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smoothcal_rx42_nophasors.py
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smoothcal_rx42_nophasors.py
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#!/usr/bin/python
# -*- coding: utf-8 -*-
import sys
import os
import numpy as numpy
import math
#import lofar.expion.parmdbmain
import scipy.signal
import pyrap.tables as pt
import lofar.parmdb
def median_smooth(ampl, half_window):
ampl_tot_copy = numpy.copy(ampl)
ndata = len(ampl)
flags = numpy.zeros(ndata, dtype=bool)
sol = numpy.zeros(ndata + 2 * half_window)
sol[half_window:half_window + ndata] = ampl
for i in range(0, half_window):
# Mirror at left edge.
idx = min(ndata - 1, half_window - i)
sol[i] = ampl[idx]
# Mirror at right edge
idx = max(0, ndata - 2 - i)
sol[ndata + half_window + i] = ampl[idx]
# fix oct 2012
median_array = scipy.signal.medfilt(sol, half_window * 2 - 1)
ampl_tot_copy = median_array[half_window:ndata + half_window]
return ampl_tot_copy
def my_solflag(ampl, half_window, threshold):
ampl_tot_copy = numpy.copy(ampl)
ndata = len(ampl)
flags = numpy.zeros(ndata, dtype=bool)
sol = numpy.zeros(ndata + 2 * half_window)
sol[half_window:half_window + ndata] = ampl
for i in range(0, half_window):
# Mirror at left edge.
idx = min(ndata - 1, half_window - i)
sol[i] = ampl[idx]
# Mirror at right edge
idx = max(0, ndata - 2 - i)
sol[ndata + half_window + i] = ampl[idx]
# fix oct 2012
median_array = scipy.signal.medfilt(sol, half_window * 2 - 1)
sol_flag = numpy.zeros(ndata + 2 * half_window, dtype=bool)
sol_flag_val = numpy.zeros(ndata + 2 * half_window, dtype=bool)
for i in range(half_window, half_window + ndata):
# Compute median of the absolute distance to the median.
window = sol[i - half_window:i + half_window + 1]
window_flag = sol_flag[i - half_window:i + half_window + 1]
window_masked = window[~window_flag]
if len(window_masked) < math.sqrt(len(window)):
# Not enough data to get accurate statistics.
continue
median = numpy.median(window_masked)
q = 1.4826 * numpy.median(numpy.abs(window_masked - median))
# Flag sample if it is more than 1.4826 * threshold * the
# median distance away from the median.
if abs(sol[i] - median) > threshold * q:
sol_flag[i] = True
mask = sol_flag[half_window:half_window + ndata]
for i in range(len(mask)):
if mask[i]:
ampl_tot_copy[i] = median_array[half_window + i] # fixed 2012
return ampl_tot_copy
def my_solflag_inv(ampl, half_window, threshold):
ampl_tot_copy = 1. / numpy.copy(ampl)
ndata = len(ampl)
flags = numpy.zeros(ndata, dtype=bool)
sol = numpy.zeros(ndata + 2 * half_window)
sol[half_window:half_window + ndata] = 1. / ampl # fixed 2012
for i in range(0, half_window):
# Mirror at left edge.
idx = min(ndata - 1, half_window - i)
sol[i] = 1. / ampl[idx] # fixed 2012
# Mirror at right edge
idx = max(0, ndata - 2 - i)
sol[ndata + half_window + i] = 1. / ampl[idx] # fixed 2012
# fix 2012 oct
median_array = scipy.signal.medfilt(sol, half_window * 2 - 1)
sol_flag = numpy.zeros(ndata + 2 * half_window, dtype=bool)
sol_flag_val = numpy.zeros(ndata + 2 * half_window, dtype=bool)
for i in range(half_window, half_window + ndata):
# Compute median of the absolute distance to the median.
window = sol[i - half_window:i + half_window + 1]
window_flag = sol_flag[i - half_window:i + half_window + 1]
window_masked = window[~window_flag]
if len(window_masked) < math.sqrt(len(window)):
# Not enough data to get accurate statistics.
continue
median = numpy.median(window_masked)
q = 1.4826 * numpy.median(numpy.abs(window_masked - median))
# Flag sample if it is more than 1.4826 * threshold * the
# median distance away from the median.
if abs(sol[i] - median) > threshold * q:
sol_flag[i] = True
mask = sol_flag[half_window:half_window + ndata]
for i in range(len(mask)):
if mask[i]:
ampl_tot_copy[i] = median_array[half_window + i] # fixed 2012
return 1. / ampl_tot_copy
def median_window_filter(ampl, half_window, threshold):
ampl_tot_copy = numpy.copy(ampl)
ndata = len(ampl)
flags = numpy.zeros(ndata, dtype=bool)
sol = numpy.zeros(ndata+2*half_window)
sol[half_window:half_window+ndata] = ampl
for i in range(0, half_window):
# Mirror at left edge.
idx = min(ndata-1, half_window-i)
#print i, idx
sol[i] = ampl[idx]
# Mirror at right edge
idx = max(0, ndata-2-i)
sol[ndata+half_window+i] = ampl[idx]
#print 'sol', sol
#fix oct 2012
median_array = scipy.signal.medfilt(sol,half_window*2-1)
sol_flag = numpy.zeros(ndata+2*half_window, dtype=bool)
sol_flag_val = numpy.zeros(ndata+2*half_window, dtype=bool)
for i in range(half_window, half_window + ndata):
# Compute median of the absolute distance to the median.
window = sol[i-half_window:i+half_window+1]
window_flag = sol_flag[i-half_window:i+half_window+1]
window_masked = window[~window_flag]
if len(window_masked) < math.sqrt(len(window)):
# Not enough data to get accurate statistics.
continue
median = numpy.median(window_masked)
q = 1.4826 * numpy.median(numpy.abs(window_masked - median))
# Flag sample if it is more than 1.4826 * threshold * the
# median distance away from the median.
if abs(sol[i] - median) > (threshold * q):
sol_flag[i] = True
mask = sol_flag[half_window:half_window + ndata]
for i in range(len(mask)):
if mask[i]:
ampl_tot_copy[i] = median_array[half_window+i] # fixed 2012
return ampl_tot_copy
msname = str(sys.argv[1])
instrument_name = str(sys.argv[2])
instrument_name_smoothed = str(sys.argv[3]) # msname +'.instrument_smoothed'
##### EDIT THESE PARAMETERS BELOW #####
gain = 'Gain'
output_phasezero = False # if True the phases will be set to zero (for amplitude solutions transfer
# if False the phases will be left untouched
window = 5
#######################################
anttab = pt.table(msname + '/ANTENNA')
antenna_list = anttab.getcol('NAME')
anttab.close()
#antenna_list =['CS001HBA0', 'CS001HBA1', 'CS002HBA0', 'CS002HBA1', 'CS003HBA0', \
# 'CS003HBA1', 'CS004HBA0', 'CS004HBA1', 'CS005HBA0', 'CS005HBA1', \
# 'CS006HBA0', 'CS006HBA1', 'CS007HBA0', 'CS007HBA1', 'CS011HBA0', \
# 'CS011HBA1', 'CS021HBA0', 'CS021HBA1', 'CS024HBA0', 'CS024HBA1', \
# 'CS026HBA0', 'CS026HBA1', 'CS030HBA0', 'CS030HBA1', 'CS031HBA0', \
# 'CS031HBA1', 'CS032HBA0', 'CS032HBA1', 'CS101HBA0', 'CS101HBA1', \
# 'CS103HBA0', 'CS103HBA1', 'CS201HBA0', 'CS201HBA1', 'CS301HBA0', \
# 'CS301HBA1', 'CS302HBA0', 'CS302HBA1', 'CS401HBA0', 'CS401HBA1', \
# 'CS501HBA0', 'CS501HBA1', 'RS106HBA', 'RS205HBA', 'RS208HBA', \
# 'RS305HBA', 'RS306HBA', 'RS307HBA', 'RS310HBA', 'RS406HBA', \
# 'RS407HBA', 'RS409HBA', 'RS503HBA', 'RS508HBA', 'RS509HBA']
pdb = lofar.parmdb.parmdb(instrument_name)
parms = pdb.getValuesGrid('*')
key_names = parms.keys()
#antenna_list = []
pol_list = []
sol_par = []
dir_list = []
# create the antenna+polarizations list
for ii in range(len(key_names)):
string_a = str(key_names[ii])
split_a = string_a.split(':')
if gain == split_a[0]:
print split_a
#antenna_list.append(split_a[4])
pol_list.append(split_a[1] + ':' + split_a[2])
sol_par.append(split_a[3])
#if gain == 'DirectionalGain':
# antenna_list[-1] = antenna_list[-1]+':'+split_a[5]
pol_list = numpy.unique(pol_list)
sol_par = numpy.unique(sol_par)
print 'Stations available:', antenna_list
print 'Polarizations:', pol_list, sol_par, gain
for pol in pol_list:
for antenna in antenna_list:
print 'smoothing [antenna, polarization]:', antenna, pol
real_val = parms[gain + ':' + pol + ':Real:'
+ antenna]['values'][:, :]
imag_val = parms[gain + ':' + pol + ':Imag:'
+ antenna]['values'][:, :]
# Check is we have freq dependent solutions
print 'Found ', numpy.shape(real_val[0, :])[0], \
'solution(s) along the frequency axis'
# loop over channels
for chan in range(numpy.shape(real_val[0, :])[0]):
amp = numpy.sqrt((real_val[:,chan])**2 + (imag_val[:,chan])**2)
phase = numpy.arctan2(imag_val[:,chan],real_val[:,chan])
amp = median_window_filter(amp,window,6)
amp = 1./amp
amp = median_window_filter(amp,window,6)
amp = 1./amp
amp = median_window_filter(amp,window,3.5)
amp = 1./amp
amp = median_window_filter(amp,window,4.25)
amp = 1./amp
# ---------------------------------------------------------------
if output_phasezero:
print 'set phases to zero'
parms[gain + ':' + pol + ':Imag:' + antenna]['values'][:,chan] = numpy.copy(amp*numpy.sin(0.0))
parms[gain + ':' + pol + ':Real:' + antenna]['values'][:,chan] = numpy.copy(amp*numpy.cos(0.0))
else:
parms[gain + ':' + pol + ':Imag:' + antenna]['values'][:,chan] = numpy.copy(amp*numpy.sin(phase))
parms[gain + ':' + pol + ':Real:' + antenna]['values'][:,chan] = numpy.copy(amp*numpy.cos(phase))
print 'writing the new database:', instrument_name_smoothed
print 'check your results with: parmdbplot.py', instrument_name_smoothed
print 'compare with: parmdbplot.py', instrument_name
pdbnew = lofar.parmdb.parmdb(instrument_name_smoothed, create=True)
pdbnew.addValues(parms)
pdbnew.flush()