11_数据库记录遍历-iObjects Python with JupyterHub for K8s

原创
2019/09/02 09:53
阅读数 167

数据库记录遍历-iObjects Python with JupyterHub for K8s

使用Python对iObjects的空间数据库进行遍历,可以进一步加入处理操作。

from iobjectspy import (density_based_clustering, 
                        Unit)
import os
import sys
# 设置示例数据路径
#example_data_dir = ''
example_data_dir = '/home/jovyan/data/smdata/'

# 设置结果输出路径
out_dir = os.path.join(example_data_dir, 'out')
if not os.path.exists(out_dir):
    os.makedirs(out_dir)


def progress_func(step_event):
    sys.stdout.write('%s,%s \n' % (step_event.title, step_event.message))


def dbscan_test():
    """ 使用成都市某日17时到20时的出租车上车点数据taxi_up_webMercator,进行DBSCAN密度聚类,其中每类包含最少点数为50个, 搜索半径为100米"""

    result = density_based_clustering(os.path.join(example_data_dir, 'example_dbscan.udb/taxi_up_webMercator'), 50, 100,
                                      Unit.METER, out_data=os.path.join(out_dir, 'out_dbscan.udb'),
                                      progress=progress_func)
    if result is not None:
        if isinstance(result, str):
            print('DBSCAN密度聚类成功,结果数据集为 ' + result)
        else:
            print('DBSCAN密度聚类成功,结果数据集为 ' + result.name)
    else:
        print('DBSCAN密度聚类失败')
if __name__ == '__main__':
    dbscan_test()
java -cp /opt/conda/lib/python3.6/site-packages/iobjectspy/_jsuperpy/jars/com.supermap.jsuperpy-9.1.1.jar com.supermap.jsuperpy.ApplicationExample 127.0.0.1 50505
[iObjectsPy]: Connection gateway-service successful, Python callback port bind 35767

Adding record to dataset...,Dealing with [500/37528]... 
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Adding record to dataset...,Dealing with [37500/37528]... 
Adding record to dataset...,Dealing with [37528/37528]... 
Proceeding point cluster analysis,Proceeding point cluster operation(376/37528) 
Proceeding point cluster analysis,Proceeding point cluster operation(1126/37528) 
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Proceeding point cluster analysis,Saving cluster type information(376/37528) 
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Proceeding point cluster analysis,Saving cluster type information(13135/37528) 
Proceeding point cluster analysis,Saving cluster type information(13886/37528) 
Proceeding point cluster analysis,Saving cluster type information(14636/37528) 
Proceeding point cluster analysis,Saving cluster type information(15387/37528) 
Proceeding point cluster analysis,Saving cluster type information(16138/37528) 
Proceeding point cluster analysis,Saving cluster type information(16888/37528) 
Proceeding point cluster analysis,Saving cluster type information(17639/37528) 
Proceeding point cluster analysis,Saving cluster type information(18389/37528) 
Proceeding point cluster analysis,Saving cluster type information(19140/37528) 
Proceeding point cluster analysis,Saving cluster type information(19890/37528) 
Proceeding point cluster analysis,Saving cluster type information(20641/37528) 
Proceeding point cluster analysis,Saving cluster type information(21391/37528) 
Proceeding point cluster analysis,Saving cluster type information(22142/37528) 
Proceeding point cluster analysis,Saving cluster type information(22893/37528) 
Proceeding point cluster analysis,Saving cluster type information(23643/37528) 
Proceeding point cluster analysis,Saving cluster type information(24394/37528) 
Proceeding point cluster analysis,Saving cluster type information(25144/37528) 
Proceeding point cluster analysis,Saving cluster type information(25895/37528) 
Proceeding point cluster analysis,Saving cluster type information(26645/37528) 
Proceeding point cluster analysis,Saving cluster type information(27396/37528) 
Proceeding point cluster analysis,Saving cluster type information(28147/37528) 
Proceeding point cluster analysis,Saving cluster type information(28897/37528) 
Proceeding point cluster analysis,Saving cluster type information(29648/37528) 
Proceeding point cluster analysis,Saving cluster type information(30398/37528) 
Proceeding point cluster analysis,Saving cluster type information(31149/37528) 
Proceeding point cluster analysis,Saving cluster type information(31899/37528) 
Proceeding point cluster analysis,Saving cluster type information(32650/37528) 
Proceeding point cluster analysis,Saving cluster type information(33400/37528) 
Proceeding point cluster analysis,Saving cluster type information(34151/37528) 
Proceeding point cluster analysis,Saving cluster type information(34902/37528) 
Proceeding point cluster analysis,Saving cluster type information(35652/37528) 
Proceeding point cluster analysis,Saving cluster type information(36403/37528) 
Proceeding point cluster analysis,Saving cluster type information(37153/37528) 
DBSCAN密度聚类成功,结果数据集为 taxi_up_webMercator_dbscan
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