04_Numpy互操做-iObjects Python with JupyterHub for K8s

Numpy互操做-iObjects Python with JupyterHub for K8s

Numpy是Python科學計算的經常使用庫，也在機器學習領域具備重要的做用。Numpy主要實現矩陣運算功能，這裏的教程將地理空間數據集與Numpy實現相互轉換，從而能夠將Python的更多通用庫用於地理空間分析，包括矢量和柵格數據的轉換。進一步，能夠利用Pandas和GeoPandas的相關功能。

• 鏡像構建-iObjects Python with JupyterHub for K8s
• Pandas快速入門
• GeoPandas官方中文文檔--譯著

from iobjectspy import (datasetvector_to_numpy_array,
                        numpy_array_to_datasetvector,
                        datasetraster_to_numpy_array,
                        numpy_array_to_datasetraster)
import numpy as np
import os

# 設置示例數據路徑
#example_data_dir = ''
example_data_dir = '/home/jovyan/data/smdata/'

# 設置結果輸出路徑
out_dir = os.path.join(example_data_dir, 'out')

if not os.path.exists(out_dir):
    os.makedirs(out_dir)


def vector_to_numpy_test():
    """讀取矢量數據集 Town_P 到 numpy 數組中"""

    narray1 = datasetvector_to_numpy_array(os.path.join(example_data_dir, 'example_data.udb/Landuse_R'), export_spatial=True)
    print(narray1.dtype)
    print(narray1[:10])

    narray = datasetvector_to_numpy_array(os.path.join(example_data_dir, 'example_data.udb/Town_P'), export_spatial=True)

    if narray is None:
        print('讀取矢量數據集到 numpy 數組失敗')
    else:
        print('ndarray.ndim : ' + str(narray.ndim))
        print('ndarray.dtype : ' + str(narray.dtype))
        print(narray[:10])
        print(narray['SmX'][:10])
        print('讀取矢量數據集到 numpy 數組成功')

        xy_array = np.c_[narray['SmX'], narray['SmY']][:10]
        print(xy_array.ndim)
        print(xy_array.dtype)
        print(xy_array)

        try:
            # 使用 hdbscan 作層次聚類,並將結果在 matplotlib 顯示

            % matplotlib inline
            from hdbscan import HDBSCAN
            import matplotlib.pyplot as plt

            xy_c = np.c_[narray['SmX'], narray['SmY']]
            hdb = HDBSCAN(min_cluster_size=10).fit(xy_c)
            hdb_labels = hdb.labels_
            n_clusters_hdb_ = len(set(hdb_labels)) - (1 if -1 in hdb_labels else 0)

            hdb_unique_labels = set(hdb_labels)
            hdb_colors = plt.cm.Spectral(np.linspace(0, 1, len(hdb_unique_labels)))
            fig = plt.figure(figsize=plt.figaspect(1))
            hdb_axis = fig.add_subplot('111')
            for k, col in zip(hdb_unique_labels, hdb_colors):
                if k == -1:
                    col = 'k'
                hdb_axis.plot(xy_c[hdb_labels == k, 0], xy_c[hdb_labels == k, 1], 'o', markerfacecolor=col,
                              markeredgecolor='k', markersize=6)

            hdb_axis.set_title('HDBSCAN\nEstimated number of clusters: %d' % n_clusters_hdb_)
            plt.show()

        except ImportError:
            pass


def numpy_to_vector_test():
    """寫入數據到矢量數據集中"""

    narray = np.empty(10, dtype=[('ID', 'int32'), ('X', 'float64'), ('Y', 'float64'), ('NAME', 'U10')])

    narray[0] = 1, 116.380351, 39.93393099, '什剎海'
    narray[1] = 2, 116.365305, 39.89622499, '廣安門內'
    narray[2] = 3, 116.427342, 39.89467499, '崇文門外'
    narray[3] = 4, 116.490881, 39.96567299, '酒仙橋'
    narray[4] = 5, 116.447486, 39.93767799, '三里屯'
    narray[5] = 6, 116.347435, 40.08078599, '回龍觀'
    narray[6] = 7, 116.407196, 39.83895899, '大紅門'
    narray[7] = 8, 116.396915, 39.88371499, '天橋'
    narray[8] = 9, 116.334522, 40.03594199, '清河'
    narray[9] = 10, 116.03008, 39.87852799, '潭柘寺'

    print(narray)
    result = numpy_array_to_datasetvector(narray, os.path.join(out_dir, 'out_numpy_array.udb'), x_col='X', y_col='Y')
    if result is not None:
        if isinstance(result, str):
            if isinstance(result, str):
                print('從 numpy 數組中寫入數據到矢量數據集 %s 成功' % result)
            else:
                print('從 numpy 數組中寫入數據到矢量數據集 %s 成功' % result.name)
    else:
        print('從 numpy 數組中寫入數據到矢量數據集失敗')


def raster_to_numpy_test():
    """從柵格數據 DEM 中讀取數據到 numpy 數組中"""
    narray = datasetraster_to_numpy_array(os.path.join(example_data_dir, 'example_data.udb/DEM'))

    if narray is None:
        print('讀取柵格數據集到 numpy 數組失敗')
    else:
        print('ndarray.ndim : ' + str(narray.ndim))
        print('ndarray.dtype : ' + str(narray.dtype))
        print(narray)
        print('讀取柵格數據集到 numpy 數組成功')

    numpy_array_to_datasetraster(narray, 0.001, 0.001, os.path.join(out_dir, 'out_numpy_array.udb'), as_grid=False)


def numpy_to_raster_test():
    """從 numpy 的數組二進制文件中加載數據,寫道柵格數據集中"""

    narray = np.load(os.path.join(example_data_dir, 'dem.npy'))
    print(narray)

    result = numpy_array_to_datasetraster(narray, 0.001, 0.001, os.path.join(example_data_dir, 'out_numpy_array.udb'),
                                          as_grid=True)

    if result is not None:
        if isinstance(result, str):
            print('從 numpy 數組中寫入數據到柵格數據集 %s 成功' % result)
        else:
            print('從 numpy 數組中寫入數據到柵格數據集 %s 成功' % result.name)
    else:
        print('從 numpy 數組中寫入數據到柵格數據集失敗')

if __name__ == '__main__':
    # 讀取矢量數據集到 numpy 數組成功
    vector_to_numpy_test()

    # 從 numpy 數組中寫入數據到矢量數據集中成功
    numpy_to_vector_test()

    # 讀取柵格數據集到 numpy 數組成功
    raster_to_numpy_test()

    # 從 numpy 數組中寫入數據到柵格數據集中成功
    numpy_to_raster_test()

[('LANDTYPE', '<U4'), ('Area', '<f4'), ('Area_1', '<i2'), ('SmX', '<f8'), ('SmY', '<f8'), ('SmPerimeter', '<f8'), ('SmArea', '<f8')]
[('用材林', 132., 132, 116.47779337, 40.87251703, 0.75917921, 1.40894401e-02)
 ('用材林',  97.,  97, 116.6540059 , 40.94696274, 0.4945153 , 1.03534475e-02)
 ('灌叢',  36.,  36, 116.58451795, 40.98712283, 0.25655489, 3.89923745e-03)
 ('灌叢',  36.,  36, 116.89611418, 40.76792703, 0.59237713, 3.81791878e-03)
 ('用材林',   1.,   1, 116.37943683, 40.91435429, 0.03874328, 7.08450886e-05)
 ('灌叢', 126., 126, 116.49117083, 40.78302383, 0.53664074, 1.34577856e-02)
 ('用材林',  83.,  83, 116.69943237, 40.74456848, 0.39696365, 8.83225363e-03)
 ('用材林', 128., 128, 116.8129727 , 40.69116153, 0.56949408, 1.35877743e-02)
 ('用材林',  29.,  29, 116.24543769, 40.71076092, 0.30082509, 3.07221559e-03)
 ('灌叢', 467., 467, 116.43290772, 40.50875567, 1.91745792, 4.95537433e-02)]
ndarray.ndim : 1
ndarray.dtype : [('NAME', '<U9'), ('SmX', '<f8'), ('SmY', '<f8')]
[('百尺竿鄉', 115.917748, 39.53525099) ('什剎海', 116.380351, 39.93393099)
 ('月壇', 116.344828, 39.91476099) ('廣安門內', 116.365305, 39.89622499)
 ('牛街', 116.36388 , 39.88680299) ('崇文門外', 116.427342, 39.89467499)
 ('永定門外', 116.402249, 39.86559299) ('崔各莊', 116.515447, 39.99966499)
 ('小關', 116.411727, 39.97737199) ('潘家園', 116.467911, 39.87179299)]
[115.917748 116.380351 116.344828 116.365305 116.36388  116.427342
 116.402249 116.515447 116.411727 116.467911]
讀取矢量數據集到 numpy 數組成功
2
float64
[[115.917748    39.53525099]
 [116.380351    39.93393099]
 [116.344828    39.91476099]
 [116.365305    39.89622499]
 [116.36388     39.88680299]
 [116.427342    39.89467499]
 [116.402249    39.86559299]
 [116.515447    39.99966499]
 [116.411727    39.97737199]
 [116.467911    39.87179299]]
[( 1, 116.380351, 39.93393099, '什剎海')
 ( 2, 116.365305, 39.89622499, '廣安門內')
 ( 3, 116.427342, 39.89467499, '崇文門外')
 ( 4, 116.490881, 39.96567299, '酒仙橋') ( 5, 116.447486, 39.93767799, '三里屯')
 ( 6, 116.347435, 40.08078599, '回龍觀') ( 7, 116.407196, 39.83895899, '大紅門')
 ( 8, 116.396915, 39.88371499, '天橋') ( 9, 116.334522, 40.03594199, '清河')
 (10, 116.03008 , 39.87852799, '潭柘寺')]
從 numpy 數組中寫入數據到矢量數據集 NewDataset 成功
DEM
ndarray.ndim : 2
ndarray.dtype : int16
[[ 360  357  353 ... 1373 1320 1265]
 [ 349  353  355 ... 1354 1292 1259]
 [ 353  358  356 ... 1375 1341 1319]
 ...
 [ 741  756  745 ...  819  836  860]
 [ 700  713  718 ...  821  848  872]
 [ 684  696  696 ...  828  854  880]]
讀取柵格數據集到 numpy 數組成功
[[ 360  357  353 ... 1373 1320 1265]
 [ 349  353  355 ... 1354 1292 1259]
 [ 353  358  356 ... 1375 1341 1319]
 ...
 [ 741  756  745 ...  819  836  860]
 [ 700  713  718 ...  821  848  872]
 [ 684  696  696 ...  828  854  880]]
從 numpy 數組中寫入數據到柵格數據集 NewDataset 成功