https://github.com/marcelovilla/gdal-py-snippets
Small different-purpose programs using gdal-python bindings (and a fair amount of NumPy)
https://github.com/marcelovilla/gdal-py-snippets
gdal gdal-python numpy ogr pandas python raster shapefile
Last synced: 7 months ago
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Small different-purpose programs using gdal-python bindings (and a fair amount of NumPy)
- Host: GitHub
- URL: https://github.com/marcelovilla/gdal-py-snippets
- Owner: marcelovilla
- License: mit
- Created: 2018-08-14T21:43:12.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2019-06-26T01:17:27.000Z (over 6 years ago)
- Last Synced: 2025-01-23T05:29:35.355Z (8 months ago)
- Topics: gdal, gdal-python, numpy, ogr, pandas, python, raster, shapefile
- Language: Python
- Homepage:
- Size: 2.36 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# gdal-py-snippets
This repository presents a wide range of different-purpose small examples using the gdal-python bindings (both `gdal` and `ogr`). It uses a fair amount of `numpy` as well, along with other third party packages such as `pandas`.
The programs presented in this repository were developed using Python 3.6.5 and the following packages versions:
* `gdal` version: 2.3.3
* `numpy` version: 1.14.3
* `pandas` version: 0.24.2
### Data
All the snippets in this repository use open data. Here is a table with the description of each dataset used* and its sources:
| Dataset | Description | Type | Extension | Source |
|---------------------------|---------------------------------------------------------|--------|-----------|--------------------|
| COL_msk_alt | Elevation for Colombia (country mask) | Raster | .vrt | [DIVA_GIS][1] |
| wc2.0_10m_prec_01 | Average January global precipitation from 1970 to 2000. | Raster | .tif | [WorldClim][2] |
| COL_rails | Colombian railroads | Vector | .shp | [DIVA_GIS][1] |
| ne_110m_admin_0_countries | Administrative boundaries (countries) of the world. | Vector | .shp | [Natural Earth][3] |
*I do not own any of the datasets here presented.
### Snippets
Here is a brief explanation for each snippet:
#### 1. [attribute_table_to_csv][4]
Saves a shapefile's attribute table to a given csv file using `ogr` and `pandas`. The program iterates through each feature in the shapefile and then iterates through each one of its fields, storing the values in a `DataFrame`. The `DataFrame` is then converted to a csv file.
#### 2. [compute_proximity][5]
Computes proximity (euclidean distance) in pixels from each cell to a target value using the `gdal.ComputeProximity()` function. The input raster is the output from [rasterize][9].
#### 3. [euclidean_distance][6]
Computes the euclidean distance (in pixels) from each cell to a target value. Similar to [compute_proximity][5] but instead of using `gdal`'s built-in function an own implementation of the distance computation is presented. In this case, the target values are the rasterized lines produced in [rasterize][9]. This snippet uses `numpy` to vectorize the distance computation (following [Pythagoras' theorem][7]). It creates a distance matrix to each target value (*i.e.* each value different from NoData). Then, the minimum distance value for each cell is retrieved and a single distance matrix is returned.
#### 4. [extract_lowest_cell_values][8]
Extracts an arbitrary percentage (5% in this case) of the lowest cell values in the raster.
#### 5. [get_fields_info][9]
Loops through each field in a shapefile and prints information about them.
#### 6. [get_pixel_coordinates][10]
Creates two grids with the corresponding x and y coordinates of each pixel center in a raster.
#### 7. [get_pixel_values][11]
Gets an array of pixel values correspondent to a set of points within a raster's extent. Points are presented as a tuple of 1D arrays with x and y coordinates. It converts coordinates to array indices and retrieve the values by indexing the data array.
#### 7. [raster_up_sample][12]*
Resamples (up-samples) a raster to a higher resolution. In order to do this it reads the original `GeoTransform` and changes the pixel width and pixel height to be proportionally smaller. Then it reads the data from the original dataset specifying `buf_xsize` and `buf_ysize` so the array where the data is going to be stored fits the new dimensions. When specifying a bigger buffer than the original raster dimensions in the `ReadAsArray()` method, original values are repeated to fit the new dimensions.
*This snippet was slightly adapted from one example presented by Chris Garrard in [Geoprocessing with Python's][13] 9th chapter.
#### 8. [rasterize][14]
Rasterizes a shapefile. It uses `ogr` to open a shapefile and get its extent. Then it creates an empty raster with the same extent as the shapefile and an arbitrary value for the pixel resolution. It uses the `gdal.RasterizeLayer()` function to burn the raster´s band (*i.e.* first and only band) with a 1 where the shapefile presents a feature. The rest is set as NoData.
[1]: https://www.diva-gis.org/gdata
[2]: http://worldclim.org/version2
[3]: https://www.naturalearthdata.com/downloads/110m-cultural-vectors/110m-admin-0-countries/
[4]: https://github.com/marcelovilla9/gdal-py-snippets/blob/master/scripts/attribute_table_to_csv.py
[5]: https://github.com/marcelovilla9/gdal-py-snippets/blob/master/scripts/compute_proximity.py
[6]: https://github.com/marcelovilla9/gdal-py-snippets/blob/master/scripts/euclidean_distance.py
[7]: https://en.wikipedia.org/wiki/Pythagorean_theorem
[8]: https://github.com/marcelovilla9/gdal-py-snippets/blob/master/scripts/extract_lowest_cell_values.py
[9]: https://github.com/marcelovilla9/gdal-py-snippets/blob/master/scripts/get_fields_info.py
[10]: https://github.com/marcelovilla9/gdal-py-snippets/blob/master/scripts/get_pixel_coordinates.py
[11]: https://github.com/marcelovilla9/gdal-py-snippets/blob/master/scripts/get_pixel_values.py
[12]: https://github.com/marcelovilla9/gdal-py-snippets/blob/master/scripts/raster_up_sample.py
[13]: https://www.manning.com/books/geoprocessing-with-python
[14]: https://github.com/marcelovilla9/gdal-py-snippets/blob/master/scripts/rasterize.py