pykirill 2024.0.8

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🐗 pykirill

Documentation

This is my personal Python package, pykirill, which includes a collection of utilities and functions that I frequently use during scientific exploration. This package is especially designed to be portable, making it suitable for environments like Google Colab where setup needs to be minimal.

Installation

To install pykirill, you can use pip directly from GitHub. This method ensures you always get the latest version. Here are the steps to follow:

pip install git+https://github.com/kirilledition/pykirill.git

Usage

Here are quick examples of how to use pykirill:

Plotting

from pykirill import plotting

plotting.setup()

axm = plotting.SubplotsManager(4)

for trajectory_fragment in range(4):
  frame_values = ...

  ax = axm.nextax()
  ax.hist(frame_values)
  ax.set_title(f"Histogram of intensity values of {trajectory_fragment}")
  ax.set_xlabel("Intensity")
  ax.set_ylabel("Frequency")

axm.show()

Transforms

from pykirill import transforms

# For NumPy arrays
x = np.array([1, 2, 3, 4], dtype=np.float32)
log_scaled_x = transforms.log_scale(x)

# For Pandas DataFrames
log_scaled_df = df.apply(transforms.log_scale)

License

pykirill is open-sourced under the MIT license. The details can be found in the LICENSE file.

TODO

• Refine docstrings (add formated usage examples, add docstrings to modules, add latex formatting if needed)
• Create better overview page (Installation, usage, link to license)
• Change license to ensure citation
• Add tips and tricks page for better datascience code??