```
# -*- coding: utf-8 -*-
# Copyright (c) 2015, PyRETIS Development Team.
# Distributed under the LGPLv2.1+ License. See LICENSE for more info.
"""Methods for analysis of crossings for flux data.
Important methods defined here
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
analyse_flux (:py:func:`.analyse_flux`)
Run analysis for simulation flux data. This will calculate
the initial flux for a simulation.
"""
import numpy as np
from pyretis.analysis.analysis import running_average, block_error_corr
__all__ = ['analyse_flux']
[docs]def analyse_flux(fluxdata, settings):
"""Run the analysis on the given flux data.
This will run the flux analysis and collect the results into a
structure which is convenient for plotting and reporting the
results.
Parameters
----------
fluxdata : list of tuples of integers
The contents of this array is the data obtained from a MD
simulation for the fluxes.
settings : dict
This dict contains the settings for the analysis. Note that
this dictionary also needs some settings from the simulation,
in particular the number of cycles, the interfaces and
information about the time step.
Returns
-------
results : dict
This dict contains the results from the flux analysis.
The keys are defined in the `results` variable.
"""
end_step = settings['simulation']['endcycle']
time_step = settings['engine']['timestep']
interfaces = [i for i in settings['simulation']['interfaces']]
results = {'eff_cross': [], # effective crossings times
'ncross': None, # number of crossings
'neffcross': [], # number of effective crossings
'times': {}, # time spent in the different states
'flux': [], # store raw flux data
'runflux': [], # running average of flux
'errflux': [], # block error analysis
'interfaces': interfaces,
'totalcycle': end_step, # store total number of cycles
'cross_time': [], # steps per crossing
'neffc/nc': [], # Effective crossings per crossing
'pMD': [], #
'1-p': [], #
'teffMD': [], #
'corrMD': []} #
if len(fluxdata) < 1:
return results
ret = _effective_crossings(fluxdata, len(results['interfaces']), end_step)
results['eff_cross'] = ret[0]
results['ncross'] = ret[1]
results['neffcross'] = ret[2]
results['times'] = ret[3]
analysis = settings['analysis']
for i in range(len(results['interfaces'])):
time, ncross, flux = _calculate_flux(results['eff_cross'][i],
results['times']['OA'],
analysis['skipcross'],
time_step)
results['flux'].append(np.column_stack((time, ncross, flux)))
# now it's also a good time to obtain running averages etc.:
results['runflux'].append(running_average(flux))
block_error = block_error_corr(flux,
maxblock=analysis['maxblock'],
blockskip=analysis['blockskip'])
results['errflux'].append(block_error)
# do some additional statistics:
results['cross_time'] = [np.divide(float(end_step), float(neff))
for neff in results['neffcross']]
results['neffc/nc'] = [np.divide(float(neff), float(ncr)) for neff, ncr
in zip(results['neffcross'], results['ncross'])]
for flux, error in zip(results['runflux'], results['errflux']):
results['pMD'].append(flux[-1] * time_step)
results['1-p'].append(np.divide(float(1.0 - results['pMD'][-1]),
results['pMD'][-1]))
results['teffMD'].append(end_step * error[4]**2)
results['corrMD'].append(np.divide(results['teffMD'][-1],
results['1-p'][-1]))
return results
def _effective_crossings(fluxdata, nint, end_step):
"""Analyse flux data and obtain effective crossings.
Parameters
----------
fluxdata : list of tuples of ints
The contents of this array is the data obtained from a
``md-flux`` simulation.
nint : int
The number of interfaces used.
end_step : int
This is the last step done in the simulation.
Returns
-------
eff_cross : list of lists
`eff_cross[i]` is the effective crossings times for
interface `i`.
ncross : list of ints
`ncross[i]` is the number of crossings for interface `i`.
neffcross : list of ints
`neffcross[i]` is the number of effective crossings for
interface `i`.
time_in_state : dict
The time spent in the different states which are labelled with
the keys 'A', 'B', 'OA', 'OB'. 'O' is taken to mean the
'overall' state.
Note
----
We do here `intf - 1`. This is just to be compatible with the old
FORTRAN code where the interfaces are numbered 1, 2, 3 rather than
0, 1, 2. If this is to be changed in the future the `-1` can just
be removed. Such a change will also require changes to the writer
for flux data!
"""
# First line is used to determine if we start in B or A
overallstate_a = not (fluxdata[0][1] == 2 and fluxdata[0][2] < 0)
firstcross = [True] * nint
ncross = [0] * nint
neffcross = [0] * nint
eff_cross = [[] for _ in range(nint)]
end = {'A': 0, 'B': 0, 'OA': 0, 'OB': 0}
start = {'A': 0, 'B': 0, 'OA': 0, 'OB': 0}
time_in_state = {'A': 0, 'B': 0, 'OA': 0, 'OB': 0}
time, intf, sign = None, None, None
for (time, intf, sign) in fluxdata:
if sign > 0: # positive direction
if intf - 1 == 0: # moving out of A
end['A'] = time
time_in_state['A'] += (end['A'] - start['A'])
elif intf - 1 == 2: # moving into B
start['B'] = time
if overallstate_a: # if we came from A
end['OA'] = time
start['OB'] = time
time_in_state['OA'] += (end['OA'] - start['OA'])
overallstate_a = False
ncross[intf - 1] += 1
if firstcross[intf - 1]:
firstcross[intf - 1] = False
neffcross[intf - 1] += 1
eff_cross[intf - 1].append((time - time_in_state['OB'], time))
elif sign < 0:
if intf - 1 == 0: # moving into A
firstcross = [True] * nint
start['A'] = time
if not overallstate_a: # if we came from B
end['OB'] = time
start['OA'] = time
time_in_state['OB'] += (end['OB'] - start['OB'])
overallstate_a = not overallstate_a
elif intf - 1 == 2: # moving out of B
end['B'] = time
time_in_state['B'] += (end['B'] - start['B'])
# Now, just add up the remaining:
state = 'OA' if overallstate_a else 'OB'
time_in_state[state] += (end_step - start[state])
if intf - 1 == 0 and sign < 0:
# Note that the sign < 0 works for sign=None
time_in_state['A'] += (end_step - start['A'])
elif intf - 1 == 2 and sign > 0:
# Note that the sign > 0 works for sign=None
time_in_state['B'] += (end_step - start['B'])
return eff_cross, ncross, neffcross, time_in_state
def _calculate_flux(effective_cross, time_in_state, time_window, time_step):
"""Calculate the flux in different time windows.
Parameters
----------
effective_cross : list
The number of effective crossings, obtained from
``_effective_crossings``.
time_in_state : int
Time spent in over-all state ``A``.
time_window : int
This is the time window we consider for calculating the flux.
time_step : float
This is the time-step for the simulation.
Returns
-------
time : np.array
The times for which we have calculated the flux.
ncross : np.array
The number of crossings within a time window.
flux : np.array
The flux within a time window.
"""
max_windows = int(1.0 * time_in_state / time_window)
ncross = np.zeros(max_windows, dtype=np.int)
for crossing in effective_cross:
idx = int(np.floor((crossing[0] - 0.0) / time_window))
if idx >= max_windows:
idx = max_windows - 1
ncross[idx] += 1
flux = (1.0 * ncross) / (time_step * time_window)
time = np.arange(1, max_windows+1) * time_window
return time, ncross, flux
```

© Copyright 2018, The PyRETIS team.

Created using Sphinx 1.8.2.