Blocks = []
for fname in fnames:
# Read.
fpath = root + fname
Reader = fitsGBT.Reader(fpath)
Data = Reader.read(0,0)
Blocks.append(Data)
for Data in Blocks:
# Preprocess.
rotate_pol.rotate(Data, (-5, -7, -8, -6))
cal_scale.scale_by_cal(Data, True, False, False, False, True)
flag_data.flag_data(Data, 5, 0.1, 40)
rebin_freq.rebin(Data, 8, True, True)
rebin_time.rebin(Data, 4)
#rotate_pol.rotate(Data, (1, 2, 3, 4))
def model(n_time, centre, width, amp_xx, amp_yy, amp_xy, amp_yx,
off_xx, off_yy, off_yx, off_xy, slope_xx, slope_yy, slope_xy,
slope_yx, gain_xx, gain_yy, quad_xx, quad_yy):
# Preliminaries.
time = np.arange(n_time, dtype=float) - centre
out = np.empty((4, 2, n_time), dtype=float)
# Generate a unit gaussian.
gauss = np.exp(- time**2 / (2 * width**2))
# Generate the four time series.
out[0,:,:] = amp_xx * gauss + off_xx + slope_xx * time
out[3,:,:] = amp_yy * gauss + off_yy + slope_yy * time
out[1,:,:] = amp_xy * gauss + off_xy + slope_xy * time
Blocks = []
for fname in fnames:
# Read.
fpath = root + fname
Reader = fitsGBT.Reader(fpath)
Data = Reader.read(0, 0)
Blocks.append(Data)
for Data in Blocks:
# Preprocess.
rotate_pol.rotate(Data, (-5, -7, -8, -6))
cal_scale.scale_by_cal(Data, True, False, False, False, True)
flag_data.flag_data(Data, 5, 0.1, 40)
rebin_freq.rebin(Data, 8, True, True)
rebin_time.rebin(Data, 4)
#rotate_pol.rotate(Data, (1, 2, 3, 4))
def model(n_time, centre, width, amp_xx, amp_yy, amp_xy, amp_yx, off_xx,
off_yy, off_yx, off_xy, slope_xx, slope_yy, slope_xy, slope_yx,
gain_xx, gain_yy, quad_xx, quad_yy):
# Preliminaries.
time = np.arange(n_time, dtype=float) - centre
out = np.empty((4, 2, n_time), dtype=float)
# Generate a unit gaussian.
gauss = np.exp(-time**2 / (2 * width**2))
# Generate the four time series.
out[0, :, :] = amp_xx * gauss + off_xx + slope_xx * time
out[3, :, :] = amp_yy * gauss + off_yy + slope_yy * time
inds_total.sort()
# Seems to be nessisary for fitsdata[inds] to be the right type
inds = sp.array(inds_total)
testhdulist[1].data = fitsdata[inds]
testhdulist.writeto(test_file_name)
#### A series of test data files created from guppi data.
guppi_file_name = os.getenv("GBT_DATA") + "/GBT10B_036/42_wigglez15hrst_ralongmap_230-237.fits"
Reader = fitsGBT.Reader(guppi_file_name)
Blocks = Reader.read((0, 1), None)
for Data in Blocks:
rebin_freq.rebin(Data, 32, True, True)
rebin_time.rebin(Data, 2)
split_Blocks = ()
for Data in Blocks:
split_Blocks += split_bands.split(Data, 2, 32, 25)
comb_Blocks = copy.deepcopy(split_Blocks)
for Data in comb_Blocks:
combine_cal.combine(Data, sub_mean=False)
rot_Blocks = copy.deepcopy(comb_Blocks)
for Data in rot_Blocks:
rotate_pol.rotate(Data)
# Measure some parameters from the noise.
# Sort into 2 bands
inds_total.sort()
# Seems to be nessisary for fitsdata[inds] to be the right type
inds = sp.array(inds_total)
testhdulist[1].data = fitsdata[inds]
testhdulist.writeto(test_file_name)
#### A series of test data files created from guppi data.
guppi_file_name = (os.getenv('GBT_DATA') +
'/GBT10B_036/42_wigglez15hrst_ralongmap_230-237.fits')
Reader = fitsGBT.Reader(guppi_file_name)
Blocks = Reader.read((0,1), None)
for Data in Blocks:
rebin_freq.rebin(Data, 32, True, True)
rebin_time.rebin(Data, 2)
split_Blocks = ()
for Data in Blocks:
split_Blocks += split_bands.split(Data, 2, 32, 25)
comb_Blocks = copy.deepcopy(split_Blocks)
for Data in comb_Blocks:
combine_cal.combine(Data, sub_mean=False)
rot_Blocks = copy.deepcopy(comb_Blocks)
for Data in rot_Blocks:
rotate_pol.rotate(Data)
# Measure some parameters from the noise.
# Sort into 2 bands
