aimfast modules¶
amifast.aimfast module¶
-
aimfast.aimfast.compare_models(models, tolerance=0.2, plot=True, all_sources=False, closest_only=False, prefix=None, flux_plot='log')[source]¶ Plot model1 source properties against that of model2
- models : dict
- Tigger formatted model files e.g {model1: model2}.
- tolerance : float
- Tolerace in detecting source from model 2 (in arcsec).
- plot : bool
- Output html plot from which a png can be obtained.
- all_source: bool
- Compare all sources in the catalog (else only point-like source)
- closest_only: bool
- Returns the closest source only as the matching source
- flux_plot: str
- The type of output flux comparison plot (options:log,snr,inout)
- prefix : str
- Prefix for output htmls
- results : dict
- Dictionary of source properties from each model.
-
aimfast.aimfast.compare_residuals(residuals, skymodel=None, points=None, inline=False, area_factor=None, prefix=None, fov_factor=None)[source]¶
-
aimfast.aimfast.fitsInfo(fitsname=None)[source]¶ Get fits header info.
- fitsname : fits file
- Restored image (cube)
- fitsinfo : dict
- Dictionary of fits information e.g. {‘wcs’: wcs, ‘ra’: ra, ‘dec’: dec, ‘dra’: dra, ‘ddec’: ddec, ‘raPix’: raPix, ‘decPix’: decPix, ‘b_size’: beam_size, ‘numPix’: numPix, ‘centre’: centre, ‘skyArea’: skyArea}
-
aimfast.aimfast.generate_default_config(configfile)[source]¶ Generate default config file for running source finders
-
aimfast.aimfast.get_aimfast_data(filename='fidelity_results.json', dir='.')[source]¶ Extracts data from the json data file
-
aimfast.aimfast.get_box(wcs, radec, w)[source]¶ Get box of width w around source coordinates radec.
- radec : tuple
- RA and DEC in degrees.
- w : int
- Width of box.
- wcs : astLib.astWCS.WCS instance
- World Coordinate System.
- box : tuple
- A box centred at radec.
-
aimfast.aimfast.get_detected_sources_properties(model_1, model_2, area_factor, all_sources=False, closest_only=False)[source]¶ Extracts the output simulation sources properties.
- models_1 : file
- Tigger formatted or txt model 1 file.
- models_2 : file
- Tigger formatted or txt model 2 file.
- area_factor : float
- Area factor to multiply the psf size around source.
- all_source: bool
- Compare all sources in the catalog (else only point-like source)
- closest_only: bool
- Returns the closest source only as the matching source
- (targets_flux, targets_scale, targets_position) : tuple
- Tuple of target flux, morphology and astrometry information
-
aimfast.aimfast.get_source_overlay(sources1, sources2)[source]¶ Get source from models compare for overlay
-
aimfast.aimfast.get_src_scale(source_shape)[source]¶ Get scale measure of the source in arcsec.
- source_shape : lsm object
- Source shape object from model
- (scale_out_arc_sec, scale_out_err_arc_sec) : tuple
- Output source scale with error value
-
aimfast.aimfast.image_dynamic_range(fitsname, residual, area_factor=6)[source]¶ Gets the dynamic range in a restored image.
- fitsname : fits file
- Restored image (cube).
- residual : fits file
- Residual image (cube).
- area_factor: int
- Factor to multiply the beam area.
- DR : dict
- DRs - dynamic range values.
-
aimfast.aimfast.json_dump(data_dict, filename='fidelity_results.json')[source]¶ Dumps the computed dictionary results into a json file.
- data_dict : dict
- Dictionary with output results to save.
- filename : str
- Name of file json file where fidelity results will be dumped. Default is ‘fidelity_results.json’ in the current directory.
If the fidelity_results.json file exists, it will be append, and only repeated image assessments will be replaced.
-
aimfast.aimfast.measure_psf(psffile, arcsec_size=20)[source]¶ Measure point spread function after deconvolution.
- psfile : fits file
- Point spread function file.
- arcsec_size : float
- Cross section size
- r0 : float
- Average psf size.
-
aimfast.aimfast.model_dynamic_range(lsmname, fitsname, beam_size=5, area_factor=2)[source]¶ Gets the dynamic range using model lsm and residual fits.
- fitsname : fits file
- Residual image (cube).
- lsmname : lsm.html or .txt file
- Model .lsm.html from pybdsm (or .txt converted tigger file).
- beam_size : float
- Average beam size in arcsec.
- area_factor : float
- Factor to multiply the beam area.
- DR : dict
- DRs - dynamic range values.
-
aimfast.aimfast.noise_sigma(noise_image)[source]¶ Determines the noise sigma level in a dirty image with no source
- noise_image : file
- Noise image (cube).
- noise_std : float
- Noise image standard deviation
-
aimfast.aimfast.normality_testing(data, test_normality='normaltest', data_range=None)[source]¶ Performs a normality test on the image data.
- data : numpy.array
- Residual residual array. i.e. fitsio.open(fitsname)[0].data
- test_normality : str
- Perform normality testing using either shapiro or normaltest.
- data_range : int
- Range of data to perform normality testing.
- normality : dict
- dictionary of stats props. e.g. {‘NORM’: (123.3, 0.012)} whereby the first element is the statistics (or average if data_range specified) of the datasets and second element is the p-value.
-
aimfast.aimfast.plot_aimfast_stats(fidelity_results_file, units='micro', prefix='')[source]¶ Plot stats results if more that one residual images where assessed
-
aimfast.aimfast.plot_astrometry(models, label=None, tolerance=0.2, phase_centre=None, all_sources=False)[source]¶ Plot model-model positions from lsm.html/txt models
- models : dict
- Tigger/text formatted model files e.g {model1: model2}.
- label : str
- Use this label instead of the FITS image path when saving data.
- tolerance: float
- Radius around the source to be cross matched.
- phase_centre : str
- Phase centre of catalog (if not already embeded)
- all_source: bool
- Compare all sources in the catalog (else only point-like source)
-
aimfast.aimfast.plot_photometry(models, label=None, tolerance=0.2, phase_centre=None, all_sources=False, flux_plot='log')[source]¶ Plot model-model fluxes from lsm.html/txt models
- models : dict
- Tigger/text formatted model files e.g {model1: model2}.
- label : str
- Use this label instead of the FITS image path when saving data.
- tolerance: float
- Radius around the source to be cross matched (in arcsec).
- phase_centre : str
- Phase centre of catalog (if not already embeded)
- all_source: bool
- Compare all sources in the catalog (else only point-like source)
-
aimfast.aimfast.plot_residuals_noise(res_noise_images, skymodel=None, label=None, area_factor=2.0, points=100)[source]¶ Plot residual-residual or noise data
- res_noise_images: dict
- Dictionary of residual images to plot {res1.fits: res2.fits}.
- skymodel: file
- Skymodel file to locate on source residuals (lsm.html/txt)
- label : str
- Use this label instead of the FITS image path when saving data.
- area_factor : float
- Factor to multiply the beam area.
- points: int
- Number of data point to generate in case of random residuals.
-
aimfast.aimfast.residual_image_stats(fitsname, test_normality=None, data_range=None, threshold=None, chans=None, mask=None)[source]¶ Gets statistcal properties of a residual image.
- fitsname : file
- Residual image (cube).
- test_normality : str
- Perform normality testing using either shapiro or normaltest.
- data_range : int, optional
- Range of data to perform normality testing.
- threshold : float, optional
- Cut-off threshold to select channels in a cube
- chans : str, optional
- Channels to compute stats (e.g. 1;0~50;100~200)
- mask : file
- Fits mask to get stats in image
- props : dict
Dictionary of stats properties. e.g. {‘MEAN’: 0.0, ‘STDDev’: 0.1, ‘RMS’: 0.1,
‘SKEW’: 0.2, ‘KURT’: 0.3, ‘MAD’: 0.4, ‘MAX’: 0.7}
If normality_test=True, dictionary of stats props becomes e.g. {‘MEAN’: 0.0, ‘STDDev’: 0.1, ‘SKEW’: 0.2, ‘KURT’: 0.3, ‘MAD’: 0.4, ‘RMS’: 0.5, ‘SLIDING_STDDev’: 0.6, ‘MAX’: 0.7, ‘NORM’: (123.3,0.012)} whereby the first element is the statistics (or average if data_range specified) of the datasets and second element is the p-value.
-
aimfast.aimfast.targets_not_matching(sources1, sources2, matched_names, flux_units='milli')[source]¶ Plot model-model fluxes from lsm.html/txt models
- sources1: list
- List of sources from model 1
- sources2: list
- List of sources Sources from model 2
- matched_names: dict
- Dict of names from model 2 that matched that of model 1
- flux_units: str
- Units of flux density for tabulated values
- target_no_match1: dict
- Sources from model 1 that have no match in model 2
- target_no_match2: dict
- Sources from model 2 that have no match in model 1