Fixing spelling mistakes.

docs/index.rst

@@ -9,7 +9,7 @@ The position of the sigmoid curve is captured by its inflection point, or **infl
     :align: center
     :width: 60 %
 
-Alternatively, the dilution at which the measurement drops below a cutoff value can be measured.
+Alternatively, the dilution at which the measurement drops below a cut-off value can be measured.
 This is known as an **endpoint titer**.
 
 Investigators often measure all samples at many dilutions (typically 8-12).
@@ -22,9 +22,9 @@ Applied to a SARS-CoV-2 and human seasonal coronavirus ELISA dataset, just 3 dil
 Highlights
 ----------
 
-* Simple interface aimed at enabling investiagtors with basic knowledge of python to conduct analyses.
+* Simple interface aimed at enabling investigators with basic knowledge of python to conduct analyses.
 * Substantially decrease workload associated with serial dilution assays.
-* Flexibly incorporate different prior knowledge about experimental systems to faciliate inference.
+* Flexibly incorporate different prior knowledge about experimental systems to facilitate inference.
 
 Contents
 --------

docs/inference.rst

@@ -1,9 +1,9 @@
-Baysian Inference
-=================
+Bayesian Inference
+==================
 
 :py:mod:`ititer` uses Bayesian inference to infer posterior distributions of sigmoid curve parameters.
 
-Formally, the response of sample `i`, :math:`y_i`, is modelled as a function of log dilution using a four paramter logistic curve, :math:`x_i`:
+Formally, the response of sample `i`, :math:`y_i`, is modelled as a function of log dilution using a four parameter logistic curve, :math:`x_i`:
 
 .. math::
 
@@ -28,7 +28,7 @@ Standardizing
 Log dilution is standardized to have mean of zero and standard deviation of one for inference.
 
 Response is left unstandardized to help set priors for `c` and `d`.
-For instance, it is often known *a priori* that at theoretical minimum or maximul concentrations the response will have some known value.
+For instance, it is often known *a priori* that at theoretical minimum or maximum concentrations the response will have some known value.
 
 Priors
 ------

docs/quick.rst

@@ -1,5 +1,5 @@
-Quickstart guide
-================
+Quick start guide
+=================
 
 Installation
 ------------

docs/tutorial.rst

@@ -150,7 +150,7 @@ Inflection titers
 -----------------
 
 The degree to which a sigmoid curve is shifted left or right on the x-axis is often the point of interest to compare between samples.
-This is described by the inflection point of the curve, caculated by :py:meth:`~.ititer.Sigmoid.inflections`:
+This is described by the inflection point of the curve, calculated by :py:meth:`~.ititer.Sigmoid.inflections`:
 
 .. code-block:: python
 
@@ -194,8 +194,8 @@ Endpoint titers
 ---------------
 
 Endpoint titers can also be computed.
-An endpoint titer is the dilution at which the response drops below a certain value, known as the cutoff.
-Choice of cutoff is somewhat arbitrary, but is usually some low absolute value, or a low proportion of the maximal response.
+An endpoint titer is the dilution at which the response drops below a certain value, known as the cut-off.
+Choice of cut-off is somewhat arbitrary, but is usually some low absolute value, or a low proportion of the maximal response.
 Use :py:meth:`~.ititer.Sigmoid.endpoints` to compute endpoints:
 
 .. code-block:: python

ititer/ititer.py

@@ -51,7 +51,7 @@ def inverse_logit(
     x: Union[Iterable[Real], Real], a: Real, b: Real, c: Real, d: Real
 ) -> Union[Iterable, Real]:
     """
-    Inverse logit function at point x.
+    Inverse logit function.
 
     :param x: x.
     :param a: Location of inflection point on x-axis.
@@ -175,14 +175,14 @@ def fit(
         """
         Fit parameters of the sigmoid curve to data.
 
-        :param log_diluton: Log diluton values.
+        :param log_dilution: Log dilution values.
         :param response: Response values.
         :param sample_labels: Sample labels.
         :param data: Optional DataFrame. If supplied then `log_dilutions`,
             `response`, and `sample_labels` should be columns in the DataFrame.
         :param draws: Number of samples to draw from the posterior distribution.
         :param kwds: Passed to :py:func:`pymc3.sample`.
-        :param prior_predictive: Sample from the prior predicitve distribution.
+        :param prior_predictive: Sample from the prior predictive distribution.
             The returned Sigmoid object has a `prior_predictive` attribute.
         :returns: Sigmoid object with `posterior` attribute.
         """
@@ -316,8 +316,8 @@ def plot_sample(
         :param points: Whether to plot the data as well.
         :param mean: Show the mean of the posterior distribution, rather than samples
             from the posterior.
-        :param scatter_kwds: Passed to :py:func:`matplotlib.pyplot.scatter`. Used to control the
-            appearance of the data points.
+        :param scatter_kwds: Passed to :py:func:`matplotlib.pyplot.scatter`.
+            Used to control the appearance of the data points.
         :param line_kwds: Passed to :py:func:`matplotlib.pyplot.plot`. Used to control the
             appearance of the lines.
         :param step: Show every step'th sample from the posterior. Only has an
@@ -474,8 +474,8 @@ def endpoints(
         """
         Compute endpoints for each sample, given some response. An endpoint is
         the dilution at which a particular value of the response is obtained,
-        known as the cutoff. The cutoff is either in absolute units, or given as
-        a proportion of `d`.
+        known as the cut-off. The cut-off is either in absolute units, or given
+        as a proportion of `d`.
 
         Must supply exactly one of either `cutoff_proportion` or `cutoff_absolute`.
 

test/test_ititer.py

@@ -172,7 +172,7 @@ def test_passing_multidimensional_response(self):
 
     def test_passing_multidimensional_sample_labels(self):
         """
-        Passing multidimensional arrays to Sigmoid.fit for sample_lables should
+        Passing multidimensional arrays to Sigmoid.fit for sample_labels should
         raise a ValueError.
         """
         with self.assertRaisesRegex(ValueError, "sample_labels not 1 dimensional"):
@@ -506,7 +506,7 @@ class TestTestDf(unittest.TestCase):
 
     def test_is_dataframe(self):
         """
-        ititer.od should be a pandas DataFrame.
+        ititer.load_example_data() should return a pandas DataFrame.
         """
         self.assertIsInstance(it.load_example_data(), pd.DataFrame)