Fix typos

Author: StefRe

README.md

@@ -70,7 +70,7 @@ The best way to run the tests is in an interactive python session:
 
 ```python
 import matplotlib
-matplotib.use('agg')
+matplotlib.use('agg')
 from probscale import tests
 tests.test()
 ```

docs/tutorial/closer_look_at_plot_pos.ipynb

@@ -137,7 +137,7 @@
    "metadata": {},
    "source": [
     "Now let's create probability plots on both Weibull and normal probability scales.\n",
-    "Additionally, we'll compute the plotting positions two different but commone ways for each plot.\n",
+    "Additionally, we'll compute the plotting positions two different but common ways for each plot.\n",
     "\n",
     "First, in blue circles, we'll show the data with Weibull (α=0, β=0) plotting positions.\n",
     "Weibull plotting positions are commonly use in fields such as hydrology and water resources engineering.\n",
@@ -179,12 +179,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This demostrates that the different formulations of the plotting positions vary  most at the extreme values of the dataset. \n",
+    "This demonstrates that the different formulations of the plotting positions vary  most at the extreme values of the dataset. \n",
     "\n",
     "### Hazen plotting positions\n",
     "\n",
     "Next, let's compare the Hazen/Type 5 (α=0.5, β=0.5) formulation to Cunnane.\n",
-    "Hazen plotting positions (shown as red triangles) represet a piece-wise linear interpolation of the emperical cumulative distribution function of the dataset.\n",
+    "Hazen plotting positions (shown as red triangles) represent a piece-wise linear interpolation of the empirical cumulative distribution function of the dataset.\n",
     "\n",
     "Given the values of α and β=0.5 vary only slightly from the Cunnane values, the plotting position predictably are similar."
    ]

docs/tutorial/closer_look_at_viz.ipynb

@@ -123,7 +123,7 @@
    "source": [
     "### Quantile plots\n",
     "\n",
-    "Quantile plots are similar to propbabilty plots.\n",
+    "Quantile plots are similar to probability plots.\n",
     "The main differences is that plotting positions are converted into quantiles or $Z$-scores based on a probability distribution.\n",
     "The default distribution is the standard-normal distribution.\n",
     "Using a different distribution is covered further down.\n",
@@ -215,16 +215,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In these case of P-P plots and simple Q-Q plots, the `probplot` function doesn't offer much convencience compared to writing raw matplotlib commands.\n",
+    "In these case of P-P plots and simple Q-Q plots, the `probplot` function doesn't offer much convenience compared to writing raw matplotlib commands.\n",
     "However, this changes when you start making probability plots and using more advanced options.\n",
     "\n",
     "### Probability plots\n",
     "\n",
     "Visually, the curve of plots on probability and quantile scales should be the same.\n",
-    "The difference is that the axis ticks are placed and labeled based on non-exceedance probailities rather than the more abstract quantiles of the distribution.\n",
+    "The difference is that the axis ticks are placed and labeled based on non-exceedance probabilities rather than the more abstract quantiles of the distribution.\n",
     "\n",
     "Unsurprisingly, a picture explains this much better.\n",
-    "Let's build off of the previos plot:"
+    "Let's build off of the previous plot:"
    ]
   },
   {
@@ -384,7 +384,7 @@
     "    plottype='qq',\n",
     "    probax='x',\n",
     "    problabel='Theoretical Quantiles',\n",
-    "    datalabel='Emperical Quantiles',\n",
+    "    datalabel='Empirical Quantiles',\n",
     "    scatter_kws=dict(label='Bill amounts')\n",
     ")\n",
     "\n",
@@ -541,7 +541,7 @@
    "cell_type": "raw",
    "metadata": {},
    "source": [
-    "The ``probplot`` function calls the :func:`viz.plot_plos` function to compute each dataset's plotting positions.\n",
+    "The ``probplot`` function calls the :func:`viz.plot_pos` function to compute each dataset's plotting positions.\n",
     "\n",
     "You should read that function's docstring for more detailed information.\n",
     "But the high-level overview is that there are a couple of parameters (``alpha`` and ``beta``) that you can tweak in the plotting positions calculation.\n",
@@ -571,7 +571,7 @@
     "\n",
     "fig, (ax1, ax2, ax3) = pyplot.subplots(figsize=(6, 6), nrows=3,\n",
     "                                       sharey=True, sharex=True)\n",
-    "fig = probscale.probplot(x, ax=ax1, problabel='Cunnuane (default) plotting positions',\n",
+    "fig = probscale.probplot(x, ax=ax1, problabel='Cunnane (default) plotting positions',\n",
     "                         **common_opts)\n",
     "\n",
     "fig = probscale.probplot(x, ax=ax2, problabel='Weibull plotting positions',\n",
@@ -596,7 +596,7 @@
     "By default, the data series assumes that `linestyle='none'` and `marker='o'`.\n",
     "These can be overwritten through `scatter_kws`\n",
     "\n",
-    "Revisting the previous example, we can customize it like so:"
+    "Revisiting the previous example, we can customize it like so:"
    ]
   },
   {

docs/tutorial/getting_started.ipynb

@@ -15,7 +15,7 @@
     "\n",
     "`conda install --channel=conda-forge mpl-probscale`\n",
     "\n",
-    "Fairly recent builds of the development verions are available on my channel:\n",
+    "Fairly recent builds of the development version are available on my channel:\n",
     "\n",
     "`conda install --channel=conda-forge mpl-probscale`\n",
     "\n",
@@ -126,7 +126,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Probabilty Scales "
+    "### Probability Scales "
    ]
   },
   {
@@ -311,8 +311,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Percentile and Quanitile plots\n",
-    "For convenience, you can do percetile and quantile plots with the same function."
+    "### Percentile and Quantile plots\n",
+    "For convenience, you can do percentile and quantile plots with the same function."
    ]
   },
   {
@@ -369,7 +369,7 @@
     "        .pipe(seaborn.FacetGrid, hue='sex', col='time', row='smoker', margin_titles=True, aspect=1., size=4)\n",
     "        .map(probscale.probplot, 'pct', bestfit=True, scatter_kws=dict(alpha=0.75), probax='y')\n",
     "        .add_legend()\n",
-    "        .set_ylabels('Non-Exceedance Probabilty')\n",
+    "        .set_ylabels('Non-Exceedance Probability')\n",
     "        .set_xlabels('Tips as percent of total bill')\n",
     "        .set(ylim=(0.5, 99.5), xlim=(0, 100))\n",
     ")"

probscale/formatters.py

@@ -15,7 +15,7 @@ def _sig_figs(cls, x, n, expthresh=5, forceint=False):
         x : int or float
             The number you want to format.
         n : int
-            The number of significan figures it should have.
+            The number of significant figures it should have.
         expthresh : int, optional (default = 5)
             The absolute value of the order of magnitude at which numbers
             are formatted in exponential notation.
@@ -97,7 +97,7 @@ def __call__(self, x, pos=None):
 
 class PctFormatter(_FormatterMixin):
     """
-    Formatter class for MPL axes to display probalities as percentages.
+    Formatter class for MPL axes to display probabilities as percentages.
 
     Examples
     --------
@@ -119,7 +119,7 @@ class PctFormatter(_FormatterMixin):
 
 class ProbFormatter(_FormatterMixin):
     """
-    Formatter class for MPL axes to display probalities as decimals.
+    Formatter class for MPL axes to display probabilities as decimals.
 
     Examples
     --------

probscale/probscale.py

@@ -13,8 +13,8 @@
 
 class _minimal_norm(object):
     """
-    A basic implmentation of a normal distribution, minimally
-    API-complient with scipt.stats.norm
+    A basic implementation of a normal distribution, minimally
+    API-compliant with scipy.stats.norm
 
     """
 
@@ -71,7 +71,7 @@ class ProbScale(ScaleBase):
         The axis whose scale will be set.
     dist : scipy.stats probability distribution, optional
         The distribution whose ppf/cdf methods should be used to compute
-        the tick positions. By default, a minimal implimentation of the
+        the tick positions. By default, a minimal implementation of the
         ``scipy.stats.norm`` class is used so that scipy is not a
         requirement.
 

probscale/transforms.py

@@ -59,18 +59,18 @@ def __init__(self, dist, as_pct=True, out_of_bounds='mask'):
 
 class ProbTransform(_ProbTransformMixin):
     """
-    MPL axes tranform class to convert quantiles to probabilities
+    MPL axes transform class to convert quantiles to probabilities
     or percents.
 
     Parameters
     ----------
     dist : scipy.stats distribution
-        The distribution whose ``cdf`` and ``pdf`` methods wiil set the
+        The distribution whose ``cdf`` and ``pdf`` methods will set the
         scale of the axis.
     as_pct : bool, optional (True)
         Toggles the formatting of the probabilities associated with the
-        tick labels as percentanges (0 - 100) or fractions (0 - 1).
-    out_of_bounds : string, optionals ('mask' or 'clip')
+        tick labels as percentages (0 - 100) or fractions (0 - 1).
+    out_of_bounds : string, optional ('mask' or 'clip')
         Determines how data outside the range of valid values is
         handled. The default behavior is to mask the data.
         Alternatively, the data can be clipped to values arbitrarily
@@ -93,18 +93,18 @@ def inverted(self):
 
 class QuantileTransform(_ProbTransformMixin):
     """
-    MPL axes tranform class to convert probabilities or percents to
+    MPL axes transform class to convert probabilities or percents to
     quantiles.
 
     Parameters
     ----------
     dist : scipy.stats distribution
-        The distribution whose ``cdf`` and ``pdf`` methods wiil set the
+        The distribution whose ``cdf`` and ``pdf`` methods will set the
         scale of the axis.
     as_pct : bool, optional (True)
         Toggles the formatting of the probabilities associated with the
-        tick labels as percentanges (0 - 100) or fractions (0 - 1).
-    out_of_bounds : string, optionals ('mask' or 'clip')
+        tick labels as percentages (0 - 100) or fractions (0 - 1).
+    out_of_bounds : string, optional ('mask' or 'clip')
         Determines how data outside the range of valid values is
         handled. The default behavior is to mask the data.
         Alternatively, the data can be clipped to values arbitrarily

probscale/validate.py

@@ -24,7 +24,7 @@ def axes_object(ax):
 def axis_name(axis, axname):
     """
     Checks that an axis name is in ``{'x', 'y'}``. Raises an error on
-    an invalid value. Returns the lower case verion of valid values.
+    an invalid value. Returns the lower case version of valid values.
 
     """
 
@@ -39,7 +39,7 @@ def axis_name(axis, axname):
 def fit_argument(arg, argname):
     """
     Checks that an axis options is in ``{'x', y', 'both', None}``.
-    Raises an error on an invalid value. Returns the lower case verion
+    Raises an error on an invalid value. Returns the lower case version
     of valid values.
 
     """
@@ -62,7 +62,7 @@ def axis_type(axtype):
       - *qq* - quantile axis
       - *prob* - probability axis
 
-    Raises an error on an invalid value. Returns the lower case verion
+    Raises an error on an invalid value. Returns the lower case version
     of valid values.
 
     """

probscale/viz.py

@@ -28,13 +28,13 @@ def probplot(data, ax=None, plottype='prob', dist=None, probax='x',
     plottype : string (default = 'prob')
         Type of plot to be created. Options are:
 
-           - 'prob': probabilty plot
+           - 'prob': probability plot
            - 'pp': percentile plot
            - 'qq': quantile plot
 
 
     dist : scipy distribution, optional
-        A distribtion to compute the scale's tick positions. If not
+        A distribution to compute the scale's tick positions. If not
         specified, a standard normal distribution will be used.
 
     probax : string, optional (default = 'x')
@@ -46,7 +46,8 @@ def probplot(data, ax=None, plottype='prob', dist=None, probax='x',
         respectively.
 
     datascale : string, optional (default = 'log')
-        Scale for the other axis that is not
+        Scale for the other axis that is not the probability (or
+        quantile) axis.
 
     bestfit : bool, optional (default is False)
         Specifies whether a best-fit line should be added to the plot.
@@ -81,7 +82,7 @@ def probplot(data, ax=None, plottype='prob', dist=None, probax='x',
         specified within ``scatter_kws`` and ``line_kws``.
 
         .. note::
-           Users should not specify this parameter. It is inteded to
+           Users should not specify this parameter. It is intended to
            only be used by seaborn when operating within a
            ``FacetGrid``.
 
@@ -92,7 +93,7 @@ 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 this parameter. It is inteded to
+           Users should not specify this parameter. It is intended to
            only be used by seaborn when operating within a
            ``FacetGrid``.
 
@@ -108,7 +109,7 @@ def probplot(data, ax=None, plottype='prob', dist=None, probax='x',
            - q : array of quantiles
            - x, y : arrays of data passed to function
            - xhat, yhat : arrays of modeled data plotted in best-fit line
-           - res : array of coeffcients of the best-fit line.
+           - res : array of coefficients of the best-fit line.
 
     See also
     --------
@@ -269,7 +270,7 @@ def plot_pos(data, postype=None, alpha=None, beta=None, exceedance=False):
     Compute the plotting positions for a dataset. Heavily borrows from
     ``scipy.stats.mstats.plotting_positions``.
 
-    A plottiting position is defined as: ``(i-alpha)/(n+1-alpha-beta)``
+    A plotting position is defined as: ``(i-alpha)/(n+1-alpha-beta)``
     where:
 
         - ``i`` is the rank order
@@ -326,7 +327,7 @@ def plot_pos(data, postype=None, alpha=None, beta=None, exceedance=False):
         generally slopes from the lower left to the upper right,
         and show the probability that a new observation will be
         less than a given point. By contrast, exceedance plots show
-        the probabilty that a new observation will be greater than
+        the probability that a new observation will be greater than
         a given point.
 
     Returns
@@ -384,7 +385,7 @@ def plot_pos(data, postype=None, alpha=None, beta=None, exceedance=False):
 
 
 def _set_prob_limits(ax, probax, N):
-    """ Sets the limits of a probabilty axis based the number of point.
+    """ Sets the limits of a probability axis based the number of point.
 
     Parameters
     ----------
@@ -436,7 +437,7 @@ 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.,
-        for a probablility transform, data will be transformed with
+        for a probability 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
@@ -446,7 +447,7 @@ def fit_line(x, y, xhat=None, fitprobs=None, fitlogs=None, dist=None,
         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``.
+        ``scipy.stats.norm``.
 
     estimate_ci : bool, optional (False)
         Estimate and draw a confidence band around the best-fit line
@@ -466,7 +467,7 @@ def fit_line(x, y, xhat=None, fitprobs=None, fitlogs=None, dist=None,
         Dictionary of linear fit results. Keys include:
 
           - slope
-          - intersept
+          - intercept
           - yhat_lo (lower confidence interval of the estimated y-vals)
           - yhat_hi (upper confidence interval of the estimated y-vals)