98 lines
3.7 KiB
Python
98 lines
3.7 KiB
Python
import pandas as pd
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import os
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import numpy as np
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import re
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import math
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curdir = os.getcwd()
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path_delim = '/'
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df = pd.read_excel("data/_tmp_data_21_02_2024_17_29.xlsx", sheet_name="data")
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def device_ratio_classification(ratio):
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try:
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if ratio is not None:
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if 0.16 <= ratio <= 0.23:
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return "Normal"
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if 0.23 <= ratio <= 0.25:
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return "Negative Borderline"
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if 0.25 <= ratio <= 0.31:
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return "Sickle Cell Trait"
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if 0.31 <= ratio <= 0.36:
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return "Positive for Sickle Cell. HPLC for Confirmation"
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if 0.36 <= ratio <= 0.7:
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return "Sickle Cell Disease"
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else:
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return "Invalid"
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except Exception as e:
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return "Error"
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return "Invalid"
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def find_result_with_borderline_method_1(device_ratio, device_ratio_class, borderline_metric):
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try:
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if device_ratio is not None and borderline_metric is not None:
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if device_ratio_class == "Negative Borderline":
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return "Borderline. Normal" if borderline_metric >= 2.4 else "Borderline. Sickle Cell Trait"
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elif device_ratio_class == "Positive for Sickle Cell. HPLC for Confirmation":
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return "Borderline. Sickle Cell Trait" if borderline_metric >= 1.34 else "Borderline. Sickle Cell Disease"
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except Exception as e:
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handle_exception(e)
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return "Error"
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return str(device_ratio_class)
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def device_ratio_borderline_thresholds_br_method_2(ratio):
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try:
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if ratio is not None:
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if 0.11 <= ratio <= 0.237:
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return "Normal"
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if 0.237 <= ratio <= 0.242:
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return "Negative Borderline"
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if 0.242 <= ratio <= 0.318:
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return "Sickle Cell Trait"
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if 0.318 <= ratio <= 0.356:
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return "Positive for Sickle Cell. HPLC for Confirmation"
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if 0.356 <= ratio <= 0.7:
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return "Sickle Cell Disease"
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else:
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return "Invalid"
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except Exception as e:
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handle_exception(e)
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return "Error"
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return "Invalid"
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def reclassify_with_borderline_method_2(device_ratio, device_ratio_class, led2_average):
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try:
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if device_ratio is not None and led2_average is not None:
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if device_ratio_class == "Negative Borderline":
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return "Borderline. Normal" if led2_average >= 0.15 else "Borderline. Sickle Cell Trait"
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elif device_ratio_class == "Positive for Sickle Cell. HPLC for Confirmation":
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return "Borderline. Sickle Cell Trait" if led2_average >= 0.19 else "Borderline. Sickle Cell Disease"
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except Exception as e:
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handle_exception(e)
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return "Error"
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return str(device_ratio_class)
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def handle_exception(exception):
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pass
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df["tmpDeviceRatioClass1"] = df['deviceRatio'].apply(device_ratio_classification)
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df["borderlineMetric1"] = (df['led1Average'] - df['led2Average']) / df['deviceRatio']
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df['newDeviceRatioClass1'] = df.apply(lambda row: find_result_with_borderline_method_1(
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row['deviceRatio'],
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row['tmpDeviceRatioClass1'],
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row['borderlineMetric1']
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), axis=1)
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df["tmpDeviceRatioClass2"] = df['deviceRatio'].apply(device_ratio_borderline_thresholds_br_method_2)
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df['newDeviceRatioClass2'] = df.apply(lambda row: reclassify_with_borderline_method_2(
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row['deviceRatio'],
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row['tmpDeviceRatioClass2'],
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row['led2Average']
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), axis=1)
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# clean columns
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df = df[["_id", "name", "classificationResult", "newDeviceRatioClass1", "newDeviceRatioClass2", "deviceRatio", "led1Average", "led2Average"]]
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print(df)
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writer = pd.ExcelWriter(curdir + path_delim + "data/output.xlsx", engine = 'openpyxl')
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df.to_excel(writer, sheet_name = 'op', index=False)
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writer.close() |