How to prevent stack plots repeating plots? - matplotlib

Question

I have a code that should produce two different graphs and place them into one image and cannot figure out why it returns the last mentioned graph twice. The code is as follows:
import spacepy as sp
from spacepy import pycdf
from pylab import *
from spacepy.toolbox import windowMean, normalize
from spacepy.plot.utils import annotate_xaxis
import pylab
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.mlab as mlab
import matplotlib.cbook as cbook
import matplotlib.ticker as ticker
from matplotlib.colors import LogNorm
from matplotlib.ticker import LogLocator
from matplotlib.dates import DateFormatter
from matplotlib.dates import DayLocator, HourLocator, MinuteLocator
from matplotlib import rc, rcParams
import matplotlib.dates as mdates
import datetime as dt
import bisect as bi
import seaborn as sea
import sys
import os
import multilabel as ml
import pandas as pd
sea.set_context('poster')
# sea.set_style('ticks',{'axes.facecolor':'yellow'})
sea.set_style('whitegrid')
sea.set_palette('muted',color_codes=True)
rc('text', usetex=True)
rc('font', family='Mono')
rcParams['text.latex.preamble']=[r'\usepackage{amsmath}']
MMS_1_HPCA_SURVEY_ION = pycdf.CDF(r'/home/ary/Desktop/Arya/Project/Data/MMS/1/HPCA/Survey/Ion/mms1_hpca_srvy_l2_ion_20151025120000_v1.0.0.cdf')
EPOCH_SURVEY_ION_1 = MMS_1_HPCA_SURVEY_ION['Epoch'][...]
H_Flux_SURVEY_ION_1 = MMS_1_HPCA_SURVEY_ION['mms1_hpca_hplus_flux'][...]
O_Flux_SURVEY_ION_1 = MMS_1_HPCA_SURVEY_ION['mms1_hpca_oplus_flux'][...]
Ion_Energy_SURVEY_ION_1 = MMS_1_HPCA_SURVEY_ION['mms1_hpca_ion_energy'][...]
MMS_SURVEY_ION_1_Start_time = dt.datetime(2015, 10, 25, 12, 0, 0, 908117)
MMS_SURVEY_ION_1_Finish_time = dt.datetime(2015, 10, 25, 16, 22, 24, 403623)
dt_MMS = dt.timedelta(seconds = 15)
plt.close('all')
fig_MMS, axs_MMS = plt.subplots(2,sharex=True)
cmap = plt.get_cmap(cm.jet)
cmap.set_bad('black')
sidx_MMS_1_SURVEY_ION = bi.bisect_left(EPOCH_SURVEY_ION_1,MMS_SURVEY_ION_1_Start_time)
sidx_MMS_1_SURVEY_ION = int(sidx_MMS_1_SURVEY_ION-(sidx_MMS_1_SURVEY_ION/100))
lidx_MMS_1_SURVEY_ION = bi.bisect_left(EPOCH_SURVEY_ION_1, MMS_SURVEY_ION_1_Finish_time)
lidx_MMS_1_SURVEY_ION = int(lidx_MMS_1_SURVEY_ION+((len(EPOCH_SURVEY_ION_1)-lidx_MMS_1_SURVEY_ION)/100))
if MMS_SURVEY_ION_1_Start_time.date() == MMS_SURVEY_ION_1_Finish_time.date():
stopfmt = '%H:%M'
else:
stopfmt = '%-m/%-d/%y %H:%M'
title_1 = MMS_SURVEY_ION_1_Start_time.strftime('%m/%d/%y %H:%M')+' -'+MMS_SURVEY_ION_1_Finish_time.strftime(stopfmt)
if dt_MMS.seconds !=0:
title_1 = title_1 + ' with '+str(dt_MMS.seconds)+' second time averaging'
for j, ax in enumerate(axs_MMS.T.flatten()):
flix_1 = np.array(H_Flux_SURVEY_ION_1[sidx_MMS_1_SURVEY_ION:lidx_MMS_1_SURVEY_ION,
j, :].T)
if dt_MMS==dt.timedelta(0):
fluxwin_1 = flix_1
timewin_1 = EPOCH_SURVEY_ION_1[sidx_MMS_1_SURVEY_ION:lidx_MMS_1_SURVEY_ION]
else:
fluxwin_1=[[0 for y in range(len(flix_1))] for x_1 in range(len(flix_1))]
for i, flox in enumerate(flix_1):
fluxwin_1[i], timewin_1 = windowMean(flox, EPOCH_SURVEY_ION_1[sidx_MMS_1_SURVEY_ION:lidx_MMS_1_SURVEY_ION],
winsize=dt_MMS, overlap=dt.timedelta(0))
fluxwin_1[i] = np.array(fluxwin_1[i])
for x_1 in np.where(np.diff(EPOCH_SURVEY_ION_1[sidx_MMS_1_SURVEY_ION:lidx_MMS_1_SURVEY_ION])
>dt.timedelta(hours=1))[0]+sidx_MMS_1_SURVEY_ION:
fluxwin_1[i][bi.bisect_right(timewin_1, EPOCH_SURVEY_ION_1[x_1]):bi.bisect_right(timewin_1,
EPOCH_SURVEY_ION_1[x_1+1])]=0
fluxwin_1 = np.array(fluxwin_1)
fluxwin_1[np.where(fluxwin_1<=0)] = 0
x_1 = mdates.date2num(timewin_1)
pax_1 = ax.pcolormesh(x_1, Ion_Energy_SURVEY_ION_1, fluxwin_1, shading='turkey',cmap=cmap, vmin=1,
vmax=np.nanmax(H_Flux_SURVEY_ION_1[sidx_MMS_1_SURVEY_ION:lidx_MMS_1_SURVEY_ION,:,:]),
norm=LogNorm())
sax_1 = ax.twinx()
plt.setp(sax_1.get_yticklabels(), visible=False)
sax_1.tick_params(axis='y', right='off')
ax.set_xlim(MMS_SURVEY_ION_1_Start_time, MMS_SURVEY_ION_1_Finish_time)
ax.set_yscale('log')
ax.set_yticks([10, 100, 1000,10000])
#Allows non-log formatted values to be used for ticks
ax.yaxis.set_major_formatter(plt.ScalarFormatter())
axs_MMS[0].set_ylabel('Energy (eV)')
axs_MMS[0].set_title(title_1)
for j, ax in enumerate(axs_MMS.T.flatten()):
flix_2 = np.array(O_Flux_SURVEY_ION_1[sidx_MMS_1_SURVEY_ION:lidx_MMS_1_SURVEY_ION,
j, :].T)
if dt_MMS==dt.timedelta(0):
fluxwin_2 = flix_2
timewin_2 = EPOCH_SURVEY_ION_2[sidx_MMS_1_SURVEY_ION:lidx_MMS_1_SURVEY_ION]
else:
fluxwin_2=[[0 for y in range(len(flix_2))] for x_2 in range(len(flix_2))]
for i, flox in enumerate(flix_2):
fluxwin_2[i], timewin_2 = windowMean(flox, EPOCH_SURVEY_ION_1[sidx_MMS_1_SURVEY_ION:lidx_MMS_1_SURVEY_ION],
winsize=dt_MMS, overlap=dt.timedelta(0))
fluxwin_2[i] = np.array(fluxwin_2[i])
for x_2 in np.where(np.diff(EPOCH_SURVEY_ION_1[sidx_MMS_1_SURVEY_ION:lidx_MMS_1_SURVEY_ION])
>dt.timedelta(hours=1))[0]+sidx_MMS_1_SURVEY_ION:
fluxwin_2[i][bi.bisect_right(timewin_2, EPOCH_SURVEY_ION_1[x_2]):bi.bisect_right(timewin_2,
EPOCH_SURVEY_ION_1[x_1+1])]=0
fluxwin_2 = np.array(fluxwin_2)
fluxwin_2[np.where(fluxwin_2<=0)] = 0
x_2 = mdates.date2num(timewin_2)
pax_2 = ax.pcolormesh(x_2, Ion_Energy_SURVEY_ION_1, fluxwin_2, shading='turkey',cmap=cmap, vmin=1,
vmax=np.nanmax(O_Flux_SURVEY_ION_1[sidx_MMS_1_SURVEY_ION:lidx_MMS_1_SURVEY_ION,:,:]),
norm=LogNorm())
sax_2 = ax.twinx()
plt.setp(sax_2.get_yticklabels(), visible=False)
sax_2.tick_params(axis='y', right='off')
ax.set_xlim(MMS_SURVEY_ION_1_Start_time, MMS_SURVEY_ION_1_Finish_time)
ax.set_yscale('log')
ax.set_yticks([10, 100, 1000,10000])
#Allows non-log formatted values to be used for ticks
ax.yaxis.set_major_formatter(plt.ScalarFormatter())
axs_MMS[1].set_ylabel('Energy (eV)')
cbar_ax_1 = fig_MMS.add_axes([0.93, 0.15, 0.02, 0.7])
cb_MMS_1 = fig_MMS.colorbar(pax_1, cax=cbar_ax_1)
cb_MMS_1.set_label(r'Counts sec$^{-1}$ ster$^{-1}$ cm$^{-2}$ keV$^{-1}$')
#Sets the colorbar value range
cb_MMS_1.set_clim(1, np.nanmax(H_Flux_SURVEY_ION_1[sidx_MMS_1_SURVEY_ION:lidx_MMS_1_SURVEY_ION,:,:]))
#Redraws the colorbar
cb_MMS_1.draw_all()
and the image returned looks as such:
enter image description here

Answer 1

Consider the following example which corresponds to your code:
import matplotlib.pyplot as plt
fig, axs = plt.subplots(2,sharex=True, figsize=(4,2.4))
for j, ax in enumerate(axs.T.flatten()):
x_1 = [[0,1,2],[0,1,2],[0,1,2]]
y_1 = [[0,0,0],[1,1,1],[2,2,2]]
z_1 = [[6,5,4],[2,3,4],[6,5,4]]
pax_1 = ax.pcolormesh(x_1, y_1, z_1)
axs[0].set_ylabel('Energy (eV)')
axs[0].set_title("Title1")
for j, ax in enumerate(axs.T.flatten()):
x_2 = [[3,4,5],[3,4,5],[3,4,5]]
y_2 = [[0,0,0],[1,1,1],[2,2,2]]
z_2 = [[2,1,2],[2,1,2],[2,1,2]]
pax_2 = ax.pcolormesh(x_2, y_2, z_2)
axs[1].set_ylabel('Energy (eV)')
plt.show()
Here you plot each plot both axes.
Instead you need to plot to the different axes:
import matplotlib.pyplot as plt
fig, axs = plt.subplots(2,sharex=True, figsize=(4,2.4))
x_1 = [[0,1,2],[0,1,2],[0,1,2]]
y_1 = [[0,0,0],[1,1,1],[2,2,2]]
z_1 = [[6,5,4],[2,3,4],[6,5,4]]
pax_1 = axs[0].pcolormesh(x_1, y_1, z_1)
axs[0].set_ylabel('Energy (eV)')
axs[0].set_title("Title1")
x_2 = [[3,4,5],[3,4,5],[3,4,5]]
y_2 = [[0,0,0],[1,1,1],[2,2,2]]
z_2 = [[2,1,2],[2,1,2],[2,1,2]]
pax_2 = axs[1].pcolormesh(x_2, y_2, z_2)
axs[1].set_ylabel('Energy (eV)')
plt.show()