import pandas as pd import numpy as np import os import re import xlsxwriter from datetime import datetime import math curdir = os.getcwd() path_delim = '/' precision_threshold = 0.009 accuracy_threshold = 0.02 denovix_reference_values = { "Tartrazine": { "50": 0.187191676, "75": 0.187191676, "100": 0.187191676, "125": 0.187191676, "150": 0.187191676, "175": 0.187191676, "200": 0.187191676, "225": 0.187191676, "250": 0.187191676, "275": 0.187191676, "300": 0.187191676, }, "KMnO4": { "250": 0.187191676, "350": 0.187191676, "450": 0.187191676, "550": 0.187191676, "650": 0.187191676, "750": 0.187191676, "850": 0.187191676, "950": 0.187191676, "1050": 0.187191676, "1150": 0.187191676, "1250": 0.187191676, "1350": 0.187191676, "1450": 0.187191676, "1550": 0.187191676, }, } df_reference_device = pd.DataFrame(denovix_reference_values).T df_reference_device.index.name = 'Solution' df_reference_device.columns.name = 'Wavelength' df = pd.read_excel(curdir + path_delim + "data" + path_delim + "data_03_12_2023_11_25.xlsx", sheet_name="Sheet2") texts_to_check = ['Tar', 'KM'] df['solution'] = df['name'].str.extract(f"({'|'.join(texts_to_check)})", flags=re.IGNORECASE) df['solution'] = df['solution'].replace({'Tar': 'Tartrazine', 'KM': 'KMnO4'}, regex=True) def extract_numbers(s): match = re.match(r'\d+', s) if match: return int(match.group()) else: return None df["concentration"] = df['name'].apply(extract_numbers) df['absorbance'] = df.apply(lambda row: row['led2Average'] if row['solution'] == 'Tartrazine' else row['led1Average'], axis=1) df_precision_acc = df[["deviceId", "solution", "concentration", "led1Average", "led2Average", "absorbance"]].groupby(["deviceId", "solution", "concentration"]).describe()["absorbance"][["count", "min", "max", "mean"]] df_precision_acc['precision'] = df_precision_acc['max'] - df_precision_acc['min'] df_precision_acc['precision_result'] = ['Fail' if diff > precision_threshold else 'Pass' for diff in df_precision_acc['precision']] device_precision_results = {} for index, group_df in df_precision_acc.groupby(level=[0, 1, 2]): soln = index[1] concen = index[2] result_values = group_df['precision_result'].values if index[0] not in device_precision_results: device_precision_results[index[0]] = "Pass" else: result = 'Fail' if 'Fail' in result_values else 'Pass' if result == 'Fail': device_precision_results[index[0]] = 'Fail' df_device_precicion_results = pd.DataFrame(list(device_precision_results.items()), columns=['Device', 'Result']) df_precision_acc = df_precision_acc.assign(accuracy='', accuracy_result='') device_accuracy_results = {} # Calculate accuracy based on the mean column and reference values for idx, row in df_precision_acc.iterrows(): device_id = row.name[0] solution = row.name[1] concentration = str(row.name[2]) reference_value = denovix_reference_values[solution][concentration] try: reference_value = denovix_reference_values[solution][concentration] accuracy = math.fabs(row['mean'] - reference_value) df_precision_acc.at[idx, 'accuracy'] = accuracy # Add accuracy_result column accuracy_result = 'Pass' if accuracy < accuracy_threshold else 'Fail' df_precision_acc.at[idx, 'accuracy_result'] = accuracy_result if device_id not in device_accuracy_results: device_accuracy_results[device_id] = accuracy_result else: if accuracy_result == 'Fail': device_accuracy_results[device_id] = 'Fail' except KeyError: # Handle the case where the solution or concentration is not in the dictionary df_precision_acc.at[idx, 'accuracy'] = np.nan # You can use any value to represent missing data df_precision_acc.at[idx, 'accuracy_result'] = 'Fail' # Assume 'Fail' for missing data df_device_accuracy_results = pd.DataFrame(list(device_accuracy_results.items()), columns=['Device', 'Result']) device_results = {} # Mark deviceId as "Fail" if either precision or accuracy is failing df_device_results = pd.merge(df_device_precicion_results, df_device_accuracy_results, on='Device', how='outer', suffixes=('_precision', '_accuracy')) df_device_results['Result'] = np.where((df_device_results['Result_precision'] == 'Fail') | (df_device_results['Result_accuracy'] == 'Fail'), 'Fail', 'Pass') output_filename = f'precision_{datetime.today().strftime("%d_%m_%Y_%H_%M")}.xlsx' writer = pd.ExcelWriter(output_filename, engine = 'xlsxwriter') df_device_results.to_excel(writer, sheet_name="device_results") df_device_precicion_results.to_excel(writer, sheet_name="device_precision") df_device_accuracy_results.to_excel(writer, sheet_name="device_accuracy") df_precision_acc.to_excel(writer, sheet_name="concentration") df.to_excel(writer, sheet_name="in") df_reference_device.to_excel(writer, sheet_name="reference_device") workbook = writer.book worksheet_device_results = writer.sheets['device_results'] red_format = workbook.add_format({'bg_color': '#FFC7CE', 'font_color': '#9C0006'}) green_format = workbook.add_format({'bg_color': '#C6EFCE', 'font_color': '#006100'}) worksheet_device_results.conditional_format('E2:E{}'.format(df_device_results.shape[0] + 1), {'type': 'text', 'criteria': 'containing', 'value': 'Fail', 'format': red_format}) worksheet_device_results.conditional_format('E2:E{}'.format(df_device_results.shape[0] + 1), {'type': 'text', 'criteria': 'containing', 'value': 'Pass', 'format': green_format}) ### debug each row # # Merge df and df_device_precicion_results on deviceId # df_merged = pd.merge(df, df_device_precicion_results, left_on='deviceId', right_on='Device', how='left') # # Drop the duplicate "Device" column # df_merged.drop(columns=['Device'], inplace=True) # # Save the merged DataFrame to the Excel file # df_merged.to_excel(writer, sheet_name="merged_results") writer.close()