def test_variable_rename():
"""Tests to make sure that variable renaming works"""
loc = data_locations['binary']
data_files = [os.path.join(loc, f) for f in os.listdir(loc)]
out_dir = '.'
params = {
'start': dates['binary'][0],
'stop': dates['binary'][1],
'forcing_fmt': 'binary',
'domain_fmt': 'netcdf',
'state_fmt': 'netcdf',
'domain': './metsim/data/stehekin.nc',
'state': './metsim/data/state_vic.nc',
'forcing': data_files,
'method': 'mtclim',
'scheduler': 'threading',
'time_step': "60",
'out_dir': out_dir,
'out_state': os.path.join(out_dir, 'state.nc'),
'out_vars': {
'prec': {
'out_name': 'pptrate'
},
'shortwave': {
'out_name': 'SWRadAtm'
}
},
'forcing_vars': in_vars_section['binary'],
'domain_vars': domain_section['binary']
}
ms = MetSim(params)
ms.run()
ds = ms.open_output()
assert 'pptrate' in ds.variables
assert 'SWRadAtm' in ds.variables
def test_yaml_config():
filename = './examples/example_yaml.yaml'
conf = io.read_config(DummyOpts(filename))
out_dir = tempfile.mkdtemp('results')
conf['out_dir'] = out_dir
ms = MetSim(conf)
ms.run()
assert ms.open_output() is not None
def test_examples(kind):
filename = './examples/example_{kind}.conf'.format(kind=kind)
conf = io.read_config(DummyOpts(filename))
out_dir = tempfile.mkdtemp('results')
conf['out_dir'] = out_dir
ms = MetSim(conf)
ms.run()
assert ms.open_output() is not None
def test_time_offset():
"""Tests to make sure that the time_offset option works"""
loc = data_locations['binary']
data_files = [os.path.join(loc, f) for f in os.listdir(loc)]
out_vars = [
'prec', 'temp', 'shortwave', 'longwave', 'vapor_pressure', 'wind',
'rel_humid', 'spec_humid', 'air_pressure'
]
out_dir = '.'
params = {
'start': dates['binary'][0],
'stop': dates['binary'][1],
'forcing_fmt': 'binary',
'domain_fmt': 'netcdf',
'state_fmt': 'netcdf',
'domain': './metsim/data/stehekin.nc',
'state': './metsim/data/state_vic.nc',
'forcing': data_files,
'method': 'mtclim',
'scheduler': 'threading',
'time_step': "60",
'out_dir': out_dir,
'out_state': os.path.join(out_dir, 'state.nc'),
'out_vars': {n: metsim.metsim.available_outputs[n]
for n in out_vars},
'forcing_vars': in_vars_section['binary'],
'domain_vars': domain_section['binary']
}
params1 = dict()
params1.update(params)
params2 = dict()
params2.update(params)
params1['period_ending'] = False
params2['period_ending'] = True
# Set up the MetSim object
ms1 = MetSim(params1)
ms2 = MetSim(params2)
assert ms1._times[1:] == ms2._times[:-1]
def main():
"""Runs MetSim"""
from metsim.metsim import MetSim
setup = init(parse(sys.argv[1:]))
ms = MetSim(setup)
if ms.params['nprocs'] > 1:
ms.launch()
else:
ms.run()
def test_setup(test_params, domain_file):
"""Tests the setup of the MetSim object"""
in_fmt = test_params['forcing_fmt']
loc = data_locations[in_fmt]
# Get the files and make sure the right amount exist
if in_fmt == 'binary' or in_fmt == 'ascii':
data_files = [os.path.join(loc, f) for f in os.listdir(loc)]
assert len(data_files) == 16
else:
data_files = loc
assert data_files == './metsim/data/test.nc'
test_params['forcing'] = data_files
# Test construction
ms = MetSim(test_params)
return ms
def test_coordinate_dimension_matchup():
"""
This test checks that MetSim correctely adds a coordinate
if an input dataset is missing coordinate variables for the
chunked dimensions.
"""
var_rename = OrderedDict(latitude='lat',
longitude='lon',
mask='mask',
elevation='elev',
pptrate='prec',
maxtemp='t_max',
mintemp='t_min')
filename = './examples/example_dimtest.conf'
conf = io.read_config(DummyOpts(filename))
conf['out_dir'] = tempfile.mkdtemp('results')
ms = MetSim(conf)
ds = xr.open_dataset('./metsim/data/dim_test.nc')
assert 'hru' not in ds.coords
assert 'hru' in ms.met_data.coords
def test_disaggregation_values():
"""Tests to make sure values are being generated correctly"""
# Set parameters
loc = data_locations['binary']
data_files = [os.path.join(loc, f) for f in os.listdir(loc)]
out_vars = [
'prec', 'temp', 'shortwave', 'longwave', 'vapor_pressure', 'wind',
'rel_humid'
]
params = {
'start': dates['binary'][0],
'stop': dates['binary'][1],
'in_format': 'binary',
'out_format': 'ascii',
'domain': './tests/data/stehekin.nc',
'forcings': data_files,
'method': 'mtclim',
'time_step': "60",
't_max_lr': 0.00065,
't_min_lr': 0.00065,
'out_dir': "./tmp",
'out_vars': out_vars,
'in_vars': in_vars_section['binary'],
'domain_vars': domain_section['binary']
}
# The location we will test against
loc = (1, 4)
# Set up the MetSim object
ms = MetSim(params)
ms.load(params['forcings'])
# Run MetSim and load in the validated data
ms.run([loc])
out = ms.output.isel(lat=loc[0], lon=loc[1]).to_dataframe()[:-1][out_vars]
good = pd.read_table('./tests/data/validated_48.3125_-120.5625',
names=out_vars)
good.index = out.index
# Make sure the data comes out right
assert type(out) is pd.DataFrame
for var in ms.params['out_vars']:
# Check to make sure each variable has normalized
# rmse of less than 0.02
h = max([good[var].max(), out[var].max()])
l = min([good[var].min(), out[var].min()])
nrmse = np.sqrt((good[var] - out[var]).pow(2).mean()) / (h - l)
assert nrmse < 0.02
def test_disaggregation_values():
"""Tests to make sure values are being generated correctly"""
# Set parameters
loc = data_locations['binary']
data_files = [os.path.join(loc, f) for f in os.listdir(loc)]
out_vars = [
'prec', 'temp', 'shortwave', 'longwave', 'vapor_pressure', 'wind',
'rel_humid', 'spec_humid', 'air_pressure'
]
out_dir = tempfile.mkdtemp('results')
params = {
'start': dates['binary'][0],
'stop': dates['binary'][1],
'forcing_fmt': 'binary',
'domain_fmt': 'netcdf',
'state_fmt': 'netcdf',
'domain': './metsim/data/stehekin.nc',
'state': './metsim/data/state_vic.nc',
'forcing': data_files,
'method': 'mtclim',
'scheduler': 'threading',
'time_step': "60",
'out_dir': out_dir,
'out_state': os.path.join(out_dir, 'state.nc'),
'out_vars': {n: metsim.metsim.available_outputs[n]
for n in out_vars},
'forcing_vars': in_vars_section['binary'],
'domain_vars': domain_section['binary']
}
# The location we will test against
loc = (1, 4)
def check_data(out, good, tol=0.03):
assert isinstance(out, pd.DataFrame)
for var in ms.params['out_vars'].keys():
# Check to make sure each variable has normalized
# rmse of less than 0.02
h = max([good[var].max(), out[var].max()])
l = min([good[var].min(), out[var].min()])
nrmse = np.sqrt((good[var] - out[var]).pow(2).mean()) / (h - l)
print(var, nrmse)
assert nrmse < tol
# Set up the MetSim object
ms = MetSim(params)
# Run MetSim and load in the validated data
ms.run()
ds = ms.open_output()
out = ds.isel(lat=loc[0], lon=loc[1]).to_dataframe()[out_vars]
good = pd.read_table('./metsim/data/validated_48.3125_-120.5625',
names=out_vars)
good.index = out.index
# Make sure the data comes out right
check_data(out, good)
ds.close()
# Now do 3 hourly
params['time_step'] = '180'
ms = MetSim(params)
ms.run()
ds = ms.open_output()
out = ds.isel(lat=loc[0], lon=loc[1]).to_dataframe()[out_vars]
good = pd.read_table('./metsim/data/three_hourly_48.3125_-120.5625',
names=out_vars)
good.index = out.index
# Make sure the data comes out right
check_data(out, good, tol=0.2)
ds.close()
def test_unit_conversion():
"""Tests to make sure that variable renaming works"""
loc = data_locations['binary']
data_files = [os.path.join(loc, f) for f in os.listdir(loc)]
out_dir = '.'
params = {
'start': dates['binary'][0],
'stop': dates['binary'][1],
'forcing_fmt': 'binary',
'domain_fmt': 'netcdf',
'state_fmt': 'netcdf',
'domain': './metsim/data/stehekin.nc',
'state': './metsim/data/state_vic.nc',
'forcing': data_files,
'method': 'mtclim',
'scheduler': 'threading',
'time_step': "60",
'out_dir': out_dir,
'out_state': os.path.join(out_dir, 'state.nc'),
'out_vars': {
'prec': {
'out_name': 'pptrate',
'units': 'mm s-1'
},
'temp': {
'out_name': 'airtemp',
'units': 'K'
}
},
'forcing_vars': in_vars_section['binary'],
'domain_vars': domain_section['binary']
}
params1 = dict()
params1.update(params)
params2 = dict()
params2.update(params)
params2['out_vars'] = {
'prec': {
'out_name': 'pptrate',
'units': 'mm timestep-1'
},
'temp': {
'out_name': 'airtemp',
'units': 'C'
}
}
ms1 = MetSim(params1)
ms1.run()
ds1 = ms1.open_output().load()
ds1.close()
time_step = int(params['time_step'])
sec_per_min = 60.
tol = 1e-4
ms2 = MetSim(params2)
ms2.run()
ds2 = ms2.open_output().load()
assert np.allclose(ds1['airtemp'].mean(),
ds2['airtemp'].mean() + 273.15,
atol=tol)
assert np.allclose(time_step * sec_per_min * ds1['pptrate'].mean(),
ds2['pptrate'].mean(),
atol=tol)
def main():
"""Runs MetSim"""
from metsim.metsim import MetSim
setup = init(parse(sys.argv[1:]))
ms = MetSim(setup)
ms.run()
def test_passthrough():
"""Test to make sure passing through previously estimated
variables doesn't alter the values from disaggregation"""
loc = data_locations['binary']
data_files = [os.path.join(loc, f) for f in os.listdir(loc)]
out_dir = '.'
params = {
'start': dates['binary'][0],
'stop': dates['binary'][1],
'forcing_fmt': 'binary',
'domain_fmt': 'netcdf',
'state_fmt': 'netcdf',
'domain': './metsim/data/stehekin.nc',
'state': './metsim/data/state_vic.nc',
'forcing': data_files,
'method': 'mtclim',
'scheduler': 'threading',
'time_step': "60",
'out_dir': out_dir,
'out_state': os.path.join(out_dir, 'state.nc'),
'out_vars': {
'prec': {
'out_name': 'prec'
},
'temp': {
'out_name': 'temp'
},
'longwave': {
'out_name': 'longwave'
},
'vapor_pressure': {
'out_name': 'vapor_pressure'
},
'shortwave': {
'out_name': 'shortwave'
}
},
'forcing_vars': in_vars_section['binary'],
'domain_vars': domain_section['binary']
}
# Second run will be to use mtclim and hourly disagg
params1 = dict()
params1.update(params)
params1['out_prefix'] = 'mtclim'
ms1 = MetSim(params1)
ms1.run()
with ms1.open_output() as ds:
mtclim_ds = ds.load()
# Third run will be to use passthrough and hourly disagg
# with input data from teh first run
params2 = dict()
params2.update(params)
params2['method'] = 'passthrough'
params2['out_prefix'] = 'passthrough'
params2['forcing_vars'] = OrderedDict(prec='prec',
t_max='t_max',
t_min='t_min',
wind='wind',
shortwave='shortwave',
vapor_pressure='vapor_pressure')
params2['forcing_fmt'] = 'netcdf'
params2['forcing'] = './metsim/data/passthrough.nc'
ms2 = MetSim(params2)
ms2.run()
with ms2.open_output() as ds:
passthrough_ds = ds.load()
tol = 1e-4
assert np.allclose(passthrough_ds['shortwave'].mean(),
mtclim_ds['shortwave'].mean(),
atol=tol)
assert np.allclose(passthrough_ds['vapor_pressure'].mean(),
mtclim_ds['vapor_pressure'].mean(),
atol=tol)
assert np.allclose(passthrough_ds['longwave'].mean(),
mtclim_ds['longwave'].mean(),
atol=tol)