Revision: 4990
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4990&view=rev
Author: mdboom
Date: 2008-02-29 05:13:27 -0800 (Fri, 29 Feb 2008)
Log Message:
-----------
Removing duplicate code from a faulty merge.
Modified Paths:
--------------
trunk/matplotlib/lib/matplotlib/mlab.py
Modified: trunk/matplotlib/lib/matplotlib/mlab.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/mlab.py 2008-02-25 20:27:51 UTC (rev
4989)
+++ trunk/matplotlib/lib/matplotlib/mlab.py 2008-02-29 13:13:27 UTC (rev
4990)
@@ -46,7 +46,6 @@
= 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
@@ -2114,139 +2113,6 @@
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 @@
return text
+
def rec2csv(r, fname, delimiter=',', formatd=None):
"""
Save the data from numpy record array r into a comma/space/tab
This was sent by the SourceForge.net collaborative development platform, the
world's largest Open Source development site.
-------------------------------------------------------------------------
This SF.net email is sponsored by: Microsoft
Defy all challenges. Microsoft(R) Visual Studio 2008.
http://clk.atdmt.com/MRT/go/vse0120000070mrt/direct/01/
_______________________________________________
Matplotlib-checkins mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/matplotlib-checkins
