Installation

PyTurbo_SF can be installed in several ways. Choose the method that best fits your needs.

Requirements

Python Version

PyTurbo_SF requires Python 3.12 or higher.

Dependencies

The following packages are required:

  • numpy (≥1.20.0) - Numerical computing

  • xarray (≥0.19.0) - Labeled multi-dimensional arrays

  • scipy (≥1.7.0) - Scientific computing

  • pandas (≥1.3.0) - Data manipulation

  • joblib (≥1.0.0) - Parallel computing

Optional dependencies for enhanced functionality:

  • matplotlib (≥3.5.0) - Plotting and visualization

  • dask (≥2021.10.0) - Parallel computing for large datasets

  • zarr (≥2.10.0) - Chunked, compressed array storage

  • netcdf4 (≥1.5.0) - NetCDF file support

Installation Methods

Method 2: Development Installation

For development or to get the latest features:

# Clone the repository
git clone https://github.com/aayouche/pyturbo_sf.git
cd pyturbo_sf

# Install in development mode
pip install -e .

# Or with optional dependencies
pip install -e .[dev,complete]

Method 3: From Source

To install from source without cloning:

pip install git+https://github.com/aayouche/pyturbo_sf.git

Method 4: Virtual Environment Setup

We strongly recommend using a virtual environment to avoid dependency conflicts.

Option A: Using venv (built-in Python)

# Create virtual environment
python -m venv pyturbo_env

# Activate (Linux/Mac)
source pyturbo_env/bin/activate

# Activate (Windows)
pyturbo_env\Scripts\activate

# Install with all optional dependencies (recommended)
pip install "pyturbo_sf[complete]"

# Or install base package only
pip install pyturbo_sf

Option B: Using Conda (Recommended for New Users)

If you don’t have Python installed or are new to Python, we recommend using Miniconda, a lightweight distribution that includes conda, Python, and essential packages.

  1. Install Miniconda (Linux/Mac):

# Download Miniconda installer
curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh

# Run installer
bash Miniconda3-latest-Linux-x86_64.sh

# Follow the prompts, then restart your terminal or run:
source ~/.bashrc

For Mac (Apple Silicon):

curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh
bash Miniconda3-latest-MacOSX-arm64.sh

For Windows, download the installer from https://docs.conda.io/en/latest/miniconda.html

  1. Create and activate a conda environment:

# Create a new environment with Python 3.12
conda create -n pyturbo_env python=3.12

# Activate the environment
conda activate pyturbo_env

  1. Install PyTurbo_SF with all dependencies:

# Install with all optional dependencies (recommended)
pip install "pyturbo_sf[complete]"

# Or install base package only
pip install pyturbo_sf

  1. Verify installation:

python -c "import pyturbo_sf; print(pyturbo_sf.__version__)"

Note

To deactivate the environment when you’re done:

conda deactivate

Verification

To verify your installation, run the following in Python:

import pyturbo_sf
print(f"PyTurbo_SF version: {pyturbo_sf.__version__}")

# Test basic functionality
import numpy as np
import xarray as xr

# Create simple test data
x = np.linspace(0, 10, 100)
data = np.sin(x) + 0.1 * np.random.randn(100)
ds = xr.Dataset(
    data_vars={"signal": ("x", data)},
    coords={"x": x}
)

# Test structure function calculation
bins = {'x': np.logspace(-1, 1, 10)}
result = pyturbo_sf.bin_sf_1d(
    ds=ds,
    variables_names=["signal"],
    order=2,
    bins=bins,
    fun='scalar',
    bootsize={'x':10},
    initial_nbootstrap=5,
    max_nbootstrap=10
)

print("Installation successful!")

Common Issues

ImportError: No module named ‘pyturbo_sf’

  • Ensure you’ve activated the correct environment

  • Verify installation with pip list | grep pyturbo

Memory errors with large datasets

  • Use appropriate bootsize parameters

  • Consider using Dask for larger-than-memory datasets

  • Increase system swap space if needed

Slow performance

  • Use appropriate backend parameter (‘loky’, ‘threading’, ‘multiprocessing’)

Convergence issues

  • Increase max_nbootstrap parameter

  • Adjust convergence_eps threshold

  • Check data quality and structure

Getting Help

If you encounter installation issues:

  1. Check the GitHub Issues

  2. Create a new issue with:

    • Your operating system and Python version

    • Complete error message

    • Installation method used

  3. Join our community discussions

Upgrading

To upgrade PyTurbo_SF to the latest version:

# Using pip
pip install --upgrade pyturbo_sf

To upgrade to a specific version:

pip install pyturbo_sf==1.0.7

Uninstallation

To remove PyTurbo_SF:

# Using pip
pip uninstall pyturbo_sf

Next Steps

Once installed, check out the Quick Start Guide guide to begin using PyTurbo_SF, or explore the Examples and Tutorials for detailed tutorials.