On Fri, Jun 12, 2009 at 11:21 AM, Matthias
Michler<matthiasmich...@gmx.net> wrote:
> Hi Sebastian,
>
> You are right. A large number of numpy functions is part of pylab, but I think
> this problem was solved by introducing matplotlib.pyplot, which holds all
> plotting functions of matplotlib. The module pylab imports these plotting
> functions and all the numpy-stuff in order to offer plotting + numerical
> functions by one import.
>
> kind regards Matthias
>
Matthias,
thanks for the info. thats the info I was missing.
>>> from matplotlib import pyplot
>>> len(pyplot.__dict__)
191
Now I'm somewhat wondering about the things in pylab that are not in
pyplot nor in numpy.
E.g.:
pyplot.log2 is not numpy.log2
or
pyplot.window_hanning vs. numpy.hanning
or
pyplot.chisquare (which however is in numpy.random)
In summary, could one say that some functions are "left" in pylab to
keep backwards- and/or Matlab- compatibility ?
But does window_hanning behave exactly like numpy.hanning ?
I remember that some functions where decidedly implemented differently
than in numpy -- (sqrt for sqrt(-1) => 1j -- or was this scipy vs.
numpy)
Cheers,
Sebastian
> On Friday 12 June 2009 10:49:52 Sebastian Haase wrote:
>> Hi,
>> long time ago there was a discussion on reducing the duplications of
>> functions / symbols between Numpy and Matplotlib.
>>
>> I think from this resulted the pylab module now having many fewer entries:
>> >>> import matplotlib
>> >>> matplotlib.__version__
>>
>> '0.98.5.2'
>>
>> >>> import pylab
>> >>> len(pylab.__dict__)
>>
>> 882
>>
>> However, I think these are still to many ! I wrote, already before
>> the cleanup, a "HACK"-cleanup routine, which makes a cut-down modules
>> (called P) like this:
>> # P = new.module("pylab_sparse","""pylab module minus stuff alreay in
>> numpy""") for k,v in pylab.__dict__.iteritems():
>> try:
>> if k[:2] == '__' or v is numpy.__dict__[k]:
>> continue
>> except KeyError:
>> pass
>> #P.__dict__[k] = v
>> exec("%s = pylab.%s" % (k,k))
>>
>> ((the commented out lines did not work, but they might still
>> illustrate what I want to do -- now I have this code in a separate
>> module that I can import as "P"
>>
>> This way I get:
>> >>> len(P.__dict__)
>>
>> 395
>>
>> >>> numpy.__version__
>>
>> '1.3.0'
>>
>>
>> So why are there still that many -- more than half ! -- duplications
>> between pylab and numpy ?
>>
>> Regards,
>>
>> Sebastian Haase
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