def _write_mag_file( # pylint:disable=too-many-arguments
openscmdf, metadata, header, outfile_dir, symlink_dir, fnames, force):
out_file = os.path.join(outfile_dir, fnames[0])
out_file = "{}.MAG".format(os.path.splitext(out_file)[0])
if _skip_file(out_file, force, symlink_dir):
return
writer = MAGICCData(openscmdf)
writer["todo"] = "SET"
time_steps = writer.timeseries().columns[1:] - writer.timeseries(
).columns[:-1]
step_upper = np.timedelta64(32, "D") # pylint:disable=too-many-function-args
step_lower = np.timedelta64(28, "D") # pylint:disable=too-many-function-args
if any((time_steps > step_upper) | (time_steps < step_lower)):
raise ValueError("Please raise an issue at "
"github.com/znicholls/netcdf-scm/issues "
"to discuss how to handle non-monthly data wrangling")
writer.metadata = metadata
writer.metadata["timeseriestype"] = "MONTHLY"
writer.metadata["header"] = header
logger.info("Writing file to %s", out_file)
writer.write(out_file, magicc_version=7)
symlink_file = os.path.join(symlink_dir, os.path.basename(out_file))
logger.info("Making symlink to %s", symlink_file)
os.symlink(out_file, symlink_file)
def _write_magicc_input_files( # pylint:disable=too-many-arguments,too-many-locals
openscmdf,
time_id,
outfile_dir,
symlink_dir,
force,
metadata,
header,
timeseriestype,
):
try:
var_to_write = openscmdf["variable"].unique()[0]
variable_abbreviations = {
"filename": var_to_write,
"magicc_name": _MAGICC_VARIABLE_MAP[var_to_write][0],
"magicc_internal_name": _MAGICC_VARIABLE_MAP[var_to_write][1],
}
except KeyError:
raise KeyError(
"I don't know which MAGICC variable to use for input `{}`".format(
var_to_write))
region_filters = {
"FOURBOX": [
"World|Northern Hemisphere|Land",
"World|Southern Hemisphere|Land",
"World|Northern Hemisphere|Ocean",
"World|Southern Hemisphere|Ocean",
],
"GLOBAL": ["World"],
}
for region_key, regions_to_keep in region_filters.items():
out_file = os.path.join(
outfile_dir,
(("{}_{}_{}_{}_{}_{}_{}.IN").format(
variable_abbreviations["filename"],
openscmdf["scenario"].unique()[0],
openscmdf["climate_model"].unique()[0],
openscmdf["member_id"].unique()[0],
time_id,
region_key,
variable_abbreviations["magicc_internal_name"],
).upper()),
)
symlink_file = os.path.join(symlink_dir, os.path.basename(out_file))
if _skip_file(out_file, force, symlink_dir):
return
writer = MAGICCData(openscmdf).filter(region=regions_to_keep)
writer["todo"] = "SET"
writer["variable"] = variable_abbreviations["magicc_name"]
writer.metadata = metadata
writer.metadata["header"] = header
writer.metadata["timeseriestype"] = timeseriestype
logger.info("Writing file to %s", out_file)
writer.write(out_file, magicc_version=7)
logger.info("Making symlink to %s", symlink_file)
os.symlink(out_file, symlink_file)
def writing_base_mag():
tregions = (
["World"]
+ ["World|{}".format(r) for r in ["Northern Hemisphere", "Southern Hemisphere"]]
+ ["World|{}".format(r) for r in ["Land", "Ocean"]]
)
writing_base_mag = MAGICCData(
data=np.arange(27 * len(tregions)).reshape(27, len(tregions)),
index=[
dt.datetime(2099, 1, 16, 12, 0),
dt.datetime(2099, 2, 15, 0, 0),
dt.datetime(2099, 3, 16, 12, 0),
dt.datetime(2099, 4, 16, 0, 0),
dt.datetime(2099, 5, 16, 12, 0),
dt.datetime(2099, 6, 16, 0, 0),
dt.datetime(2099, 7, 16, 12, 0),
dt.datetime(2099, 8, 16, 12, 0),
dt.datetime(2099, 9, 16, 0, 0),
dt.datetime(2099, 10, 16, 12, 0),
dt.datetime(2099, 11, 16, 0, 0),
dt.datetime(2099, 12, 16, 12, 0),
dt.datetime(2100, 1, 16, 12, 0),
dt.datetime(2100, 2, 15, 0, 0),
dt.datetime(2100, 3, 16, 12, 0),
dt.datetime(2100, 4, 16, 0, 0),
dt.datetime(2100, 5, 16, 12, 0),
dt.datetime(2100, 6, 16, 0, 0),
dt.datetime(2100, 7, 16, 12, 0),
dt.datetime(2100, 8, 16, 12, 0),
dt.datetime(2100, 9, 16, 0, 0),
dt.datetime(2100, 10, 16, 12, 0),
dt.datetime(2100, 11, 16, 0, 0),
dt.datetime(2100, 12, 16, 12, 0),
dt.datetime(2101, 1, 16, 12, 0),
dt.datetime(2101, 2, 15, 0, 0),
dt.datetime(2101, 3, 16, 12, 0),
],
columns={
"region": tregions,
"variable": "NPP",
"model": "unspecified",
"scenario": "mag test",
"unit": "gC/yr",
"todo": "SET",
},
)
writing_base_mag.metadata = {
"header": "Test mag file",
"timeseriestype": "MONTHLY",
"other info": "checking time point handling",
}
yield writing_base_mag
def test_external_forcing_only_run(package):
time = zero_emissions["time"]
forcing_external = 2.0 * np.arange(0, len(time)) / len(time)
forcing_ext = MAGICCData(
forcing_external,
columns={
"index": time,
"scenario": ["idealised"],
"model": ["unspecified"],
"climate_model": ["unspecified"],
"variable": ["Radiative Forcing|Extra"],
"unit": ["W / m^2"],
"todo": ["SET"],
"region": ["World"],
},
)
forcing_ext_filename = "EXTERNAL_RF.IN"
forcing_ext.metadata = {
"header": "External radiative forcing file for testing"
}
forcing_ext.write(join(package.run_dir, forcing_ext_filename),
package.version)
results = package.run(
rf_extra_read=1,
file_extra_rf=forcing_ext_filename,
rf_total_runmodus="QEXTRA",
endyear=max(time).year,
rf_initialization_method=
"ZEROSTARTSHIFT", # this is default but just in case
rf_total_constantafteryr=5000,
)
# MAGICC's weird last year business means that last result is just constant from previous
# year and is not treated properly
# TODO: add this in docs
validation_output = (results.filter(
variable="Radiative Forcing",
region="World").timeseries().values.squeeze()[:-1])
validation_input = forcing_external[:-1]
np.testing.assert_allclose(validation_input, validation_output, rtol=1e-5)
temperature_global = (results.filter(
variable="Surface Temperature",
region="World").timeseries().values.squeeze())
assert (temperature_global[1:] - temperature_global[:-1] >= 0).all()
def _write_mag_file_with_operation( # pylint:disable=too-many-arguments
openscmdf, metadata, header, outfile_dir, symlink_dir, fnames, force,
out_format): # pylint:disable=too-many-locals
if len(fnames) > 1:
raise AssertionError("more than one file to wrangle?"
) # pragma: no cover # emergency valve
ts = openscmdf.timeseries()
src_time_points = ts.columns
original_years = ts.columns.map(lambda x: x.year).unique()
time_id = "{}-{}".format(src_time_points[0].year, src_time_points[-1].year)
regex_search = r"{:04d}\d*-{:04d}\d*".format(src_time_points[0].year,
src_time_points[-1].year)
old_time_id = re.search(regex_search, fnames[0]).group(0)
out_file = os.path.join(outfile_dir,
fnames[0].replace(old_time_id, time_id))
out_file = "{}.MAG".format(os.path.splitext(out_file)[0])
if _skip_file(out_file, force, symlink_dir):
return
writer = MAGICCData(_do_timeseriestype_operation(
openscmdf, out_format)).filter(year=original_years)
writer["todo"] = "SET"
writer.metadata = metadata
writer.metadata["timeseriestype"] = (out_format.replace(
"mag-files-", "").replace("-", "_").upper())
writer.metadata["header"] = header
logger.info("Writing file to %s", out_file)
writer.write(out_file, magicc_version=7)
symlink_file = os.path.join(symlink_dir, os.path.basename(out_file))
logger.info("Making symlink to %s", symlink_file)
os.symlink(out_file, symlink_file)
def test_co2_emms_other_rf_run(package, emms_co2_level):
package.set_zero_config()
df = zero_emissions.timeseries()
time = zero_emissions["time"]
emms_fossil_co2 = np.zeros(len(time))
emms_fossil_co2[20:] = emms_co2_level
df.loc[(
df.index.get_level_values("variable") ==
"Emissions|CO2|MAGICC Fossil and Industrial"), :, ] = emms_fossil_co2
scen = MAGICCData(df)
forcing_external = 2.0 * np.arange(0, len(time)) / len(time)
forcing_ext = MAGICCData(
forcing_external,
columns={
"index": time,
"scenario": ["idealised"],
"model": ["unspecified"],
"climate_model": ["unspecified"],
"variable": ["Radiative Forcing|Extra"],
"unit": ["W / m^2"],
"todo": ["SET"],
"region": ["World"],
},
)
forcing_ext.metadata = {
"header": "External radiative forcing file for testing"
}
forcing_ext_filename = "EXTERNAL_RF.IN"
forcing_ext.write(join(package.run_dir, forcing_ext_filename),
package.version)
# TODO: fix endyear so it takes from scenario input by default
results = package.run(
scen,
endyear=max(time).year,
rf_extra_read=1, # fix writing of 'True'
file_extra_rf=forcing_ext_filename,
rf_total_runmodus="all",
rf_initialization_method="ZEROSTARTSHIFT",
rf_total_constantafteryr=5000,
)
np.testing.assert_allclose(
results.filter(variable="Em*CO2*Fossil*",
region="World").timeseries().values.squeeze(),
emms_fossil_co2,
)
# CO2 temperature feedbacks mean that you get a CO2 outgassing, hence CO2 forcing. As a
# result radiative forcing values don't match exactly. Numerical precision adds to this.
ext_rf_output_vals = (results.filter(
variable="Radiative Forcing",
region="World").timeseries().values.squeeze())
zero_rows = (forcing_external == 0) & (ext_rf_output_vals == 0)
greater_equal_rows = ext_rf_output_vals >= forcing_external
close_rows_denominator = forcing_external
close_rows_denominator[zero_rows] = 10**-10 # avoid divide by zero
close_rows = (np.abs(ext_rf_output_vals - forcing_external) /
close_rows_denominator <= 10**-3)
matching_rows = greater_equal_rows | close_rows
assert matching_rows.all()