How to use the abcpy.output.Journal function in abcpy

def test_add_weights(self):
        weights1 = np.zeros((2,4))
        weights2 = np.ones((2,4))

        # test whether production mode only stores the last set of parameters
        journal_prod = Journal(0)
        journal_prod.add_weights(weights1)
        journal_prod.add_weights(weights2)
        self.assertEqual(len(journal_prod.weights), 1)
        np.testing.assert_equal(journal_prod.weights[0], weights2)

        # test whether reconstruction mode stores all parameter sets
        journal_recon = Journal(1)
        journal_recon.add_weights(weights1)
        journal_recon.add_weights(weights2)
        self.assertEqual(len(journal_recon.weights), 2)
        np.testing.assert_equal(journal_recon.weights[0], weights1)
        np.testing.assert_equal(journal_recon.weights[1], weights2)

def test_add_opt_values(self):
        opt_values1 = np.zeros((2,4))
        opt_values2 = np.ones((2,4))

        # test whether production mode only stores the last set of parameters
        journal_prod = Journal(0)
        journal_prod.add_opt_values(opt_values1)
        journal_prod.add_opt_values(opt_values2)
        self.assertEqual(len(journal_prod.opt_values), 1)
        np.testing.assert_equal(journal_prod.opt_values[0], opt_values2)

        # test whether reconstruction mode stores all parameter sets
        journal_recon = Journal(1)
        journal_recon.add_opt_values(opt_values1)
        journal_recon.add_opt_values(opt_values2)
        self.assertEqual(len(journal_recon.opt_values), 2)
        np.testing.assert_equal(journal_recon.opt_values[0], opt_values1)
        np.testing.assert_equal(journal_recon.opt_values[1], opt_values2)

def test_add_weights(self):
        weights1 = np.zeros((2,4))
        weights2 = np.ones((2,4))

        # test whether production mode only stores the last set of parameters
        journal_prod = Journal(0)
        journal_prod.add_weights(weights1)
        journal_prod.add_weights(weights2)
        self.assertEqual(len(journal_prod.weights), 1)
        np.testing.assert_equal(journal_prod.weights[0], weights2)

        # test whether reconstruction mode stores all parameter sets
        journal_recon = Journal(1)
        journal_recon.add_weights(weights1)
        journal_recon.add_weights(weights2)
        self.assertEqual(len(journal_recon.weights), 2)
        np.testing.assert_equal(journal_recon.weights[0], weights1)
        np.testing.assert_equal(journal_recon.weights[1], weights2)

print(journal.parameters)
    print(journal.weights)
    
    # do post analysis
    print(journal.posterior_mean())
    print(journal.posterior_cov())
    print(journal.posterior_histogram())
    
    # print configuration
    print(journal.configuration)
    
    # save and load journal
    journal.save("experiments.jnl")
    
    from abcpy.output import Journal
    new_journal = Journal.fromFile('experiments.jnl')

print(journal.parameters)
    print(journal.weights)

    # do post analysis
    print(journal.posterior_mean())
    print(journal.posterior_cov())
    print(journal.posterior_histogram())
    
    # print configuration
    print(journal.configuration)

    # save and load journal
    journal.save("experiments.jnl")
    
    from abcpy.output import Journal
    new_journal = Journal.fromFile('experiments.jnl')

The default value is 0, meaning the intermediate results are not saved.

        Returns
        -------
        abcpy.output.Journal
            A journal containing simulation results, metadata and optionally intermediate results.
        """
        global broken_preemptively
        self.sample_from_prior(rng=self.rng)
        self.accepted_parameters_manager.broadcast(self.backend, observations)
        self.epsilon = epsilon
        self.n_samples = n_samples
        self.n_samples_per_param = n_samples_per_param

        if(journal_file is None):
            journal = Journal(full_output)
            journal.configuration["type_model"] = [type(model).__name__ for model in self.model]
            journal.configuration["type_dist_func"] = type(self.distance).__name__
            journal.configuration["type_kernel_func"] = type(self.kernel)
            journal.configuration["n_samples"] = self.n_samples
            journal.configuration["n_samples_per_param"] = self.n_samples_per_param
            journal.configuration["beta"] = beta
            journal.configuration["delta"] = delta
            journal.configuration["v"] = v
            journal.configuration["ar_cutoff"] = ar_cutoff
            journal.configuration["resample"] = resample
            journal.configuration["n_update"] = n_update
            journal.configuration["adaptcov"] = adaptcov
            journal.configuration["full_output"] = full_output
        else:
            journal = Journal.fromFile(journal_file)

full_output: integer, optional
            If full_output==1, intermediate results are included in output journal.
            The default value is 0, meaning the intermediate results are not saved.

        Returns
        -------
        abcpy.output.Journal
            A journal containing simulation results, metadata and optionally intermediate results.
        """

        self.observations_bds = self.backend.broadcast(observations)
        self.alpha = alpha
        self.n_samples = n_samples
        self.n_samples_per_param = n_samples_per_param

        journal = Journal(full_output)
        journal.configuration["type_model"] = type(self.model)
        journal.configuration["type_dist_func"] = type(self.distance)
        journal.configuration["n_samples"] = self.n_samples
        journal.configuration["n_samples_per_param"] = self.n_samples_per_param
        journal.configuration["steps"] = steps

        accepted_parameters = None
        accepted_cov_mat = None
        accepted_dist = None

        # main RSMCABC algorithm
        # print("INFO: Starting RSMCABC iterations.")
        for aStep in range(steps):

            # 0: Compute epsilon, compute new covariance matrix for Kernel,
            # and finally Drawing new new/perturbed samples using prior or MCMC Kernel

print(journal.get_stored_output_values())
    print(journal.weights)

    # do post analysis
    print(journal.posterior_mean())
    print(journal.posterior_cov())
    print(journal.posterior_histogram())

    # print configuration
    print(journal.configuration)

    # save and load journal
    journal.save("experiments.jnl")

    from abcpy.output import Journal
    new_journal = Journal.fromFile('experiments.jnl')

The default value is 0, meaning the intermediate results are not saved.

        Returns
        -------
        abcpy.output.Journal
            A journal containing simulation results, metadata and optionally intermediate results.
        """
        self.sample_from_prior(rng=self.rng)

        self.accepted_parameters_manager.broadcast(self.backend, observations)
        self.alpha = alpha
        self.n_samples = n_samples
        self.n_samples_per_param = n_samples_per_param

        if(journal_file is None):
            journal = Journal(full_output)
            journal.configuration["type_model"] = [type(model).__name__ for model in self.model]
            journal.configuration["type_dist_func"] = type(self.distance).__name__
            journal.configuration["n_samples"] = self.n_samples
            journal.configuration["n_samples_per_param"] = self.n_samples_per_param
            journal.configuration["steps"] = steps
        else:
            journal = Journal.fromFile(journal_file)

        accepted_parameters = None
        accepted_weights = None
        accepted_cov_mats = None
        accepted_dist = None
        alpha_accepted_parameters = None
        alpha_accepted_weights = None
        alpha_accepted_dist = None