chnage pref name

This commit is contained in:
Pritimay Sarkar
2024-01-16 15:29:30 +05:30
parent 7917d5073b
commit 6f4ba44e48
7 changed files with 107 additions and 19 deletions

View File

@@ -17,7 +17,7 @@ db = firestore.client()
patient_collection = db.collection("patientData")
test_collection = db.collection("testData")
query = patient_collection.where(filter=FieldFilter("createdAt", ">=", "2023-12-13")).where(filter=FieldFilter("createdAt", "<", "2023-12-14"))
query = patient_collection.where(filter=FieldFilter("createdAt", ">=", "2023-12-27")).where(filter=FieldFilter("createdAt", "<", "2023-12-28"))
#.where(filter=FieldFilter("registrationCenterName", "==", "SCS high school"))
patient_docs = query.stream()
@@ -27,12 +27,12 @@ data = []
for patient_doc in patient_docs:
patient_data = patient_doc.to_dict()
data.append(patient_data)
if "sar" in patient_data['_id']:
if "HIN" in patient_data['_id']:
print(patient_data['_id'])
doc_ref = db.collection("patientData").document(patient_doc.id)
delete_field_name = 'incubationTime'
batch.update(doc_ref, {"_idSearch": '20231214' + patient_data['_id'], delete_field_name: firestore.DELETE_FIELD, "allowFreshTest": True, "testStatus": False})
batch.update(doc_ref, {"_idSearch": '20231228' + patient_data['_id'], delete_field_name: firestore.DELETE_FIELD, "allowFreshTest": True, "testStatus": False})
batch.commit()

View File

@@ -19,7 +19,7 @@ test_collection = db.collection("testData")
query = patient_collection.where(filter = FieldFilter("_id", "in",
[
'xyx','xyz'
'957539044830HEMSAN','336694457928TRUSAN','952345130951PRIKES','867067867252SAMKES','833204928565KHUKES','621177486591TANKES','644852777735SAKKES','962439786909TANKES','735707478715MANKES','200338449939SIMKES','687100580009VAIKES','285966149202RUSKES','967578334682RAKKES','921021476789PIYKES'
]))
#.where(filter=FieldFilter("registrationCenterName", "==", "SCS high school"))
patient_docs = query.stream()
@@ -35,10 +35,10 @@ for patient_doc in patient_docs:
delete_field_name = 'incubationTime'
# batch.update(doc_ref, {
# "_idSearch": '20231209' + patient_data['_id'],
# # delete_field_name: firestore.DELETE_FIELD,
# "allowFreshTest": True,
# "testStatus": False})
batch.update(doc_ref, {
"_idSearch": '20231221' + patient_data['_id'],
delete_field_name: firestore.DELETE_FIELD,
"allowFreshTest": True,
"testStatus": False})
batch.commit()

View File

@@ -1,5 +1,5 @@
curl --location --request POST 'https://asia-south1-hpos-qa.cloudfunctions.net/registerUser' --header 'Content-Type: application/json' --header 'x-client: e16a1bd15af2ee640f5a7a18c8d8333f5f01f7c66e003bbc21ee52870f1bce27' --data-raw '{
"_id": "202311180000SMIBLR",
curl --location --request POST 'https://asia-south1-hpos-af3cc.cloudfunctions.net/registerUser' --header 'Content-Type: application/json' --header 'x-client: e16a1bd15af2ee640f5a7a18c8d8333f5f01f7c66e003bbc21ee52870f1bce27' --data-raw '{
"_id": "202401030005SMIBLR",
"aadharId": "876795956467",
"abhaId": "12434949797979",
"birthYear": "2000",

View File

@@ -4,12 +4,12 @@ import os
from google.cloud.firestore_v1.base_query import FieldFilter
from datetime import datetime, timedelta
key_path = os.getcwd() + '/keys/hpos-prod-firebase-adminsdk.json'
key_path = os.getcwd() + '/keys/hpos-af3cc-firebase-adminsdk.json'
cred = credentials.Certificate(key_path)
firebase_admin.initialize_app(cred)
def update_incubation(source_collection):
db = firestore.client()
docs = db.collection(source_collection).where(filter = FieldFilter("_id", "in", ['798951069466PRABMB','450645088078SUJBHI'])).stream()
docs = db.collection(source_collection).where(filter = FieldFilter("_id", "in", ['202401030005SMIBLR'])).stream()
for doc in docs:
doc_id = doc.id #doc.to_dict()["_id"]

View File

@@ -0,0 +1,68 @@
s = """1
276199643589NISKES
Nisha Ramesh Karsarpe
O+ve
2
652355228200CHAKES
Chanda Thakare
A+ve
3
952345130951PRIKES
Priya Premdas Devgune
O+ve
4
867067867252SAMKES
Samiksha Dilip Apurkar
A+ve
5
833204928565KHUKES
Khushi Rajahans Pantawne
A+ve
6
621177486591TANKES
Tanuja Chandrashekhar Amgaonk
aOr+ve
7
644852777735SAKKES
Sakshi Hemraj Khadase
AB+ve
8
962439786909TANKES
Tanushree Pralad Khandarkar
O+ve
9
735707478715MANKES
Manisha Ramesh Jangale
A+ve
10
200338449939SIMKES
Simran Vijay Mahajan
A+ve
11
687100580009VAIKES
Vaishnavi Prabhakar Sontakke
B+ve
12
285966149202RUSKES
Rushi Dilip Sonkusare
B+ve
13
967578334682RAKKES
Rakesh Rajesh Patil
A+ve
14
921021476789PIYKES
Piyush Duryodhan Thakre
B+ve
"""
ids = s.split('\n')
# print(ids)
selected_lines = [ids[i] for i in range(1, len(ids), 4)]
result = ""
for line in selected_lines:
# if 'SAN' in line:
print(line)

20
scripts/plot_led_adc.py Normal file
View File

@@ -0,0 +1,20 @@
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_excel("data_dev7_6.xlsx", sheet_name="removed")
# print(df)
print(df.describe())
# Plot all lines on the same graph
# df["led1Buffer"].plot(label="LED 1")
# df["led2Buffer"].plot(label="LED 2")
df["led3Buffer"].plot(label="LED 3")
# df["led4Buffer"].plot(label="LED 4")
# Add legend to the plot
plt.legend()
# Display the plot
plt.show()

View File

@@ -30,12 +30,11 @@ 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"]]
column_names = ["_id", "name", "classificationResult", "prdClassification", "slopeRatioClass", "predictedDenovixRatio", "slopeRatio", "calculatedRatio", "deviceRatio", "kitSerial", "led1Gain1", "led1Gain2", "led1Gain4", "abs1", "led1Average", "led1Buffer", "led1Sample", "led2Gain1", "led2Gain2", "led2Gain4", "abs2", "led2Average", "led2Buffer", "led2Sample", "led3Gain1", "led3Gain2", "led3Gain4", "hb3", "abs3", "led3Average", "led3Buffer", "led3Sample", "led4Gain1", "led4Gain2", "led4Gain4", "hb4", "abs4", "led4Average", "led4Average", "led4Buffer", "led4Sample", "batteryLevel", "batteryVoltage", "deviceId", "deviceSerialNumber", "appVersion", "testTime"]
common_columns = [col for col in column_names if col in df.columns]
df = df[common_columns]
print("duplicates", len(df['_id']) - len(df['_id'].drop_duplicates()))
@@ -63,7 +62,8 @@ 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_count.to_excel(writer, sheet_name = "hc_count")
df.groupby(["result"]).describe()["resultRatio"]["count"].to_excel(writer, sheet_name = "tr_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")