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Merge pull request #49 from phobson/cleanup-tutorial-figs
Cleanup tutorial figs
2 parents 18e59b3 + 316dce9 commit 5ebf75c

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+75
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‎docs/index.rst

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mpl-probscale: Real probability scales for matplotlib
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=====================================================
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.. image:: https://travis-ci.org/phobson/watershed.svg?branch=master
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:target: https://travis-ci.org/phobson/watershed
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.. image:: https://travis-ci.org/matplotlib/mpl-probscale.svg?branch=master
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:target: https://travis-ci.org/matplotlib/mpl-probscale
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.. image:: https://coveralls.io/repos/phobson/mpl-probscale/badge.svg?branch=master&service=github
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:target: https://coveralls.io/github/phobson/mpl-probscale?branch=master
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.. image:: https://coveralls.io/repos/matplotlib/mpl-probscale/badge.svg?branch=master&service=github
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:target: https://coveralls.io/github/matplotlib/mpl-probscale?branch=master
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https://github.com/phobson/mpl-probscale
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https://github.com/matplotlib/mpl-probscale
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Installation
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------------
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Development builds are available through my conda channel:
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``conda install mpl-probscale --channel=phobson``
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``conda install mpl-probscale --channel=conda-forge``
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Quickstart

‎docs/installation.rst

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.. code-block:: console
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$ git clone git://github.com/phobson/probscale
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$ git clone git://github.com/matplotlib/mpl-probscale
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Or download the `tarball`_:
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.. code-block:: console
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$ curl -OL https://github.com/phobson/probscale/tarball/master
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$ curl -OL https://github.com/matplotlib/mpl-probscale/tarball/master
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Once you have a copy of the source, you can install it with:
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$ pip install .
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.. _Github repo: https://github.com/phobson/probscale
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.. _tarball: https://github.com/phobson/probscale/tarball/master
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.. _Github repo: https://github.com/matplotlib/mpl-probscale
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.. _tarball: https://github.com/matplotlib/mpl-probscale/tarball/master

‎docs/tutorial/closer_look_at_plot_pos.ipynb

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},
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"outputs": [],
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"source": [
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"w_opts = {'label': 'Weibull (a=0, b=0)', 'marker': 'o', 'markeredgecolor': 'b'}\n",
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"c_opts = {'label': 'Cunnane (a=0.4, b=0.4)', 'marker': 's', 'markeredgecolor': 'g'}\n",
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"w_opts = {'label': 'Weibull (α=0, β=0)', 'marker': 'o', 'markeredgecolor': 'b'}\n",
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"c_opts = {'label': 'Cunnane (α=0.4, β=0.4)', 'marker': 's', 'markeredgecolor': 'g'}\n",
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"\n",
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"common_opts = {\n",
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" 'markerfacecolor': 'none',\n",
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},
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"outputs": [],
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"source": [
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"h_opts = {'label': 'Hazen (a=0.5, b=0.5)', 'marker': '^', 'markeredgecolor': 'r'}\n",
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"h_opts = {'label': 'Hazen (α=0.5, β=0.5)', 'marker': '^', 'markeredgecolor': 'r'}\n",
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"fig, (ax1, ax2) = pyplot.subplots(figsize=(10, 8), ncols=2, sharex=True, sharey=False)\n",
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"\n",
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"for dist, ax in zip([None, weibull], [ax1, ax2]):\n",
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "probscale",
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"display_name": "Python [default]",
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"language": "python",
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"name": "probscale"
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {

‎docs/tutorial/closer_look_at_viz.ipynb

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"ax.set_xlabel('Percentile')\n",
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"ax.set_ylabel('Total Bill (USD)')\n",
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"ax.set_yscale('log')\n",
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"ax.legend(loc='upper left')\n",
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"ax.set_ylim(bottom=1, top=100)\n",
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"seaborn.despine()"
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]
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},
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"fig = probscale.probplot(tips['total_bill'], ax=ax, plottype='pp', datascale='log',\n",
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" problabel='Percentile', datalabel='Total Bill (USD)',\n",
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" scatter_kws=dict(marker='.', linestyle='none', label='Bill Amount'))\n",
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"ax.set_ylim(bottom=1, top=100)\n",
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"seaborn.despine()"
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]
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},
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"ax.set_xlabel('Normal Quantiles')\n",
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"ax.set_ylabel('Total Bill (USD)')\n",
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"ax.set_yscale('log')\n",
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"ax.legend(loc='upper left')\n",
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"ax.set_ylim(bottom=1, top=100)\n",
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"seaborn.despine()"
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]
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},
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"source": [
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"fig, ax = pyplot.subplots(figsize=(6, 3))\n",
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"fig = probscale.probplot(tips['total_bill'], ax=ax, plottype='qq', datascale='log', \n",
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" problabel='Normal Quantiles', datalabel='Total Bill (USD)',\n",
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" problabel='Standard Normal Quantiles', datalabel='Total Bill (USD)',\n",
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" scatter_kws=dict(marker='.', linestyle='none', label='Bill Amount'))\n",
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"ax.legend(loc='upper left')\n",
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"\n",
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"ax.set_ylim(bottom=1, top=100)\n",
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"seaborn.despine()"
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]
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},
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"outputs": [],
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"source": [
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"fig, (ax1, ax2) = pyplot.subplots(figsize=(6, 6), ncols=2, sharex=True)\n",
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"markers = dict(marker='.', linestyle='none', label='Bill Amount')\n",
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"\n",
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"fig = probscale.probplot(tips['total_bill'], ax=ax1, plottype='pp', probax='y',\n",
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" datascale='log', problabel='Percentile', datalabel='Total Bill (USD)',\n",
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" scatter_kws=dict(marker='.', linestyle='none', label='Bill Amount'))\n",
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" datascale='log', problabel='Percentiles',\n",
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" datalabel='Total Bill (USD)', scatter_kws=markers)\n",
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"\n",
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"fig = probscale.probplot(tips['total_bill'], ax=ax2, plottype='qq', probax='y',\n",
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" datascale='log', problabel='Normal Quantiles', datalabel='Total Bill (USD)',\n",
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" scatter_kws=dict(marker='.', linestyle='none', label='Bill Amount'))\n",
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"ax.legend(loc='upper left')\n",
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" datascale='log', problabel='Standard Normal Quantiles',\n",
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" datalabel='Total Bill (USD)', scatter_kws=markers)\n",
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"\n",
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"ax1.set_xlim(left=1, right=100)\n",
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"fig.tight_layout()\n",
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"seaborn.despine()"
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]
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" scatter_kws=dict(marker='.', linestyle='none')\n",
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")\n",
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"\n",
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"fig = probscale.probplot(tips['total_bill'], ax=ax1, \n",
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" plottype='pp',\n",
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" problabel='percentile', \n",
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" **common_opts)\n",
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"fig = probscale.probplot(tips['total_bill'], ax=ax1, plottype='pp',\n",
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" problabel='Percentiles', **common_opts)\n",
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"\n",
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"fig = probscale.probplot(tips['total_bill'], ax=ax2, \n",
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" plottype='qq',\n",
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" problabel='Normal Quantiles', \n",
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" **common_opts)\n",
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"fig = probscale.probplot(tips['total_bill'], ax=ax2, plottype='qq',\n",
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" problabel='Standard Normal Quantiles', **common_opts)\n",
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"\n",
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"fig = probscale.probplot(tips['total_bill'], ax=ax3, \n",
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" plottype='prob',\n",
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" problabel='Normal Probabilities', \n",
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" **common_opts)\n",
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"fig = probscale.probplot(tips['total_bill'], ax=ax3, plottype='prob',\n",
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" problabel='Standard Normal Probabilities', **common_opts)\n",
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"\n",
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"ax3.set_xlim(left=1, right=100)\n",
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"ax3.set_ylim(bottom=0.13, top=99.87)\n",
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"fig.tight_layout()\n",
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"seaborn.despine()"
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"alpha = stats.alpha(10)\n",
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"beta = stats.beta(6, 3)\n",
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"\n",
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"fig, (ax1, ax2, ax3) = pyplot.subplots(figsize=(9, 6), ncols=3, sharey=True)\n",
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"fig, (ax1, ax2, ax3) = pyplot.subplots(figsize=(9, 6), ncols=3, sharex=True)\n",
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"fig = probscale.probplot(tips['total_bill'], ax=ax1, dist=alpha, \n",
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" problabel='Alpha Probabilities', **common_opts)\n",
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" problabel='Alpha(10) Probabilities', **common_opts)\n",
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"\n",
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"fig = probscale.probplot(tips['total_bill'], ax=ax2, dist=beta, \n",
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" problabel='Beta Probabilities', **common_opts)\n",
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" problabel='Beta(6, 1) Probabilities', **common_opts)\n",
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"\n",
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"fig = probscale.probplot(tips['total_bill'], ax=ax3, dist=None,\n",
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" problabel='Normal Probabilities', **common_opts)\n",
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" problabel='Standard Normal Probabilities', **common_opts)\n",
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"\n",
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"ax3.set_ylim(bottom=0.2, top=99.8)\n",
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"ax3.set_xlim(left=1, right=100)\n",
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"for ax in [ax1, ax2, ax3]:\n",
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" ax.set_ylim(bottom=0.2, top=99.8)\n",
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"seaborn.despine()\n",
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"fig.tight_layout()"
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]
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"alpha = stats.alpha(10)\n",
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"beta = stats.beta(6, 3)\n",
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"\n",
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"fig, (ax1, ax2, ax3) = pyplot.subplots(figsize=(9, 6), ncols=3)\n",
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"fig, (ax1, ax2, ax3) = pyplot.subplots(figsize=(9, 6), ncols=3, sharex=True)\n",
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"fig = probscale.probplot(tips['total_bill'], ax=ax1, dist=alpha, \n",
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" problabel='Alpha Quantiles', **common_opts)\n",
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" problabel='Alpha(10) Quantiles', **common_opts)\n",
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"\n",
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"fig = probscale.probplot(tips['total_bill'], ax=ax2, dist=beta, \n",
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" problabel='Beta Quantiles', **common_opts)\n",
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" problabel='Beta(6, 3) Quantiles', **common_opts)\n",
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"\n",
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"fig = probscale.probplot(tips['total_bill'], ax=ax3, dist=None,\n",
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" problabel='Normal Quantiles', **common_opts)\n",
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" problabel='Standard Normal Quantiles', **common_opts)\n",
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"\n",
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"ax.legend(loc='upper left')\n",
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"ax.set_ylim(bottom=0.1, top=99.9)\n",
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"ax1.set_xlim(left=1, right=100)\n",
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"seaborn.despine()\n",
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"fig.tight_layout()"
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" line_kws=dict(label='Best-fit line'))\n",
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"ax.legend(loc='upper left')\n",
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"ax.set_ylim(bottom=0.1, top=99.9)\n",
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"ax.set_xlim(left=1, right=100)\n",
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"seaborn.despine()"
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" scatter_kws={'label': 'Observations'},\n",
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" problabel='Probability (%)')\n",
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"ax.legend(loc='lower right')\n",
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"ax.set_ylim(bottom=-2, top=4)\n",
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"seaborn.despine(fig)"
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"numpy.random.seed(0)\n",
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"x = numpy.random.normal(size=15)\n",
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"\n",
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"fig, (ax1, ax2, ax3) = pyplot.subplots(figsize=(6, 6), nrows=3, sharex=True)\n",
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"fig, (ax1, ax2, ax3) = pyplot.subplots(figsize=(6, 6), nrows=3,\n",
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" sharey=True, sharex=True)\n",
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"fig = probscale.probplot(x, ax=ax1, problabel='Cunnuane (default) plotting positions',\n",
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" **common_opts)\n",
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"\n",
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"outputs": [],
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"fg = (\n",
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" seaborn.FacetGrid(data=tips, hue='sex', row='smoker', col='time', aspect=1.5, margin_titles=True)\n",
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" seaborn.FacetGrid(data=tips, hue='sex', row='smoker', col='time', margin_titles=True, size=4)\n",
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" .map(probscale.probplot, 'total_bill', probax='y', bestfit=True)\n",
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" .set_ylabels('Probability')\n",
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" .add_legend()\n",
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "probscale",
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"display_name": "Python [default]",
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"language": "python",
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"name": "probscale"
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"name": "python3"
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"language_info": {
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"codemirror_mode": {

‎docs/tutorial/getting_started.ipynb

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"\n",
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"## Installation\n",
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"`mpl-probscale` is developed on Python 3.5. It is also tested on Python 3.4 and even 2.7 (for the time being).\n",
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"`mpl-probscale` is developed on Python 3.6. It is also tested on Python 3.4, 3.5, and even 2.7 (for the time being).\n",
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"\n",
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"### From conda\n",
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"Official releases of `mpl-probscale` can be found on conda-forge:\n",
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"\n",
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"Fairly recent builds of the development verions are available on my channel:\n",
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"`conda install --channel=phobson mpl-probscale`\n",
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"`conda install --channel=conda-forge mpl-probscale`\n",
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"\n",
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"\n",
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"### From PyPI\n",
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"\n",
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"### From source\n",
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"`mpl-probscale` is a pure python package. It should be fairly trivial to install from source on any platform. To do that, download or clone from [github](https://github.com/phobson/mpl-probscale), unzip the archive if necessary then do:\n",
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"`mpl-probscale` is a pure python package. It should be fairly trivial to install from source on any platform. To do that, download or clone from [github](https://github.com/matplotlib/mpl-probscale), unzip the archive if necessary then do:\n",
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"```\n",
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"cd mpl-probscale # or wherever the setup.py got placed\n",
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"\n",
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"beta1 = stats.beta(a=3, b=2)\n",
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"ax2.set_xscale('prob', dist=beta1)\n",
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"ax2.set_xlabel('Beta probability scale (a=3, b=2)')\n",
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"ax2.set_xlabel('Beta probability scale (α=3, β=2)')\n",
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"\n",
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"beta2 = stats.beta(a=2, b=7)\n",
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"ax3.set_xscale('prob', dist=beta2)\n",
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"ax3.set_xlabel('Beta probability scale (a=2, b=7)')\n",
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"ax3.set_xlabel('Beta probability scale (α=2, β=7)')\n",
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"\n",
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"ax4.set_xticks(ax1.get_xticks()[12:-12])\n",
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"ax4.set_xlabel('Linear scale (for reference)')\n",
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"plot = (\n",
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" seaborn.load_dataset(\"tips\")\n",
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" .assign(pct=lambda df: 100 * df['tip'] / df['total_bill'])\n",
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" .pipe(seaborn.FacetGrid, hue='sex', col='time', row='smoker', margin_titles=True, aspect=1.25, size=4)\n",
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" .pipe(seaborn.FacetGrid, hue='sex', col='time', row='smoker', margin_titles=True, aspect=1., size=4)\n",
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" .map(probscale.probplot, 'pct', bestfit=True, scatter_kws=dict(alpha=0.75), probax='y')\n",
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" .add_legend()\n",
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" .set_ylabels('Non-Exceedance Probabilty')\n",
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" .set_xlabels('Tips as percent of total bill')\n",
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" .set(ylim=(0.5, 99.5))\n",
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" .set(ylim=(0.5, 99.5), xlim=(0, 100))\n",
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")"
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]
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}
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],
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "probscale",
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"display_name": "Python [default]",
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"language": "python",
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"name": "probscale"
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {

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