Shapefiles

Reading

Read shapefile and create a NetworkModelGrid.

There are a number of assumptions that are required about the shapefile.

• The shapefile must be a polyline shapefile.

• All polylines must be their own object (e.g. no multi-part polylines).

• Polyline endpoints match perfectly.

You might notice that there is no write_shapefile function. If this is something you need for your work, please make a GitHub issue to start this process.

param file:

File path or file-like of a valid polyline shapefile

type file:

str or file-like

param dbf:

If file is file-like, the dbf must also be passed.

type dbf:

file-like, optional

param store_polyline_vertices:

If True (default), store the vertices of the polylines in the at_link fields x_of_polyline and y_of_polyline.

type store_polyline_vertices:

bool, optional

param points_shapefile:

File path or file-like of a valid point shapefile.

type points_shapefile:

str or file-like

param points_dbf:

If file is file-like, the dbf must also be passed.

type points_dbf:

file-like, optional

param link_fields:

List of polyline shapefile attributes to import as landlab at-link fields. Default is to import all.

type link_fields:

list, optional

param node_fields:

List of point shapefile attributes to import as landlab at-node fields. Default is to import all.

type node_fields:

list, optional

param link_field_conversion:

Dictionary mapping polyline shapefile field names to desired at link field names. Default is no remapping.

type link_field_conversion:

dict, optional

param node_field_conversion:

Dictionary mapping node shapefile field names to desired at node field names. Default is no remapping.

type node_field_conversion:

dict, optional

param link_field_dtype:

Dictionary mapping node shapefile field names to desired dtype. Default is no change to dtype.

type link_field_dtype:

dict, optional

param node_field_dtype:

Dictionary mapping node shapefile field names to desired dtype. Default is no change to dtype.

type node_field_dtype:

dict, optional

param threshold:

Maximum distance between a point in the point shapefile and a polyline junction in the polyline shapefile. Units are the same as in the shapefiles. Default is zero (requiring perfect overlap).

type threshold:

float, optional

returns:

grid – The network model grid will have nodes at the endpoints of the polylines, and links that connect these nodes. Any fields associated with the shapefile will be added as at-link fields. If a point shapefile is provided those values will be added as at-node fields.

rtype:

NetworkModelGrid instance

Examples

First, we make a simple shapefile

>>> from io import BytesIO
>>> import os

>>> import shapefile
>>> shp = BytesIO()
>>> shx = BytesIO()
>>> dbf = BytesIO()
>>> w = shapefile.Writer(shp=shp, shx=shx, dbf=dbf)
>>> w.shapeType = shapefile.POLYLINE
>>> w.field("spam", "N")
>>> w.line([[[5, 5], [10, 10]]])
>>> w.record(37)
>>> w.line([[[5, 0], [5, 5]]])
>>> w.record(100)
>>> w.line([[[5, 5], [0, 10]]])
>>> w.record(239)
>>> w.close()

Now create a NetworkModelGrid with read_shapefile:

>>> from landlab.io.shapefile import read_shapefile
>>> grid = read_shapefile(shp, dbf=dbf)
>>> grid.nodes
array([0, 1, 2, 3])
>>> grid.x_of_node
array([  5.,   5.,   0.,  10.])
>>> grid.y_of_node
array([  0.,   5.,  10.,  10.])
>>> grid.nodes_at_link
array([[0, 1],
       [2, 1],
       [1, 3]])
>>> assert "spam" in grid.at_link
>>> grid.at_link["spam"]
array([100, 239, 37])

Next lets also include a points file. First create both shapefiles.

>>> shp = BytesIO()
>>> shx = BytesIO()
>>> dbf = BytesIO()
>>> w = shapefile.Writer(shp=shp, shx=shx, dbf=dbf)
>>> w.shapeType = shapefile.POLYLINE
>>> w.field("spam", "N")
>>> w.line([[[5, 5], [10, 10]]])
>>> w.record(37)
>>> w.line([[[5, 0], [5, 5]]])
>>> w.record(100)
>>> w.line([[[5, 5], [0, 10]]])
>>> w.record(239)
>>> w.close()

>>> p_shp = BytesIO()
>>> p_shx = BytesIO()
>>> p_dbf = BytesIO()
>>> p_w = shapefile.Writer(shp=p_shp, shx=p_shx, dbf=p_dbf)
>>> p_w.shapeType = shapefile.POINT
>>> p_w.field("eggs", "N")
>>> p_w.point(5, 0)
>>> p_w.record(2)
>>> p_w.point(5, 5)
>>> p_w.record(4)
>>> p_w.point(0, 10)
>>> p_w.record(8)
>>> p_w.point(10, 10)
>>> p_w.record(6)
>>> p_w.close()

Now read in both files together.

>>> grid = read_shapefile(shp, dbf=dbf, points_shapefile=p_shp, points_dbf=p_dbf)
>>> grid.nodes
array([0, 1, 2, 3])
>>> grid.x_of_node
array([  5.,   5.,   0.,  10.])
>>> grid.y_of_node
array([  0.,   5.,  10.,  10.])
>>> grid.nodes_at_link
array([[0, 1],
       [2, 1],
       [1, 3]])
>>> assert "spam" in grid.at_link
>>> grid.at_link["spam"]
array([100, 239, 37])
>>> assert "eggs" in grid.at_node
>>> grid.at_node["eggs"]
array([2, 4, 8, 6])

__call__(*args, **kwargs)

Call self as a function.

__delattr__(name, /)

Implement delattr(self, name).

__dir__()

Default dir() implementation.

__eq__(value, /)

Return self==value.

__format__(format_spec, /)

Default object formatter.

__ge__(value, /)

Return self>=value.

__get__(instance, owner=None, /)

Return an attribute of instance, which is of type owner.

__getattribute__(name, /)

Return getattr(self, name).

__getstate__()

Helper for pickle.

__gt__(value, /)

Return self>value.

__hash__()

Return hash(self).

__init__(*args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

__init_subclass__()

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__(value, /)

Return self<=value.

__lt__(value, /)

Return self<value.

__ne__(value, /)

Return self!=value.

__new__(*args, **kwargs)

Create and return a new object. See help(type) for accurate signature.

__reduce__()

Helper for pickle.

__reduce_ex__(protocol, /)

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__sizeof__()

Size of object in memory, in bytes.

__str__()

Return str(self).

__subclasshook__()

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).