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draw_funcs.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Sep 25 12:30:42 2021
@author: ccaprani
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import pandas as pd
"""
This module assumes that there is a disps.csv in the working directory which
is written by the analysedata.py script.
"""
df_disp = pd.read_csv("disps.csv")
# Base coords
xc = [12, -12]
yc = [25, -25]
zc = [0, 5, 10, 16, 20, 25]
# Channel nos.
iNode_channels = np.linspace(5, 33, 5, dtype=int)
jNode_channels = np.linspace(4, 32, 5, dtype=int)
kNode_channels = np.linspace(2, 30, 5, dtype=int)
lNode_channels = np.linspace(3, 31, 5, dtype=int)
# Displacement amplification factor
factor = 2000
def plot_quad(ax, iNode, jNode, kNode, lNode, col="r"):
# Ordering of node coords to match what np.meshgrid would produce
ax.plot_surface(
np.array([[iNode[0], lNode[0]], [jNode[0], kNode[0]]]),
np.array([[iNode[1], lNode[1]], [jNode[1], kNode[1]]]),
np.array([[iNode[2], lNode[2]], [jNode[2], kNode[2]]]),
color=col,
alpha=0.3,
edgecolor=col,
lw=2,
)
def plot_column(ax, btmNode, topNode, col="w", lw=2):
ax.plot(
(btmNode[0], topNode[0]),
(btmNode[1], topNode[1]),
(btmNode[2], topNode[2]),
color=col,
lw=lw,
)
def channel_names(idx):
return f"DYN1-{idx}X", f"DYN1-{idx}Y"
def disp_node(node, disp, factor):
x, y, z = (*node,)
xd = x + factor * disp[0]
yd = y + factor * disp[1]
return [xd, yd, z]
def draw_frame(t, ax):
ax.clear()
ax.patch.set_facecolor("k")
ax.patch.set_alpha(1.0)
ax.set_axis_off()
ax.axes.set_xlim3d(left=xc[0] - lim_margin, right=xc[1] + lim_margin)
ax.axes.set_ylim3d(bottom=yc[0] - lim_margin, top=yc[1] + lim_margin)
ax.axes.set_zlim3d(bottom=zc[0], top=zc[-1] + lim_margin)
ax.text2D(
0.5,
1.0,
f"Monash University Living Lab\nM5.9 Mansfield Earthquake (22/9/21)\nMotion (x{factor})",
ha="center",
va="center",
transform=ax.transAxes,
color="white",
)
str_time = np.datetime_as_string(np.datetime64(t), unit="ms")
text = ax.text2D(
0.5,
0.05,
str_time,
ha="center",
va="center",
transform=ax.transAxes,
color="white",
)
i = df_disp.index[df_disp.t == t][0]
az = -(i / 20) % 360
ax.view_init(elev=-20, azim=az)
iNodeBelow = [xc[0], yc[0], 0]
jNodeBelow = [xc[0], yc[1], 0]
kNodeBelow = [xc[1], yc[1], 0]
lNodeBelow = [xc[1], yc[0], 0]
for j, z in enumerate(zc[1:]):
iNode = [xc[0], yc[0], z]
jNode = [xc[0], yc[1], z]
kNode = [xc[1], yc[1], z]
lNode = [xc[1], yc[0], z]
# plot_quad(ax, iNode, jNode, kNode, lNode, "r")
cx, cy = channel_names(iNode_channels[j])
disp = df_disp[[cx, cy]].loc[i]
iNodeD = disp_node(iNode, disp, factor)
cx, cy = channel_names(jNode_channels[j])
disp = df_disp[[cx, cy]].loc[i]
jNodeD = disp_node(jNode, disp, factor)
cx, cy = channel_names(kNode_channels[j])
disp = df_disp[[cx, cy]].loc[i]
kNodeD = disp_node(kNode, disp, factor)
cx, cy = channel_names(lNode_channels[j])
disp = df_disp[[cx, cy]].loc[i]
lNodeD = disp_node(lNode, disp, factor)
plot_quad(ax, iNodeD, jNodeD, kNodeD, lNodeD)
plot_column(ax, iNodeBelow, [*iNodeD, z])
plot_column(ax, jNodeBelow, [*jNodeD, z])
plot_column(ax, kNodeBelow, [*kNodeD, z])
plot_column(ax, lNodeBelow, [*lNodeD, z])
iNodeBelow = iNodeD
jNodeBelow = jNodeD
kNodeBelow = kNodeD
lNodeBelow = lNodeD
return (ax,)