pol_polden.py
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title = "PolInt"
tip = "best of all"
onein = 2
import numpy as np
import copy as cp
#import scipy.signal as sps
from scipy.ndimage.filters import median_filter, uniform_filter
from scipy.interpolate import InterpolatedUnivariateSpline
from guiqwt import pyplot
from guidata.qt.QtGui import QMessageBox
from guidata.dataset.datatypes import DataSet
from guidata.dataset.dataitems import (IntItem, StringItem, ChoiceItem, FloatItem, BoolItem)
from guiqwt.config import _
from nodmath import nan_interpolation, plotPDF
try:
from nodfitting import curve_fit
except:
from scipy.optimize import curve_fit
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 Error(self, msg):
QMessageBox.critical(self.parent.parent(), title,
_(u"Error:")+"\n%s" % str(msg))
def Warning(self, msg):
QMessageBox.critical(self.parent.parent(), title,
_(u"Warning:")+"\n%s" % str(msg))
def compute_app(self, **args):
class FuncParam(DataSet):
size = IntItem('Size:', min=3, default=5)
frms = FloatItem('Factor of RMS:', min=0.0, max=10.0, default=2.0)
#median = BoolItem("Median:", default=True)
#error = BoolItem("ErrMap:", default=False)
name = title.replace(" ", "")
if args == {}:
param = FuncParam(_(title), "Size of median box:")
else:
param = self.parent.ScriptParameter(name, args)
# if no parameter needed set param to None. activate next line
#param = None
self.name = name
#self.parent.compute_11(name, lambda m, p: self.function(m, p), param, onein, extern=True)
if 'BMAJ' in self.parent.item.header:
size = max(5, nint(abs(self.parent.item.header['BMAJ']/self.parent.item.header['CDELT1'])))
else:
size = 5
size = str("size=%d" % size)
self.parent.compute_11(name, lambda m, p: self.function(m, p), param, onein, extern=False, setpar=size)
def smooth_sharpen(self, data, window=5, alpha=30):
from scipy.ndimage.filters import gaussian_filter
blurred = gaussian_filter(data, sigma=(window,window))
filter_blurred = gaussian_filter(blurred, 1)
sharpened = blurred + alpha * (blurred - filter_blurred)
return sharpened
def mod_median1(self, U, Q, size):
x = 1*Q
y = 1*U
mask = np.ones((size, size))
if size%2:
mask[(size-1)/2][(size-1)/2] = 0
else:
mask[(size-1)/2][(size-1)/2] = 1
try:
xm = median_filter(x, footprint=mask)
ym = median_filter(y, footprint=mask)
except:
xm = median_filter(x, size=(size, size))
ym = median_filter(y, size=(size, size))
xm1 = median_filter(x, size=(size, size))
ym1 = median_filter(y, size=(size, size))
return np.arctan2((2*ym+ym1)/3, (2*xm+xm1)/3)
def mod_median(self, angle, size):
x = np.cos(angle)
y = np.sin(angle)
mask = np.ones((size, size))
if size%2:
mask[(size-1)/2][(size-1)/2] = 0
else:
mask[(size-1)/2][(size-1)/2] = 1
try:
xm = median_filter(x, footprint=mask)
ym = median_filter(y, footprint=mask)
except:
xm = median_filter(x, size=(size, size))
ym = median_filter(y, size=(size, size))
xm1 = median_filter(x, size=(size, size))
ym1 = median_filter(y, size=(size, size))
#xm1 = uniform_filter(x, size=(size, size))
#ym1 = uniform_filter(y, size=(size, size))
return np.arctan2((2*ym+ym1)/3, (2*xm+xm1)/3)
#return np.arctan2(ym1, xm1)
#return np.arctan2(ym, xm)
def mod_mean(self, angle, size):
x = np.cos(angle)
y = np.sin(angle)
xm = uniform_filter(x, size=size)
ym = uniform_filter(y, size=size)
f0 = 2./3.
fac = f0 / float(size)**2
fac /= (float(size)/5.0)**2
xc = x*fac
yc = y*fac
return np.arctan2(ym-yc, xm-xc)
def plot(self, xy, lh, x0, txt, std, fitonly=False):
def gauss(X, a, x0, dx, off, slope):
x = X - dx
#return off + slope*x + a*np.exp(-(x*x)/(2*x0*x0))
return a*np.exp(-(x*x)/(2*x0*x0))
def fit(f, x, y, p0):
xtol = 1.49012e-08
Error = True
i0 = np.argmax(y)
y = list(y)
x = list(x)
x.pop(i0)
y.pop(i0)
x = np.array(x)
y = np.array(y)
while Error:
try:
results = curve_fit(f, x, y, p0=p0, xtol=xtol)
popt = results[0]
Error = False
except:
Error = True
xtol *= 2.0
if xtol > 1.0:
return 0.0
return popt
x = xy[1][:lh]
y = xy[0][:lh]
rms = None
p0 = (max(y), std/2, 0.0, 0.0, 0.0)
popt = fit(gauss, x, y, p0)
if popt != []:
rms = abs(popt[1])
off = popt[2]
if fitonly:
return rms
self.parent.fig = pyplot.figure("PI noise distribution")
if txt != "": pyplot.legend()
if rms != None:
pyplot.plot(x, y, "b*", label=txt)
pyplot.plot(x, gauss(x, *popt), "r-", label=str("RMS=%.2f" % rms))
pyplot.plot(x, gauss(x, *popt), "", label=str("Off=%.2f" % off))
else:
pyplot.plot(x, y, "b-", label=txt)
pyplot.xlabel("Intensity")
pyplot.ylabel("#bins")
pyplot.zlabel("distribution")
pyplot.show(mainloop=False)
return rms
def histo(self, U, Q, pang):
pa = np.arctan2(U, Q)
hdata = np.sin(pa-pang) * np.sqrt(U*U + Q*Q)
self.errordata = 1*hdata
ny, nx = hdata.shape
mask = ~np.isnan(hdata)
hdata = hdata[mask]
#med = max(2*np.median(hdata), 2*abs(hdata.min()))
med = abs(np.nanmin(hdata))
for i in range(3):
bins = min(int(np.sqrt(nx*ny)), 32)
hdata = np.where(abs(hdata) < med, hdata, med)
hist, bin_edges = np.histogram(hdata, bins=bins)
dbin = bin_edges[1] - bin_edges[0]
#hist /= dbin
bin_edges += dbin/2.0
hist = hist[1:]
bin_edges = bin_edges[1:-1]
#bin_edges = bin_edges[:-1]
lh = len(hist)
for i in range(len(hist)):
if abs(bin_edges[i]) < med: lh = i
hmax = 0.0
imax = 0
txt = "dPI"
for i in range(lh):
if hist[i] > imax:
hmax = bin_edges[i]
imax = hist[i]
med = self.plot([hist, bin_edges], lh, hmax, txt, np.std(hdata), fitonly=True) * 5
rms = self.plot([hist, bin_edges], lh, hmax, txt, np.std(hdata))
return round(rms, 5)
def function(self, ms, p):
if not hasattr(p, 'median'):
p.median = True
if not hasattr(p, 'error'):
p.error = False
for m in ms:
if m.header["MAPTYPE"] == "U":
maskU = ~np.isnan(m.data)
U = nan_interpolation(m.data)
elif m.header["MAPTYPE"] == "Q":
maskQ = ~np.isnan(m.data)
Q = nan_interpolation(m.data)
else:
return [], p
mask = maskU * maskQ
#try: size = max(5, int(m.header['BMAJ']/m.header['CDELT2']+1))
#except: size = max(3, p.size + (p.size+1)%2)
#size = max(3, p.size + (p.size+1)%2)
size = max(3, p.size)
if p.median:
pang = self.mod_median(np.arctan2(U, Q), size)
#pang = self.mod_median1(U, Q, size)
else:
pang = self.mod_mean(np.arctan2(U, Q), size)
m.data = U*np.sin(pang) + Q*np.cos(pang)
rms = self.histo(U, Q, pang)
m.header["MAPTYPE"] = 'PI'
if 'EXTNAME' in m.header:
extname = m.header['EXTNAME'].replace("Q", 'PI').replace("U", 'PI')
else:
extname = "MAP-PI"
m.header["EXTNAME"] = extname
m.header["PSIG"] = (rms, "RMS of polarized intensity")
m.header["MEDIAN"] = (size, "Median window size")
mpa = cp.deepcopy(m)
mpa.header['EXTNAME'] = 'MAP-PA'
mpa.header["MAPTYPE"] = 'PA'
mer = cp.deepcopy(m)
mer.header['EXTNAME'] = 'MAP-PI-Error'
mer.header["MAPTYPE"] = 'PError'
adata = pang * 90.0/np.pi #+ 90.0
mpa.data = np.where(np.abs(m.data) > p.frms*rms, adata, np.nan)
m.data = np.where(mask, m.data, np.nan)
mpa.data = np.where(mask, mpa.data, np.nan)
mer.data = np.where(mask, self.errordata, np.nan)
if p.error:
plotPDF(mer.data, self.parent.cmap, self.parent.Outliers, rebin=1)
return [mer, mpa, m], p
else:
return [mpa, m], p