3.4. Run eSRRF analysis using the python package

Super-Resolution Analysis with eSRRF using the Python library

In this tutorial, we will explore how to perform super-resolution analysis using the eSRRF function from the nanopyx.methods module. We'll load an image from user files using the tifffile library for analysis.

Installation

Before we begin, make sure to install the required libraries by running:

pip install nanopyx

Importing the eSRRF Function

To get started, import the eSRRF function from the nanopyx.methods module:

from nanopyx.methods import eSRRF

Loading your input image

Next, we'll load an image from the user's files using the "tifffile" library.

import tifffile

# Replace 'path/to/your/image.tif' with the actual path to your image file
image_path = 'path/to/your/image.tif'
image = tifffile.imread(image_path)

Performing eSRRF analysis

Now that we have the eSRRF function and our image loaded, let's perform the eSRRF analysis:

# Set analysis parameters
magnification = 5
radius = 1.5
sensitivity = 1
do_intensity_weighting = True

# Run eSRRF analysis
result = eSRRF(
    image,
    magnification=magnification,
    radius=radius,
    sensitivity=sensitivity,
    doIntensityWeighting=do_intensity_weighting
)

Visualizing Results

If you want to visualize or analyze the results further, you can use matplotlib or any other plotting/analysis library of your choice:

import matplotlib.pyplot as plt

# Example: Visualize the eSRRF result
plt.imshow(result, cmap='viridis')
plt.title('eSRRF Result')
plt.colorbar(label='Localization Intensity')
plt.show()

Saving eSRRF Results

Once you have obtained the eSRRF analysis results, you might want to save them for future reference or additional analysis. We can use the tifffile library to save the results as a TIFF image.

# Replace 'path/to/save/result.tif' with the desired path to save the eSRRF result
result_path = 'path/to/save/result.tif'

# Save the eSRRF result
tifffile.imsave(result_path, result)

This code snippet saves the eSRRF result to a TIFF file at the specified path. Adjust the result_path variable to the desired location and filename for saving the results.

Conclusion

In this tutorial, you learned how to import the eSRRF function from the nanopyx.methods module, load an input image using the tifffile library, perform eSRRF analysis, and save the results. You can further explore the results using visualization libraries or incorporate them into your analysis pipeline.

Feel free to adapt the code to your specific use case, and don't hesitate to explore additional features offered by nanopyx.

Happy coding!