def phase_rotate_sgraph(vis_dataset, global_dataset, rotation_parms, sel_parms, storage_parms):
"""
Rotate uvw with faceting style rephasing for multifield mosaic.
The specified phasecenter and field phase centers are assumed to be in the same frame.
This does not support east-west arrays, emphemeris objects or objects within the nearfield.
(no refocus).
Parameters
----------
vis_dataset : xarray.core.dataset.Dataset
input Visibility Dataset
Returns
-------
psf_dataset : xarray.core.dataset.Dataset
"""
#based on UVWMachine and FTMachine
#measures/Measures/UVWMachine.cc
#Important: Can not applyflags before calling rotate (uvw coordinates are also flagged). This will destroy the rotation transform.
#Performance improvements apply_rotation_matrix (jit code)
#print('1. numpy',vis_dataset.DATA[:,0,0,0].values)
from ngcasa._ngcasa_utils._store import _store
from scipy.spatial.transform import Rotation as R
import numpy as np
import copy
import dask.array as da
import xarray as xr
from ngcasa._ngcasa_utils._check_parms import _check_storage_parms, _check_sel_parms, _check_existence_sel_parms
from ._imaging_utils._check_imaging_parms import _check_rotation_parms
import time
import numba
from numba import double
import dask
import itertools
_sel_parms = copy.deepcopy(sel_parms)
_rotation_parms = copy.deepcopy(rotation_parms)
_storage_parms = copy.deepcopy(storage_parms)
assert(_check_sel_parms(_sel_parms,{'uvw_in':'UVW','uvw_out':'UVW_ROT','data_in':'DATA','data_out':'DATA_ROT'})), "######### ERROR: sel_parms checking failed"
assert(_check_existence_sel_parms(vis_dataset,{'uvw_in':_sel_parms['uvw_in'],'data_in':_sel_parms['data_in']})), "######### ERROR: sel_parms checking failed"
assert(_check_rotation_parms(_rotation_parms)), "######### ERROR: rotation_parms checking failed"
assert(_check_storage_parms(_storage_parms,'dataset.vis.zarr','phase_rotate')), "######### ERROR: storage_parms checking failed"
assert(_sel_parms['uvw_out'] != _sel_parms['uvw_in']), "######### ERROR: sel_parms checking failed sel_parms['uvw_out'] can not be the same as sel_parms['uvw_in']."
assert(_sel_parms['data_out'] != _sel_parms['data_in']), "######### ERROR: sel_parms checking failed sel_parms['data_out'] can not be the same as sel_parms['data_in']."
#Phase center
ra_image = _rotation_parms['image_phase_center'][0]
dec_image = _rotation_parms['image_phase_center'][1]
rotmat_image_phase_center = R.from_euler('XZ',[[np.pi/2 - dec_image, - ra_image + np.pi/2]]).as_matrix()[0]
image_phase_center_cosine = _directional_cosine([ra_image,dec_image])
n_fields = global_dataset.dims['field']
field_names = global_dataset.field
uvw_rotmat = np.zeros((n_fields,3,3),np.double)
phase_rotation = np.zeros((n_fields,3),np.double)
fields_phase_center = global_dataset.FIELD_PHASE_DIR.values[:,:,vis_dataset.attrs['ddi']]
#print(fields_phase_center)
#Create a rotation matrix for each field
for i_field in range(n_fields):
#Not sure if last dimention in FIELD_PHASE_DIR is the ddi number
field_phase_center = fields_phase_center[i_field,:]
# Define rotation to a coordinate system with pole towards in-direction
# and X-axis W; by rotating around z-axis over -(90-long); and around
# x-axis (lat-90).
rotmat_field_phase_center = R.from_euler('ZX',[[-np.pi/2 + field_phase_center[0],field_phase_center[1] - np.pi/2]]).as_matrix()[0]
uvw_rotmat[i_field,:,:] = np.matmul(rotmat_image_phase_center,rotmat_field_phase_center).T
if _rotation_parms['common_tangent_reprojection'] == True:
uvw_rotmat[i_field,2,0:2] = 0.0 # (Common tangent rotation needed for joint mosaics, see last part of FTMachine::girarUVW in CASA)
field_phase_center_cosine = _directional_cosine(field_phase_center)
phase_rotation[i_field,:] = np.matmul(rotmat_image_phase_center,(image_phase_center_cosine - field_phase_center_cosine))
chunk_sizes = vis_dataset[sel_parms["data_in"]].chunks
freq_chan = da.from_array(vis_dataset.coords['chan'].values, chunks=(chunk_sizes[2][0]))
n_chunks_in_each_dim = vis_dataset[_sel_parms['data_in']].data.numblocks
iter_chunks_indx = itertools.product(np.arange(n_chunks_in_each_dim[0]), np.arange(n_chunks_in_each_dim[1]),
np.arange(n_chunks_in_each_dim[2]), np.arange(n_chunks_in_each_dim[3]))
list_of_vis_data = ndim_list(n_chunks_in_each_dim)
list_of_uvw = ndim_list(n_chunks_in_each_dim[0:2]+(1,))
for c_time, c_baseline, c_chan, c_pol in iter_chunks_indx:
vis_data_and_uvw = dask.delayed(apply_phasor)(
vis_dataset[sel_parms["data_in"]].data.partitions[c_time, c_baseline, c_chan, c_pol],
vis_dataset[sel_parms["uvw_in"]].data.partitions[c_time, c_baseline, 0],
vis_dataset.field_id.data.partitions[c_time],
freq_chan.partitions[c_chan],
dask.delayed(uvw_rotmat),
dask.delayed(phase_rotation), dask.delayed(_rotation_parms['common_tangent_reprojection']))
list_of_vis_data[c_time][c_baseline][c_chan][c_pol] = da.from_delayed(vis_data_and_uvw[0], (chunk_sizes[0][c_time], chunk_sizes[1][c_baseline], chunk_sizes[2][c_chan], chunk_sizes[3][c_pol]),dtype=np.complex128)
list_of_uvw[c_time][c_baseline][0] = da.from_delayed(vis_data_and_uvw[1],(chunk_sizes[0][c_time], chunk_sizes[1][c_baseline], 3),dtype=np.float64)
vis_dataset[_sel_parms['data_out']] = xr.DataArray(da.block(list_of_vis_data), dims=vis_dataset[_sel_parms['data_in']].dims)
vis_dataset[_sel_parms['uvw_out']] = xr.DataArray(da.block(list_of_uvw), dims=vis_dataset[_sel_parms['uvw_in']].dims)
#dask.visualize(vis_dataset[_sel_parms['uvw_out']],filename='uvw_rot_dataset')
#dask.visualize(vis_dataset[_sel_parms['data_out']],filename='vis_rot_dataset')
#dask.visualize(vis_dataset,filename='vis_dataset_before_append_custom_graph')
list_xarray_data_variables = [vis_dataset[_sel_parms['uvw_out']],vis_dataset[_sel_parms['data_out']]]
return _store(vis_dataset,list_xarray_data_variables,_storage_parms)
def make_imaging_weight(vis_dataset, imaging_weights_parms,storage_parms):
"""
Creates the imaging weight data variable that has dimensions time x baseline x chan x pol (matches the visibility data variable).
The weight density can be averaged over channels or calculated independently for each channel using imaging_weights_parms['chan_mode'].
The following imaging weighting schemes are supported 'natural', 'uniform', 'briggs', 'briggs_abs'.
The imaging_weights_parms['imsize'] and imaging_weights_parms['cell'] should usually be the same values that will be used for subsequent synthesis blocks (for example making the psf).
To achieve something similar to 'superuniform' weighting in CASA tclean imaging_weights_parms['imsize'] and imaging_weights_parms['cell'] can be varied relative to the values used in subsequent synthesis blocks.
Parameters
----------
vis_dataset : xarray.core.dataset.Dataset
Input visibility dataset.
imaging_weights_parms : dictionary
imaging_weights_parms['weighting'] : {'natural', 'uniform', 'briggs', 'briggs_abs'}, default = natural
Weighting scheme used for creating the imaging weights.
imaging_weights_parms['imsize'] : list of int, length = 2
The size of the grid for gridding the imaging weights. Used when imaging_weights_parms['weighting'] is not 'natural'.
imaging_weights_parms['cell'] : list of number, length = 2, units = arcseconds
The size of the pixels in the fft of the grid (the image domain pixel size). Used when imaging_weights_parms['weighting'] is not 'natural'.
imaging_weights_parms['robust'] : number, acceptable range [-2,2], default = 0.5
Robustness parameter for Briggs weighting.
robust = -2.0 maps to uniform weighting.
robust = +2.0 maps to natural weighting.
imaging_weights_parms['briggs_abs_noise'] : number, default=1.0
Noise parameter for imaging_weights_parms['weighting']='briggs_abs' mode weighting.
imaging_weights_parms['chan_mode'] : {'continuum'/'cube'}, default = 'continuum'
When 'cube' the weights are calculated independently for each channel (perchanweightdensity=True in CASA tclean) and when 'continuum' a common weight density is calculated for all channels.
imaging_weights_parms['uvw_name'] : str, default ='UVW'
The name of uvw data variable that will be used to grid the weights. Used when imaging_weights_parms['weighting'] is not 'natural'.
imaging_weights_parms['data_name'] : str, default = 'DATA'
The name of the visibility data variable whose dimensions will be used to construct the imaging weight data variable.
imaging_weights_parms['imaging_weight_name'] : str, default ='IMAGING_WEIGHT'
The name of that will be used for the imaging weight data variable.
storage_parms : dictionary
storage_parms['to_disk'] : bool, default = False
If true the dask graph is executed and saved to disk in the zarr format.
storage_parms['append'] : bool, default = False
If storage_parms['to_disk'] is True only the dask graph associated with the function is executed and the resulting data variables are saved to an existing zarr file on disk.
Note that graphs on unrelated data to this function will not be executed or saved.
storage_parms['outfile'] : str
The zarr file to create or append to.
storage_parms['chunks_on_disk'] : dict of int, default = {}
The chunk size to use when writing to disk. This is ignored if storage_parms['append'] is True. The default will use the chunking of the input dataset.
storage_parms['chunks_return'] : dict of int, default = {}
The chunk size of the dataset that is returned. The default will use the chunking of the input dataset.
storage_parms['graph_name'] : str
The time to compute and save the data is stored in the attribute section of the dataset and storage_parms['graph_name'] is used in the label.
storage_parms['compressor'] : numcodecs.blosc.Blosc,default=Blosc(cname='zstd', clevel=2, shuffle=0)
The compression algorithm to use. Available compression algorithms can be found at https://numcodecs.readthedocs.io/en/stable/blosc.html.
Returns
-------
vis_dataset : xarray.core.dataset.Dataset
The vis_dataset will contain a new data variable for the imaging weights the name is defined by the input parameter imaging_weights_parms['imaging_weight_name'].
"""
print('######################### Start make_imaging_weights #########################')
import time
import math
import xarray as xr
import dask.array as da
import matplotlib.pylab as plt
import dask.array.fft as dafft
import dask
import copy, os
from numcodecs import Blosc
from itertools import cycle
import zarr
from ngcasa._ngcasa_utils._store import _store
from ngcasa._ngcasa_utils._check_parms import _check_storage_parms
from ._imaging_utils._check_imaging_parms import _check_imaging_weights_parms
from cngi.dio import write_zarr, append_zarr
_imaging_weights_parms = copy.deepcopy(imaging_weights_parms)
_storage_parms = copy.deepcopy(storage_parms)
assert(_check_imaging_weights_parms(vis_dataset,_imaging_weights_parms)), "######### ERROR: imaging_weights_parms checking failed"
assert(_check_storage_parms(_storage_parms,'dataset.vis.zarr','make_imaging_weights')), "######### ERROR: storage_parms checking failed"
#Check if weight or weight spectrum present
#If both default to weight spectrum
#If none create new
weight_present = 'WEIGHT' in vis_dataset.data_vars
weight_spectrum_present = 'WEIGHT_SPECTRUM' in vis_dataset.data_vars
all_dims_dict = vis_dataset.dims
vis_data_dims = vis_dataset[_imaging_weights_parms['data_name']].dims
vis_data_chunksize = vis_dataset[_imaging_weights_parms['data_name']].data.chunksize
if weight_present and weight_spectrum_present:
print('Both WEIGHT and WEIGHT_SPECTRUM data variables found, will use WEIGHT_SPECTRUM to calculate', _imaging_weights_parms['imaging_weight_name'])
imaging_weight = _match_array_shape(vis_dataset.WEIGHT_SPECTRUM,vis_dataset[_imaging_weights_parms['data_name']])
elif weight_present:
print('WEIGHT data variable found, will use WEIGHT to calculate ', _imaging_weights_parms['imaging_weight_name'])
imaging_weight = _match_array_shape(vis_dataset.WEIGHT,vis_dataset[_imaging_weights_parms['data_name']])
elif weight_spectrum_present:
print('WEIGHT_SPECTRUM data variable found, will use WEIGHT_SPECTRUM to calculate ', _imaging_weights_parms['imaging_weight_name'])
imaging_weight = _match_array_shape(vis_dataset.WEIGHT_SPECTRUM,vis_dataset[_imaging_weights_parms['data_name']])
else:
print('No WEIGHT or WEIGHT_SPECTRUM data variable found, will assume all weights are unity to calculate ', _imaging_weights_parms['imaging_weight_name'])
imaging_weight = da.ones(vis_dataset[_imaging_weights_parms['data_name']].shape,chunks=vis_data_chunksize)
vis_dataset[_imaging_weights_parms['imaging_weight_name']] = xr.DataArray(imaging_weight, dims=vis_dataset[_imaging_weights_parms['data_name']].dims)
if _imaging_weights_parms['weighting'] != 'natural':
calc_briggs_weights(vis_dataset,_imaging_weights_parms)
list_xarray_data_variables = [vis_dataset[_imaging_weights_parms['imaging_weight_name']]]
return _store(vis_dataset,list_xarray_data_variables,_storage_parms)
def make_image(vis_dataset, img_dataset, grid_parms, sel_parms, storage_parms):
"""
Creates a cube or continuum dirty image from the user specified visibility, uvw and imaging weight data. Only the prolate spheroidal convolutional gridding function is supported. See make_image_with_gcf function for creating an image with A-projection.
Parameters
----------
vis_dataset : xarray.core.dataset.Dataset
Input visibility dataset.
grid_parms : dictionary
grid_parms['image_size'] : list of int, length = 2
The image size (no padding).
grid_parms['cell_size'] : list of number, length = 2, units = arcseconds
The image cell size.
grid_parms['chan_mode'] : {'continuum'/'cube'}, default = 'continuum'
Create a continuum or cube image.
grid_parms['fft_padding'] : number, acceptable range [1,100], default = 1.2
The factor that determines how much the gridded visibilities are padded before the fft is done.
sel_parms : dictionary
sel_parms['uvw'] : str, default ='UVW'
The name of uvw data variable that will be used to grid the visibilities.
sel_parms['data'] : str, default = 'DATA'
The name of the visibility data to be gridded.
sel_parms['imaging_weight'] : str, default ='IMAGING_WEIGHT'
The name of the imaging weights to be used.
sel_parms['image'] : str, default ='DIRTY_IMAGE'
The created image name.
sel_parms['sum_weight'] : str, default ='SUM_WEIGHT'
The created sum of weights name.
storage_parms : dictionary
storage_parms['to_disk'] : bool, default = False
If true the dask graph is executed and saved to disk in the zarr format.
storage_parms['append'] : bool, default = False
If storage_parms['to_disk'] is True only the dask graph associated with the function is executed and the resulting data variables are saved to an existing zarr file on disk.
Note that graphs on unrelated data to this function will not be executed or saved.
storage_parms['outfile'] : str
The zarr file to create or append to.
storage_parms['chunks_on_disk'] : dict of int, default = {}
The chunk size to use when writing to disk. This is ignored if storage_parms['append'] is True. The default will use the chunking of the input dataset.
storage_parms['chunks_return'] : dict of int, default = {}
The chunk size of the dataset that is returned. The default will use the chunking of the input dataset.
storage_parms['graph_name'] : str
The time to compute and save the data is stored in the attribute section of the dataset and storage_parms['graph_name'] is used in the label.
storage_parms['compressor'] : numcodecs.blosc.Blosc,default=Blosc(cname='zstd', clevel=2, shuffle=0)
The compression algorithm to use. Available compression algorithms can be found at https://numcodecs.readthedocs.io/en/stable/blosc.html.
Returns
-------
image_dataset : xarray.core.dataset.Dataset
The image_dataset will contain the image created and the sum of weights.
"""
print('######################### Start make_image #########################')
import numpy as np
from numba import jit
import time
import math
import dask.array.fft as dafft
import xarray as xr
import dask.array as da
import matplotlib.pylab as plt
import dask
import copy, os
from numcodecs import Blosc
from itertools import cycle
from ngcasa._ngcasa_utils._store import _store
from ngcasa._ngcasa_utils._check_parms import _check_storage_parms, _check_sel_parms, _check_existence_sel_parms
from ._imaging_utils._check_imaging_parms import _check_grid_parms
from ._imaging_utils._gridding_convolutional_kernels import _create_prolate_spheroidal_kernel, _create_prolate_spheroidal_kernel_1D
from ._imaging_utils._standard_grid import _graph_standard_grid
from ._imaging_utils._remove_padding import _remove_padding
from ._imaging_utils._aperture_grid import _graph_aperture_grid
_grid_parms = copy.deepcopy(grid_parms)
_storage_parms = copy.deepcopy(storage_parms)
_sel_parms = copy.deepcopy(sel_parms)
assert(_check_sel_parms(_sel_parms,{'uvw':'UVW','data':'DATA','imaging_weight':'IMAGING_WEIGHT','sum_weight':'SUM_WEIGHT','image':'IMAGE','pb':'PB','weight_pb':'WEIGHT_PB'})), "######### ERROR: sel_parms checking failed"
assert(_check_existence_sel_parms(vis_dataset,{'uvw':_sel_parms['uvw'],'data':_sel_parms['data'],'imaging_weight':_sel_parms['imaging_weight']})), "######### ERROR: sel_parms checking failed"
assert(_check_grid_parms(_grid_parms)), "######### ERROR: grid_parms checking failed"
assert(_check_storage_parms(_storage_parms,'dirty_image.img.zarr','make_image')), "######### ERROR: storage_parms checking failed"
# Creating gridding kernel
_grid_parms['oversampling'] = 100
_grid_parms['support'] = 7
cgk, correcting_cgk_image = _create_prolate_spheroidal_kernel(_grid_parms['oversampling'], _grid_parms['support'], _grid_parms['image_size_padded'])
cgk_1D = _create_prolate_spheroidal_kernel_1D(_grid_parms['oversampling'], _grid_parms['support'])
_grid_parms['complex_grid'] = True
_grid_parms['do_psf'] = False
grids_and_sum_weights = _graph_standard_grid(vis_dataset, cgk_1D, _grid_parms, _sel_parms)
uncorrected_dirty_image = dafft.fftshift(dafft.ifft2(dafft.ifftshift(grids_and_sum_weights[0], axes=(0, 1)), axes=(0, 1)), axes=(0, 1))
#Remove Padding
correcting_cgk_image = _remove_padding(correcting_cgk_image,_grid_parms['image_size'])
uncorrected_dirty_image = _remove_padding(uncorrected_dirty_image,_grid_parms['image_size']).real * (_grid_parms['image_size_padded'][0] * _grid_parms['image_size_padded'][1])
#############Normalize#############
def correct_image(uncorrected_dirty_image, sum_weights, correcting_cgk):
sum_weights_copy = copy.deepcopy(sum_weights) ##Don't mutate inputs, therefore do deep copy (https://docs.dask.org/en/latest/delayed-best-practices.html).
sum_weights_copy[sum_weights_copy == 0] = 1
# corrected_image = (uncorrected_dirty_image/sum_weights[:,:,None,None])/correcting_cgk[None,None,:,:]
corrected_image = (uncorrected_dirty_image / sum_weights_copy) / correcting_cgk
return corrected_image
corrected_dirty_image = da.map_blocks(correct_image, uncorrected_dirty_image, grids_and_sum_weights[1][None, None, :, :],correcting_cgk_image[:, :, None, None])
####################################################
if _grid_parms['chan_mode'] == 'continuum':
freq_coords = [da.mean(vis_dataset.coords['chan'].values)]
chan_width = da.from_array([da.mean(vis_dataset['chan_width'].data)],chunks=(1,))
imag_chan_chunk_size = 1
elif _grid_parms['chan_mode'] == 'cube':
freq_coords = vis_dataset.coords['chan'].values
chan_width = vis_dataset['chan_width'].data
imag_chan_chunk_size = vis_dataset.DATA.chunks[2][0]
###Create Image Dataset
chunks = vis_dataset.DATA.chunks
n_imag_pol = chunks[3][0]
coords = {'d0': np.arange(_grid_parms['image_size'][0]), 'd1': np.arange(_grid_parms['image_size'][1]),
'chan': freq_coords, 'pol': np.arange(n_imag_pol), 'chan_width' : ('chan',chan_width)}
img_dataset = img_dataset.assign_coords(coords)
img_dataset[_sel_parms['sum_weight']] = xr.DataArray(grids_and_sum_weights[1], dims=['chan','pol'])
img_dataset[_sel_parms['image']] = xr.DataArray(corrected_dirty_image, dims=['d0', 'd1', 'chan', 'pol'])
list_xarray_data_variables = [img_dataset[_sel_parms['image']],img_dataset[_sel_parms['sum_weight']]]
return _store(img_dataset,list_xarray_data_variables,_storage_parms)
'''
def make_gridding_convolution_function(vis_dataset, global_dataset, gcf_parms, grid_parms, storage_parms):
"""
Currently creates a gcf to correct for the primary beams of antennas and supports heterogenous arrays (antennas with different dish sizes).
Only the airy disk and ALMA airy disk model is implemented.
In the future support will be added for beam squint, pointing corrections, w projection, and including a prolate spheroidal term.
Parameters
----------
vis_dataset : xarray.core.dataset.Dataset
Input visibility dataset.
gcf_parms : dictionary
gcf_parms['function'] : {'alma_airy'/'airy'}, default = 'alma_airy'
The primary beam model used (a function of the dish diameter and blockage diameter).
gcf_parms['list_dish_diameters'] : list of number, units = meter
A list of unique antenna dish diameters.
gcf_parms['list_blockage_diameters'] : list of number, units = meter
A list of unique feed blockage diameters (must be the same length as gcf_parms['list_dish_diameters']).
gcf_parms['unique_ant_indx'] : list of int
A list that has indeces for the gcf_parms['list_dish_diameters'] and gcf_parms['list_blockage_diameters'] lists, for each antenna.
gcf_parms['image_phase_center'] : list of number, length = 2, units = radians
The mosaic image phase center.
gcf_parms['a_chan_num_chunk'] : int, default = 3
The number of chunks in the channel dimension of the gridding convolution function data variable.
gcf_parms['oversampling'] : list of int, length = 2, default = [10,10]
The oversampling of the gridding convolution function.
gcf_parms['max_support'] : list of int, length = 2, default = [15,15]
The maximum allowable support of the gridding convolution function.
gcf_parms['support_cut_level'] : number, default = 0.025
The antennuation at which to truncate the gridding convolution function.
gcf_parms['chan_tolerance_factor'] : number, default = 0.005
It is the fractional bandwidth at which the frequency dependence of the primary beam can be ignored and determines the number of frequencies for which to calculate a gridding convolution function. Number of channels equals the fractional bandwidth devided by gcf_parms['chan_tolerance_factor'].
grid_parms : dictionary
grid_parms['image_size'] : list of int, length = 2
The image size (no padding).
grid_parms['cell_size'] : list of number, length = 2, units = arcseconds
The image cell size.
storage_parms : dictionary
storage_parms['to_disk'] : bool, default = False
If true the dask graph is executed and saved to disk in the zarr format.
storage_parms['append'] : bool, default = False
If storage_parms['to_disk'] is True only the dask graph associated with the function is executed and the resulting data variables are saved to an existing zarr file on disk.
Note that graphs on unrelated data to this function will not be executed or saved.
storage_parms['outfile'] : str
The zarr file to create or append to.
storage_parms['chunks_on_disk'] : dict of int, default = {}
The chunk size to use when writing to disk. This is ignored if storage_parms['append'] is True. The default will use the chunking of the input dataset.
storage_parms['chunks_return'] : dict of int, default = {}
The chunk size of the dataset that is returned. The default will use the chunking of the input dataset.
storage_parms['graph_name'] : str
The time to compute and save the data is stored in the attribute section of the dataset and storage_parms['graph_name'] is used in the label.
storage_parms['compressor'] : numcodecs.blosc.Blosc,default=Blosc(cname='zstd', clevel=2, shuffle=0)
The compression algorithm to use. Available compression algorithms can be found at https://numcodecs.readthedocs.io/en/stable/blosc.html.
Returns
-------
gcf_dataset : xarray.core.dataset.Dataset
"""
print('######################### Start make_gridding_convolution_function #########################')
from ngcasa._ngcasa_utils._store import _store
from ngcasa._ngcasa_utils._check_parms import _check_storage_parms
from ._imaging_utils._check_imaging_parms import _check_pb_parms
from ._imaging_utils._check_imaging_parms import _check_grid_parms, _check_gcf_parms
from ._imaging_utils._gridding_convolutional_kernels import _create_prolate_spheroidal_kernel_2D, _create_prolate_spheroidal_image_2D
from ._imaging_utils._remove_padding import _remove_padding
import numpy as np
import dask.array as da
import copy, os
import xarray as xr
import itertools
import dask
import dask.array.fft as dafft
import matplotlib.pylab as plt
_gcf_parms = copy.deepcopy(gcf_parms)
_grid_parms = copy.deepcopy(grid_parms)
_storage_parms = copy.deepcopy(storage_parms)
_gcf_parms['basline_ant'] = vis_dataset.antennas.values # n_baseline x 2 (ant pair)
_gcf_parms['freq_chan'] = vis_dataset.chan.values
_gcf_parms['pol'] = vis_dataset.pol.values
_gcf_parms['vis_data_chunks'] = vis_dataset.DATA.chunks
_gcf_parms['field_phase_dir'] = np.array(global_dataset.FIELD_PHASE_DIR.values[:,:,vis_dataset.attrs['ddi']])
assert(_check_gcf_parms(_gcf_parms)), "######### ERROR: gcf_parms checking failed"
assert(_check_grid_parms(_grid_parms)), "######### ERROR: grid_parms checking failed"
assert(_check_storage_parms(_storage_parms,'dataset.gcf.zarr','make_gcf')), "######### ERROR: user_storage_parms checking failed"
assert(not _storage_parms['append']), "######### ERROR: storage_parms['append'] = True is not available for make_gridding_convolution_function"
if _gcf_parms['function'] == 'airy':
from ._imaging_utils._make_pb_symmetric import _airy_disk_rorder
pb_func = _airy_disk_rorder
elif _gcf_parms['function'] == 'alma_airy':
from ._imaging_utils._make_pb_symmetric import _alma_airy_disk_rorder
pb_func = _alma_airy_disk_rorder
else:
assert(False), "######### ERROR: Only airy and alma_airy function has been implemented"
#For now only a_term works
_gcf_parms['a_term'] = True
_gcf_parms['ps_term'] = False
_gcf_parms['resize_conv_size'] = (_gcf_parms['max_support'] + 1)*_gcf_parms['oversampling']
#resize_conv_size = _gcf_parms['resize_conv_size']
if _gcf_parms['ps_term'] == True:
'''
ps_term = _create_prolate_spheroidal_kernel_2D(_gcf_parms['oversampling'],np.array([7,7])) #This is only used with a_term == False. Support is hardcoded to 7 until old ps code is replaced by a general function.
center = _grid_parms['image_center']
center_embed = np.array(ps_term.shape)//2
ps_term_padded = np.zeros(_grid_parms['image_size'])
ps_term_padded[center[0]-center_embed[0]:center[0]+center_embed[0],center[1]-center_embed[1] : center[1]+center_embed[1]] = ps_term
ps_term_padded_ifft = dafft.fftshift(dafft.ifft2(dafft.ifftshift(da.from_array(ps_term_padded))))
ps_image = da.from_array(_remove_padding(_create_prolate_spheroidal_image_2D(_grid_parms['image_size_padded']),_grid_parms['image_size']),chunks=_grid_parms['image_size'])
#Effecively no mapping needed if ps_term == True and a_term == False
cf_baseline_map = np.zeros((len(_gcf_parms['basline_ant']),),dtype=int)
cf_chan_map = np.zeros((len(_gcf_parms['freq_chan']),),dtype=int)
cf_pol_map = np.zeros((len(_gcf_parms['pol']),),dtype=int)
'''
if _gcf_parms['a_term'] == True:
n_unique_ant = len(_gcf_parms['list_dish_diameters'])
cf_baseline_map,pb_ant_pairs = create_cf_baseline_map(_gcf_parms['unique_ant_indx'],_gcf_parms['basline_ant'],n_unique_ant)
cf_chan_map, pb_freq = create_cf_chan_map(_gcf_parms['freq_chan'],_gcf_parms['chan_tolerance_factor'])
pb_freq = da.from_array(pb_freq,chunks=np.ceil(len(pb_freq)/_gcf_parms['a_chan_num_chunk'] ))
cf_pol_map = np.zeros((len(_gcf_parms['pol']),),dtype=int) #create_cf_pol_map(), currently treating all pols the same
pb_pol = da.from_array(np.array([0]),1)
n_chunks_in_each_dim = [pb_freq.numblocks[0],pb_pol.numblocks[0]]
iter_chunks_indx = itertools.product(np.arange(n_chunks_in_each_dim[0]), np.arange(n_chunks_in_each_dim[1]))
chan_chunk_sizes = pb_freq.chunks
pol_chunk_sizes = pb_pol.chunks
#print(pb_freq, pb_pol,pol_chunk_sizes)
list_baseline_pb = []
list_weight_baseline_pb_sqrd = []
for c_chan, c_pol in iter_chunks_indx:
#print('chan, pol ',c_chan,c_pol)
_gcf_parms['ipower'] = 1
delayed_baseline_pb = dask.delayed(make_baseline_patterns)(pb_freq.partitions[c_chan],pb_pol.partitions[c_pol],dask.delayed(pb_ant_pairs),dask.delayed(pb_func),dask.delayed(_gcf_parms),dask.delayed(_grid_parms))
list_baseline_pb.append(da.from_delayed(delayed_baseline_pb,(len(pb_ant_pairs),chan_chunk_sizes[0][c_chan], pol_chunk_sizes[0][c_pol],_grid_parms['image_size_padded'][0],_grid_parms['image_size_padded'][1]),dtype=np.double))
_gcf_parms['ipower'] = 2
delayed_weight_baseline_pb_sqrd = dask.delayed(make_baseline_patterns)(pb_freq.partitions[c_chan],pb_pol.partitions[c_pol],dask.delayed(pb_ant_pairs),dask.delayed(pb_func),dask.delayed(_gcf_parms),dask.delayed(_grid_parms))
list_weight_baseline_pb_sqrd.append(da.from_delayed(delayed_weight_baseline_pb_sqrd,(len(pb_ant_pairs),chan_chunk_sizes[0][c_chan], pol_chunk_sizes[0][c_pol],_grid_parms['image_size_padded'][0],_grid_parms['image_size_padded'][1]),dtype=np.double))
baseline_pb = da.concatenate(list_baseline_pb,axis=1)
weight_baseline_pb_sqrd = da.concatenate(list_weight_baseline_pb_sqrd,axis=1)
#Combine patterns and fft to obtain the gridding convolutional kernel
#print(weight_baseline_pb_sqrd)
dataset_dict = {}
list_xarray_data_variables = []
if (_gcf_parms['a_term'] == True) and (_gcf_parms['ps_term'] == True):
conv_kernel = da.real(dafft.fftshift(dafft.fft2(dafft.ifftshift(ps_term_padded_ifft*baseline_pb, axes=(3, 4)), axes=(3, 4)), axes=(3, 4)))
conv_weight_kernel = da.real(dafft.fftshift(dafft.fft2(dafft.ifftshift(weight_baseline_pb_sqrd, axes=(3, 4)), axes=(3, 4)), axes=(3, 4)))
list_conv_kernel = []
list_weight_conv_kernel = []
list_conv_support = []
iter_chunks_indx = itertools.product(np.arange(n_chunks_in_each_dim[0]), np.arange(n_chunks_in_each_dim[1]))
for c_chan, c_pol in iter_chunks_indx:
delayed_kernels_and_support = dask.delayed(resize_and_calc_support)(conv_kernel.partitions[:,c_chan,c_pol,:,:],conv_weight_kernel.partitions[:,c_chan,c_pol,:,:],dask.delayed(_gcf_parms),dask.delayed(_grid_parms))
list_conv_kernel.append(da.from_delayed(delayed_kernels_and_support[0],(len(pb_ant_pairs),chan_chunk_sizes[0][c_chan], pol_chunk_sizes[0][c_pol],_gcf_parms['resize_conv_size'][0],_gcf_parms['resize_conv_size'][1]),dtype=np.double))
list_weight_conv_kernel.append(da.from_delayed(delayed_kernels_and_support[1],(len(pb_ant_pairs),chan_chunk_sizes[0][c_chan], pol_chunk_sizes[0][c_pol],_gcf_parms['resize_conv_size'][0],_gcf_parms['resize_conv_size'][1]),dtype=np.double))
list_conv_support.append(da.from_delayed(delayed_kernels_and_support[2],(len(pb_ant_pairs),chan_chunk_sizes[0][c_chan], pol_chunk_sizes[0][c_pol],2),dtype=np.int))
conv_kernel = da.concatenate(list_conv_kernel,axis=1)
weight_conv_kernel = da.concatenate(list_weight_conv_kernel,axis=1)
conv_support = da.concatenate(list_conv_support,axis=1)
dataset_dict['SUPPORT'] = xr.DataArray(conv_support, dims=['conv_baseline','conv_chan','conv_pol','xy'])
dataset_dict['PS_CORR_IMAGE'] = xr.DataArray(ps_image, dims=['l','m'])
dataset_dict['WEIGHT_CONV_KERNEL'] = xr.DataArray(weight_conv_kernel, dims=['conv_baseline','conv_chan','conv_pol','u','v'])
elif (_gcf_parms['a_term'] == False) and (_gcf_parms['ps_term'] == True):
support = np.array([7,7])
dataset_dict['SUPPORT'] = xr.DataArray(support[None,None,None,:], dims=['conv_baseline','conv_chan','conv_pol','xy'])
conv_kernel = np.zeros((1,1,1,_gcf_parms['resize_conv_size'][0],_gcf_parms['resize_conv_size'][1]))
center = _gcf_parms['resize_conv_size']//2
center_embed = np.array(ps_term.shape)//2
conv_kernel[0,0,0,center[0]-center_embed[0]:center[0]+center_embed[0],center[1]-center_embed[1] : center[1]+center_embed[1]] = ps_term
dataset_dict['PS_CORR_IMAGE'] = xr.DataArray(ps_image, dims=['l','m'])
##Enabled for test
#dataset_dict['WEIGHT_CONV_KERNEL'] = xr.DataArray(conv_kernel, dims=['conv_baseline','conv_chan','conv_pol','u','v'])
elif (_gcf_parms['a_term'] == True) and (_gcf_parms['ps_term'] == False):
conv_kernel = da.real(dafft.fftshift(dafft.fft2(dafft.ifftshift(baseline_pb, axes=(3, 4)), axes=(3, 4)), axes=(3, 4)))
conv_weight_kernel = da.real(dafft.fftshift(dafft.fft2(dafft.ifftshift(weight_baseline_pb_sqrd, axes=(3, 4)), axes=(3, 4)), axes=(3, 4)))
list_conv_kernel = []
list_weight_conv_kernel = []
list_conv_support = []
iter_chunks_indx = itertools.product(np.arange(n_chunks_in_each_dim[0]), np.arange(n_chunks_in_each_dim[1]))
for c_chan, c_pol in iter_chunks_indx:
delayed_kernels_and_support = dask.delayed(resize_and_calc_support)(conv_kernel.partitions[:,c_chan,c_pol,:,:],conv_weight_kernel.partitions[:,c_chan,c_pol,:,:],dask.delayed(_gcf_parms),dask.delayed(_grid_parms))
list_conv_kernel.append(da.from_delayed(delayed_kernels_and_support[0],(len(pb_ant_pairs),chan_chunk_sizes[0][c_chan], pol_chunk_sizes[0][c_pol],_gcf_parms['resize_conv_size'][0],_gcf_parms['resize_conv_size'][1]),dtype=np.double))
list_weight_conv_kernel.append(da.from_delayed(delayed_kernels_and_support[1],(len(pb_ant_pairs),chan_chunk_sizes[0][c_chan], pol_chunk_sizes[0][c_pol],_gcf_parms['resize_conv_size'][0],_gcf_parms['resize_conv_size'][1]),dtype=np.double))
list_conv_support.append(da.from_delayed(delayed_kernels_and_support[2],(len(pb_ant_pairs),chan_chunk_sizes[0][c_chan], pol_chunk_sizes[0][c_pol],2),dtype=np.int))
conv_kernel = da.concatenate(list_conv_kernel,axis=1)
weight_conv_kernel = da.concatenate(list_weight_conv_kernel,axis=1)
conv_support = da.concatenate(list_conv_support,axis=1)
dataset_dict['SUPPORT'] = xr.DataArray(conv_support, dims=['conv_baseline','conv_chan','conv_pol','xy'])
dataset_dict['WEIGHT_CONV_KERNEL'] = xr.DataArray(weight_conv_kernel, dims=['conv_baseline','conv_chan','conv_pol','u','v'])
dataset_dict['PS_CORR_IMAGE'] = xr.DataArray(da.from_array(np.ones(_grid_parms['image_size']),chunks=_grid_parms['image_size']), dims=['l','m'])
else:
assert(False), "######### ERROR: At least 'a_term' or 'ps_term' must be true."
###########################################################
#Make phase gradient (one for each field)
field_phase_dir = _gcf_parms['field_phase_dir']
field_phase_dir = da.from_array(field_phase_dir,chunks=(np.ceil(len(field_phase_dir)/_gcf_parms['a_chan_num_chunk']),2))
phase_gradient = da.blockwise(make_phase_gradient, ("n_field","n_x","n_y"), field_phase_dir, ("n_field","2"), gcf_parms=_gcf_parms, grid_parms=_grid_parms, dtype=complex, new_axes={"n_x": _gcf_parms['resize_conv_size'][0], "n_y": _gcf_parms['resize_conv_size'][1]})
###########################################################
#coords = {'baseline': np.arange(n_unique_ant), 'chan': pb_freq, 'pol' : pb_pol, 'u': np.arange(resize_conv_size[0]), 'v': np.arange(resize_conv_size[1]), 'xy':np.arange(2), 'field':np.arange(field_phase_dir.shape[0]),'l':np.arange(_gridding_convolution_parms['imsize'][0]),'m':np.arange(_gridding_convolution_parms['imsize'][1])}
#coords = { 'conv_chan': pb_freq, 'conv_pol' : pb_pol, 'u': np.arange(resize_conv_size[0]), 'v': np.arange(resize_conv_size[1]), 'xy':np.arange(2), 'field':np.arange(field_phase_dir.shape[0]),'l':np.arange(_gridding_convolution_parms['imsize'][0]),'m':np.arange(_gridding_convolution_parms['imsize'][1])}
coords = { 'u': np.arange(_gcf_parms['resize_conv_size'][0]), 'v': np.arange(_gcf_parms['resize_conv_size'][1]), 'xy':np.arange(2), 'field':np.arange(field_phase_dir.shape[0]),'l':np.arange(_grid_parms['image_size'][0]),'m':np.arange(_grid_parms['image_size'][1])}
dataset_dict['CF_BASELINE_MAP'] = xr.DataArray(cf_baseline_map, dims=('baseline')).chunk(_gcf_parms['vis_data_chunks'][1])
dataset_dict['CF_CHAN_MAP'] = xr.DataArray(cf_chan_map, dims=('chan')).chunk(_gcf_parms['vis_data_chunks'][2])
dataset_dict['CF_POL_MAP'] = xr.DataArray(cf_pol_map, dims=('pol')).chunk(_gcf_parms['vis_data_chunks'][3])
dataset_dict['CONV_KERNEL'] = xr.DataArray(conv_kernel, dims=('conv_baseline','conv_chan','conv_pol','u','v'))
dataset_dict['PHASE_GRADIENT'] = xr.DataArray(phase_gradient, dims=('field','u','v'))
gcf_dataset = xr.Dataset(dataset_dict, coords=coords)
gcf_dataset.attrs['cell_uv'] =1/(_grid_parms['image_size_padded']*_grid_parms['cell_size']*_gcf_parms['oversampling'])
gcf_dataset.attrs['oversampling'] = _gcf_parms['oversampling']
#list_xarray_data_variables = [gcf_dataset['A_TERM'],gcf_dataset['WEIGHT_A_TERM'],gcf_dataset['A_SUPPORT'],gcf_dataset['WEIGHT_A_SUPPORT'],gcf_dataset['PHASE_GRADIENT']]
return _store(gcf_dataset,list_xarray_data_variables,_storage_parms)
def make_image_with_gcf(vis_dataset, gcf_dataset, img_dataset, grid_parms,
norm_parms, sel_parms, storage_parms):
"""
Creates a cube or continuum dirty image from the user specified visibility, uvw and imaging weight data. A gridding convolution function (gcf_dataset), primary beam image (img_dataset) and a primary beam weight image (img_dataset) must be supplied.
Parameters
----------
vis_dataset : xarray.core.dataset.Dataset
Input visibility dataset.
gcf_dataset : xarray.core.dataset.Dataset
Input gridding convolution dataset.
img_dataset : xarray.core.dataset.Dataset
Input image dataset.
grid_parms : dictionary
grid_parms['image_size'] : list of int, length = 2
The image size (no padding).
grid_parms['cell_size'] : list of number, length = 2, units = arcseconds
The image cell size.
grid_parms['chan_mode'] : {'continuum'/'cube'}, default = 'continuum'
Create a continuum or cube image.
grid_parms['fft_padding'] : number, acceptable range [1,100], default = 1.2
The factor that determines how much the gridded visibilities are padded before the fft is done.
norm_parms : dictionary
norm_parms['norm_type'] : {'none'/'flat_noise'/'flat_sky'}, default = 'flat_sky'
Gridded (and FT'd) images represent the PB-weighted sky image.
Qualitatively it can be approximated as two instances of the PB
applied to the sky image (one naturally present in the data
and one introduced during gridding via the convolution functions).
normtype='flat_noise' : Divide the raw image by sqrt(sel_parms['weight_pb']) so that
the input to the minor cycle represents the
product of the sky and PB. The noise is 'flat'
across the region covered by each PB.
normtype='flat_sky' : Divide the raw image by sel_parms['weight_pb'] so that the input
to the minor cycle represents only the sky.
The noise is higher in the outer regions of the
primary beam where the sensitivity is low.
normtype='none' : No normalization after gridding and FFT.
sel_parms : dictionary
sel_parms['uvw'] : str, default ='UVW'
The name of uvw data variable that will be used to grid the visibilities.
sel_parms['data'] : str, default = 'DATA'
The name of the visibility data to be gridded.
sel_parms['imaging_weight'] : str, default ='IMAGING_WEIGHT'
The name of the imaging weights to be used.
sel_parms['image'] : str, default ='IMAGE'
The created image name.
sel_parms['sum_weight'] : str, default ='SUM_WEIGHT'
The created sum of weights name.
sel_parms['pb'] : str, default ='PB'
The primary beam image to use for normalization.
sel_parms['weight_pb'] : str, default ='WEIGHT_PB'
The primary beam weight image to use for normalization.
storage_parms : dictionary
storage_parms['to_disk'] : bool, default = False
If true the dask graph is executed and saved to disk in the zarr format.
storage_parms['append'] : bool, default = False
If storage_parms['to_disk'] is True only the dask graph associated with the function is executed and the resulting data variables are saved to an existing zarr file on disk.
Note that graphs on unrelated data to this function will not be executed or saved.
storage_parms['outfile'] : str
The zarr file to create or append to.
storage_parms['chunks_on_disk'] : dict of int, default = {}
The chunk size to use when writing to disk. This is ignored if storage_parms['append'] is True. The default will use the chunking of the input dataset.
storage_parms['chunks_return'] : dict of int, default = {}
The chunk size of the dataset that is returned. The default will use the chunking of the input dataset.
storage_parms['graph_name'] : str
The time to compute and save the data is stored in the attribute section of the dataset and storage_parms['graph_name'] is used in the label.
storage_parms['compressor'] : numcodecs.blosc.Blosc,default=Blosc(cname='zstd', clevel=2, shuffle=0)
The compression algorithm to use. Available compression algorithms can be found at https://numcodecs.readthedocs.io/en/stable/blosc.html.
Returns
-------
image_dataset : xarray.core.dataset.Dataset
The image_dataset will contain the image created and the sum of weights.
"""
print(
'######################### Start make_image_with_gcf #########################'
)
import numpy as np
from numba import jit
import time
import math
import dask.array.fft as dafft
import xarray as xr
import dask.array as da
import matplotlib.pylab as plt
import dask
import copy, os
from numcodecs import Blosc
from itertools import cycle
from ngcasa._ngcasa_utils._store import _store
from ngcasa._ngcasa_utils._check_parms import _check_storage_parms, _check_sel_parms, _check_existence_sel_parms
from ._imaging_utils._check_imaging_parms import _check_grid_parms, _check_norm_parms
#from ._imaging_utils._gridding_convolutional_kernels import _create_prolate_spheroidal_kernel, _create_prolate_spheroidal_kernel_1D
from ._imaging_utils._standard_grid import _graph_standard_grid
from ._imaging_utils._remove_padding import _remove_padding
from ._imaging_utils._aperture_grid import _graph_aperture_grid
from ._imaging_utils._normalize import _normalize
_grid_parms = copy.deepcopy(grid_parms)
_storage_parms = copy.deepcopy(storage_parms)
_sel_parms = copy.deepcopy(sel_parms)
_norm_parms = copy.deepcopy(norm_parms)
assert (_check_sel_parms(
_sel_parms, {
'uvw': 'UVW',
'data': 'DATA',
'imaging_weight': 'IMAGING_WEIGHT',
'sum_weight': 'SUM_WEIGHT',
'image': 'IMAGE',
'pb': 'PB',
'weight_pb': 'WEIGHT_PB'
})), "######### ERROR: sel_parms checking failed"
assert (_check_existence_sel_parms(
vis_dataset, {
'uvw': _sel_parms['uvw'],
'data': _sel_parms['data'],
'imaging_weight': _sel_parms['imaging_weight']
})), "######### ERROR: sel_parms checking failed"
assert (_check_existence_sel_parms(img_dataset, {
'pb': _sel_parms['pb'],
'weight_pb': _sel_parms['weight_pb']
})), "######### ERROR: sel_parms checking failed"
assert (_check_grid_parms(_grid_parms)
), "######### ERROR: grid_parms checking failed"
assert (_check_norm_parms(_norm_parms)
), "######### ERROR: norm_parms checking failed"
assert (_check_storage_parms(
_storage_parms, 'dirty_image.img.zarr',
'make_image')), "######### ERROR: storage_parms checking failed"
# Creating gridding kernel
#cgk, correcting_cgk_image = _create_prolate_spheroidal_kernel(_grid_parms['oversampling'], _grid_parms['support'], _grid_parms['imsize_padded'])
#cgk_1D = _create_prolate_spheroidal_kernel_1D(_grid_parms['oversampling'], _grid_parms['support'])
#Standard Gridd add switch
#cgk, correcting_cgk_image = _create_prolate_spheroidal_kernel(100, 7, _grid_parms['imsize_padded'])
#cgk_1D = _create_prolate_spheroidal_kernel_1D(100, 7)
#grids_and_sum_weights = _graph_standard_grid(vis_dataset, cgk_1D, _grid_parms)
_grid_parms['grid_weights'] = False
_grid_parms['do_psf'] = False
_grid_parms['oversampling'] = np.array(gcf_dataset.oversampling)
grids_and_sum_weights = _graph_aperture_grid(vis_dataset, gcf_dataset,
_grid_parms, _sel_parms)
uncorrected_dirty_image = dafft.fftshift(dafft.ifft2(dafft.ifftshift(
grids_and_sum_weights[0], axes=(0, 1)),
axes=(0, 1)),
axes=(0, 1))
#Remove Padding
print('grid sizes', _grid_parms['image_size_padded'][0],
_grid_parms['image_size_padded'][1])
uncorrected_dirty_image = _remove_padding(
uncorrected_dirty_image, _grid_parms['image_size']).real * (
_grid_parms['image_size_padded'][0] *
_grid_parms['image_size_padded'][1])
normalized_image = _normalize(uncorrected_dirty_image,
grids_and_sum_weights[1], img_dataset,
gcf_dataset, 'forward', _norm_parms,
sel_parms)
if _grid_parms['chan_mode'] == 'continuum':
freq_coords = [da.mean(vis_dataset.coords['chan'].values)]
chan_width = da.from_array([da.mean(vis_dataset['chan_width'].data)],
chunks=(1, ))
imag_chan_chunk_size = 1
elif _grid_parms['chan_mode'] == 'cube':
freq_coords = vis_dataset.coords['chan'].values
chan_width = vis_dataset['chan_width'].data
imag_chan_chunk_size = vis_dataset.DATA.chunks[2][0]
###Create Image Dataset
chunks = vis_dataset.DATA.chunks
n_imag_pol = chunks[3][0]
coords = {
'd0': np.arange(_grid_parms['image_size'][0]),
'd1': np.arange(_grid_parms['image_size'][1]),
'chan': freq_coords,
'pol': np.arange(n_imag_pol),
'chan_width': ('chan', chan_width)
}
img_dataset = img_dataset.assign_coords(coords)
img_dataset[_sel_parms['sum_weight']] = xr.DataArray(
grids_and_sum_weights[1], dims=['chan', 'pol'])
img_dataset[_sel_parms['image']] = xr.DataArray(
normalized_image, dims=['d0', 'd1', 'chan', 'pol'])
list_xarray_data_variables = [
img_dataset[_sel_parms['image']], img_dataset[_sel_parms['sum_weight']]
]
return _store(img_dataset, list_xarray_data_variables, _storage_parms)
'''
def make_psf(vis_dataset, grid_parms, storage_parms):
"""
Creates a cube or continuum point spread function (psf) image from the user specified uvw and imaging weight data. Only the prolate spheroidal convolutional gridding function is supported (this will change in a future releases.)
Parameters
----------
vis_dataset : xarray.core.dataset.Dataset
Input visibility dataset.
grid_parms : dictionary
grid_parms['imsize'] : list of int, length = 2
The image size (no padding).
grid_parms['cell'] : list of number, length = 2, units = arcseconds
The image cell size.
grid_parms['chan_mode'] : {'continuum'/'cube'}, default = 'continuum'
Create a continuum or cube image.
grid_parms['oversampling'] : int, default = 100
The oversampling used for the convolutional gridding kernel. This will be removed in a later release and incorporated in the function that creates gridding convolutional kernels.
grid_parms['support'] : int, default = 7
The full support used for convolutional gridding kernel. This will be removed in a later release and incormporrated in the function that creates gridding convolutional kernels.
grid_parms['fft_padding'] : number, acceptable range [1,100], default = 1.2
The factor that determines how much the gridded weights are padded before the fft is done.
grid_parms['uvw_name'] : str, default ='UVW'
The name of uvw data variable that will be used to grid the imaging weights.
grid_parms['imaging_weight_name'] : str, default ='IMAGING_WEIGHT'
The name of the imaging weights to be gridded.
grid_parms['image_name'] : str, default ='PSF'
The created image name.
grid_parms['sum_weight_name'] : str, default ='PSF_SUM_WEIGHT'
The created sum of weights name.
storage_parms : dictionary
storage_parms['to_disk'] : bool, default = False
If true the dask graph is executed and saved to disk in the zarr format.
storage_parms['append'] : bool, default = False
If storage_parms['to_disk'] is True only the dask graph associated with the function is executed and the resulting data variables are saved to an existing zarr file on disk.
Note that graphs on unrelated data to this function will not be executed or saved.
storage_parms['outfile'] : str
The zarr file to create or append to.
storage_parms['chunks_on_disk'] : dict of int, default = {}
The chunk size to use when writing to disk. This is ignored if storage_parms['append'] is True. The default will use the chunking of the input dataset.
storage_parms['chunks_return'] : dict of int, default = {}
The chunk size of the dataset that is returned. The default will use the chunking of the input dataset.
storage_parms['graph_name'] : str
The time to compute and save the data is stored in the attribute section of the dataset and storage_parms['graph_name'] is used in the label.
storage_parms['compressor'] : numcodecs.blosc.Blosc,default=Blosc(cname='zstd', clevel=2, shuffle=0)
The compression algorithm to use. Available compression algorithms can be found at https://numcodecs.readthedocs.io/en/stable/blosc.html.
Returns
-------
image_dataset : xarray.core.dataset.Dataset
The image_dataset will contain the image created and the sum of weights.
"""
print('######################### Start make_psf #########################')
import numpy as np
from numba import jit
import time
import math
import dask.array.fft as dafft
import xarray as xr
import dask.array as da
import matplotlib.pylab as plt
import dask.array.fft as dafft
import dask
import copy, os
from numcodecs import Blosc
from itertools import cycle
from ngcasa._ngcasa_utils._store import _store
from ngcasa._ngcasa_utils._check_parms import _check_storage_parms
from ._imaging_utils._check_imaging_parms import _check_grid_params
from ._imaging_utils._gridding_convolutional_kernels import _create_prolate_spheroidal_kernel, _create_prolate_spheroidal_kernel_1D
from ._imaging_utils._standard_grid import _graph_standard_grid
from ._imaging_utils._remove_padding import _remove_padding
_grid_parms = copy.deepcopy(grid_parms)
_storage_parms = copy.deepcopy(storage_parms)
_grid_parms['do_psf'] = True
assert (_check_grid_params(vis_dataset,
_grid_parms,
default_image_name='PSF',
default_sum_weight_name='PSF_SUM_WEIGHT')
), "######### ERROR: grid_parms checking failed"
assert (_check_storage_parms(
_storage_parms, 'psf.img.zarr',
'make_psf')), "######### ERROR: storage_parms checking failed"
# Creating gridding kernel
cgk, correcting_cgk_image = _create_prolate_spheroidal_kernel(
_grid_parms['oversampling'], _grid_parms['support'],
_grid_parms['imsize_padded'])
cgk_1D = _create_prolate_spheroidal_kernel_1D(_grid_parms['oversampling'],
_grid_parms['support'])
grids_and_sum_weights = _graph_standard_grid(vis_dataset, cgk_1D,
_grid_parms)
uncorrected_psf_image = dafft.fftshift(dafft.ifft2(dafft.ifftshift(
grids_and_sum_weights[0], axes=(0, 1)),
axes=(0, 1)),
axes=(0, 1))
#Remove Padding
correcting_cgk_image = _remove_padding(correcting_cgk_image,
_grid_parms['imsize'])
uncorrected_psf_image = _remove_padding(
uncorrected_psf_image, _grid_parms['imsize']).real * (
_grid_parms['imsize_padded'][0] * _grid_parms['imsize_padded'][1])
#############Move this to Normalizer#############
def correct_image(uncorrected_psf_image, sum_weights, correcting_cgk):
sum_weights[sum_weights == 0] = 1
corrected_image = (
uncorrected_psf_image /
sum_weights[None, None, :, :]) / correcting_cgk[:, :, None, None]
return corrected_image
corrected_psf_image = da.map_blocks(
correct_image, uncorrected_psf_image, grids_and_sum_weights[1],
correcting_cgk_image) # ? has to be .data to paralize correctly
####################################################
if _grid_parms['chan_mode'] == 'continuum':
freq_coords = [da.mean(vis_dataset.coords['chan'].values)]
chan_width = da.from_array([da.mean(vis_dataset['chan'].data)],
chunks=(1, ))
imag_chan_chunk_size = 1
elif _grid_parms['chan_mode'] == 'cube':
freq_coords = vis_dataset.coords['chan'].values
chan_width = vis_dataset['chan'].data
imag_chan_chunk_size = vis_dataset.DATA.chunks[2][0]
###Create PSF Image Dataset
chunks = vis_dataset.DATA.chunks
n_imag_pol = chunks[3][0]
image_dict = {}
coords = {
'd0': np.arange(_grid_parms['imsize'][0]),
'd1': np.arange(_grid_parms['imsize'][1]),
'chan': freq_coords,
'pol': np.arange(n_imag_pol),
'chan_width': ('chan', chan_width)
}
image_dict[_grid_parms['sum_weight_name']] = xr.DataArray(
grids_and_sum_weights[1], dims=['chan', 'pol'])
image_dict[_grid_parms['image_name']] = xr.DataArray(
corrected_psf_image, dims=['d0', 'd1', 'chan', 'pol'])
image_dataset = xr.Dataset(image_dict, coords=coords)
list_xarray_data_variables = [
image_dataset[_grid_parms['image_name']],
image_dataset[_grid_parms['sum_weight_name']]
]
return _store(image_dataset, list_xarray_data_variables, _storage_parms)
def make_pb(img_dataset,pb_parms, storage_parms):
"""
The make_pb function currently supports rotationally symmetric airy disk primary beams. Primary beams can be generated for any number of dishes.
The make_pb_parms['list_dish_diameters'] and make_pb_parms['list_blockage_diameters'] must be specified for each dish.
Parameters
----------
img_dataset : xarray.core.dataset.Dataset
Input image dataset.
make_pb_parms : dictionary
make_pb_parms['function'] : {'airy'}, default='airy'
Only the airy disk function is currently supported.
grid_parms['imsize'] : list of int, length = 2
The image size (no padding).
grid_parms['cell'] : list of number, length = 2, units = arcseconds
The image cell size.
make_pb_parms['list_dish_diameters'] : list of number
The list of dish diameters.
make_pb_parms['list_blockage_diameters'] = list of number
The list of blockage diameters for each dish.
make_pb_parms['pb_name'] = 'PB'
The created PB name.
storage_parms : dictionary
storage_parms['to_disk'] : bool, default = False
If true the dask graph is executed and saved to disk in the zarr format.
storage_parms['append'] : bool, default = False
If storage_parms['to_disk'] is True only the dask graph associated with the function is executed and the resulting data variables are saved to an existing zarr file on disk.
Note that graphs on unrelated data to this function will not be executed or saved.
storage_parms['outfile'] : str
The zarr file to create or append to.
storage_parms['chunks_on_disk'] : dict of int, default = {}
The chunk size to use when writing to disk. This is ignored if storage_parms['append'] is True. The default will use the chunking of the input dataset.
storage_parms['chunks_return'] : dict of int, default = {}
The chunk size of the dataset that is returned. The default will use the chunking of the input dataset.
storage_parms['graph_name'] : str
The time to compute and save the data is stored in the attribute section of the dataset and storage_parms['graph_name'] is used in the label.
storage_parms['compressor'] : numcodecs.blosc.Blosc,default=Blosc(cname='zstd', clevel=2, shuffle=0)
The compression algorithm to use. Available compression algorithms can be found at https://numcodecs.readthedocs.io/en/stable/blosc.html.
Returns
-------
img_xds : xarray.core.dataset.Dataset
"""
from ngcasa._ngcasa_utils._store import _store
from ngcasa._ngcasa_utils._check_parms import _check_storage_parms
from ._imaging_utils._check_imaging_parms import _check_pb_parms
import numpy as np
import dask.array as da
import copy, os
import xarray as xr
import matplotlib.pylab as plt
_pb_parms = copy.deepcopy(pb_parms)
_storage_parms = copy.deepcopy(storage_parms)
assert(_check_pb_parms(img_dataset,_pb_parms)), "######### ERROR: user_imaging_weights_parms checking failed"
assert(_check_storage_parms(_storage_parms,'dataset.img.zarr','make_pb')), "######### ERROR: user_storage_parms checking failed"
#parameter check
#cube continuum check
if _pb_parms['function'] == 'airy':
from ._imaging_utils._make_pb_1d import _airy_disk
pb_func = _airy_disk
else:
print('Only the airy function has been implemented')
_pb_parms['ipower'] = 2
_pb_parms['center_indx'] = []
chan_chunk_size = img_dataset.chan_width.chunks[0][0]
freq_coords = da.from_array(img_dataset.coords['chan'].values, chunks=(chan_chunk_size))
pol = img_dataset.pol.values #don't want chunking here
chunksize = (_pb_parms['imsize'][0],_pb_parms['imsize'][1]) + freq_coords.chunksize + (len(pol),) + (len(_pb_parms['list_dish_diameters']),)
pb = da.map_blocks(pb_func, freq_coords, pol, _pb_parms, chunks=chunksize ,new_axis=[0,1,3,4], dtype=np.double)
## Add PB to img_dataset
'''
coords = {'d0': np.arange(pb_parms['imsize'][0]), 'd1': np.arange(_pb_parms['imsize'][1]),
'chan': freq_coords.compute(), 'pol': pol,'dish_type': np.arange(len(_pb_parms['list_dish_diameters']))}
'''
img_dataset[_pb_parms['pb_name']] = xr.DataArray(pb, dims=['d0', 'd1', 'chan', 'pol','dish_type'])
img_dataset = img_dataset.assign_coords({'dish_type': np.arange(len(_pb_parms['list_dish_diameters']))})
list_xarray_data_variables = [img_dataset[_pb_parms['pb_name']]]
return _store(img_dataset,list_xarray_data_variables,_storage_parms)
def phase_rotate(vis_dataset, global_dataset, rotation_parms, sel_parms,
storage_parms):
"""
Rotate uvw coordinates and phase rotate visibilities. For a joint mosaics rotation_parms['common_tangent_reprojection'] must be true.
The specified phasecenter and field phase centers are assumed to be in the same frame.
East-west arrays, emphemeris objects or objects within the nearfield are not supported.
Parameters
----------
vis_dataset : xarray.core.dataset.Dataset
Input visibility dataset.
global_dataset : xarray.core.dataset.Dataset
Input global dataset.
rotation_parms : dictionary
rotation_parms['image_phase_center'] : list of number, length = 2, units = radians
The phase center to rotate to (right ascension and declination).
rotation_parms['common_tangent_reprojection'] : bool, default = True
If true common tangent reprojection is used (should be true if a joint mosaic image is being created).
rotation_parms['single_precision'] : bool, default = True
If rotation_parms['single_precision'] is true then the output visibilities are cast from 128 bit complex to 64 bit complex. Mathematical operations are always done in double precision.
sel_parms : dictionary
sel_parms['uvw_in'] : str, default = 'UVW'
The uvw data variable to rotate.
sel_parms['uvw_out'] : str, default = 'UVW_ROT'
The output uvw data variable (must not be the same as sel_parms['uvw_in']).
sel_parms['data_in'] : str, default = 'DATA'
The visbility data variable to phase rotate.
sel_parms['data_out'] : str, default = 'DATA_ROT'
The output visibility data variable (must not be the same as sel_parms['data_in']).
storage_parms : dictionary
storage_parms['to_disk'] : bool, default = False
If true the dask graph is executed and saved to disk in the zarr format.
storage_parms['append'] : bool, default = False
If storage_parms['to_disk'] is True only the dask graph associated with the function is executed and the resulting data variables are saved to an existing zarr file on disk.
Note that graphs on unrelated data to this function will not be executed or saved.
storage_parms['outfile'] : str
The zarr file to create or append to.
storage_parms['chunks_on_disk'] : dict of int, default = {}
The chunk size to use when writing to disk. This is ignored if storage_parms['append'] is True. The default will use the chunking of the input dataset.
storage_parms['chunks_return'] : dict of int, default = {}
The chunk size of the dataset that is returned. The default will use the chunking of the input dataset.
storage_parms['graph_name'] : str
The time to compute and save the data is stored in the attribute section of the dataset and storage_parms['graph_name'] is used in the label.
storage_parms['compressor'] : numcodecs.blosc.Blosc,default=Blosc(cname='zstd', clevel=2, shuffle=0)
The compression algorithm to use. Available compression algorithms can be found at https://numcodecs.readthedocs.io/en/stable/blosc.html.
Returns
-------
psf_dataset : xarray.core.dataset.Dataset
"""
#based on UVWMachine and FTMachine
#measures/Measures/UVWMachine.cc
print(
'######################### Start phase_rotate #########################'
)
from ngcasa._ngcasa_utils._store import _store
from scipy.spatial.transform import Rotation as R
import numpy as np
import copy
import dask.array as da
import xarray as xr
from ngcasa._ngcasa_utils._check_parms import _check_storage_parms, _check_sel_parms, _check_existence_sel_parms
from ._imaging_utils._check_imaging_parms import _check_rotation_parms
import time
import numba
from numba import double
import dask
_sel_parms = copy.deepcopy(sel_parms)
_rotation_parms = copy.deepcopy(rotation_parms)
_storage_parms = copy.deepcopy(storage_parms)
assert (_check_sel_parms(
_sel_parms, {
'uvw_in': 'UVW',
'uvw_out': 'UVW_ROT',
'data_in': 'DATA',
'data_out': 'DATA_ROT'
})), "######### ERROR: sel_parms checking failed"
assert (_check_existence_sel_parms(vis_dataset, {
'uvw_in': _sel_parms['uvw_in'],
'data_in': _sel_parms['data_in']
})), "######### ERROR: sel_parms checking failed"
assert (_check_rotation_parms(_rotation_parms)
), "######### ERROR: rotation_parms checking failed"
assert (_check_storage_parms(
_storage_parms, 'dataset.vis.zarr',
'phase_rotate')), "######### ERROR: storage_parms checking failed"
assert (
_sel_parms['uvw_out'] != _sel_parms['uvw_in']
), "######### ERROR: sel_parms checking failed sel_parms['uvw_out'] can not be the same as sel_parms['uvw_in']."
assert (
_sel_parms['data_out'] != _sel_parms['data_in']
), "######### ERROR: sel_parms checking failed sel_parms['data_out'] can not be the same as sel_parms['data_in']."
#Phase center
ra_image = _rotation_parms['image_phase_center'][0]
dec_image = _rotation_parms['image_phase_center'][1]
rotmat_image_phase_center = R.from_euler(
'XZ', [[np.pi / 2 - dec_image, -ra_image + np.pi / 2]]).as_matrix()[0]
image_phase_center_cosine = _directional_cosine([ra_image, dec_image])
n_fields = global_dataset.dims['field']
field_names = global_dataset.field
uvw_rotmat = np.zeros((n_fields, 3, 3), np.double)
phase_rotation = np.zeros((n_fields, 3), np.double)
fields_phase_center = global_dataset.FIELD_PHASE_DIR.values[:, :,
vis_dataset.
attrs['ddi']]
#Create a rotation matrix for each field
for i_field in range(n_fields):
#Not sure if last dimention in FIELD_PHASE_DIR is the ddi number
field_phase_center = fields_phase_center[i_field, :]
# Define rotation to a coordinate system with pole towards in-direction
# and X-axis W; by rotating around z-axis over -(90-long); and around
# x-axis (lat-90).
rotmat_field_phase_center = R.from_euler('ZX', [[
-np.pi / 2 + field_phase_center[0],
field_phase_center[1] - np.pi / 2
]]).as_matrix()[0]
uvw_rotmat[i_field, :, :] = np.matmul(rotmat_image_phase_center,
rotmat_field_phase_center).T
if _rotation_parms['common_tangent_reprojection'] == True:
uvw_rotmat[
i_field, 2, 0:
2] = 0.0 # (Common tangent rotation needed for joint mosaics, see last part of FTMachine::girarUVW in CASA)
field_phase_center_cosine = _directional_cosine(field_phase_center)
phase_rotation[i_field, :] = np.matmul(
rotmat_image_phase_center,
(image_phase_center_cosine - field_phase_center_cosine))
#Apply rotation matrix to uvw
def apply_rotation_matrix(uvw, field_id, uvw_rotmat):
#print(uvw.shape,field_id.shape,uvw_rotmat.shape)
for i_time in range(uvw.shape[0]):
#print('The index is',np.where(field_names==field[i_time])[1][0],field_id[i_time,0,0])
#uvw[i_time,:,0:2] = -uvw[i_time,:,0:2] this gives the same result as casa (in the ftmachines uvw(negateUV(vb)) is used). In ngcasa we don't do this since the uvw definition in the gridder and vis.zarr are the same.
#uvw[i_time,:,:] = np.matmul(uvw[i_time,:,:],uvw_rotmat[field_id[i_time],:,:])
uvw[i_time, :, :] = uvw[i_time, :, :] @ uvw_rotmat[field_id[
i_time, 0, 0], :, :]
return uvw
uvw = da.map_blocks(apply_rotation_matrix,
vis_dataset[_sel_parms['uvw_in']].data,
vis_dataset.field_id.data[:, None, None],
uvw_rotmat,
dtype=np.double)
chan_chunk_size = vis_dataset[_sel_parms['data_in']].chunks[2][0]
freq_chan = da.from_array(vis_dataset.coords['chan'].values,
chunks=(chan_chunk_size))
vis_rot = da.map_blocks(apply_phasor,
vis_dataset[_sel_parms['data_in']].data,
uvw[:, :, :, None],
vis_dataset.field_id.data[:, None, None, None],
freq_chan[None, None, :, None],
phase_rotation,
_rotation_parms['common_tangent_reprojection'],
_rotation_parms['single_precision'],
dtype=np.complex)
vis_dataset[_sel_parms['uvw_out']] = xr.DataArray(
uvw, dims=vis_dataset[_sel_parms['uvw_in']].dims)
vis_dataset[_sel_parms['data_out']] = xr.DataArray(
vis_rot, dims=vis_dataset[_sel_parms['data_in']].dims)
list_xarray_data_variables = [
vis_dataset[_sel_parms['uvw_out']], vis_dataset[_sel_parms['data_out']]
]
return _store(vis_dataset, list_xarray_data_variables, _storage_parms)
def phase_rotate_numba(vis_dataset, global_dataset, rotation_parms, sel_parms,
storage_parms):
"""
Rotate uvw with faceting style rephasing for multifield mosaic.
The specified phasecenter and field phase centers are assumed to be in the same frame.
This does not support east-west arrays, emphemeris objects or objects within the nearfield.
(no refocus).
Parameters
----------
vis_dataset : xarray.core.dataset.Dataset
input Visibility Dataset
Returns
-------
psf_dataset : xarray.core.dataset.Dataset
"""
#based on UVWMachine and FTMachine
#measures/Measures/UVWMachine.cc
#Important: Can not applyflags before calling rotate (uvw coordinates are also flagged). This will destroy the rotation transform.
#Performance improvements apply_rotation_matrix (jit code)
#print('1. numba',vis_dataset.DATA[:,0,0,0].values)
from ngcasa._ngcasa_utils._store import _store
from scipy.spatial.transform import Rotation as R
import scipy
import numpy as np
import copy
import dask.array as da
import xarray as xr
from ngcasa._ngcasa_utils._check_parms import _check_storage_parms, _check_sel_parms, _check_existence_sel_parms
from ._imaging_utils._check_imaging_parms import _check_rotation_parms
import time
import numba
from numba import double
import dask
_sel_parms = copy.deepcopy(sel_parms)
_rotation_parms = copy.deepcopy(rotation_parms)
_storage_parms = copy.deepcopy(storage_parms)
assert (_check_sel_parms(
_sel_parms, {
'uvw_in': 'UVW',
'uvw_out': 'UVW_ROT',
'data_in': 'DATA',
'data_out': 'DATA_ROT'
})), "######### ERROR: sel_parms checking failed"
assert (_check_existence_sel_parms(vis_dataset, {
'uvw_in': _sel_parms['uvw_in'],
'data_in': _sel_parms['data_in']
})), "######### ERROR: sel_parms checking failed"
assert (_check_rotation_parms(_rotation_parms)
), "######### ERROR: rotation_parms checking failed"
assert (_check_storage_parms(
_storage_parms, 'dataset.vis.zarr',
'phase_rotate')), "######### ERROR: storage_parms checking failed"
assert (
_sel_parms['uvw_out'] != _sel_parms['uvw_in']
), "######### ERROR: sel_parms checking failed sel_parms['uvw_out'] can not be the same as sel_parms['uvw_in']."
assert (
_sel_parms['data_out'] != _sel_parms['data_in']
), "######### ERROR: sel_parms checking failed sel_parms['data_out'] can not be the same as sel_parms['data_in']."
#Phase center
ra_image = _rotation_parms['image_phase_center'][0]
dec_image = _rotation_parms['image_phase_center'][1]
rotmat_image_phase_center = R.from_euler(
'XZ', [[np.pi / 2 - dec_image, -ra_image + np.pi / 2]]).as_matrix()[0]
image_phase_center_cosine = _directional_cosine([ra_image, dec_image])
n_fields = global_dataset.dims['field']
field_names = global_dataset.field
uvw_rotmat = np.zeros((n_fields, 3, 3), np.double)
phase_rotation = np.zeros((n_fields, 3), np.double)
fields_phase_center = global_dataset.FIELD_PHASE_DIR.values[:, :,
vis_dataset.
attrs['ddi']]
#print(fields_phase_center)
#Create a rotation matrix for each field
for i_field in range(n_fields):
#Not sure if last dimention in FIELD_PHASE_DIR is the ddi number
field_phase_center = fields_phase_center[i_field, :]
# Define rotation to a coordinate system with pole towards in-direction
# and X-axis W; by rotating around z-axis over -(90-long); and around
# x-axis (lat-90).
rotmat_field_phase_center = R.from_euler('ZX', [[
-np.pi / 2 + field_phase_center[0],
field_phase_center[1] - np.pi / 2
]]).as_matrix()[0]
uvw_rotmat[i_field, :, :] = np.matmul(rotmat_image_phase_center,
rotmat_field_phase_center).T
if _rotation_parms['common_tangent_reprojection'] == True:
uvw_rotmat[
i_field, 2, 0:
2] = 0.0 # (Common tangent rotation needed for joint mosaics, see last part of FTMachine::girarUVW in CASA)
field_phase_center_cosine = _directional_cosine(field_phase_center)
phase_rotation[i_field, :] = np.matmul(
rotmat_image_phase_center,
(image_phase_center_cosine - field_phase_center_cosine))
#Apply rotation matrix to uvw
#@jit(nopython=True, cache=True, nogil=True)
def apply_rotation_matrix(uvw, field_id, uvw_rotmat):
#print(uvw.shape,field_id.shape,uvw_rotmat.shape)
for i_time in range(uvw.shape[0]):
#print('The index is',np.where(field_names==field[i_time])[1][0],field_id[i_time,0,0])
#uvw[i_time,:,0:2] = -uvw[i_time,:,0:2] this gives the same result as casa (in the ftmachines uvw(negateUV(vb)) is used). In ngcasa we don't do this since the uvw definition in the gridder and vis.zarr are the same.
#uvw[i_time,:,:] = np.matmul(uvw[i_time,:,:],uvw_rotmat[field_id[i_time],:,:])
uvw[i_time, :, :] = uvw[i_time, :, :] @ uvw_rotmat[field_id[
i_time, 0, 0], :, :]
return uvw
uvw = da.map_blocks(apply_rotation_matrix,
vis_dataset[_sel_parms['uvw_in']].data,
vis_dataset.field_id.data[:, None, None],
uvw_rotmat,
dtype=np.double)
#dask.visualize(uvw,filename='uvw')
chan_chunk_size = vis_dataset[_sel_parms['data_in']].chunks[2][0]
freq_chan = da.from_array(vis_dataset.coords['chan'].values,
chunks=(chan_chunk_size))
#print('2. numba',vis_dataset[_sel_parms['data_in']][:,0,0,0].values)
vis_rot = da.map_blocks(apply_phasor,
vis_dataset[_sel_parms['data_in']].data,
uvw[:, :, :, None],
vis_dataset.field_id.data[:, None, None, None],
freq_chan[None, None, :, None],
phase_rotation,
_rotation_parms['common_tangent_reprojection'],
dtype=np.complex)
#dask.visualize(uvw,filename='uvw_rot')
#dask.visualize(vis_rot,filename='vis_rot')
vis_dataset[_sel_parms['uvw_out']] = xr.DataArray(
uvw, dims=vis_dataset[_sel_parms['uvw_in']].dims)
vis_dataset[_sel_parms['data_out']] = xr.DataArray(
vis_rot, dims=vis_dataset[_sel_parms['data_in']].dims)
# dask.visualize(vis_dataset[_sel_parms['uvw_out']],filename='uvw_rot_dataset')
# dask.visualize(vis_dataset[_sel_parms['data_out']],filename='vis_rot_dataset')
# dask.visualize(vis_dataset,filename='vis_dataset_before_append')
list_xarray_data_variables = [
vis_dataset[_sel_parms['uvw_out']], vis_dataset[_sel_parms['data_out']]
]
return _store(vis_dataset, list_xarray_data_variables, _storage_parms)
