def optimization_actions(self):
print("Optimizing...")
self.statusBar.showMessage('Optimizing...')
lpar_new, spar_new = ff.optimize_pars(self.xi1, self.xi2,
self.lgals.ravel(), self.lpar,
self.spar)
g_limage, g_source, mua, yi1, yi2 = ll.lensed_images(
self.xi1, self.xi2, lpar_new, spar_new)
self.slider_re.setValue(round(lpar_new[2] / re_max * nbins))
self.slider_x1.setValue(round((lpar_new[0] - x1_min) / dpl))
self.slider_x2.setValue(round((lpar_new[1] - x2_min) / dpl))
self.slider_ql.setValue(round(lpar_new[4] / ql_max * nbins))
self.slider_la.setValue(round(lpar_new[5] / la_max * nbins))
self.slider_rh.setValue(round(spar_new[2] / rh_max * nbins))
self.slider_y1.setValue(round((spar_new[0] - y1_min) / dps))
self.slider_y2.setValue(round((spar_new[1] - y2_min) / dps))
self.slider_qs.setValue(round(spar_new[4] / qs_max * nbins))
self.slider_sa.setValue(round(spar_new[5] / sa_max * nbins))
print("Done.")
self.statusBar.showMessage('Done')
data_pars = {
"name": "black",
"url":
"http://raw.githubusercontent.com/linan7788626/linan7788626.github.io/master/images/black.png",
"source": {
"size": spar_new[2],
"x": spar_new[0],
"y": spar_new[1],
"ell": spar_new[4],
"ang": spar_new[5]
},
"lens": {
"theta_e": lpar_new[2],
"x": lpar_new[0],
"y": lpar_new[1],
"ell": lpar_new[4],
"ang": lpar_new[5],
}
}
# Writing JSON data
f_out_pars = "./josn_outputs/" + data_pars['name'] + '.json'
with open(f_out_pars, 'w') as f:
json.dump(data_pars, f)
# # Reading data back
# with open(f_out_pars, 'r') as f:
# data = json.load(f)
self.statusBar.showMessage('Parameters are saved to %s' % f_out_pars)
def on_draw(self):
""" Redraws the figure
"""
l_xcen = self.slider_x1.value(
) * dpl + x1_min # x position of center (also try (0.0,0.14)
l_ycen = self.slider_x2.value() * dpl + x2_min # y position of center
l_re = self.slider_re.value(
) * re_max / nbins # Einstein radius of lens.
l_rc = 0.0 # Core size of lens (in units of Einstein radius).
l_axrat = self.slider_ql.value(
) * ql_max / nbins # Axis ratio of lens.
l_pa = self.slider_la.value() * la_max / nbins # Orintation of lens.
self.lpar = np.asarray([l_xcen, l_ycen, l_re, l_rc, l_axrat, l_pa])
#----------------------------------------------------------------------
g_amp = 1.0 # peak brightness value
g_sig = self.slider_rh.value(
) * rh_max / nbins # Gaussian "sigma" (i.e., size)
g_xcen = self.slider_y1.value(
) * dps + y1_min # x position of center (also try (0.0,0.14)
g_ycen = self.slider_y2.value() * dps + y2_min # y position of center
g_axrat = self.slider_qs.value(
) * qs_max / nbins # minor-to-major axis ratio
g_pa = self.slider_sa.value(
) * sa_max / nbins # major-axis position angle (degrees) c.c.w. from x axis
self.spar = np.asarray([g_xcen, g_ycen, g_sig, g_amp, g_axrat, g_pa])
#----------------------------------------------------------------------
self.g_limage, self.g_source, mua, yi1, yi2 = ll.lensed_images(
self.xi1, self.xi2, self.lpar, self.spar)
# self.img_limg.setImage(self.g_limage)
# self.img_gsrc.setImage(self.g_source)
g_limg_ctr = self.half_ctr(self.g_limage)
g_gsrc_ctr = self.half_ctr(self.g_source)
self.img_limg.setImage(g_limg_ctr)
self.img_gsrc.setImage(g_gsrc_ctr)
def optimization_actions(self):
print "Optimizing..."
lpar_new, spar_new = ff.optimize_pars(self.xi1, self.xi2,
self.lgals.ravel(), self.lpar,
self.spar)
g_limage, g_source, mua, yi1, yi2 = ll.lensed_images(
self.xi1, self.xi2, lpar_new, spar_new)
print "Done."
self.slider_re.setValue(round(lpar_new[2] / re_max * nbins))
self.slider_x1.setValue(round((lpar_new[0] - x1_min) / dpl))
self.slider_x2.setValue(round((lpar_new[1] - x2_min) / dpl))
self.slider_ql.setValue(round(lpar_new[4] / ql_max * nbins))
self.slider_la.setValue(round(lpar_new[5] / la_max * nbins))
self.slider_rh.setValue(round(spar_new[2] / rh_max * nbins))
self.slider_y1.setValue(round((spar_new[0] - y1_min) / dps))
self.slider_y2.setValue(round((spar_new[1] - y2_min) / dps))
self.slider_qs.setValue(round(spar_new[4] / qs_max * nbins))
self.slider_sa.setValue(round(spar_new[5] / sa_max * nbins))
levels = [
0.5,
]
levels_imgs = [0.0, 0.08, 0.1, 0.2, 0.3, 0.4, 0.5]
self.axesa.clear()
self.axesa.set_xlim(y1_min, y1_max)
self.axesa.set_ylim(y2_min, y2_max)
self.axesa.set_xticks(
[ybc1 - 0.4, ybc1 - 0.2, ybc1, ybc1 + 0.2, ybc1 + 0.4])
self.axesa.set_yticks(
[ybc2 - 0.4, ybc2 - 0.2, ybc2, ybc2 + 0.2, ybc2 + 0.4])
# self.axesa.contourf(self.xi1,self.xi2,self.sgals,levels_imgs,cmap=pl.cm.gray)
self.axesa.contour(self.xi1,
self.xi2,
g_source,
levels,
colors=('deepskyblue'))
self.axesa.contour(yi1, yi2, mua, 0, colors=('r'), linewidths=2.0)
self.axesb.clear()
self.axesb.set_xlim(x1_min, x1_max)
self.axesb.set_ylim(x2_min, x2_max)
self.axesb.set_xticks(
[xbc1 - 2.0, xbc1 - 1.0, xbc1, xbc1 + 1.0, xbc1 + 2.0])
self.axesb.set_yticks(
[xbc2 - 2.0, xbc2 - 1.0, xbc2, xbc2 + 1.0, xbc2 + 2.0])
self.axesb.contourf(self.xi1,
self.xi2,
self.lgals,
levels_imgs,
cmap=pl.cm.gray)
self.axesb.contour(self.xi1,
self.xi2,
g_limage,
levels,
colors=('deepskyblue'))
self.axesb.contour(self.xi1,
self.xi2,
mua,
colors=('r'),
linewidths=2.0)
self.canvas.draw()
print "opt----", spar_new
def on_draw(self):
""" Redraws the figure
"""
l_xcen = self.slider_x1.value(
) * dpl + x1_min # x position of center (also try (0.0,0.14)
l_ycen = self.slider_x2.value() * dpl + x2_min # y position of center
l_re = self.slider_re.value(
) * re_max / nbins # Einstein radius of lens.
l_rc = 0.0 # Core size of lens (in units of Einstein radius).
l_axrat = self.slider_ql.value(
) * ql_max / nbins # Axis ratio of lens.
l_pa = self.slider_la.value() * la_max / nbins # Orintation of lens.
self.lpar = np.asarray([l_xcen, l_ycen, l_re, l_rc, l_axrat, l_pa])
#----------------------------------------------------------------------
g_amp = 1.0 # peak brightness value
g_sig = self.slider_rh.value(
) * rh_max / nbins # Gaussian "sigma" (i.e., size)
g_xcen = self.slider_y1.value(
) * dps + y1_min # x position of center (also try (0.0,0.14)
g_ycen = self.slider_y2.value() * dps + y2_min # y position of center
g_axrat = self.slider_qs.value(
) * qs_max / nbins # minor-to-major axis ratio
g_pa = self.slider_sa.value(
) * sa_max / nbins # major-axis position angle (degrees) c.c.w. from x axis
self.spar = np.asarray([g_xcen, g_ycen, g_sig, g_amp, g_axrat, g_pa])
#----------------------------------------------------------------------
g_limage, g_source, mua, yi1, yi2 = ll.lensed_images(
self.xi1, self.xi2, self.lpar, self.spar)
#--------------------------lens images contour------------------------
levels = [
0.5,
]
levels_imgs = [0.0, 0.08, 0.1, 0.2, 0.3, 0.4, 0.5]
self.axesa.clear()
self.axesa.set_xlim(y1_min, y1_max)
self.axesa.set_ylim(y2_min, y2_max)
self.axesa.set_xticks(
[ybc1 - 0.4, ybc1 - 0.2, ybc1, ybc1 + 0.2, ybc1 + 0.4])
self.axesa.set_yticks(
[ybc2 - 0.4, ybc2 - 0.2, ybc2, ybc2 + 0.2, ybc2 + 0.4])
self.axesa.contourf(self.xi1,
self.xi2,
self.sgals,
levels_imgs,
cmap=pl.cm.gray)
self.axesa.contour(self.xi1,
self.xi2,
g_source,
levels,
colors=('deepskyblue'))
self.axesa.contour(yi1, yi2, mua, 0, colors=('r'), linewidths=2.0)
self.axesb.clear()
self.axesb.set_xlim(x1_min, x1_max)
self.axesb.set_ylim(x2_min, x2_max)
self.axesb.set_xticks([xbc1 - 2, xbc1 - 1, xbc1, xbc1 + 1, xbc1 + 2])
self.axesb.set_yticks([xbc2 - 2, xbc2 - 1, xbc2, xbc2 + 1, xbc2 + 2])
self.axesb.contourf(self.xi1,
self.xi2,
self.lgals,
levels_imgs,
cmap=pl.cm.gray)
self.axesb.contour(self.xi1,
self.xi2,
g_limage,
levels,
colors=('deepskyblue'))
self.axesb.contour(self.xi1,
self.xi2,
mua,
colors=('r'),
linewidths=2.0)
self.canvas.draw()
def optimization_actions(self):
print "Optimizing..."
lpar_new, spar_new = ff.optimize_pars(self.xi1, self.xi2,
self.lgals.ravel(), self.lpar,
self.spar)
g_limage, g_source, mua, yi1, yi2 = ll.lensed_images(
self.xi1, self.xi2, lpar_new, spar_new)
print "Done."
self.slider_re.setValue(round(lpar_new[2] / 2.0 * nbins))
self.slider_x1.setValue(
round((lpar_new[0] + self.bsz / 2.0 - self.bsz / nbins / 2.0) /
self.bsz * nbins))
self.slider_x2.setValue(
round((lpar_new[1] + self.bsz / 2.0 - self.bsz / nbins / 2.0) /
self.bsz * nbins))
self.slider_ql.setValue(round(lpar_new[4] * nbins))
self.slider_la.setValue(round(lpar_new[5] / 360.0 * nbins))
self.slider_rh.setValue(round(spar_new[2] / 0.5 * nbins))
self.slider_y1.setValue(
round((spar_new[0] + self.bsz / 8.0 - self.bsz / nbins / 8.0) /
(self.bsz / 4.0) * nbins))
self.slider_y2.setValue(
round((spar_new[1] + self.bsz / 8.0 - self.bsz / nbins / 8.0) /
(self.bsz / 4.0) * nbins))
self.slider_qs.setValue(round(spar_new[4] * nbins))
self.slider_sa.setValue(round(spar_new[5] / 360.0 * nbins))
levels = [
0.5,
]
levels_imgs = [0.0, 0.08, 0.1, 0.2, 0.3, 0.4, 0.5]
self.axesa.clear()
# pl.xticks([-2.0, 1.0, 0.0, 1.0, 2.0])
# pl.yticks([-2.0, 1.0, 0.0, 1.0, 2.0])
self.axesa.set_xlim(-self.bsz / 8.0, self.bsz / 8.0)
self.axesa.set_ylim(-self.bsz / 8.0, self.bsz / 8.0)
self.axesa.contourf(self.xi1,
self.xi2,
self.sgals,
levels_imgs,
cmap=pl.cm.gray)
self.axesa.contour(self.xi1,
self.xi2,
g_source,
levels,
colors=('deepskyblue'))
self.axesa.contour(yi1, yi2, mua, 0, colors=('g'), linewidths=2.0)
self.axesb.clear()
# pl.xticks([-0.2,-0.1,0.0,0.1,0.2])
# pl.yticks([-0.2,-0.1,0.0,0.1,0.2])
self.axesb.set_xlim(-self.bsz / 2.0, self.bsz / 2.0)
self.axesb.set_ylim(-self.bsz / 2.0, self.bsz / 2.0)
self.axesb.contourf(self.xi1,
self.xi2,
self.lgals,
levels_imgs,
cmap=pl.cm.gray)
self.axesb.contour(self.xi1,
self.xi2,
g_limage,
levels,
colors=('deepskyblue'))
self.axesb.contour(self.xi1,
self.xi2,
mua,
colors=('r'),
linewidths=2.0)
self.canvas.draw()
print "opt----", spar_new
def on_draw(self):
""" Redraws the figure
"""
l_xcen = self.slider_x1.value(
) * self.bsz / nbins - self.bsz / 2.0 + self.bsz / nbins / 2.0 # x position of center (also try (0.0,0.14)
l_ycen = self.slider_x2.value(
) * self.bsz / nbins - self.bsz / 2.0 + self.bsz / nbins / 2.0 # y position of center
l_re = self.slider_re.value() * 2.0 / nbins # Einstein radius of lens.
l_rc = 0.0 # Core size of lens (in units of Einstein radius).
l_axrat = self.slider_ql.value() * 1.0 / nbins # Axis ratio of lens.
l_pa = self.slider_la.value() * 360.0 / nbins # Orintation of lens.
self.lpar = np.asarray([l_xcen, l_ycen, l_re, l_rc, l_axrat, l_pa])
#----------------------------------------------------------------------
g_amp = 1.0 # peak brightness value
g_sig = self.slider_rh.value(
) * 0.5 / nbins # Gaussian "sigma" (i.e., size)
g_xcen = self.slider_y1.value(
) * self.bsz / 4.0 / nbins - self.bsz / 8.0 + self.bsz / nbins / 8.0 # x position of center (also try (0.0,0.14)
g_ycen = self.slider_y2.value(
) * self.bsz / 4.0 / nbins - self.bsz / 8.0 + self.bsz / nbins / 8.0 # y position of center
g_axrat = self.slider_qs.value(
) * 1.0 / nbins # minor-to-major axis ratio
g_pa = self.slider_sa.value(
) * 360.0 / nbins # major-axis position angle (degrees) c.c.w. from x axis
self.spar = np.asarray([g_xcen, g_ycen, g_sig, g_amp, g_axrat, g_pa])
print "plot***", self.spar
#----------------------------------------------------------------------
g_limage, g_source, mua, yi1, yi2 = ll.lensed_images(
self.xi1, self.xi2, self.lpar, self.spar)
#--------------------------lens images contour------------------------
levels = [
0.5,
]
levels_imgs = [0.0, 0.08, 0.1, 0.2, 0.3, 0.4, 0.5]
self.axesa.clear()
self.axesa.set_xlim(-self.bsz / 8.0, self.bsz / 8.0)
self.axesa.set_ylim(-self.bsz / 8.0, self.bsz / 8.0)
self.axesa.set_xticks([-0.4, -0.2, 0.0, 0.2, 0.4])
self.axesa.set_yticks([-0.4, -0.2, 0.0, 0.2, 0.4])
self.axesa.contourf(self.xi1,
self.xi2,
self.sgals,
levels_imgs,
cmap=pl.cm.gray)
self.axesa.contour(self.xi1,
self.xi2,
g_source,
levels,
colors=('deepskyblue'))
self.axesa.contour(yi1, yi2, mua, 0, colors=('r'), linewidths=2.0)
self.axesb.clear()
# pl.xticks([-2.0, -1.0, 0.0, 1.0, 2.0])
# pl.yticks([-2.0, -1.0, 0.0, 1.0, 2.0])
self.axesb.set_xlim(-self.bsz / 2.0, self.bsz / 2.0)
self.axesb.set_ylim(-self.bsz / 2.0, self.bsz / 2.0)
self.axesb.contourf(self.xi1,
self.xi2,
self.lgals,
levels_imgs,
cmap=pl.cm.gray)
self.axesb.contour(self.xi1,
self.xi2,
g_limage,
levels,
colors=('deepskyblue'))
self.axesb.contour(self.xi1,
self.xi2,
mua,
colors=('r'),
linewidths=2.0)
self.canvas.draw()
self.slider_label_re.setText("Re: %.2f" % self.lpar[2])