How to use the pyglm.utils.experiment_helper.load_results function in PyGLM

def plot_impulse_responses(dataset, run):
        # Load the data and results
    from pyglm.models import BernoulliEigenmodelPopulation
    model = BernoulliEigenmodelPopulation(
        N=27, dt=1.0, dt_max=10.0, B=5)
    res_dir = os.path.join("results", dataset, "run%03d" % run)
    results = load_results(dataset, run, ["gibbs"])
    samples = results["gibbs"][0]

    ###########################################################
    # Get the average connectivity
    ###########################################################
    W_samples = [smpl.weight_model.W_effective for smpl in samples]
    offset = len(W_samples) // 2
    W_samples = np.array(W_samples[offset:])
    W_lim = np.amax(abs(W_samples))
    imp_lim = W_lim * np.amax(abs(model.basis.basis))

    syn_sort = np.argsort(W_samples.mean(0).sum(2).ravel())
    i_synapses = zip(*np.unravel_index(syn_sort[:3], (27,27)))
    e_synapses = zip(*np.unravel_index(syn_sort[-3:], (27,27)))
    synapses = i_synapses + e_synapses

if seed is None:
        seed = np.random.randint(2**32)

    print "Setting seed to ", seed
    np.random.seed(seed)

    ###########################################################
    # Load some example data.
    # See data/synthetic/generate.py to create more.
    ###########################################################
    dataset = "synth_nb_eigen_K50_T10000"
    run = 0.001
    train, test, true_model = load_data(dataset)
    res_dir = os.path.join("results", dataset, "run%03d" % run)
    assert os.path.exists(res_dir), "Results directory does not exist: " + res_dir
    standard_results = load_results(dataset, run=run,
                                    algorithms=["bfgs"])

    T      = train.shape[0]
    N      = true_model.N
    B      = true_model.B
    dt     = true_model.dt
    dt_max = true_model.dt_max

    # Create and fit a standard model for initialization
    init_model = standard_results["bfgs"]

    ###########################################################
    # Create a test spike-and-slab model
    ###########################################################

    # Copy the network hypers.

:return:
    """
    if seed is None:
        seed = np.random.randint(2**32)

    print "Setting seed to ", seed
    np.random.seed(seed)

    ###########################################################
    # Load some example data.
    # See data/synthetic/generate.py to create more.
    ###########################################################
    train, test, true_model = load_data(dataset)
    res_dir = os.path.join("results", dataset, "run%03d" % run)
    assert os.path.exists(res_dir), "Results directory does not exist: " + res_dir
    standard_results = load_results(dataset, run=run,
                                    algorithms=["bfgs"])

    T      = train.shape[0]
    N      = true_model.N
    M      = true_model.M
    B      = true_model.B
    dt     = true_model.dt
    dt_max = true_model.dt_max

    # Create and fit a standard model for initialization
    init_model = standard_results["bfgs"]

    ###########################################################
    # Create a test spike-and-slab model
    ###########################################################
    # Copy the network hypers.

"""
    Fit a weakly sparse
    :return:
    """
    if seed is None:
        seed = np.random.randint(2**32)

    print "Setting seed to ", seed
    np.random.seed(seed)

    train, test, _ = load_data(dataset)
    train = train.astype(np.int32)
    test = test.astype(np.int32)
    res_dir = os.path.join("results", dataset, "run%03d" % run)
    assert os.path.exists(res_dir), "Results directory does not exist: " + res_dir
    standard_results = load_results(dataset, run=run,
                                    algorithms=["bfgs"])

    T      = train.shape[0]
    N      = train.shape[1]
    B      = 5
    dt     = 1.0
    dt_max = 10.0

    # Create and fit a standard model for initialization
    init_model = standard_results["bfgs"]

    ###########################################################
    # Create a test spike-and-slab model
    ###########################################################
    # Use the initial model to set hypers
    observation_hypers = {"xi": init_model.xi}

:return:
    """
    if seed is None:
        seed = np.random.randint(2**32)

    print "Setting seed to ", seed
    np.random.seed(seed)

    ###########################################################
    # Load some example data.
    # See data/synthetic/generate.py to create more.
    ###########################################################
    train, test, true_model = load_data(dataset)
    res_dir = os.path.join("results", dataset, "run%03d" % run)
    assert os.path.exists(res_dir), "Results directory does not exist: " + res_dir
    standard_results = load_results(dataset, run=run,
                                    algorithms=["bfgs"])

    T      = train.shape[0]
    N      = true_model.N
    M      = true_model.M
    B      = true_model.B
    dt     = true_model.dt
    dt_max = true_model.dt_max

    # Create and fit a standard model for initialization
    init_model = standard_results["bfgs"]

    ###########################################################
    # Create a test spike-and-slab model
    ###########################################################
    # Copy the network hypers.

"""
    Fit a weakly sparse
    :return:
    """
    if seed is None:
        seed = np.random.randint(2**32)

    print "Setting seed to ", seed
    np.random.seed(seed)

    train, test, _ = load_data(dataset)
    train = train.astype(np.int32)
    test = test.astype(np.int32)
    res_dir = os.path.join("results", dataset, "run%03d" % run)
    assert os.path.exists(res_dir), "Results directory does not exist: " + res_dir
    standard_results = load_results(dataset, run=run,
                                    algorithms=["bfgs"])

    T      = train.shape[0]
    N      = train.shape[1]
    B      = 5
    dt     = 1.0
    dt_max = 10.0

    # Create and fit a standard model for initialization
    init_model = standard_results["bfgs"]

    ###########################################################
    # Create a test spike-and-slab model
    ###########################################################
    # Use the initial model to set hypers
    observation_hypers = {"xi": init_model.xi}

:return:
    """
    if seed is None:
        seed = np.random.randint(2**32)

    print "Setting seed to ", seed
    np.random.seed(seed)

    ###########################################################
    # Load some example data.
    # See data/synthetic/generate.py to create more.
    ###########################################################
    train, test, true_model = load_data(dataset)
    res_dir = os.path.join("results", dataset, "run%03d" % run)
    assert os.path.exists(res_dir), "Results directory does not exist: " + res_dir
    standard_results = load_results(dataset, run=run,
                                    algorithms=["bfgs"])

    T      = train.shape[0]
    N      = true_model.N
    B      = true_model.B
    dt     = true_model.dt
    dt_max = true_model.dt_max

    # Create and fit a standard model for initialization
    init_model = standard_results["bfgs"]

    ###########################################################
    # Create a test spike-and-slab model
    ###########################################################
    # Copy the network hypers.
    test_model = NegativeBinomialEigenmodelPopulation(N=N, dt=dt, dt_max=dt_max, B=B,

:return:
    """
    if seed is None:
        seed = np.random.randint(2**32)

    print "Setting seed to ", seed
    np.random.seed(seed)

    ###########################################################
    # Load some example data.
    # See data/synthetic/generate.py to create more.
    ###########################################################
    train, test, true_model = load_data(dataset)
    res_dir = os.path.join("results", dataset, "run%03d" % run)
    assert os.path.exists(res_dir), "Results directory does not exist: " + res_dir
    standard_results = load_results(dataset, run=run,
                                    algorithms=["bfgs"])

    T      = train.shape[0]
    N      = true_model.N
    B      = true_model.B
    dt     = true_model.dt
    dt_max = true_model.dt_max

    # Create and fit a standard model for initialization
    init_model = standard_results["bfgs"]

    ###########################################################
    # Create a test spike-and-slab model
    ###########################################################
    # Copy the network hypers.
    test_model = NegativeBinomialEigenmodelPopulation(N=N, dt=dt, dt_max=dt_max, B=B,