Changeset 1599 in lmdz_wrf

Timestamp:

Aug 3, 2017, 8:12:14 PM (8 years ago)

Author:

lfita

Message:

Adding:

• `Nomasked': Function to brin back a given array wthout the masked values reducing a given dimension

File:

• trunk/tools/generic_tools.py (modified) (2 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
 # 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
@@ -12319 +12320 @@
     return newlist
 
+def Nomasked(vals, dim):
+    """ Function to brin back a given array wthout the masked values reducing a given dimension
+      vals= arrtay of values
+      dim= which dimension along which if all values are maskd reduce
+    >>> arrayv = np.arange(32).reshape(4,8)
+    >>> maskv = np.zeros((4,8), dtype=bool)
+    >>> maskv[1:3,:] = True
+    >>> maarray = ma.maskedarray(arrayv, mask=maskv)
+    >>> Nomasked(maarray, 0)
+    masked_array(data =
+     [[ 0  1  2  3  4  5  6  7]
+     [24 25 26 27 28 29 30 31]],
+     mask = False, fill_value = 999999)
+    , array([False,  True,  True, False], dtype=bool))
+    """
+    fname = 'Nomasked'
+
+    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)
+    allmasked = np.zeros((Nslices), dtype=bool)
+
+    for islc in range(Nslices):
+        if all(vals.mask[tuple(slices[islc])]): allmasked[islc] = True
+
+    # First looking for the slices
+    Novals = np.sum(allmasked)
+
+    # Definition of the new array
+    newvarshape = list(varshape)
+    newvarshape[dim] = varshape[dim]-Novals
+
+    newarray = np.zeros(tuple(newvarshape), dtype=vals.dtype)
+    newmaksv = np.zeros(tuple(newvarshape), dtype=bool)
+
+    imask = 0
+    for islc in range(Nslices-1,-1,-1):
+        if not allmasked[islc]:
+            slicev = list(slices[islc])
+            slicev[dim] = imask
+            newarray[tuple(slicev)] = vals[tuple(slices[islc])]
+            imask = imask + 1
+
+    newarray = ma.array(newarray)
+
+    return newarray, allmasked
+#quit()
+
+