minor doc cleanups

Author: phobson

probscale/formatters.py

@@ -30,9 +30,9 @@ def _sig_figs(cls, x, n, expthresh=5, forceint=False):
         Examples
         --------
         >>> print(_sig_figs(1247.15, 3))
-               1250
+        '1250'
         >>> print(_sig_figs(1247.15, 7))
-               1247.150
+        '1247.150'
 
         """
 
@@ -121,7 +121,7 @@ class ProbFormatter(_FormatterMixin):
     >>> from probscale import formatters
     >>> fmt = formatters.ProbFormatter()
     >>> fmt(0.01)
-    '0,01'
+    '0.01'
     >>> fmt(0.2)
     '0.20'
     >>> try:

probscale/probscale.py

@@ -85,7 +85,7 @@ class ProbScale(ScaleBase):
         >>> from matplotlib import pyplot
         >>> import probscale
         >>> fig, ax = pyplot.subplots()
-        >>> ax.set_ylim(bottom=0.2, top=99.9)
+        >>> ax.set_ylim(bottom=0.2, top=99.8)
         >>> ax.set_yscale('prob')
 
     """

probscale/viz.py

@@ -21,10 +21,12 @@ 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
+
     dist : scipy distribution, optional
         A distribtion to compute the scale's tick positions. If not
         specified, a standard normal distribution will be used.
@@ -67,7 +69,8 @@ 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