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Astronomical Data in Python
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Lesson 1
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Lesson 2
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<div class="section" id="astronomical-data-in-python">
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<h1>Astronomical Data in Python<a class="headerlink" href="#astronomical-data-in-python" title="Permalink to this headline">¶</a></h1>
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||||
<p><em>Astronomical Data in Python</em> is an introduction to tools and practices for working with astronomical data. Topics covered include:</p>
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||||
<ul class="simple">
|
||||
<li><p>Writing queries that select and download data from a database.</p></li>
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<li><p>Using data stored in an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> or Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>.</p></li>
|
||||
<li><p>Working with coordinates and other quantities with units.</p></li>
|
||||
<li><p>Storing data in various formats.</p></li>
|
||||
<li><p>Performing database join operations that combine data from multiple tables.</p></li>
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<li><p>Visualizing data and preparing publication-quality figures.</p></li>
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||||
</ul>
|
||||
<p>As a running example, we will replicate part of the analysis in a recent paper, “<a class="reference external" href="https://arxiv.org/abs/1805.00425">Off the beaten path: Gaia reveals GD-1 stars outside of the main stream</a>” by Adrian M. Price-Whelan and Ana Bonaca.</p>
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||||
<p>This material was developed in collaboration with <a class="reference external" href="https://carpentries.org/">The Carpentries</a> and the Astronomy Curriculum Development Committee, and supported by funding from the American Institute of Physics through the American Astronomical Society.</p>
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||||
<p>I am grateful for contributions from the members of the committee – Azalee Bostroem, Rodolfo Montez, and Phil Rosenfield – and from Erin Becker, Brett Morris and Adrian Price-Whelan.</p>
|
||||
<p>The original format of this material is a series of Jupyter notebooks. Using the
|
||||
links below, you can read the notebooks on NBViewer or run them on Colab. If you
|
||||
want to run the notebooks in your own environment, you can download them from
|
||||
this repository and follow the instructions below to set up your environment.</p>
|
||||
<p>This material is also available in the form of <a class="reference external" href="https://datacarpentry.github.io/astronomy-python">Carpentries lessons</a>, but you should be
|
||||
aware that these versions might diverge in the future.</p>
|
||||
<p><strong>Prerequisites</strong></p>
|
||||
<p>This material should be accessible to people familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas. If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, that should be enough.</p>
|
||||
<p>We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we’ll use.</p>
|
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<p><strong>Notebook 1</strong></p>
|
||||
<p>This notebook demonstrates the following steps:</p>
|
||||
<ol class="simple">
|
||||
<li><p>Making a connection to the Gaia server,</p></li>
|
||||
<li><p>Exploring information about the database and the tables it contains,</p></li>
|
||||
<li><p>Writing a query and sending it to the server, and finally</p></li>
|
||||
<li><p>Downloading the response from the server as an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code>.</p></li>
|
||||
</ol>
|
||||
<p>Press this button to run this notebook on Colab:</p>
|
||||
<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb"><img src="run_on_colab_small.png"></a></p>
|
||||
<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb">or click here to read it on NBViewer</a></p>
|
||||
<p><strong>Notebook 2</strong></p>
|
||||
<p>This notebook starts with an example that does a “cone search”; that is, it selects stars that appear in a circular region of the sky.</p>
|
||||
<p>Then, to select stars in the vicinity of GD-1, we:</p>
|
||||
<ul class="simple">
|
||||
<li><p>Use <code class="docutils literal notranslate"><span class="pre">Quantity</span></code> objects to represent measurements with units.</p></li>
|
||||
<li><p>Use the <code class="docutils literal notranslate"><span class="pre">Gala</span></code> library to convert coordinates from one frame to another.</p></li>
|
||||
<li><p>Use the ADQL keywords <code class="docutils literal notranslate"><span class="pre">POLYGON</span></code>, <code class="docutils literal notranslate"><span class="pre">CONTAINS</span></code>, and <code class="docutils literal notranslate"><span class="pre">POINT</span></code> to select stars that fall within a polygonal region.</p></li>
|
||||
<li><p>Submit a query and download the results.</p></li>
|
||||
<li><p>Store the results in a FITS file.</p></li>
|
||||
</ul>
|
||||
<p>Press this button to run this notebook on Colab:</p>
|
||||
<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/02_coords.ipynb"><img src="run_on_colab_small.png"></a></p>
|
||||
<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/02_coords.ipynb">or click here to read it on NBViewer</a></p>
|
||||
<p><strong>Notebook 3</strong></p>
|
||||
<p>Here are the steps in this notebook:</p>
|
||||
<ol class="simple">
|
||||
<li><p>We’ll read back the results from the previous notebook, which we saved in a FITS file.</p></li>
|
||||
<li><p>Then we’ll transform the coordinates and proper motion data from ICRS back to the coordinate frame of GD-1.</p></li>
|
||||
<li><p>We’ll put those results into a Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>, which we’ll use to select stars near the centerline of GD-1.</p></li>
|
||||
<li><p>Plotting the proper motion of those stars, we’ll identify a region of proper motion for stars that are likely to be in GD-1.</p></li>
|
||||
<li><p>Finally, we’ll select and plot the stars whose proper motion is in that region.</p></li>
|
||||
</ol>
|
||||
<p>Press this button to run this notebook on Colab:</p>
|
||||
<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/03_motion.ipynb"><img src="run_on_colab_small.png"></a></p>
|
||||
<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/03_motion.ipynb">or click here to read it on NBViewer</a></p>
|
||||
<p><strong>Notebook 4</strong></p>
|
||||
<p>Here are the steps in this notebook:</p>
|
||||
<ol class="simple">
|
||||
<li><p>Using data from the previous notebook, we’ll identify the values of proper motion for stars likely to be in GD-1.</p></li>
|
||||
<li><p>Then we’ll compose an ADQL query that selects stars based on proper motion, so we can download only the data we need.</p></li>
|
||||
<li><p>We’ll also see how to write the results to a CSV file.</p></li>
|
||||
</ol>
|
||||
<p>That will make it possible to search a bigger region of the sky in a single query.</p>
|
||||
<p>Press this button to run this notebook on Colab:</p>
|
||||
<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/04_select.ipynb"><img src="run_on_colab_small.png"></a></p>
|
||||
<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/04_select.ipynb">or click here to read it on NBViewer</a></p>
|
||||
<p><strong>Notebook 5</strong></p>
|
||||
<p>Here are the steps in this notebook:</p>
|
||||
<ol class="simple">
|
||||
<li><p>We’ll reload the candidate stars we identified in the previous notebook.</p></li>
|
||||
<li><p>Then we’ll run a query on the Gaia server that uploads the table of candidates and uses a <code class="docutils literal notranslate"><span class="pre">JOIN</span></code> operation to select photometry data for the candidate stars.</p></li>
|
||||
<li><p>We’ll write the results to a file for use in the next notebook.</p></li>
|
||||
</ol>
|
||||
<p>Press this button to run this notebook on Colab:</p>
|
||||
<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/05_join.ipynb"><img src="run_on_colab_small.png"></a></p>
|
||||
<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/05_join.ipynb">or click here to read it on NBViewer</a></p>
|
||||
<p><strong>Notebook 6</strong></p>
|
||||
<p>Here are the steps in this notebook:</p>
|
||||
<ol class="simple">
|
||||
<li><p>We’ll reload the data from the previous notebook and make a color-magnitude diagram.</p></li>
|
||||
<li><p>Then we’ll specify a polygon in the diagram that contains stars with the photometry we expect.</p></li>
|
||||
<li><p>Then we’ll merge the photometry data with the list of candidate stars, storing the result in a Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>.</p></li>
|
||||
</ol>
|
||||
<p>Press this button to run this notebook on Colab:</p>
|
||||
<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/06_photo.ipynb"><img src="run_on_colab_small.png"></a></p>
|
||||
<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/06_photo.ipynb">or click here to read it on NBViewer</a></p>
|
||||
<p><strong>Notebook 7</strong></p>
|
||||
<p>Here are the steps in this notebook:</p>
|
||||
<ol class="simple">
|
||||
<li><p>Starting with the figure from the previous notebook, we’ll add annotations to present the results more clearly.</p></li>
|
||||
<li><p>The we’ll see several ways to customize figures to make them more appealing and effective.</p></li>
|
||||
<li><p>Finally, we’ll see how to make a figure with multiple panels or subplots.</p></li>
|
||||
</ol>
|
||||
<p>Press this button to run this notebook on Colab:</p>
|
||||
<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/07_plot.ipynb"><img src="run_on_colab_small.png"></a></p>
|
||||
<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/07_plot.ipynb">or click here to read it on NBViewer</a></p>
|
||||
<p><strong>Installation instructions</strong></p>
|
||||
<p>Coming soon.</p>
|
||||
</div>
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<div class="section" id="astronomical-data-in-python">
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<h1>Astronomical Data in Python<a class="headerlink" href="#astronomical-data-in-python" title="Permalink to this headline">¶</a></h1>
|
||||
<p><em>Astronomical Data in Python</em> is an introduction to tools and practices for working with astronomical data. Topics covered include:</p>
|
||||
<ul class="simple">
|
||||
<li><p>Writing queries that select and download data from a database.</p></li>
|
||||
<li><p>Using data stored in an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code> or Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>.</p></li>
|
||||
<li><p>Working with coordinates and other quantities with units.</p></li>
|
||||
<li><p>Storing data in various formats.</p></li>
|
||||
<li><p>Performing database join operations that combine data from multiple tables.</p></li>
|
||||
<li><p>Visualizing data and preparing publication-quality figures.</p></li>
|
||||
</ul>
|
||||
<p>As a running example, we will replicate part of the analysis in a recent paper, “<a class="reference external" href="https://arxiv.org/abs/1805.00425">Off the beaten path: Gaia reveals GD-1 stars outside of the main stream</a>” by Adrian M. Price-Whelan and Ana Bonaca.</p>
|
||||
<p>This material was developed in collaboration with <a class="reference external" href="https://carpentries.org/">The Carpentries</a> and the Astronomy Curriculum Development Committee, and supported by funding from the American Institute of Physics through the American Astronomical Society.</p>
|
||||
<p>I am grateful for contributions from the members of the committee – Azalee Bostroem, Rodolfo Montez, and Phil Rosenfield – and from Erin Becker, Brett Morris and Adrian Price-Whelan.</p>
|
||||
<p>The original format of this material is a series of Jupyter notebooks. Using the
|
||||
links below, you can read the notebooks on NBViewer or run them on Colab. If you
|
||||
want to run the notebooks in your own environment, you can download them from
|
||||
this repository and follow the instructions below to set up your environment.</p>
|
||||
<p>This material is also available in the form of <a class="reference external" href="https://datacarpentry.github.io/astronomy-python">Carpentries lessons</a>, but you should be
|
||||
aware that these versions might diverge in the future.</p>
|
||||
<p><strong>Prerequisites</strong></p>
|
||||
<p>This material should be accessible to people familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas. If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, that should be enough.</p>
|
||||
<p>We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we’ll use.</p>
|
||||
<p><strong>Notebook 1</strong></p>
|
||||
<p>This notebook demonstrates the following steps:</p>
|
||||
<ol class="simple">
|
||||
<li><p>Making a connection to the Gaia server,</p></li>
|
||||
<li><p>Exploring information about the database and the tables it contains,</p></li>
|
||||
<li><p>Writing a query and sending it to the server, and finally</p></li>
|
||||
<li><p>Downloading the response from the server as an Astropy <code class="docutils literal notranslate"><span class="pre">Table</span></code>.</p></li>
|
||||
</ol>
|
||||
<p>Press this button to run this notebook on Colab:</p>
|
||||
<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb"><img src="run_on_colab_small.png"></a></p>
|
||||
<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb">or click here to read it on NBViewer</a></p>
|
||||
<p><strong>Notebook 2</strong></p>
|
||||
<p>This notebook starts with an example that does a “cone search”; that is, it selects stars that appear in a circular region of the sky.</p>
|
||||
<p>Then, to select stars in the vicinity of GD-1, we:</p>
|
||||
<ul class="simple">
|
||||
<li><p>Use <code class="docutils literal notranslate"><span class="pre">Quantity</span></code> objects to represent measurements with units.</p></li>
|
||||
<li><p>Use the <code class="docutils literal notranslate"><span class="pre">Gala</span></code> library to convert coordinates from one frame to another.</p></li>
|
||||
<li><p>Use the ADQL keywords <code class="docutils literal notranslate"><span class="pre">POLYGON</span></code>, <code class="docutils literal notranslate"><span class="pre">CONTAINS</span></code>, and <code class="docutils literal notranslate"><span class="pre">POINT</span></code> to select stars that fall within a polygonal region.</p></li>
|
||||
<li><p>Submit a query and download the results.</p></li>
|
||||
<li><p>Store the results in a FITS file.</p></li>
|
||||
</ul>
|
||||
<p>Press this button to run this notebook on Colab:</p>
|
||||
<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/02_coords.ipynb"><img src="run_on_colab_small.png"></a></p>
|
||||
<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/02_coords.ipynb">or click here to read it on NBViewer</a></p>
|
||||
<p><strong>Notebook 3</strong></p>
|
||||
<p>Here are the steps in this notebook:</p>
|
||||
<ol class="simple">
|
||||
<li><p>We’ll read back the results from the previous notebook, which we saved in a FITS file.</p></li>
|
||||
<li><p>Then we’ll transform the coordinates and proper motion data from ICRS back to the coordinate frame of GD-1.</p></li>
|
||||
<li><p>We’ll put those results into a Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>, which we’ll use to select stars near the centerline of GD-1.</p></li>
|
||||
<li><p>Plotting the proper motion of those stars, we’ll identify a region of proper motion for stars that are likely to be in GD-1.</p></li>
|
||||
<li><p>Finally, we’ll select and plot the stars whose proper motion is in that region.</p></li>
|
||||
</ol>
|
||||
<p>Press this button to run this notebook on Colab:</p>
|
||||
<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/03_motion.ipynb"><img src="run_on_colab_small.png"></a></p>
|
||||
<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/03_motion.ipynb">or click here to read it on NBViewer</a></p>
|
||||
<p><strong>Notebook 4</strong></p>
|
||||
<p>Here are the steps in this notebook:</p>
|
||||
<ol class="simple">
|
||||
<li><p>Using data from the previous notebook, we’ll identify the values of proper motion for stars likely to be in GD-1.</p></li>
|
||||
<li><p>Then we’ll compose an ADQL query that selects stars based on proper motion, so we can download only the data we need.</p></li>
|
||||
<li><p>We’ll also see how to write the results to a CSV file.</p></li>
|
||||
</ol>
|
||||
<p>That will make it possible to search a bigger region of the sky in a single query.</p>
|
||||
<p>Press this button to run this notebook on Colab:</p>
|
||||
<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/04_select.ipynb"><img src="run_on_colab_small.png"></a></p>
|
||||
<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/04_select.ipynb">or click here to read it on NBViewer</a></p>
|
||||
<p><strong>Notebook 5</strong></p>
|
||||
<p>Here are the steps in this notebook:</p>
|
||||
<ol class="simple">
|
||||
<li><p>We’ll reload the candidate stars we identified in the previous notebook.</p></li>
|
||||
<li><p>Then we’ll run a query on the Gaia server that uploads the table of candidates and uses a <code class="docutils literal notranslate"><span class="pre">JOIN</span></code> operation to select photometry data for the candidate stars.</p></li>
|
||||
<li><p>We’ll write the results to a file for use in the next notebook.</p></li>
|
||||
</ol>
|
||||
<p>Press this button to run this notebook on Colab:</p>
|
||||
<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/05_join.ipynb"><img src="run_on_colab_small.png"></a></p>
|
||||
<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/05_join.ipynb">or click here to read it on NBViewer</a></p>
|
||||
<p><strong>Notebook 6</strong></p>
|
||||
<p>Here are the steps in this notebook:</p>
|
||||
<ol class="simple">
|
||||
<li><p>We’ll reload the data from the previous notebook and make a color-magnitude diagram.</p></li>
|
||||
<li><p>Then we’ll specify a polygon in the diagram that contains stars with the photometry we expect.</p></li>
|
||||
<li><p>Then we’ll merge the photometry data with the list of candidate stars, storing the result in a Pandas <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>.</p></li>
|
||||
</ol>
|
||||
<p>Press this button to run this notebook on Colab:</p>
|
||||
<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/06_photo.ipynb"><img src="run_on_colab_small.png"></a></p>
|
||||
<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/06_photo.ipynb">or click here to read it on NBViewer</a></p>
|
||||
<p><strong>Notebook 7</strong></p>
|
||||
<p>Here are the steps in this notebook:</p>
|
||||
<ol class="simple">
|
||||
<li><p>Starting with the figure from the previous notebook, we’ll add annotations to present the results more clearly.</p></li>
|
||||
<li><p>The we’ll see several ways to customize figures to make them more appealing and effective.</p></li>
|
||||
<li><p>Finally, we’ll see how to make a figure with multiple panels or subplots.</p></li>
|
||||
</ol>
|
||||
<p>Press this button to run this notebook on Colab:</p>
|
||||
<p><a class="reference external" href="https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/07_plot.ipynb"><img src="run_on_colab_small.png"></a></p>
|
||||
<p><a class="reference external" href="https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/07_plot.ipynb">or click here to read it on NBViewer</a></p>
|
||||
<p><strong>Installation instructions</strong></p>
|
||||
<p>Coming soon.</p>
|
||||
<div class="toctree-wrapper compound">
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<script type="text/x-thebe-config">
|
||||
{
|
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requestKernel: true,
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binderOptions: {
|
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repo: "binder-examples/jupyter-stacks-datascience",
|
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ref: "master",
|
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|
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codeMirrorConfig: {
|
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|
||||
<div class='prev-next-bottom'>
|
||||
|
||||
<a class='right-next' id="next-link" href="01_query.html" title="next page">Chapter 1</a>
|
||||
|
||||
</div>
|
||||
<footer class="footer mt-5 mt-md-0">
|
||||
<div class="container">
|
||||
<p>
|
||||
|
||||
By Allen B. Downey<br/>
|
||||
|
||||
© Copyright 2020.<br/>
|
||||
</p>
|
||||
</div>
|
||||
</footer>
|
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</main>
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|
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</div>
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</div>
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<script src="_static/js/index.30270b6e4c972e43c488.js"></script>
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|
||||
# Astronomical Data in Python
|
||||
|
||||
*Astronomical Data in Python* is an introduction to tools and practices for working with astronomical data. Topics covered include:
|
||||
|
||||
* Writing queries that select and download data from a database.
|
||||
|
||||
* Using data stored in an Astropy `Table` or Pandas `DataFrame`.
|
||||
|
||||
* Working with coordinates and other quantities with units.
|
||||
|
||||
* Storing data in various formats.
|
||||
|
||||
* Performing database join operations that combine data from multiple tables.
|
||||
|
||||
* Visualizing data and preparing publication-quality figures.
|
||||
|
||||
As a running example, we will replicate part of the analysis in a recent paper, "[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)" by Adrian M. Price-Whelan and Ana Bonaca.
|
||||
|
||||
This material was developed in collaboration with [The Carpentries](https://carpentries.org/) and the Astronomy Curriculum Development Committee, and supported by funding from the American Institute of Physics through the American Astronomical Society.
|
||||
|
||||
I am grateful for contributions from the members of the committee -- Azalee Bostroem, Rodolfo Montez, and Phil Rosenfield -- and from Erin Becker, Brett Morris and Adrian Price-Whelan.
|
||||
|
||||
The original format of this material is a series of Jupyter notebooks. Using the
|
||||
links below, you can read the notebooks on NBViewer or run them on Colab. If you
|
||||
want to run the notebooks in your own environment, you can download them from
|
||||
this repository and follow the instructions below to set up your environment.
|
||||
|
||||
This material is also available in the form of [Carpentries lessons](https://datacarpentry.github.io/astronomy-python), but you should be
|
||||
aware that these versions might diverge in the future.
|
||||
|
||||
**Prerequisites**
|
||||
|
||||
This material should be accessible to people familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas. If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, that should be enough.
|
||||
|
||||
We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we'll use.
|
||||
|
||||
**Notebook 1**
|
||||
|
||||
This notebook demonstrates the following steps:
|
||||
|
||||
1. Making a connection to the Gaia server,
|
||||
|
||||
2. Exploring information about the database and the tables it contains,
|
||||
|
||||
3. Writing a query and sending it to the server, and finally
|
||||
|
||||
4. Downloading the response from the server as an Astropy `Table`.
|
||||
|
||||
Press this button to run this notebook on Colab:
|
||||
|
||||
[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb)
|
||||
|
||||
[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb)
|
||||
|
||||
|
||||
**Notebook 2**
|
||||
|
||||
This notebook starts with an example that does a "cone search"; that is, it selects stars that appear in a circular region of the sky.
|
||||
|
||||
Then, to select stars in the vicinity of GD-1, we:
|
||||
|
||||
* Use `Quantity` objects to represent measurements with units.
|
||||
|
||||
* Use the `Gala` library to convert coordinates from one frame to another.
|
||||
|
||||
* Use the ADQL keywords `POLYGON`, `CONTAINS`, and `POINT` to select stars that fall within a polygonal region.
|
||||
|
||||
* Submit a query and download the results.
|
||||
|
||||
* Store the results in a FITS file.
|
||||
|
||||
Press this button to run this notebook on Colab:
|
||||
|
||||
[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/02_coords.ipynb)
|
||||
|
||||
[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/02_coords.ipynb)
|
||||
|
||||
|
||||
**Notebook 3**
|
||||
|
||||
Here are the steps in this notebook:
|
||||
|
||||
1. We'll read back the results from the previous notebook, which we saved in a FITS file.
|
||||
|
||||
2. Then we'll transform the coordinates and proper motion data from ICRS back to the coordinate frame of GD-1.
|
||||
|
||||
3. We'll put those results into a Pandas `DataFrame`, which we'll use to select stars near the centerline of GD-1.
|
||||
|
||||
4. Plotting the proper motion of those stars, we'll identify a region of proper motion for stars that are likely to be in GD-1.
|
||||
|
||||
5. Finally, we'll select and plot the stars whose proper motion is in that region.
|
||||
|
||||
Press this button to run this notebook on Colab:
|
||||
|
||||
[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/03_motion.ipynb)
|
||||
|
||||
[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/03_motion.ipynb)
|
||||
|
||||
|
||||
**Notebook 4**
|
||||
|
||||
Here are the steps in this notebook:
|
||||
|
||||
1. Using data from the previous notebook, we'll identify the values of proper motion for stars likely to be in GD-1.
|
||||
|
||||
2. Then we'll compose an ADQL query that selects stars based on proper motion, so we can download only the data we need.
|
||||
|
||||
3. We'll also see how to write the results to a CSV file.
|
||||
|
||||
That will make it possible to search a bigger region of the sky in a single query.
|
||||
|
||||
Press this button to run this notebook on Colab:
|
||||
|
||||
[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/04_select.ipynb)
|
||||
|
||||
[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/04_select.ipynb)
|
||||
|
||||
|
||||
**Notebook 5**
|
||||
|
||||
Here are the steps in this notebook:
|
||||
|
||||
1. We'll reload the candidate stars we identified in the previous notebook.
|
||||
|
||||
2. Then we'll run a query on the Gaia server that uploads the table of candidates and uses a `JOIN` operation to select photometry data for the candidate stars.
|
||||
|
||||
3. We'll write the results to a file for use in the next notebook.
|
||||
|
||||
Press this button to run this notebook on Colab:
|
||||
|
||||
[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/05_join.ipynb)
|
||||
|
||||
[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/05_join.ipynb)
|
||||
|
||||
|
||||
**Notebook 6**
|
||||
|
||||
Here are the steps in this notebook:
|
||||
|
||||
1. We'll reload the data from the previous notebook and make a color-magnitude diagram.
|
||||
|
||||
2. Then we'll specify a polygon in the diagram that contains stars with the photometry we expect.
|
||||
|
||||
3. Then we'll merge the photometry data with the list of candidate stars, storing the result in a Pandas `DataFrame`.
|
||||
|
||||
Press this button to run this notebook on Colab:
|
||||
|
||||
[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/06_photo.ipynb)
|
||||
|
||||
[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/06_photo.ipynb)
|
||||
|
||||
|
||||
**Notebook 7**
|
||||
|
||||
Here are the steps in this notebook:
|
||||
|
||||
1. Starting with the figure from the previous notebook, we'll add annotations to present the results more clearly.
|
||||
|
||||
2. The we'll see several ways to customize figures to make them more appealing and effective.
|
||||
|
||||
3. Finally, we'll see how to make a figure with multiple panels or subplots.
|
||||
|
||||
Press this button to run this notebook on Colab:
|
||||
|
||||
[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/07_plot.ipynb)
|
||||
|
||||
[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/07_plot.ipynb)
|
||||
|
||||
|
||||
**Installation instructions**
|
||||
|
||||
Coming soon.
|
||||
Vendored
-20
@@ -1,20 +0,0 @@
|
||||
Copyright (c) 2019 Jan Bednar
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining
|
||||
a copy of this software and associated documentation files (the
|
||||
"Software"), to deal in the Software without restriction, including
|
||||
without limitation the rights to use, copy, modify, merge, publish,
|
||||
distribute, sublicense, and/or sell copies of the Software, and to
|
||||
permit persons to whom the Software is furnished to do so, subject to
|
||||
the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be
|
||||
included in all copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
|
||||
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
|
||||
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
|
||||
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
|
||||
LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
|
||||
OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
|
||||
WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
|
||||
Vendored
-20
@@ -1,20 +0,0 @@
|
||||
Copyright (c) 2019 Jan Bednar
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining
|
||||
a copy of this software and associated documentation files (the
|
||||
"Software"), to deal in the Software without restriction, including
|
||||
without limitation the rights to use, copy, modify, merge, publish,
|
||||
distribute, sublicense, and/or sell copies of the Software, and to
|
||||
permit persons to whom the Software is furnished to do so, subject to
|
||||
the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be
|
||||
included in all copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
|
||||
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
|
||||
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
|
||||
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
|
||||
LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
|
||||
OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
|
||||
WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -1,172 +0,0 @@
|
||||
# Astronomical Data in Python
|
||||
|
||||
*Astronomical Data in Python* is an introduction to tools and practices for working with astronomical data. Topics covered include:
|
||||
|
||||
* Writing queries that select and download data from a database.
|
||||
|
||||
* Using data stored in an Astropy `Table` or Pandas `DataFrame`.
|
||||
|
||||
* Working with coordinates and other quantities with units.
|
||||
|
||||
* Storing data in various formats.
|
||||
|
||||
* Performing database join operations that combine data from multiple tables.
|
||||
|
||||
* Visualizing data and preparing publication-quality figures.
|
||||
|
||||
As a running example, we will replicate part of the analysis in a recent paper, "[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)" by Adrian M. Price-Whelan and Ana Bonaca.
|
||||
|
||||
This material was developed in collaboration with [The Carpentries](https://carpentries.org/) and the Astronomy Curriculum Development Committee, and supported by funding from the American Institute of Physics through the American Astronomical Society.
|
||||
|
||||
I am grateful for contributions from the members of the committee -- Azalee Bostroem, Rodolfo Montez, and Phil Rosenfield -- and from Erin Becker, Brett Morris and Adrian Price-Whelan.
|
||||
|
||||
The original format of this material is a series of Jupyter notebooks. Using the
|
||||
links below, you can read the notebooks on NBViewer or run them on Colab. If you
|
||||
want to run the notebooks in your own environment, you can download them from
|
||||
this repository and follow the instructions below to set up your environment.
|
||||
|
||||
This material is also available in the form of [Carpentries lessons](https://datacarpentry.github.io/astronomy-python), but you should be
|
||||
aware that these versions might diverge in the future.
|
||||
|
||||
**Prerequisites**
|
||||
|
||||
This material should be accessible to people familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas. If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, that should be enough.
|
||||
|
||||
We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we'll use.
|
||||
|
||||
**Notebook 1**
|
||||
|
||||
This notebook demonstrates the following steps:
|
||||
|
||||
1. Making a connection to the Gaia server,
|
||||
|
||||
2. Exploring information about the database and the tables it contains,
|
||||
|
||||
3. Writing a query and sending it to the server, and finally
|
||||
|
||||
4. Downloading the response from the server as an Astropy `Table`.
|
||||
|
||||
Press this button to run this notebook on Colab:
|
||||
|
||||
[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb)
|
||||
|
||||
[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb)
|
||||
|
||||
|
||||
**Notebook 2**
|
||||
|
||||
This notebook starts with an example that does a "cone search"; that is, it selects stars that appear in a circular region of the sky.
|
||||
|
||||
Then, to select stars in the vicinity of GD-1, we:
|
||||
|
||||
* Use `Quantity` objects to represent measurements with units.
|
||||
|
||||
* Use the `Gala` library to convert coordinates from one frame to another.
|
||||
|
||||
* Use the ADQL keywords `POLYGON`, `CONTAINS`, and `POINT` to select stars that fall within a polygonal region.
|
||||
|
||||
* Submit a query and download the results.
|
||||
|
||||
* Store the results in a FITS file.
|
||||
|
||||
Press this button to run this notebook on Colab:
|
||||
|
||||
[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/02_coords.ipynb)
|
||||
|
||||
[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/02_coords.ipynb)
|
||||
|
||||
|
||||
**Notebook 3**
|
||||
|
||||
Here are the steps in this notebook:
|
||||
|
||||
1. We'll read back the results from the previous notebook, which we saved in a FITS file.
|
||||
|
||||
2. Then we'll transform the coordinates and proper motion data from ICRS back to the coordinate frame of GD-1.
|
||||
|
||||
3. We'll put those results into a Pandas `DataFrame`, which we'll use to select stars near the centerline of GD-1.
|
||||
|
||||
4. Plotting the proper motion of those stars, we'll identify a region of proper motion for stars that are likely to be in GD-1.
|
||||
|
||||
5. Finally, we'll select and plot the stars whose proper motion is in that region.
|
||||
|
||||
Press this button to run this notebook on Colab:
|
||||
|
||||
[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/03_motion.ipynb)
|
||||
|
||||
[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/03_motion.ipynb)
|
||||
|
||||
|
||||
**Notebook 4**
|
||||
|
||||
Here are the steps in this notebook:
|
||||
|
||||
1. Using data from the previous notebook, we'll identify the values of proper motion for stars likely to be in GD-1.
|
||||
|
||||
2. Then we'll compose an ADQL query that selects stars based on proper motion, so we can download only the data we need.
|
||||
|
||||
3. We'll also see how to write the results to a CSV file.
|
||||
|
||||
That will make it possible to search a bigger region of the sky in a single query.
|
||||
|
||||
Press this button to run this notebook on Colab:
|
||||
|
||||
[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/04_select.ipynb)
|
||||
|
||||
[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/04_select.ipynb)
|
||||
|
||||
|
||||
**Notebook 5**
|
||||
|
||||
Here are the steps in this notebook:
|
||||
|
||||
1. We'll reload the candidate stars we identified in the previous notebook.
|
||||
|
||||
2. Then we'll run a query on the Gaia server that uploads the table of candidates and uses a `JOIN` operation to select photometry data for the candidate stars.
|
||||
|
||||
3. We'll write the results to a file for use in the next notebook.
|
||||
|
||||
Press this button to run this notebook on Colab:
|
||||
|
||||
[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/05_join.ipynb)
|
||||
|
||||
[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/05_join.ipynb)
|
||||
|
||||
|
||||
**Notebook 6**
|
||||
|
||||
Here are the steps in this notebook:
|
||||
|
||||
1. We'll reload the data from the previous notebook and make a color-magnitude diagram.
|
||||
|
||||
2. Then we'll specify a polygon in the diagram that contains stars with the photometry we expect.
|
||||
|
||||
3. Then we'll merge the photometry data with the list of candidate stars, storing the result in a Pandas `DataFrame`.
|
||||
|
||||
Press this button to run this notebook on Colab:
|
||||
|
||||
[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/06_photo.ipynb)
|
||||
|
||||
[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/06_photo.ipynb)
|
||||
|
||||
|
||||
**Notebook 7**
|
||||
|
||||
Here are the steps in this notebook:
|
||||
|
||||
1. Starting with the figure from the previous notebook, we'll add annotations to present the results more clearly.
|
||||
|
||||
2. The we'll see several ways to customize figures to make them more appealing and effective.
|
||||
|
||||
3. Finally, we'll see how to make a figure with multiple panels or subplots.
|
||||
|
||||
Press this button to run this notebook on Colab:
|
||||
|
||||
[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/07_plot.ipynb)
|
||||
|
||||
[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/07_plot.ipynb)
|
||||
|
||||
|
||||
**Installation instructions**
|
||||
|
||||
Coming soon.
|
||||
@@ -1,169 +0,0 @@
|
||||
# Astronomical Data in Python
|
||||
|
||||
*Astronomical Data in Python* is an introduction to tools and practices for working with astronomical data. Topics covered include:
|
||||
|
||||
* Writing queries that select and download data from a database.
|
||||
|
||||
* Using data stored in an Astropy `Table` or Pandas `DataFrame`.
|
||||
|
||||
* Working with coordinates and other quantities with units.
|
||||
|
||||
* Storing data in various formats.
|
||||
|
||||
* Performing database join operations that combine data from multiple tables.
|
||||
|
||||
* Visualizing data and preparing publication-quality figures.
|
||||
|
||||
As a running example, we will replicate part of the analysis in a recent paper, "[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)" by Adrian M. Price-Whelan and Ana Bonaca.
|
||||
|
||||
This material was developed in collaboration with [The Carpentries](https://carpentries.org/) and the Astronomy Curriculum Development Committee, and supported by funding from the American Institute of Physics through the American Astronomical Society.
|
||||
|
||||
I am grateful for contributions from the members of the committee -- Azalee Bostroem, Rodolfo Montez, and Phil Rosenfield -- and from Erin Becker, Brett Morris and Adrian Price-Whelan.
|
||||
|
||||
The original format of this material is a series of Jupyter notebooks. Using the
|
||||
links below, you can read the notebooks on NBViewer or run them on Colab. If you
|
||||
want to run the notebooks in your own environment, you can download them from
|
||||
this repository and follow the instructions below to set up your environment.
|
||||
|
||||
### Prerequisites
|
||||
|
||||
This material should be accessible to people familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas. If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, that should be enough.
|
||||
|
||||
We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we'll use.
|
||||
|
||||
### Notebook 1
|
||||
|
||||
This notebook demonstrates the following steps:
|
||||
|
||||
1. Making a connection to the Gaia server,
|
||||
|
||||
2. Exploring information about the database and the tables it contains,
|
||||
|
||||
3. Writing a query and sending it to the server, and finally
|
||||
|
||||
4. Downloading the response from the server as an Astropy `Table`.
|
||||
|
||||
Press this button to run this notebook on Colab:
|
||||
|
||||
[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb)
|
||||
|
||||
[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/01_query.ipynb)
|
||||
|
||||
|
||||
### Notebook 2
|
||||
|
||||
This notebook starts with an example that does a "cone search"; that is, it selects stars that appear in a circular region of the sky.
|
||||
|
||||
Then, to select stars in the vicinity of GD-1, we:
|
||||
|
||||
* Use `Quantity` objects to represent measurements with units.
|
||||
|
||||
* Use the `Gala` library to convert coordinates from one frame to another.
|
||||
|
||||
* Use the ADQL keywords `POLYGON`, `CONTAINS`, and `POINT` to select stars that fall within a polygonal region.
|
||||
|
||||
* Submit a query and download the results.
|
||||
|
||||
* Store the results in a FITS file.
|
||||
|
||||
Press this button to run this notebook on Colab:
|
||||
|
||||
[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/02_coords.ipynb)
|
||||
|
||||
[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/02_coords.ipynb)
|
||||
|
||||
|
||||
### Notebook 3
|
||||
|
||||
Here are the steps in this notebook:
|
||||
|
||||
1. We'll read back the results from the previous notebook, which we saved in a FITS file.
|
||||
|
||||
2. Then we'll transform the coordinates and proper motion data from ICRS back to the coordinate frame of GD-1.
|
||||
|
||||
3. We'll put those results into a Pandas `DataFrame`, which we'll use to select stars near the centerline of GD-1.
|
||||
|
||||
4. Plotting the proper motion of those stars, we'll identify a region of proper motion for stars that are likely to be in GD-1.
|
||||
|
||||
5. Finally, we'll select and plot the stars whose proper motion is in that region.
|
||||
|
||||
Press this button to run this notebook on Colab:
|
||||
|
||||
[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/03_motion.ipynb)
|
||||
|
||||
[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/03_motion.ipynb)
|
||||
|
||||
|
||||
### Notebook 4
|
||||
|
||||
Here are the steps in this notebook:
|
||||
|
||||
1. Using data from the previous notebook, we'll identify the values of proper motion for stars likely to be in GD-1.
|
||||
|
||||
2. Then we'll compose an ADQL query that selects stars based on proper motion, so we can download only the data we need.
|
||||
|
||||
3. We'll also see how to write the results to a CSV file.
|
||||
|
||||
That will make it possible to search a bigger region of the sky in a single query.
|
||||
|
||||
Press this button to run this notebook on Colab:
|
||||
|
||||
[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/04_select.ipynb)
|
||||
|
||||
[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/04_select.ipynb)
|
||||
|
||||
|
||||
### Notebook 5
|
||||
|
||||
Here are the steps in this notebook:
|
||||
|
||||
1. We'll reload the candidate stars we identified in the previous notebook.
|
||||
|
||||
2. Then we'll run a query on the Gaia server that uploads the table of candidates and uses a `JOIN` operation to select photometry data for the candidate stars.
|
||||
|
||||
3. We'll write the results to a file for use in the next notebook.
|
||||
|
||||
Press this button to run this notebook on Colab:
|
||||
|
||||
[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/05_join.ipynb)
|
||||
|
||||
[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/05_join.ipynb)
|
||||
|
||||
|
||||
### Notebook 6
|
||||
|
||||
Here are the steps in this notebook:
|
||||
|
||||
1. We'll reload the data from the previous notebook and make a color-magnitude diagram.
|
||||
|
||||
2. Then we'll specify a polygon in the diagram that contains stars with the photometry we expect.
|
||||
|
||||
3. Then we'll merge the photometry data with the list of candidate stars, storing the result in a Pandas `DataFrame`.
|
||||
|
||||
Press this button to run this notebook on Colab:
|
||||
|
||||
[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/06_photo.ipynb)
|
||||
|
||||
[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/06_photo.ipynb)
|
||||
|
||||
|
||||
### Notebook 7
|
||||
|
||||
Here are the steps in this notebook:
|
||||
|
||||
1. Starting with the figure from the previous notebook, we'll add annotations to present the results more clearly.
|
||||
|
||||
2. The we'll see several ways to customize figures to make them more appealing and effective.
|
||||
|
||||
3. Finally, we'll see how to make a figure with multiple panels or subplots.
|
||||
|
||||
Press this button to run this notebook on Colab:
|
||||
|
||||
[<img src="run_on_colab_small.png">](https://colab.research.google.com/github/AllenDowney/AstronomicalData/blob/main/07_plot.ipynb)
|
||||
|
||||
[or click here to read it on NBViewer](https://nbviewer.jupyter.org/github/AllenDowney/AstronomicalData/blob/main/07_plot.ipynb)
|
||||
|
||||
|
||||
## Installation instructions
|
||||
|
||||
Coming soon.
|
||||
@@ -1,72 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# The Notebook of Last Resort"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"If you are not able to get everything installed that we need for the workshop, you have the option of running this notebook on Colab.\n",
|
||||
"\n",
|
||||
"Before you get started, you probably want to press the Save button!"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"hide-cell"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# If we're running on Colab, install libraries\n",
|
||||
"\n",
|
||||
"import sys\n",
|
||||
"IN_COLAB = 'google.colab' in sys.modules\n",
|
||||
"\n",
|
||||
"if IN_COLAB:\n",
|
||||
" !pip install astroquery astro-gala pyia"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"That should be everything you need. Now you can type code and run it in the following cells."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "raw",
|
||||
"metadata": {},
|
||||
"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": "3.8.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,136 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Astronomical Data in Python\n",
|
||||
"\n",
|
||||
"This notebook imports the libraries we need for the workshop.\n",
|
||||
"\n",
|
||||
"If any of them are missing, you'll get an error message.\n",
|
||||
"\n",
|
||||
"If you don't get any error messages, you are all set."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from wget import download"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import matplotlib as mpl\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"from matplotlib.path import Path\n",
|
||||
"from matplotlib.patches import Polygon"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import astropy.coordinates as coord\n",
|
||||
"import astropy.units as u\n",
|
||||
"from astropy.table import Table"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import gala.coordinates as gc\n",
|
||||
"from pyia import GaiaData"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Created TAP+ (v1.2.1) - Connection:\n",
|
||||
"\tHost: gea.esac.esa.int\n",
|
||||
"\tUse HTTPS: True\n",
|
||||
"\tPort: 443\n",
|
||||
"\tSSL Port: 443\n",
|
||||
"Created TAP+ (v1.2.1) - Connection:\n",
|
||||
"\tHost: geadata.esac.esa.int\n",
|
||||
"\tUse HTTPS: True\n",
|
||||
"\tPort: 443\n",
|
||||
"\tSSL Port: 443\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Note: running this import statement opens a connection\n",
|
||||
"# to a Gaia server, so it will fail if you are not connected\n",
|
||||
"# to the internet.\n",
|
||||
"\n",
|
||||
"from astroquery.gaia import Gaia"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"During the workshop, we might put some code on Slack and ask you to cut and paste it into the notebook.\n",
|
||||
"\n",
|
||||
"If you are on a Mac, you might encounter a problem: "
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": "3.8.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@@ -1,855 +0,0 @@
|
||||
/*
|
||||
* basic.css
|
||||
* ~~~~~~~~~
|
||||
*
|
||||
* Sphinx stylesheet -- basic theme.
|
||||
*
|
||||
* :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
||||
/* -- main layout ----------------------------------------------------------- */
|
||||
|
||||
div.clearer {
|
||||
clear: both;
|
||||
}
|
||||
|
||||
div.section::after {
|
||||
display: block;
|
||||
content: '';
|
||||
clear: left;
|
||||
}
|
||||
|
||||
/* -- relbar ---------------------------------------------------------------- */
|
||||
|
||||
div.related {
|
||||
width: 100%;
|
||||
font-size: 90%;
|
||||
}
|
||||
|
||||
div.related h3 {
|
||||
display: none;
|
||||
}
|
||||
|
||||
div.related ul {
|
||||
margin: 0;
|
||||
padding: 0 0 0 10px;
|
||||
list-style: none;
|
||||
}
|
||||
|
||||
div.related li {
|
||||
display: inline;
|
||||
}
|
||||
|
||||
div.related li.right {
|
||||
float: right;
|
||||
margin-right: 5px;
|
||||
}
|
||||
|
||||
/* -- sidebar --------------------------------------------------------------- */
|
||||
|
||||
div.sphinxsidebarwrapper {
|
||||
padding: 10px 5px 0 10px;
|
||||
}
|
||||
|
||||
div.sphinxsidebar {
|
||||
float: left;
|
||||
width: 270px;
|
||||
margin-left: -100%;
|
||||
font-size: 90%;
|
||||
word-wrap: break-word;
|
||||
overflow-wrap : break-word;
|
||||
}
|
||||
|
||||
div.sphinxsidebar ul {
|
||||
list-style: none;
|
||||
}
|
||||
|
||||
div.sphinxsidebar ul ul,
|
||||
div.sphinxsidebar ul.want-points {
|
||||
margin-left: 20px;
|
||||
list-style: square;
|
||||
}
|
||||
|
||||
div.sphinxsidebar ul ul {
|
||||
margin-top: 0;
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
div.sphinxsidebar form {
|
||||
margin-top: 10px;
|
||||
}
|
||||
|
||||
div.sphinxsidebar input {
|
||||
border: 1px solid #98dbcc;
|
||||
font-family: sans-serif;
|
||||
font-size: 1em;
|
||||
}
|
||||
|
||||
div.sphinxsidebar #searchbox form.search {
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
div.sphinxsidebar #searchbox input[type="text"] {
|
||||
float: left;
|
||||
width: 80%;
|
||||
padding: 0.25em;
|
||||
box-sizing: border-box;
|
||||
}
|
||||
|
||||
div.sphinxsidebar #searchbox input[type="submit"] {
|
||||
float: left;
|
||||
width: 20%;
|
||||
border-left: none;
|
||||
padding: 0.25em;
|
||||
box-sizing: border-box;
|
||||
}
|
||||
|
||||
|
||||
img {
|
||||
border: 0;
|
||||
max-width: 100%;
|
||||
}
|
||||
|
||||
/* -- search page ----------------------------------------------------------- */
|
||||
|
||||
ul.search {
|
||||
margin: 10px 0 0 20px;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
ul.search li {
|
||||
padding: 5px 0 5px 20px;
|
||||
background-image: url(file.png);
|
||||
background-repeat: no-repeat;
|
||||
background-position: 0 7px;
|
||||
}
|
||||
|
||||
ul.search li a {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
ul.search li div.context {
|
||||
color: #888;
|
||||
margin: 2px 0 0 30px;
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
ul.keywordmatches li.goodmatch a {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
/* -- index page ------------------------------------------------------------ */
|
||||
|
||||
table.contentstable {
|
||||
width: 90%;
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
table.contentstable p.biglink {
|
||||
line-height: 150%;
|
||||
}
|
||||
|
||||
a.biglink {
|
||||
font-size: 1.3em;
|
||||
}
|
||||
|
||||
span.linkdescr {
|
||||
font-style: italic;
|
||||
padding-top: 5px;
|
||||
font-size: 90%;
|
||||
}
|
||||
|
||||
/* -- general index --------------------------------------------------------- */
|
||||
|
||||
table.indextable {
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
table.indextable td {
|
||||
text-align: left;
|
||||
vertical-align: top;
|
||||
}
|
||||
|
||||
table.indextable ul {
|
||||
margin-top: 0;
|
||||
margin-bottom: 0;
|
||||
list-style-type: none;
|
||||
}
|
||||
|
||||
table.indextable > tbody > tr > td > ul {
|
||||
padding-left: 0em;
|
||||
}
|
||||
|
||||
table.indextable tr.pcap {
|
||||
height: 10px;
|
||||
}
|
||||
|
||||
table.indextable tr.cap {
|
||||
margin-top: 10px;
|
||||
background-color: #f2f2f2;
|
||||
}
|
||||
|
||||
img.toggler {
|
||||
margin-right: 3px;
|
||||
margin-top: 3px;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
div.modindex-jumpbox {
|
||||
border-top: 1px solid #ddd;
|
||||
border-bottom: 1px solid #ddd;
|
||||
margin: 1em 0 1em 0;
|
||||
padding: 0.4em;
|
||||
}
|
||||
|
||||
div.genindex-jumpbox {
|
||||
border-top: 1px solid #ddd;
|
||||
border-bottom: 1px solid #ddd;
|
||||
margin: 1em 0 1em 0;
|
||||
padding: 0.4em;
|
||||
}
|
||||
|
||||
/* -- domain module index --------------------------------------------------- */
|
||||
|
||||
table.modindextable td {
|
||||
padding: 2px;
|
||||
border-collapse: collapse;
|
||||
}
|
||||
|
||||
/* -- general body styles --------------------------------------------------- */
|
||||
|
||||
div.body {
|
||||
min-width: 450px;
|
||||
max-width: 800px;
|
||||
}
|
||||
|
||||
div.body p, div.body dd, div.body li, div.body blockquote {
|
||||
-moz-hyphens: auto;
|
||||
-ms-hyphens: auto;
|
||||
-webkit-hyphens: auto;
|
||||
hyphens: auto;
|
||||
}
|
||||
|
||||
a.headerlink {
|
||||
visibility: hidden;
|
||||
}
|
||||
|
||||
a.brackets:before,
|
||||
span.brackets > a:before{
|
||||
content: "[";
|
||||
}
|
||||
|
||||
a.brackets:after,
|
||||
span.brackets > a:after {
|
||||
content: "]";
|
||||
}
|
||||
|
||||
h1:hover > a.headerlink,
|
||||
h2:hover > a.headerlink,
|
||||
h3:hover > a.headerlink,
|
||||
h4:hover > a.headerlink,
|
||||
h5:hover > a.headerlink,
|
||||
h6:hover > a.headerlink,
|
||||
dt:hover > a.headerlink,
|
||||
caption:hover > a.headerlink,
|
||||
p.caption:hover > a.headerlink,
|
||||
div.code-block-caption:hover > a.headerlink {
|
||||
visibility: visible;
|
||||
}
|
||||
|
||||
div.body p.caption {
|
||||
text-align: inherit;
|
||||
}
|
||||
|
||||
div.body td {
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
.first {
|
||||
margin-top: 0 !important;
|
||||
}
|
||||
|
||||
p.rubric {
|
||||
margin-top: 30px;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
img.align-left, .figure.align-left, object.align-left {
|
||||
clear: left;
|
||||
float: left;
|
||||
margin-right: 1em;
|
||||
}
|
||||
|
||||
img.align-right, .figure.align-right, object.align-right {
|
||||
clear: right;
|
||||
float: right;
|
||||
margin-left: 1em;
|
||||
}
|
||||
|
||||
img.align-center, .figure.align-center, object.align-center {
|
||||
display: block;
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
img.align-default, .figure.align-default {
|
||||
display: block;
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
.align-left {
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
.align-center {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.align-default {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.align-right {
|
||||
text-align: right;
|
||||
}
|
||||
|
||||
/* -- sidebars -------------------------------------------------------------- */
|
||||
|
||||
div.sidebar {
|
||||
margin: 0 0 0.5em 1em;
|
||||
border: 1px solid #ddb;
|
||||
padding: 7px;
|
||||
background-color: #ffe;
|
||||
width: 40%;
|
||||
float: right;
|
||||
clear: right;
|
||||
overflow-x: auto;
|
||||
}
|
||||
|
||||
p.sidebar-title {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
div.admonition, div.topic, blockquote {
|
||||
clear: left;
|
||||
}
|
||||
|
||||
/* -- topics ---------------------------------------------------------------- */
|
||||
|
||||
div.topic {
|
||||
border: 1px solid #ccc;
|
||||
padding: 7px;
|
||||
margin: 10px 0 10px 0;
|
||||
}
|
||||
|
||||
p.topic-title {
|
||||
font-size: 1.1em;
|
||||
font-weight: bold;
|
||||
margin-top: 10px;
|
||||
}
|
||||
|
||||
/* -- admonitions ----------------------------------------------------------- */
|
||||
|
||||
div.admonition {
|
||||
margin-top: 10px;
|
||||
margin-bottom: 10px;
|
||||
padding: 7px;
|
||||
}
|
||||
|
||||
div.admonition dt {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
p.admonition-title {
|
||||
margin: 0px 10px 5px 0px;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
div.body p.centered {
|
||||
text-align: center;
|
||||
margin-top: 25px;
|
||||
}
|
||||
|
||||
/* -- content of sidebars/topics/admonitions -------------------------------- */
|
||||
|
||||
div.sidebar > :last-child,
|
||||
div.topic > :last-child,
|
||||
div.admonition > :last-child {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
div.sidebar::after,
|
||||
div.topic::after,
|
||||
div.admonition::after,
|
||||
blockquote::after {
|
||||
display: block;
|
||||
content: '';
|
||||
clear: both;
|
||||
}
|
||||
|
||||
/* -- tables ---------------------------------------------------------------- */
|
||||
|
||||
table.docutils {
|
||||
margin-top: 10px;
|
||||
margin-bottom: 10px;
|
||||
border: 0;
|
||||
border-collapse: collapse;
|
||||
}
|
||||
|
||||
table.align-center {
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
table.align-default {
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
table caption span.caption-number {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
table caption span.caption-text {
|
||||
}
|
||||
|
||||
table.docutils td, table.docutils th {
|
||||
padding: 1px 8px 1px 5px;
|
||||
border-top: 0;
|
||||
border-left: 0;
|
||||
border-right: 0;
|
||||
border-bottom: 1px solid #aaa;
|
||||
}
|
||||
|
||||
table.footnote td, table.footnote th {
|
||||
border: 0 !important;
|
||||
}
|
||||
|
||||
th {
|
||||
text-align: left;
|
||||
padding-right: 5px;
|
||||
}
|
||||
|
||||
table.citation {
|
||||
border-left: solid 1px gray;
|
||||
margin-left: 1px;
|
||||
}
|
||||
|
||||
table.citation td {
|
||||
border-bottom: none;
|
||||
}
|
||||
|
||||
th > :first-child,
|
||||
td > :first-child {
|
||||
margin-top: 0px;
|
||||
}
|
||||
|
||||
th > :last-child,
|
||||
td > :last-child {
|
||||
margin-bottom: 0px;
|
||||
}
|
||||
|
||||
/* -- figures --------------------------------------------------------------- */
|
||||
|
||||
div.figure {
|
||||
margin: 0.5em;
|
||||
padding: 0.5em;
|
||||
}
|
||||
|
||||
div.figure p.caption {
|
||||
padding: 0.3em;
|
||||
}
|
||||
|
||||
div.figure p.caption span.caption-number {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
div.figure p.caption span.caption-text {
|
||||
}
|
||||
|
||||
/* -- field list styles ----------------------------------------------------- */
|
||||
|
||||
table.field-list td, table.field-list th {
|
||||
border: 0 !important;
|
||||
}
|
||||
|
||||
.field-list ul {
|
||||
margin: 0;
|
||||
padding-left: 1em;
|
||||
}
|
||||
|
||||
.field-list p {
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
.field-name {
|
||||
-moz-hyphens: manual;
|
||||
-ms-hyphens: manual;
|
||||
-webkit-hyphens: manual;
|
||||
hyphens: manual;
|
||||
}
|
||||
|
||||
/* -- hlist styles ---------------------------------------------------------- */
|
||||
|
||||
table.hlist {
|
||||
margin: 1em 0;
|
||||
}
|
||||
|
||||
table.hlist td {
|
||||
vertical-align: top;
|
||||
}
|
||||
|
||||
|
||||
/* -- other body styles ----------------------------------------------------- */
|
||||
|
||||
ol.arabic {
|
||||
list-style: decimal;
|
||||
}
|
||||
|
||||
ol.loweralpha {
|
||||
list-style: lower-alpha;
|
||||
}
|
||||
|
||||
ol.upperalpha {
|
||||
list-style: upper-alpha;
|
||||
}
|
||||
|
||||
ol.lowerroman {
|
||||
list-style: lower-roman;
|
||||
}
|
||||
|
||||
ol.upperroman {
|
||||
list-style: upper-roman;
|
||||
}
|
||||
|
||||
:not(li) > ol > li:first-child > :first-child,
|
||||
:not(li) > ul > li:first-child > :first-child {
|
||||
margin-top: 0px;
|
||||
}
|
||||
|
||||
:not(li) > ol > li:last-child > :last-child,
|
||||
:not(li) > ul > li:last-child > :last-child {
|
||||
margin-bottom: 0px;
|
||||
}
|
||||
|
||||
ol.simple ol p,
|
||||
ol.simple ul p,
|
||||
ul.simple ol p,
|
||||
ul.simple ul p {
|
||||
margin-top: 0;
|
||||
}
|
||||
|
||||
ol.simple > li:not(:first-child) > p,
|
||||
ul.simple > li:not(:first-child) > p {
|
||||
margin-top: 0;
|
||||
}
|
||||
|
||||
ol.simple p,
|
||||
ul.simple p {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
dl.footnote > dt,
|
||||
dl.citation > dt {
|
||||
float: left;
|
||||
margin-right: 0.5em;
|
||||
}
|
||||
|
||||
dl.footnote > dd,
|
||||
dl.citation > dd {
|
||||
margin-bottom: 0em;
|
||||
}
|
||||
|
||||
dl.footnote > dd:after,
|
||||
dl.citation > dd:after {
|
||||
content: "";
|
||||
clear: both;
|
||||
}
|
||||
|
||||
dl.field-list {
|
||||
display: grid;
|
||||
grid-template-columns: fit-content(30%) auto;
|
||||
}
|
||||
|
||||
dl.field-list > dt {
|
||||
font-weight: bold;
|
||||
word-break: break-word;
|
||||
padding-left: 0.5em;
|
||||
padding-right: 5px;
|
||||
}
|
||||
|
||||
dl.field-list > dt:after {
|
||||
content: ":";
|
||||
}
|
||||
|
||||
dl.field-list > dd {
|
||||
padding-left: 0.5em;
|
||||
margin-top: 0em;
|
||||
margin-left: 0em;
|
||||
margin-bottom: 0em;
|
||||
}
|
||||
|
||||
dl {
|
||||
margin-bottom: 15px;
|
||||
}
|
||||
|
||||
dd > :first-child {
|
||||
margin-top: 0px;
|
||||
}
|
||||
|
||||
dd ul, dd table {
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
|
||||
dd {
|
||||
margin-top: 3px;
|
||||
margin-bottom: 10px;
|
||||
margin-left: 30px;
|
||||
}
|
||||
|
||||
dl > dd:last-child,
|
||||
dl > dd:last-child > :last-child {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
dt:target, span.highlighted {
|
||||
background-color: #fbe54e;
|
||||
}
|
||||
|
||||
rect.highlighted {
|
||||
fill: #fbe54e;
|
||||
}
|
||||
|
||||
dl.glossary dt {
|
||||
font-weight: bold;
|
||||
font-size: 1.1em;
|
||||
}
|
||||
|
||||
.optional {
|
||||
font-size: 1.3em;
|
||||
}
|
||||
|
||||
.sig-paren {
|
||||
font-size: larger;
|
||||
}
|
||||
|
||||
.versionmodified {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
.system-message {
|
||||
background-color: #fda;
|
||||
padding: 5px;
|
||||
border: 3px solid red;
|
||||
}
|
||||
|
||||
.footnote:target {
|
||||
background-color: #ffa;
|
||||
}
|
||||
|
||||
.line-block {
|
||||
display: block;
|
||||
margin-top: 1em;
|
||||
margin-bottom: 1em;
|
||||
}
|
||||
|
||||
.line-block .line-block {
|
||||
margin-top: 0;
|
||||
margin-bottom: 0;
|
||||
margin-left: 1.5em;
|
||||
}
|
||||
|
||||
.guilabel, .menuselection {
|
||||
font-family: sans-serif;
|
||||
}
|
||||
|
||||
.accelerator {
|
||||
text-decoration: underline;
|
||||
}
|
||||
|
||||
.classifier {
|
||||
font-style: oblique;
|
||||
}
|
||||
|
||||
.classifier:before {
|
||||
font-style: normal;
|
||||
margin: 0.5em;
|
||||
content: ":";
|
||||
}
|
||||
|
||||
abbr, acronym {
|
||||
border-bottom: dotted 1px;
|
||||
cursor: help;
|
||||
}
|
||||
|
||||
/* -- code displays --------------------------------------------------------- */
|
||||
|
||||
pre {
|
||||
overflow: auto;
|
||||
overflow-y: hidden; /* fixes display issues on Chrome browsers */
|
||||
}
|
||||
|
||||
pre, div[class*="highlight-"] {
|
||||
clear: both;
|
||||
}
|
||||
|
||||
span.pre {
|
||||
-moz-hyphens: none;
|
||||
-ms-hyphens: none;
|
||||
-webkit-hyphens: none;
|
||||
hyphens: none;
|
||||
}
|
||||
|
||||
div[class*="highlight-"] {
|
||||
margin: 1em 0;
|
||||
}
|
||||
|
||||
td.linenos pre {
|
||||
border: 0;
|
||||
background-color: transparent;
|
||||
color: #aaa;
|
||||
}
|
||||
|
||||
table.highlighttable {
|
||||
display: block;
|
||||
}
|
||||
|
||||
table.highlighttable tbody {
|
||||
display: block;
|
||||
}
|
||||
|
||||
table.highlighttable tr {
|
||||
display: flex;
|
||||
}
|
||||
|
||||
table.highlighttable td {
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
table.highlighttable td.linenos {
|
||||
padding-right: 0.5em;
|
||||
}
|
||||
|
||||
table.highlighttable td.code {
|
||||
flex: 1;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.highlight .hll {
|
||||
display: block;
|
||||
}
|
||||
|
||||
div.highlight pre,
|
||||
table.highlighttable pre {
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
div.code-block-caption + div {
|
||||
margin-top: 0;
|
||||
}
|
||||
|
||||
div.code-block-caption {
|
||||
margin-top: 1em;
|
||||
padding: 2px 5px;
|
||||
font-size: small;
|
||||
}
|
||||
|
||||
div.code-block-caption code {
|
||||
background-color: transparent;
|
||||
}
|
||||
|
||||
table.highlighttable td.linenos,
|
||||
div.doctest > div.highlight span.gp { /* gp: Generic.Prompt */
|
||||
user-select: none;
|
||||
}
|
||||
|
||||
div.code-block-caption span.caption-number {
|
||||
padding: 0.1em 0.3em;
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
div.code-block-caption span.caption-text {
|
||||
}
|
||||
|
||||
div.literal-block-wrapper {
|
||||
margin: 1em 0;
|
||||
}
|
||||
|
||||
code.descname {
|
||||
background-color: transparent;
|
||||
font-weight: bold;
|
||||
font-size: 1.2em;
|
||||
}
|
||||
|
||||
code.descclassname {
|
||||
background-color: transparent;
|
||||
}
|
||||
|
||||
code.xref, a code {
|
||||
background-color: transparent;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
|
||||
background-color: transparent;
|
||||
}
|
||||
|
||||
.viewcode-link {
|
||||
float: right;
|
||||
}
|
||||
|
||||
.viewcode-back {
|
||||
float: right;
|
||||
font-family: sans-serif;
|
||||
}
|
||||
|
||||
div.viewcode-block:target {
|
||||
margin: -1px -10px;
|
||||
padding: 0 10px;
|
||||
}
|
||||
|
||||
/* -- math display ---------------------------------------------------------- */
|
||||
|
||||
img.math {
|
||||
vertical-align: middle;
|
||||
}
|
||||
|
||||
div.body div.math p {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
span.eqno {
|
||||
float: right;
|
||||
}
|
||||
|
||||
span.eqno a.headerlink {
|
||||
position: absolute;
|
||||
z-index: 1;
|
||||
}
|
||||
|
||||
div.math:hover a.headerlink {
|
||||
visibility: visible;
|
||||
}
|
||||
|
||||
/* -- printout stylesheet --------------------------------------------------- */
|
||||
|
||||
@media print {
|
||||
div.document,
|
||||
div.documentwrapper,
|
||||
div.bodywrapper {
|
||||
margin: 0 !important;
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
div.sphinxsidebar,
|
||||
div.related,
|
||||
div.footer,
|
||||
#top-link {
|
||||
display: none;
|
||||
}
|
||||
}
|
||||
Vendored
-7
File diff suppressed because one or more lines are too long
@@ -1 +0,0 @@
|
||||
<svg aria-hidden="true" data-prefix="far" data-icon="copy" class="svg-inline--fa fa-copy fa-w-14" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><path fill="#777" d="M433.941 65.941l-51.882-51.882A48 48 0 0 0 348.118 0H176c-26.51 0-48 21.49-48 48v48H48c-26.51 0-48 21.49-48 48v320c0 26.51 21.49 48 48 48h224c26.51 0 48-21.49 48-48v-48h80c26.51 0 48-21.49 48-48V99.882a48 48 0 0 0-14.059-33.941zM266 464H54a6 6 0 0 1-6-6V150a6 6 0 0 1 6-6h74v224c0 26.51 21.49 48 48 48h96v42a6 6 0 0 1-6 6zm128-96H182a6 6 0 0 1-6-6V54a6 6 0 0 1 6-6h106v88c0 13.255 10.745 24 24 24h88v202a6 6 0 0 1-6 6zm6-256h-64V48h9.632c1.591 0 3.117.632 4.243 1.757l48.368 48.368a6 6 0 0 1 1.757 4.243V112z"></path></svg>
|
||||
|
Before Width: | Height: | Size: 711 B |
@@ -1,67 +0,0 @@
|
||||
/* Copy buttons */
|
||||
a.copybtn {
|
||||
position: absolute;
|
||||
top: .2em;
|
||||
right: .2em;
|
||||
width: 1em;
|
||||
height: 1em;
|
||||
opacity: .3;
|
||||
transition: opacity 0.5s;
|
||||
border: none;
|
||||
user-select: none;
|
||||
}
|
||||
|
||||
div.highlight {
|
||||
position: relative;
|
||||
}
|
||||
|
||||
a.copybtn > img {
|
||||
vertical-align: top;
|
||||
margin: 0;
|
||||
top: 0;
|
||||
left: 0;
|
||||
position: absolute;
|
||||
}
|
||||
|
||||
.highlight:hover .copybtn {
|
||||
opacity: 1;
|
||||
}
|
||||
|
||||
/**
|
||||
* A minimal CSS-only tooltip copied from:
|
||||
* https://codepen.io/mildrenben/pen/rVBrpK
|
||||
*
|
||||
* To use, write HTML like the following:
|
||||
*
|
||||
* <p class="o-tooltip--left" data-tooltip="Hey">Short</p>
|
||||
*/
|
||||
.o-tooltip--left {
|
||||
position: relative;
|
||||
}
|
||||
|
||||
.o-tooltip--left:after {
|
||||
opacity: 0;
|
||||
visibility: hidden;
|
||||
position: absolute;
|
||||
content: attr(data-tooltip);
|
||||
padding: 2px;
|
||||
top: 0;
|
||||
left: -.2em;
|
||||
background: grey;
|
||||
font-size: 1rem;
|
||||
color: white;
|
||||
white-space: nowrap;
|
||||
z-index: 2;
|
||||
border-radius: 2px;
|
||||
transform: translateX(-102%) translateY(0);
|
||||
transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
|
||||
}
|
||||
|
||||
.o-tooltip--left:hover:after {
|
||||
display: block;
|
||||
opacity: 1;
|
||||
visibility: visible;
|
||||
transform: translateX(-100%) translateY(0);
|
||||
transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
|
||||
transition-delay: .5s;
|
||||
}
|
||||
@@ -1,153 +0,0 @@
|
||||
// Localization support
|
||||
const messages = {
|
||||
'en': {
|
||||
'copy': 'Copy',
|
||||
'copy_to_clipboard': 'Copy to clipboard',
|
||||
'copy_success': 'Copied!',
|
||||
'copy_failure': 'Failed to copy',
|
||||
},
|
||||
'es' : {
|
||||
'copy': 'Copiar',
|
||||
'copy_to_clipboard': 'Copiar al portapapeles',
|
||||
'copy_success': '¡Copiado!',
|
||||
'copy_failure': 'Error al copiar',
|
||||
},
|
||||
'de' : {
|
||||
'copy': 'Kopieren',
|
||||
'copy_to_clipboard': 'In die Zwischenablage kopieren',
|
||||
'copy_success': 'Kopiert!',
|
||||
'copy_failure': 'Fehler beim Kopieren',
|
||||
}
|
||||
}
|
||||
|
||||
let locale = 'en'
|
||||
if( document.documentElement.lang !== undefined
|
||||
&& messages[document.documentElement.lang] !== undefined ) {
|
||||
locale = document.documentElement.lang
|
||||
}
|
||||
|
||||
/**
|
||||
* Set up copy/paste for code blocks
|
||||
*/
|
||||
|
||||
const runWhenDOMLoaded = cb => {
|
||||
if (document.readyState != 'loading') {
|
||||
cb()
|
||||
} else if (document.addEventListener) {
|
||||
document.addEventListener('DOMContentLoaded', cb)
|
||||
} else {
|
||||
document.attachEvent('onreadystatechange', function() {
|
||||
if (document.readyState == 'complete') cb()
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
const codeCellId = index => `codecell${index}`
|
||||
|
||||
// Clears selected text since ClipboardJS will select the text when copying
|
||||
const clearSelection = () => {
|
||||
if (window.getSelection) {
|
||||
window.getSelection().removeAllRanges()
|
||||
} else if (document.selection) {
|
||||
document.selection.empty()
|
||||
}
|
||||
}
|
||||
|
||||
// Changes tooltip text for two seconds, then changes it back
|
||||
const temporarilyChangeTooltip = (el, newText) => {
|
||||
const oldText = el.getAttribute('data-tooltip')
|
||||
el.setAttribute('data-tooltip', newText)
|
||||
setTimeout(() => el.setAttribute('data-tooltip', oldText), 2000)
|
||||
}
|
||||
|
||||
const addCopyButtonToCodeCells = () => {
|
||||
// If ClipboardJS hasn't loaded, wait a bit and try again. This
|
||||
// happens because we load ClipboardJS asynchronously.
|
||||
if (window.ClipboardJS === undefined) {
|
||||
setTimeout(addCopyButtonToCodeCells, 250)
|
||||
return
|
||||
}
|
||||
|
||||
// Add copybuttons to all of our code cells
|
||||
const codeCells = document.querySelectorAll('div.highlight pre')
|
||||
codeCells.forEach((codeCell, index) => {
|
||||
const id = codeCellId(index)
|
||||
codeCell.setAttribute('id', id)
|
||||
const pre_bg = getComputedStyle(codeCell).backgroundColor;
|
||||
|
||||
const clipboardButton = id =>
|
||||
`<a class="copybtn o-tooltip--left" style="background-color: ${pre_bg}" data-tooltip="${messages[locale]['copy']}" data-clipboard-target="#${id}">
|
||||
<img src="${DOCUMENTATION_OPTIONS.URL_ROOT}_static/copy-button.svg" alt="${messages[locale]['copy_to_clipboard']}">
|
||||
</a>`
|
||||
codeCell.insertAdjacentHTML('afterend', clipboardButton(id))
|
||||
})
|
||||
|
||||
function escapeRegExp(string) {
|
||||
return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
|
||||
}
|
||||
|
||||
// Callback when a copy button is clicked. Will be passed the node that was clicked
|
||||
// should then grab the text and replace pieces of text that shouldn't be used in output
|
||||
function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true) {
|
||||
|
||||
var regexp;
|
||||
var match;
|
||||
|
||||
// create regexp to capture prompt and remaining line
|
||||
if (isRegexp) {
|
||||
regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
|
||||
} else {
|
||||
regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
|
||||
}
|
||||
|
||||
const outputLines = [];
|
||||
var promptFound = false;
|
||||
for (const line of textContent.split('\n')) {
|
||||
match = line.match(regexp)
|
||||
if (match) {
|
||||
promptFound = true
|
||||
if (removePrompts) {
|
||||
outputLines.push(match[2])
|
||||
} else {
|
||||
outputLines.push(line)
|
||||
}
|
||||
} else {
|
||||
if (!onlyCopyPromptLines) {
|
||||
outputLines.push(line)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// If no lines with the prompt were found then just use original lines
|
||||
if (promptFound) {
|
||||
textContent = outputLines.join('\n');
|
||||
}
|
||||
|
||||
// Remove a trailing newline to avoid auto-running when pasting
|
||||
if (textContent.endsWith("\n")) {
|
||||
textContent = textContent.slice(0, -1)
|
||||
}
|
||||
return textContent
|
||||
}
|
||||
|
||||
|
||||
var copyTargetText = (trigger) => {
|
||||
var target = document.querySelector(trigger.attributes['data-clipboard-target'].value);
|
||||
return formatCopyText(target.innerText, '', false, true, true)
|
||||
}
|
||||
|
||||
// Initialize with a callback so we can modify the text before copy
|
||||
const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText})
|
||||
|
||||
// Update UI with error/success messages
|
||||
clipboard.on('success', event => {
|
||||
clearSelection()
|
||||
temporarilyChangeTooltip(event.trigger, messages[locale]['copy_success'])
|
||||
})
|
||||
|
||||
clipboard.on('error', event => {
|
||||
temporarilyChangeTooltip(event.trigger, messages[locale]['copy_failure'])
|
||||
})
|
||||
}
|
||||
|
||||
runWhenDOMLoaded(addCopyButtonToCodeCells)
|
||||
@@ -1,47 +0,0 @@
|
||||
function escapeRegExp(string) {
|
||||
return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
|
||||
}
|
||||
|
||||
// Callback when a copy button is clicked. Will be passed the node that was clicked
|
||||
// should then grab the text and replace pieces of text that shouldn't be used in output
|
||||
export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true) {
|
||||
|
||||
var regexp;
|
||||
var match;
|
||||
|
||||
// create regexp to capture prompt and remaining line
|
||||
if (isRegexp) {
|
||||
regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
|
||||
} else {
|
||||
regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
|
||||
}
|
||||
|
||||
const outputLines = [];
|
||||
var promptFound = false;
|
||||
for (const line of textContent.split('\n')) {
|
||||
match = line.match(regexp)
|
||||
if (match) {
|
||||
promptFound = true
|
||||
if (removePrompts) {
|
||||
outputLines.push(match[2])
|
||||
} else {
|
||||
outputLines.push(line)
|
||||
}
|
||||
} else {
|
||||
if (!onlyCopyPromptLines) {
|
||||
outputLines.push(line)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// If no lines with the prompt were found then just use original lines
|
||||
if (promptFound) {
|
||||
textContent = outputLines.join('\n');
|
||||
}
|
||||
|
||||
// Remove a trailing newline to avoid auto-running when pasting
|
||||
if (textContent.endsWith("\n")) {
|
||||
textContent = textContent.slice(0, -1)
|
||||
}
|
||||
return textContent
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
@@ -1,315 +0,0 @@
|
||||
/*
|
||||
* doctools.js
|
||||
* ~~~~~~~~~~~
|
||||
*
|
||||
* Sphinx JavaScript utilities for all documentation.
|
||||
*
|
||||
* :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
||||
/**
|
||||
* select a different prefix for underscore
|
||||
*/
|
||||
$u = _.noConflict();
|
||||
|
||||
/**
|
||||
* make the code below compatible with browsers without
|
||||
* an installed firebug like debugger
|
||||
if (!window.console || !console.firebug) {
|
||||
var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
|
||||
"dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
|
||||
"profile", "profileEnd"];
|
||||
window.console = {};
|
||||
for (var i = 0; i < names.length; ++i)
|
||||
window.console[names[i]] = function() {};
|
||||
}
|
||||
*/
|
||||
|
||||
/**
|
||||
* small helper function to urldecode strings
|
||||
*/
|
||||
jQuery.urldecode = function(x) {
|
||||
return decodeURIComponent(x).replace(/\+/g, ' ');
|
||||
};
|
||||
|
||||
/**
|
||||
* small helper function to urlencode strings
|
||||
*/
|
||||
jQuery.urlencode = encodeURIComponent;
|
||||
|
||||
/**
|
||||
* This function returns the parsed url parameters of the
|
||||
* current request. Multiple values per key are supported,
|
||||
* it will always return arrays of strings for the value parts.
|
||||
*/
|
||||
jQuery.getQueryParameters = function(s) {
|
||||
if (typeof s === 'undefined')
|
||||
s = document.location.search;
|
||||
var parts = s.substr(s.indexOf('?') + 1).split('&');
|
||||
var result = {};
|
||||
for (var i = 0; i < parts.length; i++) {
|
||||
var tmp = parts[i].split('=', 2);
|
||||
var key = jQuery.urldecode(tmp[0]);
|
||||
var value = jQuery.urldecode(tmp[1]);
|
||||
if (key in result)
|
||||
result[key].push(value);
|
||||
else
|
||||
result[key] = [value];
|
||||
}
|
||||
return result;
|
||||
};
|
||||
|
||||
/**
|
||||
* highlight a given string on a jquery object by wrapping it in
|
||||
* span elements with the given class name.
|
||||
*/
|
||||
jQuery.fn.highlightText = function(text, className) {
|
||||
function highlight(node, addItems) {
|
||||
if (node.nodeType === 3) {
|
||||
var val = node.nodeValue;
|
||||
var pos = val.toLowerCase().indexOf(text);
|
||||
if (pos >= 0 &&
|
||||
!jQuery(node.parentNode).hasClass(className) &&
|
||||
!jQuery(node.parentNode).hasClass("nohighlight")) {
|
||||
var span;
|
||||
var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
|
||||
if (isInSVG) {
|
||||
span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
|
||||
} else {
|
||||
span = document.createElement("span");
|
||||
span.className = className;
|
||||
}
|
||||
span.appendChild(document.createTextNode(val.substr(pos, text.length)));
|
||||
node.parentNode.insertBefore(span, node.parentNode.insertBefore(
|
||||
document.createTextNode(val.substr(pos + text.length)),
|
||||
node.nextSibling));
|
||||
node.nodeValue = val.substr(0, pos);
|
||||
if (isInSVG) {
|
||||
var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
|
||||
var bbox = node.parentElement.getBBox();
|
||||
rect.x.baseVal.value = bbox.x;
|
||||
rect.y.baseVal.value = bbox.y;
|
||||
rect.width.baseVal.value = bbox.width;
|
||||
rect.height.baseVal.value = bbox.height;
|
||||
rect.setAttribute('class', className);
|
||||
addItems.push({
|
||||
"parent": node.parentNode,
|
||||
"target": rect});
|
||||
}
|
||||
}
|
||||
}
|
||||
else if (!jQuery(node).is("button, select, textarea")) {
|
||||
jQuery.each(node.childNodes, function() {
|
||||
highlight(this, addItems);
|
||||
});
|
||||
}
|
||||
}
|
||||
var addItems = [];
|
||||
var result = this.each(function() {
|
||||
highlight(this, addItems);
|
||||
});
|
||||
for (var i = 0; i < addItems.length; ++i) {
|
||||
jQuery(addItems[i].parent).before(addItems[i].target);
|
||||
}
|
||||
return result;
|
||||
};
|
||||
|
||||
/*
|
||||
* backward compatibility for jQuery.browser
|
||||
* This will be supported until firefox bug is fixed.
|
||||
*/
|
||||
if (!jQuery.browser) {
|
||||
jQuery.uaMatch = function(ua) {
|
||||
ua = ua.toLowerCase();
|
||||
|
||||
var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
|
||||
/(webkit)[ \/]([\w.]+)/.exec(ua) ||
|
||||
/(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
|
||||
/(msie) ([\w.]+)/.exec(ua) ||
|
||||
ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
|
||||
[];
|
||||
|
||||
return {
|
||||
browser: match[ 1 ] || "",
|
||||
version: match[ 2 ] || "0"
|
||||
};
|
||||
};
|
||||
jQuery.browser = {};
|
||||
jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
|
||||
}
|
||||
|
||||
/**
|
||||
* Small JavaScript module for the documentation.
|
||||
*/
|
||||
var Documentation = {
|
||||
|
||||
init : function() {
|
||||
this.fixFirefoxAnchorBug();
|
||||
this.highlightSearchWords();
|
||||
this.initIndexTable();
|
||||
if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) {
|
||||
this.initOnKeyListeners();
|
||||
}
|
||||
},
|
||||
|
||||
/**
|
||||
* i18n support
|
||||
*/
|
||||
TRANSLATIONS : {},
|
||||
PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; },
|
||||
LOCALE : 'unknown',
|
||||
|
||||
// gettext and ngettext don't access this so that the functions
|
||||
// can safely bound to a different name (_ = Documentation.gettext)
|
||||
gettext : function(string) {
|
||||
var translated = Documentation.TRANSLATIONS[string];
|
||||
if (typeof translated === 'undefined')
|
||||
return string;
|
||||
return (typeof translated === 'string') ? translated : translated[0];
|
||||
},
|
||||
|
||||
ngettext : function(singular, plural, n) {
|
||||
var translated = Documentation.TRANSLATIONS[singular];
|
||||
if (typeof translated === 'undefined')
|
||||
return (n == 1) ? singular : plural;
|
||||
return translated[Documentation.PLURALEXPR(n)];
|
||||
},
|
||||
|
||||
addTranslations : function(catalog) {
|
||||
for (var key in catalog.messages)
|
||||
this.TRANSLATIONS[key] = catalog.messages[key];
|
||||
this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
|
||||
this.LOCALE = catalog.locale;
|
||||
},
|
||||
|
||||
/**
|
||||
* add context elements like header anchor links
|
||||
*/
|
||||
addContextElements : function() {
|
||||
$('div[id] > :header:first').each(function() {
|
||||
$('<a class="headerlink">\u00B6</a>').
|
||||
attr('href', '#' + this.id).
|
||||
attr('title', _('Permalink to this headline')).
|
||||
appendTo(this);
|
||||
});
|
||||
$('dt[id]').each(function() {
|
||||
$('<a class="headerlink">\u00B6</a>').
|
||||
attr('href', '#' + this.id).
|
||||
attr('title', _('Permalink to this definition')).
|
||||
appendTo(this);
|
||||
});
|
||||
},
|
||||
|
||||
/**
|
||||
* workaround a firefox stupidity
|
||||
* see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
|
||||
*/
|
||||
fixFirefoxAnchorBug : function() {
|
||||
if (document.location.hash && $.browser.mozilla)
|
||||
window.setTimeout(function() {
|
||||
document.location.href += '';
|
||||
}, 10);
|
||||
},
|
||||
|
||||
/**
|
||||
* highlight the search words provided in the url in the text
|
||||
*/
|
||||
highlightSearchWords : function() {
|
||||
var params = $.getQueryParameters();
|
||||
var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
|
||||
if (terms.length) {
|
||||
var body = $('div.body');
|
||||
if (!body.length) {
|
||||
body = $('body');
|
||||
}
|
||||
window.setTimeout(function() {
|
||||
$.each(terms, function() {
|
||||
body.highlightText(this.toLowerCase(), 'highlighted');
|
||||
});
|
||||
}, 10);
|
||||
$('<p class="highlight-link"><a href="javascript:Documentation.' +
|
||||
'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
|
||||
.appendTo($('#searchbox'));
|
||||
}
|
||||
},
|
||||
|
||||
/**
|
||||
* init the domain index toggle buttons
|
||||
*/
|
||||
initIndexTable : function() {
|
||||
var togglers = $('img.toggler').click(function() {
|
||||
var src = $(this).attr('src');
|
||||
var idnum = $(this).attr('id').substr(7);
|
||||
$('tr.cg-' + idnum).toggle();
|
||||
if (src.substr(-9) === 'minus.png')
|
||||
$(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
|
||||
else
|
||||
$(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
|
||||
}).css('display', '');
|
||||
if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
|
||||
togglers.click();
|
||||
}
|
||||
},
|
||||
|
||||
/**
|
||||
* helper function to hide the search marks again
|
||||
*/
|
||||
hideSearchWords : function() {
|
||||
$('#searchbox .highlight-link').fadeOut(300);
|
||||
$('span.highlighted').removeClass('highlighted');
|
||||
},
|
||||
|
||||
/**
|
||||
* make the url absolute
|
||||
*/
|
||||
makeURL : function(relativeURL) {
|
||||
return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
|
||||
},
|
||||
|
||||
/**
|
||||
* get the current relative url
|
||||
*/
|
||||
getCurrentURL : function() {
|
||||
var path = document.location.pathname;
|
||||
var parts = path.split(/\//);
|
||||
$.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
|
||||
if (this === '..')
|
||||
parts.pop();
|
||||
});
|
||||
var url = parts.join('/');
|
||||
return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
|
||||
},
|
||||
|
||||
initOnKeyListeners: function() {
|
||||
$(document).keydown(function(event) {
|
||||
var activeElementType = document.activeElement.tagName;
|
||||
// don't navigate when in search box or textarea
|
||||
if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
|
||||
&& !event.altKey && !event.ctrlKey && !event.metaKey && !event.shiftKey) {
|
||||
switch (event.keyCode) {
|
||||
case 37: // left
|
||||
var prevHref = $('link[rel="prev"]').prop('href');
|
||||
if (prevHref) {
|
||||
window.location.href = prevHref;
|
||||
return false;
|
||||
}
|
||||
case 39: // right
|
||||
var nextHref = $('link[rel="next"]').prop('href');
|
||||
if (nextHref) {
|
||||
window.location.href = nextHref;
|
||||
return false;
|
||||
}
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
};
|
||||
|
||||
// quick alias for translations
|
||||
_ = Documentation.gettext;
|
||||
|
||||
$(document).ready(function() {
|
||||
Documentation.init();
|
||||
});
|
||||
@@ -1,12 +0,0 @@
|
||||
var DOCUMENTATION_OPTIONS = {
|
||||
URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
|
||||
VERSION: '',
|
||||
LANGUAGE: 'None',
|
||||
COLLAPSE_INDEX: false,
|
||||
BUILDER: 'html',
|
||||
FILE_SUFFIX: '.html',
|
||||
LINK_SUFFIX: '.html',
|
||||
HAS_SOURCE: true,
|
||||
SOURCELINK_SUFFIX: '',
|
||||
NAVIGATION_WITH_KEYS: true
|
||||
};
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 286 B |
@@ -1,19 +0,0 @@
|
||||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<!-- Generator: Adobe Illustrator 23.0.1, SVG Export Plug-In . SVG Version: 6.00 Build 0) -->
|
||||
<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
|
||||
viewBox="0 0 44.4 44.4" style="enable-background:new 0 0 44.4 44.4;" xml:space="preserve">
|
||||
<style type="text/css">
|
||||
.st0{fill:none;stroke:#F5A252;stroke-width:5;stroke-miterlimit:10;}
|
||||
.st1{fill:none;stroke:#579ACA;stroke-width:5;stroke-miterlimit:10;}
|
||||
.st2{fill:none;stroke:#E66581;stroke-width:5;stroke-miterlimit:10;}
|
||||
</style>
|
||||
<title>logo</title>
|
||||
<g>
|
||||
<path class="st0" d="M33.9,6.4c3.6,3.9,3.4,9.9-0.5,13.5s-9.9,3.4-13.5-0.5s-3.4-9.9,0.5-13.5l0,0C24.2,2.4,30.2,2.6,33.9,6.4z"/>
|
||||
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|
||||
C26.6,21.2,32.4,22.7,35.1,27.3z"/>
|
||||
<path class="st2" d="M25.9,17.8c2.6,4.6,1.1,10.4-3.5,13s-10.4,1.1-13-3.5s-1.1-10.4,3.5-13l0,0C17.5,11.7,23.3,13.2,25.9,17.8z"/>
|
||||
<path class="st1" d="M19.2,26.4c3.1-4.3,9.1-5.2,13.3-2.1c1.1,0.8,2,1.8,2.7,3"/>
|
||||
<path class="st0" d="M19.9,19.4c-3.6-3.9-3.4-9.9,0.5-13.5s9.9-3.4,13.5,0.5"/>
|
||||
</g>
|
||||
</svg>
|
||||
|
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|
||||
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|
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|
Before Width: | Height: | Size: 1.7 KiB |
Vendored
-10872
File diff suppressed because it is too large
Load Diff
Vendored
-2
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@@ -1,297 +0,0 @@
|
||||
/*
|
||||
* language_data.js
|
||||
* ~~~~~~~~~~~~~~~~
|
||||
*
|
||||
* This script contains the language-specific data used by searchtools.js,
|
||||
* namely the list of stopwords, stemmer, scorer and splitter.
|
||||
*
|
||||
* :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
||||
var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
|
||||
|
||||
|
||||
/* Non-minified version JS is _stemmer.js if file is provided */
|
||||
/**
|
||||
* Porter Stemmer
|
||||
*/
|
||||
var Stemmer = function() {
|
||||
|
||||
var step2list = {
|
||||
ational: 'ate',
|
||||
tional: 'tion',
|
||||
enci: 'ence',
|
||||
anci: 'ance',
|
||||
izer: 'ize',
|
||||
bli: 'ble',
|
||||
alli: 'al',
|
||||
entli: 'ent',
|
||||
eli: 'e',
|
||||
ousli: 'ous',
|
||||
ization: 'ize',
|
||||
ation: 'ate',
|
||||
ator: 'ate',
|
||||
alism: 'al',
|
||||
iveness: 'ive',
|
||||
fulness: 'ful',
|
||||
ousness: 'ous',
|
||||
aliti: 'al',
|
||||
iviti: 'ive',
|
||||
biliti: 'ble',
|
||||
logi: 'log'
|
||||
};
|
||||
|
||||
var step3list = {
|
||||
icate: 'ic',
|
||||
ative: '',
|
||||
alize: 'al',
|
||||
iciti: 'ic',
|
||||
ical: 'ic',
|
||||
ful: '',
|
||||
ness: ''
|
||||
};
|
||||
|
||||
var c = "[^aeiou]"; // consonant
|
||||
var v = "[aeiouy]"; // vowel
|
||||
var C = c + "[^aeiouy]*"; // consonant sequence
|
||||
var V = v + "[aeiou]*"; // vowel sequence
|
||||
|
||||
var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0
|
||||
var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
|
||||
var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
|
||||
var s_v = "^(" + C + ")?" + v; // vowel in stem
|
||||
|
||||
this.stemWord = function (w) {
|
||||
var stem;
|
||||
var suffix;
|
||||
var firstch;
|
||||
var origword = w;
|
||||
|
||||
if (w.length < 3)
|
||||
return w;
|
||||
|
||||
var re;
|
||||
var re2;
|
||||
var re3;
|
||||
var re4;
|
||||
|
||||
firstch = w.substr(0,1);
|
||||
if (firstch == "y")
|
||||
w = firstch.toUpperCase() + w.substr(1);
|
||||
|
||||
// Step 1a
|
||||
re = /^(.+?)(ss|i)es$/;
|
||||
re2 = /^(.+?)([^s])s$/;
|
||||
|
||||
if (re.test(w))
|
||||
w = w.replace(re,"$1$2");
|
||||
else if (re2.test(w))
|
||||
w = w.replace(re2,"$1$2");
|
||||
|
||||
// Step 1b
|
||||
re = /^(.+?)eed$/;
|
||||
re2 = /^(.+?)(ed|ing)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
re = new RegExp(mgr0);
|
||||
if (re.test(fp[1])) {
|
||||
re = /.$/;
|
||||
w = w.replace(re,"");
|
||||
}
|
||||
}
|
||||
else if (re2.test(w)) {
|
||||
var fp = re2.exec(w);
|
||||
stem = fp[1];
|
||||
re2 = new RegExp(s_v);
|
||||
if (re2.test(stem)) {
|
||||
w = stem;
|
||||
re2 = /(at|bl|iz)$/;
|
||||
re3 = new RegExp("([^aeiouylsz])\\1$");
|
||||
re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
|
||||
if (re2.test(w))
|
||||
w = w + "e";
|
||||
else if (re3.test(w)) {
|
||||
re = /.$/;
|
||||
w = w.replace(re,"");
|
||||
}
|
||||
else if (re4.test(w))
|
||||
w = w + "e";
|
||||
}
|
||||
}
|
||||
|
||||
// Step 1c
|
||||
re = /^(.+?)y$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
re = new RegExp(s_v);
|
||||
if (re.test(stem))
|
||||
w = stem + "i";
|
||||
}
|
||||
|
||||
// Step 2
|
||||
re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
suffix = fp[2];
|
||||
re = new RegExp(mgr0);
|
||||
if (re.test(stem))
|
||||
w = stem + step2list[suffix];
|
||||
}
|
||||
|
||||
// Step 3
|
||||
re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
suffix = fp[2];
|
||||
re = new RegExp(mgr0);
|
||||
if (re.test(stem))
|
||||
w = stem + step3list[suffix];
|
||||
}
|
||||
|
||||
// Step 4
|
||||
re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
|
||||
re2 = /^(.+?)(s|t)(ion)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
re = new RegExp(mgr1);
|
||||
if (re.test(stem))
|
||||
w = stem;
|
||||
}
|
||||
else if (re2.test(w)) {
|
||||
var fp = re2.exec(w);
|
||||
stem = fp[1] + fp[2];
|
||||
re2 = new RegExp(mgr1);
|
||||
if (re2.test(stem))
|
||||
w = stem;
|
||||
}
|
||||
|
||||
// Step 5
|
||||
re = /^(.+?)e$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
re = new RegExp(mgr1);
|
||||
re2 = new RegExp(meq1);
|
||||
re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
|
||||
if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
|
||||
w = stem;
|
||||
}
|
||||
re = /ll$/;
|
||||
re2 = new RegExp(mgr1);
|
||||
if (re.test(w) && re2.test(w)) {
|
||||
re = /.$/;
|
||||
w = w.replace(re,"");
|
||||
}
|
||||
|
||||
// and turn initial Y back to y
|
||||
if (firstch == "y")
|
||||
w = firstch.toLowerCase() + w.substr(1);
|
||||
return w;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
var splitChars = (function() {
|
||||
var result = {};
|
||||
var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
|
||||
1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
|
||||
2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
|
||||
2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
|
||||
3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
|
||||
3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
|
||||
4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
|
||||
8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
|
||||
11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
|
||||
43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
|
||||
var i, j, start, end;
|
||||
for (i = 0; i < singles.length; i++) {
|
||||
result[singles[i]] = true;
|
||||
}
|
||||
var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
|
||||
[722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
|
||||
[1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
|
||||
[1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
|
||||
[1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
|
||||
[2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
|
||||
[2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
|
||||
[2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
|
||||
[2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
|
||||
[2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
|
||||
[2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
|
||||
[2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
|
||||
[3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
|
||||
[3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
|
||||
[3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
|
||||
[3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
|
||||
[3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
|
||||
[3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
|
||||
[4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
|
||||
[4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
|
||||
[4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
|
||||
[4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
|
||||
[5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
|
||||
[6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
|
||||
[6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
|
||||
[6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
|
||||
[6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
|
||||
[7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
|
||||
[7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
|
||||
[8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
|
||||
[8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
|
||||
[8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
|
||||
[10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
|
||||
[11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
|
||||
[12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
|
||||
[12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
|
||||
[12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
|
||||
[19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
|
||||
[42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
|
||||
[42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
|
||||
[43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
|
||||
[43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
|
||||
[43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
|
||||
[43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
|
||||
[44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
|
||||
[57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
|
||||
[64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
|
||||
[65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
|
||||
[65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
|
||||
for (i = 0; i < ranges.length; i++) {
|
||||
start = ranges[i][0];
|
||||
end = ranges[i][1];
|
||||
for (j = start; j <= end; j++) {
|
||||
result[j] = true;
|
||||
}
|
||||
}
|
||||
return result;
|
||||
})();
|
||||
|
||||
function splitQuery(query) {
|
||||
var result = [];
|
||||
var start = -1;
|
||||
for (var i = 0; i < query.length; i++) {
|
||||
if (splitChars[query.charCodeAt(i)]) {
|
||||
if (start !== -1) {
|
||||
result.push(query.slice(start, i));
|
||||
start = -1;
|
||||
}
|
||||
} else if (start === -1) {
|
||||
start = i;
|
||||
}
|
||||
}
|
||||
if (start !== -1) {
|
||||
result.push(query.slice(start));
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 90 B |
@@ -1,184 +0,0 @@
|
||||
/* Whole cell */
|
||||
div.container.cell {
|
||||
padding-left: 0;
|
||||
margin-bottom: 1em;
|
||||
}
|
||||
|
||||
/* Removing all background formatting so we can control at the div level */
|
||||
.cell_input div.highlight, .cell_input pre, .cell_output .output * {
|
||||
border: none;
|
||||
background-color: transparent;
|
||||
box-shadow: none;
|
||||
}
|
||||
|
||||
.cell_output .output pre, .cell_input pre {
|
||||
margin: 0px;
|
||||
}
|
||||
|
||||
/* Input cells */
|
||||
div.cell div.cell_input {
|
||||
padding-left: 0em;
|
||||
padding-right: 0em;
|
||||
border: 1px #ccc solid;
|
||||
background-color: #f7f7f7;
|
||||
border-left-color: green;
|
||||
border-left-width: medium;
|
||||
}
|
||||
|
||||
div.cell_input > div, div.cell_output div.output > div.highlight {
|
||||
margin: 0em !important;
|
||||
border: none !important;
|
||||
}
|
||||
|
||||
/* All cell outputs */
|
||||
.cell_output {
|
||||
padding-left: 1em;
|
||||
padding-right: 0em;
|
||||
margin-top: 1em;
|
||||
}
|
||||
|
||||
/* Outputs from jupyter_sphinx overrides to remove extra CSS */
|
||||
div.section div.jupyter_container {
|
||||
padding: .4em;
|
||||
margin: 0 0 .4em 0;
|
||||
background-color: none;
|
||||
border: none;
|
||||
-moz-box-shadow: none;
|
||||
-webkit-box-shadow: none;
|
||||
box-shadow: none;
|
||||
}
|
||||
|
||||
/* Text outputs from cells */
|
||||
.cell_output .output.text_plain,
|
||||
.cell_output .output.traceback,
|
||||
.cell_output .output.stream,
|
||||
.cell_output .output.stderr
|
||||
{
|
||||
background: #fcfcfc;
|
||||
margin-top: 1em;
|
||||
margin-bottom: 0em;
|
||||
box-shadow: none;
|
||||
}
|
||||
|
||||
.cell_output .output.text_plain,
|
||||
.cell_output .output.stream,
|
||||
.cell_output .output.stderr {
|
||||
border: 1px solid #f7f7f7;
|
||||
}
|
||||
|
||||
.cell_output .output.stderr {
|
||||
background: #fdd;
|
||||
}
|
||||
|
||||
.cell_output .output.traceback {
|
||||
border: 1px solid #ffd6d6;
|
||||
}
|
||||
|
||||
/* Math align to the left */
|
||||
.cell_output .MathJax_Display {
|
||||
text-align: left !important;
|
||||
}
|
||||
|
||||
/* Pandas tables. Pulled from the Jupyter / nbsphinx CSS */
|
||||
div.cell_output table {
|
||||
border: none;
|
||||
border-collapse: collapse;
|
||||
border-spacing: 0;
|
||||
color: black;
|
||||
font-size: 1em;
|
||||
table-layout: fixed;
|
||||
}
|
||||
div.cell_output thead {
|
||||
border-bottom: 1px solid black;
|
||||
vertical-align: bottom;
|
||||
}
|
||||
div.cell_output tr,
|
||||
div.cell_output th,
|
||||
div.cell_output td {
|
||||
text-align: right;
|
||||
vertical-align: middle;
|
||||
padding: 0.5em 0.5em;
|
||||
line-height: normal;
|
||||
white-space: normal;
|
||||
max-width: none;
|
||||
border: none;
|
||||
}
|
||||
div.cell_output th {
|
||||
font-weight: bold;
|
||||
}
|
||||
div.cell_output tbody tr:nth-child(odd) {
|
||||
background: #f5f5f5;
|
||||
}
|
||||
div.cell_output tbody tr:hover {
|
||||
background: rgba(66, 165, 245, 0.2);
|
||||
}
|
||||
|
||||
|
||||
/* Inline text from `paste` operation */
|
||||
|
||||
span.pasted-text {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
span.pasted-inline img {
|
||||
max-height: 2em;
|
||||
}
|
||||
|
||||
tbody span.pasted-inline img {
|
||||
max-height: none;
|
||||
}
|
||||
|
||||
/* Font colors for translated ANSI escape sequences
|
||||
Color values are adapted from share/jupyter/nbconvert/templates/classic/static/style.css
|
||||
*/
|
||||
div.highlight .-Color-Bold {
|
||||
font-weight: bold;
|
||||
}
|
||||
div.highlight .-Color[class*=-Black] {
|
||||
color :#3E424D
|
||||
}
|
||||
div.highlight .-Color[class*=-Red] {
|
||||
color: #E75C58
|
||||
}
|
||||
div.highlight .-Color[class*=-Green] {
|
||||
color: #00A250
|
||||
}
|
||||
div.highlight .-Color[class*=-Yellow] {
|
||||
color: yellow
|
||||
}
|
||||
div.highlight .-Color[class*=-Blue] {
|
||||
color: #208FFB
|
||||
}
|
||||
div.highlight .-Color[class*=-Magenta] {
|
||||
color: #D160C4
|
||||
}
|
||||
div.highlight .-Color[class*=-Cyan] {
|
||||
color: #60C6C8
|
||||
}
|
||||
div.highlight .-Color[class*=-White] {
|
||||
color: #C5C1B4
|
||||
}
|
||||
div.highlight .-Color[class*=-BGBlack] {
|
||||
background-color: #3E424D
|
||||
}
|
||||
div.highlight .-Color[class*=-BGRed] {
|
||||
background-color: #E75C58
|
||||
}
|
||||
div.highlight .-Color[class*=-BGGreen] {
|
||||
background-color: #00A250
|
||||
}
|
||||
div.highlight .-Color[class*=-BGYellow] {
|
||||
background-color: yellow
|
||||
}
|
||||
div.highlight .-Color[class*=-BGBlue] {
|
||||
background-color: #208FFB
|
||||
}
|
||||
div.highlight .-Color[class*=-BGMagenta] {
|
||||
background-color: #D160C4
|
||||
}
|
||||
div.highlight .-Color[class*=-BGCyan] {
|
||||
background-color: #60C6C8
|
||||
}
|
||||
div.highlight .-Color[class*=-BGWhite] {
|
||||
background-color: #C5C1B4
|
||||
}
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user