Bdtjtk

I wrote codes to demonstrate values of 3 parameters[Speed, Acceleration, Temperature] from my dataset(text file) via HeatMap plots in 24x20 matrices form. Data in text file were saved in this order:
Pointer-Speed-Acceleration-Temperature
and repeated many times and after 480times repeated again as 2nd cycle and so on.
So I extracted these main parameters and put them in lists and for each parameter in each cycle I print and save csv file and try to plot each values and also try to plot missing values(includes nan and inf) for every parameter and try to get all plots in one window with title of right cycle number and try to count missing values(not only nan but also inf) to mention next plots during getting plot for them via insnull and replacing missing values by 0 ( I prefer Nan and Inf replaced by last available value in previous cycle or in the best case replace by mean of values of last and next available cycles instead of 0 )but I've faced below error:

TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

I'm wondering if there's a elegant way to count inf and nan separately in lists and print them out in plot window via HeatMap and isnull for each cycle including counting info and save them somewhere in hard disk for other tasks.
My scripts are following:

import sys

import os

import numpy as np

import pandas as pd

#from sklearn.preprocessing import minmax_scale

import seaborn as sns

import matplotlib.pyplot as plt



def mkdf(ListOf480Numbers):

normalMatrix = np.array_split(ListOf480Numbers,8)

fixMatrix = 

for i in range(8):

    lines = np.array_split(normalMatrix[i],6)

    newMatrix = [0,0,0,0,0,0]

    for j in (1,3,5):

        newMatrix[j] = lines[j]

    for j in (0,2,4):

        newMatrix[j] = lines[j][::-1]

    fixMatrix.append(newMatrix) 

return fixMatrix



def print_df(fixMatrix):

values = 

for i in range(6):

    values.append([*fixMatrix[6][i], *fixMatrix[7][i]])

for i in range(6):

    values.append([*fixMatrix[4][i], *fixMatrix[5][i]])

for i in range(6):

    values.append([*fixMatrix[2][i], *fixMatrix[3][i]])

for i in range(6):

    values.append([*fixMatrix[0][i], *fixMatrix[1][i]])

df = pd.DataFrame(values)

return (df)



df = pd.read_csv('D:me4.TXT', header=None)

id_set = df[df.index % 4 == 0].astype('int').values

Speed = df[df.index % 4 == 1].values

Acceleration = df[df.index % 4 == 2].values

Temperature = df[df.index % 4 == 3].values

data = {'Speed': Speed[:,0], 'Acceleration': Acceleration[:,0], 'Temperature': Temperature[:,0]}

main_data = pd.DataFrame(data, columns=['Speed','Acceleration','Temperature'], index = id_set[:,0])



def plotheatmap(new_value):

Sections = mkdf(new_value)

df = print_df(Sections)

sns.heatmap(df, vmin=min_nor, vmax=max_nor, cmap ='coolwarm')

plt.title(i, fontsize=12, color='black', loc='left', style='italic')

plt.axis('off')

plt.savefig(f'{i}/{i}{count}.png')

plt.clf()

return



'''

Missing data plot

'''

Speed = np.array(Speed, dtype='float64')

Acceleration = np.array(Acceleration, dtype='float64')

Temperature = np.array(Temperature, dtype='float64')

df = pd.DataFrame({"Speed":[Speed],"Acceleration":[Acceleration],"Temperature":[Temperature]})

#pd.isnull(np.array([np.nan, 0], dtype=object))

pd.isnull(np.array([np.nan, 0], dtype=float))

df = df.replace(0,np.nan)

df = df.replace(1,np.inf)

plt.subplot2grid((3, 3), (2, 0), colspan=3)

plt.suptitle('Analysis of data in cycle Nr.{count}', fontsize=14, fontweight='bold')

plt.subplots_adjust(hspace=0.5, bottom=0.1)

sns.heatmap(df.isnull(),cbar=False)

plt.text(3, 4, r'nan={c}   inf={d}',

    verticalalignment='bottom', horizontalalignment='right', color='green', fontsize=15)



nan = np.array(df.isnull())

inf = np.array(df.isnull())

count_nan = len(df) - df.count()

print(count_nan)

c = 0

for i in nan:

    for j in i:

        if j:

            c += 1

print(f'Total nan: {c}')

d = 1

for i in inf:

    for j in i:

        if j:

            d += 1

print(f'Total inf: {d}')

plt.title(f'Total missing values: {c+d}', fontsize=12, color='black', 

loc='left', style='italic')

plt.axis('off')





cycles = int(len(main_data)/480)

print(f'Total cycles: {cycles}')

for i in main_data:

    for cycle in range(3):  

        count =  '{:04}'.format(cycle)

        j = cycle * 480

        ordered_data = mkdf(main_data.iloc[j:j+480][i])

        csv = print_df(ordered_data)

        csv.to_csv(f'{i}{count}.csv', header=None, index=None)    

        Sections = mkdf(ordered_data)

        df = print_df(Sections)

        plt.title(i, fontsize=12, color='black', loc='left', style='italic')

        plt.axis('off')

        plt.savefig(f'{i}{count}.png')

        plt.clf()

        '''

        Speed plot

        '''

        Speed_df = pd.read_csv("Speed.csv",header=None)

        plt.subplot2grid((3, 3), (0, 0), colspan=1)

        sns.heatmap(Speed_df, vmin=-1, vmax=1, cmap ="coolwarm")

        plt.title('Speed', fontsize=12, color='black', loc='left', style='italic')

        plt.axis('off')

        '''

        Speed missing plot

        '''

        Speed_df = pd.read_csv("Speed.csv",header=None)

        Speed_df = Speed_df.replace(0,np.nan)

        plt.subplot2grid((3, 3), (1, 0), colspan=1)

        sns.heatmap(Speed_df.isnull(),cbar=False)

        plt.title(f'Speed MV: {c}', fontsize=12, color='black', loc='left', style='italic')

        plt.axis('off')

        '''

        acceleration plot

        '''

        acc_df = pd.read_csv("acceleration.csv",header=None)

        plt.subplot2grid((3, 3), (0, 1), colspan=1)

        sns.heatmap(acc_df, vmin=-1, vmax=1, cmap ="coolwarm")

        plt.title('acceleration', fontsize=12, color='black', loc='left', style='italic')

        plt.axis('off')



        '''

        acceleration missing plot

        '''

        acceleration_df = pd.read_csv("acceleration.csv",header=None)

        acceleratione_df = acceleration_df.replace(0,np.nan)

        plt.subplot2grid((3, 3), (1, 1), colspan=1)

        sns.heatmap(acceleration_df.isnull(),cbar=False)

        plt.title(f'accel MV: {c}', fontsize=12, color='black', loc='left', style='italic')

        plt.axis('off')

        '''

        Temperature plot

        '''

        temp_df = pd.read_csv("temperature.csv",header=None)

        plt.subplot2grid((3, 3), (0, 2), colspan=1)

        sns.heatmap(temp_df, vmin=-40, vmax=150, cmap ="coolwarm")

        plt.title('Temperature', fontsize=12, color='black', loc='left', style='italic')

        plt.axis('off')



        '''

        Temperature missing plot

        '''

        temperature_df = pd.read_csv("temperature.csv",header=None)

        temperature_df = temperature_df.replace(0,np.nan)

        temperature_df = temperature_df.replace(0,np.inf)

        plt.subplot2grid((3, 3), (1, 2), colspan=1)

        sns.heatmap(temperature_df.isnull(),cbar=False)

        plt.title(f'temp MV: {c}', fontsize=12, color='black', loc='left', style='italic')

        plt.axis('off')    

plt.show()

My desired result is:

I also provide sample text file of dataset for 3 cycles:

dataset