Add pyproject.toml and relax dependencies

.github/workflows/ci.yml

@@ -1,41 +1,93 @@
-name: pfb-imaging Workflow
+name: pfb-imaging CI Workflow
 
 on:
   push:
     tags:
       - 'v*'
   pull_request:
 
+env:
+  POETRY_VERSION: 1.5
+
 jobs:
   test:
-    runs-on: ubuntu-latest
+    runs-on: ${{ matrix.os }}
+    continue-on-error: true
     if: "!contains(github.event.head_commit.message, '[skip ci]')"
+
+    env:
+      NUMBA_CACHE_DIR: /tmp/numba-cache
+
     strategy:
       matrix:
-        python-version: ["3.9", "3.10", "3.11"]
+        os: [ubuntu-20.04, ubuntu-22.04]
+        python-version: ["3.10", "3.11"]
 
     steps:
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version}}
 
+      - name: Install poetry
+        uses: abatilo/actions-poetry@v2
+        with:
+          poetry-version: ${{ env.POETRY_VERSION }}
+
+      - name: Check poetry install
+        run: poetry --version
+
       - name: Checkout source
         uses: actions/checkout@v3
         with:
-          fetch-depth: 1
+          fetch-depth: 0
+
+      - name: Restore repo times
+        uses: chetan/git-restore-mtime-action@v2
 
       - name: Upgrade pip and setuptools
         run: python -m pip install -U pip setuptools
 
-      # - name: Pin setuptools
-      #   run: python -m pip install setuptools==65.5
+      - name: Create Key and Numba Cache Directory
+        id: numba-key
+        run: |
+          mkdir -p ${{ env.NUMBA_CACHE_DIR }}
+          echo "timestamp=$(/bin/date -u '+%Y%m%d%H%M%S')" >> $GITHUB_OUTPUT
+
+      - name: Cache Numba Kernels
+        uses: actions/cache@v3
+        with:
+          key: numba-cache-${{ matrix.python-version }}-${{ steps.numba-key.outputs.timestamp }}
+          restore-keys: numba-cache-${{ matrix.python-version }}-
+          path: ${{ env.NUMBA_CACHE_DIR }}
+
+      - name: List the measures directory
+        run: curl ftp://ftp.astron.nl/outgoing/Measures/ > measures_dir.txt
+
+      - name: Load cached CASA Measures Data
+        id: load-cached-casa-measures
+        uses: actions/cache@v3
+        with:
+          key: casa-measures-${{ hashFiles('measures_dir.txt')}}
+          path: |
+            ~/measures
+            ~/.casarc
+
+      - name: Download and install CASA Measures Data
+        if: steps.load-cached-casa-measures.outputs.cache-hit != 'true'
+        run: |
+          mkdir -p ~/measures
+          curl ftp://ftp.astron.nl/outgoing/Measures/WSRT_Measures.ztar | tar xvzf - -C ~/measures
+          echo "measures.directory: ~/measures" > ~/.casarc
 
       - name: Install pfb-imaging
-        run: python -m pip install .[testing]
+        run: poetry install
+
+      - name: Run pfb-imaging
+        run: poetry run pfb --help
 
       - name: Run tests
-        run: py.test -s -vvv tests/
+        run: poetry run pytest -v tests/
 
   deploy:
     needs: [test]
@@ -48,27 +100,32 @@ jobs:
         with:
           python-version: "3.10"
 
-      - name: Install latest setuptools, wheel, pip
-        run: python3 -m pip install -U pip setuptools wheel
+      - name: Install poetry
+        uses: abatilo/actions-poetry@v2
+        with:
+          poetry-version: ${{ env.POETRY_VERSION }}
+
+      - name: Check poetry install
+        run: poetry --version
 
       - name: Checkout source
         uses: actions/checkout@v4
         with:
           fetch-depth: 1
 
       - name: Build distributions
-        run: python setup.py sdist bdist_wheel
+        run: poetry build
 
       - name: Publish distribution to Test PyPI
-        uses: pypa/gh-action-pypi-publish@master
+        uses: pypa/gh-action-pypi-publish@1.8.6
         with:
           user: __token__
           password: ${{ secrets.PYPI_TEST_API_TOKEN }}
           repository_url: https://test.pypi.org/legacy/
         continue-on-error: false
 
       - name: Publish distribution 📦 to PyPI
-        uses: pypa/gh-action-pypi-publish@master
+        uses: pypa/gh-action-pypi-publish@1.8.6
         with:
           user: __token__
           password: ${{ secrets.PYPI_API_TOKEN }}

pfb/opt/pcg.py

@@ -390,7 +390,6 @@ def pcg_psf(psfhat,
 
 def pcg_dds(ds_name,
             eta,  # regularisation for Hessian approximation
-            sigma,       # regularisation for preconditioner
             mask=1.0,
             use_psf=True,
             residual_name='RESIDUAL',
@@ -399,6 +398,7 @@ def pcg_dds(ds_name,
             epsilon=5e-4,
             double_accum=True,
             nthreads=1,
+            zero_model_outside_mask=False,
             tol=1e-5,
             maxit=500,
             verbosity=1,
@@ -413,94 +413,64 @@ def pcg_dds(ds_name,
     if not isinstance(ds_name, list):
         ds_name = [ds_name]
 
-    # drop_vars = ['PSF']
-    # if not use_psf:
-    #     drop_vars.append('PSFHAT')
-    drop_vars = None
+    drop_vars = ['PSF', 'PSFHAT']
     ds = xds_from_list(ds_name, nthreads=nthreads,
                        drop_vars=drop_vars)[0]
-
-    if residual_name in ds:
-        j = getattr(ds, residual_name).values * mask * ds.BEAM.values
-        ds = ds.drop_vars(residual_name)
+    beam = mask * ds.BEAM.values
+    if zero_model_outside_mask:
+        if model_name not in ds:
+            raise RuntimeError(f"Asked to zero model outside mask but {model_name} not in dds")
+        model = getattr(ds, model_name).values
+        model = np.where(mask > 0, model, 0.0)
+        print("Zeroing model outside mask")
+        resid = ds.DIRTY.values - _hessian_slice(
+                                        model,
+                                        uvw=ds.UVW.values,
+                                        weight=ds.WEIGHT.values,
+                                        vis_mask=ds.MASK.values,
+                                        freq=ds.FREQ.values,
+                                        beam=ds.BEAM.values,
+                                        cell=ds.cell_rad,
+                                        x0=ds.x0,
+                                        y0=ds.y0,
+                                        do_wgridding=do_wgridding,
+                                        epsilon=epsilon,
+                                        double_accum=double_accum,
+                                        nthreads=nthreads)
+        j = resid * beam
     else:
-        j = ds.DIRTY.values * mask * ds.BEAM.values
+        if model_name in ds:
+            model = getattr(ds, model_name).values
+        else:
+            model = np.zeros(mask.shape, dtype=float)
+
+        if residual_name in ds:
+            j = getattr(ds, residual_name).values * beam
+            ds = ds.drop_vars(residual_name)
+        else:
+            j = ds.DIRTY.values * beam
 
-    psf = ds.PSF.values
-    nx_psf, py_psf = psf.shape
     nx, ny = j.shape
-    wsum = np.sum(ds.WEIGHT.values * ds.MASK.values)
-    psf /= wsum
+    wsum = ds.wsum
     j /= wsum
-
-    # downweight edges of field compared to center
-    # this allows the PCG to downweight the fit to the edges
-    # which may be contaminated by edge effects and also
-    # stabalises the preconditioner
-    width = np.minimum(int(0.1*nx), 32)
-    taperxy = taperf((nx, ny), width)
-    # eta /= taperxy
-
-    # set precond if PSF is present
-    if 'PSFHAT' in ds and use_psf:
-        psfhat = np.abs(ds.PSFHAT.values)/wsum
-        ds.drop_vars(('PSFHAT'))
-        nx_psf, nyo2 = psfhat.shape
-        ny_psf = 2*(nyo2-1)  # is this always the case?
-        nxpadl = (nx_psf - nx)//2
-        nxpadr = nx_psf - nx - nxpadl
-        nypadl = (ny_psf - ny)//2
-        nypadr = ny_psf - ny - nypadl
-        if nx_psf != nx:
-            unpad_x = slice(nxpadl, -nxpadr)
-        else:
-            unpad_x = slice(None)
-        if ny_psf != ny:
-            unpad_y = slice(nypadl, -nypadr)
-        else:
-            unpad_y = slice(None)
-        xpad = empty_noncritical((nx_psf, ny_psf),
-                                 dtype=j.dtype)
-        xhat = empty_noncritical((nx_psf, nyo2),
-                                 dtype='c16')
-        xout = empty_noncritical((nx, ny),
-                                 dtype=j.dtype)
-        precond = partial(
-                    hess_direct_slice,
-                    xpad=xpad,
-                    xhat=xhat,
-                    xout=xout,
-                    abspsf=psfhat,
-                    taperxy=taperxy,
-                    lastsize=ny_psf,
-                    nthreads=nthreads,
-                    eta=sigma,
-                    mode='backward')
-
-        x0 = precond(j)
-
-        # get intrinsic resolution by deconvolving psf
-        upsf = precond(psf[unpad_x, unpad_y])
-        upsf /= upsf.max()
-        gaussparu = fitcleanbeam(upsf[None], level=0.25, pixsize=1.0)[0]
-        ds = ds.assign(**{
-            'UPSF': (('x', 'y'), upsf)
-        })
-        ds = ds.assign_attrs(gaussparu=gaussparu)
+    precond = None
+    if 'UPDATE' in ds:
+        x0 = ds.UPDATE.values * mask
     else:
-        # print('Not using preconditioning')
-        precond = None
         x0 = np.zeros_like(j)
 
     hess = partial(_hessian_slice,
                    uvw=ds.UVW.values,
                    weight=ds.WEIGHT.values,
                    vis_mask=ds.MASK.values,
                    freq=ds.FREQ.values,
-                   beam=ds.BEAM.values,
+                   beam=beam,
                    cell=ds.cell_rad,
                    x0=ds.x0,
                    y0=ds.y0,
+                   flip_u=ds.flip_u,
+                   flip_v=ds.flip_v,
+                   flip_w=ds.flip_w,
                    do_wgridding=do_wgridding,
                    epsilon=epsilon,
                    double_accum=double_accum,
@@ -520,11 +490,7 @@ def pcg_dds(ds_name,
             backtrack=False,
             return_resid=False)
 
-    if model_name in ds:
-        model = getattr(ds, model_name).values + x
-    else:
-        model = x
-
+    model += x
 
     resid = ds.DIRTY.values - _hessian_slice(
                                         model,

pfb/parser/fluxtractor.yaml

@@ -30,14 +30,9 @@ inputs:
   eta:
     dtype: float
     default: 1e-5
-    abbreviation: sinv
+    abbreviation: eta
     info:
       Standard deviation of assumed GRF prior
-  sigma:
-    dtype: float
-    default: 1
-    info:
-      The value that is added to |psfhat| for preconditioning.
   model-name:
     dtype: str
     default: MODEL

pfb/parser/init.yaml

@@ -121,11 +121,16 @@ inputs:
     info:
       Display progress.
       Use --no-progressbar to deactivate.
-  memory_reporting:
+  memory-reporting:
     dtype: bool
     default: false
     info:
       Report worker memory as tasks complete
+  check_ants:
+    dtype: bool
+    default: false
+    info:
+      Check that ANTENNA1 and ANTENNA2 tables are consistent with the ANTENNA table.
 
   _include:
     - (.)out.yml

pfb/parser/uncabbedcabs.yml

@@ -25,14 +25,14 @@ pfb.degrid:
   _include:
     - (.)degrid.yaml
 
-pfb.klean:
-  command: pfb.workers.klean.klean
+pfb.kclean:
+  command: pfb.workers.kclean.kclean
   flavour: python
   policies:
     pass_missing_as_none: true
 
   _include:
-    - (.)klean.yaml
+    - (.)kclean.yaml
 
 pfb.restore:
   command: pfb.workers.restore.restore

pfb/utils/misc.py

@@ -461,12 +461,6 @@ def construct_mappings(ms_name,
                 raise RuntimeError("Something went wrong constructing the "
                                    "frequency mapping. sum(fchunks != nchan)")
 
-            # nfreq_chunks = nchan_in // cpit
-            # freq_chunks = (cpit,)*nfreq_chunks
-            # rem = nchan_in - nfreq_chunks * cpit
-            # if rem:
-            #     freq_chunks += (rem,)
-
             ms_chunks[ms].append({'row': row_chunks,
                                   'chan': freq_chunks})
 

pfb/utils/stokes2vis.py

@@ -164,14 +164,15 @@ def single_stokes(
 
     # check that antpos gives the correct size table
     antmax = allants.size
-    try:
-        assert antmax == nant
-    except Exception as e:
-        raise ValueError('Inconsistent ANTENNA table. '
-                         'Shape does not match max number of antennas '
-                         'as inferred from ant1 and ant2. '
-                         f'Table size is {antpos.shape} but got {antmax}. '
-                         f'{oname}')
+    if opts.check_ants:
+        try:
+            assert antmax == nant
+        except Exception as e:
+            raise ValueError('Inconsistent ANTENNA table. '
+                            'Shape does not match max number of antennas '
+                            'as inferred from ant1 and ant2. '
+                            f'Table size is {antpos.shape} but got {antmax}. '
+                            f'{oname}')
 
     # relabel antennas by index
     # this only works because allants is sorted in ascending order