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timcornwelltimcornwell
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Move convolution_functions into griddata
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‎data_models/data_model_helpers.py‎

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from processing_library.image.operations import create_image_from_array
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from processing_components.griddata.operations import create_griddata_from_array
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from processing_components.convolution_function.operations import create_convolutionfunction_from_array
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from processing_components.griddata.convolution_functions import create_convolutionfunction_from_array
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from processing_components.image.operations import export_image_to_fits, import_image_from_fits
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from data_models.memory_data_models import Visibility, BlockVisibility, Configuration, \

‎docs/ARL_Module_View.png‎

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‎docs/ARL_calibration.rst‎

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- A single modelpartition is taken to be a vector composed of skycomponent, gaintable tuples.
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- The E step for a specific window is the sum of the window data model and the discrepancy between the observed data
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and the summed modelpartitions.
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and the summed modelpartitions.
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- The M step for a specific window is the optimisation of the skymodel vector given the window data model. This involves fitting a skycomponent and fitting for the gain phases.
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‎docs/ARL_directory_serial_workflows.rst‎

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docs/ARL_directory_arlexecute_workflows.rst renamed to docs/ARL_directory_workflows.rst

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@@ -12,7 +12,6 @@ Calibration workflows
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* Calibrate workflow: :py:mod:`workflows.arlexecute.calibration.calibration_arlexecute.calibrate_list_arlexecute_workflow`
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Imaging workflows
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=================
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=========
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* Execution framework (an interface to Dask): :py:mod:`wrappers.arlexecute.execution_support.arlexecute`
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Serial workflows
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****************
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Workflows created using the ARL processing components executed directly from python. These are higher level and fewer
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in number than processing components.
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Calibration workflows
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=====================
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* Calibrate workflow: :py:mod:`workflows.serial.calibration.calibration_serial.calibrate_list_serial_workflow`
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Imaging workflows
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=================
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* Invert: :py:mod:`workflows.serial.imaging.imaging_serial.invert_list_serial_workflow`
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* Predict: :py:mod:`workflows.serial.imaging.imaging_serial.predict_list_serial_workflow`
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* Deconvolve: :py:mod:`workflows.serial.imaging.imaging_serial.deconvolve_list_serial_workflow`
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Pipeline workflows
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==================
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* ICAL: :py:mod:`workflows.serial.pipelines.pipeline_serial.ical_list_serial_workflow`
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* Continuum imaging: :py:mod:`workflows.serial.pipelines.pipeline_serial.continuum_imaging_list_serial_workflow`
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* Spectral line imaging: :py:mod:`workflows.serial.pipelines.pipeline_serial.spectral_line_imaging_list_serial_workflow`
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Simulation workflows
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====================
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* Testing and simulation support: :py:mod:`workflows.serial.simulation.simulation_serial.simulate_list_serial_workflow`

‎docs/index.rst‎

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ARL_quickstart
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ARL_directory
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ARL_directory_workflows
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ARL_API
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ARL_directory_serial_workflows
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ARL_directory_arlexecute_workflows
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Reference material:
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processing_components/convolution_function/operations.py renamed to processing_components/griddata/convolution_functions.py

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@@ -190,7 +190,7 @@ def calculate_bounding_box_convolutionfunction(cf, fractional_level=1e-4):
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Returns a list of bounding boxes where each element is
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(z, (y0, y1), (x0, x1))
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These can be used in gridding/degridding.
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These can be used in griddata/degridding.
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:param cf:
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:param fractional_level:

‎processing_components/griddata/gridding.py‎

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""" Imaging is based on used of the FFT to perform Fourier transforms efficiently. Since the observed visibility data_models
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do not arrive naturally on grid points, the sampled points are resampled on the FFT grid using a convolution function to
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smear out the sample points. The resulting grid points are then FFT'ed. The result can be corrected for the gridding
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smear out the sample points. The resulting grid points are then FFT'ed. The result can be corrected for the griddata
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convolution function by division in the image plane of the transform.
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This module contains functions for performing the gridding process and the inverse degridding process.
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This module contains functions for performing the griddata process and the inverse degridding process.
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The GridData data model is used to hold the specification of the desired result.
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"""

processing_components/convolution_function/kernels.py renamed to processing_components/griddata/kernels.py

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from processing_library.fourier_transforms.convolutional_gridding import coordinates, grdsf
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from processing_library.image.operations import copy_image, create_w_term_like, pad_image, fft_image
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from processing_library.image.operations import create_image_from_array
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from processing_components.convolution_function.operations import create_convolutionfunction_from_image
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from processing_components.griddata.convolution_functions import create_convolutionfunction_from_image
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from processing_components.image.operations import reproject_image, create_empty_image_like
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log = logging.getLogger(__name__)
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def create_box_convolutionfunction(im, oversampling=1, support=1):
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""" Fill a box car function into a ConvolutionFunction
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Also returns the gridding correction function as an image
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Also returns the griddata correction function as an image
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:param im: Image template
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:param oversampling: Oversampling of the convolution function in uv space
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:return: gridding correction Image, gridding kernel as ConvolutionFunction
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:return: griddata correction Image, griddata kernel as ConvolutionFunction
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"""
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assert isinstance(im, Image)
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cf = create_convolutionfunction_from_image(im, oversampling=1, support=4)
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cf.data[...] = 0.0 + 0.0j
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cf.data[..., 2, 2] = 1.0 + 0.0j
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# Now calculate the gridding correction function as an image with the same coordinates as the image
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# Now calculate the griddata correction function as an image with the same coordinates as the image
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# which is necessary so that the correction function can be applied directly to the image
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nchan, npol, ny, nx = im.data.shape
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nu = numpy.abs(coordinates(nx))
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Fill the Prolate Spheroidal Wave Function into a GriData with the specified oversampling. Only the inner
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non-zero part is retained
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Also returns the gridding correction function as an image
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Also returns the griddata correction function as an image
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:param im: Image template
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:param oversampling: Oversampling of the convolution function in uv space
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:return: gridding correction Image, gridding kernel as ConvolutionFunction
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:return: griddata correction Image, griddata kernel as ConvolutionFunction
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"""
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assert isinstance(im, Image)
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# Calculate the convolution kernel. We oversample in u,v space by the factor oversampling
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norm = numpy.sum(numpy.real(cf.data[0, 0, 0, 0, 0, :, :]))
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cf.data /= norm
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# Now calculate the gridding correction function as an image with the same coordinates as the image
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# Now calculate the griddata correction function as an image with the same coordinates as the image
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# which is necessary so that the correction function can be applied directly to the image
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nchan, npol, ny, nx = im.data.shape
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nu = numpy.abs(2.0 * coordinates(nx))
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:param nw: Number of w planes
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:param wstep: Step in w (wavelengths)
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:param oversampling: Oversampling of the convolution function in uv space
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:return: gridding correction Image, gridding kernel as GridData
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:return: griddata correction Image, griddata kernel as GridData
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"""
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d2r = numpy.pi / 180.0
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# We only need the gridding correction function for the PSWF so we make
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# We only need the griddata correction function for the PSWF so we make
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# it for the shape of the image
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nchan, npol, ony, onx = im.data.shape
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‎processing_components/imaging/base.py‎

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from processing_library.imaging.imaging_params import get_frequency_map
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from processing_library.util.coordinate_support import simulate_point, skycoord_to_lmn
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from ..convolution_function.kernels import create_pswf_convolutionfunction
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from processing_components.griddata.kernels import create_pswf_convolutionfunction
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from ..griddata.gridding import grid_visibility_to_griddata, \
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fft_griddata_to_image, fft_image_to_griddata, \
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degrid_visibility_from_griddata
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log.info('advice_wide_field: Number of planes in w stack %d' % (vis_slices))
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log.info('advice_wide_field: Number of planes in w projection %d' % wprojection_planes)
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if wprojection_planes > 1:
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log.info('advice_wide_field: Recommend that wprojection gridding is used')
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log.info('advice_wide_field: Recommend that wprojection griddata is used')
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kernel = 'wprojection'
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else:
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log.info('advice_wide_field: Recommend that 2d gridding (i.e. no wprojection) is used')
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log.info('advice_wide_field: Recommend that 2d griddata (i.e. no wprojection) is used')
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kernel = '2d'
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nwpixels = int(npixels * image_fov)

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