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  • xarray · 8 ✖
id node_id number title user state locked assignee milestone comments created_at updated_at ▲ closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
1044363666 PR_kwDOAMm_X84uE2qZ 5936 Fix a missing @requires_zarr in tests ArchangeGabriel 490531 closed 0     3 2021-11-04T04:49:39Z 2021-11-04T10:12:58Z 2021-11-04T10:12:46Z CONTRIBUTOR   0 pydata/xarray/pulls/5936

When zarr is not available, this test fails with NameError: name 'zarr' is not defined.

Full trace ``` __________________________ test_zarr_storage_options ___________________________ @requires_fsspec def test_zarr_storage_options(): pytest.importorskip("aiobotocore") ds = create_test_data() store_target = "memory://test.zarr" > ds.to_zarr(store_target, storage_options={"test": "zarr_write"}) /build/python-xarray/src/xarray-0.20.0/xarray/tests/test_backends.py:2406: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /build/python-xarray/src/xarray-0.20.0/xarray/core/dataset.py:2037: in to_zarr return to_zarr( /build/python-xarray/src/xarray-0.20.0/xarray/backends/api.py:1391: in to_zarr zstore = backends.ZarrStore.open_group( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cls = <class 'xarray.backends.zarr.ZarrStore'> store = <fsspec.mapping.FSMap object at 0x7f074691cfd0>, mode = 'w-' synchronizer = None, group = None, consolidated = False consolidate_on_close = True, chunk_store = None, storage_options = None append_dim = None, write_region = None, safe_chunks = True, stacklevel = 4 @classmethod def open_group( cls, store, mode="r", synchronizer=None, group=None, consolidated=False, consolidate_on_close=False, chunk_store=None, storage_options=None, append_dim=None, write_region=None, safe_chunks=True, stacklevel=2, ): # zarr doesn't support pathlib.Path objects yet. zarr-python#601 if isinstance(store, os.PathLike): store = os.fspath(store) open_kwargs = dict( mode=mode, synchronizer=synchronizer, path=group, ) open_kwargs["storage_options"] = storage_options if chunk_store: open_kwargs["chunk_store"] = chunk_store if consolidated is None: consolidated = False if consolidated is None: try: zarr_group = zarr.open_consolidated(store, **open_kwargs) except KeyError: warnings.warn( "Failed to open Zarr store with consolidated metadata, " "falling back to try reading non-consolidated metadata. " "This is typically much slower for opening a dataset. " "To silence this warning, consider:\n" "1. Consolidating metadata in this existing store with " "zarr.consolidate_metadata().\n" "2. Explicitly setting consolidated=False, to avoid trying " "to read consolidate metadata, or\n" "3. Explicitly setting consolidated=True, to raise an " "error in this case instead of falling back to try " "reading non-consolidated metadata.", RuntimeWarning, stacklevel=stacklevel, ) zarr_group = zarr.open_group(store, **open_kwargs) elif consolidated: # TODO: an option to pass the metadata_key keyword zarr_group = zarr.open_consolidated(store, **open_kwargs) else: > zarr_group = zarr.open_group(store, **open_kwargs) E NameError: name 'zarr' is not defined /build/python-xarray/src/xarray-0.20.0/xarray/backends/zarr.py:386: NameError ``` 
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     xarray 13221727 pull
667055142 MDU6SXNzdWU2NjcwNTUxNDI= 4281 Test failure in test_serializable_locks ArchangeGabriel 490531 open 0     2 2020-07-28T13:02:56Z 2021-06-05T18:20:30Z   CONTRIBUTOR      

What happened: The mentioned test failed.

What you expected to happen: It should work?

Minimal Complete Verifiable Example:

python python setup.py build pytest

Anything else we need to know?:

Full excerpt: ``` _____ test_serializable_locks ________

def test_func():
    result = None
    workers = []
    with clean(timeout=active_rpc_timeout, **clean_kwargs) as loop:

        async def coro():
            with dask.config.set(config):
                s = False
                for i in range(5):
                    try:
                        s, ws = await start_cluster(
                            nthreads,
                            scheduler,
                            loop,
                            security=security,
                            Worker=Worker,
                            scheduler_kwargs=scheduler_kwargs,
                            worker_kwargs=worker_kwargs,
                        )
                    except Exception as e:
                        logger.error(
                            "Failed to start gen_cluster, retrying",
                            exc_info=True,
                        )
                        await asyncio.sleep(1)
                    else:
                        workers[:] = ws
                        args = [s] + workers
                        break
                if s is False:
                    raise Exception("Could not start cluster")
                if client:
                    c = await Client(
                        s.address,
                        loop=loop,
                        security=security,
                        asynchronous=True,
                        **client_kwargs,
                    )
                    args = [c] + args
                try:
                    future = func(*args)
                    if timeout:
                        future = asyncio.wait_for(future, timeout)
                    result = await future
                    if s.validate:
                        s.validate_state()
                finally:
                    if client and c.status not in ("closing", "closed"):
                        await c._close(fast=s.status == "closed")
                    await end_cluster(s, workers)
                    await asyncio.wait_for(cleanup_global_workers(), 1)

                try:
                    c = await default_client()
                except ValueError:
                    pass
                else:
                    await c._close(fast=True)

                def get_unclosed():
                    return [c for c in Comm._instances if not c.closed()] + [
                        c
                        for c in _global_clients.values()
                        if c.status != "closed"
                    ]

                try:
                    start = time()
                    while time() < start + 5:
                        gc.collect()
                        if not get_unclosed():
                            break
                        await asyncio.sleep(0.05)
                    else:
                        if allow_unclosed:
                            print(f"Unclosed Comms: {get_unclosed()}")
                        else:
                            raise RuntimeError("Unclosed Comms", get_unclosed())
                finally:
                    Comm._instances.clear()
                    _global_clients.clear()

                return result

      result = loop.run_sync(
            coro, timeout=timeout * 2 if timeout else timeout
        )

/usr/lib/python3.8/site-packages/distributed/utils_test.py:953:

/usr/lib/python3.8/site-packages/tornado/ioloop.py:532: in run_sync return future_cell[0].result() /usr/lib/python3.8/site-packages/distributed/utils_test.py:912: in coro result = await future

fut = <Task cancelled name='Task-701' coro=\<test_serializable_locks() done, defined at /build/python-xarray/src/xarray-0.16.0/xarray/tests/test_distributed.py:220>> timeout = 10

async def wait_for(fut, timeout, *, loop=None):
    """Wait for the single Future or coroutine to complete, with timeout.

    Coroutine will be wrapped in Task.

    Returns result of the Future or coroutine.  When a timeout occurs,
    it cancels the task and raises TimeoutError.  To avoid the task
    cancellation, wrap it in shield().

    If the wait is cancelled, the task is also cancelled.

    This function is a coroutine.
    """
    if loop is None:
        loop = events.get_running_loop()
    else:
        warnings.warn("The loop argument is deprecated since Python 3.8, "
                      "and scheduled for removal in Python 3.10.",
                      DeprecationWarning, stacklevel=2)

    if timeout is None:
        return await fut

    if timeout <= 0:
        fut = ensure_future(fut, loop=loop)

        if fut.done():
            return fut.result()

        fut.cancel()
        raise exceptions.TimeoutError()

    waiter = loop.create_future()
    timeout_handle = loop.call_later(timeout, _release_waiter, waiter)
    cb = functools.partial(_release_waiter, waiter)

    fut = ensure_future(fut, loop=loop)
    fut.add_done_callback(cb)

    try:
        # wait until the future completes or the timeout
        try:
            await waiter
        except exceptions.CancelledError:
            fut.remove_done_callback(cb)
            fut.cancel()
            raise

        if fut.done():
            return fut.result()
        else:
            fut.remove_done_callback(cb)
            # We must ensure that the task is not running
            # after wait_for() returns.
            # See https://bugs.python.org/issue32751
            await _cancel_and_wait(fut, loop=loop)

          raise exceptions.TimeoutError()

E asyncio.exceptions.TimeoutError

/usr/lib/python3.8/asyncio/tasks.py:490: TimeoutError ----------------------------- Captured stderr call ----------------------------- distributed.scheduler - INFO - Clear task state distributed.scheduler - INFO - Scheduler at: tcp://127.0.0.1:40483 distributed.scheduler - INFO - dashboard at: 127.0.0.1:8787 distributed.worker - INFO - Start worker at: tcp://127.0.0.1:45589 distributed.worker - INFO - Listening to: tcp://127.0.0.1:45589 distributed.worker - INFO - dashboard at: 127.0.0.1:43247 distributed.worker - INFO - Waiting to connect to: tcp://127.0.0.1:40483 distributed.worker - INFO - ------------------------------------------------- distributed.worker - INFO - Threads: 1 distributed.worker - INFO - Memory: 135.05 GB distributed.worker - INFO - Local Directory: /build/python-xarray/src/xarray-0.16.0/dask-worker-space/worker-nbb_tie4 distributed.worker - INFO - ------------------------------------------------- distributed.worker - INFO - Start worker at: tcp://127.0.0.1:40153 distributed.worker - INFO - Listening to: tcp://127.0.0.1:40153 distributed.worker - INFO - dashboard at: 127.0.0.1:33037 distributed.worker - INFO - Waiting to connect to: tcp://127.0.0.1:40483 distributed.worker - INFO - ------------------------------------------------- distributed.worker - INFO - Threads: 2 distributed.worker - INFO - Memory: 135.05 GB distributed.worker - INFO - Local Directory: /build/python-xarray/src/xarray-0.16.0/dask-worker-space/worker-qyb14fl2 distributed.worker - INFO - ------------------------------------------------- distributed.scheduler - INFO - Register worker <Worker 'tcp://127.0.0.1:45589', name: 0, memory: 0, processing: 0> distributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:45589 distributed.core - INFO - Starting established connection distributed.scheduler - INFO - Register worker <Worker 'tcp://127.0.0.1:40153', name: 1, memory: 0, processing: 0> distributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:40153 distributed.core - INFO - Starting established connection distributed.worker - INFO - Registered to: tcp://127.0.0.1:40483 distributed.worker - INFO - ------------------------------------------------- distributed.worker - INFO - Registered to: tcp://127.0.0.1:40483 distributed.worker - INFO - ------------------------------------------------- distributed.core - INFO - Starting established connection distributed.core - INFO - Starting established connection distributed.scheduler - INFO - Receive client connection: Client-01142bc3-d0d0-11ea-84a5-6cb311234570 distributed.core - INFO - Starting established connection distributed.protocol.pickle - INFO - Failed to serialize (<function apply at 0x7f8c2cb36940>, <function test_serializable_locks.\<locals>.f at 0x7f8c1da0e040>, (<class 'tuple'>, [0]), {'lock': <SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>}). Exception: dumps() got an unexpected keyword argument 'buffer_callback' distributed.protocol.core - CRITICAL - Failed to Serialize Traceback (most recent call last): File "/usr/lib/python3.8/site-packages/distributed/protocol/core.py", line 36, in dumps data = { File "/usr/lib/python3.8/site-packages/distributed/protocol/core.py", line 37, in <dictcomp> key: serialize( File "/usr/lib/python3.8/site-packages/distributed/protocol/serialize.py", line 244, in serialize raise TypeError(msg, str(x)[:10000]) TypeError: ('Could not serialize object of type tuple.', "(<function apply at 0x7f8c2cb36940>, <function test_serializable_locks.\<locals>.f at 0x7f8c1da0e040>, (<class 'tuple'>, [0]), {'lock': <SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})") distributed.comm.utils - INFO - Unserializable Message: [{'op': 'update-graph', 'tasks': {'f-e71c719fc7843a71d9b6ed77ba5c1139': <Serialize: (\<function apply at 0x7f8c2cb36940>, \<function test_serializable_locks.\<locals>.f at 0x7f8c1da0e040>, (\<class 'tuple'>, [0]), {'lock': \<SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})>, 'f-3e2e39666f6b20c1585ae513bd6c58a1': <Serialize: (\<function apply at 0x7f8c2cb36940>, \<function test_serializable_locks.\<locals>.f at 0x7f8c1da0e040>, (\<class 'tuple'>, [1]), {'lock': \<SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})>, 'f-92de0310d2e920306d74497bcdbd21f1': <Serialize: (\<function apply at 0x7f8c2cb36940>, \<function test_serializable_locks.\<locals>.f at 0x7f8c1da0e040>, (\<class 'tuple'>, [2]), {'lock': \<SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})>, 'f-f210a433c90cbabd4a839e5957c2ef7c': <Serialize: (\<function apply at 0x7f8c2cb36940>, \<function test_serializable_locks.\<locals>.f at 0x7f8c1da0e040>, (\<class 'tuple'>, [3]), {'lock': \<SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})>, 'f-5ecad7d214286d3d4cb2c0f51d71596c': <Serialize: (\<function apply at 0x7f8c2cb36940>, \<function test_serializable_locks.\<locals>.f at 0x7f8c1da0e040>, (\<class 'tuple'>, [4]), {'lock': \<SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})>, 'f-662771094d226039899ae3b5f21c6184': <Serialize: (\<function apply at 0x7f8c2cb36940>, \<function test_serializable_locks.\<locals>.f at 0x7f8c1da0e040>, (\<class 'tuple'>, [5]), {'lock': \<SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})>, 'f-6aa952faad415c3c2dab888f1e97135e': <Serialize: (\<function apply at 0x7f8c2cb36940>, \<function test_serializable_locks.\<locals>.f at 0x7f8c1da0e040>, (\<class 'tuple'>, [6]), {'lock': \<SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})>, 'f-c5a149b0b56aa271ac0e5456fcbd0c7c': <Serialize: (\<function apply at 0x7f8c2cb36940>, \<function test_serializable_locks.\<locals>.f at 0x7f8c1da0e040>, (\<class 'tuple'>, [7]), {'lock': \<SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})>, 'f-2d7b671f31682ff35e7b351af7c5ca2b': <Serialize: (\<function apply at 0x7f8c2cb36940>, \<function test_serializable_locks.\<locals>.f at 0x7f8c1da0e040>, (\<class 'tuple'>, [8]), {'lock': \<SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})>, 'f-0f7fc14155cfae899da1d79302c723c1': <Serialize: (\<function apply at 0x7f8c2cb36940>, \<function test_serializable_locks.\<locals>.f at 0x7f8c1da0e040>, (\<class 'tuple'>, [9]), {'lock': \<SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})>}, 'dependencies': {}, 'keys': ['f-e71c719fc7843a71d9b6ed77ba5c1139', 'f-3e2e39666f6b20c1585ae513bd6c58a1', 'f-92de0310d2e920306d74497bcdbd21f1', 'f-f210a433c90cbabd4a839e5957c2ef7c', 'f-5ecad7d214286d3d4cb2c0f51d71596c', 'f-662771094d226039899ae3b5f21c6184', 'f-6aa952faad415c3c2dab888f1e97135e', 'f-c5a149b0b56aa271ac0e5456fcbd0c7c', 'f-2d7b671f31682ff35e7b351af7c5ca2b', 'f-0f7fc14155cfae899da1d79302c723c1'], 'restrictions': {}, 'loose_restrictions': [], 'priority': {'f-e71c719fc7843a71d9b6ed77ba5c1139': 0, 'f-3e2e39666f6b20c1585ae513bd6c58a1': 1, 'f-92de0310d2e920306d74497bcdbd21f1': 2, 'f-f210a433c90cbabd4a839e5957c2ef7c': 3, 'f-5ecad7d214286d3d4cb2c0f51d71596c': 4, 'f-662771094d226039899ae3b5f21c6184': 5, 'f-6aa952faad415c3c2dab888f1e97135e': 6, 'f-c5a149b0b56aa271ac0e5456fcbd0c7c': 7, 'f-2d7b671f31682ff35e7b351af7c5ca2b': 8, 'f-0f7fc14155cfae899da1d79302c723c1': 9}, 'user_priority': 0, 'resources': None, 'submitting_task': None, 'retries': None, 'fifo_timeout': '100 ms', 'actors': False}] distributed.comm.utils - ERROR - ('Could not serialize object of type tuple.', "(<function apply at 0x7f8c2cb36940>, <function test_serializable_locks.\<locals>.f at 0x7f8c1da0e040>, (<class 'tuple'>, [0]), {'lock': <SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})") Traceback (most recent call last): File "/usr/lib/python3.8/site-packages/distributed/comm/utils.py", line 34, in _to_frames protocol.dumps( File "/usr/lib/python3.8/site-packages/distributed/protocol/core.py", line 36, in dumps data = { File "/usr/lib/python3.8/site-packages/distributed/protocol/core.py", line 37, in <dictcomp> key: serialize( File "/usr/lib/python3.8/site-packages/distributed/protocol/serialize.py", line 244, in serialize raise TypeError(msg, str(x)[:10000]) TypeError: ('Could not serialize object of type tuple.', "(<function apply at 0x7f8c2cb36940>, <function test_serializable_locks.\<locals>.f at 0x7f8c1da0e040>, (<class 'tuple'>, [0]), {'lock': <SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})") distributed.batched - ERROR - Error in batched write Traceback (most recent call last): File "/usr/lib/python3.8/site-packages/distributed/batched.py", line 92, in _background_send nbytes = yield self.comm.write( File "/usr/lib/python3.8/site-packages/tornado/gen.py", line 735, in run value = future.result() File "/usr/lib/python3.8/site-packages/distributed/comm/tcp.py", line 220, in write frames = await to_frames( File "/usr/lib/python3.8/site-packages/distributed/comm/utils.py", line 54, in to_frames return _to_frames() File "/usr/lib/python3.8/site-packages/distributed/comm/utils.py", line 34, in _to_frames protocol.dumps( File "/usr/lib/python3.8/site-packages/distributed/protocol/core.py", line 36, in dumps data = { File "/usr/lib/python3.8/site-packages/distributed/protocol/core.py", line 37, in <dictcomp> key: serialize( File "/usr/lib/python3.8/site-packages/distributed/protocol/serialize.py", line 244, in serialize raise TypeError(msg, str(x)[:10000]) TypeError: ('Could not serialize object of type tuple.', "(<function apply at 0x7f8c2cb36940>, <function test_serializable_locks.\<locals>.f at 0x7f8c1da0e040>, (<class 'tuple'>, [0]), {'lock': <SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})") distributed.scheduler - INFO - Remove client Client-01142bc3-d0d0-11ea-84a5-6cb311234570 distributed.scheduler - INFO - Remove client Client-01142bc3-d0d0-11ea-84a5-6cb311234570 distributed.scheduler - INFO - Close client connection: Client-01142bc3-d0d0-11ea-84a5-6cb311234570 distributed.worker - INFO - Stopping worker at tcp://127.0.0.1:45589 distributed.worker - INFO - Stopping worker at tcp://127.0.0.1:40153 distributed.scheduler - INFO - Remove worker <Worker 'tcp://127.0.0.1:45589', name: 0, memory: 0, processing: 0> distributed.core - INFO - Removing comms to tcp://127.0.0.1:45589 distributed.scheduler - INFO - Remove worker <Worker 'tcp://127.0.0.1:40153', name: 1, memory: 0, processing: 0> distributed.core - INFO - Removing comms to tcp://127.0.0.1:40153 distributed.scheduler - INFO - Lost all workers distributed.scheduler - INFO - Scheduler closing... distributed.scheduler - INFO - Scheduler closing all comms ------------------------------ Captured log call ------------------------------- ERROR asyncio:base_events.py:1707 Task exception was never retrieved future: <Task finished name='Task-702' coro=\<Client._gather.\<locals>.wait() done, defined at /usr/lib/python3.8/site-packages/distributed/client.py:1816> exception=AllExit()> Traceback (most recent call last): File "/usr/lib/python3.8/site-packages/distributed/client.py", line 1821, in wait raise AllExit() distributed.client.AllExit ERROR asyncio:base_events.py:1707 Task exception was never retrieved future: <Task finished name='Task-703' coro=\<Client._gather.\<locals>.wait() done, defined at /usr/lib/python3.8/site-packages/distributed/client.py:1816> exception=AllExit()> Traceback (most recent call last): File "/usr/lib/python3.8/site-packages/distributed/client.py", line 1821, in wait raise AllExit() distributed.client.AllExit ERROR asyncio:base_events.py:1707 Task exception was never retrieved future: <Task finished name='Task-704' coro=\<Client._gather.\<locals>.wait() done, defined at /usr/lib/python3.8/site-packages/distributed/client.py:1816> exception=AllExit()> Traceback (most recent call last): File "/usr/lib/python3.8/site-packages/distributed/client.py", line 1821, in wait raise AllExit() distributed.client.AllExit ERROR asyncio:base_events.py:1707 Task exception was never retrieved future: <Task finished name='Task-705' coro=\<Client._gather.\<locals>.wait() done, defined at /usr/lib/python3.8/site-packages/distributed/client.py:1816> exception=AllExit()> Traceback (most recent call last): File "/usr/lib/python3.8/site-packages/distributed/client.py", line 1821, in wait raise AllExit() distributed.client.AllExit ERROR asyncio:base_events.py:1707 Task exception was never retrieved future: <Task finished name='Task-706' coro=\<Client._gather.\<locals>.wait() done, defined at /usr/lib/python3.8/site-packages/distributed/client.py:1816> exception=AllExit()> Traceback (most recent call last): File "/usr/lib/python3.8/site-packages/distributed/client.py", line 1821, in wait raise AllExit() distributed.client.AllExit ERROR asyncio:base_events.py:1707 Task exception was never retrieved future: <Task finished name='Task-707' coro=\<Client._gather.\<locals>.wait() done, defined at /usr/lib/python3.8/site-packages/distributed/client.py:1816> exception=AllExit()> Traceback (most recent call last): File "/usr/lib/python3.8/site-packages/distributed/client.py", line 1821, in wait raise AllExit() distributed.client.AllExit ERROR asyncio:base_events.py:1707 Task exception was never retrieved future: <Task finished name='Task-708' coro=\<Client._gather.\<locals>.wait() done, defined at /usr/lib/python3.8/site-packages/distributed/client.py:1816> exception=AllExit()> Traceback (most recent call last): File "/usr/lib/python3.8/site-packages/distributed/client.py", line 1821, in wait raise AllExit() distributed.client.AllExit ERROR asyncio:base_events.py:1707 Task exception was never retrieved future: <Task finished name='Task-709' coro=\<Client._gather.\<locals>.wait() done, defined at /usr/lib/python3.8/site-packages/distributed/client.py:1816> exception=AllExit()> Traceback (most recent call last): File "/usr/lib/python3.8/site-packages/distributed/client.py", line 1821, in wait raise AllExit() distributed.client.AllExit ERROR asyncio:base_events.py:1707 Task exception was never retrieved future: <Task finished name='Task-710' coro=\<Client._gather.\<locals>.wait() done, defined at /usr/lib/python3.8/site-packages/distributed/client.py:1816> exception=AllExit()> Traceback (most recent call last): File "/usr/lib/python3.8/site-packages/distributed/client.py", line 1821, in wait raise AllExit() distributed.client.AllExit ERROR asyncio:base_events.py:1707 Task exception was never retrieved future: <Task finished name='Task-711' coro=\<Client._gather.\<locals>.wait() done, defined at /usr/lib/python3.8/site-packages/distributed/client.py:1816> exception=AllExit()> Traceback (most recent call last): File "/usr/lib/python3.8/site-packages/distributed/client.py", line 1821, in wait raise AllExit() distributed.client.AllExit ```

Environment:

Output of <tt>xr.show_versions()</tt> INSTALLED VERSIONS ------------------ commit: None python: 3.8.4 (default, Jul 15 2020, 10:38:22) [GCC 10.1.0] python-bits: 64 OS: Linux OS-release: 5.7.8-arch1-1 machine: x86_64 processor: byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.12.0 libnetcdf: 4.7.4 xarray: 0.16.0 pandas: 1.0.5 numpy: 1.19.1 scipy: 1.5.2 netCDF4: 1.5.4 pydap: None h5netcdf: None h5py: None Nio: None zarr: None cftime: 1.2.1 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.2 dask: 2.21.0 distributed: 2.21.0 matplotlib: 3.3.0 cartopy: None seaborn: 0.10.1 numbagg: None pint: 0.0.0 setuptools: 49.2.0 pip: None conda: None pytest: 5.4.3 IPython: None sphinx: None 
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     xarray 13221727 issue
832404698 MDU6SXNzdWU4MzI0MDQ2OTg= 5038 [tests] ImportError: Pandas requires version '0.12.3' or newer of 'xarray' (version '0.0.0' currently installed). ArchangeGabriel 490531 closed 0     11 2021-03-16T04:44:23Z 2021-03-16T20:15:38Z 2021-03-16T20:03:33Z CONTRIBUTOR      

What happened: I’m running tests of xarray while building it for packaging (Arch Linux), and they are 6 tests failures, all with this error message.

What you expected to happen: Tests should work. I’m not sure why a version 0.0.0 is reported while running tests in the build environment.

Full error log ``` __________________ TestDataArray.test_from_series_multiindex ___________________ self = <xarray.tests.test_dataarray.TestDataArray object at 0x7fcb3b23c2b0> def test_from_series_multiindex(self): # GH:3951 df = pd.DataFrame({"B": [1, 2, 3], "A": [4, 5, 6]}) df = df.rename_axis("num").rename_axis("alpha", axis=1) > actual = df.stack("alpha").to_xarray() /build/python-xarray/src/xarray-0.17.0/xarray/tests/test_dataarray.py:3697: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.9/site-packages/pandas/core/generic.py:3011: in to_xarray xarray = import_optional_dependency("xarray") _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'xarray', extra = '', raise_on_missing = True, on_version = 'raise' def import_optional_dependency( name: str, extra: str = "", raise_on_missing: bool = True, on_version: str = "raise" ): """ Import an optional dependency. By default, if a dependency is missing an ImportError with a nice message will be raised. If a dependency is present, but too old, we raise. Parameters ---------- name : str The module name. This should be top-level only, so that the version may be checked. extra : str Additional text to include in the ImportError message. raise_on_missing : bool, default True Whether to raise if the optional dependency is not found. When False and the module is not present, None is returned. on_version : str {'raise', 'warn'} What to do when a dependency's version is too old. * raise : Raise an ImportError * warn : Warn that the version is too old. Returns None * ignore: Return the module, even if the version is too old. It's expected that users validate the version locally when using ``on_version="ignore"`` (see. ``io/html.py``) Returns ------- maybe_module : Optional[ModuleType] The imported module, when found and the version is correct. None is returned when the package is not found and `raise_on_missing` is False, or when the package's version is too old and `on_version` is ``'warn'``. """ package_name = INSTALL_MAPPING.get(name) install_name = package_name if package_name is not None else name msg = ( f"Missing optional dependency '{install_name}'. {extra} " f"Use pip or conda to install {install_name}." ) try: module = importlib.import_module(name) except ImportError: if raise_on_missing: raise ImportError(msg) from None else: return None minimum_version = VERSIONS.get(name) if minimum_version: version = get_version(module) if distutils.version.LooseVersion(version) < minimum_version: assert on_version in {"warn", "raise", "ignore"} msg = ( f"Pandas requires version '{minimum_version}' or newer of '{name}' " f"(version '{version}' currently installed)." ) if on_version == "warn": warnings.warn(msg, UserWarning) return None elif on_version == "raise": > raise ImportError(msg) E ImportError: Pandas requires version '0.12.3' or newer of 'xarray' (version '0.0.0' currently installed). /usr/lib/python3.9/site-packages/pandas/compat/_optional.py:126: ImportError _______________________ TestDataset.test_sel_categorical _______________________ self = <xarray.tests.test_dataset.TestDataset object at 0x7fcb3afb8640> def test_sel_categorical(self): ind = pd.Series(["foo", "bar"], dtype="category") df = pd.DataFrame({"ind": ind, "values": [1, 2]}) > ds = df.set_index("ind").to_xarray() /build/python-xarray/src/xarray-0.17.0/xarray/tests/test_dataset.py:1432: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.9/site-packages/pandas/core/generic.py:3011: in to_xarray xarray = import_optional_dependency("xarray") _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'xarray', extra = '', raise_on_missing = True, on_version = 'raise' def import_optional_dependency( name: str, extra: str = "", raise_on_missing: bool = True, on_version: str = "raise" ): """ Import an optional dependency. By default, if a dependency is missing an ImportError with a nice message will be raised. If a dependency is present, but too old, we raise. Parameters ---------- name : str The module name. This should be top-level only, so that the version may be checked. extra : str Additional text to include in the ImportError message. raise_on_missing : bool, default True Whether to raise if the optional dependency is not found. When False and the module is not present, None is returned. on_version : str {'raise', 'warn'} What to do when a dependency's version is too old. * raise : Raise an ImportError * warn : Warn that the version is too old. Returns None * ignore: Return the module, even if the version is too old. It's expected that users validate the version locally when using ``on_version="ignore"`` (see. ``io/html.py``) Returns ------- maybe_module : Optional[ModuleType] The imported module, when found and the version is correct. None is returned when the package is not found and `raise_on_missing` is False, or when the package's version is too old and `on_version` is ``'warn'``. """ package_name = INSTALL_MAPPING.get(name) install_name = package_name if package_name is not None else name msg = ( f"Missing optional dependency '{install_name}'. {extra} " f"Use pip or conda to install {install_name}." ) try: module = importlib.import_module(name) except ImportError: if raise_on_missing: raise ImportError(msg) from None else: return None minimum_version = VERSIONS.get(name) if minimum_version: version = get_version(module) if distutils.version.LooseVersion(version) < minimum_version: assert on_version in {"warn", "raise", "ignore"} msg = ( f"Pandas requires version '{minimum_version}' or newer of '{name}' " f"(version '{version}' currently installed)." ) if on_version == "warn": warnings.warn(msg, UserWarning) return None elif on_version == "raise": > raise ImportError(msg) E ImportError: Pandas requires version '0.12.3' or newer of 'xarray' (version '0.0.0' currently installed). /usr/lib/python3.9/site-packages/pandas/compat/_optional.py:126: ImportError ____________________ TestDataset.test_sel_categorical_error ____________________ self = <xarray.tests.test_dataset.TestDataset object at 0x7fcb3b199220> def test_sel_categorical_error(self): ind = pd.Series(["foo", "bar"], dtype="category") df = pd.DataFrame({"ind": ind, "values": [1, 2]}) > ds = df.set_index("ind").to_xarray() /build/python-xarray/src/xarray-0.17.0/xarray/tests/test_dataset.py:1440: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.9/site-packages/pandas/core/generic.py:3011: in to_xarray xarray = import_optional_dependency("xarray") _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'xarray', extra = '', raise_on_missing = True, on_version = 'raise' def import_optional_dependency( name: str, extra: str = "", raise_on_missing: bool = True, on_version: str = "raise" ): """ Import an optional dependency. By default, if a dependency is missing an ImportError with a nice message will be raised. If a dependency is present, but too old, we raise. Parameters ---------- name : str The module name. This should be top-level only, so that the version may be checked. extra : str Additional text to include in the ImportError message. raise_on_missing : bool, default True Whether to raise if the optional dependency is not found. When False and the module is not present, None is returned. on_version : str {'raise', 'warn'} What to do when a dependency's version is too old. * raise : Raise an ImportError * warn : Warn that the version is too old. Returns None * ignore: Return the module, even if the version is too old. It's expected that users validate the version locally when using ``on_version="ignore"`` (see. ``io/html.py``) Returns ------- maybe_module : Optional[ModuleType] The imported module, when found and the version is correct. None is returned when the package is not found and `raise_on_missing` is False, or when the package's version is too old and `on_version` is ``'warn'``. """ package_name = INSTALL_MAPPING.get(name) install_name = package_name if package_name is not None else name msg = ( f"Missing optional dependency '{install_name}'. {extra} " f"Use pip or conda to install {install_name}." ) try: module = importlib.import_module(name) except ImportError: if raise_on_missing: raise ImportError(msg) from None else: return None minimum_version = VERSIONS.get(name) if minimum_version: version = get_version(module) if distutils.version.LooseVersion(version) < minimum_version: assert on_version in {"warn", "raise", "ignore"} msg = ( f"Pandas requires version '{minimum_version}' or newer of '{name}' " f"(version '{version}' currently installed)." ) if on_version == "warn": warnings.warn(msg, UserWarning) return None elif on_version == "raise": > raise ImportError(msg) E ImportError: Pandas requires version '0.12.3' or newer of 'xarray' (version '0.0.0' currently installed). /usr/lib/python3.9/site-packages/pandas/compat/_optional.py:126: ImportError ___________________ TestDataset.test_categorical_multiindex ____________________ self = <xarray.tests.test_dataset.TestDataset object at 0x7fcb3ae86670> def test_categorical_multiindex(self): i1 = pd.Series([0, 0]) cat = pd.CategoricalDtype(categories=["foo", "baz", "bar"]) i2 = pd.Series(["baz", "bar"], dtype=cat) df = pd.DataFrame({"i1": i1, "i2": i2, "values": [1, 2]}).set_index( ["i1", "i2"] ) > actual = df.to_xarray() /build/python-xarray/src/xarray-0.17.0/xarray/tests/test_dataset.py:1487: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.9/site-packages/pandas/core/generic.py:3011: in to_xarray xarray = import_optional_dependency("xarray") _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'xarray', extra = '', raise_on_missing = True, on_version = 'raise' def import_optional_dependency( name: str, extra: str = "", raise_on_missing: bool = True, on_version: str = "raise" ): """ Import an optional dependency. By default, if a dependency is missing an ImportError with a nice message will be raised. If a dependency is present, but too old, we raise. Parameters ---------- name : str The module name. This should be top-level only, so that the version may be checked. extra : str Additional text to include in the ImportError message. raise_on_missing : bool, default True Whether to raise if the optional dependency is not found. When False and the module is not present, None is returned. on_version : str {'raise', 'warn'} What to do when a dependency's version is too old. * raise : Raise an ImportError * warn : Warn that the version is too old. Returns None * ignore: Return the module, even if the version is too old. It's expected that users validate the version locally when using ``on_version="ignore"`` (see. ``io/html.py``) Returns ------- maybe_module : Optional[ModuleType] The imported module, when found and the version is correct. None is returned when the package is not found and `raise_on_missing` is False, or when the package's version is too old and `on_version` is ``'warn'``. """ package_name = INSTALL_MAPPING.get(name) install_name = package_name if package_name is not None else name msg = ( f"Missing optional dependency '{install_name}'. {extra} " f"Use pip or conda to install {install_name}." ) try: module = importlib.import_module(name) except ImportError: if raise_on_missing: raise ImportError(msg) from None else: return None minimum_version = VERSIONS.get(name) if minimum_version: version = get_version(module) if distutils.version.LooseVersion(version) < minimum_version: assert on_version in {"warn", "raise", "ignore"} msg = ( f"Pandas requires version '{minimum_version}' or newer of '{name}' " f"(version '{version}' currently installed)." ) if on_version == "warn": warnings.warn(msg, UserWarning) return None elif on_version == "raise": > raise ImportError(msg) E ImportError: Pandas requires version '0.12.3' or newer of 'xarray' (version '0.0.0' currently installed). /usr/lib/python3.9/site-packages/pandas/compat/_optional.py:126: ImportError _________________ TestDataset.test_from_dataframe_categorical __________________ self = <xarray.tests.test_dataset.TestDataset object at 0x7fcb3ab0b880> def test_from_dataframe_categorical(self): cat = pd.CategoricalDtype( categories=["foo", "bar", "baz", "qux", "quux", "corge"] ) i1 = pd.Series(["foo", "bar", "foo"], dtype=cat) i2 = pd.Series(["bar", "bar", "baz"], dtype=cat) df = pd.DataFrame({"i1": i1, "i2": i2, "values": [1, 2, 3]}) > ds = df.set_index("i1").to_xarray() /build/python-xarray/src/xarray-0.17.0/xarray/tests/test_dataset.py:4131: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.9/site-packages/pandas/core/generic.py:3011: in to_xarray xarray = import_optional_dependency("xarray") _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'xarray', extra = '', raise_on_missing = True, on_version = 'raise' def import_optional_dependency( name: str, extra: str = "", raise_on_missing: bool = True, on_version: str = "raise" ): """ Import an optional dependency. By default, if a dependency is missing an ImportError with a nice message will be raised. If a dependency is present, but too old, we raise. Parameters ---------- name : str The module name. This should be top-level only, so that the version may be checked. extra : str Additional text to include in the ImportError message. raise_on_missing : bool, default True Whether to raise if the optional dependency is not found. When False and the module is not present, None is returned. on_version : str {'raise', 'warn'} What to do when a dependency's version is too old. * raise : Raise an ImportError * warn : Warn that the version is too old. Returns None * ignore: Return the module, even if the version is too old. It's expected that users validate the version locally when using ``on_version="ignore"`` (see. ``io/html.py``) Returns ------- maybe_module : Optional[ModuleType] The imported module, when found and the version is correct. None is returned when the package is not found and `raise_on_missing` is False, or when the package's version is too old and `on_version` is ``'warn'``. """ package_name = INSTALL_MAPPING.get(name) install_name = package_name if package_name is not None else name msg = ( f"Missing optional dependency '{install_name}'. {extra} " f"Use pip or conda to install {install_name}." ) try: module = importlib.import_module(name) except ImportError: if raise_on_missing: raise ImportError(msg) from None else: return None minimum_version = VERSIONS.get(name) if minimum_version: version = get_version(module) if distutils.version.LooseVersion(version) < minimum_version: assert on_version in {"warn", "raise", "ignore"} msg = ( f"Pandas requires version '{minimum_version}' or newer of '{name}' " f"(version '{version}' currently installed)." ) if on_version == "warn": warnings.warn(msg, UserWarning) return None elif on_version == "raise": > raise ImportError(msg) E ImportError: Pandas requires version '0.12.3' or newer of 'xarray' (version '0.0.0' currently installed). /usr/lib/python3.9/site-packages/pandas/compat/_optional.py:126: ImportError _________________________ test_roundtrip_pandas_series _________________________ @given(numeric_series, st.text()) > def test_roundtrip_pandas_series(ser, ix_name): /build/python-xarray/src/xarray-0.17.0/properties/test_pandas_roundtrip.py:73: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /build/python-xarray/src/xarray-0.17.0/properties/test_pandas_roundtrip.py:79: in test_roundtrip_pandas_series xr.testing.assert_identical(arr, roundtripped.to_xarray()) /usr/lib/python3.9/site-packages/pandas/core/generic.py:3011: in to_xarray xarray = import_optional_dependency("xarray") _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ name = 'xarray', extra = '', raise_on_missing = True, on_version = 'raise' def import_optional_dependency( name: str, extra: str = "", raise_on_missing: bool = True, on_version: str = "raise" ): """ Import an optional dependency. By default, if a dependency is missing an ImportError with a nice message will be raised. If a dependency is present, but too old, we raise. Parameters ---------- name : str The module name. This should be top-level only, so that the version may be checked. extra : str Additional text to include in the ImportError message. raise_on_missing : bool, default True Whether to raise if the optional dependency is not found. When False and the module is not present, None is returned. on_version : str {'raise', 'warn'} What to do when a dependency's version is too old. * raise : Raise an ImportError * warn : Warn that the version is too old. Returns None * ignore: Return the module, even if the version is too old. It's expected that users validate the version locally when using ``on_version="ignore"`` (see. ``io/html.py``) Returns ------- maybe_module : Optional[ModuleType] The imported module, when found and the version is correct. None is returned when the package is not found and `raise_on_missing` is False, or when the package's version is too old and `on_version` is ``'warn'``. """ package_name = INSTALL_MAPPING.get(name) install_name = package_name if package_name is not None else name msg = ( f"Missing optional dependency '{install_name}'. {extra} " f"Use pip or conda to install {install_name}." ) try: module = importlib.import_module(name) except ImportError: if raise_on_missing: raise ImportError(msg) from None else: return None minimum_version = VERSIONS.get(name) if minimum_version: version = get_version(module) if distutils.version.LooseVersion(version) < minimum_version: assert on_version in {"warn", "raise", "ignore"} msg = ( f"Pandas requires version '{minimum_version}' or newer of '{name}' " f"(version '{version}' currently installed)." ) if on_version == "warn": warnings.warn(msg, UserWarning) return None elif on_version == "raise": > raise ImportError(msg) E ImportError: Pandas requires version '0.12.3' or newer of 'xarray' (version '0.0.0' currently installed). /usr/lib/python3.9/site-packages/pandas/compat/_optional.py:126: ImportError ```

Environment:

Output of <tt>xr.show_versions()</tt> commit: None python: 3.9.2 (default, Feb 20 2021, 18:40:11) [GCC 10.2.0] python-bits: 64 OS: Linux OS-release: 5.11.5-arch1-1 machine: x86_64 processor: byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.12.0 libnetcdf: 4.7.4 xarray: 0.0.0 pandas: 1.2.3 numpy: 1.20.1 scipy: 1.6.1 netCDF4: 1.5.5.1 pydap: None h5netcdf: None h5py: 3.1.0 Nio: None zarr: None cftime: 1.4.1 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.2 dask: 2021.03.0 distributed: 2021.03.0 matplotlib: 3.3.4 cartopy: None seaborn: 0.11.1 numbagg: None pint: 0.16.1 setuptools: 54.1.1 pip: None conda: None pytest: 6.2.2 IPython: None sphinx: None 
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   completed xarray 13221727 issue
567058587 MDU6SXNzdWU1NjcwNTg1ODc= 3778 Two strange errors in tests TestVariable using .copy() ArchangeGabriel 490531 closed 0     7 2020-02-18T17:51:22Z 2020-02-28T15:16:14Z 2020-02-28T15:16:14Z CONTRIBUTOR      

Setup is as described in #3777.

The two failures are the following ones: ``` ____ TestVariable.test_index_0d_not_a_time _______

self = <xarray.tests.test_units.TestVariable object at 0x7f3926b2ad00>

def test_index_0d_not_a_time(self):
    d = np.datetime64("NaT", "ns")
    x = self.cls(["x"], [d])

  self._assertIndexedLikeNDArray(x, d)

xarray/tests/test_variable.py:206:

self = <xarray.tests.test_units.TestVariable object at 0x7f3926b2ad00> variable = <xarray.Variable (x: 1)> <Quantity(['NaT'], 'meter')> expected_value0 = numpy.datetime64('NaT'), expected_dtype = None

def _assertIndexedLikeNDArray(self, variable, expected_value0, expected_dtype=None):
    """Given a 1-dimensional variable, verify that the variable is indexed
    like a numpy.ndarray.
    """
    assert variable[0].shape == ()
    assert variable[0].ndim == 0
    assert variable[0].size == 1
    # test identity

  assert variable.equals(variable.copy())

E AssertionError: assert False E + where False = <bound method Variable.equals of \<xarray.Variable (x: 1)>\n\<Quantity(['NaT'], 'meter')>>(<xarray.Variable (x: 1)>\n<Quantity(['NaT'], 'meter')>) E + where <bound method Variable.equals of \<xarray.Variable (x: 1)>\n\<Quantity(['NaT'], 'meter')>> = <xarray.Variable (x: 1)>\n<Quantity(['NaT'], 'meter')>.equals E + and <xarray.Variable (x: 1)>\n<Quantity(['NaT'], 'meter')> = <bound method Variable.copy of \<xarray.Variable (x: 1)>\n\<Quantity(['NaT'], 'meter')>>() E + where <bound method Variable.copy of \<xarray.Variable (x: 1)>\n\<Quantity(['NaT'], 'meter')>> = <xarray.Variable (x: 1)>\n<Quantity(['NaT'], 'meter')>.copy

xarray/tests/test_variable.py:151: AssertionError ___ TestVariable.test_equals_all_dtypes ____

self = <xarray.tests.test_units.TestVariable object at 0x7f39282b4e50>

def test_equals_all_dtypes(self):
    for v, _ in self.example_1d_objects():
        v2 = v.copy()

      assert v.equals(v2)

E AssertionError: assert False E + where False = <bound method Variable.equals of \<xarray.Variable (x: 3)>\n\<Quantity(['2000-01-01T00:00:00.000000000' '2000-01-02T00:00:00.000000000'\n '2000-01-03T00:00:00.000000000'], 'meter')>>(<xarray.Variable (x: 3)>\n<Quantity(['2000-01-01T00:00:00.000000000' '2000-01-02T00:00:00.000000000'\n '2000-01-03T00:00:00.000000000'], 'meter')>) E + where <bound method Variable.equals of \<xarray.Variable (x: 3)>\n\<Quantity(['2000-01-01T00:00:00.000000000' '2000-01-02T00:00:00.000000000'\n '2000-01-03T00:00:00.000000000'], 'meter')>> = <xarray.Variable (x: 3)>\n<Quantity(['2000-01-01T00:00:00.000000000' '2000-01-02T00:00:00.000000000'\n '2000-01-03T00:00:00.000000000'], 'meter')>.equals

xarray/tests/test_variable.py:386: AssertionError ```

I have no idea what’s wrong here.

 
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   completed xarray 13221727 issue
567062305 MDU6SXNzdWU1NjcwNjIzMDU= 3779 Three test_aggregation[int-method_median] tests failing ArchangeGabriel 490531 closed 0     2 2020-02-18T17:58:29Z 2020-02-23T19:34:35Z 2020-02-23T19:34:35Z CONTRIBUTOR      

Follow-up of #3777.

The three failing tests seems to be failing because dask_array=None, which is likely the same kind of issue as #3777: dask-dependant tests ran while dask is not available. The other printed error is strange to me, because numpy is at version 1.18.1 on this system.

``` __ TestVariable.test_aggregation[int-method_median] __

values = array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1]), axis = None, skipna = None kwargs = {}, func = <function _dask_or_eager_func.\<locals>.f at 0x7f3927bdbe50> msg = 'median is not available with skipna=False with the installed version of numpy; upgrade to numpy 1.12 or newer to use skipna=True or skipna=None'

def f(values, axis=None, skipna=None, **kwargs):
    if kwargs.pop("out", None) is not None:
        raise TypeError(f"`out` is not valid for {name}")

    values = asarray(values)

    if coerce_strings and values.dtype.kind in "SU":
        values = values.astype(object)

    func = None
    if skipna or (skipna is None and values.dtype.kind in "cfO"):
        nanname = "nan" + name
        func = getattr(nanops, nanname)
    else:
        func = _dask_or_eager_func(name, dask_module=dask_module)

    try:

      return func(values, axis=axis, **kwargs)

xarray/core/duck_array_ops.py:307:

args = (array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1]),), kwargs = {'axis': None} dispatch_args = (array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1]),)

def f(*args, **kwargs):
    if list_of_args:
        dispatch_args = args[0]
    else:
        dispatch_args = args[array_args]

  if any(isinstance(a, dask_array.Array) for a in dispatch_args):

xarray/core/duck_array_ops.py:40:

.0 = <tuple_iterator object at 0x7f3927966f70>

if any(isinstance(a, dask_array.Array) for a in dispatch_args): E AttributeError: 'NoneType' object has no attribute 'Array'

xarray/core/duck_array_ops.py:40: AttributeError

During handling of the above exception, another exception occurred:

self = <xarray.tests.test_units.TestVariable object at 0x7f3927966640> func = method_median, dtype = <class 'int'>

@pytest.mark.parametrize(
    "func",
    (
        method("all"),
        method("any"),
        method("argmax"),
        method("argmin"),
        method("argsort"),
        method("cumprod"),
        method("cumsum"),
        method("max"),
        method("mean"),
        method("median"),
        method("min"),
        pytest.param(
            method("prod"),
            marks=pytest.mark.xfail(reason="not implemented by pint"),
        ),
        method("std"),
        method("sum"),
        method("var"),
    ),
    ids=repr,
)
def test_aggregation(self, func, dtype):
    array = np.linspace(0, 1, 10).astype(dtype) * (
        unit_registry.m if func.name != "cumprod" else unit_registry.dimensionless
    )
    variable = xr.Variable("x", array)

    units = extract_units(func(array))

  expected = attach_units(func(strip_units(variable)), units)

xarray/tests/test_units.py:1389:

xarray/tests/test_units.py:374: in call return func(all_args, all_kwargs) xarray/core/common.py:46: in wrapped_func return self.reduce(func, dim, axis, skipna=skipna, kwargs) xarray/core/variable.py:1537: in reduce data = func(input_data, *kwargs)

values = array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1]), axis = None, skipna = None kwargs = {}, func = <function _dask_or_eager_func.\<locals>.f at 0x7f3927bdbe50> msg = 'median is not available with skipna=False with the installed version of numpy; upgrade to numpy 1.12 or newer to use skipna=True or skipna=None'

def f(values, axis=None, skipna=None, **kwargs):
    if kwargs.pop("out", None) is not None:
        raise TypeError(f"`out` is not valid for {name}")

    values = asarray(values)

    if coerce_strings and values.dtype.kind in "SU":
        values = values.astype(object)

    func = None
    if skipna or (skipna is None and values.dtype.kind in "cfO"):
        nanname = "nan" + name
        func = getattr(nanops, nanname)
    else:
        func = _dask_or_eager_func(name, dask_module=dask_module)

    try:
        return func(values, axis=axis, **kwargs)
    except AttributeError:
        if isinstance(values, dask_array_type):
            try:  # dask/dask#3133 dask sometimes needs dtype argument
                # if func does not accept dtype, then raises TypeError
                return func(values, axis=axis, dtype=values.dtype, **kwargs)
            except (AttributeError, TypeError):
                msg = "%s is not yet implemented on dask arrays" % name
        else:
            msg = (
                "%s is not available with skipna=False with the "
                "installed version of numpy; upgrade to numpy 1.12 "
                "or newer to use skipna=True or skipna=None" % name
            )

      raise NotImplementedError(msg)

E NotImplementedError: median is not available with skipna=False with the installed version of numpy; upgrade to numpy 1.12 or newer to use skipna=True or skipna=None

xarray/core/duck_array_ops.py:321: NotImplementedError __ TestDataArray.test_aggregation[int-method_median] ___

values = array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), axis = None, skipna = None kwargs = {}, func = <function _dask_or_eager_func.\<locals>.f at 0x7f39286dbc10> msg = 'median is not available with skipna=False with the installed version of numpy; upgrade to numpy 1.12 or newer to use skipna=True or skipna=None'

def f(values, axis=None, skipna=None, **kwargs):
    if kwargs.pop("out", None) is not None:
        raise TypeError(f"`out` is not valid for {name}")

    values = asarray(values)

    if coerce_strings and values.dtype.kind in "SU":
        values = values.astype(object)

    func = None
    if skipna or (skipna is None and values.dtype.kind in "cfO"):
        nanname = "nan" + name
        func = getattr(nanops, nanname)
    else:
        func = _dask_or_eager_func(name, dask_module=dask_module)

    try:

      return func(values, axis=axis, **kwargs)

xarray/core/duck_array_ops.py:307:

args = (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),), kwargs = {'axis': None} dispatch_args = (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),)

def f(*args, **kwargs):
    if list_of_args:
        dispatch_args = args[0]
    else:
        dispatch_args = args[array_args]

  if any(isinstance(a, dask_array.Array) for a in dispatch_args):

xarray/core/duck_array_ops.py:40:

.0 = <tuple_iterator object at 0x7f39288ad880>

if any(isinstance(a, dask_array.Array) for a in dispatch_args): E AttributeError: 'NoneType' object has no attribute 'Array'

xarray/core/duck_array_ops.py:40: AttributeError

During handling of the above exception, another exception occurred:

self = <xarray.tests.test_units.TestDataArray object at 0x7f39288ad190> func = method_median, dtype = <class 'int'>

@pytest.mark.parametrize(
    "func",
    (
        pytest.param(
            function("all"),
            marks=pytest.mark.xfail(reason="not implemented by pint yet"),
        ),
        pytest.param(
            function("any"),
            marks=pytest.mark.xfail(reason="not implemented by pint yet"),
        ),
        function("argmax"),
        function("argmin"),
        function("max"),
        function("mean"),
        pytest.param(
            function("median"),
            marks=pytest.mark.xfail(reason="not implemented by xarray"),
        ),
        function("min"),
        pytest.param(
            function("prod"),
            marks=pytest.mark.xfail(reason="not implemented by pint yet"),
        ),
        function("sum"),
        function("std"),
        function("var"),
        function("cumsum"),
        pytest.param(
            function("cumprod"),
            marks=pytest.mark.xfail(reason="not implemented by pint yet"),
        ),
        pytest.param(
            method("all"),
            marks=pytest.mark.xfail(reason="not implemented by pint yet"),
        ),
        pytest.param(
            method("any"),
            marks=pytest.mark.xfail(reason="not implemented by pint yet"),
        ),
        method("argmax"),
        method("argmin"),
        method("max"),
        method("mean"),
        method("median"),
        method("min"),
        pytest.param(
            method("prod"),
            marks=pytest.mark.xfail(
                reason="comparison of quantity with ndarrays in nanops not implemented"
            ),
        ),
        method("sum"),
        method("std"),
        method("var"),
        method("cumsum"),
        pytest.param(
            method("cumprod"),
            marks=pytest.mark.xfail(reason="pint does not implement cumprod yet"),
        ),
    ),
    ids=repr,
)
def test_aggregation(self, func, dtype):
    array = np.arange(10).astype(dtype) * (
        unit_registry.m if func.name != "cumprod" else unit_registry.dimensionless
    )
    data_array = xr.DataArray(data=array, dims="x")

    # units differ based on the applied function, so we need to
    # first compute the units
    units = extract_units(func(array))

  expected = attach_units(func(strip_units(data_array)), units)

xarray/tests/test_units.py:2226:

xarray/tests/test_units.py:374: in call return func(all_args, all_kwargs) xarray/core/common.py:46: in wrapped_func return self.reduce(func, dim, axis, skipna=skipna, kwargs) xarray/core/dataarray.py:2235: in reduce var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, kwargs) xarray/core/variable.py:1537: in reduce data = func(input_data, *kwargs)

values = array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), axis = None, skipna = None kwargs = {}, func = <function _dask_or_eager_func.\<locals>.f at 0x7f39286dbc10> msg = 'median is not available with skipna=False with the installed version of numpy; upgrade to numpy 1.12 or newer to use skipna=True or skipna=None'

def f(values, axis=None, skipna=None, **kwargs):
    if kwargs.pop("out", None) is not None:
        raise TypeError(f"`out` is not valid for {name}")

    values = asarray(values)

    if coerce_strings and values.dtype.kind in "SU":
        values = values.astype(object)

    func = None
    if skipna or (skipna is None and values.dtype.kind in "cfO"):
        nanname = "nan" + name
        func = getattr(nanops, nanname)
    else:
        func = _dask_or_eager_func(name, dask_module=dask_module)

    try:
        return func(values, axis=axis, **kwargs)
    except AttributeError:
        if isinstance(values, dask_array_type):
            try:  # dask/dask#3133 dask sometimes needs dtype argument
                # if func does not accept dtype, then raises TypeError
                return func(values, axis=axis, dtype=values.dtype, **kwargs)
            except (AttributeError, TypeError):
                msg = "%s is not yet implemented on dask arrays" % name
        else:
            msg = (
                "%s is not available with skipna=False with the "
                "installed version of numpy; upgrade to numpy 1.12 "
                "or newer to use skipna=True or skipna=None" % name
            )

      raise NotImplementedError(msg)

E NotImplementedError: median is not available with skipna=False with the installed version of numpy; upgrade to numpy 1.12 or newer to use skipna=True or skipna=None

xarray/core/duck_array_ops.py:321: NotImplementedError ___ TestDataset.test_aggregation[int-method_median] ____

values = <Quantity([0 0 0 0 0 0 0 0 0 1], 'pascal')>, axis = 0, skipna = None kwargs = {}, func = <function _dask_or_eager_func.\<locals>.f at 0x7f392619b820> msg = 'median is not available with skipna=False with the installed version of numpy; upgrade to numpy 1.12 or newer to use skipna=True or skipna=None'

def f(values, axis=None, skipna=None, **kwargs):
    if kwargs.pop("out", None) is not None:
        raise TypeError(f"`out` is not valid for {name}")

    values = asarray(values)

    if coerce_strings and values.dtype.kind in "SU":
        values = values.astype(object)

    func = None
    if skipna or (skipna is None and values.dtype.kind in "cfO"):
        nanname = "nan" + name
        func = getattr(nanops, nanname)
    else:
        func = _dask_or_eager_func(name, dask_module=dask_module)

    try:

      return func(values, axis=axis, **kwargs)

xarray/core/duck_array_ops.py:307:

args = (<Quantity([0 0 0 0 0 0 0 0 0 1], 'pascal')>,), kwargs = {'axis': 0} dispatch_args = (<Quantity([0 0 0 0 0 0 0 0 0 1], 'pascal')>,)

def f(*args, **kwargs):
    if list_of_args:
        dispatch_args = args[0]
    else:
        dispatch_args = args[array_args]

  if any(isinstance(a, dask_array.Array) for a in dispatch_args):

xarray/core/duck_array_ops.py:40:

.0 = <tuple_iterator object at 0x7f39269995e0>

if any(isinstance(a, dask_array.Array) for a in dispatch_args): E AttributeError: 'NoneType' object has no attribute 'Array'

xarray/core/duck_array_ops.py:40: AttributeError

During handling of the above exception, another exception occurred:

self = <xarray.tests.test_units.TestDataset object at 0x7f3927adf880> func = method_median, dtype = <class 'int'>

@pytest.mark.parametrize(
    "func",
    (
        pytest.param(
            function("all"),
            marks=pytest.mark.xfail(reason="not implemented by pint"),
        ),
        pytest.param(
            function("any"),
            marks=pytest.mark.xfail(reason="not implemented by pint"),
        ),
        function("argmax"),
        function("argmin"),
        function("max"),
        function("min"),
        function("mean"),
        pytest.param(
            function("median"),
            marks=pytest.mark.xfail(
                reason="np.median does not work with dataset yet"
            ),
        ),
        function("sum"),
        pytest.param(
            function("prod"),
            marks=pytest.mark.xfail(reason="not implemented by pint"),
        ),
        function("std"),
        function("var"),
        function("cumsum"),
        pytest.param(
            function("cumprod"),
            marks=pytest.mark.xfail(reason="fails within xarray"),
        ),
        pytest.param(
            method("all"), marks=pytest.mark.xfail(reason="not implemented by pint")
        ),
        pytest.param(
            method("any"), marks=pytest.mark.xfail(reason="not implemented by pint")
        ),
        method("argmax"),
        method("argmin"),
        method("max"),
        method("min"),
        method("mean"),
        method("median"),
        method("sum"),
        pytest.param(
            method("prod"),
            marks=pytest.mark.xfail(reason="not implemented by pint"),
        ),
        method("std"),
        method("var"),
        method("cumsum"),
        pytest.param(
            method("cumprod"), marks=pytest.mark.xfail(reason="fails within xarray")
        ),
    ),
    ids=repr,
)
def test_aggregation(self, func, dtype):
    unit_a = (
        unit_registry.Pa if func.name != "cumprod" else unit_registry.dimensionless
    )
    unit_b = (
        unit_registry.kg / unit_registry.m ** 3
        if func.name != "cumprod"
        else unit_registry.dimensionless
    )
    a = xr.DataArray(data=np.linspace(0, 1, 10).astype(dtype) * unit_a, dims="x")
    b = xr.DataArray(data=np.linspace(-1, 0, 10).astype(dtype) * unit_b, dims="x")
    x = xr.DataArray(data=np.arange(10).astype(dtype) * unit_registry.m, dims="x")
    y = xr.DataArray(
        data=np.arange(10, 20).astype(dtype) * unit_registry.s, dims="x"
    )

    ds = xr.Dataset(data_vars={"a": a, "b": b}, coords={"x": x, "y": y})

  actual = func(ds)

xarray/tests/test_units.py:3733:

xarray/tests/test_units.py:374: in call return func(all_args, all_kwargs) xarray/core/common.py:83: in wrapped_func return self.reduce( xarray/core/dataset.py:4230: in reduce variables[name] = var.reduce( xarray/core/variable.py:1535: in reduce data = func(input_data, axis=axis, *kwargs)

values = <Quantity([0 0 0 0 0 0 0 0 0 1], 'pascal')>, axis = 0, skipna = None kwargs = {}, func = <function _dask_or_eager_func.\<locals>.f at 0x7f392619b820> msg = 'median is not available with skipna=False with the installed version of numpy; upgrade to numpy 1.12 or newer to use skipna=True or skipna=None'

def f(values, axis=None, skipna=None, **kwargs):
    if kwargs.pop("out", None) is not None:
        raise TypeError(f"`out` is not valid for {name}")

    values = asarray(values)

    if coerce_strings and values.dtype.kind in "SU":
        values = values.astype(object)

    func = None
    if skipna or (skipna is None and values.dtype.kind in "cfO"):
        nanname = "nan" + name
        func = getattr(nanops, nanname)
    else:
        func = _dask_or_eager_func(name, dask_module=dask_module)

    try:
        return func(values, axis=axis, **kwargs)
    except AttributeError:
        if isinstance(values, dask_array_type):
            try:  # dask/dask#3133 dask sometimes needs dtype argument
                # if func does not accept dtype, then raises TypeError
                return func(values, axis=axis, dtype=values.dtype, **kwargs)
            except (AttributeError, TypeError):
                msg = "%s is not yet implemented on dask arrays" % name
        else:
            msg = (
                "%s is not available with skipna=False with the "
                "installed version of numpy; upgrade to numpy 1.12 "
                "or newer to use skipna=True or skipna=None" % name
            )

      raise NotImplementedError(msg)

E NotImplementedError: median is not available with skipna=False with the installed version of numpy; upgrade to numpy 1.12 or newer to use skipna=True or skipna=None

xarray/core/duck_array_ops.py:321: NotImplementedError ```

However I’m not much knowledgeable on all this, so I’ll defer to you for finding the root cause.

 
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   completed xarray 13221727 issue
568320746 MDU6SXNzdWU1NjgzMjA3NDY= 3783 New TestVariable.test_pad failure with pint 0.11 ArchangeGabriel 490531 closed 0     1 2020-02-20T13:59:35Z 2020-02-23T19:13:08Z 2020-02-23T19:13:08Z CONTRIBUTOR      

Since y-day, pint 0.11 was released and it results in a new test failure (in addition to the ones in #3778 and #3779): ``` ____ TestVariable.test_pad _____

self = <xarray.tests.test_units.TestVariable object at 0x7f6bfd8c1b20>

def test_pad(self):
    data = np.arange(4 * 3 * 2).reshape(4, 3, 2)
    v = self.cls(["x", "y", "z"], data)

    xr_args = [{"x": (2, 1)}, {"y": (0, 3)}, {"x": (3, 1), "z": (2, 0)}]
    np_args = [
        ((2, 1), (0, 0), (0, 0)),
        ((0, 0), (0, 3), (0, 0)),
        ((3, 1), (0, 0), (2, 0)),
    ]
    for xr_arg, np_arg in zip(xr_args, np_args):
        actual = v.pad_with_fill_value(**xr_arg)
        expected = np.pad(
            np.array(v.data.astype(float)),
            np_arg,
            mode="constant",
            constant_values=np.nan,
        )
        assert_array_equal(actual, expected)
        assert isinstance(actual._data, type(v._data))

    # for the boolean array, we pad False
    data = np.full_like(data, False, dtype=bool).reshape(4, 3, 2)
    v = self.cls(["x", "y", "z"], data)
    for xr_arg, np_arg in zip(xr_args, np_args):

      actual = v.pad_with_fill_value(fill_value=False, **xr_arg)

xarray/tests/test_variable.py:813:

xarray/core/variable.py:1210: in pad_with_fill_value array = np.pad( <array_function internals>:5: in pad ??? /usr/lib/python3.8/site-packages/pint/quantity.py:1543: in array_function return numpy_wrap("function", func, args, kwargs, types) /usr/lib/python3.8/site-packages/pint/numpy_func.py:894: in numpy_wrap return handledname /usr/lib/python3.8/site-packages/pint/numpy_func.py:671: in _pad kwargs["constant_values"] = _recursive_convert(kwargs["constant_values"], units) /usr/lib/python3.8/site-packages/pint/numpy_func.py:659: in _recursive_convert arg = unit._REGISTRY.Quantity(arg, unit) /usr/lib/python3.8/site-packages/pint/quantity.py:200: in new inst._magnitude = _to_magnitude(

value = False, force_ndarray = True, force_ndarray_like = False

def _to_magnitude(value, force_ndarray=False, force_ndarray_like=False):
    if isinstance(value, (dict, bool)) or value is None:

      raise TypeError("Invalid magnitude for Quantity: {0!r}".format(value))

E TypeError: Invalid magnitude for Quantity: False

/usr/lib/python3.8/site-packages/pint/compat.py:49: TypeError ```

 
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   completed xarray 13221727 issue
567035590 MDU6SXNzdWU1NjcwMzU1OTA= 3777 test_open_mfdataset_list_attr is ran even when dask is not available ArchangeGabriel 490531 closed 0     5 2020-02-18T17:08:23Z 2020-02-19T18:24:43Z 2020-02-19T18:24:43Z CONTRIBUTOR      

I’m currently packaging xarray (0.15.0) for ArchLinux, and I’m running the test suite with all our currently available packages (in addition to python-numpy, python-pandas and python-pytest of course):

python-netcdf4
python-scipy
python-cftime
python-bottleneck
python-matplotlib
python-seaborn
python-pint

I was greatly impressed at the automatic selection of tests depending on what is available on the system. :) I’m only seeing 10 tests failures (10 failed, 8057 passed, 1384 skipped, 518 xfailed, 277 xpassed, 5268 warnings), and amongst them one is happening because a test requiring dask is run even without it being installed: test_open_mfdataset_list_attr.

Corresponding test output: ``` ___ testopen_mfdataset_list_attr _____

@requires_netCDF4
def test_open_mfdataset_list_attr():
    """
    Case when an attribute of type list differs across the multiple files
    """
    from netCDF4 import Dataset

    with create_tmp_files(2) as nfiles:
        for i in range(2):
            f = Dataset(nfiles[i], "w") 
            f.createDimension("x", 3)
            vlvar = f.createVariable("test_var", np.int32, ("x"))
            # here create an attribute as a list 
            vlvar.test_attr = [f"string a {i}", f"string b {i}"]
            vlvar[:] = np.arange(3)
            f.close()
        ds1 = open_dataset(nfiles[0])
        ds2 = open_dataset(nfiles[1])
        original = xr.concat([ds1, ds2], dim="x")

      with xr.open_mfdataset(
            [nfiles[0], nfiles[1]], combine="nested", concat_dim="x"
        ) as actual:

xarray/tests/test_backends.py:2561:

xarray/backends/api.py:908: in open_mfdataset datasets = [open_(p, open_kwargs) for p in paths] xarray/backends/api.py:908: in <listcomp> datasets = [open_(p, open_kwargs) for p in paths] xarray/backends/api.py:538: in open_dataset ds = maybe_decode_store(store)

store = <xarray.backends.netCDF4_.NetCDF4DataStore object at 0x7f392aebc640> lock = False

def maybe_decode_store(store, lock=False):
    ds = conventions.decode_cf(
        store,
        mask_and_scale=mask_and_scale,
        decode_times=decode_times,
        concat_characters=concat_characters,
        decode_coords=decode_coords,
        drop_variables=drop_variables,
        use_cftime=use_cftime,
    )

    _protect_dataset_variables_inplace(ds, cache)

    if chunks is not None:

      from dask.base import tokenize

E ModuleNotFoundError: No module named 'dask'

xarray/backends/api.py:459: ModuleNotFoundError ``` I think this test should thus not be selected if dask is not installed. ;)

 
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   completed xarray 13221727 issue
567510745 MDExOlB1bGxSZXF1ZXN0Mzc3MTIxMjM4 3780 Avoid running test_open_mfdataset_list_attr without dask ArchangeGabriel 490531 closed 0     6 2020-02-19T11:41:25Z 2020-02-19T18:24:43Z 2020-02-19T18:24:43Z CONTRIBUTOR   0 pydata/xarray/pulls/3780

Fixes GH-3777.

  • [x] Closes #3777
 
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     xarray 13221727 pull

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