#!/usr/bin/env python
# -*- coding: utf-8 -*-
# The MIT License (MIT)
# Copyright (c) 2017 Juan Cabral
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# =============================================================================
# FUTURE
# =============================================================================
from __future__ import unicode_literals
# =============================================================================
# DOC
# =============================================================================
__doc__ = """FATS to feets compatibility testing"""
# =============================================================================
# IMPORTS
# =============================================================================
import os
import numpy as np
from .. import FeatureSpace, preprocess
from .. datasets import macho
from .core import FeetsTestCase, DATA_PATH
# =============================================================================
# CLASSES
# =============================================================================
[docs]class FATSPreprocessRegressionTestCase(FeetsTestCase):
[docs] def setUp(self):
lc = macho.load_MACHO_example()
self.time = lc.data.R.time
self.mag = lc.data.R.magnitude
self.error = lc.data.R.error
self.time2 = lc.data.B.time
self.mag2 = lc.data.B.magnitude
self.error2 = lc.data.B.error
self.preprc_path = os.path.join(DATA_PATH, "FATS_preprc.npz")
with np.load(self.preprc_path) as npz:
self.pF_time, self.pF_time2 = npz["time"], npz["time2"]
self.pF_mag, self.pF_mag2 = npz["mag"], npz["mag2"]
self.pF_error, self.pF_error2 = npz["error"], npz["error2"]
self.lc_path = os.path.join(DATA_PATH, "FATS_aligned.npz")
with np.load(self.lc_path) as npz:
self.aF_time = npz['aligned_time']
self.aF_mag = npz['aligned_mag']
self.aF_mag2 = npz['aligned_mag2']
self.aF_error = npz['aligned_error']
self.aF_error2 = npz['aligned_error2']
[docs] def test_remove_noise(self):
p_time, p_mag, p_error = preprocess.remove_noise(
self.time, self.mag, self.error)
p_time2, p_mag2, p_error2 = preprocess.remove_noise(
self.time2, self.mag2, self.error2)
self.assertArrayEqual(p_time, self.pF_time)
self.assertArrayEqual(p_time2, self.pF_time2)
self.assertArrayEqual(p_mag, self.pF_mag)
self.assertArrayEqual(p_mag2, self.pF_mag2)
self.assertArrayEqual(p_error, self.pF_error)
self.assertArrayEqual(p_error2, self.pF_error2)
[docs] def test_align(self):
a_time, a_mag, a_mag2, a_error, a_error2 = preprocess.align(
self.pF_time, self.pF_time2,
self.pF_mag, self.pF_mag2,
self.pF_error, self.pF_error2)
self.assertArrayEqual(a_time, self.aF_time)
self.assertArrayEqual(a_mag, self.aF_mag)
self.assertArrayEqual(a_mag2, self.aF_mag2)
self.assertArrayEqual(a_error, self.aF_error)
self.assertArrayEqual(a_error2, self.aF_error2)
[docs]class FATSRegressionTestCase(FeetsTestCase):
[docs] def setUp(self):
# the paths
self.lc_path = os.path.join(DATA_PATH, "FATS_aligned.npz")
self.FATS_result_path = os.path.join(DATA_PATH, "FATS_result.npz")
# recreate light curve
with np.load(self.lc_path) as npz:
self.lc = (
npz['time'],
npz['mag'],
npz['error'],
npz['mag2'],
npz['aligned_time'],
npz['aligned_mag'],
npz['aligned_mag2'],
npz['aligned_error'],
npz['aligned_error2'])
# recreate the FATS result
with np.load(self.FATS_result_path) as npz:
self.features = npz["features"]
self.features = self.features.astype("U")
self.FATS_result = dict(zip(self.features, npz["values"]))
# creates an template for all error, messages
self.err_template = ("Feature '{feature}' missmatch.")
[docs] def exclude_value_feature_evaluation(self, feature):
return "_harmonics_" in feature
[docs] def assert_feature_params(self, feature):
feature_params = {
"PeriodLS": {"atol": 1e-04},
"Period_fit": {"atol": 1e-40},
"Psi_CS": {"atol": 1e-02},
"Psi_eta": {"atol": 1e-01}}
params = {"err_msg": self.err_template.format(feature=feature)}
params .update(feature_params.get(feature, {}))
return params
[docs] def assertFATS(self, feets_result):
for feature in self.features:
if feature not in feets_result:
self.fail("Missing feature {}".format(feature))
if self.exclude_value_feature_evaluation(feature):
continue
feets_value = feets_result[feature]
FATS_value = self.FATS_result[feature]
params = self.assert_feature_params(feature)
self.assertAllClose(feets_value, FATS_value, **params)
