DEV Community

masoomjethwa
masoomjethwa

Posted on

Setting Up a Miniconda Environment for NASA PDS Data Analysis on Windows 11

#nasa #miniconda #dsa #plantery

Setting Up a Miniconda Environment for NASA PDS Data Analysis on Windows 11 (2025 Standards)

Absolutely! Let me help you craft a Miniconda environment setup guide tailored for NASA PDS (Planetary Data System) data analysis on a powerful Windows 11 laptop (32 GB RAM, Intel Core i9, no dedicated GPU). The environment will focus on modern 2025 standards and include advanced tools for:

  • Planetary datasets (spectral, imaging, tabular)
  • Higher-level Digital Signal Analysis (DSA)
  • Geospatial and remote sensing
  • Scientific computing and machine learning
  • Visualization and interactive exploration

Step 1: Install Miniconda

Download and install Miniconda for Windows:

https://docs.conda.io/en/latest/miniconda.html

Follow the Windows installer prompts and add conda to your PATH during installation for ease of use.

Step 2: Create Conda Environment

Open Anaconda Prompt or Windows Terminal and create a new environment:

----
```

bash
conda create -n pds-analysis python=3.11
Enter fullscreen mode Exit fullscreen mode

Activate it:


bash
conda activate pds-analysis
Enter fullscreen mode Exit fullscreen mode

Step 3: Install Core Scientific and Data Libraries

Install core libs for numerical analysis, data manipulation, and basic plotting:


bash
conda install numpy scipy pandas matplotlib seaborn jupyterlab ipython
Enter fullscreen mode Exit fullscreen mode

Step 4: Install NASA PDS & Planetary Science Specific Libraries

  • pds4_tools: Official NASA PDS Python toolkit for reading PDS4 data formats
  • planetarypy: Python library for planetary science computations
  • spiceypy: NASA’s SPICE toolkit Python wrapper for planetary geometry and mission planning

bash
pip install pds4_tools planetarypy spiceypy
Enter fullscreen mode Exit fullscreen mode

Step 5: Geospatial and Remote Sensing Libraries

Install libraries suitable for planetary imagery, raster/vector data, and hyperspectral analysis:


bash
conda install -c conda-forge rasterio gdal geopandas shapely fiona pyproj cartopy xarray dask
pip install spectral  # for hyperspectral image analysis
pip install scikit-image  # advanced image processing
Enter fullscreen mode Exit fullscreen mode

Step 6: Advanced Digital Signal Analysis (DSA) and ML

Leverage Python’s ML ecosystem and signal processing libraries:


bash
conda install -c conda-forge scikit-learn statsmodels xgboost lightgbm librosa
pip install PyWavelets  # wavelet transforms
pip install neurokit2  # advanced biosignal analysis tools useful for complex signal processing workflows
Enter fullscreen mode Exit fullscreen mode

Step 7: Deep Learning (Optional, CPU optimized)

If you want deep learning tools (TensorFlow and PyTorch CPU versions):


bash
conda install -c conda-forge tensorflow pytorch torchvision torchaudio cpuonly
Enter fullscreen mode Exit fullscreen mode

Step 8: Visualization and Interactive Tools

Install powerful plotting, 3D visualization, and interactive exploration libraries:


bash
conda install -c conda-forge plotly bokeh holoviews pyvista mayavi ipywidgets
pip install ipyleaflet  # interactive maps in Jupyter
Enter fullscreen mode Exit fullscreen mode

Step 9: Additional Utilities

Handy utilities for handling complex data formats and workflow automation:


bash
pip install netcdf4 h5py rasterstats geopandas fiona tqdm requests
Enter fullscreen mode Exit fullscreen mode

Step 10: Verify Installation

Check installed packages:


bash
conda list
Enter fullscreen mode Exit fullscreen mode

Launch JupyterLab to start working interactively:


bash
jupyter lab
Enter fullscreen mode Exit fullscreen mode

Optional: Create an environment.yml for Quick Reproduction 


yaml
name: pds-analysis
channels:
  - conda-forge
  - defaults
dependencies:
  - python=3.11
  - numpy
  - scipy
  - pandas
  - matplotlib
  - seaborn
  - jupyterlab
  - ipython
  - rasterio
  - gdal
  - geopandas
  - shapely
  - fiona
  - pyproj
  - cartopy
  - xarray
  - dask
  - scikit-learn
  - statsmodels
  - xgboost
  - lightgbm
  - librosa
  - tensorflow
  - pytorch
  - torchvision
  - torchaudio
  - cpuonly
  - plotly
  - bokeh
  - holoviews
  - pyvista
  - mayavi
  - ipywidgets
  - netcdf4
  - h5py
  - rasterstats
  - tqdm
  - requests
pip:
  - pds4_tools
  - planetarypy
  - spiceypy
  - spectral
  - scikit-image
  - PyWavelets
  - neurokit2
  - ipyleaflet
Enter fullscreen mode Exit fullscreen mode

Save it as environment.yml and create the environment with:


bash
conda env create -f environment.yml
conda activate pds-analysis
Enter fullscreen mode Exit fullscreen mode

Summary

This setup provides you with a robust Python environment tailored to NASA PDS data analysis and advanced planetary science workflows, optimized for a powerful Windows 11 laptop without a dedicated GPU. It covers data ingestion, geospatial analysis, digital signal processing, machine learning, and state-of-the-art visualization.

Top comments (0)