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- Switches to furo theme (#47) - Updates examples to new style (#51, #67) - Adds new examples (#54, #55, #58) - Marks the documentation unstable (#72) Signed-off-by: John Pennycook <[email protected]>
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_downloads/07075ea048bb69f84e56a96405892fba/plot_customized_navchart.ipynb
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| @@ -0,0 +1,43 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "\n# Customized Navigation Chart\n\nA customized navigation chart.\n\nIn this example, we show how to customize a navigation chart by increasing the\nnumber of axis ticks and by annotating one of the datapoints. Adjusting the\nnumber of axis ticks can improve a reader's ability to discern between two\nsimilar values, while annotations can be useful to draw attention to certain\npoints and/or provide some additional context.\n\nInstead of trying to expose all possible customization options as arguments to\n:py:func:`p3analysis.plot.navchart`, the function returns a\n:py:class:`p3analysis.plot.NavChart` object that provides direct access to library\ninternals. When using the :py:mod:`matplotlib` backend it is possible to\naccess the :py:class:`matplotlib.axes.Axes` that were used and subsequently\ncall any number of :py:mod:`matplotlib` functions. In our example, we can\nuse :py:meth:`matplotlib.axes.Axes.set_xticks` and\n:py:meth:`matplotlib.axes.Axes.set_yticks` to control the ticks, and can use\n:py:meth:`matplotlib.axes.Axes.annotate` for annotations.\n\n.. NOTE::\n :py:mod:`matplotlib` is currently the only backend supported by the P3\n Analysis Library, but this is subject to change.\n\n.. TIP::\n If you have any trouble customizing a navigation chart, or the\n :py:class:`~p3analysis.plot.backend.NavChart` object does not provide access to the\n internals you are looking for, then please [open an issue](https://github.com/intel/p3-analysis-library/issues/new/choose).\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "collapsed": false | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Initialize synthetic data\n# (not shown, but available in script download)\n\n# Read performance portability and code divergence data into pandas DataFrame\npp = pd.DataFrame(pp_data)\ncd = pd.DataFrame(cd_data)\n\n# Generate a navigation chart with custom style options\nlegend = p3analysis.plot.Legend(loc=\"center left\", bbox_to_anchor=(1.0, 0.5))\nastyle = p3analysis.plot.ApplicationStyle(markers=[\"x\", \"*\", \"s\", \"o\", \"P\"])\nnavchart = p3analysis.plot.navchart(pp, cd, size=(5, 5), legend=legend, style=astyle)\n\n# Further customize the navigation chart using matplotlib\n# In this example, we add a label and adjust the ticks\nax = navchart.get_axes()\nax.annotate(\n \"Balances performance and code re-use.\",\n xy=(0.7, 0.7),\n xytext=(0.2, 0.55),\n arrowprops=dict(facecolor='black', shrink=0.05),\n)\nax.set_xticks([x * 0.1 for x in range(0, 11)])\nax.set_yticks([y * 0.1 for y in range(0, 11)])\n\nnavchart.save(\"customized-navchart.png\")" | ||
| ] | ||
| } | ||
| ], | ||
| "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": "3.12.3" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 0 | ||
| } |
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_downloads/0c8f924ed3a2f732c6a73301834a9f3a/case-studies_python.zip
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_downloads/32adcc08533a44cfc53b5a4b6e78c2d3/plot_customized_navchart.py
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|---|---|---|
| @@ -0,0 +1,83 @@ | ||
| #!/usr/bin/env python3 | ||
| # Copyright (c) 2023 Intel Corporation | ||
| # SPDX-License-Identifier: 0BSD | ||
| """ | ||
| Customized Navigation Chart | ||
| =========================== | ||
| A customized navigation chart. | ||
| In this example, we show how to customize a navigation chart by increasing the | ||
| number of axis ticks and by annotating one of the datapoints. Adjusting the | ||
| number of axis ticks can improve a reader's ability to discern between two | ||
| similar values, while annotations can be useful to draw attention to certain | ||
| points and/or provide some additional context. | ||
| Instead of trying to expose all possible customization options as arguments to | ||
| :py:func:`p3analysis.plot.navchart`, the function returns a | ||
| :py:class:`p3analysis.plot.NavChart` object that provides direct access to library | ||
| internals. When using the :py:mod:`matplotlib` backend it is possible to | ||
| access the :py:class:`matplotlib.axes.Axes` that were used and subsequently | ||
| call any number of :py:mod:`matplotlib` functions. In our example, we can | ||
| use :py:meth:`matplotlib.axes.Axes.set_xticks` and | ||
| :py:meth:`matplotlib.axes.Axes.set_yticks` to control the ticks, and can use | ||
| :py:meth:`matplotlib.axes.Axes.annotate` for annotations. | ||
| .. NOTE:: | ||
| :py:mod:`matplotlib` is currently the only backend supported by the P3 | ||
| Analysis Library, but this is subject to change. | ||
| .. TIP:: | ||
| If you have any trouble customizing a navigation chart, or the | ||
| :py:class:`~p3analysis.plot.backend.NavChart` object does not provide access to the | ||
| internals you are looking for, then please `open an issue | ||
| <https://github.com/intel/p3-analysis-library/issues/new/choose>`_. | ||
| """ | ||
|
|
||
| # Initialize synthetic data | ||
| # (not shown, but available in script download) | ||
| # sphinx_gallery_start_ignore | ||
| from collections import defaultdict | ||
|
|
||
| import matplotlib.pyplot as plt | ||
| import pandas as pd | ||
|
|
||
| import p3analysis | ||
|
|
||
| pp_data = defaultdict(list) | ||
| cd_data = defaultdict(list) | ||
| for data in [pp_data, cd_data]: | ||
| data["problem"] += ["Synthetic"] * 5 | ||
| data["application"] += [ | ||
| "Unportable", | ||
| "Ideal", | ||
| "Per-Platform Source", | ||
| "Portability Framework", | ||
| "Specialized", | ||
| ] | ||
| pp_data["app pp"] += [0, 1, 1, 0.5, 0.7] | ||
| cd_data["divergence"] += [1, 0, 1, 0, 0.3] | ||
| # sphinx_gallery_end_ignore | ||
|
|
||
| # Read performance portability and code divergence data into pandas DataFrame | ||
| pp = pd.DataFrame(pp_data) | ||
| cd = pd.DataFrame(cd_data) | ||
|
|
||
| # Generate a navigation chart with custom style options | ||
| legend = p3analysis.plot.Legend(loc="center left", bbox_to_anchor=(1.0, 0.5)) | ||
| astyle = p3analysis.plot.ApplicationStyle(markers=["x", "*", "s", "o", "P"]) | ||
| navchart = p3analysis.plot.navchart(pp, cd, size=(5, 5), legend=legend, style=astyle) | ||
|
|
||
| # Further customize the navigation chart using matplotlib | ||
| # In this example, we add a label and adjust the ticks | ||
| ax = navchart.get_axes() | ||
| ax.annotate( | ||
| "Balances performance and code re-use.", | ||
| xy=(0.7, 0.7), | ||
| xytext=(0.2, 0.55), | ||
| arrowprops=dict(facecolor='black', shrink=0.05), | ||
| ) | ||
| ax.set_xticks([x * 0.1 for x in range(0, 11)]) | ||
| ax.set_yticks([y * 0.1 for y in range(0, 11)]) | ||
|
|
||
| navchart.save("customized-navchart.png") |
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_downloads/36eb5c73460de2b4f5af873867d4d962/plot_customized_cascade.ipynb
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,43 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "\n# Customized Cascade\n\nA customized cascade plot.\n\nIn this example, we show how to customize a cascade plot by changing the limits\nof the y-axis. Although the default limit (of 1) is useful for comparing many\nplots side-by-side, in practice it is often useful to be able to zoom-in on\nspecific regions of data. For example, when dealing with applications that do\nnot achieve very high levels of architectural efficiency, setting a lower\nmaximum value for the y-axis can improve readability.\n\nInstead of trying to expose all possible customization options as arguments to\n:py:func:`p3analysis.plot.cascade`, the function returns a\n:py:class:`p3analysis.plot.CascadePlot` object that provides direct access to library\ninternals. When using the :py:mod:`matplotlib` backend it is possible to\naccess the :py:class:`matplotlib.axes.Axes` that were used and subsequently\ncall any number of :py:mod:`matplotlib` functions. In our example, we can\nuse :py:meth:`matplotlib.axes.Axes.set_ylim` to update the y-axis.\n\n.. NOTE::\n :py:mod:`matplotlib` is currently the only backend supported by the P3\n Analysis Library, but this is subject to change.\n\n.. TIP::\n If you have any trouble customizing a plot, or the\n :py:class:`~p3analysis.plot.backend.CascadePlot` object does not provide access to\n the internals you are looking for, then please [open an issue](https://github.com/intel/p3-analysis-library/issues/new/choose).\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "collapsed": false | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Initialize synthetic performance efficiency data\n# (not shown, but available in script download)\n\n# Read performance efficiency data into pandas DataFrame\ndf = pd.DataFrame(data)\n\n# Generate a cascade plot with custom style options\nlegend = p3analysis.plot.Legend(loc=\"center left\", bbox_to_anchor=(0.91, 0.225), ncols=2)\npstyle = p3analysis.plot.PlatformStyle(colors=\"GnBu\")\nastyle = p3analysis.plot.ApplicationStyle(markers=[\"x\", \"s\", \"p\", \"h\", \"H\", \"v\"])\ncascade = p3analysis.plot.cascade(df, size=(6, 5), platform_legend=legend, platform_style=pstyle, application_style=astyle)\n\n# Further customize the cascade plot using matplotlib\n# In this example, we adjust the range of the y-axis to improve readability\n# This may be necessary for studies using architectural efficiency\ncascade.get_axes(\"eff\").set_ylim([0, 0.12])\ncascade.get_axes(\"pp\").set_ylim([0, 0.12])\n\ncascade.save(\"customized-cascade.png\")" | ||
| ] | ||
| } | ||
| ], | ||
| "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": "3.12.3" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 0 | ||
| } |
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