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')

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

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')

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