[SciPy-User] pylab
Joshua Holbrook
josh.holbrook@gmail....
Mon Jul 19 18:49:18 CDT 2010
On Mon, Jul 19, 2010 at 3:39 PM, <PHobson@geosyntec.com> wrote:
>
>
>> -----Original Message-----
>> From: scipy-user-bounces@scipy.org [mailto:scipy-user-bounces@scipy.org]
>> On Behalf Of Joshua Holbrook
>> Sent: Monday, July 19, 2010 4:24 PM
>> To: SciPy Users List
>> Subject: Re: [SciPy-User] pylab
>>
>> 2010/7/19 பழநி சே <palaniappan.chetty@gmail.com>:
>> > hi,
>> > I have a question about pylab/matplotlib, I am interested in plots and
>> > I want to know if I can have some data points in a data sets missing
>> > but still create a plot using pylab? For example (assuming all modules
>> > have been imported)
>> >
>> >>x = [1,2,3,4]
>> >>y=[10,20,30,40]
>> >>pylab.plot(x,y)
>> >>pylab.show()
>> >
>> > works fine. But what if I have one or more data points missing in my y
>> > data set? like this
>> >
>> >>x = [1,2,3,4]
>> >>y=[10,20, ,40]
>> >>pylab.plot(x,y)
>> >>pylab.show()
>> > I know that I cannot have an empty element in my list and this does not
>> work
>> >
>> > Thanks
>> > --
>> > Palani
>> >
>>
>> Hey Palani,
>>
>> I'm not extremely familiar with matplotlib, but my experience tells me
>> that, while MPL itself wouldn't really have any nice way to do this,
>> that you could import a dataset and use python/numpy to clean it up.
>> For example, you could maybe use filter() and zip() (zip's my
>> favorite toy), maybe like this:
>>
>>
>> In [30]: x
>> Out[30]: [0, 1, 2, 3, 4]
>>
>> In [31]: y
>> Out[31]: [0, 1, 4, None, 16]
>>
>> In [32]: zip(*filter(lambda x: x[1] != None, zip(x,y)))
>> Out[32]: [(0, 1, 2, 4), (0, 1, 4, 16)]
>>
>> and then you could do plot(_[0],_[1]). Alternately, and this would
>> probably be worth investigating for bigger datasets, you could maybe
>> use masked arrays
>> (http://docs.scipy.org/doc/numpy/reference/maskedarray.baseclass.html)
>> to do something similar in spirit.
>>
>> Hope that helped!
>
> As a big MPL user, that's an interesting solution. MPL and numpy were my primary gateways into Python from Matlab, so that's pretty informative for me. Given my background, I tend to take the more brute-force approach and would use the masked arrays.
>
> For the OP:
> #---
> import numpy as np
> import matplotlib.pyplot as plt
> x = np.arange(5)
> y = np.ma.MaskedArray(data=[0,1,4,None,16],mask=[0,0,0,1,0])
> fig = plt.figure()
> ax1 = fig.add_subplot(111)
> ax1.plot(x,y,'ko')
> fig.savefig('masktest.png')
>
> -paul
>
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>
So you can pass MaskedArrays to MPL and it'll filter them out on its
own? Neat! Probably faster too, for large datasets.
--Josh
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