Metadata-Version: 2.1
Name: rsr
Version: 1.0.0
Summary: Python utilities for applying the Radar Statistical Reconnaissance technique
Home-page: https://github.com/cgrima/rsr
Author: Cyril Grima
Author-email: cyril.grima@gmail.com
License: MIT
Description: # Presentation
        
        This is a Python package providing basic utilities for applying the Radar Statistical Reconnaissance.
        
        
        # Requirements
        
        
        numpy > 1.11.0
        
        
        
        # Example
        
        
        
        
        ```python
        
        import rsr
        import numpy as np
        import matplotlib.pyplot as plt
        %pylab
        
        # Load data (example is non-calibrated surface echo linear amplitudes from SHARAD orbit 0887601)
        data = np.genfromtxt('rsr/data.txt', dtype=float, delimiter=',', names=True)
        amp = data['amp']
        
        # Apply RSR to a given subset of amplitude.
        sample = amp[80000:85000]
        f = rsr.run.processor(sample, fit_model='hk')
        f.plot() # Plot results
        
        # Apply RSR along a vector of successive amplitude.
        # The RSR is applied on windows made of 1000 values. Each window is separated by
        # 500 samples (can be time consuming).
        a = rsr.run.along(amp, winsize=1000, sampling=250, nbcores=2)
        rsr.utils.plot_along(a) # Plot results
        ```
        
        
        
        
        # Citation
        
        Grima, C., Schroeder, D. M., Blankenship, D. D., and Young, D. A. (2014) [Planetary landing zone reconnaissance using ice penetrating radar data: concept validation in Antarctica][1]. Planetary and Space Science 103, 191-204.
        
        
        
          [1]: http://www.sciencedirect.com/science/article/pii/S0032063314002244
        
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
