def test_multi_agg(self):
expected_cols = [
'medDry_B', 'aveDry_B', 'maxDry_B',
'minWet_B', 'aveWet_M', 'maxWet_M',
]
wq_cols = [
('Dry_B', 'medIAN'),
('Dry_B',),
('Dry_B', 'MAXIMUM'),
('Wet_B', 'minIMUM'),
('Wet_M',),
('Wet_M', 'MAXIMUM'),
]
with utils.OverwriteState(True), utils.WorkSpace(self.ws):
wq, cols = analysis.preprocess_wq(
monitoring_locations=self.ml,
subcatchments=self.sc,
id_col='CID',
ds_col='DS_CID',
output_path=self.results,
value_columns=wq_cols
)
expected = 'expected_multi_agg.shp'
pptest.assert_shapefiles_are_close(
os.path.join(self.ws, expected),
os.path.join(self.ws, self.results),
)
nt.assert_true(isinstance(wq, numpy.ndarray))
nt.assert_list_equal(cols, expected_cols)
def test_no_wq_col_error(self):
with utils.OverwriteState(True), utils.WorkSpace(self.ws):
wq, cols = analysis.preprocess_wq(
monitoring_locations=self.ml,
subcatchments=self.sc,
id_col='CID',
ds_col='DS_CID',
output_path=self.results,
)
def test_baseline(self):
with utils.OverwriteState(True), utils.WorkSpace(self.ws):
wq, cols = analysis.preprocess_wq(
monitoring_locations=self.ml,
subcatchments=self.sc,
id_col='CID',
ds_col='DS_CID',
output_path=self.results,
value_columns=self.wq_cols
)
expected = 'expected.shp'
pptest.assert_shapefiles_are_close(
os.path.join(self.ws, expected),
os.path.join(self.ws, self.results),
)
nt.assert_true(isinstance(wq, numpy.ndarray))
nt.assert_list_equal(cols, self.expected_cols)
def propagate(subcatchments=None,
id_col=None,
ds_col=None,
monitoring_locations=None,
ml_filter=None,
ml_filter_cols=None,
value_columns=None,
streams=None,
output_path=None,
verbose=False,
asMessage=False):
"""
Propagate water quality scores upstream from the subcatchments of
a watershed.
Parameters
----------
subcatchments : str
Path to the feature class containing the subcatchments.
Attribute table must contain fields for the subcatchment ID
and the ID of the downstream subcatchment.
id_col, ds_col : str
Names of the fields in the ``subcatchments`` feature class that
specifies the subcatchment ID and the ID of the downstream
subcatchment, respectively.
monitoring_locations : str
Path to the feature class containing the monitoring locations
and water quality scores.
value_columns : list of str
List of the fields in ``monitoring_locations`` that contains
water quality score that should be propagated.
ml_filter : callable, optional
Function used to exclude (remove) monitoring locations from
from aggregation/propagation.
ml_filter_cols : str, optional
Name of any additional columns in ``monitoring_locations`` that
are required to use ``ml_filter``.
streams : str
Path to the feature class containing the streams.
output_path : str
Path to where the the new subcatchments feature class with the
propagated water quality scores should be saved.
Returns
-------
output_path : str
Examples
--------
>>> import propagator
>>> from propagator import utils
>>> with utils.WorkSpace('C:/gis/SOC.gdb'):
... propagator.propagate(
... subcatchments='subbasins',
... id_col='Catch_ID',
... ds_col='DS_ID',
... monitoring_locations='wq_data',
... value_columns=['Dry_Metals', 'Wet_Metals', 'Wet_TSS'],
... ml_filter=lambda row: row['StationType'] != 'Coastal',
... ml_filter_cols=['StationType'],
... streams='SOC_streams',
... output_path='propagated_metals'
... )
See also
--------
propagator.analysis.preprocess_wq
propagator.analysis.mark_edges
propagator.analysis.propagate_scores
propagator.analysis.aggregate_streams_by_subcatchment
propagator.utils.update_attribute_table
"""
subcatchment_output = utils.add_suffix_to_filename(output_path,
'subcatchments')
stream_output = utils.add_suffix_to_filename(output_path, 'streams')
wq, result_columns = analysis.preprocess_wq(
monitoring_locations=monitoring_locations,
ml_filter=ml_filter,
ml_filter_cols=ml_filter_cols,
subcatchments=subcatchments,
value_columns=value_columns,
id_col=id_col,
ds_col=ds_col,
output_path=subcatchment_output,
verbose=verbose,
asMessage=asMessage,
msg="Aggregating water quality data in subcatchments")
wq = analysis.mark_edges(
wq,
id_col=id_col,
ds_col=ds_col,
edge_ID='EDGE',
verbose=verbose,
asMessage=asMessage,
msg="Marking all subcatchments that flow out of the watershed")
for n, res_col in enumerate(result_columns, 1):
wq = analysis.propagate_scores(
subcatchment_array=wq,
id_col=id_col,
ds_col=ds_col,
value_column=res_col,
edge_ID='EDGE',
verbose=verbose,
asMessage=asMessage,
msg="{} of {}: Propagating {} scores".format(
n, len(result_columns), res_col))
utils.update_attribute_table(subcatchment_output, wq, id_col,
result_columns)
stream_output = analysis.aggregate_streams_by_subcatchment(
stream_layer=streams,
subcatchment_layer=subcatchment_output,
id_col=id_col,
ds_col=ds_col,
other_cols=result_columns,
agg_method='first',
output_layer=stream_output,
verbose=verbose,
asMessage=asMessage,
msg='Aggregating and associating scores with streams.',
)
return subcatchment_output, stream_output