galaxy_boxint4.py
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title = "BoxModels"
tip = "Box integration on edge-on galaxies"
onein = 1
import numpy as np
import copy as cp
from scipy.integrate import quad
from scipy import stats
try:
from nodfitting import curve_fit
except:
from scipy.optimize import curve_fit
from nodmath import map_rotate, map_interpolate, map_zoom, extract
from scipy.special import erfc
from scipy.stats import chisquare
from guiqwt import pyplot
from guiqwt.tools import SelectPointTool
from guidata.qt.QtGui import QMessageBox, QListView, QStandardItemModel, QStandardItem, QFont
from guidata.dataset.datatypes import DataSet
from guidata.dataset.dataitems import (IntItem, StringItem, ChoiceItem, FloatItem, BoolItem)
from guiqwt.config import _
from guiqwt.curve import PolygonMapItem
RAD = np.pi/180
from nodmath import nanmean
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 getparams(self):
class SetParam(DataSet):
BMAJ = FloatItem('BMAJ ["]:', min=1)
BMIN = FloatItem('BMIN ["]:', min=1)
name = self.title.replace(" ", "")
k = SetParam(_(self.title), "Give FWHM in arcsec:")
if k is not None:
if not k.edit(parent=self.parent.parent()):
return
return k.BMAJ, k.BMIN
def compute_app(self, **args):
Weight = ('No', 'Both', 'Lower', 'Upper')
Model = ("Exponential", "Gaussian", "SecHsq", "Hybrid")
base = ('Full', 'Slope', 'Offset', 'None')
self.Model = Model
class FuncParam(DataSet):
#s = StringItem('s', default="string")
#i = IntItem('i', default=0, max=100, min=0)
incl = FloatItem('Inclination [degree]', default=90.0)
posang = FloatItem('Position angle [degree]', min=-90.0, max=90.0)
#stdir = ChoiceItem("Stripes direction", (("parallel", "parallel"),
# (("rectangular", "rectangular"))))
rms = FloatItem("RMS", min=0.0)
dimgal = FloatItem("Galaxy diameter [arcsec]", min=-1.0)
boxwidth = FloatItem("BoxWidth [arcsec]")
boxheight = FloatItem("BoxHeight [arcsec]")
xnum = IntItem("#Boxes in X")
ynum = IntItem("#Boxes in Y")
noise_model = ChoiceItem(_(u"Noise model"), (("Standard", "Standard"), ("RMS", "RMS")))
#rebin = IntItem("Rebin", default=1, min=1, max=20)
#boxcenter = BoolItem("BoxCenter", default=True)
#cursor = BoolItem("Cursor", default=False)
twocomp = BoolItem("Two Components", default=False)
log = BoolItem("Log Plot", default=False)
ngraph = IntItem("#Graphs per row", default=3)
weight = ChoiceItem(_(u"Weighted fit"), zip(Weight, Weight), default=Weight[0])
model = ChoiceItem(_(u"Scale height model"), zip(Model, Model), default=Model[0])
#weight = BoolItem("Weighted fit", default=False)
baseline = ChoiceItem(_(u"Baseline"), zip(base, base), default=base[1])
outfile = StringItem('Outfile', default="boxint.out")
#inclin = FloatItem('Inclination', default=31., min=0, max=89.0)
#rms = FloatItem('Noise', default=5., min=0, max=10.0)
#overl = BoolItem("Overlay", default=True)
#choice = ChoiceItem("Unit", ("Degree", "Arcmin", "Arcsec"), default=2)
name = title.replace(" ", "")
self.title = title
if args == {}:
param = FuncParam(_(title), "Setup parameters for box integration:")
else:
param = self.parent.ScriptParameter(name, args)
# if no parameter needed set param to None. activate next line
#param = NonFalsee
self.parent.compute_11(name, lambda m, p: self.function(m, p), param, onein)
def rotate(self, m, angle):
q = {'angle': angle, 'mode': 'constant', 'reshape': False, 'prefilter': True, 'order': 3}
#if abs(angle) > 0.1:
# q['angle'] = angle+90
#else:
# q['angle'] = 0
if angle > 0: q['angle'] = -(90-angle)
else: q['angle'] = (90+angle)
param = self.parent.ScriptParameter("Rotation", q)
rows, cols = m.data.shape
if 'TMPROT' not in m.header:
rows1 = (rows+1)/2 * 2 - 1
cols1 = (cols+1)/2 * 2 - 1
posy, posx = m.header['CRPIX2'], m.header['CRPIX1']
m.data = extract(m.data, shape=(rows1, cols1), position=(nint(posy-1), nint(posx-1)))
if 'TMPROT' in m.header:
if abs(m.header['TMPROT'] - angle) > 0.1:
m.data = map_rotate(m.data, param)
self.update = True
else:
self.update = False
else:
m.data = map_rotate(m.data, param)
self.update = True
m.header['TMPROT'] = angle
m.header['CRVAL1'] = m.header['CRVAL2'] = 0.0
m.header['CTYPE1'] = m.header['CTYPE1'][:-3] + "DES"
m.header['CTYPE2'] = m.header['CTYPE2'][:-3] + "DES"
if 'LONPOLE' in m.header:
del m.header['LONPOLE']
if 'LATPOLE' in m.header:
del m.header['LATPOLE']
rows, cols = m.data.shape
#m.header['CRVAL1'] = x
#m.header['CRVAL1'] = y
m.header['CRPIX1'] = (cols+1)/2.0
m.header['CRPIX2'] = (rows+1)/2.0
#m = self.parent.removeNANedges(m)
return m
def yintpn(self, a, b, c, d, y):
e = b-a
f = b-c
g = e+f+f+d-c
g = g*y-e-f-g
g = g*y-a+c
return 0.5*g*y+b
def gaussian(self, x, a, sigma, a0, b0):
return a0 + b0*x + a*np.exp(-x**2/(2*sigma**2))
def deincl(self, m, incl, rms, dimgal):
rows, cols = m.data.shape
row2 = int(m.header['CRPIX2'])
#nrows = max(2, 1+int(m.header['BMAJ'] / m.header['CDELT1']))
nrows = max(3, int(3*m.header['BMAJ'] / abs(m.header['CDELT1'])))
p2 = nrows/2
y = 0.0*m.data[row2]
for i in range(nrows):
y += m.data[row2-p2+i]
y /= nrows
x = 3600*(np.arange(float(cols)) - m.header['CRPIX1'])*m.header['CDELT1']
ymax100 = 2*rms
xmin = 999999999999999999999.0
xmax = -xmin
imax = cols
dxm = 1.5*self.p.boxwidth/3600.0 * self.p.xnum / 2 / abs(m.header['CDELT1'])
dxm = min(dxm, cols/2)-1
for i in range(cols/2, cols/2+nint(dxm), 1):
#if y[i] > ymax100 and not np.isnan(y[i]):
if y[i] > ymax100:
xmin = x[i]
imax = i
imin = 0
for i in range(cols/2, cols/2-nint(dxm), -1):
#if y[i] > ymax100 and not np.isnan(y[i]):
if y[i] > ymax100:
xmax = x[i]
imin = i
xm = (abs(xmax) + abs(xmin))/2.0
dy = ymax100/np.nanmax(y)
if dimgal > 0:
xm = dimgal/2.0
sigma = 3600*m.header['BMAJ'] / (2.0*np.sqrt(2.0*np.log(2.0)))
#sigma1 = np.cos(RAD*incl)*xm / np.sqrt(2*np.pi)
sigma1 = np.cos(RAD*incl)*xm / (2.0*np.sqrt(2.0*np.log(2.0)))
sigma_new = np.sqrt(sigma**2 + sigma1**2)
self.galaxy_radius = xm
return sigma_new/3600.0 * 2.0*np.sqrt(2.0*np.log(2.0))
def copy(self, m):
mx = cp.copy(m)
mx.header = m.header.copy()
mx.data = 1*m.data
return mx
def overlay(self, m, p):
g = self.parent.vu
f = 3600.0
rows, cols = m.data.shape
pixw = 3600*np.sqrt(m.header['CDELT1']**2 + m.header['CDELT2']**2) / np.sqrt(2.0)
if p.cursor:
l0, b0 = self.l/g, self.b/g
else:
l0, b0 = 0.0, 0.0
l1, b1 = self.parent.get_plot_coordinates(0, 0)
l2, b2 = self.parent.get_plot_coordinates(cols, rows)
points = []
offsets = []
colors = []
COLORS = [0xffffffff, 0x00000000]
dpix = p.boxwidth / pixw
if dpix < 0.9*np.sqrt(2.):
self.Error("Stripewidth too small")
return [], p
nanz = p.xnum+1
manz = p.ynum+1
xbox = 1 - nanz%2
ybox = 1 - manz%2
n = 0
wpix = nint(p.boxwidth/(f*m.header['CDELT2']))
p.boxwidth = int(1000*wpix*m.header['CDELT2']*f)/1000.0
hpix = nint(p.boxheight/(f*m.header['CDELT2']))
p.boxheight = int(1000*hpix*m.header['CDELT2']*f)/1000.0
l1 = l0 + (-p.boxwidth/2.0*xbox + (xbox-nanz/2)*p.boxwidth)/f
l2 = l0 + (-p.boxwidth/2.0*xbox + (nanz/2)*p.boxwidth)/f
b1 = b0 + (-p.boxheight/2.0*ybox + (ybox-manz/2)*p.boxheight)/f
b2 = b0 + (-p.boxheight/2.0*ybox + (manz/2)*p.boxheight)/f
print l0, b0, l1,l2,b1,b2
self.bw = []
for j in range(nanz):
dp = (-p.boxwidth/2.0*xbox + (j-nanz/2+xbox)*p.boxwidth)/f
l = l0 + dp
points.append(([l*g,b1*g], [l*g,b2*g]))
offsets.append([n, 2*n])
colors.append(COLORS)
n += 1
#x = int(m.header['CRPIX1'] + l / m.header['CDELT1'])
x, y = self.parent.get_pixel_coordinates(l*f, 0.0)
self.bw.append(x)
self.bh = []
for i in range(manz):
dp = (-p.boxheight/2.0*ybox + (i-manz/2+ybox)*p.boxheight)/f
b = dp
points.append(([l1*g,b*g], [l2*g,b*g]))
offsets.append([n, 2*n])
colors.append(COLORS)
n += 1
#y = int(m.header['CRPIX2'] + b / m.header['CDELT2'])
x, y = self.parent.get_pixel_coordinates(0.0, b*f)
self.bh.append(y)
positions = cp.copy(points)
points = np.concatenate(points)
crv = PolygonMapItem()
crv.set_data(points, offsets, colors)
crv.Name = "Polygons"
crv.Positions = positions
crv.Color = 'white'
crv.Linewidth = 1.0
crv.Factor = self.parent.vu
self.parent.plot.add_item(crv)
self.parent.plot.replot()
def rebin(self, m, rbin, order=4, prefilter=True):
m.data = map_zoom(m.data, rbin, order=4, prefilter=True)
m.header['NAXIS2'], m.header['NAXIS1'] = m.data.shape
m.header['CRPIX1'] = (m.header['CRPIX1']-0.5) * rbin
m.header['CRPIX2'] = (m.header['CRPIX2']-0.5) * rbin
m.header['CDELT1'] /= rbin
m.header['CDELT2'] /= rbin
return m
def read_cursor(self, tool):
self.l, self.b = tool.get_coordinates()
#x, y = self.parent.get_pixel_coordinates(self.l, self.b)
self.parent.cursor_activ = True
self.overlay(self.m1, self.p)
self.statistics(self.m1.data)
self.scalefit(self.p.weight)
self.printout(self.p.outfile, p.dimgal)
self.parent.cursor_activ = False
self.parent.plot.canvas_pointer = False
self.selpos.end_callback = None
del self.selpos
self.parent.imagewidget.activate_default_tool()
def get_cursor(self):
self.cursor_activ = True
row = self.parent.listwidget.currentRow()
if row < 0: return
self.selpos = self.parent.get_tool(SelectPointTool)
self.selpos.TIP = "click on left button"
self.selpos.mode = "reuse"
self.selpos.marker = self.parent.plot.cross_marker
self.selpos.end_callback = self.read_cursor
self.selpos.activate()
#self.parent.setup_KeyEvent(self.selpos, 'SingleClick')
self.parent.setup_KeyEvent(self.selpos, 'Cursor')
if hasattr(self.selpos, 'get_active_plot'):
self.selpos.win = self.selpos.get_active_plot()
self.selpos.win.set_pointer("canvas")
def create_output_list(self):
# add source list box
self.sourcelist = QListView()
self.sourcelist.setWindowTitle('Galaxy scale height fit results')
self.sourcelist.setMinimumSize(800, 300)
self.sourcelist.setFont(QFont('Monospace'))
self.model = QStandardItemModel(self.sourcelist)
self.sourcelist.setModel(self.model)
self.parent.LView = self.sourcelist
#self.model.appendRow(QStandardItem(header))
self.N = 0
self.parent.N = True
def printout(self, filename, dimgal):
try:
f = open(filename, 'w')
filename1 = filename + ".results"
filename2 = filename + ".fitvalues"
f1 = open(filename1, 'w')
f2 = open(filename2, 'w')
except:
self.Error("no permisson to write on disk")
return
boxwidth = self.p.boxwidth
boxheight = self.p.boxheight
f.write('# Edge-on galaxy box integation: box=(%0.1f"x%.1f",x%0.1f"), effective beam=%0.4f"\n' % (boxwidth, boxheight, 3600*self.BEAM, dimgal))
f.write('\n# l["] b["] Int, Mean, Std \n')
for m in range(len(self.sum)):
for n in range(len(self.sum[m])):
l, b = self.center[m][n]
if self.noise_model == "Standard":
x = str("%.2f %.2f %.4f %.4f %.4f \n" % (l*3600, b*3600, self.sum[m][n], self.mean[m][n], self.std[m][n]))
else:
x = str("%.2f %.2f %.4f %.4f %.4f \n" % (l*3600, b*3600, self.sum[m][n], self.mean[m][n], self.RMS))
f.write(x)
f.write("\n")
f.close()
#f.write("\n")
#f.write("\n")
if hasattr(self, 'p'):
attrDic = vars(self.p)
attrNames = attrDic.keys()
attrNames.sort()
#for key in attrDic.keys():
for key in attrNames:
if key[1:8] != "DataSet":
if key[0] == "_":
Key = key[1:]
tmp = str("%s = %s\n" % (Key, attrDic[key]))
f1.write(tmp)
f1.write("\n# Results of scalefit (%s)\n" % self.fname)
f1.write(str("# effective Beam = %0.3f\" Beam(map) = %0.3f\" Diameter = %.1f\" \n" % (3600*self.BEAM, 3600*self.oBEAM, dimgal)))
f1.write("\n")
for n in range(len(self.ScaleFunct)):
l1, func, func_err = self.ScaleFunct[n]
l1 = nint(l1)
f1.write(str("l = %5d w0(%s) = %9.6g +/- %9.6g z0(%s) = %9.6g +/- %9.6g chi2 = %9.6g\n" % (l1, self.fname, abs(func[0]), func_err[0], self.fname, abs(func[1]), func_err[1], self.chi2[n])))
if self.p.twocomp:
f1.write(str("l = %5d w1(%s) = %9.6g +/- %9.6g z1(%s) = %9.6g +/- %9.6g chi2 = %9.6g\n" % (l1, self.fname, abs(func[3]), func_err[3], self.fname, abs(func[4]), func_err[4], self.chi2[n])))
self.n = n
self.PrintListBox(l1, func, func_err, self.chi2[n], self.p.twocomp, self.p.dimgal)
f1.write("\n")
f1.close()
f2.write("\n# Fit values (%s)\n" % self.fname)
if self.p.twocomp or self.model == "Hybrid":
f2.write(str("# w0, z0, x0, w1, z1, x1, [off, slope]" ))
else:
f2.write(str("# w0, z0, x0, [off, slope]"))
f2.write("\n")
for n in range(len(self.FitFunct)):
p0 = tuple(self.FitFunct[n])
try:
f2.write(str(len(p0)*"%g " % p0))
f2.write("\n")
except:
f2.write("bad fit data \n")
f2.write("\n")
f2.close()
def PrintListBox(self, l, wfunct, wfunct_err, chi2, twocomp, dimgal):
if not hasattr(self.parent, 'N'):
self.create_output_list()
text = str("# effective Beam = %0.3f\" Beam(map) = %0.3f\" Diameter = %.1f\"\n" % (3600*self.BEAM, 3600*self.oBEAM, dimgal))
if self.n == 0: self.model.appendRow(QStandardItem(text))
text = str("l = %-4d w0(%s) = %-9.4g+/- %-9.4g z0(%s) = %-9.4g+/- %-9.4g chi2 = %-9.4g" % (l, self.fname, abs(wfunct[0]), wfunct_err[0], self.fname, abs(wfunct[1]), wfunct_err[1], chi2))
self.model.appendRow(QStandardItem(text))
if self.p.twocomp:
text = str("l = %-4d w1(%s) = %-9.4g+/- %-9.4g z1(%s) = %-9.4g+/- %-9.4g chi2 = %-9.4g" % (l, self.fname, abs(wfunct[3]), wfunct_err[3], self.fname, abs(wfunct[4]), wfunct_err[4], chi2))
self.model.appendRow(QStandardItem(text))
self.model.appendRow(QStandardItem(" "))
self.sourcelist.scrollToBottom()
self.sourcelist.show()
def statistics(self, data, ldir):
if self.bw == []:
self.Error("no box-data available")
return end
self.sum = []
self.mean = []
self.std = []
self.center = []
if ldir > 0:
lrange = range(len(self.bw)-1)
else:
lrange = range(len(self.bw)-2, -1, -1)
#for i in range(len(self.bw)-1):
for i in lrange:
i1 = nint(min(self.bw[i], self.bw[i+1]))
i2 = nint(max(self.bw[i], self.bw[i+1]))
x = (i1+i2-1)/2.0
asum = []
mean = []
std = []
center = []
for j in range(len(self.bh)-1):
j1 = nint(min(self.bh[j], self.bh[j+1]))
j2 = nint(max(self.bh[j], self.bh[j+1]))
y = (j1+j2-1)/2.0
box = data[j1:j2, i1:i2]
mask = ~np.isnan(box)
if list(mask.ravel()).count(True) > 5:
asum.append(np.sum(box[mask])/self.beam)
mean.append(np.mean(box[mask]))
#mean.append(np.median(box[mask]))
mean_width = nanmean(box, axis=0)
msk = ~np.isnan(mean_width)
std.append(np.std(mean_width[msk]))
#std.append(np.std(box[mask]))
center.append(self.parent.get_plot_coordinates(x, y))
self.sum.append(asum)
self.mean.append(mean)
self.std.append(std)
self.center.append(center)
try:
self.RMS = np.nanmin(np.ravel(self.std))
except:
self.RMS = self.p.rms
def fwhm2sigma(self, beam):
return 3600*beam/(2.0*np.sqrt(2.0*np.log(2.0)))
def fwhm2sigma_old(self, header):
fwhm = 3600*np.sqrt(header['BMAJ']*header['BMIN'])
return fwhm/(2.0*np.sqrt(2.0*np.log(2.0)))
def scalefit(self, weight, model, baseline):
def integrand(x, x0, sig, h):
return np.exp(-((x-x0)**2)/sig) / np.cosh(x/h)**2
def convolv(x0, sig, h):
return quad(integrand, -5*h, 5*h, args=(x0, sig, h))[0]
def SecHsq(Z, w0, z0, Z0, off=0.0, slope=0.0):
if self.iter % 10 == 0:
self.progress.showMessage(_(str("Interation %d of stripe %d" % (self.iter, self.stripe))))
self.iter += 1
if self.iter > 1200 or self.parent.STOP:
self.stop_text = "program stopped, no solution found"
self.progress.showMessage(_(self.stop_text), 5000)
self.parent.STOP = True
return 0
z = Z - Z0
s = 1.0*self.sigma
sf = s*np.sqrt(2.0*np.pi)
if self.ioff and self.islope:
return off + slope*z + w0/sf*sech2(z, 2*s*s, z0)
elif self.islope:
return off*z + w0/sf*sech2(z, 2*s*s, z0)
elif self.ioff:
return off + w0/sf*sech2(z, 2*s*s, z0)
else:
return w0/sf*sech2(z, 2*s*s, z0)
def SecHsq2(Z, w0, z0, Z0, w1, z1, off=0.0, slope=0.0):
if self.iter % 10 == 0:
self.progress.showMessage(_(str("Interation %d of stripe %d" % (self.iter, self.stripe))))
self.iter += 1
if self.iter > 1200 or self.parent.STOP:
self.stop_text = "program stopped, no solution found"
self.progress.showMessage(_(self.stop_text), 5000)
self.parent.STOP = True
return 0
z = Z - Z0
s = 1.0*self.sigma
sf = s*np.sqrt(2.0*np.pi)
if self.ioff and self.islope:
return off + slope*z + w0/sf*sech2(z, 2*s*s, z0) + w1/sf*sech2(z, 2*s*s, z1)
elif self.islope:
return off*z + w0/sf*sech2(z, 2*s*s, z0) + w1/sf*sech2(z, 2*s*s, z1)
elif self.ioff:
return off + w0/sf*sech2(z, 2*s*s, z0) + w1/sf*sech2(z, 2*s*s, z1)
else:
return w0/sf*sech2(z, 2*s*s, z0) + w1/sf*sech2(z, 2*s*s, z1)
def Exponential(Z, w0, z0, Z0, off=0.0, slope=0.0):
z = Z - Z0
s = 1.0*self.sigma
sz = z*z / (2.0*s*s)
szp = (s*s + z*z0) / (np.sqrt(2.0)*s*z0)
szm = (s*s - z*z0) / (np.sqrt(2.0)*s*z0)
if self.ioff and self.islope:
return off + slope*z + w0/2.0 * np.exp(-sz) * (np.exp(szm*szm)*erfc(szm) + np.exp(szp*szp)*erfc(szp))
elif self.islope:
return off*z + w0/2.0 * np.exp(-sz) * (np.exp(szm*szm)*erfc(szm) + np.exp(szp*szp)*erfc(szp))
elif self.ioff:
return off + w0/2.0 * np.exp(-sz) * (np.exp(szm*szm)*erfc(szm) + np.exp(szp*szp)*erfc(szp))
else:
return w0/2.0 * np.exp(-sz) * (np.exp(szm*szm)*erfc(szm) + np.exp(szp*szp)*erfc(szp))
def Exponential2(Z, w0, z0, Z0, w1, z1, off=0.0, slope=0.0):
z = Z - Z0
s = 1.0*self.sigma
sz = z*z / (2.0*s*s)
szp = (s*s + z*z0) / (np.sqrt(2.0)*s*z0)
szm = (s*s - z*z0) / (np.sqrt(2.0)*s*z0)
szp1 = (s*s + z*z1) / (np.sqrt(2.0)*s*z1)
szm1 = (s*s - z*z1) / (np.sqrt(2.0)*s*z1)
if self.ioff and self.islope:
return off + slope*z + w0/2.0 * np.exp(-sz) * (np.exp(szm*szm)*erfc(szm) + np.exp(szp*szp)*erfc(szp)) + w1/2.0 * np.exp(-sz) * (np.exp(szm1*szm1)*erfc(szm1) + np.exp(szp1*szp1)*erfc(szp1))
elif self.islope:
return off*z + w0/2.0 * np.exp(-sz) * (np.exp(szm*szm)*erfc(szm) + np.exp(szp*szp)*erfc(szp)) + w1/2.0 * np.exp(-sz) * (np.exp(szm1*szm1)*erfc(szm1) + np.exp(szp1*szp1)*erfc(szp1))
elif self.ioff:
return off + w0/2.0 * np.exp(-sz) * (np.exp(szm*szm)*erfc(szm) + np.exp(szp*szp)*erfc(szp)) + w1/2.0 * np.exp(-sz) * (np.exp(szm1*szm1)*erfc(szm1) + np.exp(szp1*szp1)*erfc(szp1))
else:
return w0/2.0 * np.exp(-sz) * (np.exp(szm*szm)*erfc(szm) + np.exp(szp*szp)*erfc(szp)) + w1/2.0 * np.exp(-sz) * (np.exp(szm1*szm1)*erfc(szm1) + np.exp(szp1*szp1)*erfc(szp1))
def Gaussian(Z, w0, z0, Z0, off=0.0, slope=0.0):
z = Z - Z0
s = 1.0*self.sigma
sz = 2*s*s + z0*z0
if self.ioff and self.islope:
return off + slope*z + w0*(z0/np.sqrt(sz)) * np.exp(-(z*z)/sz)
elif self.islope:
return off*z + w0*(z0/np.sqrt(sz)) * np.exp(-(z*z)/sz)
elif self.ioff:
return off + w0*(z0/np.sqrt(sz)) * np.exp(-(z*z)/sz)
else:
return w0*(z0/np.sqrt(sz)) * np.exp(-(z*z)/sz)
def Gaussian2(Z, w0, z0, Z0, w1, z1, off=0.0, slope=0.0):
z = Z - Z0
s = 1.0*self.sigma
sz = 2*s*s + z0*z0
sz1 = 2*s*s + z1*z1
if self.ioff and self.islope:
return off + slope*z + w0*(z0/np.sqrt(sz)) * np.exp(-(z*z)/sz) + w1*(z1/np.sqrt(sz1)) * np.exp(-(z*z)/sz1)
elif self.islope:
return off*z + w0*(z0/np.sqrt(sz)) * np.exp(-(z*z)/sz) + w1*(z1/np.sqrt(sz1)) * np.exp(-(z*z)/sz1)
elif self.ioff:
return off + w0*(z0/np.sqrt(sz)) * np.exp(-(z*z)/sz) + w1*(z1/np.sqrt(sz1)) * np.exp(-(z*z)/sz1)
else:
return w0*(z0/np.sqrt(sz)) * np.exp(-(z*z)/sz) + w1*(z1/np.sqrt(sz1)) * np.exp(-(z*z)/sz1)
def Hybrid2(Z, wg, zg, Z0, we, ze, off=0.0, slope=0.0):
z = Z - Z0
s = 1.0*self.sigma
sze = z*z / (2.0*s*s)
szp = (s*s + z*ze) / (np.sqrt(2.0)*s*ze)
szm = (s*s - z*ze) / (np.sqrt(2.0)*s*ze)
szg = 2*s*s + zg*zg
if self.ioff and self.islope:
return off + slope*z + wg*(zg/np.sqrt(szg)) * np.exp(-(z*z)/szg) + we/2.0 * np.exp(-sze) * (np.exp(szm*szm)*erfc(szm) + np.exp(szp*szp)*erfc(szp))
elif self.islope:
return off*z + wg*(zg/np.sqrt(szg)) * np.exp(-(z*z)/szg) + we/2.0 * np.exp(-sze) * (np.exp(szm*szm)*erfc(szm) + np.exp(szp*szp)*erfc(szp))
elif self.ioff:
return off + wg*(zg/np.sqrt(szg)) * np.exp(-(z*z)/szg) + we/2.0 * np.exp(-sze) * (np.exp(szm*szm)*erfc(szm) + np.exp(szp*szp)*erfc(szp))
else:
return wg*(zg/np.sqrt(szg)) * np.exp(-(z*z)/szg) + we/2.0 * np.exp(-sze) * (np.exp(szm*szm)*erfc(szm) + np.exp(szp*szp)*erfc(szp))
def scale_fit(f, x, y, p0, sig):
xtol = 1.49012e-08
Error = True
#self.chi2 = 99999999999999.0
self.iter = 0
while Error:
self.progress.showMessage(_("stop program with ^C"), 5000)
#if 1:
try:
result = curve_fit(f, x, y, p0=p0, sigma=sig, xtol=xtol, full_output=True)
(popt, pcov, infodict, errmsg, ier) = result
Error = False
except:
#else:
Error = True
xtol *= 2.0
if xtol > self.RMS:
return
if self.parent.STOP:
self.progress.showMessage(_(self.stop_text), 5000)
self.parent.STOP = False
return [], [], self.stop_text
return popt, pcov, infodict
if baseline == "Full":
self.ioff = True
self.islope = True
elif baseline == "Slope":
self.ioff = False
self.islope = True
elif baseline == "Offset":
self.ioff = True
self.islope = False
else:
self.ioff = False
self.islope = False
Hybrid = Hybrid2
if model == "Hybrid": self.p.twocomp = True
self.fname = model
wfunct = eval(model)
wfunct2 = eval(model + "2")
sech2 = np.vectorize(convolv)
l = 0.0
self.ScaleFunct = []
self.FitFunct = []
self.dy = []
self.chi2 = []
self.parent.fig = pyplot.figure("Galaxy scale fit")
if hasattr(self, 'Nplots'): del self.Nplots
self.progress = self.parent.imagewidget.Progress
self.progress.showMessage(_("stop program with ^C"), 5000)
for m in range(len(self.sum)):
self.stripe = m+1
xdata = []
ydata = []
sigma = []
for n in range(len(self.sum[m])):
l, b = self.center[m][n]
if l > 180.0: l -= 360.0
if np.abs(self.mean[m][n]) > self.rms:
xdata.append(b)
ydata.append(self.mean[m][n])
if self.noise_model == "Standard":
sigma.append(self.std[m][n])
else:
sigma.append(self.RMS)
dy = np.array(sigma)
if weight == "Both":
#sig = np.log(np.array(sigma)) / np.log(np.nanmax(sigma))
sig = np.array(sigma) / np.nanmax(sigma)
elif weight == "Lower":
sig = np.where(np.array(xdata) >= 0.0, 10.0, 1.0)
elif weight == "Upper":
sig = np.where(np.array(xdata) <= 0.0, 10.0, 1.0)
elif weight == "No":
sig = None
xdata = 3600*np.array(xdata)
ydata = np.array(ydata)
# calculate beam with respect to geometrical projection
if hasattr(self, "galaxy_radius"):
d = self.center[m][0][0]
if d > 180: d -= 360.0
dd = np.sqrt(max(0.0, self.galaxy_radius**2 - (3600*d)**2)) / self.galaxy_radius
self.sigma = self.sigmabeam + (self.sigmaIncl - self.sigmabeam)* dd
else:
self.sigma = self.sigmabeam
try:
#if 1:
if self.p.twocomp or model == "Hybrid":
if self.ioff and self.islope:
pg = (max(ydata), self.sigma, 0.0, max(ydata)/2., 15*self.sigma, 0.0, 0.0)
elif self.ioff or self.islope:
pg = (max(ydata), self.sigma, 0.0, max(ydata)/2., 15*self.sigma, 0.0)
else:
pg = (max(ydata), self.sigma, 0.0, max(ydata)/2., 15*self.sigma)
popt, pcov, info = scale_fit(wfunct2, xdata, ydata, pg, sig)
else:
if self.ioff and self.islope:
pg = (max(ydata), self.sigma, 0.0, 0.0, 0.0)
elif self.ioff or self.islope:
pg = (max(ydata), self.sigma, 0.0, 0.0)
else:
pg = (max(ydata), self.sigma, 0.0)
popt, pcov, info = scale_fit(wfunct, xdata, ydata, pg, sig)
if type(info) == type(" "):
self.Error(info)
return
perr = np.sqrt(abs(np.diag(pcov)))
except:
#else:
self.Error("no fit result,\nchange input values")
popt = []
self.show = True
if hasattr(self, 'Nplots'): del self.Nplots
#self.plot(xdata, ydata, dy, popt, nint(l*3600), self.p)
return
self.wfunct = wfunct
self.wfunct2 = wfunct2
self.ScaleFunct.append((3600*l, popt, perr))
self.FitFunct.append([nint(3600*l)] + popt)
self.chi2.append(self.reduced_chi_sq(info, sigma, len(ydata)-len(popt)))
############print self.reduced_chi_sq(info, sigma, len(ydata)-len(popt)),
if self.p.twocomp:
yfunc = self.wfunct2(xdata, *popt)
else:
yfunc = self.wfunct(xdata, *popt)
#self.chi2.append(chisquare(ydata, yfunc, ddof=(len(ydata)-len(popt)))[0])
if m == len(self.sum)-1: self.show = True
else: self.show = False
self.plot(xdata, ydata, dy, popt, nint(l*3600), self.p)
def reduced_chi_sq(self, info, sigma, N):
res = info['fvec']
chi2 = (res/sigma)**2
return chi2.sum() / max(1, N)
def plot(self, x, y, dy, pg, l, p):
plog = p.log
if pg == []:
pyplot.figure(1)
try:
del self.Nplots
pyplot.show(mainloop=False)
except: pass
return
if not hasattr(self, 'Nplots'):
self.Nplots = 1
else:
self.Nplots += 1
N = 1*self.Nplots
#N1 = int(self.p.xnum+1)/2
N1 = p.ngraph
pyplot.subplot(1, N1, self.Nplots)
xx = np.arange(float(x[0]), float(x[-1]), 1.0)
if self.p.twocomp:
yg = self.wfunct2(xx, *pg)
else:
yg = self.wfunct(xx, *pg)
#pyplot.plot(x, y, "r+", label="Data")
if plog:
dy = np.where(y-dy > self.RMS, dy, y-0.1) # cut extreme error values
pyplot.errorbar(x, y, dy, "k+", label="Data")
#try:
# maskg = yg > np.nanmin(y[mask])
#except:
# maskg = mask
#pyplot.semilogy(xx[maskg], yg[maskg], "r-", label=self.fname)
pyplot.semilogy(xx, yg, "r-", label=self.fname)
else:
pyplot.errorbar(x, y, dy, "k+", label="Data")
pyplot.plot(xx, yg, "r-", label=self.fname)
if self.Nplots == 1:
#pyplot.legend(pos="TR")
try:
pyplot.title(str("Scale-Fit: %s@%.2fGHz" % (self.m1.header['OBJECT'], self.m1.header['CRVAL3']*1.e-9)))
except:
pyplot.title(str("Scale-Fit: %s" % (self.m1.header['OBJECT'])))
#pyplot.xlabel(str('z-distance ["] at lon=%d"' % l))
pyplot.xlabel(str('z["] at x=%d"' % l))
pyplot.ylabel("intensity")
if self.show:
if hasattr(self, 'Nplots'): del self.Nplots
pyplot.show(mainloop=False)
return
sig= -1
f = open(str("box%2.2d" % N), "w")
for i in range(len(x)):
#r = abs(x[i])
r = x[i]
text = ""
if np.sign(r) != sig:
text = "\n"
sig = np.sign(r)
if self.p.twocomp:
yg = self.wfunct2(r, *pg)
else:
yg = self.wfunct(r, *pg)
if y[i] > self.RMS and yg > self.RMS and ye > self.RMS:
text += str("%f %f %f %f %f %f\n" % (r, np.log(y[i]), np.log(yg), np.log(ye), yg-y[i], ye-y[i]))
#print r, pg[0]*np.exp(-r*r/(2*pg[1]*pg[1])) + pg[3]*np.exp(-r*r/(2*pg[4]*pg[4])),\
# pe[0]*np.exp(-abs(r)/pe[1]) + pe[2]*np.exp(-abs(r)/pe[3])
f.write(text)
f.close()
def function(self, m, p):
self.p = p
if not hasattr(p, 'rms'):
p.rms = 0.0
if not hasattr(p, 'noise_model'):
self.noise_model = "Standard"
else:
self.noise_model = p.noise_model
if not hasattr(p, 'incl'):
p.incl = 90.0
if p.incl < 70.0:
self.Error(str("Inclination < 70.0 degree will not work!"))
return [], p
if not hasattr(p, 'weight'):
p.weight = 'No'
if not hasattr(p, 'cursor'):
p.cursor = False
p.factor = 1000.0
if np.nanmax(m.data) < 0.1:
m.data *= p.factor
if not 'BMAJ' in m.header or not 'BMIN' in m.header:
self.Error("BMAJ and BMIN not found in header, please define the FWHM")
if hasattr(self, 'Nplots'): del self.Nplots
return [], p
self.beam = 2*np.pi/(8*np.log(2.0)) * (m.header['BMAJ'] * m.header['BMIN']) / abs(m.header['CDELT1'] * m.header['CDELT2'])
rows, cols = m.data.shape
m1 = self.rotate(m, p.posang)
#l = (len(range(25,rows-25))/2.0)
if m1.data == []:
return [], p
if p.incl < 90.0:
self.BEAM = self.deincl(m1, p.incl, p.rms, p.dimgal)
else:
self.BEAM = m1.header['BMAJ']
self.oBEAM = m1.header['BMAJ']
m1.header["BEAM"] = (self.BEAM, "inclination corrected beam")
#return m1, p
#if p.rebin > 1:
# m1 = self.rebin(m1, p.rebin)
self.parent.del_polygon()
self.parent.poly_visible = True
if self.update:
self.parent.add_object(m1)
self.parent.refresh_plot()
self.m1 = m1
self.sigmabeam = self.fwhm2sigma(m.header['BMAJ'])
self.sigmaIncl = self.fwhm2sigma(self.BEAM)
if p.cursor:
self.get_cursor()
return [], p
self.rms = p.rms
self.overlay(m1, p)
self.statistics(m1.data, m1.header['CDELT1'])
self.scalefit(p.weight, p.model, p.baseline)
self.printout(p.outfile, p.dimgal)
return [], p