Pandas Read CSV and animate the data

Introduction

Read CSV data, filter, interpolate and smooth it, and the animate the plotting. The file is File:Convergence dataOnly.csv.

Size			10	15	20	40	50	100	500	1000	1500	3000	3000
Nodes	n		560842	240807	172644	110324	103903	101052	100919	138706	100919	77741	69092
Triangles	n		140118	70994	48044	28684	26868	26024	25948	29970	25948	23230	22232
Tetrahedrons	n		313370	126061	92463	60451	57002	55513	55480	80746	55480	40067	34291
Displacement magnitude	max	mm	0	0	0	0	0	0	0	0	0	0	0
Displacement magnitude	min	mm	0.2	0.2	0.2	0.2	0.2	0.2	0.2	0.2	0.2	0.2	0.2
Displacement X	max	um	-31.59	-31.59	-31.59	-31.59	-31.56	-31.46	-31.47	-31.32	-31.47	-31.33	-30.91
Displacement X	min	um	1.45	1.45	1.45	1.45	1.44	1.43	1.42	1.46	1.42	1.37	1.28
Displacement Y	max	um	-12.69	-12.69	-12.69	-12.69	-12.68	-12.67	-12.67	-12.69	-12.67	-12.64	-12.57
Displacement Y	min	um	16.03	16.03	16.03	16.03	16.03	16.01	16	15.98	16	16.01	16.1
Displacement Z	max	mm	-0.2	-0.2	-0.2	-0.2	-0.2	-0.2	-0.2	-0.2	-0.2	-0.2	-0.2
Displacement Z	min	um	1.8	1.8	1.8	1.8	1.8	1.81	1.81	1.81	1.81	1.77	1.71
von Mises Stress	max	kPa	0	0	0	0	0	0	0	0	0	0	0
von Mises Stress	min	kPa	1281.81	1281.81	1281.81	1281.81	1297.14	1311.81	1286.65	1250	1286.65	1273.95	1282.35
Max Principal Stress	max	kPa	-176.22	-176.22	-176.22	-176.22	-172.74	-172.69	-171.95	-184.54	-171.95	-169.48	-180.97
Max Principal Stress	min	kPa	967.43	967.43	967.43	967.43	1014.52	955.45	1085.01	1140.49	1085.01	798.06	754.78
Min Principal Stress	max	kPa	-1345.87	-1345.87	-1345.87	-1345.87	-1358.44	-1343.18	-1306.44	-1295.94	-1306.44	-1325.73	-1359.73
Min Principal Stress	min	kPa	109.99	109.99	109.99	109.99	108.23	114.84	115.2	126.83	115.2	106.69	119.98
Max Shear Stress	max	kPa	0	0	0	0	0	0	0	0	0	0	0
Max Shear Stress	min	kPa	648.93	648.93	648.93	648.93	658.46	663.91	654.4	628.53	654.4	647.25	647.95

Read and Interpolate

• 

  Interpolated data

• 

  Original, non-interpolated data

import matplotlib.pyplot as plt
import pandas  as pd
import numpy as np

#Read, transpose, sort, drop and set the index to be the number of tetrahedrons
#and drop the columns with zero values and some parameters
df = pd.read_csv("convergence_dataOnly.csv" ).T
df.columns = ( df.iloc[0] + " " + df.iloc[1] )
df = df[3:].sort_values("Tetrahedrons n")
df.set_index("Tetrahedrons n", inplace=True)
df = df.drop('Displacement magnitude max', axis=1)
df = df.drop('von Mises Stress max', axis=1)
df = df.drop('Max Shear Stress max', axis=1)
df = df.drop('Nodes n', axis=1)
df = df.drop('Triangles n', axis=1)

#Convert values and index to numeric
df= df.apply(pd.to_numeric)
df.index = pd.to_numeric( df.index, errors='coerce' )

# Interpolate # Need to drop the one with the same number of tetrahedrons
mask = np.ones( len(df), dtype=bool)
mask[2] = False
df = df[mask]

#Get the percentage
df = df.divide( df.iloc[9] )
print(df)

df_i = df.reindex(df.index.union(np.linspace(df.index.min(),df.index.max(), df.index.shape[0]*10)))  # insert 10 NaN points between existing ones
df_i = df_i.interpolate('pchip', order=2)  # fill the gaps with values
df_i.plot( legend=False)  # draw new Dataframe