%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