finish examples in docs

Author: phobson

probscale/probscale.py

@@ -85,8 +85,8 @@ class ProbScale(ScaleBase):
         >>> from matplotlib import pyplot
         >>> import probscale
         >>> fig, ax = pyplot.subplots()
-        >>> ax.set_xlim(left=0.2, right=99.9)
-        >>> ax.set_xscale('prob')
+        >>> ax.set_ylim(bottom=0.2, top=99.9)
+        >>> ax.set_yscale('prob')
 
     """
 

probscale/tests/baseline_images/test_probscale/test_the_scale_default.png

@@ -393,6 +393,7 @@ def test_probplot_qq(plot_data):
 
 
 @pytest.mark.mpl_image_compare(baseline_dir=BASELINE_DIR, tolerance=10)
+@pytest.mark.skipif(stats is None, reason="no scipy")
 def test_probplot_qq_dist(plot_data):
     fig, ax = plt.subplots()
     norm = stats.norm(*stats.norm.fit(plot_data))

probscale/tests/test_viz.py

@@ -21,7 +21,7 @@ def probplot(data, ax=None, plottype='prob', dist=None, probax='x',
         The Axes on which to plot. If one is not provided, a new Axes
         will be created.
     plottype : string (default = 'prob')
-        Type of plot to be created. Options are:
+       Type of plot to be created. Options are:
            - 'prob': probabilty plot
            - 'pp': percentile plot
            - 'qq': quantile plot
@@ -67,7 +67,7 @@ def probplot(data, ax=None, plottype='prob', dist=None, probax='x',
     fig : matplotlib.Figure
         The figure on which the plot was drawn.
     result : dictionary of linear fit results, optional
-        Keys are:
+       Keys are:
            - q : array of quantiles
            - x, y : arrays of data passed to function
            - xhat, yhat : arrays of modeled data plotted in best-fit line
@@ -89,6 +89,8 @@ def probplot(data, ax=None, plottype='prob', dist=None, probax='x',
         :context: close-figs
 
         >>> import numpy; numpy.random.seed(0)
+        >>> from matplotlib import pyplot
+        >>> from scipy import stats
         >>> from probscale.viz import probplot
         >>> data = numpy.random.normal(loc=5, scale=1.25, size=37)
         >>> fig = probplot(data, plottype='prob', probax='y',
@@ -116,14 +118,12 @@ def probplot(data, ax=None, plottype='prob', dist=None, probax='x',
     .. plot::
         :context: close-figs
 
-        >>> from scipy import stats
         >>> norm = stats.norm(5, 1.25)
-        >>> fig = probplot(data, plottype='qq', probax='x', dist=norm,
-        ...          problabel='Theoretical Quantiles',
+        >>> fig = probplot(data, ax=ax, plottype='qq', dist=norm,
+        ...          probax='x', problabel='Theoretical Quantiles',
         ...          datalabel='Observed values', bestfit=True,
         ...          line_kws=dict(linestyle=':', linewidth=2),
         ...          scatter_kws=dict(marker='^', alpha=0.5))
-        >>> fig.axes[0].set_aspect('equal')
 
     """