Plot multiple 2D gradient Data sets with colorbar. Problem: Python plots mutiple colorbar

Question

I am working on some project where I want to compare two different solution of some equation I am solving. I have some data that are time and Space (x,y) dependent. which means I have for every time step T a data file T.dat which contains the xy and Z values of my equation I am solving. When I plot the solutions of the equation individually everything works fine. as soon as i try to make a loop to iterate over some time steps I get the problem that my Color bar doesn't get remove in the second iteration. for the first Plot it works fine but after the second one Colorbars keeps adding up in the plot.

thats how it looks like.

After four Itteration

Here is a Sample of my Code. First I imported my Libraries.

import numpy as np
import glob
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib import animation
import scipy as sp
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('jpg')
from mpl_toolkits.mplot3d import Axes3D
#%matplotlib qt
import animatplot as amp
from sklearn.preprocessing import StandardScaler

Secondly I start Importing my Datas in Dataframes (Some specifics my change but it is not relevant for my problem) (Note that the Path are mostly all the same because i wanted to test the plot function first. if this works I will put the Datas for it )

path_nonoise = r'Thesis/NCKSM/FieldData/'
path_noise_additive = r'Thesis/NCKSM/FieldData/'
path_noise_multiplicative = r'Thesis/NCKSM/FieldData/'
path_params_nonoise = r'Thesis/NCKSM/ParameterData/'
path_params_additive = r'Thesis/NCKSM/ParameterData/'
path_params_multiplicative= r'Thesis/NCKSM/ParameterData/'
all_files = len( sorted (glob.glob(path_nonoise + "/*.dat"))) #patern for the files

params_nonoise= pd.read_table(path_params_nonoise+'0'+'.dat',sep=':',names=['Params', 'Value',] )
params_Names_nonoise= np.asarray(params_nonoise['Params'].values.tolist())
params_Values_nonoise= np.asarray(params_nonoise['Value'].values.tolist())
quantum= int(params_Values_nonoise[3])
numberofdatas = np.arange(0,all_files)


#creat  a list of the names 

timesteps = np.arange(0,all_files*quantum,quantum )
nameofdata = [str(timesteps) for timesteps in timesteps]
# use your path

                                  
dfs_nonoise=[]
dfs_noise_additive=[]
dfs_noise_multiplicative=[]

for filename in nameofdata:
    foo1=pd.read_table(path_nonoise+filename+'.dat',sep='\s+',names=['X', 'Y','C','Rho'] )
    foo2=pd.read_table(path_noise_additive+filename+'.dat',sep='\s+',names=['X', 'Y','C','Rho'] )
    foo3=pd.read_table(path_noise_multiplicative+filename+'.dat',sep='\s+',names=['X', 'Y','C','Rho'] )
    dfs_nonoise.append(foo1)
    dfs_noise_additive.append(foo2)
    dfs_noise_multiplicative.append(foo3)

so far so good. Now here is how i defined my Ploting function and that's where I think the problems starts to occur. I Think that my Problemes lies in how i defined my colorbar in the function.

def plotgradient(fig,ax,X,Y,Z ,title  ):
    nbins= params_Values_nonoise[1] #this creats the gridpoints
    X_0= np.asarray(X.values.tolist())
    Y_0= np.asarray(Y.values.tolist())
    xi, yi = np.mgrid[X_0.min():X_0.max():nbins*1j, Y_0.min():Y_0.max():nbins*1j] #rearange the arrays 
    zi = np.asarray(Z.values.tolist()) #datas you need 
    t= ax.pcolormesh(xi, yi, zi.reshape(xi.shape), cmap='jet') #plottingfunction
    CS=ax.contour(xi, yi, zi.reshape(xi.shape) )
    ax.clabel(CS, inline=1, fontsize=10)
    colorbar=plt.colorbar(t, ax=ax, shrink=0.5, aspect=5) # colorbar
    ax.set_xlabel('x') #labels
    ax.set_ylabel('y')  
    ax.set_title(title) #title
    ax.grid()

now if i want to plot my datas thats what i have done. i just made a for Loop.

Fig, axes  = plt.subplots(ncols=2,nrows=1,figsize=(20,15))
my_path=r'/home/belkadi/Thesis/Plots/Plots27.01/additiveandnonoise/'
Ax1,Ax2=axes.flatten()


test=[0,1,2,3,4,5]
for i,j in zip(test,nameofdata[0:5]):
    f1=plotgradient(Fig, Ax1,dfs_nonoise[i]['X'],dfs_nonoise[i]['Y'], dfs_nonoise[i]['Rho'],'Density Gradient without noise' )
    f2=plotgradient(Fig, Ax2,dfs_nonoise[i]['X'],dfs_nonoise[i]['Y'], dfs_nonoise[i]['Rho'],'Density with noise' )
    plt.savefig(my_path+j+".png")
   
   

So. I have tried to use plt.clf() Fig.clf() plt.close() at the end of the loop. My idea was that Python doesn't delete the previous colourbar and thats why I tried to tell python he should clear clear the figure after every plot what it does is that i just delets the figure completly and i get two blank plot. Like this [Plots after plt.clf()] then i thought maybe i could clear the axis with Ax1.cla() this also didnt work.

thanks in advance for the help. And i apologize for my physicist spaghetti code.

Greetings Zino

Answer 1

I solved my problem.

Basically the problem was on how I defined my Plot function.

 def plotgradient_double(fig,ax1,ax2,X,Y,Z1,Z2 ,title1, title2 ):
    nbins= params_Values[1] #this creats t'he gridpoints
    axes= [ax1,ax2]
    xi, yi = np.mgrid[X.values.min():X.values.max():nbins*1j, Y.values.min():Y.values.max():nbins*1j] #rearange the arrays 
    #datas you need 
    t1= ax1.pcolormesh(xi, yi, Z1.values.reshape(int(nbins),int(nbins)), cmap='jet',vmax=1.) 
    t2= ax2.pcolormesh(xi, yi, Z2.values.reshape(int(nbins),int(nbins)), cmap='jet',vmin=0,vmax=1. )      #plottingfunction
    #CS1=ax1.contour(xi, yi, Z1.values.reshape(int(nbins),int(nbins)) )
    #CS2=ax2.contour(xi, yi, Z2.values.reshape(int(nbins),int(nbins)) )
    #ax1.clabel(CS1, inline=1, fontsize=10)
    #ax2.clabel(CS2, inline=1, fontsize=10)
    #colorbar=fig.colorbar(T, ax=axc, shrink=0.5, aspect=5)
    for ax in axes:
        ax.set_xlabel('x',fontsize=15) #labels
        ax.set_ylabel('y',fontsize=15)  
         #title
        #ax.grid()
    ax1.set_title(title1,fontsize=15)
    ax2.set_title(title2,fontsize=15) 
    return t1,t2

Afterwards I can creat a colorbar in the for loop to plot multiple data and delete it after every iteration.

with colorbar.remove()