def test_update_rapid_input_file():
"""
Checks RAPID input file update with valid input
"""
print("TEST 2: UPDATE NAMELIST FILE")
rapid_manager = RAPID(rapid_executable_location=RAPID_EXE_PATH,
cygwin_bin_location=CYGWIN_BIN_PATH,
use_all_processors=True,
)
rapid_manager.update_parameters(rapid_connect_file='rapid_connect.csv',
Vlat_file='m3_riv.nc',
riv_bas_id_file='riv_bas_id.csv',
k_file='k.csv',
x_file='x.csv',
Qout_file='Qout.nc'
)
original_input_file = os.path.join(INPUT_DATA_PATH,
"rapid_namelist_valid")
updated_input_file = os.path.join(OUTPUT_DATA_PATH,
"rapid_namelist-UPDATE")
copy(original_input_file, updated_input_file)
rapid_manager.update_namelist_file(updated_input_file)
updated_input_file_solution = os.path.join(COMPARE_DATA_PATH,
"rapid_namelist-UPDATE")
ok_(fcmp(updated_input_file, updated_input_file_solution))
remove_files(updated_input_file)
def test_download_usgs_daily_avg():
"""
This tests downloading USGS daily avg data
"""
print("TEST 12: TEST DOWNLOAD USGS DAILY AVERAGE DATA")
out_streamflow_file = os.path.join(OUTPUT_DATA_PATH, "gage_streamflow.csv")
out_stream_id_file = os.path.join(OUTPUT_DATA_PATH, "gage_rivid.csv")
rapid_manager = RAPID(rapid_executable_location=RAPID_EXE_PATH,
cygwin_bin_location=CYGWIN_BIN_PATH)
rapid_manager.generate_usgs_avg_daily_flows_opt(
reach_id_gage_id_file=os.path.join(INPUT_DATA_PATH,
"usgs_gage_id_rivid.csv"),
start_datetime=datetime(2000, 1, 1),
end_datetime=datetime(2000, 1, 3),
out_streamflow_file=out_streamflow_file,
out_stream_id_file=out_stream_id_file)
compare_streamflow_file = os.path.join(COMPARE_DATA_PATH,
"gage_streamflow.csv")
assert (compare_csv_decimal_files(out_streamflow_file,
compare_streamflow_file,
header=False))
compare_stream_id_file = os.path.join(COMPARE_DATA_PATH, "gage_rivid.csv")
assert (compare_csv_decimal_files(out_stream_id_file,
compare_stream_id_file,
header=False))
remove_files(out_streamflow_file, out_stream_id_file)
def test_generate_rapid_input_file():
"""
Checks RAPID input file generation with valid input
"""
print("TEST 1: GENERATE NAMELIST FILE")
rapid_manager = RAPID(rapid_executable_location=RAPID_EXE_PATH,
cygwin_bin_location=CYGWIN_BIN_PATH,
use_all_processors=True,
ZS_TauR = 24*3600, #duration of routing procedure (time step of runoff data)
ZS_dtR = 15*60, #internal routing time step
ZS_TauM = 12*24*3600, #total simulation time
ZS_dtM = 24*3600 #input time step
)
rapid_manager.update_parameters(rapid_connect_file='rapid_connect.csv',
Vlat_file='m3_riv.nc',
riv_bas_id_file='riv_bas_id.csv',
k_file='k.csv',
x_file='x.csv',
Qout_file='Qout.nc'
)
generated_input_file = os.path.join(OUTPUT_DATA_PATH,
"rapid_namelist-GENERATE")
rapid_manager.generate_namelist_file(generated_input_file)
generated_input_file_solution = os.path.join(COMPARE_DATA_PATH,
"rapid_namelist-GENERATE")
ok_(fcmp(generated_input_file, generated_input_file_solution))
remove_files(generated_input_file)
def test_download_usgs_daily_avg():
"""
This tests downloading USGS daily avg data
"""
print("TEST 12: TEST DOWNLOAD USGS DAILY AVERAGE DATA")
out_streamflow_file=os.path.join(OUTPUT_DATA_PATH,"gage_streamflow.csv")
out_stream_id_file=os.path.join(OUTPUT_DATA_PATH,"gage_rivid.csv")
rapid_manager = RAPID(rapid_executable_location=RAPID_EXE_PATH,
cygwin_bin_location=CYGWIN_BIN_PATH)
rapid_manager.generate_usgs_avg_daily_flows_opt(reach_id_gage_id_file=os.path.join(INPUT_DATA_PATH,"usgs_gage_id_rivid.csv"),
start_datetime=datetime(2000,1,1),
end_datetime=datetime(2000,1,3),
out_streamflow_file=out_streamflow_file,
out_stream_id_file=out_stream_id_file)
compare_streamflow_file=os.path.join(COMPARE_DATA_PATH,"gage_streamflow.csv")
ok_(compare_csv_decimal_files(out_streamflow_file, compare_streamflow_file, header=False))
compare_stream_id_file=os.path.join(COMPARE_DATA_PATH,"gage_rivid.csv")
ok_(compare_csv_decimal_files(out_stream_id_file, compare_stream_id_file, header=False))
remove_files(out_streamflow_file,
out_stream_id_file)
def test_generate_qinit_file():
"""
This tests the qinit file function to create an input qinit file for RAPID
"""
print("TEST 11: TEST GENERATE QINIT FILE")
rapid_manager = RAPID(rapid_executable_location=RAPID_EXE_PATH,
cygwin_bin_location=CYGWIN_BIN_PATH,
rapid_connect_file=os.path.join(
INPUT_DATA_PATH, 'rapid_connect.csv'))
#test with original rapid outpur
input_qout_file = os.path.join(COMPARE_DATA_PATH,
'Qout_nasa_lis_3hr_20020830.nc')
original_qout_file = os.path.join(OUTPUT_DATA_PATH,
'Qout_nasa_lis_3hr_20020830.nc')
copy(input_qout_file, original_qout_file)
qinit_original_rapid_qout = os.path.join(OUTPUT_DATA_PATH,
'qinit_original_rapid_qout.csv')
rapid_manager.update_parameters(Qout_file=original_qout_file)
rapid_manager.generate_qinit_from_past_qout(
qinit_file=qinit_original_rapid_qout)
qinit_original_rapid_qout_solution = os.path.join(
COMPARE_DATA_PATH, 'qinit_original_rapid_qout.csv')
ok_(
compare_csv_decimal_files(qinit_original_rapid_qout,
qinit_original_rapid_qout_solution,
header=False))
#test with CF rapid output and alternate time index
cf_input_qout_file = os.path.join(COMPARE_DATA_PATH,
'Qout_nasa_lis_3hr_20020830_CF.nc')
cf_qout_file = os.path.join(OUTPUT_DATA_PATH,
'Qout_nasa_lis_3hr_20020830_CF.nc')
copy(cf_input_qout_file, cf_qout_file)
qinit_cf_rapid_qout = os.path.join(OUTPUT_DATA_PATH,
'qinit_cf_rapid_qout.csv')
rapid_manager.update_parameters(Qout_file=cf_qout_file)
rapid_manager.generate_qinit_from_past_qout(qinit_file=qinit_cf_rapid_qout,
time_index=5)
qinit_cf_rapid_qout_solution = os.path.join(COMPARE_DATA_PATH,
'qinit_cf_rapid_qout.csv')
ok_(
compare_csv_decimal_files(qinit_cf_rapid_qout,
qinit_cf_rapid_qout_solution,
header=False))
remove_files(original_qout_file, qinit_original_rapid_qout, cf_qout_file,
qinit_cf_rapid_qout)
def test_convert_file_to_be_cf_compliant_new_format_comid_lat_lon_z():
"""
Test Convert RAPID Output to be CF Compliant for new format with COMID_LAT_LON_Z
"""
print(
"TEST 8: TEST CONVERT RAPID OUTPUT TO CF COMPLIANT (COMID_LAT_LON_Z)")
input_qout_file = os.path.join(COMPARE_DATA_PATH,
'Qout_nasa_lis_3hr_20020830.nc')
temp_qout_file = os.path.join(
OUTPUT_DATA_PATH, 'Qout_nasa_lis_3hr_20020830_test_cf_lat_lon_z.nc')
copy(input_qout_file, temp_qout_file)
rapid_manager = RAPID(rapid_executable_location=RAPID_EXE_PATH,
cygwin_bin_location=CYGWIN_BIN_PATH,
Qout_file=temp_qout_file,
rapid_connect_file=os.path.join(
INPUT_DATA_PATH, 'rapid_connect.csv'),
ZS_TauR=3 * 3600)
rapid_manager.make_output_cf_compliant(
simulation_start_datetime=datetime(2002, 8, 30),
comid_lat_lon_z_file=os.path.join(INPUT_DATA_PATH,
'comid_lat_lon_z.csv'),
project_name=
"ERA Interim (T511 Grid) 3 Hourly Runoff Based Historical flows by US Army ERDC"
)
cf_qout_file_solution = os.path.join(COMPARE_DATA_PATH,
'Qout_nasa_lis_3hr_20020830_CF.nc')
#check Qout
assert (compare_qout_files(temp_qout_file, cf_qout_file_solution))
#check other info in netcdf file
d1 = Dataset(temp_qout_file)
d2 = Dataset(cf_qout_file_solution)
# MPG: new dimensions have been introduced in RAPID. We only test for those # included in the original benchmarks.
for dim in ['time', 'rivid']:
assert (dim in d1.dimensions.keys())
# MPG: new variables have been introduced in RAPID. We only test for those
# included in the original benchmarks.
for v in [u'Qout', u'rivid', u'time', u'lon', u'lat', u'crs']:
assert (v in d1.variables.keys())
assert ((d1.variables['time'][:] == d2.variables['time'][:]).all())
assert ((d1.variables['rivid'][:] == d2.variables['rivid'][:]).all())
assert ((d1.variables['lat'][:] == d2.variables['lat'][:]).all())
assert ((d1.variables['lon'][:] == d2.variables['lon'][:]).all())
d1.close()
d2.close()
remove_files(temp_qout_file)
def test_update_rapid_numbers_input_file():
"""
Checks RAPID input file update with number validation
"""
print("TEST 4: GENERATE NUMBERS FOR NAMELIST FILE")
rapid_manager = RAPID(
rapid_executable_location=RAPID_EXE_PATH,
cygwin_bin_location=CYGWIN_BIN_PATH,
use_all_processors=True,
rapid_connect_file=os.path.join(INPUT_DATA_PATH, 'rapid_connect.csv'),
riv_bas_id_file=os.path.join(INPUT_DATA_PATH, 'riv_bas_id.csv'),
)
rapid_manager.update_reach_number_data()
rapid_manager.update_parameters(rapid_connect_file='rapid_connect.csv',
Vlat_file='m3_nasa_lis_3hr_20020830.nc',
riv_bas_id_file='riv_bas_id.csv',
k_file='k.csv',
x_file='x.csv',
Qout_file='Qout.nc')
generated_input_file = os.path.join(OUTPUT_DATA_PATH,
"rapid_namelist-GENERATE-NUMBERS")
rapid_manager.generate_namelist_file(generated_input_file)
generated_input_file_solution = os.path.join(
COMPARE_DATA_PATH, "rapid_namelist-GENERATE-NUMBERS")
assert (fcmp(generated_input_file, generated_input_file_solution))
remove_files(generated_input_file)
def test_convert_file_to_be_cf_compliant_original_format():
"""
Test Convert RAPID Output to be CF Compliant for original format
"""
print(
"TEST 10: TEST CONVERT RAPID OUTPUT TO CF COMPLIANT - ORIGINAL (COMID_LAT_LON_Z)"
)
input_qout_file = os.path.join(COMPARE_DATA_PATH,
'Qout_nasa_lis_3hr_20020830_original.nc')
temp_qout_file = os.path.join(
OUTPUT_DATA_PATH, 'Qout_nasa_lis_3hr_20020830_original_test_cf.nc')
copy(input_qout_file, temp_qout_file)
rapid_manager = RAPID(rapid_executable_location=RAPID_EXE_PATH,
cygwin_bin_location=CYGWIN_BIN_PATH,
Qout_file=temp_qout_file,
rapid_connect_file=os.path.join(
INPUT_DATA_PATH, 'rapid_connect.csv'),
ZS_TauR=3 * 3600)
rapid_manager.make_output_CF_compliant(
simulation_start_datetime=datetime(2002, 8, 30),
comid_lat_lon_z_file=os.path.join(INPUT_DATA_PATH,
'comid_lat_lon_z.csv'),
project_name=
"ERA Interim (T511 Grid) 3 Hourly Runoff Based Historical flows by US Army ERDC"
)
cf_qout_file_solution = os.path.join(COMPARE_DATA_PATH,
'Qout_nasa_lis_3hr_20020830_CF.nc')
#check Qout
assert (compare_qout_files(temp_qout_file, cf_qout_file_solution))
#check other info in netcdf file
d1 = Dataset(temp_qout_file)
d2 = Dataset(cf_qout_file_solution)
assert (d1.dimensions.keys() == d2.dimensions.keys())
assert (d1.variables.keys() == d2.variables.keys())
assert ((d1.variables['time'][:] == d2.variables['time'][:]).all())
assert ((d1.variables['rivid'][:] == d2.variables['rivid'][:]).all())
assert ((d1.variables['lat'][:] == d2.variables['lat'][:]).all())
assert ((d1.variables['lon'][:] == d2.variables['lon'][:]).all())
d1.close()
d2.close()
remove_files(temp_qout_file)
def test_update_rapid_numbers_input_file():
"""
Checks RAPID input file update with number validation
"""
print("TEST 4: GENERATE NUMBERS FOR NAMELIST FILE")
rapid_manager = RAPID(rapid_executable_location=RAPID_EXE_PATH,
cygwin_bin_location=CYGWIN_BIN_PATH,
use_all_processors=True,
rapid_connect_file=os.path.join(INPUT_DATA_PATH, 'rapid_connect.csv'),
riv_bas_id_file=os.path.join(INPUT_DATA_PATH, 'riv_bas_id.csv'),
)
rapid_manager.update_reach_number_data()
rapid_manager.update_parameters(rapid_connect_file='rapid_connect.csv',
Vlat_file='m3_nasa_lis_3hr_20020830.nc',
riv_bas_id_file='riv_bas_id.csv',
k_file='k.csv',
x_file='x.csv',
Qout_file='Qout.nc'
)
generated_input_file = os.path.join(OUTPUT_DATA_PATH,
"rapid_namelist-GENERATE-NUMBERS")
rapid_manager.generate_namelist_file(generated_input_file)
generated_input_file_solution = os.path.join(COMPARE_DATA_PATH,
"rapid_namelist-GENERATE-NUMBERS")
ok_(fcmp(generated_input_file, generated_input_file_solution))
remove_files(generated_input_file)
def test_run_rapid_simulation():
"""
Test Running RAPID Simulation
"""
print("TEST 7: TEST RUNNING RAPID SIMULATION")
generated_qout_file = os.path.join(OUTPUT_DATA_PATH, 'Qout_nasa_lis_3hr_20020830_generated.nc')
rapid_manager = RAPID(rapid_executable_location=RAPID_EXE_PATH,
cygwin_bin_location=CYGWIN_BIN_PATH,
num_processors=1,
rapid_connect_file=os.path.join(INPUT_DATA_PATH, 'rapid_connect.csv'),
riv_bas_id_file=os.path.join(INPUT_DATA_PATH, 'riv_bas_id.csv'),
Vlat_file=os.path.join(INPUT_DATA_PATH, 'm3_nasa_lis_3hr_20020830.nc'),
k_file=os.path.join(INPUT_DATA_PATH, 'k.csv'),
x_file=os.path.join(INPUT_DATA_PATH, 'x.csv'),
ZS_dtM=10800,
ZS_dtR=900,
ZS_TauM=2*86400,
ZS_TauR=10800,
Qout_file=generated_qout_file
)
rapid_manager.update_reach_number_data()
rapid_manager.run()
generated_qout_file_solution = os.path.join(COMPARE_DATA_PATH,
'Qout_nasa_lis_3hr_20020830.nc')
#check Qout
ok_(compare_qout_files(generated_qout_file, generated_qout_file_solution))
#check other info in netcdf file
d1 = Dataset(generated_qout_file)
d2 = Dataset(generated_qout_file_solution)
ok_(d1.dimensions.keys() == d2.dimensions.keys())
ok_(d1.variables.keys() == d2.variables.keys())
ok_((d1.variables['rivid'][:] == d2.variables['rivid'][:]).all())
d1.close()
d2.close()
remove_files(generated_qout_file)
def test_generate_rapid_input_file():
"""
Checks RAPID input file generation with valid input
"""
print("TEST 1: GENERATE NAMELIST FILE")
rapid_manager = RAPID(
rapid_executable_location=RAPID_EXE_PATH,
cygwin_bin_location=CYGWIN_BIN_PATH,
use_all_processors=True,
ZS_TauR=24 *
3600, #duration of routing procedure (time step of runoff data)
ZS_dtR=15 * 60, #internal routing time step
ZS_TauM=12 * 24 * 3600, #total simulation time
ZS_dtM=24 * 3600 #input time step
)
rapid_manager.update_parameters(rapid_connect_file='rapid_connect.csv',
Vlat_file='m3_riv.nc',
riv_bas_id_file='riv_bas_id.csv',
k_file='k.csv',
x_file='x.csv',
Qout_file='Qout.nc')
generated_input_file = os.path.join(OUTPUT_DATA_PATH,
"rapid_namelist-GENERATE")
rapid_manager.generate_namelist_file(generated_input_file)
generated_input_file_solution = os.path.join(COMPARE_DATA_PATH,
"rapid_namelist-GENERATE")
assert (fcmp(generated_input_file, generated_input_file_solution))
remove_files(generated_input_file)
def test_update_rapid_input_file():
"""
Checks RAPID input file update with valid input
"""
print("TEST 2: UPDATE NAMELIST FILE")
rapid_manager = RAPID(
rapid_executable_location=RAPID_EXE_PATH,
cygwin_bin_location=CYGWIN_BIN_PATH,
use_all_processors=True,
)
rapid_manager.update_parameters(rapid_connect_file='rapid_connect.csv',
Vlat_file='m3_riv.nc',
riv_bas_id_file='riv_bas_id.csv',
k_file='k.csv',
x_file='x.csv',
Qout_file='Qout.nc')
original_input_file = os.path.join(INPUT_DATA_PATH, "rapid_namelist_valid")
updated_input_file = os.path.join(OUTPUT_DATA_PATH,
"rapid_namelist-UPDATE")
copy(original_input_file, updated_input_file)
rapid_manager.update_namelist_file(updated_input_file)
updated_input_file_solution = os.path.join(COMPARE_DATA_PATH,
"rapid_namelist-UPDATE")
assert (fcmp(updated_input_file, updated_input_file_solution))
remove_files(updated_input_file)
def test_convert_file_to_be_cf_compliant_original_format():
"""
Test Convert RAPID Output to be CF Compliant for original format
"""
print("TEST 10: TEST CONVERT RAPID OUTPUT TO CF COMPLIANT - ORIGINAL (COMID_LAT_LON_Z)")
input_qout_file = os.path.join(COMPARE_DATA_PATH, 'Qout_nasa_lis_3hr_20020830_original.nc')
temp_qout_file = os.path.join(OUTPUT_DATA_PATH, 'Qout_nasa_lis_3hr_20020830_original_test_cf.nc')
copy(input_qout_file, temp_qout_file)
rapid_manager = RAPID(rapid_executable_location=RAPID_EXE_PATH,
cygwin_bin_location=CYGWIN_BIN_PATH,
Qout_file=temp_qout_file,
rapid_connect_file=os.path.join(INPUT_DATA_PATH, 'rapid_connect.csv'),
ZS_TauR=3*3600)
rapid_manager.make_output_CF_compliant(simulation_start_datetime=datetime(2002, 8, 30),
comid_lat_lon_z_file=os.path.join(INPUT_DATA_PATH, 'comid_lat_lon_z.csv'),
project_name="ERA Interim (T511 Grid) 3 Hourly Runoff Based Historical flows by US Army ERDC")
cf_qout_file_solution = os.path.join(COMPARE_DATA_PATH,
'Qout_nasa_lis_3hr_20020830_CF.nc')
#check Qout
ok_(compare_qout_files(temp_qout_file, cf_qout_file_solution))
#check other info in netcdf file
d1 = Dataset(temp_qout_file)
d2 = Dataset(cf_qout_file_solution)
ok_(d1.dimensions.keys() == d2.dimensions.keys())
ok_(d1.variables.keys() == d2.variables.keys())
ok_((d1.variables['time'][:] == d1.variables['time'][:]).all())
ok_((d1.variables['rivid'][:] == d1.variables['rivid'][:]).all())
ok_((d1.variables['lat'][:] == d1.variables['lat'][:]).all())
ok_((d1.variables['lon'][:] == d1.variables['lon'][:]).all())
d1.close()
d2.close()
remove_files(temp_qout_file)
def test_generate_qinit_file():
"""
This tests the qinit file function to create an input qinit file for RAPID
"""
print("TEST 11: TEST GENERATE QINIT FILE")
rapid_manager = RAPID(rapid_executable_location=RAPID_EXE_PATH,
cygwin_bin_location=CYGWIN_BIN_PATH,
rapid_connect_file=os.path.join(INPUT_DATA_PATH, 'rapid_connect.csv')
)
#test with original rapid outpur
input_qout_file = os.path.join(COMPARE_DATA_PATH, 'Qout_nasa_lis_3hr_20020830.nc')
original_qout_file = os.path.join(OUTPUT_DATA_PATH, 'Qout_nasa_lis_3hr_20020830.nc')
copy(input_qout_file, original_qout_file)
qinit_original_rapid_qout = os.path.join(OUTPUT_DATA_PATH, 'qinit_original_rapid_qout.csv')
rapid_manager.update_parameters(Qout_file=original_qout_file)
rapid_manager.generate_qinit_from_past_qout(qinit_file=qinit_original_rapid_qout)
qinit_original_rapid_qout_solution = os.path.join(COMPARE_DATA_PATH, 'qinit_original_rapid_qout.csv')
ok_(compare_csv_decimal_files(qinit_original_rapid_qout, qinit_original_rapid_qout_solution, header=False))
#test with CF rapid output and alternate time index
cf_input_qout_file = os.path.join(COMPARE_DATA_PATH, 'Qout_nasa_lis_3hr_20020830_CF.nc')
cf_qout_file = os.path.join(OUTPUT_DATA_PATH, 'Qout_nasa_lis_3hr_20020830_CF.nc')
copy(cf_input_qout_file, cf_qout_file)
qinit_cf_rapid_qout = os.path.join(OUTPUT_DATA_PATH, 'qinit_cf_rapid_qout.csv')
rapid_manager.update_parameters(Qout_file=cf_qout_file)
rapid_manager.generate_qinit_from_past_qout(qinit_file=qinit_cf_rapid_qout,
time_index=5)
qinit_cf_rapid_qout_solution = os.path.join(COMPARE_DATA_PATH, 'qinit_cf_rapid_qout.csv')
ok_(compare_csv_decimal_files(qinit_cf_rapid_qout, qinit_cf_rapid_qout_solution, header=False))
remove_files(original_qout_file,
qinit_original_rapid_qout,
cf_qout_file,
qinit_cf_rapid_qout
)
def test_update_rapid_numbers_forcing_input_file():
"""
Checks RAPID input file update with forcing data and number validation
"""
rapid_manager = RAPID(rapid_executable_location=RAPID_EXE_PATH,
cygwin_bin_location=CYGWIN_BIN_PATH,
use_all_processors=True,
rapid_connect_file=os.path.join(INPUT_DATA_PATH,
'rapid_connect.csv'),
riv_bas_id_file=os.path.join(INPUT_DATA_PATH,
'riv_bas_id.csv'),
for_tot_id_file=os.path.join(INPUT_DATA_PATH,
'for_tot_id.csv'),
for_use_id_file=os.path.join(INPUT_DATA_PATH,
'for_use_id.csv'),
ZS_dtF=3 * 60 * 60,
BS_opt_for=True
)
rapid_manager.update_reach_number_data()
rapid_manager.update_parameters(rapid_connect_file='rapid_connect.csv',
Vlat_file='m3_nasa_lis_3hr_20020830.nc',
riv_bas_id_file='riv_bas_id.csv',
k_file='k.csv',
x_file='x.csv',
Qout_file='Qout.nc',
Qfor_file='qfor.csv',
for_tot_id_file='for_tot_id.csv',
for_use_id_file='for_use_id.csv',
)
generated_input_file = os.path.join(OUTPUT_DATA_PATH,
"rapid_namelist-GENERATE-NUMBERS-FORCING")
rapid_manager.generate_namelist_file(generated_input_file)
generated_input_file_solution = os.path.join(COMPARE_DATA_PATH,
"rapid_namelist-GENERATE-NUMBERS-FORCING")
assert (fcmp(generated_input_file, generated_input_file_solution))
remove_files(generated_input_file)
def test_run_rapid_simulation():
"""
Test Running RAPID Simulation
"""
print("TEST 7: TEST RUNNING RAPID SIMULATION")
generated_qout_file = os.path.join(
OUTPUT_DATA_PATH, 'Qout_nasa_lis_3hr_20020830_generated.nc')
rapid_manager = RAPID(
rapid_executable_location=RAPID_EXE_PATH,
cygwin_bin_location=CYGWIN_BIN_PATH,
num_processors=1,
rapid_connect_file=os.path.join(INPUT_DATA_PATH, 'rapid_connect.csv'),
riv_bas_id_file=os.path.join(INPUT_DATA_PATH, 'riv_bas_id.csv'),
Vlat_file=os.path.join(INPUT_DATA_PATH, 'm3_nasa_lis_3hr_20020830.nc'),
k_file=os.path.join(INPUT_DATA_PATH, 'k.csv'),
x_file=os.path.join(INPUT_DATA_PATH, 'x.csv'),
ZS_dtM=10800,
ZS_dtR=900,
ZS_TauM=2 * 86400,
ZS_TauR=10800,
Qout_file=generated_qout_file)
rapid_manager.update_reach_number_data()
rapid_manager.run()
generated_qout_file_solution = os.path.join(
COMPARE_DATA_PATH, 'Qout_nasa_lis_3hr_20020830.nc')
#check Qout
assert (compare_qout_files(generated_qout_file,
generated_qout_file_solution))
#check other info in netcdf file
d1 = Dataset(generated_qout_file)
d2 = Dataset(generated_qout_file_solution)
# MPG: new dimensions have been introduced in RAPID. We only test for those # included in the original benchmarks.
for dim in ['time', 'rivid']:
assert (dim in d1.dimensions.keys())
# MPG: new variables have been introduced in RAPID. We only test for those
# included in the original benchmarks.
for v in [u'Qout', u'rivid', u'time', u'lon', u'lat', u'crs']:
assert (v in d1.variables.keys())
assert ((d1.variables['rivid'][:] == d2.variables['rivid'][:]).all())
d1.close()
d2.close()
remove_files(generated_qout_file)
def test_run_rapid_simulation():
"""
Test Running RAPID Simulation
"""
print("TEST 7: TEST RUNNING RAPID SIMULATION")
generated_qout_file = os.path.join(
OUTPUT_DATA_PATH, 'Qout_nasa_lis_3hr_20020830_generated.nc')
rapid_manager = RAPID(
rapid_executable_location=RAPID_EXE_PATH,
cygwin_bin_location=CYGWIN_BIN_PATH,
num_processors=1,
rapid_connect_file=os.path.join(INPUT_DATA_PATH, 'rapid_connect.csv'),
riv_bas_id_file=os.path.join(INPUT_DATA_PATH, 'riv_bas_id.csv'),
Vlat_file=os.path.join(INPUT_DATA_PATH, 'm3_nasa_lis_3hr_20020830.nc'),
k_file=os.path.join(INPUT_DATA_PATH, 'k.csv'),
x_file=os.path.join(INPUT_DATA_PATH, 'x.csv'),
ZS_dtM=10800,
ZS_dtR=900,
ZS_TauM=2 * 86400,
ZS_TauR=10800,
Qout_file=generated_qout_file)
rapid_manager.update_reach_number_data()
rapid_manager.run()
generated_qout_file_solution = os.path.join(
COMPARE_DATA_PATH, 'Qout_nasa_lis_3hr_20020830.nc')
#check Qout
ok_(compare_qout_files(generated_qout_file, generated_qout_file_solution))
#check other info in netcdf file
d1 = Dataset(generated_qout_file)
d2 = Dataset(generated_qout_file_solution)
ok_(d1.dimensions.keys() == d2.dimensions.keys())
ok_(d1.variables.keys() == d2.variables.keys())
ok_((d1.variables['rivid'][:] == d2.variables['rivid'][:]).all())
d1.close()
d2.close()
remove_files(generated_qout_file)
def ecmwf_rapid_multiprocess_worker(node_path, rapid_input_directory,
ecmwf_forecast, forecast_date_timestep,
watershed, subbasin, rapid_executable_location,
init_flow, initialization_time_step, conversion_flag, # modified this line CJB 20190218
# MJS I might consider a netCDF4.Dataset.variables['RO'].units check in SPT,
# and a correction of units attributes in the upstream cdo processing.
#
# dam arguments added, MJS 8/23/2020
BS_opt_dam,IS_dam_tot,IS_dam_use,
dam_tot_id_file,dam_use_id_file,dam_file):
"""
Multiprocess worker function
"""
print("In ecmwf_rapid_multiprocess_worker")
time_start_all = datetime.datetime.utcnow()
os.chdir(node_path)
ensemble_number = get_ensemble_number_from_forecast(ecmwf_forecast)
def remove_file(file_name):
"""
remove file
"""
try:
os.remove(file_name)
except OSError:
pass
#prepare ECMWF file for RAPID
print("INFO: Running all ECMWF downscaling for watershed: {0}-{1} {2} {3}"
.format(watershed,
subbasin,
forecast_date_timestep,
ensemble_number))
#set up RAPID manager
rapid_connect_file=case_insensitive_file_search(rapid_input_directory,
r'rapid_connect\.csv')
rapid_manager = RAPID(
rapid_executable_location=rapid_executable_location,
rapid_connect_file=rapid_connect_file,
riv_bas_id_file=case_insensitive_file_search(rapid_input_directory,
r'riv_bas_id.*?\.csv'),
k_file=case_insensitive_file_search(rapid_input_directory,
r'k\.csv'),
x_file=case_insensitive_file_search(rapid_input_directory,
r'x\.csv'),
ZS_dtM=3*60*60, #RAPID internal loop time interval
)
# check for forcing flows
try:
rapid_manager.update_parameters(
Qfor_file=case_insensitive_file_search(rapid_input_directory,
r'qfor\.csv'),
for_tot_id_file=case_insensitive_file_search(rapid_input_directory,
r'for_tot_id\.csv'),
for_use_id_file=case_insensitive_file_search(rapid_input_directory,
r'for_use_id\.csv'),
ZS_dtF=3*60*60, # forcing time interval
BS_opt_for=True
)
except Exception:
print('WARNING: Forcing files not found. Skipping forcing ...')
pass
rapid_manager.update_reach_number_data()
outflow_file_name = os.path.join(node_path,
'Qout_%s_%s_%s.nc' % (watershed.lower(),
subbasin.lower(),
ensemble_number))
qinit_file = ""
BS_opt_Qinit = False
print(init_flow)
if(init_flow):
#check for qinit file
past_date = (datetime.datetime.strptime(forecast_date_timestep[:11],"%Y%m%d.%H") - \
# datetime.timedelta(hours=initialization_time_step)).strftime("%Y%m%dt%H")
datetime.timedelta(hours=12)).strftime("%Y%m%dt%H")
print("Past date:" ,past_date)
qinit_file = os.path.join(rapid_input_directory, 'Qinit_%s.csv' % past_date)
BS_opt_Qinit = qinit_file and os.path.exists(qinit_file)
if not BS_opt_Qinit:
print("Error: {0} not found. Not initializing ...".format(qinit_file))
qinit_file = ""
try:
comid_lat_lon_z_file = case_insensitive_file_search(rapid_input_directory,
r'comid_lat_lon_z.*?\.csv')
except Exception:
comid_lat_lon_z_file = ""
print("WARNING: comid_lat_lon_z_file not found. Not adding lat/lon/z to output file ...")
RAPIDinflowECMWF_tool = CreateInflowFileFromECMWFRunoff()
forecast_resolution = RAPIDinflowECMWF_tool.dataIdentify(ecmwf_forecast)
time_step_count = RAPIDinflowECMWF_tool.getTimeSize(ecmwf_forecast) #new line 20190108 CJB
print(forecast_resolution)
"""
NOTE: time_step_count is the total count of forecast steps, not periods
""" # 20190108 CJB
#determine weight table from resolution
if forecast_resolution == "HRES136": #new line 20190108 CJB 150 <= T <= 240
#if forecast_resolution == "HighRes":
#HIGH RES
grid_name = RAPIDinflowECMWF_tool.getGridName(ecmwf_forecast, high_res=True)
#generate inflows for each timestep
weight_table_file = case_insensitive_file_search(rapid_input_directory,
r'weight_{0}\.csv'.format(grid_name))
inflow_file_name_1hr = os.path.join(node_path, 'm3_riv_bas_1hr_%s.nc' % ensemble_number)
inflow_file_name_3hr = os.path.join(node_path, 'm3_riv_bas_3hr_%s.nc' % ensemble_number)
inflow_file_name_6hr = os.path.join(node_path, 'm3_riv_bas_6hr_%s.nc' % ensemble_number)
qinit_3hr_file = os.path.join(node_path, 'Qinit_3hr.csv')
qinit_6hr_file = os.path.join(node_path, 'Qinit_6hr.csv')
try:
RAPIDinflowECMWF_tool.execute(ecmwf_forecast,
weight_table_file,
inflow_file_name_1hr,
grid_name,
conversion_flag, # added this line CJB 20190218
"1hr")
#from Hour 0 to 90 (the first 91 time points) are of 1 hr time interval
periods_1hr = 90 #new line 20190108 CJB
interval_1hr = 1*60*60 #1hr
duration_1hr = periods_1hr*interval_1hr #90hrs # modified line 20190108 CJB, MJS
rapid_manager.update_parameters(ZS_TauR=interval_1hr, #duration of routing procedure (time step of runoff data)
ZS_dtR=15*60, #internal routing time step
ZS_TauM=duration_1hr, #total simulation time
ZS_dtM=interval_1hr, #RAPID internal loop time interval
ZS_dtF=interval_1hr, # forcing time interval
Vlat_file=inflow_file_name_1hr,
Qout_file=outflow_file_name,
Qinit_file=qinit_file,
BS_opt_Qinit=BS_opt_Qinit,
BS_opt_dam = BS_opt_dam,
IS_dam_tot = IS_dam_tot,
IS_dam_use = IS_dam_use,
dam_tot_id_file = dam_tot_id_file,
dam_use_id_file = dam_use_id_file,
dam_file = dam_file)
rapid_manager.run()
#generate Qinit from 1hr
rapid_manager.generate_qinit_from_past_qout(qinit_3hr_file)
#then from Hour 90 to 144 (19 time points) are of 3 hour time interval
RAPIDinflowECMWF_tool.execute(ecmwf_forecast,
weight_table_file,
inflow_file_name_3hr,
grid_name,
conversion_flag, # added this line CJB 20190218
"3hr_subset")
periods_3hr = 18 #new line 20190108 CJB
interval_3hr = 3*60*60 #3hr
duration_3hr = periods_3hr*interval_3hr #54hrs # modified line 20190108 CJB, MJS
qout_3hr = os.path.join(node_path,'Qout_3hr.nc')
rapid_manager.update_parameters(ZS_TauR=interval_3hr, #duration of routing procedure (time step of runoff data)
ZS_dtR=15*60, #internal routing time step
ZS_TauM=duration_3hr, #total simulation time
ZS_dtM=interval_3hr, #RAPID internal loop time interval
ZS_dtF=interval_3hr, # forcing time interval
Vlat_file=inflow_file_name_3hr,
Qout_file=qout_3hr,
BS_opt_dam = BS_opt_dam,
IS_dam_tot = IS_dam_tot,
IS_dam_use = IS_dam_use,
dam_tot_id_file = dam_tot_id_file,
dam_use_id_file = dam_use_id_file,
dam_file = dam_file)
rapid_manager.run()
#generate Qinit from 3hr
rapid_manager.generate_qinit_from_past_qout(qinit_6hr_file)
#from Hour 144 to 240 (15 time points) are of 6 hour time interval
RAPIDinflowECMWF_tool.execute(ecmwf_forecast,
weight_table_file,
inflow_file_name_6hr,
grid_name,
conversion_flag, # added this line CJB 20190218
"6hr_subset")
periods_6hr = (time_step_count -1) - periods_3hr - periods_1hr #new line 20190108 CJB, MJS
interval_6hr = 6*60*60 #6hr
duration_6hr = periods_6hr*interval_6hr #new line 20190108 CJB, MJS
qout_6hr = os.path.join(node_path,'Qout_6hr.nc')
rapid_manager.update_parameters(ZS_TauR=interval_6hr, #duration of routing procedure (time step of runoff data)
ZS_dtR=15*60, #internal routing time step
ZS_TauM=duration_6hr, #total simulation time
ZS_dtM=interval_6hr, #RAPID internal loop time interval
ZS_dtF=interval_6hr, # forcing time interval
Vlat_file=inflow_file_name_6hr,
Qout_file=qout_6hr,
BS_opt_dam = BS_opt_dam,
IS_dam_tot = IS_dam_tot,
IS_dam_use = IS_dam_use,
dam_tot_id_file = dam_tot_id_file,
dam_use_id_file = dam_use_id_file,
dam_file = dam_file)
rapid_manager.run()
#Merge all files together at the end
cv = ConvertRAPIDOutputToCF(rapid_output_file=[outflow_file_name, qout_3hr, qout_6hr],
start_datetime=datetime.datetime.strptime(forecast_date_timestep[:11], "%Y%m%d.%H"),
time_step=[interval_1hr, interval_3hr, interval_6hr],
qinit_file=qinit_file,
comid_lat_lon_z_file=comid_lat_lon_z_file,
rapid_connect_file=rapid_connect_file,
project_name="ECMWF-RAPID Predicted flows by US Army ERDC",
output_id_dim_name='rivid',
output_flow_var_name='Qout',
print_debug=False)
cv.convert()
except Exception:
remove_file(qinit_3hr_file)
remove_file(qinit_6hr_file)
remove_file(inflow_file_name_1hr)
remove_file(inflow_file_name_3hr)
remove_file(inflow_file_name_6hr)
traceback.print_exc()
raise
remove_file(qinit_3hr_file)
remove_file(qinit_6hr_file)
remove_file(inflow_file_name_1hr)
remove_file(inflow_file_name_3hr)
remove_file(inflow_file_name_6hr)
if forecast_resolution == "HRES13": #new line 20190108 CJB 93 <= T <= 144
#if forecast_resolution == "HighRes":
#HIGH RES
grid_name = RAPIDinflowECMWF_tool.getGridName(ecmwf_forecast, high_res=True)
#generate inflows for each timestep
weight_table_file = case_insensitive_file_search(rapid_input_directory,
r'weight_{0}\.csv'.format(grid_name))
inflow_file_name_1hr = os.path.join(node_path, 'm3_riv_bas_1hr_%s.nc' % ensemble_number)
inflow_file_name_3hr = os.path.join(node_path, 'm3_riv_bas_3hr_%s.nc' % ensemble_number)
#inflow_file_name_6hr = os.path.join(node_path, 'm3_riv_bas_6hr_%s.nc' % ensemble_number)
qinit_3hr_file = os.path.join(node_path, 'Qinit_3hr.csv')
#qinit_6hr_file = os.path.join(node_path, 'Qinit_6hr.csv')
try:
RAPIDinflowECMWF_tool.execute(ecmwf_forecast,
weight_table_file,
inflow_file_name_1hr,
grid_name,
conversion_flag, # added this line CJB 20190218
"1hr")
#from Hour 0 to 90 (the first 91 time points) are of 1 hr time interval
periods_1hr = 90 #new line 20190108 CJB
interval_1hr = 1*60*60 #1hr
duration_1hr = periods_1hr*60*60 #90hrs # modified line 20190108 CJB
rapid_manager.update_parameters(ZS_TauR=interval_1hr, #duration of routing procedure (time step of runoff data)
ZS_dtR=15*60, #internal routing time step
ZS_TauM=duration_1hr, #total simulation time
ZS_dtM=interval_1hr, #RAPID internal loop time interval
ZS_dtF=interval_1hr, # forcing time interval
Vlat_file=inflow_file_name_1hr,
Qout_file=outflow_file_name,
Qinit_file=qinit_file,
BS_opt_Qinit=BS_opt_Qinit,
BS_opt_dam = BS_opt_dam,
IS_dam_tot = IS_dam_tot,
IS_dam_use = IS_dam_use,
dam_tot_id_file = dam_tot_id_file,
dam_use_id_file = dam_use_id_file,
dam_file = dam_file)
rapid_manager.run()
#generate Qinit from 1hr
rapid_manager.generate_qinit_from_past_qout(qinit_3hr_file)
#then from Hour 90 to 144 (19 time points) are of 3 hour time interval
RAPIDinflowECMWF_tool.execute(ecmwf_forecast,
weight_table_file,
inflow_file_name_3hr,
grid_name,
conversion_flag, # added this line CJB 20190218
"3hr_subset")
periods_3hr = (time_step_count - 1) - periods_1hr #new line 20190108 CJB, MJS
interval_3hr = 3*60*60 #3hr
duration_3hr = periods_3hr*interval_3hr #new line 20190108 CJB, MJS
qout_3hr = os.path.join(node_path,'Qout_3hr.nc')
rapid_manager.update_parameters(ZS_TauR=interval_3hr, #duration of routing procedure (time step of runoff data)
ZS_dtR=15*60, #internal routing time step
ZS_TauM=duration_3hr, #total simulation time
ZS_dtM=interval_3hr, #RAPID internal loop time interval
ZS_dtF=interval_3hr, # forcing time interval
Vlat_file=inflow_file_name_3hr,
Qout_file=qout_3hr,
BS_opt_dam = BS_opt_dam,
IS_dam_tot = IS_dam_tot,
IS_dam_use = IS_dam_use,
dam_tot_id_file = dam_tot_id_file,
dam_use_id_file = dam_use_id_file,
dam_file = dam_file)
rapid_manager.run()
#Merge all files together at the end
cv = ConvertRAPIDOutputToCF(rapid_output_file=[outflow_file_name, qout_3hr],
start_datetime=datetime.datetime.strptime(forecast_date_timestep[:11], "%Y%m%d.%H"),
time_step=[interval_1hr, interval_3hr],
qinit_file=qinit_file,
comid_lat_lon_z_file=comid_lat_lon_z_file,
rapid_connect_file=rapid_connect_file,
project_name="ECMWF-RAPID Predicted flows by US Army ERDC",
output_id_dim_name='rivid',
output_flow_var_name='Qout',
print_debug=False)
cv.convert()
except Exception:
remove_file(qinit_3hr_file)
remove_file(inflow_file_name_1hr)
remove_file(inflow_file_name_3hr)
traceback.print_exc()
raise
remove_file(qinit_3hr_file)
remove_file(inflow_file_name_1hr)
remove_file(inflow_file_name_3hr)
if forecast_resolution == "HRES1": #new line 20190108 CJB 0 <= T <= 90
#if forecast_resolution == "HighRes":
#HIGH RES
grid_name = RAPIDinflowECMWF_tool.getGridName(ecmwf_forecast, high_res=True)
#generate inflows for each timestep
weight_table_file = case_insensitive_file_search(rapid_input_directory,
r'weight_{0}\.csv'.format(grid_name))
inflow_file_name_1hr = os.path.join(node_path, 'm3_riv_bas_1hr_%s.nc' % ensemble_number)
#inflow_file_name_3hr = os.path.join(node_path, 'm3_riv_bas_3hr_%s.nc' % ensemble_number)
#inflow_file_name_6hr = os.path.join(node_path, 'm3_riv_bas_6hr_%s.nc' % ensemble_number)
#qinit_3hr_file = os.path.join(node_path, 'Qinit_3hr.csv')
#qinit_6hr_file = os.path.join(node_path, 'Qinit_6hr.csv')
try:
RAPIDinflowECMWF_tool.execute(ecmwf_forecast,
weight_table_file,
inflow_file_name_1hr,
grid_name,
conversion_flag, # added this line CJB 20190218
"1hr")
#from Hour 0 to 90 (the first 91 time points) are of 1 hr time interval
interval_1hr = 1*60*60 #1hr
duration_1hr = (time_step_count - 1)*interval_1hr #new line 20190108 CJB, MJS
rapid_manager.update_parameters(ZS_TauR=interval_1hr, #duration of routing procedure (time step of runoff data)
ZS_dtR=15*60, #internal routing time step
ZS_TauM=duration_1hr, #total simulation time
ZS_dtM=interval_1hr, #RAPID internal loop time interval
ZS_dtF=interval_1hr, # forcing time interval
Vlat_file=inflow_file_name_1hr,
Qout_file=outflow_file_name,
Qinit_file=qinit_file,
BS_opt_Qinit=BS_opt_Qinit,
BS_opt_dam = BS_opt_dam,
IS_dam_tot = IS_dam_tot,
IS_dam_use = IS_dam_use,
dam_tot_id_file = dam_tot_id_file,
dam_use_id_file = dam_use_id_file,
dam_file = dam_file)
rapid_manager.run()
#Merge all files together at the end
cv = ConvertRAPIDOutputToCF(rapid_output_file=[outflow_file_name],
start_datetime=datetime.datetime.strptime(forecast_date_timestep[:11], "%Y%m%d.%H"),
time_step=[interval_1hr],
qinit_file=qinit_file,
comid_lat_lon_z_file=comid_lat_lon_z_file,
rapid_connect_file=rapid_connect_file,
project_name="ECMWF-RAPID Predicted flows by US Army ERDC",
output_id_dim_name='rivid',
output_flow_var_name='Qout',
print_debug=False)
cv.convert()
except Exception:
remove_file(inflow_file_name_1hr)
traceback.print_exc()
raise
remove_file(inflow_file_name_1hr)
elif forecast_resolution == "ENS36": #new line 20190108 CJB 150 <= T <= 360
#elif forecast_resolution == "LowResFull":
#LOW RES - 3hr and 6hr timesteps
grid_name = RAPIDinflowECMWF_tool.getGridName(ecmwf_forecast, high_res=False)
#generate inflows for each timestep
weight_table_file = case_insensitive_file_search(rapid_input_directory,
r'weight_{0}\.csv'.format(grid_name))
inflow_file_name_3hr = os.path.join(node_path, 'm3_riv_bas_3hr_%s.nc' % ensemble_number)
inflow_file_name_6hr = os.path.join(node_path, 'm3_riv_bas_6hr_%s.nc' % ensemble_number)
qinit_6hr_file = os.path.join(node_path, 'Qinit_6hr.csv')
try:
RAPIDinflowECMWF_tool.execute(ecmwf_forecast,
weight_table_file,
inflow_file_name_3hr,
grid_name,
conversion_flag, # added this line CJB 20190218
"3hr_subset")
#from Hour 0 to 144 (the first 49 time points) are of 3 hr time interval
periods_3hr = 48
interval_3hr = 3*60*60 #3hr
duration_3hr = periods_3hr*interval_3hr #new line 20190108 CJB, MJS
rapid_manager.update_parameters(ZS_TauR=interval_3hr, #duration of routing procedure (time step of runoff data)
ZS_dtR=15*60, #internal routing time step
ZS_TauM=duration_3hr, #total simulation time
ZS_dtM=interval_3hr, #RAPID internal loop time interval
ZS_dtF=interval_3hr, # forcing time interval
Vlat_file=inflow_file_name_3hr,
Qout_file=outflow_file_name,
Qinit_file=qinit_file,
BS_opt_Qinit=BS_opt_Qinit,
BS_opt_dam = BS_opt_dam,
IS_dam_tot = IS_dam_tot,
IS_dam_use = IS_dam_use,
dam_tot_id_file = dam_tot_id_file,
dam_use_id_file = dam_use_id_file,
dam_file = dam_file)
rapid_manager.run()
#generate Qinit from 3hr
rapid_manager.generate_qinit_from_past_qout(qinit_6hr_file)
#from Hour 144 to 360 (36 time points) are of 6 hour time interval
RAPIDinflowECMWF_tool.execute(ecmwf_forecast,
weight_table_file,
inflow_file_name_6hr,
grid_name,
conversion_flag, # added this line CJB 20190218
"6hr_subset")
periods_6hr = (time_step_count - 1) - periods_3hr #new line 20190108 CJB, MJS
interval_6hr = 6*60*60 #6hr
duration_6hr = periods_6hr*interval_6hr #new line 20190108 CJB, MJS
qout_6hr = os.path.join(node_path,'Qout_6hr.nc')
rapid_manager.update_parameters(ZS_TauR=interval_6hr, #duration of routing procedure (time step of runoff data)
ZS_dtR=15*60, #internal routing time step
ZS_TauM=duration_6hr, #total simulation time
ZS_dtM=interval_6hr, #RAPID internal loop time interval
ZS_dtF=interval_6hr, # forcing time interval
Vlat_file=inflow_file_name_6hr,
Qout_file=qout_6hr,
BS_opt_dam = BS_opt_dam,
IS_dam_tot = IS_dam_tot,
IS_dam_use = IS_dam_use,
dam_tot_id_file = dam_tot_id_file,
dam_use_id_file = dam_use_id_file,
dam_file = dam_file)
rapid_manager.run()
#Merge all files together at the end
cv = ConvertRAPIDOutputToCF(rapid_output_file=[outflow_file_name, qout_6hr],
start_datetime=datetime.datetime.strptime(forecast_date_timestep[:11], "%Y%m%d.%H"),
time_step=[interval_3hr, interval_6hr],
qinit_file=qinit_file,
comid_lat_lon_z_file=comid_lat_lon_z_file,
rapid_connect_file=rapid_connect_file,
project_name="ECMWF-RAPID Predicted flows by US Army ERDC",
output_id_dim_name='rivid',
output_flow_var_name='Qout',
print_debug=False)
cv.convert()
except Exception:
remove_file(qinit_6hr_file)
remove_file(inflow_file_name_3hr)
remove_file(inflow_file_name_6hr)
traceback.print_exc()
raise
remove_file(qinit_6hr_file)
remove_file(inflow_file_name_3hr)
remove_file(inflow_file_name_6hr)
elif forecast_resolution == "ENS3": #new line 20190108 CJB 0 <= T <= 144
#elif forecast_resolution == "LowResFull":
#LOW RES - 3hr and 6hr timesteps
grid_name = RAPIDinflowECMWF_tool.getGridName(ecmwf_forecast, high_res=False)
#generate inflows for each timestep
weight_table_file = case_insensitive_file_search(rapid_input_directory,
r'weight_{0}\.csv'.format(grid_name))
inflow_file_name_3hr = os.path.join(node_path, 'm3_riv_bas_3hr_%s.nc' % ensemble_number)
#inflow_file_name_6hr = os.path.join(node_path, 'm3_riv_bas_6hr_%s.nc' % ensemble_number)
#qinit_6hr_file = os.path.join(node_path, 'Qinit_6hr.csv')
try:
RAPIDinflowECMWF_tool.execute(ecmwf_forecast,
weight_table_file,
inflow_file_name_3hr,
grid_name,
conversion_flag, # added this line CJB 20190218
"3hr_subset")
#from Hour 0 to 144 (the first 49 time points) are of 3 hr time interval
periods_3hr = time_step_count - 1 #new line 20190108 CJB, MJS
interval_3hr = 3*60*60 #3hr
duration_3hr = periods_3hr*interval_3hr #new line 20190108 CJB, MJS
rapid_manager.update_parameters(ZS_TauR=interval_3hr, #duration of routing procedure (time step of runoff data)
ZS_dtR=15*60, #internal routing time step
ZS_TauM=duration_3hr, #total simulation time
ZS_dtM=interval_3hr, #RAPID internal loop time interval
ZS_dtF=interval_3hr, # forcing time interval
Vlat_file=inflow_file_name_3hr,
Qout_file=outflow_file_name,
Qinit_file=qinit_file,
BS_opt_Qinit=BS_opt_Qinit,
BS_opt_dam = BS_opt_dam,
IS_dam_tot = IS_dam_tot,
IS_dam_use = IS_dam_use,
dam_tot_id_file = dam_tot_id_file,
dam_use_id_file = dam_use_id_file,
dam_file = dam_file)
rapid_manager.run()
#Merge all files together at the end
cv = ConvertRAPIDOutputToCF(rapid_output_file=[outflow_file_name],
start_datetime=datetime.datetime.strptime(forecast_date_timestep[:11], "%Y%m%d.%H"),
time_step=[interval_3hr],
qinit_file=qinit_file,
comid_lat_lon_z_file=comid_lat_lon_z_file,
rapid_connect_file=rapid_connect_file,
project_name="ECMWF-RAPID Predicted flows by US Army ERDC",
output_id_dim_name='rivid',
output_flow_var_name='Qout',
print_debug=False)
cv.convert()
#rapid_manager.make_output_CF_compliant(simulation_start_datetime=datetime.datetime.strptime(forecast_date_timestep[:11], "%Y%m%d.%H"),
# comid_lat_lon_z_file=comid_lat_lon_z_file,
# project_name="ECMWF-RAPID Predicted flows by US Army ERDC")
except Exception:
remove_file(inflow_file_name_3hr)
traceback.print_exc()
raise
#remove_file(inflow_file_name_3hr)
elif forecast_resolution == "ENS6":
#LOW RES - 6hr only
inflow_file_name = os.path.join(node_path, 'm3_riv_bas_%s.nc' % ensemble_number)
grid_name = RAPIDinflowECMWF_tool.getGridName(ecmwf_forecast, high_res=False)
#generate inflows for each timestep
weight_table_file = case_insensitive_file_search(rapid_input_directory,
r'weight_{0}\.csv'.format(grid_name))
try:
print("INFO: Converting ECMWF inflow ...")
RAPIDinflowECMWF_tool.execute(ecmwf_forecast,
weight_table_file,
inflow_file_name,
conversion_flag, # added this line CJB 20190218
grid_name)
periods_6hr = time_step_count - 1 #new line 20190108 CJB, MJS
interval = 6*60*60 #6hr
duration = periods_6hr*interval #new line 20190108 CJB
rapid_manager.update_parameters(ZS_TauR=interval, #duration of routing procedure (time step of runoff data)
ZS_dtR=15*60, #internal routing time step
ZS_TauM=duration, #total simulation time
Vlat_file=inflow_file_name,
Qout_file=outflow_file_name,
Qinit_file=qinit_file,
BS_opt_Qinit=BS_opt_Qinit,
BS_opt_dam = BS_opt_dam,
IS_dam_tot = IS_dam_tot,
IS_dam_use = IS_dam_use,
dam_tot_id_file = dam_tot_id_file,
dam_use_id_file = dam_use_id_file,
dam_file = dam_file)
rapid_manager.run()
rapid_manager.make_output_CF_compliant(simulation_start_datetime=datetime.datetime.strptime(forecast_date_timestep[:11], "%Y%m%d.%H"),
comid_lat_lon_z_file=comid_lat_lon_z_file,
project_name="ECMWF-RAPID Predicted flows by US Army ERDC")
except Exception:
remove_file(inflow_file_name)
traceback.print_exc()
raise
#clean up
remove_file(inflow_file_name)
#else:
# raise Exception("ERROR: invalid forecast resolution ...")
time_stop_all = datetime.datetime.utcnow()
print("INFO: Total time to compute: {0}".format(time_stop_all-time_start_all))
def ecmwf_rapid_multiprocess_worker(
node_path, rapid_input_directory, ecmwf_forecast,
forecast_date_timestep, watershed, subbasin, rapid_executable_location,
init_flow, initialization_time_step, BS_opt_dam, IS_dam_tot,
IS_dam_use, dam_tot_id_file, dam_use_id_file, dam_file):
"""
Multiprocess worker function
"""
time_start_all = datetime.datetime.utcnow()
os.chdir(node_path)
ensemble_number = get_ensemble_number_from_forecast(ecmwf_forecast)
def remove_file(file_name):
"""
remove file
"""
try:
os.remove(file_name)
except OSError:
pass
#prepare ECMWF file for RAPID
print("INFO: Running all ECMWF downscaling for watershed: {0}-{1} {2} {3}".
format(watershed, subbasin, forecast_date_timestep, ensemble_number))
#set up RAPID manager
rapid_connect_file = case_insensitive_file_search(rapid_input_directory,
r'rapid_connect\.csv')
rapid_manager = RAPID(
rapid_executable_location=rapid_executable_location,
rapid_connect_file=rapid_connect_file,
riv_bas_id_file=case_insensitive_file_search(rapid_input_directory,
r'riv_bas_id.*?\.csv'),
k_file=case_insensitive_file_search(rapid_input_directory, r'k\.csv'),
x_file=case_insensitive_file_search(rapid_input_directory, r'x\.csv'),
ZS_dtM=3 * 60 * 60, #RAPID internal loop time interval
)
# check for forcing flows
try:
rapid_manager.update_parameters(
Qfor_file=case_insensitive_file_search(rapid_input_directory,
r'qfor\.csv'),
for_tot_id_file=case_insensitive_file_search(
rapid_input_directory, r'for_tot_id\.csv'),
for_use_id_file=case_insensitive_file_search(
rapid_input_directory, r'for_use_id\.csv'),
ZS_dtF=3 * 60 * 60, # forcing time interval
BS_opt_for=True)
except Exception:
print('WARNING: Forcing files not found. Skipping forcing ...')
pass
rapid_manager.update_reach_number_data()
outflow_file_name = os.path.join(
node_path, 'Qout_%s_%s_%s.nc' %
(watershed.lower(), subbasin.lower(), ensemble_number))
qinit_file = ""
BS_opt_Qinit = False
if (init_flow):
#check for qinit file
past_date = (datetime.datetime.strptime(forecast_date_timestep[:11],"%Y%m%d.%H") - \
datetime.timedelta(hours=initialization_time_step)).strftime("%Y%m%dt%H")
qinit_file = os.path.join(rapid_input_directory,
'Qinit_%s.csv' % past_date)
BS_opt_Qinit = qinit_file and os.path.exists(qinit_file)
if not BS_opt_Qinit:
print("Error: {0} not found. Not initializing ...".format(
qinit_file))
qinit_file = ""
try:
comid_lat_lon_z_file = case_insensitive_file_search(
rapid_input_directory, r'comid_lat_lon_z.*?\.csv')
except Exception:
comid_lat_lon_z_file = ""
print(
"WARNING: comid_lat_lon_z_file not found. Not adding lat/lon/z to output file ..."
)
RAPIDinflowECMWF_tool = CreateInflowFileFromECMWFRunoff()
forecast_resolution = RAPIDinflowECMWF_tool.dataIdentify(ecmwf_forecast)
#determine weight table from resolution
if forecast_resolution == "HighRes":
#HIGH RES
grid_name = RAPIDinflowECMWF_tool.getGridName(ecmwf_forecast,
high_res=True)
#generate inflows for each timestep
weight_table_file = case_insensitive_file_search(
rapid_input_directory, r'weight_{0}\.csv'.format(grid_name))
inflow_file_name_1hr = os.path.join(
node_path, 'm3_riv_bas_1hr_%s.nc' % ensemble_number)
inflow_file_name_3hr = os.path.join(
node_path, 'm3_riv_bas_3hr_%s.nc' % ensemble_number)
inflow_file_name_6hr = os.path.join(
node_path, 'm3_riv_bas_6hr_%s.nc' % ensemble_number)
qinit_3hr_file = os.path.join(node_path, 'Qinit_3hr.csv')
qinit_6hr_file = os.path.join(node_path, 'Qinit_6hr.csv')
try:
RAPIDinflowECMWF_tool.execute(ecmwf_forecast, weight_table_file,
inflow_file_name_1hr, grid_name,
"1hr")
#from Hour 0 to 90 (the first 91 time points) are of 1 hr time interval
interval_1hr = 1 * 60 * 60 #1hr
duration_1hr = 90 * 60 * 60 #90hrs
rapid_manager.update_parameters(
ZS_TauR=
interval_1hr, #duration of routing procedure (time step of runoff data)
ZS_dtR=15 * 60, #internal routing time step
ZS_TauM=duration_1hr, #total simulation time
ZS_dtM=interval_1hr, #RAPID internal loop time interval
ZS_dtF=interval_1hr, # forcing time interval
Vlat_file=inflow_file_name_1hr,
Qout_file=outflow_file_name,
Qinit_file=qinit_file,
BS_opt_Qinit=BS_opt_Qinit,
BS_opt_dam=BS_opt_dam,
IS_dam_tot=IS_dam_tot,
IS_dam_use=IS_dam_use,
dam_tot_id_file=dam_tot_id_file,
dam_use_id_file=dam_use_id_file,
dam_file=dam_file)
rapid_manager.run()
#generate Qinit from 1hr
rapid_manager.generate_qinit_from_past_qout(qinit_3hr_file)
#then from Hour 90 to 144 (19 time points) are of 3 hour time interval
RAPIDinflowECMWF_tool.execute(ecmwf_forecast, weight_table_file,
inflow_file_name_3hr, grid_name,
"3hr_subset")
interval_3hr = 3 * 60 * 60 #3hr
duration_3hr = 54 * 60 * 60 #54hrs
qout_3hr = os.path.join(node_path, 'Qout_3hr.nc')
rapid_manager.update_parameters(
ZS_TauR=
interval_3hr, #duration of routing procedure (time step of runoff data)
ZS_dtR=15 * 60, #internal routing time step
ZS_TauM=duration_3hr, #total simulation time
ZS_dtM=interval_3hr, #RAPID internal loop time interval
ZS_dtF=interval_3hr, # forcing time interval
Vlat_file=inflow_file_name_3hr,
Qout_file=qout_3hr,
BS_opt_dam=BS_opt_dam, #True,
IS_dam_tot=IS_dam_tot, #1,
IS_dam_use=IS_dam_use, #1,
dam_tot_id_file=dam_tot_id_file,
dam_use_id_file=dam_use_id_file,
dam_file=dam_file)
rapid_manager.run()
#generate Qinit from 3hr
rapid_manager.generate_qinit_from_past_qout(qinit_6hr_file)
#from Hour 144 to 240 (15 time points) are of 6 hour time interval
RAPIDinflowECMWF_tool.execute(ecmwf_forecast, weight_table_file,
inflow_file_name_6hr, grid_name,
"6hr_subset")
interval_6hr = 6 * 60 * 60 #6hr
duration_6hr = 96 * 60 * 60 #96hrs
qout_6hr = os.path.join(node_path, 'Qout_6hr.nc')
rapid_manager.update_parameters(
ZS_TauR=
interval_6hr, #duration of routing procedure (time step of runoff data)
ZS_dtR=15 * 60, #internal routing time step
ZS_TauM=duration_6hr, #total simulation time
ZS_dtM=interval_6hr, #RAPID internal loop time interval
ZS_dtF=interval_6hr, # forcing time interval
Vlat_file=inflow_file_name_6hr,
Qout_file=qout_6hr,
BS_opt_dam=BS_opt_dam,
IS_dam_tot=IS_dam_tot,
IS_dam_use=IS_dam_use,
dam_tot_id_file=dam_tot_id_file,
dam_use_id_file=dam_use_id_file,
dam_file=dam_file)
rapid_manager.run()
#Merge all files together at the end
cv = ConvertRAPIDOutputToCF(
rapid_output_file=[outflow_file_name, qout_3hr, qout_6hr],
start_datetime=datetime.datetime.strptime(
forecast_date_timestep[:11], "%Y%m%d.%H"),
time_step=[interval_1hr, interval_3hr, interval_6hr],
qinit_file=qinit_file,
comid_lat_lon_z_file=comid_lat_lon_z_file,
rapid_connect_file=rapid_connect_file,
project_name="ECMWF-RAPID Predicted flows by US Army ERDC",
output_id_dim_name='rivid',
output_flow_var_name='Qout',
print_debug=False)
cv.convert()
except Exception:
remove_file(qinit_3hr_file)
remove_file(qinit_6hr_file)
remove_file(inflow_file_name_1hr)
remove_file(inflow_file_name_3hr)
remove_file(inflow_file_name_6hr)
traceback.print_exc()
raise
remove_file(qinit_3hr_file)
remove_file(qinit_6hr_file)
remove_file(inflow_file_name_1hr)
remove_file(inflow_file_name_3hr)
remove_file(inflow_file_name_6hr)
elif forecast_resolution == "LowResFull":
#LOW RES - 3hr and 6hr timesteps
grid_name = RAPIDinflowECMWF_tool.getGridName(ecmwf_forecast,
high_res=False)
#generate inflows for each timestep
weight_table_file = case_insensitive_file_search(
rapid_input_directory, r'weight_{0}\.csv'.format(grid_name))
inflow_file_name_3hr = os.path.join(
node_path, 'm3_riv_bas_3hr_%s.nc' % ensemble_number)
inflow_file_name_6hr = os.path.join(
node_path, 'm3_riv_bas_6hr_%s.nc' % ensemble_number)
qinit_6hr_file = os.path.join(node_path, 'Qinit_6hr.csv')
try:
RAPIDinflowECMWF_tool.execute(ecmwf_forecast, weight_table_file,
inflow_file_name_3hr, grid_name,
"3hr_subset")
#from Hour 0 to 144 (the first 49 time points) are of 3 hr time interval
interval_3hr = 3 * 60 * 60 #3hr
duration_3hr = 144 * 60 * 60 #144hrs
rapid_manager.update_parameters(
ZS_TauR=
interval_3hr, #duration of routing procedure (time step of runoff data)
ZS_dtR=15 * 60, #internal routing time step
ZS_TauM=duration_3hr, #total simulation time
ZS_dtM=interval_3hr, #RAPID internal loop time interval
ZS_dtF=interval_3hr, # forcing time interval
Vlat_file=inflow_file_name_3hr,
Qout_file=outflow_file_name,
Qinit_file=qinit_file,
BS_opt_Qinit=BS_opt_Qinit,
BS_opt_dam=BS_opt_dam,
IS_dam_tot=IS_dam_tot,
IS_dam_use=IS_dam_use,
dam_tot_id_file=dam_tot_id_file,
dam_use_id_file=dam_use_id_file,
dam_file=dam_file)
rapid_manager.run()
#generate Qinit from 3hr
rapid_manager.generate_qinit_from_past_qout(qinit_6hr_file)
#from Hour 144 to 360 (36 time points) are of 6 hour time interval
RAPIDinflowECMWF_tool.execute(ecmwf_forecast, weight_table_file,
inflow_file_name_6hr, grid_name,
"6hr_subset")
interval_6hr = 6 * 60 * 60 #6hr
duration_6hr = 216 * 60 * 60 #216hrs
qout_6hr = os.path.join(node_path, 'Qout_6hr.nc')
rapid_manager.update_parameters(
ZS_TauR=
interval_6hr, #duration of routing procedure (time step of runoff data)
ZS_dtR=15 * 60, #internal routing time step
ZS_TauM=duration_6hr, #total simulation time
ZS_dtM=interval_6hr, #RAPID internal loop time interval
ZS_dtF=interval_6hr, # forcing time interval
Vlat_file=inflow_file_name_6hr,
Qout_file=qout_6hr,
BS_opt_dam=BS_opt_dam,
IS_dam_tot=IS_dam_tot,
IS_dam_use=IS_dam_use,
dam_tot_id_file=dam_tot_id_file,
dam_use_id_file=dam_use_id_file,
dam_file=dam_file)
rapid_manager.run()
#Merge all files together at the end
cv = ConvertRAPIDOutputToCF(
rapid_output_file=[outflow_file_name, qout_6hr],
start_datetime=datetime.datetime.strptime(
forecast_date_timestep[:11], "%Y%m%d.%H"),
time_step=[interval_3hr, interval_6hr],
qinit_file=qinit_file,
comid_lat_lon_z_file=comid_lat_lon_z_file,
rapid_connect_file=rapid_connect_file,
project_name="ECMWF-RAPID Predicted flows by US Army ERDC",
output_id_dim_name='rivid',
output_flow_var_name='Qout',
print_debug=False)
cv.convert()
except Exception:
remove_file(qinit_6hr_file)
remove_file(inflow_file_name_3hr)
remove_file(inflow_file_name_6hr)
traceback.print_exc()
raise
remove_file(qinit_6hr_file)
remove_file(inflow_file_name_3hr)
remove_file(inflow_file_name_6hr)
elif forecast_resolution == "LowRes":
#LOW RES - 6hr only
inflow_file_name = os.path.join(node_path,
'm3_riv_bas_%s.nc' % ensemble_number)
grid_name = RAPIDinflowECMWF_tool.getGridName(ecmwf_forecast,
high_res=False)
#generate inflows for each timestep
weight_table_file = case_insensitive_file_search(
rapid_input_directory, r'weight_{0}\.csv'.format(grid_name))
try:
print("INFO: Converting ECMWF inflow ...")
RAPIDinflowECMWF_tool.execute(ecmwf_forecast, weight_table_file,
inflow_file_name, grid_name)
interval = 6 * 60 * 60 #6hr
duration = 15 * 24 * 60 * 60 #15 days
rapid_manager.update_parameters(
ZS_TauR=
interval, #duration of routing procedure (time step of runoff data)
ZS_dtR=15 * 60, #internal routing time step
ZS_TauM=duration, #total simulation time
Vlat_file=inflow_file_name,
Qout_file=outflow_file_name,
Qinit_file=qinit_file,
BS_opt_Qinit=BS_opt_Qinit,
BS_opt_dam=BS_opt_dam,
IS_dam_tot=IS_dam_tot,
IS_dam_use=IS_dam_use,
dam_tot_id_file=dam_tot_id_file,
dam_use_id_file=dam_use_id_file,
dam_file=dam_file)
rapid_manager.run()
rapid_manager.make_output_CF_compliant(
simulation_start_datetime=datetime.datetime.strptime(
forecast_date_timestep[:11], "%Y%m%d.%H"),
comid_lat_lon_z_file=comid_lat_lon_z_file,
project_name="ECMWF-RAPID Predicted flows by US Army ERDC")
except Exception:
remove_file(inflow_file_name)
traceback.print_exc()
raise
#clean up
remove_file(inflow_file_name)
else:
raise Exception("ERROR: invalid forecast resolution ...")
time_stop_all = datetime.datetime.utcnow()
print("INFO: Total time to compute: {0}".format(time_stop_all -
time_start_all))
def ecmwf_rapid_multiprocess_worker(node_path, rapid_input_directory,
ecmwf_forecast, forecast_date_timestep,
watershed, subbasin, rapid_executable_location,
init_flow):
"""
Multiprocess worker function
"""
time_start_all = datetime.datetime.utcnow()
os.chdir(node_path)
ensemble_number = get_ensemble_number_from_forecast(ecmwf_forecast)
def remove_file(file_name):
"""
remove file
"""
try:
os.remove(file_name)
except OSError:
pass
#prepare ECMWF file for RAPID
print("Running all ECMWF downscaling for watershed: {0}-{1} {2} {3}".format(watershed,
subbasin,
forecast_date_timestep,
ensemble_number))
#set up RAPID manager
rapid_connect_file=case_insensitive_file_search(rapid_input_directory,
r'rapid_connect\.csv')
rapid_manager = RAPID(rapid_executable_location=rapid_executable_location,
rapid_connect_file=rapid_connect_file,
riv_bas_id_file=case_insensitive_file_search(rapid_input_directory,
r'riv_bas_id.*?\.csv'),
k_file=case_insensitive_file_search(rapid_input_directory,
r'k\.csv'),
x_file=case_insensitive_file_search(rapid_input_directory,
r'x\.csv'),
ZS_dtM=3*60*60, #RAPID internal loop time interval
)
rapid_manager.update_reach_number_data()
outflow_file_name = os.path.join(node_path,
'Qout_%s_%s_%s.nc' % (watershed.lower(),
subbasin.lower(),
ensemble_number))
qinit_file = ""
BS_opt_Qinit = False
if(init_flow):
#check for qinit file
past_date = (datetime.datetime.strptime(forecast_date_timestep[:11],"%Y%m%d.%H") - \
datetime.timedelta(hours=12)).strftime("%Y%m%dt%H")
qinit_file = os.path.join(rapid_input_directory, 'Qinit_%s.csv' % past_date)
BS_opt_Qinit = qinit_file and os.path.exists(qinit_file)
if not BS_opt_Qinit:
qinit_file = ""
print("Error: {0} not found. Not initializing ...".format(qinit_file))
try:
comid_lat_lon_z_file = case_insensitive_file_search(rapid_input_directory,
r'comid_lat_lon_z.*?\.csv')
except Exception:
comid_lat_lon_z_file = ""
print("comid_lat_lon_z_file not found. Not adding lat/lon/z to output file ...")
RAPIDinflowECMWF_tool = CreateInflowFileFromECMWFRunoff()
forecast_resolution = RAPIDinflowECMWF_tool.dataIdentify(ecmwf_forecast)
#determine weight table from resolution
if forecast_resolution == "HighRes":
#HIGH RES
grid_name = RAPIDinflowECMWF_tool.getGridName(ecmwf_forecast, high_res=True)
#generate inflows for each timestep
weight_table_file = case_insensitive_file_search(rapid_input_directory,
r'weight_{0}\.csv'.format(grid_name))
inflow_file_name_1hr = os.path.join(node_path, 'm3_riv_bas_1hr_%s.nc' % ensemble_number)
inflow_file_name_3hr = os.path.join(node_path, 'm3_riv_bas_3hr_%s.nc' % ensemble_number)
inflow_file_name_6hr = os.path.join(node_path, 'm3_riv_bas_6hr_%s.nc' % ensemble_number)
qinit_3hr_file = os.path.join(node_path, 'Qinit_3hr.csv')
qinit_6hr_file = os.path.join(node_path, 'Qinit_6hr.csv')
try:
RAPIDinflowECMWF_tool.execute(ecmwf_forecast,
weight_table_file,
inflow_file_name_1hr,
grid_name,
"1hr")
#from Hour 0 to 90 (the first 91 time points) are of 1 hr time interval
interval_1hr = 1*60*60 #1hr
duration_1hr = 90*60*60 #90hrs
rapid_manager.update_parameters(ZS_TauR=interval_1hr, #duration of routing procedure (time step of runoff data)
ZS_dtR=15*60, #internal routing time step
ZS_TauM=duration_1hr, #total simulation time
ZS_dtM=interval_1hr, #RAPID internal loop time interval
Vlat_file=inflow_file_name_1hr,
Qout_file=outflow_file_name,
Qinit_file=qinit_file,
BS_opt_Qinit=BS_opt_Qinit)
rapid_manager.run()
#generate Qinit from 1hr
rapid_manager.generate_qinit_from_past_qout(qinit_3hr_file)
#then from Hour 90 to 144 (19 time points) are of 3 hour time interval
RAPIDinflowECMWF_tool.execute(ecmwf_forecast,
weight_table_file,
inflow_file_name_3hr,
grid_name,
"3hr_subset")
interval_3hr = 3*60*60 #3hr
duration_3hr = 54*60*60 #54hrs
qout_3hr = os.path.join(node_path,'Qout_3hr.nc')
rapid_manager.update_parameters(ZS_TauR=interval_3hr, #duration of routing procedure (time step of runoff data)
ZS_dtR=15*60, #internal routing time step
ZS_TauM=duration_3hr, #total simulation time
ZS_dtM=interval_3hr, #RAPID internal loop time interval
Vlat_file=inflow_file_name_3hr,
Qout_file=qout_3hr)
rapid_manager.run()
#generate Qinit from 3hr
rapid_manager.generate_qinit_from_past_qout(qinit_6hr_file)
#from Hour 144 to 240 (15 time points) are of 6 hour time interval
RAPIDinflowECMWF_tool.execute(ecmwf_forecast,
weight_table_file,
inflow_file_name_6hr,
grid_name,
"6hr_subset")
interval_6hr = 6*60*60 #6hr
duration_6hr = 96*60*60 #96hrs
qout_6hr = os.path.join(node_path,'Qout_6hr.nc')
rapid_manager.update_parameters(ZS_TauR=interval_6hr, #duration of routing procedure (time step of runoff data)
ZS_dtR=15*60, #internal routing time step
ZS_TauM=duration_6hr, #total simulation time
ZS_dtM=interval_6hr, #RAPID internal loop time interval
Vlat_file=inflow_file_name_6hr,
Qout_file=qout_6hr)
rapid_manager.run()
#Merge all files together at the end
cv = ConvertRAPIDOutputToCF(rapid_output_file=[outflow_file_name, qout_3hr, qout_6hr],
start_datetime=datetime.datetime.strptime(forecast_date_timestep[:11], "%Y%m%d.%H"),
time_step=[interval_1hr, interval_3hr, interval_6hr],
qinit_file=qinit_file,
comid_lat_lon_z_file=comid_lat_lon_z_file,
rapid_connect_file=rapid_connect_file,
project_name="ECMWF-RAPID Predicted flows by US Army ERDC",
output_id_dim_name='rivid',
output_flow_var_name='Qout',
print_debug=False)
cv.convert()
except Exception:
remove_file(qinit_3hr_file)
remove_file(qinit_6hr_file)
remove_file(inflow_file_name_1hr)
remove_file(inflow_file_name_3hr)
remove_file(inflow_file_name_6hr)
raise
remove_file(qinit_3hr_file)
remove_file(qinit_6hr_file)
remove_file(inflow_file_name_1hr)
remove_file(inflow_file_name_3hr)
remove_file(inflow_file_name_6hr)
elif forecast_resolution == "LowResFull":
#LOW RES - 3hr and 6hr timesteps
grid_name = RAPIDinflowECMWF_tool.getGridName(ecmwf_forecast, high_res=False)
#generate inflows for each timestep
weight_table_file = case_insensitive_file_search(rapid_input_directory,
r'weight_{0}\.csv'.format(grid_name))
inflow_file_name_3hr = os.path.join(node_path, 'm3_riv_bas_3hr_%s.nc' % ensemble_number)
inflow_file_name_6hr = os.path.join(node_path, 'm3_riv_bas_6hr_%s.nc' % ensemble_number)
qinit_6hr_file = os.path.join(node_path, 'Qinit_6hr.csv')
try:
RAPIDinflowECMWF_tool.execute(ecmwf_forecast,
weight_table_file,
inflow_file_name_3hr,
grid_name,
"3hr_subset")
#from Hour 0 to 144 (the first 49 time points) are of 3 hr time interval
interval_3hr = 3*60*60 #3hr
duration_3hr = 144*60*60 #144hrs
rapid_manager.update_parameters(ZS_TauR=interval_3hr, #duration of routing procedure (time step of runoff data)
ZS_dtR=15*60, #internal routing time step
ZS_TauM=duration_3hr, #total simulation time
ZS_dtM=interval_3hr, #RAPID internal loop time interval
Vlat_file=inflow_file_name_3hr,
Qout_file=outflow_file_name,
Qinit_file=qinit_file,
BS_opt_Qinit=BS_opt_Qinit)
rapid_manager.run()
#generate Qinit from 3hr
rapid_manager.generate_qinit_from_past_qout(qinit_6hr_file)
#from Hour 144 to 360 (36 time points) are of 6 hour time interval
RAPIDinflowECMWF_tool.execute(ecmwf_forecast,
weight_table_file,
inflow_file_name_6hr,
grid_name,
"6hr_subset")
interval_6hr = 6*60*60 #6hr
duration_6hr = 216*60*60 #216hrs
qout_6hr = os.path.join(node_path,'Qout_6hr.nc')
rapid_manager.update_parameters(ZS_TauR=interval_6hr, #duration of routing procedure (time step of runoff data)
ZS_dtR=15*60, #internal routing time step
ZS_TauM=duration_6hr, #total simulation time
ZS_dtM=interval_6hr, #RAPID internal loop time interval
Vlat_file=inflow_file_name_6hr,
Qout_file=qout_6hr)
rapid_manager.run()
#Merge all files together at the end
cv = ConvertRAPIDOutputToCF(rapid_output_file=[outflow_file_name, qout_6hr],
start_datetime=datetime.datetime.strptime(forecast_date_timestep[:11], "%Y%m%d.%H"),
time_step=[interval_3hr, interval_6hr],
qinit_file=qinit_file,
comid_lat_lon_z_file=comid_lat_lon_z_file,
rapid_connect_file=rapid_connect_file,
project_name="ECMWF-RAPID Predicted flows by US Army ERDC",
output_id_dim_name='rivid',
output_flow_var_name='Qout',
print_debug=False)
cv.convert()
except Exception:
remove_file(qinit_6hr_file)
remove_file(inflow_file_name_3hr)
remove_file(inflow_file_name_6hr)
raise
remove_file(qinit_6hr_file)
remove_file(inflow_file_name_3hr)
remove_file(inflow_file_name_6hr)
elif forecast_resolution == "LowRes":
#LOW RES - 6hr only
inflow_file_name = os.path.join(node_path, 'm3_riv_bas_%s.nc' % ensemble_number)
grid_name = RAPIDinflowECMWF_tool.getGridName(ecmwf_forecast, high_res=False)
#generate inflows for each timestep
weight_table_file = case_insensitive_file_search(rapid_input_directory,
r'weight_{0}\.csv'.format(grid_name))
try:
print("Converting ECMWF inflow ...")
RAPIDinflowECMWF_tool.execute(ecmwf_forecast,
weight_table_file,
inflow_file_name,
grid_name)
interval = 6*60*60 #6hr
duration = 15*24*60*60 #15 days
rapid_manager.update_parameters(ZS_TauR=interval, #duration of routing procedure (time step of runoff data)
ZS_dtR=15*60, #internal routing time step
ZS_TauM=duration, #total simulation time
Vlat_file=inflow_file_name,
Qout_file=outflow_file_name,
Qinit_file=qinit_file,
BS_opt_Qinit=BS_opt_Qinit)
rapid_manager.run()
rapid_manager.make_output_CF_compliant(simulation_start_datetime=datetime.datetime.strptime(forecast_date_timestep[:11], "%Y%m%d.%H"),
comid_lat_lon_z_file=comid_lat_lon_z_file,
project_name="ECMWF-RAPID Predicted flows by US Army ERDC")
except Exception:
remove_file(inflow_file_name)
raise
#clean up
remove_file(inflow_file_name)
else:
raise Exception("ERROR: invalid forecast resolution ...")
time_stop_all = datetime.datetime.utcnow()
print("Total time to compute: {0}".format(time_stop_all-time_start_all))
