Revision: 4989
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4989&view=rev
Author: mdboom
Date: 2008-02-25 12:27:51 -0800 (Mon, 25 Feb 2008)
Log Message:
-----------
Merged revisions 4947-4950,4952-4988 via svnmerge from
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
........
Modified Paths:
--------------
trunk/matplotlib/examples/units/units_sample.py
trunk/matplotlib/lib/matplotlib/axes.py
trunk/matplotlib/lib/matplotlib/mlab.py
Property Changed:
----------------
trunk/matplotlib/
Property changes on: trunk/matplotlib
___________________________________________________________________
Name: svnmerge-integrated
- /branches/v0_91_maint:1-4946,4951
+ /branches/v0_91_maint:1-4988
Modified: trunk/matplotlib/examples/units/units_sample.py
===================================================================
--- trunk/matplotlib/examples/units/units_sample.py 2008-02-25 20:24:33 UTC
(rev 4988)
+++ trunk/matplotlib/examples/units/units_sample.py 2008-02-25 20:27:51 UTC
(rev 4989)
@@ -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()
Modified: trunk/matplotlib/lib/matplotlib/axes.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/axes.py 2008-02-25 20:24:33 UTC (rev
4988)
+++ trunk/matplotlib/lib/matplotlib/axes.py 2008-02-25 20:27:51 UTC (rev
4989)
@@ -2381,7 +2381,7 @@
#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 @@
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 @@
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
Modified: trunk/matplotlib/lib/matplotlib/mlab.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/mlab.py 2008-02-25 20:24:33 UTC (rev
4988)
+++ trunk/matplotlib/lib/matplotlib/mlab.py 2008-02-25 20:27:51 UTC (rev
4989)
@@ -46,6 +46,7 @@
= 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
* rec_append_field : add a field/array to record array
@@ -2113,6 +2114,139 @@
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 @@
return text
-
def rec2csv(r, fname, delimiter=',', formatd=None):
"""
Save the data from numpy record array r into a comma/space/tab
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