def test_prepare_data_multiprof():
n_profiles = 3
testFile = paths.file_defaultlidardata()
t_values, z_values, rcss = utils.extract_data(
testFile, to_extract=["rcs_1", "rcs_2"])
rcs_1 = rcss["rcs_1"]
rcs_2 = rcss["rcs_2"]
params = utils.get_default_params()
params["predictors"] = {
"day": ["rcs_1", "rcs_2"],
"night": ["rcs_1", "rcs_2"]
}
loc, dateofday, lat, lon = utils.where_and_when(testFile)
t = 55
coords = {
"time": dt.datetime.utcfromtimestamp(t_values[t]),
"lat": lat,
"lon": lon
}
t_back = max(t - n_profiles + 1, 0)
rcss = {"rcs_1": rcs_1[t_back:t + 1, :], "rcs_2": rcs_2[t_back:t + 1, :]}
X, Z = prepare_data(coords, z_values, rcss=rcss, params=params)
assert X.shape == (438, 2) and Z.shape == (438, )
def test_extract_data_typenkeys():
testFile = paths.file_defaultlidardata()
needed_data = ["rcs_0", "rcs_1", "rcs_2"]
t_values, z_values, rcss = extract_data(testFile, to_extract=needed_data)
assert isinstance(rcss, dict) and set(rcss.keys()) == set(needed_data)
def test_extract_data_shapes():
testFile = paths.file_defaultlidardata()
t_values, z_values, rcss = extract_data(testFile, to_extract=["rcs_1"])
rcs_1 = rcss["rcs_1"]
assert t_values.shape == (288, ) and z_values.shape == (
146, ) and rcs_1.shape == (288, 146)
def test_create_file_from_source():
testFile = paths.file_defaultlidardata()
outputFile = os.path.join(paths.resultrootdir,
os.path.split(testFile)[-1][:-3] + ".out.nc")
create_file_from_source(testFile, outputFile)
assert os.path.isfile(outputFile)
def test_extract_testprofile():
testFile = paths.file_defaultlidardata()
z_values, rcs_1, rcs_2, coords = extract_testprofile(testFile,
profile_id=2,
return_coords=True)
assert z_values.shape == (146, ) and rcs_1.shape == (146, )
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_where_and_when():
testFile = paths.file_defaultlidardata()
trueLocation = 'Trappes'
trueDay = dt.datetime(2018, 8, 2)
trueLat = 48.7743
trueLon = 2.0098
location, day, lat, lon = where_and_when(testFile)
assert location == trueLocation and day == trueDay and lat == trueLat and lon == trueLon
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
def test_kabl_qualitymetrics():
testFile = paths.file_defaultlidardata()
scores = kabl_qualitymetrics(testFile)
everythingOK = all([
scores[0] < 1000.0, # errl2
scores[1] < 1000.0, # errl1
scores[2] < 3000.0, # errl0
scores[3] > 0.4, # corr
scores[4] > 1200, # ch
scores[5] < 0.4, # db
scores[6] > 0.7, # sil
scores[7] < 3, # chrono
scores[8] == 0, # n_invalid
])
assert everythingOK
def test_prepare_data_singleprof():
testFile = paths.file_defaultlidardata()
z_values, rcs_1, rcs_2, coords = utils.extract_testprofile(
testFile, profile_id=2, return_coords=True)
params = utils.get_default_params()
params["predictors"] = {
"day": ["rcs_1", "rcs_2"],
"night": ["rcs_1", "rcs_2"]
}
X, Z = prepare_data(coords,
z_values,
rcss={
"rcs_1": rcs_1,
"rcs_2": rcs_2
},
params=params)
assert X.shape == (146, 2) and Z.shape == (146, )
"""
Unitary tests of the functions in ../kabl/graphics.py
Must be executed inside the `tests/` directory
"""
# Local packages
from kabl.graphics import *
from kabl import paths
from kabl import utils
from kabl import core
# Test of quicklook_data
# ------------------------
print("\n --------------- Test of quicklook_data")
testFile = paths.file_defaultlidardata()
quicklook_data(testFile)
# Test of quicklook_testprofiles
# ------------------------
print("\n --------------- Test of quicklook_testprofiles")
testFile = paths.file_defaultlidardata()
quicklook_testprofiles(testFile)
# Test of blhs_over_profile
# ------------------------
print("\n --------------- Test of blhs_over_profile")
testFile = paths.file_defaultlidardata()
z_values, rcs_1, rcs_2, coords = utils.extract_testprofile(
testFile, profile_id=3, return_coords=True
)
from kabl import core
from kabl import utils
from kabl import graphics
from kabl import adabl
from kabl import paths
# Usual Python packages
import pickle
import numpy as np
import datetime as dt
import pytz
import sys
import time
import netCDF4 as nc
lidarFile = paths.file_defaultlidardata()
rsFile = paths.file_blhfromrs()
t_values, z_values, rcss = utils.extract_data(lidarFile,
max_height=4620,
to_extract=["rcs_1", "pbl"])
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"
def test_blh_estimation_returnlabels():
testFile = paths.file_defaultlidardata()
blh, labels = blh_estimation_returnlabels(testFile)
assert labels.shape == (288, 146) and blh.shape == (
288, ) and np.isnan(blh).sum() == 0
def test_blh_estimation():
testFile = paths.file_defaultlidardata()
blh = blh_estimation(testFile)
assert blh.shape == (288, ) and np.isnan(blh).sum() == 0