clean up tutorial notebook

__

Author: phobson

docs/tutorial/getting_started.ipynb

@@ -31,6 +31,17 @@
     "collapsed": true
    },
    "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "import warnings\n",
     "warnings.simplefilter('ignore')\n",
@@ -41,8 +52,7 @@
     "import seaborn\n",
     "\n",
     "clear_bkgd = {'axes.facecolor':'none', 'figure.facecolor':'none'}\n",
-    "seaborn.set(style='ticks', context='notebook', rc=clear_bkgd)\n",
-    "%matplotlib inline"
+    "seaborn.set(style='ticks', context='notebook', rc=clear_bkgd)"
    ]
   },
   {
@@ -94,7 +104,7 @@
     "ax2.set_xlabel(\"Base 2\")\n",
     "ax2.set_yticks([])\n",
     "\n",
-    "seaborn.despine(fig=fig, left=True)\n"
+    "seaborn.despine(fig=fig, left=True)"
    ]
   },
   {
@@ -133,14 +143,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It works after importing. Probability scales default to the standard normal distribution. Not that the formatting is a percentage-based probability."
+    "To access probability scales, simply import :py:mod:`probscale`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": false
    },
    "outputs": [],
    "source": [
@@ -156,9 +167,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "Probability scales default to the standard normal distribution (ote that the formatting is a percentage-based probability)\n",
+    "\n",
     "You can even use different probability distributions, though it can be tricky. You have to pass a frozen distribution to the `dist` kwarg in `ax.set_[x|y]scale`.\n",
     "\n",
-    "Here's a standard normal scale right next to a beta scale ($\\alpha = 1$ and $\\beta = 1$) for comparison"
+    "Here's a standard normal scale right next to two different beta scales for comparison"
    ]
   },
   {
@@ -171,9 +184,7 @@
    "outputs": [],
    "source": [
     "import paramnormal\n",
-    "\n",
-    "beta1 = paramnormal.beta(α=3, β=2)\n",
-    "beta2 = paramnormal.beta(α=2, β=7)\n",
+    "seaborn.set(style='ticks', context='notebook', rc=clear_bkgd)\n",
     "\n",
     "fig, (ax1, ax2, ax3, ax4) = pyplot.subplots(figsize=(8, 5), nrows=4)\n",
     "\n",
@@ -182,11 +193,13 @@
     "ax1.set_xlabel('Normal probability scale')\n",
     "ax1.set_yticks([])\n",
     "\n",
+    "beta1 = paramnormal.beta(α=3, β=2)\n",
     "ax2.set_xscale('prob', dist=beta1)\n",
     "ax2.set_xlim(left=2, right=98)\n",
     "ax2.set_xlabel('Beta probability scale (α=3, β=2)')\n",
     "ax2.set_yticks([])\n",
     "\n",
+    "beta2 = paramnormal.beta(α=2, β=7)\n",
     "ax3.set_xscale('prob', dist=beta2)\n",
     "ax3.set_xlim(left=2, right=98)\n",
     "ax3.set_xlabel('Beta probability scale (α=2, β=7)')\n",
@@ -197,7 +210,7 @@
     "ax4.set_xlabel('Linear scale (for reference)')\n",
     "ax4.set_yticks([])\n",
     "\n",
-    "seaborn.despine(fig=fig, left=True)\n"
+    "seaborn.despine(fig=fig, left=True)"
    ]
   },
   {
@@ -292,10 +305,17 @@
    "metadata": {},
    "source": [
     "### Percentile and Quanitile plots\n",
-    "For convenience, you can do percetile and quantile plots with the same function.\n",
-    "\n",
+    "For convenience, you can do percetile and quantile plots with the same function."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
     ".. note::\n",
-    "   The percentile and probability axes are plotted against the same values. The difference is only that \"percentiles\" are plotted on a linear scale."
+    "   The percentile and probability axes are plotted against the\n",
+    "   same values. The difference is only that \"percentiles\" \n",
+    "   are plotted on a linear scale."
    ]
   },
   {
@@ -334,4 +354,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}

probscale/probscale.py

@@ -11,7 +11,7 @@
 )
 
 
-def _mask_non_prop(a):
+def _mask_non_positives(a):
     """
     Return a Numpy array where all values outside ]0, 1[ are
     replaced with NaNs. If all values are inside ]0, 1[, the original

probscale/viz.py

@@ -136,7 +136,8 @@ def probplot(data, ax=None, axtype='prob', probax='x',
 
 
 def _fit_line(x, y, xhat=None, fitprobs=None, fitlogs=None, dist=None):
-    """ Fits a line to x-y data in various forms (raw, log, prob scales)
+    """
+    Fits a line to x-y data in various forms (linear, log, prob scales).
 
     Parameters
     ----------