112 lines
4.6 KiB
Python
112 lines
4.6 KiB
Python
import os
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import firebase_admin
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from firebase_admin import credentials
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from firebase_admin import firestore
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from google.cloud.firestore_v1.base_query import FieldFilter
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import pandas as pd
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# import datetime
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import sys
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import platform
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from datetime import datetime, timedelta
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environment = "qa"
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if __name__ == "__main__":
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if platform.system() == 'Windows':
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path_delim = '\\'
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else:
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path_delim = '/'
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key_file_path = os.getcwd() + path_delim + "keys" + path_delim + f"hpos-{environment}-firebase-adminsdk.json"
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cred = credentials.Certificate(key_file_path) # Replace with your own service account key path
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firebase_admin.initialize_app(cred)
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# Get a reference to the Firestore database
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db = firestore.client()
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# Specify the collections
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patient_collection = db.collection("patientData")
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test_collection = db.collection("testData")
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start_date = sys.argv[1] # '2023-01-12' #input("Please enter the start date (yyyy-mm-dd): ")
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end_date = sys.argv[2] #'2023-07-16' #input("Please enter the end date (yyyy-mm-dd): ")
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# if start_date == end_date:
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# start_date = datetime.strptime(start_date, "%Y-%m-%d")
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# end_date = (start_date + timedelta(days=1)).strftime("%Y-%m-%d")
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print("end_date", end_date)
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# patient_collection = db.collection("patientData")
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query = db.collection("testData").where(filter=FieldFilter("testTime", ">=", start_date)).where(filter=FieldFilter("testTime", "<", end_date))
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# query = test_collection
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# patient_docs = query.stream()
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data = []
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# for patient_doc in patient_docs:
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# patient_data = patient_doc.to_dict()
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# patient_id = patient_data["_id"]
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# test_docs = test_collection.where("_id", "==", patient_id).stream()
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# for test_doc in test_docs:
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# test_data = test_doc.to_dict()
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# combined_data = {**patient_data, **test_data}
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# for key, value in combined_data.items():
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# if value == "" and key in patient_data:
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# combined_data[key] = patient_data[key]
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# data.append(combined_data)
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test_docs = query.stream()
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for test_doc in test_docs:
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test_data = test_doc.to_dict()
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data.append(test_data)
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# Convert the data to a DataFrame
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test_df = pd.DataFrame(data)
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print(test_df)
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print(test_df.columns)
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query = db.collection("patientData").where(filter=FieldFilter("createdAt", ">=", start_date)).where(filter=FieldFilter("createdAt", "<", end_date))
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data = []
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docs = query.stream()
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for doc in docs:
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row_data = doc.to_dict()
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data.append(row_data)
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user_df = pd.DataFrame(data)
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df = pd.merge(test_df, user_df, on='_id')
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print(df.columns)
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# Save the DataFrame to a CSV file
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output_filename = f'data_{environment}_{datetime.today().strftime("%d_%m_%Y_%H_%M")}.xlsx'
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df.to_csv(output_filename, index=False)
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downloads_dir = os.path.join(os.path.expanduser("~"), "Downloads")
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output_path = os.path.join(downloads_dir, output_filename)
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writer = pd.ExcelWriter(output_path, engine = 'openpyxl')
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df = df[(df['testTime'] > start_date) & (df['testTime'] <= end_date)]
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df = df.sort_values(by=['testTime'], ascending=False)
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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", "led4Buffer", "led4Sample", "batteryLevel", "batteryVoltage", "solution", "concentration", "volume", "errorMessages", "deviceId", "deviceSerialNumber", "appVersion", "name", "testTime"]
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common_columns = [col for col in column_names if col in df.columns]
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df = df[common_columns]
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# df.rename(columns={'deviceSerialNumber': "login_id", "calculatedRatio": "calibrated_ratio", "led1Buffer": "427_buffer_intensity", "led2Buffer": "555_buffer_intensity", "led1Sample": "427_sample_intensity", "led2Sample": "555_sample_intensity", "led1Average": "427_absorbance", "led2Average": "555_absorbance"}, inplace = True)
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# df = df.reindex(sorted(df.columns), axis=1)
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df = df.drop_duplicates()
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df.to_excel(writer, sheet_name = 'data', index=False)
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writer.close()
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print(f"Data saved to '{output_path}'")
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# Close the Firebase Admin SDK
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firebase_admin.delete_app(firebase_admin.get_app())
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