Geo-Sardine 🐟

Spatial operations extend fiona and rasterio. Collection of spatial operation which i occasionally use written in python:

Interpolation with IDW (Inverse Distance Weighting) Shepard
Drape vector to raster
Spatial join between two vector
Raster wrapper, for better experience. ie: math operation between two raster, resize and resample

📘 documentation: https://sahitono.github.io/geosardine

Setup

install with pip pip install geosardine

or anaconda conda install -c sahitono geosardine

How to use it

Drape and spatial join

import geosardine as dine
import rasterio
import fiona

with rasterio.open("/home/user/data.tif") as raster, fiona.open("/home/user/data.shp") as vector:
    draped = dine.drape_geojson(vector, raster)
    joined = dine.spatial_join(vector, raster)

IDW Interpolation

import numpy as np
import geosardine as dine
xy = np.array([
        [106.8358,  -6.585 ],
        [106.6039,  -6.7226],
        [106.7589,  -6.4053],
        [106.9674,  -6.7092],
        [106.7956,  -6.5988]
])
values = np.array([132., 127.,  37.,  90., 182.])

"""
if epsg not provided, it will assume that coordinate is in wgs84 geographic
Find your epsg here https://epsg.io/
"""
interpolated = dine.interpolate.idw(xy, values, spatial_res=(0.01,0.01), epsg=4326)

# Save interpolation result to tiff
interpolated.save('idw.tif')

# shapefile or geojson can be used too
interp_file = dine.interpolate.idw("points.shp", spatial_res=(0.01,0.01), column_name="value")
interp_file.save("idw.tif")

# The result array can be accessed like this
print(interpolated.array)
"""
[[ 88.63769859  86.24219616  83.60463194 ... 101.98185127 103.37001289
  104.54621272]
 [ 90.12053232  87.79279317  85.22030848 ... 103.77118852 105.01425289
  106.05302554]
 [ 91.82987695  89.60855271  87.14722258 ... 105.70090081 106.76928067
  107.64635337]
 ...
 [127.21214817 127.33208302 127.53878268 ...  97.80436475  94.96247196
   93.12113458]
 [127.11315081 127.18465002 127.33444124 ...  95.86455668  93.19212577
   91.51135399]
 [127.0435062  127.0827023  127.19214624 ...  94.80175756  92.30685734
   90.75707134]]
"""

Raster Wrapper

Geosardine include wrapper for raster data. The benefit are:

math operation (addition, subtraction, division, multiplication) between rasters of different size, resolution and reference system. The data type result is equal to the first raster data type

for example:
```
raster1 = float32 and raster2 = int32
raster3 = raster1 - raster2
raster3 will be float32
```
resample with opencv
resize with opencv
split into tiled

from geosardine import Raster


"""
minimum parameter needed to create raster are 
1. 2D numpy array, example: np.ones(18, dtype=np.float32).reshape(3, 3, 2)
2. spatial resolution, example:  0.4 or ( 0.4,  0.4)
3. left coordinate / x minimum
4. bottom coordinate / y minimum
"""
raster1 = Raster(np.ones(18, dtype=np.float32).reshape(3, 3, 2), resolution=0.4, x_min=120, y_max=0.7)

## resample
resampled = raster.resample((0.2,0.2))
## resize
resized = raster.resize(height=16, width=16)

## math operation between raster
raster_2 = raster + resampled
raster_2 = raster - resampled
raster_2 = raster * resampled
raster_2 = raster / resampled

## math operation raster to number
raster_3 = raster + 2
raster_3 = raster - 2
raster_3 = raster * 2
raster_3 = raster / 2

### plot it using raster.array
import matplotlib.pyplot as plt
plt.imshow(raster_3)
plt.show()

Geosardine CLI

You can use it through terminal or command prompt by calling dine

$ dine --help
Usage: dine [OPTIONS] COMMAND [ARGS]...

  GeoSardine CLI

Options:
  --help  Show this message and exit.

Commands:
  drape         Drape vector to raster to obtain height value
  info          Get supported format
  join-spatial  Join attribute by location
  idw           Create raster with Inverse Distance Weighting interpolation

brendancol/geosardine