def do_average_plot_2d(self):
'''Plot the histogram for iteration self.n_iter'''
idim0 = self.dimensions[0]['idim']
idim1 = self.dimensions[1]['idim']
n_iters = self.input_h5['n_iter'][...]
iiter_start = np.searchsorted(n_iters, self.iter_start)
iiter_stop = np.searchsorted(n_iters, self.iter_stop)
binbounds_0 = self.input_h5['binbounds_{}'.format(idim0)][...]
midpoints_0 = self.input_h5['midpoints_{}'.format(idim0)][...]
binbounds_1 = self.input_h5['binbounds_{}'.format(idim1)][...]
midpoints_1 = self.input_h5['midpoints_{}'.format(idim1)][...]
for iiter in range(iiter_start, iiter_stop):
iter_hist = sum_except_along(self.input_h5['histograms'][iiter],
[idim0, idim1])
if iiter == iiter_start:
hist = iter_hist
else:
hist += iter_hist
normhistnd(hist, [binbounds_0, binbounds_1])
self._do_2d_output(hist, [idim0, idim1], [midpoints_0, midpoints_1],
[binbounds_0, binbounds_1])
def _remote_bin_iter(iiter, n_iter, dsspec, wt_dsspec, initpoint, binbounds,
ignore_out_of_range):
iter_hist_shape = tuple(len(bounds) - 1 for bounds in binbounds)
iter_hist = np.zeros(iter_hist_shape, dtype=np.float64)
dset = dsspec.get_iter_data(n_iter)
if dset is None:
return iiter, n_iter, iter_hist
else:
npts = dset.shape[1]
weights = wt_dsspec.get_iter_data(n_iter)[:, 0, 0]
# dset = dset[:,initpoint:,:]
for ipt in range(npts - initpoint):
histnd(dset[:, ipt, :],
binbounds,
weights,
out=iter_hist,
binbound_check=False,
ignore_out_of_range=ignore_out_of_range)
del weights, dset
# normalize histogram
normhistnd(iter_hist, binbounds)
return iiter, n_iter, iter_hist
def do_instant_plot_1d(self):
'''Plot the histogram for iteration self.n_iter'''
idim = self.dimensions[0]['idim']
n_iters = self.input_h5['n_iter'][...]
iiter = np.searchsorted(n_iters, self.n_iter)
binbounds = self.input_h5['binbounds_{}'.format(idim)][...]
midpoints = self.input_h5['midpoints_{}'.format(idim)][...]
hist = self.input_h5['histograms'][iiter]
# Average over other dimensions
hist = sum_except_along(hist, idim)
normhistnd(hist, [binbounds])
self._do_1d_output(hist, idim, midpoints)
def do_average_plot_1d(self):
'''Plot the average histogram for iterations self.iter_start to self.iter_stop'''
idim = self.dimensions[0]['idim']
n_iters = self.input_h5['n_iter'][...]
iiter_start = np.searchsorted(n_iters, self.iter_start)
iiter_stop = np.searchsorted(n_iters, self.iter_stop)
binbounds = self.input_h5['binbounds_{}'.format(idim)][...]
midpoints = self.input_h5['midpoints_{}'.format(idim)][...]
# hist = self.input_h5['histograms'][iiter_start:iiter_stop]
for iiter in range(iiter_start, iiter_stop):
iter_hist = sum_except_along(self.input_h5['histograms'][iiter], idim)
if iiter == iiter_start:
hist = iter_hist
else:
hist += iter_hist
del iter_hist
normhistnd(hist, [binbounds])
self._do_1d_output(hist, idim, midpoints)
def go(self):
'''Plot the evolution of the histogram for iterations self.iter_start to self.iter_stop'''
idim = self.dimensions[0]['idim']
n_iters = self.input_h5['n_iter'][...]
iiter_start = np.searchsorted(n_iters, self.iter_start)
iiter_stop = np.searchsorted(n_iters, self.iter_stop)
binbounds = self.input_h5['binbounds_{}'.format(idim)][...]
midpoints = self.input_h5['midpoints_{}'.format(idim)][...]
hists_ds = self.input_h5['histograms']
itercount = self.iter_stop - self.iter_start
# We always round down, so that we don't have a dangling partial block at the end
nblocks = itercount // self.iter_step
block_iters = np.empty((nblocks, 2), dtype=n_iters.dtype)
blocked_hists = np.zeros((nblocks, hists_ds.shape[1 + idim]), dtype=hists_ds.dtype)
for iblock, istart in enumerate(range(iiter_start, iiter_start + nblocks * self.iter_step, self.iter_step)):
istop = min(istart + self.iter_step, iiter_stop)
histslice = hists_ds[istart:istop]
# Sum over time
histslice = np.add.reduce(histslice, axis=0)
# Sum over other dimensions
blocked_hists[iblock] = sum_except_along(histslice, idim)
# Normalize
normhistnd(blocked_hists[iblock], [binbounds])
block_iters[iblock, 0] = n_iters[istart]
block_iters[iblock, 1] = n_iters[istop - 1] + 1
# enehists = -np.log(blocked_hists)
enehists = self._ener_zero(blocked_hists)
log10hists = np.log10(blocked_hists)
if self.hdf5_output_filename:
with h5py.File(self.hdf5_output_filename, 'w') as output_h5:
h5io.stamp_creator_data(output_h5)
output_h5.attrs['source_data'] = os.path.abspath(self.input_h5.filename)
output_h5.attrs['source_dimension'] = idim
output_h5['midpoints'] = midpoints
output_h5['histograms'] = blocked_hists
output_h5['n_iter'] = block_iters
if self.plot_output_filename:
if self.plotscale == 'energy':
plothist = enehists
label = r'$\Delta F(x)\,/\,kT$' + '\n' + r'$\left[-\ln\,P(x)\right]$'
elif self.plotscale == 'log10':
plothist = log10hists
label = r'$\log_{10}\ P(x)$'
else:
plothist = blocked_hists
label = r'$P(x)$'
try:
vmin, vmax = self.plotrange
except TypeError:
vmin, vmax = None, None
pyplot.figure()
norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
ax = pyplot.gca()
nui = NonUniformImage(
ax, extent=(midpoints[0], midpoints[-1], block_iters[0, -1], block_iters[-1, -1]), origin='lower', norm=norm
)
# not sure why plothist works but plothist.T doesn't, and the opposite is true
# for _do_2d_output
nui.set_data(midpoints, block_iters[:, -1], plothist)
ax.images.append(nui)
ax.set_xlim(midpoints[0], midpoints[-1])
ax.set_ylim(block_iters[0, -1], block_iters[-1, -1])
cb = pyplot.colorbar(nui)
cb.set_label(label)
pyplot.xlabel(self.dimensions[0]['label'])
pyplot.xlim(self.dimensions[0].get('lb'), self.dimensions[0].get('ub'))
pyplot.ylabel('WE Iteration')
if self.plottitle:
pyplot.title(self.plottitle)
if self.postprocess_function:
self.postprocess_function(plothist, midpoints, binbounds)
pyplot.savefig(self.plot_output_filename)