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scripts.py
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scripts.py
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from collections.abc import Callable as _Callable
#!/usr/bin/env python
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Author: Jean-Christophe Fillion-Robin (jchris.fillionr @kitware.com)
# Copyright (c) 1997-2016 California Institute of Technology.
# Copyright (c) 2016-2024 The Uncertainty Quantification Foundation.
# License: 3-clause BSD. The full license text is available at:
# - https://github.com/uqfoundation/mystic/blob/master/LICENSE
__doc__ = """
functional interfaces for mystic's visual analytics scripts
"""
__all__ = ['model_plotter','log_reader','collapse_plotter','log_converter']
# globals
__quit = False
def _get_ext(file_type):
"""get extension corresponding to ('support', 'logfile', or archive type)
file_type: one of ('logfile', 'support', or archive type)
"""
import klepto.archives as kl
if file_type == 'support': ext = '.py'
elif file_type == 'logfile': ext = '.txt'
elif file_type in ('file_archive','dir_archive'): ext = '.db'
elif file_type in (kl.file_archive,kl.dir_archive): ext = '.db'
elif file_type in ('hdf_archive','hdfdir_archive'): ext = '.h5'
elif file_type in (kl.hdf_archive,kl.hdfdir_archive): ext = '.h5'
elif file_type in ('sql_archive','sqltable_archive'): ext = '.sql'
elif file_type in (kl.sql_archive,kl.sqltable_archive): ext = '.sql'
else: ext = '' #XXX: only gets here if is a new archive type
return ext
def _type_existing(readpath, format=None):
"""determine type ('logfile', 'support', or archive type) from readpath
readpath: the string path to the stored trajectories
format: type ('logfile', 'support', or klepto.archive type) of readpath
"""
import os
if not os.path.exists(readpath):
msg = "File not found at: {0}".format(readpath)
raise FileNotFoundError(msg)
if _issupport(readpath, guess=False):
file_in = 'support'
if format not in ['support', None]:
msg = "{0} format invalid for {1}".format(format, readpath)
raise ValueError(msg)
elif _islogfile(readpath, guess=False):
file_in = 'logfile'
if format not in ['logfile', None]:
msg = "{0} format invalid for {1}".format(format, readpath)
raise ValueError(msg)
else:
if format is None:
file_in = _archive_inferred(readpath) #XXX: guess based on extension
else:
if format in ['logfile', 'support']:
msg = "{0} format invalid for {1}".format(format, readpath)
raise ValueError(msg)
file_in = _type_inferred(readpath, format)
return file_in
def _type_inferred(writepath=None, format=None):
"""guess ('logfile', 'support', or archive type) from format and writepath
writepath: the string path at which to write the trajectories
format: type ('logfile', 'support', or klepto.archive type) for writepath
"""
if writepath is None:
if format is None:
msg = 'either a format or writepath must be provided'
raise ValueError(msg)
if format is None:
import os
guess = not os.path.exists(writepath)
if _issupport(writepath, guess=guess):
file_out = 'support'
elif _islogfile(writepath, guess=guess):
file_out = 'logfile'
else:
file_out = _archive_inferred(writepath) #FIXME: is always guess
else:
import klepto.archives as kl
archives = kl.__all__[:]
[archives.remove(i) for i in ('cache','dict_archive','null_archive')]
if format in ['support','logfile'] + [kl.__dict__[a] for a in archives]:
file_out = format
elif format in archives:
file_out = kl.__dict__[format]
else:
msg = 'unknown archive format: {0}'.format(format)
raise ValueError(msg)
return file_out
def _archive_inferred(archivepath):
"""return archive class object, inferred from the archivepath extension
archivepath: the string file path to the archive
"""
import os
import klepto.archives as ka
SQL_EXT = ('.sql','.sqlite','.mdf')
ext = os.path.splitext(archivepath)[-1]
if ext in SQL_EXT or 'sql' in ext or ext.startswith(('.sq','.mysq','.pg','.post')):
return ka.sqltable_archive if _isdir(archivepath) else ka.sql_archive
HDF_EXT = ('.hdf','.h5','.hdf5')
if ext in HDF_EXT or 'hdf' in ext or ext.startswith('.h'):
return ka.hdfdir_archive if _isdir(archivepath) else ka.hdf_archive
#DBX_EXT = ('.db','.ar','.klp','.txt')
return ka.dir_archive if _isdir(archivepath) else ka.file_archive
def _logfile_to_support(logfile, support=None): #.txt -> .py
"""convert logfile to support file
logfile: the string path to the 3-column log file to read
support: the str path at which to write the support file
logfile is written with a LoggingMonitor [extension should be .txt]
support is read with read_history [extension should be .py]
if support is None, support name will be derived from logfile
"""
if support is None:
import os
support = os.path.splitext(os.path.basename(logfile))[0]+'.py'
from mystic.monitors import Monitor
from mystic.munge import logfile_reader, write_support_file
m = Monitor()
step, m._x, m._y = logfile_reader(logfile, iter=True)
if len(step) and len(step[0]) > 1:
m._id = [j for i,j in step]
write_support_file(m, support)
return
def _support_to_logfile(support, logfile=None): #.py -> .txt
"""convert support file to log file
support: the str path to the importable support file
logfile: the string path to the 3-column log file to write
logfile is written with a LoggingMonitor [extension should be .txt]
support is read with read_history [extension should be .py]
if logfile is None, logfile name will be derived from support file
"""
if logfile is None:
import os
logfile = os.path.splitext(os.path.basename(support))[0]+'.txt'
import numpy as np
from mystic.munge import read_import
from mystic.monitors import LoggingMonitor
ids,params,cost = read_import(support,'id','params','cost')
params = np.array(params).reshape(len(params),-1).T.tolist()
if not hasattr(ids, '__len__'): #XXX: ids is not 'processed'
ids = [ids]*len(cost)
id = max(set(ids)) or 0
from numbers import Integral
if not isinstance(id, Integral): id = 0
m = [LoggingMonitor(filename=logfile) for i in range(id + 1)]
if ids is None or isinstance(ids, Integral):
[m[ids or 0](p,c,id=ids) for (p,c) in zip(params,cost)]
elif id == 0: #NOTE: ids could be non-int
[m[0](p,c,id=i) for (p,c,i) in zip(params,cost,ids)]
else: #NOTE: m[None] -> m[0]
[m[i or 0](p,c,id=i) for (p,c,i) in zip(params,cost,ids)]
return
def _archive_to_logfile(archive, logfile=None, type=None, iter=False):
"""convert cached archive to log file
archive: the str path to the archive to read
logfile: the string path to the 3-column log file to write
type: the klepto archive type
iter: if True, include (iter,ids) tuple in archive keys
logfile is written with a LoggingMonitor [extension should be .txt]
archive is read with mystic.cache.archive.read [extension is .db, .h5, ...]
if logfile is None, logfile name will be derived from archive
if type is None, type will be derived from extension of archive
if iter is False, archive doesn't preserve "repeat" entries in log
(however, if iter is True, archive format is incompatible with mystic.cache)
""" #FIXME: iter=True should be default, and used in mystic.cache
import os
if not os.path.exists(archive):
msg = "File not found at: {0}".format(archive)
raise FileNotFoundError(msg)
if logfile is None:
logfile = os.path.splitext(os.path.basename(archive))[0]+'.txt'
import mystic.cache as mc
from mystic.monitors import LoggingMonitor
source = mc.archive.read(archive, type=type)
cost = list(source.values())
if iter:
step, params = list(zip(*source.keys()))
params = list(list(k) for k in params)
step = [i[-1] if len(i) == 2 else None for i in step]
id = max(set(step)) or 0
from numbers import Integral
if not isinstance(id, Integral): id = 0
m = [LoggingMonitor(filename=logfile) for i in range(id + 1)]
if id == 0: #NOTE: ids could be non-int
[m[i or 0](p,c,id=i) for (p,c,i) in zip(params,cost,step)]
else:
[m[i or 0](p,c,id=i) for (p,c,i) in zip(params,cost,step)]
else:
params = list(list(k) for k in source.keys())
step = [None]*len(cost)
# no ids, so only write with a single monitor
m = LoggingMonitor(filename=logfile)
[m(p,c) for (p,c) in zip(params,cost)]
return
def _archive_to_support(archive, support=None, type=None, iter=False):
"""convert cached archive to support file
archive: the str path to the archive to read
support: the str path at which to write the support file
type: the klepto archive type
iter: if True, include (iter,ids) tuple in archive keys
archive is read with mystic.cache.archive.read [extension is .db, .h5, ...]
support is read with read_history [extension should be .py]
if support is None, support name will be derived from archive
if type is None, type will be derived from extension of archive
if iter is False, archive doesn't preserve "repeat" entries in log
(however, if iter is True, archive format is incompatible with mystic.cache)
""" #FIXME: iter=True should be default, and used in mystic.cache
import os
if not os.path.exists(archive):
msg = "File not found at: {0}".format(archive)
raise FileNotFoundError(msg)
if support is None:
support = os.path.splitext(os.path.basename(archive))[0]+'.py'
import mystic.cache as mc
source = mc.archive.read(archive, type=type)
from mystic.monitors import Monitor
from mystic.munge import write_support_file
m = Monitor()
if iter:
m._id, m._x = list(zip(*source.keys()))
m._x = list(list(k) for k in m._x)
m._id = [i[-1] if len(i) == 2 else None for i in m._id]
else:
m._x = list(list(k) for k in source.keys())
m._y = list(source.values())
write_support_file(m, support)
return
def _logfile_to_archive(logfile, archive=None, type=None, iter=False):
"""convert log file to cached archive
archive: the str path to the archive to read
logfile: the string path to the 3-column log file to write
type: the klepto archive type
iter: if True, include (iter,ids) tuple in archive keys
archive is read with mystic.cache.archive.read [extension is .db, .h5, ...]
logfile is written with a LoggingMonitor [extension should be .txt]
if archive is None, archive name will be derived from logfile
if type is None, type will be derived from extension of archive
if iter is False, archive doesn't preserve "repeat" entries in log
(however, if iter is True, archive format is incompatible with mystic.cache)
""" #FIXME: iter=True should be default, and used in mystic.cache
try:
type_out = _type_inferred(archive, format=type)
except ValueError:
type_out = ''
if isinstance(type_out, str): # then oops... use the default
import klepto.archives as kl
type_out = kl.dir_archive if _isdir(archive) else kl.file_archive
if archive is None:
import os
ext = _get_ext(type_out)
archive = os.path.splitext(os.path.basename(logfile))[0]+ext
from mystic.munge import logfile_reader
import mystic.cache as mc
a = mc.archive.read(archive, type=type_out)
func = mc.cached(archive=a)(lambda param,**kwds: kwds['cost'])
if iter:
step, params, cost = logfile_reader(logfile, iter=True)
[func((i,tuple(p)), cost=c) for (i,p,c) in zip(step,params,cost)]
else:
params, cost = logfile_reader(logfile, iter=False)
[func(p, cost=c) for (p,c) in zip(params,cost)]
return
def _support_to_archive(support, archive=None, type=None, iter=False):
"""convert support file to cached archive
support: the str path to read the support file
archive: the str path at which to write the archive
type: the klepto archive type
iter: if True, include (iter,ids) tuple in archive keys
support is read with read_history [extension should be .py]
archive is read with mystic.cache.archive.read [extension is .db, .h5, ...]
if archive is None, archive name will be derived from support
if type is None, type will be derived from extension of archive
if iter is False, archive doesn't preserve "repeat" entries in log
(however, if iter is True, archive format is incompatible with mystic.cache)
""" #FIXME: iter=True should be default, and used in mystic.cache
try:
type_out = _type_inferred(archive, format=type)
except ValueError:
type_out = ''
if isinstance(type_out, str): # then oops... use the default
import klepto.archives as kl
type_out = kl.dir_archive if _isdir(archive) else kl.file_archive
if archive is None:
import os
ext = _get_ext(type_out)
archive = os.path.splitext(os.path.basename(support))[0]+ext
import numpy as np
from mystic.munge import read_import
import mystic.cache as mc
a = mc.archive.read(archive, type=type_out)
func = mc.cached(archive=a)(lambda param,**kwds: kwds['cost'])
if iter:
step,params,cost = read_import(support,'id''params','cost')
params = np.array(params).reshape(len(params),-1).T.tolist()
[func((i,tuple(p)), cost=c) for (i,p,c) in zip(step,params,cost)]
else:
params,cost = read_import(support,'params','cost')
params = np.array(params).reshape(len(params),-1).T.tolist()
[func(p, cost=c) for (p,c) in zip(params,cost)]
return
def _islogfile(filepath, guess=True):
"""return True if the filepath refers to a LoggingMonitor logfile
is False if filepath doesn't exist, or guess based on filepath extension
"""
if _issupport(filepath, guess): return False
if _isdir(filepath, guess=False): return False
import os
if os.path.exists(filepath):
from mystic.munge import logfile_reader
try:
logfile_reader(filepath)
return True
except: # SyntaxError, UnicodeDecodeError
return False
# file DNE, so guess from the extension
LOG_EXT = ('.txt','.log','.mon')
ext = os.path.splitext(filepath)[-1]
if ext in LOG_EXT or 'log' in ext or 'mon' in ext: return True
#DBX_EXT = ('.db','.ar','.klp','')
return False
def _issupport(filepath, guess=True):
"""return True if the filepath refers to a file written in 'support' format
is False if filepath doesn't exist, or guess based on filepath extension
"""
import os
if os.path.exists(filepath):
from mystic.munge import read_raw_file
try:
return not read_raw_file(filepath).count(None)
except RuntimeError:
return False
if not guess: return False
return os.path.splitext(filepath)[-1].startswith('.py')
def _isarchive(filepath, guess=True):
"""return True if the filepath refers to a cached archive
is False if filepath doesn't exist, or guess based on filepath extension
"""
if _issupport(filepath, guess): return False
if _islogfile(filepath, guess): return False
return guess #XXX: by default
def _isdir(filepath, guess=True):
"""return True if the filepath refers to a directory
is False if filepath doesn't exist, or guess based on filepath extension
"""
import os
if os.path.exists(filepath):
if os.path.islink(filepath):
filepath = os.readline(filepath)
return os.path.isdir(filepath)
if not guess: return False
# file DNE, so guess it's a file if it has an extension
return not os.path.splitext(filepath)[-1]
def log_converter(readpath, writepath=None, **kwds):
"""
convert between cached archives, convergence logfiles, and support logfiles
Available from the command shell as::
mystic_log_converter readpath (writepath) [options]
or as a function call::
mystic.log_converter(readpath, writepath=None, **options)
Args:
readpath (str): path of the logfile (e.g ``paramlog.py``).
writepath (str, default=None): path of converted file (e.g. ``log.txt``).
Returns:
None
Notes:
- If *writepath* is None, write file with derived name to current directory.
- The option *format* takes a string name of the file format at writepath.
Available formats are ('logfile', 'support', or a klepto.archive type).
"""
import shlex
from io import StringIO
global __quit
__quit = False
_iter = False #FIXME: if True, breaks current format of mystic.cache
#FIXME: if False, archive doesn't store (iter,ids), thus no repeated points
instance = None
# handle the special case where list is provided by sys.argv
if isinstance(readpath, (list,tuple)) and not kwds:
cmdargs = readpath # (above is used by script to parse command line)
elif isinstance(readpath, str) and not kwds:
cmdargs = shlex.split(readpath)
# 'everything else' is essentially the functional interface
else:
cmdargs = kwds.get('kwds', '')
if not cmdargs:
format = kwds.get('format', None)
# process "commandline" arguments
cmdargs = ''
cmdargs += '' if format is None else '--format={} '.format(format)
else:
cmdargs = ' ' + cmdargs
if isinstance(readpath, str):
cmdargs = readpath.split() + shlex.split(cmdargs)
else: # special case of passing in monitor instance
instance = readpath
cmdargs = shlex.split(cmdargs)
#XXX: replace with 'argparse'?
from optparse import OptionParser
def _exit(self, errno=None, msg=None):
global __quit
__quit = True
if errno or msg:
msg = msg.split(': error: ')[-1].strip()
raise IOError(msg)
OptionParser.exit = _exit
parser = OptionParser(usage=log_converter.__doc__.split('\n\nOptions:')[0])
parser.add_option("-f","--format",action="store",dest="format",\
metavar="STR",default=None,
help="format of convergence archive to write")
# import sys
# if 'mystic_log_converter.py' not in sys.argv:
f = StringIO()
parser.print_help(file=f)
f.seek(0)
if 'Options:' not in log_converter.__doc__:
log_converter.__doc__ += '\nOptions:%s' % f.read().split('Options:')[-1]
f.close()
try:
parsed_opts, parsed_args = parser.parse_args(cmdargs)
except UnboundLocalError:
pass
if __quit: return
try: # get path to archive to read
if instance:
raise NotImplementedError("cannot read monitor or file object")
else:
readpath = parsed_args[0]
except:
raise IOError("please provide path to read convergence log")
try: # get the path of the archive to write
writepath = parsed_args[1] # e.g. 'log.txt'
if "None" == writepath: writepath = None
except:
writepath = None
try: # format of the archive to write
format = parsed_opts.format # e.g. 'logfile'
if "None" == format: format = None
except:
format = None
#TODO: enable read-and-write of monitor, _iter as option, other options?
if isinstance(format, str) and "archive" in format:
import klepto.archives as kl
format = kl.__dict__[format]
file_in = _type_existing(readpath)
#if isinstance(file_in, str): # logfile or support
file_out = _type_inferred(writepath, format=format)
#else: # readpath is archive, so redo with format hint
# file_in = _type_inferred(readpath, format=format)
# file_out = _type_inferred(writepath)
# writepath in curdir, base derived from readpath name, with new format
if writepath is None:
import os
ext = _get_ext(file_out)
writepath = os.path.splitext(os.path.basename(readpath))[0]+ext
type_in = type_out = None
if not isinstance(file_in, str):
file_in,type_in = 'archive',file_in
if not isinstance(file_out, str):
file_out,type_out = 'archive',file_out
if file_in == file_out:
if file_in == 'archive':
msg = 'either a logfile or a support file is required'
else:
msg = 'file types cannot be identical'
raise TypeError(msg)
convert = eval("_{0}_to_{1}".format(file_in,file_out))
if type_in:
convert(readpath, writepath, type=type_in, iter=_iter)
elif type_out:
convert(readpath, writepath, type=type_out, iter=_iter)
else:
convert(readpath, writepath)
return
def _visual_filter(bounds, x, z=None, rtol=1e-8, ptol=1e-8):
"""apply a visual filter specified by bounds to the data within the monitor
bounds: a string specifying bounds (e.g. "0:1:.1, 0:1:.1, .5, .5, .5")
x: an array of shape (npts, params) with one param per bound
z: an array of shape (npts,) of cost
rtol: float (or list[float]) of max distance beyond range defined in bounds
ptol: float (or list[float]) of max distance from fixed plane in bounds
returns (x,z) filtered by ptol and rtol within the defined bounds
"""
# Possible input for (x,y):
# paramlog.py: x,y => (1,xi,N,1),(1,N)
# log.txt: x,y => (N,xi),(N,)
# multilog.txt: x,y => (i,j),(i,); yi is list[float], yi => (Ni,); j => xi
# xij is list[tuple[float]], xij => (Ni,) of length-1 tuple
_x = getattr(x, '_x', x) # params (x)
_y = x._y if z is None else z # cost (f(x))
select, spec, mask = _parse_input(bounds)
import numpy as np
if rtol is not None:
minmax = [s.strip().split(':')[:2] for s in spec.split(',')]
minmax = np.array([tuple(float(i) for i in mm) for mm in minmax]).T
else:
minmax = (-np.inf,np.inf)
# logical_and for distance within tolerance of selected cuts into hypercube
_x = np.array(_x)
xshape = _x.shape
_y = np.array(_y)
#yshape = _y.shape
iterate = _y.dtype is np.dtype('O')
reshape = True if (iterate or len(xshape) > 2) else False
if iterate: # 2D ndarray of lists of 1D tuple
_x = [np.array([np.array(i) for i in xi]) for xi in _x]
_y = [np.array(yi) for yi in _y]
elif reshape: # 4D ndarray
_x = list(_x)
_y = list(_y)
else: # 2D ndarray
_x = [_x]
_y = [_y]
# we can now iterate over _x,_y in all cases (iterate,reshape are same)
for i,(xi,yi) in enumerate(zip(_x,_y)):
ok = True
if reshape:
xi = xi.squeeze().T
yi = yi.squeeze().T
if ptol is not None:
ok = (abs(xi[:,mask.keys()] - mask.values()) < ptol).all(axis=1)
# logical_and for points within tolerance of selected bounds
if rtol is not None:
ok = (minmax[0] - rtol <= xi[:,select]).all(axis=1) & (xi[:,select] <= minmax[1] + rtol).all(axis=1) & ok
if ok is not True: # skip filtering when all are valid
xi = xi[ok]
#ALT: yi[ok] = np.nan
# apply same filter to cost
yi = yi[ok]
#ALT: yi[ok] = np.nan
# reshape, then save to ith element of _x,_y
# new shape is currently (-1,xi), (-1,) where N=-1
if reshape:
_x[i] = xi[None].T.tolist() # (xi,N,1)
_y[i] = yi.T.tolist()
else:
_x[i] = xi
_y[i] = yi
del xi,yi
if not reshape:
_x = _x[0]
_y = _y[0]
# return _x,_y (unless a monitor was provided)
if z is not None:
return _x, _y
# if a monitor was provided, return a monitor
m = x.__class__()
m._x = _x
m._y = _y
m._id = x._id[:] if ok is True else np.array(x._id)[ok].tolist()
#ALT: m._id = x._id
m._info = x._info[:]
# put rtol and ptol into a single sequence (for printing in info)
tol = np.zeros(len(mask)+len(select))
tol[mask.keys()] = ptol
tol[select] = rtol
m.info('FILTERED: tol=%s on "%s"' % (str(tol), bounds))
m.k = x.k #XXX: copy?
m._npts = x._npts #XXX: copy?
m.label = x.label #XXX: copy?
return m
#XXX: better if reads single id only? (e.g. same interface as read_history)
def _get_history(source, ids=None):
"""get params and cost from the given source
source is the name of the trajectory logfile (or solver instance)
if provided, ids are the list of 'run ids' to select
"""
try: # if it's a logfile, it might be multi-id
from mystic.munge import read_trajectories
step, param, cost = read_trajectories(source, iter=True)
except: # it's not a logfile, so read and convert from support format
from mystic.munge import read_history
step, param, cost = read_history(source, iter=True)
import numpy as np
param = np.array(param).reshape(len(param),-1).T.tolist()
# split (i,id) into iteration and id
if step: #XXX: ignore non-intger ids
from numbers import Integral
multinode = len(step[0]) - 1 if isinstance(step[0][-1], Integral) else 0
else: multinode = 0 #XXX: no step info, so give up
if multinode: id = [(i[1] or 0) for i in step]
else: id = [0 for i in step] #FIXME: hardwired to 0
if ids is not None:
maxid = max(id)+1
ids = [(maxid+i if i < 0 else i) for i in ids]
params = [[] for i in range(max(id) + 1)]
costs = [[] for i in range(len(params))]
# populate params for each id with the corresponding (param,cost)
for i in range(len(id)):
if ids is None or id[i] in ids: # take only the selected 'id'
params[id[i]].append(param[i])
costs[id[i]].append(cost[i])
params = [r for r in params if len(r)] # only keep selected 'ids'
costs = [r for r in costs if len(r)] # only keep selected 'ids'
# convert to support format
from mystic.munge import raw_to_support
for i in range(len(params)):
params[i], costs[i] = raw_to_support(params[i], costs[i])
return params, costs
def _get_instance(location, *args, **kwds):
"""given the import location of a model or model class, return the model
args and kwds will be passed to the constructor of the model class
"""
globals = {}
package, target = location.rsplit('.',1)
code = "from {0} import {1} as model".format(package, target)
code = compile(code, '<string>', 'exec')
exec(code, globals)
model = globals['model']
import inspect
if inspect.isclass(model):
model = model(*args, **kwds)
return model
def _parse_tol(tol, select=None):
"""parse 'tol' string into 'selected' and 'masked'
tol specifies the max distance from the plotted surface to plotted data
select contains the dimension specifications on which to plot
Examples:
>>> selected, masked = _parse_tol(".05, .1, .1, .5", [0,3])
>>> selected
(.05, .5)
>>> masked
(.1, .1)
>>> selected, masked = _parse_tol(".1")
>>> selected
.1
>>> masked
.1
"""
if tol is None:
return None,None
if type(tol) is str:
selected = eval(tol)
else:
selected = tol
if hasattr(selected, '__len__'):
masked = []
selected = [j for i,j in enumerate(selected) if i in select or masked.append(j)]
return tuple(selected),tuple(masked)
return selected,selected
def _parse_input(option):
"""parse 'option' string into 'select', 'axes', and 'mask'
select contains the dimension specifications on which to plot
axes holds the indices of the parameters selected to plot
mask is a dictionary of the parameter indices and fixed values
Examples:
>>> select, axes, mask = _parse_input("-1:10:.1, 0.0, 5.0, -50:50:.5")
>>> select
[0, 3]
>>> axes
"-1:10:.1, -50:50:.5"
>>> mask
{1: 0.0, 2: 5.0}
"""
option = option.split(',')
select = []
axes = []
mask = {}
for index,value in enumerate(option):
if ":" in value:
select.append(index)
axes.append(value)
else:
mask.update({index:float(value)})
axes = ','.join(axes)
return select, axes, mask
def _parse_axes(option, grid=True):
"""parse option string into grid axes; using modified numpy.ogrid notation
For example:
option='-1:10:.1, 0:10:.1' yields x,y=ogrid[-1:10:.1,0:10:.1],
If grid is False, accept options suitable for line plotting.
For example:
option='-1:10' yields x=ogrid[-1:10] and y=0,
option='-1:10, 2' yields x=ogrid[-1:10] and y=2,
Returns tuple (x,y) with 'x,y' defined above.
"""
option = option.split(',')
msg = "invalid format string: '{0}'".format(','.join(option))
opt = dict(zip(['x','y','z'],option))
if len(option) > 2 or len(option) < 1:
msg = "invalid format string: '{0}'".format(','.join(option))
raise ValueError(msg)
z = bool(grid)
if len(option) == 1: opt['y'] = '0'
xd = True if ':' in opt['x'] else False
yd = True if ':' in opt['y'] else False
#XXX: accepts option='3:1', '1:1', and '1:2:10' (try to catch?)
globals = {}
code = 'import numpy;'
if xd and yd:
try: # x,y form a 2D grid
code += 'x,y = numpy.ogrid[{0},{1}]'.format(opt['x'],opt['y'])
code = compile(code, '<string>', 'exec')
exec(code, globals)
x = globals['x']
y = globals['y']
except: # AttributeError:
msg = "invalid format string: '{0}'".format(','.join(option))
raise ValueError(msg)
elif xd and not z:
try:
code += 'x = numpy.ogrid[{0}]'.format(opt['x'])
code = compile(code, '<string>', 'exec')
exec(code, globals)
x = globals['x']
y = float(opt['y'])
except: # (AttributeError, SyntaxError, ValueError):
msg = "invalid format string: '{0}'".format(','.join(option))
raise ValueError(msg)
elif yd and not z:
try:
x = float(opt['x'])
code += 'y = numpy.ogrid[{0}]'.format(opt['y'])
code = compile(code, '<string>', 'exec')
exec(code, globals)
y = globals['y']
except: # (AttributeError, SyntaxError, ValueError):
msg = "invalid format string: '{0}'".format(','.join(option))
raise ValueError(msg)
else:
msg = "invalid format string: '{0}'".format(','.join(option))
raise ValueError(msg)
if not x.size or not y.size:
msg = "invalid format string: '{0}'".format(','.join(option))
raise ValueError(msg)
return x,y
def _draw_projection(x, cost, scale=True, shift=False, style=None, figure=None):
"""draw a solution trajectory (for overlay on a 1D plot)
x is the sequence of values for one parameter (i.e. a parameter trajectory)
cost is the sequence of costs (i.e. the solution trajectory)
if scale is provided, scale the intensity as 'z = log(4*z*scale+1)+2'
if shift is provided, shift the intensity as 'z = z+shift' (useful for -z's)
if style is provided, set the line style (e.g. 'w-o', 'k-', 'ro')
if figure is provided, plot to an existing figure
"""
import matplotlib.pyplot as plt
if not figure: figure = plt.figure()
ax = figure.gca()
ax.autoscale(tight=True)
if style in [None, False]:
style = 'k-o'
import numpy
if shift:
if shift is True: #NOTE: MAY NOT be the exact minimum
shift = max(-numpy.min(cost), 0.0) + 0.5 # a good guess
cost = numpy.asarray(cost)+shift
cost = numpy.asarray(cost)
if scale:
cost = numpy.log(4*cost*scale+1)+2
ax.plot(x,cost, style, linewidth=2, markersize=4)
#XXX: need to 'correct' the z-axis (or provide easy conversion)
return figure
def _draw_trajectory(x, y, cost=None, scale=True, shift=False, style=None, figure=None):
"""draw a solution trajectory (for overlay on a contour plot)
x is a sequence of values for one parameter (i.e. a parameter trajectory)
y is a sequence of values for one parameter (i.e. a parameter trajectory)
cost is the solution trajectory (i.e. costs); if provided, plot a 3D contour
if scale is provided, scale the intensity as 'z = log(4*z*scale+1)+2'
if shift is provided, shift the intensity as 'z = z+shift' (useful for -z's)
if style is provided, set the line style (e.g. 'w-o', 'k-', 'ro')
if figure is provided, plot to an existing figure
"""
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
if not figure: figure = plt.figure()
if cost: kwds = {'projection':'3d'} # 3D
else: kwds = {} # 2D
ax = figure.axes[0] if figure.axes else plt.axes(**kwds)
if style in [None, False]:
style = 'w-o' #if not scale else 'k-o'
if cost: # is 3D, cost is needed
import numpy
if shift:
if shift is True: #NOTE: MAY NOT be the exact minimum
shift = max(-numpy.min(cost), 0.0) + 0.5 # a good guess
cost = numpy.asarray(cost)+shift
if scale:
cost = numpy.asarray(cost)
cost = numpy.log(4*cost*scale+1)+2
x = numpy.asarray(x).reshape(-1)
y = numpy.asarray(y).reshape(-1)
ax.plot(x,y,cost, style, linewidth=2, markersize=4)
#XXX: need to 'correct' the z-axis (or provide easy conversion)
else: # is 2D, cost not needed
ax.plot(x,y, style, linewidth=2, markersize=4)
return figure
def _draw_slice(f, x, y=None, scale=True, shift=False):
"""plot a slice of a 2D function 'f' in 1D
x is an array used to set up the axis
y is a fixed value for the 2nd axis
if scale is provided, scale the intensity as 'z = log(4*z*scale+1)+2'
if shift is provided, shift the intensity as 'z = z+shift' (useful for -z's)
NOTE: when plotting the 'y-axis' at fixed 'x',
pass the array to 'y' and the fixed value to 'x'
"""
import numpy
if y is None:
y = 0.0
x, y = numpy.meshgrid(x, y)
plotx = True if numpy.all(y == y[0,0]) else False
z = 0*x
s,t = x.shape
for i in range(s):
for j in range(t):
xx,yy = x[i,j], y[i,j]
z[i,j] = f([xx,yy])
if shift:
if shift is True: shift = max(-numpy.min(z), 0.0) + 0.5 # exact minimum
z = z+shift
if scale: z = numpy.log(4*z*scale+1)+2
#XXX: need to 'correct' the z-axis (or provide easy conversion)
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.gca()
ax.autoscale(tight=True)
if plotx:
ax.plot(x.reshape(-1), z.reshape(-1))
else:
ax.plot(y.reshape(-1), z.reshape(-1))
return fig
def _draw_contour(f, x, y=None, surface=False, fill=True, scale=True, shift=False, density=5, kernel=None):
"""draw a contour plot for a given 2D function 'f'
x and y are arrays used to set up a 2D mesh grid
if fill is True, color fill the contours
if surface is True, plot the contours as a 3D projection
if scale is provided, scale the intensity as 'z = log(4*z*scale+1)+2'
if shift is provided, shift the intensity as 'z = z+shift' (useful for -z's)
use density to adjust the number of contour lines
if kernel is provided, apply kernel to x and y, as [xi',yi'] = kernel([xi,yi])
Using a kernel is very slow, as it calcuates inverse transform at each point
"""
import numpy
from matplotlib import cm
if y is None:
y = x
x, y = numpy.meshgrid(x, y)
if kernel:
xy = numpy.array([x,y])
xy_ = numpy.zeros_like(xy)
for i in range(xy.T.shape[0]):
xy_.T[i] = [kernel(j)[:2] for j in xy.T[i]]
del xy
# x,y = xy_
# x,y was meshgrid, but is 'skewed' due to kernel transform
# create a new grid from min,max points
x = numpy.linspace(xy_[0].min(), xy_[0].max(), xy_.shape[-1])
y = numpy.linspace(xy_[1].min(), xy_[1].max(), xy_.shape[-2])
x,y = numpy.meshgrid(x,y)
from mystic.solvers import fmin
def inverse(xi): #XXX: too simple for all cases?
cost = lambda kx: numpy.abs(numpy.array(kernel(kx)) - xi).sum()
return fmin(cost, xi, ftol=1e-2, disp=0, maxiter=20)
k = inverse
else:
k = lambda xi: xi
z = 0*x
s,t = x.shape
for i in range(s):
for j in range(t):
xx,yy = x[i,j], y[i,j]
z[i,j] = f(k([xx,yy])) #FIXME: VERY SLOW: solve x = k(x'), then f(x)
if shift:
if shift is True: shift = max(-numpy.min(z), 0.0) + 0.5 # exact minimum
z = z+shift
if scale: z = numpy.log(4*z*scale+1)+2
#XXX: need to 'correct' the z-axis (or provide easy conversion)
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
fig = plt.figure()