eff_plait.py
4.73 KB
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title = "DoubleBeamPlait"
tip = "applies sinus^2 filter to fft data"
onein = False
import numpy as np
from nodmath import extract
from guidata.dataset.datatypes import DataSet
from guidata.dataset.dataitems import (IntItem, FloatArrayItem, StringItem,
ChoiceItem, FloatItem, DictItem,
BoolItem)
from guiqwt.config import _
def nint(x):
if x > 0: return int(x+0.5)
else: return int(x-0.5)
class NOD3_App:
def __init__(self, parent):
self.parent = parent
self.parent.activateWindow()
def compute_app(self):
class Param(DataSet):
#angle = FloatItem('Angle', default=0.0)
scale = FloatItem('Scale', default=0.15, min=0.0, max=1.0)
#scale = FloatItem('Scale', default=5, min=0.0, max=100.0)
param = Param(_("Plait"), "Set scale lenght of scanning")
name = title.replace(" ", "")
self.parent.compute_11(name, lambda m, p: self.function(m, p), param, onein)
def wtfun(self, z, scale):
wt = 1.0
zsc = z*scale
if(abs(zsc) >= 1.0): return wt
thet = zsc*np.pi/2.0
wt = np.sin(thet)**2
if abs(wt) < 0.1: wt = 0.1
return wt
def wtfun0(self, z, scale):
wt = 1.0
zsc = z*scale
if(abs(zsc) >= 1.0): return wt
thet = zsc*np.pi/2.0
wt = np.sin(thet)**2
return wt
def ftnorm(self, m, fftdata):
irows, icols = fftdata.shape
xsamp = abs(m.header['CDELT1'])
ysamp = abs(m.header['CDELT2'])
nfi = icols/2
nfj = irows/2
f1i = 1.0/(xsamp*float(icols-1))
f1j = 1.0/(ysamp*float(irows-1))
for j in range(irows):
iy = j - (j/(nfj+1))*irows
y = float(iy)*f1j
for i in range(icols):
ix = i - (i/(nfi+1))*icols
x = float(ix)*f1i
sumwt = 0.0
for n in range(len(self.cosphi)):
z = x*self.cosphi[n] + y*self.sinphi[n]
sumwt += self.wtfun(z, self.scale[n])
fftdata[j][i] /= sumwt
return fftdata
def ftwt(self, m, p):
self.mask = np.isnan(m.data)
data = np.where(np.isnan(m.data), 0.0, m.data)
fftdata = np.fft.fft2(data)
irows, icols = m.data.shape
angle = m.header['PARANG']
scale = icols*abs(m.header['CDELT1'])*p.scale
#scale = abs(m.header['CDELT1'])*p.scale
phi = angle * np.pi/180.0
xsamp = abs(m.header['CDELT1'])
ysamp = abs(m.header['CDELT2'])
nfi = icols/2
nfj = irows/2
f1i = 1.0/(xsamp*float(icols-1))
f1j = 1.0/(ysamp*float(irows-1))
sinphi = np.sin(phi)
cosphi = np.cos(phi)
self.sinphi.append(sinphi)
self.cosphi.append(cosphi)
self.scale.append(scale)
for j in range(irows):
iy = j - (j/(nfj+1))*irows
y = float(iy)*f1j
for i in range(icols):
ix = i - (i/(nfi+1))*icols
x = float(ix)*f1i
z = x*cosphi + y*sinphi
wt = self.wtfun(z, scale)
fftdata[j][i] *= wt
return fftdata
def resize(self, ms):
xsize = -1
ysize = -1
for m in ms:
rows, cols = m.data.shape
xsize = max(cols, xsize)
ysize = max(rows, ysize)
MS = []
for m in ms:
#x0, y0 = int(m.header['CRPIX1']-0.5), int(m.header['CRPIX2']+0.5)
x0, y0 = nint(m.header['CRPIX1']), nint(m.header['CRPIX2'])
m.data = extract(m.data, shape=(ysize, xsize), position=(y0, x0))
MS.append(m)
return xsize, ysize, MS
def nanmean(self, x, axis=0):
x = x.copy()
Norig = x.shape[axis]
factor = 1.0-np.sum(np.isnan(x), axis)*1.0/Norig
factor = np.where(factor == 0.0, np.nan, factor)
x[np.isnan(x)] = 0
return np.mean(x,axis)/factor
def function(self, ms, p):
sum_fftdata = None
sum_data = []
self.sinphi = []
self.cosphi = []
self.scale = []
xsize, ysize, ms = self.resize(ms)
for m in ms:
if m.header['MAPTYPE'][0] != "X":
sum_data.append(m.data)
fftdata = self.ftwt(m, p)
if sum_fftdata == None:
sum_fftdata = fftdata
else:
sum_fftdata += fftdata
maver = self.nanmean(np.array(sum_data))
#m.data = maver
#return m, p
mask = np.isnan(maver)
fftdata = self.ftnorm(m, sum_fftdata)
m.data = np.fft.ifft2(fftdata).real
m.data = np.where(mask, np.nan, m.data)
return m, p