| Name | sankee JSON |
| Version |
0.2.5
JSON |
| download |
| home_page | None |
| Summary | Visualize classified time series data with interactive Sankey plots in Google Earth Engine. |
| upload_time | 2024-03-13 05:05:30 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
earth-engine
land-cover
sankey
visualization
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# sankee
[](https://developers.google.com/earth-engine/tutorials/community/intro-to-python-api)
[](https://mybinder.org/v2/gh/aazuspan/sankee/HEAD?filepath=docs%2Fexamples%2Fmodis_snow_and_ice.ipynb)
[](https://anaconda.org/conda-forge/sankee)
[](https://pypi.org/project/sankee)
[](https://github.com/aazuspan/sankee/actions/workflows/ci.yml)
[](https://codecov.io/gh/aazuspan/sankee)
[](https://sankee.readthedocs.io/en/latest/?badge=latest)
[](https://pepy.tech/project/sankee)
Visualize changes in classified time series data with interactive Sankey plots in Google Earth Engine
## Description
Use [Earth Engine](https://github.com/google/earthengine-api) to visualize changes in land cover, plant health, burn severity, or any other time series of classified imagery with interactive Sankey plots. Use a library of [built-in datasets](https://sankee.readthedocs.io/en/latest/sankee.html#premade-datasets) for convenience or define your own [custom datasets](https://sankee.readthedocs.io/en/latest/sankee.html#custom-datasets) for flexibility.
## Installation
### Pip
```bash
pip install sankee
```
### Conda
```bash
conda install -c conda-forge sankee
```
## Quickstart
### Premade Datasets
Visualize annual changes in land cover using [popular LULC datasets](https://sankee.readthedocs.io/en/latest/sankee.html#supported-datasets) with a couple lines of code. Just choose a dataset, an area of interest, and a list of years to generate a Sankey diagram from a premade dataset. Below, we can look at 30 years of vegetation recovery in the area devastated by the Mt. St. Helens eruption.
```python
import sankee
import ee
ee.Initialize()
sankee.datasets.LCMS_LC.sankify(
years=[1990, 2000, 2010, 2020],
region=ee.Geometry.Point([-122.192688, 46.25917]).buffer(2000),
max_classes=3,
title="Mt. St. Helens Recovery"
)
```
Check out the [example notebook](https://sankee.readthedocs.io/en/latest/examples/modis_snow_and_ice.html) for an interactive demo.
### Custom Datasets
Any classified images can be used by defining the dataset parameters (which pixel values correspond to which labels and colors). For example, we can look at classified [Dynamic World](https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1) scenes just two weeks apart that show substantial snow loss on Mountain Jefferson during the 2021 Heat Dome in the Pacific Northwest.
```python
import sankee
import ee
ee.Initialize()
# Load a set of classified images
img_list = [
ee.Image("GOOGLE/DYNAMICWORLD/V1/20210616T185919_20210616T190431_T10TEQ"),
ee.Image("GOOGLE/DYNAMICWORLD/V1/20210706T185919_20210706T190638_T10TEQ")
]
# Which band contains the classified data?
band = "label"
# What labels correspond to which pixel values?
labels = {
0: "Water", 1: "Trees", 2: "Grass", 3: "Flooded", 4: "Crops",
5: "Shrub / Scrub", 6: "Build", 7: "Bare", 8: "Snow / Ice",
}
# What colors should be applied to which pixel values?
palette = {
0: "#419BDF", 1: "#397D49", 2: "#88B053", 3: "#7A87C6", 4: "#E49635",
5: "#DFC35A", 6: "#C4281B", 7: "#A59B8F", 8: "#B39FE1"
}
plot = sankee.sankify(
image_list=img_list,
band=band,
labels=labels,
palette=palette,
region=ee.Geometry.Point([-121.80183, 44.67655]).buffer(3000),
max_classes=3,
title="Mt. Jefferson Snow Loss - June 2021"
)
```
### Integration with geemap
`sankee` premade datasets are usable through the [geemap](https://github.com/giswqs/geemap) interactive GUI. Check out the [documentation](https://geemap.org/notebooks/75_sankee/) and [video tutorials](https://www.youtube.com/watch?v=IZWpJYX6w8I) by [@giswqs](https://github.com/giswqs).
## Contributing
If you find bugs or have feature requests, please open an issue!
---
[Top](https://github.com/aazuspan/sankee#sankee)
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"description": "# sankee\n\n[](https://developers.google.com/earth-engine/tutorials/community/intro-to-python-api)\n[](https://mybinder.org/v2/gh/aazuspan/sankee/HEAD?filepath=docs%2Fexamples%2Fmodis_snow_and_ice.ipynb)\n[](https://anaconda.org/conda-forge/sankee)\n[](https://pypi.org/project/sankee)\n[](https://github.com/aazuspan/sankee/actions/workflows/ci.yml)\n[](https://codecov.io/gh/aazuspan/sankee)\n[](https://sankee.readthedocs.io/en/latest/?badge=latest)\n[](https://pepy.tech/project/sankee)\n\nVisualize changes in classified time series data with interactive Sankey plots in Google Earth Engine\n\n\n\n## Description\n\nUse [Earth Engine](https://github.com/google/earthengine-api) to visualize changes in land cover, plant health, burn severity, or any other time series of classified imagery with interactive Sankey plots. Use a library of [built-in datasets](https://sankee.readthedocs.io/en/latest/sankee.html#premade-datasets) for convenience or define your own [custom datasets](https://sankee.readthedocs.io/en/latest/sankee.html#custom-datasets) for flexibility.\n\n\n## Installation\n\n### Pip\n\n```bash\npip install sankee\n```\n\n### Conda\n\n```bash\nconda install -c conda-forge sankee\n```\n\n## Quickstart\n\n### Premade Datasets\n\nVisualize annual changes in land cover using [popular LULC datasets](https://sankee.readthedocs.io/en/latest/sankee.html#supported-datasets) with a couple lines of code. Just choose a dataset, an area of interest, and a list of years to generate a Sankey diagram from a premade dataset. Below, we can look at 30 years of vegetation recovery in the area devastated by the Mt. St. Helens eruption.\n\n```python\nimport sankee\nimport ee\n\nee.Initialize()\n\nsankee.datasets.LCMS_LC.sankify(\n years=[1990, 2000, 2010, 2020],\n region=ee.Geometry.Point([-122.192688, 46.25917]).buffer(2000),\n max_classes=3,\n title=\"Mt. St. Helens Recovery\"\n)\n```\n\n\n\nCheck out the [example notebook](https://sankee.readthedocs.io/en/latest/examples/modis_snow_and_ice.html) for an interactive demo.\n\n### Custom Datasets\n\nAny classified images can be used by defining the dataset parameters (which pixel values correspond to which labels and colors). For example, we can look at classified [Dynamic World](https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1) scenes just two weeks apart that show substantial snow loss on Mountain Jefferson during the 2021 Heat Dome in the Pacific Northwest.\n\n```python\nimport sankee\nimport ee\n\nee.Initialize()\n\n# Load a set of classified images\nimg_list = [\n ee.Image(\"GOOGLE/DYNAMICWORLD/V1/20210616T185919_20210616T190431_T10TEQ\"),\n ee.Image(\"GOOGLE/DYNAMICWORLD/V1/20210706T185919_20210706T190638_T10TEQ\")\n]\n\n# Which band contains the classified data?\nband = \"label\"\n\n# What labels correspond to which pixel values?\nlabels = {\n 0: \"Water\", 1: \"Trees\", 2: \"Grass\", 3: \"Flooded\", 4: \"Crops\",\n 5: \"Shrub / Scrub\", 6: \"Build\", 7: \"Bare\", 8: \"Snow / Ice\",\n}\n\n# What colors should be applied to which pixel values?\npalette = {\n 0: \"#419BDF\", 1: \"#397D49\", 2: \"#88B053\", 3: \"#7A87C6\", 4: \"#E49635\",\n 5: \"#DFC35A\", 6: \"#C4281B\", 7: \"#A59B8F\", 8: \"#B39FE1\"\n}\n\nplot = sankee.sankify(\n image_list=img_list, \n band=band, \n labels=labels,\n palette=palette,\n region=ee.Geometry.Point([-121.80183, 44.67655]).buffer(3000), \n max_classes=3,\n title=\"Mt. Jefferson Snow Loss - June 2021\"\n)\n```\n\n\n\n### Integration with geemap\n\n`sankee` premade datasets are usable through the [geemap](https://github.com/giswqs/geemap) interactive GUI. Check out the [documentation](https://geemap.org/notebooks/75_sankee/) and [video tutorials](https://www.youtube.com/watch?v=IZWpJYX6w8I) by [@giswqs](https://github.com/giswqs).\n\n\n## Contributing\n\nIf you find bugs or have feature requests, please open an issue!\n\n---\n\n[Top](https://github.com/aazuspan/sankee#sankee)\n",
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