def rxnday_func(self, ci, num_days, freq, center_mean_on_last_day):
climdex = get_package("climdex.pcic")
if num_days == 1:
return climdex.climdex_rx1day(ci, freq)
elif num_days == 5:
return climdex.climdex_rx5day(ci, freq, center_mean_on_last_day)
def threshold_func(self, threshold, ci):
climdex = get_package("climdex.pcic")
if threshold == 10.0:
return climdex.climdex_r10mm(ci)
if threshold == 20.0:
return climdex.climdex_r20mm(ci)
else:
return climdex.climdex_rnnmm(ci, threshold)
def _handler(self, request, response):
(
base_range,
cal,
date_fields,
date_format,
loglevel,
max_missing_days,
min_base_data_fraction_present,
n,
northern_hemisphere,
output_file,
prec_column,
prec_file,
prec_name,
prec_qtiles,
quantiles,
tavg_column,
tavg_file,
tavg_name,
temp_qtiles,
tmax_column,
tmax_file,
tmax_name,
tmin_column,
tmin_file,
tmin_name,
vector_name,
) = process_inputs_alpha(request.inputs, raw_inputs, self.workdir)
validate_vectors([
base_range,
date_fields,
temp_qtiles,
prec_qtiles,
max_missing_days,
])
log_handler(
self,
response,
"Starting Process",
logger,
log_level=loglevel,
process_step="start",
)
climdex = get_package("climdex.pcic")
robjects.r("library(PCICt)")
log_handler(
self,
response,
"Prepare parameters for climdexInput.raw",
logger,
log_level=loglevel,
process_step="prepare_params",
)
params = self.prepare_parameters(
request,
tmax_name,
tmin_name,
prec_name,
tavg_name,
tmax_column,
tmin_column,
prec_column,
tavg_column,
date_fields,
date_format,
cal,
prec_file,
tavg_file,
tmax_file,
tmin_file,
)
log_handler(
self,
response,
"Processing climdexInput.raw",
logger,
log_level=loglevel,
process_step="process",
)
try:
ci = climdex.climdexInput_raw(
**params,
base_range=robjects.r(base_range),
n=n,
northern_hemisphere=northern_hemisphere,
quantiles=robjects.r(quantiles),
temp_qtiles=robjects.r(temp_qtiles),
prec_qtiles=robjects.r(prec_qtiles),
max_missing_days=robjects.r(max_missing_days),
min_base_data_fraction_present=min_base_data_fraction_present,
)
except RRuntimeError as e:
raise ProcessError(msg=f"{type(e).__name__}: {str(e)}")
log_handler(
self,
response,
"Saving climdexInput as R data file",
logger,
log_level=loglevel,
process_step="save_rdata",
)
output_path = os.path.join(self.workdir, output_file)
r_valid_name(vector_name)
save_python_to_rdata(vector_name, ci, output_path)
log_handler(
self,
response,
"Building final output",
logger,
log_level=loglevel,
process_step="build_output",
)
response.outputs["climdexInput"].file = output_path
# Clear R global env
robjects.r("rm(list=ls())")
return response
def _handler(self, request, response):
climdex_input, freq, loglevel, month_type, output_file = process_inputs_alpha(
request.inputs, mmdmt_inputs, self.workdir)
log_handler(
self,
response,
"Starting Process",
logger,
log_level=loglevel,
process_step="start",
)
climdex = get_package("climdex.pcic")
vectors = []
counter = 1
total = len(climdex_input)
for input in climdex_input:
log_handler(
self,
response,
f"Loading climdexInput from R data file {counter}/{total}",
logger,
log_level=loglevel,
process_step="load_rdata",
)
cis = load_cis(input)
log_handler(
self,
response,
f"Processing {month_type} for file {counter}/{total}",
logger,
log_level=loglevel,
process_step="process",
)
for ci_name, ci in cis.items():
try:
robjects.r.assign("ci", ci)
temps = robjects.r(
f"climdex.{month_type}(ci, freq='{freq}')")
except RRuntimeError as e:
raise ProcessError(msg=f"{type(e).__name__}: {str(e)}")
vector_name = f"{month_type}_{freq}{counter}_{ci_name}"
robjects.r.assign(vector_name, temps)
vectors.append(vector_name)
counter += 1
log_handler(
self,
response,
f"Saving {month_type} vector to R data file",
logger,
log_level=loglevel,
process_step="save_rdata",
)
output_path = os.path.join(self.workdir, output_file)
robjects.r["save"](*vectors, file=output_path)
log_handler(
self,
response,
"Building final output",
logger,
log_level=loglevel,
process_step="build_output",
)
response.outputs["rda_output"].file = output_path
# Clear R global env
robjects.r("rm(list=ls())")
log_handler(
self,
response,
"Process Complete",
logger,
log_level=loglevel,
process_step="complete",
)
return response
def _handler(self, request, response):
ci_name, climdex_single_input, loglevel, output_file = process_inputs_alpha(
request.inputs, avail_indices_inputs, self.workdir)
log_handler(
self,
response,
"Starting Process",
logger,
log_level=loglevel,
process_step="start",
)
climdex = get_package("climdex.pcic")
log_handler(
self,
response,
"Loading climdexInput from R data file",
logger,
log_level=loglevel,
process_step="load_rdata",
)
ci = get_robj(climdex_single_input, ci_name)
log_handler(
self,
response,
"Processing climdex_get_available_indices",
logger,
log_level=loglevel,
process_step="process",
)
try:
robjects.r.assign("ci", ci)
avail_indices = climdex.climdex_get_available_indices(ci, False)
except RRuntimeError as e:
raise ProcessError(msg=f"{type(e).__name__}: {str(e)}")
avail_processes = self.available_processes(avail_indices)
log_handler(
self,
response,
"Building final output",
logger,
log_level=loglevel,
process_step="build_output",
)
response.outputs["avail_processes"].data = avail_processes
# Clear R global env
robjects.r("rm(list=ls())")
log_handler(
self,
response,
"Process Complete",
logger,
log_level=loglevel,
process_step="complete",
)
return response
def test_get_package_err(package):
with pytest.raises(ProcessError) as e:
get_package(package)
assert str(vars(e)["_excinfo"][1]) == f"R package, {package}, is not installed"
def test_get_package(package):
pkg = get_package(package)
assert pkg.__dict__["__rname__"] == package
def _handler(self, request, response):
(
data_file,
data_vector,
loglevel,
output_file,
quantiles_vector,
vector_name,
) = process_inputs_alpha(request.inputs, quantile_inputs, self.workdir)
validate_vectors([quantiles_vector])
r_valid_name(vector_name)
log_handler(
self,
response,
"Starting Process",
logger,
log_level=loglevel,
process_step="start",
)
climdex = get_package("climdex.pcic")
log_handler(
self,
response,
"Loading R data file",
logger,
log_level=loglevel,
process_step="load_rdata",
)
if data_file:
data = self.unpack_data_file(data_file, data_vector)
else:
data = robjects.r(data_vector)
log_handler(
self,
response,
"Processing climdex.quantile",
logger,
log_level=loglevel,
process_step="process",
)
try:
quantiles = robjects.r(quantiles_vector)
quantile_vector = climdex.climdex_quantile(data, quantiles)
except RRuntimeError as e:
raise ProcessError(msg=f"{type(e).__name__}: {str(e)}")
log_handler(
self,
response,
"Saving quantile as R data file",
logger,
log_level=loglevel,
process_step="save_rdata",
)
output_path = os.path.join(self.workdir, output_file)
save_python_to_rdata(vector_name, quantile_vector, output_path)
log_handler(
self,
response,
"Building final output",
logger,
log_level=loglevel,
process_step="build_output",
)
response.outputs["rda_output"].file = output_path
response.outputs["output_vector"].data = str(quantile_vector)
# Clear R global env
robjects.r("rm(list=ls())")
log_handler(
self,
response,
"Process Complete",
logger,
log_level=loglevel,
process_step="complete",
)
return response
def _handler(self, request, response):
climdex_input, loglevel, output_file, threshold = process_inputs_alpha(
request.inputs, rmm_inputs, self.workdir)
log_handler(
self,
response,
"Starting Process",
logger,
log_level=loglevel,
process_step="start",
)
climdex = get_package("climdex.pcic")
vectors = []
counter = 1
total = len(climdex_input)
for input in climdex_input:
log_handler(
self,
response,
f"Loading climdexInput from R data file {counter}/{total}",
logger,
log_level=loglevel,
process_step="load_rdata",
)
cis = load_cis(input)
log_handler(
self,
response,
f"Processing the annual count of days where daily precipitation is more than {threshold}mm per day for file {counter}/{total}",
logger,
log_level=loglevel,
process_step="process",
)
for ci_name, ci in cis.items():
try:
robjects.r.assign("ci", ci)
count_days = self.threshold_func(threshold, ci)
except RRuntimeError as e:
raise ProcessError(msg=f"{type(e).__name__}: {str(e)}")
vector_name = f"r{threshold}mm{counter}_{ci_name}"
robjects.r.assign(vector_name, count_days)
vectors.append(vector_name)
counter += 1
log_handler(
self,
response,
f"Saving climdex.r{threshold}mm outputs to R data file",
logger,
log_level=loglevel,
process_step="save_rdata",
)
output_path = os.path.join(self.workdir, output_file)
robjects.r["save"](*vectors, file=output_path)
log_handler(
self,
response,
"Building final output",
logger,
log_level=loglevel,
process_step="build_output",
)
response.outputs["rda_output"].file = output_path
# Clear R global env
robjects.r("rm(list=ls())")
log_handler(
self,
response,
"Process Complete",
logger,
log_level=loglevel,
process_step="complete",
)
return response