def go(self):
self.data_reader.open('r')
pi = self.progress.indicator
pi.operation = 'Initializing'
with pi:
self.output_file = h5py.File(self.output_filename, 'w')
h5io.stamp_creator_data(self.output_file)
self.iter_start = self.iter_range.iter_start
self.iter_stop = self.iter_range.iter_stop
# Construct bin boundaries
self.construct_bins(self.parse_binspec(self.binspec))
for idim, (binbounds, midpoints) in enumerate(izip(self.binbounds, self.midpoints)):
self.output_file['binbounds_{}'.format(idim)] = binbounds
self.output_file['midpoints_{}'.format(idim)] = midpoints
# construct histogram
self.construct_histogram()
# Record iteration range
iter_range = self.iter_range.iter_range()
self.output_file['n_iter'] = iter_range
self.iter_range.record_data_iter_range(self.output_file['histograms'])
self.output_file.close()
def go(self):
self.data_reader.open('r')
pi = self.progress.indicator
pi.operation = 'Initializing'
with pi:
self.output_file = h5py.File(self.output_filename, 'w')
h5io.stamp_creator_data(self.output_file)
self.iter_start = self.iter_range.iter_start
self.iter_stop = self.iter_range.iter_stop
# Construct bin boundaries
self.construct_bins(self.parse_binspec(self.binspec))
for idim, (binbounds, midpoints) in enumerate(
zip(self.binbounds, self.midpoints)):
self.output_file['binbounds_{}'.format(idim)] = binbounds
self.output_file['midpoints_{}'.format(idim)] = midpoints
# construct histogram
self.construct_histogram()
# Record iteration range
iter_range = self.iter_range.iter_range()
self.output_file['n_iter'] = iter_range
self.iter_range.record_data_iter_range(
self.output_file['histograms'])
self.output_file.close()
def go(self):
pi = self.progress.indicator
pi.operation = 'Initializing'
with pi:
self.duration = self.kinetics_file['durations'][self.iter_start-1:self.iter_stop-1]
##Only select transition events from specified istate to fstate
mask = (self.duration['istate'] == self.istate) & (self.duration['fstate'] == self.fstate)
self.duration_dsspec = DurationDataset(self.kinetics_file['durations']['duration'], mask, self.iter_start)
self.wt_dsspec = DurationDataset(self.kinetics_file['durations']['weight'], mask, self.iter_start)
self.output_file = h5py.File(self.output_filename, 'w')
h5io.stamp_creator_data(self.output_file)
# Construct bin boundaries
self.construct_bins(self.parse_binspec(self.binspec))
for idim, (binbounds, midpoints) in enumerate(izip(self.binbounds, self.midpoints)):
self.output_file['binbounds_{}'.format(idim)] = binbounds
self.output_file['midpoints_{}'.format(idim)] = midpoints
# construct histogram
self.construct_histogram()
# Record iteration range
iter_range = numpy.arange(self.iter_start, self.iter_stop, 1, dtype=(numpy.min_scalar_type(self.iter_stop)))
self.output_file['n_iter'] = iter_range
self.output_file['histograms'].attrs['iter_start'] = self.iter_start
self.output_file['histograms'].attrs['iter_stop'] = self.iter_stop
self.output_file.close()
def open_files(self):
self.output_file = h5io.WESTPAH5File(self.output_filename, 'a', creating_program=True)
h5io.stamp_creator_data(self.output_file)
self.assignments_file = h5io.WESTPAH5File(self.assignments_filename, 'r')#, driver='core', backing_store=False)
self.kinetics_file = h5io.WESTPAH5File(self.kinetics_filename, 'r')#, driver='core', backing_store=False)
if not self.iter_range.check_data_iter_range_least(self.assignments_file):
raise ValueError('assignments data do not span the requested iterations')
def go(self):
pi = self.progress.indicator
pi.operation = 'Initializing'
with pi:
self.duration = self.kinetics_file['durations'][self.iter_start-1:self.iter_stop-1]
##Only select transition events from specified istate to fstate
mask = (self.duration['istate'] == self.istate) & (self.duration['fstate'] == self.fstate)
self.duration_dsspec = DurationDataset(self.kinetics_file['durations']['duration'], mask, self.iter_start)
self.wt_dsspec = DurationDataset(self.kinetics_file['durations']['weight'], mask, self.iter_start)
self.output_file = h5py.File(self.output_filename, 'w')
h5io.stamp_creator_data(self.output_file)
# Construct bin boundaries
self.construct_bins(self.parse_binspec(self.binspec))
for idim, (binbounds, midpoints) in enumerate(izip(self.binbounds, self.midpoints)):
self.output_file['binbounds_{}'.format(idim)] = binbounds
self.output_file['midpoints_{}'.format(idim)] = midpoints
# construct histogram
self.construct_histogram()
# Record iteration range
iter_range = numpy.arange(self.iter_start, self.iter_stop, 1, dtype=(numpy.min_scalar_type(self.iter_stop)))
self.output_file['n_iter'] = iter_range
self.output_file['histograms'].attrs['iter_start'] = self.iter_start
self.output_file['histograms'].attrs['iter_stop'] = self.iter_stop
self.output_file.close()
def open_files(self):
self.output_file = h5io.WESTPAH5File(self.output_filename, 'a', creating_program=True)
h5io.stamp_creator_data(self.output_file)
self.assignments_file = h5io.WESTPAH5File(self.assignments_filename, 'r')#, driver='core', backing_store=False)
self.kinetics_file = h5io.WESTPAH5File(self.kinetics_filename, 'r')#, driver='core', backing_store=False)
if not self.iter_range.check_data_iter_range_least(self.assignments_file):
raise ValueError('assignments data do not span the requested iterations')
def open_files(self):
self.output_file = h5io.WESTPAH5File(self.output_file,
'w',
creating_program=True)
h5io.stamp_creator_data(self.output_file)
opened_files = self.generate_file_list([self.west])
self.westH5 = opened_files[self.west]
def process_args(self, args):
self.progress.process_args(args)
self.assignments_file = h5io.WESTPAH5File(args.assignments, 'r')
self.data_reader.process_args(args)
with self.data_reader:
self.iter_range.process_args(args)
self.output_file = h5io.WESTPAH5File(args.output, 'w', creating_program=True)
h5io.stamp_creator_data(self.output_file)
if not self.iter_range.check_data_iter_range_least(self.assignments_file):
raise ValueError('assignments do not span the requested iterations')
self.do_compression = args.compression
def _do_1d_output(self, hist, idim, midpoints):
enehist = self._ener_zero(hist)
log10hist = numpy.log10(hist)
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['histogram'] = hist
if self.text_output_filename:
with textio.NumericTextOutputFormatter(
self.text_output_filename) as output_file:
output_file.write_header('source data: {} dimension {}'.format(
os.path.abspath(self.input_h5.filename), idim))
output_file.write_header('column 0: midpoint of bin')
output_file.write_header('column 1: probability in bin')
output_file.write_header('column 2: -ln P')
output_file.write_header('column 3: log10 P')
numpy.savetxt(
output_file,
numpy.column_stack([midpoints, hist, enehist, log10hist]))
if self.plot_output_filename:
if self.plotscale == 'energy':
plothist = enehist
label = r'$\Delta F(x)\,/\,kT$' + '\n' + r'$\left[-\ln\,P(x)\right]$'
elif self.plotscale == 'log10':
plothist = log10hist
label = r'$\log_{10}\ P(x)$'
else:
plothist = hist
label = r'$P(x)$'
pyplot.figure()
pyplot.plot(midpoints, plothist)
pyplot.xlim(self.dimensions[0].get('lb'),
self.dimensions[0].get('ub'))
if self.plotrange:
pyplot.ylim(*self.plotrange)
pyplot.xlabel(self.dimensions[0]['label'])
pyplot.ylabel(label)
if self.plottitle:
pyplot.title(self.plottitle)
if self.postprocess_function:
self.postprocess_function(plothist, midpoints, None)
pyplot.savefig(self.plot_output_filename)
def process_args(self, args):
self.progress.process_args(args)
self.assignments_file = h5io.WESTPAH5File(args.assignments, 'r')
self.data_reader.process_args(args)
with self.data_reader:
self.iter_range.process_args(args)
self.output_file = h5io.WESTPAH5File(args.output,
'w',
creating_program=True)
h5io.stamp_creator_data(self.output_file)
if not self.iter_range.check_data_iter_range_least(
self.assignments_file):
raise ValueError(
'assignments do not span the requested iterations')
self.do_compression = args.compression
def _do_1d_output(self, hist, idim, midpoints):
enehist = self._ener_zero(hist)
log10hist = numpy.log10(hist)
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['histogram'] = hist
if self.text_output_filename:
with textio.NumericTextOutputFormatter(self.text_output_filename) as output_file:
output_file.write_header('source data: {} dimension {}'.format(os.path.abspath(self.input_h5.filename),idim))
output_file.write_header('column 0: midpoint of bin')
output_file.write_header('column 1: probability in bin')
output_file.write_header('column 2: -ln P')
output_file.write_header('column 3: log10 P')
numpy.savetxt(output_file, numpy.column_stack([midpoints,hist, enehist, log10hist]))
if self.plot_output_filename:
if self.plotscale == 'energy':
plothist = enehist
label = r'$\Delta F(x)\,/\,kT$' +'\n' + r'$\left[-\ln\,P(x)\right]$'
elif self.plotscale == 'log10':
plothist = log10hist
label = r'$\log_{10}\ P(x)$'
else:
plothist = hist
label = r'$P(x)$'
pyplot.figure()
pyplot.plot(midpoints, plothist)
pyplot.xlim(self.dimensions[0].get('lb'), self.dimensions[0].get('ub'))
if self.plotrange:
pyplot.ylim(*self.plotrange)
pyplot.xlabel(self.dimensions[0]['label'])
pyplot.ylabel(label)
if self.plottitle:
pyplot.title(self.plottitle)
if self.postprocess_function:
self.postprocess_function(plothist, midpoints, None)
pyplot.savefig(self.plot_output_filename)
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)
def _do_2d_output(self, hist, idims, midpoints, binbounds):
enehist = self._ener_zero(hist)
log10hist = numpy.log10(hist)
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_dimensions'] = numpy.array(
idims, numpy.min_scalar_type(max(idims)))
output_h5.attrs['source_dimension_labels'] = numpy.array(
[dim['label'] for dim in self.dimensions])
for idim in idims:
output_h5['midpoints_{}'.format(idim)] = midpoints
output_h5['histogram'] = hist
if self.plot_output_filename:
if self.plotscale == 'energy':
plothist = enehist
label = r'$\Delta F(\vec{x})\,/\,kT$' + '\n' + r'$\left[-\ln\,P(x)\right]$'
elif self.plotscale == 'log10':
plothist = log10hist
label = r'$\log_{10}\ P(\vec{x})$'
else:
plothist = hist
plothist[~numpy.isfinite(plothist)] = numpy.nan
label = r'$P(\vec{x})$'
try:
vmin, vmax = self.plotrange
except TypeError:
vmin, vmax = None, None
pyplot.figure()
# Transpose input so that axis 0 is displayed as x and axis 1 is displayed as y
# pyplot.imshow(plothist.T, interpolation='nearest', aspect='auto',
# extent=(midpoints[0][0], midpoints[0][-1], midpoints[1][0], midpoints[1][-1]),
# origin='lower', vmin=vmin, vmax=vmax)
# The following reproduces the former calls to imshow and colorbar
norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
ax = pyplot.gca()
nui = NonUniformImage(ax,
extent=(midpoints[0][0], midpoints[0][-1],
midpoints[1][0], midpoints[1][-1]),
origin='lower',
norm=norm)
nui.set_data(midpoints[0], midpoints[1], plothist.T)
ax.images.append(nui)
ax.set_xlim(midpoints[0][0], midpoints[0][-1])
ax.set_ylim(midpoints[1][0], midpoints[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(self.dimensions[1]['label'])
pyplot.ylim(self.dimensions[1].get('lb'),
self.dimensions[1].get('ub'))
if self.plottitle:
pyplot.title(self.plottitle)
if self.postprocess_function:
self.postprocess_function(plothist, midpoints, binbounds)
if self.plot_contour:
pyplot.contour(midpoints[0], midpoints[1], plothist.T)
pyplot.savefig(self.plot_output_filename)
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)
def _do_2d_output(self, hist, idims, midpoints, binbounds):
enehist = self._ener_zero(hist)
log10hist = numpy.log10(hist)
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_dimensions'] = numpy.array(idims, numpy.min_scalar_type(max(idims)))
output_h5.attrs['source_dimension_labels'] = numpy.array([dim['label'] for dim in self.dimensions])
for idim in idims:
output_h5['midpoints_{}'.format(idim)] = midpoints
output_h5['histogram'] = hist
if self.plot_output_filename:
if self.plotscale == 'energy':
plothist = enehist
label = r'$\Delta F(\vec{x})\,/\,kT$' +'\n' + r'$\left[-\ln\,P(x)\right]$'
elif self.plotscale == 'log10':
plothist = log10hist
label = r'$\log_{10}\ P(\vec{x})$'
else:
plothist = hist
plothist[~numpy.isfinite(plothist)] = numpy.nan
label = r'$P(\vec{x})$'
try:
vmin, vmax = self.plotrange
except TypeError:
vmin, vmax = None, None
pyplot.figure()
# Transpose input so that axis 0 is displayed as x and axis 1 is displayed as y
# pyplot.imshow(plothist.T, interpolation='nearest', aspect='auto',
# extent=(midpoints[0][0], midpoints[0][-1], midpoints[1][0], midpoints[1][-1]),
# origin='lower', vmin=vmin, vmax=vmax)
# The following reproduces the former calls to imshow and colorbar
norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
ax = pyplot.gca()
nui = NonUniformImage(ax, extent=(midpoints[0][0], midpoints[0][-1], midpoints[1][0], midpoints[1][-1]),
origin='lower', norm=norm)
nui.set_data(midpoints[0], midpoints[1], plothist.T)
ax.images.append(nui)
ax.set_xlim(midpoints[0][0], midpoints[0][-1])
ax.set_ylim(midpoints[1][0], midpoints[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(self.dimensions[1]['label'])
pyplot.ylim(self.dimensions[1].get('lb'), self.dimensions[1].get('ub'))
if self.plottitle:
pyplot.title(self.plottitle)
if self.postprocess_function:
self.postprocess_function(plothist, midpoints, binbounds)
if self.plot_contour:
pyplot.contour(midpoints[0], midpoints[1],plothist.T)
pyplot.savefig(self.plot_output_filename)
