"""
Bumps command line interface.
The functions in this module are used by the bumps command to implement
the command line interface. Bumps plugin models can use them to create
stand alone applications with a similar interface. For example, the
Refl1D application uses the following::
from . import fitplugin
import bumps.cli
bumps.cli.set_mplconfig(appdatadir='Refl1D')
bumps.cli.install_plugin(fitplugin)
bumps.cli.main()
After completing a set of fits on related systems, a post-analysis script
can use :func:`load_model` to load the problem definition and
:func:`load_best` to load the best value found in the fit. This can
be used for example in experiment design, where you look at the expected
parameter uncertainty when fitting simulated data from a range of experimental
systems.
"""
from __future__ import with_statement, print_function
__all__ = ["main", "install_plugin", "set_mplconfig", "config_matplotlib",
"load_model", "preview", "load_best", "save_best", "resynth"]
import sys
import os
import re
import warnings
import traceback
import shutil
import numpy as np
# np.seterr(all="raise")
from . import fitters
from .fitters import FitDriver, StepMonitor, ConsoleMonitor, CheckpointMonitor, nllf_scale
from .mapper import MPMapper, AMQPMapper, MPIMapper, SerialMapper
from .formatnum import format_uncertainty
from . import util
from . import initpop
from . import __version__
from . import plugin
from . import options
from .util import pushdir, push_python_path
[docs]
def install_plugin(p):
"""
Replace symbols in :mod:`bumps.plugin` with application specific
methods.
"""
for symbol in plugin.__all__:
if hasattr(p, symbol):
setattr(plugin, symbol, getattr(p, symbol))
[docs]
def load_model(path, model_options=None):
"""
Load a model file.
*path* contains the path to the model file.
*model_options* are any additional arguments to the model. The sys.argv
variable will be set such that *sys.argv[1:] == model_options*.
"""
from .fitproblem import load_problem
# Change to the target path before loading model so that data files
# can be given as relative paths in the model file. Add the directory
# to the python path (at the end) so that imports work as expected.
directory, filename = os.path.split(path)
with pushdir(directory):
# Try a specialized model loader
problem = plugin.load_model(filename)
if problem is None:
# print "loading",filename,"from",directory
# TODO: eliminate pickle!!
if filename.endswith('pickle'):
try:
import dill as pickle
except ImportError:
import pickle
# First see if it is a pickle
with open(filename, 'rb') as fd:
problem = pickle.load(fd)
else:
# Then see if it is a python model script
problem = load_problem(filename, options=model_options)
# Guard against the user changing parameters after defining the problem.
problem.model_reset()
problem.path = os.path.abspath(path)
if not hasattr(problem, 'title'):
problem.title = filename
problem.name, _ = os.path.splitext(filename)
problem.options = model_options
return problem
[docs]
def preview(problem, view=None):
"""
Show the problem plots and parameters.
"""
import pylab
problem.show()
problem.plot(view=view)
pylab.show()
[docs]
def save_best(fitdriver, problem, best, view=None):
"""
Save the fit data, including parameter values, uncertainties and plots.
*fitdriver* is the fitter that was used to drive the fit.
*problem* is a FitProblem instance.
*best* is the parameter set to save.
"""
# Make sure the problem contains the best value
# TODO: avoid recalculating if problem is already at best.
problem.setp(best)
# print "remembering best"
pardata = "".join("%s %.15g\n" % (name, value)
for name, value in zip(problem.labels(), problem.getp()))
open(problem.output_path + ".par", 'wt').write(pardata)
fitdriver.save(problem.output_path)
with util.redirect_console(problem.output_path + ".err"):
fitdriver.show()
fitdriver.plot(output_path=problem.output_path, view=view)
fitdriver.show()
# print "plotting"
PARS_PATTERN = re.compile(r"^(?P<label>.*) (?P<value>[^ ]*)\n$")
[docs]
def load_best(problem, path):
"""
Reload individual parameter values from a saved .par file.
If the label does not exist in the file, use the value from the model
as the default value. Ignore labels that do not exist in the model. In
that way we can load parameters from an old fit with minimal fuss, even
as we add, delete and move parameters in the model. If any parameters
are missing, set *problem.undefined* to the a boolean index of the
undefined parameters.
There is an interaction with --init=eps and the par file. If any parameters
are missing from the par file they will be randomized across the
entire parameter range using the equivalent of --init=lhs. That means
you can drop a # at the beginning of the line in the .par file
and that parameter will be shuffled on restart, with the remaining
parameters starting near the initial value.
"""
# WARNING: Labels are not unique! Need to track multiple instances of
# the same label.
if not os.path.isfile(path):
path = os.path.join(path, problem.name+".par")
if not os.path.isfile(path):
raise ValueError("Parameter file %s does not exist." % path)
labels = problem.labels()
targets = {label: [] for label in labels}
with open(path, 'rt') as fid:
for line in fid:
m = PARS_PATTERN.match(line)
label, value = m.group('label'), float(m.group('value'))
# Accumulate values for labels only if they appear in the model.
if label in targets:
targets[label].append(value)
# Populate model with named parameters in the order they occur in the
# parameter file. Identify the missing parameters if any, adding them
# to the the problem definition as an optional "undefined" attribute with
# one bit for each parameter. This ugly hack is to support a previous
# ugly hack in which undefined parameters are initialized with LHS but
# defined parameters are initialized with eps, cov or random.
# TODO: find a better way to "free" parameters on --pars.
# TODO: find a way to "free" parameters on --resume.
values, undefined = [], []
for label, default_value in zip(labels, problem.getp()):
remaining = targets[label]
is_empty = not remaining
# popping the next value from remaining modifies targets[label]
values.append(default_value if is_empty else remaining.pop(0))
undefined.append(is_empty)
problem.setp(np.asarray(values))
if any(undefined):
problem.undefined = np.asarray(undefined)
#CRUFT
recall_best = load_best
def store_overwrite_query_gui(path):
"""
Ask if store path should be overwritten.
Use this in a call to :func:`make_store` from a graphical user interface.
"""
import wx
msg = path + " already exists. Press 'yes' to overwrite, or 'No' to abort and restart with newpath"
msg_dlg = wx.MessageDialog(None, msg, 'Overwrite Directory',
wx.YES_NO | wx.ICON_QUESTION)
retCode = msg_dlg.ShowModal()
msg_dlg.Destroy()
if retCode != wx.ID_YES:
raise RuntimeError("Could not create path")
def store_overwrite_query(path):
"""
Ask if store path should be overwritten.
Use this in a call to :func:`make_store` from a command line interface.
"""
print(path, "already exists.")
print(
"Press 'y' to overwrite, or 'n' to abort and restart with --overwrite, --resume, or --store=newpath")
ans = input("Overwrite [y/n]? ")
if ans not in ("y", "Y", "yes"):
sys.exit(1)
def make_store(problem, opts, exists_handler):
"""
Create the store directory and populate it with the model definition file.
"""
# Determine if command line override
if opts.store:
problem.store = opts.store
if getattr(problem, 'store', None) is None:
raise RuntimeError(
"Need to specify '--store=path' on command line"
" or problem.store='path' in definition file.")
problem.output_path = os.path.join(problem.store, problem.name)
# Check if already exists
store_exists = os.path.exists(problem.output_path + '.par')
if not opts.overwrite and opts.resume is None and store_exists:
if opts.batch:
print(
problem.store + " already exists. Restart with --overwrite, --resume, or --store=newpath",
file=sys.stderr)
sys.exit(1)
exists_handler(problem.output_path)
# Create it and copy model
if not os.path.exists(problem.store):
os.mkdir(problem.store)
shutil.copy2(problem.path, problem.store)
def run_profiler(problem, steps):
"""
Model execution profiler.
Run the program with "--profiler --steps=N" to generate a function
profile chart breaking down the cost of evaluating N models.
"""
# Here is the findings from one profiling session::
# 23 ms total
# 6 ms rendering model
# 8 ms abeles
# 4 ms convolution
# 1 ms setting parameters and computing nllf
from .util import profile
p = initpop.random_init(
int(steps), initial=None, bounds=None, use_point=False,problem=problem)
# Note: map is an iterator in python 3
profile(lambda *args: list(map(*args)), problem.nllf, p)
def run_timer(mapper, problem, steps):
"""
Model execution timer.
Run the program with "--timer --steps=N" to determine the average
run time of the model. If --parallel is included, then the model
will be run in parallel on separate cores.
"""
import time
T0 = time.time()
steps = int(steps)
p = initpop.generate(
problem, init='random', pop=-steps, use_point=False)
if p.shape == (0,):
# No fitting parameters --- generate an empty population
p = np.empty((steps, 0))
mapper(p)
print("time per model eval: %g ms" % (1000 * (time.time() - T0) / steps,))
def start_remote_fit(problem, options, queue, notify):
"""
Queue remote fit.
"""
from jobqueue.client import connect
try:
from dill import dumps as dill_dumps
dumps = lambda obj: dill_dumps(obj, recurse=True)
except ImportError:
from pickle import dumps
data = dict(package='bumps',
version=__version__,
problem=dumps(problem),
options=dumps(options))
request = dict(service='fitter',
version=__version__, # fitter service version
notify=notify,
name=problem.title,
data=data)
server = connect(queue)
job = server.submit(request)
return job
# ==== Main ====
def initial_model(opts):
"""
Load and initialize the model.
*opts* are the processed command line options.
If --pars is in opts, then load the parameters from a .par file.
If --simulate is in opts, then generate random data from the model.
If --simrandom is in opts, then generate random data from a random model.
If --shake is in opts, then use a random initial state for the fit.
"""
if opts.seed is not None:
np.random.seed(opts.seed)
if opts.args:
problem = load_model(opts.args[0], opts.args[1:])
if opts.pars is not None:
load_best(problem, opts.pars)
if opts.simrandom:
problem.randomize()
if opts.simulate or opts.simrandom:
noise = None if opts.noise == "data" else float(opts.noise)
problem.simulate_data(noise=noise)
print("simulation parameters")
print(problem.summarize())
print("chisq at simulation", problem.chisq_str())
if opts.shake:
problem.randomize()
else:
problem = None
return problem
[docs]
def resynth(fitdriver, problem, mapper, opts):
"""
Generate maximum likelihood fits to resynthesized data sets.
*fitdriver* is a :class:`bumps.fitters.FitDriver` object with a fitter
already chosen.
*problem* is a :func:`bumps.fitproblem.FitProblem` object. It should
be initialized with optimal values for the parameters.
*mapper* is one of the available :mod:`bumps.mapper` classes.
*opts* is a :class:`bumps.options.BumpsOpts` object representing the command
line parameters.
"""
make_store(problem, opts, exists_handler=store_overwrite_query)
fid = open(problem.output_path + ".rsy", 'at')
fitdriver.mapper = mapper.start_mapper(problem, opts.args)
for i in range(opts.resynth):
problem.resynth_data()
best, fbest = fitdriver.fit()
scale, err = nllf_scale(problem)
print("step %d chisq %g" % (i, scale * fbest))
fid.write('%.15g ' % (scale * fbest))
fid.write(' '.join('%.15g' % v for v in best))
fid.write('\n')
problem.restore_data()
fid.close()
[docs]
def set_mplconfig(appdatadir):
r"""
Point the matplotlib config dir to %LOCALAPPDATA%\{appdatadir}\mplconfig.
"""
if hasattr(sys, 'frozen'):
if os.name == 'nt':
mplconfigdir = os.path.join(
os.environ['LOCALAPPDATA'], appdatadir, 'mplconfig')
elif sys.platform == 'darwin':
mplconfigdir = os.path.join(
os.path.expanduser('~/Library/Caches'), appdatadir, 'mplconfig')
else:
return # do nothing on linux
mplconfigdir = os.environ.setdefault('MPLCONFIGDIR', mplconfigdir)
if not os.path.exists(mplconfigdir):
os.makedirs(mplconfigdir)
[docs]
def config_matplotlib(backend=None):
"""
Setup matplotlib to use a particular backend.
The backend should be 'WXAgg' for interactive use, or 'Agg' for batch.
This distinction allows us to run in environments such as cluster computers
which do not have wx installed on the compute nodes.
This function must be called before any imports to pylab. To allow
this, modules should not import pylab at the module level, but instead
import it for each function/method that uses it. Exceptions can be made
for modules which are completely dedicated to plotting, but these modules
should never be imported at the module level.
"""
import matplotlib as mpl
# When running from a frozen environment created by py2exe, we will not
# have a range of backends available, and must set the default to WXAgg.
# With a full matplotlib distribution we can use whatever the user prefers.
if hasattr(sys, 'frozen'):
if 'MPLCONFIGDIR' not in os.environ:
raise RuntimeError(
r"MPLCONFIGDIR should be set to e.g., %LOCALAPPDATA%\YourApp\mplconfig")
if backend is None:
backend = 'WXAgg'
## CRUFT: check that backend is valid, trying alternates if an import fails
#if backend is None:
# backend = os.environ.get('MPLBACKEND', mpl.rcParams['backend'])
#import importlib
#for name in (backend, 'MacOSX', 'Qt5Agg', 'Qt4Agg', 'Gtk3Agg', 'TkAgg', 'WXAgg'):
# path = 'matplotlib.backends.backend_' + name.lower()
# try:
# importlib.import_module(path)
# backend = name
# break
# except ImportError:
# backend = None
# Specify the backend to use for plotting and import backend dependent
# classes. This must be done before importing pyplot to have an
# effect. If no backend is given, let pyplot use the default.
if backend is not None:
mpl.use(backend)
# Disable interactive mode so that plots are only updated on show() or
# draw(). The interactive function must be called before importing pyplot,
# otherwise it will have no effect.
mpl.interactive(False)
#configure the plot style
line_width = 1
pad = 2
font_family = 'Arial' if os.name == 'nt' else 'sans-serif'
font_size = 12
plot_style = {
'xtick.direction': 'in',
'ytick.direction': 'in',
'lines.linewidth': line_width,
'axes.linewidth': line_width,
'xtick.labelsize': font_size,
'ytick.labelsize': font_size,
'xtick.major.size': 5,
'ytick.major.size': 5,
'xtick.minor.size': 2.5,
'ytick.minor.size': 2.5,
'xtick.major.width': line_width,
'ytick.major.width': line_width,
'xtick.minor.width': line_width,
'ytick.minor.width': line_width,
'xtick.major.pad': pad,
'ytick.major.pad': pad,
'xtick.top': True,
'ytick.right': True,
'font.size': font_size,
'font.family': font_family,
'svg.fonttype': 'none',
'savefig.dpi': 100,
}
mpl.rcParams.update(plot_style)
def beep():
"""
Audio signal that fit is complete.
"""
if sys.platform == "win32":
try:
import winsound
winsound.MessageBeep(winsound.MB_OK)
except Exception:
pass
else:
print("\a", file=sys.__stdout__)
def run_command(c):
"""
Run an arbitrary python command.
"""
exec(c, globals())
def setup_logging():
"""Start logger"""
import logging
logging.basicConfig(level=logging.INFO)
# From http://stackoverflow.com/questions/22373927/get-traceback-of-warnings
# answered by mgab (2014-03-13)
# edited by Gareth Rees (2015-11-28)
def warn_with_traceback(message, category, filename, lineno,
file=None, line=None):
"""
Alternate warning printer which shows a traceback with the warning.
To use, set *warnings.showwarning = warn_with_traceback*.
"""
traceback.print_stack()
log = file if hasattr(file, 'write') else sys.stderr
log.write(warnings.formatwarning(message, category, filename, lineno, line))
[docs]
def main():
"""
Run the bumps program with the command line interface.
Input parameters are taken from sys.argv.
"""
# add full traceback to warnings
#warnings.showwarning = warn_with_traceback
if len(sys.argv) == 1:
sys.argv.append("-?")
# run command with bumps in the environment
if sys.argv[1] == '-m':
import runpy
sys.argv = sys.argv[2:]
runpy.run_module(sys.argv[0], run_name="__main__")
sys.exit(0)
elif sys.argv[1] == '-p':
import runpy
sys.argv = sys.argv[2:]
runpy.run_path(sys.argv[0], run_name="__main__")
sys.exit()
elif sys.argv[1] == '-c':
run_command(sys.argv[2])
sys.exit()
elif sys.argv[1] == '-i':
sys.argv = ["ipython", "--pylab"]
from IPython import start_ipython
sys.exit(start_ipython())
opts = options.getopts()
setup_logging()
if opts.edit:
from .gui.gui_app import main as gui
gui()
return
# Set up the matplotlib backend to minimize the wx/gui dependency.
# If no GUI specified and not editing, then use the default mpl
# backend for the python version.
if opts.batch or opts.remote or opts.noshow: # no interactivity
config_matplotlib(backend='agg')
else: # let preview use default graphs
config_matplotlib()
problem = initial_model(opts)
if problem is None:
print("\n!!! Model file missing from command line --- abort !!!.",
file=sys.stderr)
sys.exit(1)
# TODO: AMQP mapper as implemented requires workers started up with
# the particular problem; need to be able to transport the problem
# to the worker instead. Until that happens, the GUI shouldn't use
# the AMQP mapper.
if opts.mpi:
MPIMapper.start_worker(problem)
mapper = MPIMapper
elif opts.parallel != "" or opts.worker:
if opts.transport == 'amqp':
mapper = AMQPMapper
elif opts.transport == 'mp':
mapper = MPMapper
elif opts.transport == 'celery':
mapper = CeleryMapper
else:
raise ValueError("unknown mapper")
else:
mapper = SerialMapper
if opts.worker:
mapper.start_worker(problem)
return
if np.isfinite(float(opts.time)):
import time
start_time = time.time()
stop_time = start_time + float(opts.time)*3600
abort_test = lambda: time.time() >= stop_time
else:
abort_test = lambda: False
fitdriver = FitDriver(
opts.fit_config.selected_fitter, problem=problem, abort_test=abort_test,
**opts.fit_config.selected_values)
# Start fitter within the domain so that constraints are valid
clipped = fitdriver.clip()
if clipped:
print("Start value clipped to range for parameter", ", ".join(clipped))
if opts.time_model:
run_timer(mapper.start_mapper(problem, opts.args),
problem, steps=int(opts.steps))
elif opts.profile:
run_profiler(problem, steps=int(opts.steps))
elif opts.chisq:
if opts.cov:
fitdriver.show_cov()
print("chisq", problem.chisq_str())
#import pprint; pprint.pprint(problem.to_dict(), indent=2, width=272)
elif opts.preview:
if opts.cov:
fitdriver.show_cov()
preview(problem, view=opts.view)
elif opts.resynth > 0:
resynth(fitdriver, problem, mapper, opts)
elif opts.remote:
# Check that problem runs before submitting it remotely
# TODO: this may fail if problem requires remote resources such as GPU
print("initial chisq:", problem.chisq_str())
job = start_remote_fit(problem, opts,
queue=opts.queue, notify=opts.notify)
print("remote job:", job['id'])
else:
# Show command line arguments and initial model
print("#", " ".join(sys.argv), "--seed=%d"%opts.seed)
problem.show()
# Check that there are parameters to be fitted.
if not len(problem.getp()):
print("\n!!! No parameters selected for fitting---abort !!!\n",
file=sys.stderr)
sys.exit(1)
# Run the fit
if opts.resume == '-':
opts.resume = opts.store if os.path.exists(opts.store) else None
if opts.resume:
resume_path = os.path.join(opts.resume, problem.name)
else:
resume_path = None
make_store(problem, opts, exists_handler=store_overwrite_query)
# Redirect sys.stdout to capture progress
if opts.batch:
sys.stdout = open(problem.output_path + ".mon", "w")
# TODO: fix techical debt with checkpoint monitor implementation
# * The current checkpoint implementation is self-referential:
# checkpoint = lambda: save_best(fitdriver, ...)
# fitdriver.monitors = [..., CheckpointMonitor(checkpoint), ...]
# It is done this way because the checkpoint monitor needs the fitter
# so it can ask it to save state, but the fitter needs the list of
# monitors, including the checkpoint monitor, before it is run.
# * Figures are cumulative, with each checkpoint adding a new set
# * Figures are slow! Can they go into a separate thread? Can we
# have the problem cache the best value?
checkpoint_time = float(opts.checkpoint)*3600
def checkpoint(history):
problem = fitdriver.problem
## Use the following to save only the fitter state
fitdriver.fitter.save(problem.output_path)
## Use the following to save the fitter state plus all other
## plots and other output files. This won't work yet since
## plots are generated sequentially, with each checkpoint producing
## a completely new set of plots.
#best = history.point[0]
#save_best(fitdriver, problem, best, view=opts.view)
monitors = [ConsoleMonitor(problem)]
if checkpoint_time > 0 and np.isfinite(checkpoint_time):
mon = CheckpointMonitor(checkpoint, progress=checkpoint_time)
monitors.append(mon)
if opts.stepmon:
fid = open(problem.output_path + '.log', 'w')
mon = StepMonitor(problem, fid, fields=['step', 'value'])
monitors.append(mon)
fitdriver.monitors = monitors
#import time; t0=time.clock()
cpus = int(opts.parallel) if opts.parallel != "" else 0
fitdriver.mapper = mapper.start_mapper(problem, opts.args, cpus=cpus)
best, fbest = fitdriver.fit(resume=resume_path)
# print("time=%g"%(time.clock()-t0),file=sys.__stdout__)
# Note: keep this in sync with the checkpoint function above
save_best(fitdriver, problem, best, view=opts.view)
if opts.err or opts.cov:
fitdriver.show_err()
if opts.cov:
fitdriver.show_cov()
if opts.entropy:
fitdriver.show_entropy(opts.entropy)
mapper.stop_mapper(fitdriver.mapper)
# If in batch mode then explicitly close the monitor file on completion
if opts.batch:
sys.stdout.close()
sys.stdout = sys.__stdout__
# Display the plots
if not opts.batch and not opts.mpi and not opts.noshow:
beep()
import pylab
pylab.show()
# Allow "$python -m bumps.cli args" calling pattern
if __name__ == "__main__":
main()