Fix bug in masking for one of figures.

:

@@ -0,0 +1,29 @@
+diff --git a/icasar/aux2.py b/icasar/aux2.py
+index 9c084ea..b9c2687 100755
+--- a/icasar/aux2.py
++++ b/icasar/aux2.py
+@@ -243,13 +243,18 @@ def update_mask_sources_ifgs(mask_sources, sources, mask_ifgs, ifgs):
+             ifgs_new_mask | r2 array | as per ifgs, but with a new mask.  
+         History:
+             2020/06/26 | MEG | Written
++            2022_03_30 | MEG |  fix a bug in the number of pixels for the output array (ie. number of columns of ifgs_new_mask)
+         """
+-        n_pixs_new = len(np.argwhere(mask_new == False))                                        
+-        ifgs_new_mask = np.zeros((ifgs.shape[0], n_pixs_new))                        # initiate an array to store the modified sources as row vectors    
+-        for ifg_n, ifg in enumerate(ifgs):                                 # Loop through each source
+-            ifg_r2 = col_to_ma(ifg, mask_old)                             # turn it from a row vector into a rank 2 masked array        
+-            ifg_r2_new_mask = ma.array(ifg_r2, mask = mask_new)              # apply the new mask   
+-            ifgs_new_mask[ifg_n, :] = ma.compressed(ifg_r2_new_mask)       # convert to row vector and places in rank 2 array of modified sources
++
++        
++        for ifg_n, ifg in enumerate(ifgs):                                            # Loop through each source
++            ifg_r2 = col_to_ma(ifg, mask_old)                                         # turn it from a row vector into a rank 2 masked array        
++            ifg_r2_new_mask = ma.array(ifg_r2, mask = mask_new)                       # apply the new mask   
++            ifg_r1_new_mask = ma.compressed(ifg_r2_new_mask)                          # convert to row vector 
++            if ifg_n == 0:                                                            # if it's the first ifg..  
++                n_pixs_new = ifg_r1_new_mask.shape[0]                                 # get the new number of pixels 
++                ifgs_new_mask = np.zeros((ifgs.shape[0], n_pixs_new))                 # initiate an array of the correct size
++            ifgs_new_mask[ifg_n, :] = ifg_r1_new_mask                                 # put the row vector into the array
+         return ifgs_new_mask
+     
+     

icasar/aux.py

@@ -57,7 +57,7 @@ def plot_spatial_signals(spatial_map, pixel_mask, tcs, shape, title, ifg_dates_d
                          temporal_baselines, figures = "window",  png_path = './'):
     """
     Input:
-        spatial map | pxc matrix of c component maps (p pixels)
+        spatial map | pxc matrix of c component maps (p pixels) (i.e. images are column vectors)
         pixel_mask | mask to turn spaital maps back to regular grided masked arrays
         tcs | cxt matrix of c time courses (t long)   
         shape | tuple | the shape of the grid that the spatial maps are reshaped to

icasar/aux2.py

@@ -243,13 +243,18 @@ def apply_new_mask(ifgs, mask_old, mask_new):
             ifgs_new_mask | r2 array | as per ifgs, but with a new mask.  
         History:
             2020/06/26 | MEG | Written
+            2022_03_30 | MEG |  fix a bug in the number of pixels for the output array (ie. number of columns of ifgs_new_mask)
         """
-        n_pixs_new = len(np.argwhere(mask_new == False))                                        
-        ifgs_new_mask = np.zeros((ifgs.shape[0], n_pixs_new))                        # initiate an array to store the modified sources as row vectors    
-        for ifg_n, ifg in enumerate(ifgs):                                 # Loop through each source
-            ifg_r2 = col_to_ma(ifg, mask_old)                             # turn it from a row vector into a rank 2 masked array        
-            ifg_r2_new_mask = ma.array(ifg_r2, mask = mask_new)              # apply the new mask   
-            ifgs_new_mask[ifg_n, :] = ma.compressed(ifg_r2_new_mask)       # convert to row vector and places in rank 2 array of modified sources
+
+
+        for ifg_n, ifg in enumerate(ifgs):                                            # Loop through each source
+            ifg_r2 = col_to_ma(ifg, mask_old)                                         # turn it from a row vector into a rank 2 masked array        
+            ifg_r2_new_mask = ma.array(ifg_r2, mask = mask_new)                       # apply the new mask   
+            ifg_r1_new_mask = ma.compressed(ifg_r2_new_mask)                          # convert to row vector 
+            if ifg_n == 0:                                                            # if it's the first ifg..  
+                n_pixs_new = ifg_r1_new_mask.shape[0]                                 # get the new number of pixels 
+                ifgs_new_mask = np.zeros((ifgs.shape[0], n_pixs_new))                 # initiate an array of the correct size
+            ifgs_new_mask[ifg_n, :] = ifg_r1_new_mask                                 # put the row vector into the array
         return ifgs_new_mask
 
 

icasar/icasar_funcs.py

@@ -337,7 +337,6 @@ def baselines_from_names(self):
         S_pca = S_pca[:n_comp,:]                                                                                                        # truncate to desired number of components
         A_pca =  A_pca[:,:n_comp]
 
-
     if fig_kwargs['figures'] != "none":
         plot_pca_variance_line(PC_vals, title = '01_PCA_variance_line', **fig_kwargs)
         if spatial:
@@ -578,7 +577,7 @@ def LiCSBAS_to_ICASAR(LiCSBAS_out_folder, filtered = False, figures = False, n_c
         h5_file | string | path to h5 file.  e.g. cum_filt.h5
         figures | boolean | if True, make figures
         n_cols  | int | number of columns for figures.  May want to lower if plotting a long time series
-        crop_pixels | tuple | coords to crop images to.  x then y, 00 is top left.  e.g. (10, 500, 600, 900).  
+        crop_pixels | tuple | coords to crop images to.  x start x stop y start y stop , 00 is top left.  e.g. (10, 500, 600, 900).  
                                 x_start, x_stop, y_start, y_stop, No checking that inputted values make sense.  
                                 Note, generally better to have cropped (cliped in LiCSBAS language) to the correct area in LiCSBAS_for_LiCSAlert
         return_r3 | boolean | if True, the rank 3 data is also returns (n_ifgs x height x width).  Not used by ICASAR, so default is False
@@ -837,7 +836,7 @@ def read_img(file, length, width, dtype=np.float32, endian='little'):
 
     if mask_type == 'dem':
         mask = np.logical_or(mask_dem, mask_cum)                                                        # if dem, mask water (from DEM), and anything that's nan in cumulative (mask_cum)
-    elif mask_type == 'mask_licsbas':
+    elif mask_type == 'licsbas':
         mask = np.logical_or(mask_licsbas, np.logical_or(mask_dem, mask_cum))
 
     mask_r3 = np.repeat(mask[np.newaxis,], cumulative.shape[0], 0)