From 308f1f250255924f879e0ebc10e2518c867c3b00 Mon Sep 17 00:00:00 2001 From: Pritimay Sarkar Date: Sun, 10 Dec 2023 13:47:42 +0530 Subject: [PATCH] add 4 absorbance columns --- scripts/test_collection.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/scripts/test_collection.py b/scripts/test_collection.py index 4cb49a0..3ba84fb 100644 --- a/scripts/test_collection.py +++ b/scripts/test_collection.py @@ -8,7 +8,7 @@ from datetime import datetime import sys import matplotlib.pyplot as plt -cred = credentials.Certificate(os.getcwd() + '/' + 'keys/hpos-prod-firebase-adminsdk.json') # Replace with your own service account key path +cred = credentials.Certificate(os.getcwd() + '/' + 'keys/hpos-prod-firebase-adminsdk.json') firebase_admin.initialize_app(cred) db = firestore.client() @@ -30,7 +30,13 @@ 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"]] +# 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"])