"""Functions to simulate scans and maps."""
import numpy as np
import numpy.random as ra
import os
from astropy.io import fits
from astropy.table import Table, vstack
import astropy.units as u
from collections.abc import Iterable
from .io import mkdir_p, locations
from .utils import tqdm, njit, TWOPI
from astropy.coordinates import SkyCoord
from astropy.time import Time
import logging
try:
import matplotlib.pyplot as plt
HAS_MPL = True
except ImportError:
HAS_MPL = False
__all__ = ["simulate_scan", "save_scan", "simulate_map"]
DEFAULT_PEAK_COUNTS = 100
COUNTS_TO_K = 0.03
DEFAULT_CAL_TEMP = 5
DEFAULT_CAL_OFFSET = DEFAULT_CAL_TEMP / COUNTS_TO_K
summary_header = """
SIMPLE = T / file does conform to FITS standard
BITPIX = 8 / number of bits per data pixel
NAXIS = 0 / number of data axes
EXTEND = T / FITS dataset may contain extensions
COMMENT FITS (Flexible Image Transport System) format is defined in 'Astronomy
COMMENT and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H
HIERARCH BackendName = 'NULL ' / Backend name
CREATOR = 'NULL ' / Software (incl. version)
DATE-OBS= '2016-10-03T14:59:08.753' / Observation time
EQUINOX = 0. / Equinox of RA, Dec
EXPTIME = 0. / Total integration time (seconds)
FITSVER = 'V.1.11 ' / FITS version
LST = 0 / Local sidereal time
HIERARCH LogFileName = 'NULL ' / Name of the log file
HIERARCH NUSEBANDS = 0 / Number of sections
OBJECT = 'W51 ' / Target source name
OBSID = 'NULL ' / Observer or operator initials
PROJID = 'NULL ' / ProjectID
HIERARCH RESTFREQ1 = 22235.08 / Rest frequency (MHz)
HIERARCH RESTFREQ2 = 22235.08 / Rest frequency (MHz)
HIERARCH RESTFREQ3 = 22235.08 / Rest frequency (MHz)
HIERARCH RESTFREQ4 = 22235.08 / Rest frequency (MHz)
HIERARCH ReceiverCode = 'CCB ' / Receiver name
HIERARCH RightAscension = 5.07757730974885 / Target right ascension (radians)
HIERARCH Declination = 0.253290907695677 / Target declination (radians)
SCANGEOM= 'NULL ' / Scan geometry
SCANMODE= 'NULL ' / Mapping mode
SCANTYPE= 'NULL ' / Scan astronomical type
SCANXVEL= 0. / Tracking rate (optional,OTF)
SWTCHMOD= 'NULL ' / Switch mode
HIERARCH ScheduleName = 'NULL ' / Name of the schedule
TELESCOP= 'SRT ' / Telescope name
VDEF = 'OP ' / Radial velocity definition
VFRAME = 'LSRK ' / Radial velocity reference frame
VRAD = 0 / Radial velocity
WOBUSED = 0 / Wobbler used?
"""
_REFERENCE_MJD = 57000.5
def _apply_spectrum_to_data(spec_func, counts, nbin, bw=1000):
"""
Examples
--------
>>> res = _apply_spectrum_to_data(lambda x: np.ones(x.size), 4, 3)
>>> np.allclose(res, [4., 4., 4.])
True
>>> res = _apply_spectrum_to_data(lambda x: np.ones(x.size), [4, 2], 3)
>>> np.allclose(res, [[4., 4., 4.], [2, 2, 2]])
True
>>> _apply_spectrum_to_data(lambda x: np.ones(x.size), 4, 1)
4
"""
if nbin == 1:
return counts
single = False
if not isinstance(counts, Iterable):
counts = [counts]
single = True
counts = np.asarray(counts)
df = bw / nbin
freqs = np.arange(0, bw, df)
single_spec = spec_func(freqs)
spec = np.zeros((len(counts), len(freqs)))
for i, c in enumerate(counts):
spec[i, :] += c * single_spec
if single:
return spec[0]
return spec
def _standard_source_spectrum(counts, nbin, bw=1000, sigma=1):
def spec_func(f):
f = f - bw / 2
return np.exp(-(f**2) / (2 * sigma**2))
return _apply_spectrum_to_data(spec_func, counts, nbin, bw)
def _standard_bkg_spectrum(counts, nbin, bw=1000):
def spec_func(f):
sp = 1 + 0.1 * np.sin(TWOPI * 5 / bw * f) * (1 - f / bw)
sp -= 0.5 * f / bw
return sp
return _apply_spectrum_to_data(spec_func, counts, nbin, bw)
def create_summary(filename, key_dict=None):
if key_dict is None:
key_dict = {}
header = fits.Header.fromstring(summary_header, sep="\n")
for key, value in key_dict.items():
header[key] = value
primary_hdu = fits.PrimaryHDU(header=header)
hdul = fits.HDUList([primary_hdu])
hdul.writeto(filename, overwrite=True)
return filename
def _is_number(x):
""" "Test if a string or other is a number
Examples
--------
>>> _is_number('3')
True
>>> _is_number(3.)
True
>>> _is_number('a')
False
"""
try:
float(x)
return True
except (ValueError, TypeError):
return False
def _default_flat_shape(x):
"""A flat shape.
Examples
--------
>>> _default_flat_shape(4314)
100.0
>>> np.allclose(_default_flat_shape(np.arange(3)),
... np.array([100., 100., 100.]))
True
"""
return DEFAULT_PEAK_COUNTS + np.zeros(np.asarray(x).shape)
@njit
def _2d_gauss(x, y, sigma=2.5 / 60.0):
"""A Gaussian beam"""
return np.exp(-(x**2 + y**2) / (2 * sigma**2))
@njit
def calibrator_scan_func(x):
return DEFAULT_PEAK_COUNTS * _2d_gauss(x, 0, sigma=2.5 / 60)
def sim_crossscans(
ncross,
caldir,
scan_func=calibrator_scan_func,
srcname="DummyCal",
channel_ratio=0.8,
baseline="flat",
nbin=1,
):
src_ra = 185
src_dec = 75
speed = 2.0 # arcmin/s
dt = 0.04
dtheta = speed * dt
length = 4 / dtheta
timedelta = 0
for i in tqdm(range(ncross)):
times, ras, scan0 = simulate_scan(
dt=dt,
length=length,
speed=speed,
shape=scan_func,
noise_amplitude=0.2,
center=0,
baseline=baseline,
nbin=nbin,
)
_, _, scan1 = simulate_scan(
dt=dt,
length=length,
speed=speed,
shape=scan_func,
noise_amplitude=0.2,
center=0,
baseline=baseline,
nbin=nbin,
)
ras = ras / np.cos(np.radians(src_dec)) + src_ra
if i % 2 != 0:
ras = ras[::-1]
decs = np.zeros_like(ras) + src_dec
save_scan(
times + timedelta,
ras,
decs,
{"Ch0": scan0, "Ch1": scan1 * channel_ratio},
filename=os.path.join(caldir, "{}_Ra.fits".format(i)),
src_ra=src_ra,
src_dec=src_dec,
srcname=srcname,
counts_to_K=(COUNTS_TO_K, COUNTS_TO_K / channel_ratio),
)
timedelta += times[-1] + 1
times, decs, scan0 = simulate_scan(
dt=dt,
length=length,
speed=speed,
shape=scan_func,
noise_amplitude=0.2,
center=src_dec,
baseline=baseline,
nbin=nbin,
)
_, _, scan1 = simulate_scan(
dt=dt,
length=length,
speed=speed,
shape=scan_func,
noise_amplitude=0.2,
center=src_dec,
baseline=baseline,
nbin=nbin,
)
if i % 2 != 0:
decs = decs[::-1]
ras = np.zeros_like(decs) + src_ra
save_scan(
times + timedelta,
ras,
decs,
{"Ch0": scan0, "Ch1": scan1 * channel_ratio},
filename=os.path.join(caldir, "{}_Dec.fits".format(i)),
src_ra=src_ra,
src_dec=src_dec,
srcname=srcname,
counts_to_K=(COUNTS_TO_K, COUNTS_TO_K / channel_ratio),
)
timedelta += times[-1] + 1
create_summary(
os.path.join(caldir, "summary.fits"),
{
"RightAscension": np.radians(src_ra),
"Declination": np.radians(src_dec),
"Object": srcname,
},
)
def _default_map_shape(x, y):
"""A flat map shape.
Examples
--------
>>> _default_map_shape(4314, 234)
100
>>> res = np.array([[ 100., 100., 100., 100.],
... [ 100., 100., 100., 100.],
... [ 100., 100., 100., 100.]])
>>> np.allclose(_default_map_shape(np.zeros((3, 4)), np.ones((3, 4))), res)
True
"""
x = np.asarray(x)
y = np.asarray(y)
# It will raise a ValueError when x and y are not compatible
return DEFAULT_PEAK_COUNTS + np.zeros_like(y) * np.zeros_like(x)
def sim_position_switching(
caldir,
srcname="Dummy",
nbin=1,
offset=np.radians(3),
strategy=None,
legacy_cal_format=False,
):
dt = 0.04
src_ra = 185
src_dec = 75
if strategy is None:
strategy = [1, 1, 1]
last_time = 0
for n_on in range(strategy[0]):
times, ras, on = simulate_scan(
dt=dt,
length=0,
speed=1,
baseline=(0, 10, 0),
noise_amplitude=0.2,
center=src_dec,
nbin=nbin,
)
times += last_time
last_time = times[0]
decs = np.zeros_like(ras) + src_dec
save_scan(
times,
ras,
decs,
{"Ch0": on, "Ch1": on},
filename=os.path.join(caldir, "ON_{}.fits".format(n_on)),
src_ra=src_ra,
src_dec=src_dec,
srcname=srcname,
counts_to_K=(COUNTS_TO_K, COUNTS_TO_K),
other_keywords={"SIGNAL": "SIGNAL"},
)
for n_off in range(strategy[1]):
times, _, off = simulate_scan(
dt=dt,
length=0,
speed=1,
baseline=(0, 10, 0),
shape=lambda x: 0,
noise_amplitude=0.2,
center=src_dec,
nbin=nbin,
)
times += last_time
last_time = times[0]
save_scan(
times,
ras + offset,
decs,
{"Ch0": off, "Ch1": off},
filename=os.path.join(caldir, "OFF_{}.fits".format(n_off)),
src_ra=src_ra,
src_dec=src_dec,
srcname=srcname,
counts_to_K=(COUNTS_TO_K, COUNTS_TO_K),
other_keywords={
"RightAscension Offset": offset,
"SIGNAL": "REFERENCE",
},
)
for n_cal in range(strategy[2]):
times, _, cal = simulate_scan(
dt=dt,
length=0,
speed=1,
baseline=(0, 10, 0),
shape=lambda x: 0,
noise_amplitude=0.2,
center=src_dec,
nbin=nbin,
calon=True,
)
times += last_time
last_time = times[0]
other_keywords_cal = {
"RightAscension Offset": offset,
"SIGNAL": "REFCAL",
}
other_columns = {}
if legacy_cal_format:
other_columns = {"flag_cal": 1}
other_keywords_cal["SIGNAL"] = "REFERENCE"
save_scan(
times,
ras + offset,
decs,
{"Ch0": cal, "Ch1": cal},
filename=os.path.join(caldir, "CAL_{}.fits".format(n_cal)),
src_ra=src_ra,
src_dec=src_dec,
srcname=srcname,
counts_to_K=(COUNTS_TO_K, COUNTS_TO_K),
other_keywords=other_keywords_cal,
other_columns=other_columns,
)
create_summary(
os.path.join(caldir, "summary.fits"),
{
"RightAscension": np.radians(src_ra),
"Declination": np.radians(src_dec),
"Object": srcname,
},
)
return caldir
[docs]def simulate_scan(
dt=0.04,
length=120.0,
speed=4.0,
shape=None,
noise_amplitude=1.0,
center=0.0,
baseline="flat",
nbin=1,
calon=False,
nsamples=None,
):
"""Simulate a scan.
Parameters
----------
dt : float
The integration time in seconds
length : float
Length of the scan in arcminutes
speed : float
Speed of the scan in arcminutes / second
shape : function
Function that describes the shape of the scan. If None, a
constant scan is assumed. The zero point of the scan is in the
*center* of it
noise_amplitude : float
Noise level in counts
center : float
Center coordinate in degrees
baseline : str, number or tuple
"flat", "slope" (linearly increasing/decreasing), "messy"
(random walk), a number (which gives an amplitude to the random-walk
baseline, that is 20 for "messy"), or a tuple (m, q, messy_amp) giving
the maximum and minimum absolute-value slope and intercept, and the
random-walk amplitude.
"""
if shape is None:
shape = _default_flat_shape
if nsamples is None and length == 0:
nsamples = 100
elif nsamples is None:
nsamples = np.rint(length / speed / dt)
times = np.arange(nsamples) * dt
# In degrees!
position = np.arange(-nsamples / 2, nsamples / 2) / nsamples * length / 60
scan_baseline = _create_baseline(position, baseline)
signal = _standard_source_spectrum(shape(position), nbin)
bkg = _standard_bkg_spectrum(scan_baseline, nbin)
scan_shape = signal + bkg
scan_shape += ra.normal(0, noise_amplitude, scan_shape.shape)
if calon:
scan_shape += DEFAULT_CAL_OFFSET
return times, position + center, scan_shape
[docs]def save_scan(
times,
ra,
dec,
channels,
filename="out.fits",
other_columns=None,
other_keywords=None,
scan_type=None,
src_ra=None,
src_dec=None,
srcname="Dummy",
counts_to_K=COUNTS_TO_K,
):
"""Save a simulated scan in fitszilla format.
Parameters
----------
times : iterable
times corresponding to each bin center, in seconds
ra : iterable
RA corresponding to each bin center
dec : iterable
Dec corresponding to each bin center
channels : {'Ch0': array([...]), 'Ch1': array([...]), ...}
Dictionary containing the count array. Keys represent the name of the
channel
filename : str
Output file name
srcname : str
Name of the source
counts_to_K : float, array or dict
Conversion factor between counts and K. If array, it has to be the same
length as channels.keys()
"""
if src_ra is None:
src_ra = np.mean(ra)
if src_dec is None:
src_dec = np.mean(dec)
if other_columns is None:
other_columns = {}
if other_keywords is None:
other_keywords = {}
# If it's a single value, make it into a list
if not isinstance(counts_to_K, Iterable):
counts_to_K = counts_to_K * np.ones(len(list(channels.keys())))
# If it's a list, make it into a dict
if not hasattr(counts_to_K, "keys"):
counts_to_K = dict([(ch, counts_to_K[i]) for i, ch in enumerate(channels.keys())])
curdir = os.path.abspath(os.path.dirname(__file__))
template = os.path.abspath(os.path.join(curdir, "data", "scan_template.fits"))
lchdulist = fits.open(template)
datahdu = lchdulist["DATA TABLE"]
temphdu = lchdulist["ANTENNA TEMP TABLE"]
secthdu = lchdulist["SECTION TABLE"]
rfinput = lchdulist["RF INPUTS"]
lchdulist[0].header["SOURCE"] = "Dummy"
lchdulist[0].header["ANTENNA"] = "SRT"
lchdulist[0].header["HIERARCH RightAscension"] = np.radians(src_ra)
lchdulist[0].header["HIERARCH Declination"] = np.radians(src_dec)
if scan_type is not None:
lchdulist[0].header["HIERARCH SubScanType"] = scan_type
for key in other_keywords.keys():
lchdulist[0].header[key] = other_keywords[key]
data_table_data = Table(datahdu.data)
data_table_data.remove_column("Ch0")
data_table_data.remove_column("Ch1")
obstimes = Time((times / 86400 + _REFERENCE_MJD) * u.day, format="mjd", scale="utc")
coords = SkyCoord(ra, dec, unit=u.degree, location=locations["srt"], obstime=obstimes)
altaz = coords.altaz
el = altaz.alt.rad
az = altaz.az.rad
newtable = Table(
names=["time", "raj2000", "decj2000", "el", "az"],
data=[obstimes.value, np.radians(ra), np.radians(dec), el, az],
)
for ch in channels.keys():
newtable[ch] = channels[ch]
for col in other_columns.keys():
newtable[col] = other_columns[col]
data_table_data = vstack([data_table_data, newtable])
hdu = fits.BinTableHDU(data_table_data, header=datahdu.header)
nrows = len(data_table_data)
datahdu.data = hdu.data
temptable = Table()
for ch in channels.keys():
dummy_data = newtable[ch]
if len(dummy_data.shape) == 2:
dummy_data = np.sum(dummy_data, axis=1)
temptable[ch] = dummy_data * counts_to_K[ch]
thdu = fits.BinTableHDU.from_columns(temphdu.data.columns, nrows=nrows)
for colname in temphdu.data.columns.names:
thdu.data[colname][:] = temptable[colname]
temphdu.data = thdu.data
shape = channels["Ch0"].shape
if len(shape) == 2:
secthdu.data["bins"] = shape[1]
# Sic
rfinput.data["calibratonMark"] = DEFAULT_CAL_TEMP
lchdulist[0].header["SOURCE"] = srcname
lchdulist.writeto(filename, overwrite=True)
lchdulist.close()
def _single_value_as_tuple(value, nvals=2):
"""If a value is single, return as a tuple.
Examples
--------
>>> np.all(_single_value_as_tuple(1) == (1, 1))
True
>>> np.all(_single_value_as_tuple((1, 1, 1)) == (1, 1, 1))
True
>>> np.all(_single_value_as_tuple(1, nvals=3) == (1, 1, 1))
True
"""
if isinstance(value, Iterable):
return value
return tuple([value] * nvals)
def _create_baseline(x, baseline_kind="flat"):
"""
Parameters
----------
x : float, array-like
The x values for the baseline
baseline : str, number of tuple
"flat", "slope" (linearly increasing/decreasing), "messy"
(random walk), a number (which gives an amplitude to the random-walk
baseline, that is 20 for "messy"), or a tuple (m, q, messy_amp) giving
the maximum and minimum absolute-value slope and intercept, and the
random-walk amplitude.
"""
if baseline_kind == "flat":
mmin = mmax = 0
qmin = qmax = 0
stochastic_amp = 0
elif baseline_kind == "slope":
mmin, mmax = -5, 5
qmin, qmax = 0, 150
stochastic_amp = 0
elif baseline_kind == "messy":
mmin, mmax = 0, 0
qmin, qmax = 0, 0
stochastic_amp = 20
elif _is_number(baseline_kind):
mmin, mmax = 0, 0
qmin, qmax = 0, 0
stochastic_amp = float(baseline_kind)
elif isinstance(baseline_kind, Iterable) and not isinstance(baseline_kind, str):
m = _single_value_as_tuple(baseline_kind[0], nvals=2)
q = _single_value_as_tuple(baseline_kind[1], nvals=2)
mmin, mmax = m[0], m[1]
qmin, qmax = q[0], q[1]
stochastic_amp = float(baseline_kind[2])
else:
raise ValueError("baseline has to be 'flat', 'slope', 'messy' or a " "number")
n = len(x)
m = ra.uniform(mmin, mmax)
q = ra.uniform(qmin, qmax)
signs = np.random.choice([-1, 1], n)
stochastic = np.cumsum(signs) * stochastic_amp / np.sqrt(n)
baseline = m * x + q
return baseline + stochastic
def simulate_sun(**kwargs):
from astropy.coordinates import get_sun
coords = get_sun(Time(_REFERENCE_MJD * u.day, format="mjd", scale="utc"))
for input in ["mean_ra", "mean_dec", "count_map", "start_time"]:
_ = kwargs.pop(input, None)
mean_ra = coords.ra.to(u.deg).value
mean_dec = coords.dec.to(u.deg).value
count_map = kwargs.pop("count_map", _sun_map)
return simulate_map(mean_ra=mean_ra, mean_dec=mean_dec, count_map=count_map, **kwargs)
def _sun_map(x, y, sigma=1011 / 3600):
"""A Gaussian beam"""
import numpy as np
# It will raise ValueError when they're not compatible
map = np.zeros_like(x) * np.zeros_like(y)
map[x**2 + y**2 < sigma**2] = 100.0
return map
[docs]def simulate_map(
dt=0.04,
length_ra=120.0,
length_dec=120.0,
speed=4.0,
spacing=0.5,
count_map=None,
noise_amplitude=1.0,
width_ra=None,
width_dec=None,
outdir="sim/",
baseline="flat",
mean_ra=180,
mean_dec=70,
srcname="Dummy",
channel_ratio=1,
nbin=1,
debug=False,
start_time=0,
radec_labels_in_srcname=False,
):
"""Simulate a map.
Parameters
----------
dt : float
The integration time in seconds
length : float
Length of the scan in arcminutes
speed : float
Speed of the scan in arcminutes / second
shape : function
Function that describes the shape of the scan. If None, a
constant scan is assumed. The zero point of the scan is in the
*center* of it
noise_amplitude : float
Noise level in counts
spacing : float
Spacing between scans, in arcminutes
baseline : str
"flat", "slope" (linearly increasing/decreasing), "messy"
(random walk) or a number (which gives an amplitude to the random-walk
baseline, that is 20 for "messy").
count_map : function
Flux distribution function, centered on zero
outdir : str or iterable (str, str)
If a single string, put all files in that directory; if two strings,
put RA and DEC scans in the two directories.
channel_ratio : float
Ratio between the counts in the two channels
radec_labels_in_srcname : bool
If True, add RA and Dec labels to source name
"""
if isinstance(outdir, str):
outdir = (outdir, outdir)
outdir_ra = outdir[0]
outdir_dec = outdir[1]
mkdir_p(outdir_ra)
mkdir_p(outdir_dec)
if count_map is None:
count_map = _default_map_shape
nbins_ra = int(np.rint(length_ra / speed / dt))
nbins_dec = int(np.rint(length_dec / speed / dt))
times_ra = np.arange(nbins_ra) * dt + start_time
times_dec = np.arange(nbins_dec) * dt + start_time
ra_array = np.arange(-nbins_ra / 2, nbins_ra / 2) / nbins_ra * length_ra / 60
dec_array = np.arange(-nbins_dec / 2, nbins_dec / 2) / nbins_dec * length_dec / 60
# In degrees!
if width_dec is None:
width_dec = length_dec
if width_ra is None:
width_ra = length_ra
# Dec scans
if HAS_MPL and debug:
fig = plt.figure()
ralabel = declabel = ""
if radec_labels_in_srcname:
ralabel = "_RA"
declabel = "_Dec"
delta_decs = np.arange(-width_dec / 2, width_dec / 2 + spacing, spacing) / 60
logging.info("Simulating dec scans...")
for i_d, delta_dec in enumerate(tqdm(delta_decs)):
start_dec = mean_dec + delta_dec
counts_clean = _standard_source_spectrum(count_map(ra_array, delta_dec), nbin=nbin)
baseline0 = _standard_bkg_spectrum(_create_baseline(ra_array, baseline), nbin=nbin)
baseline1 = _standard_bkg_spectrum(_create_baseline(ra_array, baseline), nbin=nbin)
counts0 = counts_clean + ra.normal(0, noise_amplitude, counts_clean.shape) + baseline0
counts1 = counts_clean + ra.normal(0, noise_amplitude, counts_clean.shape) + baseline1
actual_ra = mean_ra + ra_array / np.cos(np.radians(start_dec))
if i_d % 2 != 0:
actual_ra = actual_ra[::-1]
fname = os.path.join(outdir_ra, "Ra{}.fits".format(i_d))
other_keywords = {"HIERARCH Declination Offset": delta_dec}
save_scan(
times_ra,
actual_ra,
np.zeros_like(actual_ra) + start_dec,
{"Ch0": counts0, "Ch1": counts1 * channel_ratio},
filename=fname,
other_keywords=other_keywords,
src_ra=mean_ra,
src_dec=mean_dec,
srcname=srcname + ralabel,
counts_to_K=(COUNTS_TO_K, COUNTS_TO_K / channel_ratio),
)
if HAS_MPL and debug:
plt.plot(ra_array, counts0)
plt.plot(ra_array, counts1)
if HAS_MPL and debug:
fig.savefig(os.path.join(outdir_ra, "allscans_ra.png"))
plt.close(fig)
fig = plt.figure()
delta_ras = np.arange(-width_ra / 2, width_ra / 2 + spacing, spacing) / 60
logging.info("Simulating RA scans...")
# RA scans
for i_r, delta_ra in enumerate(tqdm(delta_ras)):
start_ra = delta_ra / np.cos(np.radians(mean_dec)) + mean_ra
counts_clean = _standard_source_spectrum(count_map(delta_ra, dec_array), nbin=nbin)
baseline0 = _standard_bkg_spectrum(_create_baseline(dec_array, baseline), nbin=nbin)
baseline1 = _standard_bkg_spectrum(_create_baseline(dec_array, baseline), nbin=nbin)
counts0 = counts_clean + ra.normal(0, noise_amplitude, counts_clean.shape) + baseline0
counts1 = counts_clean + ra.normal(0, noise_amplitude, counts_clean.shape) + baseline1
if i_r % 2 != 0:
dec_array = dec_array[::-1]
other_keywords = {"RightAscension Offset": delta_ra}
save_scan(
times_dec,
np.zeros_like(dec_array) + start_ra,
dec_array + mean_dec,
{"Ch0": counts0, "Ch1": counts1 * channel_ratio},
other_keywords=other_keywords,
filename=os.path.join(outdir_dec, "Dec{}.fits".format(i_r)),
src_ra=mean_ra,
src_dec=mean_dec,
srcname=srcname + declabel,
)
if HAS_MPL and debug:
plt.plot(dec_array, counts0)
plt.plot(dec_array, counts1)
if HAS_MPL and debug:
fig.savefig(os.path.join(outdir_dec, "allscans_dec.png"))
plt.close(fig)
logging.info("Creating summary...")
create_summary(
os.path.join(outdir_ra, "summary.fits"),
{
"RightAscension": np.radians(mean_ra),
"Declination": np.radians(mean_dec),
"Object": srcname,
},
)
if outdir_ra == outdir_dec:
return outdir_ra, outdir_ra
create_summary(
os.path.join(outdir_dec, "summary.fits"),
{
"RightAscension": np.radians(mean_ra),
"Declination": np.radians(mean_dec),
"Object": srcname,
},
)
return outdir_ra, outdir_dec
def main_simulate(args=None):
"""Preprocess the data."""
import argparse
description = "Simulate a single scan or a map with a point source."
parser = argparse.ArgumentParser(description=description)
parser.add_argument("-s", "--source-flux", type=float, default=1, help="Source flux in Jy")
parser.add_argument(
"-n",
"--noise-amplitude",
type=float,
default=1,
help="White noise amplitude",
)
parser.add_argument(
"-b",
"--baseline",
type=str,
default="flat",
help='Baseline kind: "flat", "slope" (linearly '
'increasing/decreasing), "messy" '
"(random walk) or a number (which gives an "
"amplitude to the random-walk baseline, that "
'would be 20 for "messy")',
)
parser.add_argument(
"-g",
"--geometry",
nargs=4,
type=float,
default=[120, 120, 120, 120],
help="Geometry specification: length_ra, length_dec, "
"width_ra, width_dec, in arcmins. A square map of"
" 2 degrees would be specified as 120 120 120 "
"120. A cross-like map, 2x2 degrees wide but only"
" along 1-degree stripes, is specified as 120 120"
" 60 60",
)
parser.add_argument(
"--beam-width",
type=float,
default=2.5,
help="Gaussian beam width in arcminutes",
)
parser.add_argument(
"--spacing",
type=float,
default=0.5,
help="Spacing between scans in arcminutes " "(default 0.5)",
)
parser.add_argument(
"-o",
"--outdir-root",
type=str,
default="sim",
help="Output directory root. Here, source and "
"calibrator scans/maps will be saved in "
"outdir/gauss_ra, outdir/gauss_dec, "
"outdir/calibrator1, outdir/calibrator2, where "
"outdir is the outdir root",
)
parser.add_argument(
"--scan-speed",
type=float,
default=4.0,
help="Scan speed in arcminutes/second",
)
parser.add_argument(
"--integration-time",
type=float,
default=0.04,
help="Integration time in seconds",
)
parser.add_argument(
"--spectral-bins",
type=int,
default=1,
help="Simulate a spectrum with this number of bins",
)
parser.add_argument(
"--no-cal",
action="store_true",
default=False,
help="Don't simulate calibrators",
)
parser.add_argument(
"--sun",
action="store_true",
default=False,
help="Simulate a map of the Sun",
)
parser.add_argument(
"--debug",
action="store_true",
default=False,
help="Plot stuff and be verbose",
)
args = parser.parse_args(args)
def local_gauss_src_func(x, y):
return args.source_flux * DEFAULT_PEAK_COUNTS * _2d_gauss(x, y, sigma=args.beam_width / 60)
def calibrator_scan_func(x):
return DEFAULT_PEAK_COUNTS * _2d_gauss(x, 0, sigma=args.beam_width / 60)
if not args.no_cal:
cal1 = os.path.join(args.outdir_root, "calibrator1")
mkdir_p(cal1)
sim_crossscans(
5,
cal1,
scan_func=calibrator_scan_func,
channel_ratio=0.9,
baseline=args.baseline,
nbin=args.spectral_bins,
)
cal2 = os.path.join(args.outdir_root, "calibrator2")
mkdir_p(cal2)
sim_crossscans(
5,
cal2,
scan_func=calibrator_scan_func,
srcname="DummyCal2",
channel_ratio=0.9,
baseline=args.baseline,
nbin=args.spectral_bins,
)
func = simulate_map
count_map = local_gauss_src_func
if args.sun:
func = simulate_sun
count_map = _sun_map
func(
dt=args.integration_time,
length_ra=args.geometry[0],
length_dec=args.geometry[1],
speed=args.scan_speed,
spacing=args.spacing,
noise_amplitude=args.noise_amplitude,
width_ra=args.geometry[2],
width_dec=args.geometry[3],
outdir=(
os.path.join(args.outdir_root, "gauss_ra"),
os.path.join(args.outdir_root, "gauss_dec"),
),
baseline=args.baseline,
mean_ra=180,
mean_dec=70,
srcname="Dummy",
channel_ratio=0.9,
count_map=count_map,
nbin=args.spectral_bins,
)
