Welcome to OMpy’s documentation!¶
This is ompy, the Oslo method in python. It contains all the
functionality needed to go from a raw coincidence matrix, via unfolding
and the first-generation method, to fitting a level density and
gamma-ray strength function. It also supports uncertainty propagation by
Monte Carlo. If you want to try the package before installation, you may
simply click here to launch it on Binder.
Citing¶
If you cite OMpy, please use the version-specific DOI found by clicking the Zenodo badge above; create a new version if necessary. The DOI is to last published version; the master branch may be ahead of the published version.
The full version (including the git commit) can also be obtained from ompy.__full_version__ after installation.
An article describing the implementation more detailled will follow shortly. A draft can be read on arXiv: [A new software implementation of the Oslo method with complete uncertainty propagation](https://arxiv.org/abs/1904.13248).
Installation¶
Start off by downloading ompy:
git clone --recurse https://github.com/oslocyclotronlab/ompy/
where the --recurse flag specifies, that all submodules shall be downloaded as well.
Dependencies¶
Get and compile MultiNest (use the cmake version from github.com/JohannesBuchner/MultiNest). The goal is to create lib/libmultinest.so
git clone https://github.com/JohannesBuchner/MultiNest cd MultiNest/build cmake .. make sudo make installMultinest had following hard dependencies:
lapackandblas. To use MPI, additionally openmp has to be installed (probably does not work for MAC users, see below.). With apt-get you may fix the dependencies by:sudo apt-get install liblapack-dev libblas-dev libomp-dev
If you still get an error like:
OSError: libmultinest.so: cannot open shared object file: No such file or directory
visit http://johannesbuchner.github.io/PyMultiNest/install .
We require
python>=3.7. Make sure you use the correct python version and the correctpip. You may need to replacepythonbypython3andpipbypip3in the examples below. Runpython --versionandpip --versionto check whether you have a sufficient python version.All other dependencies can be installed automatically by
pip(see below). Alternatively, make sure to install all requirements listed inrequirements.txt, eg. usingcondaorapt-get. You may try following inconda(untested)conda install --file requirements.txt
For openMP support (optional), install
libomp. Easiest on linux/ubuntu:sudo apt-get install libomp-devor MACbrew install libomp.Many examples are written with jupyter notebooks, so you probably want to install this, too.
OMpy package¶
There are two main options on how to install OMpy. We will start off with our recommendation, that is with the -e flag is a local project in “editable” mode. This way, you will in principal not have to reinstall ompy if you pull a new version from git or create any local changes yourself.
Note: If you change any of the cython modules (*.pyx files), you will have to reinstall/recompile anyways. As they may have changed upstream, the easiest is probably if you install again every time you pull.
pip install -e .
If you want to install at the system specific path instead, use
pip install .
For debugging, you might want to compile the cython modules “manually”. The first line here is just to delete any existing cython modules in order to make sure that they will be recompiled.
rm ompy/*.so
rm ompy/*.c
python setup.py build_ext --inplace
Troubleshooting¶
Docker container¶
If you don’t succeed with the above, we also provide a Docker container via dockerhub, see https://hub.docker.com/r/oslocyclotronlab/ompy. However, for everyday usage, we think it’s easier to install the package normally on your machine
Python version¶
If you had some failed attempts, you might try to uninstall ompy before retrying the stepts above:
pip uninstall ompy
Note that we require python 3.7 or higher. If your standard python and pip link to python 2, you may have to use python3 and pip3.
Try to reinstall¶
If you changed / if after a git pull there have been any changes to one of the cython modules, you will have to reinstall/recompile anyways: pip install -e ..
OpenMP / MAC¶
If you don’t have OpenMP / have problems installing it (see above), you can install without OpenMP. Type export ompy_OpenMP=False in the terminal before the setup above.
Cloned the repo before September 2019¶
NB: Read this (only) if you have cloned the repo before October 2019: We cleaned the repository from old comits clogging the repo (big data files that should never have been there). Unfortunetely, this has the sideeffect that the history had to be rewritten: Previous commits now have a different SHA1 (git version keys). If you need anything from the previous repo, see ompy_Archive_Sept2019. This will unfortunately also destroy references in issues. The simplest way to get the new repo is to rerun the installation instructions below.
General usage¶
All the functions and classes in the package are available in the main module. You get everything by importing the package
import ompy
The overarching philosophy is that the package shall be flexible and transparent to use and modify. All of the “steps” in the Oslo method are implemented as classes with a common structure and call signature. If you understand one class, you’ll understand them all, making extending the code easy.
As the Oslo method is a complex method involving dozen of variables which can be daunting for the uninitiated, many class attributes have default values that should give satisfying results. Attributes that should be modified even though it is not strictly necessary to do so will give annoying warnings. The documentation and docstrings give in-depth explanation of each variable and its usage.
Getting started¶
[1]:
%load_ext autoreload
%autoreload 2
%matplotlib notebook
[2]:
import matplotlib.pyplot as plt
import numpy as np
import ompy as om
import logging
[3]:
om.__full_version__
[3]:
'0.8.0.dev0+97cd212'
[4]:
# For reproducability we seed the random generator.
# Note that by default several other classes in ompy, such as all
# classes with multinest calculations have a default seed, too
np.random.seed(1382398)
[5]:
# get smaller files for the online version
plt.rcParams["figure.dpi"] = 70
Loading and example raw spectra¶
The \(^{164}\mathrm{Dy}\) data used below has been gathered from following experiment: Nyhus, H. T. et al. (2010). DOI: 10.1103/physrevc.81.024325 and is reanalyzed in Renstrøm, T. et al. (2018). DOI: 10.1103/physrevc.98.054310
[6]:
# Import raw matrix into instance of om.Matrix() and plot it
raw = om.example_raw('Dy164')
# To use you own data, uncomment/adapt the line below instead
# raw = om.Matrix(path="/path/to/matrix.ending")
# Plot the entire matrix
raw_org = raw.copy() # workaround due to execution order in jupyter notebook
# (calculations are performed before plotting, but we make a cut to raw further down)
raw_org.plot();
# Note: We use the semi-colon `;` at the end of the line to silence the output
# in jupyter notebook. This is not necessary, but otherwise you get something like
# this below printed every time:
#(<matplotlib.collections.QuadMesh at 0x7fafbc422eb8>,
# <matplotlib.axes._subplots.AxesSubplot at 0x7fafc0944a20>,
# <Figure size 640x480 with 2 Axes>)
Matrix manipulation¶
The core of the Oslo method involves working with two dimensional spectra. Starting with a raw matrix of \(E_x\)-\(E_\gamma\) coincidences, you typically want to unfold the counts along the gamma-energy axis and then apply the first-generation method to obtain the matrix of first-generation, or primary, gamma rays from the decaying nucleus.
The two most important utility classes in the package are Matrix() and Vector(). They are used to store matrices (2D) or vectors (1D) of numbers, typically spectra of counts, along with energy calibration information.
As these underpin the entire package, they contain many useful functions to make life easier. Loading and saving to several formats, plotting, projections, rebinning and cutting, to mention a few. See the documentation for an exhaustive list.
Their basic structure is:
[7]:
# mat = ompy.Matrix()
mat = raw
mat.values # A 2D numpy array
mat.Ex # Array of mid-bin energy values for axis 0 (i.e. the row axis, or y axis)
mat.Eg # Array of mid-bin energy values for axis 1 (i.e. the column axis, or x axis)
print("The first gamma-ray energies:\n", mat.Eg[0:10])
The first gamma-ray energies:
[ 0. 19.364 38.728 58.092 77.456 96.82 116.184 135.548 154.912
174.276]
[8]:
# We can also create a vector, which is useful to store the NLD and gSF.
values = np.arange(11)
E = np.linspace(0, 10, num=11)
fig, ax = plt.subplots(figsize=(2,2), constrained_layout=True)
vec = om.Vector(values=values, E=E)
vec.values # A 1D numpy array
vec.E # Array of lower-bin-edge energy values for the single axis
vec.plot(ax=ax);
[9]:
# Cut away counts above the diagonal
# Remember: Think about what you do here. If you cut them away, they will not
# be used in unfolding etc. This may or may not be what you want.
# Note that the raw matrix we read in above has been cut already, so the difference here is not so large.
raw.cut_diagonal(E1=(800, 0), E2=(7500, 7300))
raw.cut('Ex', 0, 8400)
raw.plot();
Note that Matrix, Vector and several other classes contain mutable objects. If you work on them, you might want to create a deepcopy. For Matrix, Vector this can be archived by the convince method X.copy, otherwise use copy.deepcopy.
[10]:
# The "right" way if you don't want to change the original matrix
raw_big_cut = raw.copy()
raw_big_cut.cut('Ex', 0, 4000)
print(raw.Ex.max(), raw_big_cut.Ex.max())
8300.0 3980.0
[11]:
# The "wrong" way if you don't want to change the original matrix
raw_big_cut2 = raw_big_cut
raw_big_cut2.cut('Ex', 0, 2000)
print(raw_big_cut.Ex.max(), raw_big_cut2.Ex.max())
# oups!: suddenly also `raw_big_cut` was cut, not only raw_big_cut2
1940.0 1940.0
[12]:
# Plot projections
raw.plot_projection('Ex', Emin=1800, Emax=2600, kind="step");
Note that you can IPython’s has tools to quickly access information on a function, namely the ? character to explore documentation, the ?? characters to explore source code, and the Tab key (or double-tab) for auto-completion. Try it out uncommenting the function below.
[13]:
## Uncomment these lines to query a function
# ?raw.plot_projection
Unfolding¶
Get a response matrix¶
[14]:
logger = om.introspection.get_logger('response', 'INFO')
# Then do the same using OMpy functionality:
# You may need to adpot this to whereever you response matrixes are stored
folderpath = "../OCL_response_functions/oscar2017_scale1.15"
# Energy calibration of resulting response matrix:
Eg = raw.Eg
# Experimental relative FWHM at 1.33 MeV of resulting array
fwhm_abs = 30 # (30/1330 = 2.25% )
# Magne recommends 1/10 of the actual resolution for unfolding purposes
response = om.Response(folderpath)
R_ompy_view, R_tab_view = response.interpolate(Eg, fwhm_abs=fwhm_abs, return_table=True)
R_ompy_unf, R_tab_unf = response.interpolate(Eg, fwhm_abs=fwhm_abs/10, return_table=True)
R_ompy_view.plot(title="Response matrix", vmin=5e-5, vmax=5e-1,
scale="log");
2020-01-16 19:35:48,459 - ompy.response - INFO - Note: Spectra outside of 200.0 and 20000.0 are extrapolation only.
2020-01-16 19:35:52,808 - ompy.response - INFO - Note: Spectra outside of 200.0 and 20000.0 are extrapolation only.
[15]:
### Perform the unfolding
[16]:
# You can decide to log information and set the logging level (info/debug)
logger = om.introspection.get_logger('unfolder', 'INFO')
# We need to remove negative counts (unphysical) in the raw matrix before unfolding:
raw_positive = raw.copy()
raw_positive.fill_and_remove_negative(window_size=2)
# With compton subtraction and all tweaks
unfolder= om.Unfolder(response=R_ompy_unf)
unfolder.use_compton_subtraction = True # default
unfolder.response_tab = R_tab_unf
# Magne suggests some "tweaks" for a better unfolding performance. Default is 1 for all.
unfolder.FWHM_tweak_multiplier = {"fe": 1., "se": 1.1,
"de": 1.3, "511": 0.9}
unfolded = unfolder(raw_positive)
unfolded.plot();
[17]:
### Generate the first generation matrix
[18]:
firstgen = om.FirstGeneration()
primary = firstgen(unfolded)
primary.plot();
Propagating statistical uncertainties¶
In order to propagate the statistical uncertainties from the raw matrix, we use an ensemble based method. We start of my generating en enseble of raw-like matrixes. The raw counts are poisson distributed. If we had counted one another time, we would get slightly different results.
More precisely, the counts of the matrix containing prompt+bg events and the background events bg are each poisson distributed, where we have raw = (prompt+bg) - bg_ratio * bg. The ratio bg_ratio is determined by the ratio of the time gate lengths taken to obtain the prompt+bg and bg spectra. If a bg spectrum is provided to the Ensemble class, it will calculate the raw spectrum according to the equaltion above. Otherwise, the provided raw spectrum itself is
assumed to be poisson distributed.
We take the number of counts \(k_i\) in bin \(i\) of the raw matrix \(R\) as an estimate for the Poisson parameter (“the mean”) \(λ_i\) . Note that it is an unbiased estimator for \(λ_i\), since \(E(k) = λ\). To generate a member matrix \(R_l\) of the MC ensemble, we replace the counts in each bin \(i\) by a random draw from the distribution \(\operatorname{Poisson}(k_i)\).
The class Ensemble() provides this feature. Its basic usage is:
[19]:
logger = om.introspection.get_logger('ensemble', 'INFO')
# Tell the `Ensemble` class which raw spectrum, what kind of undolfer and first
# generations method to use.
# Note: This will have the same setting as above. We could for example have
# set the first generations method to use a different "valley_collection", or a
# differnt type of "multiplicity_estimation"
ensemble = om.Ensemble(raw=raw_positive)
ensemble.unfolder = unfolder
ensemble.first_generation_method = firstgen
# Generates N perturbated members; here just 10 to speed it up
# the `regernerate` flag ensures, that we don't load from disk; which might result in expected results
# if we have changed something in the input `raw` matrix.
ensemble.generate(10, regenerate=True)
2020-01-16 19:35:59,183 - ompy.ensemble - INFO - Start normalization with 3 cpus
2020-01-16 19:35:59,277 - ompy.ensemble - INFO - Generating 0
2020-01-16 19:35:59,343 - ompy.ensemble - INFO - Generating 1
2020-01-16 19:35:59,379 - ompy.ensemble - INFO - Generating 2
2020-01-16 19:36:01,739 - ompy.ensemble - INFO - Generating 3
2020-01-16 19:36:01,965 - ompy.ensemble - INFO - Generating 4
2020-01-16 19:36:02,085 - ompy.ensemble - INFO - Generating 5
2020-01-16 19:36:04,192 - ompy.ensemble - INFO - Generating 6
2020-01-16 19:36:04,553 - ompy.ensemble - INFO - Generating 7
2020-01-16 19:36:04,655 - ompy.ensemble - INFO - Generating 8
2020-01-16 19:36:06,858 - ompy.ensemble - INFO - Generating 9
The generated members are saved to disk and can be retrieved. Unfolded members can be retrieved as ensemble.get_unfolded(i), for example. Their standard deviation is ensemble.std_unfolded for the unfolded matrixes, etc.
We can now plot the standard deviation of all ensemble members for the raw, unfolded and first generation spectrum
[20]:
i_unfolded = 9
matrix = ensemble.get_unfolded(i_unfolded)
matrix.plot(title=f"Unfolded matrix #{i_unfolded}")
# Following commands plots all std. deviations
ensemble.plot();
Extract Nuclear level density and gamma strength function¶
After matrix has been cut, unfolded and firstgen’d, perhaps ensembled, its nuclear level density (nld) and gamma strength function (\(\gamma\)SF) can be extracted using the Extractor() class.
The method relies on the relation
where \(P(E_x, E_\gamma)\) is the first-generation spectrum normalized to unity for each \(E_x\) bin. Furthermore, if we assume that the \(\gamma\) decay at high \(E_x\) is dominated by dipole radiation the transmission coefficient \(\mathcal{T}\) is related to the dipole \(\gamma\)-ray strength function \(f(E_\gamma)\) by the relation
If you have reasons to assume a different multipose decomposition, you may of course calculate the transmission coefficient \(\mathcal{T}\) from the \(\gamma\)-ray strength function produced here and apply the decomposition you prefer.
[21]:
# cutout = primary.trapezoid(Ex_min=4000, Ex_max=8000, Eg_min=1000, inplace=False)
# cutout_std = ensemble.std_firstgen.trapezoid(Ex_min=4000, Ex_max=8000, Eg_min=1000, inplace=False)
# extractor = om.Extractor()
# nld, gsf = extractor.decompose(cutout, std=cutout_std)
When extracting NLD and GSF from an ensemble, a trapezoidal cutout must be performed on each ensemble member. This is achieved by Action() which allows for delayed function calls on matrices and vectors. This way we don’t cut the raw matrix at Ex_min, but this will only happen before the extraction.
[22]:
trapezoid_cut = om.Action('matrix')
trapezoid_cut.trapezoid(Ex_min=4000, Ex_max=7000, Eg_min=1000, inplace=True)
extractor = om.Extractor()
extractor.trapezoid = trapezoid_cut
# Running the lines below directy, would most probably
# result in a error like
# The AssertionError: Ex and Eg must have the same step size
#
# Why? The extraction assumes that Ex and Eg have the same binning. Thus we
# need to rebin the ensemble. This works will work inplace.
# Note: As always, be careful will mid-bin vs lower bin calibration.
# E_rebinned = ensemble.get_firstgen(0).Ex
#
E_rebinned = np.arange(100., 8500, 200)
ensemble.rebin(E_rebinned, member="firstgen")
ensemble.plot();
Now we can extract the NLD and \(\gamma SF\) for \(N\) of the samples of the ensemble.
Note: The old software extended the decomposition beyond the Ex=Eg line by a resolution dE. This is now optional and we changed the default to not do this any longer, but rather assume that the rebinning above has been performed with a binsize of approx. the FWHM of the bin with the worst resolution (usually (Ex_max, Eg_max)).
[23]:
extractor.extract_from(ensemble, regenerate=True)
The resulting nld and gsf are saved to disk and exposed as extractor.nld and extractor.gsf
[24]:
mat = ensemble.get_firstgen(0).copy()
std = ensemble.std_firstgen.copy()
trapezoid_cut.act_on(mat)
trapezoid_cut.act_on(std)
_, _, product = extractor.decompose(mat, std, product=True)
fig, ax = plt.subplots(2,1)
om.normalize_rows(mat.values)
mat.plot(ax=ax[0], scale="log", vmin=1e-3, vmax=1e-1)
product.plot(ax=ax[1], scale="log", vmin=1e-3, vmax=1e-1)
[24]:
(<matplotlib.collections.QuadMesh at 0x7f6c946104e0>,
<matplotlib.axes._subplots.AxesSubplot at 0x7f6c94a30828>,
<Figure size 448x336 with 4 Axes>)
Plotting the results before normalization¶
[25]:
extractor.plot(plot_mean=False)
[25]:
(<Figure size 448x336 with 2 Axes>,
array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f6c9449ca20>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7f6c9462d710>],
dtype=object))
Or maybe you are more used to displaying the results with std. deviations?
[26]:
extractor.plot(plot_mean=True)
[26]:
(<Figure size 448x336 with 2 Axes>,
array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f6c943fb940>,
<matplotlib.axes._subplots.AxesSubplot object at 0x7f6c94d5e7f0>],
dtype=object))
[27]:
# let's remove the nan-valued elements (unconstrained elements) for the further analysis
for nld in extractor.nld:
nld.cut_nan()
for gsf in extractor.gsf:
gsf.cut_nan()
# the "mean" nld at this stage; we'll use it later, but it's not a good estimate at this
# stage (see article)
nld_mean = extractor.ensemble_nld()
Normalization¶
Does it still look strange? probably because you are only used to see the normalized results.
1) Manual normalization¶
[28]:
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
def plot_transformed(alpha, A=1, B=1):
fig, ax = plt.subplots(1, 2, constrained_layout=True)
for nld, gsf in zip(extractor.nld, extractor.gsf):
nld.transform(const=A, alpha=alpha, inplace=False).plot(ax=ax[0], scale="log", color='k', alpha=1/10)
gsf.transform(const=B, alpha=alpha, inplace=False).plot(ax=ax[1], scale="log", color='k', alpha=1/10)
ax[0].set_title("Level density")
ax[1].set_title("γSF")
plot_transformed(alpha=0.0015)
2) Normalization through external data for one (nld, gsf) set¶
The normalization ensures that we find the physical solution, so we remove the degeneracy that is in principal inherent to decomposition of NLD and \(\gamma\)SF:
Note: This is the transformation eq (3), Schiller2000.
As external data for the normalization we commonly use: 1. the discrete leves, binned with the resolution of our data (and potentially also smoothed) 2. The NLD at Sn, derived from D0 and a spin distribution 3. The average total radiative width \(\Gamma_\gamma\).
1. Sequentially:¶
Traditionally we have choosen a sequential normalization, where the NLD is normalized first to receive a set \(\alpha\). Then we obtain the scaling parameter \(B\) of the \(\gamma\)SF from a normalization to the experimental \(\Gamma_\gamma\).
Let’s first normalize the mean nld from the extractor.
The normalization will take some time (≲ 30 seconds). The essential output of multinest is saved to disk, and some output is redirected to disk.
[29]:
normlog = om.introspection.get_logger('normalizer_nld', 'INFO')
nldnorm = om.NormalizerNLD(nld=nld_mean, discrete='../example_data/discrete_levels_Dy164.txt')
[30]:
norm_pars = om.NormalizationParameters(name="164Dy")
norm_pars.D0 = [6.8, 0.6] # eV
norm_pars.Sn = [7.658, 0.001] # MeV
norm_pars.spincutModel = 'Disc_and_EB05' # see eg. Guttormsen et al., 2017, PRC 96, 024313
norm_pars.spincutPars = {"mass":164, "NLDa":18.12, "Eshift":0.31,
"Sn": norm_pars.Sn[0], "sigma2_disc":[1.5,3.6]}
norm_pars.Jtarget = 0 # A-1 nucleus
nldnorm.normalize(limit_low=[0, 1.5], limit_high=[3, 5.5], norm_pars=norm_pars)
2020-01-16 19:36:13,431 - ompy.normalizer_nld - INFO -
---------
Normalizing nld #0
2020-01-16 19:36:14,086 - ompy.normalizer_nld - INFO - DE results:
┌───────────────────┬────────────────────┬────────────────────┬─────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞═══════════════════╪════════════════════╪════════════════════╪═════════════════════╡
│ 5.310336107841815 │ 1.5908471110306468 │ 0.6128836080973475 │ -0.6436785028696971 │
└───────────────────┴────────────────────┴────────────────────┴─────────────────────┘
2020-01-16 19:36:14,087 - ompy.normalizer_nld - INFO - Starting multinest
analysing data from multinest/nld_norm_0_.txt
2020-01-16 19:36:31,149 - ompy.normalizer_nld - INFO - Multinest results:
┌───────────────┬─────────────────┬─────────────────┬────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞═══════════════╪═════════════════╪═════════════════╪════════════════╡
│ 5.312 ± 0.038 │ 1.5907 ± 0.0098 │ 0.6130 ± 0.0059 │ -0.646 ± 0.040 │
└───────────────┴─────────────────┴─────────────────┴────────────────┘
[31]:
nldnorm.plot();
Observe that you might get strange results, i.e. unexpected results here, as you use the (potentially erroneous determinated) uncertainties of nld_mean in the normalzation, instead of the proper normalization below.
[32]:
normlog = om.introspection.get_logger('normalizer_gsf', 'INFO')
gsfnorm = om.NormalizerGSF(normalizer_nld=nldnorm, gsf=extractor.gsf[0])
# to be use for gsf normalization
norm_pars.Gg = [112., 20.] #meV
gsfnorm.norm_pars = norm_pars
gsfnorm.model_high.Efit = [4.5, 6.]
[33]:
gsfnorm.normalize()
gsfnorm.plot()
2020-01-16 19:36:31,290 - ompy.normalizer_gsf - INFO - Normalizing #0
[33]:
(<Figure size 448x336 with 1 Axes>,
<matplotlib.axes._subplots.AxesSubplot at 0x7f6c9469a048>)
It’s often instructive to plot the extrapolation of the \(\gamma\)SF; with the interactive code below, we can check the influence of choosing different fit regions. The latest choice is kept for the simultaneous normalization below.
[34]:
gsfnorm.plot_interactive()
2020-01-16 19:36:31,419 - ompy.normalizer_gsf - INFO - Normalizing #0
2. Simultaneous:¶
We now propose to normalize the NLD and \(\gamma\)SF simultaneously instead. This way, we are guaranteed to get matching combinations of the normalization parameters \(A\), \(B\) and \(\alpha\) for a given ensemble member.
[35]:
simnorm = om.introspection.get_logger('normalizer_simultan', 'INFO')
simnorm = om.NormalizerSimultan(normalizer_nld=nldnorm,
normalizer_gsf=gsfnorm)
simnorm.multinest_kwargs["n_live_points"] = 300 # running faster than the default 400 but less precise(!)
simnorm.normalize(gsf=extractor.gsf[0], nld=extractor.nld[0])
2020-01-16 19:36:32,466 - ompy.normalizer_nld - INFO - DE results:
┌────────────────────┬────────────────────┬─────────────────────┬─────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞════════════════════╪════════════════════╪═════════════════════╪═════════════════════╡
│ 3.7643483546390715 │ 1.9401560826886584 │ 0.49746926163445127 │ -0.1827625935820638 │
└────────────────────┴────────────────────┴─────────────────────┴─────────────────────┘
2020-01-16 19:36:32,508 - ompy.normalizer_gsf - INFO - Normalizing #0
2020-01-16 19:36:32,514 - ompy.normalizer_simultan - INFO - DE results/initial guess:
┌────────────────────┬────────────────────┬─────────────────────┬─────────────────────┬───────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │ B │
╞════════════════════╪════════════════════╪═════════════════════╪═════════════════════╪═══════════════════╡
│ 3.7643483546390715 │ 1.9401560826886584 │ 0.49746926163445127 │ -0.1827625935820638 │ 128.4130378428177 │
└────────────────────┴────────────────────┴─────────────────────┴─────────────────────┴───────────────────┘
2020-01-16 19:36:32,515 - ompy.normalizer_simultan - INFO - Starting multinest:
analysing data from multinest/sim_norm_0_.txt
2020-01-16 19:38:02,641 - ompy.normalizer_simultan - INFO - Multinest results:
┌─────────────┬───────────────┬───────────────┬──────────────┬─────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │ B │
╞═════════════╪═══════════════╪═══════════════╪══════════════╪═════════╡
│ 3.93 ± 0.52 │ 1.921 ± 0.050 │ 0.495 ± 0.014 │ -0.14 ± 0.23 │ 40 ± 14 │
└─────────────┴───────────────┴───────────────┴──────────────┴─────────┘
[36]:
simnorm.plot();
3. Normalization the whole ensemble¶
We have now also developed the toolset to normalize each member of the extractor ensemble separatly. This should provide a statistically more sound and robust normalization.
Why so? As the \(\chi^2\) error function is degenerate, a good minimizer should return sets of (nld, gsf) that need to be normalized with different coefficients \(A\), \(B\) and \(\alpha\). Thus we should normalize each of these sets independently, and build up an uncertainty band only after the normalization. (Instead of the traditional approach of normalizing the mean of the sets)
Again, you decide whether you normalize sequencially, or, as we recommend, to normalize simultaneously.
Note that this will this may take several minutes!
Sequential normalization¶
[37]:
normlog = om.introspection.get_logger('ensembleNormalizer', 'INFO')
ensemblenorm_seq = om.EnsembleNormalizer(extractor=extractor, normalizer_nld=nldnorm,
normalizer_gsf=gsfnorm)
ensemblenorm_seq.normalize()
2020-01-16 19:38:02,737 - ompy.ensembleNormalizer - INFO - Start normalization with 3 cpus
2020-01-16 19:38:02,858 - ompy.ensembleNormalizer - INFO -
---------
Normalizing #0
2020-01-16 19:38:02,909 - ompy.ensembleNormalizer - INFO -
---------
Normalizing #1
2020-01-16 19:38:02,954 - ompy.normalizer_nld - INFO -
---------
Normalizing nld #0
2020-01-16 19:38:02,961 - ompy.ensembleNormalizer - INFO -
---------
Normalizing #2
2020-01-16 19:38:02,982 - ompy.normalizer_nld - INFO -
---------
Normalizing nld #1
2020-01-16 19:38:03,050 - ompy.normalizer_nld - INFO -
---------
Normalizing nld #2
2020-01-16 19:38:04,031 - ompy.normalizer_nld - INFO - DE results:
┌────────────────────┬────────────────────┬────────────────────┬─────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞════════════════════╪════════════════════╪════════════════════╪═════════════════════╡
│ 3.8305851675914617 │ 1.9171967932850047 │ 0.4929099075787158 │ -0.1065935057988842 │
└────────────────────┴────────────────────┴────────────────────┴─────────────────────┘
2020-01-16 19:38:04,033 - ompy.normalizer_nld - INFO - Starting multinest
2020-01-16 19:38:04,195 - ompy.normalizer_nld - INFO - DE results:
┌────────────────────┬────────────────────┬────────────────────┬──────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞════════════════════╪════════════════════╪════════════════════╪══════════════════════╡
│ 3.8331327880501833 │ 1.9181229729650628 │ 0.4929191206039593 │ -0.10690422886975122 │
└────────────────────┴────────────────────┴────────────────────┴──────────────────────┘
2020-01-16 19:38:04,198 - ompy.normalizer_nld - INFO - Starting multinest
2020-01-16 19:38:04,421 - ompy.normalizer_nld - INFO - DE results:
┌───────────────────┬───────────────────┬─────────────────────┬─────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞═══════════════════╪═══════════════════╪═════════════════════╪═════════════════════╡
│ 3.764351801902385 │ 1.940155820648791 │ 0.49746926521790297 │ -0.1827629879224515 │
└───────────────────┴───────────────────┴─────────────────────┴─────────────────────┘
2020-01-16 19:38:04,423 - ompy.normalizer_nld - INFO - Starting multinest
analysing data from multinest/nld_norm_0_.txt
2020-01-16 19:38:25,755 - ompy.normalizer_nld - INFO - Multinest results:
┌─────────────┬───────────────┬───────────────┬──────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞═════════════╪═══════════════╪═══════════════╪══════════════╡
│ 3.88 ± 0.51 │ 1.934 ± 0.051 │ 0.499 ± 0.015 │ -0.19 ± 0.24 │
└─────────────┴───────────────┴───────────────┴──────────────┘
2020-01-16 19:38:25,810 - ompy.normalizer_gsf - INFO - Normalizing #0
2020-01-16 19:38:25,868 - ompy.ensembleNormalizer - INFO -
---------
Normalizing #3
2020-01-16 19:38:25,958 - ompy.normalizer_nld - INFO -
---------
Normalizing nld #3
analysing data from multinest/nld_norm_1_.txt
2020-01-16 19:38:27,616 - ompy.normalizer_nld - INFO - Multinest results:
┌─────────────┬───────────────┬───────────────┬──────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞═════════════╪═══════════════╪═══════════════╪══════════════╡
│ 3.98 ± 0.55 │ 1.909 ± 0.052 │ 0.495 ± 0.014 │ -0.12 ± 0.23 │
└─────────────┴───────────────┴───────────────┴──────────────┘
2020-01-16 19:38:27,676 - ompy.normalizer_gsf - INFO - Normalizing #1
2020-01-16 19:38:27,742 - ompy.ensembleNormalizer - INFO -
---------
Normalizing #4
2020-01-16 19:38:27,812 - ompy.normalizer_nld - INFO -
---------
Normalizing nld #4
2020-01-16 19:38:27,814 - ompy.normalizer_nld - INFO - DE results:
┌───────────────────┬────────────────────┬─────────────────────┬──────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞═══════════════════╪════════════════════╪═════════════════════╪══════════════════════╡
│ 3.851698375780031 │ 1.9153803821693023 │ 0.49272814463223574 │ -0.10388822618931262 │
└───────────────────┴────────────────────┴─────────────────────┴──────────────────────┘
2020-01-16 19:38:27,816 - ompy.normalizer_nld - INFO - Starting multinest
analysing data from multinest/nld_norm_2_.txt
2020-01-16 19:38:29,211 - ompy.normalizer_nld - INFO - Multinest results:
┌─────────────┬───────────────┬───────────────┬──────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞═════════════╪═══════════════╪═══════════════╪══════════════╡
│ 3.99 ± 0.52 │ 1.910 ± 0.056 │ 0.494 ± 0.015 │ -0.12 ± 0.25 │
└─────────────┴───────────────┴───────────────┴──────────────┘
2020-01-16 19:38:29,250 - ompy.normalizer_gsf - INFO - Normalizing #2
2020-01-16 19:38:29,293 - ompy.ensembleNormalizer - INFO -
---------
Normalizing #5
2020-01-16 19:38:29,359 - ompy.normalizer_nld - INFO -
---------
Normalizing nld #5
2020-01-16 19:38:29,387 - ompy.normalizer_nld - INFO - DE results:
┌───────────────────┬────────────────────┬────────────────────┬─────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞═══════════════════╪════════════════════╪════════════════════╪═════════════════════╡
│ 3.861726013691395 │ 1.9124678737912915 │ 0.4923146887074925 │ -0.0970846112341923 │
└───────────────────┴────────────────────┴────────────────────┴─────────────────────┘
2020-01-16 19:38:29,388 - ompy.normalizer_nld - INFO - Starting multinest
2020-01-16 19:38:30,983 - ompy.normalizer_nld - INFO - DE results:
┌────────────────────┬────────────────────┬─────────────────────┬──────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞════════════════════╪════════════════════╪═════════════════════╪══════════════════════╡
│ 3.8209946450179575 │ 1.9345616190068577 │ 0.49702253831327836 │ -0.17508789720810342 │
└────────────────────┴────────────────────┴─────────────────────┴──────────────────────┘
2020-01-16 19:38:30,986 - ompy.normalizer_nld - INFO - Starting multinest
analysing data from multinest/nld_norm_3_.txt
analysing data from multinest/nld_norm_5_.txt
2020-01-16 19:38:54,978 - ompy.normalizer_nld - INFO - Multinest results:
┌─────────────┬───────────────┬───────────────┬──────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞═════════════╪═══════════════╪═══════════════╪══════════════╡
│ 4.01 ± 0.55 │ 1.926 ± 0.053 │ 0.498 ± 0.014 │ -0.19 ± 0.24 │
└─────────────┴───────────────┴───────────────┴──────────────┘
2020-01-16 19:38:55,012 - ompy.normalizer_gsf - INFO - Normalizing #5
2020-01-16 19:38:55,038 - ompy.normalizer_nld - INFO - Multinest results:
┌─────────────┬───────────────┬───────────────┬──────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞═════════════╪═══════════════╪═══════════════╪══════════════╡
│ 4.08 ± 0.58 │ 1.909 ± 0.060 │ 0.499 ± 0.016 │ -0.21 ± 0.27 │
└─────────────┴───────────────┴───────────────┴──────────────┘
2020-01-16 19:38:55,043 - ompy.ensembleNormalizer - INFO -
---------
Normalizing #6
2020-01-16 19:38:55,105 - ompy.normalizer_gsf - INFO - Normalizing #3
2020-01-16 19:38:55,136 - ompy.normalizer_nld - INFO -
---------
Normalizing nld #6
2020-01-16 19:38:55,154 - ompy.ensembleNormalizer - INFO -
---------
Normalizing #7
2020-01-16 19:38:55,265 - ompy.normalizer_nld - INFO -
---------
Normalizing nld #7
analysing data from multinest/nld_norm_4_.txt
2020-01-16 19:38:56,046 - ompy.normalizer_nld - INFO - Multinest results:
┌─────────────┬───────────────┬───────────────┬──────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞═════════════╪═══════════════╪═══════════════╪══════════════╡
│ 3.99 ± 0.55 │ 1.905 ± 0.055 │ 0.494 ± 0.015 │ -0.12 ± 0.25 │
└─────────────┴───────────────┴───────────────┴──────────────┘
2020-01-16 19:38:56,078 - ompy.normalizer_gsf - INFO - Normalizing #4
2020-01-16 19:38:56,114 - ompy.ensembleNormalizer - INFO -
---------
Normalizing #8
2020-01-16 19:38:56,191 - ompy.normalizer_nld - INFO -
---------
Normalizing nld #8
2020-01-16 19:38:56,454 - ompy.normalizer_nld - INFO - DE results:
┌───────────────────┬──────────────────┬─────────────────────┬──────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞═══════════════════╪══════════════════╪═════════════════════╪══════════════════════╡
│ 3.816876317779932 │ 1.93248997944277 │ 0.49686632872678804 │ -0.17282867508789476 │
└───────────────────┴──────────────────┴─────────────────────┴──────────────────────┘
2020-01-16 19:38:56,455 - ompy.normalizer_nld - INFO - Starting multinest
2020-01-16 19:38:56,755 - ompy.normalizer_nld - INFO - DE results:
┌────────────────────┬────────────────────┬────────────────────┬──────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞════════════════════╪════════════════════╪════════════════════╪══════════════════════╡
│ 3.8342138822465777 │ 1.9246051146476373 │ 0.4942497918442618 │ -0.12906340629514734 │
└────────────────────┴────────────────────┴────────────────────┴──────────────────────┘
2020-01-16 19:38:56,756 - ompy.normalizer_nld - INFO - Starting multinest
2020-01-16 19:38:58,154 - ompy.normalizer_nld - INFO - DE results:
┌────────────────────┬────────────────────┬─────────────────────┬─────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞════════════════════╪════════════════════╪═════════════════════╪═════════════════════╡
│ 3.8389528052436397 │ 1.9332832928904529 │ 0.49678014364651946 │ -0.1712521677556156 │
└────────────────────┴────────────────────┴─────────────────────┴─────────────────────┘
2020-01-16 19:38:58,157 - ompy.normalizer_nld - INFO - Starting multinest
analysing data from multinest/nld_norm_7_.txt
2020-01-16 19:39:20,905 - ompy.normalizer_nld - INFO - Multinest results:
┌─────────────┬───────────────┬───────────────┬──────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞═════════════╪═══════════════╪═══════════════╪══════════════╡
│ 3.94 ± 0.53 │ 1.926 ± 0.051 │ 0.498 ± 0.014 │ -0.18 ± 0.24 │
└─────────────┴───────────────┴───────────────┴──────────────┘
2020-01-16 19:39:20,960 - ompy.normalizer_gsf - INFO - Normalizing #7
2020-01-16 19:39:21,006 - ompy.ensembleNormalizer - INFO -
---------
Normalizing #9
2020-01-16 19:39:21,117 - ompy.normalizer_nld - INFO -
---------
Normalizing nld #9
analysing data from multinest/nld_norm_6_.txt
2020-01-16 19:39:21,521 - ompy.normalizer_nld - INFO - Multinest results:
┌─────────────┬───────────────┬───────────────┬──────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞═════════════╪═══════════════╪═══════════════╪══════════════╡
│ 3.98 ± 0.54 │ 1.919 ± 0.054 │ 0.495 ± 0.015 │ -0.15 ± 0.24 │
└─────────────┴───────────────┴───────────────┴──────────────┘
2020-01-16 19:39:21,577 - ompy.normalizer_gsf - INFO - Normalizing #6
analysing data from multinest/nld_norm_8_.txt
2020-01-16 19:39:22,065 - ompy.normalizer_nld - INFO - Multinest results:
┌─────────────┬───────────────┬───────────────┬──────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞═════════════╪═══════════════╪═══════════════╪══════════════╡
│ 3.99 ± 0.55 │ 1.930 ± 0.053 │ 0.499 ± 0.015 │ -0.20 ± 0.23 │
└─────────────┴───────────────┴───────────────┴──────────────┘
2020-01-16 19:39:22,105 - ompy.normalizer_gsf - INFO - Normalizing #8
2020-01-16 19:39:22,422 - ompy.normalizer_nld - INFO - DE results:
┌───────────────────┬────────────────────┬────────────────────┬─────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞═══════════════════╪════════════════════╪════════════════════╪═════════════════════╡
│ 3.813899493723705 │ 1.9151868170121205 │ 0.4919696522624897 │ -0.0908778980073907 │
└───────────────────┴────────────────────┴────────────────────┴─────────────────────┘
2020-01-16 19:39:22,423 - ompy.normalizer_nld - INFO - Starting multinest
analysing data from multinest/nld_norm_9_.txt
2020-01-16 19:39:38,144 - ompy.normalizer_nld - INFO - Multinest results:
┌─────────────┬───────────────┬───────────────┬──────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞═════════════╪═══════════════╪═══════════════╪══════════════╡
│ 3.96 ± 0.53 │ 1.909 ± 0.053 │ 0.493 ± 0.014 │ -0.11 ± 0.24 │
└─────────────┴───────────────┴───────────────┴──────────────┘
2020-01-16 19:39:38,180 - ompy.normalizer_gsf - INFO - Normalizing #9
[38]:
ensemblenorm_seq.plot();
Simultaneous normalization¶
[39]:
ensemblenorm_sim = om.EnsembleNormalizer(extractor=extractor, normalizer_simultan=simnorm)
ensemblenorm_sim.normalize()
2020-01-16 19:39:38,555 - ompy.ensembleNormalizer - INFO - Start normalization with 3 cpus
2020-01-16 19:39:38,658 - ompy.ensembleNormalizer - INFO -
---------
Normalizing #0
2020-01-16 19:39:38,727 - ompy.ensembleNormalizer - INFO -
---------
Normalizing #1
2020-01-16 19:39:38,816 - ompy.ensembleNormalizer - INFO -
---------
Normalizing #2
2020-01-16 19:39:40,039 - ompy.normalizer_nld - INFO - DE results:
┌────────────────────┬────────────────────┬────────────────────┬─────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞════════════════════╪════════════════════╪════════════════════╪═════════════════════╡
│ 3.8305851675914617 │ 1.9171967932850047 │ 0.4929099075787158 │ -0.1065935057988842 │
└────────────────────┴────────────────────┴────────────────────┴─────────────────────┘
2020-01-16 19:39:40,081 - ompy.normalizer_gsf - INFO - Normalizing #0
2020-01-16 19:39:40,092 - ompy.normalizer_simultan - INFO - DE results/initial guess:
┌────────────────────┬────────────────────┬────────────────────┬─────────────────────┬────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │ B │
╞════════════════════╪════════════════════╪════════════════════╪═════════════════════╪════════════════════╡
│ 3.8305851675914617 │ 1.9171967932850047 │ 0.4929099075787158 │ -0.1065935057988842 │ 135.19226239542235 │
└────────────────────┴────────────────────┴────────────────────┴─────────────────────┴────────────────────┘
2020-01-16 19:39:40,094 - ompy.normalizer_simultan - INFO - Starting multinest:
2020-01-16 19:39:40,292 - ompy.normalizer_nld - INFO - DE results:
┌───────────────────┬───────────────────┬─────────────────────┬─────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞═══════════════════╪═══════════════════╪═════════════════════╪═════════════════════╡
│ 3.764351801902385 │ 1.940155820648791 │ 0.49746926521790297 │ -0.1827629879224515 │
└───────────────────┴───────────────────┴─────────────────────┴─────────────────────┘
2020-01-16 19:39:40,353 - ompy.normalizer_gsf - INFO - Normalizing #0
2020-01-16 19:39:40,368 - ompy.normalizer_simultan - INFO - DE results/initial guess:
┌───────────────────┬───────────────────┬─────────────────────┬─────────────────────┬────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │ B │
╞═══════════════════╪═══════════════════╪═════════════════════╪═════════════════════╪════════════════════╡
│ 3.764351801902385 │ 1.940155820648791 │ 0.49746926521790297 │ -0.1827629879224515 │ 128.41303318714756 │
└───────────────────┴───────────────────┴─────────────────────┴─────────────────────┴────────────────────┘
2020-01-16 19:39:40,373 - ompy.normalizer_simultan - INFO - Starting multinest:
2020-01-16 19:39:40,576 - ompy.normalizer_nld - INFO - DE results:
┌────────────────────┬────────────────────┬────────────────────┬──────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞════════════════════╪════════════════════╪════════════════════╪══════════════════════╡
│ 3.8331327880501833 │ 1.9181229729650628 │ 0.4929191206039593 │ -0.10690422886975122 │
└────────────────────┴────────────────────┴────────────────────┴──────────────────────┘
2020-01-16 19:39:40,656 - ompy.normalizer_gsf - INFO - Normalizing #0
2020-01-16 19:39:40,670 - ompy.normalizer_simultan - INFO - DE results/initial guess:
┌────────────────────┬────────────────────┬────────────────────┬──────────────────────┬────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │ B │
╞════════════════════╪════════════════════╪════════════════════╪══════════════════════╪════════════════════╡
│ 3.8331327880501833 │ 1.9181229729650628 │ 0.4929191206039593 │ -0.10690422886975122 │ 135.15049686492915 │
└────────────────────┴────────────────────┴────────────────────┴──────────────────────┴────────────────────┘
2020-01-16 19:39:40,679 - ompy.normalizer_simultan - INFO - Starting multinest:
analysing data from multinest/sim_norm_0_.txt
analysing data from multinest/sim_norm_2_.txt
2020-01-16 19:42:19,781 - ompy.normalizer_simultan - INFO - Multinest results:
┌─────────────┬───────────────┬───────────────┬──────────────┬─────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │ B │
╞═════════════╪═══════════════╪═══════════════╪══════════════╪═════════╡
│ 3.93 ± 0.52 │ 1.914 ± 0.048 │ 0.493 ± 0.014 │ -0.11 ± 0.22 │ 41 ± 14 │
└─────────────┴───────────────┴───────────────┴──────────────┴─────────┘
2020-01-16 19:42:19,802 - ompy.normalizer_simultan - INFO - Multinest results:
┌─────────────┬───────────────┬───────────────┬──────────────┬─────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │ B │
╞═════════════╪═══════════════╪═══════════════╪══════════════╪═════════╡
│ 4.01 ± 0.54 │ 1.897 ± 0.053 │ 0.491 ± 0.015 │ -0.07 ± 0.24 │ 46 ± 17 │
└─────────────┴───────────────┴───────────────┴──────────────┴─────────┘
2020-01-16 19:42:19,824 - ompy.ensembleNormalizer - INFO -
---------
Normalizing #3
2020-01-16 19:42:19,895 - ompy.ensembleNormalizer - INFO -
---------
Normalizing #4
2020-01-16 19:42:21,498 - ompy.normalizer_nld - INFO - DE results:
┌───────────────────┬────────────────────┬─────────────────────┬──────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞═══════════════════╪════════════════════╪═════════════════════╪══════════════════════╡
│ 3.850434233941345 │ 1.9154521123913895 │ 0.49272521499152494 │ -0.10384574748379792 │
└───────────────────┴────────────────────┴─────────────────────┴──────────────────────┘
2020-01-16 19:42:21,561 - ompy.normalizer_gsf - INFO - Normalizing #0
2020-01-16 19:42:21,568 - ompy.normalizer_simultan - INFO - DE results/initial guess:
┌───────────────────┬────────────────────┬─────────────────────┬──────────────────────┬───────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │ B │
╞═══════════════════╪════════════════════╪═════════════════════╪══════════════════════╪═══════════════════╡
│ 3.850434233941345 │ 1.9154521123913895 │ 0.49272521499152494 │ -0.10384574748379792 │ 135.0757286910683 │
└───────────────────┴────────────────────┴─────────────────────┴──────────────────────┴───────────────────┘
2020-01-16 19:42:21,569 - ompy.normalizer_simultan - INFO - Starting multinest:
2020-01-16 19:42:21,940 - ompy.normalizer_nld - INFO - DE results:
┌────────────────────┬────────────────────┬─────────────────────┬─────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞════════════════════╪════════════════════╪═════════════════════╪═════════════════════╡
│ 3.8617254176401214 │ 1.9124678611857717 │ 0.49231466923050293 │ -0.0970842619920328 │
└────────────────────┴────────────────────┴─────────────────────┴─────────────────────┘
2020-01-16 19:42:22,008 - ompy.normalizer_gsf - INFO - Normalizing #0
2020-01-16 19:42:22,018 - ompy.normalizer_simultan - INFO - DE results/initial guess:
┌────────────────────┬────────────────────┬─────────────────────┬─────────────────────┬────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │ B │
╞════════════════════╪════════════════════╪═════════════════════╪═════════════════════╪════════════════════╡
│ 3.8617254176401214 │ 1.9124678611857717 │ 0.49231466923050293 │ -0.0970842619920328 │ 135.12843284147127 │
└────────────────────┴────────────────────┴─────────────────────┴─────────────────────┴────────────────────┘
2020-01-16 19:42:22,020 - ompy.normalizer_simultan - INFO - Starting multinest:
analysing data from multinest/sim_norm_1_.txt
2020-01-16 19:42:36,115 - ompy.normalizer_simultan - INFO - Multinest results:
┌─────────────┬───────────────┬───────────────┬──────────────┬─────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │ B │
╞═════════════╪═══════════════╪═══════════════╪══════════════╪═════════╡
│ 4.01 ± 0.54 │ 1.895 ± 0.054 │ 0.491 ± 0.014 │ -0.07 ± 0.24 │ 45 ± 17 │
└─────────────┴───────────────┴───────────────┴──────────────┴─────────┘
2020-01-16 19:42:36,176 - ompy.ensembleNormalizer - INFO -
---------
Normalizing #5
2020-01-16 19:42:38,520 - ompy.normalizer_nld - INFO - DE results:
┌────────────────────┬────────────────────┬─────────────────────┬──────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞════════════════════╪════════════════════╪═════════════════════╪══════════════════════╡
│ 3.8209927129682133 │ 1.9345615767350426 │ 0.49702248718576447 │ -0.17508704894901925 │
└────────────────────┴────────────────────┴─────────────────────┴──────────────────────┘
2020-01-16 19:42:38,629 - ompy.normalizer_gsf - INFO - Normalizing #0
2020-01-16 19:42:38,645 - ompy.normalizer_simultan - INFO - DE results/initial guess:
┌────────────────────┬────────────────────┬─────────────────────┬──────────────────────┬────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │ B │
╞════════════════════╪════════════════════╪═════════════════════╪══════════════════════╪════════════════════╡
│ 3.8209927129682133 │ 1.9345615767350426 │ 0.49702248718576447 │ -0.17508704894901925 │ 130.63736461267186 │
└────────────────────┴────────────────────┴─────────────────────┴──────────────────────┴────────────────────┘
2020-01-16 19:42:38,651 - ompy.normalizer_simultan - INFO - Starting multinest:
analysing data from multinest/sim_norm_4_.txt
2020-01-16 19:44:54,804 - ompy.normalizer_simultan - INFO - Multinest results:
┌─────────────┬───────────────┬───────────────┬──────────────┬─────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │ B │
╞═════════════╪═══════════════╪═══════════════╪══════════════╪═════════╡
│ 4.05 ± 0.55 │ 1.889 ± 0.050 │ 0.490 ± 0.014 │ -0.05 ± 0.23 │ 46 ± 17 │
└─────────────┴───────────────┴───────────────┴──────────────┴─────────┘
2020-01-16 19:44:54,832 - ompy.ensembleNormalizer - INFO -
---------
Normalizing #6
analysing data from multinest/sim_norm_3_.txt
2020-01-16 19:44:55,897 - ompy.normalizer_simultan - INFO - Multinest results:
┌─────────────┬───────────────┬───────────────┬──────────────┬─────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │ B │
╞═════════════╪═══════════════╪═══════════════╪══════════════╪═════════╡
│ 4.04 ± 0.59 │ 1.892 ± 0.054 │ 0.491 ± 0.015 │ -0.07 ± 0.24 │ 46 ± 17 │
└─────────────┴───────────────┴───────────────┴──────────────┴─────────┘
2020-01-16 19:44:55,942 - ompy.ensembleNormalizer - INFO -
---------
Normalizing #7
2020-01-16 19:44:56,075 - ompy.normalizer_nld - INFO - DE results:
┌───────────────────┬───────────────────┬─────────────────────┬────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞═══════════════════╪═══════════════════╪═════════════════════╪════════════════════╡
│ 3.834208424380257 │ 1.924604953778852 │ 0.49424962094826996 │ -0.129060582690383 │
└───────────────────┴───────────────────┴─────────────────────┴────────────────────┘
2020-01-16 19:44:56,135 - ompy.normalizer_gsf - INFO - Normalizing #0
2020-01-16 19:44:56,146 - ompy.normalizer_simultan - INFO - DE results/initial guess:
┌───────────────────┬───────────────────┬─────────────────────┬────────────────────┬───────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │ B │
╞═══════════════════╪═══════════════════╪═════════════════════╪════════════════════╪═══════════════════╡
│ 3.834208424380257 │ 1.924604953778852 │ 0.49424962094826996 │ -0.129060582690383 │ 133.0983751631022 │
└───────────────────┴───────────────────┴─────────────────────┴────────────────────┴───────────────────┘
2020-01-16 19:44:56,150 - ompy.normalizer_simultan - INFO - Starting multinest:
2020-01-16 19:44:58,053 - ompy.normalizer_nld - INFO - DE results:
┌────────────────────┬────────────────────┬────────────────────┬──────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞════════════════════╪════════════════════╪════════════════════╪══════════════════════╡
│ 3.8168754218457948 │ 1.9324896717965918 │ 0.4968662318696611 │ -0.17282660034378833 │
└────────────────────┴────────────────────┴────────────────────┴──────────────────────┘
2020-01-16 19:44:58,119 - ompy.normalizer_gsf - INFO - Normalizing #0
2020-01-16 19:44:58,130 - ompy.normalizer_simultan - INFO - DE results/initial guess:
┌────────────────────┬────────────────────┬────────────────────┬──────────────────────┬───────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │ B │
╞════════════════════╪════════════════════╪════════════════════╪══════════════════════╪═══════════════════╡
│ 3.8168754218457948 │ 1.9324896717965918 │ 0.4968662318696611 │ -0.17282660034378833 │ 130.0321818713367 │
└────────────────────┴────────────────────┴────────────────────┴──────────────────────┴───────────────────┘
2020-01-16 19:44:58,132 - ompy.normalizer_simultan - INFO - Starting multinest:
analysing data from multinest/sim_norm_5_.txt
2020-01-16 19:45:04,811 - ompy.normalizer_simultan - INFO - Multinest results:
┌─────────────┬───────────────┬───────────────┬──────────────┬─────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │ B │
╞═════════════╪═══════════════╪═══════════════╪══════════════╪═════════╡
│ 4.29 ± 0.55 │ 1.847 ± 0.030 │ 0.480 ± 0.012 │ 0.11 ± 0.18 │ 62 ± 15 │
└─────────────┴───────────────┴───────────────┴──────────────┴─────────┘
2020-01-16 19:45:04,862 - ompy.ensembleNormalizer - INFO -
---------
Normalizing #8
2020-01-16 19:45:06,215 - ompy.normalizer_nld - INFO - DE results:
┌────────────────────┬────────────────────┬────────────────────┬─────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞════════════════════╪════════════════════╪════════════════════╪═════════════════════╡
│ 3.8389546886110604 │ 1.9332829078254432 │ 0.4967800553244072 │ -0.1712507117950638 │
└────────────────────┴────────────────────┴────────────────────┴─────────────────────┘
2020-01-16 19:45:06,264 - ompy.normalizer_gsf - INFO - Normalizing #0
2020-01-16 19:45:06,272 - ompy.normalizer_simultan - INFO - DE results/initial guess:
┌────────────────────┬────────────────────┬────────────────────┬─────────────────────┬────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │ B │
╞════════════════════╪════════════════════╪════════════════════╪═════════════════════╪════════════════════╡
│ 3.8389546886110604 │ 1.9332829078254432 │ 0.4967800553244072 │ -0.1712507117950638 │ 130.43172956085178 │
└────────────────────┴────────────────────┴────────────────────┴─────────────────────┴────────────────────┘
2020-01-16 19:45:06,273 - ompy.normalizer_simultan - INFO - Starting multinest:
analysing data from multinest/sim_norm_6_.txt
2020-01-16 19:47:10,117 - ompy.normalizer_simultan - INFO - Multinest results:
┌─────────────┬───────────────┬───────────────┬──────────────┬─────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │ B │
╞═════════════╪═══════════════╪═══════════════╪══════════════╪═════════╡
│ 4.04 ± 0.55 │ 1.894 ± 0.048 │ 0.490 ± 0.014 │ -0.05 ± 0.22 │ 47 ± 16 │
└─────────────┴───────────────┴───────────────┴──────────────┴─────────┘
2020-01-16 19:47:10,146 - ompy.ensembleNormalizer - INFO -
---------
Normalizing #9
2020-01-16 19:47:11,498 - ompy.normalizer_nld - INFO - DE results:
┌───────────────────┬────────────────────┬─────────────────────┬─────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │
╞═══════════════════╪════════════════════╪═════════════════════╪═════════════════════╡
│ 3.813920399920752 │ 1.9152761088992238 │ 0.49200211289130746 │ -0.0914150195585414 │
└───────────────────┴────────────────────┴─────────────────────┴─────────────────────┘
2020-01-16 19:47:11,563 - ompy.normalizer_gsf - INFO - Normalizing #0
2020-01-16 19:47:11,572 - ompy.normalizer_simultan - INFO - DE results/initial guess:
┌───────────────────┬────────────────────┬─────────────────────┬─────────────────────┬────────────────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │ B │
╞═══════════════════╪════════════════════╪═════════════════════╪═════════════════════╪════════════════════╡
│ 3.813920399920752 │ 1.9152761088992238 │ 0.49200211289130746 │ -0.0914150195585414 │ 137.86627267647992 │
└───────────────────┴────────────────────┴─────────────────────┴─────────────────────┴────────────────────┘
2020-01-16 19:47:11,574 - ompy.normalizer_simultan - INFO - Starting multinest:
analysing data from multinest/sim_norm_7_.txt
2020-01-16 19:47:24,830 - ompy.normalizer_simultan - INFO - Multinest results:
┌─────────────┬───────────────┬───────────────┬──────────────┬─────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │ B │
╞═════════════╪═══════════════╪═══════════════╪══════════════╪═════════╡
│ 3.99 ± 0.54 │ 1.912 ± 0.053 │ 0.494 ± 0.015 │ -0.13 ± 0.24 │ 42 ± 15 │
└─────────────┴───────────────┴───────────────┴──────────────┴─────────┘
analysing data from multinest/sim_norm_8_.txt
2020-01-16 19:47:52,077 - ompy.normalizer_simultan - INFO - Multinest results:
┌─────────────┬───────────────┬───────────────┬──────────────┬─────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │ B │
╞═════════════╪═══════════════╪═══════════════╪══════════════╪═════════╡
│ 3.99 ± 0.53 │ 1.913 ± 0.050 │ 0.495 ± 0.015 │ -0.13 ± 0.23 │ 41 ± 16 │
└─────────────┴───────────────┴───────────────┴──────────────┴─────────┘
analysing data from multinest/sim_norm_9_.txt
2020-01-16 19:48:48,510 - ompy.normalizer_simultan - INFO - Multinest results:
┌─────────────┬───────────────┬───────────────┬──────────────┬─────────┐
│ A │ α [MeV⁻¹] │ T [MeV] │ Eshift [MeV] │ B │
╞═════════════╪═══════════════╪═══════════════╪══════════════╪═════════╡
│ 4.01 ± 0.54 │ 1.894 ± 0.056 │ 0.490 ± 0.015 │ -0.05 ± 0.24 │ 48 ± 18 │
└─────────────┴───────────────┴───────────────┴──────────────┴─────────┘
[40]:
ensemblenorm_sim.plot();
Concepts and advanced usage¶
General usage¶
All the functions and classes in the package are available in the main module. You get everything by importing the package
import ompy
The overarching philosophy is that the package shall be flexible and transparent to use and modify. All of the “steps” in the Oslo method are implemented as classes with a common structure and call signature. If you understand one class, you’ll understand them all, making extending the code easy.
As the Oslo method is a complex method involving dozen of variables which can be daunting for the uninitiated, many class attributes have default values that should give satisfying results. Attributes that should be modified even though it is not strictly necessary to do so will give annoying warnings. The documentation and docstrings give in-depth explanation of each variable and its usage.
Normalization¶
Still working on a nice interface for the gsf normalization implementation. Test implementation only through norm_gsf classes. Does not have the same calling signatures yet.
Validation and introspection¶
An important feature of physics programs is the ability to validate that the program works as intended. This can be achieved by either running the program on problems whose solutions are already known, or by inspecting the program and confirming that each step is working as expected. OMpy uses both methods. Integration tests are performed both on artificial data satisfying the minimal assumptions required of each method (unfold, first generation method, etc.), as well as experimental data which has already been analyzed using other programs (MAMA).
In addition, the methods themselves are written in a way
which separates the uninteresting “book keeping” of each method, such as constructing arrays and normalizing rows, from the actual interesting steps performing the calculations. All parts of a method, its
initial set up, progression and tear down, can be separately inspected using the ompy.hooks submodule and logging framework. This allows the user to not only verify that each method works as intended,
but also get a visual understanding of how they work beyond their mere equational forms.
Development¶
OMpy is written with modularity in mind. We want it to be as easy as possible for the user to add custom functionality and interface OMpy with other Python packages. For example,
it may be of interest to try other unfolding algorithms than the one presently implemented. To achieve this,
one just has to write a wrapper function that has the same input and output structure as the function Unfolder.__call__(),
found in the file ompy/unfolder.py.
It is our hope and goal that OMpy will be used, and we are happy to provide support. Feedback and suggestions are also very welcome. We encourage users who implement new features to share them by opening a pull request in the Github repository.
API Reference¶
Classes¶
Abstract class for Matrix and Vector. |
|
|
Allows for delayed method calls |
|
Generates perturbated matrices to estimate uncertainty |
|
Normalizes NLD nad γSF extracted from the ensemble |
|
Extracts nld and γSF from an Ensemble or a Matrix |
First generation method from Guttormsen et al. |
|
|
Stores 2d array with energy axes (a matrix). |
|
Dataclass for Model |
|
Storage for normalization parameters + some convenience functions |
|
Normalize γSF to a given` <Γγ> (Gg) |
|
Normalizes NLD to empirical data |
|
Simultaneous normalization of nld and gsf. |
|
Interpolates response read from file for current setup |
|
Class to store the results of the Oslo Method |
|
Calculates spin distributions, spin cuts (…) |
|
Performs Guttormsen unfolding |
|
Stores 1d array with energy axes (a vector) |
Functions¶
|
division function designed to ignore / 0, i.e. |
|
Load example raw data. |
|
Fill negative channels with positive counts from neighbouring channels |
|
Function which smooths an array of counts by a Gaussian of full-width-half-maximum FWHM. |
|
Smooth a matrix with a Gaussian |
|
Returns a normalized Gaussian supported on Emids. |
|
Finds the index of the closest element in the array |
List examples |
|
|
Load discrete levels without smoothing |
|
Load discrete levels with smoothing |
|
Computes first generation matrix from nld and gSF |
|
Normalize each row to unity |
|
Rebin an array of counts from binning mids_in to binning mids_out |
|
Rebin a matrix of counts from binning mids_in to binning mids_out |
|
Maps axis to 0, 1 or 2 according to which axis is specified |
License¶
GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. https://fsf.org/ Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.
Preamble
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The licenses for most software and other practical works are designed to take away your freedom to share and change the works. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change all versions of a program–to make sure it remains free software for all its users. We, the Free Software Foundation, use the GNU General Public License for most of our software; it applies also to any other work released this way by its authors. You can apply it to your programs, too.
When we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for them if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs, and that you know you can do these things.
To protect your rights, we need to prevent others from denying you these rights or asking you to surrender the rights. Therefore, you have certain responsibilities if you distribute copies of the software, or if you modify it: responsibilities to respect the freedom of others.
For example, if you distribute copies of such a program, whether gratis or for a fee, you must pass on to the recipients the same freedoms that you received. You must make sure that they, too, receive or can get the source code. And you must show them these terms so they know their rights.
Developers that use the GNU GPL protect your rights with two steps: (1) assert copyright on the software, and (2) offer you this License giving you legal permission to copy, distribute and/or modify it.
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When you convey a covered work, you waive any legal power to forbid circumvention of technological measures to the extent such circumvention is effected by exercising rights under this License with respect to the covered work, and you disclaim any intention to limit operation or modification of the work as a means of enforcing, against the work’s users, your or third parties’ legal rights to forbid circumvention of technological measures.
Conveying Verbatim Copies.
You may convey verbatim copies of the Program’s source code as you receive it, in any medium, provided that you conspicuously and appropriately publish on each copy an appropriate copyright notice; keep intact all notices stating that this License and any non-permissive terms added in accord with section 7 apply to the code; keep intact all notices of the absence of any warranty; and give all recipients a copy of this License along with the Program.
You may charge any price or no price for each copy that you convey, and you may offer support or warranty protection for a fee.
Conveying Modified Source Versions.
You may convey a work based on the Program, or the modifications to produce it from the Program, in the form of source code under the terms of section 4, provided that you also meet all of these conditions:
a) The work must carry prominent notices stating that you modified
it, and giving a relevant date.
b) The work must carry prominent notices stating that it is
released under this License and any conditions added under section
7. This requirement modifies the requirement in section 4 to
"keep intact all notices".
c) You must license the entire work, as a whole, under this
License to anyone who comes into possession of a copy. This
License will therefore apply, along with any applicable section 7
additional terms, to the whole of the work, and all its parts,
regardless of how they are packaged. This License gives no
permission to license the work in any other way, but it does not
invalidate such permission if you have separately received it.
d) If the work has interactive user interfaces, each must display
Appropriate Legal Notices; however, if the Program has interactive
interfaces that do not display Appropriate Legal Notices, your
work need not make them do so.
A compilation of a covered work with other separate and independent works, which are not by their nature extensions of the covered work, and which are not combined with it such as to form a larger program, in or on a volume of a storage or distribution medium, is called an “aggregate” if the compilation and its resulting copyright are not used to limit the access or legal rights of the compilation’s users beyond what the individual works permit. Inclusion of a covered work in an aggregate does not cause this License to apply to the other parts of the aggregate.
Conveying Non-Source Forms.
You may convey a covered work in object code form under the terms of sections 4 and 5, provided that you also convey the machine-readable Corresponding Source under the terms of this License, in one of these ways:
a) Convey the object code in, or embodied in, a physical product
(including a physical distribution medium), accompanied by the
Corresponding Source fixed on a durable physical medium
customarily used for software interchange.
b) Convey the object code in, or embodied in, a physical product
(including a physical distribution medium), accompanied by a
written offer, valid for at least three years and valid for as
long as you offer spare parts or customer support for that product
model, to give anyone who possesses the object code either (1) a
copy of the Corresponding Source for all the software in the
product that is covered by this License, on a durable physical
medium customarily used for software interchange, for a price no
more than your reasonable cost of physically performing this
conveying of source, or (2) access to copy the
Corresponding Source from a network server at no charge.
c) Convey individual copies of the object code with a copy of the
written offer to provide the Corresponding Source. This
alternative is allowed only occasionally and noncommercially, and
only if you received the object code with such an offer, in accord
with subsection 6b.
d) Convey the object code by offering access from a designated
place (gratis or for a charge), and offer equivalent access to the
Corresponding Source in the same way through the same place at no
further charge. You need not require recipients to copy the
Corresponding Source along with the object code. If the place to
copy the object code is a network server, the Corresponding Source
may be on a different server (operated by you or a third party)
that supports equivalent copying facilities, provided you maintain
clear directions next to the object code saying where to find the
Corresponding Source. Regardless of what server hosts the
Corresponding Source, you remain obligated to ensure that it is
available for as long as needed to satisfy these requirements.
e) Convey the object code using peer-to-peer transmission, provided
you inform other peers where the object code and Corresponding
Source of the work are being offered to the general public at no
charge under subsection 6d.
A separable portion of the object code, whose source code is excluded from the Corresponding Source as a System Library, need not be included in conveying the object code work.
A “User Product” is either (1) a “consumer product”, which means any tangible personal property which is normally used for personal, family, or household purposes, or (2) anything designed or sold for incorporation into a dwelling. In determining whether a product is a consumer product, doubtful cases shall be resolved in favor of coverage. For a particular product received by a particular user, “normally used” refers to a typical or common use of that class of product, regardless of the status of the particular user or of the way in which the particular user actually uses, or expects or is expected to use, the product. A product is a consumer product regardless of whether the product has substantial commercial, industrial or non-consumer uses, unless such uses represent the only significant mode of use of the product.
“Installation Information” for a User Product means any methods, procedures, authorization keys, or other information required to install and execute modified versions of a covered work in that User Product from a modified version of its Corresponding Source. The information must suffice to ensure that the continued functioning of the modified object code is in no case prevented or interfered with solely because modification has been made.
If you convey an object code work under this section in, or with, or specifically for use in, a User Product, and the conveying occurs as part of a transaction in which the right of possession and use of the User Product is transferred to the recipient in perpetuity or for a fixed term (regardless of how the transaction is characterized), the Corresponding Source conveyed under this section must be accompanied by the Installation Information. But this requirement does not apply if neither you nor any third party retains the ability to install modified object code on the User Product (for example, the work has been installed in ROM).
The requirement to provide Installation Information does not include a requirement to continue to provide support service, warranty, or updates for a work that has been modified or installed by the recipient, or for the User Product in which it has been modified or installed. Access to a network may be denied when the modification itself materially and adversely affects the operation of the network or violates the rules and protocols for communication across the network.
Corresponding Source conveyed, and Installation Information provided, in accord with this section must be in a format that is publicly documented (and with an implementation available to the public in source code form), and must require no special password or key for unpacking, reading or copying.
Additional Terms.
“Additional permissions” are terms that supplement the terms of this License by making exceptions from one or more of its conditions. Additional permissions that are applicable to the entire Program shall be treated as though they were included in this License, to the extent that they are valid under applicable law. If additional permissions apply only to part of the Program, that part may be used separately under those permissions, but the entire Program remains governed by this License without regard to the additional permissions.
When you convey a copy of a covered work, you may at your option remove any additional permissions from that copy, or from any part of it. (Additional permissions may be written to require their own removal in certain cases when you modify the work.) You may place additional permissions on material, added by you to a covered work, for which you have or can give appropriate copyright permission.
Notwithstanding any other provision of this License, for material you add to a covered work, you may (if authorized by the copyright holders of that material) supplement the terms of this License with terms:
a) Disclaiming warranty or limiting liability differently from the
terms of sections 15 and 16 of this License; or
b) Requiring preservation of specified reasonable legal notices or
author attributions in that material or in the Appropriate Legal
Notices displayed by works containing it; or
c) Prohibiting misrepresentation of the origin of that material, or
requiring that modified versions of such material be marked in
reasonable ways as different from the original version; or
d) Limiting the use for publicity purposes of names of licensors or
authors of the material; or
e) Declining to grant rights under trademark law for use of some
trade names, trademarks, or service marks; or
f) Requiring indemnification of licensors and authors of that
material by anyone who conveys the material (or modified versions of
it) with contractual assumptions of liability to the recipient, for
any liability that these contractual assumptions directly impose on
those licensors and authors.
All other non-permissive additional terms are considered “further restrictions” within the meaning of section 10. If the Program as you received it, or any part of it, contains a notice stating that it is governed by this License along with a term that is a further restriction, you may remove that term. If a license document contains a further restriction but permits relicensing or conveying under this License, you may add to a covered work material governed by the terms of that license document, provided that the further restriction does not survive such relicensing or conveying.
If you add terms to a covered work in accord with this section, you must place, in the relevant source files, a statement of the additional terms that apply to those files, or a notice indicating where to find the applicable terms.
Additional terms, permissive or non-permissive, may be stated in the form of a separately written license, or stated as exceptions; the above requirements apply either way.
Termination.
You may not propagate or modify a covered work except as expressly provided under this License. Any attempt otherwise to propagate or modify it is void, and will automatically terminate your rights under this License (including any patent licenses granted under the third paragraph of section 11).
However, if you cease all violation of this License, then your license from a particular copyright holder is reinstated (a) provisionally, unless and until the copyright holder explicitly and finally terminates your license, and (b) permanently, if the copyright holder fails to notify you of the violation by some reasonable means prior to 60 days after the cessation.
Moreover, your license from a particular copyright holder is reinstated permanently if the copyright holder notifies you of the violation by some reasonable means, this is the first time you have received notice of violation of this License (for any work) from that copyright holder, and you cure the violation prior to 30 days after your receipt of the notice.
Termination of your rights under this section does not terminate the licenses of parties who have received copies or rights from you under this License. If your rights have been terminated and not permanently reinstated, you do not qualify to receive new licenses for the same material under section 10.
Acceptance Not Required for Having Copies.
You are not required to accept this License in order to receive or run a copy of the Program. Ancillary propagation of a covered work occurring solely as a consequence of using peer-to-peer transmission to receive a copy likewise does not require acceptance. However, nothing other than this License grants you permission to propagate or modify any covered work. These actions infringe copyright if you do not accept this License. Therefore, by modifying or propagating a covered work, you indicate your acceptance of this License to do so.
Automatic Licensing of Downstream Recipients.
Each time you convey a covered work, the recipient automatically receives a license from the original licensors, to run, modify and propagate that work, subject to this License. You are not responsible for enforcing compliance by third parties with this License.
An “entity transaction” is a transaction transferring control of an organization, or substantially all assets of one, or subdividing an organization, or merging organizations. If propagation of a covered work results from an entity transaction, each party to that transaction who receives a copy of the work also receives whatever licenses to the work the party’s predecessor in interest had or could give under the previous paragraph, plus a right to possession of the Corresponding Source of the work from the predecessor in interest, if the predecessor has it or can get it with reasonable efforts.
You may not impose any further restrictions on the exercise of the rights granted or affirmed under this License. For example, you may not impose a license fee, royalty, or other charge for exercise of rights granted under this License, and you may not initiate litigation (including a cross-claim or counterclaim in a lawsuit) alleging that any patent claim is infringed by making, using, selling, offering for sale, or importing the Program or any portion of it.
Patents.
A “contributor” is a copyright holder who authorizes use under this License of the Program or a work on which the Program is based. The work thus licensed is called the contributor’s “contributor version”.
A contributor’s “essential patent claims” are all patent claims owned or controlled by the contributor, whether already acquired or hereafter acquired, that would be infringed by some manner, permitted by this License, of making, using, or selling its contributor version, but do not include claims that would be infringed only as a consequence of further modification of the contributor version. For purposes of this definition, “control” includes the right to grant patent sublicenses in a manner consistent with the requirements of this License.
Each contributor grants you a non-exclusive, worldwide, royalty-free patent license under the contributor’s essential patent claims, to make, use, sell, offer for sale, import and otherwise run, modify and propagate the contents of its contributor version.
In the following three paragraphs, a “patent license” is any express agreement or commitment, however denominated, not to enforce a patent (such as an express permission to practice a patent or covenant not to sue for patent infringement). To “grant” such a patent license to a party means to make such an agreement or commitment not to enforce a patent against the party.
If you convey a covered work, knowingly relying on a patent license, and the Corresponding Source of the work is not available for anyone to copy, free of charge and under the terms of this License, through a publicly available network server or other readily accessible means, then you must either (1) cause the Corresponding Source to be so available, or (2) arrange to deprive yourself of the benefit of the patent license for this particular work, or (3) arrange, in a manner consistent with the requirements of this License, to extend the patent license to downstream recipients. “Knowingly relying” means you have actual knowledge that, but for the patent license, your conveying the covered work in a country, or your recipient’s use of the covered work in a country, would infringe one or more identifiable patents in that country that you have reason to believe are valid.
If, pursuant to or in connection with a single transaction or arrangement, you convey, or propagate by procuring conveyance of, a covered work, and grant a patent license to some of the parties receiving the covered work authorizing them to use, propagate, modify or convey a specific copy of the covered work, then the patent license you grant is automatically extended to all recipients of the covered work and works based on it.
A patent license is “discriminatory” if it does not include within the scope of its coverage, prohibits the exercise of, or is conditioned on the non-exercise of one or more of the rights that are specifically granted under this License. You may not convey a covered work if you are a party to an arrangement with a third party that is in the business of distributing software, under which you make payment to the third party based on the extent of your activity of conveying the work, and under which the third party grants, to any of the parties who would receive the covered work from you, a discriminatory patent license (a) in connection with copies of the covered work conveyed by you (or copies made from those copies), or (b) primarily for and in connection with specific products or compilations that contain the covered work, unless you entered into that arrangement, or that patent license was granted, prior to 28 March 2007.
Nothing in this License shall be construed as excluding or limiting any implied license or other defenses to infringement that may otherwise be available to you under applicable patent law.
No Surrender of Others’ Freedom.
If conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not convey it at all. For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program.
Use with the GNU Affero General Public License.
Notwithstanding any other provision of this License, you have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License into a single combined work, and to convey the resulting work. The terms of this License will continue to apply to the part which is the covered work, but the special requirements of the GNU Affero General Public License, section 13, concerning interaction through a network will apply to the combination as such.
Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns.
Each version is given a distinguishing version number. If the Program specifies that a certain numbered version of the GNU General Public License “or any later version” applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of the GNU General Public License, you may choose any version ever published by the Free Software Foundation.
If the Program specifies that a proxy can decide which future versions of the GNU General Public License can be used, that proxy’s public statement of acceptance of a version permanently authorizes you to choose that version for the Program.
Later license versions may give you additional or different permissions. However, no additional obligations are imposed on any author or copyright holder as a result of your choosing to follow a later version.
Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the “copyright” line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands show w' andshow c’ should show the appropriate
parts of the General Public License. Of course, your program’s commands
might be different; for a GUI interface, you would use an “about box”.
You should also get your employer (if you work as a programmer) or school, if any, to sign a “copyright disclaimer” for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see https://www.gnu.org/licenses/.
The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read https://www.gnu.org/licenses/why-not-lgpl.html.