def test_supplying_attributes(self):
fid, single_tmp = tempfile.mkstemp(suffix='.nc')
attrs = {
'y': {
'_CoordinateAxisType': 'Lat',
'_FillValue': -9999.9,
'missing_value': -9999.9,
}
}
with OrthogonalMultidimensionalTimeseries(self.single) as s:
df = s.to_dataframe()
with OrthogonalMultidimensionalTimeseries.from_dataframe(
df, single_tmp, attributes=attrs) as result_ncd:
assert 'station' in result_ncd.dimensions
assert result_ncd.variables['y']._CoordinateAxisType == 'Lat'
with self.assertRaises(AttributeError):
result_ncd.variables['y'].missing_value
with self.assertRaises(AttributeError):
result_ncd.variables['y']._FillValue
test_is_mine(OrthogonalMultidimensionalTimeseries,
single_tmp) # Try to load it again
os.close(fid)
os.remove(single_tmp)
def test_omp_dataframe(self):
single_tmp = tempfile.mkstemp(suffix='.nc')[-1]
with OrthogonalMultidimensionalTimeseries(self.single) as s:
df = s.to_dataframe()
nc = OrthogonalMultidimensionalTimeseries.from_dataframe(
df, single_tmp)
nc.close()
logger.info(single_tmp)
def test_timeseries_omp_dataframe(self):
fid, single_tmp = tempfile.mkstemp(suffix='.nc')
with OrthogonalMultidimensionalTimeseries(self.single) as s:
df = s.to_dataframe()
with OrthogonalMultidimensionalTimeseries.from_dataframe(
df, single_tmp) as result_ncd:
assert 'station' in result_ncd.dimensions
test_is_mine(OrthogonalMultidimensionalTimeseries,
single_tmp) # Try to load it again
os.close(fid)
os.remove(single_tmp)
def test_timeseries_omt_reduce_dims(self):
fid, single_tmp = tempfile.mkstemp(suffix='.nc')
with OrthogonalMultidimensionalTimeseries(self.single) as s:
df = s.to_dataframe()
with OrthogonalMultidimensionalTimeseries.from_dataframe(
df, single_tmp, reduce_dims=True) as result_ncd:
assert 'station' not in result_ncd.dimensions
assert np.ma.allclose(result_ncd.variables['pH'][:].flatten(),
self.ph)
test_is_mine(OrthogonalMultidimensionalTimeseries,
single_tmp) # Try to load it again
os.close(fid)
os.remove(single_tmp)
def test_timeseries_omt_dataframe_multi(self):
fid, single_tmp = tempfile.mkstemp(suffix='.nc')
with OrthogonalMultidimensionalTimeseries(self.multi) as s:
df = s.to_dataframe()
with OrthogonalMultidimensionalTimeseries.from_dataframe(
df, single_tmp) as result_ncd:
assert 'station' in result_ncd.dimensions
assert np.ma.allclose(
result_ncd.variables['temperature'][0, 0:7].flatten(),
[
18.61804, 13.2165, 39.30018, 17.00865, 24.95154,
35.99525, 24.33436
],
)
test_is_mine(OrthogonalMultidimensionalTimeseries,
single_tmp) # Try to load it again
os.close(fid)
os.remove(single_tmp)
def test_timeseries_omt_no_z(self):
fid, single_tmp = tempfile.mkstemp(suffix='.nc')
with OrthogonalMultidimensionalTimeseries(self.single) as s:
df = s.to_dataframe()
axes = {'z': None}
df.drop(columns=['z'], inplace=True)
with OrthogonalMultidimensionalTimeseries.from_dataframe(
df,
single_tmp,
axes=axes,
) as result_ncd:
assert 'station' in result_ncd.dimensions
assert 'z' not in result_ncd.variables
assert np.ma.allclose(result_ncd.variables['pH'][:].flatten(),
self.ph)
test_is_mine(OrthogonalMultidimensionalTimeseries,
single_tmp) # Try to load it again
os.close(fid)
os.remove(single_tmp)