def test_adabl_blh_estimation(): dataFile = paths.file_defaultlidardata() modelFile = paths.file_trainedmodel() scalerFile = paths.file_trainedscaler() blh_kabl = adabl_blh_estimation( dataFile, modelFile, scalerFile, storeInNetcdf=False ) assert blh_kabl.shape==(288,) and np.isnan(blh_kabl).sum()==0
def test_adabl_qualitymetrics(): dataFile = paths.file_defaultlidardata() modelFile = paths.file_trainedmodel() scalerFile = paths.file_trainedscaler() scores = adabl_qualitymetrics(dataFile, modelFile, scalerFile) everythingOK = all([ scores[0] < 1000.0, # errl2 scores[1] < 1000.0, # errl1 scores[2] < 3000.0, # errl0 scores[3] > 0.4, # corr scores[4] < 12, # chrono scores[5] == 0, # n_invalid ]) assert everythingOK
rcs_1 = rcss["rcs_1"] blh_mnf = rcss["pbl"] # Estimation with KABL # ---------------------- params = utils.get_default_params() params["n_clusters"] = 3 params["predictors"] = {"day": ["rcs_1"], "night": ["rcs_1", "rcs_2"]} params["n_profiles"] = 1 params["init"] = "advanced" blh_kabl = core.blh_estimation(lidarFile, storeInNetcdf=False, params=params) # Estimation with ADABL #----------------------- modelFile = paths.file_trainedmodel() scalerFile = paths.file_trainedscaler() blh_adabl = adabl.adabl_blh_estimation(lidarFile, modelFile, scalerFile, storeInNetcdf=False) # Estimation with RS #-------------------- rsdata = nc.Dataset(rsFile) t_rs = rsdata.variables['time'] blh_rs = rsdata.variables['BEST_BLH'] rsdex = np.where(np.logical_and(t_rs > t_values[0], t_rs < t_values[-1]))[0]