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-rw-r--r--wqflask/base/mrna_assay_tissue_data.py25
-rw-r--r--wqflask/wqflask/correlation/correlation_gn3_api.py60
2 files changed, 8 insertions, 77 deletions
diff --git a/wqflask/base/mrna_assay_tissue_data.py b/wqflask/base/mrna_assay_tissue_data.py
index 0220d73b..5a64afb2 100644
--- a/wqflask/base/mrna_assay_tissue_data.py
+++ b/wqflask/base/mrna_assay_tissue_data.py
@@ -52,33 +52,9 @@ class MrnaAssayTissueData(object):
# lower_symbols[gene_symbol.lower()] = True
if gene_symbol != None:
lower_symbols[gene_symbol.lower()] = True
-
- import time
- # initial_time = time.time()
- # conn,cursor = database_connector()
- # cursor.execute(query)
- # for result in cursor.fetchall():
- # symbol = result[0]
- # self.data[symbol].gene_id = result[1]
- # self.data[symbol].data_id = result[2]
- # self.data[symbol].chr = result[3]
- # self.data[symbol].mb = result[4]
- # self.data[symbol].description = result[5]
- # self.data[symbol].probe_target_description = result[6]
-
-
- # print("my loop takes>>>>",time.time()-initial_time)
- # conn.close()
- # r
-
- # takes 5 seconds
- initial_time = time.time()
results = list(g.db.execute(query).fetchall())
for result in results:
symbol = result[0]
- # if symbol is not None
- # exists = lower_symbols.get(symbol.lower())
- # if symbol.lower() in lower_symbols:
if symbol is not None and lower_symbols.get(symbol.lower()):
symbol = symbol.lower()
@@ -89,7 +65,6 @@ class MrnaAssayTissueData(object):
self.data[symbol].mb = result.Mb
self.data[symbol].description = result.description
self.data[symbol].probe_target_description = result.Probe_Target_Description
- print("time taken in the loop is",time.time()-initial_time)
###########################################################################
#Input: cursor, symbolList (list), dataIdDict(Dict)
diff --git a/wqflask/wqflask/correlation/correlation_gn3_api.py b/wqflask/wqflask/correlation/correlation_gn3_api.py
index c945f699..3c21a850 100644
--- a/wqflask/wqflask/correlation/correlation_gn3_api.py
+++ b/wqflask/wqflask/correlation/correlation_gn3_api.py
@@ -63,9 +63,6 @@ def sample_for_trait_lists(corr_results, target_dataset, this_trait, this_datase
"trait_sample_data": sample_data,
"trait_id": start_vars["trait_id"]
}
- # trait_lists = dict([(list(corr_result)[0],True) for corr_result in corr_results])
- # target_dataset.trait_data =list(filter(lambda dict_obj: dict_obj.keys()[
- # 0] in corr_results_traits, target_dataset_data))
results = map_shared_keys_to_values(
target_dataset.samplelist, target_dataset.trait_data)
correlation_results = compute_all_sample_correlation(corr_method="pearson",
@@ -77,33 +74,15 @@ def sample_for_trait_lists(corr_results, target_dataset, this_trait, this_datase
def tissue_for_trait_lists(corr_results, this_dataset, target_dataset, this_trait):
- # # print(corr_results[0])--
- # [{"awsdsd_at": {'corr_coeffient': 0.49714692782257336, 'p_value': 1.872077762359228e-05, 'num_overlap': 67}}]
-
- print("creating trait_lists")
- # corr_results = corr_results[0::]
trait_lists = dict([(list(corr_result)[0], True)
for corr_result in corr_results])
- print("finished creating trait_list")
-
traits_symbol_dict = this_dataset.retrieve_genes("Symbol")
- print("Retrieved symbol dict")
- print("creating dict here>>>>>>>>>")
- import time
- init_time = time.time()
traits_symbol_dict = dict({trait_name: symbol for (
trait_name, symbol) in traits_symbol_dict.items() if trait_lists.get(trait_name)})
- print("time taken to create this max dict is>>>>", time.time()-init_time)
- print("finished creatinf the dict")
- print("Fetching tissue datas")
primary_tissue_data, target_tissue_data = get_tissue_correlation_input(
this_trait, traits_symbol_dict)
- print("finihsed>>>>>>>>>>>>>>>>>>")
- print("Calling experimental_compute_all_tissue_correlation")
corr_results = experimental_compute_all_tissue_correlation(
primary_tissue_dict=primary_tissue_data, target_tissues_data=target_tissue_data, corr_method="pearson")
- # print('finished calling this tissue reuslts',corr_results)
-
return corr_results
@@ -123,22 +102,14 @@ def compute_correlation(start_vars, method="pearson"):
corr_input_data = {}
if corr_type == "sample":
- import time
- initial_time = time.time()
- # corr_input_data = {
- # "target_dataset": target_dataset.trait_data,
- # "target_samplelist": target_dataset.samplelist,
- # "trait_data": {
- # "trait_sample_data": sample_data,
- # "trait_id": start_vars["trait_id"]
- # }
- # }
+
sample_data = process_samples(
start_vars, this_dataset.group.samplelist)
+ initial_time = time.time()
target_dataset.get_trait_data(list(sample_data.keys()))
this_trait = retrieve_sample_data(this_trait, this_dataset)
+ print("Creating target dataset and trait took", time.time()-initial_time)
- print("Creating dataset and trait took", time.time()-initial_time)
this_trait_data = {
"trait_sample_data": sample_data,
@@ -151,15 +122,9 @@ def compute_correlation(start_vars, method="pearson"):
this_trait=this_trait_data,
target_dataset=results)
- print("computedd>>>>>>>>>>>>>")
-
print("doing sample correlation took", time.time()-initial_time)
-
- other_results_time = time.time()
- other_results = tissue_for_trait_lists(
- correlation_results, this_dataset, target_dataset, this_trait)
- print(">>>time taken for this is", time.time()-other_results_time)
-
+ # other_results = tissue_for_trait_lists(
+ # correlation_results, this_dataset, target_dataset, this_trait)
# requests_url = f"{GN3_CORRELATION_API}/sample_x/{method}"
return correlation_results
@@ -177,17 +142,9 @@ def compute_correlation(start_vars, method="pearson"):
target_tissues_data=corr_input_data[
"target_tissues_dict"],
corr_method=method)
- print("correlation y took", time.time()-initial_time)
- # initial_time = time.time()
- # correlation_results = compute_all_tissue_correlation(primary_tissue_dict=corr_input_data["primary_tissue"],
- # target_tissues_data=corr_input_data["target_tissues_dict"],
- # corr_method=method)
- # print("time taken for compute tissue is", time.time()-initial_time)
-
- # requests_url = f"{GN3_CORRELATION_API}/tissue_corr/{method}"
-
- sample_results = sample_for_trait_lists(
- correlation_results, target_dataset, this_trait, this_dataset, start_vars)
+ print("computing tissue took >>>>", time.time()-initial_time)
+ # sample_results = sample_for_trait_lists(
+ # correlation_results, target_dataset, this_trait, this_dataset, start_vars)
return correlation_results
elif corr_type == "lit":
@@ -203,7 +160,6 @@ def compute_correlation(start_vars, method="pearson"):
species=species, gene_id=this_trait_geneid)
return lit_corr_results
- print("the time taken is", time.time()-initial_time)
# requests_url = f"{GN3_CORRELATION_API}/lit_corr/{species}/{this_trait_geneid}"
# corr_input_data = geneid_dict
# corr_results = requests.post(requests_url, json=corr_input_data)