%global _empty_manifest_terminate_build 0
Name:		python-flask-tileserver
Version:	0.2.3
Release:	1
Summary:	Locally serve geospatial raster tiles in the Slippy Map standard.
License:	MIT License
URL:		https://github.com/banesullivan/flask-tileserver
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/1e/86/93ea16d71c89319c0fe03cdf643b5b514973e3e0b254e2399becadcb8dc3/flask-tileserver-0.2.3.tar.gz
BuildArch:	noarch

Requires:	python3-click
Requires:	python3-flask
Requires:	python3-Flask-Caching
Requires:	python3-furl
Requires:	python3-GDAL
Requires:	python3-large-image
Requires:	python3-large-image-source-gdal
Requires:	python3-requests
Requires:	python3-scooby
Requires:	python3-ipyleaflet

%description
# 🌐 Local Tile Server for Geospatial Rasters

[![codecov](https://codecov.io/gh/banesullivan/flask-tileserver/branch/main/graph/badge.svg?token=S0HQ64FW8G)](https://codecov.io/gh/banesullivan/flask-tileserver)
[![PyPI](https://img.shields.io/pypi/v/flask-tileserver.svg?logo=python&logoColor=white)](https://pypi.org/project/flask-tileserver/)
[![pypi-activity](https://img.shields.io/pypi/dm/flask-tileserver.svg?label=pypi%20downloads)](https://pypi.org/project/flask-tileserver/)

*Need to visualize a rather large (gigabytes) raster you have locally?* **This is for you.**

A Flask application for serving tiles from large raster files in
the [Slippy Maps standard](https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames)
(i.e., `/zoom/x/y.png`)

**Disclaimer**: I put this together over a weekend and I'm definitely going to
change a few things moving forward to make it more stable/robust. This means
that things will most likely break between minor releases (I use the
`major.minor.patch` versioning scheme).


![tile-diagram](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/tile-diagram.png)


## 🌟 Highlights

- Create a local tile server for large geospatial images
- View local raster files with `ipyleaflet`
- Extract regions of interest (ROIs) interactively

Under the hood, this uses [`large_image`](https://github.com/girder/large_image)
to launch a tile server in a background thread which will serve raster imagery
to a tile viewer (see `ipyleaflet` examples below).
This tile server can efficiently deliver varying levels of detail of your
raster imagery to your viewer; it helps to have pre-tiled, Cloud Optimized
GeoTIFFs (COG), but no wories if not as `large_image` will tile and cache for
you when opening the raster.

There is an included, standalone web viewer leveraging
[CesiumJS](https://cesium.com/platform/cesiumjs/) and [GeoJS](https://opengeoscience.github.io/geojs/).
You can use the web viewer to select and extract regions of interest from rasters.

## ⬇️ Installation

Install from PyPI: https://pypi.org/project/flask-tileserver/

```
pip install flask-tileserver
```

### 📝 A Brief Note on Installing GDAL

GDAL can be a pain in the 🍑 to install, and you may want to handle GDAL
before installing `flask-tileserver`.

If on linux, I highly recommend using the [large_image_wheels](https://github.com/girder/large_image_wheels) from Kitware.

```
pip install --find-links=https://girder.github.io/large_image_wheels --no-cache GDAL
```

Otherwise, I recommend using `conda`:

```
conda install -c conda-forge GDAL
```

## 💭 Feedback

Please share your thoughts and questions on the [Discussions](https://github.com/banesullivan/flask-tileserver/discussions) board.
If you would like to report any bugs or make feature requests, please open an issue.

If filing a bug report, please share a scooby `Report`:

```py
import tileserver
print(tileserver.Report())
```

## 🚀 Usage

### 🍃 `ipyleaflet` Tile Layers

The `TileClient` class is a nifty tool to launch a tile server as a background
thread to serve image tiles from any raster file on your local file system.
Additionally, it can be used in conjunction with the `get_leaflet_tile_layer`
utility to create an `ipyleaflet.TileLayer` for interactive visualization in
a Jupyter notebook. Here is an example:


```py
from tileserver import get_leaflet_tile_layer, TileClient
from ipyleaflet import Map

# First, create a tile server from local raster file
tile_client = TileClient('~/Desktop/TC_NG_SFBay_US_Geo.tif')

# Create ipyleaflet tile layer from that server
t = get_leaflet_tile_layer(tile_client)

# Create ipyleaflet map, add tile layer, and display
m = Map(center=tile_client.center())
m.add_layer(t)
m
```

![ipyleaflet](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/ipyleaflet.png)

#### 🥓 Two Rasters at Once

```py
from tileserver import get_leaflet_tile_layer
from ipyleaflet import Map, ScaleControl, FullScreenControl, SplitMapControl

# Create 2 tile layers from 2 separate raster files
l = get_leaflet_tile_layer('~/Desktop/TC_NG_SFBay_US_Geo.tif',
                           band=1, palette='matplotlib.Viridis_20', vmin=50, vmax=200)
r = get_leaflet_tile_layer('~/Desktop/small.tif',
                           band=2, palette='matplotlib.Plasma_6', vmin=0, vmax=150)

# Make the ipyleaflet map
m = Map(center=(37.7249511580583, -122.27230466902257), zoom=9)
control = SplitMapControl(left_layer=l, right_layer=r)
m.add_control(control)
m.add_control(ScaleControl(position='bottomleft'))
m.add_control(FullScreenControl())
m
```

![ipyleaflet-double](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/ipyleaflet.gif)


#### 🎯 Using `ipyleaflet` for ROI Extraction

I have included the `get_leaflet_roi_controls` utility to create some leaflet
UI controls for extracting regions of interest from a tile client. You can
use it as follows and then draw a polygon and click the "Extract ROI" button.

The outputs are save in your working directory by default (next to the Jupyter notebook).

```py
from tileserver import get_leaflet_tile_layer, get_leaflet_roi_controls
from tileserver import TileClient
from ipyleaflet import Map

# First, create a tile server from local raster file
tile_client = TileClient('~/Desktop/TC_NG_SFBay_US_Geo.tif')

# Create ipyleaflet tile layer from that server
t = get_leaflet_tile_layer(tile_client)

# Create ipyleaflet controls to extract an ROI
draw_control, roi_control = get_leaflet_roi_controls(tile_client)

# Create ipyleaflet map, add layers, add controls, and display
m = Map(center=(37.7249511580583, -122.27230466902257), zoom=9)
m.add_layer(t)
m.add_control(draw_control)
m.add_control(roi_control)
m
```

![ipyleaflet-draw-roi](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/ipyleaflet-draw-roi.png)


#### 🗺️ Example Datasets

A few example datasets are included with `tileserver`. A particularly
useful one has global elevation data which you can use to create high resolution Digital Elevation Models (DEMs) of a local region.

```py
from tileserver import get_leaflet_tile_layer, get_leaflet_roi_controls, examples
from ipyleaflet import Map

# Load example tile layer from publicly available DEM source
tile_client = examples.get_elevation()

# Create ipyleaflet tile layer from that server
t = get_leaflet_tile_layer(tile_client,
                           band=1, vmin=-500, vmax=5000,
                           palette='matplotlib.Plasma_6',
                           opacity=0.75)

# Create ipyleaflet controls to extract an ROI
draw_control, roi_control = get_leaflet_roi_controls(tile_client)

m = Map(zoom=2)
m.add_layer(t)
m.add_control(draw_control)
m.add_control(roi_control)
m
```

![elevation](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/elevation.png)


Then you can follow the same routine as described above to extract an ROI.

I zoomed in over Golden, Colorado and drew a polygon of the extent of the DEM I would like to create:

![golden](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/golden-roi.png)

And perform the extraction:

```py

roi_path = '...'  # Look in your working directory

r = get_leaflet_tile_layer(roi_path, band=1,
                           palette='matplotlib.Plasma_6', opacity=0.75)

m2 = Map(
        center=(39.763427033262175, -105.20614908076823),
        zoom=12,
       )
m2.add_layer(r)
m2
```

![golden-dem](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/golden-dem.png)

Here is another example with the Virtual Earth satellite imagery

```py
from tileserver import get_leaflet_tile_layer, examples
from ipyleaflet import Map

# Load example tile layer from publicly available imagery
tile_client = examples.get_virtual_earth()

# Create ipyleaflet tile layer from that server
t = get_leaflet_tile_layer(tile_client, opacity=1)

m = Map(center=(39.751343612695145, -105.22181306125279), zoom=18)
m.add_layer(t)
m
```

![kafadar](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/kafadar.png)


### 🖥️ Local Web Application

Launch the tileserver from the commandline to use the included web application where you can view the raster and extract regions of interest.

```bash
python -m tileserver path/to/raster.tif
```

![cesium-viewer](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/cesium-viewer.png)

You can use the web viewer to extract regions of interest:

![webviewer-roi](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/webviewer-roi.gif)


### Usage Notes

- `get_leaflet_tile_layer` accepts either an existing `TileClient` or a
path from which to create a `TileClient` under the hood.
- The color palette choices come from [`palettable`](https://jiffyclub.github.io/palettable/).




%package -n python3-flask-tileserver
Summary:	Locally serve geospatial raster tiles in the Slippy Map standard.
Provides:	python-flask-tileserver
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-flask-tileserver
# 🌐 Local Tile Server for Geospatial Rasters

[![codecov](https://codecov.io/gh/banesullivan/flask-tileserver/branch/main/graph/badge.svg?token=S0HQ64FW8G)](https://codecov.io/gh/banesullivan/flask-tileserver)
[![PyPI](https://img.shields.io/pypi/v/flask-tileserver.svg?logo=python&logoColor=white)](https://pypi.org/project/flask-tileserver/)
[![pypi-activity](https://img.shields.io/pypi/dm/flask-tileserver.svg?label=pypi%20downloads)](https://pypi.org/project/flask-tileserver/)

*Need to visualize a rather large (gigabytes) raster you have locally?* **This is for you.**

A Flask application for serving tiles from large raster files in
the [Slippy Maps standard](https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames)
(i.e., `/zoom/x/y.png`)

**Disclaimer**: I put this together over a weekend and I'm definitely going to
change a few things moving forward to make it more stable/robust. This means
that things will most likely break between minor releases (I use the
`major.minor.patch` versioning scheme).


![tile-diagram](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/tile-diagram.png)


## 🌟 Highlights

- Create a local tile server for large geospatial images
- View local raster files with `ipyleaflet`
- Extract regions of interest (ROIs) interactively

Under the hood, this uses [`large_image`](https://github.com/girder/large_image)
to launch a tile server in a background thread which will serve raster imagery
to a tile viewer (see `ipyleaflet` examples below).
This tile server can efficiently deliver varying levels of detail of your
raster imagery to your viewer; it helps to have pre-tiled, Cloud Optimized
GeoTIFFs (COG), but no wories if not as `large_image` will tile and cache for
you when opening the raster.

There is an included, standalone web viewer leveraging
[CesiumJS](https://cesium.com/platform/cesiumjs/) and [GeoJS](https://opengeoscience.github.io/geojs/).
You can use the web viewer to select and extract regions of interest from rasters.

## ⬇️ Installation

Install from PyPI: https://pypi.org/project/flask-tileserver/

```
pip install flask-tileserver
```

### 📝 A Brief Note on Installing GDAL

GDAL can be a pain in the 🍑 to install, and you may want to handle GDAL
before installing `flask-tileserver`.

If on linux, I highly recommend using the [large_image_wheels](https://github.com/girder/large_image_wheels) from Kitware.

```
pip install --find-links=https://girder.github.io/large_image_wheels --no-cache GDAL
```

Otherwise, I recommend using `conda`:

```
conda install -c conda-forge GDAL
```

## 💭 Feedback

Please share your thoughts and questions on the [Discussions](https://github.com/banesullivan/flask-tileserver/discussions) board.
If you would like to report any bugs or make feature requests, please open an issue.

If filing a bug report, please share a scooby `Report`:

```py
import tileserver
print(tileserver.Report())
```

## 🚀 Usage

### 🍃 `ipyleaflet` Tile Layers

The `TileClient` class is a nifty tool to launch a tile server as a background
thread to serve image tiles from any raster file on your local file system.
Additionally, it can be used in conjunction with the `get_leaflet_tile_layer`
utility to create an `ipyleaflet.TileLayer` for interactive visualization in
a Jupyter notebook. Here is an example:


```py
from tileserver import get_leaflet_tile_layer, TileClient
from ipyleaflet import Map

# First, create a tile server from local raster file
tile_client = TileClient('~/Desktop/TC_NG_SFBay_US_Geo.tif')

# Create ipyleaflet tile layer from that server
t = get_leaflet_tile_layer(tile_client)

# Create ipyleaflet map, add tile layer, and display
m = Map(center=tile_client.center())
m.add_layer(t)
m
```

![ipyleaflet](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/ipyleaflet.png)

#### 🥓 Two Rasters at Once

```py
from tileserver import get_leaflet_tile_layer
from ipyleaflet import Map, ScaleControl, FullScreenControl, SplitMapControl

# Create 2 tile layers from 2 separate raster files
l = get_leaflet_tile_layer('~/Desktop/TC_NG_SFBay_US_Geo.tif',
                           band=1, palette='matplotlib.Viridis_20', vmin=50, vmax=200)
r = get_leaflet_tile_layer('~/Desktop/small.tif',
                           band=2, palette='matplotlib.Plasma_6', vmin=0, vmax=150)

# Make the ipyleaflet map
m = Map(center=(37.7249511580583, -122.27230466902257), zoom=9)
control = SplitMapControl(left_layer=l, right_layer=r)
m.add_control(control)
m.add_control(ScaleControl(position='bottomleft'))
m.add_control(FullScreenControl())
m
```

![ipyleaflet-double](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/ipyleaflet.gif)


#### 🎯 Using `ipyleaflet` for ROI Extraction

I have included the `get_leaflet_roi_controls` utility to create some leaflet
UI controls for extracting regions of interest from a tile client. You can
use it as follows and then draw a polygon and click the "Extract ROI" button.

The outputs are save in your working directory by default (next to the Jupyter notebook).

```py
from tileserver import get_leaflet_tile_layer, get_leaflet_roi_controls
from tileserver import TileClient
from ipyleaflet import Map

# First, create a tile server from local raster file
tile_client = TileClient('~/Desktop/TC_NG_SFBay_US_Geo.tif')

# Create ipyleaflet tile layer from that server
t = get_leaflet_tile_layer(tile_client)

# Create ipyleaflet controls to extract an ROI
draw_control, roi_control = get_leaflet_roi_controls(tile_client)

# Create ipyleaflet map, add layers, add controls, and display
m = Map(center=(37.7249511580583, -122.27230466902257), zoom=9)
m.add_layer(t)
m.add_control(draw_control)
m.add_control(roi_control)
m
```

![ipyleaflet-draw-roi](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/ipyleaflet-draw-roi.png)


#### 🗺️ Example Datasets

A few example datasets are included with `tileserver`. A particularly
useful one has global elevation data which you can use to create high resolution Digital Elevation Models (DEMs) of a local region.

```py
from tileserver import get_leaflet_tile_layer, get_leaflet_roi_controls, examples
from ipyleaflet import Map

# Load example tile layer from publicly available DEM source
tile_client = examples.get_elevation()

# Create ipyleaflet tile layer from that server
t = get_leaflet_tile_layer(tile_client,
                           band=1, vmin=-500, vmax=5000,
                           palette='matplotlib.Plasma_6',
                           opacity=0.75)

# Create ipyleaflet controls to extract an ROI
draw_control, roi_control = get_leaflet_roi_controls(tile_client)

m = Map(zoom=2)
m.add_layer(t)
m.add_control(draw_control)
m.add_control(roi_control)
m
```

![elevation](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/elevation.png)


Then you can follow the same routine as described above to extract an ROI.

I zoomed in over Golden, Colorado and drew a polygon of the extent of the DEM I would like to create:

![golden](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/golden-roi.png)

And perform the extraction:

```py

roi_path = '...'  # Look in your working directory

r = get_leaflet_tile_layer(roi_path, band=1,
                           palette='matplotlib.Plasma_6', opacity=0.75)

m2 = Map(
        center=(39.763427033262175, -105.20614908076823),
        zoom=12,
       )
m2.add_layer(r)
m2
```

![golden-dem](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/golden-dem.png)

Here is another example with the Virtual Earth satellite imagery

```py
from tileserver import get_leaflet_tile_layer, examples
from ipyleaflet import Map

# Load example tile layer from publicly available imagery
tile_client = examples.get_virtual_earth()

# Create ipyleaflet tile layer from that server
t = get_leaflet_tile_layer(tile_client, opacity=1)

m = Map(center=(39.751343612695145, -105.22181306125279), zoom=18)
m.add_layer(t)
m
```

![kafadar](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/kafadar.png)


### 🖥️ Local Web Application

Launch the tileserver from the commandline to use the included web application where you can view the raster and extract regions of interest.

```bash
python -m tileserver path/to/raster.tif
```

![cesium-viewer](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/cesium-viewer.png)

You can use the web viewer to extract regions of interest:

![webviewer-roi](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/webviewer-roi.gif)


### Usage Notes

- `get_leaflet_tile_layer` accepts either an existing `TileClient` or a
path from which to create a `TileClient` under the hood.
- The color palette choices come from [`palettable`](https://jiffyclub.github.io/palettable/).




%package help
Summary:	Development documents and examples for flask-tileserver
Provides:	python3-flask-tileserver-doc
%description help
# 🌐 Local Tile Server for Geospatial Rasters

[![codecov](https://codecov.io/gh/banesullivan/flask-tileserver/branch/main/graph/badge.svg?token=S0HQ64FW8G)](https://codecov.io/gh/banesullivan/flask-tileserver)
[![PyPI](https://img.shields.io/pypi/v/flask-tileserver.svg?logo=python&logoColor=white)](https://pypi.org/project/flask-tileserver/)
[![pypi-activity](https://img.shields.io/pypi/dm/flask-tileserver.svg?label=pypi%20downloads)](https://pypi.org/project/flask-tileserver/)

*Need to visualize a rather large (gigabytes) raster you have locally?* **This is for you.**

A Flask application for serving tiles from large raster files in
the [Slippy Maps standard](https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames)
(i.e., `/zoom/x/y.png`)

**Disclaimer**: I put this together over a weekend and I'm definitely going to
change a few things moving forward to make it more stable/robust. This means
that things will most likely break between minor releases (I use the
`major.minor.patch` versioning scheme).


![tile-diagram](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/tile-diagram.png)


## 🌟 Highlights

- Create a local tile server for large geospatial images
- View local raster files with `ipyleaflet`
- Extract regions of interest (ROIs) interactively

Under the hood, this uses [`large_image`](https://github.com/girder/large_image)
to launch a tile server in a background thread which will serve raster imagery
to a tile viewer (see `ipyleaflet` examples below).
This tile server can efficiently deliver varying levels of detail of your
raster imagery to your viewer; it helps to have pre-tiled, Cloud Optimized
GeoTIFFs (COG), but no wories if not as `large_image` will tile and cache for
you when opening the raster.

There is an included, standalone web viewer leveraging
[CesiumJS](https://cesium.com/platform/cesiumjs/) and [GeoJS](https://opengeoscience.github.io/geojs/).
You can use the web viewer to select and extract regions of interest from rasters.

## ⬇️ Installation

Install from PyPI: https://pypi.org/project/flask-tileserver/

```
pip install flask-tileserver
```

### 📝 A Brief Note on Installing GDAL

GDAL can be a pain in the 🍑 to install, and you may want to handle GDAL
before installing `flask-tileserver`.

If on linux, I highly recommend using the [large_image_wheels](https://github.com/girder/large_image_wheels) from Kitware.

```
pip install --find-links=https://girder.github.io/large_image_wheels --no-cache GDAL
```

Otherwise, I recommend using `conda`:

```
conda install -c conda-forge GDAL
```

## 💭 Feedback

Please share your thoughts and questions on the [Discussions](https://github.com/banesullivan/flask-tileserver/discussions) board.
If you would like to report any bugs or make feature requests, please open an issue.

If filing a bug report, please share a scooby `Report`:

```py
import tileserver
print(tileserver.Report())
```

## 🚀 Usage

### 🍃 `ipyleaflet` Tile Layers

The `TileClient` class is a nifty tool to launch a tile server as a background
thread to serve image tiles from any raster file on your local file system.
Additionally, it can be used in conjunction with the `get_leaflet_tile_layer`
utility to create an `ipyleaflet.TileLayer` for interactive visualization in
a Jupyter notebook. Here is an example:


```py
from tileserver import get_leaflet_tile_layer, TileClient
from ipyleaflet import Map

# First, create a tile server from local raster file
tile_client = TileClient('~/Desktop/TC_NG_SFBay_US_Geo.tif')

# Create ipyleaflet tile layer from that server
t = get_leaflet_tile_layer(tile_client)

# Create ipyleaflet map, add tile layer, and display
m = Map(center=tile_client.center())
m.add_layer(t)
m
```

![ipyleaflet](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/ipyleaflet.png)

#### 🥓 Two Rasters at Once

```py
from tileserver import get_leaflet_tile_layer
from ipyleaflet import Map, ScaleControl, FullScreenControl, SplitMapControl

# Create 2 tile layers from 2 separate raster files
l = get_leaflet_tile_layer('~/Desktop/TC_NG_SFBay_US_Geo.tif',
                           band=1, palette='matplotlib.Viridis_20', vmin=50, vmax=200)
r = get_leaflet_tile_layer('~/Desktop/small.tif',
                           band=2, palette='matplotlib.Plasma_6', vmin=0, vmax=150)

# Make the ipyleaflet map
m = Map(center=(37.7249511580583, -122.27230466902257), zoom=9)
control = SplitMapControl(left_layer=l, right_layer=r)
m.add_control(control)
m.add_control(ScaleControl(position='bottomleft'))
m.add_control(FullScreenControl())
m
```

![ipyleaflet-double](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/ipyleaflet.gif)


#### 🎯 Using `ipyleaflet` for ROI Extraction

I have included the `get_leaflet_roi_controls` utility to create some leaflet
UI controls for extracting regions of interest from a tile client. You can
use it as follows and then draw a polygon and click the "Extract ROI" button.

The outputs are save in your working directory by default (next to the Jupyter notebook).

```py
from tileserver import get_leaflet_tile_layer, get_leaflet_roi_controls
from tileserver import TileClient
from ipyleaflet import Map

# First, create a tile server from local raster file
tile_client = TileClient('~/Desktop/TC_NG_SFBay_US_Geo.tif')

# Create ipyleaflet tile layer from that server
t = get_leaflet_tile_layer(tile_client)

# Create ipyleaflet controls to extract an ROI
draw_control, roi_control = get_leaflet_roi_controls(tile_client)

# Create ipyleaflet map, add layers, add controls, and display
m = Map(center=(37.7249511580583, -122.27230466902257), zoom=9)
m.add_layer(t)
m.add_control(draw_control)
m.add_control(roi_control)
m
```

![ipyleaflet-draw-roi](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/ipyleaflet-draw-roi.png)


#### 🗺️ Example Datasets

A few example datasets are included with `tileserver`. A particularly
useful one has global elevation data which you can use to create high resolution Digital Elevation Models (DEMs) of a local region.

```py
from tileserver import get_leaflet_tile_layer, get_leaflet_roi_controls, examples
from ipyleaflet import Map

# Load example tile layer from publicly available DEM source
tile_client = examples.get_elevation()

# Create ipyleaflet tile layer from that server
t = get_leaflet_tile_layer(tile_client,
                           band=1, vmin=-500, vmax=5000,
                           palette='matplotlib.Plasma_6',
                           opacity=0.75)

# Create ipyleaflet controls to extract an ROI
draw_control, roi_control = get_leaflet_roi_controls(tile_client)

m = Map(zoom=2)
m.add_layer(t)
m.add_control(draw_control)
m.add_control(roi_control)
m
```

![elevation](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/elevation.png)


Then you can follow the same routine as described above to extract an ROI.

I zoomed in over Golden, Colorado and drew a polygon of the extent of the DEM I would like to create:

![golden](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/golden-roi.png)

And perform the extraction:

```py

roi_path = '...'  # Look in your working directory

r = get_leaflet_tile_layer(roi_path, band=1,
                           palette='matplotlib.Plasma_6', opacity=0.75)

m2 = Map(
        center=(39.763427033262175, -105.20614908076823),
        zoom=12,
       )
m2.add_layer(r)
m2
```

![golden-dem](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/golden-dem.png)

Here is another example with the Virtual Earth satellite imagery

```py
from tileserver import get_leaflet_tile_layer, examples
from ipyleaflet import Map

# Load example tile layer from publicly available imagery
tile_client = examples.get_virtual_earth()

# Create ipyleaflet tile layer from that server
t = get_leaflet_tile_layer(tile_client, opacity=1)

m = Map(center=(39.751343612695145, -105.22181306125279), zoom=18)
m.add_layer(t)
m
```

![kafadar](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/kafadar.png)


### 🖥️ Local Web Application

Launch the tileserver from the commandline to use the included web application where you can view the raster and extract regions of interest.

```bash
python -m tileserver path/to/raster.tif
```

![cesium-viewer](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/cesium-viewer.png)

You can use the web viewer to extract regions of interest:

![webviewer-roi](https://raw.githubusercontent.com/banesullivan/flask-tileserver/main/imgs/webviewer-roi.gif)


### Usage Notes

- `get_leaflet_tile_layer` accepts either an existing `TileClient` or a
path from which to create a `TileClient` under the hood.
- The color palette choices come from [`palettable`](https://jiffyclub.github.io/palettable/).




%prep
%autosetup -n flask-tileserver-0.2.3

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-flask-tileserver -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.3-1
- Package Spec generated