def test_set_options_context_manger():
with set_options(foo="bar"):
assert _globals["foo"] == "bar"
assert _globals["foo"] is None
try:
set_options(foo="baz")
assert _globals["foo"] == "baz"
finally:
del _globals["foo"]
def test_set_options_context_manger():
with set_options(foo='bar'):
assert _globals['foo'] == 'bar'
assert _globals['foo'] is None
try:
set_options(foo='baz')
assert _globals['foo'] == 'baz'
finally:
del _globals['foo']
def test_set_options_context_manger():
with set_options(foo='bar'):
assert _globals['foo'] == 'bar'
assert _globals['foo'] is None
try:
set_options(foo='baz')
assert _globals['foo'] == 'baz'
finally:
del _globals['foo']
def test_random_seeds(random):
def f():
return tuple(random.randint(0, 10000) for i in range(5))
N = 10
with set_options(scheduler='processes'):
results, = compute([delayed(f, pure=False)() for i in range(N)])
assert len(set(results)) == N
def test_random_seeds(random):
def f():
return tuple(random.randint(0, 10000) for i in range(5))
N = 10
with set_options(get=get):
results, = compute([delayed(f, pure=False)() for i in range(N)])
assert len(set(results)) == N
def test_with_get():
var = [0]
def myget(dsk, keys, **kwargs):
var[0] = var[0] + 1
return dask.get(dsk, keys, **kwargs)
x = da.ones(10, chunks=(5, ))
assert x.sum().compute() == 10
assert var[0] == 0
with set_options(get=myget):
assert x.sum().compute() == 10
assert var[0] == 1
# Make sure we've cleaned up
assert x.sum().compute() == 10
assert var[0] == 1
def test_with_get():
var = [0]
def myget(dsk, keys, **kwargs):
var[0] = var[0] + 1
return dask.get(dsk, keys, **kwargs)
x = da.ones(10, chunks=(5,))
assert x.sum().compute() == 10
assert var[0] == 0
with set_options(get=myget):
assert x.sum().compute() == 10
assert var[0] == 1
# Make sure we've cleaned up
assert x.sum().compute() == 10
assert var[0] == 1
def test_arg_reductions(dfunc, func):
x = np.random.random((10, 10, 10))
a = da.from_array(x, chunks=(3, 4, 5))
assert_eq(dfunc(a), func(x))
assert_eq(dfunc(a, 0), func(x, 0))
assert_eq(dfunc(a, 1), func(x, 1))
assert_eq(dfunc(a, 2), func(x, 2))
with set_options(split_every=2):
assert_eq(dfunc(a), func(x))
assert_eq(dfunc(a, 0), func(x, 0))
assert_eq(dfunc(a, 1), func(x, 1))
assert_eq(dfunc(a, 2), func(x, 2))
pytest.raises(ValueError, lambda: dfunc(a, 3))
pytest.raises(TypeError, lambda: dfunc(a, (0, 1)))
x2 = np.arange(10)
a2 = da.from_array(x2, chunks=3)
assert_eq(dfunc(a2), func(x2))
assert_eq(dfunc(a2, 0), func(x2, 0))
assert_eq(dfunc(a2, 0, split_every=2), func(x2, 0))
def test_arg_reductions(dfunc, func):
x = np.random.random((10, 10, 10))
a = da.from_array(x, chunks=(3, 4, 5))
assert_eq(dfunc(a), func(x))
assert_eq(dfunc(a, 0), func(x, 0))
assert_eq(dfunc(a, 1), func(x, 1))
assert_eq(dfunc(a, 2), func(x, 2))
with set_options(split_every=2):
assert_eq(dfunc(a), func(x))
assert_eq(dfunc(a, 0), func(x, 0))
assert_eq(dfunc(a, 1), func(x, 1))
assert_eq(dfunc(a, 2), func(x, 2))
pytest.raises(ValueError, lambda: dfunc(a, 3))
pytest.raises(TypeError, lambda: dfunc(a, (0, 1)))
x2 = np.arange(10)
a2 = da.from_array(x2, chunks=3)
assert_eq(dfunc(a2), func(x2))
assert_eq(dfunc(a2, 0), func(x2, 0))
assert_eq(dfunc(a2, 0, split_every=2), func(x2, 0))
def test_reuse_pool():
pool = ThreadPool()
with set_options(pool=pool):
assert get({'x': (inc, 1)}, 'x') == 2
assert get({'x': (inc, 1)}, 'x') == 2
def test_dumps_loads():
with set_options(func_dumps=pickle.dumps, func_loads=pickle.loads):
assert get({'x': 1, 'y': (add, 'x', 2)}, 'y') == 3
def test_reuse_pool():
pool = multiprocessing.Pool()
with set_options(pool=pool):
assert get({'x': (inc, 1)}, 'x') == 2
assert get({'x': (inc, 1)}, 'x') == 2
def test_tree_reduce_set_options():
x = da.from_array(np.arange(242).reshape((11, 22)), chunks=(3, 4))
with set_options(split_every={0: 2, 1: 3}):
assert_max_deps(x.sum(), 2 * 3)
assert_max_deps(x.sum(axis=0), 2)
def test_reuse_pool():
pool = multiprocessing.Pool()
with set_options(pool=pool):
assert get({'x': (inc, 1)}, 'x') == 2
assert get({'x': (inc, 1)}, 'x') == 2
def test_dumps_loads():
with set_options(func_dumps=pickle.dumps, func_loads=pickle.loads):
assert get({'x': 1, 'y': (add, 'x', 2)}, 'y') == 3
def test_tree_reduce_set_options():
x = da.from_array(np.arange(242).reshape((11, 22)), chunks=(3, 4))
with set_options(split_every={0: 2, 1: 3}):
assert_max_deps(x.sum(), 2 * 3)
assert_max_deps(x.sum(axis=0), 2)
def test_dumps_loads():
with set_options(func_dumps=pickle.dumps, func_loads=pickle.loads):
assert get({"x": 1, "y": (add, "x", 2)}, "y") == 3
from dask.local import get_sync
from dask.context import set_options
import dask.dataframe as dd
import numpy as np
import pandas as pd
import xarray as xr
import datashader as ds
import pytest
set_options(get=get_sync)
df = pd.DataFrame({'x': np.array(([0.] * 10 + [1] * 10)),
'y': np.array(([0.] * 5 + [1] * 5 + [0] * 5 + [1] * 5)),
'log_x': np.array(([1.] * 10 + [10] * 10)),
'log_y': np.array(([1.] * 5 + [10] * 5 + [1] * 5 + [10] * 5)),
'i32': np.arange(20, dtype='i4'),
'i64': np.arange(20, dtype='i8'),
'f32': np.arange(20, dtype='f4'),
'f64': np.arange(20, dtype='f8'),
'empty_bin': np.array([0.] * 15 + [np.nan] * 5),
'cat': ['a']*5 + ['b']*5 + ['c']*5 + ['d']*5})
df.cat = df.cat.astype('category')
df.f32[2] = np.nan
df.f64[2] = np.nan
ddf = dd.from_pandas(df, npartitions=3)
c = ds.Canvas(plot_width=2, plot_height=2, x_range=(0, 1), y_range=(0, 1))
c_logx = ds.Canvas(plot_width=2, plot_height=2, x_range=(1, 10),
from dask. async import get_sync
from dask.context import set_options
import dask.dataframe as dd
import numpy as np
import pandas as pd
import xarray as xr
import datashader as ds
set_options(get=get_sync)
df = pd.DataFrame({
'x': np.array(([0.] * 10 + [1] * 10)),
'y': np.array(([0.] * 5 + [1] * 5 + [0] * 5 + [1] * 5)),
'log_x': np.array(([1.] * 10 + [10] * 10)),
'log_y': np.array(([1.] * 5 + [10] * 5 + [1] * 5 + [10] * 5)),
'i32': np.arange(20, dtype='i4'),
'i64': np.arange(20, dtype='i8'),
'f32': np.arange(20, dtype='f4'),
'f64': np.arange(20, dtype='f8'),
'empty_bin': np.array([0.] * 15 + [np.nan] * 5),
'cat': ['a'] * 5 + ['b'] * 5 + ['c'] * 5 + ['d'] * 5
})
df.cat = df.cat.astype('category')
df.f32[2] = np.nan
df.f64[2] = np.nan
ddf = dd.from_pandas(df, npartitions=3)
c = ds.Canvas(plot_width=2, plot_height=2, x_range=(0, 2), y_range=(0, 2))
c_logx = ds.Canvas(plot_width=2,
