def test_live_dangerously(nwalkers=32, nsteps=3000, seed=1234):
warnings.filterwarnings("error")
# Set up the random number generator.
np.random.seed(seed)
coords = np.random.randn(nwalkers, 2 * nwalkers)
proposal = moves.StretchMove()
# Test to make sure that the error is thrown if there aren't enough
# walkers.
with pytest.raises(RuntimeError):
proposal.propose(coords, np.random.randn(nwalkers), None, lambda x:
(np.zeros(len(x)), None), np.random)
# Living dangerously...
proposal.live_dangerously = True
proposal.propose(coords, np.random.randn(nwalkers), None, lambda x:
(np.zeros(len(x)), None), np.random)
all_backends = backends.get_test_backends()
def normal_log_prob(params):
return -0.5 * np.sum(params**2)
@pytest.mark.parametrize(
"backend, moves",
product(
all_backends,
[
None,
moves.GaussianMove(0.5),
[moves.StretchMove(), moves.GaussianMove(0.5)],
[(moves.StretchMove(), 0.3), (moves.GaussianMove(0.5), 0.1)],
],
),
)
def test_shapes(backend, moves, nwalkers=32, ndim=3, nsteps=10, seed=1234):
# Set up the random number generator.
np.random.seed(seed)
with backend() as be:
# Initialize the ensemble, moves and sampler.
coords = np.random.randn(nwalkers, ndim)
sampler = EnsembleSampler(nwalkers,
ndim,
normal_log_prob,
moves=moves,
def test_randomize_stretch(**kwargs):
_test_normal(moves.StretchMove(randomize_split=True), **kwargs)
def test_nsplits_stretch(**kwargs):
_test_normal(moves.StretchMove(nsplits=5), **kwargs)
def test_uniform_stretch(**kwargs):
_test_uniform(moves.StretchMove(), **kwargs)
def test_normal_stretch(**kwargs):
_test_normal(moves.StretchMove(), **kwargs)
def test_normal_stretch(blobs, **kwargs):
kwargs["blobs"] = blobs
_test_normal(moves.StretchMove(), **kwargs)
v_systemic = 100 #changed to 100 to equal exactly what we used in mathematica
# In[3]:
# x and y dims are switched in ds9 fits display versus np array shape
fits_ydim, fits_xdim = fits.open(observed_2d_vel_field_fits_file)[0].data.shape
mask_sigma = 1.
random_seed = 1234
mcmc_nwalkers = 65
mcmc_niter = 2000
mcmc_ndim = num_bins # Do not change this if fitting one ring at a time
mcmc_nthreads = 40
# Try increasing stretch scale factor a. version must be >=3 for this to be used.
mcmc_moves = moves.StretchMove(a=2)
mcmc_version = float(emcee.__version__[0])
# Option to save every batch_size iterations in case of crash<br>
# Increase this; 2 is a very low value just for testing
batch_size = 50
# option to save outputs in a particular directory
save_dir = "/Users/stephencoffey/Downloads/little-things/notebooks/stephen/fit_piecewise/bb_comparison/31bins_4/"
# In[4]:
galaxy = Galaxy(
name=name,
distance=distance,