Welcome to SAM’s documentation!

The SAM Python package is a collection of tools and functions mainly for sensor data or time series analysis, and by extension for smart asset management analysis. All functionality has been tested and documented. Using this ensures a generic approach, and will greatly speed up analysis.

Getting started

The easiest way to install the package is using pip:

pip install sam
# To install all optional dependencies: (such as pymongo, seaborn, tensorflow, etc.)
pip install sam[all]

There are different optional dependencies for SAM, if you are unsure use pip install ‘sam[all]’ other options include plotting (just use the plotting functionality), data_science (all dependencies needed for a data scientist) and data_engineering (dependencies for data engineer).

Keep in mind that the sam package is updated frequently, and after a while, your local version may be out of date with the online documentation. To be sure, run the pip install -U sam command to install the latest version.

Simple example

Below you can find a simple example on how to use one of our timeseries models. For more examples, check our example notebooks

from sam.datasets import load_rainbow_beach
from sam.models import MLPTimeseriesRegressor
from sam.feature_engineering import SimpleFeatureEngineer

data = load_rainbow_beach()
X, y = data, data["water_temperature"]

# Easily create rolling and time features to be used by the model
simple_features = SimpleFeatureEngineer(
        ("wave_height", "mean", 24),
        ("wave_height", "mean", 12),
        ("hour_of_day", "cyclical"),

# Define your model, see the docs for all parameters
model = MLPTimeseriesRegressor(
    predict_ahead=(1, 2, 3), # Multiple predict aheads are possible
    quantiles=(0.025, 0.975), # Predict quantile bounds for anomaly detection
model.fit(X, y)


A configuration file can be created as .config. This configuration file only stores api credentials for weather api’s for now. The configuration file should be placed in your working directory, and is parsed using the Python3 configparser, and an example configuration is shown below:



Main components

SAM aims to support the whole analysis from ingesting data to measuring and visualising model performance. The package is build up accordingly as can be seen in the Contents below.

Some highlights:

  • Easily train a functional model on timeseries data with sam.models.MLPTimeseriesRegressor

  • Build features from time series using the sam.feature_engineering.SimpleFeatureEngineer class

  • Automatically remove extreme values, flatlines and missing values with sam.validation.create_validation_pipe

  • Visualise the predicted quantiles using sam.visualization.sam_quantile_plot

Contributing / requesting features

Contributing works by forking the repository and creating a pull request if you want to incorporate your changes into the code. Also see the CONTRIBUTING.md file in the repository for more information.

We keep track of new features, bug reports and progress on the GitHub issues page.

Indices and tables