controls working

src/submissions/backend/excel/reports.py

@@ -1,5 +1,8 @@
+import pandas as pd
 from pandas import DataFrame
 import numpy as np
+from backend.db import models
+import json
 
 def make_report_xlsx(records:list[dict]) -> DataFrame:
     df = DataFrame.from_records(records)
@@ -10,4 +13,82 @@ def make_report_xlsx(records:list[dict]) -> DataFrame:
     print(df2.columns)
     # df2['Cost']['sum'] = df2['Cost']['sum'].apply('${:,.2f}'.format)
     df2.iloc[:, (df2.columns.get_level_values(1)=='sum') & (df2.columns.get_level_values(0)=='Cost')] = df2.iloc[:, (df2.columns.get_level_values(1)=='sum') & (df2.columns.get_level_values(0)=='Cost')].applymap('${:,.2f}'.format)
-    return df2
+    return df2
+
+
+# def split_controls_dictionary(ctx:dict, input_dict) -> list[dict]:
+#     # this will be the date in string form
+#     dict_name = list(input_dict.keys())[0]
+#     # the data associated with the date key
+#     sub_dict = input_dict[dict_name]
+#     # How many "count", "Percent", etc are in the dictionary
+#     data_size = get_dict_size(sub_dict)
+#     output = []
+#     for ii in range(data_size):
+#         new_dict = {}
+#         for genus in sub_dict:
+#             print(genus)
+#             sub_name = list(sub_dict[genus].keys())[ii]
+#             new_dict[genus] = sub_dict[genus][sub_name]
+#         output.append({"date":dict_name, "name": sub_name, "data": new_dict})
+#     return output
+        
+        
+# def get_dict_size(input:dict):
+#     return max(len(input[item]) for item in input)
+
+
+# def convert_all_controls(ctx:dict, data:list) -> dict:
+#     dfs = {}
+#     dict_list = [split_controls_dictionary(ctx, datum) for datum in data]
+#     dict_list = [item for sublist in dict_list for item in sublist]
+#     names = list(set([datum['name'] for datum in dict_list]))
+#     for name in names:
+        
+        
+#         # df = DataFrame()
+#         # entries = [{item['date']:item['data']} for item in dict_list if item['name']==name]
+#         # series_list = []
+#         # df = pd.json_normalize(entries)
+#         # for entry in entries:
+#         #     col_name = list(entry.keys())[0]
+#         #     col_dict = entry[col_name]
+#         #     series = pd.Series(data=col_dict.values(), index=col_dict.keys(), name=col_name)
+#         #     # df[col_name] = series.values
+#         #     # print(df.index)
+#         #     series_list.append(series)
+#         # df = DataFrame(series_list).T.fillna(0)
+#         # print(df)
+#         dfs['name'] = df
+#     return dfs
+
+def convert_control_by_mode(ctx:dict, control:models.Control, mode:str):
+    output = []
+    data = json.loads(getattr(control, mode))
+    for genus in data:
+        _dict = {}
+        _dict['name'] = control.name
+        _dict['submitted_date'] = control.submitted_date
+        _dict['genus'] = genus
+        _dict['target'] = 'Target' if genus.strip("*") in control.controltype.targets else "Off-target"
+        for key in data[genus]:
+            _dict[key] = data[genus][key]
+        output.append(_dict)
+    # print(output)
+    return output
+
+
+def convert_data_list_to_df(ctx:dict, input:list[dict], subtype:str|None=None) -> DataFrame:
+    df = DataFrame.from_records(input)
+    safe = ['name', 'submitted_date', 'genus', 'target']
+    print(df)
+    for column in df.columns:
+        if "percent" in column:
+            count_col = [item for item in df.columns if "count" in item][0]
+            # The actual percentage from kraken was off due to exclusion of NaN, recalculating.
+            df[column] = 100 * df[count_col] / df.groupby('submitted_date')[count_col].transform('sum')
+        if column not in safe:
+            if subtype != None and column != subtype:
+                del df[column]
+    # print(df)
+    return df