77 lines
3.4 KiB
Python
77 lines
3.4 KiB
Python
import os
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import firebase_admin
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from firebase_admin import credentials
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from firebase_admin import firestore
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from google.cloud.firestore_v1.base_query import FieldFilter
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import pandas as pd
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from datetime import datetime
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import sys
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import matplotlib.pyplot as plt
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cred = credentials.Certificate(os.getcwd() + '/' + 'keys/hpos-prod-firebase-adminsdk.json')
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firebase_admin.initialize_app(cred)
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db = firestore.client()
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patient_collection = db.collection("patientData")
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test_collection = db.collection("testData")
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start_date = sys.argv[1]
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end_date = sys.argv[2]
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query = test_collection.where(filter=FieldFilter("testTime", ">=", start_date)).where(filter=FieldFilter("testTime", "<", end_date))
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docs = query.stream()
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data = []
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for doc in docs:
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doc_data = doc.to_dict()
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data.append(doc_data)
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df = pd.DataFrame(data)
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print(df)
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print(df.size)
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# 4 abs
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#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"]]
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# 2 abs
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#df = df[["_id", "finalResult", "classificationResult", "calculatedRatio", "deviceRatio", "kitSerial", "led1Average", "led1Buffer", "led1Sample", "led2Average", "led2Buffer", "led2Sample", "deviceId", "deviceSerialNumber", "name", "testTime"]]
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df = df[["_id", "classificationResult", "calculatedRatio", "deviceRatio", "kitSerial", "led1Average", "led1Buffer", "led1Sample", "led2Average", "led2Buffer", "led2Sample", "deviceId", "deviceSerialNumber", "name", "testTime"]]
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print("duplicates", len(df['_id']) - len(df['_id'].drop_duplicates()))
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df = df.drop_duplicates()
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# df = df.groupby(["classificationResult"]).describe()
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df_count = df.groupby(["classificationResult"]).describe()["calculatedRatio"]["count"]
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print(df.groupby(["classificationResult"]).describe()["calculatedRatio"]["count"])
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print(df.groupby(["deviceId"]).describe()["calculatedRatio"]["count"])
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print(df.groupby(["deviceSerialNumber"]).describe()["calculatedRatio"]["count"])
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# print(df.groupby(["kitSerial", "classificationResult"]).describe())
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# df_device = df[df["deviceId"] == "HCV1003"]
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# df.plot(kind = 'scatter', x = 'testTime', y = 'led2Buffer')
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# plt.show()
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# df[df["deviceId"] == "HCV2013"]['led2Sample'].plot.box()
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# plt.show()
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print(df.groupby(["kitSerial"]).describe()["calculatedRatio"]["count"])
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output_filename = f'tests_{datetime.today().strftime("%d_%m_%Y_%H_%M")}.xlsx'
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downloads_dir = os.path.join(os.path.expanduser("~"), "Downloads")
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output_path = os.path.join(downloads_dir, output_filename)
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writer = pd.ExcelWriter(output_path, engine = 'openpyxl')
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df.to_excel(writer, sheet_name = 'data', index=False)
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df_count.to_excel(writer, sheet_name = "count")
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df.groupby(["deviceId"]).describe().to_excel(writer, sheet_name = "stats")
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df.groupby(["kitSerial"]).describe()["calculatedRatio"]["count"].to_excel(writer, sheet_name = "kit_count")
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df.groupby(["kitSerial", "classificationResult"]).describe()["calculatedRatio"]["count"].to_excel(writer, sheet_name = "kit_class")
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df.groupby(["deviceId", "classificationResult"]).describe()["calculatedRatio"]["count"].to_excel(writer, sheet_name = "device_class")
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writer.close()
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print(f"Data saved to '{output_path}'")
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firebase_admin.delete_app(firebase_admin.get_app())
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