diff --git a/docs/source/conf.py b/docs/source/conf.py
index 89e72477..8f78a3b9 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -12,21 +12,19 @@
 
 import os
 import sys
-
-import sphinx_rtd_theme
 from unittest import mock
 
-sys.path.insert(0, os.path.abspath('../../python'))
+sys.path.insert(0, os.path.abspath("../../python"))
 
 
 # -- Project information -----------------------------------------------------
 
-project = 'stempy'
-copyright = '2020, Kitware, INC'
-author = 'Kitware, INC'
+project = "stempy"
+copyright = "2020, Kitware, INC"
+author = "Kitware, INC"
 
 # The full version, including alpha/beta/rc tags
-release = '1.0'
+release = "1.0"
 
 
 # -- General configuration ---------------------------------------------------
@@ -34,20 +32,17 @@
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
-extensions = [
-    'sphinx.ext.autodoc',
-    'sphinx_rtd_theme',
-    'recommonmark'
-]
+extensions = ["sphinx.ext.autodoc", "sphinx_rtd_theme", "recommonmark"]
 
 # Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
+templates_path = ["_templates"]
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
 exclude_patterns = []
 
+
 # Unfortunately, if we have a class that inherits sphinx's mock
 # object, and we use that class in a default argument, we run into
 # attribute errors. Fix it by defining an explicit mock of the class.
@@ -55,8 +50,9 @@ class MockReader:
     class H5Format:
         Frame = 1
 
-sys.modules['stempy._io'] = mock.Mock()
-_io_mock = sys.modules['stempy._io']
+
+sys.modules["stempy._io"] = mock.Mock()
+_io_mock = sys.modules["stempy._io"]
 
 # We have to override these so we get don't get conflicting metaclasses
 _io_mock._sector_reader = MockReader
@@ -70,36 +66,33 @@ class H5Format:
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = 'sphinx_rtd_theme'
+html_theme = "sphinx_rtd_theme"
 
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
+html_static_path = ["_static"]
 
 # Get autodoc to mock these imports, because we don't actually need them
 # for generating the docs
-autodoc_mock_imports = [
-    'stempy._image',
-    'numpy',
-    'h5py'
-]
+autodoc_mock_imports = ["stempy._image", "numpy", "h5py"]
+
 
 # Modify this function to customize which classes/functions are skipped
 def autodoc_skip_member_handler(app, what, name, obj, skip, options):
     # Exclude any names that start with a string in this list
-    exclude_startswith_list = ['_']
+    exclude_startswith_list = ["_"]
 
-    if any([name.startswith(x) for x in exclude_startswith_list]):
+    if any(name.startswith(x) for x in exclude_startswith_list):
         return True
 
     # Exclude any names that match a string in this list
     exclude_names = [
-        'ReaderMixin',
-        'PyReader',
-        'SectorReader',
-        'get_hdf5_reader',
-        'SectorThreadedMultiPassReader',
+        "ReaderMixin",
+        "PyReader",
+        "SectorReader",
+        "get_hdf5_reader",
+        "SectorThreadedMultiPassReader",
     ]
 
     return name in exclude_names
@@ -109,4 +102,4 @@ def autodoc_skip_member_handler(app, what, name, obj, skip, options):
 def setup(app):
     # Connect the autodoc-skip-member event from apidoc to the callback
     # This allows us to customize which classes/functions are skipped
-    app.connect('autodoc-skip-member', autodoc_skip_member_handler)
+    app.connect("autodoc-skip-member", autodoc_skip_member_handler)
diff --git a/python/stempy/__init__.py b/python/stempy/__init__.py
index 9ebc766b..b775bd3a 100644
--- a/python/stempy/__init__.py
+++ b/python/stempy/__init__.py
@@ -3,5 +3,5 @@
 __version__ = get_version()
 
 __all__ = [
-    '__version__',
+    "__version__",
 ]
diff --git a/python/stempy/contrib/__init__.py b/python/stempy/contrib/__init__.py
index a63d6316..32065cb1 100644
--- a/python/stempy/contrib/__init__.py
+++ b/python/stempy/contrib/__init__.py
@@ -18,9 +18,7 @@ class FileSuffix(Enum):
 
 def check_only_one_filepath(file_path):
     if len(file_path) > 1:
-        raise ValueError(
-            "Multiple files match that input. Add scan_id to be more specific."
-        )
+        raise ValueError("Multiple files match that input. Add scan_id to be more specific.")
     elif len(file_path) == 0:
         raise FileNotFoundError("No file with those parameters can be found.")
 
@@ -125,13 +123,9 @@ def get_scan_path_version_1(
         if file_suffix == FileSuffix.STANDARD:
             file_pattern = f"FOURD_*_{str(scan_id).zfill(5)}_?????.{filetype}"
         else:
-            file_pattern = (
-                f"FOURD_*_{str(scan_id).zfill(5)}_?????{file_suffix.value}.{filetype}"
-            )
+            file_pattern = f"FOURD_*_{str(scan_id).zfill(5)}_?????{file_suffix.value}.{filetype}"
     elif scan_num is not None:
-        file_pattern = (
-            f"FOURD_*_*_{str(scan_num).zfill(5)}{file_suffix.value}.{filetype}"
-        )
+        file_pattern = f"FOURD_*_*_{str(scan_num).zfill(5)}{file_suffix.value}.{filetype}"
 
     file_path = list(directory.glob(file_pattern))
     check_only_one_filepath(file_path)
@@ -188,9 +182,7 @@ def get_scan_path(
             th=th,
             file_suffix=file_suffix,
         ),
-        1: lambda: get_scan_path_version_1(
-            directory, scan_num=scan_num, scan_id=scan_id, file_suffix=file_suffix
-        ),
+        1: lambda: get_scan_path_version_1(directory, scan_num=scan_num, scan_id=scan_id, file_suffix=file_suffix),
     }
 
     for ver in versions_to_try:
@@ -202,6 +194,4 @@ def get_scan_path(
         except FileNotFoundError:
             continue
 
-    raise FileNotFoundError(
-        "No file with those parameters can be found for any version."
-    )
+    raise FileNotFoundError("No file with those parameters can be found for any version.")
diff --git a/python/stempy/image/__init__.py b/python/stempy/image/__init__.py
index 1909c89f..87c497d3 100644
--- a/python/stempy/image/__init__.py
+++ b/python/stempy/image/__init__.py
@@ -1,17 +1,28 @@
 import warnings
 
+import h5py
+import numpy as np
+
 from stempy import _image
-from stempy import _io
 from stempy.io import (
-    get_hdf5_reader, ReaderMixin, PyReader, SectorThreadedReader,
-    SectorThreadedMultiPassReader, SparseArray
+    PyReader,
+    ReaderMixin,
+    SectorThreadedMultiPassReader,
+    SectorThreadedReader,
+    SparseArray,
+    get_hdf5_reader,
 )
 
-import h5py
-import numpy as np
 
-def create_stem_images(input, inner_radii, outer_radii, scan_dimensions=(0, 0),
-                       center=(-1, -1), frame_dimensions=None, frame_offset=0):
+def create_stem_images(
+    input,
+    inner_radii,
+    outer_radii,
+    scan_dimensions=(0, 0),
+    center=(-1, -1),
+    frame_dimensions=None,
+    frame_offset=0,
+):
     """Create a series of stem images from the input.
 
     :param input: the file reader that has already opened the data, or an
@@ -57,52 +68,75 @@ def create_stem_images(input, inner_radii, outer_radii, scan_dimensions=(0, 0),
         outer_radii = [outer_radii]
 
     # Electron counted data attributes
-    ecd_attrs = ['data', 'scan_dimensions', 'frame_dimensions']
+    ecd_attrs = ["data", "scan_dimensions", "frame_dimensions"]
 
     if isinstance(input, h5py._hl.files.File):
         # Handle h5py file
         input = get_hdf5_reader(input)
-        imgs = _image.create_stem_images(input.begin(), input.end(),
-                                         inner_radii, outer_radii,
-                                         scan_dimensions, center)
+        imgs = _image.create_stem_images(
+            input.begin(),
+            input.end(),
+            inner_radii,
+            outer_radii,
+            scan_dimensions,
+            center,
+        )
     elif issubclass(type(input), ReaderMixin):
         # Handle standard reader
-        imgs = _image.create_stem_images(input.begin(), input.end(),
-                                         inner_radii, outer_radii,
-                                         scan_dimensions, center)
-    elif hasattr(input, '_electron_counted_data'):
+        imgs = _image.create_stem_images(
+            input.begin(),
+            input.end(),
+            inner_radii,
+            outer_radii,
+            scan_dimensions,
+            center,
+        )
+    elif hasattr(input, "_electron_counted_data"):
         # Handle electron counted data with C++ object
         # This could also be a SparseArray created via electron_count()
-        imgs = _image.create_stem_images(input._electron_counted_data,
-                                         inner_radii, outer_radii, center)
+        imgs = _image.create_stem_images(input._electron_counted_data, inner_radii, outer_radii, center)
     elif isinstance(input, SparseArray):
-        imgs = _image.create_stem_images(input.data, inner_radii, outer_radii,
-                                         input.scan_shape[::-1],
-                                         input.frame_shape, center)
-    elif all([hasattr(input, x) for x in ecd_attrs]):
+        imgs = _image.create_stem_images(
+            input.data,
+            inner_radii,
+            outer_radii,
+            input.scan_shape[::-1],
+            input.frame_shape,
+            center,
+        )
+    elif all(hasattr(input, x) for x in ecd_attrs):
         # Handle electron counted data without C++ object
         # Assume this is v1 data and that each frame is one scan position
         data = input.data
         if data.ndim == 1:
             data = data[:, np.newaxis]
-        imgs = _image.create_stem_images(input.data, inner_radii, outer_radii,
-                                         input.scan_dimensions,
-                                         input.frame_dimensions, center)
+        imgs = _image.create_stem_images(
+            input.data,
+            inner_radii,
+            outer_radii,
+            input.scan_dimensions,
+            input.frame_dimensions,
+            center,
+        )
     elif isinstance(input, np.ndarray):
         # The presence of frame dimensions implies it is sparse data
         if frame_dimensions is not None:
             # Handle sparse data
             if input.ndim == 1:
                 input = input[:, np.newaxis]
-            imgs = _image.create_stem_images(input, inner_radii, outer_radii,
-                                             scan_dimensions, frame_dimensions,
-                                             center)
+            imgs = _image.create_stem_images(
+                input,
+                inner_radii,
+                outer_radii,
+                scan_dimensions,
+                frame_dimensions,
+                center,
+            )
         else:
             # Handle raw data
             # Make sure the scan dimensions were passed
             if not scan_dimensions or scan_dimensions == (0, 0):
-                msg = ('scan_dimensions must be provided for np.ndarray '
-                       'raw data input')
+                msg = "scan_dimensions must be provided for np.ndarray " "raw data input"
                 raise Exception(msg)
 
             # Should have shape (num_images, frame_height, frame_width)
@@ -111,19 +145,22 @@ def create_stem_images(input, inner_radii, outer_radii, scan_dimensions=(0, 0),
             block_size = 32
             reader = PyReader(input, image_numbers, scan_dimensions, block_size, num_images)
 
-            imgs = _image.create_stem_images(reader.begin(), reader.end(),
-                                             inner_radii, outer_radii,
-                                             scan_dimensions, center)
+            imgs = _image.create_stem_images(
+                reader.begin(),
+                reader.end(),
+                inner_radii,
+                outer_radii,
+                scan_dimensions,
+                center,
+            )
     else:
-        raise Exception('Type of input, ' + str(type(input)) +
-                        ', is not known to stempy.image.create_stem_images()')
+        raise Exception("Type of input, " + str(type(input)) + ", is not known to stempy.image.create_stem_images()")
 
     images = [np.array(img, copy=False) for img in imgs]
     return np.array(images, copy=False)
 
-def create_stem_histogram(numBins, reader, inner_radii,
-                          outer_radii, scan_dimensions=(0, 0),
-                          center=(-1, -1)):
+
+def create_stem_histogram(numBins, reader, inner_radii, outer_radii, scan_dimensions=(0, 0), center=(-1, -1)):
     """Create a histogram of the stem images generated from the input.
 
     :param numBins: the number of bins the histogram should have.
@@ -149,9 +186,7 @@ def create_stem_histogram(numBins, reader, inner_radii,
     :rtype: a tuple of length 2 of lists
     """
     # create stem images
-    imgs = _image.create_stem_images(reader.begin(), reader.end(),
-                                     inner_radii, outer_radii,
-                                     scan_dimensions, center)
+    imgs = _image.create_stem_images(reader.begin(), reader.end(), inner_radii, outer_radii, scan_dimensions, center)
 
     allBins = []
     allFreqs = []
@@ -165,6 +200,7 @@ def create_stem_histogram(numBins, reader, inner_radii,
 
     return allBins, allFreqs
 
+
 class ImageArray(np.ndarray):
     def __new__(cls, array, dtype=None, order=None):
         obj = np.asarray(array, dtype=dtype, order=order).view(cls)
@@ -172,8 +208,9 @@ def __new__(cls, array, dtype=None, order=None):
         return obj
 
     def __array_finalize__(self, obj):
-        if obj is None: return
-        self._image = getattr(obj, '_image', None)
+        if obj is None:
+            return
+        self._image = getattr(obj, "_image", None)
 
 
 def calculate_average(reader):
@@ -185,18 +222,26 @@ def calculate_average(reader):
     :return: The average image.
     :rtype: stempy.image.ImageArray
     """
-    image =  _image.calculate_average(reader.begin(), reader.end())
-    img = ImageArray(np.array(image, copy = False))
+    image = _image.calculate_average(reader.begin(), reader.end())
+    img = ImageArray(np.array(image, copy=False))
     img._image = image
 
     return img
 
 
-def electron_count(reader, darkreference=None, number_of_samples=40,
-                   background_threshold_n_sigma=4, xray_threshold_n_sigma=10,
-                   threshold_num_blocks=1, scan_dimensions=(0, 0),
-                   verbose=False, gain=None, apply_row_dark=False,
-                   apply_row_dark_use_mean=True):
+def electron_count(
+    reader,
+    darkreference=None,
+    number_of_samples=40,
+    background_threshold_n_sigma=4,
+    xray_threshold_n_sigma=10,
+    threshold_num_blocks=1,
+    scan_dimensions=(0, 0),
+    verbose=False,
+    gain=None,
+    apply_row_dark=False,
+    apply_row_dark_use_mean=True,
+):
     """Generate a list of coordinates of electron hits.
 
     :param reader: the file reader that has already opened the data.
@@ -238,11 +283,11 @@ def electron_count(reader, darkreference=None, number_of_samples=40,
         # Invert, as we will multiply in C++
         # It also must be a float32
         gain = np.power(gain, -1)
-        gain = _safe_cast(gain, np.float32, 'gain')
+        gain = _safe_cast(gain, np.float32, "gain")
 
     if isinstance(darkreference, np.ndarray):
         # Must be float32 for correct conversions
-        darkreference = _safe_cast(darkreference, np.float32, 'dark reference')
+        darkreference = _safe_cast(darkreference, np.float32, "dark reference")
 
     # Special case for threaded reader
     if isinstance(reader, (SectorThreadedReader, SectorThreadedMultiPassReader)):
@@ -262,25 +307,28 @@ def electron_count(reader, darkreference=None, number_of_samples=40,
         data = _image.electron_count(reader, options)
     else:
         deprecation_message = (
-            'Using a reader in electron_count() that is not a '
-            'SectorThreadedReader or a SectorThreadedMultiPassReader is '
-            'deprecated in stempy==1.1 and will be removed in stempy==1.2'
+            "Using a reader in electron_count() that is not a "
+            "SectorThreadedReader or a SectorThreadedMultiPassReader is "
+            "deprecated in stempy==1.1 and will be removed in stempy==1.2"
         )
-        warnings.warn(deprecation_message, category=DeprecationWarning,
-                      stacklevel=2)
+        warnings.warn(deprecation_message, category=DeprecationWarning, stacklevel=2)
 
         blocks = []
-        for i in range(threshold_num_blocks):
+        for _ in range(threshold_num_blocks):
             blocks.append(next(reader))
 
-        if darkreference is not None and hasattr(darkreference, '_image'):
+        if darkreference is not None and hasattr(darkreference, "_image"):
             darkreference = darkreference._image
 
         args = [[b._block for b in blocks]]
         if darkreference is not None:
             args.append(darkreference)
 
-        args = args + [number_of_samples, background_threshold_n_sigma, xray_threshold_n_sigma]
+        args = args + [
+            number_of_samples,
+            background_threshold_n_sigma,
+            xray_threshold_n_sigma,
+        ]
 
         if gain is not None:
             args.append(gain)
@@ -291,21 +339,20 @@ def electron_count(reader, darkreference=None, number_of_samples=40,
         xray_threshold = res.xray_threshold
 
         if verbose:
-            print('****Statistics for calculating electron thresholds****')
-            print('number of samples:', res.number_of_samples)
-            print('min sample:', res.min_sample)
-            print('max sample:', res.max_sample)
-            print('mean:', res.mean)
-            print('variance:', res.variance)
-            print('std dev:', res.std_dev)
-            print('number of bins:', res.number_of_bins)
-            print('x-ray threshold n sigma:', res.xray_threshold_n_sigma)
-            print('background threshold n sigma:',
-                res.background_threshold_n_sigma)
-            print('optimized mean:', res.optimized_mean)
-            print('optimized std dev:', res.optimized_std_dev)
-            print('background threshold:', background_threshold)
-            print('xray threshold:', xray_threshold)
+            print("****Statistics for calculating electron thresholds****")
+            print("number of samples:", res.number_of_samples)
+            print("min sample:", res.min_sample)
+            print("max sample:", res.max_sample)
+            print("mean:", res.mean)
+            print("variance:", res.variance)
+            print("std dev:", res.std_dev)
+            print("number of bins:", res.number_of_bins)
+            print("x-ray threshold n sigma:", res.xray_threshold_n_sigma)
+            print("background threshold n sigma:", res.background_threshold_n_sigma)
+            print("optimized mean:", res.optimized_mean)
+            print("optimized std dev:", res.optimized_std_dev)
+            print("background threshold:", background_threshold)
+            print("xray threshold:", xray_threshold)
 
         # Reset the reader
         reader.reset()
@@ -336,10 +383,10 @@ def electron_count(reader, darkreference=None, number_of_samples=40,
     metadata = _electron_counted_metadata_to_dict(data.metadata)
 
     kwargs = {
-        'data': np_data,
-        'scan_shape': data.scan_dimensions[::-1],
-        'frame_shape': data.frame_dimensions,
-        'metadata': {'electron_counting': metadata},
+        "data": np_data,
+        "scan_shape": data.scan_dimensions[::-1],
+        "frame_shape": data.frame_dimensions,
+        "metadata": {"electron_counting": metadata},
     }
     array = SparseArray(**kwargs)
 
@@ -348,6 +395,7 @@ def electron_count(reader, darkreference=None, number_of_samples=40,
 
     return array
 
+
 def radial_sum(reader, center=(-1, -1), scan_dimensions=(0, 0)):
     """Generate a radial sum from which STEM images can be generated.
 
@@ -367,8 +415,7 @@ def radial_sum(reader, center=(-1, -1), scan_dimensions=(0, 0)):
     :rtype: numpy.ndarray
     """
 
-    sum =  _image.radial_sum(reader.begin(), reader.end(), scan_dimensions,
-                             center)
+    sum = _image.radial_sum(reader.begin(), reader.end(), scan_dimensions, center)
 
     return np.array(sum, copy=False)
 
@@ -398,26 +445,28 @@ def maximum_diffraction_pattern(reader, darkreference=None):
 
 def com_sparse(array, crop_to=None, init_center=None, replace_nans=True):
     """Compute center of mass (COM) for counted data directly from sparse (single)
-        electron data. Empty frames will have COM set to NAN. There is an option to crop to a
-        smaller region around the initial full frame COM to improve finding the center of the zero beam. If the
-        cropped region has no counts in it then the frame is considered empty and COM will be NAN.
-        
-        :param array: A SparseArray of the electron counted data
-        :type array: stempy.io.SparseArray
-        :param crop_to: optional; The size of the region to crop around initial full frame COM for improved COM near
-                        the zero beam
-        :type crop_to: tuple of ints of length 2
-        :param init_center: optional; The initial center to use before cropping. If this is not set then cropping will be applie around 
-                            the center of mass of the each full frame.
-        :type init_center: tuple of ints of length 2
-        :param replace_nans: If true (default) empty frames will have their center of mass set as the mean of the center of mass
-                             of the the entire data set. If this is False they will be set to np.NAN.
-        :type replace_nans: bool
-        :return: The center of mass in X and Y for each scan position. If a position
-                has no data (len(electron_counts) == 0) then the center of the
-                frame is used as the center of mass.
-        :rtype: numpy.ndarray (2D)
-        """
+    electron data. Empty frames will have COM set to NAN. There is an option to crop to a
+    smaller region around the initial full frame COM to improve finding the center of the zero beam. If the
+    cropped region has no counts in it then the frame is considered empty and COM will be NAN.
+
+    :param array: A SparseArray of the electron counted data
+    :type array: stempy.io.SparseArray
+    :param crop_to: optional; The size of the region to crop around initial full frame COM for improved COM near
+                    the zero beam
+    :type crop_to: tuple of ints of length 2
+    :param init_center: optional; The initial center to use before cropping.
+                            If this is not set then cropping will be applie around
+                        the center of mass of the each full frame.
+    :type init_center: tuple of ints of length 2
+    :param replace_nans: If true (default) empty frames will have their center
+                        of mass set as the mean of the center of mass
+                        of the the entire data set. If this is False they will be set to np.NAN.
+    :type replace_nans: bool
+    :return: The center of mass in X and Y for each scan position. If a position
+            has no data (len(electron_counts) == 0) then the center of the
+            frame is used as the center of mass.
+    :rtype: numpy.ndarray (2D)
+    """
     com = np.zeros((2, array.num_scans), np.float32)
     for scan_position, frames in enumerate(array.data):
         # Combine the sparse arrays into one array.
@@ -428,7 +477,7 @@ def com_sparse(array, crop_to=None, init_center=None, replace_nans=True):
             x = ev // array.frame_shape[0]
             y = ev % array.frame_shape[1]
             mm0 = len(ev)
-            
+
             if init_center is None:
                 # Initialize center as full frame COM
                 comx0 = np.sum(x) / mm0
@@ -436,11 +485,15 @@ def com_sparse(array, crop_to=None, init_center=None, replace_nans=True):
             else:
                 comx0 = init_center[0]
                 comy0 = init_center[1]
-            
+
             if crop_to is not None:
                 # Crop around the initial center
-                keep = (x > comx0 - crop_to[0]) & (x <= comx0 + crop_to[0]) & (y > comy0 - crop_to[1]) & (
-                        y <= comy0 + crop_to[1])
+                keep = (
+                    (x > comx0 - crop_to[0])
+                    & (x <= comx0 + crop_to[0])
+                    & (y > comy0 - crop_to[1])
+                    & (y <= comy0 + crop_to[1])
+                )
                 x = x[keep]
                 y = y[keep]
                 mm = len(x)
@@ -457,23 +510,26 @@ def com_sparse(array, crop_to=None, init_center=None, replace_nans=True):
                 # Center of mass of the full frame
                 comx = np.sum(x) / mm0
                 comy = np.sum(y) / mm0
-            
-            com[:, scan_position] = (comy, comx)  # save the comx and comy. Needs to be reversed (comy, comx)
+
+            com[:, scan_position] = (
+                comy,
+                comx,
+            )  # save the comx and comy. Needs to be reversed (comy, comx)
         else:
             com[:, scan_position] = (np.nan, np.nan)  # empty frame
-            
+
     com = com.reshape((2, *array.scan_shape))
-    
+
     if replace_nans:
-        com_mean = np.nanmean(com, axis=(1,2))
-        np.nan_to_num(com[0,:,:], nan=com_mean[0], copy=False)
-        np.nan_to_num(com[1,:,:], nan=com_mean[1], copy=False)
-    
+        com_mean = np.nanmean(com, axis=(1, 2))
+        np.nan_to_num(com[0, :, :], nan=com_mean[0], copy=False)
+        np.nan_to_num(com[1, :, :], nan=com_mean[1], copy=False)
+
     return com
 
 
 def filter_bad_sectors(com, cut_off):
-    """ Missing sectors of data can greatly affect the center of mass calculation. This
+    """Missing sectors of data can greatly affect the center of mass calculation. This
     function attempts to fix that issue. Usually, the problem will be evident by a
     bimodal histogram for the horizontal center of mass. Set the cut_off to a value between
     the real values and the outliers. cut_off is a tuple where values below cut_off[0]
@@ -493,10 +549,11 @@ def filter_bad_sectors(com, cut_off):
     x, y = np.where(_)
 
     for ii, jj in zip(x, y):
-        com[0, ii, jj] = np.nanmedian(com[0, ii - 1:ii + 2, jj - 1:jj + 2])
-        com[1, ii, jj] = np.nanmedian(com[1, ii - 1:ii + 2, jj - 1:jj + 2])
+        com[0, ii, jj] = np.nanmedian(com[0, ii - 1 : ii + 2, jj - 1 : jj + 2])
+        com[1, ii, jj] = np.nanmedian(com[1, ii - 1 : ii + 2, jj - 1 : jj + 2])
     return com
 
+
 def com_dense(frames):
     """Compute the center of mass for a set of dense 2D frames.
 
@@ -511,7 +568,7 @@ def com_dense(frames):
     if frames.ndim == 2:
         frames = frames[None, :, :]
 
-    YY, XX = np.mgrid[0:frames.shape[1], 0:frames.shape[2]]
+    YY, XX = np.mgrid[0 : frames.shape[1], 0 : frames.shape[2]]
     com = np.zeros((2, frames.shape[0]), dtype=np.float64)
     for ii, dp in enumerate(frames):
         mm = dp.sum()
@@ -534,7 +591,7 @@ def calculate_sum_sparse(electron_counts, frame_dimensions):
     :return: A summed diffraction pattern.
     :rtype: numpy.ndarray
     """
-    dp = np.zeros((frame_dimensions[0] * frame_dimensions[1]), '<u8')
+    dp = np.zeros((frame_dimensions[0] * frame_dimensions[1]), "<u8")
     for ev in electron_counts:
         for ee in ev:
             dp[ee] += 1
@@ -556,11 +613,11 @@ def radial_sum_sparse(sa, center):
     """
 
     num_bins = int(max(sa.frame_shape) / 2)
-    r_sum = np.zeros((sa.num_scans, num_bins), dtype='<i8')
+    r_sum = np.zeros((sa.num_scans, num_bins), dtype="<i8")
 
     for ii, frames in enumerate(sa.data):
         # Loop over all scan positions
-        for jj, events in enumerate(frames):
+        for _, events in enumerate(frames):
             # Loop over all frames in this scan position
             x, y = np.unravel_index(events, sa.frame_shape)
             r = np.sqrt((x - center[0]) ** 2 + (y - center[1]) ** 2)
@@ -575,11 +632,11 @@ def _safe_cast(array, dtype, name=None):
     # Cast the array to a different dtype, ensuring no loss of
     # precision. Otherwise, an exception will be raised.
     if name is None:
-        name = 'array'
+        name = "array"
 
     new_array = array.astype(dtype)
     if np.any(new_array.astype(array.dtype) != array):
-        msg = f'Cannot cast {name} to {dtype} without loss of precision'
+        msg = f"Cannot cast {name} to {dtype} without loss of precision"
         raise ValueError(msg)
     return new_array
 
@@ -620,12 +677,12 @@ def phase_from_com(com, theta=0, flip=False, reg=1e-10):
     ny, nx = com.shape[1:]
 
     # Calculate Fourier coordinates for array
-    ky, kx = [np.fft.fftfreq(x) for x in [ny, nx]]
+    ky, kx = (np.fft.fftfreq(x) for x in [ny, nx])
 
     # Calculate numerator and denominator expressions for solution of
     # phase from centre of mass measurements
     numerator = ky[:, None] * np.fft.fft2(CoMx_rot) + kx[None, :] * np.fft.fft2(CoMy_rot)
-    denominator = 2 * np.pi * 1j * ((kx ** 2)[None, :] + (ky ** 2)[:, None]) + reg
+    denominator = 2 * np.pi * 1j * ((kx**2)[None, :] + (ky**2)[:, None]) + reg
     # Avoid a divide by zero for the origin of the Fourier coordinates
     numerator[0, 0] = 0
     denominator[0, 0] = 1
@@ -637,20 +694,20 @@ def phase_from_com(com, theta=0, flip=False, reg=1e-10):
 def _electron_counted_metadata_to_dict(metadata):
     # Convert the electron counted metadata to a python dict
     attributes = [
-        'threshold_calculated',
-        'background_threshold',
-        'x_ray_threshold',
-        'number_of_samples',
-        'min_sample',
-        'max_sample',
-        'mean',
-        'variance',
-        'std_dev',
-        'number_of_bins',
-        'x_ray_threshold_n_sigma',
-        'background_threshold_n_sigma',
-        'optimized_mean',
-        'optimized_std_dev',
+        "threshold_calculated",
+        "background_threshold",
+        "x_ray_threshold",
+        "number_of_samples",
+        "min_sample",
+        "max_sample",
+        "mean",
+        "variance",
+        "std_dev",
+        "number_of_bins",
+        "x_ray_threshold_n_sigma",
+        "background_threshold_n_sigma",
+        "optimized_mean",
+        "optimized_std_dev",
     ]
 
     ret = {}
@@ -659,38 +716,39 @@ def _electron_counted_metadata_to_dict(metadata):
 
     return ret
 
+
 def virtual_darkfield(array, centers_x, centers_y, radii):
     """Calculate a virtual dark field image from a set of round virtual apertures in diffraction space.
     Each aperture is defined by a center and radius and the final image is the sum of all of them.
-    
+
     :param array: The SparseArray
     :type array: SparseArray
-    
+
     :param centers_x: The center of each round aperture as the row locations
     :type centers_x: number or iterable
-    
+
     :param centers_y: The center of each round aperture as the column locations
     :type centers_y: number or iterable
-    
+
     :param radii: The radius of each aperture.
     :type radii: number or iterable
-    
+
     :rtype: np.ndarray
-    
+
     :example:
     >>> sp = stempy.io.load_electron_counts('file.h5')
     >>> df2 = stempy.image.virtual_darkfield(sp, (288, 260), (288, 160), (10, 10)) # 2 apertures
     >>> df1 = stempy.image.virtual_darkfield(sp, 260, 160, 10) # 1 aperture
-    
+
     """
-    
+
     # Change to iterable if single value
     if isinstance(centers_x, (int, float)):
-         centers_x = (centers_x,)
+        centers_x = (centers_x,)
     if isinstance(centers_y, (int, float)):
-         centers_y = (centers_y,)
+        centers_y = (centers_y,)
     if isinstance(radii, (int, float)):
-         radii = (radii,)
+        radii = (radii,)
 
     rs_image = np.zeros((array.shape[0] * array.shape[1],), dtype=array.dtype)
     for ii, events in enumerate(array.data):
@@ -698,33 +756,35 @@ def virtual_darkfield(array, centers_x, centers_y, radii):
             ev_rows = ev // array.frame_shape[0]
             ev_cols = ev % array.frame_shape[1]
             for cc_0, cc_1, rr in zip(centers_x, centers_y, radii):
-                dist = np.sqrt((ev_rows - cc_1)**2 + (ev_cols - cc_0)**2)
+                dist = np.sqrt((ev_rows - cc_1) ** 2 + (ev_cols - cc_0) ** 2)
                 rs_image[ii] += len(np.where(dist < rr)[0])
     rs_image = rs_image.reshape(array.scan_shape)
-    
+
     return rs_image
 
+
 def plot_virtual_darkfield(image, centers_x, centers_y, radii, axes=None):
     """Plot circles on the diffraction pattern corresponding to the position and size of virtual dark field apertures.
-    This has the same center and radii inputs as stempy.image.virtual_darkfield so users can check their input is physically correct.
-    
+    This has the same center and radii inputs as stempy.image.virtual_darkfield
+    so users can check their input is physically correct.
+
     :param image: The diffraction pattern to plot over
     :type image: np.ndarray, 2D
-    
+
     :param centers_x: The center of each round aperture as the row locations
     :type centers_x: iterable
-    
+
     :param centers_y: The center of each round aperture as the column locations
     :type centers_y: iterable
-    
+
     :param radii: The radius of each aperture.
     :type radii: iterable
-    
+
     :param axes: A matplotlib axes instance to use for the plotting. If None then a new plot is created.
     :type axes: matplotlib.axes._subplots.AxesSubplot
-    
+
     :rtype: matplotlib.axes._subplots.AxesSubplot
-    
+
     :example:
     >>> sp = stempy.io.load_electron_counts('file.h5')
     >>> stempy.image.plot_virtual_darkfield(sp.sum(axis=(0, 1), 260, 160, 10) # 1 aperture
@@ -735,33 +795,35 @@ def plot_virtual_darkfield(image, centers_x, centers_y, radii, axes=None):
 
     # Change to iterable if single value
     if isinstance(centers_x, (int, float)):
-         centers_x = (centers_x,)
+        centers_x = (centers_x,)
     if isinstance(centers_y, (int, float)):
-         centers_y = (centers_y,)
+        centers_y = (centers_y,)
     if isinstance(radii, (int, float)):
-         radii = (radii,)
-    
+        radii = (radii,)
+
     if not axes:
         fg, axes = plt.subplots(1, 1)
-    
-    axes.imshow(image, cmap='magma', norm=LogNorm())
-    
+
+    axes.imshow(image, cmap="magma", norm=LogNorm())
+
     # Place a circle at each apertue location
     for cc_0, cc_1, rr in zip(centers_x, centers_y, radii):
-        C = Circle((cc_0, cc_1), rr, fc='none', ec='c')
+        C = Circle((cc_0, cc_1), rr, fc="none", ec="c")
         axes.add_patch(C)
-    
+
     return axes
 
+
 def mask_real_space(array, mask):
     """Calculate a diffraction pattern from an arbitrary set of positions defined in a mask in real space
-    
+
     :param array: The sparse dataset
     :type array: SparseArray
-    
-    :param mask: The mask to apply with 0 for probe positions to ignore and 1 for probe positions to include in the sum. Must have the same scan shape as array
+
+    :param mask: The mask to apply with 0 for probe positions to ignore and 1 for probe positions to include in the sum.
+                Must have the same scan shape as array
     :type mask: np.ndarray
-    
+
     :rtype: np.ndarray
     """
     assert array.scan_shape[0] == mask.shape[0] and array.scan_shape[1] == mask.shape[1]
diff --git a/python/stempy/io/__init__.py b/python/stempy/io/__init__.py
index 22ffc9a3..214ae51e 100644
--- a/python/stempy/io/__init__.py
+++ b/python/stempy/io/__init__.py
@@ -1,26 +1,25 @@
 from collections import namedtuple
-import numpy as np
+
 import h5py
+import numpy as np
 
-from stempy._io import (
-    _reader, _sector_reader, _pyreader, _threaded_reader,
-    _threaded_multi_pass_reader
-)
+from stempy._io import _pyreader, _reader, _sector_reader, _threaded_multi_pass_reader, _threaded_reader
 
 # For exporting SparseArray
 from .sparse_array import SparseArray
 
-COMPILED_WITH_HDF5 = hasattr(_sector_reader, 'H5Format')
+COMPILED_WITH_HDF5 = hasattr(_sector_reader, "H5Format")
 
 
-class FileVersion(object):
+class FileVersion:
     VERSION1 = 1
     VERSION2 = 2
     VERSION3 = 3
     VERSION4 = 4
     VERSION5 = 5
 
-class ReaderMixin(object):
+
+class ReaderMixin:
     def __iter__(self):
         return self
 
@@ -37,16 +36,16 @@ def read(self):
         :return: The block of data that was read. Includes the header also.
         :rtype: Block (named tuple with fields 'header' and 'data')
         """
-        b = super(ReaderMixin, self).read()
+        b = super().read()
 
         # We are at the end of the stream
         if b.header.version == 0:
             return None
 
-        block = namedtuple('Block', ['header', 'data'])
+        block = namedtuple("Block", ["header", "data"])
         block._block = b
         block.header = b.header
-        block.data = np.array(b, copy = False)
+        block.data = np.array(b, copy=False)
 
         return block
 
@@ -54,15 +53,19 @@ def read(self):
 class Reader(ReaderMixin, _reader):
     pass
 
+
 class SectorReader(ReaderMixin, _sector_reader):
     pass
 
+
 class PyReader(ReaderMixin, _pyreader):
     pass
 
+
 class SectorThreadedReader(ReaderMixin, _threaded_reader):
     pass
 
+
 class SectorThreadedMultiPassReader(ReaderMixin, _threaded_multi_pass_reader):
     @property
     def num_frames_per_scan(self):
@@ -100,12 +103,8 @@ def read_frames(self, scan_position, frames_slice=None):
         if isinstance(scan_position, (list, tuple)):
             # Unravel the scan position
             scan_shape = self.scan_shape
-            if (any(not 0 <= scan_position[i] < scan_shape[i]
-                    for i in range(len(scan_position)))):
-                raise IndexError(
-                    f'Invalid position {scan_position} '
-                    f'for scan_shape {scan_shape}'
-                )
+            if any(not 0 <= scan_position[i] < scan_shape[i] for i in range(len(scan_position))):
+                raise IndexError(f"Invalid position {scan_position} " f"for scan_shape {scan_shape}")
 
             image_number = scan_position[0] * scan_shape[1] + scan_position[1]
         else:
@@ -128,18 +127,15 @@ def read_frames(self, scan_position, frames_slice=None):
             # Slice into the frames object
             try:
                 frames = frames[frames_slice]
-            except IndexError:
-                msg = (
-                    f'frames_slice "{frames_slice}" is invalid for '
-                    f'num_frames "{num_frames}"'
-                )
-                raise IndexError(msg)
+            except IndexError as e:
+                msg = f'frames_slice "{frames_slice}" is invalid for ' f'num_frames "{num_frames}"'
+                raise IndexError(msg) from e
 
         blocks = []
 
         raw_blocks = self._load_frames(image_number, frames)
         for b in raw_blocks:
-            block = namedtuple('Block', ['header', 'data'])
+            block = namedtuple("Block", ["header", "data"])
             block._block = b
             block.header = b.header
             block.data = np.array(b, copy=False)[0]
@@ -150,22 +146,24 @@ def read_frames(self, scan_position, frames_slice=None):
 
 def get_hdf5_reader(h5file):
     # the initialization is at the io.cpp
-    dset_frame=h5file['frames']
-    dset_frame_shape=dset_frame.shape
-    totalImgNum=dset_frame_shape[0]
-    scan_dimensions = dset_frame.attrs.get('scan_dimensions')
+    dset_frame = h5file["frames"]
+    dset_frame_shape = dset_frame.shape
+    totalImgNum = dset_frame_shape[0]
+    scan_dimensions = dset_frame.attrs.get("scan_dimensions")
     if scan_dimensions is None:
         # Must be an older file. Give a warning and fall back to the shape.
-        print('WARNING: "scan_dimensions" not found on "/frames"',
-              '(which may imply an older file is being loaded).',
-              'Falling back to the shape of "stem/images"')
-        dset_stem_shape = h5file['stem/images'].shape
+        print(
+            'WARNING: "scan_dimensions" not found on "/frames"',
+            "(which may imply an older file is being loaded).",
+            'Falling back to the shape of "stem/images"',
+        )
+        dset_stem_shape = h5file["stem/images"].shape
         scan_dimensions = (dset_stem_shape[2], dset_stem_shape[1])
 
-    blocksize=32
+    blocksize = 32
     # construct the consecutive image_numbers if there is no scan_positions data set in hdf5 file
-    if("scan_positions" in h5file):
-        image_numbers = h5file['scan_positions']
+    if "scan_positions" in h5file:
+        image_numbers = h5file["scan_positions"]
     else:
         image_numbers = np.arange(totalImgNum)
 
@@ -188,14 +186,14 @@ def reader(path, version=FileVersion.VERSION1, backend=None, **options):
     :rtype: stempy.io.Reader, stempy.io.SectorReader, or stempy.io.PyReader
     """
     # check if the input is the hdf5 dataset
-    if(isinstance(path, h5py._hl.files.File)):
+    if isinstance(path, h5py._hl.files.File):
         reader = get_hdf5_reader(path)
     elif version in [FileVersion.VERSION4, FileVersion.VERSION5]:
-        if backend == 'thread':
+        if backend == "thread":
             reader = SectorThreadedReader(path, version, **options)
-        elif backend == 'multi-pass':
+        elif backend == "multi-pass":
             if version != FileVersion.VERSION5:
-                raise Exception('The multi pass threaded reader only support file verison 5')
+                raise Exception("The multi pass threaded reader only support file verison 5")
 
             reader = SectorThreadedMultiPassReader(path, **options)
         elif backend is None:
@@ -208,8 +206,8 @@ def reader(path, version=FileVersion.VERSION1, backend=None, **options):
 
     return reader
 
-def save_raw_data(path, data, scan_dimensions=None, scan_positions=None,
-                  zip_data=False):
+
+def save_raw_data(path, data, scan_dimensions=None, scan_positions=None, zip_data=False):
     """Save the raw data to an HDF5 file.
 
     :param path: path to the HDF5 file.
@@ -234,20 +232,20 @@ def save_raw_data(path, data, scan_dimensions=None, scan_positions=None,
         if rdcc_nbytes < chunk_size:
             rdcc_nbytes = chunk_size
 
-    with h5py.File(path, 'a', rdcc_nbytes=rdcc_nbytes) as f:
+    with h5py.File(path, "a", rdcc_nbytes=rdcc_nbytes) as f:
         if zip_data:
             # Make each chunk the size of a frame
             chunk_shape = (1, data.shape[1], data.shape[2])
-            frames = f.create_dataset('frames', data=data, compression='gzip',
-                                      chunks=chunk_shape)
+            frames = f.create_dataset("frames", data=data, compression="gzip", chunks=chunk_shape)
         else:
-            frames = f.create_dataset('frames', data=data)
+            frames = f.create_dataset("frames", data=data)
 
         if scan_dimensions is not None:
-            frames.attrs['scan_dimensions'] = scan_dimensions
+            frames.attrs["scan_dimensions"] = scan_dimensions
 
         if scan_positions is not None:
-            f.create_dataset('scan_positions', data=scan_positions)
+            f.create_dataset("scan_positions", data=scan_positions)
+
 
 def save_electron_counts(path, array):
     """Save the electron counted data to an HDF5 file.
@@ -259,6 +257,7 @@ def save_electron_counts(path, array):
     """
     array.write_to_hdf5(path)
 
+
 def load_electron_counts(path, keep_flyback=True):
     """Load electron counted data from an HDF5 file.
 
@@ -271,6 +270,7 @@ def load_electron_counts(path, keep_flyback=True):
     """
     return SparseArray.from_hdf5(path, keep_flyback=keep_flyback)
 
+
 def save_stem_images(outputFile, images, names):
     """Save STEM images to an HDF5 file.
 
@@ -283,15 +283,16 @@ def save_stem_images(outputFile, images, names):
     :type names: a list of strings
     """
     if len(images) != len(names):
-        raise Exception('`images` and `names` must be the same length!')
+        raise Exception("`images` and `names` must be the same length!")
 
-    with h5py.File(outputFile, 'a') as f:
-        stem_group = f.require_group('stem')
-        dataset = stem_group.create_dataset('images', data=images)
-        dataset.attrs['names'] = names
+    with h5py.File(outputFile, "a") as f:
+        stem_group = f.require_group("stem")
+        dataset = stem_group.create_dataset("images", data=images)
+        dataset.attrs["names"] = names
 
 
 if COMPILED_WITH_HDF5:
+
     def write_hdf5(path, reader, format=SectorReader.H5Format.Frame):
         """write_hdf5(path, reader, format=SectorReader.H5Format.Frame)
 
diff --git a/python/stempy/io/compatibility.py b/python/stempy/io/compatibility.py
index 32ccc85e..3781cf94 100644
--- a/python/stempy/io/compatibility.py
+++ b/python/stempy/io/compatibility.py
@@ -1,8 +1,7 @@
 import numpy as np
 
 
-def convert_data_format(data, scan_positions, scan_shape, frame_shape,
-                        from_version, to_version):
+def convert_data_format(data, scan_positions, scan_shape, frame_shape, from_version, to_version):
     """Convert the data and return new (data, scan_positions) as a tuple"""
     if from_version == to_version:
         if to_version < 3:
@@ -18,7 +17,7 @@ def convert_data_format(data, scan_positions, scan_shape, frame_shape,
 
     key = (from_version, to_version)
     if key not in func_map:
-        msg = f'Conversion not implemented: {from_version} => {to_version}'
+        msg = f"Conversion not implemented: {from_version} => {to_version}"
         raise NotImplementedError(msg)
 
     return func_map[key](data, scan_positions, scan_shape)
@@ -52,10 +51,9 @@ def convert_data_v1_to_v2(data, scan_positions, scan_shape):
         # Resize the data and add on the extra rows
         new_size = ret.shape[0] + len(extra_rows)
         new_data = np.empty(new_size, dtype=object)
-        new_data[:ret.shape[0]] = ret
-        new_data[ret.shape[0]:] = extra_rows
-        new_scan_positions = np.append(scan_positions,
-                                       extra_scan_positions)
+        new_data[: ret.shape[0]] = ret
+        new_data[ret.shape[0] :] = extra_rows
+        new_scan_positions = np.append(scan_positions, extra_scan_positions)
 
         ret = new_data
         scan_positions = new_scan_positions
@@ -64,8 +62,7 @@ def convert_data_v1_to_v2(data, scan_positions, scan_shape):
 
 
 def convert_data_v1_to_v3(data, scan_positions, scan_shape):
-    data, scan_positions = convert_data_v1_to_v2(data, scan_positions,
-                                                 scan_shape)
+    data, scan_positions = convert_data_v1_to_v2(data, scan_positions, scan_shape)
     return convert_data_v2_to_v3(data, scan_positions, scan_shape)
 
 
diff --git a/python/stempy/io/sparse_array.py b/python/stempy/io/sparse_array.py
index cec4a04b..1671b807 100644
--- a/python/stempy/io/sparse_array.py
+++ b/python/stempy/io/sparse_array.py
@@ -1,8 +1,8 @@
-from collections.abc import Sequence
 import copy
-from functools import wraps
 import inspect
 import sys
+from collections.abc import Sequence
+from functools import wraps
 
 import numpy as np
 
@@ -12,9 +12,9 @@
 def _format_axis(func):
     @wraps(func)
     def wrapper(self, axis=None, *args, **kwargs):
-        if axis == 'scan':
+        if axis == "scan":
             axis = self.scan_axes
-        elif axis == 'frame':
+        elif axis == "frame":
             axis = self.frame_axes
 
         if axis is None:
@@ -23,6 +23,7 @@ def wrapper(self, axis=None, *args, **kwargs):
             axis = (axis,)
         axis = tuple(sorted(axis))
         return func(self, axis, *args, **kwargs)
+
     return wrapper
 
 
@@ -35,7 +36,7 @@ def wrapper(self, *args, **kwargs):
         if ret is not None:
             return ret
 
-        _warning(f'performing full expansion for {name}')
+        _warning(f"performing full expansion for {name}")
         prev_sparse_slicing = self.sparse_slicing
         self.sparse_slicing = False
         try:
@@ -43,6 +44,7 @@ def wrapper(self, *args, **kwargs):
         finally:
             self.sparse_slicing = prev_sparse_slicing
         return getattr(expanded, name)(*args, **kwargs)
+
     return wrapper
 
 
@@ -57,6 +59,7 @@ def wrapper(*args, **kwargs):
                 _warning(f'"{key}" is not implemented for {name}')
 
         return func(*args, **kwargs)
+
     return wrapper
 
 
@@ -86,10 +89,19 @@ class SparseArray:
     and stride. Slicing returns either a new sparse array or a dense
     array depending on the user-defined settings.
     """
+
     VERSION = 3
 
-    def __init__(self, data, scan_shape, frame_shape, dtype=np.uint32,
-                 sparse_slicing=True, allow_full_expand=False, metadata=None):
+    def __init__(
+        self,
+        data,
+        scan_shape,
+        frame_shape,
+        dtype=np.uint32,
+        sparse_slicing=True,
+        allow_full_expand=False,
+        metadata=None,
+    ):
         """Initialize a sparse array.
 
         :param data: the sparse array data, where the outer array represents
@@ -144,18 +156,18 @@ def __init__(self, data, scan_shape, frame_shape, dtype=np.uint32,
     def _validate(self):
         if self.data.ndim != 2:
             msg = (
-                'The data must have 2 dimensions, but instead has '
-                f'{self.data.ndim}. The outer dimension should be the '
-                'scan position, and the inner dimension should be the '
-                'frame index at that scan position'
+                "The data must have 2 dimensions, but instead has "
+                f"{self.data.ndim}. The outer dimension should be the "
+                "scan position, and the inner dimension should be the "
+                "frame index at that scan position"
             )
             raise Exception(msg)
 
         if len(self.data) != self.num_scans:
             msg = (
-                f'The length of the data array ({len(self.data)}) must be '
-                f'equal to the number of scans ({self.num_scans}), which '
-                f'is computed via np.prod(scan_shape)'
+                f"The length of the data array ({len(self.data)}) must be "
+                f"equal to the number of scans ({self.num_scans}), which "
+                f"is computed via np.prod(scan_shape)"
             )
             raise Exception(msg)
 
@@ -163,9 +175,9 @@ def _validate(self):
             for j, frame in enumerate(scan_frames):
                 if not np.issubdtype(frame.dtype, np.integer):
                     msg = (
-                        f'All frames of data must have integral dtype, but '
-                        f'scan_position={i} frame={j} has a dtype of '
-                        f'{frame.dtype}'
+                        f"All frames of data must have integral dtype, but "
+                        f"scan_position={i} frame={j} has a dtype of "
+                        f"{frame.dtype}"
                     )
                     raise Exception(msg)
 
@@ -185,38 +197,38 @@ def from_hdf5(cls, filepath, keep_flyback=True, **init_kwargs):
         """
         import h5py
 
-        with h5py.File(filepath, 'r') as f:
-            version = f.attrs.get('version', 1)
+        with h5py.File(filepath, "r") as f:
+            version = f.attrs.get("version", 1)
+
+            frames = f["electron_events/frames"]
+            scan_positions_group = f["electron_events/scan_positions"]
+            scan_shape = [scan_positions_group.attrs[x] for x in ["Nx", "Ny"]]
+            frame_shape = [frames.attrs[x] for x in ["Nx", "Ny"]]
 
-            frames = f['electron_events/frames']
-            scan_positions_group = f['electron_events/scan_positions']
-            scan_shape = [scan_positions_group.attrs[x] for x in ['Nx', 'Ny']]
-            frame_shape = [frames.attrs[x] for x in ['Nx', 'Ny']]
-            
             if keep_flyback:
-                data = frames[()] # load the full data set
+                data = frames[()]  # load the full data set
                 scan_positions = scan_positions_group[()]
             else:
                 # Generate the original scan indices from the scan_shape
-                orig_indices = np.ravel_multi_index([ii.ravel() for ii in np.indices(scan_shape)],scan_shape)
-                # Remove the indices of the last column 
-                crop_indices = np.delete(orig_indices, orig_indices[scan_shape[0]-1::scan_shape[0]])
+                orig_indices = np.ravel_multi_index([ii.ravel() for ii in np.indices(scan_shape)], scan_shape)
+                # Remove the indices of the last column
+                crop_indices = np.delete(orig_indices, orig_indices[scan_shape[0] - 1 :: scan_shape[0]])
                 # Load only the data needed
                 data = frames[crop_indices]
                 # Reduce the column shape by 1
                 scan_shape[0] = scan_shape[0] - 1
                 # Create the proper scan_positions without the flyback column
-                scan_positions = np.ravel_multi_index([ii.ravel() for ii in np.indices(scan_shape)],scan_shape)
-                
+                scan_positions = np.ravel_multi_index([ii.ravel() for ii in np.indices(scan_shape)], scan_shape)
+
             # Load any metadata
             metadata = {}
-            if 'metadata' in f:
-                load_h5_to_dict(f['metadata'], metadata)
+            if "metadata" in f:
+                load_h5_to_dict(f["metadata"], metadata)
 
         scan_shape = scan_shape[::-1]
-        
+
         if version >= 3:
-            # Convert to int to avoid integer division that results in 
+            # Convert to int to avoid integer division that results in
             # a float
             frames_per_scan = len(data) // np.prod(scan_shape, dtype=int)
             # Need to reshape the data, as it was flattened before saving
@@ -225,20 +237,20 @@ def from_hdf5(cls, filepath, keep_flyback=True, **init_kwargs):
         # We may need to convert the version of the data
         if version != cls.VERSION:
             kwargs = {
-                'data': data,
-                'scan_positions': scan_positions,
-                'scan_shape': scan_shape,
-                'frame_shape': frame_shape,
-                'from_version': version,
-                'to_version': cls.VERSION,
+                "data": data,
+                "scan_positions": scan_positions,
+                "scan_shape": scan_shape,
+                "frame_shape": frame_shape,
+                "from_version": version,
+                "to_version": cls.VERSION,
             }
             data = convert_data_format(**kwargs)
 
         kwargs = {
-            'data': data,
-            'scan_shape': scan_shape,
-            'frame_shape': frame_shape,
-            'metadata': metadata,
+            "data": data,
+            "scan_shape": scan_shape,
+            "frame_shape": frame_shape,
+            "metadata": metadata,
         }
         kwargs.update(init_kwargs)
         return cls(**kwargs)
@@ -252,29 +264,27 @@ def write_to_hdf5(self, path):
         import h5py
 
         data = self.data
-        with h5py.File(path, 'a') as f:
-            f.attrs['version'] = self.VERSION
+        with h5py.File(path, "a") as f:
+            f.attrs["version"] = self.VERSION
 
-            group = f.require_group('electron_events')
-            scan_positions = group.create_dataset('scan_positions',
-                                                  data=self.scan_positions)
-            scan_positions.attrs['Nx'] = self.scan_shape[1]
-            scan_positions.attrs['Ny'] = self.scan_shape[0]
+            group = f.require_group("electron_events")
+            scan_positions = group.create_dataset("scan_positions", data=self.scan_positions)
+            scan_positions.attrs["Nx"] = self.scan_shape[1]
+            scan_positions.attrs["Ny"] = self.scan_shape[0]
 
             # We can't store a 2D array that contains variable length arrays
             # in h5py currently. So flatten the data, and we will reshape it
             # when we read it back in. See h5py/h5py#876
             coordinates_type = h5py.special_dtype(vlen=np.uint32)
-            frames = group.create_dataset('frames', (np.prod(data.shape),),
-                                          dtype=coordinates_type)
+            frames = group.create_dataset("frames", (np.prod(data.shape),), dtype=coordinates_type)
             # Add the frame dimensions as attributes
-            frames.attrs['Nx'] = self.frame_shape[0]
-            frames.attrs['Ny'] = self.frame_shape[1]
+            frames.attrs["Nx"] = self.frame_shape[0]
+            frames.attrs["Ny"] = self.frame_shape[1]
 
             frames[...] = data.ravel()
 
             # Write out the metadata
-            save_dict_to_h5(self.metadata, f.require_group('metadata'))
+            save_dict_to_h5(self.metadata, f.require_group("metadata"))
 
     def to_dense(self):
         """Create and return a fully dense version of the sparse array
@@ -344,19 +354,17 @@ def reshape(self, *shape):
             shape = shape[0]
 
         if len(shape) not in (3, 4):
-            raise ValueError('Shape must be length 3 or 4')
+            raise ValueError("Shape must be length 3 or 4")
 
         scan_shape = shape[:-2]
         frame_shape = shape[-2:]
 
         # Make sure the shapes are valid
         if np.prod(scan_shape) != np.prod(self.scan_shape):
-            raise ValueError('Cannot reshape scan array of size '
-                             f'{self.scan_shape} into shape {scan_shape}')
+            raise ValueError("Cannot reshape scan array of size " f"{self.scan_shape} into shape {scan_shape}")
 
         if np.prod(frame_shape) != np.prod(self.frame_shape):
-            raise ValueError('Cannot reshape frame array of size '
-                             f'{self.frame_shape} into shape {frame_shape}')
+            raise ValueError("Cannot reshape frame array of size " f"{self.frame_shape} into shape {frame_shape}")
 
         # If we made it here, we can perform the reshaping...
         self.scan_shape = scan_shape
@@ -568,18 +576,18 @@ def bin_scans(self, bin_factor, in_place=False):
         :rtype: SparseArray
         """
         if not all(x % bin_factor == 0 for x in self.scan_shape):
-            raise ValueError(f'scan_shape must be equally divisible by '
-                             f'bin_factor {bin_factor}')
+            raise ValueError(f"scan_shape must be equally divisible by " f"bin_factor {bin_factor}")
 
         # No need to modify the data, just the scan shape and the scan
         # positions.
         new_scan_shape = tuple(x // bin_factor for x in self.scan_shape)
         all_positions = self.scan_positions
-        all_positions_reshaped = all_positions.reshape(
-            new_scan_shape[0], bin_factor, new_scan_shape[1], bin_factor)
+        all_positions_reshaped = all_positions.reshape(new_scan_shape[0], bin_factor, new_scan_shape[1], bin_factor)
 
-        new_data_shape = (np.prod(new_scan_shape),
-                          bin_factor**2 * self.num_frames_per_scan)
+        new_data_shape = (
+            np.prod(new_scan_shape),
+            bin_factor**2 * self.num_frames_per_scan,
+        )
         new_data = np.empty(new_data_shape, dtype=object)
         for i in range(new_scan_shape[0]):
             for j in range(new_scan_shape[1]):
@@ -594,12 +602,12 @@ def bin_scans(self, bin_factor, in_place=False):
             return self
 
         kwargs = {
-            'data': new_data,
-            'scan_shape': new_scan_shape,
-            'frame_shape': self.frame_shape,
-            'dtype': self.dtype,
-            'sparse_slicing': self.sparse_slicing,
-            'allow_full_expand': self.allow_full_expand,
+            "data": new_data,
+            "scan_shape": new_scan_shape,
+            "frame_shape": self.frame_shape,
+            "dtype": self.dtype,
+            "sparse_slicing": self.sparse_slicing,
+            "allow_full_expand": self.allow_full_expand,
         }
         return SparseArray(**kwargs)
 
@@ -619,8 +627,7 @@ def bin_frames(self, bin_factor, in_place=False):
         :rtype: SparseArray
         """
         if not all(x % bin_factor == 0 for x in self.frame_shape):
-            raise ValueError(f'frame_shape must be equally divisible by '
-                             f'bin_factor {bin_factor}')
+            raise ValueError(f"frame_shape must be equally divisible by " f"bin_factor {bin_factor}")
 
         # Ravel the data for easier manipulation in this function
         data = self.data.ravel()
@@ -628,13 +635,12 @@ def bin_frames(self, bin_factor, in_place=False):
         rows = data // self.frame_shape[0] // bin_factor
         cols = data % self.frame_shape[1] // bin_factor
 
-        rows *= (self.frame_shape[0] // bin_factor)
+        rows *= self.frame_shape[0] // bin_factor
         rows += cols
 
         new_data = rows
         frames_per_scan = self.data.shape[1]
-        raveled_scan_positions = np.repeat(self.scan_positions,
-                                           frames_per_scan)
+        raveled_scan_positions = np.repeat(self.scan_positions, frames_per_scan)
         new_scan_positions = raveled_scan_positions
 
         # Place duplicates in separate frames
@@ -655,10 +661,9 @@ def bin_frames(self, bin_factor, in_place=False):
             # Resize the new data and add on the extra rows
             new_size = rows.shape[0] + len(extra_rows)
             new_data = np.empty(new_size, dtype=object)
-            new_data[:rows.shape[0]] = rows
-            new_data[rows.shape[0]:] = extra_rows
-            new_scan_positions = np.append(new_scan_positions,
-                                           extra_scan_positions)
+            new_data[: rows.shape[0]] = rows
+            new_data[rows.shape[0] :] = extra_rows
+            new_scan_positions = np.append(new_scan_positions, extra_scan_positions)
 
             # Find the max number of extra frames per scan position and use
             # that.
@@ -691,12 +696,12 @@ def bin_frames(self, bin_factor, in_place=False):
             return self
 
         kwargs = {
-            'data': new_data,
-            'scan_shape': self.scan_shape,
-            'frame_shape': new_frame_shape,
-            'dtype': self.dtype,
-            'sparse_slicing': self.sparse_slicing,
-            'allow_full_expand': self.allow_full_expand,
+            "data": new_data,
+            "scan_shape": self.scan_shape,
+            "frame_shape": new_frame_shape,
+            "dtype": self.dtype,
+            "sparse_slicing": self.sparse_slicing,
+            "allow_full_expand": self.allow_full_expand,
         }
         return SparseArray(**kwargs)
 
@@ -781,6 +786,7 @@ def _split_slices(self, slices):
 
         Returns `scan_slices, frame_slices`.
         """
+
         def validate_if_advanced_indexing(obj, max_ndim, is_scan):
             """Check if the obj is advanced indexing
                (and convert to ndarray if it is)
@@ -799,12 +805,12 @@ def validate_if_advanced_indexing(obj, max_ndim, is_scan):
                 # If it is a numpy array, it is advanced indexing.
                 # Ensure that there are not too many dimensions.
                 if obj.ndim > max_ndim:
-                    msg = 'Too many advanced indexing dimensions.'
+                    msg = "Too many advanced indexing dimensions."
                     if is_scan:
                         msg += (
-                            ' Cannot perform advanced indexing on both the '
-                            'scan positions and the frame positions '
-                            'simultaneously'
+                            " Cannot perform advanced indexing on both the "
+                            "scan positions and the frame positions "
+                            "simultaneously"
                         )
                     raise IndexError(msg)
 
@@ -816,62 +822,55 @@ def validate_if_advanced_indexing(obj, max_ndim, is_scan):
 
         if not slices:
             # It's an empty tuple
-            return tuple(), tuple()
+            return (), ()
 
         # Figure out which slices belong to which parts.
         # The first slice should definitely be part of the scan.
         first_slice = slices[0]
-        first_slice = validate_if_advanced_indexing(first_slice,
-                                                    len(self.scan_shape),
-                                                    is_scan=True)
+        first_slice = validate_if_advanced_indexing(first_slice, len(self.scan_shape), is_scan=True)
 
         frame_start = 1
         if len(self.scan_shape) > 1:
             # We might have 2 slices for the scan shape
-            if not isinstance(first_slice,
-                              np.ndarray) or first_slice.ndim == 1:
+            if not isinstance(first_slice, np.ndarray) or first_slice.ndim == 1:
                 # We have another scan slice
                 frame_start += 1
 
         if frame_start == 2 and len(slices) > 1:
             # Validate the second scan slice.
-            second_slice = validate_if_advanced_indexing(slices[1], 1,
-                                                         is_scan=True)
+            second_slice = validate_if_advanced_indexing(slices[1], 1, is_scan=True)
             scan_slices = (first_slice, second_slice)
         else:
             scan_slices = (first_slice,)
 
         # If there are frame indices, validate them too
-        frame_slices = tuple()
+        frame_slices = ()
         for i in range(frame_start, len(slices)):
             max_ndim = frame_start + 2 - i
             if max_ndim == 0:
-                raise IndexError('Too many indices for frame positions')
-            frame_slices += (validate_if_advanced_indexing(slices[i], max_ndim,
-                                                           is_scan=False),)
+                raise IndexError("Too many indices for frame positions")
+            frame_slices += (validate_if_advanced_indexing(slices[i], max_ndim, is_scan=False),)
 
         # Verify that we are not doing advanced indexing on both the scan
         # positions and frame positions simultaneously.
-        if (any(isinstance(x, np.ndarray) for x in scan_slices) and
-                any(isinstance(x, np.ndarray) for x in frame_slices)):
-            msg = (
-                'Cannot perform advanced indexing on both scan positions '
-                'and frame positions simultaneously'
-            )
+        if any(isinstance(x, np.ndarray) for x in scan_slices) and any(isinstance(x, np.ndarray) for x in frame_slices):
+            msg = "Cannot perform advanced indexing on both scan positions " "and frame positions simultaneously"
             raise IndexError(msg)
 
         # Verify that if there are any 2D advanced indexing arrays, they
         # must be boolean and of the same shape.
-        first_frame_slice = (slice(None) if not frame_slices
-                             else frame_slices[0])
+        first_frame_slice = slice(None) if not frame_slices else frame_slices[0]
         for i, to_check in enumerate((first_slice, first_frame_slice)):
             req_shape = self.scan_shape if i == 0 else self.frame_shape
-            if (isinstance(to_check, np.ndarray) and to_check.ndim == 2 and
-               (to_check.dtype != np.bool_ or to_check.shape != req_shape)):
+            if (
+                isinstance(to_check, np.ndarray)
+                and to_check.ndim == 2
+                and (to_check.dtype != np.bool_ or to_check.shape != req_shape)
+            ):
                 msg = (
-                    '2D advanced indexing is only allowed for boolean arrays '
-                    'that match either the scan shape or the frame shape '
-                    '(whichever it is indexing into)'
+                    "2D advanced indexing is only allowed for boolean arrays "
+                    "that match either the scan shape or the frame shape "
+                    "(whichever it is indexing into)"
                 )
                 raise IndexError(msg)
 
@@ -882,8 +881,7 @@ def _sparse_frames(self, indices):
             indices = (indices,)
 
         if len(indices) != len(self.scan_shape):
-            msg = (f'{indices} shape does not match scan shape'
-                   f'{self.scan_shape}')
+            msg = f"{indices} shape does not match scan shape" f"{self.scan_shape}"
             raise ValueError(msg)
 
         if len(indices) == 1:
@@ -894,15 +892,13 @@ def _sparse_frames(self, indices):
         return self.data[scan_ind]
 
     def _slice_dense(self, scan_slices, frame_slices):
-        new_scan_shape = np.empty(self.scan_shape,
-                                  dtype=bool)[scan_slices].shape
-        new_frame_shape = np.empty(self.frame_shape,
-                                   dtype=bool)[frame_slices].shape
+        new_scan_shape = np.empty(self.scan_shape, dtype=bool)[scan_slices].shape
+        new_frame_shape = np.empty(self.frame_shape, dtype=bool)[frame_slices].shape
 
         result_shape = new_scan_shape + new_frame_shape
 
         if result_shape == self.shape and not self.allow_full_expand:
-            raise FullExpansionDenied('Full expansion is not allowed')
+            raise FullExpansionDenied("Full expansion is not allowed")
 
         # Create the result
         result = np.zeros(result_shape, dtype=self.dtype)
@@ -944,10 +940,8 @@ def _slice_sparse(self, scan_slices, frame_slices):
             new_frames = self.data
 
         if frame_shape_modified:
-
             # Map old frame indices to new ones. Invalid values will be -1.
-            frame_indices = np.arange(self._frame_shape_flat[0]).reshape(
-                self.frame_shape)
+            frame_indices = np.arange(self._frame_shape_flat[0]).reshape(self.frame_shape)
             sliced = frame_indices[frame_slices]
             new_frame_shape = sliced.shape
 
@@ -975,12 +969,12 @@ def _slice_sparse(self, scan_slices, frame_slices):
             new_data = copy.deepcopy(new_frames)
 
         kwargs = {
-            'data': new_data,
-            'scan_shape': new_scan_shape,
-            'frame_shape': new_frame_shape,
-            'dtype': self.dtype,
-            'sparse_slicing': self.sparse_slicing,
-            'allow_full_expand': self.allow_full_expand,
+            "data": new_data,
+            "scan_shape": new_scan_shape,
+            "frame_shape": new_frame_shape,
+            "dtype": self.dtype,
+            "sparse_slicing": self.sparse_slicing,
+            "allow_full_expand": self.allow_full_expand,
         }
         return SparseArray(**kwargs)
 
@@ -989,7 +983,7 @@ def _expand(self, indices):
             indices = (indices,)
 
         if len(indices) >= len(self.scan_shape):
-            scan_indices = indices[:len(self.scan_shape)]
+            scan_indices = indices[: len(self.scan_shape)]
             sparse_frames = self._sparse_frames(scan_indices)
 
         if len(indices) < len(self.scan_shape):
@@ -1019,12 +1013,11 @@ def _expand(self, indices):
         elif len(indices) == len(self.scan_shape) + 2:
             # The last two indices are frame indices
             frame_indices = indices[-2:]
-            flat_frame_index = (frame_indices[0] * self.frame_shape[1] +
-                                frame_indices[1])
+            flat_frame_index = frame_indices[0] * self.frame_shape[1] + frame_indices[1]
             return sum(flat_frame_index in frame for frame in sparse_frames)
 
         max_length = len(self.shape) + 1
-        raise ValueError(f'0 < len(indices) < {max_length} is required')
+        raise ValueError(f"0 < len(indices) < {max_length} is required")
 
     @staticmethod
     def _is_advanced_indexing(obj):
@@ -1053,8 +1046,7 @@ def is_int_or_bool_ndarray(x):
             return True
 
         if isinstance(obj, tuple):
-            return any(isinstance(x, Sequence) or is_int_or_bool_ndarray(x)
-                       for x in obj)
+            return any(isinstance(x, Sequence) or is_int_or_bool_ndarray(x) for x in obj)
 
         return False
 
@@ -1064,7 +1056,7 @@ def _is_none_slice(x):
 
 
 def _warning(msg):
-    print(f'Warning: {msg}', file=sys.stderr)
+    print(f"Warning: {msg}", file=sys.stderr)
 
 
 class FullExpansionDenied(Exception):
diff --git a/python/stempy/utils.py b/python/stempy/utils.py
index ce5b3dbb..fa5dac57 100644
--- a/python/stempy/utils.py
+++ b/python/stempy/utils.py
@@ -1,12 +1,14 @@
 try:
     # Use importlib metadata if available (python >=3.8)
-    from importlib.metadata import version, PackageNotFoundError
+    from importlib.metadata import PackageNotFoundError, version
 except ImportError:
     # No importlib metadata. Try to use pkg_resources instead.
     from pkg_resources import (
-        get_distribution,
         DistributionNotFound as PackageNotFoundError,
     )
+    from pkg_resources import (
+        get_distribution,
+    )
 
     def version(x):
         return get_distribution(x).version
@@ -14,7 +16,7 @@ def version(x):
 
 def get_version():
     try:
-        return version('stempy')
+        return version("stempy")
     except PackageNotFoundError:
         # package is not installed
         pass
diff --git a/setup.py b/setup.py
index d55fe476..fc7f356f 100644
--- a/setup.py
+++ b/setup.py
@@ -7,42 +7,42 @@
 def extra_cmake_args():
     # Warning: if you use paths on Windows, you should use "\\"
     # for the path delimiter to work on CI.
-    env = os.getenv('EXTRA_CMAKE_ARGS')
-    return env.split(';') if env else []
+    env = os.getenv("EXTRA_CMAKE_ARGS")
+    return env.split(";") if env else []
 
 
 cmake_args = [] + extra_cmake_args()
 
-if os.name == 'nt':
+if os.name == "nt":
     # Need to export all headers on windows...
-    cmake_args.append('-DCMAKE_WINDOWS_EXPORT_ALL_SYMBOLS=TRUE')
+    cmake_args.append("-DCMAKE_WINDOWS_EXPORT_ALL_SYMBOLS=TRUE")
 
 
-if os.getenv('USE_PYTHON_IN_PATH'):
-    python_exe = shutil.which('python')
+if os.getenv("USE_PYTHON_IN_PATH"):
+    python_exe = shutil.which("python")
     if python_exe:
         # For this program, we use find_package(Python3 ...)
-        cmake_args.append(f'-DPython3_EXECUTABLE={python_exe}')
+        cmake_args.append(f"-DPython3_EXECUTABLE={python_exe}")
 
 
-with open('requirements.txt') as f:
+with open("requirements.txt") as f:
     install_requires = f.read()
 
 setup(
-    name='stempy',
+    name="stempy",
     use_scm_version=True,
-    description='A package for the ingestion of 4D STEM data.',
-    long_description='A package for the ingestion of 4D STEM data.',
-    url='https://github.com/OpenChemistry/stempy',
-    author='Kitware Inc',
-    license='BSD 3-Clause',
+    description="A package for the ingestion of 4D STEM data.",
+    long_description="A package for the ingestion of 4D STEM data.",
+    url="https://github.com/OpenChemistry/stempy",
+    author="Kitware Inc",
+    license="BSD 3-Clause",
     classifiers=[
-        'Development Status :: 3 - Alpha',
-        'License :: OSI Approved :: BSD License',
-        'Programming Language :: Python :: 3',
+        "Development Status :: 3 - Alpha",
+        "License :: OSI Approved :: BSD License",
+        "Programming Language :: Python :: 3",
     ],
-    keywords='',
-    packages=['stempy'],
+    keywords="",
+    packages=["stempy"],
     install_requires=install_requires,
     cmake_args=cmake_args,
 )
diff --git a/tests/conftest.py b/tests/conftest.py
index d8cc8f25..a9075e51 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -6,13 +6,12 @@
 
 from stempy.io.sparse_array import SparseArray
 
-
 DATA_URLS = {
-    'electron_small': 'https://data.kitware.com/api/v1/file/6065f00d2fa25629b93bdabe/download',  # noqa
-    'electron_large': 'https://data.kitware.com/api/v1/file/6065f2792fa25629b93c0303/download',  # noqa
-    'cropped_multi_frames_v1': 'https://data.kitware.com/api/v1/file/623119814acac99f4261aa59/download',  # noqa
-    'cropped_multi_frames_v2': 'https://data.kitware.com/api/v1/file/6244e9944acac99f424743df/download',  # noqa
-    'cropped_multi_frames_v3': 'https://data.kitware.com/api/v1/file/624601cd4acac99f425c73f6/download',  # noqa
+    "electron_small": "https://data.kitware.com/api/v1/file/6065f00d2fa25629b93bdabe/download",  # noqa
+    "electron_large": "https://data.kitware.com/api/v1/file/6065f2792fa25629b93c0303/download",  # noqa
+    "cropped_multi_frames_v1": "https://data.kitware.com/api/v1/file/623119814acac99f4261aa59/download",  # noqa
+    "cropped_multi_frames_v2": "https://data.kitware.com/api/v1/file/6244e9944acac99f424743df/download",  # noqa
+    "cropped_multi_frames_v3": "https://data.kitware.com/api/v1/file/624601cd4acac99f425c73f6/download",  # noqa
 }
 
 DATA_RESPONSES = {}
@@ -34,34 +33,35 @@ def io_object(key):
 
 @pytest.fixture
 def electron_data_small():
-    return io_object('electron_small')
+    return io_object("electron_small")
 
 
 @pytest.fixture
 def electron_data_large():
-    return io_object('electron_large')
+    return io_object("electron_large")
 
 
 @pytest.fixture
 def cropped_multi_frames_data_v1():
-    return io_object('cropped_multi_frames_v1')
+    return io_object("cropped_multi_frames_v1")
 
 
 @pytest.fixture
 def cropped_multi_frames_data_v2():
-    return io_object('cropped_multi_frames_v2')
+    return io_object("cropped_multi_frames_v2")
 
 
 @pytest.fixture
 def cropped_multi_frames_data_v3():
-    return io_object('cropped_multi_frames_v3')
+    return io_object("cropped_multi_frames_v3")
 
 
 # SparseArray fixtures
 
+
 @pytest.fixture
 def sparse_array_10x10():
-    aa = np.zeros((4, 3), dtype='object')
+    aa = np.zeros((4, 3), dtype="object")
     for ii in range(4):
         aa[ii, 0] = np.array((0, 48, 72, 65))
         aa[ii, 1] = np.array((0, 48, 72, 65))
@@ -73,7 +73,7 @@ def sparse_array_10x10():
 @pytest.fixture
 def sparse_array_small(electron_data_small):
     kwargs = {
-        'dtype': np.uint64,
+        "dtype": np.uint64,
     }
     array = SparseArray.from_hdf5(electron_data_small, **kwargs)
 
@@ -109,54 +109,53 @@ def cropped_multi_frames_v2(cropped_multi_frames_data_v2):
 def cropped_multi_frames_v3(cropped_multi_frames_data_v3):
     return SparseArray.from_hdf5(cropped_multi_frames_data_v3, dtype=np.uint16)
 
+
 @pytest.fixture
 def simulate_sparse_array():
-    
     """Make a ndarray with sparse disk.
     scan_size: Real space scan size (pixels)
     frame_size: detector size (pixels)
     center: the center of the disk in diffraction space
     how_sparse: Percent sparseness (0-1). Large number means fewer electrons per frame
     disk_size: The radius in pixels of the diffraction disk
-    
-    """ 
-    
+    """
+
     scan_size = (100, 100)
-    frame_size = (100,100)
+    frame_size = (100, 100)
     center = (30, 70)
-    how_sparse = .8 # 0-1; larger is less electrons
-    disk_size = 10 # disk radius in pixels
+    how_sparse = 0.8  # 0-1; larger is less electrons
+    disk_size = 10  # disk radius in pixels
     num_frames = 2
-    
-    YY, XX = np.mgrid[0:frame_size[0], 0:frame_size[1]]
-    RR = np.sqrt((YY-center[1])**2 + (XX-center[0])**2)
+
+    YY, XX = np.mgrid[0 : frame_size[0], 0 : frame_size[1]]
+    RR = np.sqrt((YY - center[1]) ** 2 + (XX - center[0]) ** 2)
 
     RR[RR <= disk_size] = 1
     RR[RR > disk_size] = 0
 
-    sparse = np.random.rand(scan_size[0],scan_size[1],frame_size[0],frame_size[1], num_frames) * RR[:,:,None]
+    sparse = np.random.rand(scan_size[0], scan_size[1], frame_size[0], frame_size[1], num_frames) * RR[:, :, None]
 
     sparse[sparse >= how_sparse] = 1
     sparse[sparse < how_sparse] = 0
 
     sparse = sparse.astype(np.uint8)
-    
-    new_data = np.empty((sparse.shape[0],sparse.shape[1], num_frames), dtype=object)
+
+    new_data = np.empty((sparse.shape[0], sparse.shape[1], num_frames), dtype=object)
     for j in range(sparse.shape[0]):
         for k in range(sparse.shape[1]):
             for i in range(num_frames):
-                events = np.ravel_multi_index(np.where(sparse[j,k,:,:,i] == 1),frame_size)
-                new_data[j,k,i] = events
+                events = np.ravel_multi_index(np.where(sparse[j, k, :, :, i] == 1), frame_size)
+                new_data[j, k, i] = events
 
-    new_data = new_data.reshape(new_data.shape[0]*new_data.shape[1],num_frames)
+    new_data = new_data.reshape(new_data.shape[0] * new_data.shape[1], num_frames)
 
     kwargs = {
-        'data': new_data,
-        'scan_shape': sparse.shape[0:2],
-        'frame_shape': sparse.shape[2:4],
-        'dtype': sparse.dtype,
-        'sparse_slicing': True,
-        'allow_full_expand': False,
+        "data": new_data,
+        "scan_shape": sparse.shape[0:2],
+        "frame_shape": sparse.shape[2:4],
+        "dtype": sparse.dtype,
+        "sparse_slicing": True,
+        "allow_full_expand": False,
     }
-    
-    return SparseArray(**kwargs)
\ No newline at end of file
+
+    return SparseArray(**kwargs)
diff --git a/tests/test_image.py b/tests/test_image.py
index 4c18a907..d3a16acc 100644
--- a/tests/test_image.py
+++ b/tests/test_image.py
@@ -1,5 +1,3 @@
-import pytest
-
 import numpy as np
 
 from stempy.image import com_dense, com_sparse, radial_sum_sparse
@@ -15,9 +13,9 @@ def test_com_sparse(sparse_array_small, full_array_small):
     data[1][0] = np.array([0])
     data[1][1] = np.array([0, 1, 2])
     kwargs = {
-        'data': data,
-        'scan_shape': (2, 1),
-        'frame_shape': (2, 2),
+        "data": data,
+        "scan_shape": (2, 1),
+        "frame_shape": (2, 2),
     }
     array = SparseArray(**kwargs)
 
@@ -40,24 +38,23 @@ def test_com_sparse(sparse_array_small, full_array_small):
 
 
 def test_radial_sum_sparse(sparse_array_10x10):
-
     rr = radial_sum_sparse(sparse_array_10x10, center=(5, 5))
 
     assert np.array_equal(rr[0, 0, :], [0, 6, 0, 0, 3])
 
+
 def test_com_sparse_parameters(simulate_sparse_array):
-    
-    sp = simulate_sparse_array #((100,100), (100,100), (30,70), (0.8), (10))
-    
+    sp = simulate_sparse_array  # ((100,100), (100,100), (30,70), (0.8), (10))
+
     # Test no inputs. This should be the full frame COM
     com0 = com_sparse(sp)
     assert round(com0[0,].mean()) == 30
-    
+
     # Test crop_to input. Initial COM should be full frame COM
-    com1 = com_sparse(sp, crop_to=(10,10))
+    com1 = com_sparse(sp, crop_to=(10, 10))
     assert round(com1[0,].mean()) == 30
-    
+
     # Test crop_to and init_center input.
     # No counts will be in the center so all positions will be np.nan
-    com2 = com_sparse(sp, crop_to=(10,10), init_center=(1,1))
-    assert np.isnan(com2[0,0,0])
+    com2 = com_sparse(sp, crop_to=(10, 10), init_center=(1, 1))
+    assert np.isnan(com2[0, 0, 0])
diff --git a/tests/test_simple.py b/tests/test_simple.py
index f51cf447..e79119ef 100644
--- a/tests/test_simple.py
+++ b/tests/test_simple.py
@@ -12,24 +12,24 @@ def test_simple():
     # io.get_hdf5_reader() is hard-coded to that.
     shape = (32, 576, 576)
     data = np.arange(np.prod(shape)).reshape(shape)
-    dataset_name = 'frames'
+    dataset_name = "frames"
 
     # Stempy uses uint16. Let's prevent overflow...
     data %= np.iinfo(np.uint16).max
     mean = data.mean(axis=0)
 
     # Fake stuff that io.get_hdf5_reader() needs...
-    fake_scan_name = 'stem/images'
+    fake_scan_name = "stem/images"
     fake_scan = np.zeros((4, 8, 1))
 
     temp = tempfile.NamedTemporaryFile(delete=False)
     file_name = temp.name
     try:
-        with h5py.File(file_name, 'w') as wf:
+        with h5py.File(file_name, "w") as wf:
             wf.create_dataset(dataset_name, data=data)
             wf.create_dataset(fake_scan_name, data=fake_scan)
 
-        with h5py.File(file_name, 'r') as rf:
+        with h5py.File(file_name, "r") as rf:
             reader = io.get_hdf5_reader(rf)
             average = image.calculate_average(reader)
     finally:
@@ -41,5 +41,5 @@ def test_simple():
     assert np.array_equal(average, mean)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     test_simple()
diff --git a/tests/test_sparse_array.py b/tests/test_sparse_array.py
index e088884c..6a465c56 100644
--- a/tests/test_sparse_array.py
+++ b/tests/test_sparse_array.py
@@ -1,12 +1,10 @@
 import os
 import tempfile
 
-import pytest
-
 import numpy as np
+import pytest
 
-from stempy.io.sparse_array import FullExpansionDenied
-from stempy.io.sparse_array import SparseArray
+from stempy.io.sparse_array import FullExpansionDenied, SparseArray
 
 
 def test_full_expansion(sparse_array_small, full_array_small):
@@ -56,7 +54,7 @@ def test_dense_slicing(sparse_array_small, full_array_small):
         for i in range(len(slices)):
             # Try using just the first slice, then the first and the second,
             # etc. until using all slices.
-            s = slices[:i + 1]
+            s = slices[: i + 1]
 
             if len(s) == 1 and s[0] == slice(None):
                 # Skip over full expansions
@@ -74,9 +72,24 @@ def test_sparse_slicing(sparse_array_small, full_array_small):
 
     # Just test a few indices
     test_frames = [
-        (0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 7), (0, 9), (0, 11),
-        (1, 0), (1, 3), (2, 2), (3, 1), (8, 4), (9, 6), (10, 13), (17, 17),
-        (14, 16), (18, 15),
+        (0, 0),
+        (0, 1),
+        (0, 2),
+        (0, 3),
+        (0, 4),
+        (0, 7),
+        (0, 9),
+        (0, 11),
+        (1, 0),
+        (1, 3),
+        (2, 2),
+        (3, 1),
+        (8, 4),
+        (9, 6),
+        (10, 13),
+        (17, 17),
+        (14, 16),
+        (18, 15),
     ]
     for slices in TEST_SLICES:
         sliced = array[slices]
@@ -113,8 +126,7 @@ def test_frame_slicing(sparse_array_small, full_array_small):
 
     # Now test the dense slicing version
     array.sparse_slicing = False
-    assert np.array_equal(array[:, :, 100:200, 200:300],
-                          full[:, :, 100:200, 200:300])
+    assert np.array_equal(array[:, :, 100:200, 200:300], full[:, :, 100:200, 200:300])
 
 
 def test_scan_axes_arithmetic(sparse_array_small, full_array_small):
@@ -161,9 +173,9 @@ def test_bin_scans(sparse_array_small, full_array_small):
             continue
 
         binned = array.bin_scans(factor)
-        full_binned = full.reshape(shape[0] // factor, factor,
-                                   shape[1] // factor, factor,
-                                   shape[2], shape[3]).sum(axis=(1, 3))
+        full_binned = full.reshape(shape[0] // factor, factor, shape[1] // factor, factor, shape[2], shape[3]).sum(
+            axis=(1, 3)
+        )
 
         assert np.array_equal(binned.to_dense(), full_binned)
 
@@ -192,9 +204,9 @@ def test_bin_frames(sparse_array_small, full_array_small):
             continue
 
         binned = array.bin_frames(factor)
-        full_binned = full.reshape(shape[0], shape[1], shape[2] // factor,
-                                   factor, shape[3] // factor,
-                                   factor).sum(axis=(3, 5))
+        full_binned = full.reshape(shape[0], shape[1], shape[2] // factor, factor, shape[3] // factor, factor).sum(
+            axis=(3, 5)
+        )
 
         assert np.array_equal(binned.to_dense(), full_binned)
 
@@ -295,21 +307,16 @@ def test_slice_sum(sparse_array_small, full_array_small):
     data = sparse_array_small
     full = full_array_small
 
-    assert np.array_equal(data[0, 0:1, :, :].sum(axis='scan'),
-                          full[0, 0:1, :, :].sum(axis=0))
-    assert np.array_equal(data[0:1, 0:1, :, :].sum(axis='scan'),
-                          full[0:1, 0:1, :, :].sum(axis=(0, 1)))
-    assert np.array_equal(data[0:2, 0:2, :, :].sum(axis='scan'),
-                          full[0:2, 0:2, :, :].sum(axis=(0, 1)))
-    assert np.array_equal(data[0, 0, :, :].sum(),
-                          full[0, 0, :, :].sum())
+    assert np.array_equal(data[0, 0:1, :, :].sum(axis="scan"), full[0, 0:1, :, :].sum(axis=0))
+    assert np.array_equal(data[0:1, 0:1, :, :].sum(axis="scan"), full[0:1, 0:1, :, :].sum(axis=(0, 1)))
+    assert np.array_equal(data[0:2, 0:2, :, :].sum(axis="scan"), full[0:2, 0:2, :, :].sum(axis=(0, 1)))
+    assert np.array_equal(data[0, 0, :, :].sum(), full[0, 0, :, :].sum())
 
-    assert np.array_equal(data[:, :, 0:1, 0:2].sum(axis='frame'),
-                          full[:, :, 0:1, 0:2].sum(axis=(2, 3)))
-    assert np.array_equal(data[:, :, 100:200, 100:200].sum(axis='frame'),
-                          full[:, :, 100:200, 100:200].sum(axis=(2, 3)))
-    assert np.array_equal(data[:, :, 100:200:-3, 100:200:-3].sum(axis='frame'),
-                          full[:, :, 100:200:-3, 100:200:-3].sum(axis=(2, 3)))
+    assert np.array_equal(data[:, :, 0:1, 0:2].sum(axis="frame"), full[:, :, 0:1, 0:2].sum(axis=(2, 3)))
+    assert np.array_equal(data[:, :, 100:200, 100:200].sum(axis="frame"), full[:, :, 100:200, 100:200].sum(axis=(2, 3)))
+    assert np.array_equal(
+        data[:, :, 100:200:-3, 100:200:-3].sum(axis="frame"), full[:, :, 100:200:-3, 100:200:-3].sum(axis=(2, 3))
+    )
 
 
 def test_scan_ravel(sparse_array_small, full_array_small):
@@ -322,8 +329,7 @@ def test_scan_ravel(sparse_array_small, full_array_small):
 
     # Perform a few simple tests on the raveled SparseArray
     assert np.array_equal(array.sum(axis=0), full.sum(axis=(0, 1)))
-    assert np.array_equal(array.sum(axis=(1, 2)),
-                          full.sum(axis=(2, 3)).ravel())
+    assert np.array_equal(array.sum(axis=(1, 2)), full.sum(axis=(2, 3)).ravel())
 
     full = full.reshape(expected_shape)
     for i in range(expected_shape[0]):
@@ -335,14 +341,16 @@ def test_round_trip_hdf5(sparse_array_small):
     original = sparse_array_small
 
     # Store some metadata that we will test for
-    original.metadata.update({
-        'nested': {
-            'string': 's',
-        },
-        'array1d': np.arange(3, dtype=int),
-        'array2d': np.arange(9, dtype=np.float32).reshape((3, 3)),
-        'float': np.float64(7.21),
-    })
+    original.metadata.update(
+        {
+            "nested": {
+                "string": "s",
+            },
+            "array1d": np.arange(3, dtype=int),
+            "array2d": np.arange(9, dtype=np.float32).reshape((3, 3)),
+            "float": np.float64(7.21),
+        }
+    )
 
     temp = tempfile.NamedTemporaryFile(delete=False)
     try:
@@ -361,7 +369,7 @@ def test_round_trip_hdf5(sparse_array_small):
         for a, b in zip(sfa, sfb):
             assert np.array_equal(a, b)
 
-    original.metadata['float'] = np.float64(6.5)
+    original.metadata["float"] = np.float64(6.5)
     # Now, the metadata shouldn't be equal
     assert not dicts_equal(original.metadata, array.metadata)
 
@@ -373,7 +381,7 @@ def dicts_equal(d1, d2):
     for k, v1 in d1.items():
         v2 = d2[k]
 
-        if type(v1) != type(v2):
+        if type(v1) is not type(v2):
             return False
 
         if isinstance(v1, np.ndarray):
@@ -400,11 +408,11 @@ def test_multiple_frames_per_scan_position():
     data[1][0] = np.array([0])
     data[1][1] = np.array([0, 1])
     kwargs = {
-        'data': data,
-        'scan_shape': (2, 1),
-        'frame_shape': (2, 2),
-        'sparse_slicing': False,
-        'allow_full_expand': True,
+        "data": data,
+        "scan_shape": (2, 1),
+        "frame_shape": (2, 2),
+        "sparse_slicing": False,
+        "allow_full_expand": True,
     }
     array = SparseArray(**kwargs)
 
@@ -447,10 +455,8 @@ def test_multiple_frames_per_scan_position():
     assert np.array_equal(array[:, :, 0][1, 0], full[:, :, 0][1, 0])
     assert np.array_equal(array[:, :, 0, 1][0, 0], full[:, :, 0, 1][0, 0])
     assert np.array_equal(array[:, :, 0, 1:][0, 0], full[:, :, 0, 1:][0, 0])
-    assert np.array_equal(array[:, :, 0:2, 0:2][0, 0],
-                          full[:, :, 0:2, 0:2][0, 0])
-    assert np.array_equal(array[:, :, 0:2, 0:2][1, 0],
-                          full[:, :, 0:2, 0:2][1, 0])
+    assert np.array_equal(array[:, :, 0:2, 0:2][0, 0], full[:, :, 0:2, 0:2][0, 0])
+    assert np.array_equal(array[:, :, 0:2, 0:2][1, 0], full[:, :, 0:2, 0:2][1, 0])
     assert np.array_equal(array[0, :, :, 0][0], full[0, :, :, 0][0])
     assert np.array_equal(array[0, :, :, 1][0], full[0, :, :, 1][0])
     assert np.array_equal(array[0, :, 0:2, 0:2][0], full[0, :, 0:2, 0:2][0])
@@ -460,13 +466,13 @@ def test_multiple_frames_per_scan_position():
 
     # Test arithmetic
     array.allow_full_expand = False
-    axis = 'scan'
+    axis = "scan"
     assert np.array_equal(array.sum(axis=axis), [[4, 1], [1, 0]])
     assert np.array_equal(array.max(axis=axis), [[2, 1], [1, 0]])
     assert np.array_equal(array.min(axis=axis), [[2, 0], [0, 0]])
     assert np.array_equal(array.mean(axis=axis), [[2, 0.5], [0.5, 0]])
 
-    axis = 'frame'
+    axis = "frame"
     assert np.array_equal(array.sum(axis=axis), full.sum(axis=(2, 3)))
     assert np.array_equal(array.mean(axis=axis), full.mean(axis=(2, 3)))
     assert np.array_equal(array.min(axis=axis), full.min(axis=(2, 3)))
@@ -480,10 +486,10 @@ def test_multiple_frames_per_scan_position():
     data[1][1] = np.array([], dtype=int)
 
     kwargs = {
-        'data': data,
-        'scan_shape': (2, 1),
-        'frame_shape': (4, 4),
-        'sparse_slicing': False,
+        "data": data,
+        "scan_shape": (2, 1),
+        "frame_shape": (4, 4),
+        "sparse_slicing": False,
     }
     test_bin_frames_array = SparseArray(**kwargs)
     binned = test_bin_frames_array.bin_frames(2)
@@ -501,11 +507,11 @@ def test_multiple_frames_per_scan_position():
         data[i] = np.array([i % 4])
 
     kwargs = {
-        'data': data,
-        'scan_shape': (4, 4),
-        'frame_shape': (2, 2),
-        'sparse_slicing': False,
-        'allow_full_expand': True,
+        "data": data,
+        "scan_shape": (4, 4),
+        "frame_shape": (2, 2),
+        "sparse_slicing": False,
+        "allow_full_expand": True,
     }
     test_bin_scans_array = SparseArray(**kwargs)
     binned = test_bin_scans_array.bin_scans(2)
@@ -527,8 +533,7 @@ def test_multiple_frames_per_scan_position():
     assert np.array_equal(binned_4x[:], binned_twice[:])
 
 
-def test_data_conversion(cropped_multi_frames_v1, cropped_multi_frames_v2,
-                         cropped_multi_frames_v3):
+def test_data_conversion(cropped_multi_frames_v1, cropped_multi_frames_v2, cropped_multi_frames_v3):
     # These datasets were in older formats, but when they were converted
     # into a SparseArray via the fixture, they should have automatically
     # been converted to the latest version. Confirm this is the case.
@@ -618,7 +623,7 @@ def compare_with_sparse(full, sparse):
     rng = np.random.default_rng(0)
 
     num_tries = 3
-    for i in range(num_tries):
+    for _ in range(num_tries):
         scan_mask = rng.choice([True, False], array.scan_shape)
         frame_mask = rng.choice([True, False], array.frame_shape)
 
@@ -653,23 +658,15 @@ def compare_with_sparse(full, sparse):
 
     # Now test a few arithmetic operations combined with the indexing
     array.sparse_slicing = True
-    assert np.array_equal(array[scan_mask].sum(axis='scan'),
-                          full[scan_mask].sum(axis=(0,)))
-    assert np.array_equal(array[scan_mask].min(axis='scan'),
-                          full[scan_mask].min(axis=(0,)))
-    assert np.array_equal(array[scan_mask].max(axis='scan'),
-                          full[scan_mask].max(axis=(0,)))
-    assert np.allclose(array[scan_mask].mean(axis='scan'),
-                       full[scan_mask].mean(axis=(0,)))
-
-    assert np.array_equal(array[:, :, frame_mask].sum(axis='frame'),
-                          full[:, :, frame_mask].sum(axis=(2,)))
-    assert np.allclose(array[:, :, frame_mask].mean(axis='frame'),
-                       full[:, :, frame_mask].mean(axis=(2,)))
-    assert np.array_equal(array[:, :, frame_mask].min(axis='frame'),
-                          full[:, :, frame_mask].min(axis=(2,)))
-    assert np.array_equal(array[:, :, frame_mask].max(axis='frame'),
-                          full[:, :, frame_mask].max(axis=(2,)))
+    assert np.array_equal(array[scan_mask].sum(axis="scan"), full[scan_mask].sum(axis=(0,)))
+    assert np.array_equal(array[scan_mask].min(axis="scan"), full[scan_mask].min(axis=(0,)))
+    assert np.array_equal(array[scan_mask].max(axis="scan"), full[scan_mask].max(axis=(0,)))
+    assert np.allclose(array[scan_mask].mean(axis="scan"), full[scan_mask].mean(axis=(0,)))
+
+    assert np.array_equal(array[:, :, frame_mask].sum(axis="frame"), full[:, :, frame_mask].sum(axis=(2,)))
+    assert np.allclose(array[:, :, frame_mask].mean(axis="frame"), full[:, :, frame_mask].mean(axis=(2,)))
+    assert np.array_equal(array[:, :, frame_mask].min(axis="frame"), full[:, :, frame_mask].min(axis=(2,)))
+    assert np.array_equal(array[:, :, frame_mask].max(axis="frame"), full[:, :, frame_mask].max(axis=(2,)))
 
     # Now do some basic tests with multiple frames per scan
     data = np.empty((2, 2), dtype=object)
@@ -678,11 +675,11 @@ def compare_with_sparse(full, sparse):
     data[1][0] = np.array([0])
     data[1][1] = np.array([0, 1])
     kwargs = {
-        'data': data,
-        'scan_shape': (2, 1),
-        'frame_shape': (2, 2),
-        'sparse_slicing': False,
-        'allow_full_expand': True,
+        "data": data,
+        "scan_shape": (2, 1),
+        "frame_shape": (2, 2),
+        "sparse_slicing": False,
+        "allow_full_expand": True,
     }
     # The multiple frames per scan array
     m_array = SparseArray(**kwargs)
@@ -702,10 +699,8 @@ def compare_with_sparse(full, sparse):
     assert np.array_equal(m_array[[1, 0], [0]][0], position_one)
     assert np.array_equal(m_array[[1, 0], [0]][1], position_zero)
     assert np.array_equal(m_array[0, 0, [0, 1], [0, 0]], [2, 1])
-    assert np.array_equal(m_array[0, 0, [[True, False], [True, False]]],
-                          [2, 1])
-    assert np.array_equal(m_array[0, 0, [[False, True], [False, True]]],
-                          [0, 0])
+    assert np.array_equal(m_array[0, 0, [[True, False], [True, False]]], [2, 1])
+    assert np.array_equal(m_array[0, 0, [[False, True], [False, True]]], [0, 0])
     assert np.array_equal(m_array[[True, False], 0][0], position_zero)
     assert np.array_equal(m_array[[False, True], 0][0], position_one)
 
@@ -715,7 +710,8 @@ def test_keep_flyback(electron_data_small):
     assert flyback.scan_shape[1] == 50
     no_flyback = SparseArray.from_hdf5(electron_data_small, keep_flyback=False)
     assert no_flyback.scan_shape[1] == 49
-    
+
+
 # Test binning until this number
 TEST_BINNING_UNTIL = 33
 
@@ -736,10 +732,7 @@ def test_keep_flyback(electron_data_small):
     (slice(4), slice(7), slice(3), slice(5)),
     (slice(2, 8), slice(5, 9), slice(2, 20), slice(50, 200)),
     (slice(2, 18, 2), slice(3, 20, 3), slice(2, 50, 2), slice(20, 100)),
-    (slice(None, None, 2), slice(None, None, 3), slice(None, None, 4),
-     slice(None, None, 5)),
-    (slice(3, None, 2), slice(5, None, 5), slice(4, None, 4),
-     slice(20, None, 5)),
-    (slice(20, 4, -2), slice(None, None, -1), slice(4, None, -3),
-     slice(100, 3, -5)),
+    (slice(None, None, 2), slice(None, None, 3), slice(None, None, 4), slice(None, None, 5)),
+    (slice(3, None, 2), slice(5, None, 5), slice(4, None, 4), slice(20, None, 5)),
+    (slice(20, 4, -2), slice(None, None, -1), slice(4, None, -3), slice(100, 3, -5)),
 ]