Changeset 1600 in lmdz_wrf for trunk/tools/generic_tools.py

Timestamp:

Aug 3, 2017, 8:26:35 PM (7 years ago)

Author:

lfita

Message:

Adding:

• `shrinkarray_dim': Function to shrink a given array with a vector of True/False? along a given dimension

File:

• trunk/tools/generic_tools.py (modified) (5 diffs)

trunk/tools/generic_tools.py

@@ -100 +100 @@
 # multi_index_mat: Function to provide the multiple coordinates of a given value inside a matrix
 # multi_index_string: Function to provide the indeces of every repetition of a group of characters within a string
-# Nomasked: Function to brin back a given array wthout the masked values reducing a given dimension
+# Nomasked: Function to bring back a given array wthout the masked values reducing a given dimension
 # num_chainSnum: Function to pass a value to a `ChainStrNum' number
 # num_ordinal: Function to provide the ordinal of a given number, language, format and gender
@@ -122 +122 @@
 # same_shape: Function to check if two matrices have the same shape
 # search_sec_list: Function to provide the values and indices on a list which matches a section of a string
+# shrinkarray_dim: Function to shrink a given array with a vector of True/False along a given dimension
 # significant_decomposition: Function to decompose a given number by its signifcant potencies
 # singleline_printing_class: Function to print all the values of a given class in a single line to be parseavel
@@ -12321 +12322 @@
 
 def Nomasked(vals, dim):
-    """ Function to brin back a given array wthout the masked values reducing a given dimension
-      vals= arrtay of values
+    """ Function to bring back a given array wthout the masked values reducing a given dimension
+      vals= array of values
       dim= which dimension along which if all values are maskd reduce
     >>> arrayv = np.arange(32).reshape(4,8)
@@ -12337 +12338 @@
     fname = 'Nomasked'
 
+    if type(vals) != type(mamat):
+        print errormsg
+        print '  ' + fname + ': only works with masked arrays !!'
+        print '    provided type:', type(vals)
+        quit(-1)
+    if dim >= len(vals.shape):
+        print errormsg
+        print '  ' + fname + ': dimension provided too big !!'
+        print '    shape of the values:', vals.shape
+        quit(-1)
+
     varshape = vals.shape
     dimns = []
@@ -12372 +12384 @@
 
     return newarray, allmasked
+
+def shrinkarray_dim(vals, vector, dim):
+    """ Function to shrink a given array with a vector of True/False along a given dimension
+      vals= array of values
+      vector= vector of booleans of the same length as dim (True: remove)
+      dim= which dimension along which if all values are maskd reduce
+    >>> arrayv = np.arange(32).reshape(4,8)
+    >>> maskv = np.zeros((4), dtype=bool)
+    >>> maskv[1:3] = True
+    >>> shrinkarray_dim(arrayv, maskv, 0)
+    [[ 0  1  2  3  4  5  6  7]
+     [24 25 26 27 28 29 30 31]]
+    """
+    fname = 'shrinkarray_dim'
+
+    if dim >= len(vals.shape):
+        print errormsg
+        print '  ' + fname + ': dimension provided too big !!'
+        print '    shape of the values:', vals.shape
+        quit(-1)
+    if len(vector) != vals.shape[dim]:
+        print errormsg
+        print '  ' + fname + ': provided vector and length of the dimension differ !!'
+        print '    shape of the array:', vals.shape[dim], 'on dimension:', dim
+        print '    length of the vector:', len(vector)
+        quit(-1)
+
+    varshape = vals.shape
+    dimns = []
+    for id in range(len(varshape)):
+        dimns.append('d' + str(id))
+
+    # Looking for the slices along the given dimension
+    slices = provide_slices(dimns, varshape, ['d'+str(dim)])
+
+    Nslices = len(slices)
+    # First looking for the slices
+    Novals = np.sum(vector)
+
+    # Definition of the new array
+    newvarshape = list(varshape)
+    newvarshape[dim] = varshape[dim]-Novals
+
+    newarray = np.zeros(tuple(newvarshape), dtype=vals.dtype)
+
+    imask = 0
+    for islc in range(Nslices-1,-1,-1):
+        if not vector[islc]:
+            slicev = list(slices[islc])
+            slicev[dim] = imask
+            newarray[tuple(slicev)] = vals[tuple(slices[islc])]
+            imask = imask + 1
+
+    return newarray
+
 #quit()