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tvpPlot_all.py
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tvpPlot_all.py
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#!/usr/bin/env python
import matplotlib.pyplot as plt
import matplotlib
from matplotlib.colors import to_rgba_array
import numpy as np
import sys
from cmocean import cm
import argparse
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
def parseArgs():
"""
Set up the argument parser.
"""
parser = argparse.ArgumentParser(description='Plot true versus predicted for a given epoch.')
parser.add_argument('-f', '--file', default='', type=str,
help='file name in which to plot')
parser.add_argument('-s', '--save', dest='save', action='store_true', help='Save plot to file.')
parser.add_argument('-l', '--latex', dest='latex', action='store_true', help='Use latex in text.')
parser.add_argument('--maxs', dest='maxs', default=None, nargs="+" , help='Maxs of data, default: 1.0', type=float)
parser.add_argument('--mins', dest='mins', default=None, nargs="+", help='Mins of data, default: 0.0', type=float)
parser.add_argument('--unit', dest='unit', default='', help='Units of values')
parser.add_argument('--xlabel', dest='xlabel', default='')
parser.add_argument('--ylabel', dest='ylabel', default='')
return parser.parse_args()
def main():
"""
Plot true verus predicted for selected columns.
"""
args = parseArgs()
d = np.loadtxt(args.file)
if args.maxs is None:
args.maxs = [1.] * d.shape[1]
if args.mins is None:
args.mins = [0.] * d.shape[1]
pred = d[:,::2]
true = d[:,1::2]
print(args.maxs, args.mins)
for i in range(pred.shape[1]):
diff = args.maxs[i] - args.mins[i]
pred[:,i] *= diff
pred[:,i] += args.mins[i]
true[:,i] *= diff
true[:,i] += args.mins[i]
if args.latex:
font = {'family' : 'CMU Serif',
# 'weight' : 'light',
'size' : 18}
plt.rc('font', **font)
plt.rc('text', usetex=True)
plt.rc('text.latex', preamble=r'\usepackage{bm}')
print ("Median error :", np.median(np.abs(pred - true)))
print ("Median squared error :", np.median((pred - true)**2))
print ("Mean absolute error :", np.mean(np.abs(pred - true)))
print ("Root Mean Squared Error :", np.sqrt(np.mean((pred - true)**2)))
print ("Max absolute error :", np.max(np.abs(pred - true)))
fig, ax = plt.subplots(1, 1, figsize=(7, 5))
colors = np.linspace(0, 1.0, pred.shape[1])
#for i in range(pred.shape[1]):
norm_true = np.linalg.norm(true, 2, axis=-1)
norm_pred = np.linalg.norm(pred, 2, axis=-1)
print(np.argmax(np.abs(norm_true - norm_pred)))
x = np.linspace(np.min(norm_true), np.max(norm_true), 32)
ax.plot(x,np.zeros_like(x),'--', c='gray')
diffs = np.abs(norm_true - norm_pred)
diffs /= (diffs.max() - diffs.min())
im = ax.scatter(norm_true, norm_true - norm_pred, c=cm.haline_r(diffs))
im.set_cmap(cm.haline_r)
im.set_clim(np.abs(norm_true - norm_pred).min(), np.abs(norm_true - norm_pred).max())
cbar = plt.colorbar(im, ax=ax)
cbar.set_label('Absolute Difference ' + args.unit)
ax.set_xlabel(args.xlabel + ' ' + args.unit)
ax.set_ylabel(args.ylabel + ' ' + args.unit)
ax.set_ylim([-np.std(diffs), 3 * np.std(diffs)])
ax.grid(linestyle='-.')
ax2 = inset_axes(ax, width="40%", height="40%")
ax2.scatter(norm_true, norm_pred, c=cm.haline_r(diffs))
ax2.grid(linestyle='-.')
ax2.set_xlabel('True ' + args.unit)
ax2.set_ylabel('Pred. ' + args.unit)
ax2.plot(x,x, '--', c='gray')
plt.tight_layout()
# plt.show()
# im = axes[0].scatter(true, pred, s=10, c=colors, cmap=cm.matter)
# axes[0].plot(x,x, '--', color='k', markersize=20)
# # plt.colorbar(im, ax=axes[0])
# axes[0].grid(True)
# # plt.title('True vs. predicted distances for $H_2$', {'family': 'serif','fontsize': 15})
# if args.unit != '':
# axes[0].set_xlabel('True [{}]'.format(args.unit))
# axes[0].set_ylabel('Predicted [{}]'.format(args.unit))
# axes[1].set_xlabel('True [{}]'.format(args.unit))
# axes[1].set_ylabel('True - Predicted [{}]'.format(args.unit))
# else:
# axes[0].set_xlabel('True')
# axes[0].set_ylabel('Predicted')
# axes[1].set_xlabel('True')
# axes[1].set_ylabel('True - Predicted')
# diffs = true - pred
# im = axes[1].scatter(true, true - pred, s=10, c=colors, cmap=cm.matter)
# # axes[0].plot(x,x, '--', color='k', markersize=20)
# cbar = plt.colorbar(im, ax=axes[1])
# cbar.set_label('Distribution of Data')
# axes[1].grid(True)
# # plt.title('True vs. predicted distances for $H_2$', {'family': 'serif','fontsize': 15})
# axes[0].set_aspect('equal', 'box')
# axes[0].set_ylim([np.min(pred), np.max(pred)])
# axes[0].set_xlim([np.min(true), np.max(true)])
# axes[1].set_ylim([np.mean(diffs) - 10 * np.std(diffs), np.mean(diffs) + 10 * np.std(diffs)])
# axes[1].set_xlim([np.min(true), np.max(true)])
# plt.tight_layout()
if args.save:
plt.savefig('all_true_vs_pred.pdf')
plt.show()
if __name__ == '__main__':
main()