hpos-data/scripts/test_collection.py

import os
import firebase_admin
from firebase_admin import credentials
from firebase_admin import firestore
from google.cloud.firestore_v1.base_query import FieldFilter
import pandas as pd
from datetime import datetime
import sys
import matplotlib.pyplot as plt

cred = credentials.Certificate(os.getcwd() + '/' + 'keys/hpos-prod-firebase-adminsdk.json')
firebase_admin.initialize_app(cred)

db = firestore.client()

patient_collection = db.collection("patientData")
test_collection = db.collection("testData")
start_date = sys.argv[1] # '2023-09-12' #input("Please enter the start date (yyyy-mm-dd): ")
end_date = sys.argv[2] #'2023-07-16' #input("Please enter the end date (yyyy-mm-dd): ")

query = test_collection.where(filter=FieldFilter("testTime", ">=", start_date)).where(filter=FieldFilter("testTime", "<", end_date))
docs = query.stream()

data = []
for doc in docs:
    doc_data = doc.to_dict()
    data.append(doc_data)

df = pd.DataFrame(data)
print(df)
print(df.size)

# 4 abs
#df = df[["_id", "batteryLevel", "batteryVoltage", "classificationResult", "prdClassification", "predictedDenovixRatio", "calculatedRatio", "deviceRatio", "kitSerial", "abs1", "led1Average", "led1Buffer", "led1Sample", "abs2", "led2Average", "led2Buffer", "led2Sample", "abs3", "led3Average", "led3Buffer", "led3Sample", "abs4", "led4Average", "led4Buffer", "led4Sample", "deviceId", "deviceSerialNumber", "name", "testTime"]]

# 2 abs
#df = df[["_id", "finalResult", "classificationResult", "calculatedRatio", "deviceRatio", "kitSerial", "led1Average", "led1Buffer", "led1Sample", "led2Average", "led2Buffer", "led2Sample", "deviceId", "deviceSerialNumber", "name", "testTime"]]
df = df[["_id", "classificationResult", "calculatedRatio", "deviceRatio", "kitSerial", "led1Average", "led1Buffer", "led1Sample", "led2Average", "led2Buffer", "led2Sample", "deviceId", "deviceSerialNumber", "name", "testTime"]]

# df = df.groupby(["classificationResult"]).describe()
df_count = df.groupby(["classificationResult"]).describe()["calculatedRatio"]["count"]
print(df.groupby(["classificationResult"]).describe()["calculatedRatio"]["count"])
print(df.groupby(["deviceId"]).describe()["calculatedRatio"]["count"])
print(df.groupby(["deviceSerialNumber"]).describe()["calculatedRatio"]["count"])
# print(df.groupby(["kitSerial", "classificationResult"]).describe())

# df_device = df[df["deviceId"] == "HCV1003"]
# df.plot(kind = 'scatter', x = 'testTime', y = 'led2Buffer')
# plt.show()

# df[df["deviceId"] == "HCV2013"]['led2Sample'].plot.box()
# plt.show()

print(df.groupby(["kitSerial"]).describe()["calculatedRatio"]["count"])

print("duplicates", len(df['_id']) - len(df['_id'].drop_duplicates()))

output_filename = f'tests_{datetime.today().strftime("%d_%m_%Y_%H_%M")}.xlsx'

downloads_dir = os.path.join(os.path.expanduser("~"), "Downloads")
output_path = os.path.join(downloads_dir, output_filename)
writer = pd.ExcelWriter(output_path, engine = 'openpyxl')
df.to_excel(writer, sheet_name = 'data', index=False)
df_count.to_excel(writer, sheet_name = "count")
df.groupby(["deviceId"]).describe().to_excel(writer, sheet_name = "stats")
df.groupby(["kitSerial"]).describe()["calculatedRatio"]["count"].to_excel(writer, sheet_name = "kit_count")
df.groupby(["kitSerial", "classificationResult"]).describe()["calculatedRatio"]["count"].to_excel(writer, sheet_name = "kit_class")
df.groupby(["deviceId", "classificationResult"]).describe()["calculatedRatio"]["count"].to_excel(writer, sheet_name = "device_class")
writer.close()

print(f"Data saved to '{output_path}'")

firebase_admin.delete_app(firebase_admin.get_app())