def model_psf(self, model, radius, psf_resolution, shape=256, **kwargs):
"""Models the PSF given the desired model function and kwargs.
Args:
model (str):
Must be either 'airydisk' or 'gaussian'.
radius (int, float, astropy.unit.Quantity):
Radius of the PSF model that is the radius of the first zero in an AiryDisk model or the standard
deviation of the Gaussian model. Scalar values will be interpreted in units of arcseconds.
psf_resolution (int, float, astropy.unit.Quantity):
Resolution of the model PSF, equivalent to the pixel scale of the array. Scalar values will be
interpreted in units of arcseconds.
shape (int, optional):
Size of the model PSF along both axes.
kwargs are forwarded to the model function.
"""
# Check input parameters
if not isinstance(model, str):
raise SpecklepyTypeError('model_psf', 'model', type(model), 'str')
if isinstance(radius, Quantity):
self.radius = radius
elif isinstance(radius, (int, float)):
logger.warning(f"Interpreting scalar type radius as {radius} arcsec")
self.radius = Quantity(f"{radius} arcsec")
elif isinstance(radius, str):
self.radius = Quantity(radius)
else:
raise SpecklepyTypeError('model_psf', 'radius', type(radius), 'Quantity')
if isinstance(psf_resolution, Quantity):
self.psf_resolution = psf_resolution
elif isinstance(psf_resolution, (int, float)):
logger.warning(f"Interpreting scalar type psf_resolution as {psf_resolution} arcsec")
self.psf_resolution = Quantity(f"{psf_resolution} arcsec")
elif isinstance(psf_resolution, str):
self.psf_resolution = Quantity(psf_resolution)
else:
raise SpecklepyTypeError('model_psf', 'psf_resolution', type(psf_resolution), 'Quantity')
if isinstance(shape, int):
center = (shape / 2, shape / 2)
shape = (shape, shape)
elif isinstance(shape, tuple):
center = (shape[0] / 2, shape[1] / 2)
else:
raise SpecklepyTypeError('model_psf', 'shape', type(shape), 'int or tuple')
if model.lower() == 'airydisk':
model = models.AiryDisk2D(x_0=center[0], y_0=center[1], radius=float(self.radius / self.psf_resolution))
elif model.lower() == 'gaussian':
stddev = float(self.radius / self.psf_resolution)
model = models.Gaussian2D(x_mean=center[0], y_mean=center[1], x_stddev=stddev, y_stddev=stddev)
else:
raise SpecklepyValueError('model_psf', 'model', model, 'either AiryDisk or Gaussian')
y, x = np.mgrid[0:shape[0], 0:shape[1]]
self.psf = model(x, y)
self.psf = self.normalize(self.psf)
def get_header_quantity(header, key, aliases=None):
"""Extract unit quantities from FITS headers, based on the unit in the comment.
Args:
header:
Header of the FITS file.
key (str):
Key or name of the FITS header card.
aliases (dict, optional):
The aliases dictionary maps unit strings such that they are understood by Unit(str).
Returns:
quantity (Quantity):
Quantity derived from the combination of the FITS header card value and comment.
"""
# Read header card
value = header[key]
comment = header.comments[key]
# Handle empty comment
if not comment:
logger.warning("Function 'get_value()' received an empty comment string and returns scalar value.")
return value
# Apply fall back values
if aliases is None:
aliases = {'sec': 's', 'milliarcsec': 'mas', 'microns': 'micron'}
# Replace comment entries that are not understood by astropy.units by corresponding aliases
if comment in aliases.keys():
comment = aliases[comment]
return value * Unit(comment)
def __init__(self, diameter, psf_source, central_obscuration=None, psf_frame=0, **kwargs):
"""Instantiate Telescope class:
Args:
diameter (float or astropy.units.Quantity):
Telescope diameter, used to compute the light collecting area.
psf_source (str):
File name to read PSFs from or model name. Models can be either 'AiryDisk' or 'Gaussian'. The models
require central_obscuration (float, optional): Radial fraction of the
telescope aperture that is blocked by the secondary.
central_obscuration (float, optional):
Radial fraction that is centrally obscured, by the secondary mirror.
psf_frame (int, optional):
Index of the first frame to read from psf_source.
kwargs:
Are forwarded to the psf_source model.
"""
# Input parameters
if isinstance(diameter, Quantity):
self.diameter = diameter
elif isinstance(diameter, (int, float)):
logger.warning(f"Interpreting scalar type diameter as {diameter} m")
self.diameter = Quantity(f"{diameter} m")
elif isinstance(diameter, str):
self.diameter = Quantity(diameter)
else:
raise SpecklepyTypeError('Telescope', 'diameter', type(diameter), 'Quantity')
if isinstance(psf_source, str):
self.psf_source = psf_source
else:
raise SpecklepyTypeError('Telescope', 'psf_source', type(psf_source), 'str')
if isinstance(central_obscuration, float) or central_obscuration is None:
self.central_obscuration = central_obscuration
elif isinstance(central_obscuration, str):
self.central_obscuration = float(central_obscuration)
else:
raise SpecklepyTypeError('Telescope', 'central_obscuration', type(central_obscuration), 'float')
if isinstance(psf_frame, int):
self.psf_frame = psf_frame
else:
raise SpecklepyTypeError('Telescope', 'psf_frame', type(psf_frame), 'int')
# Derive secondary parameters
if self.central_obscuration is not None:
self.area = (1. - self.central_obscuration**2) * np.pi * (self.diameter / 2)**2
else:
self.area = np.pi * (self.diameter / 2)**2
if psf_source.lower() in ['airydisk', 'gaussian']:
self.model_psf(psf_source, **kwargs)
else:
psf, time_resolution, angular_resolution = self.read_psf_file(psf_source)
self.psf = psf
self.psf_resolution = angular_resolution
if time_resolution is not None:
self.timestep = time_resolution
def get_psf_variance(self):
"""Extract a radial variance profile of the speckle PSFs."""
# Check data shape
if self.data.ndim != 3:
logger.warning(
f"Aperture data need to be 3D but are {self.data.ndim}-dimensional!"
)
raise SystemExit("Aborting...")
# Initialize output radii and array
radius_map = self.make_radius_map()
rdata = np.unique(radius_map)
ydata = np.zeros(rdata.shape)
edata = np.zeros(rdata.shape)
# Extract 2D variance map
var_map = np.var(self.data, axis=0)
# Iterate over aperture radii
with warnings.catch_warnings():
warnings.simplefilter("ignore")
for index, radius in enumerate(rdata):
subset = var_map[np.where(radius_map == radius)]
ydata[index] = np.mean(subset)
edata[index] = np.mean(subset)
return rdata, ydata, edata
def apodize(self, type, radius, crop=False):
"""Apodize the Fourier object with a Gaussian or Airy disk kernel.
Args:
type (str):
Type of the apodization. Can be either `Gaussian` or `Airy`. See specklepy.core.psfmodel for details.
radius (float):
Radius of the apodization kernel. This is the standard deviation of a Gaussian kernel or the radius of
first zero in the case of an Airy function.
crop (bool, optional):
Crop corners of the PSF and set them to zero.
Returns:
apodized (np.array, dtype=np.complex128):
Apodized Fourier-plane image.
"""
# Assert image shape
if self.fourier_image.shape[0] != self.fourier_image.shape[1]:
logger.warning(
"The apodization is applied to a non-quadratic input image. This may cause some "
"unpredictable results!")
logger.info("Apodizing the object...")
if type is None and radius is None:
logger.warning(
f"Apodization is skipped for either type or radius not being defined!"
)
return self.fourier_image
# Interpret function input and compute apodization PSF
psf_model = PSFModel(type=type, radius=radius)
apodization_psf = psf_model(self.fourier_image.shape)
# Crop corners of the PSF
if crop:
threshold = apodization_psf[0, int(psf_model.center[1])]
apodization_psf -= threshold
apodization_psf = np.maximum(apodization_psf, 0.0)
# Normalize to unity
apodization_psf /= np.sum(apodization_psf)
# Transform into Fourier space
apodization_otf = otf(apodization_psf)
self.fourier_image = np.multiply(self.fourier_image, apodization_otf)
return self.fourier_image
def maximize_plot():
mng = plt.get_current_fig_manager()
backend = mpl.get_backend()
if backend == 'Qt4Agg' or backend == 'Qt5Agg':
mng.window.showMaximized()
elif backend == 'wxAgg':
mng.frame.Maximize(True)
elif backend == 'TkAgg':
try:
mng.window.state('zoomed')
except:
logger.warning("Could not maximize plot")
else:
raise RuntimeWarning(
"Maximizing plot is not possible with matplotlib backend {}".
format(backend))
def integrate_psf(self, integration_time, hdu=None):
"""Integrates psf frames over the input time.
Args:
integration_time (Quantity):
This is used for computing the number of frames 'n_frames', via floor division by the 'timestep'
attribute.
hdu (int, str, optional):
Specifier of the HD Default is None for the first HD
"""
# Check input parameters
if isinstance(integration_time, (int, float)):
logger.warning(f"Interpreting scalar type integration_time as {integration_time} s")
integration_time = integration_time * Unit('s')
elif not isinstance(integration_time, Quantity):
raise SpecklepyTypeError('integrate_psf', 'integration_time', type(integration_time), 'Quantity')
if integration_time < self.timestep:
raise ValueError(f"integrate_psf received integration time {integration_time} shorter than the time "
f"resolution of the psf source ({self.timestep})!")
n_frames = int(integration_time / self.timestep)
# Read PSF frames from source file
data = fits.getdata(self.psf_source, hdu)
self.psf_frame += 1
if self.psf_frame + n_frames < data.shape[0]:
self.psf = np.sum(data[self.psf_frame : self.psf_frame+n_frames], axis=0)
else:
self.psf = np.sum(data[self.psf_frame : ], axis=0)
self.psf += np.sum(data[ : (self.psf_frame+n_frames) % data.shape[0]], axis=0)
self.psf_frame += n_frames - 1
self.psf_frame = self.psf_frame % data.shape[0]
# Normalize the integrated PSF
self.psf = self.normalize(self.psf)
def expose(self,
photon_rate,
integration_time,
photon_rate_resolution,
debug=False):
"""Compute the number of electrons in every pixel after the exposure.
Args:
photon_rate (Quantity):
Passed to expose() method.
integration_time (Quantity):
Passed to expose() and readout() methods.
photon_rate_resolution (Quantity):
Angular resolution of the photon_rate array, used for resampling this to the detectors grid.
debug (bool, optional):
Set True for debugging. Default is False.
Returns:
electrons (Quantity):
"""
# Input parameters
if isinstance(photon_rate, (int, float)):
logger.warning(
f"Interpreting scalar type photon_rate as {photon_rate} photon/ s"
)
photon_rate = photon_rate * Unit('ph / s')
elif not isinstance(photon_rate, Quantity):
raise SpecklepyTypeError('expose', 'photon_rate',
type(photon_rate), 'Quantity')
if isinstance(integration_time, (int, float)):
logger.warning(
f"Interpreting scalar type integration_time as {integration_time} s"
)
integration_time = integration_time * Unit('s')
elif not isinstance(integration_time, Quantity):
raise SpecklepyTypeError('expose', 'integration_time',
type(integration_time), 'Quantity')
if isinstance(photon_rate_resolution, (int, float)):
logger.warning(
f"Interpreting scalar type photon_rate_resolution as {photon_rate_resolution} arcsec"
)
photon_rate_resolution = photon_rate_resolution * Unit('arcsec')
elif not isinstance(photon_rate_resolution, Quantity):
raise SpecklepyTypeError('expose', 'photon_rate_resolution',
type(photon_rate_resolution), 'Quantity')
# Resample the photon rate to the detector resolution
photon_rate = self.resample(
photon_rate=photon_rate,
photon_rate_resolution=photon_rate_resolution)
photons = photon_rate * integration_time
if debug:
imshow(photons, title='photons')
# Compute photon shot noise with Poisson statistics
photons = np.random.poisson(photons.value) * photons.unit
# Incorporate efficiencies
if self.optics_transmission is not None:
photons = photons * self.optics_transmission
electrons = photons * self.quantum_efficiency
if debug:
imshow(electrons, title='electrons')
# Limit to the saturation level of the detector
if self.saturation_level is not None:
electrons = np.minimum(
electrons, self.saturation_level) # * self.system_gain)
electrons = np.round(electrons)
self.array = electrons
return electrons
def get_photon_rate(self, photon_rate_density, photon_rate_density_resolution=None, integration_time=None,
debug=False):
"""Propagates the 'photon_rate_density' array through the telescope.
The photon_rate_density is multiplied by the telescope collecting area and then
convolved with the PSF. If the resolution of the flux array is different
from the telescopes psf_resolution, then one is resampled. If the PSF is
non-static, it will be integrated over the 'integration_time' value.
Args:
photon_rate_density (np.ndarray, dtype=Quantity):
photon_rate_density_resolution (Quantity, optional):
integration_time(Quantity, optional):
Required only if the PSF is non-static.
debug (bool, optional):
Show additional information for debugging. Default is False.
Returns:
photon_rate (Quantity): PSF-convolved photon rate array.
"""
# Input parameters
if not isinstance(photon_rate_density, Quantity):
raise SpecklepyTypeError('get_photon_rate', 'photon_rate_density', type(photon_rate_density), 'Quantity')
if photon_rate_density_resolution is not None:
if not isinstance(photon_rate_density_resolution, Quantity):
raise SpecklepyTypeError('get_photon_rate', 'photon_rate_density_resolution',
type(photon_rate_density_resolution), 'Quantity')
psf_resample_mode = True
else:
psf_resample_mode = False
if integration_time is None and hasattr(self, 'timestep'):
raise ValueError("If the PSF source of Telescope is non-static, the call function requires the "
"integration_time.")
elif isinstance(integration_time, (int, float)):
logger.warning(f"Interpreting scalar type integration_time as {integration_time} s")
integration_time = Quantity(f"{integration_time} s")
elif not isinstance(integration_time, Quantity):
raise SpecklepyTypeError('get_photon_rate', 'integration_time', type(integration_time), 'Quantity')
# Apply telescope collecting area
photon_rate = photon_rate_density * self.area
total_flux = np.sum(photon_rate)
photon_rate_unit = photon_rate.unit
# Prepare PSF if non-static
if hasattr(self, 'timestep'):
self.integrate_psf(integration_time=integration_time)
# Resample photon_rate_density to psf resolution
if psf_resample_mode:
# Compute the zoom factor
ratio = float(photon_rate_density_resolution / self.psf_resolution)
# try:
# ratio = float(photon_rate_density_resolution / self.psf_resolution)
# except UnitConversionError as e:
# raise UnitConversionError(f"The resolution values of the image ({photon_rate_density_resolution}) and "
# f"PSF ({self.psf_resolution}) have different units!")
# Zoom either the image or the PSF, depending on the zoom factor
with warnings.catch_warnings():
warnings.simplefilter('ignore')
if ratio < 1.0:
self.psf = zoom(self.psf, 1/ratio, order=1) / ratio**2
self.psf = self.normalize(self.psf)
else:
memory_sum = np.sum(photon_rate)
photon_rate = zoom(photon_rate, ratio, order=1) / ratio**2
photon_rate = photon_rate / np.sum(photon_rate) * memory_sum
# Convolve the array with the PSF
convolved = fftconvolve(photon_rate, self.psf, mode='same') * photon_rate_unit
# Report on flux conservation
logger.debug(f"Flux conservation during convolution: {total_flux} > {np.sum(convolved)}")
return convolved.decompose()
def subtract_sky_background(in_files,
out_files=None,
method='scalar',
source='sky',
mask_sources=False,
file_path=None,
tmp_dir=None,
show=False,
debug=False):
"""Estimate and subtract the sky background via different methods and sources.
TODO: Implement sky subtraction from image
Args:
in_files (specklepy.FileArchive):
File archive storing the information of all the files in the reduction.
out_files (list):
List of files to apply the sky subtraction to. If left empty, the list stored in the `in_files` FileArchive
is used.
method (str, optional):
Switch between a scalar (`scalar`) background value or a 2D image (`images`).
source (str, optional):
Source for estimating the background from. Can be either `sky` to measure from dedicated sky frames or
`science` to use the science frames themselves. Typically, these frames have a high number of sources, so
`mask_sources` should be switched on.
mask_sources (bool, optional):
In empty reference fields, this masking option should stay at `False`, since source masking is not well
tested. However, masking sources yields a more precise result.
file_path (str, optional):
Path to the files, listed in `in_files`.
tmp_dir (str, optional):
Directory to which temporary results and QA data is stored.
show (bool, optional):
Show plots of sky estimates for each sequence. They will be created and stored regardless of this choice.
debug (bool, optional):
Show debugging information.
"""
# Set logging level
if debug:
logger.setLevel('DEBUG')
# Apply fall back values
if method is None:
method = 'scalar'
logger.info(f"Sky background subtraction method: {method}")
if source is None:
source = 'sky'
logger.info(f"Sky background subtraction source: {source}")
if out_files is None:
out_files = in_files.product_files
if out_files is None:
logger.warning(
f"Output files are not declared in subtract_sky_background!")
# Identify the observing sequences
sequences = in_files.identify_sequences(source=source)
# Start the background estimates
if method == 'scalar':
# Iterate through observing sequences
for s, sequence in enumerate(sequences):
logger.info(
f"Starting observing sequence {s} :: Object {sequence.object} :: Setup {sequence.setup}"
)
# Compute weights based on the time offset to the individual sky observations
weights = sequence.compute_weights()
# Start extracting sky fluxes
sky_bkg = np.zeros(sequence.n_sky)
sky_bkg_std = np.zeros(sequence.n_sky)
for i in trange(sequence.n_sky,
desc='Estimate sky background from cube'):
file = sequence.sky_files[i]
bkg, d_bkg = estimate_sky_background(file,
method=method,
mask_sources=mask_sources,
path=file_path)
sky_bkg[i] = bkg
sky_bkg_std[i] = d_bkg
logger.debug(
f"Shapes:\nF: {sky_bkg.shape}\ndF: {sky_bkg_std.shape}")
# Compute weighted sky background for each science file
weighted_sky_bkg = np.dot(weights, sky_bkg)
weighted_sky_bkg_var = np.dot(np.square(weights),
np.square(sky_bkg_std))
# Store sky background estimates
sky_bkg_table = Table(data=[
sequence.sky_files, weighted_sky_bkg, weighted_sky_bkg_var
],
names=['FILE', 'BKG', 'VAR'])
sky_bkg_table_name = f"sky_bkg_{sequence.object}_{sequence.setup}.fits"
sky_bkg_table.write(os.path.join(tmp_dir, sky_bkg_table_name),
overwrite=True)
# Plot sky flux estimates
for i, file in enumerate(sequence.sky_files):
plt.text(sequence.sky_time_stamps[i],
sky_bkg[i],
file,
rotation=90,
alpha=.5)
for i, file in enumerate(sequence.science_files):
plt.text(sequence.science_time_stamps[i],
weighted_sky_bkg[i],
file,
rotation=90,
alpha=.66)
plt.errorbar(x=sequence.sky_time_stamps,
y=sky_bkg,
yerr=sky_bkg_std,
fmt='None',
ecolor='tab:blue',
alpha=.5)
plt.plot(sequence.sky_time_stamps,
sky_bkg,
'D',
label='Sky',
c='tab:blue')
plt.errorbar(x=sequence.science_time_stamps,
y=weighted_sky_bkg,
yerr=np.sqrt(weighted_sky_bkg_var),
fmt='None',
ecolor='tab:orange',
alpha=.66)
plt.plot(sequence.science_time_stamps,
weighted_sky_bkg,
'D',
label='Science',
c='tab:orange')
plt.xlabel('Time (s)')
plt.ylabel('Flux (counts)')
plt.legend()
save_figure(
os.path.join(tmp_dir,
sky_bkg_table_name.replace('.fits', '.png')))
if show:
plt.show()
plt.close()
# Subtract sky from product files
for i, science_file in enumerate(sequence.science_files):
for out_file in out_files:
if science_file in out_file:
science_file = out_file
logger.info(
f"Applying sky background subtraction on file {science_file}"
)
with fits.open(science_file, mode='update') as hdu_list:
hdu_list[0].data = hdu_list[0].data.astype(
float) - weighted_sky_bkg[i]
if 'VAR' in hdu_list:
hdu_list['VAR'].data = hdu_list[
'VAR'].data + weighted_sky_bkg_var[i]
else:
# Construct new HDU
shape = np.array(hdu_list[0].data.shape)[[-2, -1]]
data = np.full(shape=shape,
fill_value=weighted_sky_bkg_var[i])
hdu = fits.ImageHDU(data=data, name='VAR')
hdu_list.append(hdu)
hdu_list[0].header.set('SKYCORR', str(datetime.now()))
hdu_list[0].header.set('SKYBKG', weighted_sky_bkg[i],
"Sky background")
hdu_list[0].header.set('SKYVAR', weighted_sky_bkg_var[i],
"Sky background variance")
hdu_list.flush()
elif method in ['image', 'frame']:
raise NotImplementedError(
"Sky subtraction in image mode is not implemented yet!")
else:
raise ValueError(f"Sky subtraction method {method} is not understood!")
def get_photon_rate_density(self, field_of_view, resolution, dither=None):
"""Creates an image of the field of view.
Args:
field_of_view (Quantity or tuple, dtype=Quantity):
Size of the field of view that is covered by the output image.
resolution (Quantity):
Resolution of the image. Optimally, set it to Telescope.psf_resolution to avoid resampling the image.
dither (tuple, optional):
Dither position, relative to the (0, 0) standard phase center.
Returns:
photon_rate_density (Quantity):
2D image of the photon rate density towards the standard phase center or dithered position.
"""
# Input parameters
if isinstance(resolution, (int, float)):
logger.warning(
f"Interpreting float type resolution as {resolution} arcsec")
resolution = resolution * Unit('arcsec')
elif isinstance(resolution, Quantity):
pass
else:
raise SpecklepyTypeError('get_photon_rate_density', 'resolution',
type(resolution), 'Quantity')
self.resolution = resolution
if isinstance(field_of_view, (int, float)):
logger.warning(
f"Interpreting float type FoV as {field_of_view} arcsec")
field_of_view = field_of_view * Unit('arcsec')
elif isinstance(field_of_view, (tuple, list, Quantity)):
pass
else:
raise SpecklepyTypeError('get_photon_rate_density',
'field_of_view', type(field_of_view),
'tuple')
# Add 10% FoV to avoid dark margins
self.field_of_view = ScaledTuple(field_of_view,
scale=resolution,
scaled=True)
self.field_of_view *= 1.1
if dither is None:
phase_center = (0, 0)
elif isinstance(dither, (tuple, list)):
if not (isinstance(dither[0], (int, float))):
raise TypeError(
"Dithers should be provided as int or float. These are then interpreted as arcseconds."
)
else:
phase_center = dither
else:
raise SpecklepyTypeError('get_photon_rate_density', 'dither',
type(dither), 'tuple')
# Define image center for centering star positions around the image center
center = copy(self.field_of_view) / 2
# Create array with sky background flux
self.flux_per_pixel = (self.sky_background_flux *
self.resolution**2).decompose()
photon_rate_density = np.ones(
shape=self.field_of_view.index) * self.flux_per_pixel
# Add stars from star_table to photon_rate_density
for row in self.stars:
position = Position(row['x'],
row['y'],
scale=self.resolution.to('arcsec').value,
scaled=True)
position.offset(center)
position.offset(phase_center, scaled=True)
flux = row['flux']
try:
photon_rate_density.value[position.index] = np.maximum(
photon_rate_density.value[position.index], flux)
except IndexError:
# Star is placed outside the field of view
pass
return photon_rate_density
def __init__(self,
band,
star_table=None,
sky_background=None,
photometry_file=None):
"""Instantiate Target class.
Args:
band (str):
Name of the band. Used for extracting the band specific reference flux for magnitude 0.
star_table (str, optional):
Name of the file with the data of all stars.
sky_background (Quantity, optional):
Sky background. Int and float inputs will be interpreted as mag / arcsec**2.
photometry_file (str, optional):
Name of the file, from which the band specific reference flux is extracted.
"""
# Input parameters
if isinstance(band, str):
try:
self.band = eval(band)
except NameError:
self.band = band
else:
raise SpecklepyTypeError('Target', 'band', type(band), 'str')
if star_table is None or isinstance(star_table, str):
self.star_table = star_table
# Read star table already here?
else:
raise SpecklepyTypeError('Target', 'star_table', type(star_table),
'str')
if photometry_file is None or isinstance(photometry_file, str):
self.photometry_file = photometry_file
else:
raise SpecklepyTypeError('Target', 'photometry_file',
type(photometry_file), 'str')
self.band_reference_flux = self.get_reference_flux(
self.photometry_file, self.band)
if isinstance(sky_background, str):
sky_background = eval(sky_background)
if sky_background is None:
self.sky_background_flux = 0.0 / Unit('arcsec')**2
elif isinstance(sky_background, Quantity):
# Interpreting as mag / arcsec**2
logger.warning(
"Interpreting sky_background as in units of mag per arcsec**2."
)
self.sky_background_flux = self.magnitude_to_flux(
sky_background.value) / Unit('arcsec')**2
elif isinstance(sky_background, (int, float)):
logger.warning(
f"Interpreting scalar type sky_background as {sky_background * Unit('mag') / Unit('arcsec')**2}"
)
self.sky_background_flux = self.magnitude_to_flux(
sky_background) / Unit('arcsec')**2
else:
raise SpecklepyTypeError('Target', 'sky_background',
type(sky_background), 'Quantity')
# Initialize class attributes
self.shape = None
self.field_of_view = None
self.pixel_scale = None
self.resolution = None
self.flux_per_pixel = None
self.stars = self.read_star_table(self.star_table)
def holography(params, mode='same', debug=False):
"""Execute the holographic image reconstruction.
The holographic image reconstruction is an algorithm as outlined, eg. by Schoedel et al (2013, Section 3). This
function follows that algorithm, see comments in the code. Most of the important functions are imported from other
modules of specklepy.
Args:
params (dict):
Dictionary that carries all important parameters.
mode (str, optional):
Define the size of the output image as 'same' to the reference
image or expanding to include the 'full' covered field. Default is
'same'.
debug (bool, optional):
Set to True to inspect intermediate results.
Default is False.
Returns:
image (np.ndarray): The image reconstruction.
"""
logger.info(f"Starting holographic reconstruction...")
file_archive = FileArchive(file_list=params['PATHS']['inDir'],
cards=[],
dtypes=[])
in_files = file_archive.files
in_dir = file_archive.in_dir
tmp_dir = params['PATHS']['tmpDir']
# Input check
if mode not in ['same', 'full', 'valid']:
raise SpecklepyValueError('holography()',
argname='mode',
argvalue=mode,
expected="either 'same', 'full', or 'valid'")
if 'apodizationType' in params['APODIZATION']:
# Catch deprecated parameter name
logger.warning(
"Parameter 'apodizationType' is deprecated. Use 'type' instead!")
params['APODIZATION']['type'] = params['APODIZATION'][
'apodizationType']
if 'apodizationWidth' in params['APODIZATION']:
# Catch deprecated parameter name
logger.warning(
"Parameter 'apodizationWidth' is deprecated. Use 'radius' instead!"
)
params['APODIZATION']['radius'] = params['APODIZATION'][
'apodizationWidth']
if params['APODIZATION']['type'] is None or params['APODIZATION'][
'type'].lower() not in ['gaussian', 'airy']:
logger.error(
f"Apodization type has not been set or of wrong type ({params['APODIZATION']['type']})"
)
if params['APODIZATION']['radius'] is None or not isinstance(
params['APODIZATION']['radius'], (int, float)):
logger.error(
f"Apodization radius has not been set or of wrong type ({params['APODIZATION']['radius']})"
)
# Initialize the outfile
out_file = ReconstructionFile(filename=params['PATHS']['outFile'],
files=in_files,
cards={"RECONSTRUCTION": "Holography"},
in_dir=in_dir)
# Initialize reconstruction
reconstruction = Reconstruction(
in_files=in_files,
mode=mode,
alignment_method='ssa',
reference_image=params['PATHS']['alignmentReferenceFile'],
in_dir=in_dir,
tmp_dir=tmp_dir,
out_file=params['PATHS']['outFile'],
var_ext=params['OPTIONS']['varianceExtensionName'],
box_indexes=params['OPTIONS']['box_indexes'],
debug=debug)
# (i-ii) Align cubes
# shifts = get_shifts(files=in_files, reference_file=params['PATHS']['alignmentReferenceFile'],
# lazy_mode=True, return_image_shape=False, in_dir=in_dir, debug=debug)
shifts = reconstruction.shifts
# (iii) Compute SSA reconstruction
# image = ssa(in_files, mode=mode, outfile=out_file, in_dir=in_dir, tmp_dir=tmp_dir,
# variance_extension_name=params['OPTIONS']['varianceExtensionName'])
image = reconstruction.coadd_long_exposures()
if isinstance(image, tuple):
# SSA returned a reconstruction image and a variance image
image, image_var = image
total_flux = np.sum(image) # Stored for flux conservation
# Start iteration from steps (iv) through (xi)
while True:
# (iv) Astrometry and photometry, i.e. StarFinder
extract_sources(image=image,
fwhm=params['STARFINDER']['starfinderFwhm'],
noise_threshold=params['STARFINDER']['noiseThreshold'],
background_subtraction=True,
write_to=params['PATHS']['allStarsFile'],
star_finder='DAO',
debug=debug)
# (v) Select reference stars
print(
"\tPlease copy your desired reference stars from the all stars file into the reference star file!"
)
input("\tWhen you are done, hit a ENTER.")
# (vi) PSF extraction
ref_stars = ReferenceStars(
psf_radius=params['PSFEXTRACTION']['psfRadius'],
reference_source_file=params['PATHS']['refSourceFile'],
in_files=in_files,
save_dir=tmp_dir,
in_dir=in_dir,
field_segmentation=params['PSFEXTRACTION']['fieldSegmentation'])
if params['PSFEXTRACTION']['mode'].lower() == 'epsf':
psf_files = ref_stars.extract_epsfs(file_shifts=shifts,
debug=debug)
elif params['PSFEXTRACTION']['mode'].lower() in [
'mean', 'median', 'weighted_mean'
]:
psf_files = ref_stars.extract_psfs(
file_shifts=shifts,
mode=params['PSFEXTRACTION']['mode'].lower(),
debug=debug)
else:
raise RuntimeError(
f"PSF extraction mode '{params['PSFEXTRACTION']['mode']}' is not understood!"
)
logger.info("Saved the extracted PSFs...")
# (vii) Noise thresholding
psf_noise_mask = None
for file in psf_files:
with fits.open(file, mode='update') as hdu_list:
n_frames = hdu_list[0].header['NAXIS3']
if psf_noise_mask is None:
psf_noise_mask = get_noise_mask(
hdu_list[0].data[0],
noise_reference_margin=params['PSFEXTRACTION']
['noiseReferenceMargin'])
for index in range(n_frames):
reference = np.ma.masked_array(hdu_list[0].data[index],
mask=psf_noise_mask)
background = np.mean(reference)
noise = np.std(reference)
update = np.maximum(
hdu_list[0].data[index] - background -
params['PSFEXTRACTION']['noiseThreshold'] * noise, 0.0)
if np.sum(update) == 0.0:
raise ValueError(
"After background subtraction and noise thresholding, no signal is leftover. "
"Please reduce the noiseThreshold!")
update = update / np.sum(update) # Flux sum of order unity
hdu_list[0].data[index] = update
hdu_list.flush()
# (viii) Subtraction of secondary sources within the reference apertures
# TODO: Implement Secondary source subtraction
pass
# (ix) Estimate object, following Eq. 1 (Schoedel et al., 2013)
f_object = FourierObject(in_files,
psf_files,
shifts=shifts,
mode=mode,
in_dir=in_dir)
f_object.coadd_fft()
# (x) Apodization
f_object.apodize(type=params['APODIZATION']['type'],
radius=params['APODIZATION']['radius'])
# (xi) Inverse Fourier transform to retain the reconstructed image
image = f_object.ifft(total_flux=total_flux)
# Inspect the latest reconstruction
if debug:
imshow(image)
# Save the latest reconstruction image to outfile
out_file.data = image
# Ask the user whether the iteration shall be continued or not
answer = input(
"\tDo you want to continue with one more iteration? [yes/no]\n\t")
if answer.lower() in ['n', 'no']:
break
# Repeat astrometry and photometry, i.e. StarFinder on final image
extract_sources(image=image,
fwhm=params['STARFINDER']['starfinderFwhm'],
noise_threshold=params['STARFINDER']['noiseThreshold'],
background_subtraction=True,
write_to=params['PATHS']['allStarsFile'],
star_finder='DAO',
debug=debug)
# Finally return the image
return image
def main():
# Parse args
parser = GeneralArgParser()
args = parser.parse_args()
if args.debug:
logger.setLevel('DEBUG')
logger.debug(args)
if args.gui:
start()
# Execute the script of the corresponding command
if args.command is 'generate':
# Read parameters from file and generate exposures
target, telescope, detector, parameters = get_objects(args.parfile,
debug=args.debug)
generate_exposure(target=target,
telescope=telescope,
detector=detector,
debug=args.debug,
**parameters)
elif args.command is 'reduce':
# In setup mode
if args.setup:
run.setup(path=args.path,
instrument=args.instrument,
par_file=args.parfile,
list_file=args.filelist,
sort_by=args.sortby)
# Else start reduction following the parameter file
else:
params = config.read(args.parfile)
run.full_reduction(params, debug=args.debug)
elif args.command is 'ssa':
# Prepare path information and execute reconstruction
if args.tmpdir is not None and not os.path.isdir(args.tmpdir):
os.mkdir(args.tmpdir)
ssa(args.files,
mode=args.mode,
tmp_dir=args.tmpdir,
outfile=args.outfile,
box_indexes=args.box_indexes,
debug=args.debug)
elif args.command is 'holography':
# Read parameters from file and execute reconstruction
defaults_file = os.path.join(os.path.dirname(__file__),
'../config/holography.cfg')
defaults_file = os.path.abspath(defaults_file)
params = config.read(defaults_file)
params = config.update_from_file(params, args.parfile)
holography(params,
mode=params['OPTIONS']['reconstructionMode'],
debug=args.debug)
elif args.command is 'aperture':
if args.mode == 'psf1d':
logger.info("Extract 1D PSF profile")
analysis.get_psf_1d(args.file,
args.index,
args.radius,
args.out_file,
args.normalize,
debug=args.debug)
elif args.mode == 'variance':
logger.info("Extract 1D PSF variation")
analysis.get_psf_variation(args.file, args.index, args.radius,
args.out_file, args.normalize,
args.debug)
else:
logger.warning(f"Aperture mode {args.mode} not recognized!")
elif args.command is 'extract':
if args.out_file is None:
args.out_file = 'sources_' + os.path.basename(
args.file_name).replace('.fits', '.dat')
extract_sources(image=args.file_name,
noise_threshold=args.noise_threshold,
fwhm=args.fwhm,
image_var=args.var,
write_to=args.out_file)
elif args.command == 'plot':
plot = Plot.from_file(file_name=args.file,
extension=args.extension,
columns=args.columns,
format=args.format,
layout=args.layout,
debug=args.debug)
plot.apply_layout(layout=args.layout)
plot.save()
plot.show()
elif args.command is 'apodization':
get_resolution_parameters(wavelength=args.wavelength,
diameter=args.diameter,
pixel_scale=args.pixel_scale)
def identify_sequences(self, source='sky'):
"""Identify observation sequences.
Args:
source (str, optional):
Observation type of the images the shall be used to measure the sky background from. Options are 'sky'
(default) and 'science'.
Returns:
sequences (list of Sequence):
List of observing sequences.
"""
# Type check
if isinstance(source, str):
if source not in ['sky', 'science']:
raise SpecklepyValueError('identify sequences',
argname='source',
argvalue=source,
expected="'sky' or 'science'")
else:
raise SpecklepyTypeError('identify sequences',
argname='source',
argtype=type(source),
expected='str')
# Identify the observing sequences
sequences = []
for setup in self.setups:
for object in self.objects:
# Query names and time stamps of science and sky files
sky_files = self.filter({
'OBSTYPE': source.upper(),
'OBJECT': object,
'SETUP': setup
})
sky_time_stamps = self.filter(
{
'OBSTYPE': source.upper(),
'OBJECT': object,
'SETUP': setup
},
namekey='DATE')
science_files = self.filter({
'OBSTYPE': 'SCIENCE',
'OBJECT': object,
'SETUP': setup
})
science_time_stamps = self.filter(
{
'OBSTYPE': 'SCIENCE',
'OBJECT': object,
'SETUP': setup
},
namekey='DATE')
# Test the number of source files
if len(sky_files) == 0:
logger.warning(
f"Did not find any sky observations for object {object} in setup {setup}. No sky "
f"subtraction will be applied!")
else:
# Store the information in a new sequence
sequences.append(
Sequence(sky_files=sky_files,
science_files=science_files,
file_path=self.in_dir,
sky_time_stamps=sky_time_stamps,
science_time_stamps=science_time_stamps,
source=source,
object=object,
setup=setup))
return sequences
def __init__(self,
shape,
pixel_scale,
optics_transmission=1,
quantum_efficiency=1,
system_gain=1,
readout_noise=None,
dark_current=None,
saturation_level=None):
"""Instantiate Detector class.
Args:
shape (tuple, dtype=int):
Number of the pixels of the detector along two axes. Integer values will create a square detector array
with the same number of pixels along both axes.
pixel_scale (Quantity):
Angular size of each pixel.
optics_transmission (float, optional):
Optical transmission coefficient, scaling between 0.0 and 1.0 (default).
quantum_efficiency (float, Quantity, optional):
Quantum efficiency of the detector. Scalar type values will be interpreted units of electrons per
photon.
system_gain (float, Quantity, optional):
System gain of the detector. Scalar type values will be interpreted units of electrons per ADU.
readout_noise (float, Quantity, optional):
Read noise of the detector. Scalar type values will be interpreted units of electrons.
dark_current (float, Quantity, optional):
Dark current of the detector. Scalar type values will be interpreted units of electrons per second.
saturation_level (float, Quantity, optional):
Saturation level of the detector. Scalar type values will be interpreted units of electrons.
"""
# Input parameters
if isinstance(shape, str):
shape = eval(shape)
if isinstance(shape, tuple):
self.shape = shape
elif isinstance(shape, int):
self.shape = (shape, shape)
else:
raise SpecklepyTypeError('Detector', 'shape', type(shape), 'tuple')
if isinstance(pixel_scale, Quantity):
self.pixel_scale = pixel_scale
elif isinstance(pixel_scale, (int, float)):
logger.warning(
f"Interpreting float type pixel_scale as {pixel_scale} arcsec")
self.pixel_scale = pixel_scale * Unit('arcsec')
elif isinstance(pixel_scale, str):
self.pixel_scale = Quantity(pixel_scale)
else:
raise SpecklepyTypeError('Detector', 'pixel_scale',
type(pixel_scale), 'Quantity')
if isinstance(optics_transmission, (int, float)):
self.optics_transmission = optics_transmission
elif isinstance(optics_transmission, str):
self.optics_transmission = float(optics_transmission)
else:
raise SpecklepyTypeError('Detector', 'optics_transmission',
type(optics_transmission), 'float')
if isinstance(quantum_efficiency, Quantity):
self.quantum_efficiency = quantum_efficiency
elif isinstance(quantum_efficiency, (int, float)):
logger.warning(
f"Interpreting scalar type quantum_efficiency as {quantum_efficiency} electron/ photon"
)
self.quantum_efficiency = quantum_efficiency * Unit(
'electron / ph')
elif isinstance(quantum_efficiency, str):
self.quantum_efficiency = Quantity(quantum_efficiency)
else:
raise SpecklepyTypeError('Detector', 'quantum_efficiency',
type(quantum_efficiency), 'Quantity')
if isinstance(system_gain, Quantity):
self.system_gain = system_gain
elif isinstance(system_gain, (int, float)):
logger.warning(
f"Interpreting scalar type system_gain as {system_gain} electron/ ADU"
)
self.system_gain = system_gain * Unit('electron / adu')
elif isinstance(system_gain, str):
self.system_gain = Quantity(system_gain)
else:
raise SpecklepyTypeError('Detector', 'system_gain',
type(system_gain), 'Quantity')
if dark_current is None or isinstance(dark_current, Quantity):
self.dark_current = dark_current
elif isinstance(dark_current, (int, float)):
logger.warning(
f"Interpreting scalar type dark_current as {dark_current} electron/ s"
)
self.dark_current = dark_current * Unit('electron / s')
elif isinstance(dark_current, str):
self.dark_current = Quantity(dark_current)
else:
raise SpecklepyTypeError('Detector', 'dark_current',
type(dark_current), 'Quantity')
if readout_noise is None or isinstance(readout_noise, Quantity):
self.readout_noise = readout_noise
elif isinstance(readout_noise, (int, float)):
logger.warning(
f"Interpreting scalar type readout_noise as {readout_noise} electron"
)
self.readout_noise = readout_noise * Unit('electron')
elif isinstance(readout_noise, str):
self.readout_noise = Quantity(readout_noise)
else:
raise SpecklepyTypeError('Detector', 'readout_noise',
type(readout_noise), 'Quantity')
if isinstance(saturation_level, Quantity) or saturation_level is None:
self.saturation_level = saturation_level
elif isinstance(saturation_level, (int, float)):
logger.warning(
f"Interpreting scalar type saturation_level as {saturation_level} electron"
)
self.saturation_level = saturation_level * Unit('electron')
elif isinstance(saturation_level, str):
self.saturation_level = Quantity(saturation_level)
else:
raise SpecklepyTypeError('Detector', 'saturation_level',
type(saturation_level), 'Quantity')
# Derive secondary parameters
self.array = np.zeros(self.shape)
self.field_of_view = ScaledTuple(self.shape,
scale=self.pixel_scale).scaled
def full_reduction(params, debug=False):
"""Execute a full reduction following the parameters in the `params` dictionary.
TODO: Split this function into the parts and sort into the other modules
Args:
params (dict):
Dictionary with all the settings for reduction.
debug (bool, optional):
Show debugging information.
"""
# Set logging level
if debug:
logger.setLevel('DEBUG')
# (0) Read file list table
logger.info("Reading file list ...")
in_files = FileArchive(file_list=params['PATHS']['fileList'],
in_dir=params['PATHS']['filePath'],
out_dir=params['PATHS']['outDir'])
logger.info('\n' + str(in_files.table))
# (1) Initialize directories and reduction files
if not os.path.isdir(params['PATHS']['outDir']):
os.makedirs(params['PATHS']['outDir'])
if not os.path.isdir(params['PATHS']['tmpDir']):
os.makedirs(params['PATHS']['tmpDir'])
if 'skip' in params['PATHS'] and params['PATHS']['skip']:
product_files = glob.glob(os.path.join(params['PATHS']['outDir'], '*fits'))
else:
product_files = in_files.initialize_product_files(prefix=params['PATHS']['prefix'])
# (2) Flat fielding
if 'skip' in params['FLAT'] and params['FLAT']['skip']:
logger.info('Skipping flat fielding as requested from parameter file...')
else:
flat_files = in_files.get_flats()
if len(flat_files) == 0:
logger.warning("Did not find any flat field observations. No flat field correction will be applied!")
else:
logger.info("Starting flat field correction...")
master_flat = flat.MasterFlat(flat_files, file_name=params['FLAT']['masterFlatFile'],
file_path=params['PATHS']['filePath'], out_dir=params['PATHS']['tmpDir'])
master_flat.combine()
master_flat.run_correction(file_list=product_files, file_path=None)
# (3) Linearization
# TODO: Implement linearization
# (4) Sky subtraction
if 'skip' in params['SKY'] and params['SKY']['skip']:
logger.info('Skipping sky background subtraction as requested from parameter file...')
else:
logger.info("Starting sky subtraction...")
try:
sky.subtract_sky_background(**params['SKY'], in_files=in_files, out_files=product_files,
file_path=params['PATHS']['filePath'], tmp_dir=params['PATHS']['tmpDir'])
except RuntimeError as e:
raise RuntimeWarning(e)
# Close reduction
logger.info("Reduction finished...")
