232 lines
9.3 KiB
Python
232 lines
9.3 KiB
Python
import pandas as pd
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import numpy as np
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import os
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import re
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import xlsxwriter
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from datetime import datetime
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import math
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import matplotlib.pyplot as plt
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import io
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curdir = os.getcwd()
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path_delim = '/'
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precision_threshold = 0.009
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accuracy_threshold = 0.02
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denovix_reference_values = {
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"Tartrazine": {
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"10": 0.187191676,
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"30": 0.187191676,
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"50": 0.187447015,
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"60": 0.187191676,
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"75": 0.294739334,
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"90": 0.187191676,
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"100": 0.410113264,
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"120": 0.187191676,
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"125": 0.512526022,
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"150": 0.622883833,
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"175": 0.721095085,
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"180": 0.187191676,
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"200": 0.824065454,
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"210": 0.187191676,
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"225": 0.931385567,
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"250": 1.002224884,
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"275": 1.091996882,
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"300": 1.16558307,
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"350": 1.16558307,
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},
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"KMnO4": {
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"10": 0.187191676,
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"100": 0.187191676,
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"145": 0.187191676,
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"175": 0.187191676,
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"250": 0.077846397,
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"290": 0.359288533,
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"350": 0.111884606,
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"435": 0.187191676,
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"450": 0.138031952,
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"550": 0.175111257,
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"580": 0.628039375,
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"650": 0.208337398,
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"720": 0.187191676,
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"750": 0.238773222,
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"850": 0.267574903,
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"950": 0.299568449,
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"1050": 0.329271864,
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"1150": 0.365827844,
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"1160": 0.388015579,
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"1250": 0.388015579,
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"1350": 0.417304075,
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"1450": 0.447634285,
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"1550": 0.478220085,
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},
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}
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df_reference_device = pd.DataFrame(denovix_reference_values).T
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df_reference_device.index.name = 'Solution'
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df_reference_device.columns.name = 'Wavelength'
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df = pd.read_excel(curdir + path_delim + "data" + path_delim + "data_06_12_2023_12_47.xlsx", sheet_name="data")
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if not df.empty:
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conditions = [df['name'].str.contains('Tar', case=False, na=False),
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df['name'].str.contains('KM', case=False, na=False)]
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choices = ['Tartrazine', 'KMnO4']
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df['solution'] = np.select(conditions, choices, default=None)
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def extract_numbers(s):
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match = re.search(r'-(\d+)', s) or re.search(r'(\d+)', s)
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if match:
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return int(match.group(1))
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else:
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return None
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df["concentration"] = df['name'].apply(extract_numbers)
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df['absorbance'] = df.apply(lambda row: row['led2Average'] if row['solution'] == 'Tartrazine' else row['led1Average'], axis=1)
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df_precision_acc = df[["deviceId", "solution", "concentration", "led1Average", "led2Average", "absorbance"]].groupby(["deviceId", "solution", "concentration"]).describe()["absorbance"][["count", "min", "max", "mean"]]
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df_precision_acc['precision'] = df_precision_acc['max'] - df_precision_acc['min']
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df_precision_acc['precision_result'] = ['Fail' if diff > precision_threshold else 'Pass' for diff in df_precision_acc['precision']]
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df_precision_acc.reset_index(inplace=True)
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df_precision_acc.set_index(["deviceId", "solution", "concentration"], inplace=True)
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device_precision_results = {}
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for index, group_df in df_precision_acc.groupby(level=[0, 1, 2]):
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soln = index[1]
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concen = index[2]
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result_values = group_df['precision_result'].values
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if index[0] not in device_precision_results:
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device_precision_results[index[0]] = "Pass"
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else:
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result = 'Fail' if 'Fail' in result_values else 'Pass'
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if result == 'Fail':
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device_precision_results[index[0]] = 'Fail'
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df_device_precicion_results = pd.DataFrame(list(device_precision_results.items()), columns=['Device', 'Result'])
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df_precision_acc = df_precision_acc.assign(accuracy='', accuracy_result='')
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device_accuracy_results = {}
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for idx, row in df_precision_acc.iterrows():
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device_id = row.name[0]
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solution = row.name[1]
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concentration = str(int(row.name[2]))
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# print(concentration)
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try:
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reference_value = denovix_reference_values[solution][concentration]
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accuracy = math.fabs(row['mean'] - reference_value)
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df_precision_acc.at[idx, 'accuracy'] = accuracy
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accuracy_result = 'Pass' if accuracy < accuracy_threshold else 'Fail'
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df_precision_acc.at[idx, 'accuracy_result'] = accuracy_result
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if device_id not in device_accuracy_results:
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device_accuracy_results[device_id] = accuracy_result
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else:
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if accuracy_result == 'Fail':
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device_accuracy_results[device_id] = 'Fail'
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except KeyError:
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df_precision_acc.at[idx, 'accuracy'] = np.nan
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df_precision_acc.at[idx, 'accuracy_result'] = 'Fail'
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df_device_accuracy_results = pd.DataFrame(list(device_accuracy_results.items()), columns=['Device', 'Result'])
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device_results = {}
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df_device_results = pd.merge(df_device_precicion_results, df_device_accuracy_results, on='Device', how='outer', suffixes=('_precision', '_accuracy'))
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df_device_results['Result'] = np.where((df_device_results['Result_precision'] == 'Fail') | (df_device_results['Result_accuracy'] == 'Fail'), 'Fail', 'Pass')
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output_filename = f'data/accuracy_{datetime.today().strftime("%d_%m_%Y_%H_%M")}.xlsx'
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writer = pd.ExcelWriter(output_filename, engine = 'xlsxwriter')
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df_device_results.to_excel(writer, sheet_name="device_results")
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df_precision_acc.to_excel(writer, sheet_name="concentration")
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df.to_excel(writer, sheet_name="in")
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df_reference_device.to_excel(writer, sheet_name="reference_device")
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workbook = writer.book
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worksheet_device_results = writer.sheets['device_results']
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red_format = workbook.add_format({'bg_color': '#FFC7CE', 'font_color': '#9C0006'})
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green_format = workbook.add_format({'bg_color': '#C6EFCE', 'font_color': '#006100'})
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worksheet_device_results.conditional_format('E2:E{}'.format(df_device_results.shape[0] + 1), {'type': 'text',
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'criteria': 'containing',
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'value': 'Fail',
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'format': red_format})
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worksheet_device_results.conditional_format('E2:E{}'.format(df_device_results.shape[0] + 1), {'type': 'text',
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'criteria': 'containing',
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'value': 'Pass',
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'format': green_format})
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wks1 = workbook.add_worksheet('abs_plot')
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wks1.write(0,0,'Abs Plot')
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# Write the header
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wks1.write(0, 0, 'Solution')
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wks1.write(0, 1, 'Slope')
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wks1.write(0, 2, 'Intercept')
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row = 1
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fig, ax = plt.subplots()
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filtered_df = df[df['solution'].isin(['KMnO4', 'Tartrazine'])]
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for solution in filtered_df['solution'].unique():
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if pd.notna(solution):
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solution_data = df[df['solution'] == solution]
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concentration_means = solution_data.groupby('concentration')['absorbance'].mean()
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# Fit a linear regression model
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coefficients = np.polyfit(concentration_means.index, concentration_means.values, 1)
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print(f'Coefficients for {solution}: Slope={coefficients[0]}, Intercept={coefficients[1]}')
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ax.plot(concentration_means.index, concentration_means.values, marker='o', linestyle='-', label=f'Mean Absorbance for {solution}')
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ax.plot(concentration_means.index, np.polyval(coefficients, concentration_means.index), linestyle='--', label=f'Linear Fit for {solution}')
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annotation_text = f'Slope: {coefficients[0]:.4f}\nIntercept: {coefficients[1]:.4f}'
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ax.text(concentration_means.index[-1] + 5, np.polyval(coefficients, concentration_means.index[-1]), annotation_text, fontsize=10, verticalalignment='center')
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wks1.write(row, 0, solution)
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wks1.write(row, 1, coefficients[0])
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wks1.write(row, 2, coefficients[1])
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row += 1
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ax.set_title('Mean Absorbance for Each Solution')
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ax.set_xlabel('Concentration')
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ax.set_ylabel('Mean Absorbance')
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ax.legend()
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# plt.show()
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imgdata=io.BytesIO()
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fig.savefig(imgdata, format='png')
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wks1.insert_image(2,2, '', {'image_data': imgdata})
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### debug each row
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# # Merge df and df_device_precicion_results on deviceId
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# df_merged = pd.merge(df, df_device_precicion_results, left_on='deviceId', right_on='Device', how='left')
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# # Drop the duplicate "Device" column
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# df_merged.drop(columns=['Device'], inplace=True)
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# # Save the merged DataFrame to the Excel file
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# df_merged.to_excel(writer, sheet_name="merged_results")
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writer.close()
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else:
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# If df is empty, create an empty Excel file
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empty_output_filename = f'data/empty_excel_{datetime.today().strftime("%d_%m_%Y_%H_%M")}.xlsx'
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pd.DataFrame().to_excel(empty_output_filename, engine='xlsxwriter', index=False) |