hermes

Development

This code base is under active development.

Installation

We use poetry as our main package and dependency manager. We recommend using poetry to install hermes. To do so, clone this repo, navigate to the root directory and run poetry install. For instructions on how to install poetry see here

Alternatively, you can run pip install . inside the root directory to install hermes. If your machine is macOS and ARM64 (M1, M2), this is the recommended method.

Installing without cloning

To install hermes without cloning this repository, run the following command:

$ pip install git+ssh://git@github.com/cvelezrmc/hermes.git@scratchcv

or to run without SSH

$ pip install git+https://<my_token>@github.com/cvelezrmc/hermes.git@scratchcv

where <my_token> is your personal access GitHub Token.

goals

Consistent active learning and modeling interface aimed at enabling nonstandard analysis and acquisition policy? But with batteries included for standard BayesOpt or whatever
data acquisition and wrangling with no-work FAIR backend integration
Possibly actual ML models and bag of materials/physics tricks lives in separate module?

Base level tasks

Instrument Communication:

  Basic functions for importing data from instruments and setting them up for use in modeling

  Instrument specific functions for reading data in, sending commands and the like.

Intrinsic Data Analysis:

  Analysis of the intrinsic properties of the data

  Examples include: data pre-processing, domain-specific data manipulation,

  clustering, dimesionallity reduction, distance measures.

```
  All inputs are treated as features
```
Relational Data Analysis:

  Analysis of how the inputs are related to observations of the outputs.

  Examples include: Regression, classification, physical models.

Persistant Storage:

  Basic functions for data storage and database design/use.

Coorespondence

Austin McDannald
austin.mcdannald@nist.gov
National Institute of Standards and Technology
Material Measurement Laboratory
Materials Measurment Science Division
Data and AI-Driven Materials Science Group