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|
%global _empty_manifest_terminate_build 0
Name: python-numba-kdtree
Version: 0.1.7
Release: 1
Summary: A kdtree implementation for numba.
License: MIT
URL: https://github.com/mortacious/numba-kdtree
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d6/47/660d9f3fc314269777e25dc69650b422df4b356d4b2a3bb26cb42bf7b8fb/numba-kdtree-0.1.7.tar.gz
%description
# Numba-kdtree
A simple KD-Tree for numba using a ctypes wrapper around the scipy `ckdtree` implementation.
The KD-Tree is usable in both python and numba nopython functions.
Once the query functions are compiled by numba, the implementation is just as fast as the original scipy version.
Note: Currently only a basic subset of the original `ckdtree` interface is implemented.
## Installation
### Using pip
```
pip install numba-kdtree
```
### From source
```
git clone https://github.com/mortacious/numba-kdtree.git
cd numba-kdtree
python setup.py install
```
## Usage
```python
import numpy as np
from numba_kdtree import KDTree
data = np.random.random(3_000_000).reshape(-1, 3)
kdtree = KDTree(data, leafsize=10)
# query the nearest neighbors of the first 100 points
distances, indices = kdtree.query(data[:100], k=30)
# query all points in a radius around the first 100 points
indices = kdtree.query_radius(data[:100], r=0.5, return_sorted=True)
```
The `KDTree` can also be used from within numba functions
```python
import numpy as np
from numba import njit
from numba_kdtree import KDTree
def numba_function_with_kdtree(kdtree, data):
for i in range(data.shape[0]):
distances, indices = kdtree.query(data[0], k=30)
#<Use the computed neighbors
data = np.random.random(3_000_000).reshape(-1, 3)
kdtree = KDTree(data, leafsize=10)
numba_function_with_kdtree(kdtree, data[:10000])
```
## TODOs
- Implement all scipy `ckdtree` functions
- Fix the parallel query functions
%package -n python3-numba-kdtree
Summary: A kdtree implementation for numba.
Provides: python-numba-kdtree
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
BuildRequires: python3-cffi
BuildRequires: gcc
BuildRequires: gdb
%description -n python3-numba-kdtree
# Numba-kdtree
A simple KD-Tree for numba using a ctypes wrapper around the scipy `ckdtree` implementation.
The KD-Tree is usable in both python and numba nopython functions.
Once the query functions are compiled by numba, the implementation is just as fast as the original scipy version.
Note: Currently only a basic subset of the original `ckdtree` interface is implemented.
## Installation
### Using pip
```
pip install numba-kdtree
```
### From source
```
git clone https://github.com/mortacious/numba-kdtree.git
cd numba-kdtree
python setup.py install
```
## Usage
```python
import numpy as np
from numba_kdtree import KDTree
data = np.random.random(3_000_000).reshape(-1, 3)
kdtree = KDTree(data, leafsize=10)
# query the nearest neighbors of the first 100 points
distances, indices = kdtree.query(data[:100], k=30)
# query all points in a radius around the first 100 points
indices = kdtree.query_radius(data[:100], r=0.5, return_sorted=True)
```
The `KDTree` can also be used from within numba functions
```python
import numpy as np
from numba import njit
from numba_kdtree import KDTree
def numba_function_with_kdtree(kdtree, data):
for i in range(data.shape[0]):
distances, indices = kdtree.query(data[0], k=30)
#<Use the computed neighbors
data = np.random.random(3_000_000).reshape(-1, 3)
kdtree = KDTree(data, leafsize=10)
numba_function_with_kdtree(kdtree, data[:10000])
```
## TODOs
- Implement all scipy `ckdtree` functions
- Fix the parallel query functions
%package help
Summary: Development documents and examples for numba-kdtree
Provides: python3-numba-kdtree-doc
%description help
# Numba-kdtree
A simple KD-Tree for numba using a ctypes wrapper around the scipy `ckdtree` implementation.
The KD-Tree is usable in both python and numba nopython functions.
Once the query functions are compiled by numba, the implementation is just as fast as the original scipy version.
Note: Currently only a basic subset of the original `ckdtree` interface is implemented.
## Installation
### Using pip
```
pip install numba-kdtree
```
### From source
```
git clone https://github.com/mortacious/numba-kdtree.git
cd numba-kdtree
python setup.py install
```
## Usage
```python
import numpy as np
from numba_kdtree import KDTree
data = np.random.random(3_000_000).reshape(-1, 3)
kdtree = KDTree(data, leafsize=10)
# query the nearest neighbors of the first 100 points
distances, indices = kdtree.query(data[:100], k=30)
# query all points in a radius around the first 100 points
indices = kdtree.query_radius(data[:100], r=0.5, return_sorted=True)
```
The `KDTree` can also be used from within numba functions
```python
import numpy as np
from numba import njit
from numba_kdtree import KDTree
def numba_function_with_kdtree(kdtree, data):
for i in range(data.shape[0]):
distances, indices = kdtree.query(data[0], k=30)
#<Use the computed neighbors
data = np.random.random(3_000_000).reshape(-1, 3)
kdtree = KDTree(data, leafsize=10)
numba_function_with_kdtree(kdtree, data[:10000])
```
## TODOs
- Implement all scipy `ckdtree` functions
- Fix the parallel query functions
%prep
%autosetup -n numba-kdtree-0.1.7
%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-numba-kdtree -f filelist.lst
%dir %{python3_sitearch}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.7-1
- Package Spec generated
|
