How to use the mc3.utils.savebin function in mc3

savefile = MCMC_test.npz''')
    p = tmp_path / 'quadratic.py'
    p.write_text(u'''
def quad(p, x):
    y = p[0] + p[1]*x + p[2]*x**2.0
    return y''')
    # Create synthetic dataset:
    x  = np.linspace(0, 10, 1000)         # Independent model variable
    p0 = [3, -2.4, 0.5]                   # True-underlying model parameters
    y  = quad(p0, x)                      # Noiseless model
    uncert = np.sqrt(np.abs(y))           # Data points uncertainty
    error  = np.random.normal(0, uncert)  # Noise for the data
    data   = y + error                    # Noisy data set
    # Store data set and other inputs:
    mc3.utils.savebin([data, uncert], 'data.npz')
    mc3.utils.savebin([x],            'indp.npz')
    subprocess.call('mc3 -c MCMC.cfg'.split())
    assert "MCMC_test.npz"           in os.listdir(".")
    assert "MCMC_test_trace.png"     in os.listdir(".")
    assert "MCMC_test_pairwise.png"  in os.listdir(".")
    assert "MCMC_test_posterior.png" in os.listdir(".")
    assert "MCMC_test_model.png"     in os.listdir(".")

savefile = MCMC_test.npz''')
    p = tmp_path / 'quadratic.py'
    p.write_text(u'''
def quad(p, x):
    y = p[0] + p[1]*x + p[2]*x**2.0
    return y''')
    # Create synthetic dataset:
    x  = np.linspace(0, 10, 1000)         # Independent model variable
    p0 = [3, -2.4, 0.5]                   # True-underlying model parameters
    y  = quad(p0, x)                      # Noiseless model
    uncert = np.sqrt(np.abs(y))           # Data points uncertainty
    error  = np.random.normal(0, uncert)  # Noise for the data
    data   = y + error                    # Noisy data set
    # Store data set and other inputs:
    mc3.utils.savebin([data, uncert], 'data.npz')
    mc3.utils.savebin([x],            'indp.npz')
    subprocess.call('mc3 -c MCMC.cfg'.split())
    assert "MCMC_test.npz"           in os.listdir(".")
    assert "MCMC_test_trace.png"     in os.listdir(".")
    assert "MCMC_test_pairwise.png"  in os.listdir(".")
    assert "MCMC_test_posterior.png" in os.listdir(".")
    assert "MCMC_test_model.png"     in os.listdir(".")

def test_load_savebin_array(tmp_path):
    binfile = str(tmp_path / 'saved_bin.npz')
    data = np.arange(4)
    indata = [data]
    mu.savebin(indata, binfile)
    outdata = mu.loadbin(binfile)
    assert type(outdata[0]) == np.ndarray
    np.testing.assert_equal(outdata[0], data)

def test_loadsavebin_all(tmp_path):
    # This could be replaced with pickle files
    binfile = str(tmp_path / "saved_bin.npz")
    indata = [np.arange(4), "one", np.ones((2,2)), True, [42], (42, 42)]
    mu.savebin(indata, binfile)
    outdata = mu.loadbin(binfile)
    # Check types:
    assert type(outdata[0]) == np.ndarray
    assert type(outdata[1]) == str
    assert type(outdata[2]) == np.ndarray
    assert type(outdata[3]) == bool
    assert type(outdata[4]) == list
    assert type(outdata[5]) == tuple
    # Check values:
    np.testing.assert_equal(outdata[0], np.arange(4))
    assert outdata[1] == 'one'
    np.testing.assert_equal(outdata[2], np.ones((2,2)))
    assert outdata[3] == True
    assert outdata[4] == [42]
    assert outdata[5] == (42,42)

def test_load_savebin(tmp_path, data):
    binfile = str(tmp_path / 'saved_bin.npz')
    dtype = type(data)
    indata = [data]
    mu.savebin(indata, binfile)
    outdata = mu.loadbin(binfile)
    assert type(outdata[0]) == dtype
    np.testing.assert_equal(outdata[0], data)