3D generation in broken

[Falfat]

I have tried to run all the examples provided for generating 3D random samples, and they all returned same error below

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[48], line 2
      1 model = Gaussian(dim=3, var=3, len_scale=1.5)
----> 2 srf = SRF(model, mean=(0.5, 0, 0), generator="VectorField", seed=198412031)

File ~\AppData\Local\Programs\Python\Python38\lib\site-packages\gstools\field\srf.py:100, in SRF.__init__(self, model, mean, normalizer, trend, upscaling, generator, **generator_kwargs)
     98 # initialize attributes
     99 self.upscaling = upscaling
--> 100 self.set_generator(generator, **generator_kwargs)

File ~\AppData\Local\Programs\Python\Python38\lib\site-packages\gstools\field\srf.py:161, in SRF.set_generator(self, generator, **generator_kwargs)
    159 if generator in GENERATOR:
    160     gen = GENERATOR[generator]
--> 161     self._generator = gen(self.model, **generator_kwargs)
    162     self.value_type = self._generator.value_type
    163 else:

File ~\AppData\Local\Programs\Python\Python38\lib\site-packages\gstools\field\generator.py:395, in IncomprRandMeth.__init__(self, model, mean_velocity, mode_no, seed, verbose, sampling, **kwargs)
    391 if model.dim < 2 or model.dim > 3:
    392     raise ValueError(
    393         "Only 2D and 3D incompressible fields can be generated."
    394     )
--> 395 super().__init__(model, mode_no, seed, verbose, sampling, **kwargs)
    397 self.mean_u = mean_velocity
    398 self._value_type = "vector"

File ~\AppData\Local\Programs\Python\Python38\lib\site-packages\gstools\field\generator.py:108, in RandMeth.__init__(self, model, mode_no, seed, verbose, sampling, **kwargs)
    106 self.sampling = sampling
    107 # set model and seed
--> 108 self.update(model, seed)

File ~\AppData\Local\Programs\Python\Python38\lib\site-packages\gstools\field\generator.py:174, in RandMeth.update(self, model, seed)
    172 self._model = dcp(model)
    173 if seed is None or not np.isnan(seed):
--> 174     self.reset_seed(seed)
    175 else:
    176     self.reset_seed(self._seed)

File ~\AppData\Local\Programs\Python\Python38\lib\site-packages\gstools\field\generator.py:242, in RandMeth.reset_seed(self, seed)
    238     rad = self._rng.sample_dist(
    239         size=self._mode_no, pdf=pdf, cdf=cdf, ppf=ppf, a=0
    240     )
    241 else:
--> 242     rad = self._rng.sample_ln_pdf(
    243         ln_pdf=self.model.ln_spectral_rad_pdf,
    244         size=self._mode_no,
    245         sample_around=1.0 / self.model.len_rescaled,
    246     )
    247 # get fully spatial samples by multiplying sphere samples and radii
    248 self._cov_sample = rad * sphere_coord

File ~\AppData\Local\Programs\Python\Python38\lib\site-packages\gstools\random\rng.py:98, in RNG.sample_ln_pdf(self, ln_pdf, size, sample_around, nwalkers, burn_in, oversampling_factor)
     96 initial_state = State(burn_in_state, copy=True)
     97 initial_state.random_state = self.random.get_state()
---> 98 sampler.run_mcmc(initial_state=initial_state, nsteps=sample_size)
     99 samples = sampler.get_chain(flat=True)[:, 0]
    101 # choose samples according to size

File ~\AppData\Local\Programs\Python\Python38\lib\site-packages\emcee\ensemble.py:450, in EnsembleSampler.run_mcmc(self, initial_state, nsteps, **kwargs)
    447     initial_state = self._previous_state
    449 results = None
--> 450 for results in self.sample(initial_state, iterations=nsteps, **kwargs):
    451     pass
    453 # Store so that the ``initial_state=None`` case will work

File ~\AppData\Local\Programs\Python\Python38\lib\site-packages\emcee\ensemble.py:386, in EnsembleSampler.sample(self, initial_state, log_prob0, rstate0, blobs0, iterations, tune, skip_initial_state_check, thin_by, thin, store, progress, progress_kwargs)
    384     checkpoint_step = thin_by
    385     if store:
--> 386         self.backend.grow(iterations, state.blobs)
    388 # Set up a wrapper around the relevant model functions
    389 if self.pool is not None:

File ~\AppData\Local\Programs\Python\Python38\lib\site-packages\emcee\backends\backend.py:175, in Backend.grow(self, ngrow, blobs)
    173 self._check_blobs(blobs)
    174 i = ngrow - (len(self.chain) - self.iteration)
--> 175 a = np.empty((i, self.nwalkers, self.ndim), dtype=self.dtype)
    176 self.chain = np.concatenate((self.chain, a), axis=0)
    177 a = np.empty((i, self.nwalkers), dtype=self.dtype)

TypeError: 'float' object cannot be interpreted as an integer

[Falfat]

[MuellerSeb]

This is the exact same issue as this: #183
Which was solved in GSTools v1.3.2 here: #184

Do you use an old version of GSTools? If this is intended, use emcee with a version <3.1.

[LSchueler]

@Falfat could you solve your problem?

[Falfat]

Yes, I did. Thanks!