Fixed np.float issue by changing to np.float64

pyCHX/Create_Report.py

@@ -119,6 +119,7 @@ class create_pdf_report( object ):
 
     def __init__( self, data_dir, uid,  out_dir=None, filename=None, load=True, user=None,
                  report_type='saxs',md=None, res_h5_filename=None ):
+        from datetime import datetime
         self.data_dir = data_dir
         self.uid = uid
         self.md = md        
@@ -1638,7 +1639,7 @@ def recursively_save_dict_contents_to_group( h5file, path, dic):
         if not isinstance(key, str):
             raise ValueError("dict keys must be strings to save to hdf5")
         # save strings, numpy.int64, and numpy.float64 types
-        if isinstance(item, (np.int64, np.float64, str, np.float, float, np.float32,int)):
+        if isinstance(item, (np.int64, np.float64, str, float, np.float32,int)):  # removed depreciated np.float LW @06/11/2023
             #print( 'here' )
             h5file[path + key] = item
             if not h5file[path + key].value == item:

pyCHX/chx_compress.py

@@ -146,8 +146,8 @@ def read_compressed_eigerdata( mask, filename, beg, end,
             CAL = True
     if CAL:            
         FD = Multifile( filename, beg, end)    
-        imgsum  =  np.zeros(   FD.end- FD.beg, dtype= np.float  )     
-        avg_img = np.zeros(  [FD.md['ncols'], FD.md['nrows'] ] , dtype= np.float )     
+        imgsum  =  np.zeros(   FD.end- FD.beg, dtype= np.float64  )     
+        avg_img = np.zeros(  [FD.md['ncols'], FD.md['nrows'] ] , dtype= np.float64 )     
         imgsum, bad_frame_list_ = get_each_frame_intensityc( FD, sampling = 1, 
                 bad_pixel_threshold=bad_pixel_threshold, bad_pixel_low_threshold=bad_pixel_low_threshold,
                                         hot_pixel_threshold=hot_pixel_threshold, plot_ = False,
@@ -345,7 +345,7 @@ def segment_compress_eigerdata( images,  mask, md, filename,
 
     Nimg_ = len( images)     
     M,N = images[0].shape
-    avg_img = np.zeros( [M,N], dtype= np.float )    
+    avg_img = np.zeros( [M,N], dtype= np.float64 )    
     Nopix =  float( avg_img.size )
     n=0
     good_count = 0
@@ -531,7 +531,7 @@ def init_compress_eigerdata( images, mask, md, filename,
     fp.write( Header)  
 
     Nimg_ = len( images)
-    avg_img = np.zeros_like(    images[0], dtype= np.float ) 
+    avg_img = np.zeros_like(    images[0], dtype= np.float64 ) 
     Nopix =  float( avg_img.size )
     n=0
     good_count = 0

pyCHX/chx_correlationc.py

@@ -1283,14 +1283,14 @@ def get_data(self ):
         Return: 2-D array, shape as (len(images), len(pixellist))
         '''
 
-        data_array = np.zeros([ self.length,len(self.pixelist)], dtype=np.float)        
+        data_array = np.zeros([ self.length,len(self.pixelist)], dtype=np.float64)        # changed dtype = np.float (depreciated) to dtype = np.float64 LW @06112023
         #fra_pix = np.zeros_like( pixelist, dtype=np.float64)
         timg = np.zeros(    self.FD.md['ncols'] * self.FD.md['nrows']   , dtype=np.int32   ) 
         timg[self.pixelist] =   np.arange( 1, len(self.pixelist) + 1  ) 
 
         if self.norm_inten is not None:
             #Mean_Int_Qind = np.array( self.qind.copy(), dtype=np.float)
-            Mean_Int_Qind = np.ones( len( self.qind),  dtype = np.float)
+            Mean_Int_Qind = np.ones( len( self.qind),  dtype = np.float64)      # changed dtype = np.float (depreciated) to dtype = np.float64 LW @06112023
             noqs = len(np.unique( self.qind ))
             nopr = np.bincount(self.qind-1)
             noprs = np.concatenate( [ np.array([0]), np.cumsum(nopr) ] )
@@ -1393,14 +1393,14 @@ def get_data(self ):
         Return: 2-D array, shape as (len(images), len(pixellist))
         '''
 
-        data_array = np.zeros([ self.length,len(self.pixelist)], dtype=np.float)        
+        data_array = np.zeros([ self.length,len(self.pixelist)], dtype=np.float64)        
         #fra_pix = np.zeros_like( pixelist, dtype=np.float64)
         timg = np.zeros(    self.FD.md['ncols'] * self.FD.md['nrows']   , dtype=np.int32   ) 
         timg[self.pixelist] =   np.arange( 1, len(self.pixelist) + 1  ) 
 
         if self.mean_int_sets is not None:
             #Mean_Int_Qind = np.array( self.qind.copy(), dtype=np.float)
-            Mean_Int_Qind = np.ones( len( self.qind),  dtype = np.float)
+            Mean_Int_Qind = np.ones( len( self.qind),  dtype = np.float64)
             noqs = len(np.unique( self.qind ))
             nopr = np.bincount(self.qind-1)
             noprs = np.concatenate( [ np.array([0]), np.cumsum(nopr) ] )

pyCHX/xpcs_timepixel.py

@@ -253,7 +253,7 @@ def init_compress_timepix_data( pos, t, binstep, filename, mask=None,
     css = np.cumsum(cs)
     imgsum  =  np.zeros(   N   )      
     good_count = 0
-    avg_img = np.zeros(  [ md['sy'], md['sx'] ], dtype= np.float ) 
+    avg_img = np.zeros(  [ md['sy'], md['sx'] ], dtype= np.float64 )    # changed depreciated np.float to np.float64   LW @06/11/2023
 
     for i in tqdm( range(0,N) ):
         if i ==0:
@@ -337,7 +337,7 @@ def init_compress_timepix_data_light_duty( pos, t,  binstep, filename, mask=None
     imgsum  =  np.zeros(   N-1   ) 
     print('There are %s frames to be compressed...'%(N-1))
     good_count = 0
-    avg_img = np.zeros(  [ md['sy'], md['sx'] ], dtype= np.float ) 
+    avg_img = np.zeros(  [ md['sy'], md['sx'] ], dtype= np.float64 )  # changed depreciated np.float to np.float64   LW @06/11/2023
     for i in tqdm( range(N-1) ):
         ind1 = np.argmin( np.abs( tx[i] - t)  )
         ind2 = np.argmin( np.abs( tx[i+1] - t )  )