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GeoRasters

The GeoRasters package is a python module that provides a fast and flexible tool to work with GIS raster files. It provides the GeoRaster class, which makes working with rasters quite transparent and easy. In a way it tries to do for rasters what GeoPandas does for geometries.

It includes tools to

Merge rasters
Plot rasters
Extract information from rasters
Given a point (lat,lon) find its location in a raster
Aggregate rasters to lower resolutions
Align two rasters of different sizes to common area and size
Get all the geographical information of raster
Create GeoTiff files easily
Load GeoTiff files as masked numpy rasters
Clip raster using geometries
Get zonal statistics using geometries
Spatial analysis tools

Install

GeoRasters can be installed using pip or conda.

pip install git+git://github.com/ozak/georasters.git
pip install georasters
conda install -c conda-forge georasters
conda install -c ozak georasters

You can try it out easily using conda env and the provided scripts:

try_georasters2.yml creates a GIS functional python-2.7 environment
try_georasters3.yml creates a GIS functional python-3.5 environment.

Requirements

You need to install the following software for georasters to work.

GDAL

Example Usage: GeoRasters

import georasters as gr
import numpy as np

# Load data
raster = './data/slope.tif'
data = gr.from_file(raster)

# Plot data
data.plot()

# Get some stats
data.mean()
data.sum()
data.std()

# Convert to Pandas DataFrame
df = data.to_pandas()

# Save transformed data to GeoTiff
data2 = data**2
data2.to_tiff('./data2')

# Algebra with rasters
data3 = np.sin(data.raster) / data2
data3.plot()

# Notice that by using the data.raster object,
# you can do any mathematical operation that handles
# Numpy Masked Arrays

# Find value at point (x,y) or at vectors (X,Y)
value = data.map_pixel(x,y)
Value = data.map_pixel(X,Y)

Example Merge GeoRasters:

import os
import georasters as gr
import matplotlib.pyplot as plt

DATA = "/path/to/tiff/files"

# Import raster
raster = os.path.join(DATA, 'pre1500.tif')
data = gr.from_file(raster)
(xmin, xsize, x, ymax, y, ysize) = data.geot

# Split raster in two
data1 = gr.GeoRaster(data.raster[:data.shape[0] / 2, :],
					 data.geot,
					 nodata_value=data.nodata_value,
					 projection=data.projection,
					 datatype=data.datatype)

data2 = gr.GeoRaster(data.raster[data.shape[0] / 2:, :],
					 (xmin, xsize, x, ymax + ysize * data.shape[0] / 2, y, ysize),
					 nodata_value=data.nodata_value,
					 projection=data.projection,
					 datatype=data.datatype,)

# Plot both parts and save them
plt.figure(figsize=(12, 8))
data1.plot()
plt.savefig(os.path.join(DATA, 'data1.png'), bbox_inches='tight')

plot1

plt.figure(figsize=(12,8))
data2.plot()
plt.savefig(os.path.join(DATA,'data2.png'), bbox_inches='tight')

plot2

Generate merged raster

data3 = data1.union(data2)

# Plot it and save the figure
plt.figure(figsize=(12,8))
data3.plot()
plt.savefig(os.path.join(DATA,'data3.png'), bbox_inches='tight')

plot3

Another Merge:

Example Usage: Other functions

import georasters as gr
import numpy as np

# Get info on raster
NDV, xsize, ysize, GeoT, Projection, DataType = gr.get_geo_info(raster)

# Load raster
data = load_tiff(raster)

# Find location of point (x,y) on raster, e.g. to extract info at that location
col, row = gr.map_pixel(x,y,GeoT[1],GeoT[-1], GeoT[0],GeoT[3])
value = data[row,col]

# Agregate raster by summing over cells in order to increase pixel size by e.g. 10
gr.aggregate(data,NDV,(10,10))

# Align two rasters
data2 = load_tiff(raster2)
(alignedraster_o, alignedraster_a, GeoT_a) = gr.align_rasters(raster, raster2, how=np.mean)

# Create GeoRaster
A=gr.GeoRaster(data, GeoT, nodata_value=NDV)

# Load another raster
NDV, xsize, ysize, GeoT, Projection, DataType = gr.get_geo_info(raster2)
data = load_tiff(raster2)
B=gr.GeoRaster(data2, GeoT, nodata_value=NDV)

# Plot Raster
A.plot()

# Merge both rasters and plot
C=B.merge(A)
C.plot()

Issues

Find a bug? Report it via Github issues by providing

a link to download the smallest possible raster and vector dataset necessary to reproduce the error
python code or command to reproduce the error
information on your environment: versions of python, gdal and numpy and system memory

Copyright

This code and data is provided under Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) License and GPLv3.