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BlackMarblePy is a Python package that provides a simple way to use nighttime lights data from NASA’s Black Marble project. Black Marble is a NASA Earth Science Data Systems (ESDS) project that provides a product suite of daily, monthly and yearly global nighttime lights. This package automates the process of downloading all relevant tiles from the NASA LAADS DAAC to cover a region of interest, converting the raw files (in HDF5 format), to georeferenced rasters, and mosaicing rasters together when needed.


  • Download daily, monthly, and yearly nighttime lights data for user-specified region of interest and time.

  • Parallel downloading for faster data retrieval and automatic retry mechanism for handling network errors.

  • Access NASA Black Marble as a Xarray Dataset

    • Integrated data visualization with customization options

      • Choose between various plot types, including bar charts, line graphs, and heatmaps.

      • Customize plot appearance with color palettes, axes labels, titles, and legends.

      • Save visualizations as high-resolution images for presentations or reports.

    • Perform time series analysis on nighttime lights data.

      • Calculate zonal statistics like mean and sum.

      • Plot time series of nighttime lights data.


BlackMarblePy is available on PyPI as blackmarblepy and can installed using pip:

From PyPI#

pip install blackmarblepy

From Source#

  1. Clone or download this repository to your local machine. Then, navigate to the root directory of the repository:

    git clone
    cd blackmarblepy
  2. Create a virtual environment (optional but recommended):

    python3 -m venv venv
    source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
  3. Install the package with dependencies:

    pip install .

    Install the package in editable mode with dependencies:

    pip install -e .

    The -e flag stands for “editable,” meaning changes to the source code will immediately affect the installed package.

Building Documentation Locally#

To build the documentation locally, after (1) and (2) above, please follow these steps:

  • Install the package with documentation dependencies:

      pip install -e .[docs]
  • Build the documentation:m

      sphinx-build docs _build/html -b html

The generated documentation will be available in the _build/html directory. Open the index.html file in a web browser to view it.


Before downloading and extracting Black Marble data, define the NASA LAADS archive bearer token, and define a region of interest (i.e., gdf as a geopandas.GeoDataFrame).

from blackmarble.raster import bm_raster

# Retrieve VNP46A2 for date range into a Xarray Dataset
daily = bm_raster(
    date_range=pd.date_range("2022-01-01", "2022-03-31", freq="D"),

For more detailed information and examples, please refer to the documentation.


We welcome contributions to improve this documentation. If you find errors, have suggestions, or want to add new content, please follow our contribution guidelines.

Feedback and Issues#

If you have any feedback, encounter issues, or want to suggest improvements, please open an issue.


Gabriel Stefanini Vicente ORCID logo
Robert Marty ORCID logo


When using BlackMarblePy, your support is much appreciated! Please consider using the following citation or download bibliography.bib:

  title = {{BlackMarblePy: Georeferenced Rasters and Statistics of Nighttime Lights from NASA Black Marble}},
  author = {Gabriel {Stefanini Vicente} and Robert Marty},
  year = {2023},
  note = {{BlackMarblePy} v0.2.3},
  url = {},


Gabriel Stefanini Vicente and Robert Marty. BlackMarblePy: Georeferenced Rasters and Statistics of Nighttime Lights from NASA Black Marble. 2023. BlackMarblePy v0.2.3. URL:


This projects is licensed under the Mozilla Public License - see the LICENSE file for details.