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download coefficents

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This commit is contained in:
Pritimay Sarkar
2023-10-12 13:41:44 +05:30
parent 4131543c1b
commit 2ef22d061c
1 changed files with 1 additions and 28 deletions
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29
scripts/coefficients.py
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@@ -8,7 +8,7 @@ from datetime import datetime
import sys
# Initialize Firebase Admin SDK
cred = credentials.Certificate(os.getcwd() + '/' + 'keys/hpos-preprod-firebase-adminsdk.json') # Replace with your own service account key path
cred = credentials.Certificate(os.getcwd() + '/' + 'keys/hpos-prod-firebase-adminsdk.json') # Replace with your own service account key path
firebase_admin.initialize_app(cred)
# Get a reference to the Firestore database
@@ -16,35 +16,13 @@ db = firestore.client()
# Specify the collections
patient_collection = db.collection("devices")
# 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 = patient_collection.stream()
# Prepare data to store in CSV
data = []
for doc in docs:
doc_data = doc.to_dict()
# patient_id = patient_data["_id"]
# # Query the document from testData collection based on the common _id
# test_docs = test_collection.where("_id", "==", patient_id).stream()
# for test_doc in test_docs:
# print(test_doc)
# test_data = test_doc.to_dict()
# # Combine the data from both collections into a single dictionary
# combined_data = {**patient_data, **test_data}
# # Fill empty fields in test_data with corresponding values from patient_data
# for key, value in combined_data.items():
# if value == "" and key in patient_data:
# combined_data[key] = patient_data[key]
# data.append(combined_data)
data.append(doc_data)
# Convert the data to a DataFrame
@@ -52,11 +30,6 @@ df = pd.DataFrame(data)
print(df)
print(df.size)
# df = df[["_id", "classificationResult", "calculatedRatio", "deviceId", "deviceRatio", "kitSerial", "led1Average", "led1Buffer", "led1Sample", "led2Average", "led2Buffer", "led2Sample", "deviceSerialNumber", "name", "testTime", "resultData"]]
# # df = df.groupby(["classificationResult"]).describe()
# df_count = df.groupby(["classificationResult"]).describe()["calculatedRatio"]["count"]
# print(df.groupby(["classificationResult"]).describe()["calculatedRatio"]["count"])
# print("duplicates", len(df['_id']) - len(df['_id'].drop_duplicates()))
# Save the DataFrame to a CSV file