# -*- coding: utf-8 -*-
# Copyright (c) 2019, PyRETIS Development Team.
# Distributed under the LGPLv2.1+ License. See LICENSE for more info.
"""Methods that will output results from the analysis functions.
The Methods defined here will also run the analysis and output
according to given settings.
Important methods defined here
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
analyse_file (:py:func:`.analyse_file`)
Method to analyse a file. For example, it can be used as
>>> from pyretis.inout.analysisio import analyse_file
>>> result, raw_data = analyse_file('cross', 'cross.txt', settings)
To output the results, for instance by plotting, one can do:
>>> figures = output_results(file_type, plotter, result, raw_data)
>>> txt_file = output_results(file_type, txt_plotter, result, raw_data)
run_analysis_files (:py:func:`.run_analysis_files`)
Methods to the analysis on a set of files. It will create output
that can be used for reporting.
"""
import logging
import os
from pyretis.core.units import create_conversion_factors
from pyretis.core.pathensemble import generate_ensemble_name
from pyretis.analysis import (analyse_flux, analyse_energies, analyse_orderp,
analyse_path_ensemble, match_probabilities,
retis_flux, retis_rate)
from pyretis.inout import print_to_screen
from pyretis.inout.formats.formatter import format_number
from pyretis.inout.plotting import create_plotter
from pyretis.inout.plotting import TxtPlotter
from pyretis.inout.report import generate_report
from pyretis.inout.settings import (SECTIONS, copy_settings)
from pyretis.inout.formats.pathensemble import PathEnsembleFile
from pyretis.inout.formats.energy import EnergyFile
from pyretis.inout.formats.order import OrderFile
from pyretis.inout.formats.cross import CrossFile
from pyretis.inout.simulationio import OUTPUT_TASKS
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
logger.addHandler(logging.NullHandler())
__all__ = ['analyse_file', 'run_analysis_files']
_ANALYSIS_FUNCTIONS = {'cross': analyse_flux,
'order': analyse_orderp,
'energy': analyse_energies,
'pathensemble': analyse_path_ensemble}
# Input files for analysis
_FILES = {'md-flux': {},
'md-nve': {},
'md': {},
'tis': {},
'tis-multiple': {},
'retis': {}}
# Add files
for key in ('cross', 'energy', 'order'):
_FILES['md-flux'][key] = OUTPUT_TASKS[key]['filename']
for key in ('energy',):
_FILES['md-nve'][key] = OUTPUT_TASKS[key]['filename']
_FILES['md'][key] = OUTPUT_TASKS[key]['filename']
for key in ('pathensemble',):
for key2 in ('tis-multiple', 'tis', 'retis'):
_FILES[key2][key] = OUTPUT_TASKS[key]['filename']
def run_analysis(settings):
"""Run a predefined analysis task.
Parameters
----------
settings : dict
Simulation settings and settings for the analysis.
Returns
-------
out : dict
A dictionary with the results from the analysis. This dict
can be used to generate a report.
"""
runners = {'retis': run_retis_analysis,
'tis': run_tis_analysis,
'tis-multiple': run_tis_analysis,
'md-flux': run_mdflux_analysis}
sim = settings['simulation']
sim_task = sim['task'].lower()
report_dir = settings['analysis'].get('report-dir', None)
plotter = create_plotter(settings['analysis']['plot'],
out_dir=report_dir)
backup = settings['output'].get('backup', SECTIONS['output']['backup'])
txt_plotter = TxtPlotter(settings['analysis']['txt-output'],
backup=backup,
out_dir=report_dir)
if sim_task in runners.keys():
runner = runners[sim_task]
return runner(settings, plotter, txt_plotter)
msgtxt = 'Unknown analysis task "{}" requested!'.format(sim_task)
logger.error(msgtxt)
raise ValueError(msgtxt)
def get_path_ensemble_files(ensemble, settings, detect,
interfaces):
"""Return files for a single path ensemble.
Here, we will return the files needed to analyse a single path
ensemble and we will also return settings which can be used for
the analysis.
Parameters
----------
ensemble : int
This is the integer representing the ensemble.
settings : dict
The settings to use for an analysis/simulation
detect : float or None
The interface use for detecting if a path is successful for not.
interfaces : list of floats
The interfaces used for this particular path simulation.
Returns
-------
local_settings : dict
This dict contains settings which can be used for a initial
flux analysis.
files : list of tuples
The tuples in this list are the files which can be analysed
further, using the settings in `out[0]`.
"""
sim_task = settings['simulation']['task'].lower()
lsetting = copy_settings(settings)
lsetting['simulation']['interfaces'] = interfaces
lsetting['tis']['detect'] = detect
lsetting['tis']['ensemble_number'] = ensemble
files = []
for file_type in _FILES[sim_task]:
filename = os.path.join(generate_ensemble_name(ensemble),
_FILES[sim_task][file_type])
if os.path.isfile(filename):
files.append((file_type, filename))
logger.debug('Adding file "%s" for analysis', filename)
else:
logger.debug('Could not find file %s', filename)
return lsetting, files
def get_path_simulation_files(sim_settings):
"""Set up for analysis of TIS and RETIS simulations.
For these kinds of simulations, we expect to analyse several
directories with raw-data. For TIS we expect to find a directory
with raw-data for the initial flux (named ``flux``) and the
directories for the path simulations (named ``001``, ``002`` etc.
for ensembles [0^+], [1^+] and so on). For RETIS, we expect to
find the same directories but with a ``000`` (for the ``[0^-]``
ensemble) rather than the ``flux`` directory.
Parameters
----------
sim_settings : dict
The settings used for the (RE)TIS simulation(s). These settings
are used to get the interfaces used and the path ensembles
defined by these interfaces.
Returns
-------
all_settings : list of dict
This dict in this list contains settings which can be used for
the analysis.
all_files : list of lists of tuples
`all_files[i]` is a list of files from path ensemble
simulation `i`. For TIS, `all_files[0]` should be the files
obtained in the initial flux simulation. These files can be
analysed using the settings in `all_settings[i]`.
"""
# Check if we can do flux analysis:
all_files, all_settings = [], []
interfaces = sim_settings['simulation']['interfaces']
reactant = interfaces[0]
product = interfaces[-1]
if sim_settings['simulation']['task'] == 'tis-multiple':
all_files.append(None)
all_settings.append(None)
else: # just add the 0 ensemble
detect = None
interface = [-float('inf'), reactant, reactant]
setts, files = get_path_ensemble_files(0, sim_settings, detect,
interface)
all_files.append(files)
all_settings.append(setts)
for i, middle in enumerate(interfaces[:-1]):
try:
detect = interfaces[i + 1]
except IndexError:
detect = product
interface = [reactant, middle, product]
setts, files = get_path_ensemble_files(i + 1, sim_settings, detect,
interface)
all_files.append(files)
all_settings.append(setts)
return all_settings, all_files
def print_value_error(heading, value, rel_error, level=None):
"""Print out the matched probabilities."""
val = format_number(value, 0.1, 100)
msgtxt = '{}: {}'.format(heading, val)
print_to_screen(msgtxt.strip(), level=level)
fmt_scale = format_number(rel_error * 100, 0.1, 100)
msgtxt = '(Relative error: {} %)'.format(fmt_scale.rstrip())
print_to_screen(msgtxt, level=level)
def run_single_tis_analysis(settings, plotter, txt_plotter):
"""Run the analysis for a single TIS ensemble.
Parameters
----------
settings : dict
The settings to use for an analysis/simulation.
plotter : object like :py:class:`.Plotter`
This is the object that handles the plotting.
txt_plotter : object like :py:class:`.Plotter`
This is the object that handles the text output.
Returns
-------
out : list or dict or similar
The output from the analysis.
"""
sim = settings['simulation']
tis = settings['tis']
sett, files = get_path_ensemble_files(tis['ensemble_number'],
settings,
tis['detect'],
sim['interfaces'])
msgtxt = 'Analysing ensemble {}'.format(tis['ensemble_number'])
print_to_screen(msgtxt, level='info')
print_to_screen()
result = run_analysis_files(sett, files, plotter, txt_plotter)
report_txt = generate_report('tis', result, output='txt')[0]
print_to_screen(''.join(report_txt))
print_to_screen()
return result
def run_tis_analysis(settings, plotter, txt_plotter):
"""Run the analysis for TIS.
Parameters
----------
settings : dict
The settings to use for an analysis/simulation.
plotter : object like :py:class:`.Plotter`
This is the object that handles the plotting.
txt_plotter : object like :py:class:`.Plotter`
This is the object that handles the text output.
Returns
-------
out : list or dict or similar
The output from the analysis.
"""
if settings['simulation']['task'] == 'tis':
return run_single_tis_analysis(settings, plotter, txt_plotter)
results = {'cross': None, 'pathensemble': [], 'matched': None}
all_settings, all_files = get_path_simulation_files(settings)
nens = len(all_settings) - 1
for i, (sett, files) in enumerate(zip(all_settings, all_files)):
if i == 0:
msgtxt = ('Initial flux is not calculated here.\n'
'Remember to calculate this separately!')
logger.info(msgtxt)
print_to_screen(msgtxt, level='warning')
else:
msgtxt = 'Analysing ensemble {} of {}'.format(i, nens)
print_to_screen(msgtxt, level='info')
print_to_screen()
result = run_analysis_files(sett, files, plotter, txt_plotter)
results['pathensemble'].append(result['pathensemble'])
report_txt = generate_report('tis', result,
output='txt')[0]
print_to_screen(''.join(report_txt))
print_to_screen()
# match probabilities:
out, fig, txt = analyse_and_output_matched(results['pathensemble'],
plotter, txt_plotter,
settings)
results['matched'] = {'out': out, 'figures': fig, 'txtfile': txt}
print_to_screen('Overall results', level='success')
print_to_screen('===============', level='success')
print_to_screen()
print_value_error('TIS Crossing probability',
out['prob'], out['relerror'], level='success')
return results
def run_retis_analysis(settings, plotter, txt_plotter):
"""Run the analysis for RETIS.
Parameters
----------
settings : dict
The settings to use for an analysis/simulation.
plotter : object like :py:class:`.Plotter`
This is the object that handles the plotting.
txt_plotter : object like :py:class:`.Plotter`
This is the object that handles the text output.
"""
units = settings['system']['units']
if 'unit-system' in settings:
create_conversion_factors(units, **settings['unit-system'])
else:
create_conversion_factors(units)
all_settings, all_files = get_path_simulation_files(settings)
results = {'cross': None,
'pathensemble': [],
'matched': None}
nens = len(all_settings) - 1
print_to_screen()
for i, (sett, files) in enumerate(zip(all_settings, all_files)):
msgtxt = 'Analysing ensemble {} of {}'.format(i, nens)
print_to_screen(msgtxt, level='info')
print_to_screen()
if i == 0:
result = run_analysis_files(sett, files, plotter, txt_plotter)
results['pathensemble0'] = result['pathensemble']
report_txt = generate_report('retis0', result, output='txt')[0]
print_to_screen(''.join(report_txt))
print_to_screen()
else:
result = run_analysis_files(sett, files, plotter, txt_plotter)
results['pathensemble'].append(result['pathensemble'])
report_txt = generate_report('tis', result,
output='txt')[0]
print_to_screen(''.join(report_txt))
print_to_screen()
# flux first:
time_subcycles = settings['engine'].get('subcycles', 1)
timestep = settings['engine']['timestep'] * time_subcycles
flux, flux_error = retis_flux(results['pathensemble0']['out'],
results['pathensemble'][0]['out'],
timestep)
results['flux'] = {'value': flux, 'error': flux_error,
'unit': units}
# match probabilities:
out, fig, txt = analyse_and_output_matched(results['pathensemble'],
plotter, txt_plotter,
settings,
flux=flux)
results['matched'] = {'out': out, 'figures': fig, 'txtfile': txt}
rate, rate_error = retis_rate(out['prob'], out['relerror'],
flux, flux_error)
results['rate'] = {'value': rate, 'error': rate_error,
'unit': units}
print_to_screen('Overall results', level='success')
print_to_screen('===============', level='success')
print_to_screen()
print_value_error('RETIS Crossing probability',
out['prob'], out['relerror'], level='success')
print_to_screen()
print_value_error('Initial flux (units 1/{})'.format(units), flux,
flux_error, level='success')
print_to_screen()
print_value_error('Rate constant (units 1/{})'.format(units), rate,
rate_error, level='success')
return results
def run_mdflux_analysis(settings, plotter, txt_plotter):
"""Run the analysis for the md flux simulation.
Parameters
----------
settings : dict
The settings to use for an analysis/simulation.
plotter : object like :py:class:`.Plotter`
This is the object that handles the plotting.
txt_plotter : object like :py:class:`.Plotter`
This is the object that handles the text output.
Returns
-------
out : list or dict or similar
The output from the analysis.
"""
sim = settings['simulation']
sim_task = sim['task']
files = []
for file_type in _FILES[sim_task]:
filename = _FILES[sim_task][file_type]
if os.path.isfile(filename):
files.append((file_type, filename))
msgtxt = 'Running analysis of a MD flux simulation...'
print_to_screen(msgtxt, level='info')
print_to_screen()
result = run_analysis_files(settings, files, plotter, txt_plotter)
report_txt = generate_report('md-flux', result, output='txt')[0]
print_to_screen(''.join(report_txt))
print_to_screen()
return result
[docs]def run_analysis_files(settings, files, plotter, txt_plotter):
"""Run the analysis on a collection of files.
Parameters
----------
settings : dict
This dict contains settings which dictate how the
analysis should be performed and it should also contain
information on how the simulation was performed.
files : list of tuples
This list contains the raw files to be analysed. The
tuples are on format ('filetype', 'filename').
plotter : object like :py:class:`.Plotter`
This is the object that handles the plotting.
txt_plotter : object like :py:class:`.Plotter`
This is the object that handles the text output.
Returns
-------
out: dict
The results from the analysis.
"""
results = {}
for (file_type, file_name) in files:
result, raw_data = analyse_file(file_type, file_name, settings)
figures = output_results(file_type, plotter, result, raw_data)
txt_file = output_results(file_type, txt_plotter, result, raw_data)
results[file_type] = {'out': result,
'figures': figures,
'txtfile': txt_file}
return results
def read_first_block(file_type, file_name):
"""Read the first block of data from a file.
This method will read the first block of data from a file and
immediately return.
Parameters
----------
file_type : string
A string used for selecting the file object to use here.
file_name : string
The file to open.
Returns
-------
out : numpy.array
The raw data read from the file.
"""
first_block = None
class_map = {'energy': EnergyFile, 'order': OrderFile, 'cross': CrossFile}
try:
klass = class_map[file_type]
except KeyError:
logger.error(
'Unknown file type "%s" requested for analysis.',
file_type
)
raise ValueError('Unknown file type requested in analysis.')
with klass(file_name, 'r') as fileobj:
for block in fileobj.load():
if first_block is None:
first_block = block
else:
logger.warning(
(
'Noticed a second block in the input file "%s".\n'
'This will be ignored by the flux analysis.\n'
'Are you are running the analysis with '
'the correct input?'
),
file_name
)
break
if first_block is None:
return None
return first_block['data']
def output_results(file_type, plotter, result, rawdata):
"""Plot the results.
Plotting might here refer to both figures and text.
Parameters
----------
file_type : string
This determines what we are going to plot.
plotter : object like :py:class:`.Plotter`
This is the object that handles the plotting.
result : list of lists or dicts
This contains the results from a specific analysis.
rawdata : list of floats, lists or objects
The raw data with analysis results.
Returns
-------
out : list of strings
These are the files that the plotter created.
"""
if file_type == 'cross':
return plotter.output_flux(result)
if file_type == 'order':
return plotter.output_orderp(result, rawdata)
if file_type == 'energy':
return plotter.output_energy(result, rawdata)
if file_type == 'pathensemble':
return plotter.output_path(result, rawdata)
return None
[docs]def analyse_file(file_type, file_name, settings):
"""Run analysis on the given file.
This function is included for convenience so that we can call an
analysis like ``analyse_file('cross', 'cross.txt')`` i.e. it should
automatically open the file and apply the correct analysis
according to a given file type. Here we return a function to do the
analysis, so we are basically wrapping one of the analysis
functions. This is done in case we wish to rerun the analysis but
with different settings for instance.
Parameters
----------
file_type : string
This is the type of file we are to analyse.
file_name : string
The file name to open.
settings : dict
This dict contains settings which dictate how the
analysis should be performed and information on how the
simulation was performed.
Returns
-------
results : list or dict
The output from the analysis
raw_data : list, numpy.array or another type of object
The raw data used in the analysis.
"""
function = _ANALYSIS_FUNCTIONS.get(file_type, None)
if function is None:
msgtxt = 'Unknown file type "{}" requested!'.format(file_type)
logger.error(msgtxt)
raise ValueError(msgtxt)
results, raw_data = None, None
if file_type in ('energy', 'order', 'cross'):
# Read the first block of raw data and analyse it:
raw_data = read_first_block(file_type, file_name)
results = function(raw_data, settings)
elif file_type == 'pathensemble':
# Create a PathEnsembleFile object to use for the analysis.
ensemble_settings = {
'ensemble_number': settings['tis']['ensemble_number'],
'interfaces': settings['simulation']['interfaces'],
'detect': settings['tis']['detect'],
}
raw_data = PathEnsembleFile(file_name, 'r', ensemble_settings)
results = function(raw_data, settings)
return results, raw_data
def analyse_and_output_matched(raw_data, plotter, txt_plotter,
settings=None, flux=None):
"""Analyse and output matched probability.
This will calculate the over-all crossing probability by combining
results from many path simulations.
Parameters
----------
raw_data : list, numpy.array or another type of object
The raw data used in the analysis.
plotter : object like :py:class:`.Plotter`
This is the object that handles the plotting.
txt_plotter : object like :py:class:`.Plotter`
This is the object that handles the text output.
settings : dict
This dictionary contains settings for the analysis.
Here we make use of the following keys from the
analysis section:
* `ngrid`: The number of grid points for calculating the
crossing probability as a function of the order parameter.
* `maxblock`: The max length of the blocks for the block error
analysis. Note that this will maximum be equal the half of the
length of the data, see `block_error` in `.analysis`.
* `blockskip`: Can be used to skip certain block lengths.
A `blockskip` equal to `n` will consider every n'th block up
to `maxblock`, i.e. it will use block lengths equal to `1`,
`1+n`, `1+2n`, etc.
* `bins`: The number of bins to use for creating histograms.
flux : float, optional
The computed flux to be used only in RETIS if the running average
of the computed rate is desired.
Returns
-------
out[0] : dict
This dict contains the results from the analysis
out[1] : list of dicts
A dictionary with the figure files created (if any).
out[2] : list of strings
A list with the text files created (if any).
"""
path_results, ensemble_names, detect = [], [], []
interface_left = None
for ensemble in raw_data:
path_results.append(ensemble['out'])
ensemble_names.append(ensemble['out']['ensemble'])
detect.append(ensemble['out']['detect'])
if interface_left is None:
interface_left = ensemble['out']['interfaces'][0]
result = match_probabilities(path_results, detect, settings)
if flux is not None:
result['overall-rrun'] = flux*result['overall-prun']
# for the figure, we add the A interface:
detect_plot = [interface_left] + detect
figures = plotter.output_matched_probability(ensemble_names,
detect_plot,
result)
outtxt = txt_plotter.output_matched_probability(ensemble_names,
detect,
result)
return result, figures, outtxt