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Load .fig figure form Matlab in python. Error bars make figure look weird

I got a .fig file generated in Matlab and I would like to display it in python environment, or at least retrieve the data. It works well when the original figure does not contain error bars. But when I have error bars then it looks like a staircase. Any ideas on how I could fix it?
The code I have been using is this:
## escape disti time for ions ##
size_f =26
def plotFig(filename,fignr=1):
from scipy.io import loadmat
from numpy import size
from matplotlib.pyplot import plot,figure,xlabel,ylabel,show,clf,xlim,ylim,legend,yticks,xticks
d = loadmat(filename,squeeze_me=True, struct_as_record=False)
ax1 = d['hgS_070000'].children
if size(ax1) > 1:
legs= ax1[1]
ax1 = ax1[0]
else:
legs=0
figure(fignr)
clf()
#hold(True)
counter = 0
counter1 =0
# x=np.zeros((1000,1000))
#y=np.zeros((1000,1000))
for line in ax1.children:
if line.type == 'graph2d.lineseries':
if hasattr(line.properties,'Marker'):
mark = "%s" % line.properties.Marker
mark = mark[0]
else:
mark = '.'
if hasattr(line.properties,'LineStyle'):
linestyle = "%s" % line.properties.LineStyle
else:
linestyle = '-'
if hasattr(line.properties,'Color'):
r,g,b = line.properties.Color
else:
r = 0
g = 0
b = 1
if hasattr(line.properties,'MarkerSize'):
marker_size = line.properties.MarkerSize
else:
marker_size = 1
#if counter1 ==0:
x = line.properties.XData
y = line.properties.YData
#counter1=1
#else:
# x1 = line.properties.XData
# y1 = line.properties.YData
if counter ==0:
x = line.properties.XData
y = line.properties.YData
plt.plot(x[y>0],y[y>0],linestyle=linestyle,linewidth=3.0)
counter=1
elif counter==1:
x1 = line.properties.XData
y1 = line.properties.YData
#plt.plot(x1[y1>0],y1[y1>0],linestyle=linestyle,linewidth=3.0)
counter=2
else:
x = line.properties.XData
y = line.properties.YData
plt.plot(x[y>0],y[y>0],linestyle=linestyle,linewidth=4.0,color=[r,g,b])
plt.yscale('log')
plt.xscale('log')
elif line.type == 'text':
if counter < 1:
xlabel(r'$%s$' % line.properties.String,fontsize =18)
#xlabel('%s' % line.properties.String,fontsize = 18)
counter += 1
elif counter < 2:
ylabel(r'$%s$' % line.properties.String,fontsize = 18)
# ylabel('%s' % line.properties.String,fontsize = 18)
counter += 1
ylim(1e-7,1e-1)
#ylim(ax1.properties.YLim)
xlim((1e-2, 1e3))
plt.rcParams["figure.figsize"] = [10,8]
plt.tick_params(axis='x',labelsize=size_f)
plt.tick_params(axis='y',labelsize=size_f)
plt.ylabel(r'$p(t_{esc})$', fontsize=size_f)
plt.xlabel(r'$t_{esc} \ [sec]$', fontsize=size_f)
#plt.savefig(r'C:\Users\nikos.000\vlahos\png\escape distr_elec.png', format='png',dpi=300)#,bbox_inches='tight')
#plt.savefig(r'C:\Users\nikos.000\vlahos\eps\escape_distr_elec.eps', format='eps',dpi=300)#,bbox_inches='tight')
#### Set tick size ####
labels = [r'$10^{-2}$', r'$10^{-1}$',r'$10^{0}$',r'$10^{1}$',r'$10^{2}$', r'$10^{3}$']#,r'$10^{2}$',r'$10^{4}$']
x = [1e-2,1e-1,1e0,1e1,1e2,1e3]
plt.tick_params(axis='x',labelsize=size_f)
plt.tick_params(axis='y',labelsize=size_f)
plt.xticks(x, labels)
labels1 = [r'$10^{-6}$', r'$10^{-4}$', r'$10^{-2}$']
y = [1e-6,1e-4, 1e-2,]
plt.yticks(y, labels1)
if legs:
leg_entries = tuple(legs.properties.String)
py_locs = ['upper center','lower center','right','left','upper right','upper left','lower right','lower left','best']
MAT_locs=['north','south','east','west','northeast', 'northwest', 'southeast', 'southwest','best']
Mat2py = dict(zip(MAT_locs,py_locs))
location = legs.properties.Location
leg1=legend(leg_entries,loc=Mat2py[location],frameon=False,fontsize=size_f)
leg1.legendHandles[1].set_color('black')
#plt.xticks(fontsize=18 )
#plt.yticks(fontsize=18)
#### Save figure ####
plt.savefig(r'C:\Users\nikos.000\vlahos\png\escape distr_prot.png', format='png',dpi=300,bbox_inches='tight')
plt.savefig(r'C:\Users\nikos.000\vlahos\eps\escape_distr_prot.eps', format='eps',dpi=300,bbox_inches='tight')
show()
return
plotFig(r'C:\Users\nikos.000\neutrons\f5b P=1.fig',fignr=1)
This is how the figure looks:
The original .fig file can be found here:
https://www.dropbox.com/sh/iddxpfvxz85abci/AAAU9p0DM5OYtpI7mE-nLAuga?dl=0

Python matplotlib - set_data and set_3d_properties don't seem to be updating my plot

I am currently working on a Yee Solver script for uni, but when I try to animate my 3D graph, the graph is not what is expected. It works for a 2D plot, but I can't seem to translate that into 3D. From my understanding, set_data and set_3d_properties need a 1D array to work, which I am inputting.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
from matplotlib.widgets import Slider
# Program Variables
wv_lgth_num = 10
graph_type = '3d'
t = 0
# Physical Constants
c = 3e8
mu_r = 1
eps_r = 1
# Source Constants
f = 2e9
omega = 2*f*(np.pi)
amp = 1.0
wv_lgth = c/f
period = 1/f
# Step size
dz = wv_lgth/20
dt = ((c/f)/20)/c
#dt = ((1/f)/20)/1
# Axis Grids
z_grid = np.arange(0,wv_lgth_num*wv_lgth,dz)
t_grid = np.arange(0,10*period,dt)
# Number of steps
num_z = z_grid.size
num_t = t_grid.size
# Coefficients
coe_E = c*dt/(eps_r*dz)
coe_H = c*dt/(mu_r*dz)
# E and H Matricies
E_mat = np.zeros((num_z,num_t))
H_mat = np.zeros((num_z,num_t))
# Generating Values for E and H
for time in range(0,num_t-1):
for pos in range(0,num_z-1):
# Source Wave
if pos == 0:
H_mat[0,time] = amp*np.sin(omega*t_grid[time])
# All cases of Yee Solver
if pos == 1:
if time == 0:
H_mat[1,0] = 0
E_mat[0,0] = 0
else:
H_mat[1,time] = H_mat[1,time-1] + coe_H*(E_mat[1,time-1] - E_mat[0,time-1])
E_mat[0,time] = E_mat[0,time-1] + coe_E*(H_mat[1,time] - H_mat[0,time])
if pos > 1 and pos != num_z-1:
if time == 0:
H_mat[pos,0] = 0
E_mat[pos-1,0] = 0
if time > 0:
H_mat[pos,time] = H_mat[pos,time-1] + coe_H*(E_mat[pos,time-1] - E_mat[pos-1,time-1])
E_mat[pos-1,time] = E_mat[pos-1,time-1] + coe_E*(H_mat[pos,time] - H_mat[pos-1,time])
if pos == num_z-1:
if time == 0:
H_mat[num_z-1,0] = 0
E_mat[num_z-2,0] = 0
E_mat[num_z-1,0] = 0
if time > 0:
H_mat[num_z-1,time] = H_mat[num_z-1,time-1] + coe_H*(E_mat[num_z-1,time-1] - E_mat[num_z-2,time-1])
E_mat[num_z-2,time] = E_mat[num_z-2,time-1] + coe_E*(H_mat[num_z-1,time] - H_mat[num_z-2,time])
E_mat[num_z-1,time] = E_mat[num_z-2,time]
def update(val):
t = slider_time.val
if graph_type == '2d':
a.set_ydata(E_mat[:,t])
b.set_ydata(H_mat[:,t])
if graph_type == '3d':
a.set_3d_properties(E_mat[:,t])
a.set_data(z_grid,np.zeros((num_z,num_t))[:,t])
b.set_3d_properties(np.zeros((num_z,num_t))[:,t])
b.set_data(z_grid,H_mat[:t])
fig.canvas.draw_idle()
print(H_mat)
print(H_mat[:,t].size)
print(z_grid)
print(np.zeros((num_z,num_t))[:,t].size)
# Creating plot
if graph_type == '3d':
fig, ax = plt.subplots()
ax = plt.axes(projection='3d')
b, = ax.plot3D(z_grid,H_mat[:,t],np.zeros((num_z,num_t))[:,t], label='H')
a, = ax.plot3D(z_grid,np.zeros((num_z,num_t))[:,t],E_mat[:,t], label='E')
plt.title('Light Wave')
ax.set_xlabel('z')
ax.set_ylabel('x')
ax.set_zlabel('y')
plt.legend()
ax_time = plt.axes([0.25,0.1,0.65,0.03])
slider_time = Slider(ax_time,'Time',0,num_t-2,valinit=0,valstep=1)
slider_time.on_changed(update)
plt.show()
if graph_type == '2d':
fig, ax = plt.subplots()
plt.subplots_adjust(left=0.25, bottom=0.25)
a, = plt.plot(z_grid,E_mat[:,t], label='E (yz plane)')
b, = plt.plot(z_grid,H_mat[:,t], label='H (xz plane)')
plt.title('Light Wave')
plt.xlabel('z')
plt.ylabel('x')
plt.legend()
ax_time = plt.axes([0.25,0.1,0.65,0.03])
slider_time = Slider(ax_time,'Time',0,num_t-2,valinit=0,valstep=1)
slider_time.on_changed(update)
plt.show()
Any help would be appreciated. The middle for loop is just generating my functions, using the Yee Method.

My Matplotlib subplot title removes itself

When I run my code I create a figure, then I create a subplot in that figure. Then when I try to add a title to it using ax.set_title("title") it sometimes shows up for a split second then goes away. I have tried using plot.title aswell with no luck.
I tried recreating the error in a small example but for some reason it worked just fine there, so here is the entire source code of the code.
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import matplotlib.style as style
import plotgen
from matplotlib.widgets import Button
class plotWindow():
def __init__(self):
style.use("bmh")
self.dp = 30
self.fig = plt.figure()
self.ax = self.fig.add_subplot(1, 1, 1, label="ax1")
self.cax = 1
self.maxax = 2
self.minax = 1
plotgen.clear("plot1.txt")
plotgen.clear("plot2.txt")
axnext = plt.axes([0.80, 0.01, 0.06, 0.06])
axprev = plt.axes([0.73, 0.01, 0.06, 0.06])
bnext = Button(axnext, 'Next >')
bnext.on_clicked(self.changePlotNext)
bprev = Button(axprev, "< Previous")
bprev.on_clicked(self.changePlotPrev)
ani = animation.FuncAnimation(self.fig, self.animate, interval=500)
plt.show()
def changePlotNext(self, i):
if self.cax < self.maxax:
self.cax += 1
self.ax.set_title("Pump " + str(self.cax))
def changePlotPrev(self, i):
if self.cax > self.minax:
self.cax -= 1
self.ax.set_title("Pump " + str(self.cax))
def animate(self, i):
if self.cax == 1:
plotgen.generate("plot1.txt")
graph_data = open('plot1.txt', 'r').read()
lines = graph_data.split('\n')
xs = []
ys = []
for line in lines:
if len(line) > 1:
x, y = line.split(',')
xs.append(x)
ys.append(float(y))
self.ax.clear()
lx = len(xs)
ly = len(ys)
if len(xs) < self.dp:
pxs = xs
pys = ys
else:
pxs = xs[(lx - (self.dp - 1)):(lx - 1)]
pys = ys[(ly - (self.dp - 1)):(ly - 1)]
self.ax.plot(pxs, pys, "r")
elif self.cax == 2:
plotgen.generate("plot2.txt")
graph_data = open('plot2.txt', 'r').read()
lines = graph_data.split('\n')
xs = []
ys = []
for line in lines:
if len(line) > 1:
x, y = line.split(',')
xs.append(x)
ys.append(float(y))
self.ax.clear()
lx = len(xs)
ly = len(ys)
if len(xs) <= self.dp:
pxs = xs
pys = ys
else:
pxs = xs[(lx - (self.dp - 1)):(lx - 1)]
pys = ys[(ly - (self.dp - 1)):(ly - 1)]
self.ax.plot(pxs, pys)
plotWindow()
As you can see in my changePlotNext and changePlotPrev functions I'm trying to change the title. Sometimes they display for a split second when I change, but then it goes away. And I am very aware that I have not set a title to display before I change the plot.

In animate, you have self.ax.clear(), which is removing all artists, texts, etc. on the Axes, including the title.
A simple option, then, is to reset the title after you clear the axes. So if you add:
self.ax.set_title("Pump " + str(self.cax))
in both places, immediately after you call self.ax.clear(), your titles will still be shown.
Another option would be to stop clearing the axes, but just remove the items you need to remove. I think this is just the lines you have plotted? So, for example, you could remove the call to self.ax.clear(), and add:
for line in self.ax.lines:
line.remove()
in its place. That will remove just the plotted line, but retain the title.

matplotlib figure parameters don't appears

Here is my issue: I have an embedded matplotlib figure in a Qt5 application. When I press the button "edit axis, curve and image parameter", I select my concerned subplot, but only the tab "axis" options appears. it is missing tabs for "curve" and "image".
actual picture
whereas I should have had something like this:
targeted picture
If anyone knows why...

Probably the answer is easy:
If there is no curve (line) in the plot, there will be no "Curves" tab.
If there is no image in the plot, there will be no "Images" tab.

class View2D(MapView):
def show(self, som, what='codebook', which_dim='all', cmap=None,
col_sz=None, desnormalize=False):
(self.width, self.height, indtoshow, no_row_in_plot, no_col_in_plot,
axis_num) = self._calculate_figure_params(som, which_dim, col_sz)
self.prepare()
if not desnormalize:
codebook = som.codebook.matrix
else:
codebook = som._normalizer.denormalize_by(som.data_raw, som.codebook.matrix)
if which_dim == 'all':
names = som._component_names[0]
elif type(which_dim) == int:
names = [som._component_names[0][which_dim]]
elif type(which_dim) == list:
names = som._component_names[0][which_dim]
while axis_num < len(indtoshow):
axis_num += 1
ax = plt.subplot(no_row_in_plot, no_col_in_plot, axis_num)
ind = int(indtoshow[axis_num-1])
min_color_scale = np.mean(codebook[:, ind].flatten()) - 1 * np.std(codebook[:, ind].flatten())
max_color_scale = np.mean(codebook[:, ind].flatten()) + 1 * np.std(codebook[:, ind].flatten())
min_color_scale = min_color_scale if min_color_scale >= min(codebook[:, ind].flatten()) else \
min(codebook[:, ind].flatten())
max_color_scale = max_color_scale if max_color_scale <= max(codebook[:, ind].flatten()) else \
max(codebook[:, ind].flatten())
norm = matplotlib.colors.Normalize(vmin=min_color_scale, vmax=max_color_scale, clip=True)
mp = codebook[:, ind].reshape(som.codebook.mapsize[0],
som.codebook.mapsize[1])
# pl = plt.pcolor(mp[::-1], norm=norm, cmap='jet')
pl = plt.imshow(mp[::-1], interpolation='nearest', origin='lower',cmap='jet')
plt.axis([0, som.codebook.mapsize[1], 0, som.codebook.mapsize[0]])
plt.title(names[axis_num - 1])
ax.set_yticklabels([])
ax.set_xticklabels([])
plt.colorbar(pl)
plt.show()

Python : QWidget: Must construct a QApplication before a QPaintDevice

I am trying to plot graphs using matplotlib when clicked on a button called "generate graph" in a QT window. At first, I found a problem : I cannot close plots or control it when the QT window is opened. But I found this solution :
Cannot move Matplotlib plot window and exit it using red X button
and I test it on an empty plot and it works. However, when I put my code I get this error :
QWidget: Must construct a QApplication before a QPaintDevice
In my qt window I put :
Process = subprocess.Popen(['python', 'mygraph.py'], shell=True).communicate()
in my script mygraph.py :
def main():
print("Beginning plot for section 2.1 ...")
#Get the selected iteration and sector
iter = InterfaceVariationTRANUS("config").DropDownListDisplayIter2_1.currentIndex()
sector = InterfaceVariationTRANUS("config").DropDownListDisplaySector2_1.currentIndex()
#Access the corresponding IMPLOC file and extract the data for the selected sector
nameDirectory = InterfaceVariationTRANUS("config").nameDirectory2_1 + str(iter)
pathToDirectory = os.path.join(InterfaceVariationTRANUS("config").pathOutputDirectoryInstance, nameDirectory)
filepath = os.path.join(pathToDirectory, "IMPLOC_J.MTX")
matrix = pd.read_csv(filepath)
matrix.columns = ["Scen", "Sector", "Zone", "TotProd", "TotDem", "ProdCost", "Price", "MinRes", "MaxRes", "Adjust"]
#Removal of the noise (production equal to zero => adjust equal to zero)
#matrix.Adjust[matrix.TotProd == 0] = 0
row_index = matrix.TotProd == 0
matrix.loc[row_index,'Adjust'] = 0
#matrix.Adjust[matrix.Price == 0] = 0
row_index = matrix.Price == 0
matrix.loc[row_index,'Adjust'] =0
#matrix.Price[matrix.Price == 0] = None
row_index = matrix.Price == 0
matrix.loc[row_index,'Price'] = None
matrix2 = matrix[["Sector","Zone", "Price", "Adjust"]]
#Isolation of the data for the sector selected
nameSector = str(InterfaceVariationTRANUS("config").stockParam.list_sectors[sector])+" "+(InterfaceVariationTRANUS().stockParam.list_names_sectors[sector])
matrix3 = matrix2[matrix2["Sector"].str.contains(nameSector) == True]
matrix4 = matrix3[matrix3["Zone"].str.contains("ext_") == False]
#This boolean is used to allow for multiple graphes to be shown on the same panel.
firstPlot = False
#Plot graph and display it
if(InterfaceVariationTRANUS("config").DropDownListDisplaySector2_1.currentIndex() != InterfaceVariationTRANUS("config").currentSectorPlot2_1):
InterfaceVariationTRANUS("config").numFiguresPlot2_1 = InterfaceVariationTRANUS("config").numFiguresPlot2_1 + 1
InterfaceVariationTRANUS("config").currentSectorPlot2_1 = InterfaceVariationTRANUS("config").DropDownListDisplaySector2_1.currentIndex()
firstPlot = True
fig = plt.figure(self.numFiguresPlot2_1)
fig.canvas.set_window_title(InterfaceVariationTRANUS("config").DropDownListDisplaySector2_1.currentText())
x = np.arange(0, InterfaceVariationTRANUS("config").stockParam.nTotZones, 1)
y = pd.to_numeric(matrix4["Price"])
print("Moyenne = ")
print(y.mean(0))
z = pd.to_numeric(matrix4["Adjust"]*y)/100 + y
# plot data
if(firstPlot):
price = plt.plot(x, y, label = ("Price"))
shadowPrice = plt.plot(x, z, label = ("Price + adjust for iteration "+InterfaceVariationTRANUS("config").DropDownListDisplayIter2_1.currentText()))
plt.legend()
plt.show(block=False) #method 3
plt.draw()
if name == 'main':
main()
Where InterfaceVariationTRANUS is the class of my QT window.

Finally, I found a solution that works correctly for my problem :
import matplotlib.pyplot as plt
plt.switch_backend('Qt4Agg')
...
plt.legend()
plt.draw()
plt.show(block=False)