Removing duplicate code from a faulty merge.

mdboom · mdboom · commit a9b27633f8a1 · 2008-02-29T13:13:27.000Z
svn path=/trunk/matplotlib/; revision=4990
diff --git a/lib/matplotlib/mlab.py b/lib/matplotlib/mlab.py
@@ -45,7 +45,6 @@
       this efficiently by caching the direct FFTs.
 
 = record array helper functions =
-   * rec2txt          : pretty print a record array
    * rec2txt          : pretty print a record array
    * rec2csv          : store record array in CSV file
    * csv2rec          : import record array from CSV file with type inspection
@@ -2114,139 +2113,6 @@ def key_desc(name):
     return newrec.view(npy.recarray)
 
 
-def rec_groupby(r, groupby, stats):
-    """
-    r is a numpy record array
-
-    groupby is a sequence of record array attribute names that
-    together form the grouping key.  eg ('date', 'productcode')
-
-    stats is a sequence of (attr, func, outname) which will call x =
-    func(attr) and assign x to the record array output with attribute
-    outname.
-    Eg,  stats = ( ('sales', len, 'numsales'), ('sales', npy.mean, 'avgsale') )
-
-    return record array has dtype names for each attribute name in in
-    the the 'groupby' argument, with the associated group values, and
-    for each outname name in the stats argument, with the associated
-    stat summary output
-    """
-    # build a dictionary from groupby keys-> list of indices into r with
-    # those keys
-    rowd = dict()
-    for i, row in enumerate(r):
-        key = tuple([row[attr] for attr in groupby])
-        rowd.setdefault(key, []).append(i)
-
-    # sort the output by groupby keys
-    keys = rowd.keys()
-    keys.sort()
-
-    rows = []
-    for key in keys:
-        row = list(key)
-        # get the indices for this groupby key
-        ind = rowd[key]
-        thisr = r[ind]
-        # call each stat function for this groupby slice
-        row.extend([func(thisr[attr]) for attr, func, outname in stats])
-        rows.append(row)
-
-    # build the output record array with groupby and outname attributes
-    attrs, funcs, outnames = zip(*stats)
-    names = list(groupby)
-    names.extend(outnames)
-    return npy.rec.fromrecords(rows, names=names)
-
-
-
-def rec_summarize(r, summaryfuncs):
-    """
-    r is a numpy record array
-
-    summaryfuncs is a list of (attr, func, outname) which will
-    apply codefunc to the the array r[attr] and assign the output
-    to a new attribute name outname.  The returned record array is
-    identical to r, with extra arrays for each element in summaryfuncs
-    """
-
-    names = list(r.dtype.names)
-    arrays = [r[name] for name in names]
-
-    for attr, func, outname in summaryfuncs:
-        names.append(outname)
-        arrays.append(npy.asarray(func(r[attr])))
-
-    return npy.rec.fromarrays(arrays, names=names)
-
-def rec_join(key, r1, r2):
-    """
-    join record arrays r1 and r2 on key; key is a tuple of field
-    names.  if r1 and r2 have equal values on all the keys in the key
-    tuple, then their fields will be merged into a new record array
-    containing the intersection of the fields of r1 and r2
-    """
-
-    for name in key:
-        if name not in r1.dtype.names:
-            raise ValueError('r1 does not have key field %s'%name)
-        if name not in r2.dtype.names:
-            raise ValueError('r2 does not have key field %s'%name)
-
-    def makekey(row):
-        return tuple([row[name] for name in key])
-
-    r1d = dict([(makekey(row),i) for i,row in enumerate(r1)])
-    r2d = dict([(makekey(row),i) for i,row in enumerate(r2)])
-
-    r1keys = set(r1d.keys())
-    r2keys = set(r2d.keys())
-
-    keys = r1keys & r2keys
-
-    r1ind = npy.array([r1d[k] for k in keys])
-    r2ind = npy.array([r2d[k] for k in keys])
-
-    # Make sure that the output rows have the same relative order as r1
-    sortind = r1ind.argsort()
-
-    r1 = r1[r1ind[sortind]]
-    r2 = r2[r2ind[sortind]]
-
-    r2 = rec_drop_fields(r2, r1.dtype.names)
-
-
-    def key_desc(name):
-        'if name is a string key, use the larger size of r1 or r2 before merging'
-        dt1 = r1.dtype[name]
-        if dt1.type != npy.string_:
-            return (name, dt1.descr[0][1])
-
-        dt2 = r1.dtype[name]
-        assert dt2==dt1
-        if dt1.num>dt2.num:
-            return (name, dt1.descr[0][1])
-        else:
-            return (name, dt2.descr[0][1])
-
-
-
-    keydesc = [key_desc(name) for name in key]
-
-    newdtype = npy.dtype(keydesc +
-                         [desc for desc in r1.dtype.descr if desc[0] not in key ] +
-                         [desc for desc in r2.dtype.descr if desc[0] not in key ] )
-
-
-    newrec = npy.empty(len(r1), dtype=newdtype)
-    for field in r1.dtype.names:
-        newrec[field] = r1[field]
-
-    for field in r2.dtype.names:
-        newrec[field] = r2[field]
-
-    return newrec.view(npy.recarray)
-
 def csv2rec(fname, comments='#', skiprows=0, checkrows=0, delimiter=',',
             converterd=None, names=None, missing=None):
     """
@@ -2633,6 +2499,7 @@ def format(item, just_pad_prec_spacer):
     return text
 
 
+
 def rec2csv(r, fname, delimiter=',', formatd=None):
     """
     Save the data from numpy record array r into a comma/space/tab
