Blobs function error

General information:

• emcee version: 3.1.1
• platform: Centos7
• installation method (pip/conda/source/other?): Conda-forge

Problem description:

Expected behavior:

Blobs function

Actual behavior:

After running normally for some time, an error is reported:

378012 Traceback (most recent call last):
378013 File "/home/yuebb/.local/lib/python3.9/site-packages/emcee/ensemble.py", line 492, in compute_log_prob
378014 log_prob = np.array([float(l[0]) for l in results])
378015 File "/home/yuebb/.local/lib/python3.9/site-packages/emcee/ensemble.py", line 492, in
378016 log_prob = np.array([float(l[0]) for l in results])
378017 TypeError: 'float' object is not subscriptable
378018
378019 During handling of the above exception, another exception occurred:
378020
378021 Traceback (most recent call last):
378022 File "/public/data/yuebb/Neutrino/SkyWalker/bin/JUNOMCMC/JUNO_C13_Pool.py", line 146, in
378023 sampler.run_mcmc(starting_guesses, nsteps)
378024 File "/home/yuebb/.local/lib/python3.9/site-packages/emcee/ensemble.py", line 443, in run_mcmc
378025 for results in self.sample(initial_state, iterations=nsteps, **kwargs):
378026 File "/home/yuebb/.local/lib/python3.9/site-packages/emcee/ensemble.py", line 402, in sample
378027 state, accepted = move.propose(model, state)
378028 File "/home/yuebb/.local/lib/python3.9/site-packages/emcee/moves/red_blue.py", line 93, in propose
378029 new_log_probs, new_blobs = model.compute_log_prob_fn(q)
378030 File "/home/yuebb/.local/lib/python3.9/site-packages/emcee/ensemble.py", line 495, in compute_log_prob
378031 log_prob = np.array([float(l) for l in results])
378032 File "/home/yuebb/.local/lib/python3.9/site-packages/emcee/ensemble.py", line 495, in
378033 log_prob = np.array([float(l) for l in results])
378034 TypeError: float() argument must be a string or a number, not 'tuple'

What have you tried so far?:

Minimal example:

import emcee

def log_posterior(theta):
    deltaMSq21, sinSqTheta12, B8Flux, B8NuShift, JUNO_B12, JUNO_Li8, JUNO_C10, JUNO_Be11, JUNO_He6, JUNO_U238, JUNO_Th232, JUNO_ReactorES, JUNO_CCBG, C13CCScale, C13NCScale, ESScale, sinSqThetaW, JUNODetectorESNCEff, JUNODetectorESCCEff = theta
    
    model.setParValue("deltaMSq21", deltaMSq21*1e-5)
    model.setParValue("sinSqTheta12", sinSqTheta12)
    model.setParValue("B8Flux", B8Flux)
    model.setParValue("B8NuShift", B8NuShift)
    model.setParValue("JUNO_B12", JUNO_B12)
    model.setParValue("JUNO_Li8", JUNO_Li8)
    model.setParValue("JUNO_C10", JUNO_C10)
    model.setParValue("JUNO_Be11", JUNO_Be11)
    model.setParValue("JUNO_He6", JUNO_He6)
    model.setParValue("JUNO_U238", JUNO_U238)
    model.setParValue("JUNO_Th232", JUNO_Th232)
    model.setParValue("JUNO_ReactorES", JUNO_ReactorES)

    model.setParValue("JUNO_CCBG", JUNO_CCBG)
    model.setParValue("C13CCScale", C13CCScale)
    model.setParValue("C13NCScale", C13NCScale)
    model.setParValue("ESScale", ESScale)
    model.setParValue("sinSqThetaW", sinSqThetaW)
    model.setParValue("JUNODetectorESNCEff", JUNODetectorESNCEff)
    model.setParValue("JUNODetectorESCCEff", JUNODetectorESCCEff)

    if sinSqTheta12 < 0 or sinSqTheta12 > 1 or deltaMSq21 < 0 or B8Flux < 0 or JUNO_B12 < 0 or JUNO_Li8 < 0 or JUNO_C10 < 0 or JUNO_Be11 < 0 or JUNO_He6 < 0 or JUNO_U238 < 0 or JUNO_Th232 < 0 or JUNO_ReactorES < 0:
        return -np.inf  

    return -0.5*model.getChiSquare(), B8Flux*model.getProb(0), B8Flux*model.getProb(1), B8Flux*model.getProb(2), B8Flux*(model.getProb(1)+model.getProb(2))

#### ignoring some useless code

dtype = [("PhiE", float), ("PhiM", float), ("PhiT", float), ("PhiMT", float)]

filename = data+"_Backend.h5"
#np.save(data+"_original_data.npy", sampler.chain)  # emcee_trace)
backend = emcee.backends.HDFBackend(filename)
backend.reset(nwalkers, ndim)

# with Pool() as pool:
sampler = emcee.EnsembleSampler(nwalkers, ndim, log_posterior,  # pool=pool,
                                # sampler = emcee.EnsembleSampler(nwalkers, ndim, log_posterior,
                                #     # )#,
                                #     # moves=[(emcee.moves.WalkMove(), 1),]) # (emcee.moves.DESnookerMove(), 0.2),],)
                                moves=[(emcee.moves.DEMove(), 1), ],
                                blobs_dtype=dtype,
                                backend=backend,
                                )

sampler.run_mcmc(starting_guesses, nsteps)


# sample code goes here...

Start with the minimal Python example and the compute_log_prob entry point in emcee/ensemble.py, especially the handling shown at lines 492-495. Compare the tuple returned by log_posterior with blobs_dtype, then run sampler.run_mcmc to reproduce the failure. Done means the example completes without the reported TypeError while using blobs_dtype.