Merged revisions 4947-4950,4952-4988 via svnmerge from

mdboom · mdboom · commit f982b2aafc6e · 2008-02-25T20:27:51.000Z
https://matplotlib.svn.sourceforge.net/svnroot/matplotlib/branches/v0_91_maint ........ r4948 | jrevans | 2008-02-08 12:56:12 -0500 (Fri, 08 Feb 2008) | 2 lines Removed a reference to nx, replaced with numpy. ........ r4977 | jdh2358 | 2008-02-19 10:26:56 -0500 (Tue, 19 Feb 2008) | 2 lines added rec_groupby and rec2txt ........ r4987 | mdboom | 2008-02-25 15:21:39 -0500 (Mon, 25 Feb 2008) | 2 lines [ 1901410 ] Newbie bug report: clip_on set to False actually True ........ svn path=/trunk/matplotlib/; revision=4989
diff --git a/examples/units/units_sample.py b/examples/units/units_sample.py
@@ -8,9 +8,10 @@
 
 """
 from basic_units import cm, inch
-from pylab import figure, show, nx
+from pylab import figure, show
+import numpy
 
-cms = cm *nx.arange(0, 10, 2)
+cms = cm *numpy.arange(0, 10, 2)
 
 fig = figure()
 
diff --git a/lib/matplotlib/axes.py b/lib/matplotlib/axes.py
@@ -2381,7 +2381,7 @@ def text(self, x, y, s, fontdict=None,
 
 
         #if t.get_clip_on():  t.set_clip_box(self.bbox)
-        if kwargs.has_key('clip_on'):  t.set_clip_path(self.axesPatch)
+        if kwargs.has_key('clip_on'):  t.set_clip_box(self.bbox)
         return t
     text.__doc__ = cbook.dedent(text.__doc__) % martist.kwdocd
 
@@ -4188,6 +4188,7 @@ def scatter(self, x, y, s=20, c='b', marker='o', cmap=None, norm=None,
 
         Finally, marker can be (verts, 0), verts is a sequence of (x,y)
         vertices for a custom scatter symbol.
+<<<<<<< .working
 
             numsides is the number of sides
 
@@ -4201,6 +4202,9 @@ def scatter(self, x, y, s=20, c='b', marker='o', cmap=None, norm=None,
         Finally, marker can be (verts, 0), verts is a sequence of (x,y)
         vertices for a custom scatter symbol.
 
+=======
+
+>>>>>>> .merge-right.r4987
         s is a size argument in points squared.
 
         Any or all of x, y, s, and c may be masked arrays, in which
diff --git a/lib/matplotlib/mlab.py b/lib/matplotlib/mlab.py
@@ -45,6 +45,7 @@
       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
@@ -2113,6 +2114,139 @@ 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):
     """
@@ -2499,7 +2633,6 @@ 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
