rename axtype -> plottype & otherscale -> datascale

Author: phobson

docs/tutorial/closer_look_at_viz.ipynb

@@ -94,7 +94,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using the `probplot` function with `axtype='pp'`, it becomes:"
+    "Using the `probplot` function with `plottype='pp'`, it becomes:"
    ]
   },
   {
@@ -106,8 +106,8 @@
    "outputs": [],
    "source": [
     "fig, ax = pyplot.subplots(figsize=(6, 3))\n",
-    "fig = probscale.probplot(tips['total_bill'], ax=ax, axtype='pp',\n",
-    "                         otherscale='log', label='Bill Amount', \n",
+    "fig = probscale.probplot(tips['total_bill'], ax=ax, plottype='pp',\n",
+    "                         datascale='log', label='Bill Amount', \n",
     "                         xlabel='Percentile', ylabel='Total Bill (USD)',\n",
     "                         scatter_kws=dict(marker='.', linestyle='none'))\n",
     "ax.legend()\n",
@@ -166,8 +166,8 @@
    "outputs": [],
    "source": [
     "fig, ax = pyplot.subplots(figsize=(6, 3))\n",
-    "fig = probscale.probplot(tips['total_bill'], ax=ax, axtype='qq',\n",
-    "                         otherscale='log', label='Bill Amount', \n",
+    "fig = probscale.probplot(tips['total_bill'], ax=ax, plottype='qq',\n",
+    "                         datascale='log', label='Bill Amount', \n",
     "                         xlabel='Normal Quantiles', ylabel='Total Bill (USD)',\n",
     "                         scatter_kws=dict(marker='.', linestyle='none'))\n",
     "ax.legend()\n",
@@ -194,14 +194,14 @@
    "source": [
     "fig, (ax1, ax2) = pyplot.subplots(figsize=(6, 6), ncols=2, sharex=True)\n",
     "\n",
-    "fig = probscale.probplot(tips['total_bill'], ax=ax1, axtype='pp', probax='y',\n",
-    "                         otherscale='log', label='Bill Amount', \n",
+    "fig = probscale.probplot(tips['total_bill'], ax=ax1, plottype='pp', probax='y',\n",
+    "                         datascale='log', label='Bill Amount', \n",
     "                         ylabel='Percentile', xlabel='Total Bill (USD)',\n",
     "                         scatter_kws=dict(marker='.', linestyle='none'))\n",
     "ax.legend()\n",
     "\n",
-    "fig = probscale.probplot(tips['total_bill'], ax=ax2, axtype='qq', probax='y',\n",
-    "                         otherscale='log', label='Bill Amount', \n",
+    "fig = probscale.probplot(tips['total_bill'], ax=ax2, plottype='qq', probax='y',\n",
+    "                         datascale='log', label='Bill Amount', \n",
     "                         ylabel='Normal Quantiles', xlabel='Total Bill (USD)',\n",
     "                         scatter_kws=dict(marker='.', linestyle='none'))\n",
     "ax.legend()\n",
@@ -236,24 +236,24 @@
     "fig, (ax1, ax2, ax3) = pyplot.subplots(figsize=(9, 6), ncols=3, sharex=True)\n",
     "common_opts = dict(\n",
     "    probax='y', \n",
-    "    otherscale='log', \n",
+    "    datascale='log', \n",
     "    label='Bill Amount', \n",
     "    xlabel='Total Bill (USD)',\n",
     "    scatter_kws=dict(marker='.', linestyle='none')\n",
     ")\n",
     "\n",
     "fig = probscale.probplot(tips['total_bill'], ax=ax1, \n",
-    "                         axtype='pp',\n",
+    "                         plottype='pp',\n",
     "                         ylabel='percentile', \n",
     "                         **common_opts)\n",
     "\n",
     "fig = probscale.probplot(tips['total_bill'], ax=ax2, \n",
-    "                         axtype='qq',\n",
+    "                         plottype='qq',\n",
     "                         ylabel='Normal Quantiles', \n",
     "                         **common_opts)\n",
     "\n",
     "fig = probscale.probplot(tips['total_bill'], ax=ax3, \n",
-    "                         axtype='prob',\n",
+    "                         plottype='prob',\n",
     "                         ylabel='Normal Probabilities', \n",
     "                         **common_opts)\n",
     "ax3.set_ylim(bottom=0.13, top=99.87)\n",
@@ -285,9 +285,9 @@
    "outputs": [],
    "source": [
     "common_opts = dict(\n",
-    "    axtype='prob',\n",
+    "    plottype='prob',\n",
     "    probax='y',\n",
-    "    otherscale='log',\n",
+    "    datascale='log',\n",
     "    label='Bill Amount',\n",
     "    xlabel='Total Bill (USD)',\n",
     "    scatter_kws=dict(marker='+', linestyle='none', mew=1)\n",
@@ -328,9 +328,9 @@
    "outputs": [],
    "source": [
     "common_opts = dict(\n",
-    "    axtype='qq',\n",
+    "    plottype='qq',\n",
     "    probax='y',\n",
-    "    otherscale='log',\n",
+    "    datascale='log',\n",
     "    label='Bill Amount',\n",
     "    xlabel='Total Bill (USD)',\n",
     "    scatter_kws=dict(marker='+', linestyle='none', mew=1)\n",
@@ -387,7 +387,7 @@
     "ax.set_aspect('equal')\n",
     "\n",
     "common_opts = dict(\n",
-    "    axtype='qq',\n",
+    "    plottype='qq',\n",
     "    probax='y',\n",
     "    ylabel='Theoretical Quantiles',\n",
     "    xlabel='Emperical Quantiles',\n",
@@ -447,7 +447,7 @@
     "Adding a best-fit line to a probability plot can provide insight as to whether or not a dataset can be characterized by a distribution.\n",
     "\n",
     "This is simply done with the `bestfit=True` option in `probplot`.\n",
-    "Behind the scenes, `probplot` transforms both the x- and y-data of fed to the regression based on the plot type and scale of the data axis (controlled via `otherscale`). \n",
+    "Behind the scenes, `probplot` transforms both the x- and y-data of fed to the regression based on the plot type and scale of the data axis (controlled via `datascale`). \n",
     "\n",
     "Visual attributes of the line can be controled with the `line_kws` parameter.\n",
     "If you want label the best-fit line, that is where you specify its label.\n",
@@ -464,7 +464,7 @@
    "outputs": [],
    "source": [
     "fig, ax = pyplot.subplots(figsize=(6, 3))\n",
-    "fig = probscale.probplot(tips['total_bill'], ax=ax, axtype='pp', bestfit=True,\n",
+    "fig = probscale.probplot(tips['total_bill'], ax=ax, plottype='pp', bestfit=True,\n",
     "                         label='Bill Amount', xlabel='Percentile', ylabel='Total Bill (USD)',\n",
     "                         scatter_kws=dict(marker='.', linestyle='none', alpha=0.5),\n",
     "                         line_kws=dict(label='Best-fit line', linestyle='--', linewidth=2.5))\n",
@@ -492,8 +492,8 @@
    "outputs": [],
    "source": [
     "fig, ax = pyplot.subplots(figsize=(4, 6))\n",
-    "fig = probscale.probplot(tips['total_bill'], ax=ax, axtype='prob', bestfit=True, \n",
-    "                         probax='y', otherscale='log',label='Bill Amount',\n",
+    "fig = probscale.probplot(tips['total_bill'], ax=ax, plottype='prob', bestfit=True, \n",
+    "                         probax='y', datascale='log',label='Bill Amount',\n",
     "                         ylabel='Probabilities', xlabel='Total Bill (USD)',\n",
     "                         scatter_kws=dict(marker='+', linestyle='none', alpha=0.75, mew=1),\n",
     "                         line_kws=dict(label='Best-fit line', linewidth=2.5, zorder=0))\n",
@@ -556,7 +556,7 @@
    "outputs": [],
    "source": [
     "common_opts = dict(\n",
-    "    axtype='prob',\n",
+    "    plottype='prob',\n",
     "    probax='x',\n",
     "    label='Bill Amount',\n",
     "    ylabel='Data',\n",
@@ -613,7 +613,7 @@
    "outputs": [],
    "source": [
     "fg = seaborn.FacetGrid(data=iris, hue='species', aspect=2)\n",
-    "fg.map(probscale.probplot, 'petal_length', axtype='qq', probax='y')\n",
+    "fg.map(probscale.probplot, 'petal_length', plottype='qq', probax='y')\n",
     "fg.set_ylabels('Quantiles')"
    ]
   },

docs/tutorial/getting_started.ipynb

@@ -297,7 +297,7 @@
     "    new_sample,\n",
     "    ax=ax,\n",
     "    probax='y',        # flip the plot\n",
-    "    otherscale='log',  # scale of the non-probability axis\n",
+    "    datascale='log',  # scale of the non-probability axis\n",
     "    bestfit=True,      # draw a best-fit line\n",
     "    xlabel='Lognormal Values',  # labels and markers...\n",
     "    ylabel='Non-exceedance probability',\n",
@@ -337,9 +337,9 @@
    "source": [
     "fig, (ax1, ax2, ax3) = pyplot.subplots(nrows=3, figsize=(5.5, 7))\n",
     "\n",
-    "probscale.probplot(sample, ax=ax1, axtype='pp', xlabel='Percentiles')\n",
-    "probscale.probplot(sample, ax=ax2, axtype='qq', xlabel='Quantiles')\n",
-    "probscale.probplot(sample, ax=ax3, axtype='prob', xlabel='Probabilities')\n",
+    "probscale.probplot(sample, ax=ax1, plottype='pp', xlabel='Percentiles')\n",
+    "probscale.probplot(sample, ax=ax2, plottype='qq', xlabel='Quantiles')\n",
+    "probscale.probplot(sample, ax=ax3, plottype='prob', xlabel='Probabilities')\n",
     "\n",
     "ax2.set_xlim(left=-2.5, right=2.5)\n",
     "ax3.set_xlim(left=0.5, right=99.5)\n",
@@ -368,12 +368,12 @@
     "plot = (\n",
     "    seaborn.load_dataset(\"tips\")\n",
     "        .assign(pct=lambda df: 100 * df['tip'] / df['total_bill'])\n",
-    "        .pipe(seaborn.FacetGrid, hue='time', col='sex', row='smoker', margin_titles=True, aspect=1.75)\n",
-    "        .map(probscale.probplot, 'pct', bestfit=True, otherscale='log', scatter_kws=dict(alpha=0.75))\n",
+    "        .pipe(seaborn.FacetGrid, hue='sex', col='time', row='smoker', margin_titles=True, aspect=1.75)\n",
+    "        .map(probscale.probplot, 'pct', bestfit=True, scatter_kws=dict(alpha=0.75), probax='y')\n",
     "        .add_legend()\n",
-    "        .set_xlabels('Non-Exceedance Probabilty')\n",
-    "        .set_ylabels('Tips as percent of total bill')\n",
-    ")"
+    "        .set_ylabels('Non-Exceedance Probabilty')\n",
+    "        .set_xlabels('Tips as percent of total bill')\n",
+    ").set(ylim=(0.5, 99.5))"
    ]
   }
  ],

probscale/tests/test_viz.py

@@ -373,40 +373,40 @@ def test_bad_postype(self):
 def test_probplot_prob():
     fig, ax = plt.subplots()
     data = setup_plot_data()
-    fig = viz.probplot(data, ax=ax, xlabel='Test xlabel', otherscale='log')
+    fig = viz.probplot(data, ax=ax, xlabel='Test xlabel', datascale='log')
     nt.assert_true(isinstance(fig, plt.Figure))
 
 
 @image_comparison(baseline_images=['test_probplot_qq'], extensions=['png'])
 def test_probplot_qq():
     fig, ax = plt.subplots()
     data = setup_plot_data()
-    fig = viz.probplot(data, ax=ax, axtype='qq', ylabel='Test label',
-                       otherscale='log', scatter_kws=dict(color='r'))
+    fig = viz.probplot(data, ax=ax, plottype='qq', ylabel='Test label',
+                       datascale='log', scatter_kws=dict(color='r'))
 
 
 @image_comparison(baseline_images=['test_probplot_pp'], extensions=['png'])
 def test_probplot_pp():
     fig, ax = plt.subplots()
     data = setup_plot_data()
     scatter_kws = dict(color='b', linestyle='--', markeredgecolor='g', markerfacecolor='none')
-    fig = viz.probplot(data, ax=ax, axtype='pp', otherscale='linear',
+    fig = viz.probplot(data, ax=ax, plottype='pp', datascale='linear',
                        xlabel='test x', ylabel='test y', scatter_kws=scatter_kws)
 
 
 @image_comparison(baseline_images=['test_probplot_prob_bestfit'], extensions=['png'])
 def test_probplot_prob_bestfit():
     fig, ax = plt.subplots()
     data = setup_plot_data()
-    fig = viz.probplot(data, ax=ax, xlabel='Test xlabel', bestfit=True, otherscale='log')
+    fig = viz.probplot(data, ax=ax, xlabel='Test xlabel', bestfit=True, datascale='log')
     nt.assert_true(isinstance(fig, plt.Figure))
 
 
 @image_comparison(baseline_images=['test_probplot_qq_bestfit'], extensions=['png'])
 def test_probplot_qq_bestfit():
     fig, ax = plt.subplots()
     data = setup_plot_data()
-    fig = viz.probplot(data, ax=ax, axtype='qq', bestfit=True, ylabel='Test label', otherscale='log')
+    fig = viz.probplot(data, ax=ax, plottype='qq', bestfit=True, ylabel='Test label', datascale='log')
 
 
 @image_comparison(baseline_images=['test_probplot_pp_bestfit'], extensions=['png'])
@@ -415,7 +415,7 @@ def test_probplot_pp_bestfit():
     data = setup_plot_data()
     scatter_kws = {'marker': 's', 'color': 'red'}
     line_kws = {'linestyle': '--', 'linewidth': 3}
-    fig = viz.probplot(data, ax=ax, axtype='pp', otherscale='linear',
+    fig = viz.probplot(data, ax=ax, plottype='pp', datascale='linear',
                        xlabel='test x', bestfit=True, ylabel='test y',
                        scatter_kws=scatter_kws, line_kws=line_kws)
 
@@ -424,40 +424,40 @@ def test_probplot_pp_bestfit():
 def test_probplot_prob_probax_y():
     fig, ax = plt.subplots()
     data = setup_plot_data()
-    fig = viz.probplot(data, ax=ax, xlabel='Test xlabel', otherscale='log', probax='y')
+    fig = viz.probplot(data, ax=ax, xlabel='Test xlabel', datascale='log', probax='y')
     nt.assert_true(isinstance(fig, plt.Figure))
 
 
 @image_comparison(baseline_images=['test_probplot_qq_probax_y'], extensions=['png'])
 def test_probplot_qq_probax_y():
     fig, ax = plt.subplots()
     data = setup_plot_data()
-    fig = viz.probplot(data, ax=ax, axtype='qq', ylabel='Test label', probax='y',
-                       otherscale='log', scatter_kws=dict(color='r'))
+    fig = viz.probplot(data, ax=ax, plottype='qq', ylabel='Test label', probax='y',
+                       datascale='log', scatter_kws=dict(color='r'))
 
 
 @image_comparison(baseline_images=['test_probplot_pp_probax_y'], extensions=['png'])
 def test_probplot_pp_probax_y():
     fig, ax = plt.subplots()
     data = setup_plot_data()
     scatter_kws = dict(color='b', linestyle='--', markeredgecolor='g', markerfacecolor='none')
-    fig = viz.probplot(data, ax=ax, axtype='pp', otherscale='linear', probax='y',
+    fig = viz.probplot(data, ax=ax, plottype='pp', datascale='linear', probax='y',
                        xlabel='test x', ylabel='test y', scatter_kws=scatter_kws)
 
 
 @image_comparison(baseline_images=['test_probplot_prob_bestfit_probax_y'], extensions=['png'])
 def test_probplot_prob_bestfit_probax_y():
     fig, ax = plt.subplots()
     data = setup_plot_data()
-    fig = viz.probplot(data, ax=ax, xlabel='Test xlabel', bestfit=True, otherscale='log', probax='y')
+    fig = viz.probplot(data, ax=ax, xlabel='Test xlabel', bestfit=True, datascale='log', probax='y')
     nt.assert_true(isinstance(fig, plt.Figure))
 
 
 @image_comparison(baseline_images=['test_probplot_qq_bestfit_probax_y'], extensions=['png'])
 def test_probplot_qq_bestfit_probax_y():
     fig, ax = plt.subplots()
     data = setup_plot_data()
-    fig = viz.probplot(data, ax=ax, axtype='qq', bestfit=True, ylabel='Test label', otherscale='log', probax='y')
+    fig = viz.probplot(data, ax=ax, plottype='qq', bestfit=True, ylabel='Test label', datascale='log', probax='y')
 
 
 @image_comparison(baseline_images=['test_probplot_pp_bestfit_probax_y'], extensions=['png'])
@@ -466,7 +466,7 @@ def test_probplot_pp_bestfit_probax_y():
     data = setup_plot_data()
     scatter_kws = {'marker': 's', 'color': 'red'}
     line_kws = {'linestyle': '--', 'linewidth': 3}
-    fig = viz.probplot(data, ax=ax, axtype='pp', otherscale='linear', probax='y',
+    fig = viz.probplot(data, ax=ax, plottype='pp', datascale='linear', probax='y',
                        xlabel='test x', bestfit=True, ylabel='test y',
                        scatter_kws=scatter_kws, line_kws=line_kws)
 
@@ -478,11 +478,11 @@ def test_probplot_beta_dist_best_fit_y():
     data = setup_plot_data()
     dist = stats.beta(3, 3)
     fig = viz.probplot(data, dist=dist, ax=ax1, ylabel='Beta scale',
-                       bestfit=True, otherscale='log', probax='y')
+                       bestfit=True, datascale='log', probax='y')
     ax1.set_ylim(bottom=0.5, top=98)
 
     fig = viz.probplot(data, ax=ax2, xlabel='Default (norm)',
-                       bestfit=True, otherscale='log', probax='y')
+                       bestfit=True, datascale='log', probax='y')
     ax2.set_ylim(bottom=0.5, top=98)
 
     nt.assert_true(isinstance(fig, plt.Figure))
@@ -495,11 +495,11 @@ def test_probplot_beta_dist_best_fit_x():
     data = setup_plot_data()
     dist = stats.beta(3, 3)
     fig = viz.probplot(data, dist=dist, ax=ax1, xlabel='Beta scale',
-                       bestfit=True, otherscale='log', probax='x')
+                       bestfit=True, datascale='log', probax='x')
     ax1.set_xlim(left=0.5, right=98)
 
     fig = viz.probplot(data, ax=ax2, xlabel='Default (norm)',
-                       bestfit=True, otherscale='log', probax='x')
+                       bestfit=True, datascale='log', probax='x')
     ax2.set_xlim(left=0.5, right=98)
 
     nt.assert_true(isinstance(fig, plt.Figure))