import os
os.environ['HDF5_USE_FILE_LOCKING'] = 'FALSE'
import h5py
import hdf5plugin
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
import sys
import glob
import uvtools as uvt
from astropy.time import Time
from astropy.coordinates import EarthLocation, AltAz, Angle
from astropy.coordinates import SkyCoord as sc
import pandas
import warnings
import copy
from hera_notebook_templates import utils
import hera_qm
from hera_mc import cm_hookup
import importlib
from scipy import stats
from IPython.display import display, HTML
#warnings.filterwarnings('ignore')
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
display(HTML("<style>.container { width:100% !important; }</style>"))
#get data location
data_path = os.environ['DATA_PATH']
print(f'DATA_PATH = "{data_path}"')
statuses = os.environ['APRIORI_STATUSES']
print(f'APRIORI_STATUSES = {statuses}')
JD = os.environ['JULIANDATE']
print(f'JULIANDATE = {JD}')
utc = Time(JD, format='jd').datetime
print(f'Date = {utc.month}-{utc.day}-{utc.year}')
DATA_PATH = "/mnt/sn1/2460369" APRIORI_STATUSES = dish_maintenance,dish_ok,RF_maintenance,RF_ok,digital_ok,digital_maintenance,calibration_maintenance,calibration_triage,calibration_ok JULIANDATE = 2460369 Date = 2-28-2024
# Load in data
HHfiles, difffiles, HHautos, diffautos, uvdx, uvdy = utils.load_data(data_path,JD)
uvd = UVData()
unread = True
readInd=0
while unread and readInd<len(HHautos):
try:
uvd.read(HHautos[readInd])
unread = False
except:
readInd += 1
continue
use_ants = utils.get_use_ants(uvd,statuses,JD)
print(f'This day contains {len(use_ants)} antennas of the given status category.')
uvd.read(HHautos[::10], skip_bad_files=True, antenna_nums = use_ants)
lsts = uvd.lst_array
uvdx.select(antenna_nums=use_ants)
uvdy.select(antenna_nums=use_ants)
1293 sum files found between JDs 2460369.18985 and 2460369.64744 1293 diff files found between JDs 2460369.18985 and 2460369.64744 1215 sum auto files found between JDs 2460369.18985 and 2460369.64744 1215 diff auto files found between JDs 2460369.18985 and 2460369.64744
This day contains 252 antennas of the given status category.
Sky Coverage Map¶
Map of the sky (made using the Haslam 408MHz map). The RA/DEC range covered by this night of observation is shaded based on a 12 degree FWHM of the beam. Horizontal dashed lines represent the stripe that HERA can observe, while the shaded region is what was observed on this night. Vertical lines represent the beginning and ending LSTs of this observation. Selected sources are labelled, sources included are those in the GLEAM 4Jy catalog with a flux >10.9 Jy. Note that the map is clipped at the northern horizon.
sources = utils.gather_source_list()
utils.plot_sky_map(uvd,dec_pad=55,ra_pad=55,clip=False,sources=sources)
LST Coverage¶
Shows the LSTs (in hours) and JDs for which data is collected. Green represents data, red means no data.
utils.plot_lst_coverage(uvd)
Autocorrelations for a single file¶
This plot shows autocorrelations for one timestamp of each antenna that is active and each polarization. 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.
### plot autos
utils.plot_autos(uvdx, uvdy)
Waterfalls of Autocorrelation Amplitudes for each Antenna and Each polarization¶
These plots show autocorrelation waterfalls of each antenna that is active and whose status qualifies for this notebook. 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_wfs(uvd, pol = 0)
utils.plot_wfs(uvd, pol = 1)
