Add codespell: workflow, config and fix typos it finds

.gitignore

@@ -165,7 +165,6 @@ ehthumbs_vista.db
 Desktop.ini
 
 #dev and comparison
-/bin
 #everything from test except the one the user want to send back and an example one.
 caiman/tests/comparison/tests/
 !caiman/tests/comparison/tests/example/*

CONTRIBUTING.md

@@ -31,7 +31,7 @@ The workflow for contributing to Caiman is roughly illustrated by the numbers in
 Below we have instructions on how to do all of the above steps. While all of this may seem like a lot, some of the steps are extremely simple. Also, once you have done it once, you will have the recipe and it will be pretty easy. Finally, it is a very rewarding experience to contribute to an open source project -- we hope you'll take the plunge!
 
 ## First, create a dedicated development environment
-If you have downloaded Caiman for standard use, you probably installed it using `conda` or `mamba` as described on the README page. As a contributor, you will want to set up a dedicated development environment. This means you will be setting up a version of Caiman you will edit and tweak, uncoupled from your main installation for everyday use. To set up a development environment so you can follow the worflow outlined above, do the following:
+If you have downloaded Caiman for standard use, you probably installed it using `conda` or `mamba` as described on the README page. As a contributor, you will want to set up a dedicated development environment. This means you will be setting up a version of Caiman you will edit and tweak, uncoupled from your main installation for everyday use. To set up a development environment so you can follow the workflow outlined above, do the following:
 
 1. Fork and clone the caiman repository    
 Go to the [Caiman repo](https://github.com/flatironinstitute/CaImAn) and hit the `Fork` button at the top right of the page. You now have Caiman on your own GitHub page! On your computer, in your conda prompt, go to a directory where you want Caiman to download, and clone your personal Caiman repo: `git clone https://github.com/<your-username>/CaImAn.git` where <your-username> is replaced by your github username.

Jenkinsfile

@@ -94,7 +94,7 @@ Check console output at $BUILD_URL to view full results.
 Building $BRANCH_NAME for $CAUSE
 $JOB_DESCRIPTION
 
-Chages:
+Changes:
 $CHANGES
 
 End of build log:

bin/caiman_gui.py

@@ -109,11 +109,11 @@ def selectionchange(self,i):
     estimates.Cn = cm.local_correlations(mov, swap_dim=False)
 #Cn = estimates.Cn
 
-# We rotate our components 90 degrees right because of incompatiability of pyqtgraph and pyplot
+# We rotate our components 90 degrees right because of incompatibility of pyqtgraph and pyplot
 def rotate90(img, right=None, vector=None, sparse=False):
     # rotate the img 90 degrees
     # we first transpose the img then flip axis
-    # If right is ture, then rotate 90 degrees right, otherwise, rotate left
+    # If right is true, then rotate 90 degrees right, otherwise, rotate left
     # If vector is True, then we first reshape the spatial 1D vector to 2D then rotate
     # If vector is False, then we directly rotate the matrix
     global dims

caiman/base/movies.py

@@ -129,7 +129,7 @@ def motion_correct(self,
 
 
         Returns:
-            self: motion corected movie, it might change the object itself
+            self: motion corrected movie, it might change the object itself
 
             shifts : tuple, contains x & y shifts and correlation with template
 
@@ -208,7 +208,7 @@ def motion_correct_3d(self,
 
 
         Returns:
-            self: motion corected movie, it might change the object itself
+            self: motion corrected movie, it might change the object itself
 
             shifts : tuple, contains x, y, and z shifts and correlation with template
 
@@ -535,7 +535,7 @@ def removeBL(self, windowSize:int=100, quantilMin:int=8, in_place:bool=False, re
                 window size over which to compute the baseline (the larger the faster the algorithm and the less granular
 
             quantilMin: float
-                percentil to be used as baseline value
+                percentile to be used as baseline value
             in_place: bool
                 update movie in place
             returnBL:
@@ -764,7 +764,7 @@ def IPCA(self, components: int = 50, batch: int = 1000) -> tuple[np.ndarray, np.
         frame_size = h * w
         frame_samples = np.reshape(self, (num_frames, frame_size)).T
 
-        # run IPCA to approxiate the SVD
+        # run IPCA to approximate the SVD
         ipca_f = sklearn.decomposition.IncrementalPCA(n_components=components, batch_size=batch)
         ipca_f.fit(frame_samples)
 
@@ -1783,7 +1783,7 @@ def sbxread(filename: str, k: int = 0, n_frames=np.inf) -> np.ndarray:
     # Determine number of frames in whole file
     max_idx = os.path.getsize(filename + '.sbx') / info['recordsPerBuffer'] / info['sz'][1] * factor / 4 - 1
 
-    # Paramters
+    # Parameters
     N = max_idx + 1    # Last frame
     N = np.minimum(N, n_frames)
 
@@ -1834,7 +1834,7 @@ def sbxreadskip(filename: str, subindices: slice) -> np.ndarray:
     # Determine number of frames in whole file
     max_idx = int(os.path.getsize(filename + '.sbx') / info['recordsPerBuffer'] / info['sz'][1] * factor / 4 - 1)
 
-    # Paramters
+    # Parameters
     if isinstance(subindices, slice):
         if subindices.start is None:
             start = 0

caiman/base/rois.py

@@ -720,7 +720,7 @@ def distance_masks(M_s:list, cm_s: list[list], max_dist: float, enclosed_thr: Op
     for gt_comp, test_comp, cmgt_comp, cmtest_comp in zip(M_s[:-1], M_s[1:], cm_s[:-1], cm_s[1:]):
 
         # todo : better with a function that calls itself
-        # not to interfer with M_s
+        # not to interfere with M_s
         gt_comp = gt_comp.copy()[:, :]
         test_comp = test_comp.copy()[:, :]
 
@@ -753,8 +753,8 @@ def distance_masks(M_s:list, cm_s: list[list], max_dist: float, enclosed_thr: Op
                     # if we don't have even a union this is pointless
                     if union > 0:
 
-                        # intersection is removed from union since union contains twice the overlaping area
-                        # having the values in this format 0-1 is helpfull for the hungarian algorithm that follows
+                        # intersection is removed from union since union contains twice the overlapping area
+                        # having the values in this format 0-1 is helpful for the hungarian algorithm that follows
                         D[i, j] = 1 - 1. * intersection / \
                             (union - intersection)
                         if enclosed_thr is not None:
@@ -791,7 +791,7 @@ def find_matches(D_s, print_assignment: bool = False) -> tuple[list, list]:
         matches.append(indexes)
         DD = D.copy()
         total = []
-        # we want to extract those informations from the hungarian algo
+        # we want to extract those information from the hungarian algo
         for row, column in indexes2:
             value = DD[row, column]
             if print_assignment:
@@ -1095,7 +1095,7 @@ def extract_binary_masks_blob(A,
 
     Args:
         A: scipy.sparse matrix
-            contains the components as outputed from the CNMF algorithm
+            contains the components as outputted from the CNMF algorithm
 
         neuron_radius: float
             neuronal radius employed in the CNMF settings (gSiz)

caiman/behavior/behavior.py

@@ -178,7 +178,7 @@ def compute_optical_flow(m,
             mask selecting relevant pixels
 
         polar_coord: boolean
-            wheather to return the coordinate in polar coordinates (or cartesian)
+            whether to return the coordinate in polar coordinates (or cartesian)
 
         do_show: bool
             show flow movie

caiman/cluster.py

@@ -386,14 +386,14 @@ def setup_cluster(backend:str = 'multiprocessing',
                             'You have configured the cluster setup to not raise an exception.')
             else:
                 raise Exception(
-                    'A cluster is already runnning. Terminate with dview.terminate() if you want to restart.')
+                    'A cluster is already running. Terminate with dview.terminate() if you want to restart.')
         if platform.system() == 'Darwin':
             try:
                 if 'kernel' in get_ipython().trait_names():        # type: ignore
                                                                    # If you're on OSX and you're running under Jupyter or Spyder,
                                                                    # which already run the code in a forkserver-friendly way, this
                                                                    # can eliminate some setup and make this a reasonable approach.
-                                                                   # Otherwise, seting VECLIB_MAXIMUM_THREADS=1 or using a different
+                                                                   # Otherwise, setting VECLIB_MAXIMUM_THREADS=1 or using a different
                                                                    # blas/lapack is the way to avoid the issues.
                                                                    # See https://github.com/flatironinstitute/CaImAn/issues/206 for more
                                                                    # info on why we're doing this (for now).

caiman/components_evaluation.py

@@ -64,7 +64,7 @@ def compute_event_exceptionality(traces: np.ndarray,
             value estimate of the quality of components (the lesser the better)
 
         erfc: ndarray
-            probability at each time step of observing the N consequtive actual trace values given the distribution of noise
+            probability at each time step of observing the N consecutive actual trace values given the distribution of noise
 
         noise_est: ndarray
             the components ordered according to the fitness
@@ -394,10 +394,10 @@ def evaluate_components(Y: np.ndarray,
             value estimate of the quality of components (the lesser the better) on diff(trace)
 
         erfc_raw: ndarray
-            probability at each time step of observing the N consequtive actual trace values given the distribution of noise on the raw trace
+            probability at each time step of observing the N consecutive actual trace values given the distribution of noise on the raw trace
 
         erfc_raw: ndarray
-            probability at each time step of observing the N consequtive actual trace values given the distribution of noise on diff(trace)
+            probability at each time step of observing the N consecutive actual trace values given the distribution of noise on diff(trace)
 
         r_values: list
             float values representing correlation between component and spatial mask obtained by averaging important points

caiman/mmapping.py

@@ -90,7 +90,7 @@ def save_memmap_each(fnames: list[str],
             list of path to the filenames
 
         dview: ipyparallel dview
-            used to perform computation in parallel. If none it will be signle thread
+            used to perform computation in parallel. If none it will be single thread
 
         base_name str
             BaseName for the file to be creates. If not given the file itself is used
@@ -358,7 +358,7 @@ def save_memmap(filenames:list[str],
             x,y, and z downsampling factors (0.5 means downsampled by a factor 2)
 
         remove_init: int
-            number of frames to remove at the begining of each tif file
+            number of frames to remove at the beginning of each tif file
             (used for resonant scanning images if laser in rutned on trial by trial)
 
         idx_xy: tuple size 2 [or 3 for 3D data]
@@ -449,11 +449,11 @@ def save_memmap(filenames:list[str],
                 if slices is not None:
                     Yr = Yr[tuple(slices)]
                 else:
-                    if idx_xy is None:         #todo remove if not used, superceded by the slices parameter
+                    if idx_xy is None:         #todo remove if not used, superseded by the slices parameter
                         Yr = Yr[remove_init:]
-                    elif len(idx_xy) == 2:     #todo remove if not used, superceded by the slices parameter
+                    elif len(idx_xy) == 2:     #todo remove if not used, superseded by the slices parameter
                         Yr = Yr[remove_init:, idx_xy[0], idx_xy[1]]
-                    else:                      #todo remove if not used, superceded by the slices parameter
+                    else:                      #todo remove if not used, superseded by the slices parameter
                         Yr = Yr[remove_init:, idx_xy[0], idx_xy[1], idx_xy[2]]
 
             else:

caiman/motion_correction.py

@@ -113,7 +113,7 @@ def __init__(self, fname, min_mov=None, dview=None, max_shifts=(6, 6), niter_rig
                will quickly initialize a template with the first frames
 
            splits_rig': int
-            for parallelization split the movies in  num_splits chuncks across time
+            for parallelization split the movies in  num_splits chunks across time
 
            num_splits_to_process_rig: list,
                if none all the splits are processed and the movie is saved, otherwise at each iteration
@@ -123,13 +123,13 @@ def __init__(self, fname, min_mov=None, dview=None, max_shifts=(6, 6), niter_rig
                intervals at which patches are laid out for motion correction
 
            overlaps: tuple
-               overlap between pathes (size of patch strides+overlaps)
+               overlap between patches (size of patch strides+overlaps)
 
            pw_rigig: bool, default: False
                flag for performing motion correction when calling motion_correct
 
            splits_els':list
-               for parallelization split the movies in  num_splits chuncks across time
+               for parallelization split the movies in  num_splits chunks across time
 
            num_splits_to_process_els: list,
                if none all the splits are processed and the movie is saved  otherwise at each iteration
@@ -404,7 +404,7 @@ def apply_shifts_movie(self, fname, rigid_shifts:bool=None, save_memmap:bool=Fal
 
             rigid_shifts: bool (True)
                 apply rigid or pw-rigid shifts (must exist in the mc object)
-                deprectated (read directly from mc.pw_rigid)
+                deprecated (read directly from mc.pw_rigid)
 
             save_memmap: bool (False)
                 flag for saving the resulting file in memory mapped form
@@ -2118,7 +2118,7 @@ def tile_and_correct(img, template, strides, overlaps, max_shifts, newoverlaps=N
             strides of the patches in which the FOV is subdivided
 
         overlaps: tuple
-            amount of pixel overlaping between patches along each dimension
+            amount of pixel overlapping between patches along each dimension
 
         max_shifts: tuple
             max shifts in x and y
@@ -2127,7 +2127,7 @@ def tile_and_correct(img, template, strides, overlaps, max_shifts, newoverlaps=N
             strides between patches along each dimension when upsampling the vector fields
 
         newoverlaps:tuple
-            amount of pixel overlaping between patches along each dimension when upsampling the vector fields
+            amount of pixel overlapping between patches along each dimension when upsampling the vector fields
 
         upsample_factor_grid: int
             if newshapes or newstrides are not specified this is inferred upsampling by a constant factor the cvector field
@@ -2306,7 +2306,7 @@ def tile_and_correct(img, template, strides, overlaps, max_shifts, newoverlaps=N
 
             new_img = new_img / normalizer
 
-        else:  # in case the difference in shift between neighboring patches is larger than 0.5 pixels we do not interpolate in the overlaping area
+        else:  # in case the difference in shift between neighboring patches is larger than 0.5 pixels we do not interpolate in the overlapping area
             half_overlap_x = int(newoverlaps[0] / 2)
             half_overlap_y = int(newoverlaps[1] / 2)
             for (x, y), (idx_0, idx_1), im, (_, _), weight_mat in zip(start_step, xy_grid, imgs, total_shifts, weight_matrix):
@@ -2364,7 +2364,7 @@ def tile_and_correct_3d(img:np.ndarray, template:np.ndarray, strides:tuple, over
             strides of the patches in which the FOV is subdivided
 
         overlaps: tuple
-            amount of pixel overlaping between patches along each dimension
+            amount of pixel overlapping between patches along each dimension
 
         max_shifts: tuple
             max shifts in x, y, and z
@@ -2373,7 +2373,7 @@ def tile_and_correct_3d(img:np.ndarray, template:np.ndarray, strides:tuple, over
             strides between patches along each dimension when upsampling the vector fields
 
         newoverlaps:tuple
-            amount of pixel overlaping between patches along each dimension when upsampling the vector fields
+            amount of pixel overlapping between patches along each dimension when upsampling the vector fields
 
         upsample_factor_grid: int
             if newshapes or newstrides are not specified this is inferred upsampling by a constant factor the cvector field
@@ -2570,7 +2570,7 @@ def tile_and_correct_3d(img:np.ndarray, template:np.ndarray, strides:tuple, over
 
             new_img = new_img / normalizer
 
-        else:  # in case the difference in shift between neighboring patches is larger than 0.5 pixels we do not interpolate in the overlaping area
+        else:  # in case the difference in shift between neighboring patches is larger than 0.5 pixels we do not interpolate in the overlapping area
             half_overlap_x = int(newoverlaps[0] / 2)
             half_overlap_y = int(newoverlaps[1] / 2)
             half_overlap_z = int(newoverlaps[2] / 2)
@@ -2964,7 +2964,7 @@ def motion_correct_batch_pwrigid(fname, max_shifts, strides, overlaps, add_to_mo
             list of produced templates, one per batch
 
         shifts: list
-            inferred rigid shifts to corrrect the movie
+            inferred rigid shifts to correct the movie
 
     Raises:
         Exception 'You need to initialize the template with a good estimate. See the motion'

caiman/source_extraction/cnmf/cnmf.py

@@ -392,7 +392,7 @@ def fit_file(self, motion_correct=False, indices=None, include_eval=False):
 
     def refit(self, images, dview=None):
         """
-        Refits the data using CNMF initialized from a previous interation
+        Refits the data using CNMF initialized from a previous iteration
 
         Args:
             images