c=ls[key][0],
linestyle=ls[key][1],
linewidth=1)
plt.ylim(phase_peak[0], phase_peak[-1])
ax.axes.xaxis.set_ticklabels([])
plt.locator_params(axis='y', nbins=3)
ax.yaxis.set_label_position("right")
ax.set_ylabel(key, fontsize=35)
axis.append(ax)
d = columns[key]
pulse_off1 = d[:, peak_end[0] - 50:peak_end[0]]
pulse_off2 = d[:, peak_end[1]:peak_end[1] + 50]
pulse_off = np.concatenate((pulse_off1, pulse_off2), axis=1)
cr_off = cor.weighed_corr_map(w, pulse_off, 0, -10000)
cr_off = np.concatenate([
cr_off[:int(cr_off.shape[0] / 3), -int(cr_off.shape[1] / 3):],
cr_off[-int(cr_off.shape[0] / 3):, :int(cr_off.shape[1] / 3)]
])
cr_off = np.std(cr_off)
data_peak = d[:, peak_end[0]:peak_end[1]]
cr = cor.weighed_corr_map(w, data_peak, 1, abs(cr_off))
ax = plt.subplot(grid[1 + j * 2:3 + j * 2, 0:2])
plt.sca(ax)
norm = mpl.colors.Normalize(vmin=-1, vmax=1) #将1设为最红，-1为最蓝，中间的0为白
def plot_cor(w,
data,
data_all,
flux,
p_end,
lag,
star_name,
freq,
bin_num=1024):
phase = np.linspace(0, 360, bin_num)
fig = plt.figure(figsize=(9, 9))
grid = plt.GridSpec(18, 18, fig, wspace=0., hspace=0.)
pulse_off11 = data[:, p_end[0][0] - 50:p_end[0][0]]
pulse_off12 = data[:, p_end[0][1]:p_end[0][1] + 50]
pulse_off21 = data[:, p_end[1][0] - 50:p_end[1][0]]
pulse_off22 = data[:, p_end[1][1]:p_end[1][1] + 50]
pulse_off = np.concatenate(
(pulse_off11, pulse_off12, pulse_off21, pulse_off22), axis=1)
cr_off = cor.weighed_corr_map(w, pulse_off, lag, -10000)
cr_off = np.concatenate([
cr_off[:int(cr_off.shape[0] / 3), -int(cr_off.shape[1] / 3):],
cr_off[-int(cr_off.shape[0] / 3):, :int(cr_off.shape[1] / 3)]
])
cr_off = np.std(cr_off)
I_prof = data_all[:, 0, :].mean(
0) #(w[:,None]*data_all[:,0,peak_end[0]:peak_end[1]]).sum(0)/w.sum(0)
Q_peak = data_all[:, 1, :].mean(
0) #(w[:,None]*data[:,1,peak_end[0]:peak_end[1]]).sum(0)/w.sum(0)
U_peak = data_all[:, 2, :].mean(
0) #(w[:,None]*data[:,2,peak_end[0]:peak_end[1]]).sum(0)/w.sum(0)
L_prof = np.sqrt(Q_peak**2 + U_peak**2)
L_base = np.nanmean(
np.concatenate([
L_prof[:p_end[0][0]], L_prof[p_end[1][1]:],
L_prof[p_end[0][1]:p_end[1][0]]
],
axis=0))
L_prof = L_prof - L_base
V_prof = (w[:, None] * data_all[:, 3, :]).sum(0) / w.sum(0)
top = max(I_prof)
I_prof = I_prof / top
L_prof = L_prof / top
V_prof = V_prof / top
flux_peak = np.concatenate(
[data[:, p_end[0][0]:p_end[0][1]], data[:, p_end[1][0]:p_end[1][1]]],
axis=1)
flux_peak = flux_peak / top
cr_block = cor.weighed_corr_map(w, flux_peak, lag, abs(cr_off))
cr = []
cr.append(cr_block[-(p_end[1][1] - p_end[1][0]):, :(p_end[0][1] -
p_end[0][0])])
cr.append(cr_block[-(p_end[1][1] - p_end[1][0]):,
(p_end[0][1] - p_end[0][0]):])
cr.append(cr_block[:-(p_end[1][1] - p_end[1][0]), :(p_end[0][1] -
p_end[0][0])])
cr.append(cr_block[:-(p_end[1][1] - p_end[1][0]),
(p_end[0][1] - p_end[0][0]):])
#plt.show()
ax = []
for side, end in enumerate(p_end):
phase_peak = phase[end[0]:end[1]]
if side == 0:
axis = plt.subplot(grid[:6, 6:10]), plt.subplot(grid[-4:, :6])
else:
axis = plt.subplot(grid[:6, 10:]), plt.subplot(grid[6:14, :6])
ax.append(axis)
plt.sca(ax[side][0])
plt.locator_params(axis='y', nbins=4)
#plt.title(r'%s %s $n_{\rm{lag}}$ = %d' % (flux,freq, lag))
plt.plot(phase_peak,
I_prof[end[0]:end[1]],
label='I',
c='black',
linewidth=1)
plt.plot(phase_peak,
L_prof[end[0]:end[1]],
label='L',
c='red',
linestyle='dashed',
linewidth=1)
plt.plot(phase_peak,
V_prof[end[0]:end[1]],
label='V',
c='blue',
linestyle='dotted',
linewidth=1)
plt.xlim(phase_peak[0], phase_peak[-1])
plt.tick_params(labelbottom=False)
plt.xticks(phase[end[0]:end[1]][15:-5])
plt.locator_params(axis='x', nbins=side + 3)
if side == 0:
plt.ylabel('normalised flux')
plt.sca(ax[side][1])
plt.plot(I_prof[end[0]:end[1]], phase_peak, c='black', linewidth=1)
plt.plot(L_prof[end[0]:end[1]],
phase_peak,
c='red',
linestyle='dashed',
linewidth=1)
plt.plot(V_prof[end[0]:end[1]],
phase_peak,
c='blue',
linestyle='dotted',
linewidth=1)
ax[side][1].invert_xaxis()
plt.ylim(phase_peak[0], phase_peak[-1])
plt.locator_params(axis='x', nbins=4)
plt.yticks(phase[end[0]:end[1]][15:-5])
plt.locator_params(axis='y', nbins=side + 3)
ax[side][1].yaxis.set_major_formatter(
mpl.ticker.FormatStrFormatter('%.1f'))
#plt.ylabel('phase/$^\circ$' )
align_yaxis([ax[0][0], ax[1][0]])
align_xaxis([ax[0][1], ax[1][1]])
ax[1][0].legend()
axis_set = {
'right': [0, 0],
'top': [0, 1],
'left': [1, 0],
'bottom': [1, 1]
}
for key in axis_set:
val = axis_set[key]
axis = ax[val[0]][val[1]]
axis.spines[key].set_linestyle(dash)
axis.spines[key].set_linewidth(0.5)
axis.spines[key].set_color('k')
if val == [1, 1]:
axis.set_xticks([])
if val == [1, 0]:
axis.set_yticks([])
d = .01
kwargs = dict(transform=ax[1][0].transAxes, color='k', clip_on=False)
#ax[1][0].plot((-d,+d),(-2*d,2*d),**kwargs)
ax[1][0].plot((-6 / 8 * d, +6 / 8 * d),
(1 - 2 * 8 / 6 * d, 1 + 2 * 8 / 6 * d), **kwargs)
kwargs = dict(transform=ax[1][1].transAxes, color='k', clip_on=False)
ax[1][1].plot((-d, +d), (-2 * d, 2 * d), **kwargs)
#ax[1][1].plot((1-d,1+d),(-2*d,+2*d),**kwargs)
ax_cr = [
plt.subplot(grid[6:14, 6:10]),
plt.subplot(grid[6:14, 10:]),
plt.subplot(grid[-4:, 6:10]),
plt.subplot(grid[-4:, 10:])
]
spine_set = [['bottom', 'right'], ['left', 'bottom'], ['top', 'right'],
['top', 'left']]
for b in range(4):
plt.xticks([])
plt.yticks([])
plt.sca(ax_cr[b])
for key in spine_set[b]:
ax_cr[b].spines[key].set_linestyle(dash)
ax_cr[b].spines[key].set_linewidth(0.5)
ax_cr[b].spines[key].set_color('k')
phase_peak = phase[p_end[int(b / 3)][0]:p_end[int(b / 3)][1]]
norm = mpl.colors.Normalize(vmin=-1, vmax=1) #将1设为最红，-1为最蓝，中间的0为白
cmap = mpl.cm.seismic
cmap.set_bad(color='#404040', alpha=0.15)
plt.imshow(cr[b],
norm=norm,
cmap=cmap,
aspect='auto',
origin='lower',
extent=(phase_peak[0], phase_peak[-1], phase_peak[0],
phase_peak[-1]))
for bt in [2, 3]:
ax_cr[bt].set_xticks(
phase[p_end[int(bt / 3)][0]:p_end[int(bt / 3)][1]][15:-5])
ax_cr[bt].locator_params(axis='x', nbins=bt + 1)
ax_cr[bt].xaxis.set_major_formatter(
mpl.ticker.FormatStrFormatter('%.1f'))
kwargs = dict(transform=ax_cr[0].transAxes, color='k', clip_on=False)
ax_cr[0].plot((-6 / 4 * d, +6 / 4 * d), (-2 * d, 2 * d), **kwargs)
ax_cr[0].plot((1 - 6 / 4 * d, 1 + 6 / 4 * d), (1 - 2 * d, 1 + 2 * d),
**kwargs)
kwargs = dict(transform=ax_cr[-1].transAxes, color='k', clip_on=False)
ax_cr[-1].plot((-6 / 8 * d, +6 / 8 * d), (-4 * d, 4 * d), **kwargs)
#ax_cr[-1].plot((1-d,1+d),(1-2*d,1+2*d),**kwargs)
add = fig.add_subplot(grid[6:, 6:], frameon=False)
add.set_xlabel('phase/$^\circ$')
add.tick_params(labelcolor='none',
top=False,
bottom=False,
left=False,
right=False)
add = fig.add_subplot(grid[6:, :6], frameon=False)
add.set_ylabel('phase/$^\circ$', labelpad=12)
add.tick_params(labelcolor='none',
top=False,
bottom=False,
left=False,
right=False)
add = fig.add_subplot(grid[:6, 6:], frameon=False)
add.set_title(r'%s %s $n_{\rm{lag}}$ = %d' % (flux, freq, lag))
add.tick_params(labelcolor='none',
top=False,
bottom=False,
left=False,
right=False)
if flux == r'$|$ V $|$':
flux = 'abs_V'
#plt.savefig('%s_%s_lag=%d.pdf'%(flux,freq.replace(' ',''),lag))
plt.show()
def plot_cor(w, data_all, peak_end, lags, star_name, freq, bin_num=1024):
phase = np.linspace(0, 360, bin_num)
I_prof = np.nanmean(
data_all[:, 0, peak_end[0]:peak_end[1]], axis=0
) #(w[:,None]*data_all[:,0,peak_end[0]:peak_end[1]]).sum(0)/w.sum(0)
Q_peak = np.nanmean(
data_all[:, 1, peak_end[0]:peak_end[1]],
axis=0) #(w[:,None]*data[:,1,peak_end[0]:peak_end[1]]).sum(0)/w.sum(0)
U_peak = np.nanmean(
data_all[:, 2, peak_end[0]:peak_end[1]],
axis=0) #(w[:,None]*data[:,2,peak_end[0]:peak_end[1]]).sum(0)/w.sum(0)
L_prof = np.sqrt(Q_peak**2 + U_peak**2)
V_prof = np.nanmean(data_all[:, 3, peak_end[0]:peak_end[1]], axis=0)
top = max(I_prof)
I_prof = I_prof / top
L_prof = L_prof / top
V_prof = V_prof / top
Q = data_all[:, 1]
U = data_all[:, 2]
L = np.sqrt(Q**2 + U**2)
base_L = np.concatenate((L[:, :peak_end[0]], L[:, peak_end[1]:]), axis=1)
# print(base_L.shape,base_L)
base_L = np.nanmean(base_L, axis=1)
# print(base_L.shape)
L = L - base_L[:, None]
L = L / top
I = data_all[:, 0] / top
abs_V = abs(data_all[:, -1] / top)
phase_peak = phase[peak_end[0]:peak_end[1]]
columns = {'I': I, 'L': L, 'V': abs_V}
ls = {
'I': ['black', 'solid'],
'L': ['red', 'dashed'],
'V': ['blue', 'dotted']
}
profile = {'I': I_prof, 'L': L_prof, 'V': V_prof}
plt.rcParams['figure.figsize'] = (14.3, 28.4)
fig = plt.figure()
grid = plt.GridSpec(108, 14, fig, wspace=0.1, hspace=0.4)
for j in np.arange(6):
ax = plt.subplot(grid[8 + j * 16:8 + (j + 1) * 16, -2:])
plt.sca(ax)
plt.plot(I_prof, phase_peak, c=ls['I'][0], linewidth=1, label='I')
plt.plot(L_prof,
phase_peak,
c=ls['L'][0],
linestyle=ls['L'][1],
linewidth=1,
label='L')
plt.plot(V_prof,
phase_peak,
c=ls['V'][0],
linestyle=ls['V'][1],
linewidth=1,
label='V')
plt.ylim(phase_peak[0], phase_peak[-1])
plt.locator_params(axis='x', nbins=3)
plt.locator_params(axis='y', nbins=3)
ax.axes.xaxis.set_ticklabels([])
ax.tick_params(labelleft=False, labelright=True)
if j == 0:
plt.legend(loc='lower left',
bbox_to_anchor=(0.05, 1.02),
borderaxespad=0.0,
ncol=1,
frameon=False)
jj = -1
axis = []
for key, data in columns.items():
jj += 1
pulse_off1 = data[:, peak_end[0] - 50:peak_end[0]]
pulse_off2 = data[:, peak_end[1]:peak_end[1] + 50]
pulse_off = np.concatenate((pulse_off1, pulse_off2), axis=1)
ax1 = plt.subplot(grid[:8, 4 * jj:(jj + 1) * 4])
plt.sca(ax1)
plt.locator_params(axis='y', nbins=3)
plt.locator_params(axis='x', nbins=3)
plt.plot(phase_peak,
profile[key] / max(abs(profile[key])),
label=key,
c=ls[key][0],
linestyle=ls[key][1],
linewidth=1)
plt.xlim(phase_peak[0], phase_peak[-1])
ax1.axes.xaxis.set_ticklabels([])
axis.append(ax1)
title = key
# if key == "V":
# title = r'$|$ V $|$'
plt.title(title,
fontsize=35,
ha='center',
va='bottom',
position=(0.5, 1.025))
if jj == 0:
ax1.yaxis.set_label_position("left")
ax1.set_ylabel('normalised flux')
ax1.tick_params(labelright=False)
else:
ax1.axes.yaxis.set_ticklabels([])
for index, lag in enumerate(lags):
cr_off = cor.weighed_corr_map(w, pulse_off, lag, -10000)
cr_off = np.concatenate([
cr_off[:int(cr_off.shape[0] / 3), -int(cr_off.shape[1] / 3):],
cr_off[-int(cr_off.shape[0] / 3):, :int(cr_off.shape[1] / 3)]
])
cr_off = np.std(cr_off)
data_peak = data[:, peak_end[0]:peak_end[1]]
cr = cor.weighed_corr_map(w, data_peak, lag, abs(cr_off))
ax = plt.subplot(grid[8 + index * 16:24 + index * 16,
jj * 4:(jj + 1) * 4])
plt.sca(ax)
norm = mpl.colors.Normalize(vmin=-1, vmax=1) #将1设为最红，-1为最蓝，中间的0为白
cmap = mpl.cm.seismic
cmap.set_bad(color='#404040', alpha=0.15)
corr_map = plt.imshow(cr,
norm=norm,
cmap=cmap,
aspect='equal',
origin='lower',
extent=(phase_peak[0], phase_peak[-1],
phase_peak[0], phase_peak[-1]))
ax.axes.yaxis.set_ticklabels([])
ax.tick_params(labeltop=False)
plt.locator_params(axis='x', nbins=3)
plt.locator_params(axis='y', nbins=3)
if jj == 0:
plt.ylabel(r'$\mathrm{\mathit{n}_{\rm{lag}}}$=%d' % lag,
fontsize=35)
if lag != lags[-1]:
ax.axes.xaxis.set_ticklabels([])
add = fig.add_subplot(grid[:-4, :], frameon=False)
add.tick_params(labelcolor='none',
top=False,
bottom=False,
left=False,
right=False)
add.minorticks_off()
add.xaxis.set_label_position("bottom")
add.set_xlabel('phase/$^\circ$', fontsize=35)
add.yaxis.set_label_position("right")
add.set_ylabel('phase/$^\circ$', fontsize=35)
add.yaxis.set_label_coords(1.08, 0.5)
axis = np.array(axis)
align_yaxis(axis)
ca = fig.add_subplot(grid[-1, :-2], frameon=False)
pos = ca.get_position()
pos2 = [pos.x0, pos.y0 - 0.015, pos.width, pos.height]
ca.set_position(pos2)
plt.sca(ca)
plt.colorbar(corr_map, cax=ca, orientation='horizontal')
al = fig.add_subplot(111, frameon=False)
al.tick_params(labelcolor='none',
top=False,
bottom=False,
left=False,
right=False)
al.minorticks_off()
al.set_title('PSR %s %s' % (star_name, freq),
fontsize=35,
ha='center',
va='bottom',
position=(0.5, 1.035))
plt.savefig('%s_%s.pdf' % (star_name, freq.replace(' ', '')),
bbox_inches='tight',
pad_inches=0)
plt.show()
def cor_block(gs, data, p_end, lag, tl=False):
ssgrid = gs.subgridspec(3, 3, hspace=0, wspace=0)
pulse_off11 = data[:, p_end[0][0] - 50:p_end[0][0]]
pulse_off12 = data[:, p_end[0][1]:p_end[0][1] + 50]
pulse_off21 = data[:, p_end[1][0] - 50:p_end[1][0]]
pulse_off22 = data[:, p_end[1][1]:p_end[1][1] + 50]
pulse_off = np.concatenate(
(pulse_off11, pulse_off12, pulse_off21, pulse_off22), axis=1)
cr_off = cor.weighed_corr_map(w, pulse_off, lag, -10000)
cr_off = np.concatenate([
cr_off[:int(cr_off.shape[0] / 3), -int(cr_off.shape[1] / 3):],
cr_off[-int(cr_off.shape[0] / 3):, :int(cr_off.shape[1] / 3)]
])
cr_off = np.std(cr_off)
flux_peak = np.concatenate(
[data[:, p_end[0][0]:p_end[0][1]], data[:, p_end[1][0]:p_end[1][1]]],
axis=1)
flux_peak = flux_peak
cr_block = cor.weighed_corr_map(w, flux_peak, lag, abs(cr_off))
cr = []
cr.append(cr_block[-(p_end[1][1] - p_end[1][0]):, :(p_end[0][1] -
p_end[0][0])])
cr.append(cr_block[-(p_end[1][1] - p_end[1][0]):,
(p_end[0][1] - p_end[0][0]):])
cr.append(cr_block[:-(p_end[1][1] - p_end[1][0]), :(p_end[0][1] -
p_end[0][0])])
cr.append(cr_block[:-(p_end[1][1] - p_end[1][0]),
(p_end[0][1] - p_end[0][0]):])
ax_cr = [
plt.subplot(ssgrid[:2, 0]),
plt.subplot(ssgrid[:2, 1:]),
plt.subplot(ssgrid[-1, 0]),
plt.subplot(ssgrid[-1, 1:])
]
spine_set = [['bottom', 'right'], ['left', 'bottom'], ['top', 'right'],
['top', 'left']]
xt = {0: [70], 1: [225, 250], 2: [70], 3: [225, 250]}
yt = {0: [225, 250], 1: [225, 250], 2: [70], 3: [70]}
phase = np.linspace(0, 360, 1024)
phase_IP = phase[p_end[0][0]:p_end[0][1]]
phase_MP = phase[p_end[1][0]:p_end[1][1]]
phase_peak = {
0: (phase_IP[0], phase_IP[-1], phase_MP[0], phase_MP[-1]),
1: (phase_MP[0], phase_MP[-1], phase_MP[0], phase_MP[-1]),
2: (phase_IP[0], phase_IP[-1], phase_IP[0], phase_IP[-1]),
3: (phase_MP[0], phase_MP[-1], phase_IP[0], phase_IP[-1])
}
ax_cr[0].tick_params(bottom=False, right=False)
#ax_cr[0].tick_params(which='minor', bottom=False,right=False)
ax_cr[1].tick_params(bottom=False, left=False)
#ax_cr[1].tick_params(which='minor', bottom=False,left=False)
ax_cr[2].tick_params(top=False, right=False)
#ax_cr[1].tick_params(which='minor', top=False,right=False)
ax_cr[3].tick_params(top=False, left=False)
#ax_cr[3].tick_params(which='minor', top=False,left=False)
for b in range(4):
plt.sca(ax_cr[b])
for key in spine_set[b]:
ax_cr[b].spines[key].set_linestyle(dash)
ax_cr[b].spines[key].set_linewidth(0.5)
ax_cr[b].spines[key].set_color('k')
plt.xticks(xt[b])
plt.yticks(yt[b])
norm = mpl.colors.Normalize(vmin=-1, vmax=1) #将1设为最红，-1为最蓝，中间的0为白
cmap = mpl.cm.seismic
cmap.set_bad(color='#404040', alpha=0.15)
plt.imshow(cr[b],
norm=norm,
cmap=cmap,
aspect='auto',
origin='lower',
extent=phase_peak[b])
if tl == True:
for bt in [2, 3]:
ax_cr[bt].tick_params(labelbottom=True)
ax_cr[bt].locator_params(axis='x', nbins=bt - 1)
d = 0.03
kwargs = dict(transform=ax_cr[0].transAxes, color='k', clip_on=False)
#ax_cr[0].plot((-3/2*d,+3/2*d),(-d,d),**kwargs)
#ax_cr[0].plot((1-3/2*d,1+3/2*d),(1-d,1+d),**kwargs)
kwargs = dict(transform=ax_cr[-1].transAxes, color='k', clip_on=False)