Changeset 2193 in lmdz_wrf

Timestamp:

Oct 18, 2018, 3:31:10 PM (6 years ago)

Author:

lfita

Message:

Adding:

• `mat_dimreshape': Function to reshape a matrix on a new one according to values along their dimensions

File:

• trunk/tools/generic_tools.py (modified) (3 diffs)

trunk/tools/generic_tools.py

@@ -138 +138 @@
 # list_norepeatcombos: Function to all possible (Num-1)-combinations of a Num values without repetitions
 # lstring_values: Function to provide a new list-string from a string which is a list of word separated by a character if some values are repeated they are not included
+# mat_dimreshape: Function to reshape a matrix on a new one according to values along their dimensions
 # maxNcounts: Function to provide a value according to the maximum number of repeated values along a given axis
 # minNcounts: Function to provide a value according to the minimum number of repeated values along a given axis
@@ -9831 +9832 @@
         if shape1[idn] != shape2[idn]:
             print errormsg
-            print '  ' +fname+ ': length of',idn,'diemsion in mat1 and mat2 differ !!'
+            print '  ' +fname+ ': length of',idn,'dimension in mat1 and mat2 differ !!'
             print '    Length mat 1:', shape1[idn],' Length mat2:', shape2[idn]
             print '    shape mat 1:', shape1, 'mat 2:', shape2
@@ -14561 +14562 @@
     return rightCFtimeu
 
-def masks_mult(dimns1, mask1, dimns2, mask2, dimsuse, shapeuse, nonusemaskdims):
-    """ Function to multiply two sets of masks on coincident diensions
-      dimns1= list of dimensions names for mask 1
-      mask1= mask 1 
-      dimns2= list of dimensions names for mask 2
-      mask2= mask 2
-      dimnuse= list of dimensions to use
-      shapeuse: shape of being used
-      nonusedmaskdims: dictionary with values of the given dimennsios of the masks
-        not to be used
-    """
-    fname = 'masks_mult'
-
-    coincdims = list(set(dimns1) & set(dimns2))
-    NOTcoincdims = list(set(dimns1) - set(dimns2))
-    print coincdims
-    print NOTcoincdims
+def mat_dimreshape(dimns, values, dimnsuse, shapeuse, nonusevaluesdims):
+    """ Function to reshape a matrix on a new one according to values along their 
+        dimensions
+      dimns= list of dimensions names of the input matrix
+      values= values of the matrix
+      dimnsuse= list of dimensions to use
+      shapeuse: list with shape of being produced
+      nonusevaluesdims: dictionary to provide index values to that non-concident 
+        dimennsios of [values] from [dimnuse]
+    >>> mask1 = np.zeros((3,3,3), dtype=bool)
+    mask1[1,:,:] = True
+    print mat_dimreshape(['dt', 'dy', 'dx'], mask1, ['dz', 'dy', 'dx'], [2, 3, 3], {'dt': 0})
+
+    [[[False False False]
+      [False False False]
+      [False False False]]
+
+     [[False False False]
+      [False False False]
+      [False False False]]]
+    >>> mask2 = np.zeros((2,3), dtype=bool)
+    mask2[1,1] = True
+    mat_dimreshape(['dt', 'dy'], mask2, ['dz', 'dy', 'dx'], [3, 3, 3], {'dt': 1})
+    [[[False False False]
+      [ True  True  True]
+      [False False False]]
+
+     [[False False False]
+      [ True  True  True]
+      [False False False]]
+
+     [[False False False]
+      [ True  True  True]
+      [False False False]]]
+    """
+    fname = 'mat_dimreshape'
+
+    coincdims = list(set(dimns) & set(dimnsuse))
+    NOTcoincdims = list(set(dimnsuse) - set(dimns))
     
-    NOTsliceuse = []
+    newvalues = np.zeros(tuple(shapeuse), dtype=values.dtype)
+
+    # Slices to fill new matrix
+    newslices = provide_slices(dimnsuse, shapeuse, NOTcoincdims)
+
+    # Getting values to use to fill new matrix from values
+    shapevals = values.shape
+    slcvalues = []
     idim = 0
-    for dimn in dimsuse:
-        if not searchInlist(dimns1, dimn):
-            NOTsliceuse.append(dimn)
-            continue
-        if searchInlist(dimns2, dimn):
-            NOTsliceuse.append(dimn)
-            continue
-
-    mask = np.zeros(shapeuse, dtype=bool)
-    slices = []
-    idim = 0
-    for dimn in dimsuse:   
-        if searchInlist(NOTsliceuse, dimn):
-            slices.append(-9)
+    for dn in dimns:
+        if searchInlist(coincdims,dn):
+            slcvalues.append(slice(0,shapevals[idim]))
         else:
-            slices.append(slice(0,shapeuse[idim])) 
-        idim = idim+1
-
-    finalslc = []
-    idim = 0
-    for slc in slices:
-        if type(slc) == type(slice(0,1)):
-            finalslc.append(slc)
-        else:
-            finalslc.append(range(shapeuse[idim]))
+            if not nonusevaluesdims.has_key(dn):
+                print errormsg
+                print '  ' + fname + ": non-concident dimension '" + dn +            \
+                  "' requires a value to proceed in dictionary 'nonusevaluesdims'!!"
+                print '    values provided _______'
+                printing_dictionary(nonusevaluesdims)
+                quit(-1)
+            else:
+                slcvalues.append(nonusevaluesdims[dn])
         idim = idim + 1
 
-    print 'finalslc:', finalslc   
-
-        
-
-
-    return 
-
-mask1 = np.zeros((3,3,3), dtype=bool).reshape(3,3,3)
-mask1[1,:,:] = True
-
-mask2 = np.zeros((3,3,3), dtype=bool).reshape(3,3,3)
-mask2[1,1,1] = True
-
-masks_mult(['dt', 'dx', 'dy'], mask1, ['dt', 'dx', 'dy'], mask2, ['dz', 'dy', 'dx'], (2, 3, 3), {'dt': 0})
+    slicevals = values[tuple(slcvalues)]
+
+    # Checking for consistency
+    newvalslc = newvalues[tuple(newslices[0])]
+    matsame = same_shape(slicevals, newvalslc, quitval=False)
+    if not matsame:
+        print errormsg
+        print '  ' + fname + ": not coincident shapes of matrices !!"
+        print '    shape of example of slice for new values:', newvalslc.shape
+        print '      example of slice from the newvalues:', newslices[0]
+        print '    shape of values:', values.shape
+        print '    shape of slice from values to fill:', slicevals.shape
+        print '      slice of values:', slcvalues
+        print '    check dimensions and values provided ________'
+        print '      * dimension names of values:', dimns
+        print '      * dimensions of new values:', dimnsuse
+        print '      * shape of new values:', shapeuse
+        print '      * selected indices from values for non-coincident dimensions'
+        printing_dictionary(nonusevaluesdims)
+        quit(-1)
+
+    Nnewslices = len(newslices)
+    for islc in range(Nnewslices):
+        slc = newslices[islc]
+        newvalues[tuple(slc)] = slicevals
+
+    return newvalues
 
 #quit()