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granger causality analysis

Installers

  • noarch v0.0.4

conda install

To install this package run one of the following:
conda install dsm-72::gnr

Description

gnr

This file will become your README and also the index of your documentation.

Developer Guide

Setup

# create conda environment
$ mamba env create -f env.yml

# update conda environment
$ mamba env update -n gnr --file env.yml
# $ mamba env update -n gnr --file env.mac.yml

Install

pip install -e .

# install from pypi
pip install gnr

nbdev

# activate conda environment
$ conda activate gnr

# make sure the gnr package is installed in development mode
$ pip install -e .

 to the gnr package
$ nbdev_prepare

Note: it might be useful to use the following snippet to enable hot reloading:

%load_ext autoreload
%autoreload 2

Publishing

# publish to pypi
$ nbdev_pypi

# publish to conda
$ nbdev_conda --build_args '-c conda-forge'

Usage

Installation

Install latest from the GitHub repository:

$ pip install git+https://github.com/dsm-72/gnr.git

or from conda

$ conda install -c dsm-72 gnr

or from pypi

$ pip install gnr
df_trj = make_mock_genes_x_tbins()
df_trj.head()
| | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | |-------|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----| | wasf | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | | colq | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | | gpr1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | | chrm3 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | ... | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | | lmod2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | ... | 8 | 8 | 8 | 9 | 9 | 9 | 9 | 9 | 9 | 9 |

5 rows × 100 columns

gc_op = GrangerCausality(n_jobs=2)
df_res = gc_op.fit_transform(df_trj, fit_params={'standard_scaler':True, 'signed_correlation': True})
df_res.head()
| | wasf_y | colq_y | gpr1_y | chrm3_y | lmod2_y | tek_y | kank3_y | oca2_y | taz_y | map4k1_y | |---------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------| | wasf_x | 1.000000 | 0.683091 | 0.314458 | 0.144127 | 0.000818 | 1.000000 | 1.000000 | 0.000066 | 0.102470 | 0.006449 | | colq_x | 1.000000 | 1.000000 | 0.779284 | 1.000000 | 1.000000 | 0.001091 | 0.192685 | 0.675090 | 1.000000 | 0.806543 | | gpr1_x | 0.805541 | 0.042286 | 1.000000 | 0.892251 | 0.795418 | 0.823063 | 1.000000 | 0.542452 | 0.001091 | 0.852052 | | chrm3_x | 0.001091 | 0.073638 | 0.168425 | 1.000000 | 0.632585 | 1.000000 | 0.102470 | 0.542452 | 1.000000 | 0.367649 | | lmod2_x | 0.683091 | 0.000104 | 0.031086 | 0.220671 | 1.000000 | 0.683091 | 0.000818 | 0.367649 | 1.000000 | 0.017608 |
gc_op.plot_df_org(figsize=(4,4))

gc_op.plot_df_res(figsize=(4,4))

Documentation

Documentation can be found hosted on GitHub repository pages. Additionally you can find package manager specific guidelines on conda and pypi respectively.


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