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Merge branch 'fjosw:develop' into feature/input_2.0

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2
.github/workflows/flake8.yml vendored
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@ -21,6 +21,6 @@ jobs:
- name: flake8 Lint
uses: py-actions/flake8@v1
with:
ignore: "E501,E722"
ignore: "E501"
exclude: "__init__.py, input/__init__.py"
path: "pyerrors"

5
.github/workflows/pytest.yml vendored
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@ -6,6 +6,8 @@ on:
- master
- develop
pull_request:
schedule:
- cron: '0 4 1 * *'
jobs:
pytest:
@ -13,7 +15,7 @@ jobs:
strategy:
fail-fast: true
matrix:
python-version: ["3.6", "3.7", "3.8", "3.9"]
python-version: ["3.6", "3.7", "3.8", "3.9", "3.10"]
steps:
- name: Checkout source
@ -27,6 +29,7 @@ jobs:
- name: Install
run: |
python -m pip install --upgrade pip
pip install wheel
pip install .
pip install pytest
pip install pytest-cov

1
.gitignore vendored
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@ -8,3 +8,4 @@ examples/B1k2_pcac_plateau.p
examples/Untitled.*
core.*
*.swp
htmlcov

27
CHANGELOG.md
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@ -4,20 +4,33 @@ All notable changes to this project will be documented in this file.
## [2.0.0] - 2021-??-??
### Added
- `CObs` class added which can handle complex valued Markov chain Monte Carlo data and the corresponding error propagation
- Matrix to matrix operations like the matrix inverse now also work for complex matrices and matrices containing entries that are not `Obs` but `float` or `int`
- `Obs` objects now have methods `is_zero` and `is_zero_within_error`
- `CObs` class added which can handle complex valued Markov chain Monte Carlo data and the corresponding error propagation.
- Matrix to matrix operations like the matrix inverse now also work for complex matrices and matrices containing entries that are not `Obs` but `float` or `int`.
- The possibility to work with Monte Carlo histories which are evenly or unevenly spaced was added.
- The Corr class now has additional methods like `reverse`, `T_symmetry`, `correlate` and `reweight`.
- `linalg` module now has explicit functions `inv` and `cholesky`.
- `Obs` objects now have methods `is_zero` and `is_zero_within_error` as well as overloaded comparison operations.
- Functions to convert Obs data to or from jackknife was added.
- Alternative matrix multiplication routine `jack_matmul` was added to `linalg` module which makes use of the jackknife approximation and is much faster for large matrices.
- Additional input routines for npr data added to `input.hadrons`.
- Version number added.
### Changed
- Additional attributes can no longer be added to existing `Obs`. This makes it no longer possible to import `Obs` created with previous versions of pyerrors
- The default value for `Corr.prange` is now `None`
- The `input` module was restructured to contain one submodule per data source
- The internal bookkeeping system for ensembles/replica was changed. The separator for replica is now `|`.
- The fit functions were renamed to `least_squares` and `total_least_squares`.
- The fit functions can now deal with provided covariance matrices.
- The convention for the fit range in the Corr class has been changed.
- Obs.print was renamed to Obs.details and the output was improved.
- The default value for `Corr.prange` is now `None`.
- The `input` module was restructured to contain one submodule per data source.
- Performance of Obs.__init__ improved.
### Deprecated
- The function `plot_corrs` was deprecated as all its functionality is now contained within `Corr.show`
- The kwarg `bias_correction` in `derived_observable` was removed
- Obs no longer have an attribute `e_Q`
- Removed `fits.fit_exp`
- Removed jackknife module
## [1.1.0] - 2021-10-11
### Added
@ -77,7 +90,7 @@ All notable changes to this project will be documented in this file.
## [0.7.0] - 2020-03-10
### Added
- New fit funtions for fitting with and without x-errors added which use automatic differentiation and should be more reliable than the old ones.
- New fit functions for fitting with and without x-errors added which use automatic differentiation and should be more reliable than the old ones.
- Fitting with Bayesian priors added.
- New functions for visualization of fits which can be activated via the kwargs resplot and qqplot.
- chisquare/expected_chisquared which takes into account correlations in the data and non-linearities in the fit function can now be activated with the kwarg expected_chisquare.

18
CONTRIBUTING.md
View file

@ -9,25 +9,27 @@ and create your own branch
cd pyerrors
git checkout -b feature/my_feature
```
I find it convenient to install the package in editable mode in the local python environment
It can be convenient to install the package in editable mode in the local python environment when developing new features
```
pip install -e .
```
Please send any pull requests to the `develop` branch.
### Documentation
Please add docstrings to any new function, class or method you implement.
Please add docstrings to any new function, class or method you implement. The documentation is automatically generated from these docstrings. The startpage of the documentation is generated from the docstring of `pyerrors/__init__.py`.
### Tests
When implementing a new feature or fixing a bug please add meaningful tests to the files in the `tests` directory which cover the new code.
### Continous integration
For all pull requests tests are executed for the most recent python releases via
```
pytest --cov=pyerrors -v
pytest --cov=pyerrors -vv
```
requiring `pytest`, `pytest-cov` and `pytest-benchmark`
and the linter `flake8` is executed with the command
requiring `pytest`, `pytest-cov` and `pytest-benchmark`. To get a coverage report in html run
```
flake8 --ignore=E501,E722 --exclude=__init__.py pyerrors
pytest --cov=pyerrors --cov-report html
```
The linter `flake8` is executed with the command
```
flake8 --ignore=E501 --exclude=__init__.py pyerrors
```
Please make sure that all tests are passed for a new pull requests.

8
README.md
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@ -3,7 +3,7 @@
`pyerrors` is a python package for error computation and propagation of Markov chain Monte Carlo data.
- **Documentation:** https://fjosw.github.io/pyerrors/pyerrors.html
- **Examples**: https://github.com/fjosw/pyerrors/tree/develop/examples
- **Examples**: https://github.com/fjosw/pyerrors/tree/develop/examples (Do not work properly at the moment)
- **Contributing:** https://github.com/fjosw/pyerrors/blob/develop/CONTRIBUTING.md
- **Bug reports:** https://github.com/fjosw/pyerrors/issues
@ -16,9 +16,3 @@ to install the most recent release run
```bash
pip install git+https://github.com/fjosw/pyerrors.git@master
```
## Other implementations
There exist similar publicly available implementations of gamma method error analysis suites in
- [Fortran](https://gitlab.ift.uam-csic.es/alberto/aderrors)
- [Julia](https://gitlab.ift.uam-csic.es/alberto/aderrors.jl)
- [Python](https://github.com/mbruno46/pyobs)

91
pyerrors/__init__.py
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@ -1,29 +1,26 @@
r'''
# What is pyerrors?
`pyerrors` is a python package for error computation and propagation of Markov chain Monte Carlo data.
It is based on the **gamma method** [arXiv:hep-lat/0306017](https://arxiv.org/abs/hep-lat/0306017). Some of its features are:
- **automatic differentiation** as suggested in [arXiv:1809.01289](https://arxiv.org/abs/1809.01289) (partly based on the [autograd](https://github.com/HIPS/autograd) package)
- **treatment of slow modes** in the simulation as suggested in [arXiv:1009.5228](https://arxiv.org/abs/1009.5228)
- coherent **error propagation** for data from **different Markov chains**
- **non-linear fits with x- and y-errors** and exact linear error propagation based on automatic differentiation as introduced in [arXiv:1809.01289](https://arxiv.org/abs/1809.01289)
- **real and complex matrix operations** and their error propagation based on automatic differentiation (Cholesky decomposition, calculation of eigenvalues and eigenvectors, singular value decomposition...)
It is based on the gamma method [arXiv:hep-lat/0306017](https://arxiv.org/abs/hep-lat/0306017). Some of its features are:
- automatic differentiation for exact liner error propagation as suggested in [arXiv:1809.01289](https://arxiv.org/abs/1809.01289) (partly based on the [autograd](https://github.com/HIPS/autograd) package).
- treatment of slow modes in the simulation as suggested in [arXiv:1009.5228](https://arxiv.org/abs/1009.5228).
- coherent error propagation for data from different Markov chains.
- non-linear fits with x- and y-errors and exact linear error propagation based on automatic differentiation as introduced in [arXiv:1809.01289](https://arxiv.org/abs/1809.01289).
- real and complex matrix operations and their error propagation based on automatic differentiation (Matrix inverse, Cholesky decomposition, calculation of eigenvalues and eigenvectors, singular value decomposition...).
## Getting started
There exist similar publicly available implementations of gamma method error analysis suites in [Fortran](https://gitlab.ift.uam-csic.es/alberto/aderrors), [Julia](https://gitlab.ift.uam-csic.es/alberto/aderrors.jl) and [Python](https://github.com/mbruno46/pyobs).
## Basic example
```python
import numpy as np
import pyerrors as pe
my_obs = pe.Obs([samples], ['ensemble_name'])
my_new_obs = 2 * np.log(my_obs) / my_obs ** 2
my_new_obs.gamma_method()
print(my_new_obs)
my_obs = pe.Obs([samples], ['ensemble_name']) # Initialize an Obs object
my_new_obs = 2 * np.log(my_obs) / my_obs ** 2 # Construct derived Obs object
my_new_obs.gamma_method() # Estimate the statistical error
print(my_new_obs) # Print the result to stdout
> 0.31498(72)
iamzero = my_new_obs - my_new_obs
iamzero.gamma_method()
print(iamzero == 0.0)
> True
```
# The `Obs` class
@ -33,7 +30,6 @@ An `Obs` object can be initialized with two arguments, the first is a list conta
The samples can either be provided as python list or as numpy array.
The second argument is a list containing the names of the respective Monte Carlo chains as strings. These strings uniquely identify a Monte Carlo chain/ensemble.
Example:
```python
import pyerrors as pe
@ -43,14 +39,12 @@ my_obs = pe.Obs([samples], ['ensemble_name'])
## Error propagation
When performing mathematical operations on `Obs` objects the correct error propagation is intrinsically taken care using a first order Taylor expansion
$$\delta_f^i=\sum_\alpha \bar{f}_\alpha \delta_\alpha^i\,,\quad \delta_\alpha^i=a_\alpha^i-\bar{a}_\alpha$$
$$\delta_f^i=\sum_\alpha \bar{f}_\alpha \delta_\alpha^i\,,\quad \delta_\alpha^i=a_\alpha^i-\bar{a}_\alpha\,,$$
as introduced in [arXiv:hep-lat/0306017](https://arxiv.org/abs/hep-lat/0306017).
The required derivatives $\bar{f}_\alpha$ are evaluated up to machine precision via automatic differentiation as suggested in [arXiv:1809.01289](https://arxiv.org/abs/1809.01289).
The `Obs` class is designed such that mathematical numpy functions can be used on `Obs` just as for regular floats.
Example:
```python
import numpy as np
import pyerrors as pe
@ -61,6 +55,11 @@ my_obs2 = pe.Obs([samples2], ['ensemble_name'])
my_sum = my_obs1 + my_obs2
my_m_eff = np.log(my_obs1 / my_obs2)
iamzero = my_m_eff - my_m_eff
# Check that value and fluctuations are zero within machine precision
print(iamzero == 0.0)
> True
```
## Error estimation
@ -68,7 +67,6 @@ my_m_eff = np.log(my_obs1 / my_obs2)
The error estimation within `pyerrors` is based on the gamma method introduced in [arXiv:hep-lat/0306017](https://arxiv.org/abs/hep-lat/0306017).
After having arrived at the derived quantity of interest the `gamma_method` can be called as detailed in the following example.
Example:
```python
my_sum.gamma_method()
print(my_sum)
@ -81,9 +79,11 @@ my_sum.details()
```
The standard value for the automatic windowing procedure is $S=2$. Other values for $S$ can be passed to the `gamma_method` as parameter.
We use the following definition of the integrated autocorrelation time established in [Madras & Sokal 1988](https://link.springer.com/article/10.1007/BF01022990)
$$\tau_\mathrm{int}=\frac{1}{2}+\sum_{t=1}^{W}\rho(t)\geq \frac{1}{2}\,.$$
The window $W$ is determined via the automatic windowing procedure described in [arXiv:hep-lat/0306017](https://arxiv.org/abs/hep-lat/0306017).
The standard value for the parameter $S$ of this automatic windowing procedure is $S=2$. Other values for $S$ can be passed to the `gamma_method` as parameter.
Example:
```python
my_sum.gamma_method(S=3.0)
my_sum.details()
@ -94,19 +94,15 @@ my_sum.details()
```
The integrated autocorrelation time $\tau_\mathrm{int}$ and the autocorrelation function $\rho(W)$ can be monitored via the methods ´pyerrors.obs.Obs.plot_tauint` and ´pyerrors.obs.Obs.plot_tauint`.
The integrated autocorrelation time $\tau_\mathrm{int}$ and the autocorrelation function $\rho(W)$ can be monitored via the methods `pyerrors.obs.Obs.plot_tauint` and `pyerrors.obs.Obs.plot_tauint`.
Example:
```python
my_sum.plot_tauint()
my_sum.plot_rho()
```
If the parameter $S$ is set to zero it is assumed that dataset does not exhibit any autocorrelation and the windowsize is chosen to be zero.
In this case the error estimate is identical to the sample standard error.
### Exponential tails
Slow modes in the Monte Carlo history can be accounted for by attaching an exponential tail to the autocorrelation function $\rho$ as suggested in [arXiv:1009.5228](https://arxiv.org/abs/1009.5228). The longest autocorrelation time in the history, $\tau_\mathrm{exp}$, can be passed to the `gamma_method` as parameter. In this case the automatic windowing procedure is vacated and the parameter $S$ does not affect the error estimate.
Example:
```python
my_sum.gamma_method(tau_exp=7.2)
my_sum.details()
@ -116,20 +112,19 @@ my_sum.details()
> · Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)
```
For the full API see `pyerrors.obs.Obs.gamma_method`
For the full API see `pyerrors.obs.Obs.gamma_method`.
## Multiple ensembles/replica
Error propagation for multiple ensembles (Markov chains with different simulation parameters) is handled automatically. Ensembles are uniquely identified by their `name`.
Example:
```python
obs1 = pe.Obs([samples1], ['ensemble1'])
obs2 = pe.Obs([samples2], ['ensemble2'])
my_sum = obs1 + obs2
my_sum.details()
> Result 2.00697958e+00 +/- 0.00000000e+00 +/- 0.00000000e+00 (0.000%)
> Result 2.00697958e+00
> 1500 samples in 2 ensembles:
> · Ensemble 'ensemble1' : 1000 configurations (from 1 to 1000)
> · Ensemble 'ensemble2' : 500 configurations (from 1 to 500)
@ -137,14 +132,13 @@ my_sum.details()
`pyerrors` identifies multiple replica (independent Markov chains with identical simulation parameters) by the vertical bar `|` in the name of the data set.
Example:
```python
obs1 = pe.Obs([samples1], ['ensemble1|r01'])
obs2 = pe.Obs([samples2], ['ensemble1|r02'])
> my_sum = obs1 + obs2
> my_sum.details()
> Result 2.00697958e+00 +/- 0.00000000e+00 +/- 0.00000000e+00 (0.000%)
> Result 2.00697958e+00
> 1500 samples in 1 ensemble:
> · Ensemble 'ensemble1'
> · Replicum 'r01' : 1000 configurations (from 1 to 1000)
@ -155,7 +149,6 @@ obs2 = pe.Obs([samples2], ['ensemble1|r02'])
In order to keep track of different error analysis parameters for different ensembles one can make use of global dictionaries as detailed in the following example.
Example:
```python
pe.Obs.S_dict['ensemble1'] = 2.5
pe.Obs.tau_exp_dict['ensemble2'] = 8.0
@ -170,32 +163,43 @@ Passing arguments to the `gamma_method` still dominates over the dictionaries.
Irregular Monte Carlo chains can be initialized with the parameter `idl`.
Example:
```python
# Observable defined on configurations 20 to 519
obs1 = pe.Obs([samples1], ['ensemble1'], idl=[range(20, 520)])
obs1.details()
> Result 9.98319881e-01
> 500 samples in 1 ensemble:
> · Ensemble 'ensemble1' : 500 configurations (from 20 to 519)
# Observable defined on every second configuration between 5 and 1003
obs2 = pe.Obs([samples2], ['ensemble1'], idl=[range(5, 1005, 2)])
obs2.details()
> Result 9.99100712e-01
> 500 samples in 1 ensemble:
> · Ensemble 'ensemble1' : 500 configurations (from 5 to 1003 in steps of 2)
# Observable defined on configurations 2, 9, 28, 29 and 501
obs3 = pe.Obs([samples3], ['ensemble1'], idl=[[2, 9, 28, 29, 501]])
obs3.details()
> Result 1.01718064e+00
> 5 samples in 1 ensemble:
> · Ensemble 'ensemble1' : 5 configurations (irregular range)
```
**Warning:** Irregular Monte Carlo chains can result in odd patterns in the autocorrelation functions.
Make sure to check the autocorrelation time with e.g. `pyerrors.obs.Obs.plot_rho` or `pyerrors.obs.Obs.plot_tauint`.
For the full API see `pyerrors.obs.Obs`
For the full API see `pyerrors.obs.Obs`.
# Correlators
For the full API see `pyerrors.correlators.Corr`
For the full API see `pyerrors.correlators.Corr`.
# Complex observables
`pyerrors` can handle complex valued observables via the class `pyerrors.obs.CObs`.
`CObs` are initialized with a real and an imaginary part which both can be `Obs` valued.
Example:
```python
my_real_part = pe.Obs([samples1], ['ensemble1'])
my_imag_part = pe.Obs([samples2], ['ensemble1'])
@ -225,8 +229,8 @@ print(my_derived_cobs)
The preferred exported file format within `pyerrors` is
## Jackknife samples
For comparison with other analysis workflows `pyerrors` can generate jackknife samples from an `Obs` object.
See `pyerrors.obs.Obs.export_jackknife` for details.
For comparison with other analysis workflows `pyerrors` can generate jackknife samples from an `Obs` object or import jackknife samples into an `Obs` object.
See `pyerrors.obs.Obs.export_jackknife` and `pyerrors.obs.import_jackknife` for details.
# Input
`pyerrors.input`
@ -234,12 +238,11 @@ See `pyerrors.obs.Obs.export_jackknife` for details.
from .obs import *
from .correlators import *
from .fits import *
from .misc import *
from . import dirac
from . import input
from . import linalg
from . import misc
from . import mpm
from . import npr
from . import roots
from .version import __version__

7
pyerrors/correlators.py
View file

@ -3,7 +3,8 @@ import numpy as np
import autograd.numpy as anp
import matplotlib.pyplot as plt
import scipy.linalg
from .obs import Obs, dump_object, reweight, correlate
from .obs import Obs, reweight, correlate
from .misc import dump_object
from .fits import least_squares
from .linalg import eigh, inv, cholesky
from .roots import find_root
@ -442,7 +443,7 @@ class Corr:
if self.prange:
fitrange = self.prange
else:
fitrange = [0, self.T]
fitrange = [0, self.T - 1]
xs = [x for x in range(fitrange[0], fitrange[1] + 1) if not self.content[x] is None]
ys = [self.content[x][0] for x in range(fitrange[0], fitrange[1] + 1) if not self.content[x] is None]
@ -535,7 +536,7 @@ class Corr:
y_min = min([(x[0].value - x[0].dvalue) for x in self.content[x_range[0]: x_range[1] + 1] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)])
y_max = max([(x[0].value + x[0].dvalue) for x in self.content[x_range[0]: x_range[1] + 1] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)])
ax1.set_ylim([y_min - 0.1 * (y_max - y_min), y_max + 0.1 * (y_max - y_min)])
except:
except Exception:
pass
else:
ax1.set_ylim(y_range)

75
pyerrors/covobs.py Normal file
View file

@ -0,0 +1,75 @@
import numpy as np
class Covobs:
def __init__(self, mean, cov, name, pos=None, grad=None):
""" Initialize Covobs object.
Parameters
----------
mean : float
Mean value of the new Obs
cov : list or array
2d Covariance matrix or 1d diagonal entries
name : str
identifier for the covariance matrix
pos : int
Position of the variance belonging to mean in cov.
Is taken to be 1 if cov is 0-dimensional
grad : list or array
Gradient of the Covobs wrt. the means belonging to cov.
"""
self._set_cov(cov)
if '|' in name:
raise Exception("Covobs name must not contain replica separator '|'.")
self.name = name
if grad is None:
if pos is None:
if self.N == 1:
pos = 0
else:
raise Exception('Have to specify position of cov-element belonging to mean!')
else:
if pos > self.N:
raise Exception('pos %d too large for covariance matrix with dimension %dx%d!' % (pos, self.N, self.N))
self._grad = np.zeros((self.N, 1))
self._grad[pos] = 1.
else:
self._set_grad(grad)
self.value = mean
def errsq(self):
""" Return the variance (= square of the error) of the Covobs
"""
return float(np.dot(np.transpose(self.grad), np.dot(self.cov, self.grad)))
def _set_cov(self, cov):
self._cov = np.array(cov)
if self._cov.ndim == 0:
self.N = 1
self._cov = np.diag([self._cov])
elif self._cov.ndim == 1:
self.N = len(self._cov)
self._cov = np.diag(self._cov)
elif self._cov.ndim == 2:
self.N = self._cov.shape[0]
if self._cov.shape[1] != self.N:
raise Exception('Covariance matrix has to be a square matrix!')
else:
raise Exception('Covariance matrix has to be a 2 dimensional square matrix!')
def _set_grad(self, grad):
self._grad = np.array(grad)
if self._grad.ndim in [0, 1]:
self._grad = np.reshape(self._grad, (self.N, 1))
elif self._grad.ndim != 2:
raise Exception('Invalid dimension of grad!')
@property
def cov(self):
return self._cov
@property
def grad(self):
return self._grad

346
pyerrors/fits.py
View file

@ -1,4 +1,3 @@
import gc
from collections.abc import Sequence
import warnings
import numpy as np
@ -11,7 +10,7 @@ from scipy.odr import ODR, Model, RealData
import iminuit
from autograd import jacobian
from autograd import elementwise_grad as egrad
from .obs import Obs, derived_observable, covariance, pseudo_Obs
from .obs import Obs, derived_observable, covariance, cov_Obs
class Fit_result(Sequence):
@ -88,8 +87,6 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
priors has to be a list with an entry for every parameter in the fit. The entries can either be
Obs (e.g. results from a previous fit) or strings containing a value and an error formatted like
0.548(23), 500(40) or 0.5(0.4)
It is important for the subsequent error estimation that the e_tag for the gamma method is large
enough.
silent : bool, optional
If true all output to the console is omitted (default False).
initial_guess : list
@ -109,6 +106,11 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
corrected by effects caused by correlated input data.
This can take a while as the full correlation matrix
has to be calculated (default False).
correlated_fit : bool
If true, use the full correlation matrix in the definition of the chisquare
(only works for prior==None and when no method is given, at the moment).
const_par : list, optional
List of N Obs that are used to constrain the last N fit parameters of func.
'''
if priors is not None:
return _prior_fit(x, y, func, priors, silent=silent, **kwargs)
@ -154,6 +156,8 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
corrected by effects caused by correlated input data.
This can take a while as the full correlation matrix
has to be calculated (default False).
const_par : list, optional
List of N Obs that are used to constrain the last N fit parameters of func.
Based on the orthogonal distance regression module of scipy
'''
@ -169,10 +173,21 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
if not callable(func):
raise TypeError('func has to be a function.')
func_aug = func
if 'const_par' in kwargs:
const_par = kwargs['const_par']
if isinstance(const_par, Obs):
const_par = [const_par]
def func(p, x):
return func_aug(np.concatenate((p, [o.value for o in const_par])), x)
else:
const_par = []
for i in range(25):
try:
func(np.arange(i), x.T[0])
except:
except Exception:
pass
else:
break
@ -180,6 +195,8 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
n_parms = i
if not silent:
print('Fit with', n_parms, 'parameters')
if(len(const_par) > 0):
print('\t and %d constrained parameter%s' % (len(const_par), 's' if len(const_par) > 1 else ''), const_par)
x_f = np.vectorize(lambda o: o.value)(x)
dx_f = np.vectorize(lambda o: o.dvalue)(x)
@ -195,7 +212,7 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
if 'initial_guess' in kwargs:
x0 = kwargs.get('initial_guess')
if len(x0) != n_parms:
raise Exception('Initial guess does not have the correct length.')
raise Exception('Initial guess does not have the correct length: %d vs. %d' % (len(x0), n_parms))
else:
x0 = [1] * n_parms
@ -222,12 +239,18 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
raise Exception('The minimization procedure did not converge.')
m = x_f.size
n_parms_aug = n_parms + len(const_par)
def odr_chisquare(p):
model = func(p[:n_parms], p[n_parms:].reshape(x_shape))
chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((x_f - p[n_parms:].reshape(x_shape)) / dx_f) ** 2)
return chisq
def odr_chisquare_aug(p):
model = func_aug(np.concatenate((p[:n_parms_aug], [o.value for o in const_par])), p[n_parms_aug:].reshape(x_shape))
chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((x_f - p[n_parms_aug:].reshape(x_shape)) / dx_f) ** 2)
return chisq
if kwargs.get('expected_chisquare') is True:
W = np.diag(1 / np.asarray(np.concatenate((dy_f.ravel(), dx_f.ravel()))))
@ -254,31 +277,32 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
print('chisquare/expected_chisquare:',
output.chisquare_by_expected_chisquare)
hess_inv = np.linalg.pinv(jacobian(jacobian(odr_chisquare))(np.concatenate((out.beta, out.xplus.ravel()))))
fitp = np.concatenate((out.beta, [o.value for o in const_par]))
hess_inv = np.linalg.pinv(jacobian(jacobian(odr_chisquare_aug))(np.concatenate((fitp, out.xplus.ravel()))))
def odr_chisquare_compact_x(d):
model = func(d[:n_parms], d[n_parms:n_parms + m].reshape(x_shape))
chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((d[n_parms + m:].reshape(x_shape) - d[n_parms:n_parms + m].reshape(x_shape)) / dx_f) ** 2)
model = func_aug(d[:n_parms_aug], d[n_parms_aug:n_parms_aug + m].reshape(x_shape))
chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((d[n_parms_aug + m:].reshape(x_shape) - d[n_parms_aug:n_parms_aug + m].reshape(x_shape)) / dx_f) ** 2)
return chisq
jac_jac_x = jacobian(jacobian(odr_chisquare_compact_x))(np.concatenate((out.beta, out.xplus.ravel(), x_f.ravel())))
jac_jac_x = jacobian(jacobian(odr_chisquare_compact_x))(np.concatenate((fitp, out.xplus.ravel(), x_f.ravel())))
deriv_x = -hess_inv @ jac_jac_x[:n_parms + m, n_parms + m:]
deriv_x = -hess_inv @ jac_jac_x[:n_parms_aug + m, n_parms_aug + m:]
def odr_chisquare_compact_y(d):
model = func(d[:n_parms], d[n_parms:n_parms + m].reshape(x_shape))
chisq = anp.sum(((d[n_parms + m:] - model) / dy_f) ** 2) + anp.sum(((x_f - d[n_parms:n_parms + m].reshape(x_shape)) / dx_f) ** 2)
model = func_aug(d[:n_parms_aug], d[n_parms_aug:n_parms_aug + m].reshape(x_shape))
chisq = anp.sum(((d[n_parms_aug + m:] - model) / dy_f) ** 2) + anp.sum(((x_f - d[n_parms_aug:n_parms_aug + m].reshape(x_shape)) / dx_f) ** 2)
return chisq
jac_jac_y = jacobian(jacobian(odr_chisquare_compact_y))(np.concatenate((out.beta, out.xplus.ravel(), y_f)))
jac_jac_y = jacobian(jacobian(odr_chisquare_compact_y))(np.concatenate((fitp, out.xplus.ravel(), y_f)))
deriv_y = -hess_inv @ jac_jac_y[:n_parms + m, n_parms + m:]
deriv_y = -hess_inv @ jac_jac_y[:n_parms_aug + m, n_parms_aug + m:]
result = []
for i in range(n_parms):
result.append(derived_observable(lambda x, **kwargs: x[0], [pseudo_Obs(out.beta[i], 0.0, y[0].names[0], y[0].shape[y[0].names[0]])] + list(x.ravel()) + list(y), man_grad=[0] + list(deriv_x[i]) + list(deriv_y[i])))
result.append(derived_observable(lambda my_var, **kwargs: (my_var[0] + np.finfo(np.float64).eps) / (x.ravel()[0].value + np.finfo(np.float64).eps) * out.beta[i], list(x.ravel()) + list(y), man_grad=list(deriv_x[i]) + list(deriv_y[i])))
output.fit_parameters = result
output.fit_parameters = result + const_par
output.odr_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplus.ravel())))
output.dof = x.shape[-1] - n_parms
@ -296,9 +320,6 @@ def _prior_fit(x, y, func, priors, silent=False, **kwargs):
output.fit_function = func
if Obs.e_tag_global < 4:
warnings.warn("e_tag_global is smaller than 4, this can cause problems when calculating errors from fits with priors", RuntimeWarning)
x = np.asarray(x)
if not callable(func):
@ -307,7 +328,7 @@ def _prior_fit(x, y, func, priors, silent=False, **kwargs):
for i in range(100):
try:
func(np.arange(i), 0)
except:
except Exception:
pass
else:
break
@ -331,7 +352,7 @@ def _prior_fit(x, y, func, priors, silent=False, **kwargs):
loc_priors.append(i_prior)
else:
loc_val, loc_dval = extract_val_and_dval(i_prior)
loc_priors.append(pseudo_Obs(loc_val, loc_dval, 'p' + str(i_n)))
loc_priors.append(cov_Obs(loc_val, loc_dval ** 2, '#prior' + str(i_n) + f"_{np.random.randint(2147483647):010d}"))
output.priors = loc_priors
@ -365,13 +386,15 @@ def _prior_fit(x, y, func, priors, silent=False, **kwargs):
if not silent:
print('Method: migrad')
m = iminuit.Minuit.from_array_func(chisqfunc, x0, error=np.asarray(x0) * 0.01, errordef=1, print_level=0)
m = iminuit.Minuit(chisqfunc, x0)
m.errordef = 1
m.print_level = 0
if 'tol' in kwargs:
m.tol = kwargs.get('tol')
else:
m.tol = 1e-4
m.migrad()
params = np.asarray(m.values.values())
params = np.asarray(m.values)
output.chisquare_by_dof = m.fval / len(x)
@ -380,7 +403,7 @@ def _prior_fit(x, y, func, priors, silent=False, **kwargs):
if not silent:
print('chisquare/d.o.f.:', output.chisquare_by_dof)
if not m.get_fmin().is_valid:
if not m.fmin.is_valid:
raise Exception('The minimization procedure did not converge.')
hess_inv = np.linalg.pinv(jacobian(jacobian(chisqfunc))(params))
@ -396,7 +419,7 @@ def _prior_fit(x, y, func, priors, silent=False, **kwargs):
result = []
for i in range(n_parms):
result.append(derived_observable(lambda x, **kwargs: x[0], [pseudo_Obs(params[i], 0.0, y[0].names[0], y[0].shape[y[0].names[0]])] + list(y) + list(loc_priors), man_grad=[0] + list(deriv[i])))
result.append(derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (y[0].value + np.finfo(np.float64).eps) * params[i], list(y) + list(loc_priors), man_grad=list(deriv[i])))
output.fit_parameters = result
output.chisquare = chisqfunc(np.asarray(params))
@ -432,10 +455,21 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
if not callable(func):
raise TypeError('func has to be a function.')
func_aug = func
if 'const_par' in kwargs:
const_par = kwargs['const_par']
if isinstance(const_par, Obs):
const_par = [const_par]
def func(p, x):
return func_aug(np.concatenate((p, [o.value for o in const_par])), x)
else:
const_par = []
for i in range(25):
try:
func(np.arange(i), x.T[0])
except:
except Exception:
pass
else:
break
@ -444,6 +478,8 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
if not silent:
print('Fit with', n_parms, 'parameters')
if(len(const_par) > 0):
print('\t and %d constrained parameter%s' % (len(const_par), 's' if len(const_par) > 1 else ''), const_par)
y_f = [o.value for o in y]
dy_f = [o.dvalue for o in y]
@ -454,14 +490,44 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
if 'initial_guess' in kwargs:
x0 = kwargs.get('initial_guess')
if len(x0) != n_parms:
raise Exception('Initial guess does not have the correct length.')
raise Exception('Initial guess does not have the correct length: %d vs. %d' % (len(x0), n_parms))
else:
x0 = [0.1] * n_parms
def chisqfunc(p):
model = func(p, x)
chisq = anp.sum(((y_f - model) / dy_f) ** 2)
return chisq
if kwargs.get('correlated_fit') is True:
cov = covariance_matrix(y)
covdiag = np.diag(1. / np.sqrt(np.diag(cov)))
corr = np.copy(cov)
for i in range(len(y)):
for j in range(len(y)):
corr[i][j] = cov[i][j] / np.sqrt(cov[i][i] * cov[j][j])
condn = np.linalg.cond(corr)
if condn > 1e4:
warnings.warn("Correlation matrix may be ill-conditioned! condition number: %1.2e" % (condn), RuntimeWarning)
chol = np.linalg.cholesky(corr)
chol_inv = np.linalg.inv(chol)
chol_inv = np.dot(chol_inv, covdiag)
def chisqfunc(p):
model = func(p, x)
chisq = anp.sum(anp.dot(chol_inv, (y_f - model)) ** 2)
return chisq
def chisqfunc_aug(p):
model = func_aug(np.concatenate((p, [o.value for o in const_par])), x)
chisq = anp.sum(anp.dot(chol_inv, (y_f - model)) ** 2)
return chisq
else:
def chisqfunc(p):
model = func(p, x)
chisq = anp.sum(((y_f - model) / dy_f) ** 2)
return chisq
def chisqfunc_aug(p):
model = func_aug(np.concatenate((p, [o.value for o in const_par])), x)
chisq = anp.sum(((y_f - model) / dy_f) ** 2)
return chisq
if 'method' in kwargs:
output.method = kwargs.get('method')
@ -482,10 +548,17 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
if not silent:
print('Method: Levenberg-Marquardt')
def chisqfunc_residuals(p):
model = func(p, x)
chisq = ((y_f - model) / dy_f)
return chisq
if kwargs.get('correlated_fit') is True:
def chisqfunc_residuals(p):
model = func(p, x)
chisq = anp.dot(chol_inv, (y_f - model))
return chisq
else:
def chisqfunc_residuals(p):
model = func(p, x)
chisq = ((y_f - model) / dy_f)
return chisq
fit_result = scipy.optimize.least_squares(chisqfunc_residuals, x0, method='lm', ftol=1e-15, gtol=1e-15, xtol=1e-15)
@ -507,32 +580,42 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
print('chisquare/d.o.f.:', output.chisquare_by_dof)
if kwargs.get('expected_chisquare') is True:
W = np.diag(1 / np.asarray(dy_f))
cov = covariance_matrix(y)
A = W @ jacobian(func)(fit_result.x, x)
P_phi = A @ np.linalg.inv(A.T @ A) @ A.T
expected_chisquare = np.trace((np.identity(x.shape[-1]) - P_phi) @ W @ cov @ W)
output.chisquare_by_expected_chisquare = chisquare / expected_chisquare
if not silent:
print('chisquare/expected_chisquare:',
output.chisquare_by_expected_chisquare)
if kwargs.get('correlated_fit') is not True:
W = np.diag(1 / np.asarray(dy_f))
cov = covariance_matrix(y)
A = W @ jacobian(func)(fit_result.x, x)
P_phi = A @ np.linalg.inv(A.T @ A) @ A.T
expected_chisquare = np.trace((np.identity(x.shape[-1]) - P_phi) @ W @ cov @ W)
output.chisquare_by_expected_chisquare = chisquare / expected_chisquare
if not silent:
print('chisquare/expected_chisquare:',
output.chisquare_by_expected_chisquare)
hess_inv = np.linalg.pinv(jacobian(jacobian(chisqfunc))(fit_result.x))
fitp = np.concatenate((fit_result.x, [o.value for o in const_par]))
hess_inv = np.linalg.pinv(jacobian(jacobian(chisqfunc_aug))(fitp))
def chisqfunc_compact(d):
model = func(d[:n_parms], x)
chisq = anp.sum(((d[n_parms:] - model) / dy_f) ** 2)
return chisq
n_parms_aug = n_parms + len(const_par)
if kwargs.get('correlated_fit') is True:
def chisqfunc_compact(d):
model = func_aug(d[:n_parms_aug], x)
chisq = anp.sum(anp.dot(chol_inv, (d[n_parms_aug:] - model)) ** 2)
return chisq
jac_jac = jacobian(jacobian(chisqfunc_compact))(np.concatenate((fit_result.x, y_f)))
else:
def chisqfunc_compact(d):
model = func_aug(d[:n_parms_aug], x)
chisq = anp.sum(((d[n_parms_aug:] - model) / dy_f) ** 2)
return chisq
deriv = -hess_inv @ jac_jac[:n_parms, n_parms:]
jac_jac = jacobian(jacobian(chisqfunc_compact))(np.concatenate((fitp, y_f)))
deriv = -hess_inv @ jac_jac[:n_parms_aug, n_parms_aug:]
result = []
for i in range(n_parms):
result.append(derived_observable(lambda x, **kwargs: x[0], [pseudo_Obs(fit_result.x[i], 0.0, y[0].names[0], y[0].shape[y[0].names[0]])] + list(y), man_grad=[0] + list(deriv[i])))
result.append(derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (y[0].value + np.finfo(np.float64).eps) * fit_result.x[i], list(y), man_grad=list(deriv[i])))
output.fit_parameters = result
output.fit_parameters = result + const_par
output.chisquare = chisqfunc(fit_result.x)
output.dof = x.shape[-1] - n_parms
@ -564,10 +647,10 @@ def fit_lin(x, y, **kwargs):
return y
if all(isinstance(n, Obs) for n in x):
out = odr_fit(x, y, f, **kwargs)
out = total_least_squares(x, y, f, **kwargs)
return out.fit_parameters
elif all(isinstance(n, float) or isinstance(n, int) for n in x) or isinstance(x, np.ndarray):
out = standard_fit(x, y, f, **kwargs)
out = least_squares(x, y, f, **kwargs)
return out.fit_parameters
else:
raise Exception('Unsupported types for x')
@ -596,7 +679,7 @@ def qqplot(x, o_y, func, p):
plt.xlabel('Theoretical quantiles')
plt.ylabel('Ordered Values')
plt.legend()
plt.show()
plt.draw()
def residual_plot(x, y, func, fit_res):
@ -625,7 +708,7 @@ def residual_plot(x, y, func, fit_res):
ax1.set_xlim([xstart, xstop])
ax1.set_ylabel('Residuals')
plt.subplots_adjust(wspace=None, hspace=None)
plt.show()
plt.draw()
def covariance_matrix(y):
@ -657,150 +740,3 @@ def error_band(x, func, beta):
err = np.array(err)
return err
def ks_test(obs=None):
"""Performs a Kolmogorov–Smirnov test for the Q-values of all fit object.
If no list is given all Obs in memory are used.
Disclaimer: The determination of the individual Q-values as well as this function have not been tested yet.
"""
raise Exception('Not yet implemented')
if obs is None:
obs_list = []
for obj in gc.get_objects():
if isinstance(obj, Obs):
obs_list.append(obj)
else:
obs_list = obs
# TODO: Rework to apply to Q-values of all fits in memory
Qs = []
for obs_i in obs_list:
for ens in obs_i.e_names:
if obs_i.e_Q[ens] is not None:
Qs.append(obs_i.e_Q[ens])
bins = len(Qs)
x = np.arange(0, 1.001, 0.001)
plt.plot(x, x, 'k', zorder=1)
plt.xlim(0, 1)
plt.ylim(0, 1)
plt.xlabel('Q value')
plt.ylabel('Cumulative probability')
plt.title(str(bins) + ' Q values')
n = np.arange(1, bins + 1) / np.float64(bins)
Xs = np.sort(Qs)
plt.step(Xs, n)
diffs = n - Xs
loc_max_diff = np.argmax(np.abs(diffs))
loc = Xs[loc_max_diff]
plt.annotate(s='', xy=(loc, loc), xytext=(loc, loc + diffs[loc_max_diff]), arrowprops=dict(arrowstyle='<->', shrinkA=0, shrinkB=0))
plt.show()
print(scipy.stats.kstest(Qs, 'uniform'))
def fit_general(x, y, func, silent=False, **kwargs):
"""Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
Plausibility of the results should be checked. To control the numerical differentiation
the kwargs of numdifftools.step_generators.MaxStepGenerator can be used.
func has to be of the form
def func(a, x):
y = a[0] + a[1] * x + a[2] * np.sinh(x)
return y
y has to be a list of Obs, the dvalues of the Obs are used as yerror for the fit.
x can either be a list of floats in which case no xerror is assumed, or
a list of Obs, where the dvalues of the Obs are used as xerror for the fit.
Keyword arguments
-----------------
silent -- If true all output to the console is omitted (default False).
initial_guess -- can provide an initial guess for the input parameters. Relevant for non-linear fits
with many parameters.
"""
warnings.warn("New fit functions with exact error propagation are now available as alternative.", DeprecationWarning)
if not callable(func):
raise TypeError('func has to be a function.')
for i in range(10):
try:
func(np.arange(i), 0)
except:
pass
else:
break
n_parms = i
if not silent:
print('Fit with', n_parms, 'parameters')
global print_output, beta0
print_output = 1
if 'initial_guess' in kwargs:
beta0 = kwargs.get('initial_guess')
if len(beta0) != n_parms:
raise Exception('Initial guess does not have the correct length.')
else:
beta0 = np.arange(n_parms)
if len(x) != len(y):
raise Exception('x and y have to have the same length')
if all(isinstance(n, Obs) for n in x):
obs = x + y
x_constants = None
xerr = [o.dvalue for o in x]
yerr = [o.dvalue for o in y]
elif all(isinstance(n, float) or isinstance(n, int) for n in x) or isinstance(x, np.ndarray):
obs = y
x_constants = x
xerr = None
yerr = [o.dvalue for o in y]
else:
raise Exception('Unsupported types for x')
def do_the_fit(obs, **kwargs):
global print_output, beta0
func = kwargs.get('function')
yerr = kwargs.get('yerr')
length = len(yerr)
xerr = kwargs.get('xerr')
if length == len(obs):
assert 'x_constants' in kwargs
data = RealData(kwargs.get('x_constants'), obs, sy=yerr)
fit_type = 2
elif length == len(obs) // 2:
data = RealData(obs[:length], obs[length:], sx=xerr, sy=yerr)
fit_type = 0
else:
raise Exception('x and y do not fit together.')
model = Model(func)
odr = ODR(data, model, beta0, partol=np.finfo(np.float64).eps)
odr.set_job(fit_type=fit_type, deriv=1)
output = odr.run()
if print_output and not silent:
print(*output.stopreason)
print('chisquare/d.o.f.:', output.res_var)
print_output = 0
beta0 = output.beta
return output.beta[kwargs.get('n')]
res = []
for n in range(n_parms):
res.append(derived_observable(do_the_fit, obs, function=func, xerr=xerr, yerr=yerr, x_constants=x_constants, num_grad=True, n=n, **kwargs))
return res

5
pyerrors/input/__init__.py
View file

@ -1,5 +1,6 @@
from . import bdio
from . import hadrons
from . import sfcf
from . import openQCD
from . import json
from . import misc
from . import openQCD
from . import sfcf

2
pyerrors/input/bdio.py
View file

@ -226,7 +226,7 @@ def write_ADerrors(obs_list, file_path, bdio_path='./libbdio.so', **kwargs):
for key in keys:
try: # Try to convert key to integer
ids.append(int(key))
except: # If not possible construct a hash
except Exception: # If not possible construct a hash
ids.append(int(hashlib.sha256(key.encode('utf-8')).hexdigest(), 16) % 10 ** 8)
print('ids', ids)
nt = []

258
pyerrors/input/hadrons.py
View file

@ -1,25 +1,15 @@
#!/usr/bin/env python
# coding: utf-8
import os
import h5py
import numpy as np
from ..obs import Obs, CObs
from ..correlators import Corr
from ..npr import Npr_matrix
def _get_files(path, filestem):
ls = []
for (dirpath, dirnames, filenames) in os.walk(path):
ls.extend(filenames)
break
def _get_files(path, filestem, idl):
ls = os.listdir(path)
# Clean up file list
files = []
for line in ls:
if line.startswith(filestem):
files.append(line)
files = list(filter(lambda x: x.startswith(filestem), ls))
if not files:
raise Exception('No files starting with', filestem, 'in folder', path)
@ -30,18 +20,31 @@ def _get_files(path, filestem):
# Sort according to configuration number
files.sort(key=get_cnfg_number)
# Check that configurations are evenly spaced
cnfg_numbers = []
filtered_files = []
for line in files:
cnfg_numbers.append(get_cnfg_number(line))
no = get_cnfg_number(line)
if idl:
if no in list(idl):
filtered_files.append(line)
cnfg_numbers.append(no)
else:
filtered_files.append(line)
cnfg_numbers.append(no)
if not all(np.diff(cnfg_numbers) == np.diff(cnfg_numbers)[0]):
# Check that configurations are evenly spaced
dc = np.unique(np.diff(cnfg_numbers))
if np.any(dc < 0):
raise Exception("Unsorted files")
if len(dc) == 1:
idx = range(cnfg_numbers[0], cnfg_numbers[-1] + dc[0], dc[0])
else:
raise Exception('Configurations are not evenly spaced.')
return files, cnfg_numbers
return filtered_files, idx
def read_meson_hd5(path, filestem, ens_id, meson='meson_0', tree='meson'):
def read_meson_hd5(path, filestem, ens_id, meson='meson_0', tree='meson', idl=None):
"""Read hadrons meson hdf5 file and extract the meson labeled 'meson'
Parameters
@ -59,9 +62,11 @@ def read_meson_hd5(path, filestem, ens_id, meson='meson_0', tree='meson'):
Label of the upmost directory in the hdf5 file, default 'meson'
for outputs of the Meson module. Can be altered to read input
from other modules with similar structures.
idl : range
If specified only configurations in the given range are read in.
"""
files, cnfg_numbers = _get_files(path, filestem)
files, idx = _get_files(path, filestem, idl)
corr_data = []
infos = []
@ -78,27 +83,69 @@ def read_meson_hd5(path, filestem, ens_id, meson='meson_0', tree='meson'):
l_obs = []
for c in corr_data.T:
l_obs.append(Obs([c], [ens_id], idl=[cnfg_numbers]))
l_obs.append(Obs([c], [ens_id], idl=[idx]))
corr = Corr(l_obs)
corr.tag = r", ".join(infos)
return corr
def read_ExternalLeg_hd5(path, filestem, ens_id, order='F'):
class Npr_matrix(np.ndarray):
def __new__(cls, input_array, mom_in=None, mom_out=None):
obj = np.asarray(input_array).view(cls)
obj.mom_in = mom_in
obj.mom_out = mom_out
return obj
@property
def g5H(self):
"""Gamma_5 hermitean conjugate
Uses the fact that the propagator is gamma5 hermitean, so just the
in and out momenta of the propagator are exchanged.
"""
return Npr_matrix(self,
mom_in=self.mom_out,
mom_out=self.mom_in)
def _propagate_mom(self, other, name):
s_mom = getattr(self, name, None)
o_mom = getattr(other, name, None)
if s_mom is not None and o_mom is not None:
if not np.allclose(s_mom, o_mom):
raise Exception(name + ' does not match.')
return o_mom if o_mom is not None else s_mom
def __matmul__(self, other):
return self.__new__(Npr_matrix,
super().__matmul__(other),
self._propagate_mom(other, 'mom_in'),
self._propagate_mom(other, 'mom_out'))
def __array_finalize__(self, obj):
if obj is None:
return
self.mom_in = getattr(obj, 'mom_in', None)
self.mom_out = getattr(obj, 'mom_out', None)
def read_ExternalLeg_hd5(path, filestem, ens_id, idl=None):
"""Read hadrons ExternalLeg hdf5 file and output an array of CObs
Parameters
-----------------
path -- path to the files to read
filestem -- namestem of the files to read
ens_id -- name of the ensemble, required for internal bookkeeping
order -- order in which the array is to be reshaped,
'F' for the first index changing fastest (9 4x4 matrices) default.
'C' for the last index changing fastest (16 3x3 matrices),
----------
path : str
path to the files to read
filestem : str
namestem of the files to read
ens_id : str
name of the ensemble, required for internal bookkeeping
idl : range
If specified only configurations in the given range are read in.
"""
files, cnfg_numbers = _get_files(path, filestem)
files, idx = _get_files(path, filestem, idl)
mom = None
@ -107,9 +154,7 @@ def read_ExternalLeg_hd5(path, filestem, ens_id, order='F'):
file = h5py.File(path + '/' + hd5_file, "r")
raw_data = file['ExternalLeg/corr'][0][0].view('complex')
corr_data.append(raw_data)
if mom is not None:
assert np.allclose(mom, np.array(str(file['ExternalLeg/info'].attrs['pIn'])[3:-2].strip().split(' '), dtype=int))
else:
if mom is None:
mom = np.array(str(file['ExternalLeg/info'].attrs['pIn'])[3:-2].strip().split(' '), dtype=int)
file.close()
corr_data = np.array(corr_data)
@ -118,28 +163,29 @@ def read_ExternalLeg_hd5(path, filestem, ens_id, order='F'):
matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]):
real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[cnfg_numbers])
imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[cnfg_numbers])
real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx])
imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx])
matrix[si, sj, ci, cj] = CObs(real, imag)
matrix[si, sj, ci, cj].gamma_method()
return Npr_matrix(matrix.swapaxes(1, 2).reshape((12, 12), order=order), mom_in=mom)
return Npr_matrix(matrix, mom_in=mom)
def read_Bilinear_hd5(path, filestem, ens_id, order='F'):
def read_Bilinear_hd5(path, filestem, ens_id, idl=None):
"""Read hadrons Bilinear hdf5 file and output an array of CObs
Parameters
-----------------
path -- path to the files to read
filestem -- namestem of the files to read
ens_id -- name of the ensemble, required for internal bookkeeping
order -- order in which the array is to be reshaped,
'F' for the first index changing fastest (9 4x4 matrices) default.
'C' for the last index changing fastest (16 3x3 matrices),
----------
path : str
path to the files to read
filestem : str
namestem of the files to read
ens_id : str
name of the ensemble, required for internal bookkeeping
idl : range
If specified only configurations in the given range are read in.
"""
files, cnfg_numbers = _get_files(path, filestem)
files, idx = _get_files(path, filestem, idl)
mom_in = None
mom_out = None
@ -153,13 +199,9 @@ def read_Bilinear_hd5(path, filestem, ens_id, order='F'):
corr_data[name] = []
raw_data = file['Bilinear/Bilinear_' + str(i) + '/corr'][0][0].view('complex')
corr_data[name].append(raw_data)
if mom_in is not None:
assert np.allclose(mom_in, np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(' '), dtype=int))
else:
if mom_in is None:
mom_in = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(' '), dtype=int)
if mom_out is not None:
assert np.allclose(mom_out, np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(' '), dtype=int))
else:
if mom_out is None:
mom_out = np.array(str(file['Bilinear/Bilinear_' + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(' '), dtype=int)
file.close()
@ -173,11 +215,117 @@ def read_Bilinear_hd5(path, filestem, ens_id, order='F'):
matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
for si, sj, ci, cj in np.ndindex(rolled_array.shape[:-1]):
real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[cnfg_numbers])
imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[cnfg_numbers])
real = Obs([rolled_array[si, sj, ci, cj].real], [ens_id], idl=[idx])
imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx])
matrix[si, sj, ci, cj] = CObs(real, imag)
matrix[si, sj, ci, cj].gamma_method()
result_dict[key] = Npr_matrix(matrix.swapaxes(1, 2).reshape((12, 12), order=order), mom_in=mom_in, mom_out=mom_out)
result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out)
return result_dict
def read_Fourquark_hd5(path, filestem, ens_id, idl=None, vertices=["VA", "AV"]):
"""Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs
Parameters
----------
path : str
path to the files to read
filestem : str
namestem of the files to read
ens_id : str
name of the ensemble, required for internal bookkeeping
idl : range
If specified only configurations in the given range are read in.
vertices : list
Vertex functions to be extracted.
"""
files, idx = _get_files(path, filestem, idl)
mom_in = None
mom_out = None
vertex_names = []
for vertex in vertices:
vertex_names += _get_lorentz_names(vertex)
corr_data = {}
tree = 'FourQuarkFullyConnected/FourQuarkFullyConnected_'
for hd5_file in files:
file = h5py.File(path + '/' + hd5_file, "r")
for i in range(32):
name = (file[tree + str(i) + '/info'].attrs['gammaA'][0].decode('UTF-8'), file[tree + str(i) + '/info'].attrs['gammaB'][0].decode('UTF-8'))
if name in vertex_names:
if name not in corr_data:
corr_data[name] = []
raw_data = file[tree + str(i) + '/corr'][0][0].view('complex')
corr_data[name].append(raw_data)
if mom_in is None:
mom_in = np.array(str(file[tree + str(i) + '/info'].attrs['pIn'])[3:-2].strip().split(' '), dtype=int)
if mom_out is None:
mom_out = np.array(str(file[tree + str(i) + '/info'].attrs['pOut'])[3:-2].strip().split(' '), dtype=int)
file.close()
intermediate_dict = {}
for vertex in vertices:
lorentz_names = _get_lorentz_names(vertex)
for v_name in lorentz_names:
if vertex not in intermediate_dict:
intermediate_dict[vertex] = np.array(corr_data[v_name])
else:
intermediate_dict[vertex] += np.array(corr_data[v_name])
result_dict = {}
for key, data in intermediate_dict.items():
rolled_array = np.moveaxis(data, 0, 8)
matrix = np.empty((rolled_array.shape[:-1]), dtype=object)
for index in np.ndindex(rolled_array.shape[:-1]):
real = Obs([rolled_array[index].real], [ens_id], idl=[idx])
imag = Obs([rolled_array[index].imag], [ens_id], idl=[idx])
matrix[index] = CObs(real, imag)
result_dict[key] = Npr_matrix(matrix, mom_in=mom_in, mom_out=mom_out)
return result_dict
def _get_lorentz_names(name):
assert len(name) == 2
res = []
if not set(name) <= set(['S', 'P', 'V', 'A', 'T']):
raise Exception("Name can only contain 'S', 'P', 'V', 'A' or 'T'")
if 'S' in name or 'P' in name:
if not set(name) <= set(['S', 'P']):
raise Exception("'" + name + "' is not a Lorentz scalar")
g_names = {'S': 'Identity',
'P': 'Gamma5'}
res.append((g_names[name[0]], g_names[name[1]]))
elif 'T' in name:
if not set(name) <= set(['T']):
raise Exception("'" + name + "' is not a Lorentz scalar")
raise Exception("Tensor operators not yet implemented.")
else:
if not set(name) <= set(['V', 'A']):
raise Exception("'" + name + "' is not a Lorentz scalar")
lorentz_index = ['X', 'Y', 'Z', 'T']
for ind in lorentz_index:
res.append(('Gamma' + ind + (name[0] == 'A') * 'Gamma5',
'Gamma' + ind + (name[1] == 'A') * 'Gamma5'))
return res

453
pyerrors/input/json.py Normal file
View file

@ -0,0 +1,453 @@
import json
import gzip
import numpy as np
import getpass
import socket
import datetime
import platform
import warnings
from ..obs import Obs
from ..covobs import Covobs
from .. import version as pyerrorsversion
def create_json_string(ol, description='', indent=1):
"""Generate the string for the export of a list of Obs or structures containing Obs
to a .json(.gz) file
Parameters
----------
ol : list
List of objects that will be exported. At the moments, these objects can be
either of: Obs, list, numpy.ndarray.
All Obs inside a structure have to be defined on the same set of configurations.
description : str
Optional string that describes the contents of the json file.
indent : int
Specify the indentation level of the json file. None or 0 is permissible and
saves disk space.
"""
def _default(self, obj):
return str(obj)
my_encoder = json.JSONEncoder
_default.default = json.JSONEncoder().default
my_encoder.default = _default
class Deltalist:
def __init__(self, li):
self.cnfg = li[0]
self.deltas = li[1:]
def __repr__(self):
s = '[%d' % (self.cnfg)
for d in self.deltas:
s += ', %1.15e' % (d)
s += ']'
return s
def __str__(self):
return self.__repr__()
class Floatlist:
def __init__(self, li):
self.li = list(li)
def __repr__(self):
s = '['
for i in range(len(self.li)):
if i > 0:
s += ', '
s += '%1.15e' % (self.li[i])
s += ']'
return s
def __str__(self):
return self.__repr__()
def _gen_data_d_from_list(ol):
dl = []
for name in ol[0].mc_names:
ed = {}
ed['id'] = name
ed['replica'] = []
for r_name in ol[0].e_content[name]:
rd = {}
rd['name'] = r_name
if ol[0].is_merged.get(r_name, False):
rd['is_merged'] = True
rd['deltas'] = []
for i in range(len(ol[0].idl[r_name])):
rd['deltas'].append([ol[0].idl[r_name][i]])
for o in ol:
rd['deltas'][-1].append(o.deltas[r_name][i])
rd['deltas'][-1] = Deltalist(rd['deltas'][-1])
ed['replica'].append(rd)
dl.append(ed)
return dl
def _gen_cdata_d_from_list(ol):
dl = []
for name in ol[0].cov_names:
ed = {}
ed['id'] = name
ed['layout'] = str(ol[0].covobs[name].cov.shape).lstrip('(').rstrip(')').rstrip(',')
ed['cov'] = Floatlist(np.ravel(ol[0].covobs[name].cov))
ncov = ol[0].covobs[name].cov.shape[0]
ed['grad'] = []
for i in range(ncov):
ed['grad'].append([])
for o in ol:
ed['grad'][-1].append(o.covobs[name].grad[i][0])
ed['grad'][-1] = Floatlist(ed['grad'][-1])
dl.append(ed)
return dl
def _assert_equal_properties(ol, otype=Obs):
for o in ol:
if not isinstance(o, otype):
raise Exception("Wrong data type in list.")
for o in ol[1:]:
if not ol[0].is_merged == o.is_merged:
raise Exception("All Obs in list have to be defined on the same set of configs.")
if not ol[0].reweighted == o.reweighted:
raise Exception("All Obs in list have to have the same property 'reweighted'.")
if not ol[0].e_content == o.e_content:
raise Exception("All Obs in list have to be defined on the same set of configs.")
if not ol[0].idl == o.idl:
raise Exception("All Obs in list have to be defined on the same set of configurations.")
def write_Obs_to_dict(o):
d = {}
d['type'] = 'Obs'
d['layout'] = '1'
if o.tag:
d['tag'] = [o.tag]
if o.reweighted:
d['reweighted'] = o.reweighted
d['value'] = [o.value]
data = _gen_data_d_from_list([o])
if len(data) > 0:
d['data'] = data
cdata = _gen_cdata_d_from_list([o])
if len(cdata) > 0:
d['cdata'] = cdata
return d
def write_List_to_dict(ol):
_assert_equal_properties(ol)
d = {}
d['type'] = 'List'
d['layout'] = '%d' % len(ol)
taglist = [o.tag for o in ol]
if np.any([tag is not None for tag in taglist]):
d['tag'] = taglist
if ol[0].reweighted:
d['reweighted'] = ol[0].reweighted
d['value'] = [o.value for o in ol]
data = _gen_data_d_from_list(ol)
if len(data) > 0:
d['data'] = data
cdata = _gen_cdata_d_from_list(ol)
if len(cdata) > 0:
d['cdata'] = cdata
return d
def write_Array_to_dict(oa):
ol = np.ravel(oa)
_assert_equal_properties(ol)
d = {}
d['type'] = 'Array'
d['layout'] = str(oa.shape).lstrip('(').rstrip(')').rstrip(',')
taglist = [o.tag for o in ol]
if np.any([tag is not None for tag in taglist]):
d['tag'] = taglist
if ol[0].reweighted:
d['reweighted'] = ol[0].reweighted
d['value'] = [o.value for o in ol]
data = _gen_data_d_from_list(ol)
if len(data) > 0:
d['data'] = data
cdata = _gen_cdata_d_from_list(ol)
if len(cdata) > 0:
d['cdata'] = cdata
return d
if not isinstance(ol, list):
ol = [ol]
d = {}
d['program'] = 'pyerrors %s' % (pyerrorsversion.__version__)
d['version'] = '0.1'
d['who'] = getpass.getuser()
d['date'] = datetime.datetime.now().astimezone().strftime('%Y-%m-%d %H:%M:%S %z')
d['host'] = socket.gethostname() + ', ' + platform.platform()
if description:
d['description'] = description
d['obsdata'] = []
for io in ol:
if isinstance(io, Obs):
d['obsdata'].append(write_Obs_to_dict(io))
elif isinstance(io, list):
d['obsdata'].append(write_List_to_dict(io))
elif isinstance(io, np.ndarray):
d['obsdata'].append(write_Array_to_dict(io))
jsonstring = json.dumps(d, indent=indent, cls=my_encoder, ensure_ascii=False)
def remove_quotationmarks(s):
"""Workaround for un-quoting of delta lists, adds 5% of work
but is save, compared to a simple replace that could destroy the structure
"""
deltas = False
split = s.split('\n')
for i in range(len(split)):
if '"deltas":' in split[i] or '"cov":' in split[i] or '"grad":' in split[i]:
deltas = True
if deltas:
split[i] = split[i].replace('"[', '[').replace(']"', ']')
if split[i][-1] == ']':
deltas = False
return '\n'.join(split)
jsonstring = remove_quotationmarks(jsonstring)
return jsonstring
def dump_to_json(ol, fname, description='', indent=1, gz=True):
"""Export a list of Obs or structures containing Obs to a .json(.gz) file
Parameters
----------
ol : list
List of objects that will be exported. At the moments, these objects can be
either of: Obs, list, numpy.ndarray.
All Obs inside a structure have to be defined on the same set of configurations.
fname : str
Filename of the output file.
description : str
Optional string that describes the contents of the json file.
indent : int
Specify the indentation level of the json file. None or 0 is permissible and
saves disk space.
gz : bool
If True, the output is a gzipped json. If False, the output is a json file.
"""
jsonstring = create_json_string(ol, description, indent)
if not fname.endswith('.json') and not fname.endswith('.gz'):
fname += '.json'
if gz:
if not fname.endswith('.gz'):
fname += '.gz'
fp = gzip.open(fname, 'wb')
fp.write(jsonstring.encode('utf-8'))
else:
fp = open(fname, 'w', encoding='utf-8')
fp.write(jsonstring)
fp.close()
def import_json_string(json_string, verbose=True, full_output=False):
"""Reconstruct a list of Obs or structures containing Obs from a json string.
The following structures are supported: Obs, list, numpy.ndarray
If the list contains only one element, it is unpacked from the list.
Parameters
----------
json_string : str
json string containing the data.
verbose : bool
Print additional information that was written to the file.
full_output : bool
If True, a dict containing auxiliary information and the data is returned.
If False, only the data is returned.
"""
def _gen_obsd_from_datad(d):
retd = {}
if d:
retd['names'] = []
retd['idl'] = []
retd['deltas'] = []
retd['is_merged'] = {}
for ens in d:
for rep in ens['replica']:
retd['names'].append(rep['name'])
retd['idl'].append([di[0] for di in rep['deltas']])
retd['deltas'].append(np.array([di[1:] for di in rep['deltas']]))
retd['is_merged'][rep['name']] = rep.get('is_merged', False)
return retd
def _gen_covobsd_from_cdatad(d):
retd = {}
for ens in d:
retl = []
name = ens['id']
layouts = ens.get('layout', '1').strip()
layout = [int(ls.strip()) for ls in layouts.split(',') if len(ls) > 0]
cov = np.reshape(ens['cov'], layout)
grad = ens['grad']
nobs = len(grad[0])
for i in range(nobs):
retl.append({'name': name, 'cov': cov, 'grad': [g[i] for g in grad]})
retd[name] = retl
return retd
def get_Obs_from_dict(o):
layouts = o.get('layout', '1').strip()
if layouts != '1':
raise Exception("layout is %s has to be 1 for type Obs." % (layouts), RuntimeWarning)
values = o['value']
od = _gen_obsd_from_datad(o.get('data', {}))
cd = _gen_covobsd_from_cdatad(o.get('cdata', {}))
if od:
ret = Obs([[ddi[0] + values[0] for ddi in di] for di in od['deltas']], od['names'], idl=od['idl'])
ret.is_merged = od['is_merged']
else:
ret = Obs([], [])
ret._value = values[0]
for name in cd:
co = cd[name][0]
ret._covobs[name] = Covobs(None, co['cov'], co['name'], grad=co['grad'])
ret.names.append(co['name'])
ret.reweighted = o.get('reweighted', False)
ret.tag = o.get('tag', [None])[0]
return ret
def get_List_from_dict(o):
layouts = o.get('layout', '1').strip()
layout = int(layouts)
values = o['value']
od = _gen_obsd_from_datad(o.get('data', {}))
cd = _gen_covobsd_from_cdatad(o.get('cdata', {}))
ret = []
taglist = o.get('tag', layout * [None])
for i in range(layout):
if od:
ret.append(Obs([list(di[:, i] + values[i]) for di in od['deltas']], od['names'], idl=od['idl']))
ret[-1].is_merged = od['is_merged']
else:
ret.append(Obs([], []))
ret[-1]._value = values[i]
print('Created Obs with means= ', values[i])
for name in cd:
co = cd[name][i]
ret[-1]._covobs[name] = Covobs(None, co['cov'], co['name'], grad=co['grad'])
ret[-1].names.append(co['name'])
ret[-1].reweighted = o.get('reweighted', False)
ret[-1].tag = taglist[i]
return ret
def get_Array_from_dict(o):
layouts = o.get('layout', '1').strip()
layout = [int(ls.strip()) for ls in layouts.split(',') if len(ls) > 0]
N = np.prod(layout)
values = o['value']
od = _gen_obsd_from_datad(o.get('data', {}))
cd = _gen_covobsd_from_cdatad(o.get('cdata', {}))
ret = []
taglist = o.get('tag', N * [None])
for i in range(N):
if od:
ret.append(Obs([di[:, i] + values[i] for di in od['deltas']], od['names'], idl=od['idl']))
ret[-1].is_merged = od['is_merged']
else:
ret.append(Obs([], []))
ret[-1]._value = values[i]
for name in cd:
co = cd[name][i]
ret[-1]._covobs[name] = Covobs(None, co['cov'], co['name'], grad=co['grad'])
ret[-1].names.append(co['name'])
ret[-1].reweighted = o.get('reweighted', False)
ret[-1].tag = taglist[i]
return np.reshape(ret, layout)
json_dict = json.loads(json_string)
prog = json_dict.get('program', '')
version = json_dict.get('version', '')
who = json_dict.get('who', '')
date = json_dict.get('date', '')
host = json_dict.get('host', '')
if prog and verbose:
print('Data has been written using %s.' % (prog))
if version and verbose:
print('Format version %s' % (version))
if np.any([who, date, host] and verbose):
print('Written by %s on %s on host %s' % (who, date, host))
description = json_dict.get('description', '')
if description and verbose:
print()
print('Description: ', description)
obsdata = json_dict['obsdata']
ol = []
for io in obsdata:
if io['type'] == 'Obs':
ol.append(get_Obs_from_dict(io))
elif io['type'] == 'List':
ol.append(get_List_from_dict(io))
elif io['type'] == 'Array':
ol.append(get_Array_from_dict(io))
if full_output:
retd = {}
retd['program'] = prog
retd['version'] = version
retd['who'] = who
retd['date'] = date
retd['host'] = host
retd['description'] = description
retd['obsdata'] = ol
return retd
else:
if len(obsdata) == 1:
ol = ol[0]
return ol
def load_json(fname, verbose=True, gz=True, full_output=False):
"""Import a list of Obs or structures containing Obs from a .json.gz file.
The following structures are supported: Obs, list, numpy.ndarray
If the list contains only one element, it is unpacked from the list.
Parameters
----------
fname : str
Filename of the input file.
verbose : bool
Print additional information that was written to the file.
gz : bool
If True, assumes that data is gzipped. If False, assumes JSON file.
full_output : bool
If True, a dict containing auxiliary information and the data is returned.
If False, only the data is returned.
"""
if not fname.endswith('.json') and not fname.endswith('.gz'):
fname += '.json'
if gz:
if not fname.endswith('.gz'):
fname += '.gz'
with gzip.open(fname, 'r') as fin:
d = fin.read().decode('utf-8')
else:
if fname.endswith('.gz'):
warnings.warn("Trying to read from %s without unzipping!" % fname, UserWarning)
with open(fname, 'r', encoding='utf-8') as fin:
d = fin.read()
return import_json_string(d, verbose, full_output)

351
pyerrors/linalg.py
View file

@ -1,128 +1,18 @@
import numpy as np
from autograd import jacobian
import autograd.numpy as anp # Thinly-wrapped numpy
from .obs import derived_observable, CObs, Obs, _merge_idx, _expand_deltas_for_merge, _filter_zeroes
from functools import partial
from autograd.extend import defvjp
def derived_array(func, data, **kwargs):
"""Construct a derived Obs for a matrix valued function according to func(data, **kwargs) using automatic differentiation.
Parameters
----------
func : object
arbitrary function of the form func(data, **kwargs). For the
automatic differentiation to work, all numpy functions have to have
the autograd wrapper (use 'import autograd.numpy as anp').
data : list
list of Obs, e.g. [obs1, obs2, obs3].
man_grad : list
manually supply a list or an array which contains the jacobian
of func. Use cautiously, supplying the wrong derivative will
not be intercepted.
"""
data = np.asarray(data)
raveled_data = data.ravel()
# Workaround for matrix operations containing non Obs data
for i_data in raveled_data:
if isinstance(i_data, Obs):
first_name = i_data.names[0]
first_shape = i_data.shape[first_name]
first_idl = i_data.idl[first_name]
break
for i in range(len(raveled_data)):
if isinstance(raveled_data[i], (int, float)):
raveled_data[i] = Obs([raveled_data[i] + np.zeros(first_shape)], [first_name], idl=[first_idl])
n_obs = len(raveled_data)
new_names = sorted(set([y for x in [o.names for o in raveled_data] for y in x]))
is_merged = len(list(filter(lambda o: o.is_merged is True, raveled_data))) > 0
reweighted = len(list(filter(lambda o: o.reweighted is True, raveled_data))) > 0
new_idl_d = {}
for name in new_names:
idl = []
for i_data in raveled_data:
tmp = i_data.idl.get(name)
if tmp is not None:
idl.append(tmp)
new_idl_d[name] = _merge_idx(idl)
if not is_merged:
is_merged = (1 != len(set([len(idx) for idx in [*idl, new_idl_d[name]]])))
if data.ndim == 1:
values = np.array([o.value for o in data])
else:
values = np.vectorize(lambda x: x.value)(data)
new_values = func(values, **kwargs)
new_r_values = {}
for name in new_names:
tmp_values = np.zeros(n_obs)
for i, item in enumerate(raveled_data):
tmp = item.r_values.get(name)
if tmp is None:
tmp = item.value
tmp_values[i] = tmp
tmp_values = np.array(tmp_values).reshape(data.shape)
new_r_values[name] = func(tmp_values, **kwargs)
if 'man_grad' in kwargs:
deriv = np.asarray(kwargs.get('man_grad'))
if new_values.shape + data.shape != deriv.shape:
raise Exception('Manual derivative does not have correct shape.')
else:
deriv = jacobian(func)(values, **kwargs)
final_result = np.zeros(new_values.shape, dtype=object)
d_extracted = {}
for name in new_names:
d_extracted[name] = []
for i_dat, dat in enumerate(data):
ens_length = len(new_idl_d[name])
d_extracted[name].append(np.array([_expand_deltas_for_merge(o.deltas[name], o.idl[name], o.shape[name], new_idl_d[name]) for o in dat.reshape(np.prod(dat.shape))]).reshape(dat.shape + (ens_length, )))
for i_val, new_val in np.ndenumerate(new_values):
new_deltas = {}
for name in new_names:
ens_length = d_extracted[name][0].shape[-1]
new_deltas[name] = np.zeros(ens_length)
for i_dat, dat in enumerate(d_extracted[name]):
new_deltas[name] += np.tensordot(deriv[i_val + (i_dat, )], dat)
new_samples = []
new_means = []
new_idl = []
if is_merged:
filtered_names, filtered_deltas, filtered_idl_d = _filter_zeroes(new_names, new_deltas, new_idl_d)
else:
filtered_names = new_names
filtered_deltas = new_deltas
filtered_idl_d = new_idl_d
for name in filtered_names:
new_samples.append(filtered_deltas[name])
new_means.append(new_r_values[name][i_val])
new_idl.append(filtered_idl_d[name])
final_result[i_val] = Obs(new_samples, filtered_names, means=new_means, idl=new_idl)
final_result[i_val]._value = new_val
final_result[i_val].is_merged = is_merged
final_result[i_val].reweighted = reweighted
return final_result
from .obs import derived_observable, CObs, Obs, import_jackknife
def matmul(*operands):
"""Matrix multiply all operands.
Supports real and complex valued matrices and is faster compared to
standard multiplication via the @ operator.
Parameters
----------
operands : numpy.ndarray
Arbitrary number of 2d-numpy arrays which can be real or complex
Obs valued.
This implementation is faster compared to standard multiplication via the @ operator.
"""
if any(isinstance(o[0, 0], CObs) for o in operands):
extended_operands = []
@ -152,8 +42,8 @@ def matmul(*operands):
def multi_dot_i(operands):
return multi_dot(operands, 'Imag')
Nr = derived_array(multi_dot_r, extended_operands)
Ni = derived_array(multi_dot_i, extended_operands)
Nr = derived_observable(multi_dot_r, extended_operands, array_mode=True)
Ni = derived_observable(multi_dot_i, extended_operands, array_mode=True)
res = np.empty_like(Nr)
for (n, m), entry in np.ndenumerate(Nr):
@ -166,7 +56,142 @@ def matmul(*operands):
for op in operands[1:]:
stack = stack @ op
return stack
return derived_array(multi_dot, operands)
return derived_observable(multi_dot, operands, array_mode=True)
def jack_matmul(*operands):
"""Matrix multiply both operands making use of the jackknife approximation.
Parameters
----------
operands : numpy.ndarray
Arbitrary number of 2d-numpy arrays which can be real or complex
Obs valued.
For large matrices this is considerably faster compared to matmul.
"""
def _exp_to_jack(matrix):
base_matrix = np.empty_like(matrix)
for index, entry in np.ndenumerate(matrix):
base_matrix[index] = entry.export_jackknife()
return base_matrix
def _imp_from_jack(matrix, name, idl):
base_matrix = np.empty_like(matrix)
for index, entry in np.ndenumerate(matrix):
base_matrix[index] = import_jackknife(entry, name, [idl])
return base_matrix
def _exp_to_jack_c(matrix):
base_matrix = np.empty_like(matrix)
for index, entry in np.ndenumerate(matrix):
base_matrix[index] = entry.real.export_jackknife() + 1j * entry.imag.export_jackknife()
return base_matrix
def _imp_from_jack_c(matrix, name, idl):
base_matrix = np.empty_like(matrix)
for index, entry in np.ndenumerate(matrix):
base_matrix[index] = CObs(import_jackknife(entry.real, name, [idl]),
import_jackknife(entry.imag, name, [idl]))
return base_matrix
if any(isinstance(o.flat[0], CObs) for o in operands):
name = operands[0].flat[0].real.names[0]
idl = operands[0].flat[0].real.idl[name]
r = _exp_to_jack_c(operands[0])
for op in operands[1:]:
if isinstance(op.flat[0], CObs):
r = r @ _exp_to_jack_c(op)
else:
r = r @ op
return _imp_from_jack_c(r, name, idl)
else:
name = operands[0].flat[0].names[0]
idl = operands[0].flat[0].idl[name]
r = _exp_to_jack(operands[0])
for op in operands[1:]:
if isinstance(op.flat[0], Obs):
r = r @ _exp_to_jack(op)
else:
r = r @ op
return _imp_from_jack(r, name, idl)
def einsum(subscripts, *operands):
"""Wrapper for numpy.einsum
Parameters
----------
subscripts : str
Subscripts for summation (see numpy documentation for details)
operands : numpy.ndarray
Arbitrary number of 2d-numpy arrays which can be real or complex
Obs valued.
"""
def _exp_to_jack(matrix):
base_matrix = []
for index, entry in np.ndenumerate(matrix):
base_matrix.append(entry.export_jackknife())
return np.asarray(base_matrix).reshape(matrix.shape + base_matrix[0].shape)
def _exp_to_jack_c(matrix):
base_matrix = []
for index, entry in np.ndenumerate(matrix):
base_matrix.append(entry.real.export_jackknife() + 1j * entry.imag.export_jackknife())
return np.asarray(base_matrix).reshape(matrix.shape + base_matrix[0].shape)
def _imp_from_jack(matrix, name, idl):
base_matrix = np.empty(shape=matrix.shape[:-1], dtype=object)
for index in np.ndindex(matrix.shape[:-1]):
base_matrix[index] = import_jackknife(matrix[index], name, [idl])
return base_matrix
def _imp_from_jack_c(matrix, name, idl):
base_matrix = np.empty(shape=matrix.shape[:-1], dtype=object)
for index in np.ndindex(matrix.shape[:-1]):
base_matrix[index] = CObs(import_jackknife(matrix[index].real, name, [idl]),
import_jackknife(matrix[index].imag, name, [idl]))
return base_matrix
for op in operands:
if isinstance(op.flat[0], CObs):
name = op.flat[0].real.names[0]
idl = op.flat[0].real.idl[name]
break
elif isinstance(op.flat[0], Obs):
name = op.flat[0].names[0]
idl = op.flat[0].idl[name]
break
conv_operands = []
for op in operands:
if isinstance(op.flat[0], CObs):
conv_operands.append(_exp_to_jack_c(op))
elif isinstance(op.flat[0], Obs):
conv_operands.append(_exp_to_jack(op))
else:
conv_operands.append(op)
tmp_subscripts = ','.join([o + '...' for o in subscripts.split(',')])
extended_subscripts = '->'.join([o + '...' for o in tmp_subscripts.split('->')[:-1]] + [tmp_subscripts.split('->')[-1]])
einsum_path = np.einsum_path(extended_subscripts, *conv_operands, optimize='optimal')[0]
jack_einsum = np.einsum(extended_subscripts, *conv_operands, optimize=einsum_path)
if jack_einsum.dtype == complex:
result = _imp_from_jack_c(jack_einsum, name, idl)
elif jack_einsum.dtype == float:
result = _imp_from_jack(jack_einsum, name, idl)
else:
raise Exception("Result has unexpected datatype")
if result.shape == ():
return result.flat[0]
else:
return result
def inv(x):
@ -179,7 +204,12 @@ def cholesky(x):
return _mat_mat_op(anp.linalg.cholesky, x)
def scalar_mat_op(op, obs, **kwargs):
def det(x):
"""Determinant of Obs valued matrices."""
return _scalar_mat_op(anp.linalg.det, x)
def _scalar_mat_op(op, obs, **kwargs):
"""Computes the matrix to scalar operation op to a given matrix of Obs."""
def _mat(x, **kwargs):
dim = int(np.sqrt(len(x)))
@ -221,7 +251,7 @@ def _mat_mat_op(op, obs, **kwargs):
if kwargs.get('num_grad') is True:
op_big_matrix = _num_diff_mat_mat_op(op, big_matrix, **kwargs)
else:
op_big_matrix = derived_array(lambda x, **kwargs: op(x), [big_matrix])[0]
op_big_matrix = derived_observable(lambda x, **kwargs: op(x), [big_matrix], array_mode=True)[0]
dim = op_big_matrix.shape[0]
op_A = op_big_matrix[0: dim // 2, 0: dim // 2]
op_B = op_big_matrix[dim // 2:, 0: dim // 2]
@ -232,7 +262,7 @@ def _mat_mat_op(op, obs, **kwargs):
else:
if kwargs.get('num_grad') is True:
return _num_diff_mat_mat_op(op, obs, **kwargs)
return derived_array(lambda x, **kwargs: op(x), [obs])[0]
return derived_observable(lambda x, **kwargs: op(x), [obs], array_mode=True)[0]
def eigh(obs, **kwargs):
@ -271,31 +301,6 @@ def svd(obs, **kwargs):
return (u, s, vh)
def slogdet(obs, **kwargs):
"""Computes the determinant of a matrix of Obs via np.linalg.slogdet."""
def _mat(x):
dim = int(np.sqrt(len(x)))
if np.sqrt(len(x)) != dim:
raise Exception('Input has to have dim**2 entries')
mat = []
for i in range(dim):
row = []
for j in range(dim):
row.append(x[j + dim * i])
mat.append(row)
(sign, logdet) = anp.linalg.slogdet(np.array(mat))
return sign * anp.exp(logdet)
if isinstance(obs, np.ndarray):
return derived_observable(_mat, (1 * (obs.ravel())).tolist(), **kwargs)
elif isinstance(obs, list):
return derived_observable(_mat, obs, **kwargs)
else:
raise TypeError('Unproper type of input.')
# Variants for numerical differentiation
def _num_diff_mat_mat_op(op, obs, **kwargs):
@ -500,51 +505,3 @@ def _num_diff_svd(obs, **kwargs):
res_mat2.append(row)
return (np.array(res_mat0) @ np.identity(mid_index), np.array(res_mat1) @ np.identity(mid_index), np.array(res_mat2) @ np.identity(shape[1]))
# This code block is directly taken from the current master branch of autograd and remains
# only until the new version is released on PyPi
_dot = partial(anp.einsum, '...ij,...jk->...ik')
# batched diag
def _diag(a):
return anp.eye(a.shape[-1]) * a
# batched diagonal, similar to matrix_diag in tensorflow
def _matrix_diag(a):
reps = anp.array(a.shape)
reps[:-1] = 1
reps[-1] = a.shape[-1]
newshape = list(a.shape) + [a.shape[-1]]
return _diag(anp.tile(a, reps).reshape(newshape))
# https://arxiv.org/pdf/1701.00392.pdf Eq(4.77)
# Note the formula from Sec3.1 in https://people.maths.ox.ac.uk/gilesm/files/NA-08-01.pdf is incomplete
def grad_eig(ans, x):
"""Gradient of a general square (complex valued) matrix"""
e, u = ans # eigenvalues as 1d array, eigenvectors in columns
n = e.shape[-1]
def vjp(g):
ge, gu = g
ge = _matrix_diag(ge)
f = 1 / (e[..., anp.newaxis, :] - e[..., :, anp.newaxis] + 1.e-20)
f -= _diag(f)
ut = anp.swapaxes(u, -1, -2)
r1 = f * _dot(ut, gu)
r2 = -f * (_dot(_dot(ut, anp.conj(u)), anp.real(_dot(ut, gu)) * anp.eye(n)))
r = _dot(_dot(anp.linalg.inv(ut), ge + r1 + r2), ut)
if not anp.iscomplexobj(x):
r = anp.real(r)
# the derivative is still complex for real input (imaginary delta is allowed), real output
# but the derivative should be real in real input case when imaginary delta is forbidden
return r
return vjp
defvjp(anp.linalg.eig, grad_eig)
# End of the code block from autograd.master

50
pyerrors/misc.py
View file

@ -1,18 +1,56 @@
import pickle
import numpy as np
from .obs import Obs
def dump_object(obj, name, **kwargs):
"""Dump object into pickle file.
Parameters
----------
obj : object
object to be saved in the pickle file
name : str
name of the file
path : str
specifies a custom path for the file (default '.')
"""
if 'path' in kwargs:
file_name = kwargs.get('path') + '/' + name + '.p'
else:
file_name = name + '.p'
with open(file_name, 'wb') as fb:
pickle.dump(obj, fb)
def load_object(path):
"""Load object from pickle file.
Parameters
----------
path : str
path to the file
"""
with open(path, 'rb') as file:
return pickle.load(file)
def gen_correlated_data(means, cov, name, tau=0.5, samples=1000):
""" Generate observables with given covariance and autocorrelation times.
Parameters
----------
means -- list containing the mean value of each observable.
cov -- covariance matrix for the data to be geneated.
name -- ensemble name for the data to be geneated.
tau -- can either be a real number or a list with an entry for
every dataset.
samples -- number of samples to be generated for each observable.
means : list
list containing the mean value of each observable.
cov : numpy.ndarray
covariance matrix for the data to be generated.
name : str
ensemble name for the data to be geneated.
tau : float or list
can either be a real number or a list with an entry for
every dataset.
samples : int
number of samples to be generated for each observable.
"""
assert len(means) == cov.shape[-1]

108
pyerrors/npr.py
View file

@ -1,108 +0,0 @@
import warnings
import numpy as np
from .linalg import inv, matmul
from .dirac import gamma, gamma5
L = None
T = None
class Npr_matrix(np.ndarray):
def __new__(cls, input_array, mom_in=None, mom_out=None):
obj = np.asarray(input_array).view(cls)
obj.mom_in = mom_in
obj.mom_out = mom_out
return obj
@property
def g5H(self):
"""Gamma_5 hermitean conjugate
Returns gamma_5 @ M.T.conj() @ gamma_5 and exchanges in and out going
momenta. Works only for 12x12 matrices.
"""
if self.shape != (12, 12):
raise Exception('g5H only works for 12x12 matrices.')
extended_g5 = np.kron(np.eye(3, dtype=int), gamma5)
return Npr_matrix(matmul(extended_g5, self.conj().T, extended_g5),
mom_in=self.mom_out,
mom_out=self.mom_in)
def _propagate_mom(self, other, name):
s_mom = getattr(self, name, None)
o_mom = getattr(other, name, None)
if s_mom is not None and o_mom is not None:
if not np.allclose(s_mom, o_mom):
raise Exception(name + ' does not match.')
return o_mom if o_mom is not None else s_mom
def __matmul__(self, other):
return self.__new__(Npr_matrix,
super().__matmul__(other),
self._propagate_mom(other, 'mom_in'),
self._propagate_mom(other, 'mom_out'))
def __array_finalize__(self, obj):
if obj is None:
return
self.mom_in = getattr(obj, 'mom_in', None)
self.mom_out = getattr(obj, 'mom_out', None)
def _check_geometry():
if L is None:
raise Exception("Spatial extent 'L' not set.")
else:
if not isinstance(L, int):
raise Exception("Spatial extent 'L' must be an integer.")
if T is None:
raise Exception("Temporal extent 'T' not set.")
if not isinstance(T, int):
raise Exception("Temporal extent 'T' must be an integer.")
def inv_propagator(prop):
""" Inverts a 12x12 quark propagator"""
if prop.shape != (12, 12):
raise Exception("Only 12x12 propagators can be inverted.")
return Npr_matrix(inv(prop), prop.mom_in)
def Zq(inv_prop, fermion='Wilson'):
""" Calculates the quark field renormalization constant Zq
Parameters
----------
inv_prop : array
Inverted 12x12 quark propagator
fermion : str
Fermion type for which the tree-level propagator is used
in the calculation of Zq. Default Wilson.
"""
_check_geometry()
mom = np.copy(inv_prop.mom_in)
mom[3] /= T / L
sin_mom = np.sin(2 * np.pi / L * mom)
if fermion == 'Wilson':
p_slash = -1j * (sin_mom[0] * gamma[0] + sin_mom[1] * gamma[1] + sin_mom[2] * gamma[2] + sin_mom[3] * gamma[3]) / np.sum(sin_mom ** 2)
elif fermion == 'Continuum':
p_mom = 2 * np.pi / L * mom
p_slash = -1j * (p_mom[0] * gamma[0] + p_mom[1] * gamma[1] + p_mom[2] * gamma[2] + p_mom[3] * gamma[3]) / np.sum(p_mom ** 2)
elif fermion == 'DWF':
W = np.sum(1 - np.cos(2 * np.pi / L * mom))
s2 = np.sum(sin_mom ** 2)
p_slash = -1j * (sin_mom[0] * gamma[0] + sin_mom[1] * gamma[1] + sin_mom[2] * gamma[2] + sin_mom[3] * gamma[3])
p_slash /= 2 * (W - 1 + np.sqrt((1 - W) ** 2 + s2))
else:
raise Exception("Fermion type '" + fermion + "' not implemented")
res = 1 / 12. * np.trace(matmul(inv_prop, np.kron(np.eye(3, dtype=int), p_slash)))
res.gamma_method()
if not res.imag.is_zero_within_error(5):
warnings.warn("Imaginary part of Zq is not zero within 5 sigma")
return res
return res.real

700
pyerrors/obs.py
View file

File diff suppressed because it is too large Load diff

6
pyerrors/roots.py
View file

@ -1,6 +1,7 @@
import numpy as np
import scipy.optimize
from autograd import jacobian
from .obs import derived_observable, pseudo_Obs
from .obs import derived_observable
def find_root(d, func, guess=1.0, **kwargs):
@ -33,4 +34,5 @@ def find_root(d, func, guess=1.0, **kwargs):
da = jacobian(lambda u, v: func(v, u))(d.value, root[0])
deriv = - da / dx
return derived_observable(lambda x, **kwargs: x[0], [pseudo_Obs(root, 0.0, d.names[0], d.shape[d.names[0]]), d], man_grad=[0, deriv])
res = derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (d.value + np.finfo(np.float64).eps) * root[0], [d], man_grad=[deriv])
return res

2
setup.py
View file

@ -9,5 +9,5 @@ setup(name='pyerrors',
author_email='fabian.joswig@ed.ac.uk',
packages=find_packages(),
python_requires='>=3.6.0',
install_requires=['numpy>=1.16', 'autograd>=1.2', 'numdifftools', 'matplotlib>=3.3', 'scipy', 'iminuit<2', 'h5py']
install_requires=['numpy>=1.16', 'autograd @ git+https://github.com/HIPS/autograd.git', 'numdifftools', 'matplotlib>=3.3', 'scipy', 'iminuit>=2', 'h5py']
)

20
tests/correlators_test.py
View file

@ -79,6 +79,26 @@ def test_T_symmetry():
T_symmetric = my_corr.T_symmetry(my_corr)
def test_fit_correlator():
my_corr = pe.correlators.Corr([pe.pseudo_Obs(1.01324, 0.05, 't'), pe.pseudo_Obs(2.042345, 0.0004, 't')])
def f(a, x):
y = a[0] + a[1] * x
return y
fit_res = my_corr.fit(f)
assert fit_res[0] == my_corr[0]
assert fit_res[1] == my_corr[1] - my_corr[0]
def test_plateau():
my_corr = pe.correlators.Corr([pe.pseudo_Obs(1.01324, 0.05, 't'), pe.pseudo_Obs(1.042345, 0.008, 't')])
my_corr.plateau([0, 1], method="fit")
my_corr.plateau([0, 1], method="mean")
with pytest.raises(Exception):
my_corr.plateau()
def test_utility():
corr_content = []
for t in range(8):

94
tests/covobs_test.py Normal file
View file

@ -0,0 +1,94 @@
import autograd.numpy as np
import pyerrors as pe
import pytest
np.random.seed(0)
def test_covobs():
val = 1.123124
cov = .243423
name = 'Covariance'
co = pe.cov_Obs(val, cov, name)
co.gamma_method()
co.details()
assert (co.dvalue == np.sqrt(cov))
assert (co.value == val)
do = 2 * co
assert (do.covobs[name].grad[0] == 2)
do = co * co
assert (do.covobs[name].grad[0] == 2 * val)
assert np.array_equal(do.covobs[name].cov, co.covobs[name].cov)
pi = [16.7457, -19.0475]
cov = [[3.49591, -6.07560], [-6.07560, 10.5834]]
cl = pe.cov_Obs(pi, cov, 'rAP')
pl = pe.misc.gen_correlated_data(pi, np.asarray(cov), 'rAPpseudo')
def rAP(p, g0sq):
return -0.0010666 * g0sq * (1 + np.exp(p[0] + p[1] / g0sq))
for g0sq in [1, 1.5, 1.8]:
oc = rAP(cl, g0sq)
oc.gamma_method()
op = rAP(pl, g0sq)
op.gamma_method()
assert(np.isclose(oc.value, op.value, rtol=1e-14, atol=1e-14))
[o.gamma_method() for o in cl]
assert(pe.covariance(cl[0], cl[1]) == cov[0][1])
assert(pe.covariance(cl[0], cl[1]) == cov[1][0])
do = cl[0] * cl[1]
assert(np.array_equal(do.covobs['rAP'].grad, np.transpose([pi[1], pi[0]]).reshape(2, 1)))
def test_covobs_overloading():
covobs = pe.cov_Obs([0.5, 0.5], np.array([[0.02, 0.02], [0.02, 0.02]]), 'test')
assert (covobs[0] / covobs[1]) == 1
assert (covobs[0] - covobs[1]) == 0
my_obs = pe.pseudo_Obs(2.3, 0.2, 'obs')
assert (my_obs * covobs[0] / covobs[1]) == my_obs
covobs = pe.cov_Obs(0.0, 0.3, 'test')
assert not covobs.is_zero()
def test_covobs_name_collision():
covobs = pe.cov_Obs(0.5, 0.002, 'test')
my_obs = pe.pseudo_Obs(2.3, 0.2, 'test')
with pytest.raises(Exception):
summed_obs = my_obs + covobs
covobs2 = pe.cov_Obs(0.3, 0.001, 'test')
with pytest.raises(Exception):
summed_obs = covobs + covobs2
def test_covobs_replica_separator():
with pytest.raises(Exception):
covobs = pe.cov_Obs(0.5, 0.002, 'test|r2')
def test_covobs_init():
covobs = pe.cov_Obs(0.5, 0.002, 'test')
covobs = pe.cov_Obs([1, 2], [0.1, 0.2], 'test')
covobs = pe.cov_Obs([1, 2], np.array([0.1, 0.2]), 'test')
covobs = pe.cov_Obs([1, 2], [[0.1, 0.2], [0.1, 0.2]], 'test')
covobs = pe.cov_Obs([1, 2], np.array([[0.1, 0.2], [0.1, 0.2]]), 'test')
def test_covobs_exceptions():
with pytest.raises(Exception):
covobs = pe.cov_Obs(0.1, [[0.1, 0.2], [0.1, 0.2]], 'test')
with pytest.raises(Exception):
covobs = pe.cov_Obs(0.1, np.array([[0.1, 0.2], [0.1, 0.2]]), 'test')
with pytest.raises(Exception):
covobs = pe.cov_Obs([0.5, 0.1], np.array([[2, 1, 3], [1, 2, 3]]), 'test')
with pytest.raises(Exception):
covobs = pe.cov_Obs([0.5, 0.1], np.random.random((2, 2, 2)), 'test')

22
tests/dirac_test.py
View file

@ -10,3 +10,25 @@ def test_gamma_matrices():
assert np.allclose(matrix @ matrix, np.identity(4))
assert np.allclose(matrix, matrix.T.conj())
assert np.allclose(pe.dirac.gamma5, pe.dirac.gamma[0] @ pe.dirac.gamma[1] @ pe.dirac.gamma[2] @ pe.dirac.gamma[3])
def test_grid_dirac():
for gamma in ['Identity',
'Gamma5',
'GammaX',
'GammaY',
'GammaZ',
'GammaT',
'GammaXGamma5',
'GammaYGamma5',
'GammaZGamma5',
'GammaTGamma5',
'SigmaXT',
'SigmaXY',
'SigmaXZ',
'SigmaYT',
'SigmaYZ',
'SigmaZT']:
pe.dirac.Grid_gamma(gamma)
with pytest.raises(Exception):
pe.dirac.Grid_gamma('Not a gamma matrix')

320
tests/fits_test.py
View file

@ -2,12 +2,34 @@ import autograd.numpy as np
import math
import scipy.optimize
from scipy.odr import ODR, Model, RealData
from scipy.linalg import cholesky
from scipy.stats import norm
import pyerrors as pe
import pytest
np.random.seed(0)
def test_fit_lin():
x = [0, 2]
y = [pe.pseudo_Obs(0, 0.1, 'ensemble'),
pe.pseudo_Obs(2, 0.1, 'ensemble')]
res = pe.fits.fit_lin(x, y)
assert res[0] == y[0]
assert res[1] == (y[1] - y[0]) / (x[1] - x[0])
x = y = [pe.pseudo_Obs(0, 0.1, 'ensemble'),
pe.pseudo_Obs(2, 0.1, 'ensemble')]
res = pe.fits.fit_lin(x, y)
m = (y[1] - y[0]) / (x[1] - x[0])
assert res[0] == y[1] - x[1] * m
assert res[1] == m
def test_least_squares():
dim = 10 + int(30 * np.random.rand())
x = np.arange(dim)
@ -27,20 +49,86 @@ def test_least_squares():
y = a[0] * np.exp(-a[1] * x)
return y
out = pe.least_squares(x, oy, func)
out = pe.least_squares(x, oy, func, expected_chisquare=True, resplot=True, qqplot=True)
beta = out.fit_parameters
str(out)
repr(out)
len(out)
for i in range(2):
beta[i].gamma_method(S=1.0)
assert math.isclose(beta[i].value, popt[i], abs_tol=1e-5)
assert math.isclose(pcov[i, i], beta[i].dvalue ** 2, abs_tol=1e-3)
assert math.isclose(pe.covariance(beta[0], beta[1]), pcov[0, 1], abs_tol=1e-3)
pe.Obs.e_tag_global = 0
chi2_pyerrors = np.sum(((f(x, *[o.value for o in beta]) - y) / yerr) ** 2) / (len(x) - 2)
chi2_scipy = np.sum(((f(x, *popt) - y) / yerr) ** 2) / (len(x) - 2)
assert math.isclose(chi2_pyerrors, chi2_scipy, abs_tol=1e-10)
out = pe.least_squares(x, oy, func, const_par=[beta[1]])
assert((out.fit_parameters[0] - beta[0]).is_zero())
assert((out.fit_parameters[1] - beta[1]).is_zero())
oyc = []
for i, item in enumerate(x):
oyc.append(pe.cov_Obs(y[i], yerr[i]**2, 'cov' + str(i)))
outc = pe.least_squares(x, oyc, func)
betac = outc.fit_parameters
for i in range(2):
betac[i].gamma_method(S=1.0)
assert math.isclose(betac[i].value, popt[i], abs_tol=1e-5)
assert math.isclose(pcov[i, i], betac[i].dvalue ** 2, abs_tol=1e-3)
assert math.isclose(pe.covariance(betac[0], betac[1]), pcov[0, 1], abs_tol=1e-3)
def test_correlated_fit():
num_samples = 400
N = 10
x = norm.rvs(size=(N, num_samples))
r = np.zeros((N, N))
for i in range(N):
for j in range(N):
r[i, j] = np.exp(-0.1 * np.fabs(i - j))
errl = np.sqrt([3.4, 2.5, 3.6, 2.8, 4.2, 4.7, 4.9, 5.1, 3.2, 4.2])
errl *= 4
for i in range(N):
for j in range(N):
r[i, j] *= errl[i] * errl[j]
c = cholesky(r, lower=True)
y = np.dot(c, x)
x = np.arange(N)
for linear in [True, False]:
data = []
for i in range(N):
if linear:
data.append(pe.Obs([[i + 1 + o for o in y[i]]], ['ens']))
else:
data.append(pe.Obs([[np.exp(-(i + 1)) + np.exp(-(i + 1)) * o for o in y[i]]], ['ens']))
[o.gamma_method() for o in data]
if linear:
def fitf(p, x):
return p[1] + p[0] * x
else:
def fitf(p, x):
return p[1] * np.exp(-p[0] * x)
fitp = pe.least_squares(x, data, fitf, expected_chisquare=True)
fitpc = pe.least_squares(x, data, fitf, correlated_fit=True)
for i in range(2):
diff = fitp[i] - fitpc[i]
diff.gamma_method()
assert(diff.is_zero_within_error(sigma=1.5))
def test_total_least_squares():
dim = 10 + int(30 * np.random.rand())
@ -70,16 +158,62 @@ def test_total_least_squares():
odr.set_job(fit_type=0, deriv=1)
output = odr.run()
out = pe.total_least_squares(ox, oy, func)
out = pe.total_least_squares(ox, oy, func, expected_chisquare=True)
beta = out.fit_parameters
pe.Obs.e_tag_global = 5
str(out)
repr(out)
len(out)
for i in range(2):
beta[i].gamma_method(e_tag=5, S=1.0)
beta[i].gamma_method(S=1.0)
assert math.isclose(beta[i].value, output.beta[i], rel_tol=1e-5)
assert math.isclose(output.cov_beta[i, i], beta[i].dvalue ** 2, rel_tol=2.5e-1), str(output.cov_beta[i, i]) + ' ' + str(beta[i].dvalue ** 2)
assert math.isclose(pe.covariance(beta[0], beta[1]), output.cov_beta[0, 1], rel_tol=2.5e-1)
pe.Obs.e_tag_global = 0
out = pe.total_least_squares(ox, oy, func, const_par=[beta[1]])
diff = out.fit_parameters[0] - beta[0]
assert(diff / beta[0] < 1e-3 * beta[0].dvalue)
assert((out.fit_parameters[1] - beta[1]).is_zero())
oxc = []
for i, item in enumerate(x):
oxc.append(pe.cov_Obs(x[i], xerr[i]**2, 'covx' + str(i)))
oyc = []
for i, item in enumerate(x):
oyc.append(pe.cov_Obs(y[i], yerr[i]**2, 'covy' + str(i)))
outc = pe.total_least_squares(oxc, oyc, func)
betac = outc.fit_parameters
for i in range(2):
betac[i].gamma_method(S=1.0)
assert math.isclose(betac[i].value, output.beta[i], rel_tol=1e-3)
assert math.isclose(output.cov_beta[i, i], betac[i].dvalue ** 2, rel_tol=2.5e-1), str(output.cov_beta[i, i]) + ' ' + str(betac[i].dvalue ** 2)
assert math.isclose(pe.covariance(betac[0], betac[1]), output.cov_beta[0, 1], rel_tol=2.5e-1)
outc = pe.total_least_squares(oxc, oyc, func, const_par=[betac[1]])
diffc = outc.fit_parameters[0] - betac[0]
assert(diffc / betac[0] < 1e-3 * betac[0].dvalue)
assert((outc.fit_parameters[1] - betac[1]).is_zero())
outc = pe.total_least_squares(oxc, oy, func)
betac = outc.fit_parameters
for i in range(2):
betac[i].gamma_method(S=1.0)
assert math.isclose(betac[i].value, output.beta[i], rel_tol=1e-3)
assert math.isclose(output.cov_beta[i, i], betac[i].dvalue ** 2, rel_tol=2.5e-1), str(output.cov_beta[i, i]) + ' ' + str(betac[i].dvalue ** 2)
assert math.isclose(pe.covariance(betac[0], betac[1]), output.cov_beta[0, 1], rel_tol=2.5e-1)
outc = pe.total_least_squares(oxc, oy, func, const_par=[betac[1]])
diffc = outc.fit_parameters[0] - betac[0]
assert(diffc / betac[0] < 1e-3 * betac[0].dvalue)
assert((outc.fit_parameters[1] - betac[1]).is_zero())
def test_odr_derivatives():
@ -99,7 +233,177 @@ def test_odr_derivatives():
out = pe.total_least_squares(x, y, func)
fit1 = out.fit_parameters
with pytest.warns(DeprecationWarning):
tfit = pe.fits.fit_general(x, y, func, base_step=0.1, step_ratio=1.1, num_steps=20)
tfit = fit_general(x, y, func, base_step=0.1, step_ratio=1.1, num_steps=20)
assert np.abs(np.max(np.array(list(fit1[1].deltas.values()))
- np.array(list(tfit[1].deltas.values())))) < 10e-8
def test_r_value_persistence():
def f(a, x):
return a[0] + a[1] * x
a = pe.pseudo_Obs(1.1, .1, 'a')
assert np.isclose(a.value, a.r_values['a'])
a_2 = a ** 2
assert np.isclose(a_2.value, a_2.r_values['a'])
b = pe.pseudo_Obs(2.1, .2, 'b')
y = [a, b]
[o.gamma_method() for o in y]
fitp = pe.fits.least_squares([1, 2], y, f)
assert np.isclose(fitp[0].value, fitp[0].r_values['a'])
assert np.isclose(fitp[0].value, fitp[0].r_values['b'])
assert np.isclose(fitp[1].value, fitp[1].r_values['a'])
assert np.isclose(fitp[1].value, fitp[1].r_values['b'])
fitp = pe.fits.total_least_squares(y, y, f)
assert np.isclose(fitp[0].value, fitp[0].r_values['a'])
assert np.isclose(fitp[0].value, fitp[0].r_values['b'])
assert np.isclose(fitp[1].value, fitp[1].r_values['a'])
assert np.isclose(fitp[1].value, fitp[1].r_values['b'])
fitp = pe.fits.least_squares([1, 2], y, f, priors=y)
assert np.isclose(fitp[0].value, fitp[0].r_values['a'])
assert np.isclose(fitp[0].value, fitp[0].r_values['b'])
assert np.isclose(fitp[1].value, fitp[1].r_values['a'])
assert np.isclose(fitp[1].value, fitp[1].r_values['b'])
def test_prior_fit():
def f(a, x):
return a[0] + a[1] * x
a = pe.pseudo_Obs(0.0, 0.1, 'a')
b = pe.pseudo_Obs(1.0, 0.2, 'a')
y = [a, b]
with pytest.raises(Exception):
fitp = pe.fits.least_squares([0, 1], 1 * np.array(y), f, priors=['0.0(8)', '1.0(8)'])
[o.gamma_method() for o in y]
fitp = pe.fits.least_squares([0, 1], y, f, priors=['0.0(8)', '1.0(8)'])
fitp = pe.fits.least_squares([0, 1], y, f, priors=y, resplot=True, qqplot=True)
def test_error_band():
def f(a, x):
return a[0] + a[1] * x
a = pe.pseudo_Obs(0.0, 0.1, 'a')
b = pe.pseudo_Obs(1.0, 0.2, 'a')
x = [0, 1]
y = [a, b]
fitp = pe.fits.least_squares(x, y, f)
with pytest.raises(Exception):
pe.fits.error_band(x, f, fitp.fit_parameters)
fitp.gamma_method()
pe.fits.error_band(x, f, fitp.fit_parameters)
def fit_general(x, y, func, silent=False, **kwargs):
"""Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
Plausibility of the results should be checked. To control the numerical differentiation
the kwargs of numdifftools.step_generators.MaxStepGenerator can be used.
func has to be of the form
def func(a, x):
y = a[0] + a[1] * x + a[2] * np.sinh(x)
return y
y has to be a list of Obs, the dvalues of the Obs are used as yerror for the fit.
x can either be a list of floats in which case no xerror is assumed, or
a list of Obs, where the dvalues of the Obs are used as xerror for the fit.
Keyword arguments
-----------------
silent -- If true all output to the console is omitted (default False).
initial_guess -- can provide an initial guess for the input parameters. Relevant for non-linear fits
with many parameters.
"""
if not callable(func):
raise TypeError('func has to be a function.')
for i in range(10):
try:
func(np.arange(i), 0)
except:
pass
else:
break
n_parms = i
if not silent:
print('Fit with', n_parms, 'parameters')
global print_output, beta0
print_output = 1
if 'initial_guess' in kwargs:
beta0 = kwargs.get('initial_guess')
if len(beta0) != n_parms:
raise Exception('Initial guess does not have the correct length.')
else:
beta0 = np.arange(n_parms)
if len(x) != len(y):
raise Exception('x and y have to have the same length')
if all(isinstance(n, pe.Obs) for n in x):
obs = x + y
x_constants = None
xerr = [o.dvalue for o in x]
yerr = [o.dvalue for o in y]
elif all(isinstance(n, float) or isinstance(n, int) for n in x) or isinstance(x, np.ndarray):
obs = y
x_constants = x
xerr = None
yerr = [o.dvalue for o in y]
else:
raise Exception('Unsupported types for x')
def do_the_fit(obs, **kwargs):
global print_output, beta0
func = kwargs.get('function')
yerr = kwargs.get('yerr')
length = len(yerr)
xerr = kwargs.get('xerr')
if length == len(obs):
assert 'x_constants' in kwargs
data = RealData(kwargs.get('x_constants'), obs, sy=yerr)
fit_type = 2
elif length == len(obs) // 2:
data = RealData(obs[:length], obs[length:], sx=xerr, sy=yerr)
fit_type = 0
else:
raise Exception('x and y do not fit together.')
model = Model(func)
odr = ODR(data, model, beta0, partol=np.finfo(np.float64).eps)
odr.set_job(fit_type=fit_type, deriv=1)
output = odr.run()
if print_output and not silent:
print(*output.stopreason)
print('chisquare/d.o.f.:', output.res_var)
print_output = 0
beta0 = output.beta
return output.beta[kwargs.get('n')]
res = []
for n in range(n_parms):
res.append(pe.derived_observable(do_the_fit, obs, function=func, xerr=xerr, yerr=yerr, x_constants=x_constants, num_grad=True, n=n, **kwargs))
return res

91
tests/io_test.py Normal file
View file

@ -0,0 +1,91 @@
import os
import gzip
import numpy as np
import pyerrors as pe
import pyerrors.input.json as jsonio
def test_jsonio():
o = pe.pseudo_Obs(1.0, .2, 'one')
o2 = pe.pseudo_Obs(0.5, .1, 'two|r1')
o3 = pe.pseudo_Obs(0.5, .1, 'two|r2')
o4 = pe.merge_obs([o2, o3])
otag = 'This has been merged!'
o4.tag = otag
do = o - .2 * o4
co1 = pe.cov_Obs(1., .123, 'cov1')
co3 = pe.cov_Obs(4., .1 ** 2, 'cov3')
do *= co1 / co3
do.tag = {'A': 2}
o5 = pe.pseudo_Obs(0.8, .1, 'two|r2')
co2 = pe.cov_Obs([1, 2], [[.12, .004], [.004, .02]], 'cov2')
o5 /= co2[0]
o3 /= co2[1]
o5.tag = 2 * otag
testl = [o3, o5]
arr = np.array([o3, o5])
mat = np.array([[pe.pseudo_Obs(1.0, .1, 'mat'), pe.pseudo_Obs(0.3, .1, 'mat')], [pe.pseudo_Obs(0.2, .1, 'mat'), pe.pseudo_Obs(2.0, .4, 'mat')]])
mat[0][1].tag = ['This', 'is', 2, None]
mat[1][0].tag = '{testt}'
mat[1][1].tag = '[tag]'
tt1 = pe.Obs([np.random.rand(100)], ['t|r1'], idl=[range(2, 202, 2)])
tt2 = pe.Obs([np.random.rand(100)], ['t|r2'], idl=[range(2, 202, 2)])
tt3 = pe.Obs([np.random.rand(102)], ['qe'])
tt = tt1 + tt2 + tt3
tt.tag = 'Test Obs: Ä'
ol = [o4, do, testl, mat, arr, np.array([o]), np.array([tt, tt]), [tt, tt], co1, co2, np.array(co2), co1 / co2[0]]
fname = 'test_rw'
jsonio.dump_to_json(ol, fname, indent=1, description='[I am a tricky description]')
rl = jsonio.load_json(fname)
os.remove(fname + '.json.gz')
for o, r in zip(ol, rl):
assert np.all(o == r)
for i in range(len(ol)):
if isinstance(ol[i], pe.Obs):
o = ol[i] - rl[i]
assert(o.is_zero())
assert(ol[i].tag == rl[i].tag)
or1 = np.ravel(ol[i])
or2 = np.ravel(rl[i])
for j in range(len(or1)):
o = or1[j] - or2[j]
assert(o.is_zero())
description = {'I': {'Am': {'a': 'nested dictionary!'}}}
jsonio.dump_to_json(ol, fname, indent=0, gz=False, description=description)
rl = jsonio.load_json(fname, gz=False, full_output=True)
os.remove(fname + '.json')
for o, r in zip(ol, rl['obsdata']):
assert np.all(o == r)
assert(description == rl['description'])
def test_json_string_reconstruction():
my_obs = pe.Obs([np.random.rand(100)], ['name'])
json_string = pe.input.json.create_json_string(my_obs)
reconstructed_obs1 = pe.input.json.import_json_string(json_string)
assert my_obs == reconstructed_obs1
compressed_string = gzip.compress(json_string.encode('utf-8'))
reconstructed_string = gzip.decompress(compressed_string).decode('utf-8')
reconstructed_obs2 = pe.input.json.import_json_string(reconstructed_string)
assert reconstructed_string == json_string
assert my_obs == reconstructed_obs2

192
tests/linalg_test.py
View file

@ -7,35 +7,153 @@ import pytest
np.random.seed(0)
def test_matmul():
for dim in [4, 8]:
my_list = []
length = 1000 + np.random.randint(200)
for i in range(dim ** 2):
my_list.append(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']))
my_array = np.array(my_list).reshape((dim, dim))
tt = pe.linalg.matmul(my_array, my_array) - my_array @ my_array
for t, e in np.ndenumerate(tt):
assert e.is_zero(), t
def get_real_matrix(dimension):
base_matrix = np.empty((dimension, dimension), dtype=object)
for (n, m), entry in np.ndenumerate(base_matrix):
exponent_real = np.random.normal(0, 1)
exponent_imag = np.random.normal(0, 1)
base_matrix[n, m] = pe.Obs([np.random.normal(1.0, 0.1, 100)], ['t'])
my_list = []
length = 1000 + np.random.randint(200)
for i in range(dim ** 2):
my_list.append(pe.CObs(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']),
pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2'])))
my_array = np.array(my_list).reshape((dim, dim))
tt = pe.linalg.matmul(my_array, my_array) - my_array @ my_array
for t, e in np.ndenumerate(tt):
assert e.is_zero(), t
return base_matrix
def get_complex_matrix(dimension):
base_matrix = np.empty((dimension, dimension), dtype=object)
for (n, m), entry in np.ndenumerate(base_matrix):
exponent_real = np.random.normal(0, 1)
exponent_imag = np.random.normal(0, 1)
base_matrix[n, m] = pe.CObs(pe.Obs([np.random.normal(1.0, 0.1, 100)], ['t']),
pe.Obs([np.random.normal(1.0, 0.1, 100)], ['t']))
return base_matrix
def test_matmul():
for dim in [4, 6]:
for const in [1, pe.cov_Obs([1.0, 1.0], [[0.001,0.0001], [0.0001, 0.002]], 'norm')[1]]:
my_list = []
length = 100 + np.random.randint(200)
for i in range(dim ** 2):
my_list.append(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']))
my_array = const * np.array(my_list).reshape((dim, dim))
tt = pe.linalg.matmul(my_array, my_array) - my_array @ my_array
for t, e in np.ndenumerate(tt):
assert e.is_zero(), t
my_list = []
length = 100 + np.random.randint(200)
for i in range(dim ** 2):
my_list.append(pe.CObs(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']),
pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2'])))
my_array = np.array(my_list).reshape((dim, dim)) * const
tt = pe.linalg.matmul(my_array, my_array) - my_array @ my_array
for t, e in np.ndenumerate(tt):
assert e.is_zero(), t
def test_jack_matmul():
tt = get_real_matrix(8)
check1 = pe.linalg.jack_matmul(tt, tt) - pe.linalg.matmul(tt, tt)
[o.gamma_method() for o in check1.ravel()]
assert np.all([o.is_zero_within_error(0.1) for o in check1.ravel()])
assert np.all([o.dvalue < 0.001 for o in check1.ravel()])
trace1 = np.trace(check1)
trace1.gamma_method()
assert trace1.dvalue < 0.001
tr = np.random.rand(8, 8)
check2 = pe.linalg.jack_matmul(tt, tr) - pe.linalg.matmul(tt, tr)
[o.gamma_method() for o in check2.ravel()]
assert np.all([o.is_zero_within_error(0.1) for o in check2.ravel()])
assert np.all([o.dvalue < 0.001 for o in check2.ravel()])
trace2 = np.trace(check2)
trace2.gamma_method()
assert trace2.dvalue < 0.001
tt2 = get_complex_matrix(8)
check3 = pe.linalg.jack_matmul(tt2, tt2) - pe.linalg.matmul(tt2, tt2)
[o.gamma_method() for o in check3.ravel()]
assert np.all([o.real.is_zero_within_error(0.1) for o in check3.ravel()])
assert np.all([o.imag.is_zero_within_error(0.1) for o in check3.ravel()])
assert np.all([o.real.dvalue < 0.001 for o in check3.ravel()])
assert np.all([o.imag.dvalue < 0.001 for o in check3.ravel()])
trace3 = np.trace(check3)
trace3.gamma_method()
assert trace3.real.dvalue < 0.001
assert trace3.imag.dvalue < 0.001
tr2 = np.random.rand(8, 8) + 1j * np.random.rand(8, 8)
check4 = pe.linalg.jack_matmul(tt2, tr2) - pe.linalg.matmul(tt2, tr2)
[o.gamma_method() for o in check4.ravel()]
assert np.all([o.real.is_zero_within_error(0.1) for o in check4.ravel()])
assert np.all([o.imag.is_zero_within_error(0.1) for o in check4.ravel()])
assert np.all([o.real.dvalue < 0.001 for o in check4.ravel()])
assert np.all([o.imag.dvalue < 0.001 for o in check4.ravel()])
trace4 = np.trace(check4)
trace4.gamma_method()
assert trace4.real.dvalue < 0.001
assert trace4.imag.dvalue < 0.001
def test_einsum():
def _perform_real_check(arr):
[o.gamma_method() for o in arr]
assert np.all([o.is_zero_within_error(0.001) for o in arr])
assert np.all([o.dvalue < 0.001 for o in arr])
def _perform_complex_check(arr):
[o.gamma_method() for o in arr]
assert np.all([o.real.is_zero_within_error(0.001) for o in arr])
assert np.all([o.real.dvalue < 0.001 for o in arr])
assert np.all([o.imag.is_zero_within_error(0.001) for o in arr])
assert np.all([o.imag.dvalue < 0.001 for o in arr])
tt = [get_real_matrix(4), get_real_matrix(3)]
q = np.tensordot(tt[0], tt[1], 0)
c1 = tt[1] @ q
c2 = pe.linalg.einsum('ij,abjd->abid', tt[1], q)
check1 = c1 - c2
_perform_real_check(check1.ravel())
check2 = np.trace(tt[0]) - pe.linalg.einsum('ii', tt[0])
_perform_real_check([check2])
check3 = np.trace(tt[1]) - pe.linalg.einsum('ii', tt[1])
_perform_real_check([check3])
tt = [get_real_matrix(4), np.random.random((3, 3))]
q = np.tensordot(tt[0], tt[1], 0)
c1 = tt[1] @ q
c2 = pe.linalg.einsum('ij,abjd->abid', tt[1], q)
check1 = c1 - c2
_perform_real_check(check1.ravel())
tt = [get_complex_matrix(4), get_complex_matrix(3)]
q = np.tensordot(tt[0], tt[1], 0)
c1 = tt[1] @ q
c2 = pe.linalg.einsum('ij,abjd->abid', tt[1], q)
check1 = c1 - c2
_perform_complex_check(check1.ravel())
check2 = np.trace(tt[0]) - pe.linalg.einsum('ii', tt[0])
_perform_complex_check([check2])
check3 = np.trace(tt[1]) - pe.linalg.einsum('ii', tt[1])
_perform_complex_check([check3])
tt = [get_complex_matrix(4), np.random.random((3, 3))]
q = np.tensordot(tt[0], tt[1], 0)
c1 = tt[1] @ q
c2 = pe.linalg.einsum('ij,abjd->abid', tt[1], q)
check1 = c1 - c2
_perform_complex_check(check1.ravel())
def test_multi_dot():
for dim in [4, 8]:
for dim in [4, 6]:
my_list = []
length = 1000 + np.random.randint(200)
for i in range(dim ** 2):
my_list.append(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']))
my_array = np.array(my_list).reshape((dim, dim))
my_array = pe.cov_Obs(1.0, 0.002, 'cov') * np.array(my_list).reshape((dim, dim))
tt = pe.linalg.matmul(my_array, my_array, my_array, my_array) - my_array @ my_array @ my_array @ my_array
for t, e in np.ndenumerate(tt):
assert e.is_zero(), t
@ -45,12 +163,25 @@ def test_multi_dot():
for i in range(dim ** 2):
my_list.append(pe.CObs(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']),
pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2'])))
my_array = np.array(my_list).reshape((dim, dim))
my_array = np.array(my_list).reshape((dim, dim)) * pe.cov_Obs(1.0, 0.002, 'cov')
tt = pe.linalg.matmul(my_array, my_array, my_array, my_array) - my_array @ my_array @ my_array @ my_array
for t, e in np.ndenumerate(tt):
assert e.is_zero(), t
def test_jack_multi_dot():
for dim in [2, 4, 8]:
my_array = get_real_matrix(dim)
tt = pe.linalg.jack_matmul(my_array, my_array, my_array) - pe.linalg.matmul(my_array, my_array, my_array)
for t, e in np.ndenumerate(tt):
e.gamma_method()
assert e.is_zero_within_error(0.01)
assert e.is_zero(atol=1e-1), t
assert np.isclose(e.value, 0.0)
def test_matmul_irregular_histories():
dim = 2
length = 500
@ -58,13 +189,13 @@ def test_matmul_irregular_histories():
standard_array = []
for i in range(dim ** 2):
standard_array.append(pe.Obs([np.random.normal(1.1, 0.2, length)], ['ens1']))
standard_matrix = np.array(standard_array).reshape((dim, dim))
standard_matrix = np.array(standard_array).reshape((dim, dim)) * pe.cov_Obs(1.0, 0.002, 'cov') * pe.pseudo_Obs(0.1, 0.002, 'qr')
for idl in [range(1, 501, 2), range(250, 273), [2, 8, 19, 20, 78]]:
irregular_array = []
for i in range(dim ** 2):
irregular_array.append(pe.Obs([np.random.normal(1.1, 0.2, len(idl))], ['ens1'], idl=[idl]))
irregular_matrix = np.array(irregular_array).reshape((dim, dim))
irregular_matrix = np.array(irregular_array).reshape((dim, dim)) * pe.cov_Obs([1.0, 1.0], [[0.001,0.0001], [0.0001, 0.002]], 'norm')[0]
t1 = standard_matrix @ irregular_matrix
t2 = pe.linalg.matmul(standard_matrix, irregular_matrix)
@ -82,7 +213,7 @@ def test_irregular_matrix_inverse():
irregular_array = []
for i in range(dim ** 2):
irregular_array.append(pe.Obs([np.random.normal(1.1, 0.2, len(idl)), np.random.normal(0.25, 0.1, 10)], ['ens1', 'ens2'], idl=[idl, range(1, 11)]))
irregular_matrix = np.array(irregular_array).reshape((dim, dim))
irregular_matrix = np.array(irregular_array).reshape((dim, dim)) * pe.cov_Obs(1.0, 0.002, 'cov') * pe.pseudo_Obs(1.0, 0.002, 'ens2|r23')
invertible_irregular_matrix = np.identity(dim) + irregular_matrix @ irregular_matrix.T
@ -113,8 +244,8 @@ def test_complex_matrix_inverse():
base_matrix = np.empty((dimension, dimension), dtype=object)
matrix = np.empty((dimension, dimension), dtype=complex)
for (n, m), entry in np.ndenumerate(base_matrix):
exponent_real = np.random.normal(3, 5)
exponent_imag = np.random.normal(3, 5)
exponent_real = np.random.normal(2, 3)
exponent_imag = np.random.normal(2, 3)
base_matrix[n, m] = pe.CObs(pe.pseudo_Obs(2 + 10 ** exponent_real, 10 ** (exponent_real - 1), 't'),
pe.pseudo_Obs(2 + 10 ** exponent_imag, 10 ** (exponent_imag - 1), 't'))
@ -169,6 +300,10 @@ def test_matrix_functions():
for j in range(dim):
assert tmp[j].is_zero()
# Check eig function
e2 = pe.linalg.eig(sym)
assert np.all(np.sort(e) == np.sort(e2))
# Check svd
u, v, vh = pe.linalg.svd(sym)
diff = sym - u @ np.diag(v) @ vh
@ -176,6 +311,9 @@ def test_matrix_functions():
for (i, j), entry in np.ndenumerate(diff):
assert entry.is_zero()
# Check determinant
assert pe.linalg.det(np.diag(np.diag(matrix))) == np.prod(np.diag(matrix))
def test_complex_matrix_operations():
dimension = 4

124
tests/obs_test.py
View file

@ -9,6 +9,50 @@ import pytest
np.random.seed(0)
def test_Obs_exceptions():
with pytest.raises(Exception):
pe.Obs([np.random.rand(10)], ['1', '2'])
with pytest.raises(Exception):
pe.Obs([np.random.rand(10)], ['1'], idl=[])
with pytest.raises(Exception):
pe.Obs([np.random.rand(10), np.random.rand(10)], ['1', '1'])
with pytest.raises(Exception):
pe.Obs([np.random.rand(10), np.random.rand(10)], ['1', 1])
with pytest.raises(Exception):
pe.Obs([np.random.rand(10)], [1])
with pytest.raises(Exception):
pe.Obs([np.random.rand(4)], ['name'])
with pytest.raises(Exception):
pe.Obs([np.random.rand(5)], ['1'], idl=[[5, 3, 2 ,4 ,1]])
with pytest.raises(Exception):
pe.Obs([np.random.rand(5)], ['1'], idl=['t'])
with pytest.raises(Exception):
pe.Obs([np.random.rand(5)], ['1'], idl=[range(1, 8)])
my_obs = pe.Obs([np.random.rand(6)], ['name'])
my_obs._value = 0.0
my_obs.details()
with pytest.raises(Exception):
my_obs.plot_tauint()
with pytest.raises(Exception):
my_obs.plot_rho()
with pytest.raises(Exception):
my_obs.plot_rep_dist()
with pytest.raises(Exception):
my_obs.plot_piechart()
with pytest.raises(Exception):
my_obs.gamma_method(S='2.3')
with pytest.raises(Exception):
my_obs.gamma_method(tau_exp=2.3)
my_obs.gamma_method()
my_obs.details()
my_obs.plot_rep_dist()
my_obs += pe.Obs([np.random.rand(6)], ['name2|r1'], idl=[[1, 3, 4, 5, 6, 7]])
my_obs += pe.Obs([np.random.rand(6)], ['name2|r2'])
my_obs.gamma_method()
my_obs.details()
def test_dump():
value = np.random.normal(5, 10)
dvalue = np.abs(np.random.normal(0, 1))
@ -95,6 +139,18 @@ def test_gamma_method():
assert test_obs.e_tauint['t'] - 10.5 <= test_obs.e_dtauint['t']
def test_gamma_method_no_windowing():
for iteration in range(50):
obs = pe.Obs([np.random.normal(1.02, 0.02, 733 + np.random.randint(1000))], ['ens'])
obs.gamma_method(S=0)
assert obs.e_tauint['ens'] == 0.5
assert np.isclose(np.sqrt(np.var(obs.deltas['ens'], ddof=1) / obs.shape['ens']), obs.dvalue)
obs.gamma_method(S=1.1)
assert obs.e_tauint['ens'] > 0.5
with pytest.raises(Exception):
obs.gamma_method(S=-0.2)
def test_gamma_method_persistance():
my_obs = pe.Obs([np.random.rand(730)], ['t'])
my_obs.gamma_method()
@ -183,7 +239,7 @@ def test_covariance_is_variance():
test_obs.gamma_method()
assert np.abs(test_obs.dvalue ** 2 - pe.covariance(test_obs, test_obs)) <= 10 * np.finfo(np.float64).eps
test_obs = test_obs + pe.pseudo_Obs(value, dvalue, 'q', 200)
test_obs.gamma_method(e_tag=0)
test_obs.gamma_method()
assert np.abs(test_obs.dvalue ** 2 - pe.covariance(test_obs, test_obs)) <= 10 * np.finfo(np.float64).eps
@ -221,7 +277,7 @@ def test_gamma_method():
test_obs = pe.pseudo_Obs(value, dvalue, 't', int(1000 * (1 + np.random.rand())))
# Test if the error is processed correctly
test_obs.gamma_method(e_tag=1)
test_obs.gamma_method()
assert np.abs(test_obs.value - value) < 1e-12
assert abs(test_obs.dvalue - dvalue) < 1e-10 * dvalue
@ -241,7 +297,7 @@ def test_derived_observables():
assert np.abs(d_Obs_ad.dvalue-d_Obs_fd.dvalue) < 1000 * np.finfo(np.float64).eps * d_Obs_ad.dvalue
i_am_one = pe.derived_observable(lambda x, **kwargs: x[0] / x[1], [d_Obs_ad, d_Obs_ad])
i_am_one.gamma_method(e_tag=1)
i_am_one.gamma_method()
assert i_am_one.value == 1.0
assert i_am_one.dvalue < 2 * np.finfo(np.float64).eps
@ -290,15 +346,17 @@ def test_overloaded_functions():
for i, item in enumerate(funcs):
ad_obs = item(test_obs)
fd_obs = pe.derived_observable(lambda x, **kwargs: item(x[0]), [test_obs], num_grad=True)
ad_obs.gamma_method(S=0.01, e_tag=1)
ad_obs.gamma_method(S=0.01)
assert np.max((ad_obs.deltas['t'] - fd_obs.deltas['t']) / ad_obs.deltas['t']) < 1e-8, item.__name__
assert np.abs((ad_obs.value - item(val)) / ad_obs.value) < 1e-10, item.__name__
assert np.abs(ad_obs.dvalue - dval * np.abs(deriv[i](val))) < 1e-6, item.__name__
def test_utils():
zero_pseudo_obs = pe.pseudo_Obs(1.0, 0.0, 'null')
my_obs = pe.pseudo_Obs(1.0, 0.5, 't|r01')
my_obs += pe.pseudo_Obs(1.0, 0.5, 't|r02')
str(my_obs)
for tau_exp in [0, 5]:
my_obs.gamma_method(tau_exp=tau_exp)
my_obs.tag = "Test description"
@ -313,6 +371,8 @@ def test_utils():
my_obs.plot_piechart()
assert my_obs > (my_obs - 1)
assert my_obs < (my_obs + 1)
float(my_obs)
str(my_obs)
def test_cobs():
@ -360,6 +420,15 @@ def test_reweighting():
r_obs2 = r_obs[0] * my_obs
assert r_obs2.reweighted
my_irregular_obs = pe.Obs([np.random.rand(500)], ['t'], idl=[range(1, 1001, 2)])
assert not my_irregular_obs.reweighted
r_obs = pe.reweight(my_obs, [my_irregular_obs], all_configs=True)
r_obs = pe.reweight(my_obs, [my_irregular_obs], all_configs=False)
r_obs = pe.reweight(my_obs, [my_obs])
assert r_obs[0].reweighted
r_obs2 = r_obs[0] * my_obs
assert r_obs2.reweighted
def test_merge_obs():
my_obs1 = pe.Obs([np.random.rand(100)], ['t'])
@ -369,6 +438,16 @@ def test_merge_obs():
assert diff == -(my_obs1.value + my_obs2.value) / 2
def test_merge_obs_r_values():
a1 = pe.pseudo_Obs(1.1, .1, 'a|1')
a2 = pe.pseudo_Obs(1.2, .1, 'a|2')
a = pe.merge_obs([a1, a2])
assert np.isclose(a.r_values['a|1'], a1.value)
assert np.isclose(a.r_values['a|2'], a2.value)
assert np.isclose(a.value, np.mean([a1.value, a2.value]))
def test_correlate():
my_obs1 = pe.Obs([np.random.rand(100)], ['t'])
my_obs2 = pe.Obs([np.random.rand(100)], ['t'])
@ -397,6 +476,7 @@ def test_irregular_error_propagation():
pe.Obs([np.random.rand(6)], ['t'], idl=[[4, 18, 27, 29, 57, 80]]),
pe.Obs([np.random.rand(50)], ['t'], idl=[list(range(1, 26)) + list(range(50, 100, 2))])]
for obs1 in obs_list:
obs1.details()
for obs2 in obs_list:
assert obs1 == (obs1 / obs2) * obs2
assert obs1 == (obs1 * obs2) / obs2
@ -447,8 +527,8 @@ def test_gamma_method_irregular():
idx2 = [i + 1 for i in range(len(configs)) if configs[i] == 1]
a = pe.Obs([zero_arr, zero_arr2], ['a1', 'a2'], idl=[idx, idx2])
afull.gamma_method(e_tag=1)
a.gamma_method(e_tag=1)
afull.gamma_method()
a.gamma_method()
expe = (afull.dvalue * np.sqrt(N / np.sum(configs)))
assert (a.dvalue - 5 * a.ddvalue < expe and expe < a.dvalue + 5 * a.ddvalue)
@ -462,20 +542,20 @@ def test_gamma_method_irregular():
arr = np.random.normal(1, .2, size=N)
carr = gen_autocorrelated_array(arr, .346)
a = pe.Obs([carr], ['a'])
a.gamma_method(e_tag=1)
a.gamma_method()
ae = pe.Obs([[carr[i] for i in range(len(carr)) if i % 2 == 0]], ['a'], idl=[[i for i in range(len(carr)) if i % 2 == 0]])
ae.gamma_method(e_tag=1)
ae.gamma_method()
ao = pe.Obs([[carr[i] for i in range(len(carr)) if i % 2 == 1]], ['a'], idl=[[i for i in range(len(carr)) if i % 2 == 1]])
ao.gamma_method(e_tag=1)
ao.gamma_method()
assert(ae.e_tauint['a'] < a.e_tauint['a'])
assert((ae.e_tauint['a'] - 4 * ae.e_dtauint['a'] < ao.e_tauint['a']))
assert((ae.e_tauint['a'] + 4 * ae.e_dtauint['a'] > ao.e_tauint['a']))
def test_covariance2_symmetry():
def test_covariance_symmetry():
value1 = np.random.normal(5, 10)
dvalue1 = np.abs(np.random.normal(0, 1))
test_obs1 = pe.pseudo_Obs(value1, dvalue1, 't')
@ -484,8 +564,8 @@ def test_covariance2_symmetry():
dvalue2 = np.abs(np.random.normal(0, 1))
test_obs2 = pe.pseudo_Obs(value2, dvalue2, 't')
test_obs2.gamma_method()
cov_ab = pe.covariance2(test_obs1, test_obs2)
cov_ba = pe.covariance2(test_obs2, test_obs1)
cov_ab = pe.covariance(test_obs1, test_obs2)
cov_ba = pe.covariance(test_obs2, test_obs1)
assert np.abs(cov_ab - cov_ba) <= 10 * np.finfo(np.float64).eps
assert np.abs(cov_ab) < test_obs1.dvalue * test_obs2.dvalue * (1 + 10 * np.finfo(np.float64).eps)
@ -498,14 +578,20 @@ def test_covariance2_symmetry():
idx = [i + 1 for i in range(len(configs)) if configs[i] == 1]
a = pe.Obs([zero_arr], ['t'], idl=[idx])
a.gamma_method()
assert np.isclose(a.dvalue**2, pe.covariance2(a, a), atol=100, rtol=1e-4)
assert np.isclose(a.dvalue**2, pe.covariance(a, a), atol=100, rtol=1e-4)
cov_ab = pe.covariance2(test_obs1, a)
cov_ba = pe.covariance2(a, test_obs1)
cov_ab = pe.covariance(test_obs1, a)
cov_ba = pe.covariance(a, test_obs1)
assert np.abs(cov_ab - cov_ba) <= 10 * np.finfo(np.float64).eps
assert np.abs(cov_ab) < test_obs1.dvalue * test_obs2.dvalue * (1 + 10 * np.finfo(np.float64).eps)
def test_empty_obs():
o = pe.Obs([np.random.rand(100)], ['test'])
q = o + pe.Obs([], [])
assert q == o
def test_jackknife():
full_data = np.random.normal(1.1, 0.87, 5487)
@ -522,3 +608,11 @@ def test_jackknife():
my_new_obs = my_obs + pe.Obs([full_data], ['test2'])
with pytest.raises(Exception):
my_new_obs.export_jackknife()
def test_import_jackknife():
full_data = np.random.normal(1.105, 0.021, 754)
my_obs = pe.Obs([full_data], ['test'])
my_jacks = my_obs.export_jackknife()
reconstructed_obs = pe.import_jackknife(my_jacks, 'test')
assert my_obs == reconstructed_obs

13
tests/roots_test.py
View file

@ -15,5 +15,18 @@ def test_root_linear():
my_root = pe.roots.find_root(my_obs, root_function)
assert np.isclose(my_root.value, value)
assert np.isclose(my_root.value, my_root.r_values['t'])
difference = my_obs - my_root
assert difference.is_zero()
def test_root_linear_idl():
def root_function(x, d):
return x - d
my_obs = pe.Obs([np.random.rand(50)], ['t'], idl=[range(20, 120, 2)])
my_root = pe.roots.find_root(my_obs, root_function)
difference = my_obs - my_root
assert difference.is_zero()