Small improvements to function documentation in cnmf/temporal

Author: pgunn

caiman/source_extraction/cnmf/temporal.py

@@ -212,13 +212,14 @@ def update_temporal_components(Y, A, b, Cin, fin, bl=None, c1=None, g=None, sn=N
     YrA = YA - AA.T.dot(Cin).T
     # creating the patch of components to be computed in parallel
     parrllcomp, len_parrllcomp = caiman.source_extraction.cnmf.utilities.update_order_greedy(AA[:nr, :][:, :nr])
-    logger.info("entering the deconvolution ")
+    logger.info(f"Entering Deconvolution, {C.shape=} {S.shape=}")
     C, S, bl, YrA, c1, sn, g, lam = update_iteration(parrllcomp, len_parrllcomp, nb, C, S, bl, nr,
                                                      ITER, YrA, c1, sn, g, Cin, T, nA, dview, debug, AA, kwargs)
     ff = np.where(np.sum(C, axis=1) == 0)  # remove empty components
     if np.size(ff) > 0:  # Eliminating empty temporal components
         ff = ff[0]
-        logger.info(f'removing {len(ff)} empty spatial component(s)')
+        logger.info(f'removing {len(ff)} empty temporal component(s)')
+
         keep = list(range(A.shape[1]))
         for i in ff:
             keep.remove(i)
@@ -258,9 +259,6 @@ def update_iteration(parrllcomp, len_parrllcomp, nb, C, S, bl, nr,
         Cin: np.ndarray
             current estimate of temporal components (K x T)
 
-        g:  np.ndarray
-            Global time constant (not used)
-
         bl: np.ndarray
            baseline for fluorescence trace for each column in A
 
@@ -314,18 +312,21 @@ def update_iteration(parrllcomp, len_parrllcomp, nb, C, S, bl, nr,
         bl:  float
             same as input
 
+        YrA: np.ndarray
+            matrix of spatial component filtered raw data, after all contributions have been removed.
+            YrA corresponds to the residual trace for each component and is used for faster plotting (K x T)
+
         c1:  float
             same as input
 
-        g:   float
+        sn:  float
             same as input
 
-        sn:  float
+        g:   float
             same as input
 
-        YrA: np.ndarray
-            matrix of spatial component filtered raw data, after all contributions have been removed.
-            YrA corresponds to the residual trace for each component and is used for faster plotting (K x T)
+        lam: (undocumented)
+
 """
 
     logger = logging.getLogger("caiman")