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 = numpy.searchsorted(n_iters, self.iter_start)
iiter_stop = numpy.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 xrange(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 = numpy.zeros(iter_hist_shape, dtype=numpy.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 = numpy.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_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 = numpy.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 _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 = numpy.zeros(iter_hist_shape, dtype=numpy.float64)
dset = dsspec.get_iter_data(n_iter)
npts = dset.shape[1]
weights = wt_dsspec.get_iter_data(n_iter)
dset = dset[:,initpoint:,:]
for ipt in xrange(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_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 = numpy.searchsorted(n_iters, self.iter_start)
iiter_stop = numpy.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 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 = numpy.searchsorted(n_iters, self.iter_start)
iiter_stop = numpy.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 xrange(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 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 = numpy.searchsorted(n_iters, self.iter_start)
iiter_stop = numpy.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 xrange(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 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 = numpy.searchsorted(n_iters, self.iter_start)
iiter_stop = numpy.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 = numpy.empty((nblocks, 2), dtype=n_iters.dtype)
blocked_hists = numpy.zeros((nblocks, hists_ds.shape[1 + idim]),
dtype=hists_ds.dtype)
for iblock, istart in enumerate(
xrange(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 = numpy.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 = -numpy.log(blocked_hists)
enehists = self._ener_zero(blocked_hists)
log10hists = numpy.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)
denom = np.dot(phi_vals[:, np.newaxis], ntwids[np.newaxis, :])
assert denom.shape == (n_windows, n_segs)
denom -= free_energies[:, np.newaxis]
denom = np.exp(-denom)
denom *= n_tots[:, np.newaxis]
denom = np.sum(denom, axis=0)
histnd(vals, binbounds, n_segs * weights / denom, out=hist, binbound_check=False)
for dm in data_managers:
dm.close_backing()
# normalize the f****r
normhistnd(hist, binbounds)
phi_binctrs = phi_binbounds[:-1] + np.diff(phi_binbounds) / 2.0
psi_binctrs = psi_binbounds[:-1] + np.diff(psi_binbounds) / 2.0
vmin, vmax = 0, 10
norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
extent = (phi_binbounds[0], phi_binbounds[-1], psi_binbounds[0], psi_binbounds[-1])
min_energy = 20.0
for i in range(64):
thishist = hist[:, :, i].copy()
normhistnd(thishist, binbounds[0:2])
loghist = -np.log(thishist)
if loghist.min() < min_energy:
print(i)
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 = numpy.searchsorted(n_iters, self.iter_start)
iiter_stop = numpy.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 = numpy.empty((nblocks,2), dtype=n_iters.dtype)
blocked_hists = numpy.zeros((nblocks,hists_ds.shape[1+idim]), dtype=hists_ds.dtype)
for iblock, istart in enumerate(xrange(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 = numpy.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 = -numpy.log(blocked_hists)
enehists = self._ener_zero(blocked_hists)
log10hists = numpy.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)
