def mc_plot(result_arr, mc_arr, img_name):
img = Image_Plot(fig_x=6, fig_y=4, xpad=0.25, ypad=0.2)
img.subplots(1, 2)
scale = 1000
img.axs[0][0].errorbar(result_arr[0],
result_arr[1],
result_arr[2],
marker="s",
fmt=" ",
label="g1",
capsize=img.cap_size)
img.axs[0][0].errorbar(result_arr[3],
result_arr[4],
result_arr[5],
marker="s",
fmt=" ",
label="g2",
capsize=img.cap_size)
img.axs[0][1].errorbar(result_arr[0],
scale * (result_arr[1] - result_arr[0]),
scale * result_arr[2],
marker="s",
fmt=" ",
label="g1",
capsize=img.cap_size)
img.axs[0][1].errorbar(result_arr[3],
scale * (result_arr[4] - result_arr[3]),
scale * result_arr[5],
marker="s",
fmt=" ",
label="g2",
capsize=img.cap_size)
mc_str = "Mean\n$m_1 = %.5f (%.5f)$\n$c_1 = %.5f (%.5f)$\n$m_2 = %.5f (%.5f)$\n$c_2 = %.5f (%.5f)$" \
% (mc_arr[0, 0], mc_arr[0, 1], mc_arr[0, 2], mc_arr[0, 3],
mc_arr[1, 0], mc_arr[1, 1], mc_arr[1, 2], mc_arr[1, 3])
img.axs_text(0, 0, 0.8, 0.05, mc_str, text_fontsize=img.legend_size)
img.set_label(0, 0, 0, "EST g")
img.set_label(0, 1, 1, "$10^3 (\hat{g} - g_{true})$")
img.axs[0][0].plot([-0.1, 0.1], [-0.1, 0.1],
ls="dashed",
c="grey",
alpha=0.5)
img.axs[0][0].set_ylim(-0.05, 0.05)
img.axs[0][1].set_ylim(-0.1, 0.1)
for i in range(2):
img.set_label(0, i, 1, "TRUE g")
img.axs[0][i].legend(fontsize=img.legend_size, loc="lower right")
img.axs[0][i].set_xlim(-0.05, 0.05)
img.save_img(img_name)
img.close_img()
def plot_gf_pix(x, y, ichip, gf1, gf2, gf, gf1_scale, gf2_scale,gf_scale,dot_size=1,pic_path=None):
chip_row, chip_col = numpy.divmod(ichip, 9)
x = x + chip_col * 2112
y = y + chip_row * 4644
img = Image_Plot(xpad=0.2,ypad=0.1)
img.subplots(1, 3)
color_cm = 'bwr'
norm = plt.Normalize(vmin=numpy.min(gf1_scale[0]), vmax=numpy.max(gf1_scale[1]))
cmap = plt.get_cmap(color_cm)
cl = cmap(norm(gf1))
fig = img.axs[0][0].scatter(x, y, color=cl, s=dot_size)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm._A = []
img.figure.colorbar(sm, ax=img.axs[0][0])
norm = plt.Normalize(vmin=numpy.min(gf2_scale[0]), vmax=numpy.max(gf2_scale[1]))
cmap = plt.get_cmap(color_cm)
cl = cmap(norm(gf2))
fig = img.axs[0][1].scatter(x, y, color=cl, s=dot_size)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm._A = []
img.figure.colorbar(sm, ax=img.axs[0][1])
norm = plt.Normalize(vmin=numpy.min(gf_scale[0]), vmax=numpy.max(gf_scale[1]))
cmap = plt.get_cmap(color_cm)
cl = cmap(norm(gf))
fig = img.axs[0][2].scatter(x, y, color=cl, s=dot_size)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm._A = []
img.figure.colorbar(sm, ax=img.axs[0][2])
for i in range(3):
img.axis_sci_ticklabel(0,i,0)
img.axis_sci_ticklabel(0,i,1)
img.set_label(0,i,0,"y")
img.set_label(0,i,1,"x")
if pic_path:
img.save_img(pic_path)
# img.show_img()
img.close_img()
def plot_label(x, y, cent_xy, cent_label, title, show=None, pic_path=None):
ncent = cent_label.shape[0]
img = Image_Plot()
img.subplots(1, 1)
for i in range(ncent):
img.axs[0][0].scatter(cent_xy[0][i],
cent_xy[1][i],
color="C%d" % i,
marker="*",
s=80)
idx = cent_label[i] == 1
img.axs[0][0].scatter(x[idx],
y[idx],
edgecolors="C%d" % i,
color="none",
label="%d, %d" % (i, idx.sum()),
s=5)
img.axs[0][0].set_title(title)
img.axs[0][0].legend()
if pic_path:
img.save_img(pic_path)
if show:
img.show_img()
img.close_img()
h5f.close()
h5f = h5py.File("E:/mask_12.hdf5", "r")
mask = h5f["/data"][()]
h5f.close()
idx = mask > 0
print(idx.sum() / idx.shape[0])
matplotlib.style.use('default')
plt.rcParams['font.family'] = 'serif'
scale = [24.2423, 23.4742]
line_labels = ["$30%$ cutoff", "$60%$ cutoff"]
img = Image_Plot(legend_size=14,
fig_x=6,
fig_y=4,
plt_line_width=2,
axis_linewidth=2,
xpad=0.2)
img.subplots(1, 1)
# img.set_style()
img.axis_type(0, "major", tick_len=6, tick_width=1.2)
img.axis_type(1, "major", tick_len=6, tick_width=1.2)
bins = img.axs[0][0].hist(-mag_true, 50, ec="gray", label="Entire sample")[1]
img.axs[0][0].hist(-mag_true[idx], bins, ec="gray", label="Detected sample")
ys = img.axs[0][0].set_ylim()
for i, s in enumerate(scale):
img.axs[0][0].plot([s, s], [ys[0], ys[1]],
lw=img.plt_line_width,
ls="--",
label=line_labels[i])
# ori_detect[idx] = 1
# snr_stack = snr_stack/ori_detect
# snr_stack[idx] = -1000
#
# idx = detect_num < 1
# detect_num[idx] = 1
# result_stack = result_stack/detect_num
# result_stack[idx] = -1000
# print(result_stack)
numpy.savez("./imgs/stack_result.npz", shears, result_stack,
error_bar_stack, pk_pool, mag_auto_pool, snr_pool, snr_stack)
# plot
# matplotlib.style.use('default')
img = Image_Plot(fig_x=6, fig_y=4, ypad=0.22, xpad=0)
img.subplots(2, 2)
# img.set_style_default()
img.set_style()
img.axis_type(0, "major")
img.axis_type(1, "major")
markers = ['o', 'v', 's', 'h', 'd', 'p', "4", "*", "X", "^", ">", "+"]
colors = ["C%d" % i for i in range(10)]
plot_data = [
result_stack[:flux_num], result_stack[flux_num:2 * flux_num],
result_stack[2 * flux_num:3 * flux_num],
result_stack[3 * flux_num:4 * flux_num]
]
plot_data_err = [
error_bar_stack[:flux_num], error_bar_stack[flux_num:2 * flux_num],
error_bar_stack[2 * flux_num:3 * flux_num],
scale = st + i * dg
if bins[-1] <= ed_:
bins.append(scale)
else:
break
i += 1
bins = numpy.array(bins)
# calculate the flow
num_move = gauss_fit_fun.get_flow(mix_data, 1, gh)
scale = num_move.sum()
# N/dg
x_fit, y_fit = gh[1:], num_move / scale / dg
img = Image_Plot(plt_line_width=2.5, fig_x=12, fig_y=9)
img.subplots(1, 1)
img.axs[0][0].scatter(x_fit,
y_fit,
c="black",
s=15,
label="data flow (normalized)")
# img.axs[0][1].hist(mix_data, bins, histtype="step", linewidth=img.plt_line_width,label="PDF")
# fitting
text_content = []
fit_res_1 = scipy.optimize.curve_fit(gauss_fit_fun.gauss_fun_2_, x_fit,
y_fit)[0]
import numpy
import matplotlib.pyplot as plt
from sys import path
# import os
# my_home = os.popen("echo $HOME").readlines()[0][:-1]
# path.append('%s/work/mylib/'%my_home)
path.append("D:/Github/astrophy-research/mylib/")
from plot_tool import Image_Plot
import tool_box
parent_path = "F:/works/GGL/1/w1/"
img = Image_Plot()
img.subplots(1, 1)
file_num = 6
bin_num = 13
x = tool_box.set_bin_log(0.04, 15, bin_num + 1)
print(x)
signal = numpy.zeros((file_num, bin_num))
sigma = numpy.zeros((file_num, bin_num))
los_dist = numpy.zeros((file_num, bin_num))
for i in range(file_num):
data_path = parent_path + "cmass_result_w_1_%s.npz" % i
data = numpy.load(data_path)["arr_0"]
signal[i] = data[0]
sigma[i] = data[1]
los_dist[i] = data[-1]
dec_n_min, dec_n_max = dec_n.min(), dec_n.max()
z_n_min, z_n_max = z_n.min(), z_n.max()
ra_s, dec_s, z_s = data_s["RA"], data_s["DEC"], data_s["Z"]
ra_s_min, ra_s_max = ra_s.min(), ra_s.max()
dec_s_min, dec_s_max = dec_s.min(), dec_s.max()
z_s_min, z_s_max = z_s.min(), z_s.max()
print("North: RA: %.3f ~ %.3f, DEC: %.3f ~ %.3f, Z: %.4f ~ %.4f"%(ra_n_min, ra_n_max,dec_n_min, dec_n_max,z_n_min, z_n_max))
print("South: RA: %.3f ~ %.3f, DEC: %.3f ~ %.3f, Z: %.4f ~ %.4f"%(ra_s_min, ra_s_max,dec_s_min, dec_s_max,z_s_min, z_s_max))
# The original catalog
img = Image_Plot(fig_x=12, fig_y=9)
img.set_style()
img.plot_img(1,1)
img.axs[0][0].scatter(ra_n, dec_n, s=3, label="CMASS_NORTH")
img.axs[0][0].scatter(ra_s, dec_s, s=3, label="CMASS_SOUTH")
h5f = h5py.File("/mnt/ddnfs/data_users/hkli/CFHT/catalog/cfht_cata/cata.hdf5","r")
labels = ["w_1", "w_2", "w_3", "w_4"]
for i in range(1,5):
data = h5f[labels[i-1]].value
dec = data[:,1]
ra = data[:,0]
ra_min, ra_max = ra.min(), ra.max()
dec_min, dec_max = dec.min(), dec.max()
pts_1 = [ra_min, ra_max, ra_max, ra_min, ra_min]
nbytes = 0
# on rank 0 of comm, create the contiguous shared block
win1 = MPI.Win.Allocate_shared(nbytes, itemsize, comm=comm)
buf1, itemsize = win1.Shared_query(0)
result_min = numpy.ndarray(buffer=buf1, dtype='d', shape=(4, dilute_num))
comm.Barrier()
for tag in task_list_sub:
num_non = int(num_s * dilute_case[tag])
num_total = num_s + num_non
num_corr = num_non
img = Image_Plot(xpad=0.25, ypad=0.25)
img.subplots(1, 2)
t1 = time.time()
gh_mix, gh_mix_sig, coeff_mix, asym_mix = gglensing_tool.find_shear_grid(
mg1[:num_total],
mnu1[:num_total],
G1_bin,
G1_hist_bin,
NU1_hist_bin,
chisq_gap=50,
max_iters=50,
fit_num=20,
dg=0.002,
ax=img.axs[0][0])[:4]
t2 = time.time()
files = "mag_auto"
ch_num = 9
cuts_num = 10
x_coord = [i * cuts_num for i in range(ch_num)]
ch = [i for i in range(ch_num)]
ylabels = [
"$m_1 \\times 10^2$", "$m_2 \\times 10^2$", "$m_1 \\times 10^2$",
"$m_2 \\times 10^2$"
]
x_tick = [i * cuts_num for i in range(0, ch_num, 2)]
fmt = '%2.f%%'
xticks = mtick.FormatStrFormatter(fmt)
img = Image_Plot(fig_x=6, fig_y=4, legend_size=12, plt_line_width=2, xpad=0.20)
img.subplots(1, 2)
img.axis_type(0, "major", tick_len=6, tick_width=1.5)
img.axis_type(1, "major", tick_len=6, tick_width=1.5)
dys = (-0.1, 0.3)
legend_pos = (0.02, 0.96)
col = 0
gauss_size = [2, 4, 2, 4]
sig_scale = [1.5, 1.5, 4, 4]
color = ["C0", "C4", "C1", "C3"]
for tag, filter_name in enumerate(sex_filters):
print(filter_name, files)
shear_tag = rank
print(shear_tag, g1[shear_tag], g2[shear_tag])
h5f = h5py.File(data_path + "/data_%d_noisy_cpp.hdf5" % shear_tag, "r")
data = h5f["/data"][()]
h5f.close()
G1 = numpy.ascontiguousarray(data[:, 0], dtype=numpy.float64)
G2 = numpy.ascontiguousarray(data[:, 1], dtype=numpy.float64)
NU1 = numpy.ascontiguousarray(data[:, 2] + data[:, 3], dtype=numpy.float64)
NU2 = numpy.ascontiguousarray(data[:, 2] - data[:, 3], dtype=numpy.float64)
G1_bin = tool_box.set_bin(G1, 10, 100)
G2_bin = tool_box.set_bin(G2, 10, 100)
img = Image_Plot(xpad=0.2, ypad=0.2)
img.subplots(2, 5)
bin_type = [bins for i in range(5)]
bins_nums = [[100, 50], [100, 100], [200, 100], [200, 200], [500, 200]]
for j in range(5):
num_g, num_nu = bins_nums[j]
G1_hist_bin = set_bin(G1[:3000], bins_nums[j], 1.001, bin_type[j])
NU1_hist_bin = set_bin(NU1[:3000], bins_nums[j], 1.001, bin_type[j])
G2_hist_bin = set_bin(G2[:3000], bins_nums[j], 1.001, bin_type[j])
NU2_hist_bin = set_bin(NU2[:3000], bins_nums[j], 1.001, bin_type[j])
h5f["/shear_true"] = shear_field_grid_true
h5f.close()
# true, mean, measured, sigma, diff_percent, number
plt_data_com = [
shear_field_grid_true, shear_field_grid_mean, shear_field_grid,
shear_field_grid_mean - shear_field_grid_true,
shear_field_grid - shear_field_grid_true
]
plt_data_m = [shear_field_grid_err, num_in_grid]
plt_data_coms.append(plt_data_com)
plt_data_ms.append(plt_data_m)
img = Image_Plot(fig_x=5, fig_y=5 / 4 * 3)
img.subplots(len(grid_nx), 5)
titles = [
"True", "Mean in grid", "Measured", "Mean - True", "Measured - True"
]
for tag in range(len(grid_nx)):
nx = grid_nx[tag]
inverse = range(nx - 1, -1, -1)
grid_min = min([plt_data_coms[tag][i].min() for i in range(3)])
grid_max = min([plt_data_coms[tag][i].max() for i in range(3)])
diff_min = min([plt_data_coms[tag][i].min() for i in range(3, 5)])
diff_max = min([plt_data_coms[tag][i].max() for i in range(3, 5)])
else:
exit()
t1 = time.time()
with Pool(ncpus) as pool:
sampler = emcee.EnsembleSampler(
nwalkers,
ndim,
MCMC_program.ln_prob_g,
args=[MG, NU, mg_bins, bin_num2, inverse, ra, dec, shear_tag],
pool=pool)
pos, prob, state = sampler.run_mcmc(p0, step)
t2 = time.time()
print("Time: %2.f sec" % (t2 - t1))
# Plot the walkers
img = Image_Plot(fig_x=16, fig_y=4)
img.subplots(ndim, 1)
for i in range(nwalkers):
for j in range(ndim):
img.axs[j][0].plot(range(step),
sampler.chain[i, :, j],
color='grey',
alpha=0.6)
img.axs[j][0].plot([0, step], [parameters[j], parameters[j]])
img.save_img(parent_path + "pic/%s_mcmc_walkers_nw_%d_stp_%d_expo_%d.png" %
(shear_cmd, nwalkers, step, expo_num))
img.close_img()
# Plot the panels
samples = sampler.chain[:, 150:, :].reshape((-1, ndim))
print(samples.shape)
import matplotlib
# matplotlib.use("Agg")
from sys import path,argv
path.append("/home/hklee/work/mylib")
path.append("D:/GitHub/astrophy-research/mylib")
from plot_tool import Image_Plot
import numpy
import h5py
parent_path = "E:/works/Group_meeting/2019-11-25-shear_bias_checking/result/data_from_pi/3"
img = Image_Plot(fig_x=6,fig_y=8, ypad=0.1)
img.subplots(1,2)
img.set_style()
color_tag = [0]
x = numpy.linspace(-1, 1, 100)
pts_size = 7
for i in range(12):
if i>0:
h5f = h5py.File(parent_path+"/%d/result_noisy_bin_num_40.hdf5"%(i-1),"r")
else:
h5f = h5py.File(parent_path + "/result_all/result_noisy.hdf5", "r")
mc = h5f["/sym_mc"].value
h5f.close()
tag = 3*i + 2
if i in color_tag:
color = "C3"
else:
import numpy
from sys import path
path.append("D:/Github/astrophy-research/mylib")
path.append("D:/Github/astrophy-research/multi_shear_detect")
from Fourier_Quad import Fourier_Quad
import h5py
from plot_tool import Image_Plot
import tool_box
img = Image_Plot(fig_x=6, fig_y=8, xpad=0.1, ypad=0.2)
img.subplots(1, 3)
signal = 0.01
bin_num = [2, 4, 8, 12, 14, 16, 20, 32, 64, 96, 128, 192]
data_num = len(bin_num)
y = range(data_num)
titles = [
"Num = $10^5$,$\sigma=1$,$\mu=0.01$", "Num = $10^6$,$\sigma=1$,$\mu=0.01$",
"Num = $10^7$,$\sigma=1$,$\mu=0.01$"
]
scale = [100, 100, 1000]
x_ticks = numpy.linspace(-0.02, 0.01, 4)
for i in range(3):
h5f = h5py.File("D:/data_%d.hdf5" % (i + 1), "r")
data = h5f["/data"][()]
img.axs[0][i].errorbar(data[1, :data_num],
y,
xerr=data[2, :data_num],
capsize=img.cap_size - 1)
ys = img.axs[0][i].set_ylim()
img.axs[0][i].plot([signal, signal], [ys[0], ys[1]],
print("E-PSF")
gal_imgs = numpy.zeros((size, 4*size))
# gal_imgs_cpp = numpy.zeros((size, 4*size))
psf_imgs = numpy.zeros((size, 4*size))
# psf_imgs_cpp = numpy.zeros((size, 4*size))
for i in range(4):
psf = fq.cre_psf(psf_scale,ellip_theta=(psf_e, numpy.pi/4*i + theta))
gal = fq.convolve_psf(pts, psf_scale, 1, ellip_theta=(psf_e, numpy.pi/4*i + theta))
gal_imgs[:,int(i*size):int((i+1)*size)] = gal
psf_imgs[:,int(i*size):int((i+1)*size)] = psf
diff_psf = psf_imgs - epsf_cpp
print(diff_psf.min(), diff_psf.max())
img = Image_Plot(fig_x=4,fig_y=3)
img.subplots(3, 1)
fig = img.axs[0][0].imshow(psf_imgs)
img.figure.colorbar(fig,ax=img.axs[0][0])
fig = img.axs[1][0].imshow(epsf_cpp)
img.figure.colorbar(fig,ax=img.axs[1][0])
fig = img.axs[2][0].imshow(diff_psf)
img.figure.colorbar(fig,ax=img.axs[2][0])
img.show_img()
diff_gal = gal_imgs - gal_epsf_cpp
print(diff_gal.min(), diff_gal.max())
img = Image_Plot(fig_x=4,fig_y=3)
img.subplots(3, 1)
fig = img.axs[0][0].imshow(gal_imgs)
import numpy
from sys import path
path.append("D:/Github/astrophy-research/mylib")
path.append("D:/Github/astrophy-research/multi_shear_detect")
from Fourier_Quad import Fourier_Quad
import h5py
from plot_tool import Image_Plot
import tool_box
img = Image_Plot(xpad=0.28, ypad=0.2)
img.subplots(2,2)
data_path = "D:/TEMP/PDF_grid_test"
h5f = h5py.File(data_path + "/shear.hdf5","r")
g1t = h5f["/g1"][()]
g2t = h5f["/g2"][()]
h5f.close()
result = numpy.load(data_path + "/cache.npz")["arr_0"]
print(result.shape)
# noise free g1 ori PDF
mc = tool_box.data_fit(g1t,result[:,0],result[:,1])
label = "ori PDF_SYM noise-free\nm: %.4f(%.4f) c: %.5f(%.5f)"%(mc[0]-1, mc[1], mc[2], mc[3])
img.axs[0][0].errorbar(g1t, result[:,0],result[:,1],c="C0",fmt=" ", label=label,marker="s",ms=5)
# noise free g2 ori PDF
mc = tool_box.data_fit(g2t,result[:,2],result[:,3])
label = "ori PDF_SYM noise-free\nm: %.4f(%.4f) c: %.5f(%.5f)"%(mc[0]-1, mc[1], mc[2], mc[3])
img.axs[0][1].errorbar(g2t, result[:,2],result[:,3],c="C0",fmt=" ", label=label,marker="s",ms=5)
sigma8_bin = h5f["/sigma8"][()]
h5f.close()
print(chisqs.shape)
print(chisqs.min(), chisqs.max())
omega_cm_num = omega_cm_bin.shape[0]
omega_bm_num = omega_bm_bin.shape[0]
sigma8_num = sigma8_bin.shape[0]
my, mx = numpy.zeros((omega_cm_num, sigma8_num)), numpy.zeros(
(omega_cm_num, sigma8_num))
for i in range(omega_cm_num):
my[i] = omega_cm_bin[i]
for j in range(sigma8_num):
mx[:, j] = sigma8_bin[j]
img = Image_Plot(fig_x=4, fig_y=3, xpad=0.25, ypad=0.28)
img.subplots(2, 5)
for i in range(2):
for j in range(5):
tag = i * 2 + j
idx = chisqs[tag] > -200
print(idx.sum())
x = mx[idx].flatten()
y = my[idx].flatten()
z = chisqs[tag][idx].flatten()
img.scatter_pts(i, j, x, y, z, color_map="jet", pts_size=18)
img.axs[i][j].set_title("$\Omega_b=%.3f$" % omega_bm_bin[tag])
img.set_label(i, j, 0, "$\Omega_m$")
for j in range(5):
total_path = "E:/data/new_pdf/galsim_sample/imgs_%d" % j
data_path = total_path + "/chisq"
h5f = h5py.File(data_path + "/shear.hdf5", "r")
g1 = h5f["/g1"][()]
g2 = h5f["/g2"][()]
h5f.close()
for n in nm:
for i in range(10):
h5f = h5py.File(data_path + "/chisq_%d_%s.hdf5" % (i, n), "r")
chisq1 = h5f["/chisq1"][()]
chisq2 = h5f["/chisq2"][()]
shear_h = h5f["/shear"][()]
h5f.close()
print(chisq1.min(), chisq2.min())
idx1 = chisq1 < 10000
idx2 = chisq2 < 10000
img = Image_Plot(xpad=0.15, ypad=0.15)
img.subplots(1, 1)
img.axs[0][0].plot(shear_h[idx1], chisq1[idx1], label="g1")
img.axs[0][0].plot(shear_h[idx2], chisq2[idx2], label="g2")
img.axs[0][0].set_xlim(-0.11, 0.11)
img.axs[0][0].legend()
img.axs[0][0].set_title("g1=%.4f, g2=%.4f" % (g1[i], g2[i]))
img.set_label(0, 0, 0, "$\chi^2$")
img.set_label(0, 0, 1, "$g$")
img.save_img(data_path + "/chisq_%s_%d.png" % (n, i))
# img.show_img()
img.close_img()
import h5py
from plot_tool import Image_Plot
c_scale = 1000
m_scale = 100
names = ["%d" % i for i in range(10)]
# names = []
# names.append("total")
data_type = "noise_free"
for nm in names:
parent_path = "E:/data/weight_test/step_1_psf_4_large_sample_48x48"
data_path = parent_path + "/%s" % nm
pic_nm = parent_path + "/%s_%s.png" % (data_type, nm)
img = Image_Plot(xpad=0.2, ypad=0.1, fig_x=5, fig_y=4)
img.subplots(2, 3)
# weight_label = [5,6,7]
# weight_pow = [1,2,3]
weight_label = [4, 5, 6]
weight_pow = [1, 2, 3, 4]
xlabels = ["$w^%d$" % i for i in range(len(weight_pow) + 1)]
# weight = ["$w=\\frac{1}{P_{k0,fit}}$",
# "$w=\\frac{1}{Max(P_{k0},P_{k0,fit})}$", "$w=\\frac{1}{Flux_{true}}$"]
weight = [
"$w=\\frac{1}{P_{k0}}$", "$w=\\frac{1}{P_{k0,fit}}$",
"$w=\\frac{1}{Max(P_{k0},P_{k0,fit})}$"
]
print(xlabels)
import os
my_home = os.popen("echo $MYWORK_DIR").readlines()[0][:-1]
from sys import path, argv
path.append('%s/work/mylib/' % my_home)
import h5py
import numpy
from plot_tool import Image_Plot
src_path = argv[1]
ra_bin = [1000, 5000, 10000, 15000, 30000]
labels = []
sub_num = numpy.array([75, 34, 56, 35], dtype=numpy.intc)
img = Image_Plot(fig_x=8, fig_y=6)
img.subplots(2, 2)
for i in range(2):
for j in range(2):
tag = int(i * 2 + j)
h5f = h5py.File(src_path + "/group_predict_%d.hdf5" % tag, "r")
group_label = h5f["/data"][()]
ra_dec = h5f["/ra_dec"][()]
h5f.close()
for k in range(sub_num[tag]):
idx = group_label == k
print(k, idx.sum())
# group_label += sub_num[:tag].sum()
print(tag, group_label.min(), group_label.max(), sub_num[:tag].sum())
print("\n")
img.pts_scatter(i, j, ra_dec[:, 0], ra_dec[:, 1], group_label,
import os
# my_home = os.popen("echo $MYWORK_DIR").readlines()[0][:-1]
from sys import path, argv
# path.append('%s/work/mylib/' % my_home)
path.append("D:/GitHub/astrophy-research/mylib")
from plot_tool import Image_Plot
import h5py
import numpy
import matplotlib.pyplot as plt
import tool_box
data_path = "E:/works/correlation/CFHT/2021_6_15/cut_2.5_new/unsmooth/ODDS_0.4"
h5f = h5py.File(data_path + "/bias_test.hdf5", "r")
img = Image_Plot(fig_x=4, fig_y=3, xpad=0.14, ypad=0.2)
img.subplots(2, 4)
for i in range(2):
for j in range(2):
tag = int(2 * i + j)
data = numpy.abs(h5f["/%d" % tag][()])
scale = 1
# img.axs[0][tag].errorbar(data[:,0], data[:,1]*scale, data[:,2]*scale,fmt=" ",capsize=3,marker="s", label="$10^3 c_1$")
img.axs[0][tag].scatter(data[:, 0],
data[:, 1] * scale / data[:, 2] * scale,
marker="s",
label="$|c_1|/\delta_{c_1}$")
# img.axs[1][tag].errorbar(data[:,0], data[:,3]*scale, data[:,4]*scale,fmt=" ",capsize=3,marker="s", label="$10^3 c_2$")
img.axs[1][tag].scatter(data[:, 0],
data[:, 3] * scale / data[:, 4] * scale,
g2 = h5f["/g2"][()]
h5f.close()
h5f = h5py.File(data_path + "/new_pdf_noise_free.hdf5", "r")
ave_result = h5f["/average"][()].T
pdf_result = h5f["/PDF"][()].T
new_pdf_result1 = h5f["/new_PDF/g1"][()]
new_pdf_result2 = h5f["/new_PDF/g2"][()]
h5f.close()
shear_num = ave_result.shape[1]
img = Image_Plot(cap_size=4, xpad=0.2, ypad=0.2)
img.subplots(1, 2)
print(ave_result)
mc1 = numpy.array(tool_box.data_fit(g1, ave_result[0], ave_result[1]))
mc2 = numpy.array(tool_box.data_fit(g2, ave_result[2], ave_result[3]))
mc1[0] = mc1[0] - 1
mc2[0] = mc2[0] - 1
print(mc1)
print(mc2)
img.axs[0][0].errorbar(0,
mc1[0],
mc1[1],
label="ave m1",
marker="s",
plt_data = [[
mg2_noise_free, mg2_noise_residual + mg2_noise_free,
mg2_cross_term + mg2_noise_free
], [mn_noise_free, mn_noise_residual, mn_cross_term],
[mu_noise_free, mu_noise_residual, mu_cross_term],
[
mn_noise_free + mu_noise_free,
mn_noise_residual + mu_noise_free,
mn_cross_term + mu_noise_free
]]
labels = [["NF G1", "NR G1", "CT G1"], ["NF N", "NR N", "CT N"],
["NF U", "NR U", "CT U"], ["NF U + N", "NR U + N", "CT U + N"]]
img = Image_Plot(fig_x=5, fig_y=4, xpad=0.1, ypad=0.13)
img.subplots(2, 2)
for i in range(4):
m, n = divmod(i, 2)
img.axs[m][n].hist(plt_data[i][0][idx],
20,
label=labels[i][0],
alpha=alpha,
histtype="step",
linewidth=ls)
img.axs[m][n].hist(plt_data[i][1][idx],
20,
label=labels[i][1],
alpha=alpha,
histtype="step",
import matplotlib
matplotlib.use("Agg")
from sys import path, argv
path.append("/home/hklee/work/mylib")
path.append("/home/hkli/work/mylib")
path.append("D:/GitHub/astrophy-research/mylib")
from plot_tool import Image_Plot
import numpy
import h5py
import os
parent_path = "/mnt/ddnfs/data_users/hkli/bias_check/data_from_pi/psf_scale_test"
img = Image_Plot(fig_x=7, fig_y=2, ypad=0, xpad=0.2)
img.subplots(4, 1)
m_scale = 100
c_scale = 10000
x_tick_pos = []
for i in range(7):
noise_free_path = parent_path + "/data_%d/shear_result_noise_free_epsf_ori_pdf.hdf5" % i
noisy_path = parent_path + "/data_%d/shear_result_noisy_cpp_epsf_ori_pdf.hdf5" % i
print(i, os.path.exists(noise_free_path))
print(os.path.exists(noisy_path))
#
# continue
h5f = h5py.File(noise_free_path, "r")
sym_mc_nf = h5f["/sym_mc"][()]
mean_mc_nf = h5f["/mean_mc"][()]
h5f.close()
def plot_gf(ra, dec, gf1, gf2, pic_path, gf1_bin_num = 15, gf2_bin_num = 18,gf_bin_num = 18):
gf = numpy.sqrt(gf1**2 + gf2**2)
label_1 = numpy.zeros_like(gf1)
label_2 = numpy.zeros_like(gf2)
label_3 = numpy.zeros_like(gf)
gf1_bin = numpy.linspace(gf1.min(), gf1.max(), gf1_bin_num+1)
gf2_bin = numpy.linspace(gf2.min(), gf2.max(), gf2_bin_num+1)
gf_bin = numpy.linspace(gf.min(), gf.max(), gf_bin_num+1)
for i in range(gf1_bin_num):
idx1 = gf1 >= gf1_bin[i]
idx2 = gf1 < gf1_bin[i+1]
idx = idx1 & idx2
label_1[idx] = (gf1_bin[i]+ gf1_bin[i+1])/2
for i in range(gf2_bin_num):
idx1 = gf2 >= gf2_bin[i]
idx2 = gf2 < gf2_bin[i+1]
idx = idx1 & idx2
label_2[idx] = (gf2_bin[i]+ gf2_bin[i+1])/2
for i in range(gf_bin_num):
idx1 = gf >= gf_bin[i]
idx2 = gf < gf_bin[i+1]
idx = idx1 & idx2
label_3[idx] = (gf_bin[i]+ gf_bin[i+1])/2
img = Image_Plot()
img.subplots(1,3)
norm = plt.Normalize(vmin=numpy.min(label_1), vmax=numpy.max(label_1))
cmap = plt.get_cmap('YlOrRd',gf1_bin_num)
cl = cmap(norm(label_1))
fig = img.axs[0][0].scatter(ra, dec, color=cl, s=3)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm._A = []
img.figure.colorbar(sm, ax=img.axs[0][0])
norm = plt.Normalize(vmin=numpy.min(label_2), vmax=numpy.max(label_2))
cmap = plt.get_cmap('YlOrRd',gf2_bin_num)
cl = cmap(norm(label_2))
fig = img.axs[0][1].scatter(ra, dec, color=cl, s=3)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm._A = []
img.figure.colorbar(sm, ax=img.axs[0][1])
norm = plt.Normalize(vmin=numpy.min(label_3), vmax=numpy.max(label_3))
cmap = plt.get_cmap('YlOrRd',gf_bin_num)
cl = cmap(norm(label_3))
fig = img.axs[0][2].scatter(ra, dec, color=cl, s=3)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm._A = []
img.figure.colorbar(sm, ax=img.axs[0][2])
img.save_img(pic_path)
# img.show_img()
img.close_img()
pic_nm = "./result/galsim/mc_%s_ellip_mix.png"%psf_tag
pic_nm_pdf = "./result/galsim/mc_%s_ellip_mix.pdf"%psf_tag
else:
numpy.savez("./result/galsim/cache_%s_ellip_%.2f.npz"%(psf_tag,ellip), mcs)
pic_nm = "./result/galsim/mc_%s_ellip_%.2f.png"%(psf_tag, ellip)
pic_nm_pdf = "./result/galsim/mc_%s_ellip_%.2f.pdf"%(psf_tag, ellip)
y = numpy.zeros((si_num*sr_num,))
x = numpy.zeros((si_num*sr_num,))
for i in range(si_num):
for j in range(sr_num):
tag = int(i*sr_num + j)
y[tag] = sersic_index[i]
x[tag] = scale_radius[j]
titles = ["$m_1$","$c_1$","$m_2$", "$c_2$"]
img = Image_Plot(xpad=0.3,ypad=0.3,fig_x=4,fig_y=3)
img.subplots(2, 2)
for i in range(2):
for j in range(2):
tag = int(i*2 + j)
z = mcs[tag].flatten()
img.scatter_pts(i,j,x,y,z,pts_size=30,color_map="bwr")
img.set_label(i,j,0,"Sersic Index")
img.set_label(i,j,1,"Scale Radius")
img.axs[i][j].set_title(titles[tag],fontsize=img.xy_lb_size)
img.save_img(pic_nm)
img.save_img(pic_nm_pdf)
# img.show_img()
plt.show()
num_g = 6
result = numpy.zeros((5,num_g))
dz = 0.1
for i in range(num_g):
z1 = redshift.min() + i*dz
z2 = z1+dz
idx_1 = redshift >= z1
idx_2 = redshift < z2
idx = idx_1 & idx_2
# img = Image_Plot()
# img.subplots(1, 2)
left, right = -0.1, 0.1
gt, gt_sig = fq.fmin_g_new(mgt[idx], mnut[idx], 8, left=left, right=right)[:2]
gx, gx_sig = fq.fmin_g_new(mgx[idx], mnux[idx], 8, left=left, right=right)[:2]
print("gamma_t: %.4f (%.4f).Num: %d.\nMead dist: %.4f Mpc/h. Z bin [%.4f, %.4f].\n"
%(gt, gt_sig,idx.sum(),dist_len[idx].mean(),z1, z2))
result[0, i] = gt
result[1, i] = gt_sig
result[2, i] = gx
result[3, i] = gx_sig
result[4, i] = dist_len[idx].mean()
# img.show_img()
img = Image_Plot(fig_x=12, fig_y=8)
img.subplots(1, 1)
img.axs[0][0].errorbar(result[4],result[0], result[1], c="C4", label="$\gamma_t$")
img.set_label(0,0,0,"$\gamma_t$",size=15)
img.set_label(0,0,1,"$Dist_S\ [\\rm{Mpc \cdot h^{-1}}]$",size=15)
img.show_img()
h5f = h5py.File(
data_path +
"/cuts_pi_all_sample_w_maxpkfit_sq/sym/sex2_1.5/flux2_ex3/total.hdf5", "r")
mc1_mpk_pk_fit = h5f["/mc1"][()][:, ch]
mc2_mpk_pk_fit = h5f["/mc2"][()][:, ch]
h5f.close()
h5f = h5py.File(
data_path +
"/cuts_pi_all_sample_w_maxpkfit_sq/sym/sex2_1.5/mag_true/total.hdf5", "r")
mc1_mpk_tmag = h5f["/mc1"][()][:, ch]
mc2_mpk_tmag = h5f["/mc2"][()][:, ch]
h5f.close()
matplotlib.rcParams["font.family"] = "serif"
img = Image_Plot(fig_x=6, fig_y=4, xpad=0.2, ypad=0.2, legend_size=13)
img.subplots(1, 1)
img.axis_type(0, "major", tick_len=6, tick_width=1.5)
img.axis_type(1, "major", tick_len=6, tick_width=1.5)
img.axs[0][0].errorbar(x_coord,
100 * mc1_tf_pk_fit[0],
100 * mc1_tf_pk_fit[1],
linewidth=img.plt_line_width - 0.5,
capsize=img.cap_size,
marker="o",
fillstyle="none",
c="C2",
label="$m_1$, weight=$F^{-2}$")
img.axs[0][0].errorbar(x_coord,
100 * mc2_tf_pk_fit[0],
data_path = "E:/works/correlation/CFHT/cut_2.5/deblend_1"
h5f = h5py.File(data_path + "/chisq_diff_expo.hdf5","r")
chisq = h5f["/chisq"][()]
omg_b = h5f["/omega_bm_bin"][()]
omg_c = h5f["/omega_cm_bin"][()]
sig8 = h5f["/sigma8"][()]
x, y = numpy.zeros_like(chisq[0]), numpy.zeros_like(chisq[0])
for i in range(len(sig8)):
x[:,i] = sig8[i]
for i in range(len(omg_c)):
y[i] = omg_c[i]
img = Image_Plot(fig_x=5,fig_y=4,xpad=0.25,ypad=0.25)
img.subplots(2,5)
for i in range(2):
for j in range(5):
chisq_i = -chisq[int(i*5 + j)]
idx = chisq_i < 70
xi = x[idx].flatten()
yi = y[idx].flatten()
zi = chisq_i[idx].flatten()
idx = zi == zi.min()
xi_min, yi_min = xi[idx], yi[idx]
print(xi_min, yi_min)
img.scatter_pts(i,j, xi,yi,numpy.log10(zi),color_map='jet',pts_size=5)
