How to use abcpy - 10 common examples

def test(self):
        B1 = Binomial([10, 0.2])
        N1 = Normal([0.03, 0.01])
        N2 = Normal([0.1, N1])
        graph1 = Normal([B1, N2])
        graph2 = Normal([1, N2])

        statistics_calculator = Identity(degree=2, cross=False)
        distance_calculator = LogReg(statistics_calculator)
        backend = Backend()

        sampler = RejectionABC([graph1, graph2], [distance_calculator, distance_calculator], backend)

        rng = np.random.RandomState(1)

        sampler.sample_from_prior(rng=rng)

        y_sim = sampler.simulate(1, rng=rng)

        self.assertTrue(isinstance(y_sim, list))

        self.assertTrue(len(y_sim)==2)

        self.assertTrue(isinstance(y_sim[0][0], np.ndarray))

def test(self):
        model = Normal([1, 0.1])
        Manager = AcceptedParametersManager([model])
        backend = Backend()
        Manager.update_kernel_values(backend, [1])
        self.assertEqual(Manager.kernel_parameters_bds.value(),[1])

def test(self):
        B1 = Binomial([10, 0.2])
        N1 = Normal([0.03, 0.01])
        N2 = Normal([0.1, N1])
        graph1 = Normal([B1, N2])
        graph2 = Normal([1, N2])

        statistics_calculator = Identity(degree=2, cross=False)
        distance_calculator = LogReg(statistics_calculator)
        backend = Backend()

        sampler = RejectionABC([graph1, graph2], [distance_calculator, distance_calculator], backend)

        rng = np.random.RandomState(1)

        sampler.sample_from_prior(rng=rng)

        y_sim = sampler.simulate(1, rng=rng)

        self.assertTrue(isinstance(y_sim, list))

        self.assertTrue(len(y_sim)==2)

        self.assertTrue(isinstance(y_sim[0][0], np.ndarray))

'''
    If an exception is raised in a setUpModule then none of 
    the tests in the module will be run. 
    
    This is useful because the slaves run in a while loop on initialization
    only responding to the master's commands and will never execute anything else.

    On termination of master, the slaves call quit() that raises a SystemExit(). 
    Because of the behaviour of setUpModule, it will not run any unit tests
    for the slave and we now only need to write unit-tests from the master's 
    point of view. 
    '''
    global rank,backend_mpi
    comm = MPI.COMM_WORLD
    rank = comm.Get_rank()
    backend_mpi = BackendMPI()

'''
    If an exception is raised in a setUpModule then none of 
    the tests in the module will be run. 
    
    This is useful because the teams run in a while loop on initialization
    only responding to the scheduler's commands and will never execute anything else.

    On termination of scheduler, the teams call quit() that raises a SystemExit(). 
    Because of the behaviour of setUpModule, it will not run any unit tests
    for the team and we now only need to write unit-tests from the scheduler's 
    point of view. 
    '''
    global rank,backend_mpi
    comm = MPI.COMM_WORLD
    rank = comm.Get_rank()
    backend_mpi = BackendMPI(process_per_model=2)

def test_Raises(self):
        N1 = Normal([0.1, 0.01])
        N2 = Normal([0.3, N1])
        kernel = MultivariateNormalKernel([N1,N2,N1])
        with self.assertRaises(ValueError):
            JointPerturbationKernel([kernel])

def setUp(self):
        self.stat_calc1 = Identity(degree = 1, cross = 0)
        self.stat_calc2 = Identity(degree= 1, cross = 0)
        self.likfun1 = SynLikelihood(self.stat_calc1)
        self.likfun2 = SynLikelihood(self.stat_calc2)
        ## Define Models
        # define a uniform prior distribution
        self.mu = Uniform([[-5.0], [5.0]], name='mu')
        self.sigma = Uniform([[0.0], [10.0]], name='sigma')
        # define a Gaussian model
        self.model1 = Normal([self.mu,self.sigma])
        self.model2 = Normal([self.mu,self.sigma])

        #Check whether wrong sized distnacefuncs gives an error
        self.assertRaises(ValueError, ProductCombination, [self.model1,self.model2], [self.likfun1])

        self.jointapprox_lhd = ProductCombination([self.model1, self.model2], [self.likfun1, self.likfun2])

def test_sample(self):
        # setup backend
        dummy = BackendDummy()

        # define a uniform prior distribution
        mu = Uniform([[-5.0], [5.0]], name='mu')
        sigma = Uniform([[0.0], [10.0]], name='sigma')
        # define a Gaussian model
        self.model = Normal([mu,sigma])

        # define sufficient statistics for the model
        stat_calc = Identity(degree=2, cross=0)
        
        # define a distance function
        dist_calc = Euclidean(stat_calc)

        # create fake observed data
        y_obs = [np.array(9.8)]

        # use the rejection sampling scheme
        sampler = RejectionABC([self.model], [dist_calc], dummy, seed = 1)
        journal = sampler.sample([y_obs], 10, 1, 10)
        mu_sample = np.array(journal.get_parameters()['mu'])

def setUp(self):
        # define prior and model
        sigma = Uniform([[10], [20]])
        mu = Normal([0, 1])
        self.Y = Normal([mu, sigma])

        # define backend
        self.backend = Backend()

        # define statistics
        self.statistics_cal = Identity(degree=3, cross=False)

        if has_torch:
            # Initialize statistics learning
            self.statisticslearning = TripletDistanceLearning([self.Y], self.statistics_cal, self.backend,
                                                              n_samples=100, n_samples_per_param=1, seed=1, n_epochs=10)

def setUp(self):
        # find spark and initialize it
        self.backend = BackendDummy()

        # define a uniform prior distribution
        mu = Uniform([[-5.0], [5.0]], name='mu')
        sigma = Uniform([[0.0], [10.0]], name='sigma')
        # define a Gaussian model
        self.model = Normal([mu, sigma])

        # define a distance function
        stat_calc = Identity(degree=2, cross=0)
        self.dist_calc = Euclidean(stat_calc)

        # create fake observed data
        #self.observation = self.model.forward_simulate(1, np.random.RandomState(1))[0].tolist()
        self.observation = [np.array(9.8)]