Getting started#

Installation Guide for macOS & Linux#

TEEHR requires the following dependencies:

  • Python 3.10 or later

  • Java 8 or later for Spark (we use 17)

The easiest way to install TEEHR is from PyPI using pip. If using pip to install TEEHR, we recommend installing TEEHR in a virtual environment. The code below creates a new virtual environment and installs TEEHR in it.

# Create directory for your code and create a new virtual environment.
mkdir teehr_examples
cd teehr_examples
python3 -m venv .venv
source .venv/bin/activate

# Install using pip.
# Starting with version 0.4.1 TEEHR is available in PyPI
pip install teehr

# Download the required JAR files for Spark to interact with AWS S3.
python -m teehr.utils.install_spark_jars

Installation Guide for Windows#

Currently, TEEHR dependencies require users install on Linux or macOS. To use TEEHR on Windows, we recommend Windows Subsystem for Linux (WSL). Learn more about WSL.

  1. Install Linux on Windows via WSL (in Windows terminal)

    • Detailed instructions can be found here: How to Install Linux on Windows with WSL

    • Summary:

      • Run the following command in PowerShell or Windows Terminal. This will install necessary features to run Windows subsystem for Linux (WSL) and install the Ubuntu-22.04 Linux distribution.

      wsl --install -d Ubuntu-22.04

      • Restart your machine.

    • Validate install:

      • Check what Ubuntu version you have installed using the following command:

      wsl -l -v

  2. Launch Ubuntu (in Windows terminal)

    • You can launch your default WSL installation from the terminal using the following command:

    wsl

    • Upon entering the command, you should notice your terminal prompt/CWD update to display your current windows directory relative to your Linux file system (i.e. ‘/mnt/c/Users/{your_username}$’. To access your home directory in Linux, enter the following command:

    cd ~/

  3. Set-up Python on Linux (within WSL terminal)

    • Update and upgrade Ubuntu:

    sudo apt update && sudo apt upgrade
sudo apt-get install wget ca-certificates

    • Install some key default development packages (within WSL terminal):

    sudo apt install -y build-essential git curl libexpat1-dev libssl-dev zlib1g-dev libncurses5-dev libbz2-dev liblzma-dev libsqlite3-dev libffi-dev tcl-dev linux-headers-generic libgdbm-dev libreadline-dev tk tk-dev

    • Install pyenv to manage Python versions (within WSL terminal):

    curl https://pyenv.run | bash
echo 'export PYENV_ROOT="$HOME/.pyenv"' >> ~/.bashrc
echo 'command -v pyenv >/dev/null || export PATH="$PYENV_ROOT/bin:$PATH"' >> ~/.bashrc
echo 'eval "$(pyenv init -)"' >> ~/.bashrc
# Reload bashrc
source ~/.bashrc
# Confirm installation
pyenv --version

    • Install the required Python version [>=3.10.12] (within WSL terminal):

    pyenv install 3.10.12
pyenv rehash
# Set the global Python version to 3.10.12
pyenv global 3.10.12
# Confirm installation
python --version

  4. Set-up Linux dependencies (within WSL terminal)

    • Install Java 17 (within WSL terminal):

    sudo apt install openjdk-17-jre-headless
# Confirm installation
java -version

  5. Set-up VSCode in WSL

    • Follow the official VSCode Instructions here: VSCode for WSL.

      • NOTE: An installation of VSCode on Windows is required to utilize VSCode in your WSL distribution. The provided link details the full set-up process – including installation on Windows.

  6. Set-up TEEHR (within WSL terminal)

    • Create a new virtual environment:

    mkdir teehr_examples
cd teehr_examples
python3 -m venv .venv
source .venv/bin/activate
pip install teehr
python -m teehr.utils.install_spark_jars

Set-up Guide for Docker#

If you do not want to install TEEHR in your own virtual environment, you can use Docker:

docker build -t teehr:v0.4.13 .
docker run -it --rm --volume $HOME:$HOME -p 8888:8888 teehr:v0.4.13 jupyter lab --ip 0.0.0.0 $HOME

Project Objectives#

  • Easy integration into research workflows

  • Use of modern and efficient data structures and computing platforms

  • Scalable for rapid execution of large-domain/large-sample evaluations

  • Simplified exploration of performance trends and potential drivers (e.g., climate, time-period, regulation, and basin characteristics)

  • Inclusion of common and emergent evaluation methods (e.g., error statistics, skill scores, categorical metrics, hydrologic signatures, uncertainty quantification, and graphical methods)

  • Open source and community-extensible development

Why TEEHR?#

TEEHR is a python package that provides a framework for the evaluation of hydrologic model performance. It is designed to enable iterative and exploratory analysis of hydrologic data, and facilitates this through:

  • Scalability - TEEHR’s computational engine is built on PySpark, allowing it to take advantage of your available compute resources.

  • Data Integrity - TEEHR’s internal data model (The TEEHR Framework) makes it easier to work with and validate the various data making up your evaluation, such as model outputs, observations, location attributes, and more.

  • Flexibility - TEEHR is designed to be flexible and extensible, allowing you to easily customize metrics, add bootstrapping, and group and filter your data in a variety of ways.

TEEHR Evaluation Example#

The following is an example of initializing a TEEHR Evaluation, cloning a dataset from the TEEHR S3 bucket, and calculating two versions of KGE (one with bootstrap uncertainty and one without).

import teehr
from pathlib import Path

# Initialize an Evaluation object
ev = teehr.Evaluation(
   dir_path=Path(Path().home(), "temp", "quick_start_example"),
   create_dir=True
)

# Clone the example data from S3
ev.clone_from_s3("e0_2_location_example")

# Define a bootstrapper with custom parameters.
boot = teehr.Bootstrappers.CircularBlock(
   seed=50,
   reps=500,
   block_size=10,
   quantiles=[0.05, 0.95]
)
kge = teehr.DeterministicMetrics.KlingGuptaEfficiency(bootstrap=boot)
kge.output_field_name = "BS_KGE"

include_metrics = [kge, teehr.DeterministicMetrics.KlingGuptaEfficiency()]

# Get the currently available fields to use in the query.
flds = ev.joined_timeseries.field_enum()

metrics_df = ev.metrics.query(
   include_metrics=include_metrics,
   group_by=[flds.primary_location_id],
   order_by=[flds.primary_location_id]
).to_pandas()

metrics_df

For a full list of metrics currently available in TEEHR, see the Available Metrics documentation.