Tests utils

resmda/reservoir_simulator.py

@@ -29,9 +29,20 @@ def __dir__():
 class Simulator:
     """A small 2D Reservoir Simulator.
 
-    2D connection-based, single phase, single component system using the Darcy
-    assumption, backward Euler for time discretization, and finite volume for
-    space discretization.
+    2D connection-based, single-phase, single-component system using the Darcy
+    assumption, employing backward Euler for time discretization and finite
+    volume for space discretization. It simulates a single-phase fluid (likely
+    water) with compressibility in a reservoir with constant porosity and a
+    heterogeneous permeability field. The simulator utilizes SciPy's
+    ``sparse.linalg.solve`` for pressure solutions and calculates
+    transmissibilities for inter-block connections. Well modeling is handled
+    through the Peaceman well model for well indices, with constant pressure
+    boundary conditions for injection and production wells. The simulation
+    operates on a 2D grid with user-defined dimensions (nx, ny), uses flexible
+    time steps, and starts from a specified initial pressure condition.
+    Physical processes accounted for include fluid density changes with
+    pressure (modeling a slightly compressible fluid) while assuming constant
+    fluid viscosity."
 
     Created by following the course
     **AESM304A - Flow and Simulation of Subsurface processes** at
@@ -60,7 +71,9 @@ class Simulator:
     wells : {ndarray, None}, default: None
         Nd array of shape ``(nwells, 3)``, indicating well locations (with cell
         indices) and pressure. If None, the default is used, which is
+
             np.array([[0, 0, 180], [self.nx-1, self.ny-1, 120]])
+
         corresponding to a well in the first and in the last cell, with a
         pressure of 180 and 120, respectively.
     dx, dz : floats, default: 50.0, 10.0

tests/test_data_assimilation.py

@@ -0,0 +1,5 @@
+from resmda import data_assimilation
+
+
+def test_all_dir():
+    assert set(data_assimilation.__all__) == set(dir(data_assimilation))

tests/test_reservoir_simulator.py

@@ -0,0 +1,5 @@
+from resmda import reservoir_simulator
+
+
+def test_all_dir():
+    assert set(reservoir_simulator.__all__) == set(dir(reservoir_simulator))

tests/test_utils.py

@@ -1,10 +1,90 @@
+import scooby
 import numpy as np
+from numpy.testing import assert_allclose
 
 from resmda import utils
 
 
+def test_gaussian_covariance():
+    # Small length, no variance => eye
+    assert_allclose(utils.gaussian_covariance(4, 4, [0.1, 0.1], 0, 0),
+                    np.eye(4*4))
+
+    # Small length, some variance => still eye
+    assert_allclose(
+        utils.gaussian_covariance(
+            nx=4, ny=4, length=[0.1, 0.1], theta=0, variance=1
+        ),
+        np.eye(4*4)
+    )
+
+    # Simply check some values with variance
+    x = utils.gaussian_covariance(4, 4, [2, 1], 30, 2)
+    assert_allclose(np.diag(x), 1.)
+    assert_allclose(np.diag(x, -1)[3::4], 0)
+    for i in range(3):
+        assert_allclose(np.diag(x, -1)[i::4], 0.00024027168)
+    assert_allclose(np.diag(x, -4), 0.58600724)
+    for i in [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]:
+        assert_allclose(np.diag(x, -i), 0)
+
+    # Simply check some values without variance
+    x = utils.gaussian_covariance(4, 4, [1, 2], 0, 0)
+    assert_allclose(np.diag(x), 1.)
+    assert_allclose(np.diag(x, -1)[0], 0.20833333)
+    assert_allclose(np.diag(x, -3)[1], 0.13466999)
+    assert_allclose(np.diag(x, -4)[0], 0.6848958)
+    assert_allclose(np.diag(x, -5)[0], 0.13466999)
+    assert_allclose(np.diag(x, -7)[1], 0.030032475)
+    assert_allclose(np.diag(x, -8)[0], 0.20833333)
+    assert_allclose(np.diag(x, -9)[0], 0.030032475)
+    assert_allclose(np.diag(x, -11)[1], 0.0004445008)
+    assert_allclose(np.diag(x, -12)[0], 0.016493056)
+    assert_allclose(np.diag(x, -13)[0], 0.0004445008)
+    for i in [2, 6, 10, 14, 15]:
+        assert_allclose(np.diag(x, -i), 0)
+
+
+def test_localization_matrix():
+    # [[ 0,  0,  0,  0],
+    #  [ 4,  5,  0,  0],
+    #  [ 8,  9, 10,  0],
+    #  [12, 13, 14, 15]]
+    tril = np.tril(np.arange(16).reshape(4, 4))
+    full = tril + np.tril(tril, -1).T
+    triu = np.triu(full)
+
+    data_positions = np.array([[1, 0]], dtype=int)
+    solution = np.array([[[4]], [[5]], [[9]], [[13]]])
+    outtril = utils.localization_matrix(tril, data_positions, (4, 1))
+    assert_allclose(solution, outtril)
+    outtriu = utils.localization_matrix(triu, data_positions, (4, 1), 'upper')
+    assert_allclose(solution, outtriu)
+    outfull = utils.localization_matrix(full, data_positions, (4, 1), 'full')
+    assert_allclose(solution, outfull)
+
+
 def test_random():
     assert isinstance(utils.rng(), np.random.Generator)
     assert isinstance(utils.rng(11), np.random.Generator)
     rng = np.random.default_rng()
     assert rng == utils.rng(rng)
+
+
+def test_Report(capsys):
+    out, _ = capsys.readouterr()  # Empty capsys
+
+    # Reporting is done by the external package scooby.
+    # We just ensure the shown packages do not change (core and optional).
+    out1 = utils.Report()
+    out2 = scooby.Report(
+            core=['numpy', 'scipy', 'numba', 'resmda'],
+            optional=['matplotlib', 'IPython'],
+            ncol=3)
+
+    # Ensure they're the same; exclude time to avoid errors.
+    assert out1.__repr__()[115:] == out2.__repr__()[115:]
+
+
+def test_all_dir():
+    assert set(utils.__all__) == set(dir(utils))