[Matplotlib-users] Problem doing multiple independent plots

[Thomas Caswell]

Bill,

Where on the matplotlib website would you have expected to find this
information?

Asking not to be passive aggressive, but so we can update the docs to put
this information there so the next person can find it ;)

Tom

On Wed, Sep 27, 2017 at 8:23 AM William Ray Wing <wrw at mac.com> wrote:

> On Sep 27, 2017, at 9:44 AM, Benjamin Root <ben.v.root at gmail.com> wrote:
>
> You might want to check out my Anatomy of Matplotlib tutorial:
> https://github.com/matplotlib/AnatomyOfMatplotlib
>
> You can use the Jupyter notebooks and follow along with the recordings
> available on YouTube: https://www.youtube.com/watch?v=rARMKS8jE9g  (Scipy
> 2017) and https://www.youtube.com/watch?v=MKucn8NtVeI  (Scipy 2015 -
> which covered more, but spent less time on each aspect).
>
> There is also a book, "Mastering Matplotlib", which goes into deep depths
> with respect to the API and logic behind the library:
> https://www.amazon.com/Mastering-matplotlib-Duncan-M-McGreggor/dp/1783987545
>
>
> Thanks, that book may be just what I’ve been searching for.  I’ll check it
> out.
> Bill
>
> I hope that helps!
> Ben Root
>
>
> On Wed, Sep 27, 2017 at 8:37 AM, William Ray Wing <wrw at mac.com> wrote:
>
>>
>> On Sep 26, 2017, at 6:20 PM, Ryan May <rmay31 at gmail.com> wrote:
>>
>> William,
>>
>> Don't use plt to call all of the methods, but directly use them off of
>> the Figure (e.g. fig1) and Axes (e.g. ax1) instances you create:
>>
>>
>> Thanks, that did it.  Very grateful.
>>
>> Parenthetically, I wish there were a matplotlib book that explained the
>> underlying logic of mil plotting.  Every one I’ve looked into so far is
>> full of specific cookbook examples, but they don’t explain what the various
>> calls really do, what order they need to be called in (and why), and how
>> they affect each other.  Cookbooks are fine for doing things by rote, but
>> they don’t provide understanding. If anyone on this list knows of such a
>> book, I’d really appreciate hearing.
>>
>> Thanks,
>> Bill
>>
>> import numpy as np, matplotlib.pyplot as plt
>>
>> def problem(xdata, ydata, i):
>>     color_dic = {1: "red", 2: "green", 3: "blue", 4: "cyan"}
>>     fig1, ax1 = plt.subplots()
>>     ax1.plot(xdata, ydata, linestyle = '-', color = color_dic[i])
>>     fig1.savefig('Plot for run_num ' + str(i))
>>     return
>>
>> def problem_alt(xdata, ydata, i):
>>     return
>>
>> t = np.arange(0.0, 2.0, 0.01)
>> fig2, ax2 = plt.subplots()
>>
>> for i in range(0,4):
>>     i = i+1
>>     problem(t, np.sin(i*np.pi*3*t), i)
>>     problem_alt(t, np.sin(i*np.pi*3*t), i)
>>     ax2.set_xlim(xmin = 0.0, xmax = 20.0)
>>     ax2.plot((t+i*3), np.sin(i*np.pi*3*(t+i*3)))
>>
>> fig2.savefig("Global Plot")
>>
>> At least, I think that's what you're going for. Note I removed some extra
>> calls to figure() and subplot() that I don't think were helping you.
>>
>> Ryan
>>
>> On Tue, Sep 26, 2017 at 3:04 PM, William Ray Wing <wrw at mac.com> wrote:
>>
>>> Below is a simplified version of a much more elaborate analysis code,
>>> but it will illustrate the problem I’m having.  What I want to do is
>>> repetitively call an analysis function from my main code and plot the
>>> results of that analysis (a curve fit, although thats immaterial here)
>>> while in that function.  Back in the main code, I want to plot the results
>>> of all the curve fits on a single plot.  They share a common x axis, but
>>> appear at different points along it.  What seems to be happening is that
>>> the gets set in the function, and doesn’t get set back in the main code.
>>>
>>> Note that there are two versions of the “problem” function, problem and
>>> problem_alt.  If you change the main code (move the #), you get the plot I
>>> want at the end of the main.
>>>
>>> There must be something I can call or set to recover the settings
>>> associated with figure(2), but I can’t seem to figure it out.  Any help
>>> would be appreciated.
>>>
>>> Thanks,
>>> Bill Wing
>>>
>>> #! /usr/bin/env python
>>> # -*- coding: utf-8 -*-
>>> #
>>> #   A simple skeleton of the program to work out the plotting problem
>>> #
>>> import numpy as np, matplotlib.pyplot as plt
>>> #
>>> # skeleton subroutines
>>> #
>>>
>>> def problem(xdata, ydata, i):
>>>     color_dic = {1: "red", 2: "green", 3: "blue", 4: "cyan"}
>>>     plt.figure(1)
>>>     fig1, ax1 = plt.subplots()
>>>     plt.subplot()
>>>     plt.plot(xdata, ydata, linestyle = '-', color = color_dic[i])
>>>     plt.savefig('Plot for run_num ' + str(i))
>>>     return
>>>
>>> def problem_alt(xdata, ydata, i):
>>>     return
>>>
>>>
>>> t = np.arange(0.0, 2.0, 0.01)
>>> plt.figure(2)
>>> fig2, ax2 = plt.subplots()
>>>
>>> for i in range(0,4):
>>>     i = i+1
>>>     problem(t, np.sin(i*np.pi*3*t), i)
>>>     problem_alt(t, np.sin(i*np.pi*3*t), i)
>>>     ax2.set_xlim(xmin = 0.0, xmax = 20.0)
>>>     plt.subplot()
>>>     plt.plot((t+i*3), np.sin(i*np.pi*3*(t+i*3)))
>>>
>>> plt.savefig("Global Plot")
>>>
>>> _______________________________________________
>>> Matplotlib-users mailing list
>>> Matplotlib-users at python.org
>>> https://mail.python.org/mailman/listinfo/matplotlib-users
>>>
>>
>>
>>
>> --
>> Ryan May
>>
>>
>>
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>>
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