axes[row][1].plot(
frames,
intercepts,
label=voltage,
linestyle=linestyle,
color="red"
)
#axes[row][0].set_ylabel(r"Slope $a$")
#axes[row][1].set_ylabel(r"Intercept $b$")
axes[row][1].yaxis.tick_right()
axes[row][1].yaxis.set_label_position("right")
for ax in axes[2]:
ax.set_xlabel("Frame index")
#axes[1].legend(loc="center left")
axes[0][0].set_title(r"Slope $a$")
axes[0][1].set_title(r"Intercept $b$")
plt.grid(True)
plt.tight_layout()
#plt.show(); import sys; sys.exit()
from utils import save_tex
save_tex(
__file__,
addFigureDims=True,
extra=set([
#r"legend style={font=\tiny}",
r"ticklabel style={font=\footnotesize}"
])
)
for fl in fluxes:
selection = flux == fl
sorting = np.argsort(frms[selection])
ax.errorbar(
frms[selection][sorting],
sharpness[selection][sorting],
yerr=uncertainty[selection][sorting],
fmt="x-",
label=str(fl)
)
ax.set_xlabel("Frames")
if findex == 0:
ax.set_ylabel("Edge sharpness")
ax.set_ylim(0.1, 1.2)
plt.tight_layout()
#plt.show(); import sys; sys.exit()
from utils import save_tex
#"""
save_tex(
__file__,
subdir="jaw",
addFigureDims=True,
extra=set([
r"legend style={font=\footnotesize}",
r"ticklabel style={font=\footnotesize}"
])
)
#"""
ax.add_patch(
Rectangle(
*roi_props["rect_args"],
ec="red",
fill=False
)
)
ax.annotate(
"$s={}$".format(slice_index),
(5,300),
color="white",
fontsize="xx-large"
)
#ax.set_title("{}".format(name))
ax.set_axis_off()
ax.grid(False)
fig.subplots_adjust(right=0.85)
cbar_ax = fig.add_axes([0.9, 0.1, 0.05, 0.8])
fig.colorbar(img, cax=cbar_ax)
# Tweak padding and save via toolbar
plt.show(); import sys; sys.exit()
from utils import save_tex
save_tex(
__file__,
subdir="jaw",
addFigureDims=True
)
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
from utils import save_tex
style.use("ggplot")
fig, ax = plt.subplots(1, 1)
for material in ["water", "lead"]:
data = np.loadtxt(
"../data/mass-attenuation-coeff_{}.dat".format(material),
delimiter=" ",
usecols=(0, 1)
)
ax.loglog(*data.T, label=material)
ax.set_xlim(0.001, 20)
ax.set_xlabel(r"Photon energy, MeV")
ax.set_ylabel(r"$\mu/\rho$, cm$^2$ g$^{-1}$")
plt.legend(loc="upper right")
plt.tight_layout()
#plt.show()
save_tex(
__file__,
addFigureDims=True,
extra=set([
r"ticklabel style={font=\footnotesize}"
])
)
import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np
fig = plt.figure()
style.use('ggplot')
plt.plot()
plt.grid(True)
#plt.show()
from utils import save_tex
save_tex(__file__)
fig = plt.figure()
style.use('ggplot')
parameters = [
(3.0, 3.0),
(5.0, 1.8)
]
for kappa, sigma_d in parameters:
r = kappa*sigma_d
x = np.arange(-r, r+1)
gaussian_kernel = np.exp(-x**2/(2.0*sigma_d**2))
plt.plot(x, gaussian_kernel, 'o',
markersize=2,
label="$\\sigma_d={},\\kappa={}$".format(sigma_d, kappa)
)
plt.xlabel('Distance from kernel center')
plt.ylabel('Weight')
plt.legend(loc="lower center")
plt.grid(True)
#plt.show(); import sys; sys.exit()
from utils import save_tex
save_tex(
__file__,
addFigureDims=True
)
sharpness[fm_selection][fl_selection][frms_sorting],
yerr=uncertainty[fm_selection][fl_selection][frms_sorting],
fmt=".-",
label=flux_str,
markersize=1.0
)
ax.grid(True)
ax.set_ylim(ylim)
ax.set_xlim(200, 1039)
ax.set_xlabel("Frames")
if fm == 0:
ax.set_ylabel("Edge sharpness")
if fm == legendfm:
print("Don't forget to set the legend location to at={(0.5,0.03)}")
print("Also, adding 'forget plot' to the markplots of the subplot containing the legend,")
print(r"i.e. \addplot [color0, mark=-, mark size=3, only marks, forget plot]")
ax.legend(loc=loc)
plt.tight_layout()
save_tex(
__file__,
subdir="jaw",
addFigureDims=True,
suffix=suffix+str(fm),
extra=set([
r"legend style={font=\tiny}",
r"ticklabel style={font=\footnotesize}",
r"legend columns=2"
])
)
if fmode == "postfiltered" or fmode == "weighted": # 0.1639, 0.1637
print fmode, np.mean(volume[roi])
var1 = np.var(volume[roi])
nl_data.append(var1)
# Plot noise level versus edge sharpness and annotate.
for es, esd, nl in zip(es_data[0], es_data[1], nl_data):
# Different color for each dot
plt.errorbar(es, nl, xerr=esd, fmt='o', markersize=3)
for fmode, x, y in zip(FILTER_MODES, es_data[0], nl_data):
plt.annotate(fmode, xy=(x, y))
ax.set_ylabel("Noise level")
ax.set_xlabel("Edge sharpness")
ax.set_xlim(0.2, 1.0)
ax.set_ylim(0, 0.0011)
ax.grid(True)
#plt.show(); import sys; sys.exit()
from utils import save_tex
save_tex(
__file__,
subdir="johann",
addFigureDims=True,
extra=set([
r"ticklabel style={font=\tiny}"
])
)
import numpy as np
fig = plt.figure()
style.use('ggplot')
x, y = np.loadtxt(
"../data/nose/csv/croppedjaw_normed_masked_volume_var-mean.csv",
delimiter=';',
unpack=True
)
# account for the normalization when loaded to ImFusionSuite.
xmin, xmax = [-1.75781,19.5312]
xrange_ = xmax - xmin
plt.plot(x*xrange_ + xmin, y, '.', markersize=1)
plt.semilogy()
plt.grid(True)
plt.xlabel("Voxel mean value")
plt.ylabel("Variance")
plt.xlim(-1,20)
plt.ylim(4e-6, 3e-4)
#plt.show()
from utils import save_tex
save_tex(
__file__,
addFigureDims=True,
subdir="nose"
)
rmses[fr_selection][fl_selection][tvGrad_sorting],
"o-",
markersize=1,
label=mant_exp_from_int(fl)[2],
)
ax.set_title("{} frames".format(fr))
ax.grid(True)
ax.semilogx()
ax.set_xlabel("TV parameter")
if i == 0:
ax.set_ylabel("RMSE")
ax.legend(loc="upper center", ncol=2)
# plt.show()
# """
save_tex(
__file__,
addFigureDims=True,
subdir="jaw",
suffix="-mlem" if "mlem" in file else "-sirt",
extra=set(
[
r"legend style={font=\tiny}",
r"ticklabel style={font=\tiny}"
# r"legend columns=2"
]
),
)
# """
ax.semilogy()
ax.set_title(irmode)
ax.grid(True)
ax.set_ylim(1e-7, 0.01)
#conserves the grid, other than ax.xaxis.set_ticks([])
# Both approaches allow setting an xlabel
ax.xaxis.set_ticklabels([])
#ax.set_xlabel("Frames")
if i == 0:
ax.set_ylabel("Noise level")
legend = ax.legend(loc="lower center", ncol=2)
plt.tight_layout()
plt.show();import sys; sys.exit()
from utils import save_tex
#"""
# legend needs to be removed from second subplot. Tweak at={(0.5,0.03)}.
save_tex(
__file__,
subdir="jaw",
addFigureDims=True,
extra=set([
r"legend style={font=\tiny}",
r"ticklabel style={font=\tiny}",
r"legend columns=2",
r"xticklabels={,,}"
])
)
#"""
ax.plot(
sigma_var[sigma_var != 0],
var1[sigma_var != 0],
'o',
markersize=2
)
ax.plot(
0,
var1[sigma_var == 0],
'*',
markersize=4
)
ax.set_xlim(-0.1, 3)
ax.set_xlabel("$\sigma$")
ax.set_ylabel("Noise level")
"""
plt.grid(True)
#plt.show(); import sys; sys.exit()
from utils import save_tex
save_tex(
__file__,
addFigureDims=True,
extra=set([
r"ticklabel style={font=\footnotesize}",
r"every x tick scale label/.style={at={(1,0)},yshift=-3pt,anchor=north,inner sep=0pt}"
])
)
def gauss(x, *coeffs):
A, mu, sigma = coeffs
return A * np.exp(-0.5 * (x-mu)**2 / sigma**2)
from scipy.optimize import curve_fit
popt, pcov = curve_fit(gauss, x, y, p0=(540, 10800, 100))
#print popt
#print np.mean(x)
#print np.var(x)
plt.bar(x,y,width=8)
fine_x = np.arange(10400, 11200)
plt.plot(fine_x, gauss(fine_x, *popt)
#label="Fit curve $f(x)=A\\cdot\\exp(\\frac{1}{2\\pi\\sigma^2}\ (x-\\mu)^2)$"
)
plt.grid(True)
plt.xlim(10500, 11200)
plt.xlabel("Gray value")
plt.ylabel("Counts")
#plt.legend(loc="upper left")
#plt.show()
from utils import save_tex
save_tex(
__file__,
addFigureDims=True,
draw_rectangles=True #required to draw bar plot
)
