easyidp.shp.read_shp¶

easyidp.shp.read_shp(shp_path, shp_proj=None, encoding='utf-8', return_proj=False)¶

read shp file to python numpy object

Parameters:

shp_path (str) – the file path of *.shp
shp_proj (str | pyproj object) – by default None, will read automatically from prj file with the same name of shp filename, or give manually by read_shp(..., shp_proj=pyproj.CRS.from_epsg(4326), ...) or read_shp(..., shp_proj=r'path/to/{shp_name}.prj', ...)
encoding (str) – by default ‘utf-8’, for some chinese characters, ‘gbk’ may required
return_proj (bool, optional) – by default False, if given as true, will return extra pyproj.CRS object of current shp file.

Returns:

list[np.ndarray] – Polygon coordinates for each shape in original order.
list[dict] – Attribute records of each shape in original order.

dict – Field map in format {"FIELD_NAME": int_id}.

{'id1': np.array([[x1,y1],[x2,y2],...]),
 'id2': np.array([[x1,y1],[x2,y2],...]),...}

pyproj.CRS, optional – once set return_proj=True

Example

The example shp file has the following columns:

[0] ID	[1] MASSIFID	[2] CROPTYPE	[3] CROPDATE	[4] CROPAREA	[5] ATTID
23010…0000	23010…0000	小麦	2018-09-01	61525.26302
23010…0012	23010…0012	蔬菜	2018-09-01	2802.33512
23010…0014	23010…0014	玉米	2018-09-01	6960.7745
23010…0061	23010…0061	牧草	2018-09-01	25349.08639
23010…0062	23010…0062	玉米	2018-09-01	71463.27666
…	…	…	…	…	…
23010…0582	23010…0582	胡萝卜	2018-09-01	288.23876
23010…0577	23010…0577	杂豆	2018-09-01	2001.80384
23010…0583	23010…0583	大豆	2018-09-01	380.41704
23010…0584	23010…0584	其它	2018-09-01	9133.25998
23010…0585	23010…0585	其它	2018-09-01	1704.27193

First, prepare data:

>>> import easyidp as idp
>>> testdata = idp.data.TestData()
>>> data_path = testdata.shp.complex_shp

Then read geometry and attributes:

>>> polygons, records, fields = idp.shp.read_shp(data_path, encoding='gbk')
>>> polygons[0]
array([[ 45.83319255, 126.84383445],
       [ 45.83222256, 126.84212197],
       ...,
       [ 45.83321205, 126.84381378],
       [ 45.83319255, 126.84383445]])
>>> records[0]
{'ID': '230104112201809010000000000', 'MASSIFID': '2301041120000000000', ...}
>>> fields
{'ID': 0, 'MASSIFID': 1, 'CROPTYPE': 2, ...}