Fix typos in docstrings, RST documentation, and source comments

README.rst

@@ -38,7 +38,7 @@ Getting this Code
 
 .. image:: https://i.imgur.com/h4mWwM0.png
     :target: https://www.youtube.com/watch?v=uZ_yKs11W18
-    :alt: Introduction and Installation Guide to Stoner Pythin Package
+    :alt: Introduction and Installation Guide to Stoner Python Package
     :width: 320
 
 Anaconda Python is the recommended distribution of python and the conda packages are the preferred packaging system for
@@ -48,8 +48,8 @@ be obtained from the `phygbu` channel (although for full functionality, other pa
 Compatibility
 --------------
 
-Version 0.10.x is compatbile with Python 3.9-3.13. Version 0.11.x (currently in development) will be compatible with
-Python 3.11-3.13 and will add support for 3.14 when it is supported within the Anaconda distrubtion.
+Version 0.10.x is compatible with Python 3.9-3.13. Version 0.11.x (currently in development) will be compatible with
+Python 3.11-3.13 and will add support for 3.14 when it is supported within the Anaconda distribution.
 
 The last version that supported Python 2.7 was 0.9.x
 
@@ -124,7 +124,7 @@ ImageFile
 ---------
 
 **Stoner.ImageFile** is the top-level class for managing image data. It is the equivalent of **Stoner.Data** and maintains
-metadta and comes with a number of methods to manipulate image data. The image data is stored internally as a masked numpy
+metadata and comes with a number of methods to manipulate image data. The image data is stored internally as a masked numpy
 array and where possible the masking is taken into account when carrying out image analysis tasks. Through some abuse of
 the Python class system, functions in the scpy.ndimage and scikit-image modules are mapped into methods of the ImageFile
 class allowing a very rich set of operations on the data sets. The default IO methods handle tiff and png images and can
@@ -137,7 +137,7 @@ DataFolder
 structure. It provides methods to list. filter and group data according to filename patterns or metadata and then to execute
 a function on each file or group of files and then collect metadata from each file in turn. A key feature of DataFolder is
 its ability to work with the collated metadata from the individual files that are held in the DataFolder.
-In combination with its ability to walk through a complete heirarchy of groups of
+In combination with its ability to walk through a complete hierarchy of groups of
 **Data** objects, the handling of the common metadata provides powerful tools for quickly writing data reduction scripts.
 
 ImageFolder
@@ -159,8 +159,8 @@ Other Modules and Classes
 -------------------------
 
 The **Stoner.HDF5** module provides some additional classes to manipulate *Data* and *DataFolder* objects within HDF5
-format files. HDF5 is a common chouse for storing data from large scale facilties, although providing a way to handle
-arbitary HDF5 files is beyond the scope of this package at this time - the format is much too complex and flexible to make that
+format files. HDF5 is a common choice for storing data from large scale facilities, although providing a way to handle
+arbitrary HDF5 files is beyond the scope of this package at this time - the format is much too complex and flexible to make that
 an easy task. Rather it provides a way to work with large numbers of experimental sets using just a single file which may be less
 brutal to your computer's OS than having directory trees with millions of individual files.
 
@@ -227,7 +227,7 @@ Online documentation for all versions can be found on the ReadTheDocs pages `onl
 
 Version 0.11 is the current (in development) stable release (i.e. releases pass all unit tests.) New features include:
 
-    *   Move away from mixin classes and multiple inheritence to dynamically binding functions into the class from
+    *   Move away from mixin classes and multiple inheritance to dynamically binding functions into the class from
         modules.
     *   Continued restructuring of the class layout and rationalisation of the naming conventions.
 
@@ -242,7 +242,7 @@ Version 0.10 is the old stable release. New Features in 0.10 include:
         dictionary with methods to prune and filter in the virtual tree of sub-folders.
     *   Refactoring of the **ImageArray** and **ImageFile** so that binding of external functions as methods is done at
         class definition time rather than at runtime with overly complex __getattr__ methods. The longer term goal is to
-        depricate the use of ImageArray in favour of just using ImageFile.
+        deprecate the use of ImageArray in favour of just using ImageFile.
     *   Introduce interactive selection of boxes, lines and mask regions for interactive Matplotlib backends.
     *   Fix some long standing bugs which could lead to shared metadata dictionaries and race conditions
 
@@ -271,7 +271,7 @@ Version 0.8 is the ancient release. The main new features were:
 
 0.8.2 was the final release of the 0.8.0 branch
 
-No further relases will be made to 0.8.x - 0.10.x
+No further releases will be made to 0.8.x - 0.10.x
 
 Versions 0.7.x and earlier are now pre-historic!
 

Stoner/Image/attrs.py

@@ -230,7 +230,7 @@ class MaskProxy:
 
     The string representation of the mask is an ascii art version of the mask where . is unmasked and X is masked.
 
-    Conversion to a boolean is equaivalent to testing whether **any** elements of the mask are True.
+    Conversion to a boolean is equivalent to testing whether **any** elements of the mask are True.
 
     The mask also supports the invert and negate operators which both return the inverse of the mask (but do not
     change the mask itself - unlike :py:meth:`MaskProxy.invert`).
@@ -367,7 +367,7 @@ def select(self, **kwargs):
             -   left-clicking the mouse sets a new vertex
             -   right-clicking the mouse removes the last set vertex
             -   pressing "i" inverts the mask (i.e. controls whether the shape the user is drawing is masked or clear)
-            -   pressing "p" sets polygon mode (the default) - each vertex is then the corener of a polygon. The
+            -   pressing "p" sets polygon mode (the default) - each vertex is then the corner of a polygon. The
                 polygon
                 vertices are defined in order going around the shape.
             -   pressing "r" sets rectangular mode. The first vertex defined is one corner. With only two vertices the

Stoner/Image/core.py

@@ -212,7 +212,7 @@ class ImageArray(np.ma.MaskedArray, metadataObject):
             The title of the image (defaults to the filename).
         mask (:py:class:`numpy.ndarray of bool`):
             The underlying mask data of the image. Masked elements (i.e. where mask=True) are ignored for many
-            image operations. Indexing them will return the mask fill value (typically NaN, ot -1 or -MAXINT)
+            image operations. Indexing them will return the mask fill value (typically NaN, or -1 or -MAXINT)
             draw (:py:class:`Stoner.Image.attrs.DrawProxy`):
             A special object that allows the user to manipulate the image data by making use of
             :py:mod:`skimage.draw` functions as well as some additional drawing functions.
@@ -1184,7 +1184,7 @@ def get_filename(self, mode):
 
     @classmethod
     def load(cls, *args, **kwargs):
-        """Create a :py:class:`ImageFile` from file abnd guessing a better subclass if necessary.
+        """Create a :py:class:`ImageFile` from file and guessing a better subclass if necessary.
 
         Args:
             filename (string or None):

Stoner/Image/imagefuncs.py

@@ -399,7 +399,7 @@ def convert(image, dtype, force_copy=False, uniform=False, normalise=True):
             elif (
                 dtypeobj_in.itemsize <= dtypeobj.itemsize and dtypeobj.itemsize == 8
             ):  # f64->int64 needs care to avoid overruns!
-                image *= 2**54  # float64 has 52bits of mantissa, so this will avoid precission loss for a +/-1 range
+                image *= 2**54  # float64 has 52bits of mantissa, so this will avoid precision loss for a +/-1 range
                 np.rint(image, out=image)
                 image = image.astype(dtype)
                 np.clip(image, -(2**54), 2**54 - 1, out=image)
@@ -581,7 +581,7 @@ def gridimage(im, points=None, xi=None, method="linear", fill_value=None, rescal
             Image data to be worked with.
         points (tuple of (x-co-ords,yco-ordsa)):
             The actual sampled data coordinates
-        xi (tupe of (2D array,2D array)):
+        xi (tuple of (2D array,2D array)):
             The regular grid coordinates (as generated by e.g. :py:func:`np.meshgrid`)
 
     Keyword Arguments:
@@ -673,7 +673,7 @@ def imshow(
         mask_alpha (float,None):
             Alpha of mask to use
         mask_color (matplotlib colour):
-            Colour of masked regons - defaults to red.
+            Colour of masked regions - defaults to red.
         **kwargs:
             Other keywords are passed to imshow()
 
@@ -969,7 +969,7 @@ def fix(x, mx):
 
 
 def radial_coordinates(im, centre=(None, None), pixel_size=(1, 1), angle=False):
-    """Rerurn a map of the radial coordinates of an image from a given centre, with adjustments for pixel size.
+    """Return a map of the radial coordinates of an image from a given centre, with adjustments for pixel size.
 
     Args:
         im (ImargeArray,ImageFile):
@@ -1602,7 +1602,7 @@ def save(image, filename=None, **kwargs):
             integer data will be converted to np.float32 type for saving. if forcetype then preserve and save as
             int type (will be unsigned).
         **kwargs:
-            Other keyword aruments
+            Other keyword arguments
 
     Notes:
         Metadata will be preserved in .png and .tif format.

Stoner/Image/kerr.py

@@ -279,15 +279,15 @@ def HcMap(  # pylint: disable=invalid-name
                 the threshold value for the intensity switching. This will need to
                 be tuned for each stack
             correct_drift(bol):
-                whether to correct drift on the image stack before proceding
+                whether to correct drift on the image stack before proceeding
             baseimage(int or ImageArray):
                 we use drift correction from the baseimage.
             saturation_end(bool):
                 last image in stack is closest to saturation
             saturation_white(bool):
                 bright pixels are saturated dark pixels are not yet switched
             quiet: bool
-                choose wether to output status updates as print messages
+                choose whether to output status updates as print messages
             extra_info(bool):
                 choose whether to return intermediate calculation steps as an extra dictionary
         Returns:

Stoner/Image/widgets.py

@@ -37,7 +37,7 @@ def _straight_ellipse(p, data):
 
 
 def _rotated_ellipse(p, data):
-    """Calcualte a rotated ellipse."""
+    """Calculate a rotated ellipse."""
     xc, yc, a, b, phi = p
     x, y = data.T
     t1 = ((x - xc) * np.cos(phi) + (y - yc) * np.sin(phi)) ** 2 / a**2
@@ -188,7 +188,7 @@ def __init__(self):
         self.kwargs = {}
 
     def __call__(self, image, **kwargs):
-        """Actuall do the region selection.
+        """Actually do the region selection.
 
         Args:
             image (ImageArray, ImageFile):
@@ -198,7 +198,7 @@ def __call__(self, image, **kwargs):
 
         Returns:
             list:
-                The x and y range coordinates, in te order left-x, right-x, top-y, bottom-y
+                The x and y range coordinates, in the order left-x, right-x, top-y, bottom-y
 
         """
         self.fig = image.imshow()
@@ -289,7 +289,7 @@ def __call__(self, image, **kwargs):
 
         Returns:
             mask_array:
-                A boolean array of te same shape as the image that can be used as a mask.
+                A boolean array of the same shape as the image that can be used as a mask.
 
         """
         self.fig = image.imshow()
@@ -379,7 +379,7 @@ def on_click(self, event):
                 self.vertices.append((event.xdata, event.ydata))
             else:
                 self.finished = True
-        elif event.button == 3 and len(self.vertices) > 0:  # Right click toi remove last vertex
+        elif event.button == 3 and len(self.vertices) > 0:  # Right click to remove last vertex
             del self.vertices[-1]
             if len(self.vertices) == 0:
                 self.ov_layer.set_array(np.zeros(self.shape + (4,)))

Stoner/analysis/columns.py

@@ -57,7 +57,7 @@ def __get_math_val(datafile: Data, col: Index) -> Tuple[Data, str]:
 
     Args:
         datafile (Data):
-            If not being used as a bound menthod, specifies the instance of Data to work with.
+            If not being used as a bound method, specifies the instance of Data to work with.
         col (various):
             If col can be interpreted as col_a column index then return the first matching column.
             If col is col_a 1D array of the same length as the data then just return the data. If col is col_a
@@ -97,7 +97,7 @@ def add(
 
     Args:
         datafile (Data):
-            If not being used as a bound menthod, specifies the instance of Data to work with.
+            If not being used as a bound method, specifies the instance of Data to work with.
         col_a (index):
             First column to work with
         col_b (index, float or 1D array):
@@ -148,7 +148,7 @@ def diffsum(
 
     Args:
         datafile (Data):
-            If not being used as a bound menthod, specifies the instance of Data to work with.
+            If not being used as a bound method, specifies the instance of Data to work with.
         col_a (index):
             First column to work with
         col_b (index, float or 1D array):
@@ -199,7 +199,7 @@ def divide(
 
     Args:
         datafile (Data):
-            If not being used as a bound menthod, specifies the instance of Data to work with.
+            If not being used as a bound method, specifies the instance of Data to work with.
         col_a (index):
             First column to work with
         col_b (index, float or 1D array):
@@ -244,7 +244,7 @@ def max(  # pylint: disable=redefined-builtin
 
     Args:
         datafile (Data):
-            If not being used as a bound menthod, specifies the instance of Data to work with.
+            If not being used as a bound method, specifies the instance of Data to work with.
         column (index):
             Column to look for the maximum in
 
@@ -284,7 +284,7 @@ def mean(
 
     Args:
         datafile (Data):
-            If not being used as a bound menthod, specifies the instance of Data to work with.
+            If not being used as a bound method, specifies the instance of Data to work with.
         column (index):
             Column to look for the maximum in
 
@@ -338,7 +338,7 @@ def min(  # pylint: disable=redefined-builtin
 
     Args:
         datafile (Data):
-            If not being used as a bound menthod, specifies the instance of Data to work with.
+            If not being used as a bound method, specifies the instance of Data to work with.
         column (index):
             Column to look for the maximum in
 
@@ -380,7 +380,7 @@ def multiply(
 
     Args:
         datafile (Data):
-            If not being used as a bound menthod, specifies the instance of Data to work with.
+            If not being used as a bound method, specifies the instance of Data to work with.
         col_a (index):
             First column to work with
         col_b (index, float or 1D array):
@@ -424,7 +424,7 @@ def span(datafile: Data, column: Optional[Index] = None, bounds: Optional[Callab
 
     Args:
         datafile (Data):
-            If not being used as a bound menthod, specifies the instance of Data to work with.
+            If not being used as a bound method, specifies the instance of Data to work with.
         column (index):
             Column to look for the maximum in
 
@@ -457,7 +457,7 @@ def std(
 
     Args:
         datafile (Data):
-            If not being used as a bound menthod, specifies the instance of Data to work with.
+            If not being used as a bound method, specifies the instance of Data to work with.
         column (index):
             Column to look for the maximum in
 
@@ -518,7 +518,7 @@ def subtract(
 
     Args:
         datafile (Data):
-            If not being used as a bound menthod, specifies the instance of Data to work with.
+            If not being used as a bound method, specifies the instance of Data to work with.
         col_a (index):
             First column to work with
         col_b (index, float or 1D array):

Stoner/analysis/filtering.py

@@ -28,7 +28,7 @@ def _bin_weighted(x, y_vals, bin_edges, y_errs=None):
 
     Keyword Arguments:
         y_errs (2D array (N,m) floats or None):
-            uncertainities in y values. Default value is None in which case all vbalues equally weighted.
+            uncertainties in y values. Default value is None in which case all vbalues equally weighted.
 
     Returns:
         tuple[1D array of x, 2D array of y, 2D array of y_errs, binned_counts]
@@ -72,7 +72,7 @@ def _bin_weighted(x, y_vals, bin_edges, y_errs=None):
 
         binned_y[:, j] = sum_yw / sum_w
 
-        # Calculate the weighted uncertainity
+        # Calculate the weighted uncertainty
         variance = (y_vals[:, j] - binned_y[:, j][bin_indices]) ** 2
 
         variance_n = variance / binned_counts[:, j][bin_indices] * w / sum_w[bin_indices]
@@ -1056,7 +1056,7 @@ def spline(
         result (bool or column index):
             Whether to add the smoothed data to the dataset and if so where.
         replace (Boolean or column index or None):
-            If True then the y-column data is repalced, if a column index then the
+            If True then the y-column data is replaced, if a column index then the
             new data is added after the specified index, if False then the new y-data is returned and if None,
             then spline object is returned.
         header (string):

Stoner/analysis/fitting/classes.py

@@ -278,7 +278,7 @@ def fit_values(self):
 
     @property
     def perr(self):
-        """Return standar error from covariance matrix."""
+        """Return standard error from covariance matrix."""
         if "perr" in self.results and self.results["perr"] is not None:
             return self.results["perr"]
         return np.sqrt(np.diag(self.pcov))