def compute_geom_constraints(dirname, inversion_rdiffs, max_fluences):
filename = lambda name: os.path.join(dirname, name)
print output.div_line
print "computing geometry parameters domain constraints"
count_rm = params.ext_opt_geom_resolution[0]
count_rb = params.ext_opt_geom_resolution[1]
Rm = np.linspace(params.ext_opt_geom_mediumradius[0], params.ext_opt_geom_mediumradius[1], count_rm)
Rb = np.linspace(params.ext_opt_geom_beamradius[0], params.ext_opt_geom_beamradius[1], count_rb)
inversion_rdiff_max = params.ext_opt_inversion_rdiff_max
fluence_max = params.ext_opt_fluence_max
contours = [
(max_fluences.T, fluence_max, "damage"),
]
contour_comps = [1.0]
rm_depop = np.interp(inversion_rdiff_max, inversion_rdiffs[::-1], Rm[::-1])
xvals = [(rm_depop, "depopulation")]
xval_comps = [-1.0]
if params.graphs:
print output.status_writing
dirname = os.path.join(dirname, output.opt_geom_rel_path)
dirname = output.init_dir(dirname)
plot.plot_contour(filename("constraints"), "Geometry Parameters Domain Constraints", (Rm, None, None, output.medium_radius_label), (Rb, None, None, output.beam_radius_label), contours, xvals=xvals)
return (contours, xvals, None), (contour_comps, xval_comps, None)
def compute_pump_constraints(dirname, inversion_rdiffs, max_fluences):
filename = lambda name: os.path.join(dirname, name)
print output.div_line
print "computing pumping parameters domain constraints"
active_medium = core.create_medium(None)
count_tau = params.ext_opt_pump_resolution[0]
count_pwr = params.ext_opt_pump_resolution[1]
Tau = np.linspace(params.ext_opt_pump_duration[0], params.ext_opt_pump_duration[1], count_tau)
Pwr = np.linspace(params.ext_opt_pump_power[0], params.ext_opt_pump_power[1], count_pwr)
inversion_rdiff_max = params.ext_opt_inversion_rdiff_max
fluence_max = params.ext_opt_fluence_max
contours = [
(inversion_rdiffs.T, inversion_rdiff_max, "depopulation"),
(max_fluences.T, fluence_max, "damage"),
]
contour_comps = [1.0, 1.0]
if params.graphs:
print output.status_writing
dirname = os.path.join(dirname, output.opt_pump_rel_path)
graph_types = [
(dirname, Pwr,output. pump_power_label),
(os.path.join(dirname, output.alt_plot_rel_path), Pwr * params.pump_efficiency / active_medium.volume, output.eff_power_density_label),
]
for dirname, Y, ylabel in graph_types:
dirname = output.init_dir(dirname)
plot.plot_contour(filename("constraints"), "Pumping Parameters Domain Constraints", (Tau, None, None, output.pump_duration_label), (Y, None, None, ylabel), contours)
return (contours, None, None), (contour_comps, None, None)
def fexc_finh_sweep(neuronp, filt_tau=.01, k_trans=5, max_u=6., max_f=1000.,
npts=10, fname_pre='', max_proc=None, close=False):
alpha = max_u/max_f
fexc = np.linspace(0, max_f, npts)
finh = np.linspace(0, max_f, npts)
fexc_g, finh_g = np.meshgrid(fexc, finh)
lam_g = fexc_g+finh_g
u_g = alpha*(fexc_g-finh_g)
s_g = np.sqrt(lam_g/(2.*neuronp['tau_syn']))
tuning_th = th_lif_fi(u_g,
neuronp['tau_m'], neuronp['tref'], neuronp['xt'])
T = 2.
dt = .0001
io_collector = LIF_IO_Collector(
dt=dt, T=T, alpha=alpha, neuronp=neuronp, filt_tau=filt_tau,
k_trans=k_trans)
tuning, dev1s_l, dev1s_u = io_collector.collect_io_stats(
fexc=fexc_g, finh=finh_g, ret_devs=True, max_proc=max_proc)
# E[u]
fig, ax = plot_contour(
fexc_g, finh_g, u_g,
contourfp={'cmap': plt.cm.BrBG}, contourp={'colors': 'r'},
figp={'figsize': (8, 6)},
xlabel=r'$f_{exc}$', xlabelp={'fontsize': 20},
ylabel=r'$f_{inh}$', ylabelp={'fontsize': 20},
title=r'$E[u]$', titlep={'fontsize': 20})
plot_scatter(
fexc_g, finh_g, scatterp={'c': 'm', 'marker': '+', 'alpha': .5}, ax=ax,
xlim=(fexc[0], fexc[-1]), ylim=(finh[0], finh[-1]), close=close,
fname=FIGDIR+fname_pre+'fe_fi_u.png', savep={'dpi': 200})
# a(E[u]))
fig, ax = plot_contour(
fexc_g, finh_g, tuning_th,
contourfp={'cmap': plt.cm.copper}, contourp={'colors': 'r'},
figp={'figsize': (8, 6)},
xlabel=r'$f_{exc}$', xlabelp={'fontsize': 20},
ylabel=r'$f_{inh}$', ylabelp={'fontsize': 20},
title=r'$a(E[u])$', titlep={'fontsize': 20})
plot_scatter(
fexc_g, finh_g, scatterp={'c': 'm', 'marker': '+', 'alpha': .5}, ax=ax,
xlim=(fexc[0], fexc[-1]), ylim=(finh[0], finh[-1]), close=close,
fname=FIGDIR+fname_pre+'fe_fi_tuning_th.png', savep={'dpi': 200})
# sqrt(Var(u))
fig, ax = plot_contour(
fexc_g, finh_g, s_g,
contourfp={'cmap': plt.cm.PuOr}, contourp={'colors': 'r'},
figp={'figsize': (8, 6)},
xlabel=r'$f_{exc}$', xlabelp={'fontsize': 20},
ylabel=r'$f_{inh}$', ylabelp={'fontsize': 20},
title=r'$\sigma(f_{exc}+f_{inh})$', titlep={'fontsize': 20})
plot_scatter(fexc_g, finh_g,
scatterp={'c': 'm', 'marker': '+', 'alpha': .5}, ax=ax,
xlim=(fexc[0], fexc[-1]), ylim=(finh[0], finh[-1]),
close=close,
fname=FIGDIR+fname_pre+'fe_fi_noise.png', savep={'dpi': 200})
# E[a(u)]
fig, ax = plot_contour(
fexc_g, finh_g, tuning,
contourfp={'cmap': plt.cm.copper}, contourp={'colors': 'r'},
figp={'figsize': (8, 6)},
xlabel=r'$f_{exc}$', xlabelp={'fontsize': 20},
ylabel=r'$f_{inh}$', ylabelp={'fontsize': 20},
title=r'$E[a(u)]$', titlep={'fontsize': 20})
plot_scatter(
fexc_g, finh_g, scatterp={'c': 'm', 'marker': '+', 'alpha': .5}, ax=ax,
xlim=(fexc[0], fexc[-1]), ylim=(finh[0], finh[-1]), close=close,
fname=FIGDIR+fname_pre+'fe_fi_noisy_tuning.png', savep={'dpi': 200})
# Var(a(u))
fig, ax = plot_contour(
fexc_g, finh_g, dev1s_l,
contourfp={'cmap': plt.cm.winter}, contourp={'colors': 'r'},
subplotp=(1, 2, 1), figp={'figsize': (16, 6)},
xlabel=r'$f_{exc}$', xlabelp={'fontsize': 20},
ylabel=r'$f_{inh}$', ylabelp={'fontsize': 20},
title=r'$-\sigma\%(a(u))$', titlep={'fontsize': 20})
plot_scatter(
fexc_g, finh_g, scatterp={'c': 'm', 'marker': '+', 'alpha': .5}, ax=ax,
xlim=(fexc[0], fexc[-1]), ylim=(finh[0], finh[-1]))
fig, ax = plot_contour(
fexc_g, finh_g, dev1s_u,
contourfp={'cmap': plt.cm.winter}, contourp={'colors': 'r'}, fig=fig,
subplotp=(1, 2, 2),
xlabel=r'$f_{exc}$', xlabelp={'fontsize': 20},
ylabel=r'$f_{inh}$', ylabelp={'fontsize': 20},
title=r'$+\sigma\%(a(u))$', titlep={'fontsize': 20})
plot_scatter(
fexc_g, finh_g, scatterp={'c': 'm', 'marker': '+', 'alpha': .5}, ax=ax,
xlim=(fexc[0], fexc[-1]), ylim=(finh[0], finh[-1]), close=close,
fname=FIGDIR+fname_pre+'fe_fi_noisy_tuning.png', savep={'dpi': 200})
contour_hs_1 = c[1]
contour_v_50 = hdc_contour_50.coordinates[0][0]
contour_hs_50 = hdc_contour_50.coordinates[0][1]
c = sort_points_to_form_continous_line(hdc_contour_50.coordinates[0][0],
hdc_contour_50.coordinates[0][1],
do_search_for_optimal_start=True)
contour_v_50 = c[0]
contour_hs_50 = c[1]
fig = plt.figure(figsize=(5, 5), dpi=150)
ax = fig.add_subplot(111)
# Plot the two lower contours.
plot_contour(x=contour_v_lowest,
y=contour_hs_lowest,
ax=ax,
line_style='r-')
plot_contour(x=contour_v_1,
y=contour_hs_1,
ax=ax,
line_style='r-')
# Compute the median hs conditonal on v.
x = np.linspace(0, 35, 100)
d1 = fit.mul_var_dist.distributions[1]
a = d1.scale.a
b = d1.scale.b
c = d1.scale.c
y = a + b * np.power(x, c)
# Plot the 50-year contour and the sample.
plot_mesh(num_nodes_x, num_nodes_y, length_x, length_y)
# if quad_order is not None:
# fill = raw['fill'].reshape((
# (num_nodes_x - 1) * quad_order,
# (num_nodes_y - 1) * quad_order,
# ))
# boundary = raw['boundary'].reshape((
# (num_nodes_x - 1) * quad_order,
# (num_nodes_y - 1) * quad_order,
# ))
# plot_contour(gpt_mesh, fill, plot_fill=True)
# plot_contour(gpt_mesh, boundary, plot_boundary=True)
if 1:
phi = np.asarray(raw['phi'])
phi[phi <= 0] = 0
phi[phi > 0] = 1
multipliers = phi.reshape((num_nodes_x, num_nodes_y), order='F')
plot_contour(mesh, multipliers, plot_fill=True)
else:
multipliers = raw['multipliers'].reshape(
(num_nodes_x - 1, num_nodes_y - 1))
plot_contour(mesh, multipliers, plot_fill=True)
plot_save(save='save/save%03i.png' % counter)
counter += 1
filename = 'save/data%03i.pkl' % counter
import movie
hs_outside, v_outside, hs_inside, v_inside = \
points_outside(contour_hs_50,
contour_v_50,
np.asarray(df[df.columns[2]].values),
np.asarray(df[df.columns[1]].values))
print('Number of points outside the contour: ' + str(len(hs_outside)))
#%%
fig = plt.figure(figsize=(5, 5), dpi=150)
ax = fig.add_subplot(111)
# Plot the 1-year contour.
plot_contour(x=contour_v_1,
y=contour_hs_1,
ax=ax,
contour_label=str(T1) + '-yr contour',
x_label=label_v,
y_label=label_hs,
line_style='b--')
# Plot the 50-year contour and the sample.
plotted_sample = PlottedSample(x=np.asarray(sample_v),
y=np.asarray(sample_hs),
ax=ax,
x_inside=v_inside,
y_inside=hs_inside,
x_outside=v_outside,
y_outside=hs_outside,
return_period=T50)
plot_contour(x=contour_v_50,
y=contour_hs_50,
from outer import encContour
from height import height
from plot import plot_contour
from matplotlib import pyplot as plt
import cv2
import numpy as np
import copy
import math
import sys
cnt1 = encContour('man_front.jpg')
cnt2 = encContour('man_side.jpg')
image = cv2.imread('man_front.jpg')
plot_contour(cnt1= cnt1, cnt2= cnt2)
#cv2.drawContours(image,cnt1,-1,(255),3)
#cv2.imshow('front image',image)
#cv2.waitKey(0)
#print((cnt))
'''
#test for encContour
image = cv2.imread(url)
cnt = encContour(url)
cv2.drawContours(image,cnt,-1,(255),3)
cv2.imshow('image',image)
cv2.waitKey(0)
cv2.destroyAllWindows()
'''
hdc_contour_1.coordinates[0][1],
do_search_for_optimal_start=True)
contour_hs_1 = c[0]
contour_tz_1 = c[1]
c = sort_points_to_form_continous_line(hdc_contour_20.coordinates[0][0],
hdc_contour_20.coordinates[0][1],
do_search_for_optimal_start=True)
contour_hs_20 = c[0]
contour_tz_20 = c[1]
fig = plt.figure(figsize=(5, 5), dpi=150)
ax = fig.add_subplot(111)
# Plot the lowest density contour.
plot_contour(x=contour_tz_lowest, y=contour_hs_lowest, ax=ax, line_style='r-')
# Compute the median tz conditonal on hs.
hs = np.linspace(0, 14, 100)
d1 = fit.mul_var_dist.distributions[1]
c1 = d1.scale.a
c2 = d1.scale.b
tz = c1 + c2 * np.sqrt(np.divide(hs, 9.81))
# Plot the 1-year contour.
plot_contour(x=contour_tz_1, y=contour_hs_1, ax=ax, line_style='r-')
# Plot the 20-year contour and the sample.
plotted_sample = PlottedSample(x=np.asarray(sample_tz),
y=np.asarray(sample_hs),
ax=ax,
# Delete interval centers that were not used.
interval_centers = np.delete(interval_centers, deleted_centers)
# Create Figure 1 of the paper.
fig = plt.figure(figsize=(10, 5), dpi=150)
# Plot dataset A and the 20-year contour.
ax = fig.add_subplot(121)
plotted_sample_a = PlottedSample(x=np.asarray(a_tz),
y=np.asarray(a_hs),
ax=ax,
return_period=return_period_20)
plot_contour(x=contour_tz_20,
y=contour_hs_20,
ax=ax,
contour_label=str(return_period_20) + '-yr contour',
x_label=label_tz,
y_label=label_hs,
line_style='b-',
plotted_sample=plotted_sample_a)
plt.legend(['20-year contour', '10 years of observations'],
loc='upper left',
frameon=False)
plt.title('Dataset A')
# Plot dataset D and the 50-year contour.
ax2 = fig.add_subplot(122)
plotted_sample_d = PlottedSample(x=np.asarray(d_v),
y=np.asarray(d_hs),
ax=ax2,
return_period=return_period_50)
plot_contour(x=contour_v_50,
from outer import encContour
from height import height
from plot import plot_contour
from matplotlib import pyplot as plt
import cv2
import numpy as np
import copy
import math
import sys
cnt1 = encContour('man_front.jpg')
cnt2 = encContour('man_side.jpg')
image = cv2.imread('man_front.jpg')
plot_contour(cnt1=cnt1, cnt2=cnt2)
#cv2.drawContours(image,cnt1,-1,(255),3)
#cv2.imshow('front image',image)
#cv2.waitKey(0)
#print((cnt))
'''
#test for encContour
image = cv2.imread(url)
cnt = encContour(url)
cv2.drawContours(image,cnt,-1,(255),3)
cv2.imshow('image',image)
cv2.waitKey(0)
cv2.destroyAllWindows()
'''
# Plot the environmental contours.
fig = plt.figure(figsize=(5, 5), dpi=150)
ax = fig.add_subplot(111)
for i, contour in enumerate(contours):
if i == 0:
plotted_sample = PlottedSample(x=np.asarray(dataset_d_v),
y=np.asarray(dataset_d_hs),
ax=ax,
label='dataset D')
contour_label = str(return_period) + '-yr contour'
plot_contour(x=contour.c[0],
y=contour.c[1],
ax=ax,
contour_label=contour_label,
x_label=label_v,
y_label=label_hs,
line_style='b-',
alpha=0.4,
plotted_sample=plotted_sample)
else:
plot_contour(x=contour.c[0],
y=contour.c[1],
line_style='b-',
alpha=0.4,
ax=ax)
if DO_COMPUTE_CONFIDENCE_INTERVAL and DO_PLOT_ANGLE_LINES:
for j, (line_v, line_hs) in enumerate(zip(theta_line_v,
theta_line_hs)):
if i == 0:
plt.plot(line_v, line_hs, 'r-')
# Find datapoints that exceed the 20-yr contour.
v_outside, hs_outside, v_inside, hs_inside = \
points_outside(contour_v_50,
contour_hs_50,
np.asarray(sample_v),
np.asarray(sample_hs))
print('Number of points outside the contour: ' + str(len(v_outside)))
fig = plt.figure(figsize=(5, 5), dpi=150)
ax = fig.add_subplot(111)
# Plot the 1-year contour.
plot_contour(x=contour_v_1,
y=contour_hs_1,
ax=ax,
contour_label=str(return_period_1) + '-yr contour',
line_style='b--')
# Compute the median hs conditonal on v.
x = np.linspace(0, 35, 100)
d1 = fit.mul_var_dist.distributions[1]
a = d1.scale.a
b = d1.scale.b
c = d1.scale.c
y = a + b * np.power(x, c)
# Plot the 50-year contour and the sample.
plotted_sample = PlottedSample(x=np.asarray(sample_v),
y=np.asarray(sample_hs),
ax=ax,
fig = plt.figure(figsize=(5, 5), dpi=150)
ax = fig.add_subplot(111)
plotted_sample = PlottedSample(x=np.asarray(sample_tz),
y=np.asarray(sample_hs),
ax=ax,
x_inside=tz_inside,
y_inside=hs_inside,
x_outside=tz_outside,
y_outside=hs_outside,
return_period=T20)
# Plot the 1-year contour.
plot_contour(x=contour_tz_1,
y=contour_hs_1,
ax=ax,
contour_label=str(T1) + '-yr contour',
x_label=label_tz,
y_label=label_hs,
line_style='b--',
plotted_sample=plotted_sample)
# Plot the 20-year contour and the sample.
plot_contour(x=contour_tz_20[~nan_mask],
y=contour_hs_20[~nan_mask],
ax=ax,
contour_label=str(T20) + '-yr contour',
x_label=label_tz,
y_label=label_hs,
line_style='b-') #,
# plotted_sample=plotted_sample)
plt.title('Dataset ' + DATASET_CHAR)
plt.show()
contour_v_50 = hdc_contour_50.coordinates[0][1]
contour_hs_50 = hdc_contour_50.coordinates[0][0]
c = sort_points_to_form_continous_line(hdc_contour_50.coordinates[0][0],
hdc_contour_50.coordinates[0][1],
do_search_for_optimal_start=True)
contour_v_50 = c[1]
contour_hs_50 = c[0]
fig = plt.figure(figsize=(5, 5), dpi=150)
ax = fig.add_subplot(111)
# Plot the two lower contours.
plot_contour(x=contour_v_lowest,
y=contour_hs_lowest,
ax=ax,
contour_label=str(return_period_lowest) + '-yr contour',
line_style='r-')
plot_contour(x=contour_v_1,
y=contour_hs_1,
ax=ax,
contour_label=str(return_period_1) + '-yr contour',
line_style='r-')
# Plot the 50-year contour and the sample.
plotted_sample = PlottedSample(x=np.asarray(sample_v),
y=np.asarray(sample_hs),
ax=ax,
return_period=return_period_50)
plot_contour(x=contour_v_50,
contour_hs_1 = c[0]
contour_tz_1 = c[1]
c = sort_points_to_form_continous_line(hdc_contour_20.coordinates[0][0],
hdc_contour_20.coordinates[0][1],
do_search_for_optimal_start=True)
contour_hs_20 = c[0]
contour_tz_20 = c[1]
fig = plt.figure(figsize=(5, 5), dpi=150)
ax = fig.add_subplot(111)
# Plot the lowest density contour.
plot_contour(x=contour_tz_lowest,
y=contour_hs_lowest,
ax=ax,
contour_label=str(return_period_lowest) + '-yr contour',
line_style='r-')
# Plot the 1-year contour.
plot_contour(x=contour_tz_1,
y=contour_hs_1,
ax=ax,
contour_label=str(return_period_1) + '-yr contour',
line_style='r-')
# Plot the 20-year contour and the sample.
plotted_sample = PlottedSample(x=np.asarray(sample_tz),
y=np.asarray(sample_hs),
ax=ax,
return_period=return_period_20)
# Find datapoints that exceed the 20-yr contour.
hs_outside, tz_outside, hs_inside, tz_inside = \
points_outside(contour_hs_20,
contour_tz_20,
np.asarray(sample_hs),
np.asarray(sample_tz))
print('Number of points outside the contour: ' + str(len(hs_outside)))
fig = plt.figure(figsize=(5, 5), dpi=150)
ax = fig.add_subplot(111)
# Plot the 1-year contour.
plot_contour(x=contour_tz_1,
y=contour_hs_1,
ax=ax,
contour_label=str(return_period_1) + '-yr contour',
line_style='b--')
# Compute the median tz conditonal on hs.
hs = np.linspace(0, 14, 100)
d1 = fit.mul_var_dist.distributions[1]
c1 = d1.scale.a
c2 = d1.scale.b
tz = c1 + c2 * np.sqrt(np.divide(hs, 9.81))
# Plot the 20-year contour and the sample.
plotted_sample = PlottedSample(x=np.asarray(sample_tz),
y=np.asarray(sample_hs),
ax=ax,
x_inside=tz_inside,
# Find datapoints that exceed the 20-yr contour.
hs_outside, tz_outside, hs_inside, tz_inside = \
points_outside(contour_hs_20,
contour_tz_20,
np.asarray(sample_hs),
np.asarray(sample_tz))
print('Number of points outside the contour: ' + str(len(hs_outside)))
fig = plt.figure(figsize=(5, 5), dpi=150)
ax = fig.add_subplot(111)
# Plot the 1-year contour.
plot_contour(x=contour_tz_1,
y=contour_hs_1,
ax=ax,
contour_label=str(return_period_1) + '-yr contour',
x_label=label_tz,
y_label=label_hs,
line_style='b--')
# Plot the 20-year contour and the sample.
plotted_sample = PlottedSample(x=np.asarray(sample_tz),
y=np.asarray(sample_hs),
ax=ax,
x_inside=tz_inside,
y_inside=hs_inside,
x_outside=tz_outside,
y_outside=hs_outside,
return_period=return_period_20)
plot_contour(x=contour_tz_20,
y=contour_hs_20,
# Find datapoints that exceed the 20/50-yr contour.
x_outside, y_outside, x_inside, y_inside = \
points_outside(contour_x_long,
contour_y_long,
np.asarray(sample_x),
np.asarray(sample_y))
print('Number of points outside the contour: ' + str(len(x_outside)))
fig = plt.figure(figsize=(5, 5), dpi=150)
ax = fig.add_subplot(111)
# Plot the 1-year contour.
plot_contour(x=contour_x_1,
y=contour_y_1,
ax=ax,
contour_label='1-yr contour',
line_style='b--')
# Plot the 20/50-year contour and the sample.
plotted_sample = PlottedSample(x=np.asarray(sample_x),
y=np.asarray(sample_y),
ax=ax,
x_inside=x_inside,
y_inside=y_inside,
x_outside=x_outside,
y_outside=y_outside,
return_period=return_period_long_tr)
plot_contour(
x=contour_x_long,
y=contour_y_long,