def test_run_catchmod_subcatchment_flows():
""" test the subcatchment flows returned sum to the returned total flows """
catchment = catchment_from_json(os.path.join(os.path.dirname(__file__),
"data", "thames.json"),
n=2)
dates = pandas.date_range("1920-01-01", periods=200, freq="D")
rainfall = np.random.randint(0, 20, [200, 2])
pet = np.random.randint(0, 5, [200, 2])
flow_total = run_catchmod(catchment,
rainfall,
pet,
dates,
output_total=True)
flow_subcatchment = run_catchmod(catchment,
rainfall,
pet,
dates,
output_total=False)
np.testing.assert_allclose(flow_total, flow_subcatchment.sum(axis=2))
# test correct flow reshape in __main__
flow_reshape = flow_subcatchment.reshape(
(len(dates), len(catchment.subcatchments) * 2), order='C')
np.testing.assert_allclose(flow_reshape[:, 0], flow_subcatchment[:, 0, 0])
np.testing.assert_allclose(flow_reshape[:, 3], flow_subcatchment[:, 1, 0])
def test_run_catchmod(leap, total_flows):
""" Test the shape of output is correct if when there are leap dates/no leap days
and for returning total flows or individual subcatchment flows
"""
shape = [200, 4]
catchment = catchment_from_json(os.path.join(os.path.dirname(__file__),
"data", "thames.json"),
n=shape[1])
dates = pandas.date_range("1920-01-01", periods=200, freq="D")
if leap:
# remove input data for leap days
num_leap = sum(
[1 for date in dates if (date.month == 2 and date.day == 29)])
shape[0] -= num_leap
rainfall = np.ones(shape)
pet = np.zeros(shape)
flow = run_catchmod(catchment, rainfall, pet, dates, total_flows)
assert (flow.shape[0] == len(dates))
if total_flows:
assert (len(flow.shape) == 2)
assert (flow.shape[1] == rainfall.shape[1])
else:
assert (len(flow.shape) == 3)
assert (flow.shape[2] == len(catchment.subcatchments))
assert (flow.shape[1] == rainfall.shape[1])
def test_run_catchmod(leap):
shape = [200, 3]
catchment = catchment_from_json(os.path.join(os.path.dirname(__file__), "data", "thames.json"), n=shape[1])
dates = pandas.date_range("1920-01-01", periods=200, freq="D")
if leap:
# remove input data for leap days
num_leap = sum([1 for date in dates if (date.month == 2 and date.day == 29)])
shape[0] -= num_leap
rainfall = np.ones(shape)
pet = np.zeros(shape)
flow = run_catchmod(catchment, rainfall, pet, dates)
assert(flow.shape[0] == len(dates))
assert(flow.shape[1] == rainfall.shape[1])
def run(ctx, parameters, rainfall, pet, output):
import pandas
import numpy as np
from pycatchmod.io.json import catchment_from_json
from pycatchmod.io.excel import excel_parameter_adjustment
from pycatchmod import run_catchmod
# load rainfall and pet data
rainfall = pandas_read(rainfall)
pet = pandas_read(pet)
num_scenarios = rainfall.shape[1]
# load catchmod model
catchment = catchment_from_json(parameters, n=num_scenarios)
excel_parameter_adjustment(catchment)
idx0 = rainfall.index[0]
idxN = rainfall.index[-1]
dates = pandas.date_range(idx0, idxN, freq="D")
# run catchmod
flows = run_catchmod(catchment, rainfall.values, pet.values, dates=dates)
print("Shape", flows.shape)
df = pandas.DataFrame(flows, index=dates)
df.index.name = "Date"
# write output
if output.endswith((".h5", ".hdf", ".hdf5")):
df.to_hdf(output, key="flows", compress="zlib")
elif output.endswith(".csv"):
df.to_csv(output)
else:
raise ValueError("Unrecognised output format.")
print("Results:", output)
def run(ctx, parameters, rainfall, pet, output):
import pandas
import numpy as np
from pycatchmod.io.json import catchment_from_json
from pycatchmod.io.excel import excel_parameter_adjustment
from pycatchmod import run_catchmod
# load rainfall and pet data
rainfall = pandas_read(rainfall)
pet = pandas_read(pet)
num_scenarios = rainfall.shape[1]
# load catchmod model
catchment = catchment_from_json(parameters, n=num_scenarios)
excel_parameter_adjustment(catchment)
idx0 = rainfall.index[0]
idxN = rainfall.index[-1]
dates = pandas.date_range(idx0, idxN, freq="D")
# run catchmod
flows = run_catchmod(catchment, rainfall.values, pet.values, dates=dates)
print("Shape", flows.shape)
df = pandas.DataFrame(flows, index=rainfall.index)
df.index.name = "Date"
# write output
if output.endswith((".h5", ".hdf", ".hdf5")):
df.to_hdf(output, key="flows", compress="zlib")
elif output.endswith(".csv"):
df.to_csv(output)
else:
raise ValueError("Unrecognised output format.")
print("Results:", output)
def test_json(thames_json):
catchment = catchment_from_json(thames_json)
assert (catchment.name == 'Thames')
assert (len(catchment.subcatchments) == 3)
assert (catchment.subcatchments[0].area == 3900.0)
def run(ctx, parameters, rainfall, pet, total, output, output_key,
rainfall_key, pet_key, complib, complevel, output_mode):
import pandas
import numpy as np
from pycatchmod.io.json import catchment_from_json
from pycatchmod.io.excel import excel_parameter_adjustment
from pycatchmod import run_catchmod
print("Loading input data...")
# load rainfall and pet data
rainfall = pandas_read(rainfall, rainfall_key)
pet = pandas_read(pet, pet_key)
num_scenarios = rainfall.shape[1]
# load catchmod model
catchment = catchment_from_json(parameters, n=num_scenarios)
excel_parameter_adjustment(catchment)
idx0 = rainfall.index[0]
idxN = rainfall.index[-1]
dates = pandas.period_range(idx0, idxN, freq="D")
# run catchmod
print("Running catchmod...")
flows = run_catchmod(catchment,
rainfall.values,
pet.values,
dates=dates,
output_total=total)
print("Shape:", flows.shape)
if total:
df = pandas.DataFrame(flows, index=dates)
df.index.name = "Date"
else:
flows = flows.reshape(
(len(dates), len(catchment.subcatchments) * num_scenarios),
order='C')
df = pandas.DataFrame(
flows,
columns=pandas.MultiIndex.from_product([
range(num_scenarios),
[x.name for x in catchment.subcatchments]
],
names=('Scenarios',
'Subcatchments')),
index=dates)
# write output
if output.endswith((".h5", ".hdf", ".hdf5")):
df.to_hdf(output,
key=output_key,
mode=output_mode,
complib=complib,
complevel=complevel)
elif output.endswith(".csv"):
# workaround for bug in pandas
# see https://github.com/pandas-dev/pandas/issues/15982
df.reset_index().to_csv(output, index=False)
else:
raise ValueError("Unrecognised output format.")
print("Results:", output)
def test_json(thames_json):
catchment = catchment_from_json(thames_json)
assert(catchment.name == 'Thames')
assert(len(catchment.subcatchments) == 3)
assert(catchment.subcatchments[0].area == 3900.0)