceptions.ConnectionError - Getting Around Corporate Proxies", - "v4upgrade": true - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/_posts/python/chart-studio/proxy-configuration.md b/_posts/python/chart-studio/proxy-configuration.md deleted file mode 100644 index 3fc3f221d..000000000 --- a/_posts/python/chart-studio/proxy-configuration.md +++ /dev/null @@ -1,77 +0,0 @@ ---- -jupyter: - jupytext: - notebook_metadata_filter: all - text_representation: - extension: .md - format_name: markdown - format_version: '1.1' - jupytext_version: 1.1.7 - kernelspec: - display_name: Python 3 - 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: 3.6.5 - plotly: - v4upgrade: true - name: Requests Behind Corporate Proxies - permalink: python/proxy-configuration/ - description: How to configure Plotly's Python API to work with corporate proxies - layout: base - language: python - thumbnail: thumbnail/net.jpg - display_as: chart_studio - order: 10 ---- - - -If you are behind a corporate firewall, you may see the error message: -``` -requests.exceptions.ConnectionError: ('Connection aborted.', TimeoutError(10060, ...) -``` -Plotly uses the requests module to communicate with the Plotly server. You can configure proxies by setting the environment variables HTTP_PROXY and HTTPS_PROXY. -``` -$ export HTTP_PROXY="http://10.10.1.10:3128" -$ export HTTPS_PROXY="http://10.10.1.10:1080" -``` -To use HTTP Basic Auth with your proxy, use the http://user:password@host/ syntax: - -``` -$ export HTTP_PROXY="http://user:pass@10.10.1.10:3128/" -``` - -Note that proxy URLs must include the scheme. - -You may also see this error if your proxy variable is set but you are no longer behind the corporate proxy. Check if a proxy variable is set with: - -``` -$ echo $HTTP_PROXY -$ echo $HTTPS_PROXY -``` -**Still not working?** - -[Log an issue](https://github.com/plotly/plotly.py) - -Contact [support@plot.ly]() - -Get in touch with your IT department, and ask them about corporate proxies. - -[Requests documentation on configuring proxies](http://docs.python-requests.org/en/latest/user/advanced/#proxies) the requests documentation. - -Plotly for IPython Notebooks is also available for [offline use](https://plotly.com/python/offline/). - -[Chart Studio Enterprise](https://plotly.com/product/enterprise) is available for behind-the-firewall corporate installations. - - -```python - -``` diff --git a/_posts/python/chart-studio/regen.sh b/_posts/python/chart-studio/regen.sh deleted file mode 100755 index 646c673f4..000000000 --- a/_posts/python/chart-studio/regen.sh +++ /dev/null @@ -1,7 +0,0 @@ -#!/bin/bash - -ls *ipynb | sed 's/\.ipynb$//g' | while read NB; do - - jupyter nbconvert $NB.ipynb --to html --template nb.tpl --output 2019-07-03-$NB.html - - done diff --git a/_posts/r/2019-07-03-is-plotly-free-r.md b/_posts/r/2019-07-03-is-plotly-free-r.md index 7f45927de..5eefab193 100644 --- a/_posts/r/2019-07-03-is-plotly-free-r.md +++ b/_posts/r/2019-07-03-is-plotly-free-r.md @@ -1,7 +1,7 @@ --- name: Is Plotly for R Free? permalink: r/is-plotly-free/ -description: Plotly's open-source graphing libraries are free to use, work offline and don't require any account registration. Plotly also has commercial offerings, such as Dash Enterprise and Chart Studio Enterprise. +description: Plotly's open-source graphing libraries are free to use, work offline and don't require any account registration. Plotly also has commercial offerings, such as Dash Enterprise. layout: base no_in_language: true language: r diff --git a/_posts/r/README.md b/_posts/r/README.md index 9044ee1e2..9ddd8b2d9 100644 --- a/_posts/r/README.md +++ b/_posts/r/README.md @@ -1,8 +1,8 @@ # Contribute to Plotly's [R Documentation](https://plotly.com/r/) -These are the instructions for contributing to the subset of the documentation for Plotly's R graphing library which deals with Chart Studio. +These are the instructions for contributing to the documentation for Plotly's R graphing library. -In order to contribute to the majority of Plotly's R graphing library documentation (which is not related to Chart Studio), please visit the [plotly.r-docs](https://github.com/plotly/plotly.r-docs) repository. +For the majority of Plotly's R graphing library documentation, please visit the [plotly.r-docs](https://github.com/plotly/plotly.r-docs) repository. ## Initial Steps: 1. Clone the repo: @@ -54,15 +54,6 @@ In order to contribute to the majority of Plotly's R graphing library documentat knitr::opts_chunk$set(message = FALSE, warning=FALSE) ``` - - If your example needs to authenticate with Chart Studio, use the following R code snippet instead: - - ``` - ```{r, echo = FALSE, message=FALSE} - knitr::opts_chunk$set(message = FALSE, warning=FALSE) - Sys.setenv("plotly_username"="RPlotBot") - Sys.setenv("plotly_api_key"="q0lz6r5efr")``` - ``` - - To include R code and plots in your tutorial, format the code snippets and plots in the following format: ``` diff --git a/_posts/r/chart-studio/2015-04-09-static-image_r_index.Rmd b/_posts/r/chart-studio/2015-04-09-static-image_r_index.Rmd deleted file mode 100644 index fc9a5a5d1..000000000 --- a/_posts/r/chart-studio/2015-04-09-static-image_r_index.Rmd +++ /dev/null @@ -1,75 +0,0 @@ ---- -description: How to export R graphs as static images using Chart Studio. -display_as: chart_studio -language: r -layout: base -name: Exporting Graphs As Static Images Using Chart Studio -order: 2 -output: - html_document: - keep_md: true -page_type: example_index -permalink: r/chart-studio-image-export/ -sitemap: false -thumbnail: thumbnail/png-export.png ---- - -```{r, echo = FALSE, message=FALSE} -knitr::opts_chunk$set(message = FALSE, warning=FALSE) -Sys.setenv("plotly_username"="RPlotBot") -Sys.setenv("plotly_api_key"="q0lz6r5efr") -``` -### Supported File Formats - -With the `plotly` R package, you can export graphs you create as static images in the `.png` and/or `.jpg`/`.jpeg` file formats for free using the [Chart Studio web service](https://chart-studio.plot.ly/create/#/). - -Currently, exporting graphs you create as static images in the `.eps`, `.svg`, and/or `.pdf` format is a feature that is available only to users of [Chart Studio Enterprise](https://plotly.com/online-chart-maker/). - -**Note:** It is important to be aware that R graphs containing WebGL-based traces (i.e. of type `scattergl`, `heatmapgl`, `contourgl`, `scatter3d`, `surface`, `mesh3d`, `scatterpolargl`, `cone`, `streamtube`, `splom`, and/or `parcoords`) will include encapsulated rasters instead of vectors for some parts of the image if they are exported as static images in a vector format like `.eps`, `.svg`, and/or `.pdf`. - -### Exporting Chart Studio Charts As Static Images - -To export your R graphs as static images using the Chart Studio web service, you can use the built-in `plotly_IMAGE()` function. - -#### Create A Chart Studio Account And Get An API Key - -To use the `plotly_IMAGE()` function, you will need to have a [Chart Studio account](https://chart-studio.plot.ly/Auth/login/?action=signup#/) and an API key (which can be found [in your Chart Studio account online settings](https://plotly.com/settings/api)). Learn more about [getting started with Chart Studio in R](https://plotly.com/r/getting-started-with-chart-studio). - -#### Set Environment Variables In Your R Session - -Let the R session know about your Chart Studio authorization credentials by setting environment variables using [`Sys.setenv()`](https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/Sys.setenv). - -```{r, eval = FALSE} -Sys.setenv("plotly_username" = "YOUR USER NAME") -Sys.setenv("plotly_api_key" = "YOUR API KEY1") -``` - -#### Use The Development Version Of The `plotly` R Package - -You will also need to be using the development version of the `plotly` R package in order to use the `plotly_IMAGE()` function. This can be installed from GitHub using the [`devtools`](https://cran.r-project.org/web/packages/devtools/index.html) R package by running the following command in your R session: - -```r -devtools::install_github("plotly/plotly.R") -``` - -#### Export R Graph As Static Image - -The `plotly_IMAGE()` function exports your R plots as static images using the Chart Studio web service. The image will be stored in a file in the working directory of your R session. - -```{r} -library(plotly) -p <- plot_ly(x = c(1,2,3,4), y = c(2,4,1,3), type = 'scatter', mode = 'lines') -plotly_IMAGE(p, format = "png", out_file = "output.png") -``` - -![](https://images.plot.ly/plotly-documentation/images/output.png) - -### Alternative Methods Of Exporting Graphs As Static Images In R - -#### Local Image Export - -As an alternative to using the Chart Studio web service to export your R graphs as static images, you can [use the built-in `orca()` function](https://plotly.com/r/static-image-export) to export images locally. - -#### Embed R Charts in RMarkdown Documents - -See [Embedding Graphs in RMarkdown](https://plotly.com/r/embedding-graphs-in-rmarkdown/) to learn more about embedding R charts in RMarkdown (.Rmd) files. diff --git a/_posts/r/chart-studio/2015-04-09-static-image_r_index.md b/_posts/r/chart-studio/2015-04-09-static-image_r_index.md deleted file mode 100644 index 654e6d953..000000000 --- a/_posts/r/chart-studio/2015-04-09-static-image_r_index.md +++ /dev/null @@ -1,73 +0,0 @@ ---- -description: How to export R graphs as static images using Chart Studio. -display_as: chart_studio -language: r -layout: base -name: Exporting Graphs As Static Images Using Chart Studio -order: 2 -output: - html_document: - keep_md: true -page_type: example_index -permalink: r/chart-studio-image-export/ -sitemap: false -thumbnail: thumbnail/png-export.png ---- - - -### Supported File Formats - -With the `plotly` R package, you can export graphs you create as static images in the `.png` and/or `.jpg`/`.jpeg` file formats for free using the [Chart Studio web service](https://chart-studio.plot.ly/create/#/). - -Currently, exporting graphs you create as static images in the `.eps`, `.svg`, and/or `.pdf` format is a feature that is available only to users of [Chart Studio Enterprise](https://plotly.com/online-chart-maker/). - -**Note:** It is important to be aware that R graphs containing WebGL-based traces (i.e. of type `scattergl`, `heatmapgl`, `contourgl`, `scatter3d`, `surface`, `mesh3d`, `scatterpolargl`, `cone`, `streamtube`, `splom`, and/or `parcoords`) will include encapsulated rasters instead of vectors for some parts of the image if they are exported as static images in a vector format like `.eps`, `.svg`, and/or `.pdf`. - -### Exporting Chart Studio Charts As Static Images - -To export your R graphs as static images using the Chart Studio web service, you can use the built-in `plotly_IMAGE()` function. - -#### Create A Chart Studio Account And Get An API Key - -To use the `plotly_IMAGE()` function, you will need to have a [Chart Studio account](https://chart-studio.plot.ly/Auth/login/?action=signup#/) and an API key (which can be found [in your Chart Studio account online settings](https://plotly.com/settings/api)). Learn more about [getting started with Chart Studio in R](https://plotly.com/r/getting-started-with-chart-studio). - -#### Set Environment Variables In Your R Session - -Let the R session know about your Chart Studio authorization credentials by setting environment variables using [`Sys.setenv()`](https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/Sys.setenv). - - -```r -Sys.setenv("plotly_username" = "YOUR USER NAME") -Sys.setenv("plotly_api_key" = "YOUR API KEY") -``` - -#### Use The Development Version Of The `plotly` R Package - -You will also need to be using the development version of the `plotly` R package in order to use the `plotly_IMAGE()` function. This can be installed from GitHub using the [`devtools`](https://cran.r-project.org/web/packages/devtools/index.html) R package by running the following command in your R session: - -```r -devtools::install_github("plotly/plotly.R") -``` - -#### Export R Graph As Static Image - -The `plotly_IMAGE()` function exports your R plots as static images using the Chart Studio web service. The image will be stored in a file in the working directory of your R session. - - -```r -library(plotly) -p <- plot_ly(x = c(1,2,3,4), y = c(2,4,1,3), type = 'scatter', mode = 'lines') -plotly_IMAGE(p, format = "png", out_file = "output.png") -``` - -![](https://images.plot.ly/plotly-documentation/images/output.png) - -### Alternative Methods Of Exporting Graphs As Static Images In R - -#### Local Image Export - -As an alternative to using the Chart Studio web service to export your R graphs as static images, you can [use the built-in `orca()` function](https://plotly.com/r/static-image-export) to export images locally. - -#### Embed R Charts in RMarkdown Documents - -See [Embedding Graphs in RMarkdown](https://plotly.com/r/embedding-graphs-in-rmarkdown/) to learn more about embedding R charts in RMarkdown (.Rmd) files. diff --git a/_posts/r/chart-studio/2015-07-29-dashboard-index.html b/_posts/r/chart-studio/2015-07-29-dashboard-index.html deleted file mode 100755 index 53bb278c5..000000000 --- a/_posts/r/chart-studio/2015-07-29-dashboard-index.html +++ /dev/null @@ -1,5 +0,0 @@ ---- -permalink: r/dashboard/ -redirect_to: https://plotly.com/dash/ -sitemap: false ---- diff --git a/_posts/r/chart-studio/2015-07-30-filenames.Rmd b/_posts/r/chart-studio/2015-07-30-filenames.Rmd deleted file mode 100644 index f9985f9e7..000000000 --- a/_posts/r/chart-studio/2015-07-30-filenames.Rmd +++ /dev/null @@ -1,52 +0,0 @@ ---- -description: How to update graphs stored in Chart Studio with R. -display_as: chart_studio -language: r -layout: base -name: Updating Graphs Stored In Chart Studio -order: 8 -output: - html_document: - keep_md: true -permalink: r/file-options/ -thumbnail: thumbnail/horizontal-bar.jpg ---- - -```{r, echo = FALSE, message=FALSE} -knitr::opts_chunk$set(message = FALSE, warning=FALSE) -Sys.setenv("plotly_username"="RPlotBot") -Sys.setenv("plotly_api_key"="q0lz6r5efr") -``` -### Save R Plot To Chart Studio - -Using the `plotly` R package, you can create a Chart Studio figure based on your R chart. Simply pass your chart as a parameter to the `api_create()` function: - -```{r} -library(plotly) -p <- plot_ly(data = iris, x = ~Sepal.Length, y = ~Petal.Length) -api_create(p) -``` - -### How To Overwrite An Existing Plot - -By default, when you call `api_create()`, a new plot is created in your Chart Studio account with its own unique URL. - -If you would like to overwrite an existing plot in your Chart Studio account and keep the same URL, then supply a `filename` as an extra parameter to the `api_create()` function. This will keep the same URL for the plot. - -```{r} -api_create(p, filename = "name-of-my-plotly-file") -``` - -### Saving Plots In Folders - -If the `filename` parameter contains the character "/", then the `api_create()` function will save that plot in a folder in your Chart Studio account. - -This option is only available for [Chart Studio Enterprise subscribers](https://plotly.com/online-chart-maker/) - -```{r} -api_create(p, filename="r-docs/name-of-my-chart-studio-file") -``` - -### Viewing Saved Plots - -View the R graphs you have saved in your Chart Studio account at [https://plotly.com/organize](https://plotly.com/organize). \ No newline at end of file diff --git a/_posts/r/chart-studio/2015-07-30-filenames.md b/_posts/r/chart-studio/2015-07-30-filenames.md deleted file mode 100644 index bd863fc9b..000000000 --- a/_posts/r/chart-studio/2015-07-30-filenames.md +++ /dev/null @@ -1,57 +0,0 @@ ---- -description: How to update graphs stored in Chart Studio with R. -display_as: chart_studio -language: r -layout: base -name: Updating Graphs Stored In Chart Studio -order: 8 -output: - html_document: - keep_md: true -permalink: r/file-options/ -thumbnail: thumbnail/horizontal-bar.jpg ---- - - -### Save R Plot To Chart Studio - -Using the `plotly` R package, you can create a Chart Studio figure based on your R chart. Simply pass your chart as a parameter to the `api_create()` function: - - -```r -library(plotly) -p <- plot_ly(data = iris, x = ~Sepal.Length, y = ~Petal.Length) -api_create(p) -``` - - - -### How To Overwrite An Existing Plot - -By default, when you call `api_create()`, a new plot is created in your Chart Studio account with its own unique URL. - -If you would like to overwrite an existing plot in your Chart Studio account and keep the same URL, then supply a `filename` as an extra parameter to the `api_create()` function. This will keep the same URL for the plot. - - -```r -api_create(p, filename = "name-of-my-plotly-file") -``` - - - -### Saving Plots In Folders - -If the `filename` parameter contains the character "/", then the `api_create()` function will save that plot in a folder in your Chart Studio account. - -This option is only available for [Chart Studio Enterprise subscribers](https://plotly.com/online-chart-maker/) - - -```r -api_create(p, filename="r-docs/name-of-my-chart-studio-file") -``` - - - -### Viewing Saved Plots - -View the R graphs you have saved in your Chart Studio account at [https://plotly.com/organize](https://plotly.com/organize). diff --git a/_posts/r/chart-studio/2015-07-30-get-requests.Rmd b/_posts/r/chart-studio/2015-07-30-get-requests.Rmd deleted file mode 100644 index cb04046e5..000000000 --- a/_posts/r/chart-studio/2015-07-30-get-requests.Rmd +++ /dev/null @@ -1,56 +0,0 @@ ---- -description: How to download Chart Studio users' public graphs and data into an R session. -display_as: chart_studio -language: r -layout: base -name: Working With Chart Studio Graphs -order: 5 -output: - html_document: - keep_md: true -permalink: r/working-with-chart-studio-graphs/ -redirect_from: -- r/get-requests/ -thumbnail: thumbnail/hover.jpg ---- - -```{r, echo = FALSE, message=FALSE} -knitr::opts_chunk$set(message = FALSE, warning=FALSE) -Sys.setenv("plotly_username"="RPlotBot") -Sys.setenv("plotly_api_key"="q0lz6r5efr") -``` -### Download Chart Studio Graphs Into R Sessions - -Download Chart Studio figures directly into your R session with the `api_download_plot()` function. This takes the `plot_id` of the Chart Studio plot and the `username` of the plot's creator as arguments. - -For example, to download [https://plotly.com/~cpsievert/559](https://plotly.com/~cpsievert/559) into R, call: - -```{r} -library(plotly) -fig <- api_download_plot("559", "cpsievert") -fig -``` - -### Update The Layout on A Downloaded Graph - -Once the figure is downloaded from Chart Studio into your R session, you can update its layout just like you would any other figure you create with the `plotly` R package. - -**Note:** If you were to re-upload this figure to Chart Studio, a new figure would be created unless you specify the same `filename` as the figure that you downloaded. In that case, the existing figure will be overwritten. - -```{r} -p <- layout(fig, title = paste("Modified on ", Sys.time())) -p -``` - -### Adding a Trace to a Subplot Figure - -```{r} -fig <- api_download_plot("6343", "chelsea_lyn") - -p <- add_lines(fig, x = c(1, 2), y = c(1, 2), xaxis = "x2", yaxis = "y2") -p -``` - -### Reference - -See the documentation for [getting started with Chart Studio in R](https://plotly.com/r/getting-started-with-chart-studio). \ No newline at end of file diff --git a/_posts/r/chart-studio/2015-07-30-get-requests.md b/_posts/r/chart-studio/2015-07-30-get-requests.md deleted file mode 100644 index 8958b7ffb..000000000 --- a/_posts/r/chart-studio/2015-07-30-get-requests.md +++ /dev/null @@ -1,64 +0,0 @@ ---- -description: How to download Chart Studio users' public graphs and data into an R session. -display_as: chart_studio -language: r -layout: base -name: Working With Chart Studio Graphs -order: 5 -output: - html_document: - keep_md: true -permalink: r/working-with-chart-studio-graphs/ -redirect_from: -- r/get-requests/ -thumbnail: thumbnail/hover.jpg ---- - - -### Download Chart Studio Graphs Into R Sessions - -Download Chart Studio figures directly into your R session with the `api_download_plot()` function. This takes the `plot_id` of the Chart Studio plot and the `username` of the plot's creator as arguments. - -For example, to download [https://plotly.com/~cpsievert/559](https://plotly.com/~cpsievert/559) into R, call: - - -```r -library(plotly) -fig <- api_download_plot("559", "cpsievert") -fig -``` - -
- - -### Update The Layout on A Downloaded Graph - -Once the figure is downloaded from Chart Studio into your R session, you can update its layout just like you would any other figure you create with the `plotly` R package. - -**Note:** If you were to re-upload this figure to Chart Studio, a new figure would be created unless you specify the same `filename` as the figure that you downloaded. In that case, the existing figure will be overwritten. - - -```r -p <- layout(fig, title = paste("Modified on ", Sys.time())) -p -``` - -
- - -### Adding a Trace to a Subplot Figure - - -```r -fig <- api_download_plot("6343", "chelsea_lyn") - -p <- add_lines(fig, x = c(1, 2), y = c(1, 2), xaxis = "x2", yaxis = "y2") -p -``` - -
- - -### Reference - -See the documentation for [getting started with Chart Studio in R](https://plotly.com/r/getting-started-with-chart-studio). diff --git a/_posts/r/chart-studio/2015-07-30-privacy.Rmd b/_posts/r/chart-studio/2015-07-30-privacy.Rmd deleted file mode 100644 index 2746cf957..000000000 --- a/_posts/r/chart-studio/2015-07-30-privacy.Rmd +++ /dev/null @@ -1,59 +0,0 @@ ---- -description: How to set the privacy settings of Chart Studio graphs in R. -display_as: chart_studio -language: r -layout: base -name: Privacy Settings For Chart Studio Graphs -order: 7 -output: - html_document: - keep_md: true -permalink: r/privacy/ -thumbnail: thumbnail/privacy.jpg ---- - -```{r, echo = FALSE, message=FALSE} -knitr::opts_chunk$set(message = FALSE, warning = FALSE) -Sys.setenv("plotly_username"="RPlotBot") -Sys.setenv("plotly_api_key"="q0lz6r5efr") -``` -#### Default Privacy -The `plotly` R package renders plots entirely **locally** by default. - -However, you can also choose to publish plots on the web using Chart Studio via the `api_create()` function. - -By default, the `api_create()` function creates public graphs. With a [Chart Studio Enterprise subscription](https://plotly.com/online-chart-maker/), you can easily make graphs private by using the `sharing` argument of the `api_create()` function. - -### Public Graph - -Please note, this is the default privacy option. - -```{r} -library(plotly) -p <- plot_ly(x = c(0, 2, 4), y = c(0, 4, 2), type = 'scatter', mode = 'markers+lines') -chart_link = api_create(p, filename = "public-graph") -chart_link -``` - -Below is the URL of this public plot. Anyone can view public plots even if they are not logged into Plotly.
Try it out: [https://plotly.com/~RPlotBot/4545](https://plotly.com/~RPlotBot/4545) - -### Private Graph -```{r} -library(plotly) -p <- plot_ly(x = c(0, 2, 4), y = c(0, 4, 2), type = 'scatter', mode = 'markers+lines') -chart_link = api_create(p, filename = "private-graph", sharing = "private") -chart_link -``` - -Below is the URL of the private plot above. Only the owner can view the private plot. You won't be able to view this plot.
Try it out: [https://plotly.com/~RPlotBot/4549/](https://plotly.com/~RPlotBot/4549/) - -### Secret Graph -```{r} -library(plotly) -p <- plot_ly(x = c(0, 2, 4), y = c(0, 4, 2), type = 'scatter', mode = 'markers+lines') -secret_graph = api_create(p, filename = "secret-graph-file", sharing = "secret") -secret_graph -``` - -Below is the URL of this secret plot. Anyone with the secret link can view this chart. However, it will not appear in the Plotly feed, your profile, or search engines.
Try it out: -[https://plotly.com/~RPlotBot/4553/?share_key=62AMQ8YBpZebu6Y5OYsukj](https://plotly.com/~RPlotBot/4553/?share_key=62AMQ8YBpZebu6Y5OYsukj) \ No newline at end of file diff --git a/_posts/r/chart-studio/2015-07-30-privacy.md b/_posts/r/chart-studio/2015-07-30-privacy.md deleted file mode 100644 index 077277f26..000000000 --- a/_posts/r/chart-studio/2015-07-30-privacy.md +++ /dev/null @@ -1,64 +0,0 @@ ---- -description: How to set the privacy settings of Chart Studio graphs in R. -display_as: chart_studio -language: r -layout: base -name: Privacy Settings For Chart Studio Graphs -order: 7 -output: - html_document: - keep_md: true -permalink: r/privacy/ -thumbnail: thumbnail/privacy.jpg ---- - - -#### Default Privacy -The `plotly` R package renders plots entirely **locally** by default. - -However, you can also choose to publish plots on the web using Chart Studio via the `api_create()` function. - -By default, the `api_create()` function creates public graphs. With a [Chart Studio Enterprise subscription](https://plotly.com/online-chart-maker/), you can easily make graphs private by using the `sharing` argument of the `api_create()` function. - -### Public Graph - -Please note, this is the default privacy option. - - -```r -library(plotly) -p <- plot_ly(x = c(0, 2, 4), y = c(0, 4, 2), type = 'scatter', mode = 'markers+lines') -chart_link = api_create(p, filename = "public-graph") -chart_link -``` - - - -Below is the URL of this public plot. Anyone can view public plots even if they are not logged into Plotly.
Try it out: [https://plotly.com/~RPlotBot/4545](https://plotly.com/~RPlotBot/4545) - -### Private Graph - -```r -library(plotly) -p <- plot_ly(x = c(0, 2, 4), y = c(0, 4, 2), type = 'scatter', mode = 'markers+lines') -chart_link = api_create(p, filename = "private-graph", sharing = "private") -chart_link -``` - - - -Below is the URL of the private plot above. Only the owner can view the private plot. You won't be able to view this plot.
Try it out: [https://plotly.com/~RPlotBot/4549/](https://plotly.com/~RPlotBot/4549/) - -### Secret Graph - -```r -library(plotly) -p <- plot_ly(x = c(0, 2, 4), y = c(0, 4, 2), type = 'scatter', mode = 'markers+lines') -secret_graph = api_create(p, filename = "secret-graph-file", sharing = "secret") -secret_graph -``` - - - -Below is the URL of this secret plot. Anyone with the secret link can view this chart. However, it will not appear in the Plotly feed, your profile, or search engines.
Try it out: -[https://plotly.com/~RPlotBot/4553/?share_key=62AMQ8YBpZebu6Y5OYsukj](https://plotly.com/~RPlotBot/4553/?share_key=62AMQ8YBpZebu6Y5OYsukj) diff --git a/_posts/r/chart-studio/2015-08-10-knitr.Rmd b/_posts/r/chart-studio/2015-08-10-knitr.Rmd deleted file mode 100644 index 98f87d20a..000000000 --- a/_posts/r/chart-studio/2015-08-10-knitr.Rmd +++ /dev/null @@ -1,80 +0,0 @@ ---- -description: How to embed R graphs in RMarkdown files. -display_as: chart_studio -language: r -layout: base -name: Embedding Graphs in RMarkdown Files -order: 3 -output: - html_document: - keep_md: true -page_type: example_index -permalink: r/embedding-graphs-in-rmarkdown/ -redirect_from: -- r/embedding-plotly-graphs-in-HTML -- r/knitr/ -thumbnail: thumbnail/ipythonnb.jpg ---- - -```{r, echo = FALSE, message=FALSE} -knitr::opts_chunk$set(message = FALSE) -Sys.setenv("plotly_username"="RPlotBot") -Sys.setenv("plotly_api_key"="q0lz6r5efr") -``` -### Embedding R Graphs in RMarkdown files - -If you are creating R charts in an [RMarkdown](http://rmarkdown.rstudio.com/) environment with HTML output (such as RStudio), simply printing a graph you created using the `plotly` R package in a code chunk will result in an interactive HTML graph in the viewer. - -When using RMarkdown with non-HTML output, printing a graph you created using the `plotly` R package will result in a `.png` screenshot of the graph being generated. - -```{r} -library(plotly) -p <- plot_ly(economics, x = ~date, y = ~unemploy / pop) -p -``` - -Sometimes, you may want to print a _list_ of graphs in an RMarkdown document. - -If, for some reason, you don't want to use the [`subplot()` function](https://plotly.com/r/subplots/), you can render a list of `htmlwidgets` in a single code chunk using the `tagList()` function from the [`htmltools`](https://cran.r-project.org/web/packages/htmltools/index.html) package: - -```{r} -htmltools::tagList(list(p, p)) -``` - -Another way to print multiple graphs in an RMarkdown document with the `plotly` R package is by using the [`lapply`](https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/lapply) function: - -```{r} -library(plotly) - -htmltools::tagList(lapply(1:3, function(x) { plot_ly(x = rnorm(10)) })) -``` - -Alternatively, you can use a `for` loop instead of `lapply`: - -```{r} -library(plotly) - -l <- htmltools::tagList() -for (i in 1:3) { - l[[i]] <- plot_ly(x = rnorm(10)) -} -l -``` - -### Embedding Chart Studio Graphs in RMarkdown Files - -When you publish your plots to Chart Studio via the `api_create()` function, a figure object is returned to your R session. - -When a Chart Studio figure object is rendered in an RMarkdown document, it is embedded as an `iframe`, displaying the plot as it appears on your Chart Studio account. - -```{r, echo="FALSE", results='hide'} -f <- api_create(p) -class(f) -f -``` - -You can control the height and width of that `iframe` through the `height`/`width` [knitr chunk options](http://yihui.name/knitr/options/), but the figure object also contains the relevant URL so you have complete control over embedding your figure. - -This [post](http://help.plot.ly/embed-graphs-in-websites/) has more details on how to embed Chart Studio graphs within HTML `iframes`, but you could also use Chart Studio's built-in image export by simply adding a `.png` or `.jpeg` file extension to the end of the figure's URL. - -For example, view the static image of at . \ No newline at end of file diff --git a/_posts/r/chart-studio/2015-08-10-knitr.md b/_posts/r/chart-studio/2015-08-10-knitr.md deleted file mode 100644 index 379b7cbb3..000000000 --- a/_posts/r/chart-studio/2015-08-10-knitr.md +++ /dev/null @@ -1,103 +0,0 @@ ---- -description: How to embed R graphs in RMarkdown files. -display_as: chart_studio -language: r -layout: base -name: Embedding Graphs in RMarkdown Files -order: 3 -output: - html_document: - keep_md: true -page_type: example_index -permalink: r/embedding-graphs-in-rmarkdown/ -redirect_from: -- r/embedding-plotly-graphs-in-HTML -- r/knitr/ -thumbnail: thumbnail/ipythonnb.jpg ---- - - -### Embedding R Graphs in RMarkdown files - -If you are creating R charts in an [RMarkdown](http://rmarkdown.rstudio.com/) environment with HTML output (such as RStudio), simply printing a graph you created using the `plotly` R package in a code chunk will result in an interactive HTML graph in the viewer. - -When using RMarkdown with non-HTML output, printing a graph you created using the `plotly` R package will result in a `.png` screenshot of the graph being generated. - - -``` -library(plotly) -p <- plot_ly(economics, x = ~date, y = ~unemploy / pop) -p -``` - -
- - -Sometimes, you may want to print a _list_ of graphs in an RMarkdown document. - -If, for some reason, you don't want to use the [`subplot()` function](https://plotly.com/r/subplots/), you can render a list of `htmlwidgets` in a single code chunk using the `tagList()` function from the [`htmltools`](https://cran.r-project.org/web/packages/htmltools/index.html) package: - - -```r -htmltools::tagList(list(p, p)) -``` - -
- -
- - -Another way to print multiple graphs in an RMarkdown document with the `plotly` R package is by using the [`lapply`](https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/lapply) function: - - -```r -library(plotly) - -htmltools::tagList(lapply(1:3, function(x) { plot_ly(x = rnorm(10)) })) -``` - -
- -
- -
- - -Alternatively, you can use a `for` loop instead of `lapply`: - - -```r -library(plotly) - -l <- htmltools::tagList() -for (i in 1:3) { - l[[i]] <- plot_ly(x = rnorm(10)) -} -l -``` - -
- -
- -
- - -### Embedding Chart Studio Graphs in RMarkdown Files - -When you publish your plots to Chart Studio via the `api_create()` function, a figure object is returned to your R session. - -When a Chart Studio figure object is rendered in an RMarkdown document, it is embedded as an `iframe`, displaying the plot as it appears on your Chart Studio account. - - -```r -f <- api_create(p) -class(f) -f -``` - -You can control the height and width of that `iframe` through the `height`/`width` [knitr chunk options](http://yihui.name/knitr/options/), but the figure object also contains the relevant URL so you have complete control over embedding your figure. - -This [post](http://help.plot.ly/embed-graphs-in-websites/) has more details on how to embed Chart Studio graphs within HTML `iframes`, but you could also use Chart Studio's built-in image export by simply adding a `.png` or `.jpeg` file extension to the end of the figure's URL. - -For example, view the static image of at . diff --git a/_posts/r/chart-studio/2015-08-10-plotly-offline.html b/_posts/r/chart-studio/2015-08-10-plotly-offline.html deleted file mode 100644 index a08b0b4d2..000000000 --- a/_posts/r/chart-studio/2015-08-10-plotly-offline.html +++ /dev/null @@ -1,5 +0,0 @@ ---- -permalink: r/offline/ -redirect_to: r/getting-started -sitemap: false ---- \ No newline at end of file diff --git a/_posts/r/chart-studio/2016-02-20-jupyter-notebook-r.html b/_posts/r/chart-studio/2016-02-20-jupyter-notebook-r.html deleted file mode 100644 index c091a68cc..000000000 --- a/_posts/r/chart-studio/2016-02-20-jupyter-notebook-r.html +++ /dev/null @@ -1,242 +0,0 @@ ---- -description: How to embed R graphs in Jupyter notebeooks. -display_as: chart_studio -language: r -layout: base -name: Embed Graphs In Jupyter Notebooks -order: 4 -page_type: example_index -permalink: r/using-r-in-jupyter-notebooks/ -sitemap: false -thumbnail: thumbnail/png-export.png ---- - -

Embedding R Graphs in Jupyter Notebooks

- -

This tutorial should help you get up and running with embedding R charts inside a Jupyter notebook.

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Install Python


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Head on over to https://www.python.org/downloads/ and install Python.

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Install Jupyter

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Simply run the following command in your console:

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pip install jupyter
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Use pip3 for python 3.x. See here for more details.

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Install IRKernel

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Next we'll install a R Kernel so that we can use R commands inside a Jupyter notebook. This is similar to installing a R package. Run the following code in your R session:


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install.packages(c('repr', 'IRdisplay', 'pbdZMQ', 'devtools'))
-devtools::install_github('IRkernel/IRkernel')
-IRkernel::installspec()
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See here for details.

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Install Pandoc

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Pandoc is required to successfully render an R chart in a Jupyter notebook. You could either:


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  • Download and install Pandoc from here.
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  • Or use the *.exe files in \bin\pandoc from your R-Studio installation folder.
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Make sure that both pandoc.exe and pandoc-citeproc are available in your local python installation folder (or Jupyter environment if you have setup a separate environment).

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Run Jupyter

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Run this in the terminal / console:


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jupyter notebook
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You should see something like this pop up in a new browser window:


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Create a notebook

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Click on New >> R to create a new Jupyter notebook using the R kernel.


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You should now have something like this:


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Examples:

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Here are some examples on how to use Plotly's R graphing library inside of a Jupyter notebook.

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Scatter plot

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In [8]:
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# Scatter Plot
-library(plotly)
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-set.seed(123)
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-x <- rnorm(1000)
-y <- rchisq(1000, df = 1, ncp = 0)
-group <- sample(LETTERS[1:5], size = 1000, replace = T)
-size <- sample(1:5, size = 1000, replace = T)
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-ds <- data.frame(x, y, group, size)
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-p <- plot_ly(ds, x = x, y = y, mode = "markers", split = group, size = size) %>%
-  layout(title = "Scatter Plot")
-embed_notebook(p)
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Filled Line Chart

Apart from plots and figures, tables and text output can shown as well. Just like in R-Markdown.

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In [10]:
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# Filled Line Chart
-library(plotly)
-library(PerformanceAnalytics)
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-#Load data
-data(managers)
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-# Convert to data.frame
-managers.df <- as.data.frame(managers)
-managers.df$Dates <- index(managers)
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-# See first few rows
-head(managers.df)
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-# Plot
-p <- plot_ly(managers.df, x = ~Dates, y = ~HAM1, type = "scatter", mode = "lines", name = "Manager 1", fill = "tonexty") %>%
-  layout(title = "Time Series plot")
-embed_notebook(p)
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Out[10]:
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- - - - - - - - - - -
HAM1HAM2HAM3HAM4HAM5HAM6EDHEC LS EQSP500 TRUS 10Y TRUS 3m TRDates
1996-01-310.0074NA0.03490.0222NANANA0.0340.00380.004561996-01-31
1996-02-290.0193NA0.03510.0195NANANA0.0093-0.035320.003981996-02-29
1996-03-310.0155NA0.0258-0.0098NANANA0.0096-0.010570.003711996-03-31
1996-04-30-0.0091NA0.04490.0236NANANA0.0147-0.017390.004281996-04-30
1996-05-310.0076NA0.03530.0028NANANA0.0258-0.005430.004431996-05-31
1996-06-30-0.0039NA-0.0303-0.0019NANANA0.00380.015070.004121996-06-30
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Heatmap

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In [15]:
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# Heatmap
-library(plotly)
-library(mlbench)
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-# Get Sonar data
-data(Sonar)
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-# Use only numeric data
-rock <- as.matrix(subset(Sonar, Class == "R")[,1:59])
-mine <- as.matrix(subset(Sonar, Class == "M")[,1:59])
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-# For rocks
-p1 <- plot_ly(z = rock, type = "heatmap", showscale = F)
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-# For mines
-p2 <- plot_ly(z = mine, type = "heatmap", name = "test") %>%
-  layout(title = "Mine vs Rock")
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-# Plot together
-p3 <- subplot(p1, p2)
-embed_notebook(p3)
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diff --git a/_posts/r/chart-studio/2017-07-17-configuration-options.Rmd b/_posts/r/chart-studio/2017-07-17-configuration-options.Rmd deleted file mode 100644 index 2b04edcf8..000000000 --- a/_posts/r/chart-studio/2017-07-17-configuration-options.Rmd +++ /dev/null @@ -1,42 +0,0 @@ ---- -name: Configuration Options For Embedded Chart Studio Graphs -permalink: r/configuration-options/ -description: How to set configuration options of embedded Chart Studio graphs in R. Examples of both online and offline configurations. -layout: base -language: r -thumbnail: thumbnail/modebar-icons.png -display_as: chart_studio -order: 6 -output: - html_document: - keep_md: true ---- - -```{r, echo = FALSE, message=FALSE} -knitr::opts_chunk$set(message = FALSE, warning=FALSE) -``` -#### Online Configuration Options - -Configuration options for graphs created with the `plotly` R package are overridden when those graphs are published to Chart Studio using the `api_create()` function. - -To set configutation options for charts published to Chart STudio, you can edit the plot's embed url. - -Visit our [embed tutorial](http://help.plot.ly/embed-graphs-in-websites/#step-8-customize-the-iframe) for more information on customizing the embed URL to remove the "Edit Chart" link, hide the modebar, or autosize the plot. - -#### Offline Configuration Options - -Add the 'Edit Chart' link: -```{r, results = 'hide'} -library(plotly) -p <- plot_ly(data = iris, x = ~Sepal.Length, y = ~Petal.Length) - -htmlwidgets::saveWidget(config(p, showLink = T), "graph.html") -``` - -Remove the 'mode bar': -```{r, results = 'hide'} -htmlwidgets::saveWidget(config(p, displayModeBar = FALSE), "graph.html") -``` - -#### Reference -Arguments are documented [here](https://github.com/plotly/plotly.js/blob/master/src/plot_api/plot_config.js). \ No newline at end of file diff --git a/_posts/r/chart-studio/2017-07-17-configuration-options.md b/_posts/r/chart-studio/2017-07-17-configuration-options.md deleted file mode 100644 index 872cf6f6d..000000000 --- a/_posts/r/chart-studio/2017-07-17-configuration-options.md +++ /dev/null @@ -1,42 +0,0 @@ ---- -name: Configuration Options For Embedded Chart Studio Graphs -permalink: r/configuration-options/ -description: How to set configuration options of embedded Chart Studio graphs in R. Examples of both online and offline configurations. -layout: base -language: r -thumbnail: thumbnail/modebar-icons.png -display_as: chart_studio -order: 6 -output: - html_document: - keep_md: true ---- - - -#### Online Configuration Options - -Configuration options for graphs created with the `plotly` R package are overridden when those graphs are published to Chart Studio using the `api_create()` function. - -To set configutation options for charts published to Chart STudio, you can edit the plot's embed url. - -Visit our [embed tutorial](http://help.plot.ly/embed-graphs-in-websites/#step-8-customize-the-iframe) for more information on customizing the embed URL to remove the "Edit Chart" link, hide the modebar, or autosize the plot. - -#### Offline Configuration Options - -Add the 'Edit Chart' link: - -```r -library(plotly) -p <- plot_ly(data = iris, x = ~Sepal.Length, y = ~Petal.Length) - -htmlwidgets::saveWidget(config(p, showLink = T), "graph.html") -``` - -Remove the 'mode bar': - -```r -htmlwidgets::saveWidget(config(p, displayModeBar = FALSE), "graph.html") -``` - -#### Reference -Arguments are documented [here](https://github.com/plotly/plotly.js/blob/master/src/plot_api/plot_config.js). diff --git a/_posts/r/chart-studio/2019-12-18-chart-studio-index.html b/_posts/r/chart-studio/2019-12-18-chart-studio-index.html deleted file mode 100644 index b621f8d5e..000000000 --- a/_posts/r/chart-studio/2019-12-18-chart-studio-index.html +++ /dev/null @@ -1,26 +0,0 @@ ---- -permalink: r/chart-studio/ -description: Plotly's R graphing library makes interactive, publication-quality graphs online. Tutorials and tips about fundamental features of Plotly's R API. -name: More Chart Studio Docs -layout: langindex -language: r -display_as: chart_studio -thumbnail: thumbnail/mixed.jpg -page_type: example_index ---- - -
-
- -
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Plotly R Chart Studio Integration

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{{page.description}}

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- -{% assign languagelist = site.posts | where:"language","r" -|where:"display_as","chart_studio" | where: "layout","base" | sort: "order" %} -{% include posts/documentation_eg.html %} diff --git a/_posts/r/chart-studio/2020-01-17-getting-started-with-chart-studio.Rmd b/_posts/r/chart-studio/2020-01-17-getting-started-with-chart-studio.Rmd deleted file mode 100644 index 593b842f9..000000000 --- a/_posts/r/chart-studio/2020-01-17-getting-started-with-chart-studio.Rmd +++ /dev/null @@ -1,131 +0,0 @@ ---- -name: Getting Started with Chart Studio -permalink: r/getting-started-with-chart-studio/ -description: Get started with Chart Studio and Plotly's R graphing library. -page_type: example_index -display_as: chart_studio -layout: base -language: r -thumbnail: thumbnail/bubble.jpg -order: 1 -output: - html_document: - keep_md: true ---- - -```{r, echo = FALSE, message=FALSE} -knitr::opts_chunk$set(message = FALSE, warning=FALSE) -``` - -# Getting Started with Chart Studio and the `plotly` R Package - -`plotly` is an R package for creating interactive web-based graphs via the open source JavaScript graphing library [plotly.js](http://plot.ly/javascript). - -As of version 2.0 (November 17, 2015), R graphs created with the `plotly` R package are, by default, rendered *locally* through the [htmlwidgets](http://www.htmlwidgets.org/) framework. - -## Initialization for Online Plotting - -You can choose to publish charts you create with the `plotly` R package to the web using [Chart Studio](https://plotly.com/online-chart-maker). In order to do so, follow these steps: - -1 - [Create a free Chart Studio account](https://plotly.com/api_signup):
-A Chart Studio account is required to publish R charts to the web using Chart Studio. It's free to get started, and you control the privacy of your charts. - -2 - Store your Chart Studio authentication credentials as environment variables in your R session
-Your Chart Studio authentication credentials consist of your Chart Studio username and your Chart Studio API key, which can be found [in your online settings](https://plotly.com/settings/api). - -Use the [`Sys.setenv()`](https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/Sys.setenv) function to set these credentials as environment variables in your R session. - -```r -Sys.setenv("plotly_username"="your_plotly_username") -Sys.setenv("plotly_api_key"="your_api_key") -``` - -Save these commands in your [.Rprofile](http://www.statmethods.net/interface/customizing.html) file if you want them to be run every time you start a new R session. - -3 - Use the `api_create()` function to publish R charts to Chart Studio: - -Use the `filename` attribute to set the title of the file that will be generated in your Chart Studio account. - -```r -library(plotly) -p <- plot_ly(midwest, x = ~percollege, color = ~state, type = "box") -api_create(p, filename = "r-docs-midwest-boxplots") -``` - -4 (optional) - Suppress auto open behavior: - -When following the instructions above, executing `api_create(p)` will auto open the created Chart Studio URL in the browser. To suppress this behavior, set the `browser` option to `false` in your R session. - -```r -options(browser = 'false') -api_create(p, filename = "r-docs-midwest-boxplots") -``` - -## Special Instructions for Chart Studio Enterprise Users - -### Where To Find Your API Key - -Your API key for your free Chart Studio account will be different than the API key for your [Chart Studio Enterprise](https://plotly.com/product/enterprise/) account. - -Visit to find your Chart Studio Enterprise account API key. - -Remember to replace "your-company.com" with the URL of your company's Chart Studio Enterprise server. - -### Set the `plotly_domain` environment variable - -The URL that the `plotly` package uses to communicate with Chart Studio will be different if your company has a Chart Studio Enterprise server. In order to make your R session aware of the new URL, set the `plotly_domain` environment variable equal to the URL of your Chart Studio Enterprise server using the `Sys.setenv()` function. - -Save the following command in your [.Rprofile](http://www.statmethods.net/interface/customizing.html) so that it runs every time you start a new R session: - -```r -Sys.setenv("plotly_domain"="https://plotly.your-company.com") -``` - -Remember to replace "your-company" with the URL of your company's Chart Studio Enterprise server. - -## Chart Studio Plot Privacy Modes - -Chart Studio plots can be set to three different type of privacy modes: `public`, `private`, or `secret`. - -* **public:** - - Anyone can view this graph. - It will appear in your Chart Studio profile and can be indexed by search engines. - Being logged in to a Chart Studio account is not required to view this chart. - -* **private:** - - Only you can view this plot. - It will not appear in the public Chart Studio feed, your Chart Studio profile, or be indexed by search engines. - Being logged into your Chart Studio account is required to view this graph. - You can privately share this graph with other Chart Studio users. They will also need to be logged in to their Chart Studio account to view this plot. - This option is only available to Chart Studio Enterprise subscribers. - -* **secret:** - - Anyone with this secret link can view this chart. - It will not appear in the public Chart Studio feed, your Chart Studio profile, or be indexed by search engines. - If it is embedded inside a webpage or an IPython notebook, anybody who is viewing that page will be able to view the graph. - You do not need to be logged in to your Chart Studio account view this plot. - This option is only available to Chart Studio Enterprise subscribers. - -By default all Chart Studio plots you create with the `plotly` R package are set to `public`. Users with free Chart Studio accounts are limited to creating `public` plots. - -### Appending Static Image File Types to Chart Studio Plot URLs - -You can also view the static image version of any public Chart Studio graph by appending `.png` or `.jpeg` to the end of the URL for the graph. - -For example, view the static image of at . - -[Chart Studio Enterprise](https://plotly.com/online_chart_maker) users can also use this method to get static images in the `.pdf`, `.svg`, and `.eps` file formats. - -## Private Charts In Chart Studio - -If you have private storage needs, please learn more about [Chart Studio Enterprise](https://plotly.com/online-chart-maker/). - -If you're a [Chart Studio Enterprise subscriber](https://plotly.com/settings/subscription/?modal=true&utm_source=api-docs&utm_medium=support-oss) and would like the setting for your plots to be private, you can specify sharing as private: - -```r -api_create(filename = "private-graph", sharing = "private") -``` -For more information regarding the privacy of plots published to Chart Studio using the `plotly` R package, please visit [our Chart Studio privacy documentation](https://plotly.com/r/privacy/) diff --git a/_posts/r/chart-studio/2020-01-17-getting-started-with-chart-studio.md b/_posts/r/chart-studio/2020-01-17-getting-started-with-chart-studio.md deleted file mode 100644 index d323df85f..000000000 --- a/_posts/r/chart-studio/2020-01-17-getting-started-with-chart-studio.md +++ /dev/null @@ -1,129 +0,0 @@ ---- -name: Getting Started with Chart Studio -permalink: r/getting-started-with-chart-studio/ -description: Get started with Chart Studio and Plotly's R graphing library. -page_type: example_index -display_as: chart_studio -layout: base -language: r -thumbnail: thumbnail/bubble.jpg -order: 1 -output: - html_document: - keep_md: true ---- - - - -# Getting Started with Chart Studio and the `plotly` R Package - -`plotly` is an R package for creating interactive web-based graphs via the open source JavaScript graphing library [plotly.js](http://plot.ly/javascript). - -As of version 2.0 (November 17, 2015), R graphs created with the `plotly` R package are, by default, rendered *locally* through the [htmlwidgets](http://www.htmlwidgets.org/) framework. - -## Initialization for Online Plotting - -You can choose to publish charts you create with the `plotly` R package to the web using [Chart Studio](https://plotly.com/online-chart-maker). In order to do so, follow these steps: - -1 - [Create a free Chart Studio account](https://plotly.com/api_signup):
-A Chart Studio account is required to publish R charts to the web using Chart Studio. It's free to get started, and you control the privacy of your charts. - -2 - Store your Chart Studio authentication credentials as environment variables in your R session
-Your Chart Studio authentication credentials consist of your Chart Studio username and your Chart Studio API key, which can be found [in your online settings](https://plotly.com/settings/api). - -Use the [`Sys.setenv()`](https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/Sys.setenv) function to set these credentials as environment variables in your R session. - -```r -Sys.setenv("plotly_username"="your_plotly_username") -Sys.setenv("plotly_api_key"="your_api_key") -``` - -Save these commands in your [.Rprofile](http://www.statmethods.net/interface/customizing.html) file if you want them to be run every time you start a new R session. - -3 - Use the `api_create()` function to publish R charts to Chart Studio: - -Use the `filename` attribute to set the title of the file that will be generated in your Chart Studio account. - -```r -library(plotly) -p <- plot_ly(midwest, x = ~percollege, color = ~state, type = "box") -api_create(p, filename = "r-docs-midwest-boxplots") -``` - -4 (optional) - Suppress auto open behavior: - -When following the instructions above, executing `api_create(p)` will auto open the created Chart Studio URL in the browser. To suppress this behavior, set the `browser` option to `false` in your R session. - -```r -options(browser = 'false') -api_create(p, filename = "r-docs-midwest-boxplots") -``` - -## Special Instructions for Chart Studio Enterprise Users - -### Where To Find Your API Key - -Your API key for your free Chart Studio account will be different than the API key for your [Chart Studio Enterprise](https://plotly.com/product/enterprise/) account. - -Visit to find your Chart Studio Enterprise account API key. - -Remember to replace "your-company.com" with the URL of your company's Chart Studio Enterprise server. - -### Set the `plotly_domain` environment variable - -The URL that the `plotly` package uses to communicate with Chart Studio will be different if your company has a Chart Studio Enterprise server. In order to make your R session aware of the new URL, set the `plotly_domain` environment variable equal to the URL of your Chart Studio Enterprise server using the `Sys.setenv()` function. - -Save the following command in your [.Rprofile](http://www.statmethods.net/interface/customizing.html) so that it runs every time you start a new R session: - -```r -Sys.setenv("plotly_domain"="https://plotly.your-company.com") -``` - -Remember to replace "your-company" with the URL of your company's Chart Studio Enterprise server. - -## Chart Studio Plot Privacy Modes - -Chart Studio plots can be set to three different type of privacy modes: `public`, `private`, or `secret`. - -* **public:** - - Anyone can view this graph. - It will appear in your Chart Studio profile and can be indexed by search engines. - Being logged in to a Chart Studio account is not required to view this chart. - -* **private:** - - Only you can view this plot. - It will not appear in the public Chart Studio feed, your Chart Studio profile, or be indexed by search engines. - Being logged into your Chart Studio account is required to view this graph. - You can privately share this graph with other Chart Studio users. They will also need to be logged in to their Chart Studio account to view this plot. - This option is only available to Chart Studio Enterprise subscribers. - -* **secret:** - - Anyone with this secret link can view this chart. - It will not appear in the public Chart Studio feed, your Chart Studio profile, or be indexed by search engines. - If it is embedded inside a webpage or an IPython notebook, anybody who is viewing that page will be able to view the graph. - You do not need to be logged in to your Chart Studio account view this plot. - This option is only available to Chart Studio Enterprise subscribers. - -By default all Chart Studio plots you create with the `plotly` R package are set to `public`. Users with free Chart Studio accounts are limited to creating `public` plots. - -### Appending Static Image File Types to Chart Studio Plot URLs - -You can also view the static image version of any public Chart Studio graph by appending `.png` or `.jpeg` to the end of the URL for the graph. - -For example, view the static image of at . - -[Chart Studio Enterprise](https://plotly.com/online_chart_maker) users can also use this method to get static images in the `.pdf`, `.svg`, and `.eps` file formats. - -## Private Charts In Chart Studio - -If you have private storage needs, please learn more about [Chart Studio Enterprise](https://plotly.com/online-chart-maker/). - -If you're a [Chart Studio Enterprise subscriber](https://plotly.com/settings/subscription/?modal=true&utm_source=api-docs&utm_medium=support-oss) and would like the setting for your plots to be private, you can specify sharing as private: - -```r -api_create(filename = "private-graph", sharing = "private") -``` -For more information regarding the privacy of plots published to Chart Studio using the `plotly` R package, please visit [our Chart Studio privacy documentation](https://plotly.com/r/privacy/) diff --git a/_posts/r/chart-studio/Plotly-Jupyter-Example.ipynb b/_posts/r/chart-studio/Plotly-Jupyter-Example.ipynb deleted file mode 100644 index 8c0ff62e4..000000000 --- a/_posts/r/chart-studio/Plotly-Jupyter-Example.ipynb +++ /dev/null @@ -1,208 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Scatter plot" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Scatter plot\n", - "library(plotly)\n", - "\n", - "set.seed(123)\n", - "\n", - "x <- rnorm(1000)\n", - "y <- rchisq(1000, df = 1, ncp = 0)\n", - "color <- sample(LETTERS[1:5], size = 1000, replace = T)\n", - "size <- sample(1:5, size = 1000, replace = T)\n", - "\n", - "ds <- data.frame(x, y, color, size)\n", - "\n", - "p <- plot_ly(ds, x = ~x, y = ~y, color = ~color, size = ~size) %>% \n", - " layout(title = \"Scatter plot in\")\n", - "embed_notebook(p)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Filled line chart\n", - "Apart from plots and figures, *tables* and *text output* can shown as well. Just like in *R-Markdown*.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
HAM1HAM2HAM3HAM4HAM5HAM6EDHEC LS EQSP500 TRUS 10Y TRUS 3m TRDates
1996-01-310.0074NA0.03490.0222NANANA0.0340.00380.004561996-01-31
1996-02-290.0193NA0.03510.0195NANANA0.0093-0.035320.003981996-02-29
1996-03-310.0155NA0.0258-0.0098NANANA0.0096-0.010570.003711996-03-31
1996-04-30-0.0091NA0.04490.0236NANANA0.0147-0.017390.004281996-04-30
1996-05-310.0076NA0.03530.0028NANANA0.0258-0.005430.004431996-05-31
1996-06-30-0.0039NA-0.0303-0.0019NANANA0.00380.015070.004121996-06-30
\n" - ], - "text/latex": [ - "\\begin{tabular}{r|lllllllllll}\n", - " & HAM1 & HAM2 & HAM3 & HAM4 & HAM5 & HAM6 & EDHEC LS EQ & SP500 TR & US 10Y TR & US 3m TR & Dates\\\\\n", - "\\hline\n", - "\t1996-01-31 & 0.0074 & NA & 0.0349 & 0.0222 & NA & NA & NA & 0.034 & 0.0038 & 0.00456 & 1996-01-31\\\\\n", - "\t1996-02-29 & 0.0193 & NA & 0.0351 & 0.0195 & NA & NA & NA & 0.0093 & -0.03532 & 0.00398 & 1996-02-29\\\\\n", - "\t1996-03-31 & 0.0155 & NA & 0.0258 & -0.0098 & NA & NA & NA & 0.0096 & -0.01057 & 0.00371 & 1996-03-31\\\\\n", - "\t1996-04-30 & -0.0091 & NA & 0.0449 & 0.0236 & NA & NA & NA & 0.0147 & -0.01739 & 0.00428 & 1996-04-30\\\\\n", - "\t1996-05-31 & 0.0076 & NA & 0.0353 & 0.0028 & NA & NA & NA & 0.0258 & -0.00543 & 0.00443 & 1996-05-31\\\\\n", - "\t1996-06-30 & -0.0039 & NA & -0.0303 & -0.0019 & NA & NA & NA & 0.0038 & 0.01507 & 0.00412 & 1996-06-30\\\\\n", - "\\end{tabular}\n" - ], - "text/plain": [ - " HAM1 HAM2 HAM3 HAM4 HAM5 HAM6 EDHEC LS EQ SP500 TR\n", - "1996-01-31 0.0074 NA 0.0349 0.0222 NA NA NA 0.0340\n", - "1996-02-29 0.0193 NA 0.0351 0.0195 NA NA NA 0.0093\n", - "1996-03-31 0.0155 NA 0.0258 -0.0098 NA NA NA 0.0096\n", - "1996-04-30 -0.0091 NA 0.0449 0.0236 NA NA NA 0.0147\n", - "1996-05-31 0.0076 NA 0.0353 0.0028 NA NA NA 0.0258\n", - "1996-06-30 -0.0039 NA -0.0303 -0.0019 NA NA NA 0.0038\n", - " US 10Y TR US 3m TR Dates\n", - "1996-01-31 0.00380 0.00456 1996-01-31\n", - "1996-02-29 -0.03532 0.00398 1996-02-29\n", - "1996-03-31 -0.01057 0.00371 1996-03-31\n", - "1996-04-30 -0.01739 0.00428 1996-04-30\n", - "1996-05-31 -0.00543 0.00443 1996-05-31\n", - "1996-06-30 0.01507 0.00412 1996-06-30" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Filled line Chart\n", - "library(plotly)\n", - "library(PerformanceAnalytics)\n", - "\n", - "#Load data\n", - "data(managers)\n", - "\n", - "# Convert to data.frame\n", - "managers.df <- as.data.frame(managers)\n", - "managers.df$Dates <- index(managers)\n", - "\n", - "# See first few rows\n", - "head(managers.df)\n", - "\n", - "# Plot\n", - "p <- plot_ly(managers.df, x = ~Dates, y = ~HAM1, name = \"Manager 1\") %>% add_lines() \n", - " layout(title = \"Time Series plot\")\n", - "embed_notebook(p)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Heat map" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Heat map\n", - "library(plotly)\n", - "library(mlbench)\n", - "\n", - "# Get Sonar data\n", - "data(Sonar)\n", - "\n", - "# Use only numeric data\n", - "rock <- as.matrix(subset(Sonar, Class == \"R\")[,1:59])\n", - "mine <- as.matrix(subset(Sonar, Class == \"M\")[,1:59])\n", - "\n", - "# For rocks\n", - "p1 <- plot_ly(z = rock, type = \"heatmap\", showscale = F)\n", - " \n", - "# For mines\n", - "p2 <- plot_ly(z = mine, type = \"heatmap\", name = \"test\") %>% \n", - " layout(title = \"Mine vs Rock\")\n", - "\n", - "# Plot together\n", - "p3 <- subplot(p1, p2)\n", - "embed_notebook(p3)\n", - "\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "R", - "language": "R", - "name": "ir" - }, - "language_info": { - "codemirror_mode": "r", - "file_extension": ".r", - "mimetype": "text/x-r-source", - "name": "R", - "pygments_lexer": "r", - "version": "3.2.3" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/_posts/r/chart-studio/sending-data/2015-04-09-add-traces.html b/_posts/r/chart-studio/sending-data/2015-04-09-add-traces.html deleted file mode 100755 index 24aa0fdbe..000000000 --- a/_posts/r/chart-studio/sending-data/2015-04-09-add-traces.html +++ /dev/null @@ -1,14 +0,0 @@ ---- -name: Add new traces to a chart -description: NOT RECOMMENDED
When updating a chart's data remotely, we recommend overwriting all of the chart's data instead of adding new traces. -plot_url: http://i.imgur.com/RzrURdn.gif -arrangement: horizontal -language: r -suite: sending-data -order: 2 -sitemap: false ---- -# Learn about API authentication here: https://plotly.com/r/getting-started -# Find your api_key here: https://plotly.com/settings/api - -api_create(plot_ly(x = c(1, 2), y = c(1, 2)), filename="name-of-my-plotly-file", fileopt='append') diff --git a/_posts/r/chart-studio/sending-data/2015-04-09-extend.html b/_posts/r/chart-studio/sending-data/2015-04-09-extend.html deleted file mode 100755 index 74f2f3048..000000000 --- a/_posts/r/chart-studio/sending-data/2015-04-09-extend.html +++ /dev/null @@ -1,14 +0,0 @@ ---- -name: Add data to an existing trace -description: Add data to an existing trace by setting fileopt='extend'.
This method is used for embedded systems that may not have the memory for a full overwrite of the chart data in one API call. -plot_url: http://i.imgur.com/2LhVSX6.gif -arrangement: horizontal -language: r -suite: sending-data -order: 1 -sitemap: false ---- -# Learn about API authentication here: https://plotly.com/r/getting-started -# Find your api_key here: https://plotly.com/settings/api - -api_create(plot_ly(x = c(1, 2), y = c(1, 2)), filename="name-of-my-plotly-file", fileopt='extend') diff --git a/_posts/r/chart-studio/sending-data/2015-04-09-overwrite.html b/_posts/r/chart-studio/sending-data/2015-04-09-overwrite.html deleted file mode 100755 index 0dfd414be..000000000 --- a/_posts/r/chart-studio/sending-data/2015-04-09-overwrite.html +++ /dev/null @@ -1,14 +0,0 @@ ---- -name: Overwrite chart data with new data -description: The simplest and recommended way to update a chart remotely.
You can overwrite a chart's data with new data remotely, simply by including its file name in the filename kwarg.
Note that setting a filename overwrites the entire chart (i.e., style & layout settings are not preserved). -plot_url: http://i.imgur.com/VuobuN3.gif -arrangement: horizontal -language: r -suite: sending-data -order: 0 -sitemap: false ---- -# Learn about API authentication here: https://plotly.com/r/getting-started -# Find your api_key here: https://plotly.com/settings/api - -api_create(plot_ly(x = c(1, 2), y = c(1, 2)), filename='overwrite example') diff --git a/_posts/r/chart-studio/sending-data/2015-04-09-sending-data_index.html b/_posts/r/chart-studio/sending-data/2015-04-09-sending-data_index.html deleted file mode 100644 index 8433a60f3..000000000 --- a/_posts/r/chart-studio/sending-data/2015-04-09-sending-data_index.html +++ /dev/null @@ -1,14 +0,0 @@ ---- -description: How to send data to charts in Python. 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