def setUp(self):
self.testfile = 'tiny'
self.testfilepath = os.path.expanduser(
os.sep.join(('~', '.xarray_tutorial_data', self.testfile)))
with suppress(OSError):
os.remove('{}.nc'.format(self.testfilepath))
with suppress(OSError):
os.remove('{}.md5'.format(self.testfilepath))
def setUp(self):
self.testfile = 'tiny'
self.testfilepath = os.path.expanduser(os.sep.join(
('~', '.xarray_tutorial_data', self.testfile)))
with suppress(OSError):
os.remove('{}.nc'.format(self.testfilepath))
with suppress(OSError):
os.remove('{}.md5'.format(self.testfilepath))
def test_dataset_getattr(self):
# Test that pickling/unpickling converts the dask backend
# to numpy in neither the data variables nor the non-index coords
data = build_dask_array('data')
nonindex_coord = build_dask_array('coord')
ds = Dataset(data_vars={'a': ('x', data)},
coords={'y': ('x', nonindex_coord)})
with suppress(AttributeError):
getattr(ds, 'NOTEXIST')
assert kernel_call_count == 0
def test_dataset_getattr(self):
# Test that pickling/unpickling converts the dask backend
# to numpy in neither the data variables nor the non-index coords
data = build_dask_array('data')
nonindex_coord = build_dask_array('coord')
ds = Dataset(data_vars={'a': ('x', data)},
coords={'y': ('x', nonindex_coord)})
with suppress(AttributeError):
getattr(ds, 'NOTEXIST')
self.assertEquals(kernel_call_count, 0)
def test_dataarray_getattr(self):
# ipython/jupyter does a long list of getattr() calls to when trying to
# represent an object.
# Make sure we're not accidentally computing dask variables.
data = build_dask_array('data')
nonindex_coord = build_dask_array('coord')
a = DataArray(data, dims=['x'], coords={'y': ('x', nonindex_coord)})
with suppress(AttributeError):
getattr(a, 'NOTEXIST')
self.assertEquals(kernel_call_count, 0)
def test_dataarray_getattr(self):
# ipython/jupyter does a long list of getattr() calls to when trying to
# represent an object.
# Make sure we're not accidentally computing dask variables.
data = build_dask_array('data')
nonindex_coord = build_dask_array('coord')
a = DataArray(data, dims=['x'],
coords={'y': ('x', nonindex_coord)})
with suppress(AttributeError):
getattr(a, 'NOTEXIST')
assert kernel_call_count == 0
from __future__ import division
from __future__ import print_function
import pickle
import numpy as np
import pandas as pd
import pytest
import xarray as xr
from xarray import Variable, DataArray, Dataset
import xarray.ufuncs as xu
from xarray.core.pycompat import suppress
from . import TestCase, requires_dask
from xarray.tests import unittest, mock
with suppress(ImportError):
import dask
import dask.array as da
class DaskTestCase(TestCase):
def assertLazyAnd(self, expected, actual, test):
with dask.set_options(get=dask.get):
test(actual, expected)
if isinstance(actual, Dataset):
for k, v in actual.variables.items():
if k in actual.dims:
self.assertIsInstance(var.data, np.ndarray)
else:
self.assertIsInstance(var.data, da.Array)
elif isinstance(actual, DataArray):
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function
import numpy as np
import pytest
from xarray import Variable
from xarray.coding import strings
from xarray.core import indexing
from xarray.core.pycompat import bytes_type, suppress, unicode_type
from . import (
IndexerMaker, assert_array_equal, assert_identical, raises_regex,
requires_dask)
with suppress(ImportError):
import dask.array as da
def test_vlen_dtype():
dtype = strings.create_vlen_dtype(unicode_type)
assert dtype.metadata['element_type'] == unicode_type
assert strings.is_unicode_dtype(dtype)
assert not strings.is_bytes_dtype(dtype)
assert strings.check_vlen_dtype(dtype) is unicode_type
dtype = strings.create_vlen_dtype(bytes_type)
assert dtype.metadata['element_type'] == bytes_type
assert not strings.is_unicode_dtype(dtype)
assert strings.is_bytes_dtype(dtype)
assert strings.check_vlen_dtype(dtype) is bytes_type