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add r2

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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
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59
cloud-functions/python/functions/performance/main.py
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@@ -15,6 +15,7 @@ import re
import xlsxwriter
import math
import matplotlib.pyplot as plt
from scipy.stats import linregress
initialize_app()
@@ -59,27 +60,27 @@ def performance(request):
if 'file' not in request.files:
print('No file part')
else:
file = request.files['file']
file = request.files['file']
if file.filename == '':
print('No selected file')
# Save the file to a temporary location
temp_filepath = '/tmp/temp_file.csv'
file.save(temp_filepath)
if file.filename == '':
print('No selected file')
# Save the file to a temporary location
temp_filepath = '/tmp/temp_file.csv'
file.save(temp_filepath)
# Load the file into a Pandas DataFrame
try:
df_reference_device_raw = pd.read_csv(temp_filepath) # Adjust the read method based on your file type (e.g., read_excel for Excel files)
# Now you can work with the DataFrame (e.g., perform analysis or display it)
print(df_reference_device_raw.head())
print('File uploaded and loaded into DataFrame successfully')
except Exception as e:
print(f'Error loading the file: {str(e)}')
finally:
# Remove the temporary file
os.remove(temp_filepath)
# Load the file into a Pandas DataFrame
try:
df_reference_device_raw = pd.read_csv(temp_filepath) # Adjust the read method based on your file type (e.g., read_excel for Excel files)
# Now you can work with the DataFrame (e.g., perform analysis or display it)
print(df_reference_device_raw.head())
print('File uploaded and loaded into DataFrame successfully')
except Exception as e:
print(f'Error loading the file: {str(e)}')
finally:
# Remove the temporary file
os.remove(temp_filepath)
path_delim = "/"
@@ -283,6 +284,7 @@ def performance(request):
wks1.write(0, 0, 'Solution')
wks1.write(0, 1, 'Slope')
wks1.write(0, 2, 'Intercept')
wks1.write(0, 3, 'R^2')
row = 1
fig, ax = plt.subplots()
@@ -295,33 +297,38 @@ def performance(request):
concentration_means = solution_data.groupby('concentration')['absorbance'].mean()
# 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))
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})
wks1.insert_image(6,0, '', {'image_data': imgdata})
### debug each row