linear fit in excal
This commit is contained in:
Pritimay Sarkar
@@ -5,6 +5,8 @@ import re
|
||||
import xlsxwriter
|
||||
from datetime import datetime
|
||||
import math
|
||||
import matplotlib.pyplot as plt
|
||||
import io
|
||||
|
||||
curdir = os.getcwd()
|
||||
path_delim = '/'
|
||||
@@ -13,45 +15,62 @@ 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,
|
||||
},
|
||||
}
|
||||
"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,
|
||||
},
|
||||
}
|
||||
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")
|
||||
df = pd.read_excel(curdir + path_delim + "data" + path_delim + "data_06_12_2023_12_47.xlsx", sheet_name="data")
|
||||
|
||||
texts_to_check = ['Tar', 'KM']
|
||||
texts_to_check = ['T', 'K']
|
||||
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)
|
||||
df['solution'] = df['solution'].replace({'T': 'Tartrazine', 'K': 'KMnO4'}, regex=True)
|
||||
|
||||
def extract_numbers(s):
|
||||
match = re.match(r'\d+', s)
|
||||
@@ -68,6 +87,9 @@ 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]
|
||||
@@ -87,11 +109,11 @@ 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])
|
||||
concentration = str(int(row.name[2]))
|
||||
# print(concentration)
|
||||
reference_value = denovix_reference_values[solution][concentration]
|
||||
|
||||
try:
|
||||
@@ -99,7 +121,6 @@ for idx, row in df_precision_acc.iterrows():
|
||||
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:
|
||||
@@ -108,24 +129,20 @@ for idx, row in df_precision_acc.iterrows():
|
||||
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_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 = {}
|
||||
|
||||
# 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'
|
||||
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_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")
|
||||
@@ -146,6 +163,61 @@ worksheet_device_results.conditional_format('E2:E{}'.format(df_device_results.sh
|
||||
'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()
|
||||
|
||||
unique_solutions = df['solution'].unique()
|
||||
|
||||
for solution in unique_solutions:
|
||||
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 the coefficients
|
||||
print(f'Coefficients for {solution}: Slope={coefficients[0]}, Intercept={coefficients[1]}')
|
||||
|
||||
# Plot the mean absorbance
|
||||
ax.plot(concentration_means.index, concentration_means.values, marker='o', linestyle='-', label=f'Mean Absorbance for {solution}')
|
||||
|
||||
# Plot the linear fit
|
||||
ax.plot(concentration_means.index, np.polyval(coefficients, concentration_means.index), linestyle='--', label=f'Linear Fit for {solution}')
|
||||
|
||||
# Add coefficients next to the linear fit trendline
|
||||
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')
|
||||
|
||||
# Write the coefficients to the worksheet
|
||||
wks1.write(row, 0, solution)
|
||||
wks1.write(row, 1, coefficients[0])
|
||||
wks1.write(row, 2, coefficients[1])
|
||||
|
||||
row += 1
|
||||
|
||||
# writer.save()
|
||||
|
||||
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')
|
||||
|
||||
Reference in New Issue
Block a user