Input/Output (IO)¶

This section documents various methods you can use to bring in data and write output to a file.

Module contents¶

Modules that read/write ModelGrids from various file formats.

read_esri_ascii(asc_file, grid=None, reshape=False, name=None, halo=0)[source]

Read RasterModelGrid from an ESRI ASCII file.

Read data from asc_file, an ESRI ASCII file, into a RasterModelGrid. asc_file is either the name of the data file or is a file-like object.

The grid and data read from the file are returned as a tuple (grid, data) where grid is an instance of RasterModelGrid and data is a numpy array of doubles with that has been reshaped to have the number of rows and columns given in the header.

Parameters:	asc_file (str of file-like) – Data file to read. reshape (boolean, optional) – Reshape the returned array, otherwise return a flattened array. name (str, optional) – Add data to the grid as a named field. grid (grid , optional) – Adds data to an existing grid instead of creating a new one. halo (integer, optional) – Adds outer border of depth halo to the grid.
Returns:	(grid, data) – A newly-created RasterModel grid and the associated node data.
Return type:	tuple
Raises:	`DataSizeError` – Data are not the same size as indicated by the header file. `MismatchGridDataSizeError` – If a grid is passed, and the size of the grid does not agree with the size of the data. `MismatchGridXYSpacing` – If a grid is passed, and the cellsize listed in the heading does not match the grid dx and dy. `MismatchGridXYLowerLeft` – If a grid is passed and the xllcorner and yllcorner do not match that of the grid.

Examples

Assume that fop is the name of a file that contains text below (make sure you have your path correct): ncols 3 nrows 4 xllcorner 1. yllcorner 2. cellsize 10. NODATA_value -9999 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. ——– >>> from landlab.io import read_esri_ascii >>> (grid, data) = read_esri_ascii(‘fop’) # doctest: +SKIP >>> #grid is an object of type RasterModelGrid with 4 rows and 3 cols >>> #data contains an array of length 4*3 that is equal to >>> # [9., 10., 11., 6., 7., 8., 3., 4., 5., 0., 1., 2.] >>> (grid, data) = read_esri_ascii(‘fop’, halo=1) # doctest: +SKIP >>> #now the data has a nodata_value ring of -9999 around it. So array is >>> # [-9999, -9999, -9999, -9999, -9999, -9999, >>> # -9999, 9., 10., 11., -9999, >>> # -9999, 6., 7., 8., -9999, >>> # -9999, 3., 4., 5., -9999, >>> # -9999, 0., 1., 2. -9999, >>> # -9999, -9999, -9999, -9999, -9999, -9999]

read_asc_header(asc_file)[source]

Read header information from an ESRI ASCII raster file.

The header contains the following variables,

ncols: Number of cell columns
nrows: Number of cell rows
xllcenter or xllcorner: X (column) coordinate of lower-left

coordinate of grid (by center or lower-left corner of the cell)
yllcenter, yllcorner: Y (row) coordinate of lower-left

coordinate of grid (by center or lower-left corner of the cell)
cellsize: Grid spacing between rows and columns
nodata_value: No-data value (optional)

Parameters:	asc_file (file_like) – File-like object from which to read header.
Returns:	Header as key-value pairs.
Return type:	dict
Raises:	`MissingRequiredKeyError` – The header is missing a required key. `KeyTypeError` – The header has the key but its values is of the wrong type.

Examples

>>> from io import StringIO
>>> from landlab.io.esri_ascii import read_asc_header
>>> contents = StringIO('''
...     nrows 100
...     ncols 200
...     cellsize 1.5
...     xllcenter 0.5
...     yllcenter -0.5
... ''')
>>> hdr = read_asc_header(contents)
>>> hdr['nrows'], hdr['ncols']
(100, 200)
>>> hdr['cellsize']
1.5
>>> hdr['xllcenter'], hdr['yllcenter']
(0.5, -0.5)

MissingRequiredKey is raised if the header does not contain all of the necessary keys.

>>> contents = StringIO('''
...     ncols 200
...     cellsize 1.5
...     xllcenter 0.5
...     yllcenter -0.5
... ''')
>>> read_asc_header(contents) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
MissingRequiredKeyError: nrows

KeyTypeError is raises if a value is of the wrong type. For instance, nrows and ncols must be int.

>>> contents = StringIO('''
...     nrows 100.5
...     ncols 200
...     cellsize 1.5
...     xllcenter 0.5
...     yllcenter -0.5
... ''')
>>> read_asc_header(contents) # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
KeyTypeError: Unable to convert nrows to <type 'int'>

read_shapefile(file, dbf=None, store_polyline_vertices=True, points_shapefile=None, points_dbf=None, link_fields=None, node_fields=None, link_field_conversion=None, node_field_conversion=None, link_field_dtype=None, node_field_dtype=None, threshold=0.0)[source]

Read shapefile and create a NetworkModelGrid.

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

The shape file 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.

Parameters:	file (str or file-like) – File path or file-like of a valid polyline shapefile dbf (file-like, optional) – If file is file-like, the dbf must also be passed. store_polyline_vertices (bool, optional) – If True (default), store the vertices of the polylines in the at_link fields `x_of_polyline` and `y_of_polyline`. points_shapefile (str or file-like) – File path or file-like of a valid point shapefile. points_dbf (file-like, optional) – If file is file-like, the dbf must also be passed. link_fields (list, optional) – List of polyline shapefile attributes to import as landlab at-link fields. Default is to import all. node_fields (list, optional) – List of point shapefile attributes to import as landlab at-node fields. Default is to import all. link_field_conversion (dict, optional) – Dictionary mapping polyline shapefile field names to desired at link field names. Default is no remapping. node_field_conversion (dict, optional) – Dictionary mapping node shapefile field names to desired at node field names. Default is no remapping. link_field_dtype (dict, optional) – Dictionary mapping node shapefile field names to desired dtype. Default is no change to dtype. node_field_dtype (dict, optional) – Dictionary mapping node shapefile field names to desired dtype. Default is no change to dtype. threshold (float, optional) – 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).
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.
Return type:	NetworkModelGrid instance

Examples

First, we make a simple shapefile

>>> from io import BytesIO
>>> 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 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])

write_esri_ascii(path, fields, names=None, clobber=False)[source]

Write landlab fields to ESRI ASCII.

Write the data and grid information for fields to path in the ESRI ASCII format.

Parameters:	path (str) – Path to output file. fields (field-like) – Landlab field object that holds a grid and associated values. names (iterable of str, optional) – Names of the fields to include in the output file. If not provided, write all fields. clobber (boolean) – If path exists, clobber the existing file, otherwise raise an exception.

Examples

>>> import numpy as np
>>> import os
>>> import tempfile
>>> from landlab import RasterModelGrid
>>> from landlab.io.esri_ascii import write_esri_ascii

>>> grid = RasterModelGrid((4, 5), xy_spacing=(2., 2.))
>>> _ = grid.add_field("air__temperature", np.arange(20.), at="node")
>>> with tempfile.TemporaryDirectory() as tmpdirname:
...     fname = os.path.join(tmpdirname, 'test.asc')
...     files = write_esri_ascii(fname, grid)
>>> for file in files:
...     print(os.path.basename(file))
test.asc

>>> _ = grid.add_field("land_surface__elevation", np.arange(20.), at="node")
>>> with tempfile.TemporaryDirectory() as tmpdirname:
...     fname = os.path.join(tmpdirname, 'test.asc')
...     files = write_esri_ascii(fname, grid)
>>> files.sort()
>>> for file in files:
...     print(os.path.basename(file))
test_air__temperature.asc
test_land_surface__elevation.asc

write_obj(path, grid, field_for_z='topographic__elevation', clobber=False)[source]

Write landlab grid and one field to Wavefront OBJ.

Parameters:	path (str, or file-like) – Path to output file. grid (Landlab grid object) – Landlab grid object that includes associated values. field_for_z (str, optional) – Name of a field to use for the z-values of the OBJ file. clobber (boolean, optional) – If path exists, clobber the existing file, otherwise raise an exception.
Returns:	The input path used to write the OBJ file.
Return type:	str or file-like

Examples

>>> import io
>>> from landlab import HexModelGrid, RasterModelGrid
>>> from landlab.io.obj import write_obj

>>> grid = HexModelGrid((3, 2), spacing=2.)
>>> z = grid.add_zeros("topographic__elevation", at="node")
>>> z[3] = 1.0

>>> obj_file = write_obj(io.StringIO(), grid)
>>> print(obj_file.getvalue())
# landlabgrid
#
g landlabgrid
v 1.0 0.0 0.0
v 3.0 0.0 0.0
v 0.0 1.732051 0.0
v 2.0 1.732051 1.0
v 4.0 1.732051 0.0
v 1.0 3.464102 0.0
v 3.0 3.464102 0.0
f 4// 1// 2//
f 4// 3// 1//
f 5// 4// 2//
f 6// 3// 4//
f 7// 4// 5//
f 7// 6// 4//
<BLANKLINE>