Merge pull request #49 from phobson/cleanup-tutorial-figs

Cleanup tutorial figs
matplotlib · Feb 9, 2017 · 5ebf75c · 5ebf75c
2 parents 18e59b3 + 316dce9
commit 5ebf75c
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diff --git a/docs/index.rst b/docs/index.rst
@@ -7,13 +7,13 @@
 mpl-probscale: Real probability scales for matplotlib
 =====================================================
 
-.. image:: https://travis-ci.org/phobson/watershed.svg?branch=master
-    :target: https://travis-ci.org/phobson/watershed
+.. image:: https://travis-ci.org/matplotlib/mpl-probscale.svg?branch=master
+    :target: https://travis-ci.org/matplotlib/mpl-probscale
 
-.. image:: https://coveralls.io/repos/phobson/mpl-probscale/badge.svg?branch=master&service=github
-  :target: https://coveralls.io/github/phobson/mpl-probscale?branch=master
+.. image:: https://coveralls.io/repos/matplotlib/mpl-probscale/badge.svg?branch=master&service=github
+  :target: https://coveralls.io/github/matplotlib/mpl-probscale?branch=master
 
-https://github.com/phobson/mpl-probscale
+https://github.com/matplotlib/mpl-probscale
 
 Installation
 ------------
@@ -34,7 +34,7 @@ Development builds
 
 Development builds are available through my conda channel:
 
-``conda install mpl-probscale --channel=phobson``
+``conda install mpl-probscale --channel=conda-forge``
 
 
 Quickstart

diff --git a/docs/installation.rst b/docs/installation.rst
@@ -32,13 +32,13 @@ You can either clone the public repository:
 
 .. code-block:: console
 
-    $ git clone git://github.com/phobson/probscale
+    $ git clone git://github.com/matplotlib/mpl-probscale
 
 Or download the `tarball`_:
 
 .. code-block:: console
 
-    $ curl  -OL https://github.com/phobson/probscale/tarball/master
+    $ curl  -OL https://github.com/matplotlib/mpl-probscale/tarball/master
 
 Once you have a copy of the source, you can install it with:
 
@@ -47,5 +47,5 @@ Once you have a copy of the source, you can install it with:
     $ pip install .
 
 
-.. _Github repo: https://github.com/phobson/probscale
-.. _tarball: https://github.com/phobson/probscale/tarball/master
+.. _Github repo: https://github.com/matplotlib/mpl-probscale
+.. _tarball: https://github.com/matplotlib/mpl-probscale/tarball/master
diff --git a/docs/tutorial/closer_look_at_plot_pos.ipynb b/docs/tutorial/closer_look_at_plot_pos.ipynb
@@ -154,8 +154,8 @@
    },
    "outputs": [],
    "source": [
-    "w_opts = {'label': 'Weibull (a=0, b=0)',     'marker': 'o', 'markeredgecolor': 'b'}\n",
-    "c_opts = {'label': 'Cunnane (a=0.4, b=0.4)', 'marker': 's', 'markeredgecolor': 'g'}\n",
+    "w_opts = {'label': 'Weibull (α=0, β=0)',     'marker': 'o', 'markeredgecolor': 'b'}\n",
+    "c_opts = {'label': 'Cunnane (α=0.4, β=0.4)', 'marker': 's', 'markeredgecolor': 'g'}\n",
     "\n",
     "common_opts = {\n",
     "    'markerfacecolor': 'none',\n",
@@ -198,7 +198,7 @@
    },
    "outputs": [],
    "source": [
-    "h_opts = {'label': 'Hazen (a=0.5, b=0.5)', 'marker': '^', 'markeredgecolor': 'r'}\n",
+    "h_opts = {'label': 'Hazen (α=0.5, β=0.5)', 'marker': '^', 'markeredgecolor': 'r'}\n",
     "fig, (ax1, ax2) = pyplot.subplots(figsize=(10, 8), ncols=2, sharex=True, sharey=False)\n",
     "\n",
     "for dist, ax in zip([None, weibull], [ax1, ax2]):\n",
@@ -280,9 +280,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "probscale",
+   "display_name": "Python [default]",
    "language": "python",
-   "name": "probscale"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {

diff --git a/docs/tutorial/closer_look_at_viz.ipynb b/docs/tutorial/closer_look_at_viz.ipynb
@@ -90,7 +90,7 @@
     "ax.set_xlabel('Percentile')\n",
     "ax.set_ylabel('Total Bill (USD)')\n",
     "ax.set_yscale('log')\n",
-    "ax.legend(loc='upper left')\n",
+    "ax.set_ylim(bottom=1, top=100)\n",
     "seaborn.despine()"
    ]
   },
@@ -113,6 +113,7 @@
     "fig = probscale.probplot(tips['total_bill'], ax=ax, plottype='pp', datascale='log',\n",
     "                         problabel='Percentile', datalabel='Total Bill (USD)',\n",
     "                         scatter_kws=dict(marker='.', linestyle='none', label='Bill Amount'))\n",
+    "ax.set_ylim(bottom=1, top=100)\n",
     "seaborn.despine()"
    ]
   },
@@ -148,7 +149,7 @@
     "ax.set_xlabel('Normal Quantiles')\n",
     "ax.set_ylabel('Total Bill (USD)')\n",
     "ax.set_yscale('log')\n",
-    "ax.legend(loc='upper left')\n",
+    "ax.set_ylim(bottom=1, top=100)\n",
     "seaborn.despine()"
    ]
   },
@@ -169,9 +170,10 @@
    "source": [
     "fig, ax = pyplot.subplots(figsize=(6, 3))\n",
     "fig = probscale.probplot(tips['total_bill'], ax=ax, plottype='qq', datascale='log', \n",
-    "                         problabel='Normal Quantiles', datalabel='Total Bill (USD)',\n",
+    "                         problabel='Standard Normal Quantiles', datalabel='Total Bill (USD)',\n",
     "                         scatter_kws=dict(marker='.', linestyle='none', label='Bill Amount'))\n",
-    "ax.legend(loc='upper left')\n",
+    "\n",
+    "ax.set_ylim(bottom=1, top=100)\n",
     "seaborn.despine()"
    ]
   },
@@ -194,15 +196,17 @@
    "outputs": [],
    "source": [
     "fig, (ax1, ax2) = pyplot.subplots(figsize=(6, 6), ncols=2, sharex=True)\n",
+    "markers = dict(marker='.', linestyle='none', label='Bill Amount')\n",
     "\n",
     "fig = probscale.probplot(tips['total_bill'], ax=ax1, plottype='pp', probax='y',\n",
-    "                         datascale='log', problabel='Percentile', datalabel='Total Bill (USD)',\n",
-    "                         scatter_kws=dict(marker='.', linestyle='none', label='Bill Amount'))\n",
+    "                         datascale='log', problabel='Percentiles',\n",
+    "                         datalabel='Total Bill (USD)', scatter_kws=markers)\n",
     "\n",
     "fig = probscale.probplot(tips['total_bill'], ax=ax2, plottype='qq', probax='y',\n",
-    "                         datascale='log', problabel='Normal Quantiles', datalabel='Total Bill (USD)',\n",
-    "                         scatter_kws=dict(marker='.', linestyle='none', label='Bill Amount'))\n",
-    "ax.legend(loc='upper left')\n",
+    "                         datascale='log', problabel='Standard Normal Quantiles',\n",
+    "                         datalabel='Total Bill (USD)', scatter_kws=markers)\n",
+    "\n",
+    "ax1.set_xlim(left=1, right=100)\n",
     "fig.tight_layout()\n",
     "seaborn.despine()"
    ]
@@ -239,20 +243,16 @@
     "    scatter_kws=dict(marker='.', linestyle='none')\n",
     ")\n",
     "\n",
-    "fig = probscale.probplot(tips['total_bill'], ax=ax1, \n",
-    "                         plottype='pp',\n",
-    "                         problabel='percentile', \n",
-    "                         **common_opts)\n",
+    "fig = probscale.probplot(tips['total_bill'], ax=ax1, plottype='pp',\n",
+    "                         problabel='Percentiles',  **common_opts)\n",
     "\n",
-    "fig = probscale.probplot(tips['total_bill'], ax=ax2, \n",
-    "                         plottype='qq',\n",
-    "                         problabel='Normal Quantiles', \n",
-    "                         **common_opts)\n",
+    "fig = probscale.probplot(tips['total_bill'], ax=ax2, plottype='qq',\n",
+    "                         problabel='Standard Normal Quantiles',  **common_opts)\n",
     "\n",
-    "fig = probscale.probplot(tips['total_bill'], ax=ax3, \n",
-    "                         plottype='prob',\n",
-    "                         problabel='Normal Probabilities', \n",
-    "                         **common_opts)\n",
+    "fig = probscale.probplot(tips['total_bill'], ax=ax3, plottype='prob',\n",
+    "                         problabel='Standard Normal Probabilities',  **common_opts)\n",
+    "\n",
+    "ax3.set_xlim(left=1, right=100)\n",
     "ax3.set_ylim(bottom=0.13, top=99.87)\n",
     "fig.tight_layout()\n",
     "seaborn.despine()"
@@ -291,17 +291,19 @@
     "alpha = stats.alpha(10)\n",
     "beta = stats.beta(6, 3)\n",
     "\n",
-    "fig, (ax1, ax2, ax3) = pyplot.subplots(figsize=(9, 6), ncols=3, sharey=True)\n",
+    "fig, (ax1, ax2, ax3) = pyplot.subplots(figsize=(9, 6), ncols=3, sharex=True)\n",
     "fig = probscale.probplot(tips['total_bill'], ax=ax1, dist=alpha, \n",
-    "                         problabel='Alpha Probabilities', **common_opts)\n",
+    "                         problabel='Alpha(10) Probabilities', **common_opts)\n",
     "\n",
     "fig = probscale.probplot(tips['total_bill'], ax=ax2, dist=beta, \n",
-    "                         problabel='Beta Probabilities', **common_opts)\n",
+    "                         problabel='Beta(6, 1) Probabilities', **common_opts)\n",
     "\n",
     "fig = probscale.probplot(tips['total_bill'], ax=ax3, dist=None,\n",
-    "                         problabel='Normal Probabilities', **common_opts)\n",
+    "                         problabel='Standard Normal Probabilities', **common_opts)\n",
     "\n",
-    "ax3.set_ylim(bottom=0.2, top=99.8)\n",
+    "ax3.set_xlim(left=1, right=100)\n",
+    "for ax in [ax1, ax2, ax3]:\n",
+    "    ax.set_ylim(bottom=0.2, top=99.8)\n",
     "seaborn.despine()\n",
     "fig.tight_layout()"
    ]
@@ -332,18 +334,17 @@
     "alpha = stats.alpha(10)\n",
     "beta = stats.beta(6, 3)\n",
     "\n",
-    "fig, (ax1, ax2, ax3) = pyplot.subplots(figsize=(9, 6), ncols=3)\n",
+    "fig, (ax1, ax2, ax3) = pyplot.subplots(figsize=(9, 6), ncols=3, sharex=True)\n",
     "fig = probscale.probplot(tips['total_bill'], ax=ax1, dist=alpha, \n",
-    "                         problabel='Alpha Quantiles', **common_opts)\n",
+    "                         problabel='Alpha(10) Quantiles', **common_opts)\n",
     "\n",
     "fig = probscale.probplot(tips['total_bill'], ax=ax2, dist=beta, \n",
-    "                         problabel='Beta Quantiles', **common_opts)\n",
+    "                         problabel='Beta(6, 3) Quantiles', **common_opts)\n",
     "\n",
     "fig = probscale.probplot(tips['total_bill'], ax=ax3, dist=None,\n",
-    "                         problabel='Normal Quantiles', **common_opts)\n",
+    "                         problabel='Standard Normal Quantiles', **common_opts)\n",
     "\n",
-    "ax.legend(loc='upper left')\n",
-    "ax.set_ylim(bottom=0.1, top=99.9)\n",
+    "ax1.set_xlim(left=1, right=100)\n",
     "seaborn.despine()\n",
     "fig.tight_layout()"
    ]
@@ -494,6 +495,7 @@
     "                         line_kws=dict(label='Best-fit line'))\n",
     "ax.legend(loc='upper left')\n",
     "ax.set_ylim(bottom=0.1, top=99.9)\n",
+    "ax.set_xlim(left=1, right=100)\n",
     "seaborn.despine()"
    ]
   },
@@ -524,6 +526,7 @@
     "                         scatter_kws={'label': 'Observations'},\n",
     "                         problabel='Probability (%)')\n",
     "ax.legend(loc='lower right')\n",
+    "ax.set_ylim(bottom=-2, top=4)\n",
     "seaborn.despine(fig)"
    ]
   },
@@ -566,7 +569,8 @@
     "numpy.random.seed(0)\n",
     "x = numpy.random.normal(size=15)\n",
     "\n",
-    "fig, (ax1, ax2, ax3) = pyplot.subplots(figsize=(6, 6), nrows=3, sharex=True)\n",
+    "fig, (ax1, ax2, ax3) = pyplot.subplots(figsize=(6, 6), nrows=3,\n",
+    "                                       sharey=True, sharex=True)\n",
     "fig = probscale.probplot(x, ax=ax1, problabel='Cunnuane (default) plotting positions',\n",
     "                         **common_opts)\n",
     "\n",
@@ -697,7 +701,7 @@
    "outputs": [],
    "source": [
     "fg = (\n",
-    "    seaborn.FacetGrid(data=tips, hue='sex', row='smoker', col='time', aspect=1.5, margin_titles=True)\n",
+    "    seaborn.FacetGrid(data=tips, hue='sex', row='smoker', col='time', margin_titles=True, size=4)\n",
     "        .map(probscale.probplot, 'total_bill', probax='y', bestfit=True)\n",
     "        .set_ylabels('Probability')\n",
     "        .add_legend()\n",
@@ -708,9 +712,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "probscale",
+   "display_name": "Python [default]",
    "language": "python",
-   "name": "probscale"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {

diff --git a/docs/tutorial/getting_started.ipynb b/docs/tutorial/getting_started.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "## Installation\n",
     "\n",
-    "`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",
+    "`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",
     "\n",
     "### From conda\n",
     "Official releases of `mpl-probscale` can be found on conda-forge:\n",
@@ -17,7 +17,7 @@
     "\n",
     "Fairly recent builds of the development verions are available on my channel:\n",
     "\n",
-    "`conda install --channel=phobson mpl-probscale`\n",
+    "`conda install --channel=conda-forge mpl-probscale`\n",
     "\n",
     "\n",
     "### From PyPI\n",
@@ -27,7 +27,7 @@
     "\n",
     "### From source\n",
     "\n",
-    "`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",
+    "`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",
     "\n",
     "```\n",
     "cd mpl-probscale # or wherever the setup.py got placed\n",
@@ -208,11 +208,11 @@
     "\n",
     "beta1 = stats.beta(a=3, b=2)\n",
     "ax2.set_xscale('prob', dist=beta1)\n",
-    "ax2.set_xlabel('Beta probability scale (a=3, b=2)')\n",
+    "ax2.set_xlabel('Beta probability scale (α=3, β=2)')\n",
     "\n",
     "beta2 = stats.beta(a=2, b=7)\n",
     "ax3.set_xscale('prob', dist=beta2)\n",
-    "ax3.set_xlabel('Beta probability scale (a=2, b=7)')\n",
+    "ax3.set_xlabel('Beta probability scale (α=2, β=7)')\n",
     "\n",
     "ax4.set_xticks(ax1.get_xticks()[12:-12])\n",
     "ax4.set_xlabel('Linear scale (for reference)')\n",
@@ -366,22 +366,22 @@
     "plot = (\n",
     "    seaborn.load_dataset(\"tips\")\n",
     "        .assign(pct=lambda df: 100 * df['tip'] / df['total_bill'])\n",
-    "        .pipe(seaborn.FacetGrid, hue='sex', col='time', row='smoker', margin_titles=True, aspect=1.25, size=4)\n",
+    "        .pipe(seaborn.FacetGrid, hue='sex', col='time', row='smoker', margin_titles=True, aspect=1., size=4)\n",
     "        .map(probscale.probplot, 'pct', bestfit=True, scatter_kws=dict(alpha=0.75), probax='y')\n",
     "        .add_legend()\n",
     "        .set_ylabels('Non-Exceedance Probabilty')\n",
     "        .set_xlabels('Tips as percent of total bill')\n",
-    "        .set(ylim=(0.5, 99.5))\n",
+    "        .set(ylim=(0.5, 99.5), xlim=(0, 100))\n",
     ")"
    ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "probscale",
+   "display_name": "Python [default]",
    "language": "python",
-   "name": "probscale"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {