from astropy.io import fits
import numpy as np
from scipy.optimize import curve_fit
from .utils import HAS_MPL
from .io import adjust_temperature_size
import logging
if HAS_MPL:
import matplotlib.pyplot as plt
[docs]def exptau(airmass, tatm, tau, t0):
"""Function to fit to the T vs airmass data."""
bx = np.exp(-tau * airmass)
return tatm * (1 - bx) + t0
[docs]def calculate_opacity(file, plot=True, tatm=None, tau0=None, t0=None):
"""Calculate opacity from a skydip scan.
Atmosphere temperature is fixed, from Buffa et al.'s calculations.
Parameters
----------
file : str
File name of the skydip scan in Fits format
plot : bool
Plot diagnostics about the fit
Other parameters
----------------
tatm : float
Atmospheric temperature (fixed in the fit). The default value is
calculated from an empyrical formula
tau0 : float
Initial opacity in the fit. The default value is
np.log(2 / (1 + np.sqrt(1 - 4 * (t30 - t90) / tatm))), where
t30 and t90 are the Tsys values calculated at 30 and 90 degrees of
elevation respectively.
t0 : float
Initial value for Tsys in the fit.
Returns
-------
opacities : dict
Dictionary containing the opacities calculated for each channel, plus
the time in the middle of the observation.
"""
with fits.open(file) as hdulist:
data = hdulist["DATA TABLE"].data
tempdata = hdulist["ANTENNA TEMP TABLE"].data
rfdata = hdulist["RF INPUTS"].data
time = np.mean(data["Time"])
freq = (rfdata["frequency"] + rfdata["bandwidth"] / 2)[0]
elevation = data["el"]
airmass = 1 / np.sin(elevation)
if tatm is None:
airtemp = np.median(data["weather"][:, 1])
tatm = 0.683 * (airtemp + 273.15) + 78
el30 = np.argmin(np.abs(elevation - np.radians(30)))
el90 = np.argmin(np.abs(elevation - np.radians(90)))
results = {"time": time}
for ch in ["Ch0", "Ch1"]:
temp = tempdata[ch]
temp = adjust_temperature_size(temp, airmass)
if plot and HAS_MPL:
fig = plt.figure(ch)
plt.scatter(airmass, temp, c="k")
if tau0 is None:
t90 = temp[el90]
t30 = temp[el30]
tau0 = np.log(2 / (1 + np.sqrt(1 - 4 * (t30 - t90) / tatm)))
if t0 is None:
t0 = freq / 1e3
init_par = [tatm, tau0, t0]
epsilon = 1.0e-5
par, _ = curve_fit(
exptau,
airmass,
temp,
p0=init_par,
maxfev=10000000,
bounds=(
[tatm - epsilon, -np.inf, -np.inf],
[tatm + epsilon, np.inf, np.inf],
),
)
logging.info(f"{file}: The opacity for channel {ch} is {par[1]}")
if plot and HAS_MPL:
plt.plot(airmass, exptau(airmass, *par), color="r", zorder=10)
plt.xlabel("Airmass")
plt.ylabel("T (K)")
plt.title("T_atm: {:.2f}; tau: {:.4f}; t0: {:.2f}".format(*par))
plt.savefig(file.replace(".fits", "_fit_{}.png".format(ch)))
plt.close(fig)
results[ch] = par[1]
return results
[docs]def main_opacity(args=None):
import argparse
description = "Calculate opacity from a skydip scan and plot the fit " "results"
parser = argparse.ArgumentParser(description=description)
parser.add_argument("files", nargs="+", help="File to inspect", default=None, type=str)
parser.add_argument("--tatm", type=float, default=None, help="Atmospheric temperature")
parser.add_argument(
"--tau0",
type=float,
default=None,
help="Initial value for tau (to be fit)",
)
parser.add_argument(
"--t0",
type=float,
default=None,
help="Initial value for Tsys (to be fitted)",
)
args = parser.parse_args(args)
for f in args.files:
_ = calculate_opacity(f, tatm=args.tatm, tau0=args.tau0, t0=args.t0)
