Initial hacky commit of zarr support.

setup.py

@@ -9,14 +9,11 @@
     readme = readme_file.read()
 
 requirements = [
-    'dask[array] == 2021.2.0',
     'donfig >= 0.4.0',
-    'numpy >= 1.14.0, <= 1.19.5',  # breakage in newer numpy + numerical errors
     'numba >= 0.43.0',
     'scipy >= 1.2.0',
     'threadpoolctl >= 1.0.0',
-    'dask-ms == 0.2.6',
-    'zarr >= 2.3.1'
+    'dask-ms[xarray,zarr,s3]'
 ]
 
 extras_require = {'testing': ['pytest',

tricolour/apps/tricolour/app.py

@@ -21,7 +21,9 @@
                               ResourceProfiler,
                               CacheProfiler, visualize)
 import numpy as np
-from daskms import xds_from_ms, xds_from_table, xds_to_table
+from daskms import (xds_from_storage_ms,
+                    xds_from_storage_table,
+                    xds_to_storage_table)
 from threadpoolctl import threadpool_limits
 
 from tricolour.apps.tricolour.strat_executor import StrategyExecutor
@@ -229,10 +231,10 @@ def support_tables(ms):
     """
 
     # Get datasets for sub-tables partitioned by row when variably shaped
-    support = {t: xds_from_table("::".join((ms, t)), group_cols="__row__")
+    support = {t: xds_from_storage_table("::".join((ms, t)), group_cols="__row__")
                for t in ["FIELD", "POLARIZATION", "SPECTRAL_WINDOW"]}
     # These columns have fixed shapes
-    support.update({t: xds_from_table("::".join((ms, t)))[0]
+    support.update({t: xds_from_storage_table("::".join((ms, t)))[0]
                     for t in ["ANTENNA", "DATA_DESCRIPTION"]})
 
     # Reify all values upfront
@@ -291,7 +293,7 @@ def _main(args):
     if args.subtract_model_column is not None:
         columns.append(args.subtract_model_column)
 
-    xds = list(xds_from_ms(args.ms,
+    xds = list(xds_from_storage_ms(args.ms,
                            columns=tuple(columns),
                            group_cols=group_cols,
                            index_cols=index_cols,
@@ -347,6 +349,7 @@ def _main(args):
         field_dict = {i: fn for i, fn in enumerate(fieldnames)}
 
     # List which hold our dask compute graphs for each dataset
+    writable_xds = []
     write_computes = []
     original_stats = []
     final_stats = []
@@ -386,7 +389,7 @@ def _main(args):
         # Generate unflagged defaults if we should ignore existing flags
         # otherwise take flags from the dataset
         if args.ignore_flags is True:
-            flags = da.full_like(vis, False, dtype=np.bool)
+            flags = da.full_like(vis, False, dtype=np.bool_)
             log.critical("Completely ignoring measurement set "
                          "flags as per '-if' request. "
                          "Strategy WILL NOT or with original flags, even if "
@@ -471,10 +474,13 @@ def _main(args):
         # Create new dataset containing new flags
         new_ds = ds.assign(FLAG=(("row", "chan", "corr"), corr_flags))
 
-        # Write back to original dataset
-        writes = xds_to_table(new_ds, args.ms, "FLAG")
-        # original should also have .compute called because we need stats
-        write_computes.append(writes)
+        # Append to list of datasets we intend to write to disk.
+        writable_xds.append(new_ds)
+
+    # Write back to original dataset
+    write_computes = xds_to_storage_table(
+        writable_xds, args.ms, columns=("FLAG",), rechunk=True
+    )
 
     if len(write_computes) > 0:
         # Combine stats from all datasets

tricolour/mask.py

@@ -60,7 +60,7 @@ def load_mask(filename, dilate):
     # Load mask
     mask = np.load(filename)
 
-    if mask.dtype[0] != np.bool or mask.dtype[1] != np.float64:
+    if mask.dtype[0] != np.bool_ or mask.dtype[1] != np.float64:
         raise ValueError("Mask %s is not a valid static mask "
                          "with labelled channel axis "
                          "[dtype == (bool, float64)]" % filename)

tricolour/packing.py

@@ -90,7 +90,7 @@ def _create_window_dask(name, ntime, nchan, nbl, ncorr, token,
 
     graph = HighLevelGraph.from_collections(collection_name, layers, ())
     chunks = ((0,),)  # One chunk containing single zarr array object
-    return da.Array(graph, collection_name, chunks, dtype=np.object)
+    return da.Array(graph, collection_name, chunks, dtype=object)
 
 
 def create_vis_windows(ntime, nchan, nbl, ncorr, token,
@@ -343,7 +343,7 @@ def pack_data(time_inv, ubl,
                            flags, ("row", "chan", "corr"),
                            vis_win_obj, ("windim",),
                            flag_win_obj, ("windim",),
-                           dtype=np.bool)
+                           dtype=np.bool_)
 
     # Expose visibility data at it's full resolution
     vis_windows = da.blockwise(_packed_windows, _WINDOW_SCHEMA,

tricolour/tests/test_flagging_additional.py

@@ -131,7 +131,7 @@ def test_apply_static_mask(wsrt_ants, unique_baselines,
                                   accumulation_mode="or")
 
     # Check that first mask's flags are applied
-    chan_sel = np.zeros(chan_freqs.shape[0], dtype=np.bool)
+    chan_sel = np.zeros(chan_freqs.shape[0], dtype=np.bool_)
     chan_sel[[2, 10]] = True
 
     assert np.all(new_flags[:, :, :, chan_sel] == 1)
@@ -144,7 +144,7 @@ def test_apply_static_mask(wsrt_ants, unique_baselines,
                                   accumulation_mode="or")
 
     # Check that both mask's flags have been applied
-    chan_sel = np.zeros(chan_freqs.shape[0], dtype=np.bool)
+    chan_sel = np.zeros(chan_freqs.shape[0], dtype=np.bool_)
     chan_sel[[2, 10, 4, 11, 5]] = True
 
     assert np.all(new_flags[:, :, :, chan_sel] == 1)
@@ -157,7 +157,7 @@ def test_apply_static_mask(wsrt_ants, unique_baselines,
                                   accumulation_mode="override")
 
     # Check that only last mask's flags applied
-    chan_sel = np.zeros(chan_freqs.shape[0], dtype=np.bool)
+    chan_sel = np.zeros(chan_freqs.shape[0], dtype=np.bool_)
     chan_sel[[4, 11, 5]] = True
 
     assert np.all(new_flags[:, :, :, chan_sel] == 1)
@@ -176,7 +176,7 @@ def test_apply_static_mask(wsrt_ants, unique_baselines,
                                   uvrange=uvrange)
 
     # Check that both mask's flags have been applied
-    chan_sel = np.zeros(chan_freqs.shape[0], dtype=np.bool)
+    chan_sel = np.zeros(chan_freqs.shape[0], dtype=np.bool_)
     chan_sel[[2, 10, 4, 11, 5]] = True
 
     # Select baselines based on the uvrange

tricolour/window_statistics.py

@@ -123,21 +123,21 @@ def window_stats(flag_window, ubls, chan_freqs,
                          field_name, None,
                          ddid, None,
                          nchanbins, None,
-                         meta=np.empty((0,), dtype=np.object))
+                         meta=np.empty((0,), dtype=object))
 
     # Create an empty stats object if the user hasn't supplied one
     if prev_stats is None:
         def _window_stat_creator():
             return WindowStatistics(nchanbins)
 
         prev_stats = da.blockwise(_window_stat_creator, (),
-                                  meta=np.empty((), dtype=np.object))
+                                  meta=np.empty((), dtype=object))
 
     # Combine per-baseline stats into a single stats object
     return da.blockwise(_combine_baseline_window_stats, (),
                         stats, ("bl",),
                         prev_stats, (),
-                        meta=np.empty((), dtype=np.object))
+                        meta=np.empty((), dtype=object))
 
 
 def _combine_window_stats(*args):
@@ -167,7 +167,7 @@ def combine_window_stats(window_stats):
     args = (v for ws in window_stats for v in (ws, ()))
 
     return da.blockwise(_combine_window_stats, (),
-                        *args, dtype=np.object)
+                        *args, dtype=object)
 
 
 class WindowStatistics(object):