Spatial operations extend fiona and rasterio

# Geo-Sardine :fish:

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

:blue_book: 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
"""
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:

1. 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

2. resample with opencv

3. resize with opencv

4. 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


## Project details

Uploaded Source
Uploaded Python 3