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gleb.py
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import numpy as np
import pandas as pd
import re
import os
# df = pd.read_csv('drive/My Drive/P.csv', sep=';', decimal=',', dtype={'PatientKey': 'Int32'}, encoding='utf-8',
# parse_dates=['BirthDate', 'LaboratoryResultsDate', 'MinLaboratoryResultsDate'])
def Gleb(df):
def lab_result_proc(s):
'''
The best method for describing RegEx:
r"""(?x)\d + # the integral part
\. # the decimal point
\d * # some fractional digits"""
'''
pattern_1 = r'^\^?\s?[<>]?=?\s?(-?\d+[.,]?\d*).*$'
pattern_2 = r'^(-?\d+[.,]?\d*)\s?-\s?(-?\d+[.,]?\d*)$'
pattern_3 = r'.*\n?.*>=?\s?(-?\d+[.,]?\d*).*\n?.*'
pattern_4 = r'.*\n?.*<=?\s?(-?\d+[.,]?\d*).*\n?.*'
res_val = s['ValueText']
res_num = re.fullmatch(pattern_1, res_val) if isinstance(res_val, str) else False
res_val = float(res_num.group(1).replace(',', '.')) if res_num else np.nan
ref_min = s['RefMin']
ref_max = s['RefMax']
ref_text = s['RefText']
ref_range = re.fullmatch(pattern_2, ref_text) if isinstance(ref_text, str) else False
ref_great = re.fullmatch(pattern_3, ref_text) if isinstance(ref_text, str) else False
ref_less = re.fullmatch(pattern_4, ref_text) if isinstance(ref_text, str) else False
ref_min = ref_min if np.isnan(ref_min) == False \
else float(ref_range.group(1).replace(',', '.')) if ref_range \
else float(ref_great.group(1).replace(',', '.')) if ref_great \
else ref_min
ref_max = ref_max if np.isnan(ref_max) == False \
else float(ref_range.group(2).replace(',', '.')) if ref_range \
else float(ref_less.group(1).replace(',', '.')) if ref_less \
else ref_max
return res_val, ref_min, ref_max
def lab_result_value(s):
result, _, _ = lab_result_proc(s)
return result
def lab_result_min(s):
_, result, _ = lab_result_proc(s)
return result
def lab_result_max(s):
_, _, result = lab_result_proc(s)
return result
def path_lab_result_proc(s):
pattern_neg = r'(?is).*не обнар.*|.*отриц.*|.*отсут.*|.*не выявл.*|.*не найден.*|.*neg.*|.*norm.*|.*none.*|.*not found.*|^-*$'
pattern_pos = r'(?is).*обнар.*|.*полож.*|.*присут.*|.*выявл.*|.*pos.*|^\+*$'
res_val = s['Value']
ref_min = s['RefMin']
ref_max = s['RefMax']
result = 0
if pd.notnull(res_val):
if isinstance(res_val, str):
if re.fullmatch(pattern_neg, res_val):
result = 1
elif re.fullmatch(pattern_pos, res_val):
result = 2
elif pd.isnull(ref_min) and pd.notnull(ref_max):
if (res_val <= ref_max):
result = 1
else:
result = 2
elif pd.notnull(ref_min) and pd.isnull(ref_max):
if (ref_min <= res_val):
result = 1
else:
result = 2
elif pd.notnull(ref_min) and pd.notnull(ref_max) and isinstance(ref_min, float) and isinstance(ref_max,
float):
if (ref_min <= res_val <= ref_max):
result = 1
else:
result = 2
return result
# 'Есть патология' if result == 2 else 'Нет патологии' if result == 1 else 'Не определенно'
def age_group(elem):
if elem['Age'] < 1:
return 1
elif 1 <= elem['Age'] <= 3:
return 2
elif 4 <= elem['Age'] <= 12:
return 3
elif 13 <= elem['Age'] < 18:
return 4
elif 18 <= elem['Age'] <= 29:
return 5
elif 30 <= elem['Age'] <= 45:
return 6
elif 46 <= elem['Age'] <= 59 and elem['Gender'] == 1:
return 7
elif 46 <= elem['Age'] <= 64 and elem['Gender'] == 0:
return 8
elif 60 <= elem['Age'] and elem['Gender'] == 1:
return 9
elif 65 <= elem['Age'] and elem['Gender'] == 0:
return 10
def path_lab_result_proc_2(s):
pattern_neg = r'''(?is).*не обнар.*|.*отриц.*|.*отсут.*|.*не выявл.*|.*не найден.*|.*neg.*|.*norm.*|.*none.*|.*not found.*|
.*небольшое количество.*|.*незначительно.*|.*единичные в препарате.*|
.*полная.*|.*прозрачная.*|.*желтый.*|.*оттенки желтого.*|^-*$'''
pattern_pos = r'(?is).*обнар.*|.*полож.*|.*присут.*|.*выявл.*|.*pos.*|^\+*$'
res_val = s['ValueText']
result = 0
if pd.notnull(res_val):
if isinstance(res_val, str):
if re.fullmatch(pattern_neg, res_val):
result = 1
elif re.fullmatch(pattern_pos, res_val):
result = 2
return result
filter3 = (df['Value'].isnull())
df.loc[filter3, 'Value'] = df.loc[filter3].apply(lab_result_value, axis=1)
filter4 = (df['RefMin'].isnull())
df.loc[filter4, 'RefMin'] = df.loc[filter4].apply(lab_result_min, axis=1)
filter5 = (df['RefMax'].isnull())
df.loc[filter5, 'RefMax'] = df.loc[filter5].apply(lab_result_max, axis=1)
df['Pathology'] = df.apply(path_lab_result_proc, axis=1)
filter3 = (df['Value'].isnull())
df.loc[filter3, 'Pathology'] = df.loc[filter3].apply(path_lab_result_proc_2, axis=1)
filter3 = (df['Value'].isnull())
df['Pathology'] = df['Pathology'] - 1
df.loc[filter3, 'Value'] = df.loc[filter3, 'Pathology']
df['Value'] = df.apply(lambda row: 0 if row['ValueText'] == row['RefText'] else row['Value'], axis=1)
df = df[(df.Value <= 5000) | (df.Value.isnull())]
data = df.pivot_table(index=['PatientKey']
, columns=['LaboratoryMethodsKey']
, values=['Value', 'Pathology']
, aggfunc='first'
, fill_value=-1
)
data.reset_index(col_level=1).to_csv('lena.csv', encoding='utf-8', index=False)
df_1 = pd.read_csv('lena.csv', sep=',', encoding='utf-8', header=1)
a = ["PatientKey",
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