import numpy as np
from scipy.stats import circmean

import unittest

from flasc import circular_statistics as cs


class TestDataframeManipulations(unittest.TestCase):
    def test_empty_array(self):
        # Try empty array with all statistics
        mean_wd, median_wd, std_wd, min_wd, max_wd = (
            cs.calculate_wd_statistics([], calc_median_min_max_std=True)
        )
        self.assertTrue(np.isnan(mean_wd))
        self.assertTrue(np.isnan(median_wd))
        self.assertTrue(np.isnan(std_wd))
        self.assertTrue(np.isnan(min_wd))
        self.assertTrue(np.isnan(max_wd))

        # Try empty array with just mean
        mean = cs.calculate_wd_statistics([], calc_median_min_max_std=False)
        self.assertTrue(np.isnan(mean))

    def test_single_value(self):
        # Single value
        mean = cs.calculate_wd_statistics([12], calc_median_min_max_std=False)
        self.assertAlmostEqual(mean, 12.0)

        # Single value with wrapping
        mean = cs.calculate_wd_statistics([-5], calc_median_min_max_std=False)
        self.assertAlmostEqual(mean, 355.0)

        # Multiple values without wrapping
        mean_wd, median_wd, std_wd, min_wd, max_wd = (
            cs.calculate_wd_statistics([12], calc_median_min_max_std=True)
        )
        self.assertAlmostEqual(mean_wd, 12.0)
        self.assertAlmostEqual(median_wd, 12.0)
        self.assertAlmostEqual(std_wd, 0.0)
        self.assertAlmostEqual(min_wd, 12.0)
        self.assertAlmostEqual(max_wd, 12.0)

        # Multiple values with wrapping
        mean_wd, median_wd, std_wd, min_wd, max_wd = (
            cs.calculate_wd_statistics([365], calc_median_min_max_std=True)
        )
        self.assertAlmostEqual(mean_wd, 5.0)
        self.assertAlmostEqual(median_wd, 5.0)
        self.assertAlmostEqual(std_wd, 0.0)
        self.assertAlmostEqual(min_wd, 5.0)
        self.assertAlmostEqual(max_wd, 5.0)

    def test_mean_median_min_max_std(self):
        angles_array = np.array(
            [
                [355.0, 341.2, 2.1],
                [13.2, 344.9, 356.1],
                [2.2, 334.0, 356.5]
            ]
        )
        mean_array, median_array, std_array, min_array, max_array = (
            cs.calculate_wd_statistics(
                angles_array,
                axis=0,
                calc_median_min_max_std=True,
            )
        )

        # Compare to SciPy solutions
        means_scipy = circmean(angles_array, axis=0, high=360.0)
        self.assertAlmostEqual(mean_array[0], means_scipy[0], places=4)
        self.assertAlmostEqual(mean_array[1], means_scipy[1], places=4)
        self.assertAlmostEqual(mean_array[2], means_scipy[2], places=4)

        # Compare to precalculated solutions
        self.assertAlmostEqual(mean_array[0], 3.461333, places=4)
        self.assertAlmostEqual(mean_array[1], 340.03507657, places=4)
        self.assertAlmostEqual(mean_array[2], 358.2326068, places=4)

        self.assertAlmostEqual(median_array[0], 2.2, places=4)
        self.assertAlmostEqual(median_array[1], 341.2, places=4)
        self.assertAlmostEqual(median_array[2], 356.5, places=4)

        self.assertAlmostEqual(std_array[0], 7.48390866, places=4)
        self.assertAlmostEqual(std_array[1], 4.52572892, places=4)
        self.assertAlmostEqual(std_array[2], 2.73901848, places=4)

        self.assertAlmostEqual(min_array[0], 355.0, places=4)
        self.assertAlmostEqual(min_array[1], 334.0, places=4)
        self.assertAlmostEqual(min_array[2], 356.1, places=4)

        self.assertAlmostEqual(max_array[0], 13.2, places=4)
        self.assertAlmostEqual(max_array[1], 344.9, places=4)
        self.assertAlmostEqual(max_array[2], 2.1, places=4)

import numpy as np
from scipy.stats import circmean

import unittest

from flasc import circular_statistics as cs


class TestDataframeManipulations(unittest.TestCase):
    def test_empty_array(self):
        # Try empty array with all statistics
        mean_wd, median_wd, std_wd, min_wd, max_wd = (
            cs.calculate_wd_statistics([], calc_median_min_max_std=True)
        )
        self.assertTrue(np.isnan(mean_wd))
        self.assertTrue(np.isnan(median_wd))
        self.assertTrue(np.isnan(std_wd))
        self.assertTrue(np.isnan(min_wd))
        self.assertTrue(np.isnan(max_wd))

        # Try empty array with just mean
        mean = cs.calculate_wd_statistics([], calc_median_min_max_std=False)
        self.assertTrue(np.isnan(mean))

    def test_single_value(self):
        # Single value
        mean = cs.calculate_wd_statistics([12], calc_median_min_max_std=False)
        self.assertAlmostEqual(mean, 12.0)

        # Single value with wrapping
        mean = cs.calculate_wd_statistics([-5], calc_median_min_max_std=False)
        self.assertAlmostEqual(mean, 355.0)

        # Multiple values without wrapping
        mean_wd, median_wd, std_wd, min_wd, max_wd = (
            cs.calculate_wd_statistics([12], calc_median_min_max_std=True)
        )
        self.assertAlmostEqual(mean_wd, 12.0)
        self.assertAlmostEqual(median_wd, 12.0)
        self.assertAlmostEqual(std_wd, 0.0)
        self.assertAlmostEqual(min_wd, 12.0)
        self.assertAlmostEqual(max_wd, 12.0)

        # Multiple values with wrapping
        mean_wd, median_wd, std_wd, min_wd, max_wd = (
            cs.calculate_wd_statistics([365], calc_median_min_max_std=True)
        )
        self.assertAlmostEqual(mean_wd, 5.0)
        self.assertAlmostEqual(median_wd, 5.0)
        self.assertAlmostEqual(std_wd, 0.0)
        self.assertAlmostEqual(min_wd, 5.0)
        self.assertAlmostEqual(max_wd, 5.0)

    def test_mean_median_min_max_std(self):
        angles_array = np.array(
            [
                [355.0, 341.2, 2.1],
                [13.2, 344.9, 356.1],
                [2.2, 334.0, 356.5]
            ]
        )
        mean_array, median_array, std_array, min_array, max_array = (
            cs.calculate_wd_statistics(
                angles_array,
                axis=0,
                calc_median_min_max_std=True,
            )
        )

        # Compare to SciPy solutions
        means_scipy = circmean(angles_array, axis=0, high=360.0)
        self.assertAlmostEqual(mean_array[0], means_scipy[0], places=4)
        self.assertAlmostEqual(mean_array[1], means_scipy[1], places=4)
        self.assertAlmostEqual(mean_array[2], means_scipy[2], places=4)

        # Compare to precalculated solutions
        self.assertAlmostEqual(mean_array[0], 3.461333, places=4)
        self.assertAlmostEqual(mean_array[1], 340.03507657, places=4)
        self.assertAlmostEqual(mean_array[2], 358.2326068, places=4)

        self.assertAlmostEqual(median_array[0], 2.2, places=4)
        self.assertAlmostEqual(median_array[1], 341.2, places=4)
        self.assertAlmostEqual(median_array[2], 356.5, places=4)

        self.assertAlmostEqual(std_array[0], 7.48390866, places=4)
        self.assertAlmostEqual(std_array[1], 4.52572892, places=4)
        self.assertAlmostEqual(std_array[2], 2.73901848, places=4)

        self.assertAlmostEqual(min_array[0], 355.0, places=4)
        self.assertAlmostEqual(min_array[1], 334.0, places=4)
        self.assertAlmostEqual(min_array[2], 356.1, places=4)

        self.assertAlmostEqual(max_array[0], 13.2, places=4)
        self.assertAlmostEqual(max_array[1], 344.9, places=4)
        self.assertAlmostEqual(max_array[2], 2.1, places=4)

# Try empty array with all statistics # Try empty array with just mean # Single value # Single value with wrapping # Multiple values without wrapping # Multiple values with wrapping # Compare to SciPy solutions # Compare to precalculated solutions