Question

我正在使用pyyaml（以及其他库）生成一个夹具集，我想用manage.py loaddata加载到我的django项目中。当我运行loaddata时，有些东西不适用于我正在制作的YAML（或者使用GeoDjango，或者我认为的其他东西）。

我收到此错误：Cannot set Neighborhood GeometryProxy (MULTIPOLYGON) with value of type: <type 'list'>

这是我尝试加载的数据示例。下面是模型定义

- fields:
    external_id: unincorporated-catalina-island
    name: Unincorporated Catalina Island
    region: 5
    shape:
    - - - [-118.604432, 33.47871]
.... 
        - [-118.604375, 33.478642]
        - [-118.604325, 33.478558]
        - [-118.603603, 33.47794]   
   model: geo.neighborhood

模型：

from django.db import models
from django.contrib.gis.db import models as geo_models 
class Area(ABase):
   """
   An area defines a geographic area of any size.  This abstract class is
   subclassed in order to define the type of area being modeled, e.g.
   a wide region or a smaller neighborhood.
   """
   name = models.CharField(max_length=200)
   external_id = models.CharField(max_length=200, blank=True, unique=True,
      help_text="The ID of this area in a third party datasource")
   shape = geo_models.MultiPolygonField()
   def __unicode__(self):
      return self.name
   class Meta(ABase.Meta):
      abstract = True
      ordering = ['name']

我认为我没有正确格式化形状字段。 JSON源代码在这里：

然后我尝试给YAML文件提供整个几何图形，如下所示：

- fields:
    external_id: lake-los-angeles
    name: Lake Los Angeles
    region: 2
    shape:
      coordinates:
      - - - [-117.845055, 34.631392]
         ...
          - [-117.845055, 34.631392]
      type: MultiPolygon
  model: geo.neighborhood

但是这会产生与上面几乎相同的错误：

Cannot set Neighborhood GeometryProxy (MULTIPOLYGON) with value of type: <type 'dict'>

继续这个过程，我尝试了最简单的手工制作的YAML文件版本（编辑 here ，为了简洁起见，删除了一大块中间坐标）：

-  {model: geo.region, fields: {province: 1, id: &angeles-forest 1,
      name: Angeles Forest, external_id: angeles-forest, shape: [ [ [ [ -118.298947, 34.157699 ], [ -118.298830, 34.157683 ], [ -118.298638, 34.157808 ], [ -118.298481, 34.157914 ], [ -118.298172, 34.158122 ], [ -118.297935, 34.158293 ], [ -118.297878, 34.158342 ], [ -118.297854, 34.158477 ], [ -118.297837, 34.158577 ], [ -118.297744, 34.158575 ],  [ -118.299082, 34.157728 ], [ -118.298947, 34.157699 ] ] ] ]}}

这会产生与上述相同的错误，抱怨<type 'list'>

对于loaddata来说，YAML文件的多边形（此处为＆＃39; shape＆＃39;）字段应该是什么样的？

Answer 1

GEOS正在寻找WKT（Well Known Text）格式，它与上面的geoJSON / YAML格式接近但不相同。

实际的解决方案是使用geomet将JSON输入转换为WKT，然后可以舒适地驻留在YAML文件中......带括号的乐趣！

那么成功的格式如下所示：

- fields: {external_id: toluca-lake, name: Toluca Lake, region: 9, shape: 'MULTIPOLYGON
      (((-118.357158 34.164806, -118.357154 34.163097, -118.357151 34.161287, -118.356036
      34.161287, -118.354978 34.161288, -118.354682 34.161288, -118.354020 34.161288,
      -118.353103 34.161289, -118.353034 34.161128, -118.352925 34.160873, -118.352156
      34.159076, -118.352138 34.159033, -118.351812 34.158271, -118.351554 34.157668,
      -118.351235 34.156925, -118.350751 34.155794, -118.350196 34.154497, -118.349988
      34.154012, -118.349958 34.153941, -118.349830 34.153812, -118.349756 34.153629,
      -118.349673 34.153425, -118.349643 34.153350, -118.349664 34.153256, -118.349216
      34.152209, -118.348450 34.150419, -118.348067 34.149523, -118.347680 34.148618,
      -118.347555 34.148327, -118.347308 34.147748, -118.346800 34.146562, -118.346767
      34.146485, -118.346624 34.146151, -118.346446 34.145735, -118.346430 34.145696,
      -118.345949 34.144573, -118.345903 34.144218, -118.345691 34.142572, -118.345678
      34.142466, -118.345665 34.142367, -118.345665 34.142367, -118.345698 34.142356,
      -118.346425 34.142207, -118.346907 34.142174, -118.347168 34.142177, -118.347168
      34.142177, -118.347522 34.142180, -118.348435 34.142262, -118.351608 34.142887,
      -118.352051 34.142964, -118.354160 34.143211, -118.354603 34.143227, -118.357641
      34.143337, -118.357783 34.143342, -118.357967 34.143348, -118.358339 34.143269,
      -118.358930 34.143143, -118.359082 34.143111, -118.359198 34.143088, -118.361136
      34.142712, -118.361258 34.142688, -118.361267 34.142686, -118.361264 34.142680,
      -118.361340 34.142669, -118.361411 34.142907, -118.361711 34.143307, -118.362211
      34.144107, -118.362911 34.145107, -118.363350 34.145900, -118.363863 34.146805,
      -118.364611 34.148106, -118.365011 34.148806, -118.365312 34.149206, -118.365512
      34.149606, -118.366012 34.150406, -118.366612 34.151206, -118.367112 34.152206,
      -118.367609 34.153023, -118.367885 34.153506, -118.368500 34.154522, -118.368612
      34.154706, -118.369145 34.155691, -118.369652 34.156644, -118.370237 34.157613,
      -118.369112 34.157606, -118.368112 34.157606, -118.367012 34.157606, -118.365929
      34.157604, -118.364912 34.157606, -118.363793 34.157613, -118.362712 34.157606,
      -118.361601 34.157613, -118.361623 34.159427, -118.361612 34.161206, -118.361612
      34.163106, -118.361612 34.164806, -118.360512 34.164806, -118.359412 34.164806,
      -118.358211 34.164806, -118.357211 34.164806, -118.357158 34.164806)))'}
  model: geo.neighborhood

Django loaddata：YAML灯具+ GeoDjango MultiPolygon

1 个答案: