I have a question about the use of vectorize in new.instancemethod:
Using vectorize with *args requires adjustment to the number of
arguments, nin = nargs. This works when it is used with a function.
However, I don't manage to set nin when using vectorize with a method
created with new.instancemethod.
I would like to do:
def funcm(self,x,*args):
...
class D(object):
def __init__(self):
vecfunc = vectorize(funcm)
vecfunc.nin = 2
self.funcm = new.instancemethod(vecfunc,self,A)
But, calling D().funcm still causes a value error. I managed to do it
by setting vecfunc.nin = None.
What is the correct or best way to do this? I needed it when I wanted
to correct scipy.stats.distributions.
Below (and as attachment) is a script that summarizes what I figured
out about the use of vectorize with *args.
I did not find any documentation for this case, I got the information
by trial and error and hints in the bug reports.
Josef
'''
Nin adjustment in np.vectorize with *args
=========================================
* Problem: simple use of vectorize with *args raises exception::
File "C:\Programs\Python24\Lib\site-packages\numpy\lib
\function_base.py", line 1636, in __call__
raise ValueError, "mismatch between python function inputs"\
ValueError: mismatch between python function inputs and received
arguments
* solution adjust nin directly, either set nin to the correct number
of arguments
or set nin to None
* with functions both ways of adjusting nin work, (case 2 and 3)
* with new.instance method, the only way, I managed to get it to work
is by
setting nin = None (case E). Setting nin = nargs did not work (case
C and D)
with functions
--------------
* case 0: function without vectorize
* case 1: function with vectorize -> broken
* case 2: function with vectorize, with nin adjustment -> works
* case 3: function with vectorize, with nin adjustment, nin = None ->
works
with class and new.instancemethod
---------------------------------
* case A: class without vectorize -> works
* case B: class with vectorize -> broken: argument miss match
* case C: class with vectorize with nin adjustment -> broken
* case D: class with vectorize with nin adjustment -> broken
* case E: class with vectorize with nin adjustment, nin = None ->
works
Motivation:
-----------
vectorize (sgf) in scipy.stats.distribution does not work in cases
where there
is no correct nin adjustment.
Example for new.instancemethod, that looks broken, is self._ppf in
class rv_discrete.
'''
import new
import numpy as np
from numpy import vectorize
def func1(x,*args):
print 'args = ', args
print 'x = ', x
return np.sum(x)
def funcm(self,x,*args):
''' function for use as instance method'''
print 'args = ', args
print 'x = ', x
return np.sum(x)
# case 0: function without vectorize
print 'func1(1,*(2,))'
func1(1,*(2,))
# case 1: function with vectorize -> broken
vecfunc = vectorize(func1)
print 'vecfunc(1):'
vecfunc(1)
print 'vecfunc(1,3):'
try:
vecfunc(1,3)
except ValueError,e:
print e
print 'vecfunc(1,*(2,))'
try:
vecfunc(1,*(2,))
except ValueError,e:
print e
# case 2: function with vectorize, with nin adjustment -> works
vecfunc2 = vectorize(func1)
vecfunc2.nin = 2
print 'vecfunc2(1):'
vecfunc2(1)
print 'vecfunc2(1,3):'
vecfunc2(1,3)
print 'vecfunc2(1,*(2,))'
vecfunc2(1,*(2,))
# case 3: function with vectorize, with nin adjustment, nin = None ->
works
vecfunc3 = vectorize(func1)
vecfunc3.nin = None
print 'vecfunc3(1):'
vecfunc3(1)
print 'vecfunc3(1,3):'
vecfunc3(1,3)
print 'vecfunc3(1,*(2,))'
vecfunc3(1,*(2,))
print 'vecfunc3(1,*(2,5))'
vecfunc3(1,*(2,5))
# with class and new.instancemethod
# case A: class without vectorize -> works
class A(object):
def __init__(self):
self.funcm = new.instancemethod(funcm,self,A)
aa = A()
#print dir(aav)
print 'A: aav.funcm(5)'
aa.funcm(5)
print 'A: aav.funcm(5,2)'
aa.funcm(5,2)
print 'A: aav.funcm(5,*(2,))'
aa.funcm(5,*(2,))
# case B: class with vectorize -> broken: argument miss match
class B(object):
def __init__(self):
self.funcm = new.instancemethod(vectorize(funcm),self,A)
aav = B()
#print dir(aav)
print 'B: aav.funcm(5)'
aav.funcm(5)
print 'B: aav.funcm(5,2)'
try:
aav.funcm(5,2)
except ValueError,e:
print e
print 'B: aav.funcm(5,*(2,))'
try:
aav.funcm(5,*(2,))
except ValueError,e:
print e
# case C: class with vectorize with nin adjustment -> broken
# AttributeError: 'instancemethod' object has no attribute 'nin'
class C(object):
def __init__(self):
self.funcm = new.instancemethod(vectorize(funcm),self,A)
#self.funcm.nin = 2
try:
self.funcm.nin = 2
except AttributeError,e:
print e
aav = C()
#print dir(aav)
print 'C: aav.funcm(5)'
aav.funcm(5)
print 'C: aav.funcm(5,2)'
try:
aav.funcm(5,2)
except ValueError,e:
print e
print 'C: aav.funcm(5,*(2,))'
try:
aav.funcm(5,*(2,))
except ValueError,e:
print e
# case D: class with vectorize with nin adjustment -> broken
# nin is not correctly used by vectorize
class D(object):
def __init__(self):
# define the vectorized function
vecfunc = vectorize(funcm)
vecfunc.nin = 2
self.funcm = new.instancemethod(vecfunc,self,A)
#self.funcm.nin = 2
try:
self.funcm.nin = 2
except AttributeError,e:
print e
aav = D()
#print dir(aav)
print 'D: aav.funcm(5)'
aav.funcm(5)
print 'D: aav.funcm(5,2)'
try:
aav.funcm(5,2)
except ValueError,e:
print e
print 'D: aav.funcm(5,*(2,))'
try:
aav.funcm(5,*(2,))
except ValueError,e:
print e
# case E: class with vectorize with nin adjustment, nin = None ->
works
# nin is calculated by vectorize
class E(object):
def __init__(self):
vecfunc = vectorize(funcm,otypes='d')
vecfunc.nin = None # remove nin at let vectorize do the work
self.funcm = new.instancemethod(vecfunc,self,A)
#self.funcm.nin = 2
try:
self.funcm.nin = 2
except AttributeError,e:
print e
aav = E()
#print dir(aav)
print 'E: aav.funcm(5)'
aav.funcm(5)
print 'E: aav.funcm(5,2)'
try:
aav.funcm(5,2)
except ValueError,e:
print e
print 'E: aav.funcm(5,*(2,))'
try:
aav.funcm(5,*(2,))
except ValueError,e:
print e
aav.funcm([1,2,5.2])
aav.funcm([1,2,5.2],*(2,))
res = aav.funcm(np.array([1,2,5.2]),*(2,))
print res
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