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rh_plot.py
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# -*- coding: utf-8 -*-
from matplotlib.backends.backend_agg import FigureCanvasAgg
from matplotlib.figure import Figure
import matplotlib.pyplot as plt
import numpy as np
def plot_two_curves(rh1, rh2, data, path):
fig = Figure(figsize=(10, 10), dpi=50)
canvas = FigureCanvasAgg(fig)
ax = fig.add_subplot(111)
ax.set_xticks([]); ax.set_yticks([])
ax.imshow(data, cmap='gray')
ax.plot(rh1[1], rh1[0], color='red', linewidth=1)
ax.plot(rh2[1], rh2[0], color='blue', linewidth=1)
ax.set_xlim((0,data.shape[1]))
ax.set_ylim((data.shape[0],0))
fig.savefig(path,dpi=50, bbox_inches='tight')
def plot_results(curves, data, path):
n_components = len(curves)
fig = Figure(figsize=(10, 10), dpi=100)
canvas = FigureCanvasAgg(fig)
ax = fig.add_subplot(111)
ax.set_xticks([]); ax.set_yticks([])
ax.imshow(data, cmap='gray')
randOrder = np.arange(n_components)
np.random.shuffle(randOrder)
for counter, rh in enumerate(curves):
rgba = plt.cm.Spectral(float(np.clip(randOrder[counter], 0, n_components))/n_components)
rgb = rgba[0:3]
ax.plot(rh.y, rh.x, color=rgb, linewidth=0.5)
ax.set_xlim((0,data.shape[1]))
ax.set_ylim((data.shape[0],0))
fig.savefig(path,dpi=300, bbox_inches='tight')
def plot_candidates(curves, data, path):
n_components = len(curves)
fig = Figure(figsize=(10, 10), dpi=100)
canvas = FigureCanvasAgg(fig)
ax = fig.add_subplot(111)
ax.set_xticks([]); ax.set_yticks([])
ax.imshow(data, cmap='gray')
randOrder = np.arange(n_components)
np.random.shuffle(randOrder)
for counter, rh in enumerate(curves):
rgba = plt.cm.Spectral(float(np.clip(randOrder[counter], 0, n_components))/n_components)
rgb = rgba[0:3]
ax.plot(rh[1], rh[0], color=rgb)
ax.set_xlim((0,data.shape[1]))
ax.set_ylim((data.shape[0],0))
fig.savefig(path+"candidates_all.png",dpi=100, bbox_inches='tight')
for counter, rh in enumerate(curves):
fig = Figure(figsize=(10, 10), dpi=100)
canvas = FigureCanvasAgg(fig)
ax = fig.add_subplot(111)
ax.set_xticks([]); ax.set_yticks([])
ax.imshow(data, cmap='gray')
rgba = plt.cm.Spectral(float(np.clip(randOrder[counter], 0, n_components))/n_components)
rgb = rgba[0:3]
ax.plot(rh[1], rh[0], color=rgb, linewidth=2)
fig.savefig(path+"candidates_"+str(counter)+".png",dpi=100, bbox_inches='tight')
def plot_colored_state(curves, values, min_val, max_val, data, name_path, title=None):
n_components = len(curves)
fig = Figure(figsize=(20, 20), dpi=150)
canvas = FigureCanvasAgg(fig)
ax = fig.add_subplot(111)
ax.set_xticks([]); ax.set_yticks([])
im = ax.imshow(data, cmap='gray')
#print im.origin
randOrder = np.arange(n_components)
np.random.shuffle(randOrder)
for counter, rh in enumerate(curves):
rgba = plt.cm.Spectral(float(np.clip(values[counter], min_val, max_val))/(max_val-min_val))
rgb = rgba[0:3]
ax.plot(rh[1], rh[0], color=rgb)
if title is not None:
ax.set_title(title)
#im.set_extent((0,data.shape[1],data.shape[0],0))
ax.set_xlim((0,data.shape[1]))
ax.set_ylim((data.shape[0],0))
fig.savefig(name_path,dpi=150, bbox_inches='tight')
def plot_colored_candidates(curves, values, data, path):
n_components = len(curves)
fig = Figure(figsize=(10, 10), dpi=100)
canvas = FigureCanvasAgg(fig)
ax = fig.add_subplot(111)
ax.set_xticks([]); ax.set_yticks([])
ax.imshow(data, cmap='gray')
randOrder = np.arange(n_components)
np.random.shuffle(randOrder)
for counter, rh in enumerate(curves):
rgba = plt.cm.Spectral(float(np.clip(values[counter], min(values), max(values)))/(max(values)-min(values)))
rgb = rgba[0:3]
ax.plot(rh.y, rh.x, color=rgb)
ax.set_xlim((0,data.shape[1]))
ax.set_ylim((data.shape[0],0))
fig.savefig(path+"candidates_all.png",dpi=100, bbox_inches='tight')
for counter, rh in enumerate(curves):
fig = Figure(figsize=(10, 10), dpi=100)
canvas = FigureCanvasAgg(fig)
ax = fig.add_subplot(111)
ax.set_xticks([]); ax.set_yticks([])
ax.imshow(data, cmap='gray')
rgba = plt.cm.Spectral(float(np.clip(values[counter], min(values), max(values)))/(max(values)-min(values)))
rgb = rgba[0:3]
ax.plot(rh.y, rh.x, color=rgb, linewidth=2)
ax.set_title('{0:.{1}f}'.format(values[counter], 4))
fig.savefig(path+"candidates_"+str(counter)+".png",dpi=100, bbox_inches='tight')
def sa_plots(candidate_arr, summary, classes):
import numpy as np
for j,s in summary.items():
n_components = len(s['sols'][0])
randOrder = np.arange(n_components)
np.random.shuffle(randOrder)
for i,sol in enumerate(s['sols']):
if i%5 != 0:
continue
sol = np.where(sol)[0]
fig = plt.figure(figsize=(10,5))
grid = plt.GridSpec(5, 4, hspace=0.1, wspace=0.5)
ax1 = fig.add_subplot(grid[0:5, 0:2])
ax2 = fig.add_subplot(grid[1:4, 2:4])
# fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(classes, cmap='gray')
# roothairs = [candidate_arr[i_cand] for i_cand in sol]
for rh_id in sol:
rh = candidate_arr[rh_id]
rgba = plt.cm.Spectral(float(np.clip(randOrder[rh_id], 0, n_components))/n_components)
rgb = rgba[0:3]
ax1.plot(rh.curve.y, rh.curve.x, '-', color=rgb, linewidth=2)
#ax.plot(l.y[0],l.x[0],'o', color=rgb, linewidth=3)
#ax.plot(l.y[-1],l.x[-1],'o', color=rgb, linewidth=3)
# ax1.set_frame_on(False)
ax1.set_xticks([]); ax1.set_yticks([])
T_arr = s['T'][:i+1]
sub_cost_arr = np.exp(s['sub_cost'][:i+1])
cost_arr = s['cost'][:i+1]
best_arr = s['best'][:i+1]
ax2.plot(list(range(len(T_arr))), list(zip(*sub_cost_arr))[0],'r-',linewidth=0.5)
ax2.plot(list(range(len(T_arr))), list(zip(*sub_cost_arr))[1],'b-',linewidth=0.5)
ax2.plot(list(range(len(T_arr))), list(zip(*sub_cost_arr))[2],'g-',linewidth=0.5)
ax2.plot(list(range(len(T_arr))), list(zip(*sub_cost_arr))[3],'orange',linewidth=0.5)
ax2.plot(list(range(len(T_arr))), cost_arr,'k-',linewidth=1)
ax2.plot(list(range(len(T_arr))), best_arr,'k:',linewidth=1)
ax2.set_xlim(0,len(s['sols']))
ax2.set_ylim(1,max(s['cost']))
ax2.set_xlabel('iteration')
ax2.set_ylabel('cost')
ax2.legend(['Strain Energy','Completeness','Min. Distance','Max. Distance','Overall'])
plt.savefig('C:/Projects/Roothair/Conferences/IPPS 2019/main_for_figures/sa_plots/fig_'+str(j)+'_'+str(i)+'.png',dpi=300)
plt.close()
break