pygeotools 0.1.4

Libraries and command-line utilities for geospatial data processing/analysis

# pygeotools Libraries and utilities for geospatial data processing/analysis

## Overview

## Features - quickly resample rasters to common resolution/extent/projection - various functions for NumPy masked arrays - simple coordinate transformations - automatic projection determination

### pygeotools/lib - libraries containing many useful functions - geolib - coordinate transformations, raster to vector, vector to raster - malib - NumPy Masked Array operations, DEMStack class - warplib - on-the-fly GDAL warp operations - iolib - file input/output, primarily wrappers for GDAL I/O, write out masked arrays - timelib - time conversions, useful for raster time series analysis - filtlib - raster filtering

### pygeotools - executable command-line utilities (run with no arguments for usage) - warptool.py - make_stack.py - ndvtrim.py - apply_mask.py - …

## Examples

### Warping multiple datasets to common grid and computing difference ` from pygeotools.lib import iolib, warplib, malib fn1 = 'raster1.tif' fn2 = 'raster2.tif' ds_list = warplib.memwarp_multi_fn([fn1, fn2], res='max', extent='intersection', t_srs='first', r='cubic') r1 = iolib.ds_getma(ds_list[0]) r2 = iolib.ds_getma(ds_list[1]) rdiff = r1 - r2 malib.print_stats(rdiff) out_fn = 'raster_diff.tif' iolib.writeGTiff(rdiff, out_fn, ds_list[0]) ` or, from the command line…

Warp all to match raster1.tif projection with common intersection and largest pixel size:

warptool.py -tr max -te intersection -t_srs first raster1.tif raster2.tif raster3.tif

Create version of raster1.tif that matches resolution, extent, and projection of raster2.tif:

warptool.py -tr raster2.tif -te raster2.tif -t_srs raster2.tif raster1.tif

Reproject and clip to user-defined extent, preserving original resolution of each input raster:

warptool.py -tr source -te ‘439090 5285360 458630 5306450’ -t_srs EPSG:32610 raster1.tif raster2.tif

### Creating a time series “stack” object: ` from pygeotools.lib import malib fn_list = ['20080101_dem.tif', '20090101_dem.tif', '20100101_dem.tif'] s = malib.DEMStack(fn_list, res='min', extent='union') #Stack standard deviation s.stack_std #Stack linear trend s.stack_trend ` or, from the command line…

make_stack.py -tr ‘min’ -te ‘union’ 20*.tif

## Documentation

Is in the works…

## Installation

Install the latest release from PyPI:

pip install pygeotools

Note: by default, this will deploy executable scripts in /usr/local/bin

### Building from source

Clone the repository and install:

git clone https://github.com/dshean/pygeotools.git pip install -e pygeotools/

The -e flag (“editable mode”, setuptools “develop mode”) will allow you to modify source code and immediately see changes.

### Core requirements - [GDAL/OGR](http://www.gdal.org/) - [NumPy](http://www.numpy.org/) - [SciPy](https://www.scipy.org/)

### Optional requirements (needed for some functionality) - [matplotlib](http://matplotlib.org/) - [NASA Ames Stereo Pipeline (ASP)](https://ti.arc.nasa.gov/tech/asr/intelligent-robotics/ngt/stereo/)

## Disclaimer

This originated as a poorly written, poorly organized personal repo that I am finally cleaning up and distributing. There are some useful things that work very well, other things that were hastily written for a one-off task several years ago, and some confusing things that were never finished. I have no tests. The minor changes needed for migration to this repo have undoubtedly broken some things.

Contributions, bug reports, and general feedback are all welcome. My time is limited, I have bad habits, and I could really use some help. Thanks in advance.

This was all originally developed for Python 2.X, but should now also work with Python 3.X thanks to [@dlilien](https://github.com/dlilien)

## License

This project is licensed under the terms of the MIT License.