How to use the mc3.sample function in mc3

def test_mcmc_params_error(capsys):
    output = mc3.sample(data=data, uncert=uncert, func=quad, sampler=sampler,
        indparams=[x], pstep=pstep, nsamples=1e4, burnin=100)
    captured = capsys.readouterr()
    assert output is None
    assert "'params' is a required argument" in captured.out

def test_mcmc_deprecation_parname(capsys):
    output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
        sampler=sampler, indparams=[x], pstep=pstep,
        nsamples=1e3, burnin=2, parname=pnames)
    captured = capsys.readouterr()
    assert output is not None
    assert "parname argument is deprecated. Use pnames instead." \
        in captured.out

def test_mcmc_deprecation_chireturn(capsys):
    output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
        sampler=sampler, indparams=[x], stepsize=pstep,
        nsamples=1e3, burnin=2, ncpu=7, chireturn=True)
    captured = capsys.readouterr()
    assert output is not None
    assert "chireturn argument is deprecated." in captured.out

def test_mcmc_gr(capsys):
    output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
        sampler=sampler, indparams=[x],
        pstep=pstep, nsamples=1e4, burnin=100,
        grtest=True)
    captured = capsys.readouterr()
    assert output is not None
    assert "Gelman-Rubin statistics for free parameters:" in captured.out

def test_mcmc_deprecation_leastsq(capsys, lm, leastsq):
    if leastsq is True and lm is False:
        ls = 'trf'
    elif leastsq in [True, 'lm']:
        ls = 'lm'
    else:
        ls = None
    output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
        sampler=sampler, indparams=[x], stepsize=pstep,
        nsamples=1e3, burnin=2, ncpu=7, leastsq=leastsq, lm=lm)
    captured = capsys.readouterr()
    assert output is not None
    if isinstance(leastsq, bool):
        assert "leastsq as boolean is deprecated.  See docs for new usage.  " \
           "Set\nleastsq={}".format(repr(ls)) in captured.out
    if isinstance(lm, bool):
        assert "lm argument is deprecated.  See new usage of leastsq.  " \
               "Set\nleastsq={}".format(repr(ls)) in captured.out

def test_mcmc_pnames(capsys):
    output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
        sampler=sampler, indparams=[x],
        pstep=pstep, nsamples=1e4, burnin=100,
        pnames=pnames)
    captured = capsys.readouterr()
    assert output is not None
    assert "constant"  in captured.out
    assert "linear"    in captured.out
    assert "quadratic" in captured.out

def test_mcmc_gr_break_iterations(capsys):
    output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
        sampler=sampler, indparams=[x],
        pstep=pstep, nsamples=1e4, burnin=100,
        grtest=True, grbreak=1.1, grnmin=5000.0)
    captured = capsys.readouterr()
    assert output is not None
    assert "All parameters satisfy the GR convergence threshold of 1.1" \
           in captured.out

def test_dynesty_priors_gauss():
    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.sample(data, uncert, func=quad, params=np.copy(params),
        sampler=sampler, indparams=[x], pmin=pmin, pmax=pmax,
        pstep=pstep, maxiter=5000,
        prior=prior, priorlow=priorlow, priorup=priorup)
    assert output is not None
    assert -2*output['best_log_post'] > output['best_chisq']
    assert np.all(-2*output['log_post'] > output['chisq'])

def test_mcmc_pnames_texnames(capsys):
    output = mc3.sample(data, uncert, func=quad, params=np.copy(params),
        sampler=sampler, indparams=[x],
        pstep=pstep, nsamples=1e4, burnin=100,
        pnames=pnames, texnames=texnames)
    captured = capsys.readouterr()
    assert output is not None
    assert "constant"  in captured.out
    assert "linear"    in captured.out
    assert "quadratic" in captured.out

grbreak = 1.01
grnmin  = 0.5

# Logging:
log = 'MCMC_tutorial.log'

# File outputs:
savefile = 'MCMC_tutorial.npz'
plots    = True
rms      = True

# Carter & Winn (2009) Wavelet-likelihood method:
wlike = False

# Run the MCMC:
mc3_output = mc3.sample(data=data, uncert=uncert, func=func, params=params,
     indparams=indparams, pmin=pmin, pmax=pmax, pstep=pstep,
     pnames=pnames, texnames=texnames,
     prior=prior, priorlow=priorlow, priorup=priorup,
     sampler=sampler, nsamples=nsamples,  nchains=nchains,
     ncpu=ncpu, burnin=burnin, thinning=thinning,
     leastsq=leastsq, chisqscale=chisqscale,
     grtest=grtest, grbreak=grbreak, grnmin=grnmin,
     hsize=hsize, kickoff=kickoff,
     wlike=wlike, log=log,
     plots=plots, savefile=savefile, rms=rms)