def get_context_data(self, **kwargs):
# Call the base implementation first to get the context
context = super(CompoundListView, self).get_context_data(**kwargs)
# add data from the DataFrame to the context
comp_tracer_list_df = qs2df.get_compound_synonym_list_df()
# convert DataFrame to a list of dictionary
data = qs2df.df_to_list_of_dict(comp_tracer_list_df)
context["df"] = data
return context
def get_context_data(self, **kwargs):
# Call the base implementation first to get the context
context = super(StudyListView, self).get_context_data(**kwargs)
# add data from the DataFrame to the context
stud_list_stats_df = qs2df.get_study_list_stats_df()
# convert DataFrame to a list of dictionary
data = qs2df.df_to_list_of_dict(stud_list_stats_df)
context["df"] = data
return context
def get_context_data(self, **kwargs):
# Call the base implementation first to get the context
context = super(SampleListView, self).get_context_data(**kwargs)
# add data from the DataFrame to the context
all_anim_msrun_df = qs2df.get_animal_msrun_all_df()
# convert DataFrame to a list of dictionary
data = qs2df.df_to_list_of_dict(all_anim_msrun_df)
context["df"] = data
return context
def study_summary(request):
"""
function-based view for studies based summary data, including selected
data fileds for animal, tissue, sample, and MSRun
get DataFrame for summary data, then convert to JSON format
"""
all_stud_msrun_df = qs2df.get_study_msrun_all_df()
# convert DataFrame to a list of dictionary
data = qs2df.df_to_list_of_dict(all_stud_msrun_df)
context = {"df": data}
return render(request, "DataRepo/study_summary.html", context)
def get_context_data(self, **kwargs):
# Call the base implementation first to get the context
context = super(StudyDetailView, self).get_context_data(**kwargs)
pk = self.kwargs.get("pk")
per_stud_msrun_df = qs2df().get_per_study_msrun_df(pk)
per_stud_stat_df = qs2df().get_per_study_stat_df(pk)
# convert DataFrame to a list of dictionary
data = qs2df.df_to_list_of_dict(per_stud_msrun_df)
stats_data = qs2df.df_to_list_of_dict(per_stud_stat_df)
context["df"] = data
context["stats_df"] = stats_data
return context
def get_context_data(self, **kwargs):
# Call the base implementation first to get the context
context = super(CompoundDetailView, self).get_context_data(**kwargs)
# add data from the DataFrame to the context
anim_list_stats_df = qs2df.get_animal_list_stats_df()
pk = self.kwargs.get("pk")
per_tracer_anim_list_stats_df = anim_list_stats_df[
anim_list_stats_df["tracer_compound_id"] == pk]
# convert DataFrame to a list of dictionary
tracer_data = qs2df.df_to_list_of_dict(per_tracer_anim_list_stats_df)
context["tracer_df"] = tracer_data
context["measured"] = (
PeakGroup.objects.filter(compounds__id__exact=pk).count() > 0)
return context
def test_study_list_stat_df(self):
"""
get data from the data frame for selected study with selected columns,
then convert the data to dictionary to compare with the example data.
"""
example_study_dict = self.get_example_study_dict()
study_list_stats_df = qs2df.get_study_list_stats_df()
stud1_list_stats_df = study_list_stats_df[
study_list_stats_df["study"] == "obob_fasted"
]
selected_columns = list(example_study_dict.keys())
stud1_list_stats_dict = stud1_list_stats_df[selected_columns].loc[0].to_dict()
self.assertEqual(stud1_list_stats_dict, example_study_dict)
def test_animal_list_stat_df(self):
"""
get data from the data frame for selected animal with selected columns,
then convert the data to dictionary to compare with the example data.
test studies as an unordered list
"""
example_animal_dict = self.get_example_animal_dict()
example_studies = self.get_example_study_list()
anim_list_stats_df = qs2df.get_animal_list_stats_df()
anim1_list_stats_df = anim_list_stats_df[anim_list_stats_df["animal"] == "971"]
selected_columns = list(example_animal_dict.keys())
anim1_list_stats_dict = anim1_list_stats_df[selected_columns].iloc[0].to_dict()
studies = anim1_list_stats_df["studies"].iloc[0].tolist()
self.assertEqual(anim1_list_stats_dict, example_animal_dict)
self.assertEqual(len(studies), len(example_studies))
self.assertEqual(any(studies), any(example_studies))
def test_comp_tracer_list_df(self):
"""
get data from the data frame for selected compound with selected columns,
then convert the data to dictionary to compare with the example data.
"""
example_compound_dict = self.get_example_compound_dict()
comp_tracer_list_df = qs2df.get_compound_synonym_list_df()
comp1_tracer_list_df = comp_tracer_list_df[
comp_tracer_list_df["compound_name"] == "lysine"
]
selected_columns = list(example_compound_dict.keys())
comp1_tracer_dict = comp1_tracer_list_df[selected_columns].iloc[0].to_dict()
self.assertEqual(comp1_tracer_dict, example_compound_dict)
# check synonym values
expected_comp_symnonym_list = ["Lysine", "lys", "lysine"]
comp_symnonym_in_df = comp1_tracer_list_df.iloc[0].synonyms.tolist()
self.assertEqual(len(comp_symnonym_in_df), len(expected_comp_symnonym_list))
self.assertEqual(any(comp_symnonym_in_df), any(comp_symnonym_in_df))
def test_animal_sample_msrun_df(self):
"""
get data from the data frame for selected sample with selected columns,
then convert the data to dictionary to compare with the example data.
test studies as an unordered list
"""
# get data for examples
example_sample1_dict = self.get_example_sample1_dict()
example_sample2_dict = self.get_example_sample2_dict()
example_studies = self.get_example_study_list()
anim_msrun_df = qs2df.get_animal_msrun_all_df()
# sample1 test
sam1_msrun_df = anim_msrun_df[anim_msrun_df["sample"] == "BAT-xz971"]
sam1_columns = list(example_sample1_dict.keys())
sam1_msrun_dict = sam1_msrun_df[sam1_columns].iloc[0].to_dict()
sam1_studies = sam1_msrun_df["studies"].iloc[0].tolist()
self.assertEqual(sam1_msrun_dict, example_sample1_dict)
self.assertEqual(len(sam1_studies), len(example_studies))
self.assertEqual(any(sam1_studies), any(example_studies))
# sample2 test
sam2_msrun_df = anim_msrun_df[anim_msrun_df["sample"] == "Br-t1-no-tracer-age"]
sam2_columns = list(example_sample2_dict.keys())
sam2_msrun_sel_dict = sam2_msrun_df[sam2_columns].iloc[0].to_dict()
sam2_msrun_all_dict = sam2_msrun_df.iloc[0].to_dict()
self.assertEqual(sam2_msrun_sel_dict, example_sample2_dict)
# test values for age and sample_time_collected
# expected values
expected_sam2_age_week = 6.0
expected_sam2_time_collected_mins = 150.0
# verify the values from the Dataframe
sam2_age_to_week = (sam2_msrun_all_dict["age"]).days // 7
# age: timedelta64[ns] type in DataFrame vs. isoformat in json output
sam2_msrun_df_json = sam2_msrun_df.to_json(
orient="records", date_format="iso", date_unit="ns"
)
sam2_msrun_df_json_data = json.loads(sam2_msrun_df_json)
sam2_age_in_json_data = sam2_msrun_df_json_data[0]["age"]
sam2_age_in_json_data_to_weeks = (
dateparse.parse_duration(sam2_age_in_json_data).days // 7
)
self.assertEqual(sam2_age_to_week, expected_sam2_age_week)
self.assertEqual(sam2_age_in_json_data_to_weeks, expected_sam2_age_week)
# sample_time_collected: timedelta64[ns] type in DataFrame vs. isoformat in json output
sam2_time_collected_to_mins = (
sam2_msrun_all_dict["sample_time_collected"]
).seconds // 60
sam2_time_collected_in_json_data = sam2_msrun_df_json_data[0][
"sample_time_collected"
]
sam2_time_collected_in_json_data_to_mins = (
dateparse.parse_duration(sam2_time_collected_in_json_data).seconds // 60
)
self.assertEqual(sam2_time_collected_to_mins, expected_sam2_time_collected_mins)
self.assertEqual(
sam2_time_collected_in_json_data_to_mins, expected_sam2_time_collected_mins
)
# sample2 has no tracer and MSRun data
self.assertTrue(sam2_msrun_all_dict["tracer"] is pd.NA)
self.assertTrue(sam2_msrun_all_dict["msrun_id"] is pd.NA)
self.assertTrue(sam2_msrun_all_dict["msrun_owner"] is pd.NA)