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# This scripts generate the random distributions for testing purposes.
# You can copy this tests to iarray by hand:
# $ cp test_*.iarray $IARRAY_DIR/tests/data/
import pytest
import iarray as ia
import numpy as np
# Rand
@pytest.mark.parametrize(
"shape, chunks, blocks, dtype, plainbuffer",
[
([20, 20, 20], [10, 12, 5], [2, 3, 2], np.float64, False),
([12, 31, 11, 22], [4, 3, 5, 2], [2, 2, 2, 2], np.float32, False),
([10, 12, 5], None, None, np.float64, False),
([4, 3, 5, 2], None, None, np.float32, False),
([10, 12, 5], None, None, np.float64, True),
([4, 3, 5, 2], None, None, np.float32, True),
],
)
def test_rand(shape, chunks, blocks, dtype, plainbuffer):
store = ia.Store(chunks, blocks, plainbuffer=plainbuffer)
size = int(np.prod(shape))
a = ia.random.random_sample(shape, store=store, dtype=dtype)
b = np.random.rand(size).reshape(shape).astype(dtype)
c = ia.numpy2iarray(b, store=store)
assert ia.random.kstest(a, c)
# Randn
@pytest.mark.parametrize(
"shape, chunks, blocks, dtype, plainbuffer",
[
([20, 20, 20], [10, 12, 5], [2, 3, 2], np.float64, False),
([10, 10, 8, 10], [4, 3, 5, 2], [2, 2, 2, 2], np.float32, False),
([10, 12, 5], None, None, np.float64, False),
([4, 3, 5, 2], None, None, np.float32, False),
([10, 12, 5], None, None, np.float64, True),
([4, 3, 5, 2], None, None, np.float32, True),
],
)
def test_randn(shape, chunks, blocks, dtype, plainbuffer):
store = ia.Store(chunks, blocks, plainbuffer=plainbuffer)
size = int(np.prod(shape))
a = ia.random.standard_normal(shape, store=store, dtype=dtype)
b = np.random.standard_normal(size).reshape(shape).astype(dtype)
c = ia.numpy2iarray(b, store=store)
assert ia.random.kstest(a, c)
# Beta
@pytest.mark.parametrize(
"alpha, beta, shape, chunks, blocks, dtype, plainbuffer",
[
(3, 4, [20, 20, 30], [10, 12, 5], [2, 3, 4], np.float64, False),
(0.1, 5, [12, 13, 8, 7], [4, 3, 5, 2], [2, 2, 5, 2], np.float32, False),
(3, 0.2, [10, 12, 5], None, None, np.float64, False),
(0.5, 0.05, [4, 3, 5, 2], None, None, np.float32, False),
(3, 0.2, [10, 12, 5], None, None, np.float64, True),
(0.5, 0.05, [4, 3, 5, 2], None, None, np.float32, True),
],
)
def test_beta(alpha, beta, shape, chunks, blocks, dtype, plainbuffer):
store = ia.Store(chunks, blocks, plainbuffer=plainbuffer)
size = int(np.prod(shape))
a = ia.random.beta(shape, alpha, beta, store=store, dtype=dtype)
b = np.random.beta(alpha, beta, size).reshape(shape).astype(dtype)
c = ia.numpy2iarray(b, store=store)
assert ia.random.kstest(a, c)
# Lognormal
@pytest.mark.parametrize(
"mu, sigma, shape, chunks, blocks, dtype, plainbuffer",
[
(3, 4, [20, 20, 20], [10, 12, 5], [2, 3, 2], np.float64, False),
(0.1, 5, [10, 20, 10, 20], [4, 3, 5, 2], [2, 2, 2, 2], np.float32, False),
(3, 0.2, [10, 12, 5], None, None, np.float64, False),
(0.5, 0.05, [4, 3, 5, 2], None, None, np.float32, False),
(3, 0.2, [10, 12, 5], None, None, np.float64, True),
(0.5, 0.05, [4, 3, 5, 2], None, None, np.float32, True),
],
)
def test_lognormal(mu, sigma, shape, chunks, blocks, dtype, plainbuffer):
store = ia.Store(chunks, blocks, plainbuffer=plainbuffer)
size = int(np.prod(shape))
a = ia.random.lognormal(shape, mu, sigma, store=store, dtype=dtype)
b = np.random.lognormal(mu, sigma, size).reshape(shape).astype(dtype)
c = ia.numpy2iarray(b, store=store)
assert ia.random.kstest(a, c)
# Exponential
@pytest.mark.parametrize(
"beta, shape, chunks, blocks, dtype, plainbuffer",
[
(3, [20, 20, 12], [10, 12, 5], [3, 5, 2], np.float64, False),
(0.1, [10, 20, 20, 10], [4, 10, 8, 2], [2, 5, 3, 2], np.float32, False),
(3, [10, 12, 5], None, None, np.float64, False),
(0.5, [4, 3, 5, 2], None, None, np.float32, False),
(3, [10, 12, 5], None, None, np.float64, True),
(0.5, [4, 3, 5, 2], None, None, np.float32, True),
],
)
def test_exponential(beta, shape, chunks, blocks, dtype, plainbuffer):
store = ia.Store(chunks, blocks, plainbuffer=plainbuffer)
size = int(np.prod(shape))
a = ia.random.exponential(shape, beta, store=store, dtype=dtype)
b = np.random.exponential(beta, size).reshape(shape).astype(dtype)
c = ia.numpy2iarray(b, store=store)
assert ia.random.kstest(a, c)
# Uniform
@pytest.mark.parametrize(
"a_, b_, shape, chunks, blocks, dtype, plainbuffer",
[
(3, 5, [20, 20, 20], [10, 12, 10], [2, 3, 2], np.float64, False),
(0.1, 0.2, [10, 20, 20, 10], [4, 10, 8, 2], [2, 5, 3, 2], np.float32, False),
(-3, -2, [10, 12, 5], None, None, np.float64, False),
(0.5, 1000, [4, 3, 5, 2], None, None, np.float32, False),
(-3, -2, [10, 12, 5], None, None, np.float64, True),
(0.5, 1000, [4, 3, 5, 2], None, None, np.float32, True),
],
)
def test_uniform(a_, b_, shape, chunks, blocks, dtype, plainbuffer):
store = ia.Store(chunks, blocks, plainbuffer=plainbuffer)
size = int(np.prod(shape))
a = ia.random.uniform(shape, a_, b_, store=store, dtype=dtype)
b = np.random.uniform(a_, b_, size).reshape(shape).astype(dtype)
c = ia.numpy2iarray(b, store=store)
assert ia.random.kstest(a, c)
# Normal
@pytest.mark.parametrize(
"mu, sigma, shape, chunks, blocks, dtype, plainbuffer",
[
(3, 5, [20, 20, 12], [10, 12, 5], [3, 5, 2], np.float64, False),
(0.1, 0.2, [10, 20, 20, 10], [4, 10, 8, 2], [2, 5, 3, 2], np.float32, False),
(-3, 2, [10, 12, 5], None, None, np.float64, False),
(0.5, 1000, [4, 3, 5, 2], None, None, np.float32, False),
(-3, 2, [10, 12, 5], None, None, np.float64, True),
(0.5, 1000, [4, 3, 5, 2], None, None, np.float32, True),
],
)
def test_normal(mu, sigma, shape, chunks, blocks, dtype, plainbuffer):
store = ia.Store(chunks, blocks, plainbuffer=plainbuffer)
size = int(np.prod(shape))
a = ia.random.normal(shape, mu, sigma, store=store, dtype=dtype)
b = np.random.normal(mu, sigma, size).reshape(shape).astype(dtype)
c = ia.numpy2iarray(b, store=store)
assert ia.random.kstest(a, c)
# Bernoulli (compare against np.random.binomial)
@pytest.mark.parametrize(
"p, shape, chunks, blocks, dtype, plainbuffer",
[
(0.7, [20, 20, 12], [10, 12, 5], [3, 5, 2], np.float64, False),
(0.01, [10, 20, 20, 10], [4, 10, 8, 2], [2, 5, 3, 2], np.float32, False),
(0.15, [10, 12, 5], None, None, np.float64, False),
(0.6, [4, 3, 5, 2], None, None, np.float32, False),
(0.15, [10, 12, 5], None, None, np.float64, True),
(0.6, [4, 3, 5, 2], None, None, np.float32, True),
],
)
def test_bernoulli(p, shape, chunks, blocks, dtype, plainbuffer):
store = ia.Store(chunks, blocks, plainbuffer=plainbuffer)
size = int(np.prod(shape))
a = ia.random.bernoulli(shape, p, store=store, dtype=dtype)
b = np.random.binomial(1, p, size).reshape(shape).astype(dtype)
c = ia.numpy2iarray(b, store=store)
assert ia.random.kstest(a, c)
# Binomial
@pytest.mark.parametrize(
"n, p, shape, chunks, blocks, dtype, plainbuffer",
[
(3, 0.7, [20, 20, 12], [10, 12, 5], [3, 5, 2], np.float64, False),
(10, 0.01, [10, 20, 20, 10], [4, 10, 8, 2], [2, 5, 3, 2], np.float32, False),
(1000, 0.15, [10, 12, 5], None, None, np.float64, False),
(5, 0.6, [4, 3, 5, 2], None, None, np.float32, False),
(1000, 0.15, [10, 12, 5], None, None, np.float64, True),
(5, 0.6, [4, 3, 5, 2], None, None, np.float32, True),
],
)
def test_binomial(n, p, shape, chunks, blocks, dtype, plainbuffer):
store = ia.Store(chunks, blocks, plainbuffer=plainbuffer)
size = int(np.prod(shape))
a = ia.random.binomial(
shape, n, p, store=store, random_gen=ia.RandomGen.MERSENNE_TWISTER, dtype=dtype
)
b = np.random.binomial(n, p, size).reshape(shape).astype(dtype)
c = ia.numpy2iarray(b, store=store)
assert ia.random.kstest(a, c)
# Poisson
@pytest.mark.parametrize(
"lamb, shape, chunks, blocks, dtype, plainbuffer",
[
(3, [20, 20, 12], [10, 12, 5], [3, 5, 2], np.float64, False),
(0.01, [10, 20, 20, 10], [4, 10, 8, 2], [2, 5, 3, 2], np.float32, False),
(0.15, [10, 12, 5], None, None, np.float64, False),
(5, [4, 3, 5, 2], None, None, np.float32, False),
(0.15, [10, 12, 5], None, None, np.float64, True),
(5, [4, 3, 5, 2], None, None, np.float32, True),
],
)
def test_poisson(lamb, shape, chunks, blocks, dtype, plainbuffer):
store = ia.Store(chunks, blocks, plainbuffer=plainbuffer)
size = int(np.prod(shape))
a = ia.random.poisson(shape, lamb, store=store, random_gen=ia.RandomGen.SOBOL, dtype=dtype)
b = np.random.poisson(lamb, size).reshape(shape).astype(dtype)
c = ia.numpy2iarray(b, store=store)
assert ia.random.kstest(a, c)