-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathcode.py
250 lines (183 loc) · 6.87 KB
/
code.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
import numpy as np
import numpy.ma as ma
from scipy import fftpack
from astropy.io import fits
ecf = 1.47e11
n = 17.0
# degree to radian
theta = np.linspace(0., 17/(n-1.0)*360.0)
#
arcsec2rad = np.pi/180.0/3600.0
pixel = 1.2 # in arcsec
tpixel = pixel*arcsec2rad
x = 1.0*np.sin(theta)
y = 1.0*np.cos(theta)
# Fourier space binning
tet_1grid = np.append((1.2+10**(0.15*np.arange(21.0))), 1200.0)
nq_1grid = tet_1grid.size
q_p_minmax = 2.0*np.pi/tet_1grid
k_1_minmax = q_p_minmax/2.0/np.pi
q_p_1grid = np.zeros(nq_1grid-1)
for ik in np.arange(nq_1grid-1) :
q_p_1grid[ik] = 0.5 * (q_p_minmax[ik]+q_p_minmax[ik+1])
def read_fits_file ( filename, show_hdu_info=False ):
data, hdr = fits.getdata(filename, header = True)
return hdr, data;
def dist1D(nrows):
"""Returns a 1-D array in which the value of each element is proportional to its frequency.
"""
#result = np.linspace(-nrows/2+1, nrows/2, nrows)
result = fftpack.fftfreq(nrows, d = 1.0/float(nrows))
#result = fftpack.fftshift(result)
result = np.sqrt(result**2)
return result
def calc_del_flux (flux, weight) :
del_flux = flux - flux.mean()
del_flux *= weight
del_flux = del_flux - del_flux.mean()
return del_flux
def calc_k_w (nx_tot, ny_tot) :
k_w = np.zeros([nx_tot,ny_tot])
k_x = dist1D(nx_tot)
k_y = dist1D(ny_tot)
for i in np.arange(nx_tot) :
k_w[i,:] = np.sqrt((k_x[i]/nx_tot)**2.0+(k_y[:]/ny_tot)**2.0)/pixel
return k_w
def calc_f_clip (cmask, nx_tot, ny_tot) :
hn0 = np.where(cmask != 0)
n1 = len(hn0)
f_clip = float(n1)/nx_tot/ny_tot
return f_clip
def auto_power ( flux, fab, t_ch, cmask, f_clip, k_w, outfile = None, writefits = None ) :
s_e = flux.shape
nx_tot = s_e[0]
ny_tot = s_e[1]
nx21 = nx_tot/2+1
ny21 = ny_tot/2+1
area = float(nx_tot)*float(ny_tot)*(pixel/3600.0)**2.0/3282.8
mask = (cmask != 0)
weight = np.zeros([nx_tot,ny_tot])
weight = cmask
#weight_masked = ma.masked_array(weight, ~mask, filled_value = 0.0)
#print weight_masked.shape, t_ch.shape
#t_ch_masked = ma.masked_array(t_ch, ~mask)
#weight_masked = t_ch_masked / t_ch_masked.mean()
del_flux = calc_del_flux (flux, weight)
del_flux_ab = calc_del_flux (fab, weight)
# Remove axis in Fourier Space and compute fft
#msk_fft = np.ones([nx_tot,ny_tot])
#msk_fft[:,0] = 0.0
#msk_fft[0,:] = 0.0
#msk_fft = np.roll(msk_fft,-nx21, axis = 0)
#msk_fft = np.roll(msk_fft,-ny21, axis = 1)
del_flux_fft = fftpack.fft2(del_flux)
del_flux_ab_fft = fftpack.fft2(del_flux_ab)
amp = del_flux_fft
ampab = del_flux_ab_fft
#amp = fftpack.fftshift(del_flux_fft)
#ampab= fftpack.fftshift(del_flux_ab_fft)
#amp = np.roll(np.roll(del_flux_fft, -ny21, axis=1), -nx21, axis = 0)
#ampab = np.roll(np.roll(del_flux_ab_fft, -ny21, axis=1), -nx21, axis = 0)
###
pairs = np.zeros(nq_1grid-1)
power = np.zeros(nq_1grid-1)
sig_p = np.zeros(nq_1grid-1)
powerab = np.zeros(nq_1grid-1)
sig_ab = np.zeros(nq_1grid-1)
k_1_minmax = q_p_minmax/2.0/np.pi
for iq in np.arange(nq_1grid-1) :
hp = np.where(((k_w >= k_1_minmax[iq+1]) & (k_w < k_1_minmax[iq])))
if (len(hp) >1) :
power[iq] = np.mean(np.abs(amp[hp])**2.0)*area/f_clip
pairs[iq] = len(hp)
powerab[iq] = np.mean(np.abs(ampab[hp])**2.0)*area/f_clip
sig_p = power/(np.sqrt(0.5*pairs))
sig_pab = powerab/(np.sqrt(0.5*pairs))
# computing P(A+B) - P(A-B)
pclean = power - powerab
sig_plc = np.sqrt(sig_pab**2.0 + sig_p**2.0)
if outfile :
f = open(outfile,'w')
for iq in np.arange(nq_1grid-1) :
print>>f, 2.*np.pi/q_p_1grid[iq], pairs[iq]
f.close()
return (amp, ampab, power, powerab, pairs, pclean, sig_plc )
def cross_power ( amp1, amp2, power1, power2, f_clip, k_w, writefits = None ) :
pairs = np.zeros(nq_1grid-1)
power = np.zeros(nq_1grid-1)
sig_p = np.zeros(nq_1grid-1)
amp_x = amp1.real* amp2.real + amp1.imag * amp2.imag
for iq in np.arange(nq_1grid -1) :
hp = where (k_w >= k_1_minmax[iq+1] & k_w < k_1_minmax[iq])
if (len(hp) >1) :
power_x[iq] = mean(amp_x[hp])*area/f_clip
pairs_x[iq] = len(hp)
sig_x = np.sqrt (0.5*power1*power2/(0.5*pairs_x))
if writefits :
pyfits.writeto(writefits, amp_x*area/f_clip)
return (power_x, sig_x)
def coherence (power_x,pclean1,pclean2,sig_pcl1,sig_pcl2) :
'''
!p.multi=[0]
!p.charsize=1.
!p.symsize=.1
SET_PLOT, 'PS'
DEVICE, FILE='C1X.ps', /COLOR, BITS=8,SCALE_FACTOR=2, YSIZE=13, Xsize=13,/landsc
;window, 4, xsize=500,ysize=400
cirx1=(power1x*power1x)/(pcleanir1*pclean)
a=(2*power1x/(pcleanir1*pclean)*sig_m1x)^2
b=((power1x^2/pcleanir1*alog(abs(pclean)))*sig_pcl)^2
c=((power1x^2/pclean*alog(abs(pcleanir1)))*sig_plcir1)^2
pippo=sqrt(a+b+c)
sig_c1=pippo;cirx1*sqrt(12/pairs1x)
plot,2*!pi/q_p_1grid,cirx1,psym=sym(1),xran=[10,500],yrange=[.001,10],/xstyle,/xlog,/ylog,xtitle=TEXTOIDL('2\pi\q'),ytitle=TEXTOIDL('C_{1,X_{1}}(q)')
xyouts, 20, 0.800, 'Ch1 vs 0.5-2 keV'
errplot,2*!pi/q_p_1grid,cirx1-sig_c1,cirx1+sig_c1
DEVICE, /close
'''
return 0
def plot_ps ( power, k, plotfile=None ) :
import matplotlib.pyplot as plt
plt.loglog(k, power)
plt.xlabel(r'$k$')
plt.ylabel(r'$P(k)$')
if plotfile :
plt.savefig(plotfile)
else:
plt.show()
def main():
folder = '../Chandra/'
flux_hdr,flux = read_fits_file (folder+'deltaf.fits')
t_x_hdr, t_x = read_fits_file (folder+'exp.fits')
xab_hdr, xab = read_fits_file(folder+'deltaf_ab.fits')
print ("The mean flux is %e " % ( np.mean(flux) ))
#cmask = t_ch1 / t_ch1 #;(readfits(folder+''mask_rep_corr.fits',hea5));[0:1131,*]
cmask_hdr, cmask = read_fits_file(folder+'mask.fits')
cmask = ( cmask == 1.0 ).astype(int)
# Fixing the mask after projection
flux *= cmask/ecf
xab *= cmask/ecf
print ("The mean flux after masking is %e " % ( np.mean(flux) ))
print ("flux array size =", flux.shape)
s_e = flux.shape
nx_tot = s_e[0]
ny_tot = s_e[1]
nx21 = nx_tot/2+1
ny21 = ny_tot/2+1
flux = flux/(pixel/3600.0)**2.0*3282.8 # from cts/s/pix to cts/s/steradians
xab = xab/(pixel/3600.0)**2.0*3282.8
# Defining binning in Real and Fourier space
#exp = np.zeros([nx_tot,ny_tot])
#rx = np.zeros([nx_tot,ny_tot])
#n_k = 1000
#nx_center = nx21 -1
#ny_center = ny21 -1
k_w = calc_k_w (nx_tot, ny_tot)
k_0 = 1.0 / (np.sqrt(2.0) * nx_tot * pixel)
k_f = 1.01 * np.max(k_w)
f_clip = calc_f_clip ( cmask, nx_tot, ny_tot )
(amp, ampab, power, power_ab, pairs, pclean, sig_plc ) = auto_power ( flux, xab, t_x, cmask, f_clip, k_w, outfile = None, writefits = None )
k_p_1grid = 2.*np.pi/q_p_1grid
plot_ps(k_p_1grid, pclean)
if __name__ == "__main__":
main()