Experiment Module

Experiment module for DRIADA.

This module provides the core Experiment class and utilities for loading, saving, and generating synthetic experimental data.

Core data structures and utilities for managing neural experiments, including data loading/saving, spike reconstruction, wavelet analysis, and synthetic data generation.

Module Components

• Core Experiment Classes
• Data Loading and Saving
• Spike Reconstruction Methods
• Wavelet Event Detection
• Synthetic Data Generation

Quick Links

Core Classes

• Experiment - Main experiment container

• Neuron - Single neuron analysis

• Core Experiment Classes - Experiment management

Loading and Saving

• load_experiment() - Load from various formats

• load_exp_from_aligned_data() - Load pre-aligned data

• save_exp_to_pickle() - Save experiments

• load_exp_from_pickle() - Load pickled experiments

• Data Loading and Saving - All I/O functions

Spike Reconstruction

• reconstruct_spikes() - Main reconstruction function

• wavelet_reconstruction() - Wavelet-based method

• threshold_reconstruction() - Threshold-based method

• Spike Reconstruction Methods - Spike deconvolution methods

Wavelet Analysis

• extract_wvt_events() - Extract wavelet events

• get_cwt_ridges() - Find CWT ridges

• Wavelet Event Detection - Wavelet event detection

Synthetic Data Generation

• generate_synthetic_exp() - Basic synthetic data

• generate_circular_manifold_exp() - Head direction cells

• generate_2d_manifold_exp() - 2D place cells

• generate_mixed_population_exp() - Mixed populations

• Synthetic Data Generation - All synthetic data generators

Usage Example

from driada.experiment import Experiment, load_exp_from_pickle

# Load saved experiment
# Replace with your actual experiment file path
# sample_pkl_path = "path/to/your/experiment.pkl"
# exp = load_exp_from_pickle(sample_pkl_path)

# Or create synthetic data
from driada.experiment import generate_circular_manifold_exp
exp = generate_circular_manifold_exp(
    n_neurons=50,
    duration=600,
    noise_std=0.1
)

# Access data
calcium_data = exp.calcium  # MultiTimeSeries

# Reconstruct spikes
from driada.experiment import reconstruct_spikes
spikes = reconstruct_spikes(exp.calcium, fps=exp.fps, method='wavelet')