In [4]:
utils.plot_lst_coverage(uvd)
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
import matplotlib.patches as mpatches
import matplotlib.gridspec as gridspec
import numpy as np
from pyuvdata import UVCal, UVData, UVFlag
import pyuvdata
import os
import sys
import glob
import uvtools as uvt
from astropy.time import Time
from astropy.coordinates import EarthLocation, SkyCoord, AltAz, Angle
import pandas
import warnings
import copy
from hera_notebook_templates import utils
import hera_qm
from hera_mc import cm_hookup
import h5py
import importlib
from scipy import stats
import scipy
import pandas as pd
from IPython.display import display, HTML
#warnings.filterwarnings('ignore')
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
#get data location
JD = os.environ['JULIANDATE']
data_path = os.environ['DATA_PATH']
nb_outdir = os.environ['NB_OUTDIR']
utc = Time(JD, format='jd').datetime
print(f'JD = {JD}')
print(f'Date = {utc.month}-{utc.day}-{utc.year}')
print(f'data_path = "{data_path}"')
JD = 2459797 Date = 8-5-2022 data_path = "/mnt/sn1/2459797"
# Load in data
HHfiles, difffiles, uvdx, uvdy = utils.load_data_ds(data_path,JD)
uvd = UVData()
uvd_diff = UVData()
uvd.read(HHfiles[0])
use_ants = [int(ant) for ant in uvd.get_ants()]
bls = [(ant, ant) for ant in use_ants]
uvd.read(HHfiles[::10], skip_bad_files=True, bls=bls)
uvd_diff.read(difffiles[::10], skip_bad_files=True, bls=bls)
lsts = uvd.lst_array
flagfile = glob.glob(os.path.join(HHfiles[0].split('zen')[0],'zen.{}*total_stage_1_threshold_flags.h5'.format(JD)))
uvf = UVFlag()
uvf.read(flagfile)
bls = [(ant, ant) for ant in uvd.get_ants()]
times_uvf = np.unique(uvf.time_array)
times_uvd = np.unique(uvd.time_array)
idx_times = [np.where(time_uvd == times_uvf)[0][0] for time_uvd in times_uvd]
uvd.flag_array[:,0,:,:] = np.repeat(uvf.flag_array[idx_times], len(bls), axis=0)
372 sum files found between JDs 2459797.25304 and 2459797.33603 372 diff files found between JDs 2459797.25304 and 2459797.33603
Shows the LSTs (in hours) and JDs for which data is collected. Green represents data, red means no data.
utils.plot_lst_coverage(uvd)
Delay spectrum CLEANed using uvtools.dspec.high_pass_fourier_filter with 7th-order Blackman-Harris window function. Odd/even visibilities are used to remove noise bias.
_data_cleaned_sq, d_even, d_odd = utils.clean_ds(bls, uvd, uvd_diff, N_threads=14)
These plots show autocorrelation delay spectrum waterfalls of each antenna that is active and whose status qualifies for this notebook. For nn/ee polarization, the autocorrelation delay spectrum is normalized by the max of the delay spectrum. For ne polarization, the autocorrelation delay spectrum is normalized by max(sqrt(|nn| |ee|)). ne and en are the same for autocorrelations, and thus only ne is shown here. The delay spectra are presented in dB with 10log10($|\tilde{V}|$).
For each node, antennas are ordered by SNAP number, and within that by SNAP input number. The antenna number label color corresponds to the a priori status of that antenna.
utils.plot_wfds(uvd, _data_cleaned_sq, 0)
utils.plot_wfds(uvd, _data_cleaned_sq, 1)
utils.plot_wfds(uvd, _data_cleaned_sq, 2)
