filter_immerg.py
5.22 KB
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
title = "ImMerge"
tip = "add missing flux to maps"
onein = 2
import numpy as np
#from scipy import fftpack
import scipy.ndimage as spi
from guidata.qt.QtGui import QMessageBox
from guidata.dataset.datatypes import DataSet
from guidata.dataset.dataitems import (IntItem, FloatArrayItem, StringItem,
ChoiceItem, FloatItem, DictItem,
BoolItem)
from guiqwt.config import _
from nodfitting import gaussian, correlate
from nodmath import nan_interpolation, subpixel_shift
def nextpow2(i):
n = 1
while n < i: n *= 2
return n
class NOD3_App:
def __init__(self, parent):
self.parent = parent
self.parent.activateWindow()
def Error(self, msg):
QMessageBox.critical(self.parent.parent(), title,
_(u"Error:")+"\n%s" % str(msg))
def compute_app(self, **args):
class FuncParam(DataSet):
center = BoolItem(u"Center:", default=False)
improve = BoolItem(u"Improve:", default=False)
name = title.replace(" ", "")
if args == {}:
param = FuncParam(_(title), "Image Merging:")
else:
param = self.parent.ScriptParameter(name, args)
# if no parameter needed set param to None. activate next line
#param = None
self.parent.compute_11(name, lambda m, p: self.function(m, p), param, onein)
def def_ring(self, shape, r):
rows, cols = shape
r1 = rows/2
r2 = rows - r1
c1 = cols/2
c2 = cols - c1
y,x = np.ogrid[-r1:+r2, -c1:+c2]
mask = x*x + y*y <= r*r
print mask.shape, shape
return mask
def weight(self, fac, ampl, phas, beam):
amp = np.fft.ifftshift(np.where(ampl[0] < fac*ampl[1], fac*ampl[1], ampl[0]))
ang = np.fft.ifftshift(np.where(ampl[0] < fac*ampl[1], phas[1], phas[0]))
data = np.fft.ifft2(amp)
data.real = amp*np.cos(ang)
data.imag = amp*np.sin(ang)
ifftdata = np.fft.ifft2(data)
iamp = np.abs(ifftdata)
iang = np.angle(ifftdata)
return iamp*np.cos(iang) + iamp*np.sin(iang)
def get_beam(self, header):
b = (header['BMAJ'] + header['BMIN']) / 2.0
b /= (2.0*np.sqrt(2.0*np.log(2.0)))
b /= (abs(header['CDELT1']) + abs(header['CDELT2'])) / 2.0
return b
def polyfit(self, data1, data2, fmax=0.0, out=False, sort=False, order=2):
d1 = 1*np.ravel(data1)
d2 = 1*np.ravel(data2)
if d1.max() < 1:
self.Error("numbers too small, please rescale data")
if out:
return None, None, None
else:
return None
if sort:
d1.sort()
d2.sort()
mask1 = np.where((data1 < fmax) | np.isnan(data1), True, False)
mask2 = np.where((data2 < fmax) | np.isnan(data2), True, False)
mask = ~np.array([any(tup) for tup in zip(mask1.ravel(), mask2.ravel())])
d1 = d1[mask]
d2 = d2[mask]
pf = np.polyfit(d1, d2, order)
if out:
return pf, d1, d2
else:
return pf
def correlate_plot(self, data1, data2, fmax=0.0, plot=True, order=2):
from guiqwt import pyplot
##a, b, d1, d2 = correlate(data1, data2, fmax=fmax, out=True)
pf, d1, d2 = self.polyfit(data1, data2, fmax=fmax, out=True, order=order)
self.pf = pf
#print pf
x = np.arange(np.nanmin(d1), np.nanmax(d1))
p = np.poly1d(pf)
if not plot:
return p
# plotting
self.parent.fig = pyplot.figure("TT-plot")
fit = []
#for i in range(len(pf), 0, -1):
lpf = len(pf)
for i in range(lpf):
fit.append(str(_(u"x^%d : %10.3g \r") % (i, pf[lpf-i-1])))
pyplot.plot(d1, d2, "g+", label="Data points")
pyplot.legend(pos="TL")
pyplot.plot(x, p(x), "r-", label=fit[0])
for i in range(1, lpf):
pyplot.plot(x[:2], p(x)[:2], " ", label=fit[i])
pyplot.ylabel("VLA")
pyplot.xlabel("Effelsberg")
pyplot.zlabel("TT-plot")
pyplot.show(mainloop=False)
return p
def function(self, ms, p):
hpbw = []
for m in ms:
hpbw.append(np.sqrt(m.header['BMAJ'] * m.header['BMIN']))
if hpbw[0] > hpbw[1]:
ms.reverse()
ampl = []
phas = []
beam = []
masks = []
i = 0
for m in ms:
masks.append(np.isnan(m.data))
data = nan_interpolation(m.data)
ms[i].data = data
beam.append(self.get_beam(m.header))
i += 1
fac = beam[0]**2 / beam[1]**2
m = ms[0]
m1 = ms[1]
b = np.sqrt(beam[1]**2 - beam[0]**2)
smooth = spi.gaussian_filter(m.data, (b, b)) / fac
if p.center:
dx, dy, m1.data = subpixel_shift(smooth, m1.data, clip=0.1)
m.data += fac*(m1.data - smooth)
if p.improve:
smooth = spi.gaussian_filter(m.data, (b, b)) / fac
m.data += fac*(m1.data - smooth)
mask = np.array([any(tup) for tup in zip(masks[0].ravel(), masks[1].ravel())])
mask = mask.reshape(m.data.shape)
m.data = np.where(mask, np.nan, m.data)
return m, p