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test if tests will pass on github actions
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lucas-carmo committed Sep 10, 2024
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288 changes: 288 additions & 0 deletions tests/test_data/test_model.py
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# tests RAFT FOWT functionality and results

import pytest
import numpy as np
from numpy.testing import assert_allclose
import yaml
import pickle
import raft
import os

'''
Define files for testing
'''
# Name of the subfolder where the test data is located
test_dir = 'test_data'

# List of input file names to be tested
list_files = [
'VolturnUS-S.yaml',
'OC3spar.yaml',
'VolturnUS-S_farm.yaml'
]


# Get the directory of the current file
current_dir = os.path.dirname(os.path.abspath(__file__))

# To avoid problems with different platforms, get the full path of the file
list_files = [os.path.join(current_dir, test_dir, file) for file in list_files]


# Single test case used in test_solveStatics and test_solveDynamics
# Multiple cases from the yaml file are tested in test_analyzeCases only

'''
Aux functions
'''
# Function used to create FOWT instance
# Not explicitly inside the fixture below so that we can also run this file as a script
#
def create_model(file):
with open(file) as f:
design = yaml.load(f, Loader=yaml.FullLoader)

if 'array_mooring' in design: # Relative paths may be different in different platforms, so we make sure the path is correct
if design['array_mooring']['file']:
design['array_mooring']['file'] = os.path.join(current_dir, test_dir, design['array_mooring']['file'])

model = raft.Model(design)
return model

# Define a fixture to loop fowt instances with the index to loop the desired values as well
# Could also zip the lists with the desired values, but I think the approach below is simpler
@pytest.fixture(params=enumerate(list_files))
def index_and_model(request):
index, file = request.param
model = create_model(file)
return index, model

'''
Test functions
'''
#===== model.solveStatics for different loading conditions
cases4solveStatics = {
'wind': {'wind_speed': 8, 'wind_heading': 30, 'turbulence': 0, 'turbine_status': 'operating', 'yaw_misalign': 0, 'wave_spectrum': 'JONSWAP', 'wave_period': 0, 'wave_height': 0, 'wave_heading': 0, 'current_speed': 0, 'current_heading': 0},
'wave': {'wind_speed': 0, 'wind_heading': 0, 'turbulence': 0, 'turbine_status': 'operating', 'yaw_misalign': 0, 'wave_spectrum': 'JONSWAP', 'wave_period': 10, 'wave_height': 4, 'wave_heading': -30, 'current_speed': 0, 'current_heading': 0},
'current': {'wind_speed': 0, 'wind_heading': 0, 'turbulence': 0, 'turbine_status': 'operating', 'yaw_misalign': 0, 'wave_spectrum': 'JONSWAP', 'wave_period': 0, 'wave_height': 0, 'wave_heading': 0, 'current_speed': 0.6, 'current_heading': 15},
'wind_wave_current': {'wind_speed': 8, 'wind_heading': 30, 'turbulence': 0, 'turbine_status': 'operating', 'yaw_misalign': 0, 'wave_spectrum': 'JONSWAP', 'wave_period': 10, 'wave_height': 4, 'wave_heading': -30, 'current_speed': 0.6, 'current_heading': 15}
}

desired_X0 = {
'wind': [
np.array([ 1.27750843e+01, 1.04270725e+01, -5.01403771e-01, -3.48692268e-02, 5.90533519e-02, -3.22418223e-02]),
np.array([ 1.10831732e+01, 5.22389760e+00, -8.09325191e-01, -2.37567722e-02, 4.02685757e-02, -8.38412801e-02]),
np.array([ 1.67861341e+01, 1.12637020e+01, 6.65451797e-01, -3.01629231e-02, 5.68383850e-02, -5.12690113e-02, 1.61811048e+03, 1.07595392e+01, 1.03868611e+00, -3.13622101e-02, 5.90181250e-02, 1.65113590e-02])
],
'wave': [
np.array([ 1.69712005e-02, -1.93781208e-17, -4.28261180e-01, -1.21300094e-18, 2.26746861e-05, -2.30847610e-23]),
np.array([-1.64267049e-05, -2.83795893e-15, -6.65861624e-01, 3.88717546e-19, -5.94238978e-11, -4.02571352e-17]),
np.array([-5.01177348e-01, 1.11798952e-15, 8.82461053e-01, 4.91932000e-17, 4.39038724e-04, 8.69456218e-19, 1.60050118e+03, 9.82053320e-16, 8.82460768e-01, 4.27743746e-17, -4.39066827e-04, -8.32305085e-19])
],
'current': [
np.array([ 3.07647856e+00, 8.09230061e-01, -4.29676672e-01, 6.33390732e-04, -2.49217661e-03, 3.80888009e-03]),
np.array([ 3.86072176e+00, 9.22694246e-01, -6.74898762e-01, -2.64759824e-04, 9.82529767e-04, -1.03532699e-05]),
np.array([ 3.24739802e+00, 1.08484956e+00, 8.42959914e-01, 7.16963134e-04, -1.22097638e-03, -5.87434156e-03, 1.60424961e+03, 1.10109258e+00, 9.21764906e-01, 7.58137041e-04, -2.11268701e-03, 6.56575162e-03])
],
'wind_wave_current': [
np.array([ 1.49894720e+01, 1.16765061e+01, -5.14161071e-01, -3.42338575e-02, 5.66634437e-02, -2.76885509e-02]),
np.array([ 1.52428293e+01, 5.61793710e+00, -8.60576419e-01, -2.40342388e-02, 4.11894593e-02, -8.77292315e-02]),
np.array([ 2.05127673e+01, 1.23010332e+01, 6.26628389e-01, -2.94743425e-02, 5.49413694e-02, -5.38145777e-02, 1.62214085e+03, 1.22293955e+01, 1.07721320e+00, -3.05889945e-02, 5.76177298e-02, 2.63915249e-02])
]
}

def solveStatics(index_and_model, test_case_key):
'''
We test only the mean offsets and linearized mooring properties.
'''
index, model = index_and_model
testCase = cases4solveStatics[test_case_key]
model.solveStatics(testCase)
for i, fowt in enumerate(model.fowtList):
assert_allclose(fowt.r6, desired_X0[test_case_key][index][6*i:6*(i+1)], rtol=1e-05, atol=1e-10)

def test_solveStatics_Wind(index_and_model):
solveStatics(index_and_model, 'wind')

def test_solveStatics_Wave(index_and_model):
solveStatics(index_and_model, 'wave')

def test_solveStatics_Current(index_and_model):
solveStatics(index_and_model, 'current')

def test_solveStatics_Wind_Wave_Current(index_and_model):
solveStatics(index_and_model, 'wind_wave_current')



#===== model.solveEigen for different cases
cases4solveEigen = {
'unloaded': {'wind_speed': 0, 'wind_heading': 0, 'turbulence': 0, 'turbine_status': 'idle', 'yaw_misalign': 0, 'wave_spectrum': 'JONSWAP', 'wave_period': 0, 'wave_height': 0, 'wave_heading': 0, 'current_speed': 0, 'current_heading': 0},
'loaded': {'wind_speed': 8, 'wind_heading': 30, 'turbulence': 0, 'turbine_status': 'operating', 'yaw_misalign': 0, 'wave_spectrum': 'JONSWAP', 'wave_period': 10, 'wave_height': 4, 'wave_heading': -30, 'current_speed': 0.6, 'current_heading': 15}
}

desired_fn = {
'unloaded': [
np.array([0.00780613, 0.00781769, 0.06073888, 0.03861193, 0.03862018, 0.01239692]),
np.array([0.00796903, 0.00796903, 0.03245079, 0.03383781, 0.03384323, 0.15347415]),
np.array([0.01074625, 0.00716318, 0.05084381, 0.03748606, 0.03783757, 0.01574022, 0.00756192, 0.00704588, 0.05086277, 0.03748700, 0.03779494, 0.01547133])
],
'loaded': [
np.array([0.00983469, 0.00711507, 0.06075487, 0.03837915, 0.03917206, 0.01327898]),
np.array([0.00730761, 0.00938691, 0.03246216, 0.03384494, 0.03390347, 0.15560606]),
np.array([0.01065828, 0.00721512, 0.05086059, 0.03788729, 0.03835768, 0.01772042, 0.00740785, 0.00644214, 0.05081994, 0.03679016, 0.03751815, 0.01330817])
],
}

desired_modes = {
'unloaded': [
np.array([[-9.99999802e-01, -3.58177566e-14, -5.47524722e-08, -4.39990138e-13, -9.90311437e-01, -2.35694740e-15],
[-2.09525757e-20, 9.99999800e-01, -1.41067090e-16, 9.90300308e-01, -1.02414825e-14, -1.13370282e-01],
[ 1.27034965e-06, 1.14269752e-18, -1.00000000e+00, -5.06576752e-16, -3.61024872e-05, -9.89217552e-20],
[-1.92236308e-20, 6.27336513e-04, 2.26769079e-18, -1.38943513e-01, 1.44181432e-15, -1.78996143e-04],
[ 6.30061473e-04, 2.26235065e-17, 1.61898091e-07, -6.16893988e-14, -1.38864162e-01, -2.13805104e-17],
[ 3.40207703e-20, -8.15805381e-05, 4.18327175e-18, 1.97802394e-05, -7.70377924e-18, -9.93552790e-01]]),
np.array([[ 9.99999999e-01, -5.75186507e-10, -3.66356915e-10, -8.67852276e-14, 9.99898193e-01, -2.99686750e-14],
[ 3.92738961e-10, -9.99999999e-01, -3.31616203e-17, 9.99898194e-01, 3.23260804e-14, 2.06096172e-03],
[ 7.45752595e-11, -2.85321785e-18, 1.00000000e+00, -2.72406265e-18, 2.31375465e-10, -1.10751480e-16],
[ 2.12335674e-14, -5.40653002e-05, 4.24138273e-19, -1.42689340e-02, -4.31499026e-16, -2.94481141e-05],
[-5.40646648e-05, 3.10972493e-14, -4.68816970e-12, -1.23789014e-15, 1.42689404e-02, -1.94677827e-16],
[ 1.95726867e-18, -5.58669353e-09, 9.44626575e-17, 4.97628953e-06, -2.81458719e-18, -9.99997876e-01]]),
np.array([[-7.07063968e-01, 1.71887064e-16, -1.30340226e-02, -2.82542470e-14, 6.94863002e-01, 3.85153580e-16, 7.07066034e-01, -6.53986496e-17, 1.25007531e-02, 2.80098589e-14, 6.92313443e-01, 5.76639921e-16],
[-7.25250255e-15, 7.07099398e-01, 1.75093624e-14, -6.97995773e-01, -3.82099078e-14, 6.98639459e-01, 7.80689701e-14, -7.07100871e-01, -1.97524808e-14, 6.97890097e-01, -2.17875817e-14, 6.96282021e-01],
[ 7.78086989e-03, -1.31547996e-19, -7.06986675e-01, 3.69809461e-15, -3.37914294e-02, 1.87852753e-17, -7.58851866e-03, -1.14893765e-18, 7.06996195e-01, -3.54499811e-15, -4.84772086e-02, 1.82168142e-17],
[-4.42008293e-18, 3.85533504e-04, -1.95389309e-16, 1.13310010e-01, 6.59117155e-15, 2.20799443e-03, 4.26954391e-17, -3.95913134e-04, -2.00243504e-16, -1.13574765e-01, 3.57431861e-15, 2.50916481e-03],
[ 4.66972697e-05, -6.69897247e-19, -2.08076011e-04, -5.37783516e-15, 1.26573873e-01, -7.03266775e-19, -1.90337265e-04, 7.23965014e-19, 1.52684219e-04, 5.23638666e-15, 1.35481349e-01, -4.06301593e-18],
[ 4.59941229e-17, -3.22196475e-03, 8.14219936e-18, -2.53785685e-03, -1.51955686e-16, 1.09079638e-01, -3.47492945e-16, 2.85012022e-03, -5.76771539e-18, 2.46481410e-03, -4.41443842e-17, 1.23225467e-01],
[ 7.07063969e-01, 1.23317808e-16, 1.30340204e-02, 5.92119539e-15, 6.94866599e-01, 5.63370023e-16, 7.07066037e-01, -2.39412617e-17, 1.25007650e-02, 7.52362237e-15, -6.92309081e-01, 1.94827127e-16],
[ 8.83463633e-17, -7.07099273e-01, -2.67391452e-17, -6.97934777e-01, 8.19753287e-15, -6.98638992e-01, -9.46449353e-15, -7.07100982e-01, -4.28411155e-18, -6.97951671e-01, -9.99882707e-15, 6.96282772e-01],
[ 7.78087658e-03, 3.67247159e-19, -7.06986551e-01, 1.17996039e-15, 3.37916947e-02, 1.50031146e-18, 7.58851869e-03, -5.80489109e-19, -7.06996319e-01, 6.76458394e-16, -4.84769765e-02, 8.65225494e-18],
[ 1.23387652e-19, -3.85533550e-04, 3.22484756e-18, 1.13300083e-01, -1.34298598e-15, -2.20798839e-03, -4.64803567e-18, -3.95913299e-04, 3.40496951e-18, 1.13584760e-01, 1.62276251e-15, 2.50916234e-03],
[-4.66966628e-05, 9.52249103e-19, 2.08075991e-04, 1.35742924e-15, 1.26574617e-01, 5.81538035e-18, -1.90337282e-04, -1.14034186e-18, 1.52684170e-04, 1.19298438e-15, -1.35480553e-01, 4.27926093e-19],
[-2.06368074e-18, -3.22196433e-03, 3.25247512e-19, 2.53764084e-03, -2.87786844e-17, 1.09079545e-01, -6.15608121e-17, -2.85012079e-03, -4.84193205e-19, 2.46503742e-03, 3.86906564e-17, -1.23225573e-01]])
],
'loaded': [
np.array([[-9.81986152e-01, -1.88286824e-01, 5.59946554e-03, 2.61193228e-01, -9.72955253e-01, -3.08726352e-01],
[ 1.88900743e-01, -9.82107189e-01, 2.98408290e-03, 9.54498808e-01, 1.89261310e-01, -6.94110416e-01],
[ 4.31605153e-03, 3.58498427e-03, 9.99979861e-01, -2.03285632e-02, 2.34980450e-02, 2.70719416e-02],
[ 2.08031966e-04, -5.07233886e-04, -5.66479516e-05, -1.37388601e-01, -2.53976025e-02, 1.24094800e-02],
[ 9.11364493e-04, 1.07208858e-04, 1.20997966e-04, 3.76971113e-02, -1.27830009e-01, 7.71061585e-03],
[-4.52439252e-04, -6.48214886e-04, -2.48529247e-05, 4.15507018e-04, 7.24248385e-04, 6.49578656e-01]]),
np.array([[-9.64427970e-01, -2.63275240e-01, -2.19940087e-02, 5.55127926e-01, 6.76736640e-01, -5.39080693e-03],
[ 2.64046519e-01, -9.64654426e-01, -1.04287809e-02, -8.31628275e-01, 7.35752801e-01, 1.95933294e-03],
[ 1.25597856e-02, 1.12293654e-02, 9.99703591e-01, 1.34671593e-03, 2.21214750e-02, -1.64280220e-03],
[ 1.39293939e-05, -8.52234984e-05, 2.02695624e-04, 1.18481555e-02, -1.04808808e-02, -1.78310745e-04],
[ 5.46588215e-05, 3.29682334e-05, -4.22850783e-04, 7.92105029e-03, 9.65143171e-03, -9.99432432e-05],
[ 6.12997995e-04, -1.37073518e-03, 1.17914717e-04, 4.73951683e-03, -1.72558944e-03, 9.99982180e-01]]),
np.array([[-7.72780208e-01, 1.35468412e-01, -1.77495566e-02, 4.22842581e-01, -7.32826057e-01, 1.07921591e-01, 5.95578887e-01, -1.87722800e-01, 4.79726175e-03, -1.83129519e-03, -1.72205796e-02, -1.44056530e-01],
[-2.28519471e-03, -9.77326843e-01, -1.74843237e-04, -8.88837415e-01, -6.53268410e-01, -9.54807874e-01, 2.05369622e-01, -6.41688285e-02, 3.56670017e-05, -3.02582744e-03, 2.46120155e-02, 6.30228760e-02],
[ 8.50862335e-03, -1.27740285e-03, -9.69677742e-01, -2.66634251e-02, 5.60030809e-02, -1.43192612e-02, -6.36785160e-03, 1.98135470e-03, 2.43677208e-01, -3.31096809e-03, 1.06565881e-02, 1.48729311e-03],
[-2.14147548e-07, -5.13051234e-04, 2.28569369e-05, 1.46348523e-01, 1.03860372e-01, -8.38713717e-03, 1.11521615e-04, -4.39100546e-05, -3.88676030e-06, 3.89544548e-04, -4.17391596e-03, -1.20687744e-04],
[ 3.50229130e-05, 4.81508566e-05, -2.96471440e-04, 7.95770662e-02, -1.38834167e-01, -2.81132282e-03, -2.08014641e-04, 7.50030410e-05, 1.16277632e-04, 1.13920287e-03, -7.01170226e-03, -8.83950465e-05],
[-8.25205229e-05, 2.97095677e-03, 5.83651248e-05, -4.00418833e-03, -2.30212529e-03, -2.40166282e-01, -2.55349690e-04, -1.02689336e-04, -1.45133749e-05, -7.17160421e-05, 1.72592987e-04, -3.61621868e-03],
[ 6.33918522e-01, 1.62082701e-01, -3.39536374e-03, 5.09072525e-02, -5.43724190e-02, 8.05114239e-02, 7.01183592e-01, -2.94123355e-01, -1.51879157e-02, -2.87259623e-01, 9.22402696e-01, 2.36651590e-01],
[-2.89806557e-02, -1.40197163e-02, 6.27951672e-04, -7.98679610e-03, 2.09851086e-03, 1.10471065e-01, -3.33697652e-01, -9.34933041e-01, 2.50812940e-03, -9.42830766e-01, -3.36459026e-01, -9.52181619e-01],
[ 6.12262049e-03, 1.54700079e-03, 2.43717017e-01, -2.73706238e-03, 5.41124848e-03, 7.14441628e-04, 7.03656560e-03, -1.60801493e-03, 9.69722323e-01, 6.75971380e-03, 4.42384895e-02, 4.03303739e-03],
[-7.31124374e-05, -2.43839230e-05, -2.50078305e-05, 9.22017629e-04, -4.24008169e-04, 4.83394158e-06, -2.41322773e-04, -4.82461803e-04, -9.63686678e-05, 1.57899450e-01, 5.19950014e-02, -6.52497993e-04],
[-6.60885996e-05, -3.13030222e-05, -4.59073676e-06, 5.58053584e-03, -3.54713531e-03, -2.21919732e-05, -1.89441252e-04, -6.23442736e-05, -1.80039164e-04, -5.95345943e-02, 1.73855081e-01, 1.09011091e-03],
[-1.74789101e-03, 2.81715003e-04, -9.04124903e-07, 9.27870178e-05, 1.81765117e-06, -6.72796711e-03, -2.50526577e-03, -5.23512364e-03, -4.85249735e-06, 2.19802909e-03, 1.10782634e-05, 1.12199961e-01]])
],
}

def solveEigen(index_and_model, test_case_key):
index, model = index_and_model
testCase = cases4solveEigen[test_case_key]
model.solveStatics(testCase)
fns, modes = model.solveEigen()
assert_allclose(fns, desired_fn[test_case_key][index], rtol=1e-05, atol=1e-5)
assert_allclose(modes, desired_modes[test_case_key][index], rtol=1e-05, atol=1e-5) # Include this again after moorpy dev stuff is merged

def test_solveEigen_unloaded(index_and_model):
solveEigen(index_and_model, 'unloaded')

def test_solveEigen_loaded(index_and_model):
solveEigen(index_and_model, 'loaded')


#===== model.analyzeCases for multiple environmental conditions specified in the yaml file
def test_analyzeCases(index_and_model, plotPSDs=False, flagSaveValues=False):
'''Solve cases listed in the yaml file
Set flagSaveValues to true to replace the true values file with the values calculated below
'''
index, model = index_and_model
true_values_file = list_files[index].replace('.yaml', '_true_analyzeCases.pkl')
metrics2check = ['wave_PSD', 'surge_PSD', 'sway_PSD', 'heave_PSD', 'roll_PSD', 'pitch_PSD', 'yaw_PSD', 'AxRNA_PSD', 'Mbase_PSD', 'Tmoor_PSD']

model.analyzeCases()

# Save or read the true values
if flagSaveValues:
with open(true_values_file, 'wb') as f:
pickle.dump(model.results['case_metrics'], f)
return # If saving, we don't need to check the results
else:
with open(true_values_file, 'rb') as f:
true_values = pickle.load(f)

# Check compute results against previously computed true values
nCases = len(model.results['case_metrics'])
for iCase in range(nCases):
for ifowt in range(model.nFOWT):
for imetric, metric in enumerate(metrics2check):
if metric in model.results['case_metrics'][iCase][ifowt]:
assert_allclose(model.results['case_metrics'][iCase][ifowt][metric], true_values[iCase][ifowt][metric], rtol=1e-05, atol=1e-3)
elif 'array_mooring' in model.results['case_metrics'][iCase] and metric in model.results['case_metrics'][iCase]['array_mooring']:
assert_allclose(model.results['case_metrics'][iCase]['array_mooring'][metric], true_values[iCase]['array_mooring'][metric], rtol=1e-05, atol=1e-3)

if plotPSDs:
import matplotlib.pyplot as plt
for ifowt in range(model.nFOWT):
fig, ax = plt.subplots(3, 3, figsize=(15, 10))
for iCase in range(nCases):
for imetric, metric in enumerate(metrics2check):
if metric in model.results['case_metrics'][iCase][ifowt]:
y = model.results['case_metrics'][iCase][ifowt][metric]
elif 'array_mooring' in model.results['case_metrics'][iCase] and metric in model.results['case_metrics'][iCase]['array_mooring']:
y = model.results['case_metrics'][iCase]['array_mooring'][metric]

if metric == 'Tmoor_PSD':
if iCase == 0:
fig2, ax2 = plt.subplots(y.shape[0], 1, figsize=(15, 10))
for i in range(y.shape[0]):
ax2[i].plot(model.w, y[i, :])
ax2[i].set_ylabel(f'Line channel {i+1}')
ax2[i].set_xlabel('Frequency (Hz)')
ax2[0].set_title(f'{metric}')
else:
# assert_allclose(model.results['case_metrics'][iCase][ifowt][metric], true_values[idxTrueValues][ifowt][metric], rtol=1e-05, atol=1e-5)
ax[imetric//3, imetric%3].plot(model.w, y, label=f'Case {iCase+1}')
ax[imetric//3, imetric%3].set_ylabel(metric)
ax[imetric//3, imetric%3].set_xlabel('Frequency (Hz)')
plt.show()

'''
To run as a script. Useful for debugging.
'''
if __name__ == "__main__":
index = 2

model = create_model(list_files[index])
test_solveStatics_Wind((index,model))

model = create_model(list_files[index])
test_solveStatics_Wave((index,model))

model = create_model(list_files[index])
test_solveStatics_Current((index,model))

model = create_model(list_files[index])
test_solveStatics_Wind_Wave_Current((index,model))

model = create_model(list_files[index])
test_solveEigen_unloaded((index,model))

model = create_model(list_files[index])
test_solveEigen_loaded((index,model))

model = create_model(list_files[index])
test_analyzeCases((index,model), plotPSDs=True, flagSaveValues=False)

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