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Temp disc #28

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Temp disc #28

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6109337
added temp_disc and edited existing code for testing
egmclaurin Aug 29, 2022
35d8fd6
added plot_multi_freqs function to utils, to be used in nightly data_…
egmclaurin Oct 4, 2022
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185 changes: 181 additions & 4 deletions hera_notebook_templates/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@
import scipy
from pyuvdata import UVCal, UVData, UVFlag, utils

from astropy.time import Time
from astropy.time import Time, TimeDelta
from astropy.coordinates import EarthLocation, AltAz, Angle
from astropy.coordinates import SkyCoord as sc
from astropy.io import fits
Expand All @@ -37,11 +37,10 @@
import matplotlib.gridspec as gridspec
from matplotlib.ticker import FormatStrFormatter
from matplotlib.lines import Line2D
from matplotlib import colors
from matplotlib import colors, cm

import hera_qm
from hera_mc import cm_hookup, geo_sysdef
from hera_mc import cm_active
from hera_mc import cm_hookup, geo_sysdef, cm_active, mc
import uvtools as uvt
from uvtools import dspec

Expand Down Expand Up @@ -2985,3 +2984,181 @@ def most_common_flag_rationale(self, jds=None):
return sorted(mms.items(), key=lambda item: item[1])[-1][0]

return np.nan

def plot_multi_freqs(uvd,uvdx,uvdy,HHautos,JD,freqind = [0,200,400,600,800,1000,1200,1400,1530],pols=['NN','EE'], use_database = True, array_signal = 'antenna'):
"""Plot power vs time for a collection of individual frequencies; also create an accompanying power vs frequency plot for a single time within the night.

Parameters:
----------
uvd: UVData Object
Object containing autos from sum files. Used for getting antenna information.
uvdx: UVData Object
Sample observations from a single time during the desired night, on the xx (NN) polarization.
uvdy:
Sample observations from a single time during the desired night, on the yy (EE) polarization.
freqind: List
Frequency array index values. Allows for evenly spaced frequencies to be easily chosen and plotted.
Default values are [0,200,400,600,800,1000,1200,1400,1530], which correspond to freqeuncies [46.9, 71.3, 95.7, 120.2, 144.6, 169.0,
193.4, 217.8, 233.7] MHz.
pols: List
Polarizations to plot. Can include any polarization strings accepted by pyuvdata.
use_database: Boolean
Option to attempt using the database to determine the array's commanded observing mode on the desired night. Default is True.
array_signal: String
Used to manually specify the expected array mode if use_database is set to False, or if database doesn't return an array mode.
Must be one of these values: 'antenna','noise','load','digital_noise_same','digital_noise_different'. Default is 'antenna'.

Returns:
-------
None
"""

if use_database is True:
try:
db = mc.connect_to_mc_db(None)
session = db.sessionmaker()
startJD = float(HHautos[0].split('zen.')[1].split('.sum')[0])
stopJD = float(HHautos[-1].split('zen.')[1].split('.sum')[0])
start_time = Time(startJD,format='jd')
stop_time = Time(stopJD,format='jd')
# get initial state by looking for commands up to 3 hours before the starttime
# this logic can be improved after an upcoming hera_mc PR
# which will return the most recent command before a particular time.
search_start_time = start_time - TimeDelta(3*3600, format="sec")
initial_command_res = session.get_array_signal_source(starttime=search_start_time, stoptime=start_time)
if len(initial_command_res) == 0:
initial_source = "Unknown"
elif len(initial_command_res) == 1:
initial_source = initial_command_res[0].source
else:
# multiple commands
times = []
sources = []
for obj in initial_command_res:
times.append(obj.time)
sources.append(obj.source)
initial_source = sources[np.argmax(times)]
except Exception as e:
print(e)
initial_source = None

if initial_source is None:
print(f"Database not found. Assuming array mode is '{array_signal}'.")
elif initial_source == 'Unknown':
print(f"Array mode not found. Assuming array mode is {array_signal}")
else:
print(f"Using database; array mode is '{initial_source}'. Some antennas may work in a different mode due to communication error.")
else:
if array_signal not in ['antenna','noise','load','digital_noise_same','digital_noise_different']:
raise ValueError("array_signal must be one of these values: 'antenna','noise','load','digital_noise_same','digital_noise_different'")
else:
print(f"use_database set to False. Assuming array mode is '{array_signal}'.")


plt.subplots_adjust(hspace=.0)
cmap = cm.get_cmap("plasma")
loc = EarthLocation.from_geocentric(*uvd.telescope_location, unit='m')
freq_array = uvd.freq_array[0]*1e-6
freqs_use = freq_array[freqind]
times = uvdx.time_array
lsts = uvd.lst_array*3.819719
inds = np.unique(lsts,return_index=True)[1]
lsts = np.asarray([lsts[ind] for ind in sorted(inds)])
color_ind = np.linspace(0,1,len(freqind))

nodes, antDict, inclNodes = generate_nodeDict(uvdx)
ants = uvdx.get_ants()
sorted_ants = sort_antennas(uvdx)
#maxants = 0
#for node in nodes:
#n = len(nodes[node]['ants'])
#if n>maxants:
#maxants = n

#Nants = len(ants)
#Nside = maxants
#Yside = len(inclNodes)

#t_index = 0
#jd = times[t_index]
# utc = Time(jd, format='jd').datetime

h = cm_active.get_active(at_date=JD, float_format="jd")

state_plots = {'antenna': (0, 4e7, 'black', 1), 'noise' : (0, 4e6, 'cornflowerblue', 2), 'load' : (0,4e6,'green',2), 'digital_noise_same' : (1e7,4e7,'indigo',2), 'digital_noise_different': (1e7,4e7,'palevioletred',2)}
if use_database is True and (initial_source is not None and initial_source != "Unknown"):
mymin, mymax, my_color, my_linewidth = state_plots[initial_source]
else:
mymin, mymax, my_color, my_linewidth = state_plots[array_signal]

for i,n in enumerate(inclNodes):
ants = nodes[n]['ants']
j = 0
k = 0
for _,a in enumerate(sorted_ants):
if a not in ants:
continue
snaploc = antDict[a]['snapLocs']
snapinput = antDict[a]['snapInput']
fig = plt.figure(constrained_layout=True, figsize=(20, 4))
subfigs = fig.subfigures(1, 2, wspace=0.07, width_ratios=[2, 1])
axsLeft = subfigs[0].subplots(1, 2, sharey=True)
dat = np.abs(uvd.get_data(a,a))
status = get_ant_status(h, a)
abb = status_abbreviations[status]
for j,p in enumerate(pols):
ax = axsLeft[j]
with warnings.catch_warnings():
warnings.simplefilter("ignore")
for f,freq in enumerate(freqind):
ax.plot(dat[:,freq,j],label=np.around(freq_array[freq],1),color=cmap(color_ind[f]),linewidth=0.95)
if a==0 and j==0:
ax.legend()
ax.set_title(f'{p}', fontsize = 14)
ax.set_xlabel('LST (hours)')
ax.set_ylabel('Power (amp)')
xticks = [int(i) for i in np.linspace(0,len(lsts)-1, 5)]
ax.set_xticks(xticks)
ax.set_xticklabels(np.around(lsts[xticks], 2))
sm = cm.ScalarMappable(cmap=cmap)
if np.mean(dat)>mymin and np.mean(dat)<mymax:
ax.set_ylim(mymin, mymax)
ax.spines['left'].set_color(my_color)
ax.spines['left'].set_linewidth(my_linewidth)
ax.spines['right'].set_color(my_color)
ax.spines['right'].set_linewidth(my_linewidth)
ax.spines['bottom'].set_color(my_color)
ax.spines['bottom'].set_linewidth(my_linewidth)
ax.spines['top'].set_color(my_color)
ax.spines['top'].set_linewidth(my_linewidth)
else:
ax.set_ylim(auto=True)
ax.spines['left'].set_color('red')
ax.spines['left'].set_linewidth(2)
ax.spines['right'].set_color('red')
ax.spines['right'].set_linewidth(2)
ax.spines['bottom'].set_color('red')
ax.spines['bottom'].set_linewidth(2)
ax.spines['top'].set_color('red')
ax.spines['top'].set_linewidth(2)

axsRight = subfigs[1].subplots(1, 1, sharex=True)
sm = cm.ScalarMappable(cmap=cmap)
axsRight.vlines(freqs_use,55,85,
color = cmap(color_ind),
linewidth = 1,
linestyle = '--')
axsRight.set_ylim(55,85)
px, = axsRight.plot(freq_array, 10*np.log10(np.abs(uvdx.get_data((a, a))[0]+1000)), color='#006837', alpha=0.75, linewidth=1.2)
py, = axsRight.plot(freq_array, 10*np.log10(np.abs(uvdy.get_data((a, a))[0])), color='#a6611a', alpha=0.75, linewidth=1.2)
axsRight.grid(False, which='both')
axsRight.set_title(f'Auto-correlation (both pols)', fontsize=14)
axsRight.set_xlabel('freq (MHz)')
axsRight.set_ylabel(r'$10\cdot\log$(amp)')
axsRight.set_xticks(freqs_use)

if k == 0:
axsRight.legend([px, py], ['NN', 'EE'])
fig.suptitle(f'Antenna {a} ({abb})\nNode {n} Snap {snaploc[0]} Input {snapinput[0]}',fontsize=20,backgroundcolor=status_colors[status],y=1.2)
plt.show()
plt.close()
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