proc_tnt.py
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title = "TnT"
tip = "seperates thermal and non-thermal emission"
onein = 2
import numpy as np
import copy as cp
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, map_zoom, same_size, register_translation, 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):
nonth = FloatItem('Non-thermal index', default=1.0, min=0.1)
freq = FloatItem('Frequency', default=6.0, min=0.0)
proj = ChoiceItem("Projection", (("CAR", "CAR"), #("ARC", "ARC"),
("SIN", "SIN"),
("TAN", "TAN"), ("NCP", "NCP"), ("SFL", "SFL"),
("AIT", "AIT")))
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 same_proj(self, m1, m2, proj):
m1.header['CTYPE1'] = m1.header['CTYPE1'][:-3] + proj
m1.header['CTYPE2'] = m1.header['CTYPE2'][:-3] + proj
m2.header['CTYPE1'] = m2.header['CTYPE1'][:-3] + proj
m2.header['CTYPE2'] = m2.header['CTYPE2'][:-3] + proj
return m1, m2
def convolve(self, m1, m2):
f = 2.0*np.sqrt(2.0*np.log(2.0))
hpbwx1 = m1.header["BMAJ"]
hpbwy1 = m1.header["BMIN"]
hpbwx2 = m2.header["BMAJ"]
hpbwy2 = m2.header["BMIN"]
delx1 = abs(m1.header['CDELT1'])
dely1 = abs(m1.header['CDELT2'])
sigmax = np.sqrt((hpbwx2/delx1)**2 - (hpbwx1/delx1)**2) / f
sigmay = np.sqrt((hpbwy2/dely1)**2 - (hpbwy1/dely1)**2) / f
fac = (hpbwx2*hpbwy2) / (hpbwx1*hpbwy1)
m1.data = spi.gaussian_filter(m1.data, (sigmay, sigmax)) * fac
m1.header['BMAJ'] = m2.header['BMAJ']
m1.header['BMIN'] = m2.header['BMIN']
return m1
def resample(self, m1, m2):
dx1, dy1 = m1.header['CDELT1'], m1.header['CDELT2']
dx2, dy2 = m2.header['CDELT1'], m2.header['CDELT2']
rebin = (abs(dx2/dx1), abs(dy2/dy1))
m2.data = map_zoom(m2.data, rebin, order=3, prefilter=True)
m2.header['NAXIS2'], m2.header['NAXIS1'] = m2.data.shape
m2.header['CRPIX1'] = (m2.header['CRPIX1'] - 0.5) * rebin[1] + 0.5
m2.header['CRPIX2'] = (m2.header['CRPIX2'] - 0.5) * rebin[0] + 0.5
m2.header['CDELT1'] /= rebin[1]
m2.header['CDELT2'] /= rebin[0]
return m2
def seperate_tnt_1(self, m1, m2, m3, bth, bnt, fghz3):
fghz1 = m1.header['CRVAL3']*1.e-9
fghz2 = m2.header['CRVAL3']*1.e-9
d = 1.0 / ((fghz1*fghz2)**bth - (fghz1*fghz2)**bnt)
data_th = (d*fghz2**bnt) * m1.data - (d*fghz1**bnt) * m2.data
data_nt = (d*fghz1**bth) * m2.data - (d*fghz2**bth) * m1.data
data = data_th*fghz3**bth + data_nt*fghz3**bnt
return data_th, data_nt, data
def seperate_tnt(self, m1, m2, m3, bth, bnt, fghz3):
fghz1 = m1.header['CRVAL3']*1.e-9
fghz2 = m2.header['CRVAL3']*1.e-9
x = fghz1/fghz2
Cth = x**bnt
Cnt = x**bth
C = 1.0 / (Cnt - Cth)
data_th = +C * (m1.data - Cth*m2.data)
data_nt = -C * (m1.data - Cnt*m2.data)
m3.header = m1.header.copy()
m3.header['CRVAL3'] = fghz3*1.e9
xn = fghz3/fghz2
m3.data = data_th * xn**bth + data_nt * xn**bnt
return data_th, data_nt, m3.data
def center_of_gravity(self, data1, data2, clip=None):
mask1 = data1 > clip*np.nanmax(data1)
mask2 = data2 > clip*np.nanmax(data2)
d1 = np.where(mask1*mask2, data1, np.nan)
d2 = np.where(mask1*mask2, data2, np.nan)
rows, cols = d1.shape
x, y = np.mgrid[:rows,:cols]
b1x = np.nansum(d1*x)/np.nansum(d1)
b1y = np.nansum(d1*y)/np.nansum(d1)
rows, cols = d2.shape
x, y = np.mgrid[:rows,:cols]
b2x = np.nansum(d2*x)/np.nansum(d2)
b2y = np.nansum(d2*y)/np.nansum(d2)
return b2x-b1x, b2y-b1y
def subpixel_shift(self, data1, data2):
#shift, error, diffphase = register_translation(data1, data2, 1000)
#print shift, error
shift = center_of_gravity(data1, data2, clip=0.1)
data2 = nan_interpolation(data2)
return np.fft.ifft2(spi.fourier_shift(np.fft.fft2(data2), shift)).real
def function(self, ms, p):
hpbw = []
i = 0
for m in ms:
hpbw.append(np.sqrt(m.header['BMAJ'] + m.header['BMIN']))
ms[i].data = nan_interpolation(m.data)
i += 1
if hpbw[0] > hpbw[1]:
ms.reverse()
m1 = ms[0] # high freq
m2 = ms[1] # low freq
m3 = cp.copy(m1)
m1, m2 = self.same_proj(m1, m2, p.proj)
m1 = self.convolve(m1, m2)
m2 = self.resample(m1, m2)
#return [m1, m2], p
out, ms = same_size([m1, m2], True)
m1 = ms[0]
m2 = ms[1]
if out > 0:
self.Error("one or more maps have different scales or coordinate systems")
return [], p
#return [m1, m2], p
m1.data = nan_interpolation(m1.data)
m2.data = nan_interpolation(m2.data)
dx, dy, m1.data = subpixel_shift(m2.data, m1.data)
m1.data, m2.data, m3.data = self.seperate_tnt(m1, m2, m3, -0.1, -abs(p.nonth), p.freq)
m1.header["MAPTYPE"] = "TH"
m2.header["MAPTYPE"] = "NT"
m1.header["CRVAL3"] = 1e9
m2.header["CRVAL3"] = 1e9
m3.header["MAPTYPE"] = "I"
return [m1, m2, m3], p