def test_sp_voronoi():
nnodes = 100
np.random.seed(0)
x = np.random.rand(nnodes)
np.random.seed(1)
y = np.random.rand(nnodes)
mg = VoronoiDelaunayGrid(x, y)
np.random.seed(2)
z = mg.add_field('node', 'topographic__elevation',
np.random.rand(nnodes) / 10000., copy=False)
fr = FlowRouter(mg)
spe = StreamPowerEroder(mg, os.path.join(_THIS_DIR,
'drive_sp_params_voronoi.txt'))
for i in range(10):
z[mg.core_nodes] += 0.01
fr.route_flow()
spe.erode(mg, 1.)
z_tg = np.array([4.35994902e-05, 2.59262318e-06, 5.49662478e-05,
4.36094902e-05, 4.20367802e-05, 2.16664196e-03,
1.23484520e-02, 4.07654109e-02, 5.69974645e-02,
4.71086393e-02, 5.55056467e-02, 5.53655860e-02,
4.87984556e-02, 6.62132007e-02, 7.33132951e-02,
6.10003971e-02, 8.53975293e-05, 4.90709812e-02,
7.84728490e-02, 8.26091298e-02, 5.05246090e-05,
3.69289510e-02, 7.56743264e-02, 2.52850671e-02,
6.80517443e-02, 5.96745309e-05, 3.86335644e-02,
4.12526935e-02, 7.78476139e-02, 7.18165632e-02,
7.62359762e-02, 6.65702961e-02, 9.04684278e-02,
7.37203974e-02, 9.16862054e-02, 4.11168411e-02,
4.36498947e-02, 9.04430763e-02, 1.42123575e-02,
8.02734138e-02, 8.34895711e-02, 4.51564589e-02,
6.47124169e-02, 9.52317640e-02, 6.01917122e-05,
7.11667309e-02, 8.00463179e-02, 9.40668605e-02,
9.33247720e-02, 7.34011920e-02, 7.30924467e-02,
6.21387674e-02, 9.66278589e-02, 8.64266637e-02,
9.06100497e-02, 7.19991365e-02, 1.07162442e-02,
8.68685360e-02, 7.40515066e-02, 2.73264483e-02,
9.55406046e-02, 6.01817121e-05, 3.28663723e-02,
8.60706344e-02, 9.56285286e-02, 4.24741937e-02,
5.64139004e-03, 8.72373392e-02, 9.04304841e-02,
8.83708170e-02, 5.04000439e-05, 6.47845229e-02,
8.77304766e-02, 4.40894724e-02, 8.08029139e-02,
4.52732792e-02, 6.35806712e-02, 6.71004941e-02,
7.32682350e-02, 4.88067087e-02, 2.14920377e-02,
2.55509418e-06, 8.25346959e-02, 8.12610977e-02,
3.75778123e-02, 2.20818912e-02, 2.45940192e-02,
4.43365163e-02, 4.93255141e-02, 6.23517572e-02,
5.99594648e-02, 4.17977098e-06, 4.96450779e-02,
3.72952724e-02, 2.26332108e-03, 1.69925430e-02,
1.99835635e-02, 6.61481327e-05, 1.70477133e-05,
8.81652236e-05] )
assert_array_almost_equal(mg.at_node['topographic__elevation'], z_tg)
def test_sp_voronoi():
nnodes = 100
np.random.seed(0)
x = np.random.rand(nnodes)
np.random.seed(1)
y = np.random.rand(nnodes)
mg = VoronoiDelaunayGrid(x, y)
np.random.seed(2)
z = mg.add_field('node', 'topographic__elevation',
np.random.rand(nnodes) / 10000., copy=False)
fr = FlowRouter(mg)
spe = StreamPowerEroder(mg, os.path.join(_THIS_DIR,
'drive_sp_params_voronoi.txt'))
for i in range(10):
z[mg.core_nodes] += 0.01
fr.route_flow()
spe.erode(mg, 1.)
z_tg = np.array([4.35994902e-05, 2.59262318e-06, 5.49662478e-05,
6.56738615e-03, 4.20367802e-05, 1.21371424e-02,
2.16596169e-02, 4.73320898e-02, 6.00389761e-02,
5.22007356e-02, 5.37507115e-02, 5.95794752e-02,
5.29862904e-02, 6.76465914e-02, 7.31720024e-02,
6.18730861e-02, 8.53975293e-05, 5.32189275e-02,
7.34302556e-02, 8.07385044e-02, 5.05246090e-05,
4.08940657e-02, 7.39971005e-02, 3.31915602e-02,
6.72650419e-02, 5.96745309e-05, 4.72752445e-02,
3.60359567e-02, 7.59432065e-02, 7.24461985e-02,
7.80305760e-02, 4.93866869e-02, 8.69642467e-02,
7.21627626e-02, 8.96368291e-02, 4.65142080e-02,
6.07720217e-02, 8.83372939e-02, 2.35887558e-02,
7.97616193e-02, 8.35615355e-02, 4.61809032e-02,
6.34634214e-02, 9.25711770e-02, 4.11717225e-03,
7.24493623e-02, 7.97908053e-02, 9.10375623e-02,
9.13155023e-02, 7.10567915e-02, 7.35271752e-02,
6.13091341e-02, 9.45498463e-02, 8.48532386e-02,
8.82702021e-02, 7.14969941e-02, 2.22640943e-02,
8.53311932e-02, 7.49161159e-02, 3.48837223e-02,
9.30132692e-02, 6.01817121e-05, 3.87455443e-02,
8.44673586e-02, 9.35213577e-02, 6.76075824e-02,
1.58614508e-02, 8.51346837e-02, 8.83645680e-02,
8.69944117e-02, 5.04000439e-05, 5.02319084e-02,
8.63882765e-02, 5.00991880e-02, 7.65156630e-02,
5.07591983e-02, 6.54909962e-02, 6.91505342e-02,
7.33358371e-02, 5.30109890e-02, 2.99074601e-02,
2.55509418e-06, 8.21523907e-02, 8.09368483e-02,
4.35073025e-02, 3.04096109e-02, 3.26298627e-02,
4.92259177e-02, 5.48690358e-02, 6.44732130e-02,
6.28133567e-02, 4.17977098e-06, 5.37149677e-02,
4.32828136e-02, 1.30559903e-02, 2.62405261e-02,
2.86079272e-02, 6.61481327e-05, 1.70477133e-05,
8.81652236e-05])
assert_array_almost_equal(mg.at_node['topographic__elevation'], z_tg)
def test_sp_voronoi():
nnodes = 100
np.random.seed(0)
x = np.random.rand(nnodes)
np.random.seed(1)
y = np.random.rand(nnodes)
mg = VoronoiDelaunayGrid(x, y)
np.random.seed(2)
z = mg.add_field('node',
'topographic__elevation',
np.random.rand(nnodes) / 10000.,
copy=False)
fr = FlowRouter(mg)
spe = StreamPowerEroder(
mg, os.path.join(_THIS_DIR, 'drive_sp_params_voronoi.txt'))
for i in range(10):
z[mg.core_nodes] += 0.01
fr.route_flow()
spe.erode(mg, 1.)
z_tg = np.array([
4.35994902e-05, 2.59262318e-06, 5.49662478e-05, 4.36094902e-05,
4.20367802e-05, 2.16664196e-03, 1.23484520e-02, 4.07654109e-02,
5.69974645e-02, 4.71086393e-02, 5.55056467e-02, 5.53655860e-02,
4.87984556e-02, 6.62132007e-02, 7.33132951e-02, 6.10003971e-02,
8.53975293e-05, 4.90709812e-02, 7.84728490e-02, 8.26091298e-02,
5.05246090e-05, 3.69289510e-02, 7.56743264e-02, 2.52850671e-02,
6.80517443e-02, 5.96745309e-05, 3.86335644e-02, 4.12526935e-02,
7.78476139e-02, 7.18165632e-02, 7.62359762e-02, 6.65702961e-02,
9.04684278e-02, 7.37203974e-02, 9.16862054e-02, 4.11168411e-02,
4.36498947e-02, 9.04430763e-02, 1.42123575e-02, 8.02734138e-02,
8.34895711e-02, 4.51564589e-02, 6.47124169e-02, 9.52317640e-02,
6.01917122e-05, 7.11667309e-02, 8.00463179e-02, 9.40668605e-02,
9.33247720e-02, 7.34011920e-02, 7.30924467e-02, 6.21387674e-02,
9.66278589e-02, 8.64266637e-02, 9.06100497e-02, 7.19991365e-02,
1.07162442e-02, 8.68685360e-02, 7.40515066e-02, 2.73264483e-02,
9.55406046e-02, 6.01817121e-05, 3.28663723e-02, 8.60706344e-02,
9.56285286e-02, 4.24741937e-02, 5.64139004e-03, 8.72373392e-02,
9.04304841e-02, 8.83708170e-02, 5.04000439e-05, 6.47845229e-02,
8.77304766e-02, 4.40894724e-02, 8.08029139e-02, 4.52732792e-02,
6.35806712e-02, 6.71004941e-02, 7.32682350e-02, 4.88067087e-02,
2.14920377e-02, 2.55509418e-06, 8.25346959e-02, 8.12610977e-02,
3.75778123e-02, 2.20818912e-02, 2.45940192e-02, 4.43365163e-02,
4.93255141e-02, 6.23517572e-02, 5.99594648e-02, 4.17977098e-06,
4.96450779e-02, 3.72952724e-02, 2.26332108e-03, 1.69925430e-02,
1.99835635e-02, 6.61481327e-05, 1.70477133e-05, 8.81652236e-05
])
assert_array_almost_equal(mg.at_node['topographic__elevation'], z_tg)
def test_voronoi_from_dict():
x = [0, 0.1, 0.2, 0.3, 1, 1.1, 1.2, 1.3, 2, 2.1, 2.2, 2.3]
y = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]
params = {
"x": x,
"y": y,
"axis_name": ("spam", "eggs"),
"axis_units": ("smoot", "parsec"),
"xy_of_reference": (12345, 678910),
}
mg = VoronoiDelaunayGrid.from_dict(params)
assert mg.axis_units == ("smoot", "parsec")
assert mg.axis_name == ("spam", "eggs")
assert mg.xy_of_reference == (12345, 678910)
true_x = np.array([0.0, 1.0, 2.0, 0.1, 1.1, 2.1, 0.2, 1.2, 2.2, 0.3, 1.3, 2.3])
true_y = np.array([0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0])
true_nodes_at_node = np.array(
[
[1, 3, -1, -1, -1, -1],
[2, 4, 3, 0, -1, -1],
[5, 4, 1, -1, -1, -1],
[4, 6, 0, 1, -1, -1],
[5, 7, 6, 3, 1, 2],
[8, 7, 4, 2, -1, -1],
[7, 9, 3, 4, -1, -1],
[8, 10, 9, 6, 4, 5],
[11, 10, 7, 5, -1, -1],
[10, 6, 7, -1, -1, -1],
[11, 9, 7, 8, -1, -1],
[10, 8, -1, -1, -1, -1],
]
)
assert_array_equal(mg.node_x, true_x)
assert_array_equal(mg.node_y, true_y)
assert_array_equal(mg.adjacent_nodes_at_node, true_nodes_at_node)
def test_voronoi_from_dict():
x = [0.0, 0.1, 0.2, 0.3, 1.0, 1.1, 1.2, 1.3, 2.0, 2.1, 2.2, 2.3]
y = [0.0, 1.0, 2.0, 3.0, 0.0, 1.0, 2.0, 3.0, 0.0, 1.0, 2.0, 3.0]
params = {
"x": x,
"y": y,
"xy_axis_name": ("spam", "eggs"),
"xy_axis_units": ("smoot", "parsec"),
"xy_of_reference": (12345, 678910),
}
mg = VoronoiDelaunayGrid.from_dict(params)
assert mg.axis_units == ("smoot", "parsec")
assert mg.axis_name == ("spam", "eggs")
assert mg.xy_of_reference == (12345, 678910)
true_x = np.array([0.0, 1.0, 2.0, 0.1, 1.1, 2.1, 0.2, 1.2, 2.2, 0.3, 1.3, 2.3])
true_y = np.array([0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0])
true_nodes_at_node = np.array(
[
[1, 3, -1, -1, -1, -1],
[2, 4, 3, 0, -1, -1],
[5, 4, 1, -1, -1, -1],
[4, 6, 0, 1, -1, -1],
[5, 7, 6, 3, 1, 2],
[8, 7, 4, 2, -1, -1],
[7, 9, 3, 4, -1, -1],
[8, 10, 9, 6, 4, 5],
[11, 10, 7, 5, -1, -1],
[10, 6, 7, -1, -1, -1],
[11, 9, 7, 8, -1, -1],
[10, 8, -1, -1, -1, -1],
]
)
assert_array_equal(mg.x_of_node, true_x)
assert_array_equal(mg.y_of_node, true_y)
assert_array_equal(mg.adjacent_nodes_at_node, true_nodes_at_node)
from landlab import VoronoiDelaunayGrid # , RasterModelGrid
from landlab.components.flow_routing.route_flow_dn import FlowRouter
from landlab.components.stream_power.stream_power import StreamPowerEroder
from landlab.plot.imshow import imshow_node_grid
import numpy as np
from matplotlib.pyplot import figure, show
nnodes = 10000
x, y = np.random.rand(nnodes), np.random.rand(nnodes)
mg = VoronoiDelaunayGrid(x,y)
#mg = RasterModelGrid(4,5)
z = mg.add_field('node', 'topographic__elevation', np.random.rand(nnodes)/10000., copy=False)
fr = FlowRouter(mg)
spe = StreamPowerEroder(mg, 'drive_sp_params_voronoi.txt')
for i in xrange(100):
z[mg.core_nodes] += 0.01
fr.route_flow()
spe.erode(mg, 1.)
imshow_node_grid(mg, 'topographic__elevation')
show()
def test_not_implemented_voroni():
x = [0, 0.1, 0.2, 0.3, 1, 1.1, 1.2, 1.3, 2, 2.1, 2.2, 2.3]
y = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]
vmg = VoronoiDelaunayGrid(x, y)
with pytest.raises(NotImplementedError):
flow_direction_dinf.flow_directions_dinf(vmg)
import numpy as np
from matplotlib.pyplot import show
from six.moves import range
from landlab import VoronoiDelaunayGrid
from landlab.components import FlowAccumulator, StreamPowerEroder
from landlab.plot.imshow import imshow_node_grid
nnodes = 10000
x, y = np.random.rand(nnodes), np.random.rand(nnodes)
mg = VoronoiDelaunayGrid(x, y)
z = mg.add_field("node",
"topographic__elevation",
np.random.rand(nnodes) / 10000.,
copy=False)
fr = FlowAccumulator(mg)
spe = StreamPowerEroder(mg, "drive_sp_params_voronoi.txt")
for i in range(100):
z[mg.core_nodes] += 0.01
fr.run_one_step()
spe.erode(mg, 1.)
imshow_node_grid(mg, "topographic__elevation")
show()
items={
"grid_element": np.array([["node"], ["link"]]),
"element_id": np.array([[1], [3]]),
},
data_vars={"mean_elevation": (["time"], np.array([100]))},
attrs=["not a dict"],
)
# Should return except AttributeError:
# raise TypeError(('Attributes (attrs) passed to DataRecord'
# 'must be a dictionary'))
#
# test bad loc and id:
rmg = RasterModelGrid((3, 3))
hmg = HexModelGrid(3, 2, 1.0)
radmg = RadialModelGrid(num_shells=1, dr=1.0, xy_of_center=(0.0, 0.0))
vdmg = VoronoiDelaunayGrid(np.random.rand(25), np.random.rand(25))
my_items_bad_loc = {
"grid_element": np.array(["node", "bad_loc"]),
"element_id": np.array([1, 3]),
}
my_items_bad_loc2 = {"grid_element": "bad_loc", "element_id": np.array([1, 3])}
my_items_bad_loc3 = {
"grid_element": np.array(["node", "node", "node"]),
"element_id": np.array([1, 3]),
}
my_items_bad_id = {
"grid_element": np.array(["node", "link"]),
"element_id": np.array([1, 300]),
}
my_items_bad_id2 = {
"grid_element": np.array(["node", "link"]),
#
# We can create **grid** objects with the following lines of code.
# In[ ]:
import numpy as np
from landlab import RasterModelGrid, VoronoiDelaunayGrid, HexModelGrid
# a square-cell raster, 3 rows x 4 columns, unit spacing
smg = RasterModelGrid((3, 4), 1.)
rmg = RasterModelGrid((3, 4), (1., 2.)) # a rectangular-cell raster
hmg = HexModelGrid(3, 4, 1.)
# ^a hexagonal grid with 3 rows, 4 columns from the base row, & node spacing of
# 1.
x = np.random.rand(100) * 100.
y = np.random.rand(100) * 100.
vmg = VoronoiDelaunayGrid(x, y)
# ^a Voronoi-cell grid with 100 randomly positioned nodes within a 100.x100.
# square
# All these various `ModelGrid` objects contains various data items (known as
# *attributes*). These include, for example: * number nodes and links in the
# grid * *x* and *y* coordinates of each each node * starting ("tail") and
# ending ("head") node IDs of each link * IDs of links that are active * IDs of
# core nodes * etc.
#
# From here on we'll focus on the square raster grid as its geometry is a bit
# easier to think through, but all of the following applies to all grid types.
#
# ## Understanding the topology of Landlab grids
#
# All grids consist of two interlocked sets of *points* joined by *lines*
import numpy as np
from matplotlib.pyplot import show
from six.moves import range
from landlab import VoronoiDelaunayGrid
from landlab.components import FlowAccumulator, StreamPowerEroder
from landlab.plot.imshow import imshow_grid
nnodes = 10000
x, y = np.random.rand(nnodes), np.random.rand(nnodes)
mg = VoronoiDelaunayGrid(x, y)
z = mg.add_field(
"node", "topographic__elevation", np.random.rand(nnodes) / 10000., copy=False
)
fr = FlowAccumulator(mg)
spe = StreamPowerEroder(mg, "drive_sp_params_voronoi.txt")
for i in range(100):
z[mg.core_nodes] += 0.01
fr.run_one_step()
spe.erode(mg, 1.)
imshow_grid(mg, "topographic__elevation")
show()
def test_misc():
grid = RasterModelGrid((3, 3))
# test bad dimension:
with pytest.raises(ValueError):
DataRecord(
grid,
time=[0.0],
data_vars={"mean_elev": (["time"], [100.0]), "test": (["bad_dim"], [12])},
)
# should return ValueError('Data variable dimensions must be time and/or'
# 'item_id')
# test bad time format:
with pytest.raises(TypeError):
DataRecord(grid=grid, time="bad_time")
# should return TypeError: Time must be a list or an array of length 1
# test bad datavars format:
with pytest.raises(TypeError):
DataRecord(grid, time=[0.0], data_vars=["not a dict"])
# should return TypeError(('Data variables (data_vars) passed to'
# ' DataRecord must be a dictionary (see '
# 'documentation for valid structure)'))
# test bad items format:
with pytest.raises(TypeError):
DataRecord(
grid,
time=[0.0],
items=["not a dict"],
data_vars={"mean_elevation": (["time"], np.array([100]))},
attrs={"time_units": "y"},
)
# Should return TypeError(('You must provide an ''items'' dictionary '
# '(see documentation for required format)'))
#
with pytest.raises(TypeError):
"""Test bad items keys"""
DataRecord(
grid,
time=[0.0],
items={
"grid_element": np.array([["node"], ["link"]]),
"bad_key": np.array([[1], [3]]),
},
data_vars={"mean_elevation": (["time"], np.array([100]))},
attrs={"time_units": "y"},
)
# Should return TypeError(('You must provide an ''items'' dictionary '
# '(see documentation for required format)'))
with pytest.raises(TypeError):
"""Test bad attrs"""
DataRecord(
grid,
time=[0.0],
items={
"grid_element": np.array([["node"], ["link"]]),
"element_id": np.array([[1], [3]]),
},
data_vars={"mean_elevation": (["time"], np.array([100]))},
attrs=["not a dict"],
)
# Should return except AttributeError:
# raise TypeError(('Attributes (attrs) passed to DataRecord'
# 'must be a dictionary'))
#
# test bad loc and id:
rmg = RasterModelGrid((3, 3))
hmg = HexModelGrid((3, 2), spacing=1.0)
radmg = RadialModelGrid(n_rings=1, nodes_in_first_ring=5, xy_of_center=(0.0, 0.0))
vdmg = VoronoiDelaunayGrid(np.random.rand(25), np.random.rand(25))
my_items_bad_loc = {
"grid_element": np.array(["node", "bad_loc"]),
"element_id": np.array([1, 3]),
}
my_items_bad_loc2 = {"grid_element": "bad_loc", "element_id": np.array([1, 3])}
my_items_bad_loc3 = {
"grid_element": np.array(["node", "node", "node"]),
"element_id": np.array([1, 3]),
}
my_items_bad_id = {
"grid_element": np.array(["node", "link"]),
"element_id": np.array([1, 300]),
}
my_items_bad_id2 = {
"grid_element": np.array(["node", "link"]),
"element_id": np.array([1, -300]),
}
my_items_bad_id3 = {
"grid_element": np.array(["node", "link"]),
"element_id": np.array([1, 2.0]),
}
my_items_bad_id4 = {
"grid_element": np.array(["node", "link"]),
"element_id": np.array([1, 2, 3]),
}
with pytest.raises(ValueError):
DataRecord(rmg, items=my_items_bad_loc)
with pytest.raises(ValueError):
DataRecord(hmg, items=my_items_bad_loc)
with pytest.raises(ValueError):
DataRecord(radmg, items=my_items_bad_loc)
with pytest.raises(ValueError):
DataRecord(vdmg, items=my_items_bad_loc)
# should return ValueError(('One or more of the grid elements provided is/are'
# ' not permitted location for this grid type.'))
with pytest.raises(ValueError):
DataRecord(rmg, items=my_items_bad_loc2)
# should return ValueError: Location provided: bad_loc is not a permitted
# location for this grid type.
with pytest.raises(ValueError):
DataRecord(rmg, items=my_items_bad_loc3)
# should return ValueError(('grid_element passed to DataRecord must be '
# ' the same length as the number of items or 1.'))
with pytest.raises(ValueError):
DataRecord(rmg, items=my_items_bad_id)
with pytest.raises(ValueError):
DataRecord(hmg, items=my_items_bad_id)
with pytest.raises(ValueError):
DataRecord(radmg, items=my_items_bad_id)
with pytest.raises(ValueError):
DataRecord(vdmg, items=my_items_bad_id)
# should return ValueError(('An item residing at ' + at + ' has an '
# 'element_id larger than the number of'+ at + 'on the grid.'))
with pytest.raises(ValueError):
DataRecord(rmg, items=my_items_bad_id2)
# should return ValueError(('An item residing at ' + at + ' has '
# 'an element id below zero. This is not permitted.'))
with pytest.raises(ValueError):
DataRecord(rmg, items=my_items_bad_id3)
# should return ValueError(('You have passed a non-integer element_id to '
# 'DataRecord, this is not permitted.'))
with pytest.raises(ValueError):
DataRecord(rmg, items=my_items_bad_id4)