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SF.net SVN: matplotlib: [5066] trunk/matplotlib
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From: <jd...@us...> - 2008-04-23 17:45:51
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Revision: 5066
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=5066&view=rev
Author: jdh2358
Date: 2008-04-23 10:44:15 -0700 (Wed, 23 Apr 2008)
Log Message:
-----------
added manuels scatter pie example
Modified Paths:
--------------
trunk/matplotlib/lib/matplotlib/mlab.py
trunk/matplotlib/unit/mlab_unit.py
Added Paths:
-----------
trunk/matplotlib/examples/scatter_piecharts.py
Added: trunk/matplotlib/examples/scatter_piecharts.py
===================================================================
--- trunk/matplotlib/examples/scatter_piecharts.py (rev 0)
+++ trunk/matplotlib/examples/scatter_piecharts.py 2008-04-23 17:44:15 UTC (rev 5066)
@@ -0,0 +1,40 @@
+"""
+This example makes custom 'pie charts' as the markers for a scatter plotqu
+
+Thanks to Manuel Metz for the example
+"""
+import math
+import numpy as np
+import matplotlib.pyplot as plt
+
+# first define the ratios
+r1 = 0.2 # 20%
+r2 = r1 + 0.4 # 40%
+
+# define some sizes of the scatter marker
+sizes = [60,80,120]
+
+# calculate the points of the first pie marker
+#
+# these are just the origin (0,0) +
+# some points on a circle cos,sin
+x = [0] + np.cos(np.linspace(0, 2*math.pi*r1, 10)).tolist()
+y = [0] + np.sin(np.linspace(0, 2*math.pi*r1, 10)).tolist()
+xy1 = zip(x,y)
+
+# ...
+x = [0] + np.cos(np.linspace(2*math.pi*r1, 2*math.pi*r2, 10)).tolist()
+y = [0] + np.sin(np.linspace(2*math.pi*r1, 2*math.pi*r2, 10)).tolist()
+xy2 = zip(x,y)
+
+x = [0] + np.cos(np.linspace(2*math.pi*r2, 2*math.pi, 10)).tolist()
+y = [0] + np.sin(np.linspace(2*math.pi*r2, 2*math.pi, 10)).tolist()
+xy3 = zip(x,y)
+
+
+fig = plt.figure()
+ax = fig.add_subplot(111)
+ax.scatter( np.arange(3), np.arange(3), marker=(xy1,0), s=sizes, facecolor='blue' )
+ax.scatter( np.arange(3), np.arange(3), marker=(xy2,0), s=sizes, facecolor='green' )
+ax.scatter( np.arange(3), np.arange(3), marker=(xy3,0), s=sizes, facecolor='red' )
+plt.show()
Modified: trunk/matplotlib/lib/matplotlib/mlab.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/mlab.py 2008-04-23 16:54:21 UTC (rev 5065)
+++ trunk/matplotlib/lib/matplotlib/mlab.py 2008-04-23 17:44:15 UTC (rev 5066)
@@ -87,6 +87,7 @@
import numpy as npy
+
from matplotlib import nxutils
from matplotlib import cbook
@@ -2143,10 +2144,10 @@
def csv2rec(fname, comments='#', skiprows=0, checkrows=0, delimiter=',',
- converterd=None, names=None, missing=None):
+ converterd=None, names=None, missing='', missingd=None):
"""
Load data from comma/space/tab delimited file in fname into a
- numpy record array and return the record array.
+ numpy (m)record array and return the record array.
If names is None, a header row is required to automatically assign
the recarray names. The headers will be lower cased, spaces will
@@ -2172,13 +2173,24 @@
names, if not None, is a list of header names. In this case, no
header will be read from the file
+ missingd - is a dictionary mapping munged column names to field values
+ which signify that the field does not contain actual data and should
+ be masked, e.g. '0000-00-00' or 'unused'
+
+ missing - a string whose value signals a missing field regardless of
+ the column it appears in, e.g. 'unused'
+
if no rows are found, None is returned -- see examples/loadrec.py
"""
if converterd is None:
converterd = dict()
+ if missingd is None:
+ missingd = {}
+
import dateutil.parser
+ import datetime
parsedate = dateutil.parser.parse
@@ -2226,14 +2238,28 @@
process_skiprows(reader)
- dateparser = dateutil.parser.parse
+ def ismissing(name, val):
+ "Should the value val in column name be masked?"
- def myfloat(x):
- if x==missing:
- return npy.nan
+ if val == missing or val == missingd.get(name) or val == '':
+ return True
else:
- return float(x)
+ return False
+ def with_default_value(func, default):
+ def newfunc(name, val):
+ if ismissing(name, val):
+ return default
+ else:
+ return func(val)
+ return newfunc
+
+ dateparser = dateutil.parser.parse
+ mydateparser = with_default_value(dateparser, datetime.date(1,1,1))
+ myfloat = with_default_value(float, npy.nan)
+ myint = with_default_value(int, -1)
+ mystr = with_default_value(str, '')
+
def mydate(x):
# try and return a date object
d = dateparser(x)
@@ -2241,16 +2267,16 @@
if d.hour>0 or d.minute>0 or d.second>0:
raise ValueError('not a date')
return d.date()
+ mydate = with_default_value(mydate, datetime.date(1,1,1))
-
- def get_func(item, func):
+ def get_func(name, item, func):
# promote functions in this order
- funcmap = {int:myfloat, myfloat:mydate, mydate:dateparser, dateparser:str}
- try: func(item)
+ funcmap = {myint:myfloat, myfloat:mydate, mydate:mydateparser, mydateparser:mystr}
+ try: func(name, item)
except:
- if func==str:
+ if func==mystr:
raise ValueError('Could not find a working conversion function')
- else: return get_func(item, funcmap[func]) # recurse
+ else: return get_func(name, item, funcmap[func]) # recurse
else: return func
@@ -2266,7 +2292,7 @@
converters = None
for i, row in enumerate(reader):
if i==0:
- converters = [int]*len(row)
+ converters = [myint]*len(row)
if checkrows and i>checkrows:
break
#print i, len(names), len(row)
@@ -2276,10 +2302,10 @@
if func is None:
func = converterd.get(name)
if func is None:
- if not item.strip(): continue
+ #if not item.strip(): continue
func = converters[j]
if len(item.strip()):
- func = get_func(item, func)
+ func = get_func(name, item, func)
converters[j] = func
return converters
@@ -2307,7 +2333,7 @@
item = itemd.get(item, item)
cnt = seen.get(item, 0)
if cnt>0:
- names.append(item + '%d'%cnt)
+ names.append(item + '_%d'%cnt)
else:
names.append(item)
seen[item] = cnt+1
@@ -2327,15 +2353,24 @@
# iterate over the remaining rows and convert the data to date
# objects, ints, or floats as approriate
rows = []
+ rowmasks = []
for i, row in enumerate(reader):
if not len(row): continue
if row[0].startswith(comments): continue
- rows.append([func(val) for func, val in zip(converters, row)])
+ rows.append([func(name, val) for func, name, val in zip(converters, names, row)])
+ rowmasks.append([ismissing(name, val) for name, val in zip(names, row)])
fh.close()
if not len(rows):
return None
- r = npy.rec.fromrecords(rows, names=names)
+ if npy.any(rowmasks):
+ try: from numpy.ma import mrecords
+ except ImportError:
+ raise RuntimeError('numpy 1.05 or later is required for masked array support')
+ else:
+ r = mrecords.fromrecords(rows, names=names, mask=rowmasks)
+ else:
+ r = npy.rec.fromrecords(rows, names=names)
return r
@@ -2529,26 +2564,59 @@
-def rec2csv(r, fname, delimiter=',', formatd=None):
+def rec2csv(r, fname, delimiter=',', formatd=None, missing='',
+ missingd=None):
"""
- Save the data from numpy record array r into a comma/space/tab
+ Save the data from numpy (m)recarray r into a comma/space/tab
delimited file. The record array dtype names will be used for
column headers.
fname - can be a filename or a file handle. Support for gzipped
files is automatic, if the filename ends in .gz
+
+ See csv2rec and rec2csv for information about missing and
+ missingd, which can be used to fill in masked values into your CSV
+ file.
"""
+
+ if missingd is None:
+ missingd = dict()
+
+ def with_mask(func):
+ def newfunc(val, mask, mval):
+ if mask:
+ return mval
+ else:
+ return func(val)
+ return newfunc
+
formatd = get_formatd(r, formatd)
funcs = []
for i, name in enumerate(r.dtype.names):
- funcs.append(csvformat_factory(formatd[name]).tostr)
+ funcs.append(with_mask(csvformat_factory(formatd[name]).tostr))
fh, opened = cbook.to_filehandle(fname, 'w', return_opened=True)
writer = csv.writer(fh, delimiter=delimiter)
header = r.dtype.names
writer.writerow(header)
+
+ # Our list of specials for missing values
+ mvals = []
+ for name in header:
+ mvals.append(missingd.get(name, missing))
+
+ ismasked = False
+ if len(r):
+ row = r[0]
+ ismasked = hasattr(row, '_fieldmask')
+
for row in r:
- writer.writerow([func(val) for func, val in zip(funcs, row)])
+ if ismasked:
+ row, rowmask = row.item(), row._fieldmask.item()
+ else:
+ rowmask = [False] * len(row)
+ writer.writerow([func(val, mask, mval) for func, val, mask, mval
+ in zip(funcs, row, rowmask, mvals)])
if opened:
fh.close()
Modified: trunk/matplotlib/unit/mlab_unit.py
===================================================================
--- trunk/matplotlib/unit/mlab_unit.py 2008-04-23 16:54:21 UTC (rev 5065)
+++ trunk/matplotlib/unit/mlab_unit.py 2008-04-23 17:44:15 UTC (rev 5066)
@@ -55,5 +55,27 @@
print 'repr(dt.type)',repr(dt.type)
self.failUnless( numpy.all(ra[name] == ra2[name]) ) # should not fail with numpy 1.0.5
+ def test_csv2rec_masks(self):
+ # Make sure masked entries survive roundtrip
+
+ csv = """date,age,weight,name
+2007-01-01,12,32.2,"jdh1"
+0000-00-00,0,23,"jdh2"
+2007-01-03,,32.5,"jdh3"
+2007-01-04,12,NaN,"jdh4"
+2007-01-05,-1,NULL,"""
+ missingd = dict(date='0000-00-00', age='-1', weight='NULL')
+ fh = StringIO.StringIO(csv)
+ r1 = mlab.csv2rec(fh, missingd=missingd)
+ fh = StringIO.StringIO()
+ mlab.rec2csv(r1, fh, missingd=missingd)
+ fh.seek(0)
+ r2 = mlab.csv2rec(fh, missingd=missingd)
+
+ self.failUnless( numpy.all( r2['date'].mask == [0,1,0,0,0] ))
+ self.failUnless( numpy.all( r2['age'].mask == [0,0,1,0,1] ))
+ self.failUnless( numpy.all( r2['weight'].mask == [0,0,0,0,1] ))
+ self.failUnless( numpy.all( r2['name'].mask == [0,0,0,0,1] ))
+
if __name__=='__main__':
unittest.main()
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