def rolling_functions_tests(p, d):
# Old-fashioned rolling API
assert_eq(pd.rolling_count(p, 3), dd.rolling_count(d, 3))
assert_eq(pd.rolling_sum(p, 3), dd.rolling_sum(d, 3))
assert_eq(pd.rolling_mean(p, 3), dd.rolling_mean(d, 3))
assert_eq(pd.rolling_median(p, 3), dd.rolling_median(d, 3))
assert_eq(pd.rolling_min(p, 3), dd.rolling_min(d, 3))
assert_eq(pd.rolling_max(p, 3), dd.rolling_max(d, 3))
assert_eq(pd.rolling_std(p, 3), dd.rolling_std(d, 3))
assert_eq(pd.rolling_var(p, 3), dd.rolling_var(d, 3))
# see note around test_rolling_dataframe for logic concerning precision
assert_eq(pd.rolling_skew(p, 3),
dd.rolling_skew(d, 3),
check_less_precise=True)
assert_eq(pd.rolling_kurt(p, 3),
dd.rolling_kurt(d, 3),
check_less_precise=True)
assert_eq(pd.rolling_quantile(p, 3, 0.5), dd.rolling_quantile(d, 3, 0.5))
assert_eq(pd.rolling_apply(p, 3, mad), dd.rolling_apply(d, 3, mad))
with ignoring(ImportError):
assert_eq(pd.rolling_window(p, 3, 'boxcar'),
dd.rolling_window(d, 3, 'boxcar'))
# Test with edge-case window sizes
assert_eq(pd.rolling_sum(p, 0), dd.rolling_sum(d, 0))
assert_eq(pd.rolling_sum(p, 1), dd.rolling_sum(d, 1))
# Test with kwargs
assert_eq(pd.rolling_sum(p, 3, min_periods=3),
dd.rolling_sum(d, 3, min_periods=3))
def basic_rolling_tests(p, d): # Works for series or df
# New rolling API
eq(p.rolling(3).count(), d.rolling(3).count())
eq(p.rolling(3).sum(), d.rolling(3).sum())
eq(p.rolling(3).mean(), d.rolling(3).mean())
eq(p.rolling(3).median(), d.rolling(3).median())
eq(p.rolling(3).min(), d.rolling(3).min())
eq(p.rolling(3).max(), d.rolling(3).max())
eq(p.rolling(3).std(), d.rolling(3).std())
eq(p.rolling(3).var(), d.rolling(3).var())
# see note around test_rolling_dataframe for logic concerning precision
eq(p.rolling(3).skew(), d.rolling(3).skew(), check_less_precise=True)
eq(p.rolling(3).kurt(), d.rolling(3).kurt(), check_less_precise=True)
eq(p.rolling(3).quantile(0.5), d.rolling(3).quantile(0.5))
eq(p.rolling(3).apply(mad), d.rolling(3).apply(mad))
with ignoring(ImportError):
eq(p.rolling(3, win_type='boxcar').sum(),
d.rolling(3, win_type='boxcar').sum())
# Test with edge-case window sizes
eq(p.rolling(0).sum(), d.rolling(0).sum())
eq(p.rolling(1).sum(), d.rolling(1).sum())
# Test with kwargs
eq(p.rolling(3, min_periods=2).sum(), d.rolling(3, min_periods=2).sum())
# Test with center
eq(p.rolling(3, center=True).max(), d.rolling(3, center=True).max())
eq(p.rolling(3, center=False).std(), d.rolling(3, center=False).std())
eq(p.rolling(6, center=True).var(), d.rolling(6, center=True).var())
# see note around test_rolling_dataframe for logic concerning precision
eq(p.rolling(7, center=True).skew(), d.rolling(7, center=True).skew(),
check_less_precise=True)
def rolling_functions_tests(p, d):
# Old-fashioned rolling API
assert_eq(pd.rolling_count(p, 3), dd.rolling_count(d, 3))
assert_eq(pd.rolling_sum(p, 3), dd.rolling_sum(d, 3))
assert_eq(pd.rolling_mean(p, 3), dd.rolling_mean(d, 3))
assert_eq(pd.rolling_median(p, 3), dd.rolling_median(d, 3))
assert_eq(pd.rolling_min(p, 3), dd.rolling_min(d, 3))
assert_eq(pd.rolling_max(p, 3), dd.rolling_max(d, 3))
assert_eq(pd.rolling_std(p, 3), dd.rolling_std(d, 3))
assert_eq(pd.rolling_var(p, 3), dd.rolling_var(d, 3))
# see note around test_rolling_dataframe for logic concerning precision
assert_eq(pd.rolling_skew(p, 3),
dd.rolling_skew(d, 3), check_less_precise=True)
assert_eq(pd.rolling_kurt(p, 3),
dd.rolling_kurt(d, 3), check_less_precise=True)
assert_eq(pd.rolling_quantile(p, 3, 0.5), dd.rolling_quantile(d, 3, 0.5))
assert_eq(pd.rolling_apply(p, 3, mad), dd.rolling_apply(d, 3, mad))
with ignoring(ImportError):
assert_eq(pd.rolling_window(p, 3, 'boxcar'),
dd.rolling_window(d, 3, 'boxcar'))
# Test with edge-case window sizes
assert_eq(pd.rolling_sum(p, 0), dd.rolling_sum(d, 0))
assert_eq(pd.rolling_sum(p, 1), dd.rolling_sum(d, 1))
# Test with kwargs
assert_eq(pd.rolling_sum(p, 3, min_periods=3),
dd.rolling_sum(d, 3, min_periods=3))
def test_reductions_2D(dtype):
x = np.arange(1, 122).reshape((11, 11)).astype(dtype)
a = da.from_array(x, chunks=(4, 4))
b = a.sum(keepdims=True)
assert b._keys() == [[(b.name, 0, 0)]]
reduction_2d_test(da.sum, a, np.sum, x)
reduction_2d_test(da.prod, a, np.prod, x)
reduction_2d_test(da.mean, a, np.mean, x)
reduction_2d_test(da.var, a, np.var, x, False) # Difference in dtype algo
reduction_2d_test(da.std, a, np.std, x, False) # Difference in dtype algo
reduction_2d_test(da.min, a, np.min, x, False)
reduction_2d_test(da.max, a, np.max, x, False)
reduction_2d_test(da.any, a, np.any, x, False)
reduction_2d_test(da.all, a, np.all, x, False)
reduction_2d_test(da.nansum, a, np.nansum, x)
with ignoring(AttributeError):
reduction_2d_test(da.nanprod, a, np.nanprod, x)
reduction_2d_test(da.nanmean, a, np.mean, x)
reduction_2d_test(da.nanvar, a, np.nanvar, x,
False) # Difference in dtype algo
reduction_2d_test(da.nanstd, a, np.nanstd, x,
False) # Difference in dtype algo
reduction_2d_test(da.nanmin, a, np.nanmin, x, False)
reduction_2d_test(da.nanmax, a, np.nanmax, x, False)
def test_reductions_2D_nans():
# chunks are a mix of some/all/no NaNs
x = np.full((4, 4), np.nan)
x[:2, :2] = np.array([[1, 2], [3, 4]])
x[2, 2] = 5
x[3, 3] = 6
a = da.from_array(x, chunks=(2, 2))
reduction_2d_test(da.sum, a, np.sum, x, False, False)
reduction_2d_test(da.prod, a, np.prod, x, False, False)
reduction_2d_test(da.mean, a, np.mean, x, False, False)
reduction_2d_test(da.var, a, np.var, x, False, False)
reduction_2d_test(da.std, a, np.std, x, False, False)
reduction_2d_test(da.min, a, np.min, x, False, False)
reduction_2d_test(da.max, a, np.max, x, False, False)
reduction_2d_test(da.any, a, np.any, x, False, False)
reduction_2d_test(da.all, a, np.all, x, False, False)
reduction_2d_test(da.nansum, a, np.nansum, x, False, False)
with ignoring(AttributeError):
reduction_2d_test(da.nanprod, a, np.nanprod, x, False, False)
reduction_2d_test(da.nanmean, a, np.nanmean, x, False, False)
reduction_2d_test(da.nanvar, a, np.nanvar, x, False, False)
reduction_2d_test(da.nanstd, a, np.nanstd, x, False, False)
reduction_2d_test(da.nanmin, a, np.nanmin, x, False, False)
reduction_2d_test(da.nanmax, a, np.nanmax, x, False, False)
def test_reductions_1D_int():
x = np.arange(5).astype('i4')
a = da.from_array(x, chunks=(2,))
reduction_1d_test(da.sum, a, np.sum, x)
reduction_1d_test(da.prod, a, np.prod, x)
reduction_1d_test(da.mean, a, np.mean, x)
reduction_1d_test(da.var, a, np.var, x)
reduction_1d_test(da.std, a, np.std, x)
reduction_1d_test(da.min, a, np.min, x, False)
reduction_1d_test(da.max, a, np.max, x, False)
reduction_1d_test(da.any, a, np.any, x, False)
reduction_1d_test(da.all, a, np.all, x, False)
reduction_1d_test(da.nansum, a, np.nansum, x)
with ignoring(AttributeError):
reduction_1d_test(da.nanprod, a, np.nanprod, x)
reduction_1d_test(da.nanmean, a, np.mean, x)
reduction_1d_test(da.nanvar, a, np.var, x)
reduction_1d_test(da.nanstd, a, np.std, x)
reduction_1d_test(da.nanmin, a, np.nanmin, x, False)
reduction_1d_test(da.nanmax, a, np.nanmax, x, False)
assert eq(da.argmax(a, axis=0), np.argmax(x, axis=0))
assert eq(da.argmin(a, axis=0), np.argmin(x, axis=0))
assert eq(da.nanargmax(a, axis=0), np.nanargmax(x, axis=0))
assert eq(da.nanargmin(a, axis=0), np.nanargmin(x, axis=0))
def test_reductions_2D(dtype):
x = np.arange(1, 122).reshape((11, 11)).astype(dtype)
a = da.from_array(x, chunks=(4, 4))
b = a.sum(keepdims=True)
assert b._keys() == [[(b.name, 0, 0)]]
reduction_2d_test(da.sum, a, np.sum, x)
reduction_2d_test(da.prod, a, np.prod, x)
reduction_2d_test(da.mean, a, np.mean, x)
reduction_2d_test(da.var, a, np.var, x, False) # Difference in dtype algo
reduction_2d_test(da.std, a, np.std, x, False) # Difference in dtype algo
reduction_2d_test(da.min, a, np.min, x, False)
reduction_2d_test(da.max, a, np.max, x, False)
reduction_2d_test(da.any, a, np.any, x, False)
reduction_2d_test(da.all, a, np.all, x, False)
reduction_2d_test(da.nansum, a, np.nansum, x)
with ignoring(AttributeError):
reduction_2d_test(da.nanprod, a, np.nanprod, x)
reduction_2d_test(da.nanmean, a, np.mean, x)
reduction_2d_test(da.nanvar, a, np.nanvar, x, False) # Difference in dtype algo
reduction_2d_test(da.nanstd, a, np.nanstd, x, False) # Difference in dtype algo
reduction_2d_test(da.nanmin, a, np.nanmin, x, False)
reduction_2d_test(da.nanmax, a, np.nanmax, x, False)
def test_reductions_2D_nans():
# chunks are a mix of some/all/no NaNs
x = np.full((4, 4), np.nan)
x[:2, :2] = np.array([[1, 2], [3, 4]])
x[2, 2] = 5
x[3, 3] = 6
a = da.from_array(x, chunks=(2, 2))
reduction_2d_test(da.sum, a, np.sum, x, False)
reduction_2d_test(da.prod, a, np.prod, x, False)
reduction_2d_test(da.mean, a, np.mean, x, False)
reduction_2d_test(da.var, a, np.var, x, False)
reduction_2d_test(da.std, a, np.std, x, False)
reduction_2d_test(da.min, a, np.min, x, False)
reduction_2d_test(da.max, a, np.max, x, False)
reduction_2d_test(da.any, a, np.any, x, False)
reduction_2d_test(da.all, a, np.all, x, False)
reduction_2d_test(da.nansum, a, np.nansum, x, False)
with ignoring(AttributeError):
reduction_2d_test(da.nanprod, a, np.nanprod, x, False)
reduction_2d_test(da.nanmean, a, np.nanmean, x, False)
reduction_2d_test(da.nanvar, a, np.nanvar, x, False)
reduction_2d_test(da.nanstd, a, np.nanstd, x, False)
reduction_2d_test(da.nanmin, a, np.nanmin, x, False)
reduction_2d_test(da.nanmax, a, np.nanmax, x, False)
def basic_rolling_tests(p, d): # Works for series or df
# New rolling API
eq(p.rolling(3).count(), d.rolling(3).count())
eq(p.rolling(3).sum(), d.rolling(3).sum())
eq(p.rolling(3).mean(), d.rolling(3).mean())
eq(p.rolling(3).median(), d.rolling(3).median())
eq(p.rolling(3).min(), d.rolling(3).min())
eq(p.rolling(3).max(), d.rolling(3).max())
eq(p.rolling(3).std(), d.rolling(3).std())
eq(p.rolling(3).var(), d.rolling(3).var())
# see note around test_rolling_dataframe for logic concerning precision
eq(p.rolling(3).skew(), d.rolling(3).skew(), check_less_precise=True)
eq(p.rolling(3).kurt(), d.rolling(3).kurt(), check_less_precise=True)
eq(p.rolling(3).quantile(0.5), d.rolling(3).quantile(0.5))
eq(p.rolling(3).apply(mad), d.rolling(3).apply(mad))
with ignoring(ImportError):
eq(
p.rolling(3, win_type='boxcar').sum(),
d.rolling(3, win_type='boxcar').sum())
# Test with edge-case window sizes
eq(p.rolling(0).sum(), d.rolling(0).sum())
eq(p.rolling(1).sum(), d.rolling(1).sum())
# Test with kwargs
eq(p.rolling(3, min_periods=2).sum(), d.rolling(3, min_periods=2).sum())
# Test with center
eq(p.rolling(3, center=True).max(), d.rolling(3, center=True).max())
eq(p.rolling(3, center=False).std(), d.rolling(3, center=False).std())
eq(p.rolling(6, center=True).var(), d.rolling(6, center=True).var())
# see note around test_rolling_dataframe for logic concerning precision
eq(p.rolling(7, center=True).skew(),
d.rolling(7, center=True).skew(),
check_less_precise=True)
def test_reductions_1D_int():
x = np.arange(5).astype('i4')
a = da.from_array(x, chunks=(2, ))
reduction_1d_test(da.sum, a, np.sum, x)
reduction_1d_test(da.prod, a, np.prod, x)
reduction_1d_test(da.mean, a, np.mean, x)
reduction_1d_test(da.var, a, np.var, x)
reduction_1d_test(da.std, a, np.std, x)
reduction_1d_test(da.min, a, np.min, x, False)
reduction_1d_test(da.max, a, np.max, x, False)
reduction_1d_test(da.any, a, np.any, x, False)
reduction_1d_test(da.all, a, np.all, x, False)
reduction_1d_test(da.nansum, a, np.nansum, x)
with ignoring(AttributeError):
reduction_1d_test(da.nanprod, a, np.nanprod, x)
reduction_1d_test(da.nanmean, a, np.mean, x)
reduction_1d_test(da.nanvar, a, np.var, x)
reduction_1d_test(da.nanstd, a, np.std, x)
reduction_1d_test(da.nanmin, a, np.nanmin, x, False)
reduction_1d_test(da.nanmax, a, np.nanmax, x, False)
assert eq(da.argmax(a, axis=0), np.argmax(x, axis=0))
assert eq(da.argmin(a, axis=0), np.argmin(x, axis=0))
assert eq(da.nanargmax(a, axis=0), np.nanargmax(x, axis=0))
assert eq(da.nanargmin(a, axis=0), np.nanargmin(x, axis=0))
def test_reductions_2D_int():
x = np.arange(1, 122).reshape((11, 11)).astype('i4')
a = da.from_array(x, chunks=(4, 4))
reduction_2d_test(da.sum, a, np.sum, x)
reduction_2d_test(da.prod, a, np.prod, x)
reduction_2d_test(da.mean, a, np.mean, x)
reduction_2d_test(da.var, a, np.var, x, False) # Difference in dtype algo
reduction_2d_test(da.std, a, np.std, x, False) # Difference in dtype algo
reduction_2d_test(da.min, a, np.min, x, False)
reduction_2d_test(da.max, a, np.max, x, False)
reduction_2d_test(da.any, a, np.any, x, False)
reduction_2d_test(da.all, a, np.all, x, False)
reduction_2d_test(da.nansum, a, np.nansum, x)
with ignoring(AttributeError):
reduction_2d_test(da.nanprod, a, np.nanprod, x)
reduction_2d_test(da.nanmean, a, np.mean, x)
reduction_2d_test(da.nanvar, a, np.nanvar, x, False) # Difference in dtype algo
reduction_2d_test(da.nanstd, a, np.nanstd, x, False) # Difference in dtype algo
reduction_2d_test(da.nanmin, a, np.nanmin, x, False)
reduction_2d_test(da.nanmax, a, np.nanmax, x, False)
assert eq(da.argmax(a, axis=0), np.argmax(x, axis=0))
assert eq(da.argmin(a, axis=0), np.argmin(x, axis=0))
assert eq(da.nanargmax(a, axis=0), np.nanargmax(x, axis=0))
assert eq(da.nanargmin(a, axis=0), np.nanargmin(x, axis=0))
assert eq(da.argmax(a, axis=1), np.argmax(x, axis=1))
assert eq(da.argmin(a, axis=1), np.argmin(x, axis=1))
assert eq(da.nanargmax(a, axis=1), np.nanargmax(x, axis=1))
assert eq(da.nanargmin(a, axis=1), np.nanargmin(x, axis=1))
def test_reductions_2D_int():
x = np.arange(1, 122).reshape((11, 11)).astype('i4')
a = da.from_array(x, chunks=(4, 4))
reduction_2d_test(da.sum, a, np.sum, x)
reduction_2d_test(da.prod, a, np.prod, x)
reduction_2d_test(da.mean, a, np.mean, x)
reduction_2d_test(da.var, a, np.var, x, False) # Difference in dtype algo
reduction_2d_test(da.std, a, np.std, x, False) # Difference in dtype algo
reduction_2d_test(da.min, a, np.min, x, False)
reduction_2d_test(da.max, a, np.max, x, False)
reduction_2d_test(da.any, a, np.any, x, False)
reduction_2d_test(da.all, a, np.all, x, False)
reduction_2d_test(da.nansum, a, np.nansum, x)
with ignoring(AttributeError):
reduction_2d_test(da.nanprod, a, np.nanprod, x)
reduction_2d_test(da.nanmean, a, np.mean, x)
reduction_2d_test(da.nanvar, a, np.nanvar, x,
False) # Difference in dtype algo
reduction_2d_test(da.nanstd, a, np.nanstd, x,
False) # Difference in dtype algo
reduction_2d_test(da.nanmin, a, np.nanmin, x, False)
reduction_2d_test(da.nanmax, a, np.nanmax, x, False)
assert eq(da.argmax(a, axis=0), np.argmax(x, axis=0))
assert eq(da.argmin(a, axis=0), np.argmin(x, axis=0))
assert eq(da.nanargmax(a, axis=0), np.nanargmax(x, axis=0))
assert eq(da.nanargmin(a, axis=0), np.nanargmin(x, axis=0))
assert eq(da.argmax(a, axis=1), np.argmax(x, axis=1))
assert eq(da.argmin(a, axis=1), np.argmin(x, axis=1))
assert eq(da.nanargmax(a, axis=1), np.nanargmax(x, axis=1))
assert eq(da.nanargmin(a, axis=1), np.nanargmin(x, axis=1))
def test_tokenize_ordered_dict():
with ignoring(ImportError):
from collections import OrderedDict
a = OrderedDict([('a', 1), ('b', 2)])
b = OrderedDict([('a', 1), ('b', 2)])
c = OrderedDict([('b', 2), ('a', 1)])
assert tokenize(a) == tokenize(b)
assert tokenize(a) != tokenize(c)
def test_tokenize_ordered_dict():
with ignoring(ImportError):
from collections import OrderedDict
a = OrderedDict([('a', 1), ('b', 2)])
b = OrderedDict([('a', 1), ('b', 2)])
c = OrderedDict([('b', 2), ('a', 1)])
assert tokenize(a) == tokenize(b)
assert tokenize(a) != tokenize(c)
def test_profiler_works_under_error():
div = lambda x, y: x / y
dsk = {"x": (div, 1, 1), "y": (div, "x", 2), "z": (div, "y", 0)}
with ignoring(ZeroDivisionError):
with prof:
out = get(dsk, "z")
assert all(len(v) == 5 for v in prof.results)
assert len(prof.results) == 2
def test_profiler_works_under_error():
div = lambda x, y: x / y
dsk = {'x': (div, 1, 1), 'y': (div, 'x', 2), 'z': (div, 'y', 0)}
with ignoring(ZeroDivisionError):
with prof:
out = get(dsk, 'z')
assert all(len(v) == 5 for v in prof.results)
assert len(prof.results) == 2
def test_profiler_works_under_error():
div = lambda x, y: x / y
dsk = {'x': (div, 1, 1), 'y': (div, 'x', 2), 'z': (div, 'y', 0)}
with ignoring(ZeroDivisionError):
with prof:
out = get(dsk, 'z')
assert all(len(v) == 5 for v in prof.results)
assert len(prof.results) == 2
def test_profiler_works_under_error():
div = lambda x, y: x / y
dsk = {"x": (div, 1, 1), "y": (div, "x", 2), "z": (div, "y", 0)}
with ignoring(ZeroDivisionError):
with prof:
get(dsk, "z")
assert all(len(v) == 5 for v in prof.results)
assert len(prof.results) == 2
def test_nan():
x = np.array([[1, np.nan, 3, 4], [5, 6, 7, np.nan], [9, 10, 11, 12]])
d = da.from_array(x, chunks=(2, 2))
assert eq(np.nansum(x), da.nansum(d))
assert eq(np.nansum(x, axis=0), da.nansum(d, axis=0))
assert eq(np.nanmean(x, axis=1), da.nanmean(d, axis=1))
assert eq(np.nanmin(x, axis=1), da.nanmin(d, axis=1))
assert eq(np.nanmax(x, axis=(0, 1)), da.nanmax(d, axis=(0, 1)))
assert eq(np.nanvar(x), da.nanvar(d))
assert eq(np.nanstd(x, axis=0), da.nanstd(d, axis=0))
assert eq(np.nanargmin(x, axis=0), da.nanargmin(d, axis=0))
assert eq(np.nanargmax(x, axis=0), da.nanargmax(d, axis=0))
with ignoring(AttributeError):
assert eq(np.nanprod(x), da.nanprod(d))
def test_nan():
x = np.array([[1, np.nan, 3, 4],
[5, 6, 7, np.nan],
[9, 10, 11, 12]])
d = da.from_array(x, chunks=(2, 2))
assert eq(np.nansum(x), da.nansum(d))
assert eq(np.nansum(x, axis=0), da.nansum(d, axis=0))
assert eq(np.nanmean(x, axis=1), da.nanmean(d, axis=1))
assert eq(np.nanmin(x, axis=1), da.nanmin(d, axis=1))
assert eq(np.nanmax(x, axis=(0, 1)), da.nanmax(d, axis=(0, 1)))
assert eq(np.nanvar(x), da.nanvar(d))
assert eq(np.nanstd(x, axis=0), da.nanstd(d, axis=0))
assert eq(np.nanargmin(x, axis=0), da.nanargmin(d, axis=0))
assert eq(np.nanargmax(x, axis=0), da.nanargmax(d, axis=0))
with ignoring(AttributeError):
assert eq(np.nanprod(x), da.nanprod(d))
def test_dtype_complex():
x = np.arange(24).reshape((4, 6)).astype('f4')
y = np.arange(24).reshape((4, 6)).astype('i8')
z = np.arange(24).reshape((4, 6)).astype('i2')
a = da.from_array(x, chunks=(2, 3))
b = da.from_array(y, chunks=(2, 3))
c = da.from_array(z, chunks=(2, 3))
def eq(a, b):
return (isinstance(a, np.dtype) and
isinstance(b, np.dtype) and
str(a) == str(b))
assert eq(a._dtype, x.dtype)
assert eq(b._dtype, y.dtype)
assert eq((a + 1)._dtype, (x + 1).dtype)
assert eq((a + b)._dtype, (x + y).dtype)
assert eq(a.T._dtype, x.T.dtype)
assert eq(a[:3]._dtype, x[:3].dtype)
assert eq((a.dot(b.T))._dtype, (x.dot(y.T)).dtype)
assert eq(stack([a, b])._dtype, np.vstack([x, y]).dtype)
assert eq(concatenate([a, b])._dtype, np.concatenate([x, y]).dtype)
assert eq(b.std()._dtype, y.std().dtype)
assert eq(c.sum()._dtype, z.sum().dtype)
assert eq(a.min()._dtype, a.min().dtype)
assert eq(b.std()._dtype, b.std().dtype)
assert eq(a.argmin(axis=0)._dtype, a.argmin(axis=0).dtype)
assert eq(da.sin(c)._dtype, np.sin(z).dtype)
assert eq(da.exp(b)._dtype, np.exp(y).dtype)
assert eq(da.floor(a)._dtype, np.floor(x).dtype)
assert eq(da.isnan(b)._dtype, np.isnan(y).dtype)
with ignoring(ImportError):
assert da.isnull(b)._dtype == 'bool'
assert da.notnull(b)._dtype == 'bool'
x = np.array([('a', 1)], dtype=[('text', 'S1'), ('numbers', 'i4')])
d = da.from_array(x, chunks=(1,))
assert eq(d['text']._dtype, x['text'].dtype)
assert eq(d[['numbers', 'text']]._dtype, x[['numbers', 'text']].dtype)
def update_bar(elapsed, prev_completed, prev_estimated, pb):
total = 0
completed = 0
estimated = 0.0
time_guess = 0.0
# update
with pb._lock:
for k, v in pb.task_data.items():
total += v.total
completed += v.completed
if v.completed > 0:
avg_time = v.time_sum / v.completed
estimated += avg_time * v.total
time_guess += v.time_sum
# If we've completed some new tasks, update our estimate
# otherwise use previous estimate. This prevents jumps
# relative to the elapsed time
if completed != prev_completed:
estimated = estimated * elapsed / time_guess
else:
estimated = prev_estimated
# For the first 10 seconds, tell the user estimates improve over time
# then display the bar
if elapsed < 10.0:
fraction = 0.0
bar = " estimate improves over time"
else:
# Print out the progress bar
fraction = elapsed / estimated if estimated > 0.0 else 0.0
bar = "#" * int(pb._width * fraction)
percent = int(100 * fraction)
msg = "\r[{0:{1}.{1}}] | {2}% Complete (Estimate) | {3} / ~{4}".format(
bar, pb._width, percent, format_time(elapsed),
"???" if estimated == 0.0 else format_time(estimated))
with ignoring(ValueError):
pb._file.write(msg)
pb._file.flush()
return completed, estimated
def test_dtype_complex():
x = np.arange(24).reshape((4, 6)).astype('f4')
y = np.arange(24).reshape((4, 6)).astype('i8')
z = np.arange(24).reshape((4, 6)).astype('i2')
a = da.from_array(x, chunks=(2, 3))
b = da.from_array(y, chunks=(2, 3))
c = da.from_array(z, chunks=(2, 3))
def eq(a, b):
return (isinstance(a, np.dtype) and isinstance(b, np.dtype)
and str(a) == str(b))
assert eq(a._dtype, x.dtype)
assert eq(b._dtype, y.dtype)
assert eq((a + 1)._dtype, (x + 1).dtype)
assert eq((a + b)._dtype, (x + y).dtype)
assert eq(a.T._dtype, x.T.dtype)
assert eq(a[:3]._dtype, x[:3].dtype)
assert eq((a.dot(b.T))._dtype, (x.dot(y.T)).dtype)
assert eq(stack([a, b])._dtype, np.vstack([x, y]).dtype)
assert eq(concatenate([a, b])._dtype, np.concatenate([x, y]).dtype)
assert eq(b.std()._dtype, y.std().dtype)
assert eq(c.sum()._dtype, z.sum().dtype)
assert eq(a.min()._dtype, a.min().dtype)
assert eq(b.std()._dtype, b.std().dtype)
assert eq(a.argmin(axis=0)._dtype, a.argmin(axis=0).dtype)
assert eq(da.sin(c)._dtype, np.sin(z).dtype)
assert eq(da.exp(b)._dtype, np.exp(y).dtype)
assert eq(da.floor(a)._dtype, np.floor(x).dtype)
assert eq(da.isnan(b)._dtype, np.isnan(y).dtype)
with ignoring(ImportError):
assert da.isnull(b)._dtype == 'bool'
assert da.notnull(b)._dtype == 'bool'
x = np.array([('a', 1)], dtype=[('text', 'S1'), ('numbers', 'i4')])
d = da.from_array(x, chunks=(1, ))
assert eq(d['text']._dtype, x['text'].dtype)
assert eq(d[['numbers', 'text']]._dtype, x[['numbers', 'text']].dtype)
def rolling_functions_tests(p, d):
# Old-fashioned rolling API
eq(pd.rolling_count(p, 3), dd.rolling_count(d, 3))
eq(pd.rolling_sum(p, 3), dd.rolling_sum(d, 3))
eq(pd.rolling_mean(p, 3), dd.rolling_mean(d, 3))
eq(pd.rolling_median(p, 3), dd.rolling_median(d, 3))
eq(pd.rolling_min(p, 3), dd.rolling_min(d, 3))
eq(pd.rolling_max(p, 3), dd.rolling_max(d, 3))
eq(pd.rolling_std(p, 3), dd.rolling_std(d, 3))
eq(pd.rolling_var(p, 3), dd.rolling_var(d, 3))
eq(pd.rolling_skew(p, 3), dd.rolling_skew(d, 3))
eq(pd.rolling_kurt(p, 3), dd.rolling_kurt(d, 3))
eq(pd.rolling_quantile(p, 3, 0.5), dd.rolling_quantile(d, 3, 0.5))
eq(pd.rolling_apply(p, 3, mad), dd.rolling_apply(d, 3, mad))
with ignoring(ImportError):
eq(pd.rolling_window(p, 3, 'boxcar'), dd.rolling_window(d, 3, 'boxcar'))
# Test with edge-case window sizes
eq(pd.rolling_sum(p, 0), dd.rolling_sum(d, 0))
eq(pd.rolling_sum(p, 1), dd.rolling_sum(d, 1))
# Test with kwargs
eq(pd.rolling_sum(p, 3, min_periods=3), dd.rolling_sum(d, 3, min_periods=3))
def rolling_functions_tests(p, d):
# Old-fashioned rolling API
eq(pd.rolling_count(p, 3), dd.rolling_count(d, 3))
eq(pd.rolling_sum(p, 3), dd.rolling_sum(d, 3))
eq(pd.rolling_mean(p, 3), dd.rolling_mean(d, 3))
eq(pd.rolling_median(p, 3), dd.rolling_median(d, 3))
eq(pd.rolling_min(p, 3), dd.rolling_min(d, 3))
eq(pd.rolling_max(p, 3), dd.rolling_max(d, 3))
eq(pd.rolling_std(p, 3), dd.rolling_std(d, 3))
eq(pd.rolling_var(p, 3), dd.rolling_var(d, 3))
eq(pd.rolling_skew(p, 3), dd.rolling_skew(d, 3))
eq(pd.rolling_kurt(p, 3), dd.rolling_kurt(d, 3))
eq(pd.rolling_quantile(p, 3, 0.5), dd.rolling_quantile(d, 3, 0.5))
eq(pd.rolling_apply(p, 3, mad), dd.rolling_apply(d, 3, mad))
with ignoring(ImportError):
eq(pd.rolling_window(p, 3, "boxcar"), dd.rolling_window(d, 3, "boxcar"))
# Test with edge-case window sizes
eq(pd.rolling_sum(p, 0), dd.rolling_sum(d, 0))
eq(pd.rolling_sum(p, 1), dd.rolling_sum(d, 1))
# Test with kwargs
eq(pd.rolling_sum(p, 3, min_periods=3), dd.rolling_sum(d, 3, min_periods=3))
def basic_rolling_tests(p, d): # Works for series or df
# New rolling API
eq(p.rolling(3).count(), d.rolling(3).count())
eq(p.rolling(3).sum(), d.rolling(3).sum())
eq(p.rolling(3).mean(), d.rolling(3).mean())
eq(p.rolling(3).median(), d.rolling(3).median())
eq(p.rolling(3).min(), d.rolling(3).min())
eq(p.rolling(3).max(), d.rolling(3).max())
eq(p.rolling(3).std(), d.rolling(3).std())
eq(p.rolling(3).var(), d.rolling(3).var())
eq(p.rolling(3).skew(), d.rolling(3).skew())
eq(p.rolling(3).kurt(), d.rolling(3).kurt())
eq(p.rolling(3).quantile(0.5), d.rolling(3).quantile(0.5))
eq(p.rolling(3).apply(mad), d.rolling(3).apply(mad))
with ignoring(ImportError):
eq(p.rolling(3, win_type="boxcar").sum(), d.rolling(3, win_type="boxcar").sum())
# Test with edge-case window sizes
eq(p.rolling(0).sum(), d.rolling(0).sum())
eq(p.rolling(1).sum(), d.rolling(1).sum())
# Test with kwargs
eq(p.rolling(3, min_periods=2).sum(), d.rolling(3, min_periods=2).sum())
def rolling_tests(p, d):
eq(pd.rolling_count(p, 3), dd.rolling_count(d, 3))
eq(pd.rolling_sum(p, 3), dd.rolling_sum(d, 3))
eq(pd.rolling_mean(p, 3), dd.rolling_mean(d, 3))
eq(pd.rolling_median(p, 3), dd.rolling_median(d, 3))
eq(pd.rolling_min(p, 3), dd.rolling_min(d, 3))
eq(pd.rolling_max(p, 3), dd.rolling_max(d, 3))
eq(pd.rolling_std(p, 3), dd.rolling_std(d, 3))
eq(pd.rolling_var(p, 3), dd.rolling_var(d, 3))
eq(pd.rolling_skew(p, 3), dd.rolling_skew(d, 3))
eq(pd.rolling_kurt(p, 3), dd.rolling_kurt(d, 3))
eq(pd.rolling_quantile(p, 3, 0.5), dd.rolling_quantile(d, 3, 0.5))
mad = lambda x: np.fabs(x - x.mean()).mean()
eq(pd.rolling_apply(p, 3, mad), dd.rolling_apply(d, 3, mad))
with ignoring(ImportError):
eq(pd.rolling_window(p, 3, "boxcar"), dd.rolling_window(d, 3, "boxcar"))
# Test with edge-case window sizes
eq(pd.rolling_sum(p, 0), dd.rolling_sum(d, 0))
eq(pd.rolling_sum(p, 1), dd.rolling_sum(d, 1))
# Test with kwargs
eq(pd.rolling_sum(p, 3, min_periods=3), dd.rolling_sum(d, 3, min_periods=3))
def test_reductions_1D(dtype):
x = np.arange(5).astype(dtype)
a = da.from_array(x, chunks=(2,))
reduction_1d_test(da.sum, a, np.sum, x)
reduction_1d_test(da.prod, a, np.prod, x)
reduction_1d_test(da.mean, a, np.mean, x)
reduction_1d_test(da.var, a, np.var, x)
reduction_1d_test(da.std, a, np.std, x)
reduction_1d_test(da.min, a, np.min, x, False)
reduction_1d_test(da.max, a, np.max, x, False)
reduction_1d_test(da.any, a, np.any, x, False)
reduction_1d_test(da.all, a, np.all, x, False)
reduction_1d_test(da.nansum, a, np.nansum, x)
with ignoring(AttributeError):
reduction_1d_test(da.nanprod, a, np.nanprod, x)
reduction_1d_test(da.nanmean, a, np.mean, x)
reduction_1d_test(da.nanvar, a, np.var, x)
reduction_1d_test(da.nanstd, a, np.std, x)
reduction_1d_test(da.nanmin, a, np.nanmin, x, False)
reduction_1d_test(da.nanmax, a, np.nanmax, x, False)
def test_reductions_1D(dtype):
x = np.arange(5).astype(dtype)
a = da.from_array(x, chunks=(2, ))
reduction_1d_test(da.sum, a, np.sum, x)
reduction_1d_test(da.prod, a, np.prod, x)
reduction_1d_test(da.mean, a, np.mean, x)
reduction_1d_test(da.var, a, np.var, x)
reduction_1d_test(da.std, a, np.std, x)
reduction_1d_test(da.min, a, np.min, x, False)
reduction_1d_test(da.max, a, np.max, x, False)
reduction_1d_test(da.any, a, np.any, x, False)
reduction_1d_test(da.all, a, np.all, x, False)
reduction_1d_test(da.nansum, a, np.nansum, x)
with ignoring(AttributeError):
reduction_1d_test(da.nanprod, a, np.nanprod, x)
reduction_1d_test(da.nanmean, a, np.mean, x)
reduction_1d_test(da.nanvar, a, np.var, x)
reduction_1d_test(da.nanstd, a, np.std, x)
reduction_1d_test(da.nanmin, a, np.nanmin, x, False)
reduction_1d_test(da.nanmax, a, np.nanmax, x, False)
def basic_rolling_tests(p, d): # Works for series or df
# New rolling API
eq(p.rolling(3).count(), d.rolling(3).count())
eq(p.rolling(3).sum(), d.rolling(3).sum())
eq(p.rolling(3).mean(), d.rolling(3).mean())
eq(p.rolling(3).median(), d.rolling(3).median())
eq(p.rolling(3).min(), d.rolling(3).min())
eq(p.rolling(3).max(), d.rolling(3).max())
eq(p.rolling(3).std(), d.rolling(3).std())
eq(p.rolling(3).var(), d.rolling(3).var())
eq(p.rolling(3).skew(), d.rolling(3).skew())
eq(p.rolling(3).kurt(), d.rolling(3).kurt())
eq(p.rolling(3).quantile(0.5), d.rolling(3).quantile(0.5))
eq(p.rolling(3).apply(mad), d.rolling(3).apply(mad))
with ignoring(ImportError):
eq(p.rolling(3, win_type='boxcar').sum(),
d.rolling(3, win_type='boxcar').sum())
# Test with edge-case window sizes
eq(p.rolling(0).sum(), d.rolling(0).sum())
eq(p.rolling(1).sum(), d.rolling(1).sum())
# Test with kwargs
eq(p.rolling(3, min_periods=2).sum(), d.rolling(3, min_periods=2).sum())
def rolling_tests(p, d):
eq(pd.rolling_count(p, 3), dd.rolling_count(d, 3))
eq(pd.rolling_sum(p, 3), dd.rolling_sum(d, 3))
eq(pd.rolling_mean(p, 3), dd.rolling_mean(d, 3))
eq(pd.rolling_median(p, 3), dd.rolling_median(d, 3))
eq(pd.rolling_min(p, 3), dd.rolling_min(d, 3))
eq(pd.rolling_max(p, 3), dd.rolling_max(d, 3))
eq(pd.rolling_std(p, 3), dd.rolling_std(d, 3))
eq(pd.rolling_var(p, 3), dd.rolling_var(d, 3))
eq(pd.rolling_skew(p, 3), dd.rolling_skew(d, 3))
eq(pd.rolling_kurt(p, 3), dd.rolling_kurt(d, 3))
eq(pd.rolling_quantile(p, 3, 0.5), dd.rolling_quantile(d, 3, 0.5))
mad = lambda x: np.fabs(x - x.mean()).mean()
eq(pd.rolling_apply(p, 3, mad), dd.rolling_apply(d, 3, mad))
with ignoring(ImportError):
eq(pd.rolling_window(p, 3, 'boxcar'),
dd.rolling_window(d, 3, 'boxcar'))
# Test with edge-case window sizes
eq(pd.rolling_sum(p, 0), dd.rolling_sum(d, 0))
eq(pd.rolling_sum(p, 1), dd.rolling_sum(d, 1))
# Test with kwargs
eq(pd.rolling_sum(p, 3, min_periods=3), dd.rolling_sum(d, 3,
min_periods=3))
def import_or_none(path):
with ignoring():
return pytest.importorskip(path)
return None
def test_ignoring_deprecated():
with pytest.warns(FutureWarning, match="contextlib.suppress"):
with ignoring(ValueError):
pass
def import_or_none(path):
with ignoring(BaseException):
return pytest.importorskip(path)
return None
def test_isnull_result_is_an_array():
# regression test for https://github.com/dask/dask/issues/3822
arr = da.from_array(np.arange(3, dtype=np.int64), chunks=-1)
with ignoring(ImportError):
result = da.isnull(arr[0]).compute()
assert type(result) is np.ndarray
def _close_job(cls, job_id):
if job_id:
with ignoring(RuntimeError): # deleting job when job already gone
cls._call(shlex.split(cls.cancel_command) + [job_id])
logger.debug("Closed job %s", job_id)
def main(scheduler, host, worker_port, listen_address, contact_address,
nanny_port, nthreads, nprocs, nanny, name, pid_file, resources,
dashboard, bokeh, bokeh_port, scheduler_file, dashboard_prefix,
tls_ca_file, tls_cert, tls_key, dashboard_address, **kwargs):
g0, g1, g2 = gc.get_threshold(
) # https://github.com/dask/distributed/issues/1653
gc.set_threshold(g0 * 3, g1 * 3, g2 * 3)
enable_proctitle_on_current()
enable_proctitle_on_children()
if bokeh_port is not None:
warnings.warn(
"The --bokeh-port flag has been renamed to --dashboard-address. "
"Consider adding ``--dashboard-address :%d`` " % bokeh_port)
dashboard_address = bokeh_port
if bokeh is not None:
warnings.warn(
"The --bokeh/--no-bokeh flag has been renamed to --dashboard/--no-dashboard. "
)
dashboard = bokeh
sec = Security(
**{
k: v
for k, v in [
("tls_ca_file", tls_ca_file),
("tls_worker_cert", tls_cert),
("tls_worker_key", tls_key),
] if v is not None
})
if nprocs > 1 and worker_port != 0:
logger.error(
"Failed to launch worker. You cannot use the --port argument when nprocs > 1."
)
exit(1)
if nprocs > 1 and not nanny:
logger.error(
"Failed to launch worker. You cannot use the --no-nanny argument when nprocs > 1."
)
exit(1)
if contact_address and not listen_address:
logger.error(
"Failed to launch worker. "
"Must specify --listen-address when --contact-address is given")
exit(1)
if nprocs > 1 and listen_address:
logger.error("Failed to launch worker. "
"You cannot specify --listen-address when nprocs > 1.")
exit(1)
if (worker_port or host) and listen_address:
logger.error(
"Failed to launch worker. "
"You cannot specify --listen-address when --worker-port or --host is given."
)
exit(1)
try:
if listen_address:
(host, worker_port) = get_address_host_port(listen_address,
strict=True)
if contact_address:
# we only need this to verify it is getting parsed
(_, _) = get_address_host_port(contact_address, strict=True)
else:
# if contact address is not present we use the listen_address for contact
contact_address = listen_address
except ValueError as e:
logger.error("Failed to launch worker. " + str(e))
exit(1)
if nanny:
port = nanny_port
else:
port = worker_port
if not nthreads:
nthreads = CPU_COUNT // nprocs
if pid_file:
with open(pid_file, "w") as f:
f.write(str(os.getpid()))
def del_pid_file():
if os.path.exists(pid_file):
os.remove(pid_file)
atexit.register(del_pid_file)
if resources:
resources = resources.replace(",", " ").split()
resources = dict(pair.split("=") for pair in resources)
resources = valmap(float, resources)
else:
resources = None
loop = IOLoop.current()
if nanny:
kwargs.update({
"worker_port": worker_port,
"listen_address": listen_address
})
t = Nanny
else:
if nanny_port:
kwargs["service_ports"] = {"nanny": nanny_port}
t = Worker
if (not scheduler and not scheduler_file
and dask.config.get("scheduler-address", None) is None):
raise ValueError("Need to provide scheduler address like\n"
"dask-worker SCHEDULER_ADDRESS:8786")
with ignoring(TypeError, ValueError):
name = int(name)
nannies = [
t(scheduler,
scheduler_file=scheduler_file,
nthreads=nthreads,
loop=loop,
resources=resources,
security=sec,
contact_address=contact_address,
host=host,
port=port,
dashboard_address=dashboard_address if dashboard else None,
service_kwargs={"dashboard": {
"prefix": dashboard_prefix
}},
name=name if nprocs == 1 or name is None or name == "" else
str(name) + "-" + str(i),
**kwargs) for i in range(nprocs)
]
@gen.coroutine
def close_all():
# Unregister all workers from scheduler
if nanny:
yield [n.close(timeout=2) for n in nannies]
def on_signal(signum):
logger.info("Exiting on signal %d", signum)
close_all()
@gen.coroutine
def run():
yield nannies
yield [n.finished() for n in nannies]
install_signal_handlers(loop, cleanup=on_signal)
try:
loop.run_sync(run)
except TimeoutError:
# We already log the exception in nanny / worker. Don't do it again.
raise TimeoutError("Timed out starting worker.") from None
except KeyboardInterrupt:
pass
finally:
logger.info("End worker")
def test_isnull():
x = np.array([1, np.nan])
a = from_array(x, chunks=(2,))
with ignoring(ImportError):
assert eq(isnull(a), np.isnan(x))
assert eq(notnull(a), ~np.isnan(x))
def import_or_none(path):
with ignoring():
return pytest.importorskip(path)
return None
def import_or_none(path):
with ignoring(BaseException):
return pytest.importorskip(path)
return None
class RandomState(object):
"""
Mersenne Twister pseudo-random number generator
This object contains state to deterministically generate pseudo-random
numbers from a variety of probability distributions. It is identical to
``Random_State_class`` except that all functions also take a ``chunks=``
keyword argument.
Examples
--------
>>> import dask.array as da
>>> state = da.random.RandomState(1234) # a seed
>>> x = state.normal(10, 0.1, size=3, chunks=(2,))
>>> x.compute()
array([ 10.01867852, 10.04812289, 9.89649746])
See Also:
Random_State_class
"""
def __init__(self, seed=None):
self._numpy_state = Random_State_class(seed)
def seed(self, seed=None):
self._numpy_state.seed(seed)
def _wrap(self, func, *args, **kwargs):
""" Wrap numpy random function to produce dask.array random function
extra_chunks should be a chunks tuple to append to the end of chunks
"""
size = kwargs.pop('size', None)
chunks = kwargs.pop('chunks')
extra_chunks = kwargs.pop('extra_chunks', ())
if size is not None and not isinstance(size, (tuple, list)):
size = (size, )
args_shapes = {
ar.shape
for ar in args if isinstance(ar, (Array, np.ndarray))
}
args_shapes.union({
ar.shape
for ar in kwargs.values() if isinstance(ar, (Array, np.ndarray))
})
shapes = list(args_shapes)
if size is not None:
shapes += [size]
# broadcast to the final size(shape)
size = broadcast_shapes(*shapes)
chunks = normalize_chunks(chunks, size)
slices = slices_from_chunks(chunks)
def _broadcast_any(ar, shape, chunks):
if isinstance(ar, Array):
return broadcast_to(ar, shape).rechunk(chunks)
if isinstance(ar, np.ndarray):
return np.ascontiguousarray(np.broadcast_to(ar, shape))
# Broadcast all arguments, get tiny versions as well
# Start adding the relevant bits to the graph
dsk = {}
dsks = []
lookup = {}
small_args = []
for i, ar in enumerate(args):
if isinstance(ar, (np.ndarray, Array)):
res = _broadcast_any(ar, size, chunks)
if isinstance(res, Array):
dsks.append(res.dask)
lookup[i] = res.name
elif isinstance(res, np.ndarray):
name = 'array-{}'.format(tokenize(res))
lookup[i] = name
dsk[name] = res
small_args.append(ar[tuple(0 for _ in ar.shape)])
else:
small_args.append(ar)
small_kwargs = {}
for key, ar in kwargs.items():
if isinstance(ar, (np.ndarray, Array)):
res = _broadcast_any(ar, size, chunks)
if isinstance(res, Array):
dsks.append(res.dask)
lookup[key] = res.name
elif isinstance(res, np.ndarray):
name = 'array-{}'.format(tokenize(res))
lookup[key] = name
dsk[name] = res
small_kwargs[key] = ar[tuple(0 for _ in ar.shape)]
else:
small_kwargs[key] = ar
# Get dtype
small_kwargs['size'] = (0, )
dtype = func(xoroshiro128plus.RandomState(), *small_args,
**small_kwargs).dtype
sizes = list(product(*chunks))
state_data = random_state_data(len(sizes), self._numpy_state)
token = tokenize(state_data, size, chunks, args, kwargs)
name = 'da.random.{0}-{1}'.format(func.__name__, token)
keys = product([name],
*([range(len(bd))
for bd in chunks] + [[0]] * len(extra_chunks)))
blocks = product(*[range(len(bd)) for bd in chunks])
vals = []
for state, size, slc, block in zip(state_data, sizes, slices, blocks):
arg = []
for i, ar in enumerate(args):
if i not in lookup:
arg.append(ar)
else:
if isinstance(ar, Array):
arg.append((lookup[i], ) + block)
else: # np.ndarray
arg.append((getitem, lookup[i], slc))
kwrg = {}
for k, ar in kwargs.items():
if k not in lookup:
kwrg[k] = ar
else:
if isinstance(ar, Array):
kwrg[k] = (lookup[k], ) + block
else: # np.ndarray
kwrg[k] = (getitem, lookup[k], slc)
vals.append((_apply_random, func.__name__, state, size, arg, kwrg))
dsk.update(dict(zip(keys, vals)))
dsk = sharedict.merge((name, dsk), *dsks)
return Array(dsk, name, chunks + extra_chunks, dtype=dtype)
@doc_wraps(Random_State_class.beta)
def beta(self, a, b, size=None, chunks=None):
return self._wrap(Random_State_class.beta,
a,
b,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.binomial)
def binomial(self, n, p, size=None, chunks=None):
return self._wrap(Random_State_class.binomial,
n,
p,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.chisquare)
def chisquare(self, df, size=None, chunks=None):
return self._wrap(Random_State_class.chisquare,
df,
size=size,
chunks=chunks)
with ignoring(AttributeError):
@doc_wraps(Random_State_class.choice)
def choice(self, a, size=None, replace=True, p=None, chunks=None):
dsks = []
# Normalize and validate `a`
if isinstance(a, Integral):
# On windows the output dtype differs if p is provided or
# absent, see https://github.com/numpy/numpy/issues/9867
dummy_p = np.array([1]) if p is not None else p
dtype = np.random.choice(1, size=(), p=dummy_p).dtype
len_a = a
if a < 0:
raise ValueError("a must be greater than 0")
else:
a = asarray(a).rechunk(a.shape)
dtype = a.dtype
if a.ndim != 1:
raise ValueError("a must be one dimensional")
len_a = len(a)
dsks.append(a.dask)
a = a.__dask_keys__()[0]
# Normalize and validate `p`
if p is not None:
if not isinstance(p, Array):
# If p is not a dask array, first check the sum is close
# to 1 before converting.
p = np.asarray(p)
if not np.isclose(p.sum(), 1, rtol=1e-7, atol=0):
raise ValueError("probabilities do not sum to 1")
p = asarray(p)
else:
p = p.rechunk(p.shape)
if p.ndim != 1:
raise ValueError("p must be one dimensional")
if len(p) != len_a:
raise ValueError("a and p must have the same size")
dsks.append(p.dask)
p = p.__dask_keys__()[0]
if size is None:
size = ()
elif not isinstance(size, (tuple, list)):
size = (size, )
chunks = normalize_chunks(chunks, size)
sizes = list(product(*chunks))
state_data = random_state_data(len(sizes), self._numpy_state)
name = 'da.random.choice-%s' % tokenize(state_data, size, chunks,
a, replace, p)
keys = product([name], *(range(len(bd)) for bd in chunks))
dsk = {
k: (_choice, state, a, size, replace, p)
for k, state, size in zip(keys, state_data, sizes)
}
return Array(sharedict.merge((name, dsk), *dsks),
name,
chunks,
dtype=dtype)
# @doc_wraps(Random_State_class.dirichlet)
# def dirichlet(self, alpha, size=None, chunks=None):
@doc_wraps(Random_State_class.exponential)
def exponential(self, scale=1.0, size=None, chunks=None):
return self._wrap(Random_State_class.exponential,
scale,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.f)
def f(self, dfnum, dfden, size=None, chunks=None):
return self._wrap(Random_State_class.f,
dfnum,
dfden,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.gamma)
def gamma(self, shape, scale=1.0, size=None, chunks=None):
return self._wrap(Random_State_class.gamma,
shape,
scale,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.geometric)
def geometric(self, p, size=None, chunks=None):
return self._wrap(Random_State_class.geometric,
p,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.gumbel)
def gumbel(self, loc=0.0, scale=1.0, size=None, chunks=None):
return self._wrap(Random_State_class.gumbel,
loc,
scale,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.hypergeometric)
def hypergeometric(self, ngood, nbad, nsample, size=None, chunks=None):
return self._wrap(Random_State_class.hypergeometric,
ngood,
nbad,
nsample,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.laplace)
def laplace(self, loc=0.0, scale=1.0, size=None, chunks=None):
return self._wrap(Random_State_class.laplace,
loc,
scale,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.logistic)
def logistic(self, loc=0.0, scale=1.0, size=None, chunks=None):
return self._wrap(Random_State_class.logistic,
loc,
scale,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.lognormal)
def lognormal(self, mean=0.0, sigma=1.0, size=None, chunks=None):
return self._wrap(Random_State_class.lognormal,
mean,
sigma,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.logseries)
def logseries(self, p, size=None, chunks=None):
return self._wrap(Random_State_class.logseries,
p,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.multinomial)
def multinomial(self, n, pvals, size=None, chunks=None):
return self._wrap(Random_State_class.multinomial,
n,
pvals,
size=size,
chunks=chunks,
extra_chunks=((len(pvals), ), ))
@doc_wraps(Random_State_class.negative_binomial)
def negative_binomial(self, n, p, size=None, chunks=None):
return self._wrap(Random_State_class.negative_binomial,
n,
p,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.noncentral_chisquare)
def noncentral_chisquare(self, df, nonc, size=None, chunks=None):
return self._wrap(Random_State_class.noncentral_chisquare,
df,
nonc,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.noncentral_f)
def noncentral_f(self, dfnum, dfden, nonc, size=None, chunks=None):
return self._wrap(Random_State_class.noncentral_f,
dfnum,
dfden,
nonc,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.normal)
def normal(self, loc=0.0, scale=1.0, size=None, chunks=None):
return self._wrap(Random_State_class.normal,
loc,
scale,
size=size,
chunks=chunks,
method='zig')
@doc_wraps(Random_State_class.pareto)
def pareto(self, a, size=None, chunks=None):
return self._wrap(Random_State_class.pareto,
a,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.poisson)
def poisson(self, lam=1.0, size=None, chunks=None):
return self._wrap(Random_State_class.poisson,
lam,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.power)
def power(self, a, size=None, chunks=None):
return self._wrap(Random_State_class.power,
a,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.randint)
def randint(self, low, high=None, size=None, chunks=None):
return self._wrap(Random_State_class.randint,
low,
high,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.random_integers)
def random_integers(self, low, high=None, size=None, chunks=None):
return self._wrap(Random_State_class.random_integers,
low,
high,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.random_sample)
def random_sample(self, size=None, chunks=None):
return self._wrap(Random_State_class.random_sample,
size=size,
chunks=chunks)
random = random_sample
@doc_wraps(Random_State_class.rayleigh)
def rayleigh(self, scale=1.0, size=None, chunks=None):
return self._wrap(Random_State_class.rayleigh,
scale,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.standard_cauchy)
def standard_cauchy(self, size=None, chunks=None):
return self._wrap(Random_State_class.standard_cauchy,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.standard_exponential)
def standard_exponential(self, size=None, chunks=None):
return self._wrap(Random_State_class.standard_exponential,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.standard_gamma)
def standard_gamma(self, shape, size=None, chunks=None):
return self._wrap(Random_State_class.standard_gamma,
shape,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.standard_normal)
def standard_normal(self, size=None, chunks=None):
return self._wrap(Random_State_class.standard_normal,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.standard_t)
def standard_t(self, df, size=None, chunks=None):
return self._wrap(Random_State_class.standard_t,
df,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.tomaxint)
def tomaxint(self, size=None, chunks=None):
return self._wrap(Random_State_class.tomaxint,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.triangular)
def triangular(self, left, mode, right, size=None, chunks=None):
return self._wrap(Random_State_class.triangular,
left,
mode,
right,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.uniform)
def uniform(self, low=0.0, high=1.0, size=None, chunks=None):
return self._wrap(Random_State_class.uniform,
low,
high,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.vonmises)
def vonmises(self, mu, kappa, size=None, chunks=None):
return self._wrap(Random_State_class.vonmises,
mu,
kappa,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.wald)
def wald(self, mean, scale, size=None, chunks=None):
return self._wrap(Random_State_class.wald,
mean,
scale,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.weibull)
def weibull(self, a, size=None, chunks=None):
return self._wrap(Random_State_class.weibull,
a,
size=size,
chunks=chunks)
@doc_wraps(Random_State_class.zipf)
def zipf(self, a, size=None, chunks=None):
return self._wrap(Random_State_class.zipf, a, size=size, chunks=chunks)
def test_isnull():
x = np.array([1, np.nan])
a = da.from_array(x, chunks=(2, ))
with ignoring(ImportError):
assert_eq(da.isnull(a), np.isnan(x))
assert_eq(da.notnull(a), ~np.isnan(x))
def test_query():
df = pd.DataFrame({'x': [1, 2, 3, 4], 'y': [5, 6, 7, 8]})
a = dd.from_pandas(df, npartitions=2)
q = a.query('x**2 > y')
with ignoring(ImportError):
assert eq(q, df.query('x**2 > y'))
def test_query():
df = pd.DataFrame({"x": [1, 2, 3, 4], "y": [5, 6, 7, 8]})
a = dd.from_pandas(df, npartitions=2)
q = a.query("x**2 > y")
with ignoring(ImportError):
assert eq(q, df.query("x**2 > y"))
def test_isnull_result_is_an_array():
# regression test for https://github.com/dask/dask/issues/3822
arr = da.from_array(np.arange(3, dtype=np.int64), chunks=-1)
with ignoring(ImportError):
result = da.isnull(arr[0]).compute()
assert type(result) is np.ndarray
