Waven

This project provides a Python package designed to analyze neuronal responses in the visual cortex to visual stimuli. Using a Gabor transform of the stimulus, the package enables users to extract tuning curves for key visual features such as azimuth, elevation, orientation, spatial frequency, phase, size, and drift speed.

General Documentation and tutorial
can be found here https://waven.readthedocs.io/en/latest/

Stimulus Generation package
check out https://github.com/mwshinn/zebra_noise

Waven Analysis package
packages required:

• python 3.8
• scipy
• scikit learn
• skimage
• tifffile
• pandas
• torch

installation procedure:
in conda type :

conda env create -f environment.yml
python3 -m pip install --index-url https://test.pypi.org/simple/ --no-deps waven

GUI Documentation:
https://docs.google.com/presentation/d/1nEv07CzCwYUoozucwwqi6qgS_t0jBy7KwqHKKoh2f2U/edit?usp=sharing

Example script
check out example.py

GUI

Tutorial

Setting up the parameters**

	
	import waven.WaveletGenerator as wg
	import waven.Analysis_Utils as au
	import waven.LoadPinkNoise as lpn
	import waven.zebraGUI as ui
	import numpy as np
	import gc
	import os

	# List of default parameters for the Gabor Library
	gabor_param={
	    "N_thetas":"8",
	    "Sigmas": "[2, 3, 4, 5, 6, 8]",
	    "Frequencies": "[0.015, 0.04, 0.07, 0.1]",
	    "Phases": "[0, 90]",
	    "NX": "135",
	    "NY": "54",
	    "Save Path":"/home/sophie/Documents/POSTDOC/TEMP/gabors_library.npy"
	}

	# List of default parameters
	param_defaults = {
	    "Path Directory": "/media/sophie/Expansion1/UCL/datatest/videos",
	    "Dirs": "/media/sophie/Seagate Basic/datasets",
	    "Experiment Info": "('SS002', '2024-07-23', 3)",
	    "Number of Planes": "1",
	    "Block End": "0",
	    "screen_x":"4096",
	    "screen_y":"1536",
	    "NX": "135",
	    "NY": "54",
	    "Resolution":"1.3671",
	    "Sigmas": "[2, 3, 4, 5, 6, 8]",
	    "Frequencies": "[0.015, 0.04, 0.07, 0.1]",
	    "Visual Coverage":"[-135, 45, 34, -34]",
	    "Analysis Coverage": "[-135, 0, 34, -34]",
	    "Number of Frames": "18000",
	    "Number of Trials to Keep": "3",
	    "Movie Path": "/home/sophie/Documents/POSTDOC/TEMP/videos/perlin_stimulus_10min.mp4",
	    "Library Path": "/home/sophie/Documents/POSTDOC/TEMP/gabors_library.npy",
	    "Spks Path": "None"
	}

Here is a quick explanation of each parameter:

	
	"""
	Parameters Gabor Library:
	    N_thetas (int): number of orientatuion equally spaced between 0 and 180 degree.
	    Sigmas (list): standart deviation of theb gabor filters expressed in pixels (radius of the gaussian half peak wigth).
	    Frequencies (list): spatial frequencies expressed in pixels per cycles.
	    Phases (list): 0 and pi/2.
	    NX (int): number of azimuth positions (pix) (x shape of the downsampled stimuli).
	    NY (int): number of elevation positions (pix) (y shape of the downsampled stimuli).
	    Save Path (string): where to save the gabor library

	Parameters alignement:
	    Dirs (string): where the raw data are.
	    Experiment Info: (mouse name, data, experiment number)
	    Number of Planes (int): number of acquisition planes.
	    Block End (int): timeframe where the experiment starts.
	    Number of Frames (int): number of frames stim 30 Hz -> 1800 frame/min.
	    Number of Trials to Keep(int): Number of Trials to Keep.

	Parameters analysis:
	    screen_x: stimulus screen x size inn pixels.
	    screen_y: stimulus screen y size inn pixels.
	    NX (int): number of azimuth positions (pix) (x shape of the downsampled stimuli).
	    NY (int): number of elevation positions (pix) (y shape of the downsampled stimuli).
	    Resolution (float): microscope resolution (um per pixels)
	    Sigmas (list): standart deviation of theb gabor filters expressed in pixels (radius of the gaussian half peak wigth).
	    Visual Coverage (list): [azimuth left, azimuth right, elevation top , elevation bottom] in visual degree.
	    Analysis Coverage (list): [azimuth left, azimuth right, elevation top , elevation bottom] in visual degree.
	    Movie Path: path to the stimulus (.mp4)
	    Library Path: path to Gabor library (same as save path if ran)
	    Spks Path (opt): path to the spks.npy file to skip the alignement procedure, if set ignores Parameter alignment
	"""

2. To run the UI:

	
	ui.run(param_defaults,gabor_param)

documentation can be found here https://docs.google.com/presentation/d/1nEv07CzCwYUoozucwwqi6qgS_t0jBy7KwqHKKoh2f2U/edit?usp=sharing

3. To create a new Gabor library

	if f!=0:
	    freq=True
	else:
	    freq=False
	L = wg.makeFilterLibrary(xs, ys, thetas, sigmas, offsets, f, freq=freq)
	np.save(path_save, L)
	lib_path=path_save

An already made Gabor Library well suited for mice can be found here <>

4. Downsampling and adjusting the range of visual coverage to your analysis:

	
	if (visual_coverage!=analysis_coverage):
	    visual_coverage=np.array(visual_coverage)
	    analysis_coverage=np.array(analysis_coverage)
	    ratio_x=1-((visual_coverage[0]-visual_coverage[1])-(analysis_coverage[0]-analysis_coverage[1]))/(visual_coverage[0]-visual_coverage[1])
	    ratio_y=1-((visual_coverage[2]-visual_coverage[3])-(analysis_coverage[2]-analysis_coverage[3]))/(visual_coverage[2]-visual_coverage[3])
	else:
	    ratio_x=1
	    ratio_y=1

	## downsamples and wavelet transforms the stimulus
	wg.downsample_video_binary(movpath,visual_coverage,  analysis_coverage, shape=(ny, nx), chunk_size=1000, ratios=(ratio_x, ratio_y))
	path=os.path.dirname(movpath)
	videodata=np.load(movpath[:-4]+'_downsampled.npy')

	wg.waveletDecomposition(videodata, 0, sigmas, path, lib_path)
	wg.waveletDecomposition(videodata, 1, sigmas, path, lib_path)

5. Loading you neural activity and neuron positions :

	
	spks=np.load(spks_path)
	parent_dir = os.path.dirname(spks_path)
	neuron_pos=np.load(os.join(parent_dir, 'pos.npy'))
	## converts neuron position in microns
	neuron_pos=lpn.correctNeuronPos(neuron_pos, resolution)

6. Running the Analysis:

	
	## the spikes data have to be time registered to the stimulus frames
	respcorr_zebra = au.repetability_trial3(spks, neuron_pos, plotting=True)
	wavelets0, wavelets1, wavelet_c = lpn.coarseWavelet(path,False, nx, ny, 27, 11, n_theta, ns)

	## runs correlation analysis
	rfs_zebra = au.PearsonCorrelationPinkNoise(wavelet_c.reshape(18000, -1), np.mean(spks[:, :18000], axis=0),
		                                   neuron_pos, 27, 11, ns, analysis_coverage, screen_ratio, sigmas_deg,
		                                   plotting=True)
	## plot neuron receptive field
	idx=2441
	au.Plot_RF(rfs_zebra[0][idx],4, title=np.max(rfs_zebra[0][idx]))

	## plots neuron tuning curves
	tuning_curve=au.PlotTuningCurve(rfs_zebra, 2441, analysis_coverage, sigmas_deg, screen_ratio)