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Experanto

Experanto is a Python package designed for interpolating recordings and stimuli in neuroscience experiments. It enables users to load single or multiple experiments and create efficient dataloaders for machine learning applications.

Important

If you're interested in contributing or looking for a Google Summer of Code project, we discuss ideas and planned features in this ideas thread — please share your thoughts there before opening a PR.

Features

  • Unified Experiment Interface: Load and query multi-modal neuroscience data (neural responses, eye tracking, treadmill, visual stimuli) through a single Experiment class
  • Flexible Interpolation: Interpolate data at arbitrary time points with support for linear and nearest-neighbor methods
  • Multi-Session Support: Combine data from multiple recording sessions into a single dataloader
  • Configurable Preprocessing: YAML-based configuration for sampling rates, normalization, transforms, and filtering
  • PyTorch Integration: Native PyTorch Dataset and DataLoader implementations optimized for training

Docs

Docs

Installation

git clone https://github.com/sensorium-competition/experanto.git
cd experanto
pip install -e .

Note

To replicate the generate_sample example, use the following command (see allen_exporter):

pip install -e /path/to/allen_exporter

To replicate the sensorium_example (see sensorium_2023), install neuralpredictors (see neuralpredictors) as well:

pip install -e /path/to/neuralpredictors
pip install -e /path/to/sensorium_2023

Quick Start

Loading an Experiment

fromexperanto.experimentimportExperiment# Load a single experimentexp=Experiment("/path/to/experiment")
# Query data at specific time pointsimportnumpyasnptimes=np.linspace(0, 10, 100) # 100 time points over 10 seconds# Get interpolated data from all devicesdata=exp.interpolate(times)
# Or from a specific deviceresponses=exp.interpolate(times, device="responses")

Configuration

Experanto uses YAML configuration files. See configs/default.yaml for all options:

dataset:
modality_config:
responses:
sampling_rate: 8chunk_size: 16transforms:
normalization: "standardize"screen:
sampling_rate: 30chunk_size: 60transforms:
normalization: "normalize"dataloader:
batch_size: 16num_workers: 2

Documentation

Full documentation is available at Read the Docs.

Contributing

Contributions are welcome! Please read our Contributing Guide and open an issue or submit a pull request on GitHub.

License

This project is licensed under the MIT License. See the LICENSE file for details.

About

Python package to interpolate recordings and stimuli of neuroscience experiments

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License

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GitHub - urancon/experanto: Python package to interpolate recordings and stimuli of neuroscience experiments · GitHub
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Experanto
Experanto is a Python package designed for interpolating recordings and stimuli in neuroscience experiments. It enables users to load single or multiple experiments and create efficient dataloaders for machine learning applications.
Important
If you're interested in contributing or looking for a Google Summer of Code project, we discuss ideas and planned features in this ideas thread — please share your thoughts there before opening a PR.
Features
  • Unified Experiment Interface: Load and query multi-modal neuroscience data (neural responses, eye tracking, treadmill, visual stimuli) through a single Experiment class
  • Flexible Interpolation: Interpolate data at arbitrary time points with support for linear and nearest-neighbor methods
  • Multi-Session Support: Combine data from multiple recording sessions into a single dataloader
  • Configurable Preprocessing: YAML-based configuration for sampling rates, normalization, transforms, and filtering
  • PyTorch Integration: Native PyTorch Dataset and DataLoader implementations optimized for training
Docs
Docs
Installation
git clone https://github.com/sensorium-competition/experanto.git
cd experanto
pip install -e .
Note
To replicate the generate_sample example, use the following command (see allen_exporter):
pip install -e /path/to/allen_exporter
To replicate the sensorium_example (see sensorium_2023), install neuralpredictors (see neuralpredictors) as well:
pip install -e /path/to/neuralpredictors
pip install -e /path/to/sensorium_2023
Quick Start
Loading an Experiment
fromexperanto.experimentimportExperiment# Load a single experimentexp=Experiment("/path/to/experiment")
# Query data at specific time pointsimportnumpyasnptimes=np.linspace(0, 10, 100) # 100 time points over 10 seconds# Get interpolated data from all devicesdata=exp.interpolate(times)
# Or from a specific deviceresponses=exp.interpolate(times, device="responses")
Configuration
Experanto uses YAML configuration files. See configs/default.yaml for all options:
dataset:
modality_config:
responses:
sampling_rate: 8chunk_size: 16transforms:
normalization: "standardize"screen:
sampling_rate: 30chunk_size: 60transforms:
normalization: "normalize"dataloader:
batch_size: 16num_workers: 2
Documentation
Full documentation is available at Read the Docs.
Contributing
Contributions are welcome! Please read our Contributing Guide and open an issue or submit a pull request on GitHub.
License
This project is licensed under the MIT License. See the LICENSE file for details.
About
Python package to interpolate recordings and stimuli of neuroscience experiments 
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Readme
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GitHub - urancon/experanto: Python package to interpolate recordings and stimuli of neuroscience experiments · GitHub
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Experanto
Experanto is a Python package designed for interpolating recordings and stimuli in neuroscience experiments. It enables users to load single or multiple experiments and create efficient dataloaders for machine learning applications.
Important
If you're interested in contributing or looking for a Google Summer of Code project, we discuss ideas and planned features in this ideas thread — please share your thoughts there before opening a PR.
Features
  • Unified Experiment Interface: Load and query multi-modal neuroscience data (neural responses, eye tracking, treadmill, visual stimuli) through a single Experiment class
  • Flexible Interpolation: Interpolate data at arbitrary time points with support for linear and nearest-neighbor methods
  • Multi-Session Support: Combine data from multiple recording sessions into a single dataloader
  • Configurable Preprocessing: YAML-based configuration for sampling rates, normalization, transforms, and filtering
  • PyTorch Integration: Native PyTorch Dataset and DataLoader implementations optimized for training
Docs
Docs
Installation
git clone https://github.com/sensorium-competition/experanto.git
cd experanto
pip install -e .
Note
To replicate the generate_sample example, use the following command (see allen_exporter):
pip install -e /path/to/allen_exporter
To replicate the sensorium_example (see sensorium_2023), install neuralpredictors (see neuralpredictors) as well:
pip install -e /path/to/neuralpredictors
pip install -e /path/to/sensorium_2023
Quick Start
Loading an Experiment
fromexperanto.experimentimportExperiment# Load a single experimentexp=Experiment("/path/to/experiment")
# Query data at specific time pointsimportnumpyasnptimes=np.linspace(0, 10, 100) # 100 time points over 10 seconds# Get interpolated data from all devicesdata=exp.interpolate(times)
# Or from a specific deviceresponses=exp.interpolate(times, device="responses")
Configuration
Experanto uses YAML configuration files. See configs/default.yaml for all options:
dataset:
modality_config:
responses:
sampling_rate: 8chunk_size: 16transforms:
normalization: "standardize"screen:
sampling_rate: 30chunk_size: 60transforms:
normalization: "normalize"dataloader:
batch_size: 16num_workers: 2
Documentation
Full documentation is available at Read the Docs.
Contributing
Contributions are welcome! Please read our Contributing Guide and open an issue or submit a pull request on GitHub.
License
This project is licensed under the MIT License. See the LICENSE file for details.
About
Python package to interpolate recordings and stimuli of neuroscience experiments 
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GitHub - urancon/experanto: Python package to interpolate recordings and stimuli of neuroscience experiments · GitHub
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Experanto
Experanto is a Python package designed for interpolating recordings and stimuli in neuroscience experiments. It enables users to load single or multiple experiments and create efficient dataloaders for machine learning applications.
Important
If you're interested in contributing or looking for a Google Summer of Code project, we discuss ideas and planned features in this ideas thread — please share your thoughts there before opening a PR.
Features
  • Unified Experiment Interface: Load and query multi-modal neuroscience data (neural responses, eye tracking, treadmill, visual stimuli) through a single Experiment class
  • Flexible Interpolation: Interpolate data at arbitrary time points with support for linear and nearest-neighbor methods
  • Multi-Session Support: Combine data from multiple recording sessions into a single dataloader
  • Configurable Preprocessing: YAML-based configuration for sampling rates, normalization, transforms, and filtering
  • PyTorch Integration: Native PyTorch Dataset and DataLoader implementations optimized for training
Docs
Docs
Installation
git clone https://github.com/sensorium-competition/experanto.git
cd experanto
pip install -e .
Note
To replicate the generate_sample example, use the following command (see allen_exporter):
pip install -e /path/to/allen_exporter
To replicate the sensorium_example (see sensorium_2023), install neuralpredictors (see neuralpredictors) as well:
pip install -e /path/to/neuralpredictors
pip install -e /path/to/sensorium_2023
Quick Start
Loading an Experiment
fromexperanto.experimentimportExperiment# Load a single experimentexp=Experiment("/path/to/experiment")
# Query data at specific time pointsimportnumpyasnptimes=np.linspace(0, 10, 100) # 100 time points over 10 seconds# Get interpolated data from all devicesdata=exp.interpolate(times)
# Or from a specific deviceresponses=exp.interpolate(times, device="responses")
Configuration
Experanto uses YAML configuration files. See configs/default.yaml for all options:
dataset:
modality_config:
responses:
sampling_rate: 8chunk_size: 16transforms:
normalization: "standardize"screen:
sampling_rate: 30chunk_size: 60transforms:
normalization: "normalize"dataloader:
batch_size: 16num_workers: 2
Documentation
Full documentation is available at Read the Docs.
Contributing
Contributions are welcome! Please read our Contributing Guide and open an issue or submit a pull request on GitHub.
License
This project is licensed under the MIT License. See the LICENSE file for details.
About
Python package to interpolate recordings and stimuli of neuroscience experiments 
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GitHub - urancon/experanto: Python package to interpolate recordings and stimuli of neuroscience experiments · GitHub
Skip to content
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Repository files navigation
Experanto
Experanto is a Python package designed for interpolating recordings and stimuli in neuroscience experiments. It enables users to load single or multiple experiments and create efficient dataloaders for machine learning applications.
Important
If you're interested in contributing or looking for a Google Summer of Code project, we discuss ideas and planned features in this ideas thread — please share your thoughts there before opening a PR.
Features
  • Unified Experiment Interface: Load and query multi-modal neuroscience data (neural responses, eye tracking, treadmill, visual stimuli) through a single Experiment class
  • Flexible Interpolation: Interpolate data at arbitrary time points with support for linear and nearest-neighbor methods
  • Multi-Session Support: Combine data from multiple recording sessions into a single dataloader
  • Configurable Preprocessing: YAML-based configuration for sampling rates, normalization, transforms, and filtering
  • PyTorch Integration: Native PyTorch Dataset and DataLoader implementations optimized for training
Docs
Docs
Installation
git clone https://github.com/sensorium-competition/experanto.git
cd experanto
pip install -e .
Note
To replicate the generate_sample example, use the following command (see allen_exporter):
pip install -e /path/to/allen_exporter
To replicate the sensorium_example (see sensorium_2023), install neuralpredictors (see neuralpredictors) as well:
pip install -e /path/to/neuralpredictors
pip install -e /path/to/sensorium_2023
Quick Start
Loading an Experiment
fromexperanto.experimentimportExperiment# Load a single experimentexp=Experiment("/path/to/experiment")
# Query data at specific time pointsimportnumpyasnptimes=np.linspace(0, 10, 100) # 100 time points over 10 seconds# Get interpolated data from all devicesdata=exp.interpolate(times)
# Or from a specific deviceresponses=exp.interpolate(times, device="responses")
Configuration
Experanto uses YAML configuration files. See configs/default.yaml for all options:
dataset:
modality_config:
responses:
sampling_rate: 8chunk_size: 16transforms:
normalization: "standardize"screen:
sampling_rate: 30chunk_size: 60transforms:
normalization: "normalize"dataloader:
batch_size: 16num_workers: 2
Documentation
Full documentation is available at Read the Docs.
Contributing
Contributions are welcome! Please read our Contributing Guide and open an issue or submit a pull request on GitHub.
License
This project is licensed under the MIT License. See the LICENSE file for details.
About
Python package to interpolate recordings and stimuli of neuroscience experiments 
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Readme
License
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GitHub - urancon/experanto: Python package to interpolate recordings and stimuli of neuroscience experiments · GitHub
Skip to content
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Repository files navigation
Experanto
Experanto is a Python package designed for interpolating recordings and stimuli in neuroscience experiments. It enables users to load single or multiple experiments and create efficient dataloaders for machine learning applications.
Important
If you're interested in contributing or looking for a Google Summer of Code project, we discuss ideas and planned features in this ideas thread — please share your thoughts there before opening a PR.
Features
  • Unified Experiment Interface: Load and query multi-modal neuroscience data (neural responses, eye tracking, treadmill, visual stimuli) through a single Experiment class
  • Flexible Interpolation: Interpolate data at arbitrary time points with support for linear and nearest-neighbor methods
  • Multi-Session Support: Combine data from multiple recording sessions into a single dataloader
  • Configurable Preprocessing: YAML-based configuration for sampling rates, normalization, transforms, and filtering
  • PyTorch Integration: Native PyTorch Dataset and DataLoader implementations optimized for training
Docs
Docs
Installation
git clone https://github.com/sensorium-competition/experanto.git
cd experanto
pip install -e .
Note
To replicate the generate_sample example, use the following command (see allen_exporter):
pip install -e /path/to/allen_exporter
To replicate the sensorium_example (see sensorium_2023), install neuralpredictors (see neuralpredictors) as well:
pip install -e /path/to/neuralpredictors
pip install -e /path/to/sensorium_2023
Quick Start
Loading an Experiment
fromexperanto.experimentimportExperiment# Load a single experimentexp=Experiment("/path/to/experiment")
# Query data at specific time pointsimportnumpyasnptimes=np.linspace(0, 10, 100) # 100 time points over 10 seconds# Get interpolated data from all devicesdata=exp.interpolate(times)
# Or from a specific deviceresponses=exp.interpolate(times, device="responses")
Configuration
Experanto uses YAML configuration files. See configs/default.yaml for all options:
dataset:
modality_config:
responses:
sampling_rate: 8chunk_size: 16transforms:
normalization: "standardize"screen:
sampling_rate: 30chunk_size: 60transforms:
normalization: "normalize"dataloader:
batch_size: 16num_workers: 2
Documentation
Full documentation is available at Read the Docs.
Contributing
Contributions are welcome! Please read our Contributing Guide and open an issue or submit a pull request on GitHub.
License
This project is licensed under the MIT License. See the LICENSE file for details.
About
Python package to interpolate recordings and stimuli of neuroscience experiments 
Resources
Readme
License
Contributing
Contributing
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GitHub - urancon/experanto: Python package to interpolate recordings and stimuli of neuroscience experiments · GitHub
Skip to content
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Repository files navigation
Experanto
Experanto is a Python package designed for interpolating recordings and stimuli in neuroscience experiments. It enables users to load single or multiple experiments and create efficient dataloaders for machine learning applications.
Important
If you're interested in contributing or looking for a Google Summer of Code project, we discuss ideas and planned features in this ideas thread — please share your thoughts there before opening a PR.
Features
  • Unified Experiment Interface: Load and query multi-modal neuroscience data (neural responses, eye tracking, treadmill, visual stimuli) through a single Experiment class
  • Flexible Interpolation: Interpolate data at arbitrary time points with support for linear and nearest-neighbor methods
  • Multi-Session Support: Combine data from multiple recording sessions into a single dataloader
  • Configurable Preprocessing: YAML-based configuration for sampling rates, normalization, transforms, and filtering
  • PyTorch Integration: Native PyTorch Dataset and DataLoader implementations optimized for training
Docs
Docs
Installation
git clone https://github.com/sensorium-competition/experanto.git
cd experanto
pip install -e .
Note
To replicate the generate_sample example, use the following command (see allen_exporter):
pip install -e /path/to/allen_exporter
To replicate the sensorium_example (see sensorium_2023), install neuralpredictors (see neuralpredictors) as well:
pip install -e /path/to/neuralpredictors
pip install -e /path/to/sensorium_2023
Quick Start
Loading an Experiment
fromexperanto.experimentimportExperiment# Load a single experimentexp=Experiment("/path/to/experiment")
# Query data at specific time pointsimportnumpyasnptimes=np.linspace(0, 10, 100) # 100 time points over 10 seconds# Get interpolated data from all devicesdata=exp.interpolate(times)
# Or from a specific deviceresponses=exp.interpolate(times, device="responses")
Configuration
Experanto uses YAML configuration files. See configs/default.yaml for all options:
dataset:
modality_config:
responses:
sampling_rate: 8chunk_size: 16transforms:
normalization: "standardize"screen:
sampling_rate: 30chunk_size: 60transforms:
normalization: "normalize"dataloader:
batch_size: 16num_workers: 2
Documentation
Full documentation is available at Read the Docs.
Contributing
Contributions are welcome! Please read our Contributing Guide and open an issue or submit a pull request on GitHub.
License
This project is licensed under the MIT License. See the LICENSE file for details.
About
Python package to interpolate recordings and stimuli of neuroscience experiments 
Resources
Readme
License
Contributing
Contributing
Activity
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GitHub - urancon/experanto: Python package to interpolate recordings and stimuli of neuroscience experiments · GitHub
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Experanto
Experanto is a Python package designed for interpolating recordings and stimuli in neuroscience experiments. It enables users to load single or multiple experiments and create efficient dataloaders for machine learning applications.
Important
If you're interested in contributing or looking for a Google Summer of Code project, we discuss ideas and planned features in this ideas thread — please share your thoughts there before opening a PR.
Features
  • Unified Experiment Interface: Load and query multi-modal neuroscience data (neural responses, eye tracking, treadmill, visual stimuli) through a single Experiment class
  • Flexible Interpolation: Interpolate data at arbitrary time points with support for linear and nearest-neighbor methods
  • Multi-Session Support: Combine data from multiple recording sessions into a single dataloader
  • Configurable Preprocessing: YAML-based configuration for sampling rates, normalization, transforms, and filtering
  • PyTorch Integration: Native PyTorch Dataset and DataLoader implementations optimized for training
Docs
Docs
Installation
git clone https://github.com/sensorium-competition/experanto.git
cd experanto
pip install -e .
Note
To replicate the generate_sample example, use the following command (see allen_exporter):
pip install -e /path/to/allen_exporter
To replicate the sensorium_example (see sensorium_2023), install neuralpredictors (see neuralpredictors) as well:
pip install -e /path/to/neuralpredictors
pip install -e /path/to/sensorium_2023
Quick Start
Loading an Experiment
fromexperanto.experimentimportExperiment# Load a single experimentexp=Experiment("/path/to/experiment")
# Query data at specific time pointsimportnumpyasnptimes=np.linspace(0, 10, 100) # 100 time points over 10 seconds# Get interpolated data from all devicesdata=exp.interpolate(times)
# Or from a specific deviceresponses=exp.interpolate(times, device="responses")
Configuration
Experanto uses YAML configuration files. See configs/default.yaml for all options:
dataset:
modality_config:
responses:
sampling_rate: 8chunk_size: 16transforms:
normalization: "standardize"screen:
sampling_rate: 30chunk_size: 60transforms:
normalization: "normalize"dataloader:
batch_size: 16num_workers: 2
Documentation
Full documentation is available at Read the Docs.
Contributing
Contributions are welcome! Please read our Contributing Guide and open an issue or submit a pull request on GitHub.
License
This project is licensed under the MIT License. See the LICENSE file for details.
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Python package to interpolate recordings and stimuli of neuroscience experiments 
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