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kdg (Kernel Density Graph) is a package for exploring and using kernel density algorithms developed by the neurodata group.


Below we assume you have the default Python environment already configured on your computer and you intend to install kdg inside of it. If you want to create and work with Python virtual environments, please follow instructions on venv and virtual environments. We also highly recommend conda. For instructions to install this, please look at conda.

First, make sure you have the latest version of pip (the Python package manager) installed. If you do not, refer to the Pip documentation and install pip first.

Install from Github

You can manually download kdg by cloning the git repo master version and running the file. That is, unzip the compressed package folder and run the following from the top-level source directory using the Terminal:

$ git clone
$ cd kdg
$ python3 install

Or, alternatively, you can use pip:

$ git clone
$ cd kdg
$ pip install .

Python package dependencies

kdg requires the following packages:

  • scikit-learn>=0.22.0
  • scipy>=1.4.1
  • numpy==1.19.2

Hardware requirements

kdg package requires only a standard computer with enough RAM to support the in-memory operations. GPU's can speed up the networks which are powered by tensorflow's backend.


Exploring and using kernel density algorithms







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