Install¶
Currently, ProteiML is not pip installable, it may become a feature with future releases, but currently, the best way to access the tool is through our GitHub Repository
Install from Github¶
GitHub Installation and setup is very fast but requires some python proficiency To install from Github, run the following from the top-level source directory using the Terminal:
$ git clone https://github.com/sarahwaity/ProteiML
$ cd ProteiML
Setting up the development environment¶
Within a new terminal instance, navigate to the ProteiML directory and run the following commands in the command line:
$ pip install poetry
$ poetry install
$ poetry shell
Running ProteiML¶
Test your setup using the ‘streamlined_property_matrix.csv’ to make sure no errors will cause run to fail! Within a new terminal instance, navigate to the ProteiML directory and run the following commands in the command line:
$ pip install poetry
$ poetry install
$ poetry shell
Edit the Input_Data.csv to include information about your run (more information can be found in docs/)
Update data_processing/constants.py to indicate whether to use the ‘streamlined_property_matrix.csv’ or the ‘full_property_matrix.csv’. We reccomended testing it first using the streamlined property matrix, if everything runs correctly, delete outputs and run with the full property matrix!
To start the code run:
$ python main.py
for ‘streamlined_property_matrix.csv’, this should take 5 minutes or less
for ‘full_property_matrix.csv’, this should take ~6 hours.
Running the test suite locally¶
To perform testing quickly, it is recommended to use ‘streamlined_property_matrix.csv’ Within a new terminal instance, navigate to the ProteiML directory Run the following commands in the command line:
$ pip install poetry
$ poetry install
$ poetry shell
Edit the Input_Data.csv to include information about your run (more information can be found in docs/)
Update data_processing/constants.py to have AAINDEX_DB = “streamlined_property_matrix.csv”.
To start the tests run:
$ python -m pytest --cov
Passing and test coverage result should take ~5 minutes to generate.
Running Demo Data:
Python package dependencies¶
ProteiML mainly depends on the Python scientific stack.
A full list of dependencies can be found in the pyproject.toml file.
numpy
pandas
SciPy
scikit-learn
seaborn
Hardware requirements¶
ProteiML requires only a standard computer with enough RAM to support the in-memory operations, however the speed of computation can be faster CPUs.
OS Requirements¶
This package is supported for macOS and Windows.
Testing¶
ProteiML uses the Python pytest testing package. If you don’t already have
that package installed, follow the directions on the pytest homepage.
Tutorial¶
A step by step tutorial to run a demo of the platform is currently hosted on youtube!
Coming soon