'''Chemical Engineering Design Library (ChEDL). Utilities for process modeling.
Copyright (C) 2016, 2017, 2018 Caleb Bell <Caleb.Andrew.Bell@gmail.com>

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.
'''

from fluids.atmosphere import earthsun_distance, solar_irradiation, solar_position, sunrise_sunset
from fluids.numerics import assert_close, assert_close1d

try:
    from datetime import datetime, timedelta
except:
    pass
import pytest

try:
    has_pvlib = True
except:
    has_pvlib = False
try:
    import pytz
except:
    pass

from fluids.atmosphere import ATMOSPHERE_1976, airmass, hwm14, hwm93


def test_ATMOSPHERE_1976():
    # Test values from 'Atmosphere to 86 Km by 2 Km (SI units)', from
    # http://ckw.phys.ncku.edu.tw/public/pub/Notes/Languages/Fortran/FORSYTHE/www.pdas.com/m1.htm
    # as provided in atmtabs.html in http://www.pdas.com/atmosdownload.html
    H_1 = [-2000, 0, 2000, 4000, 6000, 8000, 10000, 12000, 14000, 16000, 18000, 20000, 22000, 24000, 26000, 28000, 30000, 32000, 34000, 36000, 38000, 40000, 42000, 44000, 46000, 48000, 50000, 52000, 54000, 56000, 58000, 60000, 62000, 64000, 66000, 68000, 70000, 72000, 74000, 76000, 78000, 80000, 82000, 84000, 86000]
    T_1 = [301.15, 288.15, 275.15, 262.17, 249.19, 236.22, 223.25, 216.65, 216.65, 216.65, 216.65, 216.65, 218.57, 220.56, 222.54, 224.53, 226.51, 228.49, 233.74, 239.28, 244.82, 250.35, 255.88, 261.4, 266.92, 270.65, 270.65, 269.03, 263.52, 258.02, 252.52, 247.02, 241.53, 236.04, 230.55, 225.07, 219.58, 214.26, 210.35, 206.45, 202.54, 198.64, 194.74, 190.84, 186.95]
    P_1 = [127780, 101320, 79501, 61660, 47218, 35652, 26500, 19399, 14170, 10353, 7565.2, 5529.3, 4047.5, 2971.7, 2188.4, 1616.2, 1197, 889.06, 663.41, 498.52, 377.14, 287.14, 219.97, 169.5, 131.34, 102.3, 79.779, 62.215, 48.338, 37.362, 28.724, 21.959, 16.689, 12.606, 9.4609, 7.0529, 5.2209, 3.8363, 2.8009, 2.0333, 1.4674, 1.0525, 0.75009, 0.53104, 0.37338]
    rho_1 = [1.4782, 1.225, 1.0066, 0.81935, 0.66011, 0.52579, 0.41351, 0.31194, 0.22786, 0.16647, 0.12165, 0.08891, 0.06451, 0.046938, 0.034257, 0.025076, 0.01841, 0.013555, 0.0098874, 0.0072579, 0.0053666, 0.0039957, 0.0029948, 0.0022589, 0.0017141, 0.0013167, 0.0010269, 0.00080562, 0.000639, 0.00050445, 0.00039626, 0.00030968, 0.00024071, 0.00018605, 0.00014296, 0.00010917, 0.000082829, 0.000062373, 0.000046385, 0.000034311, 0.000025239, 0.000018458, 0.000013418, 9.6939E-006, 6.9578E-006]
    c_1 = [347.89, 340.29, 332.53, 324.59, 316.45, 308.11, 299.53, 295.07, 295.07, 295.07, 295.07, 295.07, 296.38, 297.72, 299.06, 300.39, 301.71, 303.02, 306.49, 310.1, 313.67, 317.19, 320.67, 324.12, 327.52, 329.8, 329.8, 328.81, 325.43, 322.01, 318.56, 315.07, 311.55, 307.99, 304.39, 300.75, 297.06, 293.44, 290.75, 288.04, 285.3, 282.54, 279.75, 276.94, 274.1]
    mu_1 = [0.000018515, 0.000017894, 0.00001726, 0.000016612, 0.000015949, 0.000015271, 0.000014577, 0.000014216, 0.000014216, 0.000014216, 0.000014216, 0.000014216, 0.000014322, 0.00001443, 0.000014538, 0.000014646, 0.000014753, 0.000014859, 0.00001514, 0.000015433, 0.000015723, 0.000016009, 0.000016293, 0.000016573, 0.000016851, 0.000017037, 0.000017037, 0.000016956, 0.00001668, 0.000016402, 0.000016121, 0.000015837, 0.000015551, 0.000015262, 0.00001497, 0.000014675, 0.000014377, 0.000014085, 0.000013868, 0.00001365, 0.00001343, 0.000013208, 0.000012985, 0.00001276, 0.000012533]


    Ts = [ATMOSPHERE_1976(Z).T for Z in H_1]
    assert_close1d(Ts, T_1, atol=0.005)
    Ps = [ATMOSPHERE_1976(Z).P for Z in H_1]
    assert_close1d(Ps, P_1, rtol=5E-5)
    rhos = [ATMOSPHERE_1976(Z).rho for Z in H_1]
    assert_close1d(rhos, rho_1, rtol=5E-5)
    cs = [ATMOSPHERE_1976(Z).v_sonic for Z in H_1]
    assert_close1d(cs, c_1, rtol=5E-5)
    mus = [ATMOSPHERE_1976(Z).mu for Z in H_1]
    assert_close1d(mus, mu_1, rtol=5E-5)

    assert_close(ATMOSPHERE_1976(1000, dT=1).T, 282.6510223716947)

    # Check thermal conductivity with: http://www.aerospaceweb.org/design/scripts/atmosphere/
    assert_close(ATMOSPHERE_1976(1000).k, 0.0248133634493)
    # Other possible additions:
    # mean air particle speed; mean collision frequency; mean free path; mole volume; total number density


    delta_P = ATMOSPHERE_1976.pressure_integral(288.6, 84100.0, 147.0)
    assert_close(delta_P, 1451.9583061008857)


def test_airmass():
    m = airmass(lambda Z : ATMOSPHERE_1976(Z).rho, 90)
    assert_close(m, 10356.127665863998) # vs 10356
    m = airmass(lambda Z : ATMOSPHERE_1976(Z).rho, 60)
    assert_close(m, 11954.138271601627) # vs 11954

    m = airmass(lambda Z : ATMOSPHERE_1976(Z).rho, 5)
    assert_close(m, 106861.56335489497) # vs 106837

    m = airmass(lambda Z : ATMOSPHERE_1976(Z).rho, .1)
    assert_close(m, 379082.24065519444, rtol=1e-6) # vs 378596

    # airmass(lambda Z : ATMOSPHERE_1976(Z).rho, .1, RI=1.0016977377367)
    # As refractive index increases, the atmospheric mass increases drastically. An exception is being raised numerically, not sure why
    # 7966284.95792788 - that's an 800x atmospheric increase.

hwm93_compiled = True
try:
    # Just check if works
    hwm93(5E5, 45.0, 50.0, 365.0)
except:
    hwm93_compiled = False

hwm14_compiled = True
try:
    hwm14(11000.0, latitude=-45.0, longitude=-85.0, day=150, seconds=12*3600.0, geomagnetic_disturbance_index=80.0)
except:
    hwm14_compiled = False

@pytest.mark.f2py
@pytest.mark.skipif(not hwm93_compiled,
                    reason='hwm93 model is not built')
def test_hwm93():
    # pass on systems without f2py for now
    custom = hwm93(5E5, 45.0, 50.0, 365.0)
    assert_close1d(custom, [-73.00312042236328, 0.1485661268234253])

    # Test from pyhwm93
    ans = hwm93(Z=150E3, latitude=65.0, longitude=-148.0, day=90.0, seconds=12*3600.0, f107=100., f107_avg=100., geomagnetic_disturbance_index=4.0)
    assert_close1d(ans, [-110.16133880615234, -12.400712013244629])


@pytest.mark.f2py
@pytest.mark.skipif(not hwm14_compiled,
                    reason='hwm14 model is not built')
def test_hwm14():
    # Data in checkhwm14.f90; all checks out.
    # Disturbance wind model checks are not separately implemented.
    # Height profile
    HEIGHTS = [0, 25, 50, 75, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400]
    HEIGHT_PROFILE_MER = [0.031, 2.965, -6.627, 2.238, -14.253, 37.403, 42.789, 20.278, 25.027, 34.297, 40.408, 44.436, 47.092, 48.843, 49.997, 50.758, 51.259]
    HEIGHT_PROFILE_ZON = [6.271, 25.115, 96.343, 44.845, 31.59, 11.628, -33.319, -49.984, -68.588, -80.022, -87.56, -92.53, -95.806, -97.965, -99.389, -100.327, -100.946]

    winds = [hwm14(alt*1000.0, latitude=-45.0, longitude=-85.0, day=150.0, seconds=12*3600.0, geomagnetic_disturbance_index=80.0) for alt in HEIGHTS]

    winds = [[round(i, 3) for i in j] for j in winds]

    MER_CALC = [i[0] for i in winds]
    ZON_CALC = [i[1] for i in winds]

    assert_close1d(MER_CALC, HEIGHT_PROFILE_MER)
    assert_close1d(ZON_CALC, HEIGHT_PROFILE_ZON)


    # Latitude profile
    LATS = [-90, -80, -70, -60, -50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
    LAT_PROFILE_MER = [-124.197, -150.268, -124.54, -23.132, 31.377, 39.524, 56.305, 60.849, 58.117, 56.751, 51.048, 35.653, 14.832, 1.068, -2.749, -27.112, -91.199, -186.757, -166.717]
    LAT_PROFILE_ZON = [177.174, 63.864, -71.971, -105.913, -28.176, 36.532, 32.79, 34.341, 72.676, 110.18, 111.472, 90.547, 77.736, 74.993, 41.972, -140.557, 3.833, 179.951, 235.447]

    winds = [hwm14(250E3, latitude=LAT, longitude=30, day=305, seconds=18*3600, geomagnetic_disturbance_index=48) for LAT in LATS]

    winds = [[round(i, 3) for i in j] for j in winds]

    MER_CALC = [i[0] for i in winds]
    ZON_CALC = [i[1] for i in winds]

    assert_close1d(MER_CALC, LAT_PROFILE_MER)
    assert_close1d(ZON_CALC, LAT_PROFILE_ZON)


    # Time of day profile: Note the data is specified in terms of local time
    TIMES_LT = [0, 1.5, 3, 4.5, 6, 7.5, 9, 10.5, 12, 13.5, 15, 16.5, 18, 19.5, 21, 22.5, 24]
    TIMES = [(lt_hour+70/15.)*3600 for lt_hour in TIMES_LT]
    TIME_PROFILE_MER = [6.564, 28.79, 22.316, -4.946, -23.175, -11.278, 17.57, 34.192, 26.875, 9.39, -1.362, -7.168, -21.035, -41.123, -46.702, -27.048, 6.566]
    TIME_PROFILER_ZON = [-40.187, -54.899, -57.187, -47.936, -41.468, -43.648, -49.691, -44.868, -22.542, 2.052, 4.603, -24.13, -66.38, -83.942, -60.262, -36.616, -40.145]

    winds = [hwm14(125E3, latitude=45, longitude=-70, day=75, seconds=TIME, geomagnetic_disturbance_index=30) for TIME in TIMES]

    winds = [[round(i, 3) for i in j] for j in winds]

    MER_CALC = [i[0] for i in winds]
    ZON_CALC = [i[1] for i in winds]

    assert_close1d(MER_CALC, TIME_PROFILE_MER)
    assert_close1d(ZON_CALC, TIME_PROFILER_ZON)

    # Longitude profile
    LONGS = [-180, -160, -140, -120, -100, -80, -60, -40, -20, 0, 20, 40, 60, 80, 100, 120, 140, 160, 180]
    LONG_PROFILE_MER = [-0.757, -0.592, 0.033, 0.885, 1.507, 1.545, 1.041, 0.421, 0.172, 0.463, 1.049, 1.502, 1.552, 1.232, 0.757, 0.288, -0.146, -0.538, -0.757]
    LONG_PROFILE_ZON = [-16.835, -18.073, -20.107, -22.166, -22.9, -21.649, -19.089, -16.596, -14.992, -13.909, -12.395, -10.129, -7.991, -7.369, -8.869, -11.701, -14.359, -15.945, -16.835]

    winds = [hwm14(40E3, latitude=-5, longitude=LONG, day=330, seconds=6*3600, geomagnetic_disturbance_index=4) for LONG in LONGS]

    winds = [[round(i, 3) for i in j] for j in winds]

    MER_CALC = [i[0] for i in winds]
    ZON_CALC = [i[1] for i in winds]

    assert_close1d(MER_CALC, LONG_PROFILE_MER)
    assert_close1d(ZON_CALC, LONG_PROFILE_ZON)

    # Day of year profile
    DAYS = [0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220, 240, 260, 280, 300, 320, 340, 360]
    DAY_PROFILE_MER = [1.57, -5.43, -13.908, -22.489, -30.844, -39.415, -48.717, -58.582, -67.762, -74.124, -75.371, -70.021, -58.19, -41.813, -24.159, -8.838, 1.319, 5.064, 2.908]
    DAY_PROFILE_ZON = [-42.143, -36.947, -29.927, -23.077, -17.698, -14.016, -11.35, -8.72, -5.53, -2.039, 0.608, 0.85, -2.529, -9.733, -19.666, -30.164, -38.684, -43.208, -42.951]

    winds = [hwm14(200E3, latitude=-65, longitude=-135, day=DAY, seconds=21*3600, geomagnetic_disturbance_index=15) for DAY in DAYS]

    winds = [[round(i, 3) for i in j] for j in winds]

    MER_CALC = [i[0] for i in winds]
    ZON_CALC = [i[1] for i in winds]

    assert_close1d(MER_CALC, DAY_PROFILE_MER)
    assert_close1d(ZON_CALC, DAY_PROFILE_ZON)

    # Magnetic strength profile
    APS = [0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220, 240, 260]
    AP_PROFILE_MER = [18.63, 11.026, -0.395, -9.121, -13.965, -16.868, -18.476, -19.38, -19.82, -19.887, -19.685, -19.558, -19.558, -19.558]
    AP_PROFILE_ZON = [-71.801, -69.031, -83.49, -96.899, -104.811, -109.891, -112.984, -114.991, -116.293, -116.99, -117.22, -117.212, -117.212, -117.212]

    winds = [hwm14(350E3, latitude=38, longitude=125, day=280, seconds=21*3600, geomagnetic_disturbance_index=AP) for AP in APS]

    winds = [[round(i, 3) for i in j] for j in winds]

    MER_CALC = [i[0] for i in winds]
    ZON_CALC = [i[1] for i in winds]

    assert_close1d(MER_CALC, AP_PROFILE_MER)
    assert_close1d(ZON_CALC, AP_PROFILE_ZON)


@pytest.mark.pytz
def test_solar_position():
    pos = solar_position(pytz.timezone('Australia/Perth').localize(datetime(2020, 6, 6, 7, 10, 57)), -31.95265, 115.85742)
    pos_expect = [90.89617025931763, 90.89617025931763, -0.8961702593176304, -0.8961702593176304, 63.60160176917509, 79.07112321438035]
    assert_close1d(pos, pos_expect, rtol=1e-9)

    pos = solar_position(pytz.timezone('Australia/Perth').localize(datetime(2020, 6, 6, 14, 30, 0)), -31.95265, 115.85742)
    pos_expect = [63.40805686233129, 63.44000181582068, 26.591943137668704, 26.559998184179317, 325.1213762464115, 75.74674754854641]
    assert_close1d(pos, pos_expect, rtol=1e-9)

    pos = solar_position(datetime(2020, 6, 6, 14, 30, 0) - timedelta(hours=8), -31.95265, 115.85742)
    pos_expect = [63.40805686233129, 63.44000181582068, 26.591943137668704, 26.559998184179317, 325.1213762464115, 75.74674754854641]
    assert_close1d(pos, pos_expect, rtol=1e-9)

    local_time = datetime(2018, 4, 15, 6, 43, 5)
    local_time = pytz.timezone('America/Edmonton').localize(local_time)
    assert_close(solar_position(local_time, 51.0486, -114.07)[0], 90.00054676987014, rtol=1e-9)

    pos = solar_position(pytz.timezone('America/Edmonton').localize(datetime(2018, 4, 15, 20, 30, 28)), 51.0486, -114.07)
    pos_expect = [89.9995695661236, 90.54103812161853, 0.00043043387640950836, -0.5410381216185247, 286.8313781904518, 6.631429525878048]
    assert_close1d(pos, pos_expect, rtol=1e-9)


@pytest.mark.pytz
def test_earthsun_distance():
    dt = earthsun_distance(datetime(2003, 10, 17, 13, 30, 30))
    assert_close(dt, 149090925951.18338, rtol=1e-10)

    dt = earthsun_distance(datetime(2013, 1, 1, 21, 21, 0, 0))
    assert_close(dt, 147098127628.8943, rtol=1e-10)

    dt = earthsun_distance(datetime(2013, 7, 5, 8, 44, 0, 0))
    assert_close(dt, 152097326908.20578, rtol=1e-10)

    assert_close(earthsun_distance(pytz.timezone('America/Edmonton').localize(datetime(2020, 6, 6, 10, 0, 0, 0))),
                 151817805599.67142, rtol=1e-10)

    assert_close(earthsun_distance(datetime(2020, 6, 6, 10, 0, 0, 0)),
                 151812898579.44104, rtol=1e-10)




@pytest.mark.pytz
def test_solar_irradiation():
    ans = solar_irradiation(Z=1100.0, latitude=51.0486, longitude=-114.07, linke_turbidity=3,
    moment=datetime(2018, 4, 15, 19, 43, 5), surface_tilt=41.0,
    surface_azimuth=180.0)
    ans_expect = [1065.7622492480543, 945.2657257434173, 120.49652350463705, 95.31534254980346, 25.18118095483359]
    assert_close1d(ans, ans_expect, rtol=1e-5)

@pytest.mark.pytz
def test_solar_irradiation_pytz():
    import pytz
    # Providing linke_turbidity always saves .1 seconds on unit testing from loading database
    ans = solar_irradiation(Z=1100.0, latitude=51.0486, longitude=-114.07, linke_turbidity=3, moment=pytz.timezone('America/Edmonton').localize(datetime(2018, 4, 15, 13, 43, 5)), surface_tilt=41.0,  surface_azimuth=180.0)
    ans_expect = [1065.7622492480543, 945.2657257434173, 120.49652350463705, 95.31534254980346, 25.18118095483359]
    assert_close1d(ans, ans_expect, rtol=1e-5)


@pytest.mark.pytz
def test_sunrise_sunset():
    sunrise, sunset, transit = sunrise_sunset(datetime(2018, 4, 17, 13, 43, 5), 51.0486,  -114.07)
    sunrise_expected = datetime(2018, 4, 17, 12, 36, 55, 782660)
    sunset_expected = datetime(2018, 4, 18, 2, 34, 4, 249326)
    transit_expected = datetime(2018, 4, 17, 19, 35, 46, 686265)
    assert sunrise == sunrise_expected
    assert sunset == sunset_expected
    assert transit == transit_expected

@pytest.mark.pytz
def test_sunrise_sunset_pytz():
    calgary = pytz.timezone('America/Edmonton')
    sunrise, sunset, transit = sunrise_sunset(calgary.localize(datetime(2018, 4, 17)), 51.0486, -114.07)
    assert sunrise == calgary.localize(datetime(2018, 4, 16, 6, 39, 1, 570479))
    assert sunset == calgary.localize(datetime(2018, 4, 16, 20, 32, 25, 778162))
    assert transit == calgary.localize(datetime(2018, 4, 16, 13, 36, 0, 386341))