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calibration.py
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'''
Module for accessing, analyzing and plotting calibration data'''
#
# History:
# 2014-Dec-11 DG
# First written (to analyze SOLPNTCAL scans)
# 2014-Dec-12 DG
# Lots of tweaks to make and beautify the plots. Lots more to do!
# 2014-Dec-14 DG
# Added delay after dump_tsys(), to allow for files to finish writing.
# Also added error return for when RSTN data are not available.
# 2014-Dec-15 DG
# Changed to work with UDB files as well as IDB files.
# 2014-Dec-16 DG
# Added ability to run as cron job (__main__ call at the end).
# Also added a routine sp_check_qual() to do quality/sanity check,
# to verify that the calibration is okay.
# 2014-Dec-17 DG
# Converted s0 zeros to np.nan so division no longer sets a warning.
# 2014-Dec-26 DG
# Added write of today's calibration file to tomorrow, so that
# tomorrow's initial scans will be calibrated.
# 2015-Jan-25 DG
# Fixed all of the routines to work with any number of antennas (?)
# At least it should work for 4 or 8...
# 2015-May-29 DG
# Converted from using datime() to using Time() based on astropy.
# 2015-Jun-27 DG
# Changed the code to standardize on names, content, and order of indices
# of outputs for rd_miriad_tsys. Names ut_mjd, fghz, and tsys will be used,
# with units hopefully made obvious. Order of indices will be
# (nant/nbl, npol, nfreq, ntimes).
# This required some changes to several of the routines here.
# 2016-May-05 DG
# Add test in solpntanal() for 16-ant correlator data (based on date),
# and read with rd_miriad_tsys_16() routine if so.
# 2016-May-07 DG
# Fix some plotting bugs for when number of antennas is odd.
# 2016-Jun-15 DG
# Added new versions sp_apply_cal2() and sp_bg_subtract2() to work
# with new data format.
# 2016-Aug-07 DG
# Change sp_offsets() to give RA and Dec offsets for old ants. Also
# wrote offsets2ants() routine (to replace one that I wrote earlier,
# but somehow got lost...)
# 2016-Nov-20 DG
# There was a slight bug in offsets2ants(), now fixed.
# 2016-Nov-21 DG
# Had the wrong sign of the correction in offsets2ants()! Yikes! It
# was only x that was the wrong sign. (I worry that this is only for the
# equatorial dishes...)
# 2016-Nov-27 DG
# New routine to analyze interferometric (cal) pointing, called calpntanal().
# 2016-Dec-09 DG
# I had determined RA offsets in units of "cross-Dec," but in fact I want
# them in units of RA, so do not multiply by cos(Dec). Also, I now write
# out HA and Dec in degrees instead of radians.
# 2017-Jan-29 DG
# Updated calpntanal() to take a single time and find the relevant scan
# to analyze. Also added calpnt_multi(), which calls calpntanal() for all
# scans in a timerange.
# 2017-Feb-05 DG
# Made changes to calpnt_multi() and calpntanal() to improve plots.
# 2017-Feb-09 DG
# Still struggling with getting the right signs on updating offsets on antennas.
# I have verified that the old antennas 9, 10, 11 and 12 need to have the
# opposite sign for P1 correction (RA to HA involves inverting the sign). This
# sign reversal is implemented in offsets2ants()
# 2017-Apr-11 DG
# Some changes to calpnt_multi() to survive a bad scan, and write results
# to a file as it goes along. This should allow restarting where we left
# off in case of a crash or looking at results before the entire set of
# observations is done.
# 2017-Apr-28 DG
# Began work on calpntanal() to perform on either CALPNTCAL or CALPNT2M data
# with automatic adjustment based on scans in fdb file.
# 2017-May-17 DG
# Added 'auto' keyword to solpntanal(), for optionally analyzing solar
# pointing based on auto-correlation instead of total power.
# 2017-Jul-30 DG
# Added 'auto' keyword to sp_apply_cal2() and sp_bg_subtract2(), to apply
# the calibration to auto-correlation if desired.
# 2017-Oct-27 DG
# Added tsql keyword to solpnt2sql(), for overriding date of SQL record.
# 2017-Dec-03 DG
# Now tsql is set by default to 20 UT on the date of observation, which is
# the new standard. A second analysis at any time of day will overwrite the
# existing one.
# 2018-Jan-04 DG
# Added best_solpnt2sql(), to aid in getting past TP calibrations into SQL.
# Also added a find parameter to solpntanal() call.
# 2018-Jan-07 DG
# Small bug fixes to reduce crashes for unusual situations.
# 2018-Jan-11 DG
# imported ant_str2list() from util, instead of trying to use pcapture2 version
# 2018-Jan-15 DG
# Changed calpntanal() to require feed-rotation corrected data (it was not
# working well otherwise). As part of this, an alternate directory can be
# provided in either calpntanal() or calpnt_multi().
# 2018-Jun-14 DG
# Added skycal_anal() routine, which together with some code in solpntanal() will
# be used to allow fitting of lower frequencies, for which the current SOLPNTCAL
# uses too small a range of distances for fitting the large primary beam. More
# to come.
# 2019-May-17 DG
# Changes to the case of running from the command line, since the routine runs
# fine from pipeline now.
# 2019-May-21 DG
# Finally completed the skycal_anal() routine, which is now called in
# solpntanal() to improve fitting by adding pseudo-10-degree pointing offset.
# Also added code to give a "poor-man's" skycal when those are not available,
# by using the GAINCAL full attenuation setting.
# 2019-Jun-23 DG
# Change to calpntanal() to do feed rotation correction, and change to calpnt_multi()
# to append '2m' to output filename if calpnt2m = True and outfile is not specified,
# to avoid mixing 2-m and 27-m results in the same file. Also fixed a bug in
# calpnt_multi() that occurred in ax array when nrows is 1.
# 2020-May-10 DG
# Updated skycal_anal() to use util.get_idbdir() to find IDB root path.
# 2020-May-11 DG
# Further update to make this work on the DPP.
# 2020-May-29 SY
# update calpntanal() to use util.get_idbdir() to find IDB root path.
# 2020-Dec-12 DG
# Added sp_explore() routine for investigating the data and fits for
# a given antenna and frequency.
# 2021-Jan-11 DG
# Important change to default to correcting for correlator saturation
# (only affects the total power auto-correlation calibration). This
# activates the correction in the read_idb.py function readXdata().
# 2021-Jul-25 DG
# I discovered a bug in skycal_anal() where the times of SQL levels and data
# power levels could be mismatched. Now finds the common value indexes first.
# Argh. Use of median() instead of nanmedian() got me again.
# 2022-Jun-28 DG
# Found that offsets2ants() sometimes read zeros from SQL. It now bails
# with a message to try a different timestamp.
# 2023-02-17 DG
# Changes to skycal_anal() to support new calibration type (13) in cal_header.py.
# This just adds the timestamp and frequency list to the dictionary.
# 2023-03-13 DG
# Added azeloff parameter to allow SOLPNTANAL to work with Azimuth/Elevation offset data.
# 2023-04-02 DG
# Fixed a couple of places where the above change was not fully accounted for.
# 2023-11-13 DG
# Major change for newer data (after 2023-10-20) to use an entirely new solpnt
# scheme for the new feeds.
# 2024-05-01 DG
# Change behavior of __main__ call, now analyzes latest SOLPNT when called without
# arguments, or exact time when a time is given on the command line. Expected to be
# run on Pipeline.
# 2024-05-04 DG
# Updates to calpnt_multi() to work over a day boundary and to deal with data gaps.
# 2024-09-03 DG
# Update to NOT write a total power calibration if too many bad measurements.
#
if __name__ == "__main__":
''' For cron-job use, make sure we use a backend that does not require
X-server
'''
import matplotlib
matplotlib.use('Agg')
import numpy as np
import solpnt
from util import Time, nearest_val_idx, lobe, ant_str2list, get_idbdir, common_val_idx
import struct, time, glob, sys, socket, os
from disk_conv import *
import dump_tsys
def calpnt_multi(trange, fdir=None, ant_str='ant1-13', calpnt2m=False, do_plot=True, outfile=None):
''' Runs calpntanal() for all scans within the given time range.
trange Time object with start and end time over which scans
should be identified and analyzed.
Returns a list of dictionaries containing
Source name, Time, HA, Dec, RA offset and Dec offset
'''
fdb = dump_tsys.rd_fdb(trange[0])
if int(trange[1].mjd) > int(trange[0].mjd):
fdb2 = dump_tsys.rd_fdb(trange[1])
for k,v in fdb2.items():
fdb[k] = np.concatenate((fdb[k],fdb2[k]))
#calpnt2m = False # Flag to indicate whether we are doing CALPNT2M analysis
if calpnt2m:
# Search for CALPNT2M
scanidx, = np.where(fdb['PROJECTID'] == 'CALPNT2M')
if scanidx.size == 0:
print 'No CALPNT2M project IDs found for date'+t.iso[:10]
return {}
else:
# Search for CALPNTCAL
scanidx, = np.where(fdb['PROJECTID'] == 'CALPNTCAL')
if scanidx.size == 0:
print 'No CALPNTCAL project IDs found for date'+t.iso[:10]
return {}
scans,sidx = np.unique(fdb['SCANID'][scanidx],return_index=True)
tlist = Time(fdb['ST_TS'][scanidx[sidx]].astype(float).astype(int),format='lv')
telist = Time(fdb['EN_TS'][scanidx[sidx]].astype(float).astype(int),format='lv')
idx = ant_str2list(ant_str)
out = []
if outfile is None:
if calpnt2m:
outfile = '/common/tmp/Pointing/'+tlist[0].iso[:10].replace('-','')+'_calpnt2m.txt'
else:
outfile = '/common/tmp/Pointing/'+tlist[0].iso[:10].replace('-','')+'_calpnt27m.txt'
k = -1
# First find out how many pointings there will be
npt = 0
for i,t in enumerate(tlist):
if t.jd >= trange[0].jd and telist[i].jd <= trange[1].jd:
npt += 1
for i,t in enumerate(tlist):
if t.jd >= trange[0].jd and telist[i].jd <= trange[1].jd:
k += 1 # Plot-axis row pointer
if do_plot:
if k % 6 == 0:
nrows = min(npt-k,6) # Number of rows in this plot--up to 6
if calpnt2m:
f, ax = plt.subplots(nrows,2*idx.size)
f.set_size_inches(2.5*idx.size,1.5*nrows,forward=True)
plt.subplots_adjust(left=0.03, right=0.97, top=0.89, bottom=0.3, wspace=0.1, hspace=0.3)
else:
f, ax = plt.subplots(nrows,2)
f.set_size_inches(2.5,1.5*nrows,forward=True)
k = 0 # Reset row pointer to top of new plot
else:
# Kill previous row's xaxis tick labels so that title is visible
if calpnt2m:
for j in range(idx.size*2):
ax[k-1,j].xaxis.set_ticklabels([])
ax[k-1,j].set_xlabel('')
else:
for j in range(2):
ax[k-1,j].xaxis.set_ticklabels([])
ax[k-1,j].set_xlabel('')
try:
if nrows == 1:
out.append(calpntanal(t, fdir=fdir, ant_str=ant_str, calpnt2m=calpnt2m, do_plot=do_plot, ax=ax))
else:
out.append(calpntanal(t, fdir=fdir, ant_str=ant_str, calpnt2m=calpnt2m, do_plot=do_plot, ax=ax[k]))
except:
out.append({})
else:
try:
out.append(calpntanal(t, fdir=fdir, ant_str=ant_str, calpnt2m=calpnt2m))
except:
out.append({})
src = out[-1]
if src == {}:
print 'Scan starting at',t.iso,'failed.'
else:
if calpnt2m:
if k == 0:
# Print heading to screen and to file
heading = ' Source Date Time HA Dec '
for iant in idx:
heading += ' Ant {:2d} '.format(iant+1)
print heading
# Write heading to file only if the file has not been opened/created yet.
if not os.path.isfile(outfile):
f = open(outfile,'w')
f.write(heading+'\n')
f.close()
rao_deco = ''
for j in range(idx.size):
rao_deco += ' {:7.3f} {:7.3f}'.format(src['rao'][j],src['deco'][j])
line = '{:s} {:s} {:6.2f} {:6.2f}'.format(src['source'],
src['time'].iso[:19],
src['ha'],src['dec'])
line += rao_deco
else:
if k == 0:
# Print heading to screen and to file
heading = ' Source Date Time HA Dec Ant 14'
print heading
# Write heading to file only if the file has not been opened/created yet.
if not os.path.isfile(outfile):
f = open(outfile,'w')
f.write(heading+'\n')
f.close()
line = '{:s} {:s} {:6.2f} {:6.2f} {:7.3f} {:7.3f}'.format(src['source'],
src['time'].iso[:19],
src['ha'],src['dec'],
src['rao'],src['deco'])
print line
f = open(outfile,'a') # This should create the file if it does not exist, or append otherwise.
f.write(line+'\n')
f.close()
return out
def calpntanal(t, fdir=None, ant_str='ant1-13', calpnt2m=False, do_plot=True, ax=None):
''' Does a complete analysis of CALPNTCAL, reading information from the SQL
database, finding the corresponding Miriad IDB data, and doing the
gaussian fit to the beam, to return the beam and offset parameters.
t Time object with a time near the start of the desired scan (finds the scan
that starts closest time to the given time)
ax If specified as a two-element array of axes, the plots will be placed
in an already existing window, allowing reuse of the same window.
Otherwise, a new figure is created (if do_plot is True).
Returns a dictionary containing
Source name, Time, HA, Dec, RA offset and Dec offset
NB: This routine has been changed to use feed-rotation-corrected data, by
commenting out the original code with ##. Two replacement lines were
added, below comments marked with ##--.
'''
import matplotlib.pyplot as plt
from matplotlib.transforms import Bbox
import read_idb
import dbutil as db
bl2ord = read_idb.bl2ord
tdate = t.iso.replace('-','')[:8]
if fdir is None:
fdir = get_idbdir(t)+tdate+'/'
else:
if fdir[-1] != '/':
fdir += '/'
fdb = dump_tsys.rd_fdb(t)
# Set offset coordinates appropriate to the type of PROJECTID found
if calpnt2m:
# Search for CALPNT2M
scanidx, = np.where(fdb['PROJECTID'] == 'CALPNT2M')
if scanidx.size == 0:
print 'No CALPNT2M project IDs found for date'+t.iso[:10]
return {}
else:
# Found CALPNT2M, so set offset coordinates to match calpnt2m.trj
rao = np.array([-5.00, -2.0, -1.0, -0.5, 0.00, 0.5, 1.0, 2.0])
deco = np.array([-2.0, -1.0, -0.5, 0.00, 0.5, 1.0, 2.0, 5.00])
pltfac = 10.
else:
# Search for CALPNTCAL
scanidx, = np.where(fdb['PROJECTID'] == 'CALPNTCAL')
if scanidx.size == 0:
print 'No CALPNTCAL project IDs found for date'+t.iso[:10]
return {}
else:
# Found CALPNTCAL, so set offset coordinates to match calpnt.trj
rao = np.array([-1.00, -0.20, -0.10, -0.05, 0.00, 0.05, 0.10, 0.20])
deco = np.array([-0.20, -0.10, -0.05, 0.00, 0.05, 0.10, 0.20, 1.00])
pltfac = 1.
scans,sidx = np.unique(fdb['SCANID'][scanidx],return_index=True)
tlist = Time(fdb['ST_TS'][scanidx[sidx]].astype(float).astype(int),format='lv')
idx, = nearest_val_idx([t.jd],tlist.jd)
filelist = [fdir+f for f in fdb['FILE'][np.where(fdb['SCANID'] == scans[idx])]]
#import pdb; pdb.set_trace()
# Read pointing data (timerange t must be accurate)
outo = read_idb.read_idb(filelist, navg=30)
print "Number of times in data",outo['time'].shape,"(Needs to be 16+)"
# Perform feed rotation correction
out = read_idb.unrot(outo)
# Determine wanted baselines with ant 14 from ant_str
idx = ant_str2list(ant_str)
##idx1 = idx[idx>7] # Ants > 8
##idx2 = idx[idx<8] # Ants <= 8
# Determine parallactic angle for azel antennas (they are all the same, so find median).
# If 0 < abs(chi) < 30, use channel XX
# If 30 < abs(chi) < 60, use sum of channel XX and XY
# If 60 < abs(chi) < 90, use channel XY
##midtime = Time((out['time'][0] + out['time'][-1])/2.,format='jd')
##times, chi = db.get_chi(Time([midtime.lv+1,midtime.lv + 10],format='lv'))
##abschi = abs(lobe(np.median(chi[0,0:8])))
if pltfac == 1.:
# Case of 27m antenna pointing
# Do appropriate sums over frequency, polarization and baseline
pntdata = np.sum(np.abs(np.sum(np.sum(out['x'][bl2ord[idx,13],:2],1),1)),0)
tarray = np.zeros(50)
pntarray = np.zeros(50)
k = 0
tarray[0] = out['time'][0]
pntarray[0] = pntdata[0]
# Fill in time gaps with nans
for i in range(1,len(out['time'])):
dt = out['time'][i] - tarray[k]
while dt > 32./86400: # Should be 30, but add 2 s to give room for a little slop
# This is a time gap
k += 1
tarray[k] = tarray[k-1]+30./86400. # Add 30 s to "current" time
pntarray[k] = np.nan # Insert NaN
dt = out['time'][i] - tarray[k]
k += 1
tarray[k] = out['time'][i]
pntarray[k] = pntdata[i]
pntarray = pntarray[:48]
pntarray.shape = (16,3)
stdev = np.nanstd(pntarray,1)
pntdata = np.nansum(pntarray,1)
radat = pntdata[:8]
decdat = pntdata[8:]
# peak flux offset beamwidth background
low_bounds = [ 1., -0.2, 0.05, 0.]
high_bounds = [1.5*max(np.max(pntdata),1), 0.2, 0.15, 1.]
bounds = (low_bounds,high_bounds)
plsqr, xr, yr = solpnt.gausfit(rao, radat, bounds=bounds)
plsqd, xd, yd = solpnt.gausfit(deco, decdat, bounds=bounds)
midtime = Time((out['time'][0] + out['time'][-1])/2.,format='jd')
if (do_plot):
if ax is None:
f, ax = plt.subplots(1,2)
f.set_size_inches(2.5,1.5,forward=True)
ax[0].errorbar(rao,radat,yerr=stdev[:8],fmt='.')
ax[0].plot(xr,yr)
ax[0].axvline(x=0,color='k')
ax[0].axvline(x=plsqr[1],linestyle='--')
ax[1].errorbar(deco,decdat,yerr=stdev[8:],fmt='.')
ax[1].plot(xd,yd)
ax[1].axvline(x=0,color='k')
ax[1].axvline(x=plsqd[1],linestyle='--')
for j in range(2):
ax[j].set_xlim(-0.3, 0.3)
ax[j].grid()
ax[0].text(0.05,0.9,'RAO :'+str(plsqr[1])[:5],transform=ax[0].transAxes)
ax[0].text(0.55,0.9,'FWHM:'+str(plsqr[2])[:5],transform=ax[0].transAxes)
ax[0].set_xlabel('RA Offset [deg]')
ax[1].text(0.05,0.9,'DECO:'+str(plsqd[1])[:5],transform=ax[1].transAxes)
ax[1].text(0.55,0.9,'FWHM:'+str(plsqd[2])[:5],transform=ax[1].transAxes)
ax[1].set_xlabel('Dec Offset [deg]')
if ax is None:
f.suptitle('Pointing on '+out['source']+' at '+midtime.iso)
else:
ax[0].set_title(out['source']+' at')
ax[1].set_title(midtime.iso[:16])
plt.pause(0.5)
return {'source':out['source'],'ha':out['ha'][24]*180./np.pi,'dec':out['dec']*180/np.pi,
'rao':plsqr[1],'deco':plsqd[1],'time':midtime,'antidx':13}
else:
# Case of 2m antenna pointing
# Do appropriate sum over frequency and polarization but not baseline
##--This line replaces all of the lines marked with ##
pntdata = np.abs(np.sum(np.sum(out['x'][bl2ord[idx,13],:2,:,:48],1),1)) # Combines XX and YY
##if abschi >= 0 and abschi < np.pi/6:
## pntdata = np.abs(np.sum(out['x'][bl2ord[idx,13],0,:,:48],1)) # Use only XX
##elif abschi >= np.pi/6 and abschi < np.pi/3:
## pntdata1 = np.abs(np.sum(out['x'][bl2ord[idx1,13],0,:,:48],1)) # Use XX only for ants > 8
## pntdata2 = np.abs(np.sum(out['x'][bl2ord[idx2,13],:,:,:48],2)) # Use sum of XX and XY for ants <= 8
## pntdata = np.concatenate((pntdata1,np.sum(pntdata2[:,np.array([0,2])],1)),0)
##else:
## pntdata1 = np.abs(np.sum(out['x'][bl2ord[idx1,13],0,:,:48],1)) # Use XX only for ants > 8
## pntdata2 = np.abs(np.sum(out['x'][bl2ord[idx2,13],2,:,:48],1)) # Use sum of XY for ants <= 8
## pntdata = np.concatenate((pntdata1,pntdata2),0)
# Measurements are 90 s long, hence 3 consecutive 30 s points, so do final
# sum over these
pntdata.shape = (idx.size,16,3)
stdev = np.std(pntdata,2)
pntdata = np.sum(pntdata,2)
rao_fit = np.zeros(idx.size,float)
deco_fit = np.zeros(idx.size,float)
if (do_plot):
if ax is None:
f, ax = plt.subplots(1,2*idx.size)
f.set_size_inches(2.5*idx.size,1.5,forward=True)
plt.subplots_adjust(left=0.03, right=0.97, top=0.89, bottom=0.3, wspace=0.1, hspace=0.1)
for k in range(idx.size):
radat = pntdata[k,:8]
decdat = pntdata[k,8:]
plsqr, xr, yr = solpnt.gausfit(rao, radat)
plsqd, xd, yd = solpnt.gausfit(deco, decdat)
midtime = Time((out['time'][0] + out['time'][-1])/2.,format='jd')
if (do_plot):
ax[k*2+0].errorbar(rao,radat,yerr=stdev[k,:8],fmt='.')
ax[k*2+0].plot(xr,yr)
ax[k*2+0].axvline(x=0,color='k')
ax[k*2+0].axvline(x=plsqr[1],linestyle='--')
ax[k*2+1].errorbar(deco,decdat,yerr=stdev[k,8:],fmt='.')
ax[k*2+1].plot(xd,yd)
ax[k*2+1].axvline(x=0,color='k')
ax[k*2+1].axvline(x=plsqd[1],linestyle='--')
for j in range(2):
ax[k*2+j].set_xlim(-3.0, 3.0)
ax[k*2+j].grid()
ax[k*2+0].text(0.05,0.7,'RAO :'+str(plsqr[1])[:5],transform=ax[k*2+0].transAxes,fontsize=9)
ax[k*2+0].text(0.05,0.5,'FWHM:'+str(plsqr[2])[:5],transform=ax[k*2+0].transAxes,fontsize=9)
ax[k*2+0].set_xlabel('RAO [deg]',fontsize=9)
ax[k*2+1].text(-0.2,0.85,'Ant :'+str(idx[k]+1),transform=ax[k*2+1].transAxes,fontsize=9)
ax[k*2+1].text(0.05,0.7,'DECO:'+str(plsqd[1])[:5],transform=ax[k*2+1].transAxes,fontsize=9)
ax[k*2+1].text(0.05,0.5,'FWHM:'+str(plsqd[2])[:5],transform=ax[k*2+1].transAxes,fontsize=9)
ax[k*2+1].set_yticklabels([])
ax[k*2+1].set_xlabel('DECO [deg]',fontsize=9)
if k == idx.size-1:
ax[k+0].set_title(out['source']+' at',fontsize=9)
ax[k+1].set_title(midtime.iso[:16],fontsize=9)
# Adjust the pairs of subplots to group RA/Dec for each antenna together
ax[k*2+0].set_position(Bbox(ax[k*2+0].get_position().get_points() + np.array([[0.0025,0],[0.0025,0]])))
ax[k*2+1].set_position(Bbox(ax[k*2+1].get_position().get_points() + np.array([[-0.0025,0],[-0.0025,0]])))
# Set to the same amplitude scale
ymin = min(ax[k*2+0].get_ylim()[0],ax[k*2+1].get_ylim()[0])
ymax = max(ax[k*2+0].get_ylim()[1],ax[k*2+1].get_ylim()[1])
ax[k*2+0].set_ylim(ymin,ymax)
ax[k*2+1].set_ylim(ymin,ymax)
plt.pause(0.5)
rao_fit[k] = plsqr[1]
deco_fit[k] = plsqd[1]
return {'source':out['source'],'ha':out['ha'][24]*180./np.pi,'dec':out['dec']*180/np.pi,
'rao':rao_fit,'deco':deco_fit,'time':midtime,'antidx':idx}
def solpntanal(t, udb=False, auto=False, find=True, desat=False, azeloff=False):
''' Does a complete analysis of SOLPNTCAL, reading information from the SQL
database, finding and dumping the corresponding Miriad IDB data, and
doing gaussian fit to beam to return the beam parameters. The outputs
are identical dictionaries for x and y polarizations.
Added find parameter, which if False skips the find_solpnt() call, so
the Time() object t must be an actual SOLPNTCAL scan time.
'''
from copy import deepcopy
import cal_header as ch
from stateframe import extract
def fname2mjd(filename):
fstem = filename.split('/')[-1]
fstr = fstem[3:7]+'-'+fstem[7:9]+'-'+fstem[9:11]+' '+fstem[11:13]+':'+fstem[13:15]+':'+fstem[15:17]
return Time(fstr).mjd
pnt = solpnt.get_solpnt(t, find=find, azeloff=azeloff)
proc = solpnt.process_solpnt(pnt)
trange = Time([pnt['Timestamp'],pnt['Timestamp']+300.],format='lv')
skycal = {}
if trange[0].mjd < 57450:
if auto: print "Warning: 'auto' keyword does nothing for older data. Keyword ignored."
otp = dump_tsys.rd_miriad_tsys(trange,udb=udb)
elif trange[0].mjd < 58623:
# The SKYCALTEST scans may have been taken, but they were not setting the DCM table correctly
# until 2019-05-20, so earlier dates just do this almost as good approach.
poor_skycal = skycal_anal(trange[0], do_plot=False, desat=desat)
otp = dump_tsys.rd_miriad_tsys_16(trange, udb=udb, auto=auto, tref=trange[0], skycal=poor_skycal, desat=desat)
else:
# New code as of 2019-May-21, tries to read from SKYCALTEST and use it
# for a far off-Sun background measurement.
skycal = skycal_anal(trange[0], do_plot=False, last=True, desat=desat)
otp = dump_tsys.rd_miriad_tsys_16(trange, udb=udb, auto=auto, tref=trange[0], skycal=skycal, desat=desat)
if skycal != {}:
# Check if skycal record exists in SQL database, and write it if not
xml, buf = ch.read_cal(13, trange[0])
sqlmjd = Time(extract(buf, xml['Timestamp']), format='lv').mjd # Date of existing skycal record
if int(sqlmjd) != int(trange[0].mjd):
# Day numbers of existing and new records do not agree, so write a new record
ch.skycal2sql(skycal)
newskycal = deepcopy(skycal)
if auto:
# Modify the skycal to put the auto information into the standard location
newskycal['offsun'] = np.real(skycal['offsun_auto'])
newskycal['rcvr_bgd'] = np.real(skycal['rcvr_bgd_auto'])
else:
newskycal = {}
fghz = otp['fghz']
xra,xdec,xrao,xdeco = solpnt.process_tsys(otp,proc,pol=0,skycal=newskycal)
#import pdb; pdb.set_trace()
x = {'ut_mjd':otp['ut_mjd'],'fghz':fghz,'ra0':proc['ra0'],'dec0':proc['dec0'],'raparms':xra,'decparms':xdec,
'rao':xrao,'deco':xdeco,'ra':proc['rao'],'dec':proc['deco'],'antlist':proc['antlist']}
# otp = solpnt.rd_tsys(yfile,sfile)
yra,ydec,yrao,ydeco = solpnt.process_tsys(otp,proc,pol=1,skycal=newskycal)
y = {'ut_mjd':otp['ut_mjd'],'fghz':fghz,'ra0':proc['ra0'],'dec0':proc['dec0'],'raparms':yra,'decparms':ydec,
'rao':yrao,'deco':ydeco,'ra':proc['rao'],'dec':proc['deco'],'antlist':proc['antlist']}
qual = sp_check_qual(x,y)
return x,y, qual
def skycal_anal(t=None, do_plot=False, last=False, desat=False, file=''):
''' Reads the daily SKYCALTEST data and returns the far "off Sun" background for use with
total power calibration. The SKYCALTEST observation can double as a GAINCAL if needed,
but this has not yet been implemented.
Returns the receiver background power and the backend power (with 62 dB front-end attenuation
applied), for both total power and auto-correlation data.
Inputs:
t An optional Time() object with the date for which to return the SKYCAL data. Uses
today's date if omitted (or None)
do_plot A flag requesting a summary plot of the results (default = True)
NB: Only total power is plotted, not auto-correlation
last A flag specifying that the last SKYCALTEST scan should be used (normally only one
per day is obtained, so this allows a second one to be processed). Default is
False, meaning only the first SKYCALTEST scan is used. Note that if there is
only one SKYCALTEST scan, first and last are the same and this flag has no effect.
Returns:
Dictionary with keys (or None):
'offsun' Total power level on blank sky, with femattn level 0 (includes receiver noise)
'rcvr_bgd' Total power level on blank sky, with femattn level 15 (receiver noise only)
'offsun_auto' Auto correlation level on blank sky, with femattn level 0
'rcvr_bgd_auto' Auto correlation level on blank sky, with femattn level 15
'''
from gaincal2 import get_fem_level
import read_idb as ri
import socket
# lev = get_fem_level(trange,300)
# levs = np.zeros((13,2), dtype=int)
# levs[:,0] = lev['hlev'][:13,0]
# levs[:,1] = lev['vlev'][:13,0]
if t is None:
t = Time.now()
if t.mjd < 58623:
print 'SKYCAL_ANAL: No valid SKYCALTEST scans for dates prior to 2019-05-20'
print 'SKYCAL_ANAL: Using GAINCALTEST 62 dB setting instead.'
# Do a "poor-man's" skycal background by using the gaincal 62 dB setting.
# This should be almost as good for receiver noise subtraction as a true SKYCAL
from attncal import get_attncal
outdict = get_attncal(t)
if outdict[0] == {}:
print 'SKYCAL_ANAL: No available GAINCALTEST scans. Returning {} result.'
return outdict
timestamp = int(Time(outdict[0]['time'],format='jd').lv)
return {'timestamp':timestamp, 'fghz':outdict[0]['fghz'], 'rcvr_bgd': outdict[0]['rcvr'], 'rcvr_bgd_auto': outdict[0]['rcvr_auto']}
fdb = dump_tsys.rd_fdb(t)
gcidxes, = np.where(fdb['PROJECTID'] == 'SKYCALTEST')
host = socket.gethostname()
if len(gcidxes) != 0:
if last:
gcidx = gcidxes[-1]
else:
gcidx = gcidxes[0]
datadir = get_idbdir(t)
# Add date path if on pipeline
# if datadir.find('eovsa') != -1:
# datadir += fdb['FILE'][gcidx][3:11]+'/'
if host == 'pipeline': datadir += fdb['FILE'][gcidx][3:11]+'/'
if file == '':
file = datadir+fdb['FILE'][gcidx]
out = ri.read_idb([file], desat=desat)
skytrange = Time(out['time'][[0,-1]],format='jd')
nant, npol, nf, nt = out['p'].shape
if nant > 13: nant = 13
offsun = np.zeros((nant,npol,nf), dtype=float)
rcvr = np.zeros((nant,npol,nf), dtype=float)
offsun_auto = np.zeros((nant,npol,nf), dtype=complex)
rcvr_auto = np.zeros((nant,npol,nf), dtype=complex)
# Get the SKYCALTEST fem level vs. time and use it to define
# the range of times for femattn 15 (62 dB) and femattn 0 (0 dB)
hvlev = ['hlev','vlev']
lev = get_fem_level(skytrange)
# Get indexes to common times
idx1, idx2 = common_val_idx(lev['times'].jd,out['time'])
timestamp = int(Time(out['time'][idx2[0]],format='jd').lv)
for i in range(nant):
for j in range(npol):
idx0, = np.where(lev[hvlev[j]][i,idx1] == 0) # 0 dB
idx15, = np.where(lev[hvlev[j]][i,idx1] == 15) # 62 dB
# NB: time and frequency indexes are reordered for some reason.
# The next two lines are a median over times, despite what it seems.
offsun[i,j] = np.nanmedian(out['p'][i,j,:,idx2[idx0]],0)
rcvr[i,j] = np.nanmedian(out['p'][i,j,:,idx2[idx15]],0)
offsun_auto[i,j] = np.nanmedian(out['a'][i,j,:,idx2[idx0]],0)
rcvr_auto[i,j] = np.nanmedian(out['a'][i,j,:,idx2[idx15]],0)
if do_plot:
nrow = 2
ncol = (nant+1)/2
f, ax = plt.subplots(nrow, ncol, sharex='col', sharey='row')
f.set_size_inches(2*nant,7,forward=True)
f.suptitle('HPol Receiver and Sky Noise for SKYCAL scan at '+skytrange[0].iso[:19]+' UT',fontsize=18)
for ant in range(nant):
ax[ant % nrow, ant/nrow].set_title('Ant '+str(ant+1)+' Noise')
if ant % ncol == 0:
ax[ant/ncol,0].set_ylabel('Arbitrary Units')
if ant >= nant/nrow:
ax[1,ant - nant/nrow].set_xlabel('Frequency [GHz]')
ax[ant % nrow, ant/nrow].plot(offsun[ant,0])
ax[ant % nrow, ant/nrow].plot(rcvr[ant,0])
f, ax = plt.subplots(nrow, ncol, sharex='col', sharey='row')
f.set_size_inches(2*nant,7,forward=True)
f.suptitle('VPol Receiver and Sky Noise for SKYCAL scan at '+skytrange[0].iso[:19]+' UT',fontsize=18)
for ant in range(nant):
ax[ant % nrow, ant/nrow].set_title('Ant '+str(ant+1)+' Noise')
if ant % ncol == 0:
ax[ant/ncol,0].set_ylabel('Arbitrary Units')
if ant >= nant/nrow:
ax[1,ant - nant/nrow].set_xlabel('Frequency [GHz]')
ax[ant % nrow, ant/nrow].plot(offsun[ant,1])
ax[ant % nrow, ant/nrow].plot(rcvr[ant,1])
else:
print 'SKYCAL_ANAL: No SKYCALTEST scan for this date'
print 'SKYCAL_ANAL: Using GAINCALTEST 62 dB setting instead.'
# Do a "poor-man's" skycal background by using the gaincal 62 dB setting.
# This should be almost as good for receiver noise subtraction as a true SKYCAL
from attncal import get_attncal
outdict = get_attncal(t)
if outdict[0] == {}:
print 'SKYCAL_ANAL: No available SKYCAL or GAINCALTEST scans. Returning {} result.'
return outdict
timestamp = int(Time(outdict[0]['time'],format='jd').lv)
return {'timestamp':timestamp, 'fghz':outdict[0]['fghz'], 'rcvr_bgd': outdict[0]['rcvr'], 'rcvr_bgd_auto': outdict[0]['rcvr_auto']}
return {'timestamp':timestamp, 'fghz':out['fghz'], 'offsun': offsun, 'rcvr_bgd': rcvr, 'offsun_auto': offsun_auto, 'rcvr_bgd_auto': rcvr_auto}
def sp_get_calfac(x,y, do_plot=True):
''' Reads the RSTN/Penticton flux, fits to the observed frequencies, and applies
them to the antenna solar response in input dictionaries x and y to return
the calibration factors with which to MULTIPLY solar data to convert to solar
flux units. The offsun (background) spectrum for each polarization and antenna
is also returned.
if do_plot is True, also make a nice plot of the factors applied to the data
TODO: These need to be scaled for gain state
'''
import rstn
import matplotlib.pyplot as plt
t = Time(x['ut_mjd'][0],format='mjd')
frq, flux = rstn.rd_rstnflux(t)
if frq is None:
print 'Cannot continue.'
return None
nfrq, npnt, nant = x['rao'].shape
fmhz = x['fghz']*1000.
fghz = x['fghz']
s = rstn.rstn2ant(frq, flux, fmhz, t)
calfac = np.zeros((2,nfrq,nant),'float')
offsun = np.zeros((2,nfrq,nant),'float')
if do_plot:
# Set up summary plot
nrow = 2
ncol = (nant+1)/2
f, ax = plt.subplots(nrow, ncol, sharex='col', sharey='row')
f.set_size_inches(2*nant,7,forward=True)
f.suptitle('Calibration for SOLPNT scan at '+t.iso[:19]+' UT',fontsize=18)
for ant in range(nant):
ax[ant % nrow, ant/nrow].set_title('Ant '+str(ant+1)+' Solar Spectrum')
if ant % ncol == 0:
ax[ant/ncol,0].set_ylabel('Solar Flux [sfu]')
if ant >= nant/nrow:
ax[1,ant - nant/nrow].set_xlabel('Frequency [GHz]')
for ant in range(nant):
# Do flux calculation for X feed
a1 = x['raparms'][2,:,ant] # 1/e half-width of RA beam (1/10000th of a degree)
a2 = x['decparms'][2,:,ant] # 1/e half-width of Dec beam (1/10000th of a degree)
s1 = x['raparms'][0,:,ant] # RA peak flux in arb. units
s2 = x['decparms'][0,:,ant] # Dec peak flux in arb. units
o1 = x['raparms'][1,:,ant] # RA offset (1/10000th of a degree)
o2 = x['decparms'][1,:,ant] # Dec offset (1/10000th of a degree)
s01 = s1*np.exp((o2/a1)**2) # Corrects RA flux for off-pointing in Dec
s02 = s2*np.exp((o1/a2)**2) # Corrects Dec flux for off-pointing in RA
s0 = (s01 + s02)/2. # S01 should equal S02, take the mean
s0[np.where(s0 == 0)] = np.nan
calfac[0,:,ant] = s/s0 # sfu/unit
offsun[0,:,ant] = (x['raparms'][3,:,ant] + x['decparms'][3,:,ant])/2 # arb. units
if do_plot:
ax[ant % 2, ant/2].plot(fghz,s1*calfac[0,:,ant],'.',label='X-RA')
ax[ant % 2, ant/2].plot(fghz,s2*calfac[0,:,ant],'.',label='X-Dec')
# Repeat for Y feed
a1 = y['raparms'][2,:,ant]
a2 = y['decparms'][2,:,ant]
s1 = y['raparms'][0,:,ant]
s2 = y['decparms'][0,:,ant]
o1 = y['raparms'][1,:,ant]
o2 = y['decparms'][1,:,ant]
s01 = s1*np.exp((o2/a1)**2) # Corrects RA flux for off-pointing in Dec
s02 = s2*np.exp((o1/a2)**2) # Corrects Dec flux for off-pointing in RA
s0 = (s01 + s02)/2.
s0[np.where(s0 == 0)] = np.nan
calfac[1,:,ant] = s/s0 # sfu/unit
offsun[1,:,ant] = (y['raparms'][3,:,ant] + y['decparms'][3,:,ant])/2 # arb. units
if do_plot:
ax[ant % nrow, ant/nrow].plot(fghz,s1*calfac[1,:,ant],'.',label='Y-RA')
ax[ant % nrow, ant/nrow].plot(fghz,s2*calfac[1,:,ant],'.',label='Y-Dec')
ax[ant % nrow, ant/nrow].set_ylim(0,600)
ax[ant % nrow, ant/nrow].set_xlim(0,19)
ax[ant % nrow, ant/nrow].legend(loc='upper left',fontsize='small')
return calfac,offsun
def sp_check_qual(x, y):
''' Does a sanity check for quality of SOLPNTCAL, based on comparison
of measured beamsize with that expected from a 2.1-m dish
'''
fghz = x['fghz']
fout,a,aout = disk_conv(fghz)
aout = aout*10000/(2*np.sqrt(np.log(2)))
nparms,nf,nant = x['raparms'].shape
qual = np.zeros((4,nant),'bool')
for i in range(nant):
# Beamsize must be within 10 percent of nominal value
val = np.median(x['raparms'][2,:,i]/aout)
qual[0,i] = val < 1.1 and val > 0.9
val = np.median(x['decparms'][2,:,i]/aout)
qual[1,i] = val < 1.1 and val > 0.9
val = np.median(y['raparms'][2,:,i]/aout)
qual[2,i] = val < 1.1 and val > 0.9
val = np.median(y['decparms'][2,:,i]/aout)
qual[3,i] = val < 1.1 and val > 0.9
return qual
def sp_write_calfac(x, y, calfac, offsun):
''' Given input dictionaries x, y, and the calibration factors calfac and offsun,
generated by sp_get_calfac(), write standard binary files with the name
TPCALyyyymmdd_a_bbb_c.dat, where a = number of feeds (2),
bbb = number of frequencies,
c = number of antennas.
These values are needed in order to know how to read the file...
The file contains a list of bbb frequencies, and the arrays calfac and offsun,
both of dimensions [a,bbb,c].
'''
fghz = x['fghz']
nf = len(fghz)
t1 = Time(x['ut_mjd'][0],format='mjd')
datstr = t1.iso[:10].replace('-','')
dims = calfac.shape
siz = np.prod(dims)
buf = struct.pack(str(nf)+'f',*fghz)
buf += struct.pack(str(siz)+'f',*calfac.reshape(siz))
buf += struct.pack(str(siz)+'f',*offsun.reshape(siz))
filename = '/data1/TPCAL/TPCAL'+datstr+'_'+str(dims[0])+'_'+str(dims[1])+'_'+str(dims[2])+'.dat'
f = open(filename,'wb')
f.write(buf)
f.close()
return
def sp_read_calfac(t):
''' Read the contents of a SOLPNT calibration file and return
fghz, calfac, offsun arrays
'''
files = glob.glob('/dppdata1/TPCAL/*')
for file in files:
if file.find(t.iso.replace('-','')[:8]) != -1:
npol,nf,nant = file.replace('.dat','').split('_')[1:]
nsiz = int(npol)*int(nf)*int(nant)
nfi = int(nf)
f = open(file,'rb')
data = f.read()
f.close()
fghz = np.array(struct.unpack_from(nf+'f',data,0))
calfac = np.array(struct.unpack_from(str(nsiz)+'f',data,nfi*4)).reshape(int(npol),int(nf),int(nant))
offsun = np.array(struct.unpack_from(str(nsiz)+'f',data,(nfi+nsiz)*4)).reshape(int(npol),int(nf),int(nant))
return fghz, calfac, offsun
print 'Calibration file not found for date',t.iso
return None, None, None
def sp_apply_cal2(out, calfac, offsun, auto=False):
''' Given "standard" output of tp_display.rd_tsys_multi(),
and corresponding calibration data from sp_read_calfac(),
apply the calibration and return the calibrated output.
'''
fghz = out['fghz']
good = np.where(np.logical_and(fghz != 0,fghz > 2.0))
nant, npol, nf, nt = out['p'].shape
# Interchange antenna and frequency axes in offsun and calfac
osun = np.rollaxis(offsun,2,0)
cfac = np.rollaxis(calfac,2,0)
nant,npol,nf = cfac.shape
if auto:
for i in range(nt):
out['a'][:nant,:2,good,i] = (out['a'][:nant,:2,good,i]-osun[:,:,good])*cfac[:,:,good]
else:
for i in range(nt):
out['p'][:nant,:,good,i] = (out['p'][:nant,:,good,i]-osun[:,:,good])*cfac[:,:,good]
# out['tsys'][:,1,good,i] = (out['tsys'][:,1,good,i]-offsun[1,good,:])*calfac[1,good,:]
return out
def sp_apply_cal(out,fghz,calfac,offsun):
''' Given "standard" output of tp_display.rd_tsys_multi(),
and corresponding calibration data from sp_read_calfac(),
apply the calibration and return the calibrated output.
'''
good = np.where(np.logical_and(fghz != 0,fghz > 2.0))
nant, npol, nf, nt = out['tsys'].shape
# Interchange antenna and frequency axes in offsun and calfac
osun = np.rollaxis(offsun,2,0)
cfac = np.rollaxis(calfac,2,0)
for i in range(nt):
out['tsys'][:,:,good,i] = (out['tsys'][:,:,good,i]-osun[:,:,good])*cfac[:,:,good]
# out['tsys'][:,1,good,i] = (out['tsys'][:,1,good,i]-offsun[1,good,:])*calfac[1,good,:]
return out
def sp_bg_subtract2(out, idx, auto=False):
''' Given "standard" output of tp_display.rd_tsys_multi(),
and a range of time indexes idx into the arrays, use the
median of data at times indicated by idx as background.
Subtract that background from both xtsys and ytsys
'''
nant, npol, nf, nt = out['p'].shape
# Create the backgrounds (shape nf,nant)
if len(idx) == 1:
# Only one index in idx, so no median necessary
if auto:
bg = out['a'][:,:,:,idx]
else:
bg = out['p'][:,:,:,idx]
else:
# Multiple values in idx, so do median
if auto:
bg = np.median(out['a'][:,:,:,idx],3)
else:
bg = np.median(out['p'][:,:,:,idx],3)
# Perform the background subtraction
if auto:
for i in range(nt):
out['a'][:,:,:,i] -= bg
else:
for i in range(nt):
out['p'][:,:,:,i] -= bg
return out
def sp_bg_subtract(out,idx):
''' Given "standard" output of tp_display.rd_tsys_multi(),
and a range of time indexes idx into the arrays, use the
median of data at times indicated by idx as background.
Subtract that background from both xtsys and ytsys
'''
nant, npol, nf, nt = out['tsys'].shape
# Create the backgrounds (shape nf,nant)
if len(idx) == 1:
# Only one index in idx, so no median necessary
bg = out['tsys'][:,:,:,idx]
else:
# Multiple values in idx, so do median
bg = np.median(out['tsys'][:,:,:,idx],3)
# Perform the background subtraction
for i in range(nt):
out['tsys'][:,:,:,i] -= bg
return out
def sp_bsize(x,y):
''' Make nice plots of the beamsize.
'''
import matplotlib.pyplot as plt
nfrq, npnt, nant = x['rao'].shape
nrow = 2
ncol = (nant+1)/2
f, ax = plt.subplots(nrow, ncol, sharex='col', sharey='row')
f.set_size_inches(2*nant,7,forward=True)
t1 = Time(x['ut_mjd'][0],format='mjd')
f.suptitle('X-feed Beam Widths for SOLPNT scan at '+t1.iso[:19]+' UT',fontsize=18)
fout,a,aout = disk_conv()
#fgood = np.where(x['fghz'] > 2.48)[0]
fgood = np.where(x['fghz'] > 1.10)[0]
print len(fgood)
for ant in range(nant):
ax[ant % nrow, ant/nrow].set_title('Ant '+str(ant+1)+' [blue=RA, red=Dec]')
if ant % ncol == 0:
ax[ant/ncol,0].set_ylabel('Beam FWHM [deg]')
if ant >= ncol:
ax[1,ant - ncol].set_xlabel('Frequency [GHz]')
ax[ant % nrow, ant/nrow].set_ylim(0.5,20)
ax[ant % nrow, ant/nrow].set_xlim(1,20)
ax[ant % nrow, ant/nrow].set_yscale('log')
ax[ant % nrow, ant/nrow].set_xscale('log')
ax[ant % nrow, ant/nrow].plot(x['fghz'][fgood],x['raparms'][2,fgood,ant]*2*np.sqrt(np.log(2))/10000,'b.')
ax[ant % nrow, ant/nrow].plot(fout,aout,color='black')
ax[ant % nrow, ant/nrow].plot(x['fghz'][fgood],x['decparms'][2,fgood,ant]*2*np.sqrt(np.log(2))/10000,'r.')
plt.draw()
f, ax = plt.subplots(nrow, ncol, sharex='col', sharey='row')
f.set_size_inches(2*nant,7,forward=True)
t1 = Time(x['ut_mjd'][0],format='mjd')
f.suptitle('Y-feed Beam Widths for SOLPNT scan at '+t1.iso[:19]+' UT',fontsize=18)
for ant in range(nant):
ax[ant % nrow, ant/nrow].set_title('Ant '+str(ant+1)+' [blue=RA, red=Dec]')
if ant % ncol == 0:
ax[ant/ncol,0].set_ylabel('Beam FWHM [deg]')
if ant >= ncol:
ax[1,ant - ncol].set_xlabel('Frequency [GHz]')
ax[ant % nrow, ant/nrow].set_ylim(0.5,20)
ax[ant % nrow, ant/nrow].set_xlim(1,20)
ax[ant % nrow, ant/nrow].set_yscale('log')
ax[ant % nrow, ant/nrow].set_xscale('log')
ax[ant % nrow, ant/nrow].plot(y['fghz'][fgood],y['raparms'][2,fgood,ant]*2*np.sqrt(np.log(2))/10000,'b.')
ax[ant % nrow, ant/nrow].plot(fout,aout,color='black')
ax[ant % nrow, ant/nrow].plot(y['fghz'][fgood],y['decparms'][2,fgood,ant]*2*np.sqrt(np.log(2))/10000,'r.')
plt.draw()
def sp_offsets(x,y,save_plot=False):
import matplotlib.pyplot as plt
oldant = [8,9,10,12]
nfrq, npnt, nant = x['rao'].shape
nrow = (nant+1)/2
ncol = 2
f, ax = plt.subplots(nrow,ncol, sharex='col', sharey='row')
f.set_size_inches(16,2*((nant+1)/2),forward=True)
t1 = Time(x['ut_mjd'][0],format='mjd')
f.suptitle('X-feed Offsets for SOLPNT scan at '+t1.iso[:19]+' UT',fontsize=18)
user2rad = np.pi/10000./180.
cosdec = np.cos(x['dec0'])
#fgood = np.where(x['fghz'] > 2.48)[0]
fgood = np.where(x['fghz'] > 1.10)[0]
xelx = []
elx = []
#import pdb; pdb.set_trace()
for ant in range(nant):
ax[ant/ncol,ant % ncol].set_ylim(-0.5,0.5)
ax[ant/ncol,ant % ncol].set_title('Ant '+str(ant+1)+' [blue=RA, red=Dec]')
if ant >= nant-2:
ax[nant/ncol-1,ant % ncol].set_xlabel('Frequency [GHz]')
if ant % ncol == 0:
ax[ant/ncol,ant % ncol].set_ylabel('Offset [deg]')
ramed = np.median(x['raparms'][1,fgood,ant])
ax[ant/ncol,ant % ncol].plot(x['fghz'][fgood],x['raparms'][1,fgood,ant]/10000,'b.')
ax[ant/ncol,ant % ncol].plot([0,18],np.array([1,1])*ramed/10000,'b')