smiuser/hpos-data
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

add 4 absorbance columns

Browse Source
This commit is contained in:
Pritimay Sarkar
2023-12-10 13:47:42 +05:30
parent 002f62216b
commit 308f1f2502
1 changed files with 8 additions and 2 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
scripts/test_collection.py
View File

@@ -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"])