%global _empty_manifest_terminate_build 0
Name: python-ADPTC-LIB
Version: 0.0.7
Release: 1
Summary: 自适应密度峰值树聚类（Adaptive Density Peak Tree Clustering）
License: MIT License
URL: https://pypi.org/project/ADPTC-LIB/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/0d/20/58fd440c18463dc9bb222d4e6af39b8fd11fb91768230058407e2674e9f8/ADPTC_LIB-0.0.7.tar.gz
BuildArch: noarch
%description
<!--
* @Description:
* @Author: SongJ
* @Date: 2020-12-29 13:52:28
* @LastEditTime: 2021-04-12 10:44:01
* @LastEditors: SongJ
-->
## 自适应密度峰值树聚类（Adaptive Density Peak Tree Clustering）
本算法是在快速搜索与发现密度峰值聚类算法（Clustering by fast search and find of density peaks）CFSFDP的基础上进行改进的成果，主要解决的问题有:
- 手动选择聚类中心
- 单簇多密度峰值导致类簇误分
- 面向时空数据聚类时，无法顾及时空耦合
### 原理：
通过CFSFDP算法的核心概念：局部密度和斥群值，构建密度峰值树，通过直达点、连通点和切割点分离子树，达到类簇划分的目的。
<img src="https://cdn.jsdelivr.net/gh/SuilandCoder/PicStorage//img/image-20210409210616098.png" alt="image-20210409210616098" style="zoom: 80%;" />
### 使用方法：
#### 1. 安装:
```python
pip install ADPTC-LIB
```
#### 2. 空间数据聚类：
```python
import numpy as np
from ADPTC_LIB.cluster import ADPTC
from ADPTC_LIB import visual
X = np.loadtxt(r"../test_data/Aggregation.txt", delimiter="\t")
X = X[:,[0,1]]
atdpc_obj = ADPTC(X)
atdpc_obj.clustering(2)
visual.show_result(atdpc_obj.labels,X,np.array(list(atdpc_obj.core_points)))
```
#### 3. 空间属性数据聚类：
```python
from ADPTC_LIB.cluster import ADPTC
from ADPTC_LIB import visual
import xarray as xr
import os
import numpy as np
filePath = os.path.join(r'Z:\regions_daily_010deg\\05\\2013.nc')
dataset = xr.open_dataset(filePath)
pre_ds = dataset['precipitation']
lon = pre_ds.lon
lat = pre_ds.lat
lon_range = lon[(lon>-30)&(lon<70)]
lat_range = lat[(lat>30)&(lat<90)]
var = pre_ds.sel(lon=lon_range,lat = lat_range)
var = var.resample(time='1M',skipna=True).sum()
var_t = var.sel(time=var.time[0])
reduced = var_t.coarsen(lon=5).mean().coarsen(lat=5).mean()
data_nc = np.array(reduced)
spatial_eps=4
attr_eps=8
density_metric='gauss'
spre = ADPTC(data_nc)
spre.spacial_clustering_raster(spatial_eps,attr_eps,density_metric,knn_num=100,leaf_size=3000,connect_eps=0.9)
visual.show_result_2d(reduced,spre.labels)
```
#### 4.时空属性聚类：
```python
from ADPTC_LIB.cluster import ADPTC
from ADPTC_LIB import visual
import xarray as xr
import numpy as np
temp= xr.open_dataset(r'Z:\MSWX\temp\2020.nc')
temp_2020 = temp['air_temperature']
lon = temp_2020.lon
lat = temp_2020.lat
time = temp_2020.time
lon_range = lon[(lon>70)&(lon<140)]
lat_range = lat[(lat>15)&(lat<55)]
var = temp_2020.sel(lon=lon_range,lat = lat_range)
reduced = var.coarsen(lon=5).mean().coarsen(lat=5).mean()
data_nc = np.array(reduced)
s_eps = 5
t_eps = 1
attr_eps = 2.5
density_metric='gauss'
spre = ADPTC(data_nc)
spre.st_clustering_raster(s_eps,t_eps,attr_eps,density_metric,knn_num=100,leaf_size=3000,connect_eps=0.9)
visual.show_result_3d(reduced,spre,[70, 140, 15, 50],[0,12],21)
```
%package -n python3-ADPTC-LIB
Summary: 自适应密度峰值树聚类（Adaptive Density Peak Tree Clustering）
Provides: python-ADPTC-LIB
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-ADPTC-LIB
<!--
* @Description:
* @Author: SongJ
* @Date: 2020-12-29 13:52:28
* @LastEditTime: 2021-04-12 10:44:01
* @LastEditors: SongJ
-->
## 自适应密度峰值树聚类（Adaptive Density Peak Tree Clustering）
本算法是在快速搜索与发现密度峰值聚类算法（Clustering by fast search and find of density peaks）CFSFDP的基础上进行改进的成果，主要解决的问题有:
- 手动选择聚类中心
- 单簇多密度峰值导致类簇误分
- 面向时空数据聚类时，无法顾及时空耦合
### 原理：
通过CFSFDP算法的核心概念：局部密度和斥群值，构建密度峰值树，通过直达点、连通点和切割点分离子树，达到类簇划分的目的。
<img src="https://cdn.jsdelivr.net/gh/SuilandCoder/PicStorage//img/image-20210409210616098.png" alt="image-20210409210616098" style="zoom: 80%;" />
### 使用方法：
#### 1. 安装:
```python
pip install ADPTC-LIB
```
#### 2. 空间数据聚类：
```python
import numpy as np
from ADPTC_LIB.cluster import ADPTC
from ADPTC_LIB import visual
X = np.loadtxt(r"../test_data/Aggregation.txt", delimiter="\t")
X = X[:,[0,1]]
atdpc_obj = ADPTC(X)
atdpc_obj.clustering(2)
visual.show_result(atdpc_obj.labels,X,np.array(list(atdpc_obj.core_points)))
```
#### 3. 空间属性数据聚类：
```python
from ADPTC_LIB.cluster import ADPTC
from ADPTC_LIB import visual
import xarray as xr
import os
import numpy as np
filePath = os.path.join(r'Z:\regions_daily_010deg\\05\\2013.nc')
dataset = xr.open_dataset(filePath)
pre_ds = dataset['precipitation']
lon = pre_ds.lon
lat = pre_ds.lat
lon_range = lon[(lon>-30)&(lon<70)]
lat_range = lat[(lat>30)&(lat<90)]
var = pre_ds.sel(lon=lon_range,lat = lat_range)
var = var.resample(time='1M',skipna=True).sum()
var_t = var.sel(time=var.time[0])
reduced = var_t.coarsen(lon=5).mean().coarsen(lat=5).mean()
data_nc = np.array(reduced)
spatial_eps=4
attr_eps=8
density_metric='gauss'
spre = ADPTC(data_nc)
spre.spacial_clustering_raster(spatial_eps,attr_eps,density_metric,knn_num=100,leaf_size=3000,connect_eps=0.9)
visual.show_result_2d(reduced,spre.labels)
```
#### 4.时空属性聚类：
```python
from ADPTC_LIB.cluster import ADPTC
from ADPTC_LIB import visual
import xarray as xr
import numpy as np
temp= xr.open_dataset(r'Z:\MSWX\temp\2020.nc')
temp_2020 = temp['air_temperature']
lon = temp_2020.lon
lat = temp_2020.lat
time = temp_2020.time
lon_range = lon[(lon>70)&(lon<140)]
lat_range = lat[(lat>15)&(lat<55)]
var = temp_2020.sel(lon=lon_range,lat = lat_range)
reduced = var.coarsen(lon=5).mean().coarsen(lat=5).mean()
data_nc = np.array(reduced)
s_eps = 5
t_eps = 1
attr_eps = 2.5
density_metric='gauss'
spre = ADPTC(data_nc)
spre.st_clustering_raster(s_eps,t_eps,attr_eps,density_metric,knn_num=100,leaf_size=3000,connect_eps=0.9)
visual.show_result_3d(reduced,spre,[70, 140, 15, 50],[0,12],21)
```
%package help
Summary: Development documents and examples for ADPTC-LIB
Provides: python3-ADPTC-LIB-doc
%description help
<!--
* @Description:
* @Author: SongJ
* @Date: 2020-12-29 13:52:28
* @LastEditTime: 2021-04-12 10:44:01
* @LastEditors: SongJ
-->
## 自适应密度峰值树聚类（Adaptive Density Peak Tree Clustering）
本算法是在快速搜索与发现密度峰值聚类算法（Clustering by fast search and find of density peaks）CFSFDP的基础上进行改进的成果，主要解决的问题有:
- 手动选择聚类中心
- 单簇多密度峰值导致类簇误分
- 面向时空数据聚类时，无法顾及时空耦合
### 原理：
通过CFSFDP算法的核心概念：局部密度和斥群值，构建密度峰值树，通过直达点、连通点和切割点分离子树，达到类簇划分的目的。
<img src="https://cdn.jsdelivr.net/gh/SuilandCoder/PicStorage//img/image-20210409210616098.png" alt="image-20210409210616098" style="zoom: 80%;" />
### 使用方法：
#### 1. 安装:
```python
pip install ADPTC-LIB
```
#### 2. 空间数据聚类：
```python
import numpy as np
from ADPTC_LIB.cluster import ADPTC
from ADPTC_LIB import visual
X = np.loadtxt(r"../test_data/Aggregation.txt", delimiter="\t")
X = X[:,[0,1]]
atdpc_obj = ADPTC(X)
atdpc_obj.clustering(2)
visual.show_result(atdpc_obj.labels,X,np.array(list(atdpc_obj.core_points)))
```
#### 3. 空间属性数据聚类：
```python
from ADPTC_LIB.cluster import ADPTC
from ADPTC_LIB import visual
import xarray as xr
import os
import numpy as np
filePath = os.path.join(r'Z:\regions_daily_010deg\\05\\2013.nc')
dataset = xr.open_dataset(filePath)
pre_ds = dataset['precipitation']
lon = pre_ds.lon
lat = pre_ds.lat
lon_range = lon[(lon>-30)&(lon<70)]
lat_range = lat[(lat>30)&(lat<90)]
var = pre_ds.sel(lon=lon_range,lat = lat_range)
var = var.resample(time='1M',skipna=True).sum()
var_t = var.sel(time=var.time[0])
reduced = var_t.coarsen(lon=5).mean().coarsen(lat=5).mean()
data_nc = np.array(reduced)
spatial_eps=4
attr_eps=8
density_metric='gauss'
spre = ADPTC(data_nc)
spre.spacial_clustering_raster(spatial_eps,attr_eps,density_metric,knn_num=100,leaf_size=3000,connect_eps=0.9)
visual.show_result_2d(reduced,spre.labels)
```
#### 4.时空属性聚类：
```python
from ADPTC_LIB.cluster import ADPTC
from ADPTC_LIB import visual
import xarray as xr
import numpy as np
temp= xr.open_dataset(r'Z:\MSWX\temp\2020.nc')
temp_2020 = temp['air_temperature']
lon = temp_2020.lon
lat = temp_2020.lat
time = temp_2020.time
lon_range = lon[(lon>70)&(lon<140)]
lat_range = lat[(lat>15)&(lat<55)]
var = temp_2020.sel(lon=lon_range,lat = lat_range)
reduced = var.coarsen(lon=5).mean().coarsen(lat=5).mean()
data_nc = np.array(reduced)
s_eps = 5
t_eps = 1
attr_eps = 2.5
density_metric='gauss'
spre = ADPTC(data_nc)
spre.st_clustering_raster(s_eps,t_eps,attr_eps,density_metric,knn_num=100,leaf_size=3000,connect_eps=0.9)
visual.show_result_3d(reduced,spre,[70, 140, 15, 50],[0,12],21)
```
%prep
%autosetup -n ADPTC-LIB-0.0.7
%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-ADPTC-LIB -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.7-1
- Package Spec generated