move to folder

cloud-functions/python/functions/consolidation/consolidation.py

@@ -2,6 +2,7 @@ import functions_framework
 from firebase_functions import https_fn
 # import firebase_admin
 from firebase_admin import initialize_app, firestore
+from google.cloud.firestore import FieldFilter
 import pandas as pd
 import datetime
 import sys
@@ -65,7 +66,18 @@ def consolidation(request):
         test_data = test_doc.to_dict()
         data.append(test_data)
 
-    df = pd.DataFrame(data)
+    test_df = pd.DataFrame(data)
+
+    query = db.collection("patientData").where(filter=FieldFilter("createdAt", ">=", start_date)).where(filter=FieldFilter("createdAt", "<", end_date))
+    data = []
+    docs = query.stream()
+    for doc in docs:
+        row_data = doc.to_dict()
+        data.append(row_data)
+
+    user_df = pd.DataFrame(data)
+
+    df = pd.merge(test_df, user_df, on='_id')
 
     output_filename = f'data_{datetime.today().strftime("%d_%m_%Y_%H_%M")}.xlsx'
 
@@ -73,13 +85,16 @@ def consolidation(request):
     writer = pd.ExcelWriter(output_path, engine = 'openpyxl')
     df = df[(df['testTime'] > start_date) & (df['testTime'] <= end_date)]
     df = df.sort_values(by=['testTime'], ascending=False)
-    # df = df.drop(['resultData', "reportUploadTime", "userImageURL", "testType", "birthYear", "testStatus", "reportPath", "createdBy", "csvPath", "result", "mobileId", "resultRatio", "localFlag", "led2", "led1"], axis=1)
-    df = df[["_id", "errorMessages", "classificationResult",  "prdClassification", "predictedDenovixRatio", "calculatedRatio", "deviceRatio", "kitSerial", "abs1", "led1Average", "led1Buffer", "led1Sample", "abs2", "led2Average", "led2Buffer", "led2Sample", "abs3", "led3Average", "led3Buffer", "led3Sample", "abs4", "led4Average", "led4Buffer", "led4Sample", "batteryLevel", "batteryVoltage", "deviceId", "deviceSerialNumber", "name", "testTime"]]
-    df.rename(columns={'deviceSerialNumber': "loginId"}, inplace = True)
-    # df = df.reindex(sorted(df.columns), axis=1)
-
-    df = df.drop_duplicates()
     
+    column_names = ["_id", "classificationResult", "deviceRatioClass",  "prdClassification", "slopeRatioClass", "predictedDenovixRatio", "slopeRatio", "calculatedRatio", "deviceRatio", "kitSerial", "led1Gain1", "led1Gain2", "led1Gain4", "abs1", "led1Average", "led1Buffer", "led1Sample", "led2Gain1", "led2Gain2", "led2Gain4", "abs2", "led2Average", "led2Buffer", "led2Sample", "led3Gain1", "led3Gain2", "led3Gain4", "hb3", "abs3", "led3Average", "led3Buffer", "led3Sample", "led4Gain1", "led4Gain2", "led4Gain4", "hb4", "abs4", "led4Average", "led4Average", "led4Buffer", "led4Sample", "batteryLevel", "batteryVoltage", "solution", "concentration", "volume", "errorMessages", "deviceId", "deviceSerialNumber", "appVersion", "name_y", "testTime"]
+    common_columns = [col for col in column_names if col in df.columns]
+    df = df[common_columns]
+
+    df.rename(columns={'deviceSerialNumber': "loginId", "name_y": "name"}, inplace = True)
+    # df = df.reindex(sorted(df.columns), axis=1)
+    
+    df = df.drop_duplicates()
+
     df.to_excel(writer, sheet_name = 'data', index=False)
     writer.close()