fix minor doc typos

Author: phobson

.travis.yml

@@ -24,9 +24,6 @@ matrix:
         - ARGS="--mpl --verbose --pep8"
 
 before_install:
-
-  # Here we just install Miniconda, which you shouldn't have to change.
-
   - wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
   - chmod +x miniconda.sh
   - ./miniconda.sh -b -p $HOME/miniconda
@@ -35,10 +32,6 @@ before_install:
   - conda install --yes nomkl
 
 install:
-
-  # We just set up a conda environment with the right Python version. This
-  # should not need changing.
-
   - conda create --yes -n test python=$TRAVIS_PYTHON_VERSION numpy matplotlib docopt requests pyyaml
   - source activate test
   - conda install --yes --channel=conda-forge pytest-mpl pytest-cov pytest-pep8 ${EXTRATESTERS}

docs/tutorial/closer_look_at_viz.ipynb

@@ -8,7 +8,7 @@
     "\n",
     "## Overview\n",
     "\n",
-    "The `probscale.probplot` function lets you do a couple of things. They are:\n",
+    "The `probscale.probplot` function let's you do a couple of things. They are:\n",
     "\n",
     "  1. Creating percentile, quantile, or probability plots.\n",
     "  1. Placing your probability scale either axis.\n",
@@ -200,7 +200,7 @@
     "                         scatter_kws=dict(marker='.', linestyle='none', label='Bill Amount'))\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",
+    "                         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",
     "fig.tight_layout()\n",

probscale/validate.py

@@ -23,8 +23,9 @@ def axes_object(ax):
 
 def axis_name(axis, axname):
     """
-    Checks that an axis name is either 'x' or 'y'. Raises an error on
-    an invalid value. Returns the lower cased verion of valid values.
+    Checks that an axis name is in ``{'x', 'y'}``. Raises an error on
+    an invalid value. Returns the lower case verion of valid values.
+
     """
 
     valid_args = ['x', 'y']
@@ -37,9 +38,10 @@ def axis_name(axis, axname):
 
 def fit_argument(arg, argname):
     """
-    Checks that an axis options is either 'x', y', 'both', or None.
+    Checks that an axis options is in ``{'x', y', 'both', None}``.
     Raises an error on an invalid value. Returns the lower case verion
     of valid values.
+
     """
 
     valid_args = ['x', 'y', 'both', None]
@@ -55,12 +57,14 @@ def fit_argument(arg, argname):
 def axis_type(axtype):
     """
     Checks that a valid axis type is requested.
-    pp - percentile axis
-    qq - quantile axis
-    prob - probability axis
+
+      - *pp* - percentile axis
+      - *qq* - quantile axis
+      - *prob* - probability axis
 
     Raises an error on an invalid value. Returns the lower case verion
     of valid values.
+
     """
 
     if axtype.lower() not in ['pp', 'qq', 'prob']:
@@ -71,21 +75,21 @@ def axis_type(axtype):
 def axis_label(label):
     """
     Replaces None with an empty string for axis labels.
+
     """
 
-    if label is None:
-        return ''
-    else:
-        return label
+    return '' if label is None else label
 
 
 def other_options(options):
     """
     Replaces None with an empty dict for plotting options.
+
     """
 
     return dict() if options is None else options.copy()
 
+
 def estimator(value):
     if value.lower() in ['res', 'resid', 'resids', 'residual', 'residuals']:
         msg = 'Bootstrapping the residuals is not ready yet'

probscale/viz.py

@@ -1,4 +1,4 @@
-import copy
+import copy
 
 import numpy
 from matplotlib import pyplot
@@ -81,8 +81,9 @@ def probplot(data, ax=None, plottype='prob', dist=None, probax='x',
         specified within ``scatter_kws`` and ``line_kws``.
 
         .. note::
-           Users should not specify the parameter. It is inteded to only
-           be used by seaborn when operating within a FacetGrid.
+           Users should not specify this parameter. It is inteded to
+           only be used by seaborn when operating within a
+           ``FacetGrid``.
 
     label : string, optional
         A directly-specified legend label for the data series. This
@@ -91,8 +92,9 @@ def probplot(data, ax=None, plottype='prob', dist=None, probax='x',
         data series label should be specified within ``scatter_kws``.
 
         .. note::
-           Users should not specify the parameter. It is inteded to only
-           be used by seaborn when operating within a FacetGrid.
+           Users should not specify this parameter. It is inteded to
+           only be used by seaborn when operating within a
+           ``FacetGrid``.
 
 
     Returns
@@ -415,16 +417,21 @@ def fit_line(x, y, xhat=None, fitprobs=None, fitlogs=None, dist=None,
         Defines how data should be transformed. Valid values are
         'x', 'y', or 'both'. If using ``fitprobs``, variables should
         be expressed as a percentage, i.e.,
-        Probablility transform = lambda x: ``dist``.ppf(x / 100.).
-        Log transform = lambda x: numpy.log(x).
+        for a probablility transform, data will be transformed with
+        ``lambda x: dist.ppf(x / 100.)``.
+        For a log transform, ``lambda x: numpy.log(x)``.
         Take care to not pass the same value to both ``fitlogs`` and
+<<<<<<< 801b29f5fb36417255ee93a7fe704cc1360cb89c
         ``figprobs`` as both transforms will be applied.
 
+=======
+        ``fitprobs`` as both transforms will be applied.
+>>>>>>> fix minor doc typos
     dist : distribution, optional
         A fully-spec'd scipy.stats distribution-like object
         such that ``dist.ppf`` and ``dist.cdf`` can be called. If not
         provided, defaults to a minimal implementation of
-        scipt.stats.norm.
+        ``scipt.stats.norm``.
 
     estimate_ci : bool, optional (False)
         Estimate and draw a confidence band around the best-fit line
@@ -463,12 +470,12 @@ def fit_line(x, y, xhat=None, fitprobs=None, fitlogs=None, dist=None,
 
     # maybe compute ppf of x
     if fitprobs in ['x', 'both']:
-        x = dist.ppf(x/100.)
+        x = dist.ppf(x / 100.)
         xhat = dist.ppf(numpy.array(xhat)/100.)
 
     # maybe compute ppf of y
     if fitprobs in ['y', 'both']:
-        y  = dist.ppf(y/100.)
+        y  = dist.ppf(y / 100.)
 
     # maybe compute log of x
     if fitlogs in ['x', 'both']: