Merge pull request #29 from phobson/docs-2

minor doc cleanups

Author: phobson

docs/conf.py

@@ -85,9 +85,9 @@
 # built documents.
 #
 # The short X.Y version.
-version = '0.1.2'
+version = '0.2.0'
 # The full version, including alpha/beta/rc tags.
-release = '0.1.2'
+release = '0.2.0'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

docs/tutorial/closer_look_at_viz.ipynb

@@ -552,7 +552,7 @@
    "source": [
     ".. note::\n",
     "   The ``probplot`` function can take two additional aesthetic parameters:\n",
-    "   `color` and `label`. If provided `color` will override the marker face color\n",
+    "   `color` and `label`. If provided, `color` will override the marker face color\n",
     "   and line color options of the `scatter_kws` and `line_kws` parameters, respectively.\n",
     "   Similarly, the label of the scatter series will be overridden by the explicit parameter.\n",
     "   It is not recommended that `color` and `label` are used. They exist primarily for \n",
@@ -605,7 +605,7 @@
     "        Used for Gumble distributions.\n",
     "        \n",
     "These are controlled via the `pp_kws` parameter in `probplot`\n",
-    "and are discussed in much more detail in the next tutorial`.\n"
+    "and are discussed in much more detail in the next tutorial.\n"
    ]
   },
   {

probscale/formatters.py

@@ -29,10 +29,10 @@ def _sig_figs(cls, x, n, expthresh=5, forceint=False):
 
         Examples
         --------
-        >>> print(_sig_figs(1247.15, 3))
-               1250
-        >>> print(_sig_figs(1247.15, 7))
-               1247.150
+        >>> _sig_figs(1247.15, 3)
+        '1250'
+        >>> _sig_figs(1247.15, 7)
+        '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

@@ -84,8 +84,8 @@ class ProbScale(ScaleBase):
 
         >>> from matplotlib import pyplot
         >>> import probscale
-        >>> fig, ax = pyplot.subplots()
-        >>> ax.set_ylim(bottom=0.2, top=99.9)
+        >>> fig, ax = pyplot.subplots(figsize=(4, 7))
+        >>> ax.set_ylim(bottom=0.5, top=99.5)
         >>> ax.set_yscale('prob')
 
     """

probscale/tests/test_probscale.py

@@ -1,5 +1,4 @@
 import sys
-PYTHON27 = sys.version_info.major == 2
 
 import numpy
 import matplotlib.pyplot as plt
@@ -15,6 +14,10 @@
 from probscale.probscale import _minimal_norm
 
 
+PYTHON27 = sys.version_info.major == 2
+TOLERANCE = 22
+
+
 @pytest.fixture
 def mn():
     return _minimal_norm()
@@ -72,7 +75,7 @@ def test_minimal_norm_cdf(mn, mn_input):
 
 @pytest.mark.mpl_image_compare(
     baseline_dir='baseline_images/test_probscale',
-    tolerance=15
+    tolerance=TOLERANCE
 )
 @pytest.mark.skipif(PYTHON27, reason="legacy python")
 def test_the_scale_default():
@@ -85,7 +88,7 @@ def test_the_scale_default():
 
 @pytest.mark.mpl_image_compare(
     baseline_dir='baseline_images/test_probscale',
-    tolerance=15
+    tolerance=TOLERANCE
 )
 def test_the_scale_not_as_pct():
     fig, ax = plt.subplots(figsize=(4, 8))

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
@@ -111,20 +114,6 @@ def probplot(data, ax=None, plottype='prob', dist=None, probax='x',
         ...          line_kws=dict(linestyle='-', linewidth=2),
         ...          scatter_kws=dict(marker='s', alpha=0.5))
 
-
-
-    Quantile plot with a custom distribution
-
-    .. plot::
-        :context: close-figs
-
-        >>> norm = stats.norm(5, 1.25)
-        >>> 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))
-
     """
 
     if dist is None:
@@ -234,32 +223,32 @@ def plot_pos(data, postype=None, alpha=None, beta=None):
     values can also be access via the ``postype`` parameter. Available
     ``postype`` values (alpha, beta) are:
 
-        "type 4" (alpha=0, beta=1)
+       "type 4" (alpha=0, beta=1)
             Linear interpolation of the empirical CDF.
-        "type 5" or "hazen" (alpha=0.5, beta=0.5)
+       "type 5" or "hazen" (alpha=0.5, beta=0.5)
             Piecewise linear interpolation.
-        "type 6" or "weibull" (alpha=0, beta=0)
+       "type 6" or "weibull" (alpha=0, beta=0)
             Weibull plotting positions. Unbiased exceedance probability
             for all distributions. Recommended for hydrologic
             applications.
-        "type 7" (alpha=1, beta=1)
+       "type 7" (alpha=1, beta=1)
             The default values in R. Not recommended with probability
             scales as the min and max data points get plotting positions
             of 0 and 1, respectively, and therefore cannot be shown.
-        "type 8" (alpha=1/3, beta=1/3)
+       "type 8" (alpha=1/3, beta=1/3)
             Approximately median-unbiased.
-        "type 9" or "blom" (alpha=0.375, beta=0.375)
+       "type 9" or "blom" (alpha=0.375, beta=0.375)
             Approximately unbiased positions if the data are normally
             distributed.
-        "median" (alpha=0.3175, beta=0.3175)
+       "median" (alpha=0.3175, beta=0.3175)
             Median exceedance probabilities for all distributions
             (used in ``scipy.stats.probplot``).
-        "apl" or "pwm" (alpha=0.35, beta=0.35)
+       "apl" or "pwm" (alpha=0.35, beta=0.35)
             Used with probability-weighted moments.
-        "cunnane" (alpha=0.4, beta=0.4)
+       "cunnane" (alpha=0.4, beta=0.4)
             Nearly unbiased quantiles for normally distributed data.
             This is the default value.
-        "gringorten" (alpha=0.44, beta=0.44)
+       "gringorten" (alpha=0.44, beta=0.44)
             Used for Gumble distributions.
 
     Parameters

setup.py

@@ -10,7 +10,7 @@
 DESCRIPTION = "mpl-probscale: Probabily scales for matplotlib"
 LONG_DESCRIPTION = DESCRIPTION
 NAME = "probscale"
-VERSION = "0.1.2"
+VERSION = "0.2.0"
 AUTHOR = "Paul Hobson (Geosyntec Consultants)"
 AUTHOR_EMAIL = "phobson@geosyntec.com"
 URL = "https://github.com/phobson/mpl-probscale"