How to use mc3 - 10 common examples

def test_timeavg_binstep(binstep):
    maxbins = len(data) // 2
    rms, rmslo, rmshi, stderr, binsz = ms.time_avg(data, maxbins, binstep)
    assert len(rms) == len(data) // binstep // 2

def test_fit_minimal():
    output = mc3.fit(data, uncert, quad, np.copy(params), indparams=[x])
    np.testing.assert_allclose(output['best_log_post'], -54.43381306220858)
    np.testing.assert_equal(-2*output['best_log_post'], output['best_chisq'])
    np.testing.assert_allclose(output['bestp'],
        np.array([4.28263253, -2.40781859, 0.49534411]), rtol=1e-7)

def test_fit_fixed():
    pars = np.copy(params)
    pars[0] = p0[0]
    output = mc3.fit(data, uncert, quad, pars, indparams=[x],
        pstep=[0.0, 1.0, 1.0])
    assert output['bestp'][0] == pars[0]
    np.testing.assert_allclose(output['best_log_post'], -54.507722717665466)
    np.testing.assert_allclose(output['bestp'],
        np.array([4.5, -2.51456999, 0.50570154]), rtol=1e-7)

def test_fit_shared():
    output = mc3.fit(data1, uncert1, quad, np.copy(params), indparams=[x],
        pstep=[1.0, -1, 1.0])
    assert output['bestp'][1] == output['bestp'][0]
    np.testing.assert_allclose(output['best_log_post'], -51.037667264657)
    np.testing.assert_allclose(output['bestp'],
        np.array([4.58657213, 4.58657213, 0.43347714]), rtol=1e-7)

def test_fit_bounds():
    output = mc3.fit(data, uncert, quad, [4.5, -2.5, 0.5], indparams=[x],
        pmin=[4.4, -3.0, 0.4], pmax=[5.0, -2.0, 0.6], leastsq='trf')
    np.testing.assert_allclose(output['best_log_post'], -54.45536109795812)
    np.testing.assert_allclose(output['bestp'],
        np.array([4.4, -2.46545897, 0.5009366]), rtol=1e-7)

def test_fit_trf():
    output = mc3.fit(data, uncert, quad, np.copy(params), indparams=[x],
        leastsq='trf')
    np.testing.assert_allclose(output['best_log_post'], -54.43381306220856)
    np.testing.assert_allclose(output['bestp'],
        np.array([4.28263252, -2.40781858, 0.49534411]), rtol=1e-7)

def test_fit_leastsq_error(capsys):
    with pytest.raises(SystemExit):
        output = mc3.fit(data, uncert, quad, np.copy(params), indparams=[x],
            leastsq='invalid')
        captured = capsys.readouterr()
        assert "Invalid 'leastsq' input (invalid). Must select from " \
               "['lm', 'trf']." in captured.out

def test_fit_priors():
    prior    = np.array([ 4.5,  0.0,   0.0])
    priorlow = np.array([ 0.1,  0.0,   0.0])
    priorup  = np.array([ 0.1,  0.0,   0.0])
    output = mc3.fit(data, uncert, quad, np.copy(params), indparams=[x],
        prior=prior, priorlow=priorlow, priorup=priorup)
    np.testing.assert_allclose(output['best_log_post'], -54.50548056991611)
    # First parameter is closer to 4.5 than without a prior:
    np.testing.assert_allclose(output['bestp'],
        np.array([4.49340587, -2.51133157, 0.50538734]), rtol=1e-7)

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(".")