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Merge branch 'develop' into feature/boot_matmul

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Fabian Joswig 2022-01-24 10:12:32 +00:00
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2
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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"

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@ -6,14 +6,22 @@ on:
- master
- develop
pull_request:
schedule:
- cron: '0 4 1 * *'
jobs:
pytest:
runs-on: ubuntu-latest
runs-on: ${{ matrix.os }}
strategy:
fail-fast: true
fail-fast: true
matrix:
python-version: ["3.6", "3.7", "3.8", "3.9"]
os: [ubuntu-latest]
python-version: ["3.7", "3.8", "3.9", "3.10"]
include:
- os: macos-latest
python-version: 3.9
- os: windows-latest
python-version: 3.9
steps:
- name: Checkout source
@ -27,6 +35,7 @@ jobs:
- name: Install
run: |
python -m pip install --upgrade pip
pip install wheel
pip install .
pip install pytest
pip install pytest-cov

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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.

11
CONTRIBUTING.md
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@ -20,15 +20,16 @@ Please add docstrings to any new function, class or method you implement. The do
### 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 -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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@ -1,4 +1,4 @@
[![flake8](https://github.com/fjosw/pyerrors/actions/workflows/flake8.yml/badge.svg)](https://github.com/fjosw/pyerrors/actions/workflows/flake8.yml) [![pytest](https://github.com/fjosw/pyerrors/actions/workflows/pytest.yml/badge.svg)](https://github.com/fjosw/pyerrors/actions/workflows/pytest.yml) [![docs](https://github.com/fjosw/pyerrors/actions/workflows/docs.yml/badge.svg)](https://github.com/fjosw/pyerrors/actions/workflows/docs.yml) [![](https://img.shields.io/badge/python-3.6+-blue.svg)](https://www.python.org/downloads/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![flake8](https://github.com/fjosw/pyerrors/actions/workflows/flake8.yml/badge.svg)](https://github.com/fjosw/pyerrors/actions/workflows/flake8.yml) [![pytest](https://github.com/fjosw/pyerrors/actions/workflows/pytest.yml/badge.svg)](https://github.com/fjosw/pyerrors/actions/workflows/pytest.yml) [![docs](https://github.com/fjosw/pyerrors/actions/workflows/docs.yml/badge.svg)](https://github.com/fjosw/pyerrors/actions/workflows/docs.yml) [![](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
# pyerrors
`pyerrors` is a python package for error computation and propagation of Markov chain Monte Carlo data.
@ -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)

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examples/05_matrix_operations.ipynb
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@ -51,7 +51,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"The standard matrix product can be performed with @"
"The standard matrix product can be performed with `@`"
]
},
{
@ -101,13 +101,38 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Mathematical functions work elementwise"
"For large matrices overloading the standard operator `@` can become inefficient as pyerrors has to perform a large number of elementary opeations. For these situations pyerrors provides the function `linalg.matmul` which optimizes the required automatic differentiation. The function can take an arbitray number of operands."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[Obs[78.12099999999998] Obs[-22.909999999999997]]\n",
" [Obs[-22.909999999999997] Obs[7.1]]]\n"
]
}
],
"source": [
"print(pe.linalg.matmul(matrix, matrix, matrix)) # Equivalent to matrix @ matrix @ matrix but faster for large matrices"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mathematical functions work elementwise"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
@ -126,12 +151,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"For a vector of `Obs`, we again use np.asarray to end up with the correct object"
"For a vector of `Obs`, we again use `np.asarray` to end up with the correct object"
]
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 7,
"metadata": {},
"outputs": [
{
@ -160,14 +185,14 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Obs[7.2(1.7)] Obs[-1.00(45)]]\n"
"[Obs[7.2(1.7)] Obs[-1.00(46)]]\n"
]
}
],
@ -182,40 +207,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Matrix to scalar operations\n",
"If we want to apply a numpy matrix function with a scalar return value we can use `scalar_mat_op`. __Here we need to use the autograd wrapped version of numpy__ (imported as anp) to use automatic differentiation."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"det \t Obs[3.10(28)]\n",
"trace \t Obs[5.10(20)]\n",
"norm \t Obs[4.45(19)]\n"
]
}
],
"source": [
"import autograd.numpy as anp # Thinly-wrapped numpy\n",
"funcs = [anp.linalg.det, anp.trace, anp.linalg.norm]\n",
"\n",
"for i, func in enumerate(funcs):\n",
" res = pe.linalg.scalar_mat_op(func, matrix)\n",
" res.gamma_method()\n",
" print(func.__name__, '\\t', res)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For matrix operations which are not supported by autograd we can use numerical differentiation"
"`pyerrors` provides the user with wrappers to the `numpy.linalg` functions which work on `Obs` valued matrices. We can for example calculate the determinant of the matrix via"
]
},
{
@ -227,26 +219,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
"cond \t Obs[6.23(58)]\n",
"expm_cond \t Obs[4.45(19)]\n"
"3.10(28)\n"
]
}
],
"source": [
"funcs = [np.linalg.cond, scipy.linalg.expm_cond]\n",
"\n",
"for i, func in enumerate(funcs):\n",
" res = pe.linalg.scalar_mat_op(func, matrix, num_grad=True)\n",
" res.gamma_method()\n",
" print(func.__name__, ' \\t', res)"
"det = pe.linalg.det(matrix)\n",
"det.gamma_method()\n",
"print(det)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Matrix to matrix operations\n",
"For matrix operations with a matrix as return value we can use another wrapper `mat_mat_op`. Take as an example the cholesky decompostion. __Here we need to use the autograd wrapped version of numpy__ (imported as anp) to use automatic differentiation."
"The cholesky decomposition can be obtained as follows"
]
},
{
@ -259,12 +246,12 @@
"output_type": "stream",
"text": [
"[[Obs[2.025(49)] Obs[0.0]]\n",
" [Obs[-0.494(51)] Obs[0.870(29)]]]\n"
" [Obs[-0.494(50)] Obs[0.870(29)]]]\n"
]
}
],
"source": [
"cholesky = pe.linalg.mat_mat_op(anp.linalg.cholesky, matrix)\n",
"cholesky = pe.linalg.cholesky(matrix)\n",
"for (i, j), entry in np.ndenumerate(cholesky):\n",
" entry.gamma_method()\n",
"print(cholesky)"
@ -321,7 +308,7 @@
}
],
"source": [
"inv = pe.linalg.mat_mat_op(anp.linalg.inv, cholesky)\n",
"inv = pe.linalg.inv(cholesky)\n",
"for (i, j), entry in np.ndenumerate(inv):\n",
" entry.gamma_method()\n",
"print(inv)\n",
@ -334,59 +321,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Matrix to matrix operations which are not supported by autograd can also be computed with numeric differentiation"
"## Eigenvalues and eigenvectors\n",
"We can also compute eigenvalues and eigenvectors of symmetric matrices with a special wrapper `eigh`"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"orth\n",
"[[Obs[-0.9592(76)] Obs[0.283(26)]]\n",
" [Obs[0.283(26)] Obs[0.9592(76)]]]\n",
"expm\n",
"[[Obs[75(15)] Obs[-21.4(4.1)]]\n",
" [Obs[-21.4(4.1)] Obs[8.3(1.4)]]]\n",
"logm\n",
"[[Obs[1.334(57)] Obs[-0.496(61)]]\n",
" [Obs[-0.496(61)] Obs[-0.203(50)]]]\n",
"sinhm\n",
"[[Obs[37.3(7.4)] Obs[-10.8(2.1)]]\n",
" [Obs[-10.8(2.1)] Obs[3.94(68)]]]\n",
"sqrtm\n",
"[[Obs[1.996(51)] Obs[-0.341(37)]]\n",
" [Obs[-0.341(37)] Obs[0.940(14)]]]\n"
]
}
],
"source": [
"funcs = [scipy.linalg.orth, scipy.linalg.expm, scipy.linalg.logm, scipy.linalg.sinhm, scipy.linalg.sqrtm]\n",
"\n",
"for i,func in enumerate(funcs):\n",
" res = pe.linalg.mat_mat_op(func, matrix, num_grad=True)\n",
" for (i, j), entry in np.ndenumerate(res):\n",
" entry.gamma_method()\n",
" print(func.__name__)\n",
" print(res)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Eigenvalues and eigenvectors\n",
"We can also compute eigenvalues and eigenvectors of symmetric matrices with a special wrapper `eigh`"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
@ -395,8 +337,8 @@
"Eigenvalues:\n",
"[Obs[0.705(57)] Obs[4.39(19)]]\n",
"Eigenvectors:\n",
"[[Obs[-0.283(26)] Obs[-0.9592(76)]]\n",
" [Obs[-0.9592(76)] Obs[0.283(26)]]]\n"
"[[Obs[-0.283(26)] Obs[-0.9592(75)]]\n",
" [Obs[-0.9592(75)] Obs[0.283(26)]]]\n"
]
}
],
@ -421,7 +363,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 14,
"metadata": {},
"outputs": [
{
@ -451,7 +393,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@ -465,7 +407,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.9"
"version": "3.8.10"
}
},
"nbformat": 4,

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pyerrors/__init__.py
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@ -1,12 +1,14 @@
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...).
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
@ -28,7 +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
@ -44,7 +45,6 @@ The required derivatives $\bar{f}_\alpha$ are evaluated up to machine precision
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
@ -67,7 +67,6 @@ print(iamzero == 0.0)
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)
@ -82,10 +81,9 @@ my_sum.details()
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 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()
@ -105,7 +103,6 @@ In this case the error estimate is identical to the sample standard error.
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()
@ -115,13 +112,12 @@ 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'])
@ -136,7 +132,6 @@ 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'])
@ -154,7 +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
@ -167,9 +161,8 @@ Passing arguments to the `gamma_method` still dominates over the dictionaries.
## Irregular Monte Carlo chains
Irregular Monte Carlo chains can be initialized with the parameter `idl`.
`Obs` objects defined on 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)])
@ -194,20 +187,71 @@ obs3.details()
```
`Obs` objects defined on regular and irregular histories of the same ensemble can be computed with each other and the correct error propagation and estimation is automatically taken care of.
**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`
When one is not interested in single observables but correlation functions, `pyerrors` offers the `Corr` class which simplifies the corresponding error propagation and provides the user with a set of standard methods. In order to initialize a `Corr` objects one needs to arrange the data as a list of `Obs´
```python
my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3])
print(my_corr)
> x0/a Corr(x0/a)
> ------------------
> 0 0.7957(80)
> 1 0.5156(51)
> 2 0.3227(33)
> 3 0.2041(21)
```
In case the correlation functions are not defined on the outermost timeslices, for example because of fixed boundary conditions, a padding can be introduced.
```python
my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3], padding=[1, 1])
print(my_corr)
> x0/a Corr(x0/a)
> ------------------
> 0
> 1 0.7957(80)
> 2 0.5156(51)
> 3 0.3227(33)
> 4 0.2041(21)
> 5
```
The individual entries of a correlator can be accessed via slicing
```python
print(my_corr[3])
> 0.3227(33)
```
Error propagation with the `Corr` class works very similar to `Obs` objects. Mathematical operations are overloaded and `Corr` objects can be computed together with other `Corr` objects, `Obs` objects or real numbers and integers.
```python
my_new_corr = 0.3 * my_corr[2] * my_corr * my_corr + 12 / my_corr
```
`pyerrors` provides the user with a set of regularly used methods for the manipulation of correlator objects:
- `Corr.gamma_method` applies the gamma method to all entries of the correlator.
- `Corr.m_eff` to construct effective masses. Various variants for periodic and fixed temporal boundary conditions are available.
- `Corr.deriv` returns the first derivative of the correlator as `Corr`. Different discretizations of the numerical derivative are available.
- `Corr.second_deriv` returns the second derivative of the correlator as `Corr`. Different discretizations of the numerical derivative are available.
- `Corr.symmetric` symmetrizes parity even correlations functions, assuming periodic boundary conditions.
- `Corr.anti_symmetric` anti-symmetrizes parity odd correlations functions, assuming periodic boundary conditions.
- `Corr.T_symmetry` averages a correlator with its time symmetry partner, assuming fixed boundary conditions.
- `Corr.plateau` extracts a plateau value from the correlator in a given range.
- `Corr.roll` periodically shifts the correlator.
- `Corr.reverse` reverses the time ordering of the correlator.
- `Corr.correlate` constructs a disconnected correlation function from the correlator and another `Corr` or `Obs` object.
- `Corr.reweight` reweights the correlator.
`pyerrors` can also handle matrices of correlation functions and extract energy states from these matrices via a generalized eigenvalue problem (see `pyerrors.correlators.Corr.GEVP`).
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'])
@ -231,27 +275,35 @@ print(my_derived_cobs)
`pyerrors.roots`
# Matrix operations
`pyerrors.linalg`
`pyerrors` provides wrappers for `Obs`-valued matrix operations based on `numpy.linalg`. The supported functions include:
- `inv` for the matrix inverse.
- `cholseky` for the Cholesky decomposition.
- `det` for the matrix determinant.
- `eigh` for eigenvalues and eigenvectors of hermitean matrices.
- `eig` for eigenvalues of general matrices.
- `pinv` for the Moore-Penrose pseudoinverse.
- `svd` for the singular-value-decomposition.
For the full API see `pyerrors.linalg`.
# Export data
The preferred exported file format within `pyerrors` is
The preferred exported file format within `pyerrors` is json.gz
## 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`
`pyerrors` includes an `input` submodule in which input routines and parsers for the output of various numerical programs are contained. For details see `pyerrors.input`.
'''
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__

350
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, CObs
from .misc import dump_object, _assert_equal_properties
from .fits import least_squares
from .linalg import eigh, inv, cholesky
from .roots import find_root
@ -18,60 +19,68 @@ class Corr:
to iterate over all timeslices for every operation. This is especially true, when dealing with smearing matrices.
The correlator can have two types of content: An Obs at every timeslice OR a GEVP
smearing matrix at every timeslice. Other dependency (eg. spacial) are not supported.
smearing matrix at every timeslice. Other dependency (eg. spatial) are not supported.
"""
def __init__(self, data_input, padding_front=0, padding_back=0, prange=None):
# All data_input should be a list of things at different timeslices. This needs to be verified
def __init__(self, data_input, padding=[0, 0], prange=None):
""" Initialize a Corr object.
Parameters
----------
data_input : list
list of Obs or list of arrays of Obs.
padding : list, optional
List with two entries where the first labels the padding
at the front of the correlator and the second the padding
at the back.
prange : list, optional
List containing the first and last timeslice of the plateau
region indentified for this correlator.
"""
if not isinstance(data_input, list):
raise TypeError('Corr__init__ expects a list of timeslices.')
# data_input can have multiple shapes. The simplest one is a list of Obs.
# We check, if this is the case
if all([isinstance(item, Obs) for item in data_input]):
self.content = [np.asarray([item]) for item in data_input]
# Wrapping the Obs in an array ensures that the data structure is consistent with smearing matrices.
self.N = 1 # number of smearings
# data_input in the form [np.array(Obs,NxN)]
if all([(isinstance(item, Obs) or isinstance(item, CObs)) for item in data_input]):
_assert_equal_properties(data_input)
self.content = [np.asarray([item]) for item in data_input]
self.N = 1
elif all([isinstance(item, np.ndarray) or item is None for item in data_input]) and any([isinstance(item, np.ndarray) for item in data_input]):
self.content = data_input
noNull = [a for a in self.content if not (a is None)] # To check if the matrices are correct for all undefined elements
self.N = noNull[0].shape[0]
# The checks are now identical to the case above
if self.N > 1 and noNull[0].shape[0] != noNull[0].shape[1]:
raise Exception("Smearing matrices are not NxN")
if (not all([item.shape == noNull[0].shape for item in noNull])):
raise Exception("Items in data_input are not of identical shape." + str(noNull))
else: # In case its a list of something else.
else:
raise Exception("data_input contains item of wrong type")
self.tag = None
# We now apply some padding to our list. In case that our list represents a correlator of length T but is not defined at every value.
# An undefined timeslice is represented by the None object
self.content = [None] * padding_front + self.content + [None] * padding_back
self.T = len(self.content) # for convenience: will be used a lot
self.content = [None] * padding[0] + self.content + [None] * padding[1]
self.T = len(self.content)
# The attribute "range" [start,end] marks a range of two timeslices.
# This is useful for keeping track of plateaus and fitranges.
# The range can be inherited from other Corrs, if the operation should not alter a chosen range eg. multiplication with a constant.
self.prange = prange
self.gamma_method()
def __getitem__(self, idx):
"""Return the content of timeslice idx"""
if len(self.content[idx]) == 1:
if self.content[idx] is None:
return None
elif len(self.content[idx]) == 1:
return self.content[idx][0]
else:
return self.content[idx]
@property
def reweighted(self):
bool_array = np.array([list(map(lambda x: x.reweighted, o)) for o in self.content])
bool_array = np.array([list(map(lambda x: x.reweighted, o)) for o in [x for x in self.content if x is not None]])
if np.all(bool_array == 1):
return True
elif np.all(bool_array == 0):
@ -90,15 +99,15 @@ class Corr:
for j in range(self.N):
item[i, j].gamma_method(**kwargs)
# We need to project the Correlator with a Vector to get a single value at each timeslice.
# The method can use one or two vectors.
# If two are specified it returns v1@G@v2 (the order might be very important.)
# By default it will return the lowest source, which usually means unsmeared-unsmeared (0,0), but it does not have to
def projected(self, vector_l=None, vector_r=None):
def projected(self, vector_l=None, vector_r=None, normalize=False):
"""We need to project the Correlator with a Vector to get a single value at each timeslice.
The method can use one or two vectors.
If two are specified it returns v1@G@v2 (the order might be very important.)
By default it will return the lowest source, which usually means unsmeared-unsmeared (0,0), but it does not have to
"""
if self.N == 1:
raise Exception("Trying to project a Corr, that already has N=1.")
# This Exception is in no way necessary. One could just return self
# But there is no scenario, where a user would want that to happen and the error message might be more informative.
self.gamma_method()
@ -106,17 +115,32 @@ class Corr:
vector_l, vector_r = np.asarray([1.] + (self.N - 1) * [0.]), np.asarray([1.] + (self.N - 1) * [0.])
elif(vector_r is None):
vector_r = vector_l
if isinstance(vector_l, list) and not isinstance(vector_r, list):
if len(vector_l) != self.T:
raise Exception("Length of vector list must be equal to T")
vector_r = [vector_r] * self.T
if isinstance(vector_r, list) and not isinstance(vector_l, list):
if len(vector_r) != self.T:
raise Exception("Length of vector list must be equal to T")
vector_l = [vector_l] * self.T
if not vector_l.shape == vector_r.shape == (self.N,):
raise Exception("Vectors are of wrong shape!")
if not isinstance(vector_l, list):
if not vector_l.shape == vector_r.shape == (self.N,):
raise Exception("Vectors are of wrong shape!")
if normalize:
vector_l, vector_r = vector_l / np.sqrt((vector_l @ vector_l)), vector_r / np.sqrt(vector_r @ vector_r)
# if (not (0.95 < vector_r @ vector_r < 1.05)) or (not (0.95 < vector_l @ vector_l < 1.05)):
# print("Vectors are normalized before projection!")
# We always normalize before projecting! But we only raise a warning, when it is clear, they where not meant to be normalized.
if (not (0.95 < vector_r @ vector_r < 1.05)) or (not (0.95 < vector_l @ vector_l < 1.05)):
print("Vectors are normalized before projection!")
newcontent = [None if (item is None) else np.asarray([vector_l.T @ item @ vector_r]) for item in self.content]
vector_l, vector_r = vector_l / np.sqrt((vector_l @ vector_l)), vector_r / np.sqrt(vector_r @ vector_r)
else:
# There are no checks here yet. There are so many possible scenarios, where this can go wrong.
if normalize:
for t in range(self.T):
vector_l[t], vector_r[t] = vector_l[t] / np.sqrt((vector_l[t] @ vector_l[t])), vector_r[t] / np.sqrt(vector_r[t] @ vector_r[t])
newcontent = [None if (item is None) else np.asarray([vector_l.T @ item @ vector_r]) for item in self.content]
newcontent = [None if (self.content[t] is None or vector_l[t] is None or vector_r[t] is None) else np.asarray([vector_l[t].T @ self.content[t] @ vector_r[t]]) for t in range(self.T)]
return Corr(newcontent)
def sum(self):
@ -129,8 +153,6 @@ class Corr:
newcontent = [None if(item is None) else item[i, j] for item in self.content]
return Corr(newcontent)
# Obs and Matplotlib do not play nicely
# We often want to retrieve x,y,y_err as lists to pass them to something like pyplot.errorbar
def plottable(self):
"""Outputs the correlator in a plotable format.
@ -145,9 +167,6 @@ class Corr:
return x_list, y_list, y_err_list
# symmetric returns a Corr, that has been symmetrized.
# A symmetry checker is still to be implemented
# The method will not delete any redundant timeslices (Bad for memory, Great for convenience)
def symmetric(self):
""" Symmetrize the correlator around x0=0."""
if self.T % 2 != 0:
@ -184,28 +203,53 @@ class Corr:
raise Exception("Corr could not be symmetrized: No redundant values")
return Corr(newcontent, prange=self.prange)
# This method will symmetrice the matrices and therefore make them positive definit.
def smearing_symmetric(self):
"""Symmetrizes the matrices and therefore make them positive definite."""
if self.N > 1:
transposed = [None if (G is None) else G.T for G in self.content]
return 0.5 * (Corr(transposed) + self)
if self.N == 1:
raise Exception("Trying to symmetrize a smearing matrix, that already has N=1.")
# We also include a simple GEVP method based on Scipy.linalg
def GEVP(self, t0, ts, state=1):
if (self.content[t0] is None) or (self.content[ts] is None):
raise Exception("Corr not defined at t0/ts")
G0, Gt = np.empty([self.N, self.N], dtype="double"), np.empty([self.N, self.N], dtype="double")
for i in range(self.N):
for j in range(self.N):
G0[i, j] = self.content[t0][i, j].value
Gt[i, j] = self.content[ts][i, j].value
# There are two ways, the GEVP metod can be called.
# 1. return_list=False will return a single eigenvector, normalized according to V*C(t_0)*V=1
# 2. return_list=True will return a new eigenvector for every timeslice. The time t_s is used to order the vectors according to. arXiv:2004.10472 [hep-lat]
def GEVP(self, t0, ts, state=0, sorting="Eigenvalue", return_list=False):
if not return_list:
if (self.content[t0] is None) or (self.content[ts] is None):
raise Exception("Corr not defined at t0/ts")
G0, Gt = np.empty([self.N, self.N], dtype="double"), np.empty([self.N, self.N], dtype="double")
for i in range(self.N):
for j in range(self.N):
G0[i, j] = self.content[t0][i, j].value
Gt[i, j] = self.content[ts][i, j].value
sp_val, sp_vec = scipy.linalg.eig(Gt, G0)
sp_vec = sp_vec[:, np.argsort(sp_val)[-state]] # We only want the eigenvector belonging to the selected state
sp_vec = sp_vec / np.sqrt(sp_vec @ sp_vec)
return sp_vec
sp_vecs = _GEVP_solver(Gt, G0)
sp_vec = sp_vecs[state]
return sp_vec
if return_list:
all_vecs = []
for t in range(self.T):
try:
G0, Gt = np.empty([self.N, self.N], dtype="double"), np.empty([self.N, self.N], dtype="double")
for i in range(self.N):
for j in range(self.N):
G0[i, j] = self.content[t0][i, j].value
Gt[i, j] = self.content[t][i, j].value
sp_vecs = _GEVP_solver(Gt, G0)
if sorting == "Eigenvalue":
sp_vec = sp_vecs[state]
all_vecs.append(sp_vec)
else:
all_vecs.append(sp_vecs)
except Exception:
all_vecs.append(None)
if sorting == "Eigenvector":
all_vecs = _sort_vectors(all_vecs, ts)
all_vecs = [a[state] for a in all_vecs]
return all_vecs
def Eigenvalue(self, t0, state=1):
G = self.smearing_symmetric()
@ -216,13 +260,57 @@ class Corr:
LTi = inv(LT)
newcontent = []
for t in range(self.T):
Gt = G.content[t]
M = Li @ Gt @ LTi
eigenvalues = eigh(M)[0]
eigenvalue = eigenvalues[-state]
newcontent.append(eigenvalue)
if self.content[t] is None:
newcontent.append(None)
else:
Gt = G.content[t]
M = Li @ Gt @ LTi
eigenvalues = eigh(M)[0]
eigenvalue = eigenvalues[-state]
newcontent.append(eigenvalue)
return Corr(newcontent)
def Hankel(self, N, periodic=False):
"""Constructs an NxN Hankel matrix
C(t) c(t+1) ... c(t+n-1)
C(t+1) c(t+2) ... c(t+n)
.................
C(t+(n-1)) c(t+n) ... c(t+2(n-1))
Parameters
----------
N : int
Dimension of the Hankel matrix
periodic : bool, optional
determines whether the matrix is extended periodically
"""
if self.N != 1:
raise Exception("Multi-operator Prony not implemented!")
array = np.empty([N, N], dtype="object")
new_content = []
for t in range(self.T):
new_content.append(array.copy())
def wrap(i):
if i >= self.T:
return i - self.T
return i
for t in range(self.T):
for i in range(N):
for j in range(N):
if periodic:
new_content[t][i, j] = self.content[wrap(t + i + j)][0]
elif (t + i + j) >= self.T:
new_content[t] = None
else:
new_content[t][i, j] = self.content[t + i + j][0]
return Corr(new_content)
def roll(self, dt):
"""Periodically shift the correlator by dt timeslices
@ -235,7 +323,7 @@ class Corr:
def reverse(self):
"""Reverse the time ordering of the Corr"""
return Corr(self.content[::-1])
return Corr(self.content[:: -1])
def correlate(self, partner):
"""Correlate the correlator with another correlator or Obs
@ -257,7 +345,7 @@ class Corr:
new_content.append(None)
else:
new_content.append(np.array([correlate(o, partner.content[x0][0]) for o in t_slice]))
elif isinstance(partner, Obs):
elif isinstance(partner, Obs): # Should this include CObs?
new_content.append(np.array([correlate(o, partner) for o in t_slice]))
else:
raise Exception("Can only correlate with an Obs or a Corr.")
@ -328,7 +416,7 @@ class Corr:
newcontent.append(self.content[t + 1] - self.content[t])
if(all([x is None for x in newcontent])):
raise Exception("Derivative is undefined at all timeslices")
return Corr(newcontent, padding_back=1)
return Corr(newcontent, padding=[0, 1])
if symmetric:
newcontent = []
for t in range(1, self.T - 1):
@ -338,7 +426,7 @@ class Corr:
newcontent.append(0.5 * (self.content[t + 1] - self.content[t - 1]))
if(all([x is None for x in newcontent])):
raise Exception('Derivative is undefined at all timeslices')
return Corr(newcontent, padding_back=1, padding_front=1)
return Corr(newcontent, padding=[1, 1])
def second_deriv(self):
"""Return the second derivative of the correlator with respect to x0."""
@ -350,7 +438,7 @@ class Corr:
newcontent.append((self.content[t + 1] - 2 * self.content[t] + self.content[t - 1]))
if(all([x is None for x in newcontent])):
raise Exception("Derivative is undefined at all timeslices")
return Corr(newcontent, padding_back=1, padding_front=1)
return Corr(newcontent, padding=[1, 1])
def m_eff(self, variant='log', guess=1.0):
"""Returns the effective mass of the correlator as correlator object
@ -378,7 +466,7 @@ class Corr:
if(all([x is None for x in newcontent])):
raise Exception('m_eff is undefined at all timeslices')
return np.log(Corr(newcontent, padding_back=1))
return np.log(Corr(newcontent, padding=[0, 1]))
elif variant in ['periodic', 'cosh', 'sinh']:
if variant in ['periodic', 'cosh']:
@ -401,7 +489,7 @@ class Corr:
if(all([x is None for x in newcontent])):
raise Exception('m_eff is undefined at all timeslices')
return Corr(newcontent, padding_back=1)
return Corr(newcontent, padding=[0, 1])
elif variant == 'arccosh':
newcontent = []
@ -412,7 +500,7 @@ class Corr:
newcontent.append((self.content[t + 1] + self.content[t - 1]) / (2 * self.content[t]))
if(all([x is None for x in newcontent])):
raise Exception("m_eff is undefined at all timeslices")
return np.arccosh(Corr(newcontent, padding_back=1, padding_front=1))
return np.arccosh(Corr(newcontent, padding=[1, 1]))
else:
raise Exception('Unknown variant.')
@ -433,16 +521,11 @@ class Corr:
if self.N != 1:
raise Exception("Correlator must be projected before fitting")
# The default behavior is:
# 1 use explicit fitrange
# if none is provided, use the range of the corr
# if this is also not set, use the whole length of the corr (This could come with a warning!)
if fitrange is None:
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]
@ -519,7 +602,7 @@ class Corr:
if self.N != 1:
raise Exception("Correlator must be projected before plotting")
if x_range is None:
x_range = [0, self.T]
x_range = [0, self.T - 1]
fig = plt.figure()
ax1 = fig.add_subplot(111)
@ -535,7 +618,7 @@ 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)
@ -581,24 +664,45 @@ class Corr:
return
def dump(self, filename):
"""Dumps the Corr into a pickle file
def dump(self, filename, datatype="json.gz", **kwargs):
"""Dumps the Corr into a file of chosen type
Parameters
----------
filename : str
Name of the file
Name of the file to be saved.
datatype : str
Format of the exported file. Supported formats include
"json.gz" and "pickle"
path : str
specifies a custom path for the file (default '.')
"""
dump_object(self, filename)
return
if datatype == "json.gz":
from .input.json import dump_to_json
if 'path' in kwargs:
file_name = kwargs.get('path') + '/' + filename
else:
file_name = filename
dump_to_json(self, file_name)
elif datatype == "pickle":
dump_object(self, filename, **kwargs)
else:
raise Exception("Unknown datatype " + str(datatype))
def print(self, range=[0, None]):
print(self.__repr__(range))
def __repr__(self, range=[0, None]):
content_string = ""
content_string += "Corr T=" + str(self.T) + " N=" + str(self.N) + "\n" # +" filled with"+ str(type(self.content[0][0])) there should be a good solution here
if self.tag is not None:
content_string += "Description: " + self.tag + "\n"
if self.N != 1:
return content_string
# This avoids a crash for N>1. I do not know, what else to do here. I like the list representation for N==1. We could print only one "smearing" or one matrix. Printing everything will just
# be a wall of numbers.
if range[1]:
range[1] += 1
content_string += 'x0/a\tCorr(x0/a)\n------------------\n'
@ -632,7 +736,7 @@ class Corr:
newcontent.append(self.content[t] + y.content[t])
return Corr(newcontent)
elif isinstance(y, Obs) or isinstance(y, int) or isinstance(y, float):
elif isinstance(y, Obs) or isinstance(y, int) or isinstance(y, float) or isinstance(y, CObs):
newcontent = []
for t in range(self.T):
if (self.content[t] is None):
@ -655,7 +759,7 @@ class Corr:
newcontent.append(self.content[t] * y.content[t])
return Corr(newcontent)
elif isinstance(y, Obs) or isinstance(y, int) or isinstance(y, float):
elif isinstance(y, Obs) or isinstance(y, int) or isinstance(y, float) or isinstance(y, CObs):
newcontent = []
for t in range(self.T):
if (self.content[t] is None):
@ -688,9 +792,14 @@ class Corr:
raise Exception("Division returns completely undefined correlator")
return Corr(newcontent)
elif isinstance(y, Obs):
if y.value == 0:
raise Exception('Division by zero will return undefined correlator')
elif isinstance(y, Obs) or isinstance(y, CObs):
if isinstance(y, Obs):
if y.value == 0:
raise Exception('Division by zero will return undefined correlator')
if isinstance(y, CObs):
if y.is_zero():
raise Exception('Division by zero will return undefined correlator')
newcontent = []
for t in range(self.T):
if (self.content[t] is None):
@ -720,7 +829,7 @@ class Corr:
return self + (-y)
def __pow__(self, y):
if isinstance(y, Obs) or isinstance(y, int) or isinstance(y, float):
if isinstance(y, Obs) or isinstance(y, int) or isinstance(y, float) or isinstance(y, CObs):
newcontent = [None if (item is None) else item**y for item in self.content]
return Corr(newcontent, prange=self.prange)
else:
@ -801,3 +910,70 @@ class Corr:
def __rtruediv__(self, y):
return (self / y) ** (-1)
@property
def real(self):
def return_real(obs_OR_cobs):
if isinstance(obs_OR_cobs, CObs):
return obs_OR_cobs.real
else:
return obs_OR_cobs
return self._apply_func_to_corr(return_real)
@property
def imag(self):
def return_imag(obs_OR_cobs):
if isinstance(obs_OR_cobs, CObs):
return obs_OR_cobs.imag
else:
return obs_OR_cobs * 0 # So it stays the right type
return self._apply_func_to_corr(return_imag)
def _sort_vectors(vec_set, ts):
"""Helper function used to find a set of Eigenvectors consistent over all timeslices"""
reference_sorting = np.array(vec_set[ts])
N = reference_sorting.shape[0]
sorted_vec_set = []
for t in range(len(vec_set)):
if vec_set[t] is None:
sorted_vec_set.append(None)
elif not t == ts:
perms = permutation([i for i in range(N)])
best_score = 0
for perm in perms:
current_score = 1
for k in range(N):
new_sorting = reference_sorting.copy()
new_sorting[perm[k], :] = vec_set[t][k]
current_score *= abs(np.linalg.det(new_sorting))
if current_score > best_score:
best_score = current_score
best_perm = perm
sorted_vec_set.append([vec_set[t][k] for k in best_perm])
else:
sorted_vec_set.append(vec_set[t])
return sorted_vec_set
def permutation(lst): # Shamelessly copied
if len(lst) == 1:
return [lst]
ll = []
for i in range(len(lst)):
m = lst[i]
remLst = lst[:i] + lst[i + 1:]
# Generating all permutations where m is first
for p in permutation(remLst):
ll.append([m] + p)
return ll
def _GEVP_solver(Gt, G0): # Just so normalization an sorting does not need to be repeated. Here we could later put in some checks
sp_val, sp_vecs = scipy.linalg.eig(Gt, G0)
sp_vecs = [sp_vecs[:, np.argsort(sp_val)[-i]] for i in range(1, sp_vecs.shape[0] + 1)]
sp_vecs = [v / np.sqrt((v.T @ G0 @ v)) for v in sp_vecs]
return sp_vecs

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

24
pyerrors/dirac.py
View file

@ -22,6 +22,30 @@ identity = np.array(
dtype=complex)
def epsilon_tensor(i, j, k):
"""Rank-3 epsilon tensor
Based on https://codegolf.stackexchange.com/a/160375
"""
test_set = set((i, j, k))
if not (test_set <= set((1, 2, 3)) or test_set <= set((0, 1, 2))):
raise Exception("Unexpected input", i, j, k)
return (i - j) * (j - k) * (k - i) / 2
def epsilon_tensor_rank4(i, j, k, o):
"""Rank-4 epsilon tensor
Extension of https://codegolf.stackexchange.com/a/160375
"""
test_set = set((i, j, k, o))
if not (test_set <= set((1, 2, 3, 4)) or test_set <= set((0, 1, 2, 3))):
raise Exception("Unexpected input", i, j, k, o)
return (i - j) * (j - k) * (k - i) * (i - o) * (j - o) * (o - k) / 12
def Grid_gamma(gamma_tag):
"""Returns gamma matrix in Grid labeling."""
if gamma_tag == 'Identity':

180
pyerrors/fits.py
View file

@ -8,10 +8,11 @@ import scipy.stats
import matplotlib.pyplot as plt
from matplotlib import gridspec
from scipy.odr import ODR, Model, RealData
from scipy.stats import chi2
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):
@ -46,6 +47,8 @@ class Fit_result(Sequence):
my_str += 'residual variance = ' + f'{self.residual_variance:2.6f}' + '\n'
if hasattr(self, 'chisquare_by_expected_chisquare'):
my_str += '\u03C7\u00b2/\u03C7\u00b2exp = ' + f'{self.chisquare_by_expected_chisquare:2.6f}' + '\n'
if hasattr(self, 'p_value'):
my_str += 'p-value = ' + f'{self.p_value:2.4f}' + '\n'
my_str += 'Fit parameters:\n'
for i_par, par in enumerate(self.fit_parameters):
my_str += str(i_par) + '\t' + ' ' * int(par >= 0) + str(par).rjust(int(par < 0.0)) + '\n'
@ -188,7 +191,7 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
for i in range(25):
try:
func(np.arange(i), x.T[0])
except:
except Exception:
pass
else:
break
@ -301,12 +304,13 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
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 + const_par
output.odr_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplus.ravel())))
output.dof = x.shape[-1] - n_parms
output.p_value = 1 - chi2.cdf(output.odr_chisquare, output.dof)
return output
@ -329,7 +333,7 @@ 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
@ -353,7 +357,7 @@ 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
@ -387,13 +391,15 @@ 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)
@ -402,7 +408,7 @@ 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))
@ -418,7 +424,7 @@ 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))
@ -468,7 +474,7 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
for i in range(25):
try:
func(np.arange(i), x.T[0])
except:
except Exception:
pass
else:
break
@ -612,12 +618,13 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
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 + const_par
output.chisquare = chisqfunc(fit_result.x)
output.dof = x.shape[-1] - n_parms
output.p_value = 1 - chi2.cdf(output.chisquare, output.dof)
if kwargs.get('resplot') is True:
residual_plot(x, y, func, result)
@ -646,10 +653,10 @@ 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')
@ -678,7 +685,7 @@ 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):
@ -702,12 +709,12 @@ def residual_plot(x, y, func, fit_res):
ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
ax1.tick_params(direction='out')
ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
ax1.axhline(y=0.0, ls='--', color='k')
ax1.axhline(y=0.0, ls='--', color='k', marker=" ")
ax1.fill_between(x_samples, -1.0, 1.0, alpha=0.1, facecolor='k')
ax1.set_xlim([xstart, xstop])
ax1.set_ylabel('Residuals')
plt.subplots_adjust(wspace=None, hspace=None)
plt.show()
plt.draw()
def covariance_matrix(y):
@ -741,148 +748,41 @@ def error_band(x, func, beta):
return err
def ks_test(obs=None):
"""Performs a Kolmogorov–Smirnov test for the Q-values of all fit object.
def ks_test(objects=None):
"""Performs a Kolmogorov–Smirnov test for the p-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.
Parameters
----------
objects : list
List of fit results to include in the analysis (optional).
"""
raise Exception('Not yet implemented')
if obs is None:
if objects is None:
obs_list = []
for obj in gc.get_objects():
if isinstance(obj, Obs):
if isinstance(obj, Fit_result):
obs_list.append(obj)
else:
obs_list = obs
obs_list = objects
# 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])
p_values = [o.p_value for o in obs_list]
bins = len(Qs)
bins = len(p_values)
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.xlabel('p-value')
plt.ylabel('Cumulative probability')
plt.title(str(bins) + ' Q values')
plt.title(str(bins) + ' p-values')
n = np.arange(1, bins + 1) / np.float64(bins)
Xs = np.sort(Qs)
Xs = np.sort(p_values)
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()
plt.annotate('', xy=(loc, loc), xytext=(loc, loc + diffs[loc_max_diff]), arrowprops=dict(arrowstyle='<->', shrinkA=0, shrinkB=0))
plt.draw()
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
print(scipy.stats.kstest(p_values, 'uniform'))

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 = []

263
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
@ -55,14 +58,13 @@ def read_meson_hd5(path, filestem, ens_id, meson='meson_0', tree='meson'):
meson : str
label of the meson to be extracted, standard value meson_0 which
corresponds to the pseudoscalar pseudoscalar two-point function.
tree : str
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)
tree = meson.rsplit('_')[0]
corr_data = []
infos = []
for hd5_file in files:
@ -78,27 +80,69 @@ 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 +151,7 @@ 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 +160,29 @@ 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 +196,9 @@ 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 +212,117 @@ 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

477
pyerrors/input/json.py Normal file
View file

@ -0,0 +1,477 @@
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 ..correlators import Corr
from ..misc import _assert_equal_properties
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, Corr.
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 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
def write_Corr_to_dict(my_corr):
front_padding = next(i for i, j in enumerate(my_corr.content) if np.all(j))
back_padding_start = front_padding + next((i for i, j in enumerate(my_corr.content[front_padding:]) if not np.all(j)), my_corr.T)
dat = write_Array_to_dict(np.array(my_corr.content[front_padding:back_padding_start]))
dat['type'] = 'Corr'
corr_meta_data = str(front_padding) + '|' + str(my_corr.T - back_padding_start) + '|' + str(my_corr.tag)
if 'tag' in dat.keys():
dat['tag'].append(corr_meta_data)
else:
dat['tag'] = [corr_meta_data]
return dat
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))
elif isinstance(io, Corr):
d['obsdata'].append(write_Corr_to_dict(io))
else:
raise Exception("Unkown datatype.")
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, Corr.
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, Corr
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)
def get_Corr_from_dict(o):
taglist = o.get('tag')
corr_meta_data = taglist[-1].split('|')
padding_front = int(corr_meta_data[0])
padding_back = int(corr_meta_data[1])
corr_tag = corr_meta_data[2]
tmp_o = o
tmp_o['tag'] = taglist[:-1]
if len(tmp_o['tag']) == 0:
del tmp_o['tag']
dat = get_Array_from_dict(tmp_o)
my_corr = Corr(list(dat), padding=[padding_front, padding_back])
if corr_tag != 'None':
my_corr.tag = corr_tag
return my_corr
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))
elif io['type'] == 'Corr':
ol.append(get_Corr_from_dict(io))
else:
raise Exception("Unkown datatype.")
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, Corr
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)

17
pyerrors/input/misc.py
View file

@ -1,6 +1,3 @@
#!/usr/bin/env python
# coding: utf-8
import os
import fnmatch
import re
@ -12,11 +9,12 @@ from ..obs import Obs
def read_pbp(path, prefix, **kwargs):
"""Read pbp format from given folder structure. Returns a list of length nrw
Keyword arguments
-----------------
r_start -- list which contains the first config to be read for each replicum
r_stop -- list which contains the last config to be read for each replicum
Parameters
----------
r_start : list
list which contains the first config to be read for each replicum
r_stop : list
list which contains the last config to be read for each replicum
"""
extract_nfct = 1
@ -66,7 +64,6 @@ def read_pbp(path, prefix, **kwargs):
tmp_array = []
with open(path + '/' + ls[rep], 'rb') as fp:
# header
t = fp.read(4) # number of reweighting factors
if rep == 0:
nrw = struct.unpack('i', t)[0]
@ -74,7 +71,7 @@ def read_pbp(path, prefix, **kwargs):
deltas.append([])
else:
if nrw != struct.unpack('i', t)[0]:
raise Exception('Error: different number of reweighting factors for replicum', rep)
raise Exception('Error: different number of factors for replicum', rep)
for k in range(nrw):
tmp_array.append([])

308
pyerrors/input/openQCD.py
View file

@ -1,6 +1,3 @@
#!/usr/bin/env python
# coding: utf-8
import os
import fnmatch
import re
@ -39,13 +36,14 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
if not ls:
raise Exception('Error, directory not found')
# Exclude files with different names
for exc in ls:
if not fnmatch.fnmatch(exc, prefix + '*' + postfix + '.dat'):
ls = list(set(ls) - set([exc]))
if len(ls) > 1:
ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0]))
if 'files' in kwargs:
ls = kwargs.get('files')
else:
for exc in ls:
if not fnmatch.fnmatch(exc, prefix + '*' + postfix + '.dat'):
ls = list(set(ls) - set([exc]))
if len(ls) > 1:
ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0]))
replica = len(ls)
if 'r_start' in kwargs:
@ -64,9 +62,9 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
else:
r_stop = [None] * replica
print('Read reweighting factors from', prefix[:-1], ',', replica, 'replica', end='')
print('Read reweighting factors from', prefix[:-1], ',',
replica, 'replica', end='')
# Adjust replica names to new bookmarking system
if names is None:
rep_names = []
for entry in ls:
@ -85,7 +83,6 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
tmp_array = []
with open(path + '/' + ls[rep], 'rb') as fp:
# header
t = fp.read(4) # number of reweighting factors
if rep == 0:
nrw = struct.unpack('i', t)[0]
@ -94,7 +91,7 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
for k in range(nrw):
deltas.append([])
else:
if ((nrw != struct.unpack('i', t)[0] and (not version == '2.0')) or (nrw != struct.unpack('i', t)[0] / 2 and version == '2.0')): # little weird if-clause due to the /2 operation needed.
if ((nrw != struct.unpack('i', t)[0] and (not version == '2.0')) or (nrw != struct.unpack('i', t)[0] / 2 and version == '2.0')):
raise Exception('Error: different number of reweighting factors for replicum', rep)
for k in range(nrw):
@ -106,7 +103,6 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
for i in range(nrw):
t = fp.read(4)
nfct.append(struct.unpack('i', t)[0])
# print('nfct: ', nfct) # Hasenbusch factor, 1 for rat reweighting
else:
for i in range(nrw):
nfct.append(1)
@ -119,7 +115,6 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
if not struct.unpack('i', fp.read(4))[0] == 0:
print('something is wrong!')
# body
while 0 < 1:
t = fp.read(4)
if len(t) < 4:
@ -135,8 +130,11 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
for j in range(tmpd['n'][0]):
tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw[j])))
if print_err:
print(config_no, i, j, np.mean(np.exp(-np.asarray(tmp_rw[j]))), np.std(np.exp(-np.asarray(tmp_rw[j]))))
print('Sources:', np.exp(-np.asarray(tmp_rw[j])))
print(config_no, i, j,
np.mean(np.exp(-np.asarray(tmp_rw[j]))),
np.std(np.exp(-np.asarray(tmp_rw[j]))))
print('Sources:',
np.exp(-np.asarray(tmp_rw[j])))
print('Partial factor:', tmp_nfct)
elif version == '1.6' or version == '1.4':
tmp_nfct = 1.0
@ -146,7 +144,9 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
tmp_rw = struct.unpack('d' * nsrc[i], t)
tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw)))
if print_err:
print(config_no, i, j, np.mean(np.exp(-np.asarray(tmp_rw))), np.std(np.exp(-np.asarray(tmp_rw))))
print(config_no, i, j,
np.mean(np.exp(-np.asarray(tmp_rw))),
np.std(np.exp(-np.asarray(tmp_rw))))
print('Sources:', np.exp(-np.asarray(tmp_rw)))
print('Partial factor:', tmp_nfct)
tmp_array[i].append(tmp_nfct)
@ -165,11 +165,14 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
return result
def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwargs):
def extract_t0(path, prefix, dtr_read, xmin,
spatial_extent, fit_range=5, **kwargs):
"""Extract t0 from given .ms.dat files. Returns t0 as Obs.
It is assumed that all boundary effects have sufficiently decayed at x0=xmin.
The data around the zero crossing of t^2<E> - 0.3 is fitted with a linear function
It is assumed that all boundary effects have
sufficiently decayed at x0=xmin.
The data around the zero crossing of t^2<E> - 0.3
is fitted with a linear function
from which the exact root is extracted.
Only works with openQCD v 1.2.
@ -180,14 +183,17 @@ def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwar
prefix : str
Ensemble prefix
dtr_read : int
Determines how many trajectories should be skipped when reading the ms.dat files.
Determines how many trajectories should be skipped
when reading the ms.dat files.
Corresponds to dtr_cnfg / dtr_ms in the openQCD input file.
xmin : int
First timeslice where the boundary effects have sufficiently decayed.
First timeslice where the boundary
effects have sufficiently decayed.
spatial_extent : int
spatial extent of the lattice, required for normalization.
fit_range : int
Number of data points left and right of the zero crossing to be included in the linear fit. (Default: 5)
Number of data points left and right of the zero
crossing to be included in the linear fit. (Default: 5)
r_start : list
list which contains the first config to be read for each replicum.
r_stop: list
@ -204,7 +210,6 @@ def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwar
if not ls:
raise Exception('Error, directory not found')
# Exclude files with different names
for exc in ls:
if not fnmatch.fnmatch(exc, prefix + '*.ms.dat'):
ls = list(set(ls) - set([exc]))
@ -216,7 +221,6 @@ def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwar
r_start = kwargs.get('r_start')
if len(r_start) != replica:
raise Exception('r_start does not match number of replicas')
# Adjust Configuration numbering to python index
r_start = [o - 1 if o else None for o in r_start]
else:
r_start = [None] * replica
@ -235,7 +239,6 @@ def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwar
for rep in range(replica):
with open(path + '/' + ls[rep], 'rb') as fp:
# Read header
t = fp.read(12)
header = struct.unpack('iii', t)
if rep == 0:
@ -254,7 +257,6 @@ def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwar
Ysl = []
# Read body
while 0 < 1:
t = fp.read(4)
if(len(t) < 4):
@ -273,7 +275,9 @@ def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwar
Ysum.append([])
for i, item in enumerate(Ysl):
Ysum[-1].append([np.mean(item[current + xmin:current + tmax - xmin]) for current in range(0, len(item), tmax)])
Ysum[-1].append([np.mean(item[current + xmin:
current + tmax - xmin])
for current in range(0, len(item), tmax)])
t2E_dict = {}
for n in range(nn + 1):
@ -286,10 +290,13 @@ def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwar
new_obs = Obs(samples, [(w.split('.'))[0] for w in ls])
t2E_dict[n * dn * eps] = (n * dn * eps) ** 2 * new_obs / (spatial_extent ** 3) - 0.3
zero_crossing = np.argmax(np.array([o.value for o in t2E_dict.values()]) > 0.0)
zero_crossing = np.argmax(np.array(
[o.value for o in t2E_dict.values()]) > 0.0)
x = list(t2E_dict.keys())[zero_crossing - fit_range: zero_crossing + fit_range]
y = list(t2E_dict.values())[zero_crossing - fit_range: zero_crossing + fit_range]
x = list(t2E_dict.keys())[zero_crossing - fit_range:
zero_crossing + fit_range]
y = list(t2E_dict.values())[zero_crossing - fit_range:
zero_crossing + fit_range]
[o.gamma_method() for o in y]
fit_result = fit_lin(x, y)
@ -313,12 +320,6 @@ def _parse_array_openQCD2(d, n, size, wa, quadrupel=False):
return arr
# mimic the read_array routine of openQCD-2.0.
# fp is the opened file handle
# returns the dict array
# at this point we only parse a 2d array
# d = 2
# n = [nfct[irw], 2*nsrc[irw]]
def _read_array_openQCD2(fp):
t = fp.read(4)
d = struct.unpack('i', t)[0]
@ -343,3 +344,232 @@ def _read_array_openQCD2(fp):
arr = _parse_array_openQCD2(d, n, size, tmp, quadrupel=True)
return {'d': d, 'n': n, 'size': size, 'arr': arr}
def read_qtop(path, prefix, c, dtr_cnfg=1, version="1.2", **kwargs):
"""Read qtop format from given folder structure.
Parameters
----------
path:
path of the measurement files
prefix:
prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat
c: double
Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L
dtr_cnfg: int
(optional) parameter that specifies the number of trajectories
between two configs.
if it is not set, the distance between two measurements
in the file is assumed to be
the distance between two configurations.
steps: int
(optional) (maybe only necessary for openQCD2.0)
nt step size, guessed if not given
version: str
version string of the openQCD (sfqcd) version used to create
the ensemble
L: int
spatial length of the lattice in L/a.
HAS to be set if version != sfqcd, since openQCD does not provide
this in the header
r_start: list
offset of the first ensemble, making it easier to match
later on with other Obs
r_stop: list
last configurations that need to be read (per replicum)
files: list
specify the exact files that need to be read
from path, practical if e.g. only one replicum is needed
names: list
Alternative labeling for replicas/ensembles.
Has to have the appropriate length
"""
known_versions = ["1.0", "1.2", "1.4", "1.6", "2.0", "sfqcd"]
if version not in known_versions:
raise Exception("Unknown openQCD version.")
if "steps" in kwargs:
steps = kwargs.get("steps")
if version == "sfqcd":
if "L" in kwargs:
supposed_L = kwargs.get("L")
else:
if "L" not in kwargs:
raise Exception("This version of openQCD needs you to provide the spatial length of the lattice as parameter 'L'.")
else:
L = kwargs.get("L")
r_start = 1
if "r_start" in kwargs:
r_start = kwargs.get("r_start")
if "r_stop" in kwargs:
r_stop = kwargs.get("r_stop")
if "files" in kwargs:
files = kwargs.get("files")
else:
found = []
files = []
for (dirpath, dirnames, filenames) in os.walk(path + "/"):
# print(filenames)
found.extend(filenames)
break
for f in found:
if fnmatch.fnmatch(f, prefix + "*" + ".ms.dat"):
files.append(f)
print(files)
rep_names = []
deltas = []
idl = []
for rep, file in enumerate(files):
with open(path + "/" + file, "rb") as fp:
t = fp.read(12)
header = struct.unpack('<iii', t)
# step size in integration steps "dnms"
dn = header[0]
# number of measurements, so "ntot"/dn
nn = header[1]
# lattice T/a
tmax = header[2]
if version == "sfqcd":
t = fp.read(12)
Ls = struct.unpack('<iii', t)
if(Ls[0] == Ls[1] and Ls[1] == Ls[2]):
L = Ls[0]
if not (supposed_L == L):
raise Exception("It seems the length given in the header and by you contradict each other")
else:
raise Exception("Found more than one spatial length in header!")
print('dnms:', dn)
print('nn:', nn)
print('tmax:', tmax)
t = fp.read(8)
eps = struct.unpack('d', t)[0]
print('eps:', eps)
Q = []
ncs = []
while 0 < 1:
t = fp.read(4)
if(len(t) < 4):
break
ncs.append(struct.unpack('i', t)[0])
# Wsl
t = fp.read(8 * tmax * (nn + 1))
# Ysl
t = fp.read(8 * tmax * (nn + 1))
# Qsl, which is asked for in this method
t = fp.read(8 * tmax * (nn + 1))
# unpack the array of Qtops,
# on each timeslice t=0,...,tmax-1 and the
# measurement number in = 0...nn (see README.qcd1)
tmpd = struct.unpack('d' * tmax * (nn + 1), t)
Q.append(tmpd)
if not len(set([ncs[i] - ncs[i - 1] for i in range(1, len(ncs))])):
raise Exception("Irregularities in stepsize found")
else:
if 'steps' in kwargs:
if steps != ncs[1] - ncs[0]:
raise Exception("steps and the found stepsize are not the same")
else:
steps = ncs[1] - ncs[0]
print(len(Q))
print('max_t:', dn * (nn) * eps)
t_aim = (c * L) ** 2 / 8
print('t_aim:', t_aim)
index_aim = round(t_aim / eps / dn)
print('index_aim:', index_aim)
Q_sum = []
for i, item in enumerate(Q):
Q_sum.append([sum(item[current:current + tmax])
for current in range(0, len(item), tmax)])
print(len(Q_sum))
print(len(Q_sum[0]))
Q_round = []
for i in range(len(Q) // dtr_cnfg):
Q_round.append(round(Q_sum[dtr_cnfg * i][index_aim]))
if len(Q_round) != len(ncs) // dtr_cnfg:
raise Exception("qtops and ncs dont have the same length")
truncated_file = file[:-7]
print(truncated_file)
idl_start = 1
if "r_start" in kwargs:
Q_round = Q_round[r_start[rep]:]
idl_start = r_start[rep]
if "r_stop" in kwargs:
Q_round = Q_round[:r_stop[rep]]
idl_stop = idl_start + len(Q_round)
# keyword "names" prevails over "ens_name"
if "names" not in kwargs:
try:
idx = truncated_file.index('r')
except Exception:
if "names" not in kwargs:
raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.")
if "ens_name" in kwargs:
ens_name = kwargs.get("ens_name")
else:
ens_name = truncated_file[:idx]
rep_names.append(ens_name + '|' + truncated_file[idx:])
else:
names = kwargs.get("names")
rep_names = names
deltas.append(np.array(Q_round))
idl.append(range(idl_start, idl_stop))
result = Obs(deltas, rep_names, idl=idl)
return result
def read_qtop_sector(target=0, **kwargs):
"""target: int
specifies the topological sector to be reweighted to (default 0)
q_top: Obs
alternatively takes args of read_qtop method as kwargs
"""
if "q_top" in kwargs:
qtop = kwargs.get("q_top")
else:
if "path" in kwargs:
path = kwargs.get("path")
del kwargs["path"]
else:
raise Exception("If you are not providing q_top, please provide path")
if "prefix" in kwargs:
prefix = kwargs.get("prefix")
del kwargs["prefix"]
else:
raise Exception("If you are not providing q_top, please provide prefix")
if "c" in kwargs:
c = kwargs.get("c")
del kwargs["c"]
else:
raise Exception("If you are not providing q_top, please provide c")
if "version" in kwargs:
version = kwargs.get("version")
del kwargs["version"]
else:
version = "1.2"
if "dtr_cnfg" in kwargs:
dtr_cnfg = kwargs.get("dtr_cnfg")
del kwargs["dtr_cnfg"]
else:
dtr_cnfg = 1
qtop = read_qtop(path, prefix, c, dtr_cnfg=dtr_cnfg,
version=version, **kwargs)
names = qtop.names
print(names)
print(qtop.deltas.keys())
proj_qtop = []
for n in qtop.deltas:
proj_qtop.append(np.array([1 if int(qtop.value + q) == target else 0 for q in qtop.deltas[n]]))
result = Obs(proj_qtop, qtop.names)
return result

542
pyerrors/input/sfcf.py
View file

@ -6,17 +6,62 @@ import fnmatch
import re
import numpy as np # Thinly-wrapped numpy
from ..obs import Obs
from . import utils
def read_sfcf(path, prefix, name, **kwargs):
"""Read sfcf C format from given folder structure.
def read_sfcf(path, prefix, name, quarks='.*', noffset=0, wf=0, wf2=0,
version="1.0c", **kwargs):
"""Read sfcf format from given folder structure.
Parameters
----------
im -- if True, read imaginary instead of real part of the correlation function.
single -- if True, read a boundary-to-boundary correlation function with a single value
b2b -- if True, read a time-dependent boundary-to-boundary correlation function
names -- Alternative labeling for replicas/ensembles. Has to have the appropriate length
path : str
Path to the measurement files.
prefix : str
Ensemble prefix for which the data is to be extracted.
name : str
Name of the correlation function to be extracted from the file
quarks: str, optional
Label of the quarks used in the sfcf input file. e.g. "quark quark"
for version 0.0 this does NOT need to be given with the typical " - "
that is present in the output file,
this is done automatically for this version
noffset: int, optional
Offset of the source (only relevant when wavefunctions are used)
wf: int, optional
ID of wave function
wf2: int, optional
ID of the second wavefunction
(only relevant for boundary-to-boundary correlation functions)
im: bool, optional
if True, read imaginary instead of real part
of the correlation function.
b2b: bool, optional
if True, read a time-dependent boundary-to-boundary
correlation function
single: bool, optional
if True, read time independent boundary to boundary
correlation function
names: list, optional
Alternative labeling for replicas/ensembles.
Has to have the appropriate length
ens_name : str, optional
replaces the name of the ensemble
version: str, optional
version of SFCF, with which the measurement was done.
if the compact output option (-c) was specified,
append a "c" to the version (e.g. "1.0c")
if the append output option (-a) was specified,
append an "a" to the version. Currently supported versions
are "0.0", "1.0", "2.0", "1.0c", "2.0c", "1.0a" and "2.0a".
replica: list, optional
list of replica to be read, default is all
files: list, optional
list of files to be read per replica, default is all.
for non-compact output format, hand the folders to be read here.
check_configs: list, optional
list of list of supposed configs, eg. [range(1,1000)]
for one replicum with 1000 configs
"""
if kwargs.get('im'):
im = 1
@ -29,266 +74,283 @@ def read_sfcf(path, prefix, name, **kwargs):
b2b = 1
single = 1
else:
b2b = 0
if kwargs.get('b2b'):
b2b = 1
else:
b2b = 0
single = 0
if "replica" in kwargs:
reps = kwargs.get("replica")
if kwargs.get('b2b'):
b2b = 1
compact = True
appended = False
known_versions = ["0.0", "1.0", "2.0", "1.0c", "2.0c", "1.0a", "2.0a"]
if version not in known_versions:
raise Exception("This version is not known!")
if(version[-1] == "c"):
appended = False
compact = True
version = version[:-1]
elif(version[-1] == "a"):
appended = True
compact = False
version = version[:-1]
else:
compact = False
appended = False
read = 0
T = 0
start = 0
ls = []
for (dirpath, dirnames, filenames) in os.walk(path):
ls.extend(dirnames)
break
if "replica" in kwargs:
ls = reps
else:
for (dirpath, dirnames, filenames) in os.walk(path):
if not appended:
ls.extend(dirnames)
else:
ls.extend(filenames)
break
# Exclude folders with different names
for exc in ls:
if not fnmatch.fnmatch(exc, prefix + '*'):
ls = list(set(ls) - set([exc]))
if not ls:
raise Exception('Error, directory not found')
for exc in ls:
if fnmatch.fnmatch(exc, prefix + '*'):
ls = list(set(ls) - set(exc))
raise Exception('No matching directories found.')
if len(ls) > 1:
ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0]))
replica = len(ls)
print('Read', part, 'part of', name, 'from', prefix, ',', replica, 'replica')
if not appended:
replica = len(ls)
else:
replica = len([file.split(".")[-1] for file in ls]) // len(set([file.split(".")[-1] for file in ls]))
print("Read", part, "part of '" + str(name) + "' with prefix '" + str(prefix) + "' (" + str(replica) + " replica)")
if 'names' in kwargs:
new_names = kwargs.get('names')
if len(new_names) != len(set(new_names)):
raise Exception("names are not unique!")
if len(new_names) != replica:
raise Exception('Names does not have the required length', replica)
else:
# Adjust replica names to new bookmarking system
new_names = []
for entry in ls:
idx = entry.index('r')
new_names.append(entry[:idx] + '|' + entry[idx:])
if not appended:
for entry in ls:
try:
idx = entry.index('r')
except Exception:
raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.")
print(replica, 'replica')
for i, item in enumerate(ls):
print(item)
sub_ls = []
for (dirpath, dirnames, filenames) in os.walk(path + '/' + item):
sub_ls.extend(dirnames)
break
for exc in sub_ls:
if fnmatch.fnmatch(exc, 'cfg*'):
sub_ls = list(set(sub_ls) - set(exc))
sub_ls.sort(key=lambda x: int(x[3:]))
no_cfg = len(sub_ls)
print(no_cfg, 'configurations')
if 'ens_name' in kwargs:
new_names.append(kwargs.get('ens_name') + '|' + entry[idx:])
else:
new_names.append(entry[:idx] + '|' + entry[idx:])
else:
if i == 0:
with open(path + '/' + item + '/' + sub_ls[0] + '/' + name) as fp:
for k, line in enumerate(fp):
if read == 1 and not line.strip() and k > start + 1:
break
if read == 1 and k >= start:
T += 1
if '[correlator]' in line:
read = 1
start = k + 7 + b2b
T -= b2b
for exc in ls:
if not fnmatch.fnmatch(exc, prefix + '*.' + name):
ls = list(set(ls) - set([exc]))
ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1]))
for entry in ls:
myentry = entry[:-len(name) - 1]
try:
idx = myentry.index('r')
except Exception:
raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.")
deltas = []
for j in range(T):
deltas.append([])
sublength = len(sub_ls)
for j in range(T):
deltas[j].append(np.zeros(sublength))
for cnfg, subitem in enumerate(sub_ls):
with open(path + '/' + item + '/' + subitem + '/' + name) as fp:
for k, line in enumerate(fp):
if(k >= start and k < start + T):
floats = list(map(float, line.split()))
deltas[k - start][i][cnfg] = floats[1 + im - single]
result = []
for t in range(T):
result.append(Obs(deltas[t], new_names))
return result
def read_sfcf_c(path, prefix, name, quarks='.*', noffset=0, wf=0, wf2=0, **kwargs):
"""Read sfcf c format from given folder structure.
Parameters
----------
quarks -- Label of the quarks used in the sfcf input file
noffset -- Offset of the source (only relevant when wavefunctions are used)
wf -- ID of wave function
wf2 -- ID of the second wavefunction (only relevant for boundary-to-boundary correlation functions)
im -- if True, read imaginary instead of real part of the correlation function.
b2b -- if True, read a time-dependent boundary-to-boundary correlation function
names -- Alternative labeling for replicas/ensembles. Has to have the appropriate length
ens_name : str
replaces the name of the ensemble
"""
if kwargs.get('im'):
im = 1
part = 'imaginary'
else:
im = 0
part = 'real'
if kwargs.get('b2b'):
b2b = 1
else:
b2b = 0
T = 0
ls = []
for (dirpath, dirnames, filenames) in os.walk(path):
ls.extend(dirnames)
break
if not ls:
raise Exception('Error, directory not found')
# Exclude folders with different names
for exc in ls:
if not fnmatch.fnmatch(exc, prefix + '*'):
ls = list(set(ls) - set([exc]))
if len(ls) > 1:
ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0])) # New version, to cope with ids, etc.
replica = len(ls)
if 'names' in kwargs:
new_names = kwargs.get('names')
if len(new_names) != replica:
raise Exception('Names does not have the required length', replica)
else:
# Adjust replica names to new bookmarking system
new_names = []
for entry in ls:
idx = entry.index('r')
if 'ens_name' in kwargs:
new_names.append(kwargs.get('ens_name') + '|' + entry[idx:])
if 'ens_name' in kwargs:
new_names.append(kwargs.get('ens_name') + '|' + myentry[idx:])
else:
new_names.append(myentry[:idx] + '|' + myentry[idx:])
idl = []
if not appended:
for i, item in enumerate(ls):
sub_ls = []
if "files" in kwargs:
sub_ls = kwargs.get("files")
sub_ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1]))
else:
new_names.append(entry[:idx] + '|' + entry[idx:])
for (dirpath, dirnames, filenames) in os.walk(path + '/' + item):
if compact:
sub_ls.extend(filenames)
else:
sub_ls.extend(dirnames)
break
print('Read', part, 'part of', name, 'from', prefix[:-1], ',', replica, 'replica')
for i, item in enumerate(ls):
sub_ls = []
for (dirpath, dirnames, filenames) in os.walk(path + '/' + item):
sub_ls.extend(filenames)
break
for exc in sub_ls:
if not fnmatch.fnmatch(exc, prefix + '*'):
sub_ls = list(set(sub_ls) - set([exc]))
sub_ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1]))
first_cfg = int(re.findall(r'\d+', sub_ls[0])[-1])
last_cfg = len(sub_ls) + first_cfg - 1
for cfg in range(1, len(sub_ls)):
if int(re.findall(r'\d+', sub_ls[cfg])[-1]) != first_cfg + cfg:
last_cfg = cfg + first_cfg - 1
break
no_cfg = last_cfg - first_cfg + 1
print(item, ':', no_cfg, 'evenly spaced configurations (', first_cfg, '-', last_cfg, ') ,', len(sub_ls) - no_cfg, 'configs omitted\n')
if i == 0:
pattern = 'name ' + name + '\nquarks ' + quarks + '\noffset ' + str(noffset) + '\nwf ' + str(wf)
if b2b:
pattern += '\nwf_2 ' + str(wf2)
with open(path + '/' + item + '/' + sub_ls[0], 'r') as file:
content = file.read()
match = re.search(pattern, content)
if match:
start_read = content.count('\n', 0, match.start()) + 5 + b2b
end_match = re.search(r'\n\s*\n', content[match.start():])
T = content[match.start():].count('\n', 0, end_match.start()) - 4 - b2b
assert T > 0
print(T, 'entries, starting to read in line', start_read)
for exc in sub_ls:
if compact:
if not fnmatch.fnmatch(exc, prefix + '*'):
sub_ls = list(set(sub_ls) - set([exc]))
sub_ls.sort(key=lambda x:
int(re.findall(r'\d+', x)[-1]))
else:
if not fnmatch.fnmatch(exc, 'cfg*'):
sub_ls = list(set(sub_ls) - set([exc]))
sub_ls.sort(key=lambda x: int(x[3:]))
rep_idl = []
no_cfg = len(sub_ls)
for cfg in sub_ls:
try:
if compact:
rep_idl.append(int(cfg.split("n")[-1]))
else:
rep_idl.append(int(cfg[3:]))
except Exception:
raise Exception("Couldn't parse idl from directroy, problem with file " + cfg)
rep_idl.sort()
print(item, ':', no_cfg, ' configurations')
idl.append(rep_idl)
if i == 0:
if compact:
pattern = 'name ' + name + '\nquarks ' + quarks + '\noffset ' + str(noffset) + '\nwf ' + str(wf)
if b2b:
pattern += '\nwf_2 ' + str(wf2)
with open(path + '/' + item + '/' + sub_ls[0], 'r') as file:
content = file.read()
match = re.search(pattern, content)
if match:
# the start and end point of the correlator
# in question is extracted for later use in
# the other files
start_read = content.count('\n', 0, match.start()) + 5 + b2b
end_match = re.search(r'\n\s*\n', content[match.start():])
T = content[match.start():].count('\n', 0, end_match.start()) - 4 - b2b
assert T > 0
print(T, 'entries, starting to read in line', start_read)
else:
raise Exception('Correlator with pattern\n' + pattern + '\nnot found.')
else:
raise Exception('Correlator with pattern\n' + pattern + '\nnot found.')
# this part does the same as above,
# but for non-compactified versions of the files
with open(path + '/' + item + '/' + sub_ls[0] + '/' + name) as fp:
for k, line in enumerate(fp):
if version == "0.0":
# check if this is really the right file
# by matching pattern similar to above
pattern = "# " + name + " : offset " + str(noffset) + ", wf " + str(wf)
# if b2b, a second wf is needed
if b2b:
pattern += ", wf_2 " + str(wf2)
qs = quarks.split(" ")
pattern += " : " + qs[0] + " - " + qs[1]
if read == 1 and not line.strip() and k > start + 1:
break
if read == 1 and k >= start:
T += 1
deltas = []
for j in range(T):
deltas.append([])
if version == "0.0":
if pattern in line:
# print(line)
read = 1
start = k + 1
else:
if '[correlator]' in line:
read = 1
start = k + 7 + b2b
T -= b2b
print(str(T) + " entries found.")
deltas = []
for j in range(T):
deltas.append([])
sublength = no_cfg
for j in range(T):
deltas[j].append(np.zeros(sublength))
for t in range(T):
deltas[t].append(np.zeros(no_cfg))
# we iterate through all measurement files in the path given...
if compact:
for cfg in range(no_cfg):
with open(path + '/' + item + '/' + sub_ls[cfg]) as fp:
lines = fp.readlines()
if(start_read + T > len(lines)):
raise Exception("EOF before end of correlator data! Maybe " + path + '/' + item + '/' + sub_ls[cfg] + " is corrupted?")
for k in range(start_read - 6, start_read + T):
if k == start_read - 5 - b2b:
if lines[k].strip() != 'name ' + name:
raise Exception('Wrong format',
sub_ls[cfg])
if(k >= start_read and k < start_read + T):
floats = list(map(float, lines[k].split()))
deltas[k - start_read][i][cfg] = floats[-2:][im]
else:
for cnfg, subitem in enumerate(sub_ls):
with open(path + '/' + item + '/' + subitem + '/' + name) as fp:
for k, line in enumerate(fp):
if(k >= start and k < start + T):
floats = list(map(float, line.split()))
if version == "0.0":
deltas[k - start][i][cnfg] = floats[im]
else:
deltas[k - start][i][cnfg] = floats[1 + im - single]
for cfg in range(no_cfg):
with open(path + '/' + item + '/' + sub_ls[cfg]) as fp:
for k, line in enumerate(fp):
if k == start_read - 5 - b2b:
if line.strip() != 'name ' + name:
raise Exception('Wrong format', sub_ls[cfg])
if(k >= start_read and k < start_read + T):
floats = list(map(float, line.split()))
deltas[k - start_read][i][cfg] = floats[-2:][im]
else:
if "files" in kwargs:
ls = kwargs.get("files")
else:
for exc in ls:
if not fnmatch.fnmatch(exc, prefix + '*.' + name):
ls = list(set(ls) - set([exc]))
ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1]))
if len(ls) == 0:
raise Exception('File(s) for correlator ' + name + ' not found.')
pattern = 'name ' + name + '\nquarks ' + quarks + '\noffset ' + str(noffset) + '\nwf ' + str(wf)
if b2b:
pattern += '\nwf_2 ' + str(wf2)
for rep, file in enumerate(ls):
rep_idl = []
with open(path + '/' + file, 'r') as fp:
content = fp.readlines()
data_starts = []
for linenumber, line in enumerate(content):
if "[run]" in line:
data_starts.append(linenumber)
if len(set([data_starts[i] - data_starts[i - 1] for i in
range(1, len(data_starts))])) > 1:
raise Exception("Irregularities in file structure found, not all runs have the same output length")
chunk = content[:data_starts[1]]
for linenumber, line in enumerate(chunk):
if line.startswith("gauge_name"):
gauge_line = linenumber
elif line.startswith("[correlator]"):
corr_line = linenumber
found_pat = ""
for li in chunk[corr_line + 1:corr_line + 6 + b2b]:
found_pat += li
if re.search(pattern, found_pat):
start_read = corr_line + 7 + b2b
T = len(chunk) - 1 - start_read
if rep == 0:
deltas = []
for t in range(T):
deltas.append([])
for t in range(T):
deltas[t].append(np.zeros(len(data_starts)))
for cnfg in range(len(data_starts)):
start = data_starts[cnfg]
stop = start + data_starts[1]
chunk = content[start:stop]
try:
rep_idl.append(int(chunk[gauge_line].split("n")[-1]))
except Exception:
raise Exception("Couldn't parse idl from directroy, problem with chunk around line " + gauge_line)
found_pat = ""
for li in chunk[corr_line + 1:corr_line + 6 + b2b]:
found_pat += li
if re.search(pattern, found_pat):
for t, line in enumerate(chunk[start_read:start_read + T]):
floats = list(map(float, line.split()))
deltas[t][rep][cnfg] = floats[-2:][im]
idl.append(rep_idl)
if "check_configs" in kwargs:
print("Checking for missing configs...")
che = kwargs.get("check_configs")
if not (len(che) == len(idl)):
raise Exception("check_configs has to be the same length as replica!")
for r in range(len(idl)):
print("checking " + new_names[r])
utils.check_idl(idl[r], che[r])
print("Done")
result = []
for t in range(T):
result.append(Obs(deltas[t], new_names))
return result
def read_qtop(path, prefix, **kwargs):
"""Read qtop format from given folder structure.
Parameters
----------
target -- specifies the topological sector to be reweighted to (default 0)
full -- if true read the charge instead of the reweighting factor.
"""
if 'target' in kwargs:
target = kwargs.get('target')
else:
target = 0
if kwargs.get('full'):
full = 1
else:
full = 0
ls = []
for (dirpath, dirnames, filenames) in os.walk(path):
ls.extend(filenames)
break
if not ls:
raise Exception('Error, directory not found')
# Exclude files with different names
for exc in ls:
if not fnmatch.fnmatch(exc, prefix + '*'):
ls = list(set(ls) - set([exc]))
if len(ls) > 1:
ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0])) # New version, to cope with ids, etc.
replica = len(ls)
print('Read Q_top from', prefix[:-1], ',', replica, 'replica')
deltas = []
for rep in range(replica):
tmp = []
with open(path + '/' + ls[rep]) as fp:
for k, line in enumerate(fp):
floats = list(map(float, line.split()))
if full == 1:
tmp.append(floats[1])
else:
if int(floats[1]) == target:
tmp.append(1.0)
else:
tmp.append(0.0)
deltas.append(np.array(tmp))
rep_names = []
for entry in ls:
truncated_entry = entry.split('.')[0]
idx = truncated_entry.index('r')
rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
result = Obs(deltas, rep_names)
result.append(Obs(deltas[t], new_names, idl=idl))
return result

24
pyerrors/input/utils.py Normal file
View file

@ -0,0 +1,24 @@
"""Utilities for the input"""
def check_idl(idl, che):
"""Checks if list of configurations is contained in an idl
Parameters
----------
idl : range or list
idl of the current replicum
che : list
list of configurations to be checked against
"""
missing = []
for c in che:
if c not in idl:
missing.append(c)
# print missing configurations such that it can directly be parsed to slurm terminal
if not (len(missing) == 0):
print(len(missing), "configs missing")
miss_str = str(missing[0])
for i in missing[1:]:
miss_str += "," + str(i)
print(miss_str)

587
pyerrors/linalg.py
View file

@ -1,121 +1,6 @@
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, import_jackknife
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 = {name: (len(list(filter(lambda o: o.is_merged.get(name, False) is True, raveled_data))) > 0) for name in new_names}
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[name]:
is_merged[name] = (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 = []
for name in new_names:
if is_merged[name]:
filtered_deltas, filtered_idl_d = _filter_zeroes(new_deltas[name], new_idl_d[name])
else:
filtered_deltas = new_deltas[name]
filtered_idl_d = new_idl_d[name]
new_samples.append(filtered_deltas)
new_idl.append(filtered_idl_d)
new_means.append(new_r_values[name][i_val])
final_result[i_val] = Obs(new_samples, new_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):
@ -157,8 +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):
@ -171,57 +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(a, b):
def jack_matmul(*operands):
"""Matrix multiply both operands making use of the jackknife approximation.
Parameters
----------
a : numpy.ndarray
First matrix, can be real or complex Obs valued
b : numpy.ndarray
Second matrix, can be real or complex Obs valued
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.
"""
if any(isinstance(o[0, 0], CObs) for o in [a, b]):
def _exp_to_jack(matrix):
base_matrix = np.empty_like(matrix)
for (n, m), entry in np.ndenumerate(matrix):
base_matrix[n, m] = entry.real.export_jackknife() + 1j * entry.imag.export_jackknife()
return base_matrix
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):
base_matrix = np.empty_like(matrix)
for (n, m), entry in np.ndenumerate(matrix):
base_matrix[n, m] = CObs(import_jackknife(entry.real, name),
import_jackknife(entry.imag, name))
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
j_a = _exp_to_jack(a)
j_b = _exp_to_jack(b)
r = j_a @ j_b
return _imp_from_jack(r, a.ravel()[0].real.names[0])
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:
def _exp_to_jack(matrix):
base_matrix = np.empty_like(matrix)
for (n, m), entry in np.ndenumerate(matrix):
base_matrix[n, m] = entry.export_jackknife()
return base_matrix
name = operands[0].flat[0].names[0]
idl = operands[0].flat[0].idl[name]
def _imp_from_jack(matrix, name):
base_matrix = np.empty_like(matrix)
for (n, m), entry in np.ndenumerate(matrix):
base_matrix[n, m] = import_jackknife(entry, name)
return base_matrix
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)
j_a = _exp_to_jack(a)
j_b = _exp_to_jack(b)
r = j_a @ j_b
return _imp_from_jack(r, a.ravel()[0].names[0])
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 boot_matmul(a, b):
@ -320,12 +290,15 @@ 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)))
if np.sqrt(len(x)) != dim:
raise Exception('Input has to have dim**2 entries')
mat = []
for i in range(dim):
@ -338,8 +311,6 @@ def scalar_mat_op(op, obs, **kwargs):
if isinstance(obs, np.ndarray):
raveled_obs = (1 * (obs.ravel())).tolist()
elif isinstance(obs, list):
raveled_obs = obs
else:
raise TypeError('Unproper type of input.')
return derived_observable(_mat, raveled_obs, **kwargs)
@ -359,10 +330,7 @@ def _mat_mat_op(op, obs, **kwargs):
A[n, m] = entry
B[n, m] = 0.0
big_matrix = np.block([[A, -B], [B, A]])
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]
@ -371,15 +339,11 @@ def _mat_mat_op(op, obs, **kwargs):
res[n, m] = CObs(op_A[n, m], op_B[n, m])
return res
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):
"""Computes the eigenvalues and eigenvectors of a given hermitian matrix of Obs according to np.linalg.eigh."""
if kwargs.get('num_grad') is True:
return _num_diff_eigh(obs, **kwargs)
w = derived_observable(lambda x, **kwargs: anp.linalg.eigh(x)[0], obs)
v = derived_observable(lambda x, **kwargs: anp.linalg.eigh(x)[1], obs)
return w, v
@ -387,305 +351,18 @@ def eigh(obs, **kwargs):
def eig(obs, **kwargs):
"""Computes the eigenvalues of a given matrix of Obs according to np.linalg.eig."""
if kwargs.get('num_grad') is True:
return _num_diff_eig(obs, **kwargs)
# Note: Automatic differentiation of eig is implemented in the git of autograd
# but not yet released to PyPi (1.3)
w = derived_observable(lambda x, **kwargs: anp.real(anp.linalg.eig(x)[0]), obs)
return w
def pinv(obs, **kwargs):
"""Computes the Moore-Penrose pseudoinverse of a matrix of Obs."""
if kwargs.get('num_grad') is True:
return _num_diff_pinv(obs, **kwargs)
return derived_observable(lambda x, **kwargs: anp.linalg.pinv(x), obs)
def svd(obs, **kwargs):
"""Computes the singular value decomposition of a matrix of Obs."""
if kwargs.get('num_grad') is True:
return _num_diff_svd(obs, **kwargs)
u = derived_observable(lambda x, **kwargs: anp.linalg.svd(x, full_matrices=False)[0], obs)
s = derived_observable(lambda x, **kwargs: anp.linalg.svd(x, full_matrices=False)[1], obs)
vh = derived_observable(lambda x, **kwargs: anp.linalg.svd(x, full_matrices=False)[2], obs)
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):
"""Computes the matrix to matrix operation op to a given matrix of Obs elementwise
which is suitable for numerical differentiation."""
def _mat(x, **kwargs):
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)
return op(np.array(mat))[kwargs.get('i')][kwargs.get('j')]
if isinstance(obs, np.ndarray):
raveled_obs = (1 * (obs.ravel())).tolist()
elif isinstance(obs, list):
raveled_obs = obs
else:
raise TypeError('Unproper type of input.')
dim = int(np.sqrt(len(raveled_obs)))
res_mat = []
for i in range(dim):
row = []
for j in range(dim):
row.append(derived_observable(_mat, raveled_obs, i=i, j=j, **kwargs))
res_mat.append(row)
return np.array(res_mat) @ np.identity(dim)
def _num_diff_eigh(obs, **kwargs):
"""Computes the eigenvalues and eigenvectors of a given hermitian matrix of Obs according to np.linalg.eigh
elementwise which is suitable for numerical differentiation."""
def _mat(x, **kwargs):
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)
n = kwargs.get('n')
res = np.linalg.eigh(np.array(mat))[n]
if n == 0:
return res[kwargs.get('i')]
else:
return res[kwargs.get('i')][kwargs.get('j')]
if isinstance(obs, np.ndarray):
raveled_obs = (1 * (obs.ravel())).tolist()
elif isinstance(obs, list):
raveled_obs = obs
else:
raise TypeError('Unproper type of input.')
dim = int(np.sqrt(len(raveled_obs)))
res_vec = []
for i in range(dim):
res_vec.append(derived_observable(_mat, raveled_obs, n=0, i=i, **kwargs))
res_mat = []
for i in range(dim):
row = []
for j in range(dim):
row.append(derived_observable(_mat, raveled_obs, n=1, i=i, j=j, **kwargs))
res_mat.append(row)
return (np.array(res_vec) @ np.identity(dim), np.array(res_mat) @ np.identity(dim))
def _num_diff_eig(obs, **kwargs):
"""Computes the eigenvalues of a given matrix of Obs according to np.linalg.eig
elementwise which is suitable for numerical differentiation."""
def _mat(x, **kwargs):
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)
n = kwargs.get('n')
res = np.linalg.eig(np.array(mat))[n]
if n == 0:
# Discard imaginary part of eigenvalue here
return np.real(res[kwargs.get('i')])
else:
return res[kwargs.get('i')][kwargs.get('j')]
if isinstance(obs, np.ndarray):
raveled_obs = (1 * (obs.ravel())).tolist()
elif isinstance(obs, list):
raveled_obs = obs
else:
raise TypeError('Unproper type of input.')
dim = int(np.sqrt(len(raveled_obs)))
res_vec = []
for i in range(dim):
# Note: Automatic differentiation of eig is implemented in the git of autograd
# but not yet released to PyPi (1.3)
res_vec.append(derived_observable(_mat, raveled_obs, n=0, i=i, **kwargs))
return np.array(res_vec) @ np.identity(dim)
def _num_diff_pinv(obs, **kwargs):
"""Computes the Moore-Penrose pseudoinverse of a matrix of Obs elementwise which is suitable
for numerical differentiation."""
def _mat(x, **kwargs):
shape = kwargs.get('shape')
mat = []
for i in range(shape[0]):
row = []
for j in range(shape[1]):
row.append(x[j + shape[1] * i])
mat.append(row)
return np.linalg.pinv(np.array(mat))[kwargs.get('i')][kwargs.get('j')]
if isinstance(obs, np.ndarray):
shape = obs.shape
raveled_obs = (1 * (obs.ravel())).tolist()
else:
raise TypeError('Unproper type of input.')
res_mat = []
for i in range(shape[1]):
row = []
for j in range(shape[0]):
row.append(derived_observable(_mat, raveled_obs, shape=shape, i=i, j=j, **kwargs))
res_mat.append(row)
return np.array(res_mat) @ np.identity(shape[0])
def _num_diff_svd(obs, **kwargs):
"""Computes the singular value decomposition of a matrix of Obs elementwise which
is suitable for numerical differentiation."""
def _mat(x, **kwargs):
shape = kwargs.get('shape')
mat = []
for i in range(shape[0]):
row = []
for j in range(shape[1]):
row.append(x[j + shape[1] * i])
mat.append(row)
res = np.linalg.svd(np.array(mat), full_matrices=False)
if kwargs.get('n') == 1:
return res[1][kwargs.get('i')]
else:
return res[kwargs.get('n')][kwargs.get('i')][kwargs.get('j')]
if isinstance(obs, np.ndarray):
shape = obs.shape
raveled_obs = (1 * (obs.ravel())).tolist()
else:
raise TypeError('Unproper type of input.')
mid_index = min(shape[0], shape[1])
res_mat0 = []
for i in range(shape[0]):
row = []
for j in range(mid_index):
row.append(derived_observable(_mat, raveled_obs, shape=shape, n=0, i=i, j=j, **kwargs))
res_mat0.append(row)
res_mat1 = []
for i in range(mid_index):
res_mat1.append(derived_observable(_mat, raveled_obs, shape=shape, n=1, i=i, **kwargs))
res_mat2 = []
for i in range(mid_index):
row = []
for j in range(shape[1]):
row.append(derived_observable(_mat, raveled_obs, shape=shape, n=2, i=i, j=j, **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

66
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]
@ -32,3 +70,19 @@ def gen_correlated_data(means, cov, name, tau=0.5, samples=1000):
data.append(np.sqrt(1 - a ** 2) * rand[i] + a * data[-1])
corr_data = np.array(data) - np.mean(data, axis=0) + means
return [Obs([dat], [name]) for dat in corr_data.T]
def _assert_equal_properties(ol, otype=Obs):
if not isinstance(ol[0], otype):
raise Exception("Wrong data type in list.")
for o in ol[1:]:
if not isinstance(o, otype):
raise Exception("Wrong data type in list.")
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.")

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

640
pyerrors/obs.py
View file

@ -4,8 +4,10 @@ import numpy as np
import autograd.numpy as anp # Thinly-wrapped numpy
from autograd import jacobian
import matplotlib.pyplot as plt
from scipy.stats import skew, skewtest, kurtosis, kurtosistest
import numdifftools as nd
from itertools import groupby
from .covobs import Covobs
class Obs:
@ -41,7 +43,7 @@ class Obs:
'ddvalue', 'reweighted', 'S', 'tau_exp', 'N_sigma',
'e_dvalue', 'e_ddvalue', 'e_tauint', 'e_dtauint',
'e_windowsize', 'e_rho', 'e_drho', 'e_n_tauint', 'e_n_dtauint',
'idl', 'is_merged', 'tag', '__dict__']
'idl', 'is_merged', 'tag', '_covobs', '__dict__']
S_global = 2.0
S_dict = {}
@ -67,16 +69,21 @@ class Obs:
already subtracted from the samples
"""
if means is None:
if means is None and len(samples):
if len(samples) != len(names):
raise Exception('Length of samples and names incompatible.')
if idl is not None:
if len(idl) != len(names):
raise Exception('Length of idl incompatible with samples and names.')
if len(names) != len(set(names)):
raise Exception('names are not unique.')
if not all(isinstance(x, str) for x in names):
raise TypeError('All names have to be strings.')
name_length = len(names)
if name_length > 1:
if name_length != len(set(names)):
raise Exception('names are not unique.')
if not all(isinstance(x, str) for x in names):
raise TypeError('All names have to be strings.')
else:
if not isinstance(names[0], str):
raise TypeError('All names have to be strings.')
if min(len(x) for x in samples) <= 4:
raise Exception('Samples have to have at least 5 entries.')
@ -84,48 +91,53 @@ class Obs:
self.shape = {}
self.r_values = {}
self.deltas = {}
self._covobs = {}
self.idl = {}
if idl is not None:
for name, idx in sorted(zip(names, idl)):
if isinstance(idx, range):
self.idl[name] = idx
elif isinstance(idx, (list, np.ndarray)):
dc = np.unique(np.diff(idx))
if np.any(dc < 0):
raise Exception("Unsorted idx for idl[%s]" % (name))
if len(dc) == 1:
self.idl[name] = range(idx[0], idx[-1] + dc[0], dc[0])
if len(samples):
if idl is not None:
for name, idx in sorted(zip(names, idl)):
if isinstance(idx, range):
self.idl[name] = idx
elif isinstance(idx, (list, np.ndarray)):
dc = np.unique(np.diff(idx))
if np.any(dc < 0):
raise Exception("Unsorted idx for idl[%s]" % (name))
if len(dc) == 1:
self.idl[name] = range(idx[0], idx[-1] + dc[0], dc[0])
else:
self.idl[name] = list(idx)
else:
self.idl[name] = list(idx)
else:
raise Exception('incompatible type for idl[%s].' % (name))
else:
for name, sample in sorted(zip(names, samples)):
self.idl[name] = range(1, len(sample) + 1)
raise Exception('incompatible type for idl[%s].' % (name))
else:
for name, sample in sorted(zip(names, samples)):
self.idl[name] = range(1, len(sample) + 1)
if means is not None:
for name, sample, mean in sorted(zip(names, samples, means)):
self.shape[name] = len(self.idl[name])
if len(sample) != self.shape[name]:
raise Exception('Incompatible samples and idx for %s: %d vs. %d' % (name, len(sample), self.shape[name]))
self.r_values[name] = mean
self.deltas[name] = sample
else:
for name, sample in sorted(zip(names, samples)):
self.shape[name] = len(self.idl[name])
if len(sample) != self.shape[name]:
raise Exception('Incompatible samples and idx for %s: %d vs. %d' % (name, len(sample), self.shape[name]))
self.r_values[name] = np.mean(sample)
self.deltas[name] = sample - self.r_values[name]
self.is_merged = {}
self.N = sum(list(self.shape.values()))
self._value = 0
self.N = 0
if means is not None:
for name, sample, mean in sorted(zip(names, samples, means)):
self.shape[name] = len(self.idl[name])
self.N += self.shape[name]
self.r_values[name] = mean
self.deltas[name] = sample
else:
for name, sample in sorted(zip(names, samples)):
self.shape[name] = len(self.idl[name])
self.N += self.shape[name]
if len(sample) != self.shape[name]:
raise Exception('Incompatible samples and idx for %s: %d vs. %d' % (name, len(sample), self.shape[name]))
self.r_values[name] = np.mean(sample)
self.deltas[name] = sample - self.r_values[name]
self._value += self.shape[name] * self.r_values[name]
self._value /= self.N
self._value = 0
if means is None:
for name in self.names:
self._value += self.shape[name] * self.r_values[name]
self._value /= self.N
self.is_merged = {}
else:
self._value = 0
self.is_merged = {}
self.N = 0
self._dvalue = 0.0
self.ddvalue = 0.0
@ -145,6 +157,14 @@ class Obs:
def e_names(self):
return sorted(set([o.split('|')[0] for o in self.names]))
@property
def cov_names(self):
return sorted(set([o for o in self.covobs.keys()]))
@property
def mc_names(self):
return sorted(set([o.split('|')[0] for o in self.names if o not in self.cov_names]))
@property
def e_content(self):
res = {}
@ -154,6 +174,10 @@ class Obs:
res[e_name].append(e_name)
return res
@property
def covobs(self):
return self._covobs
def gamma_method(self, **kwargs):
"""Estimate the error and related properties of the Obs.
@ -219,8 +243,7 @@ class Obs:
_parse_kwarg('tau_exp')
_parse_kwarg('N_sigma')
for e, e_name in enumerate(self.e_names):
for e, e_name in enumerate(self.mc_names):
r_length = []
for r_name in e_content[e_name]:
if isinstance(self.idl[r_name], range):
@ -306,11 +329,16 @@ class Obs:
self._dvalue += self.e_dvalue[e_name] ** 2
self.ddvalue += (self.e_dvalue[e_name] * self.e_ddvalue[e_name]) ** 2
self._dvalue = np.sqrt(self.dvalue)
for e_name in self.cov_names:
self.e_dvalue[e_name] = np.sqrt(self.covobs[e_name].errsq())
self.e_ddvalue[e_name] = 0
self._dvalue += self.e_dvalue[e_name]**2
self._dvalue = np.sqrt(self._dvalue)
if self._dvalue == 0.0:
self.ddvalue = 0.0
else:
self.ddvalue = np.sqrt(self.ddvalue) / self.dvalue
self.ddvalue = np.sqrt(self.ddvalue) / self._dvalue
return
def _calc_gamma(self, deltas, idx, shape, w_max, fft):
@ -362,17 +390,19 @@ class Obs:
if self.value == 0.0:
percentage = np.nan
else:
percentage = np.abs(self.dvalue / self.value) * 100
print('Result\t %3.8e +/- %3.8e +/- %3.8e (%3.3f%%)' % (self.value, self.dvalue, self.ddvalue, percentage))
percentage = np.abs(self._dvalue / self.value) * 100
print('Result\t %3.8e +/- %3.8e +/- %3.8e (%3.3f%%)' % (self.value, self._dvalue, self.ddvalue, percentage))
if len(self.e_names) > 1:
print(' Ensemble errors:')
for e_name in self.e_names:
for e_name in self.mc_names:
if len(self.e_names) > 1:
print('', e_name, '\t %3.8e +/- %3.8e' % (self.e_dvalue[e_name], self.e_ddvalue[e_name]))
if self.tau_exp[e_name] > 0:
print(' t_int\t %3.8e +/- %3.8e tau_exp = %3.2f, N_sigma = %1.0i' % (self.e_tauint[e_name], self.e_dtauint[e_name], self.tau_exp[e_name], self.N_sigma[e_name]))
else:
print(' t_int\t %3.8e +/- %3.8e S = %3.2f' % (self.e_tauint[e_name], self.e_dtauint[e_name], self.S[e_name]))
for e_name in self.cov_names:
print('', e_name, '\t %3.8e' % (self.e_dvalue[e_name]))
if ens_content is True:
if len(self.e_names) == 1:
print(self.N, 'samples in', len(self.e_names), 'ensemble:')
@ -380,32 +410,31 @@ class Obs:
print(self.N, 'samples in', len(self.e_names), 'ensembles:')
my_string_list = []
for key, value in sorted(self.e_content.items()):
my_string = ' ' + "\u00B7 Ensemble '" + key + "' "
if len(value) == 1:
my_string += f': {self.shape[value[0]]} configurations'
if isinstance(self.idl[value[0]], range):
my_string += f' (from {self.idl[value[0]].start} to {self.idl[value[0]][-1]}' + int(self.idl[value[0]].step != 1) * f' in steps of {self.idl[value[0]].step}' + ')'
else:
my_string += ' (irregular range)'
else:
sublist = []
for v in value:
my_substring = ' ' + "\u00B7 Replicum '" + v[len(key) + 1:] + "' "
my_substring += f': {self.shape[v]} configurations'
if isinstance(self.idl[v], range):
my_substring += f' (from {self.idl[v].start} to {self.idl[v][-1]}' + int(self.idl[v].step != 1) * f' in steps of {self.idl[v].step}' + ')'
if key not in self.covobs:
my_string = ' ' + "\u00B7 Ensemble '" + key + "' "
if len(value) == 1:
my_string += f': {self.shape[value[0]]} configurations'
if isinstance(self.idl[value[0]], range):
my_string += f' (from {self.idl[value[0]].start} to {self.idl[value[0]][-1]}' + int(self.idl[value[0]].step != 1) * f' in steps of {self.idl[value[0]].step}' + ')'
else:
my_substring += ' (irregular range)'
sublist.append(my_substring)
my_string += ' (irregular range)'
else:
sublist = []
for v in value:
my_substring = ' ' + "\u00B7 Replicum '" + v[len(key) + 1:] + "' "
my_substring += f': {self.shape[v]} configurations'
if isinstance(self.idl[v], range):
my_substring += f' (from {self.idl[v].start} to {self.idl[v][-1]}' + int(self.idl[v].step != 1) * f' in steps of {self.idl[v].step}' + ')'
else:
my_substring += ' (irregular range)'
sublist.append(my_substring)
my_string += '\n' + '\n'.join(sublist)
my_string += '\n' + '\n'.join(sublist)
else:
my_string = ' ' + "\u00B7 Covobs '" + key + "' "
my_string_list.append(my_string)
print('\n'.join(my_string_list))
def print(self, level=1):
warnings.warn("Method 'print' renamed to 'details'", DeprecationWarning)
self.details(level > 1)
def is_zero_within_error(self, sigma=1):
"""Checks whether the observable is zero within 'sigma' standard errors.
@ -416,11 +445,19 @@ class Obs:
Works only properly when the gamma method was run.
"""
return self.is_zero() or np.abs(self.value) <= sigma * self.dvalue
return self.is_zero() or np.abs(self.value) <= sigma * self._dvalue
def is_zero(self):
"""Checks whether the observable is zero within machine precision."""
return np.isclose(0.0, self.value) and all(np.allclose(0.0, delta) for delta in self.deltas.values())
def is_zero(self, rtol=1.e-5, atol=1.e-8):
"""Checks whether the observable is zero within a given tolerance.
Parameters
----------
rtol : float
Relative tolerance (for details see numpy documentation).
atol : float
Absolute tolerance (for details see numpy documentation).
"""
return np.isclose(0.0, self.value, rtol, atol) and all(np.allclose(0.0, delta, rtol, atol) for delta in self.deltas.values()) and all(np.allclose(0.0, delta.errsq(), rtol, atol) for delta in self.covobs.values())
def plot_tauint(self, save=None):
"""Plot integrated autocorrelation time for each ensemble.
@ -433,8 +470,8 @@ class Obs:
if not hasattr(self, 'e_dvalue'):
raise Exception('Run the gamma method first.')
fig = plt.figure()
for e, e_name in enumerate(self.e_names):
for e, e_name in enumerate(self.mc_names):
fig = plt.figure()
plt.xlabel(r'$W$')
plt.ylabel(r'$\tau_\mathrm{int}$')
length = int(len(self.e_n_tauint[e_name]))
@ -459,13 +496,20 @@ class Obs:
plt.ylim(bottom=0.0)
plt.draw()
if save:
fig.savefig(save)
fig.savefig(save + "_" + str(e))
def plot_rho(self):
"""Plot normalized autocorrelation function time for each ensemble."""
def plot_rho(self, save=None):
"""Plot normalized autocorrelation function time for each ensemble.
Parameters
----------
save : str
saves the figure to a file named 'save' if.
"""
if not hasattr(self, 'e_dvalue'):
raise Exception('Run the gamma method first.')
for e, e_name in enumerate(self.e_names):
for e, e_name in enumerate(self.mc_names):
fig = plt.figure()
plt.xlabel('W')
plt.ylabel('rho')
length = int(len(self.e_drho[e_name]))
@ -482,12 +526,14 @@ class Obs:
plt.plot([-0.5, xmax], [0, 0], 'k--', lw=1)
plt.xlim(-0.5, xmax)
plt.draw()
if save:
fig.savefig(save + "_" + str(e))
def plot_rep_dist(self):
"""Plot replica distribution for each ensemble with more than one replicum."""
if not hasattr(self, 'e_dvalue'):
raise Exception('Run the gamma method first.')
for e, e_name in enumerate(self.e_names):
for e, e_name in enumerate(self.mc_names):
if len(self.e_content[e_name]) == 1:
print('No replica distribution for a single replicum (', e_name, ')')
continue
@ -513,22 +559,28 @@ class Obs:
expand : bool
show expanded history for irregular Monte Carlo chains (default: True).
"""
for e, e_name in enumerate(self.e_names):
for e, e_name in enumerate(self.mc_names):
plt.figure()
r_length = []
tmp = []
tmp_expanded = []
for r, r_name in enumerate(self.e_content[e_name]):
tmp.append(self.deltas[r_name] + self.r_values[r_name])
if expand:
tmp.append(_expand_deltas(self.deltas[r_name], list(self.idl[r_name]), self.shape[r_name]) + self.r_values[r_name])
tmp_expanded.append(_expand_deltas(self.deltas[r_name], list(self.idl[r_name]), self.shape[r_name]) + self.r_values[r_name])
r_length.append(len(tmp_expanded[-1]))
else:
tmp.append(self.deltas[r_name] + self.r_values[r_name])
r_length.append(len(tmp[-1]))
r_length.append(len(tmp[-1]))
e_N = np.sum(r_length)
x = np.arange(e_N)
y = np.concatenate(tmp, axis=0)
y_test = np.concatenate(tmp, axis=0)
if expand:
y = np.concatenate(tmp_expanded, axis=0)
else:
y = y_test
plt.errorbar(x, y, fmt='.', markersize=3)
plt.xlim(-0.5, e_N - 0.5)
plt.title(e_name)
plt.title(e_name + f'\nskew: {skew(y_test):.3f} (p={skewtest(y_test).pvalue:.3f}), kurtosis: {kurtosis(y_test):.3f} (p={kurtosistest(y_test).pvalue:.3f})')
plt.draw()
def plot_piechart(self):
@ -536,10 +588,10 @@ class Obs:
ensemble to the error and returns a dictionary containing the fractions."""
if not hasattr(self, 'e_dvalue'):
raise Exception('Run the gamma method first.')
if self.dvalue == 0.0:
if self._dvalue == 0.0:
raise Exception('Error is 0.0')
labels = self.e_names
sizes = [i ** 2 for i in list(self.e_dvalue.values())] / self.dvalue ** 2
sizes = [self.e_dvalue[name] ** 2 for name in labels] / self._dvalue ** 2
fig1, ax1 = plt.subplots()
ax1.pie(sizes, labels=labels, startangle=90, normalize=True)
ax1.axis('equal')
@ -547,22 +599,32 @@ class Obs:
return dict(zip(self.e_names, sizes))
def dump(self, name, **kwargs):
"""Dump the Obs to a pickle file 'name'.
def dump(self, filename, datatype="json.gz", **kwargs):
"""Dump the Obs to a file 'name' of chosen format.
Parameters
----------
name : str
filename : str
name of the file to be saved.
datatype : str
Format of the exported file. Supported formats include
"json.gz" and "pickle"
path : str
specifies a custom path for the file (default '.')
"""
if 'path' in kwargs:
file_name = kwargs.get('path') + '/' + name + '.p'
file_name = kwargs.get('path') + '/' + filename
else:
file_name = name + '.p'
with open(file_name, 'wb') as fb:
pickle.dump(self, fb)
file_name = filename
if datatype == "json.gz":
from .input.json import dump_to_json
dump_to_json([self], file_name)
elif datatype == "pickle":
with open(file_name + '.p', 'wb') as fb:
pickle.dump(self, fb)
else:
raise Exception("Unknown datatype " + str(datatype))
def export_jackknife(self):
"""Export jackknife samples from the Obs
@ -584,9 +646,9 @@ class Obs:
name = self.names[0]
full_data = self.deltas[name] + self.r_values[name]
n = full_data.size
mean = np.mean(full_data)
mean = self.value
tmp_jacks = np.zeros(n + 1)
tmp_jacks[0] = self.value
tmp_jacks[0] = mean
tmp_jacks[1:] = (n * mean - full_data) / (n - 1)
return tmp_jacks
@ -597,15 +659,15 @@ class Obs:
return 'Obs[' + str(self) + ']'
def __str__(self):
if self.dvalue == 0.0:
if self._dvalue == 0.0:
return str(self.value)
fexp = np.floor(np.log10(self.dvalue))
fexp = np.floor(np.log10(self._dvalue))
if fexp < 0.0:
return '{:{form}}({:2.0f})'.format(self.value, self.dvalue * 10 ** (-fexp + 1), form='.' + str(-int(fexp) + 1) + 'f')
return '{:{form}}({:2.0f})'.format(self.value, self._dvalue * 10 ** (-fexp + 1), form='.' + str(-int(fexp) + 1) + 'f')
elif fexp == 0.0:
return '{:.1f}({:1.1f})'.format(self.value, self.dvalue)
return '{:.1f}({:1.1f})'.format(self.value, self._dvalue)
else:
return '{:.0f}({:2.0f})'.format(self.value, self.dvalue)
return '{:.0f}({:2.0f})'.format(self.value, self._dvalue)
# Overload comparisons
def __lt__(self, other):
@ -759,9 +821,6 @@ class Obs:
def arctanh(self):
return derived_observable(lambda x: anp.arctanh(x[0]), [self])
def sinc(self):
return derived_observable(lambda x: anp.sinc(x[0]), [self])
class CObs:
"""Class for a complex valued observable."""
@ -907,7 +966,7 @@ def _merge_idx(idl):
g = groupby(idl)
if next(g, True) and not next(g, False):
return idl[0]
except:
except Exception:
pass
if np.all([type(idx) is range for idx in idl]):
@ -976,7 +1035,7 @@ def _filter_zeroes(deltas, idx, eps=Obs.filter_eps):
return deltas, idx
def derived_observable(func, data, **kwargs):
def derived_observable(func, data, array_mode=False, **kwargs):
"""Construct a derived Obs according to func(data, **kwargs) using automatic differentiation.
Parameters
@ -1009,32 +1068,27 @@ def derived_observable(func, data, **kwargs):
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
if not all(isinstance(x, Obs) for x in raveled_data):
for i in range(len(raveled_data)):
if isinstance(raveled_data[i], (int, float)):
raveled_data[i] = cov_Obs(raveled_data[i], 0.0, "###dummy_covobs###")
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])
allcov = {}
for o in raveled_data:
for name in o.cov_names:
if name in allcov:
if not np.allclose(allcov[name], o.covobs[name].cov):
raise Exception('Inconsistent covariance matrices for %s!' % (name))
else:
allcov[name] = o.covobs[name].cov
n_obs = len(raveled_data)
new_names = sorted(set([y for x in [o.names for o in raveled_data] for y in x]))
new_cov_names = sorted(set([y for x in [o.cov_names for o in raveled_data] for y in x]))
new_sample_names = sorted(set(new_names) - set(new_cov_names))
is_merged = {name: (len(list(filter(lambda o: o.is_merged.get(name, False) is True, raveled_data))) > 0) for name in new_names}
is_merged = {name: (len(list(filter(lambda o: o.is_merged.get(name, False) is True, raveled_data))) > 0) for name in new_sample_names}
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[name]:
is_merged[name] = (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])
@ -1043,21 +1097,24 @@ def derived_observable(func, data, **kwargs):
new_values = func(values, **kwargs)
multi = 0
if isinstance(new_values, np.ndarray):
multi = 1
multi = int(isinstance(new_values, np.ndarray))
new_r_values = {}
for name in new_names:
new_idl_d = {}
for name in new_sample_names:
idl = []
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[i] = item.r_values.get(name, item.value)
tmp_idl = item.idl.get(name)
if tmp_idl is not None:
idl.append(tmp_idl)
if multi > 0:
tmp_values = np.array(tmp_values).reshape(data.shape)
new_r_values[name] = func(tmp_values, **kwargs)
new_idl_d[name] = _merge_idx(idl)
if not is_merged[name]:
is_merged[name] = (1 != len(set([len(idx) for idx in [*idl, new_idl_d[name]]])))
if 'man_grad' in kwargs:
deriv = np.asarray(kwargs.get('man_grad'))
@ -1090,26 +1147,71 @@ def derived_observable(func, data, **kwargs):
final_result = np.zeros(new_values.shape, dtype=object)
if array_mode is True:
class _Zero_grad():
def __init__(self, N):
self.grad = np.zeros((N, 1))
new_covobs_lengths = dict(set([y for x in [[(n, o.covobs[n].N) for n in o.cov_names] for o in raveled_data] for y in x]))
d_extracted = {}
g_extracted = {}
for name in new_sample_names:
d_extracted[name] = []
ens_length = len(new_idl_d[name])
for i_dat, dat in enumerate(data):
d_extracted[name].append(np.array([_expand_deltas_for_merge(o.deltas.get(name, np.zeros(ens_length)), o.idl.get(name, new_idl_d[name]), o.shape.get(name, ens_length), new_idl_d[name]) for o in dat.reshape(np.prod(dat.shape))]).reshape(dat.shape + (ens_length, )))
for name in new_cov_names:
g_extracted[name] = []
zero_grad = _Zero_grad(new_covobs_lengths[name])
for i_dat, dat in enumerate(data):
g_extracted[name].append(np.array([o.covobs.get(name, zero_grad).grad for o in dat.reshape(np.prod(dat.shape))]).reshape(dat.shape + (new_covobs_lengths[name], 1)))
for i_val, new_val in np.ndenumerate(new_values):
new_deltas = {}
for j_obs, obs in np.ndenumerate(data):
for name in obs.names:
new_deltas[name] = new_deltas.get(name, 0) + deriv[i_val + j_obs] * _expand_deltas_for_merge(obs.deltas[name], obs.idl[name], obs.shape[name], new_idl_d[name])
new_grad = {}
if array_mode is True:
for name in new_sample_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)
for name in new_cov_names:
new_grad[name] = 0
for i_dat, dat in enumerate(g_extracted[name]):
new_grad[name] += np.tensordot(deriv[i_val + (i_dat, )], dat)
else:
for j_obs, obs in np.ndenumerate(data):
for name in obs.names:
if name in obs.cov_names:
new_grad[name] = new_grad.get(name, 0) + deriv[i_val + j_obs] * obs.covobs[name].grad
else:
new_deltas[name] = new_deltas.get(name, 0) + deriv[i_val + j_obs] * _expand_deltas_for_merge(obs.deltas[name], obs.idl[name], obs.shape[name], new_idl_d[name])
new_covobs = {name: Covobs(0, allcov[name], name, grad=new_grad[name]) for name in new_grad}
if not set(new_covobs.keys()).isdisjoint(new_deltas.keys()):
raise Exception('The same name has been used for deltas and covobs!')
new_samples = []
new_means = []
new_idl = []
new_names_obs = []
for name in new_names:
if is_merged[name]:
filtered_deltas, filtered_idl_d = _filter_zeroes(new_deltas[name], new_idl_d[name])
else:
filtered_deltas = new_deltas[name]
filtered_idl_d = new_idl_d[name]
if name not in new_covobs:
if is_merged[name]:
filtered_deltas, filtered_idl_d = _filter_zeroes(new_deltas[name], new_idl_d[name])
else:
filtered_deltas = new_deltas[name]
filtered_idl_d = new_idl_d[name]
new_samples.append(filtered_deltas)
new_idl.append(filtered_idl_d)
new_means.append(new_r_values[name][i_val])
final_result[i_val] = Obs(new_samples, new_names, means=new_means, idl=new_idl)
new_samples.append(filtered_deltas)
new_idl.append(filtered_idl_d)
new_means.append(new_r_values[name][i_val])
new_names_obs.append(name)
final_result[i_val] = Obs(new_samples, new_names_obs, means=new_means, idl=new_idl)
for name in new_covobs:
final_result[i_val].names.append(name)
final_result[i_val]._covobs = new_covobs
final_result[i_val]._value = new_val
final_result[i_val].is_merged = is_merged
final_result[i_val].reweighted = reweighted
@ -1172,22 +1274,24 @@ def reweight(weight, obs, **kwargs):
"""
result = []
for i in range(len(obs)):
if sorted(weight.names) != sorted(obs[i].names):
if len(obs[i].cov_names):
raise Exception('Error: Not possible to reweight an Obs that contains covobs!')
if not set(obs[i].names).issubset(weight.names):
raise Exception('Error: Ensembles do not fit')
for name in weight.names:
for name in obs[i].names:
if not set(obs[i].idl[name]).issubset(weight.idl[name]):
raise Exception('obs[%d] has to be defined on a subset of the configs in weight.idl[%s]!' % (i, name))
new_samples = []
w_deltas = {}
for name in sorted(weight.names):
for name in sorted(obs[i].names):
w_deltas[name] = _reduce_deltas(weight.deltas[name], weight.idl[name], obs[i].idl[name])
new_samples.append((w_deltas[name] + weight.r_values[name]) * (obs[i].deltas[name] + obs[i].r_values[name]))
tmp_obs = Obs(new_samples, sorted(weight.names), idl=[obs[i].idl[name] for name in sorted(weight.names)])
tmp_obs = Obs(new_samples, sorted(obs[i].names), idl=[obs[i].idl[name] for name in sorted(obs[i].names)])
if kwargs.get('all_configs'):
new_weight = weight
else:
new_weight = Obs([w_deltas[name] + weight.r_values[name] for name in sorted(weight.names)], sorted(weight.names), idl=[obs[i].idl[name] for name in sorted(weight.names)])
new_weight = Obs([w_deltas[name] + weight.r_values[name] for name in sorted(obs[i].names)], sorted(obs[i].names), idl=[obs[i].idl[name] for name in sorted(obs[i].names)])
result.append(derived_observable(lambda x, **kwargs: x[0] / x[1], [tmp_obs, new_weight], **kwargs))
result[-1].reweighted = True
@ -1213,6 +1317,8 @@ def correlate(obs_a, obs_b):
if sorted(obs_a.names) != sorted(obs_b.names):
raise Exception('Ensembles do not fit')
if len(obs_a.cov_names) or len(obs_b.cov_names):
raise Exception('Error: Not possible to correlate Obs that contain covobs!')
for name in obs_a.names:
if obs_a.shape[name] != obs_b.shape[name]:
raise Exception('Shapes of ensemble', name, 'do not fit')
@ -1243,71 +1349,7 @@ def covariance(obs1, obs2, correlation=False, **kwargs):
The gamma method has to be applied first to both observables.
If abs(covariance(obs1, obs2)) > obs1.dvalue * obs2.dvalue, the covariance
is constrained to the maximum value in order to make sure that covariance
matrices are positive semidefinite.
Parameters
----------
obs1 : Obs
First Obs
obs2 : Obs
Second Obs
correlation : bool
if true the correlation instead of the covariance is
returned (default False)
"""
if set(obs1.names).isdisjoint(set(obs2.names)):
return 0.
for name in sorted(set(obs1.names + obs2.names)):
if (obs1.shape.get(name) != obs2.shape.get(name)) and (obs1.shape.get(name) is not None) and (obs2.shape.get(name) is not None):
raise Exception('Shapes of ensemble', name, 'do not fit')
if (1 != len(set([len(idx) for idx in [obs1.idl[name], obs2.idl[name], _merge_idx([obs1.idl[name], obs2.idl[name]])]]))):
raise Exception('Shapes of ensemble', name, 'do not fit')
if not hasattr(obs1, 'e_names') or not hasattr(obs2, 'e_names'):
raise Exception('The gamma method has to be applied to both Obs first.')
dvalue = 0
for e_name in obs1.e_names:
if e_name not in obs2.e_names:
continue
gamma = 0
r_length = []
for r_name in obs1.e_content[e_name]:
if r_name not in obs2.e_content[e_name]:
continue
r_length.append(len(obs1.deltas[r_name]))
gamma += np.sum(obs1.deltas[r_name] * obs2.deltas[r_name])
e_N = np.sum(r_length)
tau_combined = (obs1.e_tauint[e_name] + obs2.e_tauint[e_name]) / 2
dvalue += gamma / e_N * (1 + 1 / e_N) / e_N * 2 * tau_combined
if np.abs(dvalue / obs1.dvalue / obs2.dvalue) > 1.0:
dvalue = np.sign(dvalue) * obs1.dvalue * obs2.dvalue
if correlation:
dvalue = dvalue / obs1.dvalue / obs2.dvalue
return dvalue
def covariance2(obs1, obs2, correlation=False, **kwargs):
"""Alternative implementation of the covariance of two observables.
covariance(obs, obs) is equal to obs.dvalue ** 2
The gamma method has to be applied first to both observables.
If abs(covariance(obs1, obs2)) > obs1.dvalue * obs2.dvalue, the covariance
is constrained to the maximum value in order to make sure that covariance
matrices are positive semidefinite.
is constrained to the maximum value.
Keyword arguments
-----------------
@ -1352,7 +1394,7 @@ def covariance2(obs1, obs2, correlation=False, **kwargs):
if set(obs1.names).isdisjoint(set(obs2.names)):
return 0.
if not hasattr(obs1, 'e_names') or not hasattr(obs2, 'e_names'):
if not hasattr(obs1, 'e_dvalue') or not hasattr(obs2, 'e_dvalue'):
raise Exception('The gamma method has to be applied to both Obs first.')
dvalue = 0
@ -1361,9 +1403,9 @@ def covariance2(obs1, obs2, correlation=False, **kwargs):
e_n_tauint = {}
e_rho = {}
for e_name in obs1.e_names:
for e_name in obs1.mc_names:
if e_name not in obs2.e_names:
if e_name not in obs2.mc_names:
continue
idl_d = {}
@ -1408,12 +1450,19 @@ def covariance2(obs1, obs2, correlation=False, **kwargs):
# Make sure no entry of tauint is smaller than 0.5
e_n_tauint[e_name][e_n_tauint[e_name] < 0.5] = 0.500000000001
window = max(obs1.e_windowsize[e_name], obs2.e_windowsize[e_name])
window = min(obs1.e_windowsize[e_name], obs2.e_windowsize[e_name])
# Bias correction hep-lat/0306017 eq. (49)
e_dvalue[e_name] = 2 * (e_n_tauint[e_name][window] + obs1.tau_exp[e_name] * np.abs(e_rho[e_name][window + 1])) * (1 + (2 * window + 1) / e_N) * e_gamma[e_name][0] / e_N
dvalue += e_dvalue[e_name]
for e_name in obs1.cov_names:
if e_name not in obs2.cov_names:
continue
dvalue += float(np.dot(np.transpose(obs1.covobs[e_name].grad), np.dot(obs1.covobs[e_name].cov, obs2.covobs[e_name].grad)))
if np.abs(dvalue / obs1.dvalue / obs2.dvalue) > 1.0:
dvalue = np.sign(dvalue) * obs1.dvalue * obs2.dvalue
@ -1423,70 +1472,6 @@ def covariance2(obs1, obs2, correlation=False, **kwargs):
return dvalue
def covariance3(obs1, obs2, correlation=False, **kwargs):
"""Another alternative implementation of the covariance of two observables.
covariance2(obs, obs) is equal to obs.dvalue ** 2
Currently only works if ensembles are identical.
The gamma method has to be applied first to both observables.
If abs(covariance2(obs1, obs2)) > obs1.dvalue * obs2.dvalue, the covariance
is constrained to the maximum value in order to make sure that covariance
matrices are positive semidefinite.
Keyword arguments
-----------------
correlation -- if true the correlation instead of the covariance is
returned (default False)
plot -- if true, the integrated autocorrelation time for each ensemble is
plotted.
"""
for name in sorted(set(obs1.names + obs2.names)):
if (obs1.shape.get(name) != obs2.shape.get(name)) and (obs1.shape.get(name) is not None) and (obs2.shape.get(name) is not None):
raise Exception('Shapes of ensemble', name, 'do not fit')
if (1 != len(set([len(idx) for idx in [obs1.idl[name], obs2.idl[name], _merge_idx([obs1.idl[name], obs2.idl[name]])]]))):
raise Exception('Shapes of ensemble', name, 'do not fit')
if not hasattr(obs1, 'e_names') or not hasattr(obs2, 'e_names'):
raise Exception('The gamma method has to be applied to both Obs first.')
tau_exp = []
S = []
for e_name in sorted(set(obs1.e_names + obs2.e_names)):
t_1 = obs1.tau_exp.get(e_name)
t_2 = obs2.tau_exp.get(e_name)
if t_1 is None:
t_1 = 0
if t_2 is None:
t_2 = 0
tau_exp.append(max(t_1, t_2))
S_1 = obs1.S.get(e_name)
S_2 = obs2.S.get(e_name)
if S_1 is None:
S_1 = Obs.S_global
if S_2 is None:
S_2 = Obs.S_global
S.append(max(S_1, S_2))
check_obs = obs1 + obs2
check_obs.gamma_method(tau_exp=tau_exp, S=S)
if kwargs.get('plot'):
check_obs.plot_tauint()
check_obs.plot_rho()
cov = (check_obs.dvalue ** 2 - obs1.dvalue ** 2 - obs2.dvalue ** 2) / 2
if np.abs(cov / obs1.dvalue / obs2.dvalue) > 1.0:
cov = np.sign(cov) * obs1.dvalue * obs2.dvalue
if correlation:
cov = cov / obs1.dvalue / obs2.dvalue
return cov
def pseudo_Obs(value, dvalue, name, samples=1000):
"""Generate a pseudo Obs with given value, dvalue and name
@ -1519,39 +1504,7 @@ def pseudo_Obs(value, dvalue, name, samples=1000):
return res
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 import_jackknife(jacks, name):
def import_jackknife(jacks, name, idl=None):
"""Imports jackknife samples and returns an Obs
Parameters
@ -1565,7 +1518,7 @@ def import_jackknife(jacks, name):
length = len(jacks) - 1
prj = (np.ones((length, length)) - (length - 1) * np.identity(length))
samples = jacks[1:] @ prj
new_obs = Obs([samples], [name])
new_obs = Obs([samples], [name], idl=idl)
new_obs._value = jacks[0]
return new_obs
@ -1583,6 +1536,8 @@ def merge_obs(list_of_obs):
replist = [item for obs in list_of_obs for item in obs.names]
if (len(replist) == len(set(replist))) is False:
raise Exception('list_of_obs contains duplicate replica: %s' % (str(replist)))
if any([len(o.cov_names) for o in list_of_obs]):
raise Exception('Not possible to merge data that contains covobs!')
new_dict = {}
idl_dict = {}
for o in list_of_obs:
@ -1595,3 +1550,46 @@ def merge_obs(list_of_obs):
o.is_merged = {name: np.any([oi.is_merged.get(name, False) for oi in list_of_obs]) for name in o.names}
o.reweighted = np.max([oi.reweighted for oi in list_of_obs])
return o
def cov_Obs(means, cov, name, grad=None):
"""Create an Obs based on mean(s) and a covariance matrix
Parameters
----------
mean : list of floats or float
N mean value(s) of the new Obs
cov : list or array
2d (NxN) Covariance matrix, 1d diagonal entries or 0d covariance
name : str
identifier for the covariance matrix
grad : list or array
Gradient of the Covobs wrt. the means belonging to cov.
"""
def covobs_to_obs(co):
"""Make an Obs out of a Covobs
Parameters
----------
co : Covobs
Covobs to be embedded into the Obs
"""
o = Obs([], [])
o._value = co.value
o.names.append(co.name)
o._covobs[co.name] = co
o._dvalue = np.sqrt(co.errsq())
return o
ol = []
if isinstance(means, (float, int)):
means = [means]
for i in range(len(means)):
ol.append(covobs_to_obs(Covobs(means[i], cov, name, pos=i, grad=grad)))
if ol[0].covobs[name].N != len(means):
raise Exception('You have to provide %d mean values!' % (ol[0].N))
if len(ol) == 1:
return ol[0]
return ol

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
pyerrors/version.py
View file

@ -1 +1 @@
__version__ = "2.0.0"
__version__ = "2.0.0+dev"

4
pytest.ini
View file

@ -1,4 +0,0 @@
[pytest]
filterwarnings =
ignore::RuntimeWarning:autograd.*:
ignore::RuntimeWarning:numdifftools.*:

4
setup.py
View file

@ -3,11 +3,11 @@
from setuptools import setup, find_packages
setup(name='pyerrors',
version='2.0.0',
version='2.0.0+dev',
description='Error analysis for lattice QCD',
author='Fabian Joswig',
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']
)

73
tests/correlators_test.py
View file

@ -31,6 +31,13 @@ def test_function_overloading():
assert np.isclose(con[0].dvalue, t2.dvalue)
assert np.allclose(con[0].deltas['t'], t2.deltas['t'])
np.arcsin(corr_a)
np.arccos(corr_a)
np.arctan(corr_a)
np.arcsinh(corr_a)
np.arccosh(corr_a + 1.1)
np.arctanh(corr_a)
def test_modify_correlator():
corr_content = []
@ -47,16 +54,20 @@ def test_modify_correlator():
corr.roll(np.random.randint(100))
corr.deriv(symmetric=True)
corr.deriv(symmetric=False)
corr.deriv().deriv()
corr.second_deriv()
corr.second_deriv().second_deriv()
def test_m_eff():
my_corr = pe.correlators.Corr([pe.pseudo_Obs(10, 0.1, 't'), pe.pseudo_Obs(9, 0.05, 't'), pe.pseudo_Obs(8, 0.1, 't'), pe.pseudo_Obs(7, 0.05, 't')])
my_corr = pe.correlators.Corr([pe.pseudo_Obs(10, 0.1, 't'), pe.pseudo_Obs(9, 0.05, 't'), pe.pseudo_Obs(9, 0.1, 't'), pe.pseudo_Obs(10, 0.05, 't')])
my_corr.m_eff('log')
my_corr.m_eff('cosh')
my_corr.m_eff('sinh')
my_corr.m_eff('arccosh')
with pytest.warns(RuntimeWarning):
my_corr.m_eff('sinh')
def test_reweighting():
my_corr = pe.correlators.Corr([pe.pseudo_Obs(10, 0.1, 't'), pe.pseudo_Obs(0, 0.05, 't')])
@ -79,6 +90,52 @@ 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_padded_correlator():
my_list = [pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']) for o in range(8)]
my_corr = pe.Corr(my_list, padding=[7, 3])
my_corr.reweighted
[o for o in my_corr]
def test_corr_exceptions():
obs_a = pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test'])
obs_b= pe.Obs([np.random.normal(0.1, 0.1, 99)], ['test'])
with pytest.raises(Exception):
pe.Corr([obs_a, obs_b])
obs_a = pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test'])
obs_b= pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test'], idl=[range(1, 200, 2)])
with pytest.raises(Exception):
pe.Corr([obs_a, obs_b])
obs_a = pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test'])
obs_b= pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test2'])
with pytest.raises(Exception):
pe.Corr([obs_a, obs_b])
def test_utility():
corr_content = []
for t in range(8):
@ -90,10 +147,20 @@ def test_utility():
corr.print([2, 4])
corr.show()
corr.dump('test_dump')
corr.dump('test_dump', datatype="pickle", path='.')
corr.dump('test_dump', datatype="pickle")
new_corr = pe.load_object('test_dump.p')
os.remove('test_dump.p')
for o_a, o_b in zip(corr.content, new_corr.content):
assert np.isclose(o_a[0].value, o_b[0].value)
assert np.isclose(o_a[0].dvalue, o_b[0].dvalue)
assert np.allclose(o_a[0].deltas['t'], o_b[0].deltas['t'])
corr.dump('test_dump', datatype="json.gz", path='.')
corr.dump('test_dump', datatype="json.gz")
new_corr = pe.input.json.load_json('test_dump')
os.remove('test_dump.json.gz')
for o_a, o_b in zip(corr.content, new_corr.content):
assert np.isclose(o_a[0].value, o_b[0].value)
assert np.isclose(o_a[0].dvalue, o_b[0].dvalue)
assert np.allclose(o_a[0].deltas['t'], o_b[0].deltas['t'])

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')

51
tests/dirac_test.py
View file

@ -10,3 +10,54 @@ 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')
def test_epsilon_tensor():
check = {(1, 2, 3) : 1.0,
(3, 1, 2) : 1.0,
(2, 3, 1) : 1.0,
(1, 1, 1) : 0.0,
(3, 2, 1) : -1.0,
(1, 3, 2) : -1.0,
(1, 1, 3) : 0.0}
for key, value in check.items():
assert pe.dirac.epsilon_tensor(*key) == value
with pytest.raises(Exception):
pe.dirac.epsilon_tensor(0, 1, 3)
def test_epsilon_tensor_rank4():
check = {(1, 4, 3, 2) : -1.0,
(1, 2, 3, 4) : 1.0,
(2, 1, 3, 4) : -1.0,
(4, 3, 2, 1) : 1.0,
(3, 2, 4, 3) : 0.0,
(0, 1, 2, 3) : 1.0,
(1, 1, 1, 1) : 0.0,
(1, 2, 3, 1) : 0.0}
for key, value in check.items():
assert pe.dirac.epsilon_tensor_rank4(*key) == value
with pytest.raises(Exception):
pe.dirac.epsilon_tensor_rank4(0, 1, 3, 4)

286
tests/fits_test.py
View file

@ -10,6 +10,26 @@ 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)
@ -29,9 +49,13 @@ 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)
@ -46,6 +70,21 @@ def test_least_squares():
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
@ -54,17 +93,15 @@ def test_least_squares():
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))
r[i, j] = np.exp(-0.8 * 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 = []
@ -84,12 +121,12 @@ def test_least_squares():
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))
assert(diff.is_zero_within_error(sigma=5))
def test_total_least_squares():
@ -120,9 +157,13 @@ 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
str(out)
repr(out)
len(out)
for i in range(2):
beta[i].gamma_method(S=1.0)
assert math.isclose(beta[i].value, output.beta[i], rel_tol=1e-5)
@ -135,6 +176,44 @@ def test_total_least_squares():
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():
x = []
@ -153,7 +232,196 @@ 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 test_ks_test():
def f(a, x):
y = a[0] + a[1] * x
return y
fit_res = []
for i in range(20):
data = []
for j in range(10):
data.append(pe.pseudo_Obs(j + np.random.normal(0.0, 0.25), 0.25, 'test'))
my_corr = pe.Corr(data)
fit_res.append(my_corr.fit(f, silent=True))
pe.fits.ks_test()
pe.fits.ks_test(fit_res)
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

126
tests/io_test.py Normal file
View file

@ -0,0 +1,126 @@
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
def test_json_corr_io():
my_list = [pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']) for o in range(8)]
rw_list = pe.reweight(pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']), my_list)
for obs_list in [my_list, rw_list]:
for tag in [None, "test"]:
obs_list[3].tag = tag
for fp in [0, 2]:
for bp in [0, 7]:
for corr_tag in [None, 'my_Corr_tag']:
my_corr = pe.Corr(obs_list, padding=[fp, bp])
my_corr.tag = corr_tag
pe.input.json.dump_to_json(my_corr, 'corr')
recover = pe.input.json.load_json('corr')
os.remove('corr.json.gz')
assert np.all([o.is_zero() for o in [x for x in (my_corr - recover) if x is not None]])
assert my_corr.tag == recover.tag
assert my_corr.reweighted == recover.reweighted
def test_json_corr_2d_io():
obs_list = [np.array([[pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test'), pe.pseudo_Obs(0.0, 0.1 * i, 'test')], [pe.pseudo_Obs(0.0, 0.1 * i, 'test'), pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test')]]) for i in range(4)]
for tag in [None, "test"]:
obs_list[3][0, 1].tag = tag
for padding in [0, 1]:
my_corr = pe.Corr(obs_list, padding=[padding, padding])
my_corr.tag = tag
pe.input.json.dump_to_json(my_corr, 'corr')
recover = pe.input.json.load_json('corr')
os.remove('corr.json.gz')
assert np.all([np.all([o.is_zero() for o in q]) for q in [x.ravel() for x in (my_corr - recover) if x is not None]])
assert my_corr.tag == recover.tag

163
tests/linalg_test.py
View file

@ -29,25 +29,26 @@ def get_complex_matrix(dimension):
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
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 = 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
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():
@ -55,28 +56,104 @@ def test_jack_matmul():
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
tt2 = get_complex_matrix(8)
check2 = pe.linalg.jack_matmul(tt2, tt2) - pe.linalg.matmul(tt2, tt2)
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.real.is_zero_within_error(0.1) for o in check2.ravel()])
assert np.all([o.imag.is_zero_within_error(0.1) 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.real.dvalue < 0.001
assert trace2.imag.dvalue < 0.001
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
@ -86,12 +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
@ -99,13 +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)
@ -123,7 +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
@ -154,8 +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'))
@ -210,6 +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
@ -217,6 +311,11 @@ 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))
pe.linalg.pinv(matrix[:,:3])
def test_complex_matrix_operations():
dimension = 4

14
tests/mpm_test.py Normal file
View file

@ -0,0 +1,14 @@
import numpy as np
import pyerrors as pe
import pytest
np.random.seed(0)
def test_mpm():
corr_content = []
for t in range(8):
f = 0.8 * np.exp(-0.4 * t)
corr_content.append(pe.pseudo_Obs(np.random.normal(f, 1e-2 * f), 1e-2 * f, 't'))
res = pe.mpm.matrix_pencil_method(corr_content)

130
tests/obs_test.py
View file

@ -9,14 +9,64 @@ 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))
test_obs = pe.pseudo_Obs(value, dvalue, 't')
test_obs.dump('test_dump')
test_obs.dump('test_dump', datatype="pickle", path=".")
test_obs.dump('test_dump', datatype="pickle")
new_obs = pe.load_object('test_dump.p')
os.remove('test_dump.p')
assert test_obs.deltas['t'].all() == new_obs.deltas['t'].all()
assert test_obs == new_obs
test_obs.dump('test_dump', dataype="json.gz", path=".")
test_obs.dump('test_dump', dataype="json.gz")
new_obs = pe.input.json.load_json("test_dump")
os.remove('test_dump.json.gz')
assert test_obs == new_obs
def test_comparison():
@ -61,6 +111,12 @@ def test_function_overloading():
assert np.sqrt(b ** 2) == b
assert np.sqrt(b) ** 2 == b
np.arcsin(1 / b)
np.arccos(1 / b)
np.arctan(1 / b)
np.arctanh(1 / b)
np.sinc(1 / b)
def test_overloading_vectorization():
a = np.random.randint(1, 100, 10)
@ -309,8 +365,10 @@ def test_overloaded_functions():
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"
@ -325,6 +383,8 @@ 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():
@ -372,6 +432,23 @@ 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
my_covobs = pe.cov_Obs(1.0, 0.003, 'cov')
with pytest.raises(Exception):
pe.reweight(my_obs, [my_covobs])
my_obs2 = pe.Obs([np.random.rand(1000)], ['t2'])
with pytest.raises(Exception):
pe.reweight(my_obs, [my_obs + my_obs2])
with pytest.raises(Exception):
pe.reweight(my_irregular_obs, [my_obs])
def test_merge_obs():
my_obs1 = pe.Obs([np.random.rand(100)], ['t'])
@ -379,6 +456,22 @@ def test_merge_obs():
merged = pe.merge_obs([my_obs1, my_obs2])
diff = merged - my_obs2 - my_obs1
assert diff == -(my_obs1.value + my_obs2.value) / 2
with pytest.raises(Exception):
pe.merge_obs([my_obs1, my_obs1])
my_covobs = pe.cov_Obs(1.0, 0.003, 'cov')
with pytest.raises(Exception):
pe.merge_obs([my_obs1, my_covobs])
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():
@ -401,6 +494,17 @@ def test_correlate():
corr3 = pe.correlate(my_obs5, my_obs6)
assert my_obs5.idl == corr3.idl
my_new_obs = pe.Obs([np.random.rand(100)], ['q3'])
with pytest.raises(Exception):
pe.correlate(my_obs1, my_new_obs)
my_covobs = pe.cov_Obs(1.0, 0.003, 'cov')
with pytest.raises(Exception):
pe.correlate(my_covobs, my_covobs)
r_obs = pe.reweight(my_obs1, [my_obs1])[0]
with pytest.warns(RuntimeWarning):
pe.correlate(r_obs, r_obs)
def test_irregular_error_propagation():
obs_list = [pe.Obs([np.random.rand(100)], ['t']),
@ -409,6 +513,7 @@ 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
@ -487,7 +592,7 @@ def test_gamma_method_irregular():
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')
@ -496,8 +601,8 @@ 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)
@ -510,12 +615,18 @@ 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)
assert np.abs(cov_ab) < test_obs1.dvalue * a.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():
@ -542,4 +653,3 @@ def test_import_jackknife():
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()