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test_pydl.py
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test_pydl.py
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# Licensed under a 3-clause BSD style license - see LICENSE.rst
# -*- coding: utf-8 -*-
"""Test the top-level pydl functions.
"""
import pytest
import numpy as np
try:
from astropy.tests.compat import assert_allclose
except ImportError:
from numpy.testing import assert_allclose
from astropy.tests.helper import raises
from astropy.utils.data import get_pkg_data_filename
from ..file_lines import file_lines
from ..median import median
from ..pcomp import pcomp
from ..rebin import rebin
from ..smooth import smooth
from ..uniq import uniq
def test_file_lines():
#
# Find the test files
#
line_numbers = (1, 42, 137)
plainfiles = [get_pkg_data_filename('t/this-file-contains-{0:d}-lines.txt'.format(l)) for l in line_numbers]
gzfiles = [get_pkg_data_filename('t/this-file-contains-{0:d}-lines.txt.gz'.format(l)) for l in line_numbers]
for i, p in enumerate(plainfiles):
n = file_lines(p)
assert n == line_numbers[i]
for i, p in enumerate(gzfiles):
n = file_lines(p, compress=True)
assert n == line_numbers[i]
#
# Test list passing
#
n = file_lines(plainfiles)
assert tuple(n) == line_numbers
n = file_lines(gzfiles, compress=True)
assert tuple(n) == line_numbers
#
# Make sure empty files work
#
n = file_lines(get_pkg_data_filename('t/this-file-is-empty.txt'))
assert n == 0
def test_median():
odd_data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
dtype=np.float32)
even_data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
dtype=np.float32)
assert median(odd_data) == 7
assert median(odd_data, even=True) == 7
assert median(even_data) == 7
assert median(even_data, even=True) == 6.5
assert (median(odd_data, 3) == odd_data).all()
with raises(ValueError):
foo = median(np.ones((9, 9, 9)), 3)
odd_data2 = np.vstack((odd_data, odd_data, odd_data, odd_data, odd_data))
assert (median(odd_data2, 3) == odd_data2).all()
assert (median(odd_data2, axis=0) == odd_data).all()
assert (median(odd_data2, axis=1) ==
7*np.ones((odd_data2.shape[0],), dtype=odd_data2.dtype)).all()
def test_pcomp():
test_data_file = get_pkg_data_filename('t/pcomp_data.txt')
test_data = np.loadtxt(test_data_file, dtype='d', delimiter=',')
with raises(ValueError):
foo = pcomp(np.arange(10))
pcomp_data = test_data[0:20, :]
m = 4
n = 20
means = np.tile(pcomp_data.mean(0), n).reshape(pcomp_data.shape)
newarray = pcomp_data - means
foo = pcomp(newarray, covariance=True)
#
# This array is obtained from the IDL version of PCOMP.
# It is only accurate up to an overall sign on each column.
#
derived = test_data[20:40, :]
for k in range(m):
assert_allclose(abs(foo.derived[:, k]), abs(derived[:, k]), 1e-4)
coefficients = test_data[40:44, :]
coefficientsT = coefficients.T
for k in range(m):
assert_allclose(abs(foo.coefficients[:, k]),
abs(coefficientsT[:, k]),
1e-4)
eigenvalues = test_data[44, :]
assert_allclose(foo.eigenvalues, eigenvalues, 1e-4)
variance = test_data[45, :]
assert_allclose(foo.variance, variance, 1e-4)
#
# Test the standardization.
#
foo = pcomp(pcomp_data, standardize=True, covariance=True)
# for k in range(m):
# assert_allclose(abs(foo.derived[:, k]), abs(derived[:, k]), 1e-4)
# for k in range(m):
# assert_allclose(abs(foo.coefficients[:, k]),
# abs(coefficientsT[:, k]),
# 1e-4)
eigenvalues = test_data[46, :]
assert_allclose(foo.eigenvalues, eigenvalues, 1e-4)
variance = test_data[47, :]
assert_allclose(foo.variance, variance, 1e-4)
# assert_allclose(foo.derived[0, :], np.array([-1.64153312,
# -9.12322038,
# 1.41790708,
# -8.29359322]))
#
# Make sure correlation is working at least.
#
foo = pcomp(pcomp_data, standardize=True)
assert_allclose(foo.eigenvalues, np.array([2.84968632e+00,
1.00127640e+00,
1.48380121e-01,
6.57156222e-04]))
assert_allclose(foo.variance, np.array([7.12421581e-01,
2.50319100e-01,
3.70950302e-02,
1.64289056e-04]))
def test_rebin():
x = np.arange(40)
with raises(ValueError):
r = rebin(x, d=(10, 10))
with raises(ValueError):
r = rebin(x, d=(70,))
with raises(ValueError):
r = rebin(x, d=(30,))
x = np.array([[1.0, 2.0], [2.0, 3.0]])
rexpect = np.array([[1.0, 2.0], [1.5, 2.5], [2.0, 3.0], [2.0, 3.0]])
r = rebin(x, d=(4, 2))
assert np.allclose(r, rexpect)
rexpect = np.array([[1.0, 1.5, 2.0, 2.0], [2.0, 2.5, 3.0, 3.0]])
r = rebin(x, d=(2, 4))
assert np.allclose(r, rexpect)
rexpect = np.array([[1.0, 2.0], [1.0, 2.0], [2.0, 3.0], [2.0, 3.0]])
r = rebin(x, d=(4, 2), sample=True)
assert np.allclose(r, rexpect)
rexpect = np.array([[1.0, 1.0, 2.0, 2.0], [2.0, 2.0, 3.0, 3.0]])
r = rebin(x, d=(2, 4), sample=True)
assert np.allclose(r, rexpect)
x = np.arange(10)
rexpect = np.array([0.0, 2.0, 4.0, 6.0, 8.0])
r = rebin(x, d=(5,), sample=True)
assert np.allclose(r, rexpect)
x = np.array([[1.0, 2.0, 3.0, 4.0],
[2.0, 3.0, 4.0, 5.0],
[3.0, 4.0, 5.0, 6.0],
[4.0, 5.0, 6.0, 7.0]])
rexpect = np.array([[2.0, 4.0], [4.0, 6.0]])
r = rebin(x, d=(2, 2))
assert np.allclose(r, rexpect)
rexpect = np.array([[1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 4.5],
[3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 6.5]])
r = rebin(x, d=(2, 8))
assert np.allclose(r, rexpect)
@pytest.mark.xfail
def test_rebin_int():
"""Test rebin on integers. Comparing to IDL code similar to this::
IDL> seed = 100
IDL> array = FIX(RANDOMN(seed, 10, 20) * 100, TYPE=1) ; UINT8
IDL> array_rebin = REBIN(array, 5, 10)
"""
array = np.array([[188, 186, 25, 212, 34, 98, 3, 235, 155, 148],
[107, 166, 4, 41, 101, 190, 39, 154, 153, 239],
[135, 181, 92, 161, 213, 136, 35, 61, 80, 164],
[123, 248, 8, 157, 96, 118, 99, 1, 109, 246],
[226, 71, 183, 27, 46, 99, 8, 239, 66, 25],
[ 27, 219, 37, 130, 5, 81, 65, 250, 96, 14],
[ 71, 157, 156, 136, 47, 225, 247, 191, 49, 12],
[231, 133, 9, 38, 243, 2, 235, 145, 23, 22],
[146, 38, 49, 89, 42, 57, 220, 214, 135, 47],
[101, 116, 122, 209, 141, 37, 158, 224, 245, 82],
[ 15, 47, 51, 250, 207, 193, 209, 228, 110, 1],
[ 59, 232, 216, 224, 24, 118, 190, 10, 107, 27],
[ 84, 193, 112, 206, 113, 171, 138, 117, 244, 20],
[ 5, 31, 128, 214, 200, 119, 59, 27, 57, 10],
[226, 71, 177, 85, 0, 68, 54, 207, 141, 250],
[ 52, 119, 121, 177, 165, 99, 68, 29, 137, 200],
[172, 91, 181, 187, 87, 250, 45, 154, 58, 83],
[ 56, 175, 189, 35, 203, 223, 243, 187, 252, 97],
[186, 172, 207, 128, 61, 231, 89, 57, 131, 222],
[206, 96, 29, 60, 3, 8, 221, 55, 60, 17]], dtype=np.uint8)
array_rebin = np.array([[161, 70, 105, 107, 173],
[171, 104, 140, 49, 149],
[135, 94, 57, 140, 50],
[148, 84, 129, 204, 26],
[100, 117, 69, 204, 127],
[ 88, 185, 135, 159, 61],
[ 78, 165, 150, 85, 82],
[116, 140, 83, 89, 181],
[123, 148, 190, 157, 122],
[165, 105, 75, 105, 107]], dtype=np.uint8)
ar = rebin(array, (10, 5))
assert (array_rebin == ar).all()
def test_rebin_int_sample():
"""Similar to test_rebin_int(), but using the sample option.
"""
array = np.array([[188, 186, 25, 212, 34, 98, 3, 235, 155, 148],
[107, 166, 4, 41, 101, 190, 39, 154, 153, 239],
[135, 181, 92, 161, 213, 136, 35, 61, 80, 164],
[123, 248, 8, 157, 96, 118, 99, 1, 109, 246],
[226, 71, 183, 27, 46, 99, 8, 239, 66, 25],
[ 27, 219, 37, 130, 5, 81, 65, 250, 96, 14],
[ 71, 157, 156, 136, 47, 225, 247, 191, 49, 12],
[231, 133, 9, 38, 243, 2, 235, 145, 23, 22],
[146, 38, 49, 89, 42, 57, 220, 214, 135, 47],
[101, 116, 122, 209, 141, 37, 158, 224, 245, 82],
[ 15, 47, 51, 250, 207, 193, 209, 228, 110, 1],
[ 59, 232, 216, 224, 24, 118, 190, 10, 107, 27],
[ 84, 193, 112, 206, 113, 171, 138, 117, 244, 20],
[ 5, 31, 128, 214, 200, 119, 59, 27, 57, 10],
[226, 71, 177, 85, 0, 68, 54, 207, 141, 250],
[ 52, 119, 121, 177, 165, 99, 68, 29, 137, 200],
[172, 91, 181, 187, 87, 250, 45, 154, 58, 83],
[ 56, 175, 189, 35, 203, 223, 243, 187, 252, 97],
[186, 172, 207, 128, 61, 231, 89, 57, 131, 222],
[206, 96, 29, 60, 3, 8, 221, 55, 60, 17]], dtype=np.uint8)
array_sample = np.array([[188, 25, 34, 3, 155],
[135, 92, 213, 35, 80],
[226, 183, 46, 8, 66],
[ 71, 156, 47, 247, 49],
[146, 49, 42, 220, 135],
[ 15, 51, 207, 209, 110],
[ 84, 112, 113, 138, 244],
[226, 177, 0, 54, 141],
[172, 181, 87, 45, 58],
[186, 207, 61, 89, 131]], dtype=np.uint8)
ars = rebin(array, (10, 5), sample=True)
assert (array_sample == ars).all()
def test_smooth():
test_data_file = get_pkg_data_filename('t/smooth_data.txt')
noise = np.loadtxt(test_data_file, dtype='d')
#
# Test smooth function
#
x = 8.0*np.arange(100)/100.0 - 4.0
y = np.sin(x) + 0.1*noise
s = smooth(y, 5)
assert s.shape == (100,)
s_edge = smooth(y, 5, True)
assert s_edge.shape == (100,)
s_w = smooth(y, 1)
assert (s_w == y).all()
def test_uniq():
items = np.array([1, 2, 3, 1, 5, 6, 1, 7, 3, 2, 5, 9, 11, 1])
items_sorted = np.sort(items)
items_argsorted = np.argsort(items)
#
# Test pre-sorted array.
#
u1 = uniq(items_sorted)
assert (u1 == np.array([3, 5, 7, 9, 10, 11, 12, 13])).all()
#
# Test arg-sorted array.
#
u2 = uniq(items, items_argsorted)
assert (u2 == np.array([13, 9, 8, 10, 5, 7, 11, 12])).all()
assert (items_sorted[u1] == items[u2]).all()
#
# Test degenerate case of all identical items.
#
identical_items = np.ones((10,), dtype=items.dtype)
u = uniq(identical_items)
assert (u == np.array([9])).all()
u = uniq(identical_items, np.arange(10, dtype=items.dtype))
assert (u == np.array([9])).all()