Recreate white-noise stimuli

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Project description

RecreateWhiteNoiseStimuliWithPython

Code for regenerating the random-number-based contrast values applied in experiments with white-noise stimulation in the Gollisch Lab. Used for both temporal and spatiotemporal stimuli, and for both binary and Gaussian white noise.

The random-number sequences are needed for analyzing the spike responses of recorded cells under white-noise stimulation.

Installation

A full installation of the package is done from command-line using pip with

pip install retinawhitenoise

Manual installation

If the pip package is not available for a given operating system or Python version, the package and Cython code can be compiled manually instead.

pip install https://github.com/gollischlab/RecreateWhiteNoiseStimuliWithPython/archive/main.tar.gz

Windows might require the Microsoft C++ Build Tools to be installed (including "Windows SDK" and "MSVC ... C++ x64/x86 build tools"). For details, see here.

Usage

The Python package retinawhitenoise offers two modules binarystimulus and gaussianstimulus, each offering routines to recreate, save (to disk), and load stimulus sequences.

Instructions on how to use them with example code and explanations is included in the jupyter notebook examples.ipynb.

Project details

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1.0.1

Jan 20, 2024

This version

1.0.0

Nov 20, 2023

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retinawhitenoise 1.0.0

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Algorithm	Hash digest
SHA256	`7326a8a42f55d2f0ef8783d415a03154f59c2bf6d12282dcb0513324fa64183a`
MD5	`6f6a339bccf37ff0f74f9f1fb7a0257a`
BLAKE2b-256	`ca21f26c35117464e8f4bee99ccbaee5b7cc0f94df706001794cf9e23dd8bcac`

Algorithm	Hash digest
SHA256	`b076cdaca5468f62de2543131ff496d7e8759748abaf33b0c542d6885d09973f`
MD5	`e3732c063bb5b07e0d74c717bd75e310`
BLAKE2b-256	`307d135c64325b897ede4564dde9970a98c5b65b31524358c6e94401e1537cb5`

Algorithm	Hash digest
SHA256	`3ba1b7e69e70adc863e1d2570cfd12a1c92b5054ff0220be9a6494b0ab0ffd29`
MD5	`bb4477de166cca4710436ca962204aa0`
BLAKE2b-256	`df0d0303a075b392fe070646563fee9552d9c9c70ca59f7be8e5395b3d7e267b`

Algorithm	Hash digest
SHA256	`dca12b12f1057642791d2fee8d2971a4b27ed025e2fff186ec6eaf40f833cc06`
MD5	`553f1cadbae13321c9a53dd9a057231b`
BLAKE2b-256	`f7d504a42c5742747cde3146fddf44d5e5171c7cadea969ebcb8ae966e0f3c4e`

Algorithm	Hash digest
SHA256	`92e5fe318c32f85f30fa62cbe6b0c5cf0d644a986ac6761da19a4c508a7fe704`
MD5	`fc952eb87f3cd906611d7e1d530552b2`
BLAKE2b-256	`3f405684ee6017634cb249327829afd8233a32e00c1758448e97411a69a17045`

Algorithm	Hash digest
SHA256	`522b2a73ba650819307777123197070204694a9acf59a047567d3a5a77b435e6`
MD5	`25fa8b7efc9c7b1dc87268d9aacd2115`
BLAKE2b-256	`926077a0c098a79ef6116d5fe63f5f2aa05f3a79184fd7e6a967749895e989c9`

Algorithm	Hash digest
SHA256	`cef6c221506d21c16764c4df3f08369d5cc102f1f2e45abce66bb5ed69541835`
MD5	`e19aa3b6c0f819da43e5e74d1b0117a4`
BLAKE2b-256	`3aad7239501fc92bdcb7592f40c3a0f038e13e3dd65e05faa8c58b62b0e2abb6`

Algorithm	Hash digest
SHA256	`6800c3ac189ef689c732bf5fd68ea8b50b9a69b6b44d592c09ed11acade554fc`
MD5	`51d91a18d3bac247e99a41aa67ffd0de`
BLAKE2b-256	`656a8b82717944f3c556d84c3b97db431fe6d414f32ebf1b06fe7dd3988ed4ce`

Algorithm	Hash digest
SHA256	`a2c5235cbaf55211531091376c2bf713a7f1763874f401ba140c007a0bfbd3ce`
MD5	`896694f3ebe76cf0a3aaf4e77daa61f5`
BLAKE2b-256	`ca78923fe19b38eeb42477312f54e0ebf4469dd53a2895ce3f45720ebfb251b5`

Algorithm	Hash digest
SHA256	`03aa0b9f8cc01f453d3e8441e6c76c1167c030b3dfb6f506d2622de0f34f2402`
MD5	`2194934cba36b72653e7b9db39f65ef9`
BLAKE2b-256	`1277df6a34383f8f777724c20c1a38c39964ada15a86d1570163c634bff3379d`