def GetMetapopSpeciesData(speciesName):
years = np.array([2006, 2007, 2012, 2013])
r('source("inSituDataMethods/createSpeciesRaster.R")')
r('sampled <- getAllSampledSpeciesMatrix(\'' + speciesName + '\')')
r('observed <- getAllObservedSpeciesMatrix(\'' + speciesName + '\')')
Observed = np.array(r.get("observed"))
Sampled = np.array(r.get("sampled"))
return MetapopSpeciesData(speciesName, years, Observed, Sampled)
def load_predict_func(file_path):
"""Load Predict Function"""
LOG.info("Loading predict function from rds file {}".format(file_path))
try:
r.source("../mlfmodelserver/deserialize_model.R")
return r.get(r.deserialize_model(file_path))
except Exception as generic_exception:
LOG.error(
"Exception occured while unpickling {}".format(generic_exception))
raise generic_exception
def convert_hgnc2ensembl(hgnc_id):
init_biomaRt()
v = R.c(hgnc_id)
res = R.getBM(attributes=R.c("ensembl_gene_id"), filters="hgnc_symbol", values=v, mart=__mart)
try:
return R.get("ensembl_gene_id", res)[0]
except:
print 'Error convert_hgnc2ensembl: '+str(hgnc_id)+' not found in database'
return None
def convert_list_ensembl2hgnc(ensembl_id_list):
init_biomaRt()
v = R.c(ensembl_id_list)
res = R.getBM(attributes=R.c("hgnc_symbol"), filters="ensembl_gene_id", values=v, mart=__mart)
try:
return R.get("hgnc_symbol", res)
except:
print 'Error convert_ensembl2hgnc: '+str(ensembl_id)+' not found in database'
return None
def _read_ExpressionSet_RData(RData):
"""Read ExpressionSet RData to Rpy2 robjects.
RData: Path to the input RData file.
ExpressionSet must be the only object in the RData.
Return Rpy2's eSet object, assayData, featureData, phenotypeData.
"""
importr('Biobase')
rdata = r.load(RData)
eSet = r.get(rdata) # rpy2 ExpressionSet object (assumed)
assayData = r.assayData(eSet) # rpy2 environment object
fData = r.fData(eSet) # rpy2 DataFrame object
pData = r.pData(eSet) # rpy2 DataFrame object
return eSet, assayData, fData, pData
def execute_r(
r_code: str,
packages: list,
inputs: dict,
outputs: list,
overwrite=False,
side_effect=False,
) -> dict:
"""
Execute R code and return the result.
"""
call = (r_env if side_effect else r_env.eval
) # Ignore print statements if side_effect is False
for pkg in packages:
call(f"library({pkg})")
for k, val in inputs.items():
if overwrite or not r_env.exists(k)[0]:
r_env.assign(k, val)
call(r_code)
return {o: convert(r_env.get(o)) for o in outputs}
def getEOdataframeAtTraps():
pandas2ri.activate()
r('source("EOData/EOData_Traps.R")')
r('EOMatrix <- getEOMatrix_Traps()')
EO_df = pandas2ri.ri2py_dataframe(r.get("EOMatrix"))
return EO_df
def GetInitialPresenceProbabilitySDM(speciesName):
r('source("SDM/SDM_Metapop.R")')
r('P <- getInitialPresenceGuessSDM(\'' + speciesName + '\')')
P = np.array(r.get("P"))
return P
def getMetapopVariables(res):
pandas2ri.activate()
r('source("EOData/EOData_Metapop.R")')
r('EOMatrix <- getEOMatrix_Metapop()')
EOMatrix = np.array(r.get("EOMatrix"))
return MetapopVariables(EOMatrix)