View source for 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]].

{| class="wikitable"
!  !!  !!  !!  !!  !!  !!  !!  !!  !!  !!  !!  !!  !! 
|-
| 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 ==

<gallery>
Pandas csv interpolatedData.png|thumb|Interpolated data
Pandas csv Data.png|thumb|Original, non-interpolated data
</gallery>

<syntaxhighlight lang="python">
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
</syntaxhighlight >

== Animate the data ==



=== Animate 1 line updating ===

<syntaxhighlight lang="python">
</syntaxhighlight >

=== Animate N lines updating ===

<syntaxhighlight lang="python">
from matplotlib.animation import FuncAnimation

x = df_i.index.to_numpy()
y = df_i.to_numpy()

fig, ax = plt.subplots()
ax.set(xlim=(0, max(x)), ylim=(.5, 1.5))
lines = ax.plot(np.empty((0, y.shape[1])), np.empty((0, y.shape[1])), lw=2)

def animate(i):
    for line_k, y_k in zip(lines, y.T):
        line_k.set_data(x[:i], y_k[:i])
    plt.savefig("image{i}.png".format(i=1), dpi=50 )
    return lines

anim = FuncAnimation(fig, animate, frames=x.size, interval=100, repeat=False)
plt.show()
</syntaxhighlight>

=== Animate x + t dimensional ===

Brushing Up Science demonstrates many beautiful animation procedures. This is one (https://brushingupscience.com/2016/06/21/matplotlib-animations-the-easy-way/):

<syntaxhighlight lang="python">
fig, ax = plt.subplots(figsize=(5, 3))
ax.set(xlim=(-3, 3), ylim=(-1, 1))

x = np.linspace(-3, 3, 91)
t = np.linspace(1, 25, 30)
X2, T2 = np.meshgrid(x, t)

sinT2 = np.sin(2*np.pi*T2/T2.max())
F = 0.9*sinT2*np.sinc(X2*(1 + sinT2))

line = ax.plot(x, F[0, :], color='k', lw=2)[0]

def animate(i):
    line.set_ydata(F[i, :])

anim = FuncAnimation( fig, animate, interval=100, frames=len(t)-1 )

plt.draw()
plt.show()
</syntaxhighlight>

=== Animate 2 ===

Add the new points in the update function. The clear command clears everything, so this in not very neat.

<syntaxhighlight lang="python">
from random import randint

import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

# create empty lists for the x and y data
x = []
y = []

# create the figure and axes objects
fig, ax = plt.subplots()


def animate(i):
    pt = randint(1,9) # grab a random integer to be the next y-value in the animation
    x.append(i)
    y.append(pt)
    ax.clear()
    ax.set_xlim([0,20])
    ax.set_ylim([0,10])
    ax.plot(x,y)

# run the animation
ani = FuncAnimation(fig, animate, frames=20, interval=100, repeat=False)

plt.show()
</syntaxhighlight>

=== Animate 3 ===

<syntaxhighlight lang="python">
</syntaxhighlight>

== References ==