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SimBA Installation

Requirements

  1. Python 3.6 <-- VALIDATED WITH 3.6.0
  2. Git
  3. FFmpeg
  4. Microsoft Windows operating system

Installing SimBA Option 1

Install SimBAxTF-development version

Open bash or command prompt and run the following commands on current working directory

pip install simba-uw-tf-dev

Note: If you are seeing error messages related to some dependency conflicts, then you need to either downgrade your pypi package or instruct SimBA to ignore these dependency conflicts - either works. To find more information on how to do this, click HERE

How to launch SimBA ( installed using pip install simba-uw-tf-dev)

  1. Open up command prompt anywhere.

  2. In the command prompt type

simba
  1. Hit Enter.

Note: If you installed SimBA on a virtual environment (anaconda), after installation, you may have to run run conda install shapely for SimBA to work.

Installing SimBA using Anaconda

Click here for a detail step by step guide on how to install using anaconda.

  1. Open up terminal of your environment

  2. In the terminal type

pip install simba-uw-tf-dev

  1. It will error out when running simba. To fix it, first uninstall shapely.

pip uninstall shapely

  1. Then, install shapely with conda command:

conda install -c conda-forge shapely

Installing on MacOS

Requirements

  • XCode installed
  • Homebrew
  • ffmpeg
  • Python 3.6
  • Anaconda

Installation process

  1. Create an environment for simba using anaconda terminal.

  2. In the terminal type, pip install simba-uw-tf-dev

  3. Then, conda install -c anaconda python.app

  4. Then, conda install matplotlib

  5. Then, conda uninstall shapely

  6. Then, conda install -c conda-forge shapely

  7. Then, pip install shap

  8. Lastly, pip install h5py

  9. In the terminal, type in simba to test if it works.

python dependencies

package ver.
Pillow 5.4.1
deeplabcut 2.0.9
eli5 0.10.1
imblearn 0.5.0
imutils 0.5.2
matplotlib 3.0.3
Shapely 1.6.4.post2
deepposekit 0.3.5
dtreeviz 0.8.1
opencv_python 3.4.5.20
numpy 1.18.1
imgaug 0.4.0
pandas 0.25.3
scikit_image 0.14.2
scipy 1.1.0
seaborn 0.9.0
sklearn 1.1.0
scikit-learn 0.22.1
tensorflow_gpu 0.14.1
scikit-learn 0.22.1
tqdm 4.30.0
yellowbrick 0.9.1
xgboost 0.9
tabulate 0.8.3
tables ≥ 3.5.1
dash 1.14.0
dash color picker 0.0.1
dash daqs 0.5.0
h5py 2.9.0
numba 0.48.0
numexpr 2.6.9
plotly 4.9.0
statsmodels 0.9.0
cefpython3 66.0
pyarrow 0.17.1
shap 0.35.0

Author Simon N, JJ Choong