import pandas as pd import numpy as np import os import re import xlsxwriter from datetime import datetime import math import matplotlib.pyplot as plt import io curdir = os.getcwd() path_delim = '/' precision_threshold = 0.009 accuracy_threshold = 0.02 denovix_reference_values = { "Tartrazine": { "10": 0.187191676, "30": 0.187191676, "50": 0.187447015, "60": 0.187191676, "75": 0.294739334, "90": 0.187191676, "100": 0.410113264, "120": 0.187191676, "125": 0.512526022, "150": 0.622883833, "175": 0.721095085, "180": 0.187191676, "200": 0.824065454, "210": 0.187191676, "225": 0.931385567, "250": 1.002224884, "275": 1.091996882, "300": 1.16558307, "350": 1.16558307, }, "KMnO4": { "10": 0.187191676, "100": 0.187191676, "145": 0.187191676, "175": 0.187191676, "250": 0.077846397, "290": 0.359288533, "350": 0.111884606, "435": 0.187191676, "450": 0.138031952, "550": 0.175111257, "580": 0.628039375, "650": 0.208337398, "720": 0.187191676, "750": 0.238773222, "850": 0.267574903, "950": 0.299568449, "1050": 0.329271864, "1150": 0.365827844, "1160": 0.388015579, "1250": 0.388015579, "1350": 0.417304075, "1450": 0.447634285, "1550": 0.478220085, }, "HB": { "2": 0.187191676, "4": 0.187191676, "5": 0.187191676, "6": 0.187191676, "8": 0.077846397, }, } 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_06_12_2023_12_47.xlsx", sheet_name="data") if not df.empty: conditions = [df['name'].str.contains('Tar', case=False, na=False), df['name'].str.contains('KM', case=False, na=False), df['name'].str.contains('HB', case=False, na=False)] choices = ['Tartrazine', 'KMnO4', "HB"] df['solution'] = np.select(conditions, choices, default=None) def extract_numbers(s): if "HB" in s: match = re.search(r'-(\d+)$', s) else: match = re.search(r'-(\d+)', s) or re.search(r'(\d+)', s) if match: return int(match.group(1)) 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']] df_precision_acc.reset_index(inplace=True) df_precision_acc.set_index(["deviceId", "solution", "concentration"], inplace=True) 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 = {} for idx, row in df_precision_acc.iterrows(): device_id = row.name[0] solution = row.name[1] concentration = str(int(row.name[2])) # print(concentration) try: reference_value = denovix_reference_values[solution][concentration] accuracy = math.fabs(row['mean'] - reference_value) df_precision_acc.at[idx, 'accuracy'] = accuracy 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: df_precision_acc.at[idx, 'accuracy'] = np.nan df_precision_acc.at[idx, 'accuracy_result'] = 'Fail' df_device_accuracy_results = pd.DataFrame(list(device_accuracy_results.items()), columns=['Device', 'Result']) device_results = {} 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'data/accuracy_{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_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}) wks1 = workbook.add_worksheet('abs_plot') wks1.write(0,0,'Abs Plot') # Write the header wks1.write(0, 0, 'Solution') wks1.write(0, 1, 'Slope') wks1.write(0, 2, 'Intercept') row = 1 fig, ax = plt.subplots() filtered_df = df[df['solution'].isin(['KMnO4', 'Tartrazine', 'HB'])] for solution in filtered_df['solution'].unique(): if pd.notna(solution): solution_data = df[df['solution'] == solution] concentration_means = solution_data.groupby('concentration')['absorbance'].mean() # Fit a linear regression model coefficients = np.polyfit(concentration_means.index, concentration_means.values, 1) print(f'Coefficients for {solution}: Slope={coefficients[0]}, Intercept={coefficients[1]}') ax.plot(concentration_means.index, concentration_means.values, marker='o', linestyle='-', label=f'Mean Absorbance for {solution}') ax.plot(concentration_means.index, np.polyval(coefficients, concentration_means.index), linestyle='--', label=f'Linear Fit for {solution}') annotation_text = f'Slope: {coefficients[0]:.4f}\nIntercept: {coefficients[1]:.4f}' ax.text(concentration_means.index[-1] + 5, np.polyval(coefficients, concentration_means.index[-1]), annotation_text, fontsize=10, verticalalignment='center') wks1.write(row, 0, solution) wks1.write(row, 1, str(coefficients[0])) wks1.write(row, 2, str(coefficients[1])) row += 1 ax.set_title('Mean Absorbance for Each Solution') ax.set_xlabel('Concentration') ax.set_ylabel('Mean Absorbance') ax.legend() # plt.show() imgdata=io.BytesIO() fig.savefig(imgdata, format='png') wks1.insert_image(2,2, '', {'image_data': imgdata}) ### 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() else: empty_output_filename = f'data/empty_excel_{datetime.today().strftime("%d_%m_%Y_%H_%M")}.xlsx' pd.DataFrame().to_excel(empty_output_filename, engine='xlsxwriter', index=False)