def test_load_cis_err(r_file):
with pytest.raises(ProcessError) as e:
load_cis(r_file)
assert (
str(vars(e)["_excinfo"][1]) == "RRuntimeError: Data file must be a RDS file or "
"a Rdata file containing a ClimdexInput object of the given name"
)
def _handler(self, request, response):
climdex_input, freq, func, loglevel, output_file = process_inputs_alpha(
request.inputs, temp_pctl_inputs, self.workdir)
log_handler(
self,
response,
"Starting Process",
logger,
log_level=loglevel,
process_step="start",
)
robjects.r("library(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 climdex.{func} for file {counter}/{total}",
logger,
log_level=loglevel,
process_step="process",
)
for ci_name, ci in cis.items():
try:
robjects.r.assign("ci", ci)
mothly_pct = robjects.r(f"climdex.{func}(ci, '{freq}')")
except RRuntimeError as e:
raise ProcessError(msg=f"{type(e).__name__}: {str(e)}")
vector_name = f"{func}{counter}_{ci_name}"
robjects.r.assign(vector_name, mothly_pct)
vectors.append(vector_name)
counter += 1
log_handler(
self,
response,
f"Saving {func} as 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):
(
center_mean_on_last_day,
climdex_input,
freq,
loglevel,
num_days,
output_file,
) = process_inputs_alpha(request.inputs, rxnday_inputs, self.workdir)
log_handler(
self,
response,
"Starting Process",
logger,
log_level=loglevel,
process_step="start",
)
robjects.r("library(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 Monthly Maximum {num_days}-day Precipitation for file {counter}/{total}",
logger,
log_level=loglevel,
process_step="process",
)
for ci_name, ci in cis.items():
try:
robjects.r.assign("ci", ci)
rxnday = self.rxnday_func(ci, num_days, freq,
center_mean_on_last_day)
except RRuntimeError as e:
raise ProcessError(msg=f"{type(e).__name__}: {str(e)}")
vector_name = f"rx{num_days}day{counter}_{ci_name}"
robjects.r.assign(vector_name, rxnday)
vectors.append(vector_name)
counter += 1
log_handler(
self,
response,
f"Saving rx{num_days}day 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):
climdex_input, loglevel, output_file = process_inputs_alpha(
request.inputs, sdii_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 mean daily diurnal temperature range for file {counter}/{total}",
logger,
log_level=loglevel,
process_step="process",
)
for ci_name, ci in cis.items():
try:
robjects.r.assign("ci", ci)
sdii = climdex.climdex_sdii(ci)
except RRuntimeError as e:
raise ProcessError(msg=f"{type(e).__name__}: {str(e)}")
vector_name = f"sdii{counter}_{ci_name}"
robjects.r.assign(vector_name, sdii)
vectors.append(vector_name)
counter += 1
log_handler(
self,
response,
"Saving dtr 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):
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
def test_load_cis(r_file):
cis = load_cis(r_file)
assert len(cis) > 0
for ci in cis.values():
assert ci.rclass[0] == "climdexInput"
