missing_obs = util.make_obslist(
'/Users/mike_e_dubs/MWA/INS/Long_Run/Original_Jackknife_Revamp_Complete/missing_obs.txt'
)
maxlist = []
quadlist = np.zeros([1, 336])
linlist = np.zeros([1, 336])
for obs in obslist:
if obs not in missing_obs:
pow = np.load('%s/cfg-%s.npy' % (args.ppd_dir, obs))
pow_max = np.amax(pow)
if not os.path.exists('%s/%s_ms_poly_coeff_order_2_XX.npy' %
(args.outdir, obs)):
read_paths = util.read_paths_construct(args.ins_dir, 'original',
obs, 'INS')
ins = INS(read_paths=read_paths,
order=2,
coeff_write=True,
outpath=args.outdir,
obs=obs)
coeff = np.load('%s/%s_ms_poly_coeff_order_2_XX.npy' %
(args.outdir, obs))
med = np.absolute(coeff[:-1])
maxlist.append(pow_max)
quadlist = np.vstack([quadlist, med[:1]])
linlist = np.vstack([linlist, med[1:2]])
print('The minimum peak is %f' % min(maxlist))
print('The maximum peak is %f' % max(maxlist))
from SSINS import INS, plot_lib, util
import numpy as np
import matplotlib.pyplot as plt
inpath = '/Users/mike_e_dubs/General/1061318984'
outpath = '%s/figs' % inpath
obs = '1061318984'
read_paths = util.read_paths_construct(inpath, None, obs, 'INS')
ins = INS(read_paths=read_paths, obs=obs, outpath=outpath)
vmax = [None, 150]
fig, ax = plt.subplots(figsize=(14, 8), nrows=2)
fig.suptitle('Narrowband SSINS')
for i in range(2):
plot_lib.image_plot(fig,
ax[i],
ins.data[:, 0, :, 0],
cbar_label='Amplitude (UNCALIB)',
freq_array=ins.freq_array[0],
vmax=vmax[i])
fig.savefig('%s/1061318984_None_INS_data_XX.png' % outpath)
from SSINS import util, INS, INS_helpers, plot_lib, MF
from SSINS import Catalog_Plot as cp
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
basedir = '/Users/jonj/Test_folder/lwa'
obs = [
'LWA_50to51', 'LWA_51to52', 'LWA_52to53', 'LWA_53to54', 'LWA_54to55',
'LWA_55to56', 'LWA_56to57', 'LWA_57to58', 'LWA_58to59', 'LWA_59to60'
]
outpath = '%s/test_combines' % basedir
insarray = []
for x in obs:
read_paths = util.read_paths_construct(basedir, 'None', x, 'INS')
insarray.append(
INS(obs=x,
outpath=outpath,
read_paths=read_paths,
flag_choice='orginal'))
inscombined = INS_helpers.INS_concat(insarray, axis=0)
inscombined.obs = 'LWA_50to60'
inscombined.outpath = outpath
inscombined.vis_units = insarray[0].vis_units
inscombined.pols = insarray[0].pols
inscombined.save()
cp.INS_plot(inscombined, vmax=.05, ms_vmax=5, ms_vmin=-5)
shape_dict = {'44MHZ': [44e6, 45e6], '40.6MHZ': [40.4e6, 40.7e6]}
sig_thresh = 4.35
'TV7': [1.81e8, 1.88e8],
'TV8': [1.88e8, 1.95e8],
'broad6': [1.72e8, 1.83e8],
'broad7': [1.79e8, 1.9e8],
'broad8': [1.86e8, 1.97e8]
}
shapes = [
'TV6', 'TV7', 'TV8', 'broad6', 'broad7', 'broad8', 'streak', 'point',
'total'
]
occ_dict = {sig: {shape: {} for shape in shapes} for sig in sig_list}
for obs in obslist:
flist = glob.glob('%s/metadata/%s*' % (basedir, obs))
if len(flist):
read_paths = util.read_paths_construct(basedir, 'original', obs, 'INS')
for sig_thresh in sig_list:
ins = INS(read_paths=read_paths,
obs=obs,
outpath=outdir,
flag_choice='original')
mf = MF(ins,
sig_thresh=sig_thresh,
N_thresh=15,
shape_dict=shape_dict)
mf.apply_match_test(apply_N_thresh=True)
occ_dict[sig_thresh]['total'][obs] = np.mean(ins.data.mask[:, 0, :,
0],
axis=0)
if len(ins.match_events):
event_frac = util.event_fraction(ins.match_events,
from __future__ import division
from SSINS import INS, plot_lib, util, MF
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
obs = 'zen.2458098.37904.xx.HH'
indir = '/Users/mike_e_dubs/HERA/INS/IDR2_Prelim_Nocut/HERA_IDR2_Prelim_Set_nocut'
outpath = '/Users/mike_e_dubs/General/%s' % obs
read_paths = util.read_paths_construct(indir, None, obs, 'INS')
indir2 = '/Users/mike_e_dubs/HERA/INS/IDR2_OR/HERA_IDR2_Prelim_Set_OR_original'
read_paths_orig = util.read_paths_construct(indir2, 'original', obs, 'INS')
ins2 = INS(read_paths=read_paths_orig,
obs=obs,
flag_choice='original',
outpath=outpath)
ins = INS(read_paths=read_paths, obs=obs, outpath=outpath)
aspect = ins.data.shape[2] / ins.data.shape[0]
fig, ax = plt.subplots(figsize=(16, 9), ncols=2)
#plot_lib.image_plot(fig, ax[0], ins.data[:, 0, :, 0], freq_array=ins.freq_array[0],
#cbar_label='Amplitude (UNCALIB)', aspect=aspect, vmax=0.03,
#ylabel='Time (10 s)')
plot_lib.image_plot(fig,
ax[0],
ins.data_ms[:, 0, :, 0],
freq_array=ins.freq_array[0],
inpath = '/Users/mike_e_dubs/MWA/Data/uvfits/1061313128.uvfits'
inpath2 = '/Users/mike_e_dubs/MWA/Data/uvfits/1061313128_noflag.uvfits'
outpath = '/Users/mike_e_dubs/General/1061313128'
if not os.path.exists('%s/arrs/%s_original_INS_data.npym' % (outpath, obs)):
ss = SS(obs=obs,
inpath=inpath,
bad_time_indices=[0, -1, -2, -3],
read_kwargs={'ant_str': 'cross'},
flag_choice='original',
outpath=outpath)
ss.INS_prepare()
ss.INS.save()
ins = ss.INS
else:
read_paths = util.read_paths_construct(outpath, 'original', obs, 'INS')
ins = INS(read_paths=read_paths,
outpath=outpath,
obs=obs,
flag_choice='original')
fig, ax = plt.subplots(figsize=(8, 9))
plot_lib.image_plot(fig,
ax,
ins.data_ms[:, 0, :, 0],
cmap=cm.coolwarm,
cbar_label='Deviation ($\hat{\sigma}$)',
aspect=ins.data.shape[2] / ins.data.shape[0],
freq_array=ins.freq_array[0],
mask_color='black')
fig.savefig('%s/1061313128_AOFlagger_INS_ms.png' % outpath)
help='The output directory',
required=True)
args = parser.parse_args()
if args.shape_dict is not None:
with open(args.shape_dict, 'rb') as file:
shape_dict = pickle.load(file)
else:
shape_dict = {}
obslist = util.make_obslist(args.obsfile)
bright_dict = {args.sig: {shape: {} for shape in args.shapes}}
for obs in obslist:
read_paths = util.read_paths_construct(args.indir, args.flag_choice, obs,
'INS')
ins = INS(obs=obs,
outpath=args.outdir,
read_paths=read_paths,
flag_choice=args.flag_choice)
mf = MF(ins,
sig_thresh=args.sig,
N_thresh=args.N,
shape_dict=shape_dict,
streak=args.streak,
point=args.point)
mf.apply_match_test(apply_N_thresh=True)
ins.match_events = util.red_event_sort(ins.match_events,
[('TV6', 'broad6'),
('TV7', 'broad7'),
('TV8', 'broad8')],
os.makedirs(figpath)
shape_dict = {
'TV4': [1.74e8, 1.82e8],
'TV5': [1.82e8, 1.9e8],
'TV6': [1.9e8, 1.98e8],
'dig1': [1.125e8, 1.15625e8],
'dig2': [1.375e8, 1.40625e8],
'dig3': [1.625e8, 1.65625e8],
'dig4': [1.875e8, 1.90625e8]
}
obslist = util.make_obslist(obslist_path)
for obs in obslist:
raw_reads = util.read_paths_construct(rawpath, 'None', obs, 'INS')
OR_reads = util.read_paths_construct(ORpath, 'original', obs, 'INS')
raw_ins = INS(obs=obs,
flag_choice=None,
read_paths=raw_reads,
order=0,
outpath=outpath)
OR_ins = INS(obs=obs, flag_choice='original', read_paths=OR_reads)
fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(14, 8))
fig.suptitle('%s Incoherent Noise Spectrum Comparison' % obs)
attr = ['data', 'data_ms']
cbar_label = ['UNCALIB', 'Deviation ($\hat{\sigma}$)']
kwargs = [{
'cmap': cm.viridis,
'vmax': 0.1
from SSINS import util
from SSINS import MF
from SSINS import plot_lib as pl
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
import os
# 'TV7_ext': [1.845e8 - 5.15e6, 1.845e8 + 5.15e6]
basedir = '/Users/mike_e_dubs/MWA/INS/Long_Run/All'
obs = '1066742016'
outpath = '/Users/mike_e_dubs/General/Movie'
if not os.path.exists(outpath):
os.makedirs(outpath)
flag_choice = 'None'
read_paths = util.read_paths_construct(basedir, flag_choice, obs, 'INS')
shape_dict = {
'TV6': [1.74e8, 1.81e8],
'TV7': [1.81e8, 1.88e8],
'TV8': [1.88e8, 1.95e8]
}
order = 0
ins = INS(obs=obs, read_paths=read_paths, outpath=outpath, order=order)
mf = MF(ins, shape_dict=shape_dict, sig_thresh=5)
mf.apply_match_test(order=order)
ins.data.mask = False
ins.data_ms = ins.mean_subtract(order=order)
ins.outpath = '%s_0' % outpath
labels = ['', '_ms']
titles = ['', ' (Mean-Subtracted)']
cbar_labels = [ins.vis_units, 'Deviation ($\hat{\sigma}$)']
