def get_Tsys(hot_scan, sky_scan, scanLen=10.0,
deltaT=0.5,
Tamb=290.0,
direc='/media/Disk1/nov11_2015',
config='/media/Disk1/nov11_2015/Config/gbt_nov11.xml'):
"""
Gets Tsys dictionary given hot and sky scans
"""
bf = BinFile(get_gbtscan_file(direc=direc, scan=hot_scan)[0],
number_packets=3000)
bf.load_sti_cross_correlate(scanLen, deltaT)
hot = bf.sti_totpower
bf = BinFile(get_gbtscan_file(direc=direc, scan=sky_scan)[0],
number_packets=3000)
bf.load_sti_cross_correlate(scanLen, deltaT)
sky = bf.sti_totpower
bf.read_xml_config(config)
pixels = bf.pixeldic.keys()
pixels.sort()
Tsys = {}
for pixel in pixels:
row, col = bf.pixeldic[pixel]
H = numpy.abs(hot[row, col, :, :]).mean(axis=1).mean(axis=0)
S = numpy.abs(sky[row, col, :, :]).mean(axis=1).mean(axis=0)
Tsys[pixel] = 290. * S /(H - S)
return Tsys
def get_spec(on_scan, off_scan, Tsys, scanLen=30.0,
deltaT=0.1,
direc='/media/Disk1/nov11_2015',
config='/media/Disk1/nov11_2015/Config/gbt_nov11.xml'):
"""
Gets spec dictionary given ON and off scans and
Tsys dictionary
"""
bf = BinFile(get_gbtscan_file(direc=direc, scan=on_scan)[0],
number_packets=3000)
bf.load_sti_cross_correlate(scanLen, deltaT)
on = bf.sti_totpower
bf = BinFile(get_gbtscan_file(direc=direc, scan=off_scan)[0],
number_packets=3000)
bf.load_sti_cross_correlate(scanLen, deltaT)
off = bf.sti_totpower
bf.read_xml_config(config)
pixels = bf.pixeldic.keys()
pixels.sort()
spec = {}
for pixel in pixels:
row, col = bf.pixeldic[pixel]
spec[pixel] = Tsys[pixel] * (numpy.abs(on[row, col, :, :]).mean(axis=1) - numpy.abs(off[row, col, :, :]).mean(axis=1))/numpy.abs(off[row, col, :, :]).mean(axis=1)
return spec
def get_dispersion_correction(on_scan, off_scan, scanLen=30.0,
deltaT=0.1,
direc='/media/Disk1/nov14_2015'):
"""
Gets the linear fits for c_y and c_z
given On and Off scans
"""
bf = BinFile(get_gbtscan_file(direc=direc, scan=on_scan)[0],
number_packets=3000)
bf.load_sti_cross_correlate(scanLen, deltaT)
Ron = bf.sti_cc.mean(axis=3)
Ron = numpy.delete(Ron, bf.bad_inputs, axis=0)
Ron = numpy.delete(Ron, bf.bad_inputs, axis=1)
bf = BinFile(get_gbtscan_file(direc=direc, scan=off_scan)[0],
number_packets=3000)
bf.load_sti_cross_correlate(scanLen, deltaT)
Roff = bf.sti_cc.mean(axis=3)
Roff = numpy.delete(Roff, bf.bad_inputs, axis=0)
Roff = numpy.delete(Roff, bf.bad_inputs, axis=1)
alpha_y = numpy.zeros(bf.num_bins)
alpha_z = numpy.zeros(bf.num_bins)
for b in range(bf.num_bins):
onoff = Ron[:, :, b] - Roff[:, :, b]
Rxy = onoff[:12, 12:24]
Rxz = onoff[:12, 24:]
alpha_y[b] = numpy.angle(Rxy.sum())
alpha_z[b] = numpy.angle(Rxz.sum())
x = numpy.arange(1, bf.num_bins+1)
c_y = numpy.polyfit(x, numpy.unwrap(alpha_y), 1)
c_z = numpy.polyfit(x, numpy.unwrap(alpha_z), 1)
return c_y, c_z