automatic import of python-pymartiniopeneuler20.03

1 files changed, 1173 insertions, 0 deletions

diff --git a/python-pymartini.spec b/python-pymartini.spec
new file mode 100644
index 0000000..dfdf44e
--- /dev/null
+++ b/python-pymartini.spec
@@ -0,0 +1,1173 @@
+%global _empty_manifest_terminate_build 0
+Name:		python-pymartini
+Version:	0.4.4
+Release:	1
+Summary:	A Python port of Martini for fast terrain mesh generation
+License:	MIT
+URL:		https://github.com/kylebarron/pymartini
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/8d/4d/0b67081813ea0862663ebd9e34a3a9492291b787b0e4496944911f36b014/pymartini-0.4.4.tar.gz
+
+Requires:	python3-numpy
+Requires:	python3-pytest
+Requires:	python3-pytest-benchmark
+Requires:	python3-imageio
+
+%description
+# pymartini
+
+A Cython port of [Martini][martini] for fast RTIN terrain mesh generation, 2-3x
+faster than Martini in Node. The only dependency is Numpy.
+
+[![][image_url]][example]
+
+[image_url]: https://raw.githubusercontent.com/kylebarron/pymartini/master/assets/grca_wireframe.jpg
+[example]: https://kylebarron.dev/quantized-mesh-encoder
+
+A wireframe rendering of the Grand Canyon. The mesh is created using
+`pymartini`, encoded using [`quantized-mesh-encoder`][quantized-mesh-encoder],
+served on-demand using [`dem-tiler`][dem-tiler], and rendered with
+[deck.gl](https://deck.gl).
+
+[quantized-mesh-encoder]: https://github.com/kylebarron/quantized-mesh-encoder
+[dem-tiler]: https://github.com/kylebarron/dem-tiler
+
+## Install
+
+With pip:
+
+```
+pip install pymartini
+```
+
+or with Conda:
+
+```
+conda install -c conda-forge pymartini
+```
+
+## Using
+
+### Example
+
+The API is modeled after Martini.
+
+```py
+from pymartini import Martini
+
+# set up mesh generator for a certain 2^k+1 grid size
+# Usually either 257 or 513
+martini = Martini(257)
+
+# generate RTIN hierarchy from terrain data (an array of size^2 length)
+tile = martini.create_tile(terrain)
+
+# get a mesh (vertices and triangles indices) for a 10m error
+vertices, triangles = tile.get_mesh(10)
+```
+
+### API
+
+The `Martini` class and `create_tile` and `get_mesh` methods are a direct port
+from the JS Martini library.
+
+Additionally I include two helper functions: `decode_ele` to decode a Mapbox
+Terrain RGB or Terrarium PNG array to elevations; and `rescale_positions`, which
+adds elevations to each vertex and optionally linearly rescales each vertex's XY
+coordinates to a new bounding box.
+
+#### `Martini`
+
+A class to instantiate constants needed for the `create_tile` and `get_mesh`
+steps. As noted in the benchmarks below, instantiating the `Martini` class is
+the slowest of the three functions. If you're planning to create many meshes of
+the same size, create one `Martini` class and create many tiles from it.
+
+##### Arguments
+
+- `grid_size` (`int`, default `257`): the grid size to use when generating the
+  mesh. Must be 2^k+1. If your source heightmap is 256x256 pixels, use
+  `grid_size=257` and backfill the border pixels.
+
+##### Returns
+
+Returns a `Martini` instance on which you can call `create_tile`.
+
+#### `Martini.create_tile`
+
+Generate RTIN hierarchy from terrain data. This is faster than creating the
+`Martini` instance, but slower than creating a mesh for a given max error. If
+you need to create many meshes with different errors for the same tile, you
+should reuse a `Tile` instance.
+
+##### Arguments
+
+- `terrain` (numpy `ndarray`): an array of dtype `float32` representing the
+  input heightmap. The array can either be flattened, of shape (2^k+1 \* 2^k+1)
+  or a two-dimensional array of shape (2^k+1, 2^k+1). Note that for a 2D array
+  pymartini expects indices in (columns, rows) order, so you might need to
+  transpose your array first. Currently an error will be produced if the dtype
+  of your input array is not `np.float32`.
+
+##### Returns
+
+Returns a `Tile` instance on which you can call `get_mesh`.
+
+#### `Tile.get_mesh`
+
+Get a mesh for a given max error.
+
+##### Arguments
+
+- `max_error` (`float`, default `0`): the maximum vertical error for each
+  triangle in the output mesh. For example if the units of the input heightmap
+  is meters, using `max_error=5` would mean that the mesh is continually refined
+  until every triangle approximates the surface of the heightmap within 5
+  meters.
+
+##### Returns
+
+Returns a tuple of (`vertices`, `triangles`).
+
+Each is a flat numpy array. Vertices represents the interleaved **2D**
+coordinates of each vertex, e.g. `[x0, y0, x1, y1, ...]`. If you need 3D
+coordinates, you can use the `rescale_positions` helper function described
+below.
+
+`triangles` represents _indices_ within the `vertices` array. So `[0, 1, 3, ...]` would use the first, second, and fourth vertices within the `vertices`
+array as a single triangle.
+
+#### `decode_ele`
+
+A helper function to decode a PNG terrain tile into elevations.
+
+##### Arguments
+
+- `png` (`np.ndarray`): Ndarray of elevations encoded in three channels,
+  representing red, green, and blue. Must be of shape (`tile_size`, `tile_size`,
+  `>=3`) or (`>=3`, `tile_size`, `tile_size`), where `tile_size` is usually 256
+  or 512
+- `encoding` (`str`): Either 'mapbox' or 'terrarium', the two main RGB
+  encodings for elevation values
+- `backfill` (`bool`, default `True`): Whether to create an array of size
+  (`tile_size + 1`, `tile_size + 1`), backfilling the bottom and right edges. This is used
+  because Martini needs a grid of size `2^n + 1`
+
+##### Returns
+
+- (`np.ndarray`) Array with decoded elevation values. If `backfill` is `True`,
+  returned shape is (`tile_size + 1`, `tile_size + 1`), otherwise returned shape
+  is (`tile_size`, `tile_size`), where `tile_size` is the shape of the input
+  array.
+
+##### Example
+
+```py
+from imageio import imread
+from pymartini import decode_ele
+
+path = './test/data/fuji.png'
+fuji = imread(path)
+terrain = decode_ele(fuji, 'mapbox')
+```
+
+#### `rescale_positions`
+
+A helper function to rescale the `vertices` output and add elevations. The
+output is a numpy ndarray of the form `[[x1, y1, z1], [x2, y2, z2], ...]`.
+
+##### Arguments
+
+- `vertices`: (`np.array`) vertices output from Martini
+- `terrain`: (`np.ndarray`) 2d heightmap array of elevations as output by
+  `decode_ele`. Expected to have shape (`grid_size`, `grid_size`). **`terrain`
+  is expected to be the exact same array passed to `Martini.create_tile`.** If
+  you use a different or transposed array, the mesh will look weird. See
+  [#15](https://github.com/kylebarron/pymartini/issues/15). If you need to
+  transpose your array, do it before passing to `Martini.create_tile`.
+- `bounds`: (`List[float]`, default `None`) linearly rescale position values to
+  this extent, expected to be [minx, miny, maxx, maxy]. If not provided, no
+  rescaling is done
+- `flip_y`: (`bool`, default `False`) Flip y coordinates. Can be useful when
+  original data source is a PNG, since the origin of a PNG is the top left.
+
+##### Example
+
+```py
+from imageio import imread
+from pymartini import decode_ele, Martini, rescale_positions
+
+path = './test/data/terrarium.png'
+png = imread(path)
+terrain = decode_ele(png, 'mapbox')
+martini = Martini(png.shape[0] + 1)
+tile = martini.create_tile(terrain)
+vertices, triangles = tile.get_mesh(10)
+
+# Use mercantile to find the bounds in WGS84 of this tile
+import mercantile
+bounds = mercantile.bounds(mercantile.Tile(385, 803, 11))
+
+# Rescale positions to WGS84
+rescaled = rescale_positions(
+    vertices,
+    terrain,
+    bounds=bounds,
+    flip_y=True
+    column_row=True
+)
+```
+
+## `Martini` or `Delatin`?
+
+Two popular algorithms for terrain mesh generation are the **"Martini"**
+algorithm, found in the JavaScript [`martini`][martini] library and this Python
+`pymartini` library, and the **"Delatin"** algorithm, found in the
+C++ [`hmm`][hmm] library, the Python [`pydelatin`][pydelatin] library, and the JavaScript
+[`delatin`][delatin] library.
+
+Which to use?
+
+For most purposes, use `pydelatin` over `pymartini`. A good breakdown from [a
+Martini issue][martini_desc_issue]:
+
+> Martini:
+>
+> - Only works on square 2^n+1 x 2^n+1 grids.
+> - Generates a hierarchy of meshes (pick arbitrary detail after a single run)
+> - Optimized for meshing speed rather than quality.
+>
+> Delatin:
+>
+> - Works on arbitrary raster grids.
+> - Generates a single mesh for a particular detail.
+> - Optimized for quality (as few triangles as possible for a given error).
+
+[hmm]: https://github.com/fogleman/hmm
+[pydelatin]: https://github.com/kylebarron/pydelatin
+[delatin]: https://github.com/mapbox/delatin
+[martini_desc_issue]: https://github.com/mapbox/martini/issues/15#issuecomment-700475731
+
+## Correctness
+
+`pymartini` passes the (only) test case included in the original Martini JS
+library. I also wrote a few extra conformance tests to compare output by
+`pymartini` and Martini. I've found some small differences in float values at
+the end of the second step.
+
+This second step, `martini.create_tile(terrain)`, computes the maximum error of
+every possible triangle and accumulates them. Thus, small float errors appear to
+be magnified by the summation of errors into larger triangles. These errors
+appear to be within `1e-5` of the JS output. I'm guessing that this variance is
+greater than normal float rounding errors, due to this summation behavior.
+
+These differences are larger when using 512px tiles compared to 256px tiles,
+which reinforces my hypothesis that the differences have something to do with
+small low-level float or bitwise operations differences between Python and
+JavaScript.
+
+If you'd like to explore this in more detail, look at the `Tile.update()` in
+`martini.pyx` and the corresponding Martini code.
+
+## Type Checking
+
+As of `pymartini` 0.4.0, types are provided, which can be used with a checker
+like [`mypy`](https://mypy.readthedocs.io/). If you wish to get the full
+benefit, make sure to [enable Numpy's mypy
+plugin](https://numpy.org/devdocs/reference/typing.html#examples).
+
+## Benchmark
+
+Preparation steps are about 3x faster in Python than in Node; generating the
+mesh is about 2x faster in Python than in Node.
+
+### Python
+
+```bash
+git clone https://github.com/kylebarron/pymartini
+cd pymartini
+pip install '.[test]'
+python bench.py
+```
+
+```
+init tileset: 14.860ms
+create tile: 5.862ms
+mesh (max_error=30): 1.010ms
+vertices: 9700.0, triangles: 19078.0
+mesh 0: 18.350ms
+mesh 1: 17.581ms
+mesh 2: 15.245ms
+mesh 3: 13.853ms
+mesh 4: 11.284ms
+mesh 5: 12.360ms
+mesh 6: 8.293ms
+mesh 7: 8.342ms
+mesh 8: 7.166ms
+mesh 9: 5.678ms
+mesh 10: 5.886ms
+mesh 11: 5.092ms
+mesh 12: 3.732ms
+mesh 13: 3.420ms
+mesh 14: 3.524ms
+mesh 15: 3.101ms
+mesh 16: 2.892ms
+mesh 17: 2.358ms
+mesh 18: 2.250ms
+mesh 19: 2.293ms
+mesh 20: 2.281ms
+20 meshes total: 155.559ms
+```
+
+### JS (Node)
+
+```bash
+git clone https://github.com/mapbox/martini
+cd martini
+npm install
+node -r esm bench.js
+```
+
+```
+init tileset: 54.293ms
+create tile: 17.307ms
+mesh: 6.230ms
+vertices: 9704, triangles: 19086
+mesh 0: 43.181ms
+mesh 1: 33.102ms
+mesh 2: 30.735ms
+mesh 3: 25.935ms
+mesh 4: 20.643ms
+mesh 5: 17.511ms
+mesh 6: 15.066ms
+mesh 7: 13.334ms
+mesh 8: 11.180ms
+mesh 9: 9.651ms
+mesh 10: 9.240ms
+mesh 11: 10.996ms
+mesh 12: 7.520ms
+mesh 13: 6.617ms
+mesh 14: 5.860ms
+mesh 15: 5.693ms
+mesh 16: 4.907ms
+mesh 17: 4.469ms
+mesh 18: 4.267ms
+mesh 19: 4.267ms
+mesh 20: 3.619ms
+20 meshes total: 290.256ms
+```
+
+## License
+
+This library is ported from Mapbox's [Martini][martini], which is licensed under
+the ISC License. My additions are licensed under the MIT license.
+
+ISC License
+
+Copyright (c) 2019, Mapbox
+
+Permission to use, copy, modify, and/or distribute this software for any purpose
+with or without fee is hereby granted, provided that the above copyright notice
+and this permission notice appear in all copies.
+
+THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
+REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
+FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
+INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER
+TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF
+THIS SOFTWARE.
+
+[martini]: https://github.com/mapbox/martini
+
+
+%package -n python3-pymartini
+Summary:	A Python port of Martini for fast terrain mesh generation
+Provides:	python-pymartini
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+BuildRequires:	python3-cffi
+BuildRequires:	gcc
+BuildRequires:	gdb
+%description -n python3-pymartini
+# pymartini
+
+A Cython port of [Martini][martini] for fast RTIN terrain mesh generation, 2-3x
+faster than Martini in Node. The only dependency is Numpy.
+
+[![][image_url]][example]
+
+[image_url]: https://raw.githubusercontent.com/kylebarron/pymartini/master/assets/grca_wireframe.jpg
+[example]: https://kylebarron.dev/quantized-mesh-encoder
+
+A wireframe rendering of the Grand Canyon. The mesh is created using
+`pymartini`, encoded using [`quantized-mesh-encoder`][quantized-mesh-encoder],
+served on-demand using [`dem-tiler`][dem-tiler], and rendered with
+[deck.gl](https://deck.gl).
+
+[quantized-mesh-encoder]: https://github.com/kylebarron/quantized-mesh-encoder
+[dem-tiler]: https://github.com/kylebarron/dem-tiler
+
+## Install
+
+With pip:
+
+```
+pip install pymartini
+```
+
+or with Conda:
+
+```
+conda install -c conda-forge pymartini
+```
+
+## Using
+
+### Example
+
+The API is modeled after Martini.
+
+```py
+from pymartini import Martini
+
+# set up mesh generator for a certain 2^k+1 grid size
+# Usually either 257 or 513
+martini = Martini(257)
+
+# generate RTIN hierarchy from terrain data (an array of size^2 length)
+tile = martini.create_tile(terrain)
+
+# get a mesh (vertices and triangles indices) for a 10m error
+vertices, triangles = tile.get_mesh(10)
+```
+
+### API
+
+The `Martini` class and `create_tile` and `get_mesh` methods are a direct port
+from the JS Martini library.
+
+Additionally I include two helper functions: `decode_ele` to decode a Mapbox
+Terrain RGB or Terrarium PNG array to elevations; and `rescale_positions`, which
+adds elevations to each vertex and optionally linearly rescales each vertex's XY
+coordinates to a new bounding box.
+
+#### `Martini`
+
+A class to instantiate constants needed for the `create_tile` and `get_mesh`
+steps. As noted in the benchmarks below, instantiating the `Martini` class is
+the slowest of the three functions. If you're planning to create many meshes of
+the same size, create one `Martini` class and create many tiles from it.
+
+##### Arguments
+
+- `grid_size` (`int`, default `257`): the grid size to use when generating the
+  mesh. Must be 2^k+1. If your source heightmap is 256x256 pixels, use
+  `grid_size=257` and backfill the border pixels.
+
+##### Returns
+
+Returns a `Martini` instance on which you can call `create_tile`.
+
+#### `Martini.create_tile`
+
+Generate RTIN hierarchy from terrain data. This is faster than creating the
+`Martini` instance, but slower than creating a mesh for a given max error. If
+you need to create many meshes with different errors for the same tile, you
+should reuse a `Tile` instance.
+
+##### Arguments
+
+- `terrain` (numpy `ndarray`): an array of dtype `float32` representing the
+  input heightmap. The array can either be flattened, of shape (2^k+1 \* 2^k+1)
+  or a two-dimensional array of shape (2^k+1, 2^k+1). Note that for a 2D array
+  pymartini expects indices in (columns, rows) order, so you might need to
+  transpose your array first. Currently an error will be produced if the dtype
+  of your input array is not `np.float32`.
+
+##### Returns
+
+Returns a `Tile` instance on which you can call `get_mesh`.
+
+#### `Tile.get_mesh`
+
+Get a mesh for a given max error.
+
+##### Arguments
+
+- `max_error` (`float`, default `0`): the maximum vertical error for each
+  triangle in the output mesh. For example if the units of the input heightmap
+  is meters, using `max_error=5` would mean that the mesh is continually refined
+  until every triangle approximates the surface of the heightmap within 5
+  meters.
+
+##### Returns
+
+Returns a tuple of (`vertices`, `triangles`).
+
+Each is a flat numpy array. Vertices represents the interleaved **2D**
+coordinates of each vertex, e.g. `[x0, y0, x1, y1, ...]`. If you need 3D
+coordinates, you can use the `rescale_positions` helper function described
+below.
+
+`triangles` represents _indices_ within the `vertices` array. So `[0, 1, 3, ...]` would use the first, second, and fourth vertices within the `vertices`
+array as a single triangle.
+
+#### `decode_ele`
+
+A helper function to decode a PNG terrain tile into elevations.
+
+##### Arguments
+
+- `png` (`np.ndarray`): Ndarray of elevations encoded in three channels,
+  representing red, green, and blue. Must be of shape (`tile_size`, `tile_size`,
+  `>=3`) or (`>=3`, `tile_size`, `tile_size`), where `tile_size` is usually 256
+  or 512
+- `encoding` (`str`): Either 'mapbox' or 'terrarium', the two main RGB
+  encodings for elevation values
+- `backfill` (`bool`, default `True`): Whether to create an array of size
+  (`tile_size + 1`, `tile_size + 1`), backfilling the bottom and right edges. This is used
+  because Martini needs a grid of size `2^n + 1`
+
+##### Returns
+
+- (`np.ndarray`) Array with decoded elevation values. If `backfill` is `True`,
+  returned shape is (`tile_size + 1`, `tile_size + 1`), otherwise returned shape
+  is (`tile_size`, `tile_size`), where `tile_size` is the shape of the input
+  array.
+
+##### Example
+
+```py
+from imageio import imread
+from pymartini import decode_ele
+
+path = './test/data/fuji.png'
+fuji = imread(path)
+terrain = decode_ele(fuji, 'mapbox')
+```
+
+#### `rescale_positions`
+
+A helper function to rescale the `vertices` output and add elevations. The
+output is a numpy ndarray of the form `[[x1, y1, z1], [x2, y2, z2], ...]`.
+
+##### Arguments
+
+- `vertices`: (`np.array`) vertices output from Martini
+- `terrain`: (`np.ndarray`) 2d heightmap array of elevations as output by
+  `decode_ele`. Expected to have shape (`grid_size`, `grid_size`). **`terrain`
+  is expected to be the exact same array passed to `Martini.create_tile`.** If
+  you use a different or transposed array, the mesh will look weird. See
+  [#15](https://github.com/kylebarron/pymartini/issues/15). If you need to
+  transpose your array, do it before passing to `Martini.create_tile`.
+- `bounds`: (`List[float]`, default `None`) linearly rescale position values to
+  this extent, expected to be [minx, miny, maxx, maxy]. If not provided, no
+  rescaling is done
+- `flip_y`: (`bool`, default `False`) Flip y coordinates. Can be useful when
+  original data source is a PNG, since the origin of a PNG is the top left.
+
+##### Example
+
+```py
+from imageio import imread
+from pymartini import decode_ele, Martini, rescale_positions
+
+path = './test/data/terrarium.png'
+png = imread(path)
+terrain = decode_ele(png, 'mapbox')
+martini = Martini(png.shape[0] + 1)
+tile = martini.create_tile(terrain)
+vertices, triangles = tile.get_mesh(10)
+
+# Use mercantile to find the bounds in WGS84 of this tile
+import mercantile
+bounds = mercantile.bounds(mercantile.Tile(385, 803, 11))
+
+# Rescale positions to WGS84
+rescaled = rescale_positions(
+    vertices,
+    terrain,
+    bounds=bounds,
+    flip_y=True
+    column_row=True
+)
+```
+
+## `Martini` or `Delatin`?
+
+Two popular algorithms for terrain mesh generation are the **"Martini"**
+algorithm, found in the JavaScript [`martini`][martini] library and this Python
+`pymartini` library, and the **"Delatin"** algorithm, found in the
+C++ [`hmm`][hmm] library, the Python [`pydelatin`][pydelatin] library, and the JavaScript
+[`delatin`][delatin] library.
+
+Which to use?
+
+For most purposes, use `pydelatin` over `pymartini`. A good breakdown from [a
+Martini issue][martini_desc_issue]:
+
+> Martini:
+>
+> - Only works on square 2^n+1 x 2^n+1 grids.
+> - Generates a hierarchy of meshes (pick arbitrary detail after a single run)
+> - Optimized for meshing speed rather than quality.
+>
+> Delatin:
+>
+> - Works on arbitrary raster grids.
+> - Generates a single mesh for a particular detail.
+> - Optimized for quality (as few triangles as possible for a given error).
+
+[hmm]: https://github.com/fogleman/hmm
+[pydelatin]: https://github.com/kylebarron/pydelatin
+[delatin]: https://github.com/mapbox/delatin
+[martini_desc_issue]: https://github.com/mapbox/martini/issues/15#issuecomment-700475731
+
+## Correctness
+
+`pymartini` passes the (only) test case included in the original Martini JS
+library. I also wrote a few extra conformance tests to compare output by
+`pymartini` and Martini. I've found some small differences in float values at
+the end of the second step.
+
+This second step, `martini.create_tile(terrain)`, computes the maximum error of
+every possible triangle and accumulates them. Thus, small float errors appear to
+be magnified by the summation of errors into larger triangles. These errors
+appear to be within `1e-5` of the JS output. I'm guessing that this variance is
+greater than normal float rounding errors, due to this summation behavior.
+
+These differences are larger when using 512px tiles compared to 256px tiles,
+which reinforces my hypothesis that the differences have something to do with
+small low-level float or bitwise operations differences between Python and
+JavaScript.
+
+If you'd like to explore this in more detail, look at the `Tile.update()` in
+`martini.pyx` and the corresponding Martini code.
+
+## Type Checking
+
+As of `pymartini` 0.4.0, types are provided, which can be used with a checker
+like [`mypy`](https://mypy.readthedocs.io/). If you wish to get the full
+benefit, make sure to [enable Numpy's mypy
+plugin](https://numpy.org/devdocs/reference/typing.html#examples).
+
+## Benchmark
+
+Preparation steps are about 3x faster in Python than in Node; generating the
+mesh is about 2x faster in Python than in Node.
+
+### Python
+
+```bash
+git clone https://github.com/kylebarron/pymartini
+cd pymartini
+pip install '.[test]'
+python bench.py
+```
+
+```
+init tileset: 14.860ms
+create tile: 5.862ms
+mesh (max_error=30): 1.010ms
+vertices: 9700.0, triangles: 19078.0
+mesh 0: 18.350ms
+mesh 1: 17.581ms
+mesh 2: 15.245ms
+mesh 3: 13.853ms
+mesh 4: 11.284ms
+mesh 5: 12.360ms
+mesh 6: 8.293ms
+mesh 7: 8.342ms
+mesh 8: 7.166ms
+mesh 9: 5.678ms
+mesh 10: 5.886ms
+mesh 11: 5.092ms
+mesh 12: 3.732ms
+mesh 13: 3.420ms
+mesh 14: 3.524ms
+mesh 15: 3.101ms
+mesh 16: 2.892ms
+mesh 17: 2.358ms
+mesh 18: 2.250ms
+mesh 19: 2.293ms
+mesh 20: 2.281ms
+20 meshes total: 155.559ms
+```
+
+### JS (Node)
+
+```bash
+git clone https://github.com/mapbox/martini
+cd martini
+npm install
+node -r esm bench.js
+```
+
+```
+init tileset: 54.293ms
+create tile: 17.307ms
+mesh: 6.230ms
+vertices: 9704, triangles: 19086
+mesh 0: 43.181ms
+mesh 1: 33.102ms
+mesh 2: 30.735ms
+mesh 3: 25.935ms
+mesh 4: 20.643ms
+mesh 5: 17.511ms
+mesh 6: 15.066ms
+mesh 7: 13.334ms
+mesh 8: 11.180ms
+mesh 9: 9.651ms
+mesh 10: 9.240ms
+mesh 11: 10.996ms
+mesh 12: 7.520ms
+mesh 13: 6.617ms
+mesh 14: 5.860ms
+mesh 15: 5.693ms
+mesh 16: 4.907ms
+mesh 17: 4.469ms
+mesh 18: 4.267ms
+mesh 19: 4.267ms
+mesh 20: 3.619ms
+20 meshes total: 290.256ms
+```
+
+## License
+
+This library is ported from Mapbox's [Martini][martini], which is licensed under
+the ISC License. My additions are licensed under the MIT license.
+
+ISC License
+
+Copyright (c) 2019, Mapbox
+
+Permission to use, copy, modify, and/or distribute this software for any purpose
+with or without fee is hereby granted, provided that the above copyright notice
+and this permission notice appear in all copies.
+
+THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
+REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
+FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
+INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER
+TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF
+THIS SOFTWARE.
+
+[martini]: https://github.com/mapbox/martini
+
+
+%package help
+Summary:	Development documents and examples for pymartini
+Provides:	python3-pymartini-doc
+%description help
+# pymartini
+
+A Cython port of [Martini][martini] for fast RTIN terrain mesh generation, 2-3x
+faster than Martini in Node. The only dependency is Numpy.
+
+[![][image_url]][example]
+
+[image_url]: https://raw.githubusercontent.com/kylebarron/pymartini/master/assets/grca_wireframe.jpg
+[example]: https://kylebarron.dev/quantized-mesh-encoder
+
+A wireframe rendering of the Grand Canyon. The mesh is created using
+`pymartini`, encoded using [`quantized-mesh-encoder`][quantized-mesh-encoder],
+served on-demand using [`dem-tiler`][dem-tiler], and rendered with
+[deck.gl](https://deck.gl).
+
+[quantized-mesh-encoder]: https://github.com/kylebarron/quantized-mesh-encoder
+[dem-tiler]: https://github.com/kylebarron/dem-tiler
+
+## Install
+
+With pip:
+
+```
+pip install pymartini
+```
+
+or with Conda:
+
+```
+conda install -c conda-forge pymartini
+```
+
+## Using
+
+### Example
+
+The API is modeled after Martini.
+
+```py
+from pymartini import Martini
+
+# set up mesh generator for a certain 2^k+1 grid size
+# Usually either 257 or 513
+martini = Martini(257)
+
+# generate RTIN hierarchy from terrain data (an array of size^2 length)
+tile = martini.create_tile(terrain)
+
+# get a mesh (vertices and triangles indices) for a 10m error
+vertices, triangles = tile.get_mesh(10)
+```
+
+### API
+
+The `Martini` class and `create_tile` and `get_mesh` methods are a direct port
+from the JS Martini library.
+
+Additionally I include two helper functions: `decode_ele` to decode a Mapbox
+Terrain RGB or Terrarium PNG array to elevations; and `rescale_positions`, which
+adds elevations to each vertex and optionally linearly rescales each vertex's XY
+coordinates to a new bounding box.
+
+#### `Martini`
+
+A class to instantiate constants needed for the `create_tile` and `get_mesh`
+steps. As noted in the benchmarks below, instantiating the `Martini` class is
+the slowest of the three functions. If you're planning to create many meshes of
+the same size, create one `Martini` class and create many tiles from it.
+
+##### Arguments
+
+- `grid_size` (`int`, default `257`): the grid size to use when generating the
+  mesh. Must be 2^k+1. If your source heightmap is 256x256 pixels, use
+  `grid_size=257` and backfill the border pixels.
+
+##### Returns
+
+Returns a `Martini` instance on which you can call `create_tile`.
+
+#### `Martini.create_tile`
+
+Generate RTIN hierarchy from terrain data. This is faster than creating the
+`Martini` instance, but slower than creating a mesh for a given max error. If
+you need to create many meshes with different errors for the same tile, you
+should reuse a `Tile` instance.
+
+##### Arguments
+
+- `terrain` (numpy `ndarray`): an array of dtype `float32` representing the
+  input heightmap. The array can either be flattened, of shape (2^k+1 \* 2^k+1)
+  or a two-dimensional array of shape (2^k+1, 2^k+1). Note that for a 2D array
+  pymartini expects indices in (columns, rows) order, so you might need to
+  transpose your array first. Currently an error will be produced if the dtype
+  of your input array is not `np.float32`.
+
+##### Returns
+
+Returns a `Tile` instance on which you can call `get_mesh`.
+
+#### `Tile.get_mesh`
+
+Get a mesh for a given max error.
+
+##### Arguments
+
+- `max_error` (`float`, default `0`): the maximum vertical error for each
+  triangle in the output mesh. For example if the units of the input heightmap
+  is meters, using `max_error=5` would mean that the mesh is continually refined
+  until every triangle approximates the surface of the heightmap within 5
+  meters.
+
+##### Returns
+
+Returns a tuple of (`vertices`, `triangles`).
+
+Each is a flat numpy array. Vertices represents the interleaved **2D**
+coordinates of each vertex, e.g. `[x0, y0, x1, y1, ...]`. If you need 3D
+coordinates, you can use the `rescale_positions` helper function described
+below.
+
+`triangles` represents _indices_ within the `vertices` array. So `[0, 1, 3, ...]` would use the first, second, and fourth vertices within the `vertices`
+array as a single triangle.
+
+#### `decode_ele`
+
+A helper function to decode a PNG terrain tile into elevations.
+
+##### Arguments
+
+- `png` (`np.ndarray`): Ndarray of elevations encoded in three channels,
+  representing red, green, and blue. Must be of shape (`tile_size`, `tile_size`,
+  `>=3`) or (`>=3`, `tile_size`, `tile_size`), where `tile_size` is usually 256
+  or 512
+- `encoding` (`str`): Either 'mapbox' or 'terrarium', the two main RGB
+  encodings for elevation values
+- `backfill` (`bool`, default `True`): Whether to create an array of size
+  (`tile_size + 1`, `tile_size + 1`), backfilling the bottom and right edges. This is used
+  because Martini needs a grid of size `2^n + 1`
+
+##### Returns
+
+- (`np.ndarray`) Array with decoded elevation values. If `backfill` is `True`,
+  returned shape is (`tile_size + 1`, `tile_size + 1`), otherwise returned shape
+  is (`tile_size`, `tile_size`), where `tile_size` is the shape of the input
+  array.
+
+##### Example
+
+```py
+from imageio import imread
+from pymartini import decode_ele
+
+path = './test/data/fuji.png'
+fuji = imread(path)
+terrain = decode_ele(fuji, 'mapbox')
+```
+
+#### `rescale_positions`
+
+A helper function to rescale the `vertices` output and add elevations. The
+output is a numpy ndarray of the form `[[x1, y1, z1], [x2, y2, z2], ...]`.
+
+##### Arguments
+
+- `vertices`: (`np.array`) vertices output from Martini
+- `terrain`: (`np.ndarray`) 2d heightmap array of elevations as output by
+  `decode_ele`. Expected to have shape (`grid_size`, `grid_size`). **`terrain`
+  is expected to be the exact same array passed to `Martini.create_tile`.** If
+  you use a different or transposed array, the mesh will look weird. See
+  [#15](https://github.com/kylebarron/pymartini/issues/15). If you need to
+  transpose your array, do it before passing to `Martini.create_tile`.
+- `bounds`: (`List[float]`, default `None`) linearly rescale position values to
+  this extent, expected to be [minx, miny, maxx, maxy]. If not provided, no
+  rescaling is done
+- `flip_y`: (`bool`, default `False`) Flip y coordinates. Can be useful when
+  original data source is a PNG, since the origin of a PNG is the top left.
+
+##### Example
+
+```py
+from imageio import imread
+from pymartini import decode_ele, Martini, rescale_positions
+
+path = './test/data/terrarium.png'
+png = imread(path)
+terrain = decode_ele(png, 'mapbox')
+martini = Martini(png.shape[0] + 1)
+tile = martini.create_tile(terrain)
+vertices, triangles = tile.get_mesh(10)
+
+# Use mercantile to find the bounds in WGS84 of this tile
+import mercantile
+bounds = mercantile.bounds(mercantile.Tile(385, 803, 11))
+
+# Rescale positions to WGS84
+rescaled = rescale_positions(
+    vertices,
+    terrain,
+    bounds=bounds,
+    flip_y=True
+    column_row=True
+)
+```
+
+## `Martini` or `Delatin`?
+
+Two popular algorithms for terrain mesh generation are the **"Martini"**
+algorithm, found in the JavaScript [`martini`][martini] library and this Python
+`pymartini` library, and the **"Delatin"** algorithm, found in the
+C++ [`hmm`][hmm] library, the Python [`pydelatin`][pydelatin] library, and the JavaScript
+[`delatin`][delatin] library.
+
+Which to use?
+
+For most purposes, use `pydelatin` over `pymartini`. A good breakdown from [a
+Martini issue][martini_desc_issue]:
+
+> Martini:
+>
+> - Only works on square 2^n+1 x 2^n+1 grids.
+> - Generates a hierarchy of meshes (pick arbitrary detail after a single run)
+> - Optimized for meshing speed rather than quality.
+>
+> Delatin:
+>
+> - Works on arbitrary raster grids.
+> - Generates a single mesh for a particular detail.
+> - Optimized for quality (as few triangles as possible for a given error).
+
+[hmm]: https://github.com/fogleman/hmm
+[pydelatin]: https://github.com/kylebarron/pydelatin
+[delatin]: https://github.com/mapbox/delatin
+[martini_desc_issue]: https://github.com/mapbox/martini/issues/15#issuecomment-700475731
+
+## Correctness
+
+`pymartini` passes the (only) test case included in the original Martini JS
+library. I also wrote a few extra conformance tests to compare output by
+`pymartini` and Martini. I've found some small differences in float values at
+the end of the second step.
+
+This second step, `martini.create_tile(terrain)`, computes the maximum error of
+every possible triangle and accumulates them. Thus, small float errors appear to
+be magnified by the summation of errors into larger triangles. These errors
+appear to be within `1e-5` of the JS output. I'm guessing that this variance is
+greater than normal float rounding errors, due to this summation behavior.
+
+These differences are larger when using 512px tiles compared to 256px tiles,
+which reinforces my hypothesis that the differences have something to do with
+small low-level float or bitwise operations differences between Python and
+JavaScript.
+
+If you'd like to explore this in more detail, look at the `Tile.update()` in
+`martini.pyx` and the corresponding Martini code.
+
+## Type Checking
+
+As of `pymartini` 0.4.0, types are provided, which can be used with a checker
+like [`mypy`](https://mypy.readthedocs.io/). If you wish to get the full
+benefit, make sure to [enable Numpy's mypy
+plugin](https://numpy.org/devdocs/reference/typing.html#examples).
+
+## Benchmark
+
+Preparation steps are about 3x faster in Python than in Node; generating the
+mesh is about 2x faster in Python than in Node.
+
+### Python
+
+```bash
+git clone https://github.com/kylebarron/pymartini
+cd pymartini
+pip install '.[test]'
+python bench.py
+```
+
+```
+init tileset: 14.860ms
+create tile: 5.862ms
+mesh (max_error=30): 1.010ms
+vertices: 9700.0, triangles: 19078.0
+mesh 0: 18.350ms
+mesh 1: 17.581ms
+mesh 2: 15.245ms
+mesh 3: 13.853ms
+mesh 4: 11.284ms
+mesh 5: 12.360ms
+mesh 6: 8.293ms
+mesh 7: 8.342ms
+mesh 8: 7.166ms
+mesh 9: 5.678ms
+mesh 10: 5.886ms
+mesh 11: 5.092ms
+mesh 12: 3.732ms
+mesh 13: 3.420ms
+mesh 14: 3.524ms
+mesh 15: 3.101ms
+mesh 16: 2.892ms
+mesh 17: 2.358ms
+mesh 18: 2.250ms
+mesh 19: 2.293ms
+mesh 20: 2.281ms
+20 meshes total: 155.559ms
+```
+
+### JS (Node)
+
+```bash
+git clone https://github.com/mapbox/martini
+cd martini
+npm install
+node -r esm bench.js
+```
+
+```
+init tileset: 54.293ms
+create tile: 17.307ms
+mesh: 6.230ms
+vertices: 9704, triangles: 19086
+mesh 0: 43.181ms
+mesh 1: 33.102ms
+mesh 2: 30.735ms
+mesh 3: 25.935ms
+mesh 4: 20.643ms
+mesh 5: 17.511ms
+mesh 6: 15.066ms
+mesh 7: 13.334ms
+mesh 8: 11.180ms
+mesh 9: 9.651ms
+mesh 10: 9.240ms
+mesh 11: 10.996ms
+mesh 12: 7.520ms
+mesh 13: 6.617ms
+mesh 14: 5.860ms
+mesh 15: 5.693ms
+mesh 16: 4.907ms
+mesh 17: 4.469ms
+mesh 18: 4.267ms
+mesh 19: 4.267ms
+mesh 20: 3.619ms
+20 meshes total: 290.256ms
+```
+
+## License
+
+This library is ported from Mapbox's [Martini][martini], which is licensed under
+the ISC License. My additions are licensed under the MIT license.
+
+ISC License
+
+Copyright (c) 2019, Mapbox
+
+Permission to use, copy, modify, and/or distribute this software for any purpose
+with or without fee is hereby granted, provided that the above copyright notice
+and this permission notice appear in all copies.
+
+THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
+REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
+FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
+INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER
+TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF
+THIS SOFTWARE.
+
+[martini]: https://github.com/mapbox/martini
+
+
+%prep
+%autosetup -n pymartini-0.4.4
+
+%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-pymartini -f filelist.lst
+%dir %{python3_sitearch}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.4-1
+- Package Spec generated