def close(self):
if self.fig is not None:
plt.rtdSubPlots = []
fig = self.fig
fig.clear()
self.fig = None
plt.close(fig)
def graph(train_df, test_df, p_forecast, f_forecast, metric, key):
fig = plt.figure(figsize=(40,10))
forecast_ds = np.array(f_forecast["ds"])
print(len(forecast_ds))
print(len(train_df))
forecast_ds = forecast_ds[int(train_df["values"].count()):]
plt.plot(np.array(train_df["ds"]), np.array(train_df["y"]),'b', label="train", linewidth=3)
plt.plot(np.array(test_df["ds"]), np.array(test_df["y"]), 'k', label="test", linewidth=3)
plt.savefig( "../testing/compare_fourier_prophet/" + str(key) + "_raw_" + metric + ".png", transparent=True)
prophet = np.array(p_forecast["yhat"])
prophet_upper = np.array(p_forecast["yhat_upper"])
prophet_lower = np.array(p_forecast["yhat_lower"])
fourier = f_forecast["yhat"]
fourier = fourier[len(train_df["values"]):]
print(len(forecast_ds))
print(len(fourier))
plt.plot(forecast_ds, fourier, 'g', label="fourier_yhat", linewidth=3)
plt.savefig( "../testing/compare_fourier_prophet/" + str(key) + "_fourier_" + metric + ".png", transparent=True)
prophet = prophet[len(train_df["values"]):]
prophet_upper = prophet_upper[len(train_df["values"]):]
prophet_lower = prophet_lower[len(train_df["values"]):]
plt.plot(forecast_ds, prophet, '*y', label="prophet_yhat", linewidth=3)
plt.plot(forecast_ds, prophet_upper, 'y', label="yhat_upper", linewidth=3)
plt.plot(forecast_ds, prophet_lower, 'y', label="yhat_lower", linewidth=3)
plt.plot()
plt.xlabel("Timestamp")
plt.ylabel("Value")
plt.legend(loc=1)
plt.title("Prophet Model Forecast")
plt.savefig( "../testing/compare_fourier_prophet/" + str(key) + "_compare_" + metric + ".png", transparent=True)
plt.close()
fig = plt.figure(figsize=(40,10))
forecast_ds = np.array(f_forecast["ds"])
forecast_ds = forecast_ds[len(train_df["values"]):]
plt.plot(np.array(train_df["ds"]), np.array(train_df["y"]),'b', label="train", linewidth=3)
plt.plot(np.array(test_df["ds"]), np.array(test_df["y"]), 'k', label="test", linewidth=3)
prophet = np.array(p_forecast["yhat"])
prophet_upper = np.array(p_forecast["yhat_upper"])
prophet_lower = np.array(p_forecast["yhat_lower"])
prophet = prophet[len(train_df["values"]):]
prophet_upper = prophet_upper[len(train_df["values"]):]
prophet_lower = prophet_lower[len(train_df["values"]):]
plt.plot(forecast_ds, prophet, '*y', label="prophet_yhat", linewidth=3)
plt.plot(forecast_ds, prophet_upper, 'y', label="yhat_upper", linewidth=3)
plt.plot(forecast_ds, prophet_lower, 'y', label="yhat_lower", linewidth=3)
plt.savefig( "../testing/compare_fourier_prophet/" + str(key) + "_prophet_" + metric + ".png", transparent=True)
plt.close()
def fxcor_subplot(result, GCs, stars):
'''
Makes subplot of TDR vs VERR and VREL.
Returns a figure object
'''
plt.close('all')
fig = plt.figure(figsize=(6,6))
gs = gridspec.GridSpec(70,40,bottom=0.10,left=0.15,right=0.98, top = 0.95)
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
R_vel = plt.subplot(gs[10:40,0:40])
R_err = plt.subplot(gs[40:70,0:40])
R_hist = plt.subplot(gs[0:10,0:40])
R_hist.axis('off')
plt.setp(R_vel.get_xticklabels(), visible=False)
x = result.TDR
y = result.VREL_helio
R_vel.scatter(x, y, s=10, c='gray', edgecolor='none', alpha = 0.6, label = 'All')
x = GCs.TDR
y = GCs.VREL_helio
R_vel.scatter(x, y, s=11, c='orange', edgecolor='none', alpha = 0.8, label = 'GCs')
x = stars.TDR
y = stars.VREL_helio
R_vel.scatter(x, y, s=11, c='green', edgecolor='none', alpha = 0.8, label = 'Stars')
R_vel.set_xlim(1,20)
R_vel.set_ylim(-2000,5000)
R_vel.set_ylabel(r'$v$ $[km \, s^{-1}]$')
plt.setp(R_vel.get_yticklabels()[0], visible=False)
x = result.TDR
y = result.VERR
R_err.scatter(x, y,s=10, c='gray', edgecolor='none', alpha = 0.6)
x = GCs.TDR
y = GCs.VERR
R_err.scatter(x, y,s=11, c='orange', edgecolor='none', alpha = 0.8)
x = stars.TDR
y = stars.VERR
R_err.scatter(x, y,s=11, c='green', edgecolor='none', alpha = 0.8)
R_err.set_ylim(2,80)
R_err.set_xlim(1,20)
R_err.set_ylabel(r'$\delta v$ $[km \, s^{-1}]$')
R_err.set_xlabel(r'TDR')
plt.setp(R_err.get_yticklabels()[-1], visible=False)
R_vel.legend()
R_hist.hist([GCs.TDR,stars.TDR], range = (1,20), bins = 50, normed=True,
color=['orange','green'])
return fig
def display_digit(X_data, y_data=None, i=0):
"""Display the Xi image, optionally with the corresponding yi label."""
plt.close('all')
Xi = X_data[i]
side = np.sqrt(Xi.size).astype(int)
data = np.array(Xi).reshape((side, side))
plt.imshow(data, cmap='Greys', interpolation='nearest')
plt.title("y = " + str(y_data[i]))
plt.show()
def plot_score_dist(spacing, std_along, prob_miss, max_distance):
from matplotlib.pylab import plt
plt.close("Score Dist")
plt.figure("Score Dist")
d = np.linspace(0, max_distance, 500)
plt.plot(d, [score_dist(di, spacing, std_along, prob_miss) for di in d])
plt.vlines(spacing, 0, 1)
plt.vlines(spacing * 2, 0, 1, ls='--')
plt.annotate("Miss-detect the next mine", (spacing * 2, 0.5), (12, 0),
textcoords='offset points')
plt.ylabel('$p(d)$')
plt.xlabel('$d$')
plt.grid()
plt.xticks(np.arange(max_distance))
plt.xlim(0, max_distance)
plt.savefig('score_dist.pdf')
def augmentation_visualize_and_save(config,
images,
images_names,
path,
times: int = 2):
"""
Visualization of image enhancements.
:param config: configuration from yaml file.
:param images: images to be augmented.
:param images_names: corresponding names of the images.
:param path: the root where the augmented pictures will be saved.
:param times: how many times each image getting augmented.
:return:
"""
rows = len(images)
cols = times + 1
for (index, image), name in zip(enumerate(images), images_names):
plt.subplot(rows, cols, index * cols + 1)
plt.axis('off')
plt.title(name)
_image = bgr2rgb_using_opencv(image)
plt.imshow(_image)
for col in range(1, cols):
plt.subplot(rows, cols, index * cols + col + 1)
plt.axis('off')
plt.title("Augmented NO. " + str(col))
# augment image
augmented_image = augment_image_using_imgaug(_image, config)
plt.imshow(augmented_image)
# Save the full figure
isExists = os.path.exists(path)
if not isExists:
os.makedirs(path)
now_time = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
savefig(os.path.join(path, "%s_Comp.png" % now_time), dpi=600)
# Clear the current figure
plt.clf()
plt.cla()
plt.close()
def make_radar(skill: int, strength: int, defence: int, willpower: int,
attack: int, stamina: int):
'''
:return: PNG type image binary content
'''
value = [skill, strength, defence, willpower, attack, stamina]
if not all(map(lambda x: isinstance(x, int) and 0 < x <= 100, value)):
return
font = FontProperties(fname=settings.PINGFANG_FONT, size=23)
plt.figure(figsize=(4.8, 4.8)) # 图片大小
name = [
'技术\n ', '力量 ', '防守 ', '\n意志力', ' 进攻 ', ' 耐力 '
] # 标签
theta = np.linspace(0, 2 * np.pi, len(name),
endpoint=False) # 将圆周根据标签的个数等比分
theta = np.concatenate((theta, [theta[0]])) # 闭合
value = np.concatenate((value, [value[0]])) # 闭合
ax = plt.subplot(111, projection='polar') # 构建图例
ax.set_theta_zero_location('N') # 设置极轴方向
ax.fill(theta, value, color="#EF2D55", alpha=0.35) # 填充色,透明度
for i in [20, 40, 60, 80, 100]: # 绘等分线
ax.plot(theta, [i] * (6 + 1), 'k-', lw=1,
color='#8989A3') # 之所以 n +1，是因为要闭合！
ax.plot(theta, value, 'ro-', 'k-', lw=1, alpha=0.75,
color='#FF465C') # 绘数据图
ax.set_thetagrids(theta * 180 / np.pi,
name,
fontproperties=font,
color='#8989A3') # 替换标签
ax.set_ylim(0, 100) # 设置极轴的区间
ax.spines['polar'].set_visible(False) # 去掉最外围的黑圈
ax.grid(True, color='#8989A3', linestyle='-', linewidth=1)
ax.set_yticks([])
buf = io.BytesIO()
plt.savefig(buf, transparent=True) # 透明
plt.close('all') # 关闭所有绘图
buf.seek(0)
return buf
def exact():
W = Lea.fastMax(W0 + U, 0)
for k in range(1, 21):
if k % 5 == 0:
plt.plot(W.support(), W.pmf(), label="k={}".format(k))
W = Lea.fastMax(W + U, 0)
return W.support(), W.pmf()
plt.figure()
plt.axis([0, 20, 0, 0.3])
plt.title("Exact")
xex, yex = exact()
plt.legend()
tikz_save('waiting_time_1.tex', figureheight='5cm', figurewidth='5cm')
plt.close()
plt.figure()
plt.axis([0, 20, 0, 0.3])
plt.title("Simulation")
xsim, ysim = simulate()
plt.legend()
tikz_save('waiting_time_2.tex', figureheight='5cm', figurewidth='5cm')
plt.close()
plt.figure()
plt.axis([0, 20, 0, 0.3])
plt.title("Sim/Exact")
plt.plot(xsim, ysim, label="sim")
plt.plot(xex, yex, label="ex")
plt.legend()
plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
train_scores_mean + train_scores_std, alpha=0.1,
color="r")
plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
test_scores_mean + test_scores_std, alpha=0.1, color="g")
plt.plot(train_sizes, train_scores_mean, '-', color="r",
label="Training score")
plt.plot(train_sizes, test_scores_mean, '-', color="g",
label="Cross-validation score")
plt.ylim(0,1.2)
plt.xlim(0,200)
plt.legend(loc="best")
plt.xlabel("Train test size")
plt.savefig("learning_curves.png")
plt.close("all")
# plot the model vs the predictions
for what_plot in [0,1,2,3]:
fig=plt.figure(figsize=(16,8))
#
ax1 = fig.add_subplot(1,2,1); ax2 = fig.add_subplot(1,2,2)
ax1.tick_params(labelsize=20); ax2.tick_params(labelsize=20)
#
if(what_plot==3):
# actual data
ax1.scatter(X_test[models_features[i]]["Budget"],X_test[models_response[i]],c=X_test[models_features[i]]["AveStudioShare"],s=2.**((X_test[models_features[i]]["TheatersOpening"])+4),cmap="Reds",alpha=0.8)
#
# predictions
n_clients=2000,
sessions_budget=2000000,
seed=seed,
sessions_per_feedback=4,
antithetic=True,
lr=1e-3,
noise_std=1e-2)
click_model2sessions2trajectory = foltr.tolist()
click_model2sessions2trajectory09 = foltr09.tolist()
click_model2sessions2trajectory05 = foltr05.tolist()
# click_model2sessions2trajectory_b = b.tolist()
sns.set(style="darkgrid")
plt.close('all')
# rcParams['figure.figsize'] = 12, 2
rcParams['figure.figsize'] = 28, 3
f, ax = plt.subplots(nrows=1, ncols=3, sharex=True)
linear, two_layer = click_model2sessions2trajectory
linear09, two_layer09 = click_model2sessions2trajectory09
linear05, two_layer05 = click_model2sessions2trajectory05
m = common_params['sessions_per_feedback'] * common_params['n_clients']
mid = 0
for row, model in enumerate([PERFECT_MODEL, NAVIGATIONAL_MODEL, INFORMATIONAL_MODEL]):
if n_clients == 2000:
Linear_ys = np.zeros(250)
Linear_ys09 = np.zeros(250)
Linear_ys05 = np.zeros(250)
def plotting(self,pressurelattice,invasionlattice,pressure,number_of_clusters):
from matplotlib.pylab import plt
#Plotting the invasionlattice
plt.figure(2)
plt.imshow(invasionlattice, cmap='Greys',interpolation='nearest')
plt.title("Invasion lattice")
plt.colorbar()
plt.savefig(self.pathsimulation + "invasionlattice.png", bbox_inches="tight")
plt.close()
#plotting the pressure
plt.figure(5)
plt.plot(pressure)
plt.xlabel('Time')
plt.ylabel('Pressure')
plt.title('P(t)')
plt.savefig(self.pathsimulation +"pressure.png", bbox_inches="tight")
plt.close()
#plotting the clusterlattice
plt.figure(6)
plt.imshow(self.lw, interpolation='nearest')
plt.title("Clusterlattice")
plt.colorbar()
plt.savefig(self.pathsimulation +"clusterlattice.png", bbox_inches="tight")
plt.close()
#Temporal diagram
plt.figure(7)
plt.imshow(self.temporalplot,interpolation='nearest')
plt.title('Temporal diagram')
plt.colorbar()
plt.savefig(self.pathsimulation +"temporal.png", bbox_inches="tight")
plt.close()
#Plotting pressure distribution in the cell.
plt.figure(3)
plt.hist(pressurelattice.ravel(), bins=30, fc='k', ec='k')
plt.savefig(self.pathsimulation +"pressuredistribution.png", bbox_inches="tight")
plt.close()
#Plotting the number of clusters as a function of interation.
plt.figure(1)
plt.plot(number_of_clusters)
plt.xlabel('Iteration number/time')
plt.ylabel('Number of clusters')
plt.title('Number_of_cluster(t)')
plt.savefig(self.pathsimulation +"clusterN.png", bbox_inches="tight")
plt.close()
def plotting(self, pressurelattice, invasionlattice, pressure,
number_of_clusters):
from matplotlib.pylab import plt
#Plotting the invasionlattice
plt.figure(2)
plt.imshow(invasionlattice, cmap='Greys', interpolation='nearest')
plt.title("Invasion lattice")
plt.colorbar()
plt.savefig(self.pathsimulation + "invasionlattice.png",
bbox_inches="tight")
plt.close()
#plotting the pressure
plt.figure(5)
plt.plot(pressure)
plt.xlabel('Time')
plt.ylabel('Pressure')
plt.title('P(t)')
plt.savefig(self.pathsimulation + "pressure.png", bbox_inches="tight")
plt.close()
#plotting the clusterlattice
plt.figure(6)
plt.imshow(self.lw, interpolation='nearest')
plt.title("Clusterlattice")
plt.colorbar()
plt.savefig(self.pathsimulation + "clusterlattice.png",
bbox_inches="tight")
plt.close()
#Temporal diagram
plt.figure(7)
plt.imshow(self.temporalplot, interpolation='nearest')
plt.title('Temporal diagram')
plt.colorbar()
plt.savefig(self.pathsimulation + "temporal.png", bbox_inches="tight")
plt.close()
#Plotting pressure distribution in the cell.
plt.figure(3)
plt.hist(pressurelattice.ravel(), bins=30, fc='k', ec='k')
plt.savefig(self.pathsimulation + "pressuredistribution.png",
bbox_inches="tight")
plt.close()
#Plotting the number of clusters as a function of interation.
plt.figure(1)
plt.plot(number_of_clusters)
plt.xlabel('Iteration number/time')
plt.ylabel('Number of clusters')
plt.title('Number_of_cluster(t)')
plt.savefig(self.pathsimulation + "clusterN.png", bbox_inches="tight")
plt.close()
def fit_galaxy(galaxy_im, psf_ave, psf_std=None, source_params=None, background_rms=0.04, pix_sz = 0.08,
exp_time = 300., fix_n=None, image_plot = True, corner_plot=True,
deep_seed = False, galaxy_msk=None, galaxy_std=None, flux_corner_plot = False,
tag = None, no_MCMC= False, pltshow = 1, return_Chisq = False, dump_result = False, pso_diag=False):
'''
A quick fit for the QSO image with (so far) single sersice + one PSF. The input psf noise is optional.
Parameter
--------
galaxy_im: An array of the QSO image.
psf_ave: The psf image.
psf_std: The psf noise, optional.
source_params: The prior for the source. Default is given.
background_rms: default as 0.04
exp_time: default at 2400.
deep_seed: if Ture, more mcmc steps will be performed.
tag: The name tag for save the plot
Return
--------
Will output the fitted image (Set image_plot = True), the corner_plot and the flux_ratio_plot.
source_result, ps_result, image_ps, image_host
To do
--------
'''
# data specifics need to set up based on the data situation
background_rms = background_rms # background noise per pixel (Gaussian)
exp_time = exp_time # exposure time (arbitrary units, flux per pixel is in units #photons/exp_time unit)
numPix = len(galaxy_im) # cutout pixel size
deltaPix = pix_sz
if psf_ave is not None:
psf_type = 'PIXEL' # 'gaussian', 'pixel', 'NONE'
kernel = psf_ave
# if psf_std is not None:
# kwargs_numerics = {'subgrid_res': 1, 'psf_error_map': True} #Turn on the PSF error map
# else:
kwargs_numerics = {'supersampling_factor': 1, 'supersampling_convolution': False}
if source_params is None:
# here are the options for the host galaxy fitting
fixed_source = []
kwargs_source_init = []
kwargs_source_sigma = []
kwargs_lower_source = []
kwargs_upper_source = []
# Disk component, as modelled by an elliptical Sersic profile
if fix_n == None:
fixed_source.append({}) # we fix the Sersic index to n=1 (exponential)
kwargs_source_init.append({'R_sersic': 0.3, 'n_sersic': 2., 'e1': 0., 'e2': 0., 'center_x': 0., 'center_y': 0.})
kwargs_source_sigma.append({'n_sersic': 0.5, 'R_sersic': 0.1, 'e1': 0.1, 'e2': 0.1, 'center_x': 0.1, 'center_y': 0.1})
kwargs_lower_source.append({'e1': -0.5, 'e2': -0.5, 'R_sersic': 0.01, 'n_sersic': 0.3, 'center_x': -10, 'center_y': -10})
kwargs_upper_source.append({'e1': 0.5, 'e2': 0.5, 'R_sersic': 3., 'n_sersic': 7., 'center_x': 10, 'center_y': 10})
elif fix_n is not None:
fixed_source.append({'n_sersic': fix_n})
kwargs_source_init.append({'R_sersic': 0.3, 'n_sersic': fix_n, 'e1': 0., 'e2': 0., 'center_x': 0., 'center_y': 0.})
kwargs_source_sigma.append({'n_sersic': 0.001, 'R_sersic': 0.1, 'e1': 0.1, 'e2': 0.1, 'center_x': 0.1, 'center_y': 0.1})
kwargs_lower_source.append({'e1': -0.5, 'e2': -0.5, 'R_sersic': 0.01, 'n_sersic': fix_n, 'center_x': -10, 'center_y': -10})
kwargs_upper_source.append({'e1': 0.5, 'e2': 0.5, 'R_sersic': 3, 'n_sersic': fix_n, 'center_x': 10, 'center_y': 10})
source_params = [kwargs_source_init, kwargs_source_sigma, fixed_source, kwargs_lower_source, kwargs_upper_source]
else:
source_params = source_params
kwargs_params = {'source_model': source_params}
#==============================================================================
#Doing the QSO fitting
#==============================================================================
kwargs_data = sim_util.data_configure_simple(numPix, deltaPix, exp_time, background_rms, inverse=True)
data_class = ImageData(**kwargs_data)
if psf_ave is not None:
kwargs_psf = {'psf_type': psf_type, 'kernel_point_source': kernel}
else:
kwargs_psf = {'psf_type': 'NONE'}
psf_class = PSF(**kwargs_psf)
data_class.update_data(galaxy_im)
light_model_list = ['SERSIC_ELLIPSE'] * len(source_params[0])
lightModel = LightModel(light_model_list=light_model_list)
kwargs_model = { 'source_light_model_list': light_model_list}
# numerical options and fitting sequences
kwargs_constraints = {}
kwargs_likelihood = {'check_bounds': True, #Set the bonds, if exceed, reutrn "penalty"
'source_marg': False, #In likelihood_module.LikelihoodModule -- whether to fully invert the covariance matrix for marginalization
'check_positive_flux': True,
'image_likelihood_mask_list': [galaxy_msk]
}
kwargs_data['image_data'] = galaxy_im
if galaxy_std is not None:
kwargs_data['noise_map'] = galaxy_std
if psf_std is not None:
kwargs_psf['psf_error_map'] = psf_std
image_band = [kwargs_data, kwargs_psf, kwargs_numerics]
multi_band_list = [image_band]
kwargs_data_joint = {'multi_band_list': multi_band_list, 'multi_band_type': 'multi-linear'} # 'single-band', 'multi-linear', 'joint-linear'
fitting_seq = FittingSequence(kwargs_data_joint, kwargs_model, kwargs_constraints, kwargs_likelihood, kwargs_params)
if deep_seed == False:
fitting_kwargs_list = [
['PSO', {'sigma_scale': 0.8, 'n_particles': 50, 'n_iterations': 50}],
['MCMC', {'n_burn': 10, 'n_run': 10, 'walkerRatio': 50, 'sigma_scale': .1}]
]
elif deep_seed == True:
fitting_kwargs_list = [
['PSO', {'sigma_scale': 0.8, 'n_particles': 100, 'n_iterations': 80}],
['MCMC', {'n_burn': 10, 'n_run': 15, 'walkerRatio': 50, 'sigma_scale': .1}]
]
elif deep_seed == 'very_deep':
fitting_kwargs_list = [
['PSO', {'sigma_scale': 0.8, 'n_particles': 150, 'n_iterations': 150}],
['MCMC', {'n_burn': 10, 'n_run': 20, 'walkerRatio': 50, 'sigma_scale': .1}]
]
if no_MCMC == True:
fitting_kwargs_list = [fitting_kwargs_list[0],
]
start_time = time.time()
chain_list = fitting_seq.fit_sequence(fitting_kwargs_list)
kwargs_result = fitting_seq.best_fit()
ps_result = kwargs_result['kwargs_ps']
source_result = kwargs_result['kwargs_source']
if no_MCMC == False:
sampler_type, samples_mcmc, param_mcmc, dist_mcmc = chain_list[1]
# chain_list, param_list, samples_mcmc, param_mcmc, dist_mcmc = fitting_seq.fit_sequence(fitting_kwargs_list)
# lens_result, source_result, lens_light_result, ps_result, cosmo_temp = fitting_seq.best_fit()
end_time = time.time()
print(end_time - start_time, 'total time needed for computation')
print('============ CONGRATULATION, YOUR JOB WAS SUCCESSFUL ================ ')
# this is the linear inversion. The kwargs will be updated afterwards
imageModel = ImageModel(data_class, psf_class, source_model_class=lightModel,kwargs_numerics=kwargs_numerics)
imageLinearFit = ImageLinearFit(data_class=data_class, psf_class=psf_class,
source_model_class=lightModel,
kwargs_numerics=kwargs_numerics)
image_reconstructed, error_map, _, _ = imageLinearFit.image_linear_solve(kwargs_source=source_result, kwargs_ps=ps_result)
# image_host = [] #!!! The linear_solver before and after could have different result for very faint sources.
# for i in range(len(source_result)):
# image_host_i = imageModel.source_surface_brightness(source_result,de_lensed=True,unconvolved=False, k=i)
# print("image_host_i", source_result[i])
# print("total flux", image_host_i.sum())
# image_host.append(image_host_i)
# let's plot the output of the PSO minimizer
modelPlot = ModelPlot(multi_band_list, kwargs_model, kwargs_result,
arrow_size=0.02, cmap_string="gist_heat", likelihood_mask_list=[galaxy_msk])
if pso_diag == True:
f, axes = chain_plot.plot_chain_list(chain_list,0)
if pltshow == 0:
plt.close()
else:
plt.show()
reduced_Chisq = imageLinearFit.reduced_chi2(image_reconstructed, error_map)
if image_plot:
f, axes = plt.subplots(1, 3, figsize=(16, 16), sharex=False, sharey=False)
modelPlot.data_plot(ax=axes[0])
modelPlot.model_plot(ax=axes[1])
modelPlot.normalized_residual_plot(ax=axes[2], v_min=-6, v_max=6)
f.tight_layout()
#f.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0., hspace=0.05)
if tag is not None:
f.savefig('{0}_fitted_image.pdf'.format(tag))
if pltshow == 0:
plt.close()
else:
plt.show()
image_host = []
for i in range(len(source_result)):
image_host_i = imageModel.source_surface_brightness(source_result,de_lensed=True,unconvolved=False, k=i)
# print("image_host_i", source_result[i])
# print("total flux", image_host_i.sum())
image_host.append(image_host_i)
if corner_plot==True and no_MCMC==False:
# here the (non-converged) MCMC chain of the non-linear parameters
if not samples_mcmc == []:
n, num_param = np.shape(samples_mcmc)
plot = corner.corner(samples_mcmc, labels=param_mcmc, show_titles=True)
if tag is not None:
plot.savefig('{0}_para_corner.pdf'.format(tag))
if pltshow == 0:
plt.close()
else:
plt.show()
if flux_corner_plot ==True and no_MCMC==False:
param = Param(kwargs_model, kwargs_fixed_source=source_params[2], **kwargs_constraints)
mcmc_new_list = []
labels_new = ["host{0} flux".format(i) for i in range(len(source_params[0]))]
for i in range(len(samples_mcmc)):
kwargs_out = param.args2kwargs(samples_mcmc[i])
kwargs_light_source_out = kwargs_out['kwargs_source']
kwargs_ps_out = kwargs_out['kwargs_ps']
image_reconstructed, _, _, _ = imageLinearFit.image_linear_solve(kwargs_source=kwargs_light_source_out, kwargs_ps=kwargs_ps_out)
fluxs = []
for j in range(len(source_params[0])):
image_j = imageModel.source_surface_brightness(kwargs_light_source_out,unconvolved= False, k=j)
fluxs.append(np.sum(image_j))
mcmc_new_list.append( fluxs )
if int(i/1000) > int((i-1)/1000) :
print(len(samples_mcmc), "MCMC samplers in total, finished translate:", i )
plot = corner.corner(mcmc_new_list, labels=labels_new, show_titles=True)
if tag is not None:
plot.savefig('{0}_HOSTvsQSO_corner.pdf'.format(tag))
if pltshow == 0:
plt.close()
else:
plt.show()
if galaxy_std is None:
noise_map = np.sqrt(data_class.C_D+np.abs(error_map))
else:
noise_map = np.sqrt(galaxy_std**2+np.abs(error_map))
if dump_result == True:
if flux_corner_plot==True and no_MCMC==False:
trans_paras = [source_params[2], mcmc_new_list, labels_new, 'source_params[2], mcmc_new_list, labels_new']
else:
trans_paras = []
picklename= tag + '.pkl'
best_fit = [source_result, image_host, 'source_result, image_host']
# pso_fit = [chain_list, param_list, 'chain_list, param_list']
# mcmc_fit = [samples_mcmc, param_mcmc, dist_mcmc, 'samples_mcmc, param_mcmc, dist_mcmc']
chain_list_result = [chain_list, 'chain_list']
pickle.dump([best_fit, chain_list_result, trans_paras], open(picklename, 'wb'))
if return_Chisq == False:
return source_result, image_host, noise_map
elif return_Chisq == True:
return source_result, image_host, noise_map, reduced_Chisq
def fit_qso(QSO_im, psf_ave, psf_std=None, source_params=None,ps_param=None, background_rms=0.04, pix_sz = 0.168,
exp_time = 300., fix_n=None, image_plot = True, corner_plot=True, supersampling_factor = 2,
flux_ratio_plot=False, deep_seed = False, fixcenter = False, QSO_msk=None, QSO_std=None,
tag = None, no_MCMC= False, pltshow = 1, return_Chisq = False, dump_result = False, pso_diag=False):
'''
A quick fit for the QSO image with (so far) single sersice + one PSF. The input psf noise is optional.
Parameter
--------
QSO_im: An array of the QSO image.
psf_ave: The psf image.
psf_std: The psf noise, optional.
source_params: The prior for the source. Default is given. If [], means no Sersic light.
background_rms: default as 0.04
exp_time: default at 2400.
deep_seed: if Ture, more mcmc steps will be performed.
tag: The name tag for save the plot
Return
--------
Will output the fitted image (Set image_plot = True), the corner_plot and the flux_ratio_plot.
source_result, ps_result, image_ps, image_host
To do
--------
'''
# data specifics need to set up based on the data situation
background_rms = background_rms # background noise per pixel (Gaussian)
exp_time = exp_time # exposure time (arbitrary units, flux per pixel is in units #photons/exp_time unit)
numPix = len(QSO_im) # cutout pixel size
deltaPix = pix_sz
psf_type = 'PIXEL' # 'gaussian', 'pixel', 'NONE'
kernel = psf_ave
kwargs_numerics = {'supersampling_factor': supersampling_factor, 'supersampling_convolution': False}
if source_params is None:
# here are the options for the host galaxy fitting
fixed_source = []
kwargs_source_init = []
kwargs_source_sigma = []
kwargs_lower_source = []
kwargs_upper_source = []
if fix_n == None:
fixed_source.append({}) # we fix the Sersic index to n=1 (exponential)
kwargs_source_init.append({'R_sersic': 0.3, 'n_sersic': 2., 'e1': 0., 'e2': 0., 'center_x': 0., 'center_y': 0.})
kwargs_source_sigma.append({'n_sersic': 0.5, 'R_sersic': 0.5, 'e1': 0.1, 'e2': 0.1, 'center_x': 0.1, 'center_y': 0.1})
kwargs_lower_source.append({'e1': -0.5, 'e2': -0.5, 'R_sersic': 0.1, 'n_sersic': 0.3, 'center_x': -10, 'center_y': -10})
kwargs_upper_source.append({'e1': 0.5, 'e2': 0.5, 'R_sersic': 3., 'n_sersic': 7., 'center_x': 10, 'center_y': 10})
elif fix_n is not None:
fixed_source.append({'n_sersic': fix_n})
kwargs_source_init.append({'R_sersic': 0.3, 'n_sersic': fix_n, 'e1': 0., 'e2': 0., 'center_x': 0., 'center_y': 0.})
kwargs_source_sigma.append({'n_sersic': 0.001, 'R_sersic': 0.5, 'e1': 0.1, 'e2': 0.1, 'center_x': 0.1, 'center_y': 0.1})
kwargs_lower_source.append({'e1': -0.5, 'e2': -0.5, 'R_sersic': 0.1, 'n_sersic': fix_n, 'center_x': -10, 'center_y': -10})
kwargs_upper_source.append({'e1': 0.5, 'e2': 0.5, 'R_sersic': 3, 'n_sersic': fix_n, 'center_x': 10, 'center_y': 10})
source_params = [kwargs_source_init, kwargs_source_sigma, fixed_source, kwargs_lower_source, kwargs_upper_source]
else:
source_params = source_params
if ps_param is None:
center_x = 0.0
center_y = 0.0
point_amp = QSO_im.sum()/2.
fixed_ps = [{}]
kwargs_ps = [{'ra_image': [center_x], 'dec_image': [center_y], 'point_amp': [point_amp]}]
kwargs_ps_init = kwargs_ps
kwargs_ps_sigma = [{'ra_image': [0.05], 'dec_image': [0.05]}]
kwargs_lower_ps = [{'ra_image': [-0.6], 'dec_image': [-0.6]}]
kwargs_upper_ps = [{'ra_image': [0.6], 'dec_image': [0.6]}]
ps_param = [kwargs_ps_init, kwargs_ps_sigma, fixed_ps, kwargs_lower_ps, kwargs_upper_ps]
else:
ps_param = ps_param
#==============================================================================
#Doing the QSO fitting
#==============================================================================
kwargs_data = sim_util.data_configure_simple(numPix, deltaPix, exp_time, background_rms, inverse=True)
data_class = ImageData(**kwargs_data)
kwargs_psf = {'psf_type': psf_type, 'kernel_point_source': kernel}
psf_class = PSF(**kwargs_psf)
data_class.update_data(QSO_im)
point_source_list = ['UNLENSED'] * len(ps_param[0])
pointSource = PointSource(point_source_type_list=point_source_list)
if fixcenter == False:
kwargs_constraints = {'num_point_source_list': [1] * len(ps_param[0])
}
elif fixcenter == True:
kwargs_constraints = {'joint_source_with_point_source': [[i, i] for i in range(len(ps_param[0]))],
'num_point_source_list': [1] * len(ps_param[0])
}
if source_params == []: #fitting image as Point source only.
kwargs_params = {'point_source_model': ps_param}
lightModel = None
kwargs_model = {'point_source_model_list': point_source_list }
imageModel = ImageModel(data_class, psf_class, point_source_class=pointSource, kwargs_numerics=kwargs_numerics)
kwargs_likelihood = {'check_bounds': True, #Set the bonds, if exceed, reutrn "penalty"
'image_likelihood_mask_list': [QSO_msk]
}
elif source_params != []:
kwargs_params = {'source_model': source_params,
'point_source_model': ps_param}
light_model_list = ['SERSIC_ELLIPSE'] * len(source_params[0])
lightModel = LightModel(light_model_list=light_model_list)
kwargs_model = { 'source_light_model_list': light_model_list,
'point_source_model_list': point_source_list
}
imageModel = ImageModel(data_class, psf_class, source_model_class=lightModel,
point_source_class=pointSource, kwargs_numerics=kwargs_numerics)
# numerical options and fitting sequences
kwargs_likelihood = {'check_bounds': True, #Set the bonds, if exceed, reutrn "penalty"
'source_marg': False, #In likelihood_module.LikelihoodModule -- whether to fully invert the covariance matrix for marginalization
'check_positive_flux': True,
'image_likelihood_mask_list': [QSO_msk]
}
kwargs_data['image_data'] = QSO_im
if QSO_std is not None:
kwargs_data['noise_map'] = QSO_std
if psf_std is not None:
kwargs_psf['psf_error_map'] = psf_std
image_band = [kwargs_data, kwargs_psf, kwargs_numerics]
multi_band_list = [image_band]
kwargs_data_joint = {'multi_band_list': multi_band_list, 'multi_band_type': 'multi-linear'} # 'single-band', 'multi-linear', 'joint-linear'
fitting_seq = FittingSequence(kwargs_data_joint, kwargs_model, kwargs_constraints, kwargs_likelihood, kwargs_params)
if deep_seed == False:
fitting_kwargs_list = [
['PSO', {'sigma_scale': 0.8, 'n_particles': 100, 'n_iterations': 60}],
['MCMC', {'n_burn': 10, 'n_run': 10, 'walkerRatio': 50, 'sigma_scale': .1}]
]
elif deep_seed == True:
fitting_kwargs_list = [
['PSO', {'sigma_scale': 0.8, 'n_particles': 250, 'n_iterations': 250}],
['MCMC', {'n_burn': 100, 'n_run': 200, 'walkerRatio': 10, 'sigma_scale': .1}]
]
if no_MCMC == True:
fitting_kwargs_list = [fitting_kwargs_list[0],
]
start_time = time.time()
chain_list = fitting_seq.fit_sequence(fitting_kwargs_list)
kwargs_result = fitting_seq.best_fit()
ps_result = kwargs_result['kwargs_ps']
source_result = kwargs_result['kwargs_source']
if no_MCMC == False:
sampler_type, samples_mcmc, param_mcmc, dist_mcmc = chain_list[1]
end_time = time.time()
print(end_time - start_time, 'total time needed for computation')
print('============ CONGRATULATION, YOUR JOB WAS SUCCESSFUL ================ ')
imageLinearFit = ImageLinearFit(data_class=data_class, psf_class=psf_class,
source_model_class=lightModel,
point_source_class=pointSource,
kwargs_numerics=kwargs_numerics)
image_reconstructed, error_map, _, _ = imageLinearFit.image_linear_solve(kwargs_source=source_result, kwargs_ps=ps_result)
# this is the linear inversion. The kwargs will be updated afterwards
modelPlot = ModelPlot(multi_band_list, kwargs_model, kwargs_result,
arrow_size=0.02, cmap_string="gist_heat", likelihood_mask_list=[QSO_msk])
image_host = [] #!!! The linear_solver before and after LensModelPlot could have different result for very faint sources.
for i in range(len(source_result)):
image_host.append(imageModel.source_surface_brightness(source_result, de_lensed=True,unconvolved=False,k=i))
image_ps = []
for i in range(len(ps_result)):
image_ps.append(imageModel.point_source(ps_result, k = i))
if pso_diag == True:
f, axes = chain_plot.plot_chain_list(chain_list,0)
if pltshow == 0:
plt.close()
else:
plt.show()
# let's plot the output of the PSO minimizer
reduced_Chisq = imageLinearFit.reduced_chi2(image_reconstructed, error_map)
if image_plot:
f, axes = plt.subplots(3, 3, figsize=(16, 16), sharex=False, sharey=False)
modelPlot.data_plot(ax=axes[0,0], text="Data")
modelPlot.model_plot(ax=axes[0,1])
modelPlot.normalized_residual_plot(ax=axes[0,2], v_min=-6, v_max=6)
modelPlot.decomposition_plot(ax=axes[1,0], text='Host galaxy', source_add=True, unconvolved=True)
modelPlot.decomposition_plot(ax=axes[1,1], text='Host galaxy convolved', source_add=True)
modelPlot.decomposition_plot(ax=axes[1,2], text='All components convolved', source_add=True, lens_light_add=True, point_source_add=True)
modelPlot.subtract_from_data_plot(ax=axes[2,0], text='Data - Point Source', point_source_add=True)
modelPlot.subtract_from_data_plot(ax=axes[2,1], text='Data - host galaxy', source_add=True)
modelPlot.subtract_from_data_plot(ax=axes[2,2], text='Data - host galaxy - Point Source', source_add=True, point_source_add=True)
f.tight_layout()
#f.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0., hspace=0.05)
if tag is not None:
f.savefig('{0}_fitted_image.pdf'.format(tag))
if pltshow == 0:
plt.close()
else:
plt.show()
if corner_plot==True and no_MCMC==False:
# here the (non-converged) MCMC chain of the non-linear parameters
if not samples_mcmc == []:
n, num_param = np.shape(samples_mcmc)
plot = corner.corner(samples_mcmc, labels=param_mcmc, show_titles=True)
if tag is not None:
plot.savefig('{0}_para_corner.pdf'.format(tag))
plt.close()
# if pltshow == 0:
# plt.close()
# else:
# plt.show()
if flux_ratio_plot==True and no_MCMC==False:
param = Param(kwargs_model, kwargs_fixed_source=source_params[2], kwargs_fixed_ps=ps_param[2], **kwargs_constraints)
mcmc_new_list = []
if len(ps_param[2]) == 1:
labels_new = ["Quasar flux"] + ["host{0} flux".format(i) for i in range(len(source_params[0]))]
else:
labels_new = ["Quasar{0} flux".format(i) for i in range(len(ps_param[2]))] + ["host{0} flux".format(i) for i in range(len(source_params[0]))]
if len(samples_mcmc) > 10000:
trans_steps = [len(samples_mcmc)-10000, len(samples_mcmc)]
else:
trans_steps = [0, len(samples_mcmc)]
for i in range(trans_steps[0], trans_steps[1]):
kwargs_out = param.args2kwargs(samples_mcmc[i])
kwargs_light_source_out = kwargs_out['kwargs_source']
kwargs_ps_out = kwargs_out['kwargs_ps']
image_reconstructed, _, _, _ = imageLinearFit.image_linear_solve(kwargs_source=kwargs_light_source_out, kwargs_ps=kwargs_ps_out)
flux_quasar = []
if len(ps_param[0]) == 1:
image_ps_j = imageModel.point_source(kwargs_ps_out)
flux_quasar.append(np.sum(image_ps_j))
else:
for j in range(len(ps_param[0])):
image_ps_j = imageModel.point_source(kwargs_ps_out, k=j)
flux_quasar.append(np.sum(image_ps_j))
fluxs = []
for j in range(len(source_params[0])):
image_j = imageModel.source_surface_brightness(kwargs_light_source_out,unconvolved= False, k=j)
fluxs.append(np.sum(image_j))
mcmc_new_list.append(flux_quasar + fluxs )
if int(i/1000) > int((i-1)/1000) :
print(len(samples_mcmc), "MCMC samplers in total, finished translate:", i )
plot = corner.corner(mcmc_new_list, labels=labels_new, show_titles=True)
if tag is not None:
plot.savefig('{0}_HOSTvsQSO_corner.pdf'.format(tag))
if pltshow == 0:
plt.close()
else:
plt.show()
if QSO_std is None:
noise_map = np.sqrt(data_class.C_D+np.abs(error_map))
else:
noise_map = np.sqrt(QSO_std**2+np.abs(error_map))
if dump_result == True:
if flux_ratio_plot==True and no_MCMC==False:
trans_paras = [mcmc_new_list, labels_new, 'mcmc_new_list, labels_new']
else:
trans_paras = []
picklename= tag + '.pkl'
best_fit = [source_result, image_host, ps_result, image_ps,'source_result, image_host, ps_result, image_ps']
chain_list_result = [chain_list, 'chain_list']
kwargs_fixed_source=source_params[2]
kwargs_fixed_ps=ps_param[2]
classes = data_class, psf_class, lightModel, pointSource
material = multi_band_list, kwargs_model, kwargs_result, QSO_msk, kwargs_fixed_source, kwargs_fixed_ps, kwargs_constraints, kwargs_numerics, classes
pickle.dump([best_fit, chain_list_result, trans_paras, material], open(picklename, 'wb'))
if return_Chisq == False:
return source_result, ps_result, image_ps, image_host, noise_map
elif return_Chisq == True:
return source_result, ps_result, image_ps, image_host, noise_map, reduced_Chisq
GCs = pd.read_csv('/Volumes/VINCE/OAC/GCs_903.csv', dtype = {'ID': object}, comment = '#')
# ----------------------------------
rep1 = GCs[GCs.Alt1.isin(GCs.ID)]
df1 = pd.DataFrame()
df2 = pd.DataFrame()
for j in range(0,len(rep1)):
df1.iloc[j] = rep1.iloc[j]
x = VIMOS['VREL_helio']
xerr = VIMOS['VERR']
y = SchuberthMatch['HRV']
yerr = SchuberthMatch['e.1']
print 'rms (VIMOS - Schuberth) GCs = ', np.std(x-y)
plt.close('all')
plt.figure(figsize=(6,6))
plt.errorbar(x, y, yerr= yerr, xerr = xerr, fmt = 'o', c ='black', label = 'Schuberth et al.')
plt.plot([-200, 2200], [-200, 2200], '--k')
plt.xlim(-200,2200)
plt.ylim(-200,2200)
x = VIMOS['r_auto']
y = SchuberthMatch['Rmag']
plt.scatter(x, y, c ='black')
def fit_qso_multiband(QSO_im_list, psf_ave_list, psf_std_list=None, source_params=None,ps_param=None,
background_rms_list=[0.04]*5, pix_sz = 0.168,
exp_time = 300., fix_n=None, image_plot = True, corner_plot=True,
flux_ratio_plot=True, deep_seed = False, fixcenter = False, QSO_msk_list=None,
QSO_std_list=None, tag = None, no_MCMC= False, pltshow = 1, new_band_seq=None):
'''
A quick fit for the QSO image with (so far) single sersice + one PSF. The input psf noise is optional.
Parameter
--------
QSO_im: An array of the QSO image.
psf_ave: The psf image.
psf_std: The psf noise, optional.
source_params: The prior for the source. Default is given.
background_rms: default as 0.04
exp_time: default at 2400.
deep_seed: if Ture, more mcmc steps will be performed.
tag: The name tag for save the plot
Return
--------
Will output the fitted image (Set image_plot = True), the corner_plot and the flux_ratio_plot.
source_result, ps_result, image_ps, image_host
To do
--------
'''
# data specifics need to set up based on the data situation
background_rms_list = background_rms_list # background noise per pixel (Gaussian)
exp_time = exp_time # exposure time (arbitrary units, flux per pixel is in units #photons/exp_time unit)
numPix = len(QSO_im_list[0]) # cutout pixel size
deltaPix = pix_sz
psf_type = 'PIXEL' # 'gaussian', 'pixel', 'NONE'
kernel_list = psf_ave_list
if new_band_seq == None:
new_band_seq= range(len(QSO_im_list))
# if psf_std_list is not None:
# kwargs_numerics_list = [{'subgrid_res': 1, 'psf_subgrid': False, 'psf_error_map': True}] * len(QSO_im_list) #Turn on the PSF error map
# else:
kwargs_numerics_list = [{'supersampling_factor': 1, 'supersampling_convolution': False}] * len(QSO_im_list)
if source_params is None:
# here are the options for the host galaxy fitting
fixed_source = []
kwargs_source_init = []
kwargs_source_sigma = []
kwargs_lower_source = []
kwargs_upper_source = []
# Disk component, as modelled by an elliptical Sersic profile
if fix_n == None:
fixed_source.append({}) # we fix the Sersic index to n=1 (exponential)
kwargs_source_init.append({'R_sersic': 0.3, 'n_sersic': 2., 'e1': 0., 'e2': 0., 'center_x': 0., 'center_y': 0.})
kwargs_source_sigma.append({'n_sersic': 0.5, 'R_sersic': 0.5, 'e1': 0.1, 'e2': 0.1, 'center_x': 0.1, 'center_y': 0.1})
kwargs_lower_source.append({'e1': -0.5, 'e2': -0.5, 'R_sersic': 0.1, 'n_sersic': 0.3, 'center_x': -10, 'center_y': -10})
kwargs_upper_source.append({'e1': 0.5, 'e2': 0.5, 'R_sersic': 3., 'n_sersic': 7., 'center_x': 10, 'center_y': 10})
elif fix_n is not None:
fixed_source.append({'n_sersic': fix_n})
kwargs_source_init.append({'R_sersic': 0.3, 'n_sersic': fix_n, 'e1': 0., 'e2': 0., 'center_x': 0., 'center_y': 0.})
kwargs_source_sigma.append({'n_sersic': 0.001, 'R_sersic': 0.5, 'e1': 0.1, 'e2': 0.1, 'center_x': 0.1, 'center_y': 0.1})
kwargs_lower_source.append({'e1': -0.5, 'e2': -0.5, 'R_sersic': 0.1, 'n_sersic': fix_n, 'center_x': -10, 'center_y': -10})
kwargs_upper_source.append({'e1': 0.5, 'e2': 0.5, 'R_sersic': 3, 'n_sersic': fix_n, 'center_x': 10, 'center_y': 10})
source_params = [kwargs_source_init, kwargs_source_sigma, fixed_source, kwargs_lower_source, kwargs_upper_source]
else:
source_params = source_params
if ps_param is None:
center_x = 0.0
center_y = 0.0
point_amp = QSO_im_list[0].sum()/2.
fixed_ps = [{}]
kwargs_ps = [{'ra_image': [center_x], 'dec_image': [center_y], 'point_amp': [point_amp]}]
kwargs_ps_init = kwargs_ps
kwargs_ps_sigma = [{'ra_image': [0.01], 'dec_image': [0.01]}]
kwargs_lower_ps = [{'ra_image': [-10], 'dec_image': [-10]}]
kwargs_upper_ps = [{'ra_image': [10], 'dec_image': [10]}]
ps_param = [kwargs_ps_init, kwargs_ps_sigma, fixed_ps, kwargs_lower_ps, kwargs_upper_ps]
else:
ps_param = ps_param
kwargs_params = {'source_model': source_params,
'point_source_model': ps_param}
#==============================================================================
#Doing the QSO fitting
#==============================================================================
kwargs_data_list, data_class_list = [], []
for i in range(len(QSO_im_list)):
kwargs_data_i = sim_util.data_configure_simple(numPix, deltaPix, exp_time, background_rms_list[i], inverse=True)
kwargs_data_list.append(kwargs_data_i)
data_class_list.append(ImageData(**kwargs_data_i))
kwargs_psf_list = []
psf_class_list = []
for i in range(len(QSO_im_list)):
kwargs_psf_i = {'psf_type': psf_type, 'kernel_point_source': kernel_list[i]}
kwargs_psf_list.append(kwargs_psf_i)
psf_class_list.append(PSF(**kwargs_psf_i))
data_class_list[i].update_data(QSO_im_list[i])
light_model_list = ['SERSIC_ELLIPSE'] * len(source_params[0])
lightModel = LightModel(light_model_list=light_model_list)
point_source_list = ['UNLENSED']
pointSource = PointSource(point_source_type_list=point_source_list)
imageModel_list = []
for i in range(len(QSO_im_list)):
kwargs_data_list[i]['image_data'] = QSO_im_list[i]
# if QSO_msk_list is not None:
# kwargs_numerics_list[i]['mask'] = QSO_msk_list[i]
if QSO_std_list is not None:
kwargs_data_list[i]['noise_map'] = QSO_std_list[i]
# if psf_std_list is not None:
# kwargs_psf_list[i]['psf_error_map'] = psf_std_list[i]
image_band_list = []
for i in range(len(QSO_im_list)):
imageModel_list.append(ImageModel(data_class_list[i], psf_class_list[i], source_model_class=lightModel,
point_source_class=pointSource, kwargs_numerics=kwargs_numerics_list[i]))
image_band_list.append([kwargs_data_list[i], kwargs_psf_list[i], kwargs_numerics_list[i]])
multi_band_list = [image_band_list[i] for i in range(len(QSO_im_list))]
# numerical options and fitting sequences
kwargs_model = { 'source_light_model_list': light_model_list,
'point_source_model_list': point_source_list
}
if fixcenter == False:
kwargs_constraints = {'num_point_source_list': [1]
}
elif fixcenter == True:
kwargs_constraints = {'joint_source_with_point_source': [[0, 0]],
'num_point_source_list': [1]
}
kwargs_likelihood = {'check_bounds': True, #Set the bonds, if exceed, reutrn "penalty"
'source_marg': False, #In likelihood_module.LikelihoodModule -- whether to fully invert the covariance matrix for marginalization
'check_positive_flux': True,
'image_likelihood_mask_list': [QSO_msk_list]
}
# mpi = False # MPI possible, but not supported through that notebook.
# The Params for the fitting. kwargs_init: initial input. kwargs_sigma: The parameter uncertainty. kwargs_fixed: fixed parameters;
#kwargs_lower,kwargs_upper: Lower and upper limits.
kwargs_data_joint = {'multi_band_list': multi_band_list, 'multi_band_type': 'multi-linear'} # 'single-band', 'multi-linear', 'joint-linear'
fitting_seq = FittingSequence(kwargs_data_joint, kwargs_model, kwargs_constraints, kwargs_likelihood, kwargs_params)
if deep_seed == False:
fitting_kwargs_list = [
['PSO', {'sigma_scale': 0.8, 'n_particles': 80, 'n_iterations': 60, 'compute_bands': [True]+[False]*(len(QSO_im_list)-1)}],
['align_images', {'n_particles': 10, 'n_iterations': 10, 'compute_bands': [False]+[True]*(len(QSO_im_list)-1)}],
['PSO', {'sigma_scale': 0.8, 'n_particles': 100, 'n_iterations': 200, 'compute_bands': [True]*len(QSO_im_list)}],
['MCMC', {'n_burn': 10, 'n_run': 20, 'walkerRatio': 50, 'sigma_scale': .1}]
]
elif deep_seed == True:
fitting_kwargs_list = [
['PSO', {'sigma_scale': 0.8, 'n_particles': 150, 'n_iterations': 60, 'compute_bands': [True]+[False]*(len(QSO_im_list)-1)}],
['align_images', {'n_particles': 20, 'n_iterations': 20, 'compute_bands': [False]+[True]*(len(QSO_im_list)-1)}],
['PSO', {'sigma_scale': 0.8, 'n_particles': 150, 'n_iterations': 200, 'compute_bands': [True]*len(QSO_im_list)}],
['MCMC', {'n_burn': 20, 'n_run': 40, 'walkerRatio': 50, 'sigma_scale': .1}]
]
if no_MCMC == True:
del fitting_kwargs_list[-1]
start_time = time.time()
# lens_result, source_result, lens_light_result, ps_result, cosmo_temp, chain_list, param_list, samples_mcmc, param_mcmc, dist_mcmc = fitting_seq.fit_sequence(fitting_kwargs_list)
chain_list, param_list, samples_mcmc, param_mcmc, dist_mcmc = fitting_seq.fit_sequence(fitting_kwargs_list)
lens_result, source_result, lens_light_result, ps_result, cosmo_temp = fitting_seq.best_fit()
end_time = time.time()
print(end_time - start_time, 'total time needed for computation')
print('============ CONGRATULATION, YOUR JOB WAS SUCCESSFUL ================ ')
source_result_list, ps_result_list = [], []
image_reconstructed_list, error_map_list, image_ps_list, image_host_list, shift_RADEC_list=[], [], [], [],[]
imageLinearFit_list = []
for k in range(len(QSO_im_list)):
# this is the linear inversion. The kwargs will be updated afterwards
imageLinearFit_k = ImageLinearFit(data_class_list[k], psf_class_list[k], source_model_class=lightModel,
point_source_class=pointSource, kwargs_numerics=kwargs_numerics_list[k])
image_reconstructed_k, error_map_k, _, _ = imageLinearFit_k.image_linear_solve(kwargs_source=source_result, kwargs_ps=ps_result)
imageLinearFit_list.append(imageLinearFit_k)
[kwargs_data_k, kwargs_psf_k, kwargs_numerics_k] = fitting_seq.multi_band_list[k]
# data_class_k = data_class_list[k] #ImageData(**kwargs_data_k)
# psf_class_k = psf_class_list[k] #PSF(**kwargs_psf_k)
# imageModel_k = ImageModel(data_class_k, psf_class_k, source_model_class=lightModel,
# point_source_class=pointSource, kwargs_numerics=kwargs_numerics_list[k])
imageModel_k = imageModel_list[k]
modelPlot = ModelPlot(multi_band_list[k], kwargs_model, lens_result, source_result,
lens_light_result, ps_result, arrow_size=0.02, cmap_string="gist_heat", likelihood_mask=QSO_im_list[k])
print("source_result", 'for', "k", source_result)
image_host_k = []
for i in range(len(source_result)):
image_host_k.append(imageModel_list[k].source_surface_brightness(source_result,de_lensed=True,unconvolved=False, k=i))
image_ps_k = imageModel_k.point_source(ps_result)
# let's plot the output of the PSO minimizer
image_reconstructed_list.append(image_reconstructed_k)
source_result_list.append(source_result)
ps_result_list.append(ps_result)
error_map_list.append(error_map_k)
image_ps_list.append(image_ps_k)
image_host_list.append(image_host_k)
if 'ra_shift' in fitting_seq.multi_band_list[k][0].keys():
shift_RADEC_list.append([fitting_seq.multi_band_list[k][0]['ra_shift'], fitting_seq.multi_band_list[k][0]['dec_shift']])
else:
shift_RADEC_list.append([0,0])
if image_plot:
f, axes = plt.subplots(3, 3, figsize=(16, 16), sharex=False, sharey=False)
modelPlot.data_plot(ax=axes[0,0], text="Data")
modelPlot.model_plot(ax=axes[0,1])
modelPlot.normalized_residual_plot(ax=axes[0,2], v_min=-6, v_max=6)
modelPlot.decomposition_plot(ax=axes[1,0], text='Host galaxy', source_add=True, unconvolved=True)
modelPlot.decomposition_plot(ax=axes[1,1], text='Host galaxy convolved', source_add=True)
modelPlot.decomposition_plot(ax=axes[1,2], text='All components convolved', source_add=True, lens_light_add=True, point_source_add=True)
modelPlot.subtract_from_data_plot(ax=axes[2,0], text='Data - Point Source', point_source_add=True)
modelPlot.subtract_from_data_plot(ax=axes[2,1], text='Data - host galaxy', source_add=True)
modelPlot.subtract_from_data_plot(ax=axes[2,2], text='Data - host galaxy - Point Source', source_add=True, point_source_add=True)
f.tight_layout()
if tag is not None:
f.savefig('{0}_fitted_image_band{1}.pdf'.format(tag,new_band_seq[k]))
if pltshow == 0:
plt.close()
else:
plt.show()
if corner_plot==True and no_MCMC==False and k ==0:
# here the (non-converged) MCMC chain of the non-linear parameters
if not samples_mcmc == []:
n, num_param = np.shape(samples_mcmc)
plot = corner.corner(samples_mcmc, labels=param_mcmc, show_titles=True)
if tag is not None:
plot.savefig('{0}_para_corner.pdf'.format(tag))
if pltshow == 0:
plt.close()
else:
plt.show()
if flux_ratio_plot==True and no_MCMC==False:
param = Param(kwargs_model, kwargs_fixed_source=source_params[2], kwargs_fixed_ps=fixed_ps, **kwargs_constraints)
mcmc_new_list = []
labels_new = [r"Quasar flux", r"host_flux", r"source_x", r"source_y"]
# transform the parameter position of the MCMC chain in a lenstronomy convention with keyword arguments #
for i in range(len(samples_mcmc)/10):
kwargs_lens_out, kwargs_light_source_out, kwargs_light_lens_out, kwargs_ps_out, kwargs_cosmo = param.getParams(samples_mcmc[i+ len(samples_mcmc)/10*9])
image_reconstructed, _, _, _ = imageLinearFit_list[k].image_linear_solve(kwargs_source=kwargs_light_source_out, kwargs_ps=kwargs_ps_out)
image_ps = imageModel_list[k].point_source(kwargs_ps_out)
flux_quasar = np.sum(image_ps)
image_disk = imageModel_list[k].source_surface_brightness(kwargs_light_source_out,de_lensed=True,unconvolved=False, k=0)
flux_disk = np.sum(image_disk)
source_x = kwargs_ps_out[0]['ra_image']
source_y = kwargs_ps_out[0]['dec_image']
if flux_disk>0:
mcmc_new_list.append([flux_quasar, flux_disk, source_x, source_y])
plot = corner.corner(mcmc_new_list, labels=labels_new, show_titles=True)
if tag is not None:
plot.savefig('{0}_HOSTvsQSO_corner_band{1}.pdf'.format(tag,new_band_seq[k]))
if pltshow == 0:
plt.close()
else:
plt.show()
errp_list = []
for k in range(len(QSO_im_list)):
if QSO_std_list is None:
errp_list.append(np.sqrt(data_class_list[k].C_D+np.abs(error_map_list[k])))
else:
errp_list.append(np.sqrt(QSO_std_list[k]**2+np.abs(error_map_list[k])))
return source_result_list, ps_result_list, image_ps_list, image_host_list, errp_list, shift_RADEC_list, fitting_seq #fitting_seq.multi_band_list
def plot_spectrum(result, correct = True, interactive = False):
plt.close('all')
plt.ioff()
if interactive:
plt.ion()
hdu = fits.open(result['ORIGINALFILE'])
galaxy = gaussian_filter(hdu[1].data, 1)
thumbnail = hdu['THUMBNAIL'].data
twoD = hdu['2D'].data
header = hdu[0].header
header1 = hdu[1].header
hdu.close()
lamRange = header1['CRVAL1'] + np.array([0., header1['CD1_1'] * (header1['NAXIS1'] - 1)])
if correct:
zp = 1. + (result['VREL'] / 299792.458)
else:
zp = 1.
wavelength = np.linspace(lamRange[0],lamRange[1], header1['NAXIS1']) / zp
ymin, ymax = np.min(galaxy), np.max(galaxy)
ylim = [ymin, ymax] + np.array([-0.02, 0.1])*(ymax-ymin)
ylim[0] = 0.
xmin, xmax = np.min(wavelength), np.max(wavelength)
### Define multipanel size and properties
fig = plt.figure(figsize=(8,6))
gs = gridspec.GridSpec(200,130,bottom=0.10,left=0.10,right=0.95)
### Plot the object in the sky
ax_obj = fig.add_subplot(gs[0:70,105:130])
ax_obj.imshow(thumbnail, cmap = 'gray', interpolation = 'nearest')
ax_obj.set_xticks([])
ax_obj.set_yticks([])
### Plot the 2D spectrum
ax_2d = fig.add_subplot(gs[0:11,0:100])
ix_start = header['START_{}'.format(int(result['DETECT']))]
ix_end = header['END_{}'.format(int(result['DETECT']))]
ax_2d.imshow(twoD, cmap='spectral',
aspect = "auto", origin = 'lower', extent=[xmin, xmax, 0, 1],
vmin = -0.2, vmax=0.2)
ax_2d.set_xticks([])
ax_2d.set_yticks([])
### Add spectra subpanels
ax_spectrum = fig.add_subplot(gs[11:85,0:100])
ax_blue = fig.add_subplot(gs[110:200,0:50])
ax_red = fig.add_subplot(gs[110:200,51:100])
### Plot some atomic lines
line_wave = [4861., 5175., 5892., 6562.8, 8498., 8542., 8662.]
# ['Hbeta', 'Mgb', 'NaD', 'Halpha', 'CaT', 'CaT', 'CaT']
for i in range(len(line_wave)):
x = [line_wave[i], line_wave[i]]
y = [ylim[0], ylim[1]]
ax_spectrum.plot(x, y, c= 'gray', linewidth=1.0)
ax_blue.plot(x, y, c= 'gray', linewidth=1.0)
ax_red.plot(x, y, c= 'gray', linewidth=1.0)
### Plot the spectrum
ax_spectrum.plot(wavelength, galaxy, 'k', linewidth=1.3)
ax_spectrum.set_ylim(ylim)
ax_spectrum.set_xlim([xmin,xmax])
ax_spectrum.set_ylabel(r'Arbitrary Flux')
ax_spectrum.set_xlabel(r'Restframe Wavelength [ $\AA$ ]')
### Plot blue part of the spectrum
x1, x2 = 300, 750
ax_blue.plot(wavelength[x1:x2], galaxy[x1:x2], 'k', linewidth=1.3)
ax_blue.set_xlim(wavelength[x1],wavelength[x2])
ax_blue.set_ylim(galaxy[x1:x2].min(), galaxy[x1:x2].max())
ax_blue.set_yticks([])
### Plot red part of the spectrum
x1, x2 = 1400, 1500
ax_red.plot(wavelength[x1:x2], galaxy[x1:x2], 'k', linewidth=1.3)
ax_red.set_xlim(wavelength[x1],wavelength[x2])
ax_red.set_ylim(galaxy[x1:x2].min(), galaxy[x1:x2].max())
ax_red.set_yticks([])
### Plot text
#if interactive:
textplot = fig.add_subplot(gs[80:200,105:130])
kwarg = {'va' : 'center', 'ha' : 'left', 'size' : 'medium'}
textplot.text(0.1, 1.0,r'ID = {} \, {}'.format(result.ID, int(result.DETECT)),**kwarg)
textplot.text(0.1, 0.9,r'$v =$ {}'.format(int(result.VREL)), **kwarg)
textplot.text(0.1, 0.8,r'$\delta \, v = $ {}'.format(int(result.VERR)), **kwarg)
textplot.text(0.1, 0.7,r'SN1 = {0:.2f}'.format(result.SN1), **kwarg)
textplot.text(0.1, 0.6,r'SN2 = {0:.2f}'.format(result.SN2), **kwarg)
textplot.text(0.1, 0.5,r'TDR = {0:.2f}'.format(result.TDR), **kwarg)
textplot.text(0.1, 0.4,r'SG = {}'.format(result.SG), **kwarg)
textplot.axis('off')
return fig
