#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Program to compare models using different compute engines.
This program lets you compare results between OpenCL and DLL versions
of the code and between precision (half, fast, single, double, quad),
where fast precision is single precision using native functions for
trig, etc., and may not be completely IEEE 754 compliant. This lets
make sure that the model calculations are stable, or if you need to
tag the model as double precision only.
Run using ./compare.sh (Linux, Mac) or compare.bat (Windows) in the
sasmodels root to see the command line options.
Note that there is no way within sasmodels to select between an
OpenCL CPU device and a GPU device, but you can do so by setting the
PYOPENCL_CTX environment variable ahead of time. Start a python
interpreter and enter::
import pyopencl as cl
cl.create_some_context()
This will prompt you to select from the available OpenCL devices
and tell you which string to use for the PYOPENCL_CTX variable.
On Windows you will need to remove the quotes.
"""
from __future__ import print_function
import sys
import os
import math
import datetime
import traceback
import re
import numpy as np # type: ignore
from . import core
from . import kerneldll
from . import kernelcl
from .data import plot_theory, empty_data1D, empty_data2D, load_data
from .direct_model import DirectModel, get_mesh
from .generate import FLOAT_RE, set_integration_size
from .weights import plot_weights
# pylint: disable=unused-import
try:
from typing import Optional, Dict, Any, Callable, Tuple
except ImportError:
pass
else:
from .modelinfo import ModelInfo, Parameter, ParameterSet
from .data import Data
Calculator = Callable[[float], np.ndarray]
# pylint: enable=unused-import
USAGE = """
usage: sascomp model [options...] [key=val]
Generate and compare SAS models. If a single model is specified it shows
a plot of that model. Different models can be compared, or the same model
with different parameters. The same model with the same parameters can
be compared with different calculation engines to see the effects of precision
on the resultant values.
model or model1,model2 are the names of the models to compare (see below).
Options (* for default):
=== data generation ===
-data="path" uses q, dq from the data file
-noise=0 sets the measurement error dI/I
-res=0 sets the resolution width dQ/Q if calculating with resolution
-lowq*/-midq/-highq/-exq use q values up to 0.05, 0.2, 1.0, 10.0
-q=min:max alternative specification of qrange
-nq=128 sets the number of Q points in the data set
-1d*/-2d computes 1d or 2d data
-zero indicates that q=0 should be included
=== model parameters ===
-preset*/-random[=seed] preset or random parameters
-sets=n generates n random datasets with the seed given by -random=seed
-pars/-nopars* prints the parameter set or not
-default/-demo* use demo vs default parameters
-sphere[=150] set up spherical integration over theta/phi using n points
=== calculation options ===
-mono*/-poly force monodisperse or allow polydisperse random parameters
-cutoff=1e-5* cutoff value for including a point in polydispersity
-magnetic/-nonmagnetic* suppress magnetism
-accuracy=Low accuracy of the resolution calculation Low, Mid, High, Xhigh
-neval=1 sets the number of evals for more accurate timing
-ngauss=0 overrides the number of points in the 1-D gaussian quadrature
=== precision options ===
-engine=default uses the default calcution precision
-single/-double/-half/-fast sets an OpenCL calculation engine
-single!/-double!/-quad! sets an OpenMP calculation engine
=== plotting ===
-plot*/-noplot plots or suppress the plot of the model
-linear/-log*/-q4 intensity scaling on plots
-hist/-nohist* plot histogram of relative error
-abs/-rel* plot relative or absolute error
-title="note" adds note to the plot title, after the model name
-weights shows weights plots for the polydisperse parameters
-profile shows the sld profile if the model has a plottable sld profile
=== output options ===
-edit starts the parameter explorer
-help/-html shows the model docs instead of running the model
=== environment variables ===
-DSAS_MODELPATH=path sets directory containing custom models
-DSAS_OPENCL=vendor:device|none sets the target OpenCL device
-DXDG_CACHE_HOME=~/.cache sets the pyopencl cache root (linux only)
-DSAS_COMPILER=tinycc|msvc|mingw|unix sets the DLL compiler
-DSAS_OPENMP=1 turns on OpenMP for the DLLs
-DSAS_DLL_PATH=path sets the path to the compiled modules
The interpretation of quad precision depends on architecture, and may
vary from 64-bit to 128-bit, with 80-bit floats being common (1e-19 precision).
On unix and mac you may need single quotes around the DLL computation
engines, such as -engine='single!,double!' since !, is treated as a history
expansion request in the shell.
Key=value pairs allow you to set specific values for the model parameters.
Key=value1,value2 to compare different values of the same parameter. The
value can be an expression including other parameters.
Items later on the command line override those that appear earlier.
Examples:
# compare single and double precision calculation for a barbell
sascomp barbell -engine=single,double
# generate 10 random lorentz models, with seed=27
sascomp lorentz -sets=10 -seed=27
# compare ellipsoid with R = R_polar = R_equatorial to sphere of radius R
sascomp sphere,ellipsoid radius_polar=radius radius_equatorial=radius
# model timing test requires multiple evals to perform the estimate
sascomp pringle -engine=single,double -timing=100,100 -noplot
"""
# Update docs with command line usage string. This is separate from the usual
# doc string so that we can display it at run time if there is an error.
# lin
__doc__ = (__doc__ # pylint: disable=redefined-builtin
+ """
Program description
-------------------
""" + USAGE)
kerneldll.ALLOW_SINGLE_PRECISION_DLLS = True
[docs]def build_math_context():
# type: () -> Dict[str, Callable]
"""build dictionary of functions from math module"""
return dict((k, getattr(math, k))
for k in dir(math) if not k.startswith('_'))
#: list of math functions for use in evaluating parameters
MATH = build_math_context()
# CRUFT python 2.6
if not hasattr(datetime.timedelta, 'total_seconds'):
def delay(dt):
"""Return number date-time delta as number seconds"""
return dt.days * 86400 + dt.seconds + 1e-6 * dt.microseconds
else:
[docs] def delay(dt):
"""Return number date-time delta as number seconds"""
return dt.total_seconds()
[docs]class push_seed(object):
"""
Set the seed value for the random number generator.
When used in a with statement, the random number generator state is
restored after the with statement is complete.
:Parameters:
*seed* : int or array_like, optional
Seed for RandomState
:Example:
Seed can be used directly to set the seed::
>>> from numpy.random import randint
>>> push_seed(24)
<...push_seed object at...>
>>> print(randint(0,1000000,3))
[242082 899 211136]
Seed can also be used in a with statement, which sets the random
number generator state for the enclosed computations and restores
it to the previous state on completion::
>>> with push_seed(24):
... print(randint(0,1000000,3))
[242082 899 211136]
Using nested contexts, we can demonstrate that state is indeed
restored after the block completes::
>>> with push_seed(24):
... print(randint(0,1000000))
... with push_seed(24):
... print(randint(0,1000000,3))
... print(randint(0,1000000))
242082
[242082 899 211136]
899
The restore step is protected against exceptions in the block::
>>> with push_seed(24):
... print(randint(0,1000000))
... try:
... with push_seed(24):
... print(randint(0,1000000,3))
... raise Exception()
... except Exception:
... print("Exception raised")
... print(randint(0,1000000))
242082
[242082 899 211136]
Exception raised
899
"""
def __init__(self, seed=None):
# type: (Optional[int]) -> None
self._state = np.random.get_state()
np.random.seed(seed)
def __enter__(self):
# type: () -> None
pass
def __exit__(self, exc_type, exc_value, trace):
# type: (Any, BaseException, Any) -> None
np.random.set_state(self._state)
[docs]def tic():
# type: () -> Callable[[], float]
"""
Timer function.
Use "toc=tic()" to start the clock and "toc()" to measure
a time interval.
"""
then = datetime.datetime.now()
return lambda: delay(datetime.datetime.now() - then)
[docs]def set_beam_stop(data, radius, outer=None):
# type: (Data, float, float) -> None
"""
Add a beam stop of the given *radius*. If *outer*, make an annulus.
"""
if hasattr(data, 'qx_data'):
q = np.sqrt(data.qx_data**2 + data.qy_data**2)
data.mask = (q < radius)
if outer is not None:
data.mask |= (q >= outer)
else:
data.mask = (data.x < radius)
if outer is not None:
data.mask |= (data.x >= outer)
[docs]def parameter_range(p, v):
# type: (str, float) -> Tuple[float, float]
"""
Choose a parameter range based on parameter name and initial value.
"""
# process the polydispersity options
if p.endswith('_pd_n'):
return 0., 100.
elif p.endswith('_pd_nsigma'):
return 0., 5.
elif p.endswith('_pd_type'):
raise ValueError("Cannot return a range for a string value")
elif any(s in p for s in ('theta', 'phi', 'psi')):
# orientation in [-180,180], orientation pd in [0,45]
if p.endswith('_pd'):
return 0., 180.
else:
return -180., 180.
elif p.endswith('_pd'):
return 0., 1.
elif 'sld' in p:
return -0.5, 10.
elif p == 'background':
return 0., 10.
elif p == 'scale':
return 0., 1.e3
elif v < 0.:
return 2.*v, -2.*v
else:
return 0., (2.*v if v > 0. else 1.)
def _randomize_one(model_info, name, value):
# type: (ModelInfo, str, float) -> float
# type: (ModelInfo, str, str) -> str
"""
Randomize a single parameter.
"""
# Set the amount of polydispersity/angular dispersion, but by default pd_n
# is zero so there is no polydispersity. This allows us to turn on/off
# pd by setting pd_n, and still have randomly generated values
if name.endswith('_pd'):
par = model_info.parameters[name[:-3]]
if par.type == 'orientation':
# Let oriention variation peak around 13 degrees; 95% < 42 degrees
return 180*np.random.beta(2.5, 20)
else:
# Let polydispersity peak around 15%; 95% < 0.4; max=100%
return np.random.beta(1.5, 7)
# pd is selected globally rather than per parameter, so set to 0 for no pd
# In particular, when multiple pd dimensions, want to decrease the number
# of points per dimension for faster computation
if name.endswith('_pd_n'):
return 0
# Don't mess with distribution type for now
if name.endswith('_pd_type'):
return 'gaussian'
# type-dependent value of number of sigmas; for gaussian use 3.
if name.endswith('_pd_nsigma'):
return 3.
# background in the range [0.01, 1]
if name == 'background':
return 10**np.random.uniform(-2, 0)
# scale defaults to 0.1% to 30% volume fraction
if name == 'scale':
return 10**np.random.uniform(-3, -0.5)
# If it is a list of choices, pick one at random with equal probability
# In practice, the model specific random generator will override.
par = model_info.parameters[name]
if len(par.limits) > 2: # choice list
return np.random.randint(len(par.limits))
# If it is a fixed range, pick from it with equal probability.
# For logarithmic ranges, the model will have to override.
if np.isfinite(par.limits).all():
return np.random.uniform(*par.limits)
# If the paramter is marked as an sld use the range of neutron slds
# TODO: ought to randomly contrast match a pair of SLDs
if par.type == 'sld':
return np.random.uniform(-0.5, 12)
# Limit magnetic SLDs to a smaller range, from zero to iron=5/A^2
if par.name.endswith('_M0'):
return np.random.uniform(0, 5)
# Guess at the random length/radius/thickness. In practice, all models
# are going to set their own reasonable ranges.
if par.type == 'volume':
if ('length' in par.name or
'radius' in par.name or
'thick' in par.name):
return 10**np.random.uniform(2, 4)
# In the absence of any other info, select a value in [0, 2v], or
# [-2|v|, 2|v|] if v is negative, or [0, 1] if v is zero. Mostly the
# model random parameter generators will override this default.
low, high = parameter_range(par.name, value)
limits = (max(par.limits[0], low), min(par.limits[1], high))
return np.random.uniform(*limits)
def _random_pd(model_info, pars):
# type: (ModelInfo, Dict[str, float]) -> None
"""
Generate a random dispersity distribution for the model.
1% no shape dispersity
85% single shape parameter
13% two shape parameters
1% three shape parameters
If oriented, then put dispersity in theta, add phi and psi dispersity
with 10% probability for each.
"""
pd = [p for p in model_info.parameters.kernel_parameters if p.polydisperse]
pd_volume = []
pd_oriented = []
for p in pd:
if p.type == 'orientation':
pd_oriented.append(p.name)
elif p.length_control is not None:
n = int(pars.get(p.length_control, 1) + 0.5)
pd_volume.extend(p.name+str(k+1) for k in range(n))
elif p.length > 1:
pd_volume.extend(p.name+str(k+1) for k in range(p.length))
else:
pd_volume.append(p.name)
u = np.random.rand()
n = len(pd_volume)
if u < 0.01 or n < 1:
pass # 1% chance of no polydispersity
elif u < 0.86 or n < 2:
pars[np.random.choice(pd_volume)+"_pd_n"] = 35
elif u < 0.99 or n < 3:
choices = np.random.choice(len(pd_volume), size=2)
pars[pd_volume[choices[0]]+"_pd_n"] = 25
pars[pd_volume[choices[1]]+"_pd_n"] = 10
else:
choices = np.random.choice(len(pd_volume), size=3)
pars[pd_volume[choices[0]]+"_pd_n"] = 25
pars[pd_volume[choices[1]]+"_pd_n"] = 10
pars[pd_volume[choices[2]]+"_pd_n"] = 5
if pd_oriented:
pars['theta_pd_n'] = 20
if np.random.rand() < 0.1:
pars['phi_pd_n'] = 5
if np.random.rand() < 0.1:
if any(p.name == 'psi' for p in model_info.parameters.kernel_parameters):
#print("generating psi_pd_n")
pars['psi_pd_n'] = 5
## Show selected polydispersity
#for name, value in pars.items():
# if name.endswith('_pd_n') and value > 0:
# print(name, value, pars.get(name[:-5], 0), pars.get(name[:-2], 0))
[docs]def randomize_pars(model_info, pars):
# type: (ModelInfo, ParameterSet) -> ParameterSet
"""
Generate random values for all of the parameters.
Valid ranges for the random number generator are guessed from the name of
the parameter; this will not account for constraints such as cap radius
greater than cylinder radius in the capped_cylinder model, so
:func:`constrain_pars` needs to be called afterward..
"""
# Note: the sort guarantees order of calls to random number generator
random_pars = dict((p, _randomize_one(model_info, p, v))
for p, v in sorted(pars.items()))
if model_info.random is not None:
random_pars.update(model_info.random())
_random_pd(model_info, random_pars)
return random_pars
[docs]def limit_dimensions(model_info, pars, maxdim):
# type: (ModelInfo, ParameterSet, float) -> None
"""
Limit parameters of units of Ang to maxdim.
"""
for p in model_info.parameters.call_parameters:
value = pars[p.name]
if p.units == 'Ang' and value > maxdim:
pars[p.name] = maxdim*10**np.random.uniform(-3, 0)
[docs]def constrain_pars(model_info, pars):
# type: (ModelInfo, ParameterSet) -> None
"""
Restrict parameters to valid values.
This includes model specific code for models such as capped_cylinder
which need to support within model constraints (cap radius more than
cylinder radius in this case).
Warning: this updates the *pars* dictionary in place.
"""
# TODO: move the model specific code to the individual models
name = model_info.id
# if it is a product model, then just look at the form factor since
# none of the structure factors need any constraints.
if '*' in name:
name = name.split('*')[0]
# Suppress magnetism for python models (not yet implemented)
if callable(model_info.Iq):
pars.update(suppress_magnetism(pars))
if name == 'barbell':
if pars['radius_bell'] < pars['radius']:
pars['radius'], pars['radius_bell'] = pars['radius_bell'], pars['radius']
elif name == 'capped_cylinder':
if pars['radius_cap'] < pars['radius']:
pars['radius'], pars['radius_cap'] = pars['radius_cap'], pars['radius']
elif name == 'guinier':
# Limit guinier to an Rg such that Iq > 1e-30 (single precision cutoff)
# I(q) = A e^-(Rg^2 q^2/3) > e^-(30 ln 10)
# => ln A - (Rg^2 q^2/3) > -30 ln 10
# => Rg^2 q^2/3 < 30 ln 10 + ln A
# => Rg < sqrt(90 ln 10 + 3 ln A)/q
#q_max = 0.2 # mid q maximum
q_max = 1.0 # high q maximum
rg_max = np.sqrt(90*np.log(10) + 3*np.log(pars['scale']))/q_max
pars['rg'] = min(pars['rg'], rg_max)
elif name == 'pearl_necklace':
if pars['radius'] < pars['thick_string']:
pars['radius'], pars['thick_string'] = pars['thick_string'], pars['radius']
elif name == 'rpa':
# Make sure phi sums to 1.0
if pars['case_num'] < 2:
pars['Phi1'] = 0.
pars['Phi2'] = 0.
elif pars['case_num'] < 5:
pars['Phi1'] = 0.
total = sum(pars['Phi'+c] for c in '1234')
for c in '1234':
pars['Phi'+c] /= total
[docs]def parlist(model_info, pars, is2d):
# type: (ModelInfo, ParameterSet, bool) -> str
"""
Format the parameter list for printing.
"""
is2d = True
lines = []
parameters = model_info.parameters
magnetic = False
magnetic_pars = []
for p in parameters.user_parameters(pars, is2d):
if any(p.id.endswith(x) for x in ('_M0', '_mtheta', '_mphi')):
continue
if p.id.startswith('up:'):
magnetic_pars.append("%s=%s"%(p.id, pars.get(p.id, p.default)))
continue
fields = dict(
value=pars.get(p.id, p.default),
pd=pars.get(p.id+"_pd", 0.),
n=int(pars.get(p.id+"_pd_n", 0)),
nsigma=pars.get(p.id+"_pd_nsgima", 3.),
pdtype=pars.get(p.id+"_pd_type", 'gaussian'),
relative_pd=p.relative_pd,
M0=pars.get(p.id+'_M0', 0.),
mphi=pars.get(p.id+'_mphi', 0.),
mtheta=pars.get(p.id+'_mtheta', 0.),
)
lines.append(_format_par(p.name, **fields))
magnetic = magnetic or fields['M0'] != 0.
if magnetic and magnetic_pars:
lines.append(" ".join(magnetic_pars))
return "\n".join(lines)
#return "\n".join("%s: %s"%(p, v) for p, v in sorted(pars.items()))
def _format_par(name, value=0., pd=0., n=0, nsigma=3., pdtype='gaussian',
relative_pd=False, M0=0., mphi=0., mtheta=0.):
# type: (str, float, float, int, float, str) -> str
line = "%s: %g"%(name, value)
if pd != 0. and n != 0:
if relative_pd:
pd *= value
line += " +/- %g (%d points in [-%g,%g] sigma %s)"\
% (pd, n, nsigma, nsigma, pdtype)
if M0 != 0.:
line += " M0:%.3f mtheta:%.1f mphi:%.1f" % (M0, mtheta, mphi)
return line
[docs]def suppress_pd(pars, suppress=True):
# type: (ParameterSet) -> ParameterSet
"""
If suppress is True complete eliminate polydispersity of the model to test
models more quickly. If suppress is False, make sure at least one
parameter is polydisperse, setting the first polydispersity parameter to
15% if no polydispersity is given (with no explicit demo parameters given
in the model, there will be no default polydispersity).
"""
pars = pars.copy()
#print("pars=", pars)
if suppress:
for p in pars:
if p.endswith("_pd_n"):
pars[p] = 0
else:
any_pd = False
first_pd = None
for p in pars:
if p.endswith("_pd_n"):
pd = pars.get(p[:-2], 0.)
any_pd |= (pars[p] != 0 and pd != 0.)
if first_pd is None:
first_pd = p
if not any_pd and first_pd is not None:
if pars[first_pd] == 0:
pars[first_pd] = 35
if first_pd[:-2] not in pars or pars[first_pd[:-2]] == 0:
pars[first_pd[:-2]] = 0.15
return pars
[docs]def suppress_magnetism(pars, suppress=True):
# type: (ParameterSet) -> ParameterSet
"""
If suppress is True complete eliminate magnetism of the model to test
models more quickly. If suppress is False, make sure at least one sld
parameter is magnetic, setting the first parameter to have a strong
magnetic sld (8/A^2) at 60 degrees (with no explicit demo parameters given
in the model, there will be no default magnetism).
"""
pars = pars.copy()
if suppress:
for p in pars:
if p.endswith("_M0"):
pars[p] = 0
else:
any_mag = False
first_mag = None
for p in pars:
if p.endswith("_M0"):
any_mag |= (pars[p] != 0)
if first_mag is None:
first_mag = p
if not any_mag and first_mag is not None:
pars[first_mag] = 8.
return pars
[docs]def time_calculation(calculator, pars, evals=1):
# type: (Calculator, ParameterSet, int) -> Tuple[np.ndarray, float]
"""
Compute the average calculation time over N evaluations.
An additional call is generated without polydispersity in order to
initialize the calculation engine, and make the average more stable.
"""
# initialize the code so time is more accurate
if evals > 1:
calculator(**suppress_pd(pars))
toc = tic()
# make sure there is at least one eval
value = calculator(**pars)
for _ in range(evals-1):
value = calculator(**pars)
average_time = toc()*1000. / evals
#print("I(q)",value)
return value, average_time
[docs]def make_data(opts):
# type: (Dict[str, Any], float) -> Tuple[Data, np.ndarray]
"""
Generate an empty dataset, used with the model to set Q points
and resolution.
*opts* contains the options, with 'qmax', 'nq', 'res',
'accuracy', 'is2d' and 'view' parsed from the command line.
"""
qmin, qmax, nq, res = opts['qmin'], opts['qmax'], opts['nq'], opts['res']
if opts['is2d']:
q = np.linspace(-qmax, qmax, nq) # type: np.ndarray
data = empty_data2D(q, resolution=res)
data.accuracy = opts['accuracy']
set_beam_stop(data, qmin)
index = ~data.mask
else:
if opts['view'] == 'log' and not opts['zero']:
q = np.logspace(math.log10(qmin), math.log10(qmax), nq)
else:
q = np.linspace(qmin, qmax, nq)
if opts['zero']:
q = np.hstack((0, q))
# TODO: provide command line control of lambda and Delta lambda/lambda
#L, dLoL = 5, 0.14/np.sqrt(6) # wavelength and 14% triangular FWHM
L, dLoL = 0, 0
data = empty_data1D(q, resolution=res, L=L, dL=L*dLoL)
index = slice(None, None)
return data, index
DTYPE_MAP = {
'half': '16',
'fast': 'fast',
'single': '32',
'double': '64',
'quad': '128',
'f16': '16',
'f32': '32',
'f64': '64',
'float16': '16',
'float32': '32',
'float64': '64',
'float128': '128',
'longdouble': '128',
}
[docs]def eval_opencl(model_info, data, dtype='single', cutoff=0.):
# type: (ModelInfo, Data, str, float) -> Calculator
"""
Return a model calculator using the OpenCL calculation engine.
"""
[docs]def eval_ctypes(model_info, data, dtype='double', cutoff=0.):
# type: (ModelInfo, Data, str, float) -> Calculator
"""
Return a model calculator using the DLL calculation engine.
"""
model = core.build_model(model_info, dtype=dtype, platform="dll")
calculator = DirectModel(data, model, cutoff=cutoff)
calculator.engine = "OMP%s"%DTYPE_MAP[str(model.dtype)]
return calculator
[docs]def make_engine(model_info, data, dtype, cutoff, ngauss=0):
# type: (ModelInfo, Data, str, float) -> Calculator
"""
Generate the appropriate calculation engine for the given datatype.
Datatypes with '!' appended are evaluated using external C DLLs rather
than OpenCL.
"""
if ngauss:
set_integration_size(model_info, ngauss)
if dtype != "default" and not dtype.endswith('!') and not kernelcl.use_opencl():
raise RuntimeError("OpenCL not available " + kernelcl.OPENCL_ERROR)
model = core.build_model(model_info, dtype=dtype, platform="ocl")
calculator = DirectModel(data, model, cutoff=cutoff)
engine_type = calculator._model.__class__.__name__.replace('Model', '').upper()
bits = calculator._model.dtype.itemsize*8
precision = "fast" if getattr(calculator._model, 'fast', False) else str(bits)
calculator.engine = "%s[%s]" % (engine_type, precision)
return calculator
def _show_invalid(data, theory):
# type: (Data, np.ma.ndarray) -> None
"""
Display a list of the non-finite values in theory.
"""
if not theory.mask.any():
return
if hasattr(data, 'x'):
bad = zip(data.x[theory.mask], theory[theory.mask])
print(" *** ", ", ".join("I(%g)=%g"%(x, y) for x, y in bad))
[docs]def compare(opts, limits=None, maxdim=np.inf):
# type: (Dict[str, Any], Optional[Tuple[float, float]]) -> Tuple[float, float]
"""
Preform a comparison using options from the command line.
*limits* are the limits on the values to use, either to set the y-axis
for 1D or to set the colormap scale for 2D. If None, then they are
inferred from the data and returned. When exploring using Bumps,
the limits are set when the model is initially called, and maintained
as the values are adjusted, making it easier to see the effects of the
parameters.
*maxdim* is the maximum value for any parameter with units of Angstrom.
"""
for k in range(opts['sets']):
if k > 0:
# print a separate seed for each dataset for better reproducibility
new_seed = np.random.randint(1000000)
print("=== Set %d uses -random=%i ==="%(k+1, new_seed))
np.random.seed(new_seed)
opts['pars'] = parse_pars(opts, maxdim=maxdim)
if opts['pars'] is None:
return
result = run_models(opts, verbose=True)
if opts['plot']:
if opts['is2d'] and k > 0:
import matplotlib.pyplot as plt
plt.figure()
limits = plot_models(opts, result, limits=limits, setnum=k)
if opts['show_weights']:
base, _ = opts['engines']
base_pars, _ = opts['pars']
model_info = base._kernel.info
dim = base._kernel.dim
plot_weights(model_info, get_mesh(model_info, base_pars, dim=dim))
if opts['show_profile']:
import pylab
base, comp = opts['engines']
base_pars, comp_pars = opts['pars']
have_base = base._kernel.info.profile is not None
have_comp = (
comp is not None
and comp._kernel.info.profile is not None
and base_pars != comp_pars
)
if have_base or have_comp:
pylab.figure()
if have_base:
plot_profile(base._kernel.info, **base_pars)
if have_comp:
plot_profile(comp._kernel.info, label='comp', **comp_pars)
pylab.legend()
if opts['plot']:
import matplotlib.pyplot as plt
plt.show()
return limits
[docs]def plot_profile(model_info, label='base', **args):
# type: (ModelInfo, List[Tuple[float, np.ndarray, np.ndarray]]) -> None
"""
Plot the profile returned by the model profile method.
*model_info* defines model parameters, etc.
*mesh* is a list of tuples containing (*value*, *dispersity*, *weights*)
for each parameter, where (*dispersity*, *weights*) pairs are the
distributions to be plotted.
"""
import pylab
args = dict((k, v) for k, v in args.items()
if "_pd" not in k
and ":" not in k
and k not in ("background", "scale", "theta", "phi", "psi"))
args = args.copy()
args.pop('scale', 1.)
args.pop('background', 0.)
z, rho = model_info.profile(**args)
#pylab.interactive(True)
pylab.plot(z, rho, '-', label=label)
pylab.grid(True)
#pylab.show()
[docs]def run_models(opts, verbose=False):
# type: (Dict[str, Any]) -> Dict[str, Any]
"""
Process a parameter set, return calculation results and times.
"""
base, comp = opts['engines']
base_n, comp_n = opts['count']
base_pars, comp_pars = opts['pars']
base_data, comp_data = opts['data']
comparison = comp is not None
base_time = comp_time = None
base_value = comp_value = resid = relerr = None
# Base calculation
try:
base_raw, base_time = time_calculation(base, base_pars, base_n)
base_value = np.ma.masked_invalid(base_raw)
if verbose:
print("%s t=%.2f ms, intensity=%.0f"
% (base.engine, base_time, base_value.sum()))
_show_invalid(base_data, base_value)
except ImportError:
traceback.print_exc()
# Comparison calculation
if comparison:
try:
comp_raw, comp_time = time_calculation(comp, comp_pars, comp_n)
comp_value = np.ma.masked_invalid(comp_raw)
if verbose:
print("%s t=%.2f ms, intensity=%.0f"
% (comp.engine, comp_time, comp_value.sum()))
_show_invalid(base_data, comp_value)
except ImportError:
traceback.print_exc()
# Compare, but only if computing both forms
if comparison:
resid = (base_value - comp_value)
relerr = resid/np.where(comp_value != 0., abs(comp_value), 1.0)
if verbose:
_print_stats("|%s-%s|"
% (base.engine, comp.engine) + (" "*(3+len(comp.engine))),
resid)
_print_stats("|(%s-%s)/%s|"
% (base.engine, comp.engine, comp.engine),
relerr)
return dict(base_value=base_value, comp_value=comp_value,
base_time=base_time, comp_time=comp_time,
resid=resid, relerr=relerr)
def _print_stats(label, err):
# type: (str, np.ma.ndarray) -> None
# work with trimmed data, not the full set
sorted_err = np.sort(abs(err.compressed()))
if len(sorted_err) == 0:
print(label + " no valid values")
return
p50 = int((len(sorted_err)-1)*0.50)
p98 = int((len(sorted_err)-1)*0.98)
data = [
"max:%.3e"%sorted_err[-1],
"median:%.3e"%sorted_err[p50],
"98%%:%.3e"%sorted_err[p98],
"rms:%.3e"%np.sqrt(np.mean(sorted_err**2)),
"zero-offset:%+.3e"%np.mean(sorted_err),
]
print(label+" "+" ".join(data))
[docs]def plot_models(opts, result, limits=None, setnum=0):
# type: (Dict[str, Any], Dict[str, Any], Optional[Tuple[float, float]]) -> Tuple[float, float]
"""
Plot the results from :func:`run_model`.
"""
import matplotlib.pyplot as plt
base_value, comp_value = result['base_value'], result['comp_value']
base_time, comp_time = result['base_time'], result['comp_time']
resid, relerr = result['resid'], result['relerr']
have_base, have_comp = (base_value is not None), (comp_value is not None)
base, comp = opts['engines']
base_data, comp_data = opts['data']
use_data = (opts['datafile'] is not None) and (have_base ^ have_comp)
# Plot if requested
view = opts['view']
#view = 'log'
if limits is None:
vmin, vmax = np.inf, -np.inf
if have_base:
vmin = min(vmin, base_value.min())
vmax = max(vmax, base_value.max())
if have_comp:
vmin = min(vmin, comp_value.min())
vmax = max(vmax, comp_value.max())
limits = vmin, vmax
if have_base:
if have_comp:
plt.subplot(131)
plot_theory(base_data, base_value, view=view, use_data=use_data, limits=limits)
plt.title("%s t=%.2f ms"%(base.engine, base_time))
#cbar_title = "log I"
if have_comp:
if have_base:
plt.subplot(132)
if not opts['is2d'] and have_base:
plot_theory(comp_data, base_value, view=view, use_data=use_data, limits=limits)
plot_theory(comp_data, comp_value, view=view, use_data=use_data, limits=limits)
plt.title("%s t=%.2f ms"%(comp.engine, comp_time))
#cbar_title = "log I"
if have_base and have_comp:
plt.subplot(133)
if not opts['rel_err']:
err, errstr, errview = resid, "abs err", "linear"
else:
err, errstr, errview = abs(relerr), "rel err", "log"
if (err == 0.).all():
errview = 'linear'
if 0: # 95% cutoff
sorted_err = np.sort(err.flatten())
cutoff = sorted_err[int(sorted_err.size*0.95)]
err[err > cutoff] = cutoff
#err,errstr = base/comp,"ratio"
# Note: base_data only since base and comp have same q values (though
# perhaps different resolution), and we are plotting the difference
# at each q
plot_theory(base_data, None, resid=err, view=errview, use_data=use_data)
plt.xscale('log' if view == 'log' and not opts['is2d'] else 'linear')
plt.legend(['P%d'%(k+1) for k in range(setnum+1)], loc='best')
plt.title("max %s = %.3g"%(errstr, abs(err).max()))
#cbar_title = errstr if errview=="linear" else "log "+errstr
#if is2D:
# h = plt.colorbar()
# h.ax.set_title(cbar_title)
fig = plt.gcf()
extra_title = ' '+opts['title'] if opts['title'] else ''
fig.suptitle(":".join(opts['name']) + extra_title)
if have_base and have_comp and opts['show_hist']:
plt.figure()
v = relerr
v[v == 0] = 0.5*np.min(np.abs(v[v != 0]))
plt.hist(np.log10(np.abs(v)), normed=1, bins=50)
plt.xlabel('log10(err), err = |(%s - %s) / %s|'
% (base.engine, comp.engine, comp.engine))
plt.ylabel('P(err)')
plt.title('Distribution of relative error between calculation engines')
return limits
# ===========================================================================
#
# Set of command line options.
# Normal options such as -plot/-noplot are specified as 'name'.
# For options such as -nq=500 which require a value use 'name='.
#
OPTIONS = [
# Plotting
'plot', 'noplot',
'weights', 'profile',
'linear', 'log', 'q4',
'rel', 'abs',
'hist', 'nohist',
'title=',
# Data generation
'data=', 'noise=', 'res=', 'nq=', 'q=',
'lowq', 'midq', 'highq', 'exq', 'zero',
'2d', '1d',
# Parameter set
'preset', 'random', 'random=', 'sets=',
'demo', 'default', # TODO: remove demo/default
'nopars', 'pars',
'sphere', 'sphere=', # integrate over a sphere in 2d with n points
# Calculation options
'poly', 'mono', 'cutoff=',
'magnetic', 'nonmagnetic',
'accuracy=', 'ngauss=',
'neval=', # for timing...
# Precision options
'engine=',
'half', 'fast', 'single', 'double', 'single!', 'double!', 'quad!',
# Output options
'help', 'html', 'edit',
]
NAME_OPTIONS = (lambda: set(k for k in OPTIONS if not k.endswith('=')))()
VALUE_OPTIONS = (lambda: [k[:-1] for k in OPTIONS if k.endswith('=')])()
[docs]def columnize(items, indent="", width=79):
# type: (List[str], str, int) -> str
"""
Format a list of strings into columns.
Returns a string with carriage returns ready for printing.
"""
column_width = max(len(w) for w in items) + 1
num_columns = (width - len(indent)) // column_width
num_rows = len(items) // num_columns
items = items + [""] * (num_rows * num_columns - len(items))
columns = [items[k*num_rows:(k+1)*num_rows] for k in range(num_columns)]
lines = [" ".join("%-*s"%(column_width, entry) for entry in row)
for row in zip(*columns)]
output = indent + ("\n"+indent).join(lines)
return output
[docs]def get_pars(model_info, use_demo=False):
# type: (ModelInfo, bool) -> ParameterSet
"""
Extract demo parameters from the model definition.
"""
# Get the default values for the parameters
pars = {}
for p in model_info.parameters.call_parameters:
parts = [('', p.default)]
if p.polydisperse:
parts.append(('_pd', 0.0))
parts.append(('_pd_n', 0))
parts.append(('_pd_nsigma', 3.0))
parts.append(('_pd_type', "gaussian"))
for ext, val in parts:
if p.length > 1:
dict(("%s%d%s" % (p.id, k, ext), val)
for k in range(1, p.length+1))
else:
pars[p.id + ext] = val
# Plug in values given in demo
if use_demo and model_info.demo:
pars.update(model_info.demo)
return pars
INTEGER_RE = re.compile("^[+-]?[1-9][0-9]*$")
[docs]def isnumber(s):
# type: (str) -> bool
"""Return True if string contains an int or float"""
match = FLOAT_RE.match(s)
isfloat = (match and not s[match.end():])
return isfloat or INTEGER_RE.match(s)
# For distinguishing pairs of models for comparison
# key-value pair separator =
# shell characters | & ; <> $ % ' " \ # `
# model and parameter names _
# parameter expressions - + * / . ( )
# path characters including tilde expansion and windows drive ~ / :
# not sure about brackets [] {}
# maybe one of the following @ ? ^ ! ,
PAR_SPLIT = ','
[docs]def parse_opts(argv):
# type: (List[str]) -> Dict[str, Any]
"""
Parse command line options.
"""
MODELS = core.list_models()
flags = [arg for arg in argv
if arg.startswith('-')]
values = [arg for arg in argv
if not arg.startswith('-') and '=' in arg]
positional_args = [arg for arg in argv
if not arg.startswith('-') and '=' not in arg]
models = "\n ".join("%-15s"%v for v in MODELS)
if len(positional_args) == 0:
print(USAGE)
print("\nAvailable models:")
print(columnize(MODELS, indent=" "))
return None
invalid = [o[1:] for o in flags
if not (o[1:] in NAME_OPTIONS
or any(o.startswith('-%s='%t) for t in VALUE_OPTIONS)
or o.startswith('-D'))]
if invalid:
print("Invalid options: %s"%(", ".join(invalid)))
return None
name = positional_args[-1]
# pylint: disable=bad-whitespace,C0321
# Interpret the flags
opts = {
'plot' : True,
'view' : 'log',
'is2d' : False,
'qmin' : None,
'qmax' : 0.05,
'nq' : 128,
'res' : '0.0',
'noise' : 0.0,
'accuracy' : 'Low',
'cutoff' : '0.0',
'seed' : -1, # default to preset
'mono' : True,
# Default to magnetic a magnetic moment is set on the command line
'magnetic' : False,
'show_pars' : False,
'show_hist' : False,
'rel_err' : True,
'explore' : False,
'use_demo' : True,
'zero' : False,
'html' : False,
'title' : None,
'datafile' : None,
'sets' : 0,
'engine' : 'default',
'count' : '1',
'show_weights' : False,
'show_profile' : False,
'sphere' : 0,
'ngauss' : '0',
}
for arg in flags:
if arg == '-noplot': opts['plot'] = False
elif arg == '-plot': opts['plot'] = True
elif arg == '-linear': opts['view'] = 'linear'
elif arg == '-log': opts['view'] = 'log'
elif arg == '-q4': opts['view'] = 'q4'
elif arg == '-1d': opts['is2d'] = False
elif arg == '-2d': opts['is2d'] = True
elif arg == '-exq': opts['qmax'] = 10.0
elif arg == '-highq': opts['qmax'] = 1.0
elif arg == '-midq': opts['qmax'] = 0.2
elif arg == '-lowq': opts['qmax'] = 0.05
elif arg == '-zero': opts['zero'] = True
elif arg.startswith('-nq='): opts['nq'] = int(arg[4:])
elif arg.startswith('-q='):
opts['qmin'], opts['qmax'] = [float(v) for v in arg[3:].split(':')]
elif arg.startswith('-res='): opts['res'] = arg[5:]
elif arg.startswith('-noise='): opts['noise'] = float(arg[7:])
elif arg.startswith('-sets='): opts['sets'] = int(arg[6:])
elif arg.startswith('-accuracy='): opts['accuracy'] = arg[10:]
elif arg.startswith('-cutoff='): opts['cutoff'] = arg[8:]
elif arg.startswith('-title='): opts['title'] = arg[7:]
elif arg.startswith('-data='): opts['datafile'] = arg[6:]
elif arg.startswith('-engine='): opts['engine'] = arg[8:]
elif arg.startswith('-neval='): opts['count'] = arg[7:]
elif arg.startswith('-ngauss='): opts['ngauss'] = arg[8:]
elif arg.startswith('-random='):
opts['seed'] = int(arg[8:])
opts['sets'] = 0
elif arg == '-random':
opts['seed'] = np.random.randint(1000000)
opts['sets'] = 0
elif arg.startswith('-sphere'):
opts['sphere'] = int(arg[8:]) if len(arg) > 7 else 150
opts['is2d'] = True
elif arg == '-preset': opts['seed'] = -1
elif arg == '-mono': opts['mono'] = True
elif arg == '-poly': opts['mono'] = False
elif arg == '-magnetic': opts['magnetic'] = True
elif arg == '-nonmagnetic': opts['magnetic'] = False
elif arg == '-pars': opts['show_pars'] = True
elif arg == '-nopars': opts['show_pars'] = False
elif arg == '-hist': opts['show_hist'] = True
elif arg == '-nohist': opts['show_hist'] = False
elif arg == '-rel': opts['rel_err'] = True
elif arg == '-abs': opts['rel_err'] = False
elif arg == '-half': opts['engine'] = 'half'
elif arg == '-fast': opts['engine'] = 'fast'
elif arg == '-single': opts['engine'] = 'single'
elif arg == '-double': opts['engine'] = 'double'
elif arg == '-single!': opts['engine'] = 'single!'
elif arg == '-double!': opts['engine'] = 'double!'
elif arg == '-quad!': opts['engine'] = 'quad!'
elif arg == '-edit': opts['explore'] = True
elif arg == '-demo': opts['use_demo'] = True
elif arg == '-default': opts['use_demo'] = False
elif arg == '-weights': opts['show_weights'] = True
elif arg == '-profile': opts['show_profile'] = True
elif arg == '-html': opts['html'] = True
elif arg == '-help': opts['html'] = True
elif arg.startswith('-D'):
var, val = arg[2:].split('=')
os.environ[var] = val
# pylint: enable=bad-whitespace,C0321
# Magnetism forces 2D for now
if opts['magnetic']:
opts['is2d'] = True
# Force random if sets is used
if opts['sets'] >= 1 and opts['seed'] < 0:
opts['seed'] = np.random.randint(1000000)
if opts['sets'] == 0:
opts['sets'] = 1
# Create the computational engines
if opts['qmin'] is None:
opts['qmin'] = 0.001*opts['qmax']
comparison = any(PAR_SPLIT in v for v in values)
if PAR_SPLIT in name:
names = name.split(PAR_SPLIT, 2)
comparison = True
else:
names = [name]*2
try:
model_info = [core.load_model_info(k) for k in names]
except ImportError as exc:
print(str(exc))
print("Could not find model; use one of:\n " + models)
return None
if PAR_SPLIT in opts['ngauss']:
opts['ngauss'] = [int(k) for k in opts['ngauss'].split(PAR_SPLIT, 2)]
comparison = True
else:
opts['ngauss'] = [int(opts['ngauss'])]*2
if PAR_SPLIT in opts['engine']:
opts['engine'] = opts['engine'].split(PAR_SPLIT, 2)
comparison = True
else:
opts['engine'] = [opts['engine']]*2
if PAR_SPLIT in opts['count']:
opts['count'] = [int(k) for k in opts['count'].split(PAR_SPLIT, 2)]
comparison = True
else:
opts['count'] = [int(opts['count'])]*2
if PAR_SPLIT in opts['cutoff']:
opts['cutoff'] = [float(k) for k in opts['cutoff'].split(PAR_SPLIT, 2)]
comparison = True
else:
opts['cutoff'] = [float(opts['cutoff'])]*2
if PAR_SPLIT in opts['res']:
opts['res'] = [float(k) for k in opts['res'].split(PAR_SPLIT, 2)]
comparison = True
else:
opts['res'] = [float(opts['res'])]*2
if opts['datafile'] is not None:
data0 = load_data(os.path.expanduser(opts['datafile']))
data = data0, data0
else:
# Hack around the fact that make_data doesn't take a pair of resolutions
res = opts['res']
opts['res'] = res[0]
data0, _ = make_data(opts)
if res[0] != res[1]:
opts['res'] = res[1]
data1, _ = make_data(opts)
else:
data1 = data0
opts['res'] = res
data = data0, data1
base = make_engine(model_info[0], data[0], opts['engine'][0],
opts['cutoff'][0], opts['ngauss'][0])
if comparison:
comp = make_engine(model_info[1], data[1], opts['engine'][1],
opts['cutoff'][1], opts['ngauss'][1])
else:
comp = None
# pylint: disable=bad-whitespace
# Remember it all
opts.update({
'data' : data,
'name' : names,
'info' : model_info,
'engines' : [base, comp],
'values' : values,
})
# pylint: enable=bad-whitespace
# Set the integration parameters to the half sphere
if opts['sphere'] > 0:
set_spherical_integration_parameters(opts, opts['sphere'])
return opts
[docs]def set_spherical_integration_parameters(opts, steps):
# type: (Dict[str, Any], int) -> None
"""
Set integration parameters for spherical integration over the entire
surface in theta-phi coordinates.
"""
# Set the integration parameters to the half sphere
opts['values'].extend([
#'theta=90',
'theta_pd=%g'%(90/np.sqrt(3)),
'theta_pd_n=%d'%steps,
'theta_pd_type=rectangle',
#'phi=0',
'phi_pd=%g'%(180/np.sqrt(3)),
'phi_pd_n=%d'%(2*steps),
'phi_pd_type=rectangle',
#'background=0',
])
if 'psi' in opts['info'][0].parameters:
opts['values'].extend([
#'psi=0',
'psi_pd=%g'%(180/np.sqrt(3)),
'psi_pd_n=%d'%(2*steps),
'psi_pd_type=rectangle',
])
[docs]def parse_pars(opts, maxdim=np.inf):
# type: (Dict[str, Any], float) -> Tuple[Dict[str, float], Dict[str, float]]
"""
Generate a parameter set.
The default values come from the model, or a randomized model if a seed
value is given. Next, evaluate any parameter expressions, constraining
the value of the parameter within and between models. If *maxdim* is
given, limit parameters with units of Angstrom to this value.
Returns a pair of parameter dictionaries for base and comparison models.
"""
model_info, model_info2 = opts['info']
# Get demo parameters from model definition, or use default parameters
# if model does not define demo parameters
pars = get_pars(model_info, opts['use_demo'])
pars2 = get_pars(model_info2, opts['use_demo'])
pars2.update((k, v) for k, v in pars.items() if k in pars2)
# randomize parameters
#pars.update(set_pars) # set value before random to control range
if opts['seed'] > -1:
pars = randomize_pars(model_info, pars)
limit_dimensions(model_info, pars, maxdim)
if model_info != model_info2:
pars2 = randomize_pars(model_info2, pars2)
limit_dimensions(model_info2, pars2, maxdim)
# Share values for parameters with the same name
for k, v in pars.items():
if k in pars2:
pars2[k] = v
else:
pars2 = pars.copy()
constrain_pars(model_info, pars)
constrain_pars(model_info2, pars2)
pars = suppress_pd(pars, opts['mono'])
pars2 = suppress_pd(pars2, opts['mono'])
pars = suppress_magnetism(pars, not opts['magnetic'])
pars2 = suppress_magnetism(pars2, not opts['magnetic'])
# Fill in parameters given on the command line
presets = {}
presets2 = {}
for arg in opts['values']:
k, v = arg.split('=', 1)
if k not in pars and k not in pars2:
# extract base name without polydispersity info
s = set(p.split('_pd')[0] for p in pars)
print("%r invalid; parameters are: %s"%(k, ", ".join(sorted(s))))
return None
v1, v2 = v.split(PAR_SPLIT, 2) if PAR_SPLIT in v else (v, v)
if v1 and k in pars:
presets[k] = float(v1) if isnumber(v1) else v1
if v2 and k in pars2:
presets2[k] = float(v2) if isnumber(v2) else v2
# If pd given on the command line, default pd_n to 35
for k, v in list(presets.items()):
if k.endswith('_pd'):
presets.setdefault(k+'_n', 35.)
for k, v in list(presets2.items()):
if k.endswith('_pd'):
presets2.setdefault(k+'_n', 35.)
# Evaluate preset parameter expressions
# Note: need to replace ':' with '_' in parameter names and expressions
# in order to support math on magnetic parameters.
context = MATH.copy()
context['np'] = np
context.update((k.replace(':', '_'), v) for k, v in pars.items())
context.update((k, v) for k, v in presets.items() if isinstance(v, float))
#for k,v in sorted(context.items()): print(k, v)
for k, v in presets.items():
if not isinstance(v, float) and not k.endswith('_type'):
presets[k] = eval(v.replace(':', '_'), context)
context.update(presets)
context.update((k.replace(':', '_'), v) for k, v in presets2.items() if isinstance(v, float))
for k, v in presets2.items():
if not isinstance(v, float) and not k.endswith('_type'):
presets2[k] = eval(v.replace(':', '_'), context)
# update parameters with presets
pars.update(presets) # set value after random to control value
pars2.update(presets2) # set value after random to control value
#import pprint; pprint.pprint(model_info)
if opts['show_pars']:
if model_info.name != model_info2.name or pars != pars2:
print("==== %s ====="%model_info.name)
print(str(parlist(model_info, pars, opts['is2d'])))
print("==== %s ====="%model_info2.name)
print(str(parlist(model_info2, pars2, opts['is2d'])))
else:
print(str(parlist(model_info, pars, opts['is2d'])))
return pars, pars2
[docs]def show_docs(opts):
# type: (Dict[str, Any]) -> None
"""
show html docs for the model
"""
from .generate import make_html
from . import rst2html
info = opts['info'][0]
html = make_html(info)
path = os.path.dirname(info.filename)
url = "file://" + path.replace("\\", "/")[2:] + "/"
rst2html.view_html_wxapp(html, url)
[docs]def explore(opts):
# type: (Dict[str, Any]) -> None
"""
explore the model using the bumps gui.
"""
import wx # type: ignore
from bumps.names import FitProblem # type: ignore
from bumps.gui.app_frame import AppFrame # type: ignore
from bumps.gui import signal
is_mac = "cocoa" in wx.version()
# Create an app if not running embedded
app = wx.App() if wx.GetApp() is None else None
model = Explore(opts)
problem = FitProblem(model)
frame = AppFrame(parent=None, title="explore", size=(1000, 700))
if not is_mac:
frame.Show()
frame.panel.set_model(model=problem)
frame.panel.Layout()
frame.panel.aui.Split(0, wx.TOP)
def _reset_parameters(event):
model.revert_values()
signal.update_parameters(problem)
frame.Bind(wx.EVT_TOOL, _reset_parameters, frame.ToolBar.GetToolByPos(1))
if is_mac:
frame.Show()
# If running withing an app, start the main loop
if app:
app.MainLoop()
[docs]class Explore(object):
"""
Bumps wrapper for a SAS model comparison.
The resulting object can be used as a Bumps fit problem so that
parameters can be adjusted in the GUI, with plots updated on the fly.
"""
def __init__(self, opts):
# type: (Dict[str, Any]) -> None
from bumps.cli import config_matplotlib # type: ignore
from . import bumps_model
config_matplotlib()
self.opts = opts
opts['pars'] = list(opts['pars'])
p1, p2 = opts['pars']
m1, m2 = opts['info']
self.fix_p2 = m1 != m2 or p1 != p2
model_info = m1
pars, pd_types = bumps_model.create_parameters(model_info, **p1)
# Initialize parameter ranges, fixing the 2D parameters for 1D data.
if not opts['is2d']:
for p in model_info.parameters.user_parameters({}, is2d=False):
for ext in ['', '_pd', '_pd_n', '_pd_nsigma']:
k = p.name+ext
v = pars.get(k, None)
if v is not None:
v.range(*parameter_range(k, v.value))
else:
for k, v in pars.items():
v.range(*parameter_range(k, v.value))
self.pars = pars
self.starting_values = dict((k, v.value) for k, v in pars.items())
self.pd_types = pd_types
self.limits = None
[docs] def revert_values(self):
# type: () -> None
"""
Restore starting values of the parameters.
"""
for k, v in self.starting_values.items():
self.pars[k].value = v
[docs] def model_update(self):
# type: () -> None
"""
Respond to signal that model parameters have been changed.
"""
pass
[docs] def numpoints(self):
# type: () -> int
"""
Return the number of points.
"""
return len(self.pars) + 1 # so dof is 1
[docs] def parameters(self):
# type: () -> Any # Dict/List hierarchy of parameters
"""
Return a dictionary of parameters.
"""
return self.pars
[docs] def nllf(self):
# type: () -> float
"""
Return cost.
"""
# pylint: disable=no-self-use
return 0. # No nllf
[docs] def plot(self, view='log'):
# type: (str) -> None
"""
Plot the data and residuals.
"""
pars = dict((k, v.value) for k, v in self.pars.items())
pars.update(self.pd_types)
self.opts['pars'][0] = pars
if not self.fix_p2:
self.opts['pars'][1] = pars
result = run_models(self.opts)
limits = plot_models(self.opts, result, limits=self.limits)
if self.limits is None:
vmin, vmax = limits
self.limits = vmax*1e-7, 1.3*vmax
import pylab
pylab.clf()
plot_models(self.opts, result, limits=self.limits)
[docs]def main(*argv):
# type: (*str) -> None
"""
Main program.
"""
opts = parse_opts(argv)
if opts is not None:
if opts['seed'] > -1:
print("Randomize using -random=%i"%opts['seed'])
np.random.seed(opts['seed'])
if opts['html']:
show_docs(opts)
elif opts['explore']:
opts['pars'] = parse_pars(opts)
if opts['pars'] is None:
return
explore(opts)
else:
compare(opts)
if __name__ == "__main__":
main(*sys.argv[1:])