lenstronomy.Data package¶
Submodules¶
lenstronomy.Data.coord_transforms module¶
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class
lenstronomy.Data.coord_transforms.
Coordinates
(transform_pix2angle, ra_at_xy_0, dec_at_xy_0)[source]¶ Bases:
object
class to handle linear coordinate transformations of a square pixel image
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coordinate_grid
(nx, ny)[source]¶ Parameters: - nx – number of pixels in x-direction
- ny – number of pixels in y-direction
Returns: 2d arrays with coordinates in RA/DEC with ra_coord[y-axis, x-axis]
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map_coord2pix
(ra, dec)[source]¶ maps the (ra,dec) coordinates of the system into the pixel coordinate of the image
Parameters: - ra – relative RA coordinate as defined by the coordinate frame
- dec – relative DEC coordinate as defined by the coordinate frame
Returns: (x, y) pixel coordinates
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map_pix2coord
(x, y)[source]¶ maps the (x,y) pixel coordinates of the image into the system coordinates
Parameters: - x – pixel coordinate (can be 1d numpy array), defined in the center of the pixel
- y – pixel coordinate (can be 1d numpy array), defined in the center of the pixel
Returns: relative (RA, DEC) coordinates of the system
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pixel_area
¶ angular area of a pixel in the image :return: area [arcsec^2]
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pixel_width
¶ size of pixel :return: sqrt(pixel_area)
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radec_at_xy_0
¶ Returns: RA, DEC coordinate at (0,0) pixel coordinate
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shift_coordinate_system
(x_shift, y_shift, pixel_unit=False)[source]¶ shifts the coordinate system :param x_shift: shift in x (or RA) :param y_shift: shift in y (or DEC) :param pixel_unit: bool, if True, units of pixels in input, otherwise RA/DEC :return: updated data class with change in coordinate system
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transform_angle2pix
¶ Returns: transformation matrix from angular to pixel coordinates
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transform_pix2angle
¶ Returns: transformation matrix from pixel to angular coordinates
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xy_at_radec_0
¶ Returns: pixel coordinate at angular (0,0) point
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class
lenstronomy.Data.coord_transforms.
Coordinates1D
(transform_pix2angle, ra_at_xy_0, dec_at_xy_0)[source]¶ Bases:
lenstronomy.Data.coord_transforms.Coordinates
coordinate grid described in 1-d arrays
lenstronomy.Data.imaging_data module¶
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class
lenstronomy.Data.imaging_data.
ImageData
(image_data, exposure_time=None, background_rms=None, noise_map=None, gradient_boost_factor=None, ra_at_xy_0=0, dec_at_xy_0=0, transform_pix2angle=None, ra_shift=0, dec_shift=0)[source]¶ Bases:
lenstronomy.Data.pixel_grid.PixelGrid
,lenstronomy.Data.image_noise.ImageNoise
class to handle the data, coordinate system and masking, including convolution with various numerical precisions
The Data() class is initialized with keyword arguments:
- ‘image_data’: 2d numpy array of the image data
- ‘transform_pix2angle’ 2x2 transformation matrix (linear) to transform a pixel shift into a coordinate shift (x, y) -> (ra, dec)
- ‘ra_at_xy_0’ RA coordinate of pixel (0,0)
- ‘dec_at_xy_0’ DEC coordinate of pixel (0,0)
optional keywords for shifts in the coordinate system: - ‘ra_shift’: shifts the coordinate system with respect to ‘ra_at_xy_0’ - ‘dec_shift’: shifts the coordinate system with respect to ‘dec_at_xy_0’
optional keywords for noise properties: - ‘background_rms’: rms value of the background noise - ‘exp_time’: float, exposure time to compute the Poisson noise contribution - ‘exposure_map’: 2d numpy array, effective exposure time for each pixel. If set, will replace ‘exp_time’ - ‘noise_map’: Gaussian noise (1-sigma) for each individual pixel. If this keyword is set, the other noise properties will be ignored.
the likelihood for the data given model P(data|model) is defined in the function below. Please make sure that your definitions and units of ‘exposure_map’, ‘background_rms’ and ‘image_data’ are in accordance with the likelihood function. In particular, make sure that the Poisson noise contribution is defined in the count rate.
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data
¶ Returns: 2d numpy array of data
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log_likelihood
(model, mask, additional_error_map=0)[source]¶ computes the likelihood of the data given the model p(data|model) The Gaussian errors are estimated with the covariance matrix, based on the model image. The errors include the background rms value and the exposure time to compute the Poisson noise level (in Gaussian approximation).
Parameters: - model – the model (same dimensions and units as data)
- mask – bool (1, 0) values per pixel. If =0, the pixel is ignored in the likelihood
- additional_error_map – additional error term (in same units as covariance matrix). This can e.g. come from model errors in the PSF estimation.
Returns: the natural logarithm of the likelihood p(data|model)
lenstronomy.Data.psf module¶
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class
lenstronomy.Data.psf.
PSF
(psf_type='NONE', fwhm=None, truncation=5, pixel_size=None, kernel_point_source=None, psf_error_map=None, point_source_supersampling_factor=1, kernel_point_source_init=None)[source]¶ Bases:
object
Point Spread Function class. This class describes and manages products used to perform the PSF modeling (convolution for extended surface brightness and painting of PSF’s for point sources).
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fwhm
¶ Returns: full width at half maximum of kernel (in units of pixel)
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kernel_pixel
¶ returns the convolution kernel for a uniform surface brightness on a pixel size
Returns: 2d numpy array
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kernel_point_source
¶
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kernel_point_source_supersampled
(supersampling_factor, updata_cache=True)[source]¶ generates (if not already available) a supersampled PSF with ood numbers of pixels centered
Parameters: - supersampling_factor – int >=1, supersampling factor relative to pixel resolution
- updata_cache – boolean, if True, updates the cached supersampling PSF if generated. Attention, this will overwrite a previously used supersampled PSF if the resolution is changing.
Returns: super-sampled PSF as 2d numpy array
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psf_error_map
¶
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