[DATALAD RUNCMD] run codespell throughout fixing typo automagically

=== Do not change lines below ===
{
 "chain": [],
 "cmd": "codespell -w",
 "exit": 0,
 "extra_inputs": [],
 "inputs": [],
 "outputs": [],
 "pwd": "."
}
^^^ Do not change lines above ^^^

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.

bin/caiman_gui.py

@@ -109,7 +109,7 @@ 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

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/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

@@ -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
@@ -129,7 +129,7 @@ def __init__(self, fname, min_mov=None, dview=None, max_shifts=(6, 6), niter_rig
                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/deconvolution.py

@@ -151,7 +151,7 @@ def constrained_foopsi(fluor, bl=None,  c1=None, g=None,  sn=None, p=None, metho
 
                 c1 = c[0]
 
-                # remove intial calcium to align with the other foopsi methods
+                # remove initial calcium to align with the other foopsi methods
                 # it is added back in function constrained_foopsi_parallel of temporal.py
                 c -= c1 * g**np.arange(len(fluor))
             elif p == 2:
@@ -323,7 +323,7 @@ def cvxpy_foopsi(fluor, g, sn, b=None, c1=None, bas_nonneg=True, solvers=None):
             should the baseline be estimated
 
         solvers: tuple of two strings
-            primary and secondary solvers to be used. Can be choosen between ECOS, SCS, CVXOPT
+            primary and secondary solvers to be used. Can be chosen between ECOS, SCS, CVXOPT
 
     Returns:
         c: estimated calcium trace
@@ -332,7 +332,7 @@ def cvxpy_foopsi(fluor, g, sn, b=None, c1=None, bas_nonneg=True, solvers=None):
 
         c1: esimtated initial calcium value
 
-        g: esitmated parameters of the autoregressive model
+        g: estimated parameters of the autoregressive model
 
         sn: estimated noise level
 
@@ -501,7 +501,7 @@ def _nnls(KK, Ky, s=None, mask=None, tol=1e-9, max_iter=None):
         w = np.argmax(l)
         P[w] = True
 
-        try:  # likely unnnecessary try-except-clause for robustness sake
+        try:  # likely unnecessary try-except-clause for robustness sake
             #mu = np.linalg.inv(KK[P][:, P]).dot(Ky[P])
             mu = np.linalg.solve(KK[P][:, P], Ky[P])
         except:

caiman/source_extraction/cnmf/estimates.py

@@ -473,7 +473,7 @@ def nb_view_components_3d(self, Yr=None, image_type='mean', dims=None,
 
             image_type: 'mean'|'max'|'corr'
                 image to be overlaid to neurons (average of shapes,
-                maximum of shapes or nearest neigbor correlation of raw data)
+                maximum of shapes or nearest neighbor correlation of raw data)
 
             max_projection: bool
                 plot max projection along specified axis if True, o/w plot layers
@@ -981,7 +981,7 @@ class label for neuron shapes
         Returns:
             self: Estimates object
                 self.idx_components contains the indeced of components above
-                the required treshold.
+                the required threshold.
         """
         dims = params.get('data', 'dims')
         gSig = params.get('init', 'gSig')
@@ -1415,7 +1415,7 @@ def remove_small_large_neurons(self, min_size_neuro, max_size_neuro,
 
         Returns:
             neurons_to_keep: np.array
-                indeces of components with size within the acceptable range
+                indices of components with size within the acceptable range
         '''
         if self.A_thr is None:
             raise Exception('You need to compute thresholded components before calling remove_duplicates: use the threshold_components method')