def to_dict(self, block):
result = {}
for f_name, f in self.fields.iteritems():
if f.field_type == FieldType.HIDDEN:
continue
with stopwatch(name="Serializing block field %s" % f_name,
threshold=0.01):
raw_val = getattr(block, f_name)
result[f_name] = f.value_to_dict(raw_val, block)
params_protype = {
str(param_name): param_field.to_dict()
for param_name, param_field in self.params.iteritems()
}
result["_params_prototype"] = params_protype
result["_params_prototype_list"] = params_protype.values()
for f_name, f in self.params.iteritems():
with stopwatch(name="Serializing block param %s" % f_name,
threshold=0.01):
raw_val = getattr(block, f.name)
result[f_name] = f.value_to_dict(raw_val, block)
result["actions"] = [{
"code": ar.name,
"title": ar.user_title,
} for ar in block.get_user_actions()]
result["out"] = block.out_manager.to_dict(block)
result["inputs"] = block.input_manager.to_dict()
return result
def to_dict(self, block):
result = {}
for f_name, f in self.fields.iteritems():
if f.field_type == FieldType.HIDDEN:
continue
with stopwatch(name="Serializing block field %s" % f_name, threshold=0.01):
raw_val = getattr(block, f_name)
result[f_name] = f.value_to_dict(raw_val, block)
params_protype = {
str(param_name): param_field.to_dict()
for param_name, param_field in self.params.iteritems()
}
result["_params_prototype"] = params_protype
result["_params_prototype_list"] = params_protype.values()
for f_name, f in self.params.iteritems():
with stopwatch(name="Serializing block param %s" % f_name, threshold=0.01):
raw_val = getattr(block, f.name)
result[f_name] = f.value_to_dict(raw_val, block)
result["actions"] = [{
"code": ar.name,
"title": ar.user_title,
} for ar in block.get_user_actions()]
result["out"] = block.out_manager.to_dict(block)
result["inputs"] = block.input_manager.to_dict()
return result
def apply_ranking(
exp, block,
es, ranking_name,
result_table,
pheno_class_column=None, options=None
):
if not options:
options = {}
if not pheno_class_column:
pheno_class_column = es.pheno_metadata["user_class_title"]
R.r['source'](R_LIB_CUSTOM_PATH + '/ranking.Methods.r')
func = R.r[ranking_name]
assay_df = es.get_assay_data_frame()
x = com.convert_to_r_matrix(assay_df)
y = es.get_pheno_column_as_r_obj(pheno_class_column)
log.debug("Computing ranking: `%s` options: `%s`", ranking_name, options)
with stopwatch(name="Computing ranking: `%s` options: `%s`" % (ranking_name, options),
threshold=0.01):
ranking_list = list(func(R.r['t'](x), y, **options))
ranking_fixed = map(lambda a: int(a - 1), ranking_list)
df = pd.DataFrame(
index=assay_df.index,
data=[len(assay_df)] * len(assay_df), columns=["rank"]
)
for rank, row_num in enumerate(ranking_fixed):
df.ix[row_num, "rank"] = rank
result_table.store_table(df)
return [result_table], {}
def apply_ranking(exp,
block,
es,
ranking_name,
result_table,
pheno_class_column=None,
options=None):
if not options:
options = {}
if not pheno_class_column:
pheno_class_column = es.pheno_metadata["user_class_title"]
R.r['source'](R_LIB_CUSTOM_PATH + '/ranking.Methods.r')
func = R.r[ranking_name]
assay_df = es.get_assay_data_frame()
x = com.convert_to_r_matrix(assay_df)
y = es.get_pheno_column_as_r_obj(pheno_class_column)
log.debug("Computing ranking: `%s` options: `%s`", ranking_name, options)
with stopwatch(name="Computing ranking: `%s` options: `%s`" %
(ranking_name, options),
threshold=0.01):
ranking_list = list(func(R.r['t'](x), y, **options))
ranking_fixed = map(lambda a: int(a - 1), ranking_list)
df = pd.DataFrame(index=assay_df.index,
data=[len(assay_df)] * len(assay_df),
columns=["rank"])
for rank, row_num in enumerate(ranking_fixed):
df.ix[row_num, "rank"] = rank
result_table.store_table(df)
return [result_table], {}
def apply_ranking(
exp, block,
es, ranking_name,
result_table,
pheno_class_column=None, options=None
):
if not options:
options = {}
if not pheno_class_column:
pheno_class_column = es.pheno_metadata["user_class_title"]
R.r['source'](R_LIB_CUSTOM_PATH + '/ranking.Methods.r')
func = R.r[ranking_name]
if settings.CELERY_DEBUG:
import sys
sys.path.append('/Migration/skola/phd/projects/miXGENE/mixgene_project/wrappers/pycharm-debug.egg')
import pydevd
pydevd.settrace('localhost', port=6901, stdoutToServer=True, stderrToServer=True)
assay_df = es.get_assay_data_frame()
cols = assay_df.columns
# We must rename cols to be unique for R
out_genes = {}
out_cols = []
for i, g in enumerate(cols):
g = g.split('.')[0]
if g in out_genes:
new_g = g + '__' + str(i)
out_genes[g].append(new_g)
out_cols.append(new_g)
else:
out_genes[g] = [g]
out_cols.append(g)
assay_df.columns = out_cols
assay_df = assay_df.T
x = com.convert_to_r_matrix(assay_df)
y = es.get_pheno_column_as_r_obj(pheno_class_column)
exp.log(block.uuid, "Computing ranking: `%s` options: `%s`" % (ranking_name, options))
log.debug("Computing ranking: `%s` options: `%s`", ranking_name, options)
with stopwatch(name="Computing ranking: `%s` options: `%s`" % (ranking_name, options),
threshold=0.01):
ranking_list = list(func(R.r['t'](x), y, **options))
ranking_fixed = map(lambda a: int(a - 1), ranking_list)
df = pd.DataFrame(
index=assay_df.index,
data=[len(assay_df)] * len(assay_df), columns=["rank"]
)
for rank, row_num in enumerate(ranking_fixed):
df.ix[row_num, "rank"] = rank
result_table.store_table(df)
return [result_table], {}
