1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
|
.. _users-guide-index:
############
User's Guide
############
Bumps is designed to determine the ideal model parameters for a given
set of measurements, and provide uncertainty on the parameter values.
This is an inverse problem, where measured data can be predicted from
theory, but theory cannot be directly inferred from measured data. This
means that bumps must search through parameter space, calling the theory
function many times to find the parameter values that are most consistent
with the data.
Unlike traditional Levenburg-Marquardt fitting programs, Bumps does not
require normally distributed measurement uncertainty. If a measurement
comes from counting statistics, for example, you can define your model with
poisson probability rather than gaussian probability. Parameter values
can have constraints. For example, if the size of a sample is known to
within 5%, the size parameter in the model can set to a gaussian distribution
with a standard deviation of 5%. Simple bounds are also supported. Parameter
expressions allow you to set the value of a parameter based on other
parameters, which allows simultaneous fitting of multiple datasets to
different models without having to define a specialized fit function.
Bumps includes Markov chain Monte Carlo (MCMC) methods to compute the
joint distribution of parameter probabilities. These methods require
hundreds of thousand function calls to explore the search space, so
for moderately complex problems, you need to run in parallel. Bumps
can fully utilize multiple cores on one computer, or through MPI, it
runs on supercomputing clusters.
..
# Data handling has been removed so that we can ship a pure python package.
In addition to inverse problem solving, bumps has acquired code for
theory building and data handling. For example, many problems have
measurements in which the instrument resolution plays a role, and
the theory function must be convolved with a data dependent resolution
function.
:ref:`intro-guide`
Model scripts associate a sample description with data and fitting
options to define the system you wish to refine.
:ref:`data-guide`
Data management is the responsibility of the modeller. Bumps
provides a generic data loader :mod:`bumps.data` with a key-value
header section followed by columns of numeric data, but it is up to
the model script to compute the theory along with any resolution
effects and compare that with the data. The :class:`bumps.curve.Curve`
class associates a theory function with measurements with Gaussian
uncertainty, and :class:`bumps.curve.PoissonCurve` does the same for
measurements following Poisson statistics.
:ref:`parameter-guide`
The adjustable values in each component of the system are defined
by :class:`Parameter <bumps.parameter>` objects. When you
set the range on a parameter, the system will be able to automatically
adjust the value in order to find the best match between theory
and data.
:ref:`fitting-guide`
One or more experiments can be combined into a
:class:`FitProblem <bumps.fitproblem.FitProblem>`. This is then
given to one of the many fitters, such as
:class:`DEFit <bumps.fitters.PTFit>`, which adjust the fitting
parameters, trying to find the best fit. See :ref:`optimizer-guide`
for a description of available optimizers and :ref:`option-guide` for
a description of the bumps options. Entropy can be calculated when
the fit is complete. See :ref:`entropy-guide`.
.. toctree::
:hidden:
intro.rst
data.rst
experiment.rst
parameter.rst
fitting.rst
optimizer.rst
options.rst
entropy.rst
|
