Merge pull request #1449 from pints-team/del-operator

Test failures after updates in github actions

CHANGELOG.md

@@ -23,6 +23,7 @@ All notable changes to this project will be documented in this file.
 - [#1424](https://github.com/pints-team/pints/pull/1424) Removed the `TriangleWaveTransform` class previously used in some optimisers.
 
 ### Fixed
+- [#1449](https://github.com/pints-team/pints/pull/1449) Fixed a bug in `MarkovJumpModel.interpolate_mol_counts`.
 
 
 ## [0.4.0] - 2021-12-07

docs/source/conf.py

@@ -72,7 +72,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+language = 'en'
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.

pints/__init__.py

@@ -278,4 +278,4 @@ def version(formatted=False):
 #
 # Remove any imported modules, so we don't expose them as part of pints
 #
-del(os, sys)
+del os, sys

pints/_evaluation.py

@@ -248,7 +248,7 @@ def _clean(self):
             if w.exitcode is not None:  # pragma: no cover
                 w.join()
                 cleaned += 1
-                del(self._workers[k], w)
+                del self._workers[k], w
         if cleaned:     # pragma: no cover
             gc.collect()
         return cleaned

pints/_mcmc/_dream.py

@@ -350,7 +350,7 @@ def _draw(self, i):
         Select 2 random chains, not including chain i.
         """
         r1, r2 = np.random.choice(self._n_chains, 2, replace=False)
-        while(r1 == i or r2 == i or r1 == r2):
+        while r1 == i or r2 == i or r1 == r2:
             r1, r2 = np.random.choice(self._n_chains, 2, replace=False)
         return r1, r2
 

pints/_mcmc/_hamiltonian.py

@@ -277,7 +277,7 @@ def tell(self, reply):
         # Check reply, copy gradient
         energy = float(energy)
         gradient = pints.vector(gradient)
-        assert(gradient.shape == (self._n_parameters, ))
+        assert gradient.shape == (self._n_parameters, )
 
         # Energy = -log_pdf, so flip both signs!
         energy = -energy

pints/_mcmc/_mala.py

@@ -230,7 +230,7 @@ def tell(self, reply):
         # Check reply, copy gradient
         fx = float(fx)
         log_gradient = pints.vector(log_gradient)
-        assert(log_gradient.shape == (self._n_parameters, ))
+        assert log_gradient.shape == (self._n_parameters, )
 
         # First point?
         if self._current is None:

pints/_mcmc/_monomial_gamma_hamiltonian.py

@@ -429,7 +429,7 @@ def tell(self, reply):
         # Check reply, copy gradient
         energy = float(energy)
         gradient = pints.vector(gradient)
-        assert(gradient.shape == (self._n_parameters, ))
+        assert gradient.shape == (self._n_parameters, )
 
         # Energy = -log_pdf, so flip both signs!
         energy = -energy

pints/_mcmc/_population.py

@@ -135,7 +135,7 @@ def _initialise(self):
         # Ask all inner samplers for first point, which should be x0
         for chain in self._chains:
             x0 = chain.ask()
-            assert(np.all(x0 == self._x0))
+            assert np.all(x0 == self._x0)
 
         # Update sampler state
         self._running = True

pints/_mcmc/_relativistic.py

@@ -452,7 +452,7 @@ def tell(self, reply):
         # Check reply, copy gradient
         energy = float(energy)
         gradient = pints.vector(gradient)
-        assert(gradient.shape == (self._n_parameters, ))
+        assert gradient.shape == (self._n_parameters, )
 
         # Energy = -log_pdf, so flip both signs!
         energy = -energy

pints/_mcmc/_slice_rank_shrinking.py

@@ -200,7 +200,7 @@ def tell(self, reply):
         # Check reply, copy gradient
         fx = float(fx)
         grad = pints.vector(grad)
-        assert(grad.shape == (self._n_parameters, ))
+        assert grad.shape == (self._n_parameters, )
 
         # Very first call
         if self._current is None:

pints/_optimisers/__init__.py

@@ -409,7 +409,7 @@ def __init__(
             self._function = function
         else:
             self._function = pints.ProbabilityBasedError(function)
-        del(function)
+        del function
 
         # Create optimiser
         if method is None:

pints/_optimisers/_cmaes.py

@@ -84,7 +84,7 @@ def _initialise(self):
         """
         Initialises the optimiser for the first iteration.
         """
-        assert(not self._running)
+        assert not self._running
 
         # Import cma (may fail!)
         # Only the first time this is called in a running program incurs

pints/_optimisers/_pso.py

@@ -103,7 +103,7 @@ def _initialise(self):
         """
         Initialises the optimiser for the first iteration.
         """
-        assert(not self._running)
+        assert not self._running
 
         # Initialize swarm
         self._xs = []     # Particle coordinate vectors

pints/_optimisers/_snes.py

@@ -94,7 +94,7 @@ def _initialise(self):
         """
         Initialises the optimiser for the first iteration.
         """
-        assert(not self._running)
+        assert not self._running
 
         # Shorthands
         d = self._n_parameters

pints/_optimisers/_xnes.py

@@ -100,7 +100,7 @@ def _initialise(self):
         """
         Initialises the optimiser for the first iteration.
         """
-        assert(not self._running)
+        assert not self._running
 
         # Shorthands
         d = self._n_parameters

pints/_transformation.py

@@ -778,7 +778,7 @@ def __init__(self, lower_or_boundaries, upper=None):
 
         # Cache dimension
         self._n_parameters = boundaries.n_parameters()
-        del(boundaries)
+        del boundaries
 
     def elementwise(self):
         """ See :meth:`Transformation.elementwise()`. """

pints/plot/_surface.py

@@ -60,7 +60,7 @@ def surface(points, values, boundaries=None, markers='+', figsize=None):
     # Extract x and y points
     x = points[:, 0]
     y = points[:, 1]
-    del(points)
+    del points
 
     # Check boundaries
     if boundaries is None:

pints/tests/test_log_priors.py

@@ -824,9 +824,9 @@ def test_multivariate_normal_sampling(self):
     def test_student_t_prior(self):
         # Test two specific function values
         p1 = pints.StudentTLogPrior(0, 2, 10)
-        self.assertEqual(p1([0]), -3.342305863833964)
+        self.assertAlmostEqual(p1([0]), -3.342305863833964)
         p2 = pints.StudentTLogPrior(10, 5, 10)
-        self.assertEqual(p2([10]), -3.27120468204877)
+        self.assertAlmostEqual(p2([10]), -3.27120468204877)
 
         # Test exceptions
         self.assertRaises(ValueError, pints.StudentTLogPrior, 0, 0, 10)

pints/tests/test_opt_optimisation_controller.py

@@ -68,7 +68,7 @@ def cb(i, opt):
             csv = np.genfromtxt(p, delimiter=',', skip_header=1)[:-1]
             lb = csv[:, 2]
             lg = csv[:, 3]
-            del(csv)
+            del csv
 
         fb, fg = np.array(fb), np.array(fg)
 

pints/tests/test_toy_simple_egg_box_logpdf.py

@@ -91,7 +91,7 @@ def test_sensitivity_bounds_distance(self):
         l, dl = f.evaluateS1([-1, -7])
         self.assertEqual(l, f([-1, -7]))
         self.assertAlmostEqual(l, -46.269777289511559)
-        self.assertEqual(dl[0], -4.6662126879796366)
+        self.assertAlmostEqual(dl[0], -4.6662126879796366)
         self.assertAlmostEqual(dl[1], -2.6666666666666639)
         self.assertTrue(np.array_equal(f.suggested_bounds(),
                                        [[-30.0, -30.0], [30.0, 30.0]]))

pints/tests/test_toy_stochastic_degradation_model.py

@@ -7,7 +7,9 @@
 # copyright notice and full license details.
 #
 import unittest
+
 import numpy as np
+
 from pints.toy.stochastic import DegradationModel
 
 
@@ -67,21 +69,28 @@ def test_mean_variance(self):
 
     def test_errors(self):
         model = DegradationModel(20)
+
         # parameters, times cannot be negative
         times = np.linspace(0, 100, 101)
         parameters = [-0.1]
-        self.assertRaises(ValueError, model.mean, parameters, times)
-        self.assertRaises(ValueError, model.variance, parameters, times)
+        self.assertRaisesRegex(ValueError, 'constant must be positive',
+                               model.mean, parameters, times)
+        self.assertRaisesRegex(ValueError, 'constant must be positive',
+                               model.variance, parameters, times)
 
         times_2 = np.linspace(-10, 10, 21)
         parameters_2 = [0.1]
-        self.assertRaises(ValueError, model.mean, parameters_2, times_2)
-        self.assertRaises(ValueError, model.variance, parameters_2, times_2)
+        self.assertRaisesRegex(ValueError, 'Negative times',
+                               model.mean, parameters_2, times_2)
+        self.assertRaisesRegex(ValueError, 'Negative times',
+                               model.variance, parameters_2, times_2)
 
         # this model should have 1 parameter
         parameters_3 = [0.1, 1]
-        self.assertRaises(ValueError, model.mean, parameters_3, times)
-        self.assertRaises(ValueError, model.variance, parameters_3, times)
+        self.assertRaisesRegex(ValueError, 'only 1 parameter',
+                               model.mean, parameters_3, times)
+        self.assertRaisesRegex(ValueError, 'only 1 parameter',
+                               model.variance, parameters_3, times)
 
 
 if __name__ == '__main__':

pints/tests/test_toy_markov_jump_model.py → pints/tests/test_toy_stochastic_markov_jump_model.py

@@ -28,7 +28,7 @@ def test_start_with_zero(self):
     def test_start_with_twenty(self):
         # Run small simulation
         model = DegradationModel(20)
-        times = [0, 1, 2, 100, 1000]
+        times = [0, 1, 2, 100, 1000, 10000]
         parameters = [0.1]
         values = model.simulate(parameters, times)
         self.assertEqual(len(values), len(times))
@@ -52,27 +52,60 @@ def test_simulate(self):
         # Check interpolation function works as expected
         temp_time = np.array([np.random.uniform(time[0], time[1])])
         self.assertEqual(
-            model.interpolate_mol_counts(time, mol_count, temp_time)[0],
-            20)
+            model.interpolate_mol_counts(time, mol_count, temp_time)[0], 20)
         temp_time = np.array([np.random.uniform(time[1], time[2])])
         self.assertEqual(
-            model.interpolate_mol_counts(time, mol_count, temp_time)[0],
-            19)
+            model.interpolate_mol_counts(time, mol_count, temp_time)[0], 19)
+
+        # Check interpolation works if 1 time is given
+        time, mol, out = [1], [7], [0, 1, 2, 3]
+        interpolated = model.interpolate_mol_counts(time, mol, out)
+        self.assertEqual(list(interpolated), [7, 7, 7, 7])
+
+        # Check if no times are given
+        self.assertRaisesRegex(ValueError, 'At least one time',
+                               model.interpolate_mol_counts, [], [], out)
+
+        # Check if times and count don't match
+        self.assertRaisesRegex(ValueError, 'must match',
+                               model.interpolate_mol_counts, [1], [2, 3], out)
+
+        # Decreasing output times
+        self.assertRaisesRegex(
+            ValueError, 'must be non-decreasing',
+            model.interpolate_mol_counts, [1, 2], [2, 3], [1, 2, 3, 4, 0])
+
+        # Check extrapolation outside of output times
+        # Note: step-wise "interpolation", no actual interpolation!
+        time = [10, 11]
+        mol = [5, 6]
+        out = [0, 10, 10.5, 11, 20]
+        val = model.interpolate_mol_counts(time, mol, out)
+        self.assertEqual(list(val), [5, 5, 5, 6, 6])
+
+        time = [10]
+        mol = [5]
+        out = [0, 10, 10.5, 11, 20]
+        val = model.interpolate_mol_counts(time, mol, out)
+        self.assertEqual(list(val), [5, 5, 5, 5, 5])
 
     def test_errors(self):
         model = DegradationModel(20)
         # times cannot be negative
         times_2 = np.linspace(-10, 10, 21)
         parameters_2 = [0.1]
-        self.assertRaises(ValueError, model.simulate, parameters_2, times_2)
+        self.assertRaisesRegex(ValueError, 'Negative times',
+                               model.simulate, parameters_2, times_2)
 
         # this model should have 1 parameter
         times = np.linspace(0, 100, 101)
         parameters_3 = [0.1, 1]
-        self.assertRaises(ValueError, model.simulate, parameters_3, times)
+        self.assertRaisesRegex(ValueError, 'should have 1 parameter',
+                               model.simulate, parameters_3, times)
 
         # Initial value can't be negative
-        self.assertRaises(ValueError, DegradationModel, -1)
+        self.assertRaisesRegex(ValueError, 'Initial molecule count',
+                               DegradationModel, -1)
 
 
 if __name__ == '__main__':

pints/toy/stochastic/_degradation_model.py

@@ -19,7 +19,7 @@ class DegradationModel(MarkovJumpModel):
     .. math::
         A \xrightarrow{k} 0
 
-    Extends :class:`pints.ForwardModel`, :class:`pints.toy.ToyModel`.
+    Extends :class:`pints.MarkovJumpModel`.
 
     Parameters
     ----------
@@ -29,14 +29,12 @@ class DegradationModel(MarkovJumpModel):
     def __init__(self, initial_molecule_count=20):
         V = [[-1]]
         init_list = [initial_molecule_count]
-        super(DegradationModel, self).__init__(init_list,
-                                               V, self._propensities)
+        super(DegradationModel, self).__init__(
+            init_list, V, self._propensities)
 
     @staticmethod
     def _propensities(xs, ks):
-        return [
-            xs[0] * ks[0],
-        ]
+        return [xs[0] * ks[0]]
 
     def mean(self, parameters, times):
         r"""