smiuser/hpos-data
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

add r2

Browse Source
This commit is contained in:
Pritimay Sarkar
2024-02-10 20:25:44 +05:30
parent c9dc69dffa
commit e8eefcd632
1 changed files with 33 additions and 26 deletions
Download Patch File Download Diff File Expand all files Collapse all files

23
cloud-functions/python/functions/performance/main.py
View File

@@ -15,6 +15,7 @@ import re
import xlsxwriter import xlsxwriter
import math import math
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from scipy.stats import linregress
initialize_app() initialize_app()
@@ -59,7 +60,7 @@ def performance(request):
if 'file' not in request.files: if 'file' not in request.files:
print('No file part') print('No file part')
else:
file = request.files['file'] file = request.files['file']
if file.filename == '': if file.filename == '':
@@ -283,6 +284,7 @@ def performance(request):
wks1.write(0, 0, 'Solution') wks1.write(0, 0, 'Solution')
wks1.write(0, 1, 'Slope') wks1.write(0, 1, 'Slope')
wks1.write(0, 2, 'Intercept') wks1.write(0, 2, 'Intercept')
wks1.write(0, 3, 'R^2')
row = 1 row = 1
fig, ax = plt.subplots() fig, ax = plt.subplots()
@@ -295,33 +297,38 @@ def performance(request):
concentration_means = solution_data.groupby('concentration')['absorbance'].mean() concentration_means = solution_data.groupby('concentration')['absorbance'].mean()
# Fit a linear regression model
coefficients = np.polyfit(concentration_means.index, concentration_means.values, 1) coefficients = np.polyfit(concentration_means.index, concentration_means.values, 1)
print(f'Coefficients for {solution}: Slope={coefficients[0]}, Intercept={coefficients[1]}') # Use linregress to get additional statistics including R-squared
slope, intercept, r_value, p_value, std_err = linregress(concentration_means.index, concentration_means.values)
print(f'For {solution}: Slope={slope:.4f}, Intercept={intercept:.4f}, R^2={r_value**2:.4f}')
ax.plot(concentration_means.index, concentration_means.values, marker='o', linestyle='-', label=f'Mean Absorbance for {solution}') 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}') 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}' annotation_text = f'Slope: {slope:.4f}\nIntercept: {intercept:.4f}\nR^2: {r_value**2:.4f}'
ax.text(concentration_means.index[-1] + 5, np.polyval(coefficients, concentration_means.index[-1]), annotation_text, fontsize=10, verticalalignment='center') 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, 0, solution)
wks1.write(row, 1, str(coefficients[0])) wks1.write(row, 1, str(slope))
wks1.write(row, 2, str(coefficients[1])) wks1.write(row, 2, str(intercept))
wks1.write(row, 3, str(r_value**2))
row += 1 row += 1
# writer.save()
ax.set_title('Mean Absorbance for Each Solution') ax.set_title('Mean Absorbance for Each Solution')
ax.set_xlabel('Concentration') ax.set_xlabel('Concentration')
ax.set_ylabel('Mean Absorbance') ax.set_ylabel('Mean Absorbance')
ax.legend() ax.legend()
# plt.show()
imgdata=io.BytesIO() imgdata=io.BytesIO()
fig.savefig(imgdata, format='png') fig.savefig(imgdata, format='png')
wks1.insert_image(2,2, '', {'image_data': imgdata}) wks1.insert_image(6,0, '', {'image_data': imgdata})
### debug each row ### debug each row