Add code to subtract sources
Tammo Jan Dijkema
parent
a1968962fa
commit
963aa7f071
File diff suppressed because one or more lines are too long
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@ -25,6 +25,10 @@ Per observation, the following attributes are used:
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* extent_lonlat Extent of image in longitude and latitude (w.r.t. WGS84 ellipsoid)
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[lon_min, lon_max, lat_min, lat_max]
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* height Height (w.r.t. station phase centre) of the image plane in metres
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* subtracted List of sources subtracted from visibilities
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For sky images, the following attributes are used:
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* subtracted List of sources subtracted from visibilities
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"""
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import datetime
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@ -61,7 +65,8 @@ def get_new_obsname(h5file: h5py.File):
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def write_hdf5(filename: str, xst_data: np.ndarray, visibilities: np.ndarray, sky_img: np.ndarray,
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ground_img: np.ndarray, station_name: str, subband: int, rcu_mode: int, frequency: float,
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obstime: datetime.datetime, extent: List[float], extent_lonlat: List[float],
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height: float, bodies_lmn: Dict[str, Tuple[float]], calibration_info: Dict[str, str]):
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height: float, bodies_lmn: Dict[str, Tuple[float]], calibration_info: Dict[str, str],
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subtracted: List[str]):
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"""
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Write an HDF5 file with all data
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@ -81,6 +86,7 @@ def write_hdf5(filename: str, xst_data: np.ndarray, visibilities: np.ndarray, sk
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height (float): Height of ground image (in metres)
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bodies_lmn (Dict[str, Tuple[float]]): lmn coordinates of some objects on the sky
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calibration_info (Dict[str, str]): Calibration metadata
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subtracted (List[str]): List of sources subtracted
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Returns:
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None
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@ -91,7 +97,7 @@ def write_hdf5(filename: str, xst_data: np.ndarray, visibilities: np.ndarray, sk
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>>> write_hdf5("test/test.h5", xst_data, visibilities, sky_img, ground_img, "DE603", \
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297, 3, 150e6, datetime.datetime.now(), [-150, 150, -150, 150], \
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[11.709, 11.713, 50.978, 50.981], 1.5, {'Cas A': (0.3, 0.5, 0.2)}, \
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{'CalTableHeader.Calibration.Date': '20181214'})
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{'CalTableHeader.Calibration.Date': '20181214'}, ["Cas A", "Sun"])
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"""
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short_station_name = station_name[:5]
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@ -110,13 +116,15 @@ def write_hdf5(filename: str, xst_data: np.ndarray, visibilities: np.ndarray, sk
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obs_group.create_dataset("calibrated_data", data=visibilities, compression="gzip")
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for key, value in calibration_info.items():
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obs_group["calibrated_data"].attrs[key] = value
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obs_group.create_dataset("sky_img", data=sky_img, compression="gzip")
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dataset_sky_img = obs_group.create_dataset("sky_img", data=sky_img, compression="gzip")
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dataset_sky_img.attrs["subtracted"] = subtracted
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ground_img_group = obs_group.create_group("ground_images")
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dataset_ground_img = ground_img_group.create_dataset("ground_img000", data=ground_img, compression="gzip")
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dataset_ground_img.attrs["extent"] = extent
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dataset_ground_img.attrs["extent_lonlat"] = extent_lonlat
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dataset_ground_img.attrs["height"] = height
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dataset_ground_img.attrs["subtracted"] = str(subtracted)
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def merge_hdf5(src_filename: str, dest_filename: str, obslist: List[str] = None):
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@ -1,5 +1,6 @@
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"""Functions for working with LOFAR single station data"""
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from typing import Dict, List
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import numpy as np
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from numpy.linalg import norm, lstsq
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import numexpr as ne
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@ -7,7 +8,8 @@ import numba
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from astropy.coordinates import SkyCoord, SkyOffsetFrame, CartesianRepresentation
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__all__ = ["nearfield_imager", "sky_imager", "ground_imager", "skycoord_to_lmn", "calibrate", "simulate_sky_source"]
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__all__ = ["nearfield_imager", "sky_imager", "ground_imager", "skycoord_to_lmn", "calibrate", "simulate_sky_source",
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"subtract_sources"]
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__version__ = "1.5.0"
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SPEED_OF_LIGHT = 299792458.0
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@ -180,13 +182,25 @@ def simulate_sky_source(lmn_coord: np.array, baselines: np.array, freq: float):
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return np.exp(2j * np.pi * freq * baselines.dot(np.array(lmn_coord)) / SPEED_OF_LIGHT)
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def simulate_nearfield_source(pqr_coord: np.array, baselines: np.array, freq: float):
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def subtract_sources(vis: np.array, baselines: np.array, freq: float, lmn_dict: Dict[str, np.array],
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sources=["Cas A", "Cyg A", "Sun"]):
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"""
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Simulate visibilities for a nearfield source
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Subtract sky sources from visibilities
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Args:
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pqr_coord (np.array): l, m, n coordinate
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vis (np.array): visibility matrix, shape [n_ant, n_ant]
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lmn_dict (Dict[str, np.array]): dictionary with lmn coordinates
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baselines (np.array): baseline distances in metres, shape (n_ant, n_ant)
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freq (float): Frequency in Hz
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sources (List[str]): list with source names to subtract (should all be in lmn_dict).
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Default ["Cas A", "Sun"]
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Returns:
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vis (np.array): visibility matrix with sources subtracted
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"""
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pass
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modelvis = [simulate_sky_source(lmn_dict[srcname], baselines, freq) for srcname in lmn_dict
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if srcname in sources]
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residual, _ = calibrate(vis, modelvis)
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return residual
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@ -28,7 +28,7 @@ from lofarantpos.db import LofarAntennaDatabase
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import lofarantpos
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from .maputil import get_map, make_leaflet_map
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from .lofarimaging import nearfield_imager, sky_imager, skycoord_to_lmn
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from .lofarimaging import nearfield_imager, sky_imager, skycoord_to_lmn, subtract_sources
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from .hdf5util import write_hdf5
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@ -565,7 +565,8 @@ def make_xst_plots(xst_data: np.ndarray,
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sky_only: bool = False,
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opacity: float = 0.6,
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hdf5_filename: str = None,
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outputpath: str = "results"):
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outputpath: str = "results",
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subtract: List[str] = None):
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"""
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Create sky and ground plots for an XST file
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@ -584,6 +585,7 @@ def make_xst_plots(xst_data: np.ndarray,
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opacity: Opacity for map overlay. Defaults to 0.6.
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hdf5_filename: Filename where hdf5 results can be written. Defaults to outputpath + '/results.h5'
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outputpath: Directory where results can be saved. Defaults to 'results'
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subtract: List of sources to subtract. Defaults to None
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Returns:
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@ -594,7 +596,8 @@ def make_xst_plots(xst_data: np.ndarray,
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>>> obstime = datetime.datetime(2017, 7, 20, 9, 58, 16)
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>>> sky_fig, ground_fig, leafletmap = make_xst_plots(xst_data, "DE603", obstime, 297, \
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3, caltable_dir="test/CalTables", \
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hdf5_filename="test/test.h5")
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hdf5_filename="test/test.h5", \
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subtract=["Cas A", "Sun"])
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Maximum at -6m east, 70m north of station center (lat/long 50.97998, 11.71118)
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>>> type(leafletmap)
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@ -650,10 +653,6 @@ def make_xst_plots(xst_data: np.ndarray,
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baselines = station_xyz[:, np.newaxis, :] - station_xyz[np.newaxis, :, :]
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# Fourier transform
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# visibilities = cube_xx[2,:,:]
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sky_img = sky_imager(visibilities_stokesI, baselines, freq, npix_l, npix_m)
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obstime_astropy = Time(obstime)
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# Determine positions of Cas A and Cyg A
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station_earthlocation = EarthLocation.from_geocentric(*(db.phase_centres[station_name] * u.m))
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@ -679,11 +678,20 @@ def make_xst_plots(xst_data: np.ndarray,
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if body_coord.transform_to(AltAz(location=station_earthlocation, obstime=obstime_astropy)).alt > 0:
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marked_bodies_lmn[body_name] = skycoord_to_lmn(marked_bodies[body_name], zenith)
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if subtract is not None:
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visibilities_stokesI = subtract_sources(visibilities_stokesI, baselines, freq, marked_bodies_lmn, subtract)
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sky_img = sky_imager(visibilities_stokesI, baselines, freq, npix_l, npix_m)
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marked_bodies_lmn_only3 = {k: v for (k, v) in marked_bodies_lmn.items() if k in ('Cas A', 'Cyg A', 'Sun')}
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# Plot the resulting sky image
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sky_fig = plt.figure(figsize=(10, 10))
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if sky_vmin is None and subtract is not None:
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# Tendency to oversubtract, we don't want to see that
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sky_vmin = np.quantile(sky_img, 0.05)
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make_sky_plot(sky_img, marked_bodies_lmn_only3, title=f"Sky image for {station_name}",
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subtitle=f"SB {subband} ({freq / 1e6:.1f} MHz), {str(obstime)[:16]}", fig=sky_fig,
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vmin=sky_vmin, vmax=sky_vmax)
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@ -731,10 +739,9 @@ def make_xst_plots(xst_data: np.ndarray,
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[maxpixel_p, maxpixel_q, _] = pqr_to_xyz.T @ np.array([maxpixel_x, maxpixel_y, height])
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maxpixel_lon, maxpixel_lat, _ = lofargeotiff.pqr_to_longlatheight([maxpixel_p, maxpixel_q], station_name)
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# Show location of maximum if not at the image border
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if 2 < maxpixel_xpix < npix_x - 2 and 2 < maxpixel_ypix < npix_y - 2:
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print(f"Maximum at {maxpixel_x:.0f}m east, {maxpixel_y:.0f}m north of station center " +
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f"(lat/long {maxpixel_lat:.5f}, {maxpixel_lon:.5f})")
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# Show location of maximum
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print(f"Maximum at {maxpixel_x:.0f}m east, {maxpixel_y:.0f}m north of station center " +
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f"(lat/long {maxpixel_lat:.5f}, {maxpixel_lon:.5f})")
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tags = {"generated_with": f"lofarimaging v{__version__}",
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"subband": subband,
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@ -752,7 +759,7 @@ def make_xst_plots(xst_data: np.ndarray,
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leaflet_map = make_leaflet_map(folium_overlay, lon_center, lat_center, lon_min, lat_min, lon_max, lat_max)
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write_hdf5(hdf5_filename, xst_data, visibilities, sky_img, ground_img, station_name, subband, rcu_mode,
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freq, obstime, extent, extent_lonlat, height, marked_bodies_lmn, calibration_info)
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freq, obstime, extent, extent_lonlat, height, marked_bodies_lmn, calibration_info, subtract)
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return sky_fig, ground_fig, leaflet_map
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