And fix a number of other import path deprecations visible through the testing framework

Author: pgunn

caiman/source_extraction/cnmf/initialization.py

@@ -14,8 +14,6 @@
 from multiprocessing import current_process
 import numpy as np
 import scipy
-import scipy.ndimage as nd
-from scipy.ndimage import center_of_mass, correlate
 import scipy.sparse as spr
 from skimage.morphology import disk
 from sklearn.decomposition import NMF, FastICA
@@ -409,7 +407,7 @@ def initialize_components(Y, K=30, gSig=[5, 5], gSiz=None, ssub=1, tsub=1, nIter
     if Ain.size > 0:
         Cin = resize(Cin, [K, T])
         center = np.asarray(
-            [center_of_mass(a.reshape(d, order='F')) for a in Ain.T])
+            [scipy.ndimage.center_of_mass(a.reshape(d, order='F')) for a in Ain.T])
     else:
         Cin = np.empty((K, T), dtype=np.float32)
         center = []
@@ -1063,10 +1061,10 @@ def imblur(Y, sig=5, siz=11, nDimBlur=None, kernel=None, opencv=True):
                 h /= np.sqrt(h.dot(h))
                 shape = [1] * len(Y.shape)
                 shape[i] = -1
-                X = correlate(X, h.reshape(shape), mode='constant')
+                X = scipy.ndimage.correlate(X, h.reshape(shape), mode='constant')
 
     else:
-        X = correlate(Y, kernel[..., np.newaxis], mode='constant')
+        X = scipy.ndimage.correlate(Y, kernel[..., np.newaxis], mode='constant')
         # for t in range(np.shape(Y)[-1]):
         #    X[:,:,t] = correlate(Y[:,:,t],kernel,mode='constant', cval=0.0)
 
@@ -1115,7 +1113,7 @@ def hals(Y, A, C, b, f, bSiz=3, maxIter=5):
     if bSiz is not None:
         if isinstance(bSiz, (int, float)):
              bSiz = [bSiz] * len(dims)
-        ind_A = nd.filters.uniform_filter(np.reshape(A,
+        ind_A = scipy.ndimage.uniform_filter(np.reshape(A,
                 dims + (K,), order='F'), size=bSiz + [0])
         ind_A = np.reshape(ind_A > 1e-10, (np.prod(dims), K), order='F')
     else:

caiman/tests/test_toydata.py

@@ -3,13 +3,13 @@
 import numpy.testing as npt
 import numpy as np
 import os
-from scipy.ndimage.filters import gaussian_filter
+import scipy.ndimage
 
+from caiman import save_memmap, load_memmap
+from caiman.paths import fn_relocated, generate_fname_tot
 import caiman.source_extraction.cnmf.params
 from caiman.source_extraction import cnmf as cnmf
 from caiman.utils.visualization import get_contours
-from caiman.paths import fn_relocated, generate_fname_tot
-from caiman import save_memmap, load_memmap
 
 TOYDATA_DIMS = {
     2: (20, 30),
@@ -34,8 +34,8 @@ def gen_data(D=3, noise=.5, T=300, framerate=30, firerate=2.):
         trueA[tuple(centers[i]) + (i,)] = 1.
     tmp = np.zeros(dims)
     tmp[tuple(d // 2 for d in dims)] = 1.
-    z = np.linalg.norm(gaussian_filter(tmp, sig).ravel())
-    trueA = 10 * gaussian_filter(trueA, sig + (0,)) / z
+    z = np.linalg.norm(scipy.ndimage.gaussian_filter(tmp, sig).ravel())
+    trueA = 10 * scipy.ndimage.gaussian_filter(trueA, sig + (0,)) / z
     Yr = bkgrd + noise * np.random.randn(*(np.prod(dims), T)) + \
         trueA.reshape((-1, 4), order='F').dot(trueC)
     return Yr, trueC, trueS, trueA, centers, dims
@@ -95,7 +95,6 @@ def get_params_dicts(D: int):
         }
     }
 
-
 def pipeline(D, params_dict, name):
     #%% GENERATE GROUND TRUTH DATA
     Yr, trueC, trueS, trueA, centers, dims = gen_data(D)

caiman/utils/sbx_utils.py

@@ -35,7 +35,7 @@ def _check_keys(checkdict: dict) -> None:
     """
 
     for key in checkdict:
-        if isinstance(checkdict[key], scipy.io.matlab.mio5_params.mat_struct):
+        if isinstance(checkdict[key], scipy.io.matlab.mat_struct):
             checkdict[key] = _todict(checkdict[key])
 
 
@@ -47,7 +47,7 @@ def _todict(matobj) -> dict:
     ret = {}
     for strg in matobj._fieldnames:
         elem = matobj.__dict__[strg]
-        if isinstance(elem, scipy.io.matlab.mio5_params.mat_struct):
+        if isinstance(elem, scipy.io.matlab.mat_struct):
             ret[strg] = _todict(elem)
         else:
             ret[strg] = elem