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add r2 in plot

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This commit is contained in:
Pritimay Sarkar
2024-02-10 20:24:43 +05:30
parent d802048c3a
commit 283de77d37
1 changed files with 21 additions and 5 deletions
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26
scripts/device_accuracy.py
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@@ -7,6 +7,7 @@ from datetime import datetime
import math
import matplotlib.pyplot as plt
import io
from scipy.stats import linregress
curdir = os.getcwd()
path_delim = '/'
@@ -186,6 +187,10 @@ if not df.empty:
wks1.write(0, 0, 'Solution')
wks1.write(0, 1, 'Slope')
wks1.write(0, 2, 'Intercept')
wks1.write(0, 3, 'R^2')
# Create a new DataFrame to store linear fit results
df_linearfit_results = pd.DataFrame(columns=['Solution', 'Slope', 'Intercept', 'R^2'])
row = 1
fig, ax = plt.subplots()
@@ -200,20 +205,27 @@ if not df.empty:
# Fit a linear regression model
coefficients = np.polyfit(concentration_means.index, concentration_means.values, 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'Coefficients for {solution}: Slope={coefficients[0]}, Intercept={coefficients[1]}')
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, 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')
wks1.write(row, 0, solution)
wks1.write(row, 1, str(coefficients[0]))
wks1.write(row, 2, str(coefficients[1]))
wks1.write(row, 1, str(slope))
wks1.write(row, 2, str(intercept))
wks1.write(row, 3, str(r_value**2))
# Add the linear fit results to the new DataFrame
df_linearfit_results = df_linearfit_results.append({'Solution': solution, 'Slope': slope, 'Intercept': intercept, 'R^2': r_value**2}, ignore_index=True)
row += 1
ax.set_title('Mean Absorbance for Each Solution')
@@ -224,7 +236,11 @@ if not df.empty:
imgdata=io.BytesIO()
fig.savefig(imgdata, format='png')
wks1.insert_image(2,2, '', {'image_data': imgdata})
wks1.insert_image(6,0, '', {'image_data': imgdata})
# Write linear fit results to Excel
df_linearfit_results.to_excel(writer, sheet_name="linearfit_results")
### debug each row
# # Merge df and df_device_precicion_results on deviceId