77 lines
2.9 KiB
Python
77 lines
2.9 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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from datetime import datetime
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import sys
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# Initialize Firebase Admin SDK
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cred = credentials.Certificate(os.getcwd() + '/' + 'keys/hpos-prod-firebase-adminsdk.json') # 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] # '2023-09-12' #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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query = test_collection.where(filter=FieldFilter("testTime", ">=", start_date)).where(filter=FieldFilter("testTime", "<", end_date))
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docs = query.stream()
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# Prepare data to store in CSV
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data = []
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for doc in docs:
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doc_data = doc.to_dict()
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# patient_id = patient_data["_id"]
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# # Query the document from testData collection based on the common _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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# print(test_doc)
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# test_data = test_doc.to_dict()
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# # Combine the data from both collections into a single dictionary
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# combined_data = {**patient_data, **test_data}
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# # Fill empty fields in test_data with corresponding values from patient_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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data.append(doc_data)
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# Convert the data to a DataFrame
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df = pd.DataFrame(data)
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print(df)
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print(df.size)
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df = df[["_id", "classificationResult", "calculatedRatio", "deviceId", "deviceRatio", "kitSerial", "led1Average", "led1Buffer", "led1Sample", "led2Average", "led2Buffer", "led2Sample", "deviceSerialNumber", "name", "testTime"]]
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# df = df.groupby(["classificationResult"]).describe()
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df_count = df.groupby(["classificationResult"]).describe()["calculatedRatio"]["count"]
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print(df.groupby(["classificationResult"]).describe()["calculatedRatio"]["count"])
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print("duplicates", len(df['_id']) - len(df['_id'].drop_duplicates()))
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# Save the DataFrame to a CSV file
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output_filename = f'tests_{datetime.today().strftime("%d_%m_%Y_%H_%M")}.xlsx'
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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.to_excel(writer, sheet_name = 'data', index=False)
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df_count.to_excel(writer, sheet_name = "count")
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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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