def test_reading():
mesh = icepack.read_msh("tests/rectangle.msh")
assert mesh.n_active_cells() == 16
assert mesh.n_vertices() == 25
boundary_ids = mesh.get_boundary_ids()
assert len(boundary_ids) == 2
assert boundary_ids.count(1) == 1
assert boundary_ids.count(2) == 1
#!/usr/bin/env python3
import numpy as np
import matplotlib.pyplot as plt
import icepack, icepack.plot
# This function pulls in the mesh and observational data that we'll use.
import preprocess
preprocess.main()
# Read in the observational data.
vx_obs = icepack.read_arc_ascii_grid(open("ross-vx.txt", "r"))
vy_obs = icepack.read_arc_ascii_grid(open("ross-vy.txt", "r"))
h_obs = icepack.read_arc_ascii_grid(open("ross-h.txt", "r"))
mesh = icepack.read_msh("ross.msh")
fig, ax = plt.subplots()
ax.set_aspect('equal')
icepack.plot.plot_mesh(ax, mesh)
plt.show(fig)
discretization = icepack.make_discretization(mesh, 1)
v = icepack.interpolate(discretization, vx_obs, vy_obs)
h = icepack.interpolate(discretization, h_obs)
# Make a dumb guess for the ice temperature. In "real life", you would want to
# use an inverse method that would tune the temperature to fit observations.
theta = icepack.interpolate(discretization, lambda x: 253.0)
# Solve for the ice velocity, assuming this guess for the temperature.
import icepack
mesh = icepack.read_msh("tests/rectangle.msh")
mesh.refine_global(2)
discretization = icepack.make_discretization(mesh, 1)
def test_discretization():
assert discretization.triangulation is not None
def test_interpolating():
u = icepack.interpolate(discretization, lambda x: x[0] * x[1])
assert u.discretization is discretization
assert icepack.max(u) <= 1.0
def test_algebra():
u = icepack.interpolate(discretization, lambda x: x[0] - x[1])
v = icepack.interpolate(discretization, lambda x: x[0] * x[1])
w = icepack.interpolate(discretization,
lambda x: x[0] - x[1] + x[0] * x[1])
assert icepack.dist(u + v, w) / icepack.norm(w) < 1.0e-8
w = icepack.interpolate(discretization,
lambda x: x[0] - x[1] - x[0] * x[1])
assert icepack.dist(u - v, w) / icepack.norm(w) < 1.0e-8
w = icepack.interpolate(discretization, lambda x: 2 * x[0] * x[1])
assert icepack.dist(2.0 * v, w) / icepack.norm(w) < 1.0e-8
import icepack
import matplotlib.pyplot as plt
import numpy as np
# First we'll call a helper script to generate a simple example mesh. This is
# in the script `make_mesh.py` in this folder.
import make_mesh
length, width = 20.0e3, 20.0e3
make_mesh.main(length, width, "rectangle.geo")
# This function reads in an unstructured mesh from a file stored in the gmsh
# `.msh` format.
mesh = icepack.read_msh("rectangle.msh")
# The `mesh` object is a python wrapper around a data structure from the
# library deal.II, which provides all of the underlying finite element
# modelling tools that icepack is built on.
print("Data type of mesh objects: {}".format(type(mesh)))
# Mesh objects have several operations for querying their size and contents.
print("Number of vertices: {}".format(mesh.n_vertices()))
print("Number of cells: {}".format(mesh.n_active_cells()))
# We can access the vertex locations too. We can make a scatter plot of all the
# vertex locations as a sanity check.
X = [x[0] for x in mesh.get_vertices()]
Y = [x[1] for x in mesh.get_vertices()]
fig, ax = plt.subplots()
ax.scatter(X, Y)
def test_refining():
mesh = icepack.read_msh("tests/rectangle.msh")
num_cells = mesh.n_active_cells()
mesh.refine_global(1)
assert mesh.n_active_cells() == 4 * num_cells
#!/usr/bin/env python3
import numpy as np
import matplotlib.pyplot as plt
import icepack, icepack.plot
# This function pulls in the mesh and observational data that we'll use.
import preprocess
preprocess.main()
# Read in the observational data.
vx_obs = icepack.read_arc_ascii_grid(open("ross-vx.txt", "r"))
vy_obs = icepack.read_arc_ascii_grid(open("ross-vy.txt", "r"))
h_obs = icepack.read_arc_ascii_grid(open("ross-h.txt", "r"))
mesh = icepack.read_msh("ross.msh")
fig, ax = plt.subplots()
ax.set_aspect('equal')
icepack.plot.plot_mesh(ax, mesh)
plt.show(fig)
discretization = icepack.make_discretization(mesh, 1)
v = icepack.interpolate(discretization, vx_obs, vy_obs)
h = icepack.interpolate(discretization, h_obs)
# Make a dumb guess for the ice temperature. In "real life", you would want to
# use an inverse method that would tune the temperature to fit observations.
theta = icepack.interpolate(discretization, lambda x: 253.0)
# Solve for the ice velocity, assuming this guess for the temperature.
