'''Chemical Engineering Design Library (ChEDL). Utilities for process modeling. Copyright (C) 2016, 2017, 2018 Caleb Bell 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))