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...

26 commits

Author SHA1 Message Date
Fabian Joswig
3c36ab08c8 [Version] Bump version to 2.15.0-dev 2025-03-09 12:37:42 +01:00
Fabian Joswig
b2847a1f80 [Release] Bump version to 2.14.0 and update CHANGELOG 2025-03-09 12:35:29 +01:00
s-kuberski
17792418ed
[Fix] Removed the possibility to create an Obs from data on several replica (#258)
* [Fix] Removed the possibility to create an Obs from data on several replica

* [Fix] extended tests and corrected a small bug in the previous commit

---------

Co-authored-by: Simon Kuberski <simon.kuberski@cern.ch>
2025-02-25 16:58:44 +01:00
Fabian Joswig
dd4f8525f7
[CI] Add ARM runner and bump macos runner python version to 3.12 (#260) 2025-02-19 18:23:56 +01:00
s-kuberski
5f5438b563
[Feat] Introduce checks of the provided inverse matrix for correlated fits (#259)
Co-authored-by: Simon Kuberski <simon.kuberski@cern.ch>
2025-02-19 18:15:55 +01:00
s-kuberski
6ed6ce6113
[fix] Corrected an error message (#257)
Co-authored-by: Simon Kuberski <simon.kuberski@cern.ch>
2025-02-13 19:43:56 +01:00
Fabian Joswig
7eabd68c5f
[CI] Speed up test workflow install phase by using uv (#254)
* [CI] Speed up install phase by using uv

* [CI] Use uv in examples workflow

* [CI] Fix yml syntax

* [CI] Install uv into system env

* [CI] Add system install for examples workflow
2025-01-10 09:36:05 +01:00
Justus Kuhlmann
9ff34c27d7
Fix/sfcf ensname (#253)
* correct strings in _get_rep_names, add option for rep_sep

* doc

* add test for rep name getters
2025-01-06 10:46:49 +01:00
Fabian Joswig
997d360db3
[ci] Add ruff workflow (#250)
* [ci] Add ruff workflow

* [ci] Add src for ruff workflow

* [ci] Rename ruff worklow

* [ci] Adjust on for ruff workflow
2024-12-24 17:52:08 +01:00
Fabian Joswig
3eac9214b4
[Fix] Ruff rules and more precise Excpetion types (#248)
* [Fix] Fix test for membership should be 'not in' (E713)

* [Fix] Fix module imported but unused (F401)

* [Fix] More precise Exception types in dirac, obs and correlator
2024-12-24 15:35:59 +01:00
Fabian Joswig
d908508120 [docs] Simplify README 2024-12-18 13:00:06 +01:00
Justus Kuhlmann
b1448a2703
Fix plateaus in correlator (#247) 2024-12-05 22:08:48 +01:00
Fabian Joswig
30bfb55981
[Feat] Provide derivatives for pow (#246)
* [Feat] Provide manual derivatives for __pow__

* [Feat] Also applied changes to rpow

* [Test] Another pow test added.
2024-11-26 17:52:27 +01:00
Fabian Joswig
0ce765a99d [Version] Bumped version to 2.14.0-dev 2024-11-03 17:07:29 +01:00
Fabian Joswig
c057ecffda [Release] Updated changelog and bumped version 2024-11-03 17:03:06 +01:00
Fabian Joswig
47fd72b814
[Build] Release workflow added. (#244) 2024-11-03 16:57:20 +01:00
Fabian Joswig
b43a2cbd34
[ci] Add python 3.13 to pytest workflow. (#242)
* [ci] Add python 3.13 to pytest workflow.

* [ci] Remove py and pyarrow from pytest workflow
2024-10-14 23:27:24 +02:00
s-kuberski
4b1bb0872a
fix: corrected bug that prevented combined fits with multiple x-obs in some cases (#241)
* fix: corrected bug that prevented combined fits with multiple x-obs in some cases

* made test more complex

* [Fix] Slightly increase tolerance for matrix function test.

* Adapt test_merge_idx to compare lists

---------

Co-authored-by: Simon Kuberski <simon.kuberski@cern.ch>
Co-authored-by: Fabian Joswig <fjosw@users.noreply.github.com>
2024-09-13 19:15:59 +02:00
Pia Leonie Jones Petrak
1d6f7f65c0
Feature/corr matrix and inverse cov matrix as input in least squares function for correlated fits (#223)
* feat: corr_matrix kwargs as input for least squares fit

* feat/tests: inverse covariance matrix and correlation matrix kwargs as input for least squares function

* feat/tests/example: reduced new kwargs to 'inv_chol_cov_matrix' and outsourced the inversion & cholesky decomposition of the covariance matrix (function 'invert_corr_cov_cholesky(corr, covdiag)')

* tests: added tests for inv_chol_cov_matrix kwarg for the case of combined fits

* fix: renamed covdiag to inverrdiag needed for the cholesky decomposition and corrected its documentation

* examples: added an example of a correlated combined fit to the least_squares documentation

* feat/tests/fix(of typos): added function 'sort_corr()' (and a test of it) to sort correlation matrix according to a list of alphabetically sorted keys

* docs: added more elaborate documentation/example of sort_corr(), fixed typos in documentation of invert_corr_cov_cholesky()
2024-09-13 08:35:10 +02:00
Fabian Joswig
3830e3f777 [Build] Bump version to 2.13.0-dev 2024-08-22 22:08:40 +02:00
Fabian Joswig
041d53e5ae
[Release] Prepare v2.12.0 (#240)
* [docs] Changelog updated.

* [build] Bump version.
2024-08-22 22:04:54 +02:00
Fabian Joswig
55cd782909
[Build] Remove python3.8 and add support for numpy 2 (#239)
* [build] Remove python 3.8 and bump dependency version.

* [Build] Remove python 3.8 from ci and update README python badge.

* [ci] Temporarily remove -Werror from pytest workflow.

* [ci] Remove python 3.8 from examples workflow.

* [Build] Bump further dependency versions.
2024-08-22 21:59:07 +02:00
Justus Kuhlmann
7ca9d4ee41
corrected sfcf_read_multi behaviour (#238) 2024-08-15 19:00:52 +02:00
Justus Kuhlmann
d17513f043
bugfix: read bb and bib/bi corr in one with keyed_out (#237) 2024-06-19 12:55:30 +02:00
Justus Kuhlmann
0e8d68a1f0
erase print rep data (#235) 2024-05-13 22:27:17 +02:00
Fabian Joswig
fce6bcd1f8 [build] Bump version to v2.12.0-dev 2024-04-25 20:55:35 +02:00
28 changed files with 1097 additions and 314 deletions

View file

@ -17,7 +17,7 @@ jobs:
fail-fast: false
matrix:
os: [ubuntu-latest]
python-version: ["3.8", "3.10", "3.12"]
python-version: ["3.10", "3.12"]
steps:
- name: Checkout source
@ -27,17 +27,17 @@ jobs:
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
- name: uv
uses: astral-sh/setup-uv@v5
- name: Install
run: |
sudo apt-get update
sudo apt-get install dvipng texlive-latex-extra texlive-fonts-recommended cm-super
python -m pip install --upgrade pip
pip install wheel
pip install .
pip install pytest
pip install nbmake
pip install -U matplotlib!=3.7.0 # Exclude version 3.7.0 of matplotlib as this breaks local imports of style files.
uv pip install wheel --system
uv pip install . --system
uv pip install pytest nbmake --system
uv pip install -U matplotlib!=3.7.0 --system # Exclude version 3.7.0 of matplotlib as this breaks local imports of style files.
- name: Run tests
run: pytest -vv --nbmake examples/*.ipynb

View file

@ -17,10 +17,12 @@ jobs:
fail-fast: false
matrix:
os: [ubuntu-latest]
python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
include:
- os: macos-latest
python-version: "3.10"
python-version: "3.12"
- os: ubuntu-24.04-arm
python-version: "3.12"
steps:
- name: Checkout source
@ -30,19 +32,15 @@ jobs:
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
- name: uv
uses: astral-sh/setup-uv@v5
- name: Install
run: |
python -m pip install --upgrade pip
pip install wheel
pip install .
pip install pytest
pip install pytest-cov
pip install pytest-benchmark
pip install hypothesis
pip install py
pip install pyarrow
pip freeze
uv pip install wheel --system
uv pip install . --system
uv pip install pytest pytest-cov pytest-benchmark hypothesis --system
uv pip freeze --system
- name: Run tests
run: pytest --cov=pyerrors -vv -Werror
run: pytest --cov=pyerrors -vv

58
.github/workflows/release.yml vendored Normal file
View file

@ -0,0 +1,58 @@
name: Release
on:
workflow_dispatch:
release:
types: [published]
jobs:
build:
name: Build sdist and wheel
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
name: Checkout repository
- uses: actions/setup-python@v5
with:
python-version: "3.12"
- name: Install pypa/build
run: >-
python3 -m
pip install
build
--user
- name: Build wheel and source tarball
run: python3 -m build
- name: Upload artifacts
uses: actions/upload-artifact@v4
with:
name: python-package-distributions
path: dist/
if-no-files-found: error
publish:
needs: [build]
name: Upload to PyPI
runs-on: ubuntu-latest
environment:
name: pypi
url: https://pypi.org/p/pyerrors
permissions:
id-token: write
steps:
- name: Download artifacts
uses: actions/download-artifact@v4
with:
name: python-package-distributions
path: dist/
- name: Sanity check
run: ls -la dist/
- name: Publish to PyPI
uses: pypa/gh-action-pypi-publish@release/v1

15
.github/workflows/ruff.yml vendored Normal file
View file

@ -0,0 +1,15 @@
name: ruff
on:
push:
branches:
- master
- develop
pull_request:
jobs:
ruff:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: astral-sh/ruff-action@v2
with:
src: "./pyerrors"

View file

@ -2,6 +2,39 @@
All notable changes to this project will be documented in this file.
## [2.14.0] - 2025-03-09
### Added
- Explicit checks of the provided inverse matrix for correlated fits #259
### Changed
- Compute derivative for pow explicitly instead of relying on autograd. This results in a ~4x speedup for pow operations #246
- More explicit exception types #248
### Fixed
- Removed the possibility to create an Obs from data on several replica #258
- Fix range in `set_prange` #247
- Fix ensemble name handling in sfcf input modules #253
- Correct error message for fit shape mismatch #257
## [2.13.0] - 2024-11-03
### Added
- Allow providing lower triangular matrix constructed from a Cholesky decomposition in least squares function for correlated fits.
### Fixed
- Corrected bug that prevented combined fits with multiple x-obs in some cases.
## [2.12.0] - 2024-08-22
### Changed
- Support for numpy 2 was added via a new autograd release
- Support for python<3.9 was dropped and dependencies were updated.
### Fixed
- Minor bug fixes in input.sfcf
## [2.11.1] - 2024-04-25
### Fixed

View file

@ -1,4 +1,4 @@
[![pytest](https://github.com/fjosw/pyerrors/actions/workflows/pytest.yml/badge.svg)](https://github.com/fjosw/pyerrors/actions/workflows/pytest.yml) [![](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![arXiv](https://img.shields.io/badge/arXiv-2209.14371-b31b1b.svg)](https://arxiv.org/abs/2209.14371) [![DOI](https://img.shields.io/badge/DOI-10.1016%2Fj.cpc.2023.108750-blue)](https://doi.org/10.1016/j.cpc.2023.108750)
[![](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![arXiv](https://img.shields.io/badge/arXiv-2209.14371-b31b1b.svg)](https://arxiv.org/abs/2209.14371) [![DOI](https://img.shields.io/badge/DOI-10.1016%2Fj.cpc.2023.108750-blue)](https://doi.org/10.1016/j.cpc.2023.108750)
# pyerrors
`pyerrors` is a python framework for error computation and propagation of Markov chain Monte Carlo data from lattice field theory and statistical mechanics simulations.
@ -14,11 +14,6 @@ Install the most recent release using pip and [pypi](https://pypi.org/project/py
python -m pip install pyerrors # Fresh install
python -m pip install -U pyerrors # Update
```
Install the most recent release using conda and [conda-forge](https://anaconda.org/conda-forge/pyerrors):
```bash
conda install -c conda-forge pyerrors # Fresh install
conda update -c conda-forge pyerrors # Update
```
## Contributing
We appreciate all contributions to the code, the documentation and the examples. If you want to get involved please have a look at our [contribution guideline](https://github.com/fjosw/pyerrors/blob/develop/CONTRIBUTING.md).

File diff suppressed because one or more lines are too long

View file

@ -481,12 +481,12 @@ from .obs import *
from .correlators import *
from .fits import *
from .misc import *
from . import dirac
from . import input
from . import linalg
from . import mpm
from . import roots
from . import integrate
from . import special
from . import dirac as dirac
from . import input as input
from . import linalg as linalg
from . import mpm as mpm
from . import roots as roots
from . import integrate as integrate
from . import special as special
from .version import __version__
from .version import __version__ as __version__

View file

@ -101,7 +101,7 @@ class Corr:
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
noNull = [a for a in self.content if a is not None] # To check if the matrices are correct for all undefined elements
self.N = noNull[0].shape[0]
if self.N > 1 and noNull[0].shape[0] != noNull[0].shape[1]:
raise ValueError("Smearing matrices are not NxN.")
@ -141,7 +141,7 @@ class Corr:
def gamma_method(self, **kwargs):
"""Apply the gamma method to the content of the Corr."""
for item in self.content:
if not (item is None):
if item is not None:
if self.N == 1:
item[0].gamma_method(**kwargs)
else:
@ -159,7 +159,7 @@ class Corr:
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.")
raise ValueError("Trying to project a Corr, that already has N=1.")
if vector_l is None:
vector_l, vector_r = np.asarray([1.] + (self.N - 1) * [0.]), np.asarray([1.] + (self.N - 1) * [0.])
@ -167,16 +167,16 @@ class Corr:
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")
raise ValueError("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")
raise ValueError("Length of vector list must be equal to T")
vector_l = [vector_l] * self.T
if not isinstance(vector_l, list):
if not vector_l.shape == vector_r.shape == (self.N,):
raise Exception("Vectors are of wrong shape!")
raise ValueError("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)
newcontent = [None if _check_for_none(self, item) else np.asarray([vector_l.T @ item @ vector_r]) for item in self.content]
@ -201,7 +201,7 @@ class Corr:
Second index to be picked.
"""
if self.N == 1:
raise Exception("Trying to pick item from projected Corr")
raise ValueError("Trying to pick item from projected Corr")
newcontent = [None if (item is None) else item[i, j] for item in self.content]
return Corr(newcontent)
@ -212,8 +212,8 @@ class Corr:
timeslice and the error on each timeslice.
"""
if self.N != 1:
raise Exception("Can only make Corr[N=1] plottable")
x_list = [x for x in range(self.T) if not self.content[x] is None]
raise ValueError("Can only make Corr[N=1] plottable")
x_list = [x for x in range(self.T) if self.content[x] is not None]
y_list = [y[0].value for y in self.content if y is not None]
y_err_list = [y[0].dvalue for y in self.content if y is not None]
@ -222,9 +222,9 @@ class Corr:
def symmetric(self):
""" Symmetrize the correlator around x0=0."""
if self.N != 1:
raise Exception('symmetric cannot be safely applied to multi-dimensional correlators.')
raise ValueError('symmetric cannot be safely applied to multi-dimensional correlators.')
if self.T % 2 != 0:
raise Exception("Can not symmetrize odd T")
raise ValueError("Can not symmetrize odd T")
if self.content[0] is not None:
if np.argmax(np.abs([o[0].value if o is not None else 0 for o in self.content])) != 0:
@ -237,7 +237,7 @@ class Corr:
else:
newcontent.append(0.5 * (self.content[t] + self.content[self.T - t]))
if (all([x is None for x in newcontent])):
raise Exception("Corr could not be symmetrized: No redundant values")
raise ValueError("Corr could not be symmetrized: No redundant values")
return Corr(newcontent, prange=self.prange)
def anti_symmetric(self):
@ -245,7 +245,7 @@ class Corr:
if self.N != 1:
raise TypeError('anti_symmetric cannot be safely applied to multi-dimensional correlators.')
if self.T % 2 != 0:
raise Exception("Can not symmetrize odd T")
raise ValueError("Can not symmetrize odd T")
test = 1 * self
test.gamma_method()
@ -259,7 +259,7 @@ class Corr:
else:
newcontent.append(0.5 * (self.content[t] - self.content[self.T - t]))
if (all([x is None for x in newcontent])):
raise Exception("Corr could not be symmetrized: No redundant values")
raise ValueError("Corr could not be symmetrized: No redundant values")
return Corr(newcontent, prange=self.prange)
def is_matrix_symmetric(self):
@ -292,7 +292,7 @@ class Corr:
def matrix_symmetric(self):
"""Symmetrizes the correlator matrices on every timeslice."""
if self.N == 1:
raise Exception("Trying to symmetrize a correlator matrix, that already has N=1.")
raise ValueError("Trying to symmetrize a correlator matrix, that already has N=1.")
if self.is_matrix_symmetric():
return 1.0 * self
else:
@ -336,10 +336,10 @@ class Corr:
'''
if self.N == 1:
raise Exception("GEVP methods only works on correlator matrices and not single correlators.")
raise ValueError("GEVP methods only works on correlator matrices and not single correlators.")
if ts is not None:
if (ts <= t0):
raise Exception("ts has to be larger than t0.")
raise ValueError("ts has to be larger than t0.")
if "sorted_list" in kwargs:
warnings.warn("Argument 'sorted_list' is deprecated, use 'sort' instead.", DeprecationWarning)
@ -371,9 +371,9 @@ class Corr:
if sort is None:
if (ts is None):
raise Exception("ts is required if sort=None.")
raise ValueError("ts is required if sort=None.")
if (self.content[t0] is None) or (self.content[ts] is None):
raise Exception("Corr not defined at t0/ts.")
raise ValueError("Corr not defined at t0/ts.")
Gt = _get_mat_at_t(ts)
reordered_vecs = _GEVP_solver(Gt, G0, method=method, chol_inv=chol_inv)
if kwargs.get('auto_gamma', False) and vector_obs:
@ -391,14 +391,14 @@ class Corr:
all_vecs.append(None)
if sort == "Eigenvector":
if ts is None:
raise Exception("ts is required for the Eigenvector sorting method.")
raise ValueError("ts is required for the Eigenvector sorting method.")
all_vecs = _sort_vectors(all_vecs, ts)
reordered_vecs = [[v[s] if v is not None else None for v in all_vecs] for s in range(self.N)]
if kwargs.get('auto_gamma', False) and vector_obs:
[[[o.gm() for o in evn] for evn in ev if evn is not None] for ev in reordered_vecs]
else:
raise Exception("Unknown value for 'sort'. Choose 'Eigenvalue', 'Eigenvector' or None.")
raise ValueError("Unknown value for 'sort'. Choose 'Eigenvalue', 'Eigenvector' or None.")
if "state" in kwargs:
return reordered_vecs[kwargs.get("state")]
@ -435,7 +435,7 @@ class Corr:
"""
if self.N != 1:
raise Exception("Multi-operator Prony not implemented!")
raise NotImplementedError("Multi-operator Prony not implemented!")
array = np.empty([N, N], dtype="object")
new_content = []
@ -502,7 +502,7 @@ class Corr:
correlator or a Corr of same length.
"""
if self.N != 1:
raise Exception("Only one-dimensional correlators can be safely correlated.")
raise ValueError("Only one-dimensional correlators can be safely correlated.")
new_content = []
for x0, t_slice in enumerate(self.content):
if _check_for_none(self, t_slice):
@ -516,7 +516,7 @@ class Corr:
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.")
raise TypeError("Can only correlate with an Obs or a Corr.")
return Corr(new_content)
@ -583,7 +583,7 @@ class Corr:
Available choice: symmetric, forward, backward, improved, log, default: symmetric
"""
if self.N != 1:
raise Exception("deriv only implemented for one-dimensional correlators.")
raise ValueError("deriv only implemented for one-dimensional correlators.")
if variant == "symmetric":
newcontent = []
for t in range(1, self.T - 1):
@ -592,7 +592,7 @@ class Corr:
else:
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')
raise ValueError('Derivative is undefined at all timeslices')
return Corr(newcontent, padding=[1, 1])
elif variant == "forward":
newcontent = []
@ -602,7 +602,7 @@ class Corr:
else:
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")
raise ValueError("Derivative is undefined at all timeslices")
return Corr(newcontent, padding=[0, 1])
elif variant == "backward":
newcontent = []
@ -612,7 +612,7 @@ class Corr:
else:
newcontent.append(self.content[t] - self.content[t - 1])
if (all([x is None for x in newcontent])):
raise Exception("Derivative is undefined at all timeslices")
raise ValueError("Derivative is undefined at all timeslices")
return Corr(newcontent, padding=[1, 0])
elif variant == "improved":
newcontent = []
@ -622,7 +622,7 @@ class Corr:
else:
newcontent.append((1 / 12) * (self.content[t - 2] - 8 * self.content[t - 1] + 8 * self.content[t + 1] - self.content[t + 2]))
if (all([x is None for x in newcontent])):
raise Exception('Derivative is undefined at all timeslices')
raise ValueError('Derivative is undefined at all timeslices')
return Corr(newcontent, padding=[2, 2])
elif variant == 'log':
newcontent = []
@ -632,11 +632,11 @@ class Corr:
else:
newcontent.append(np.log(self.content[t]))
if (all([x is None for x in newcontent])):
raise Exception("Log is undefined at all timeslices")
raise ValueError("Log is undefined at all timeslices")
logcorr = Corr(newcontent)
return self * logcorr.deriv('symmetric')
else:
raise Exception("Unknown variant.")
raise ValueError("Unknown variant.")
def second_deriv(self, variant="symmetric"):
r"""Return the second derivative of the correlator with respect to x0.
@ -656,7 +656,7 @@ class Corr:
$$f(x) = \tilde{\partial}^2_0 log(f(x_0))+(\tilde{\partial}_0 log(f(x_0)))^2$$
"""
if self.N != 1:
raise Exception("second_deriv only implemented for one-dimensional correlators.")
raise ValueError("second_deriv only implemented for one-dimensional correlators.")
if variant == "symmetric":
newcontent = []
for t in range(1, self.T - 1):
@ -665,7 +665,7 @@ class Corr:
else:
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")
raise ValueError("Derivative is undefined at all timeslices")
return Corr(newcontent, padding=[1, 1])
elif variant == "big_symmetric":
newcontent = []
@ -675,7 +675,7 @@ class Corr:
else:
newcontent.append((self.content[t + 2] - 2 * self.content[t] + self.content[t - 2]) / 4)
if (all([x is None for x in newcontent])):
raise Exception("Derivative is undefined at all timeslices")
raise ValueError("Derivative is undefined at all timeslices")
return Corr(newcontent, padding=[2, 2])
elif variant == "improved":
newcontent = []
@ -685,7 +685,7 @@ class Corr:
else:
newcontent.append((1 / 12) * (-self.content[t + 2] + 16 * self.content[t + 1] - 30 * self.content[t] + 16 * self.content[t - 1] - self.content[t - 2]))
if (all([x is None for x in newcontent])):
raise Exception("Derivative is undefined at all timeslices")
raise ValueError("Derivative is undefined at all timeslices")
return Corr(newcontent, padding=[2, 2])
elif variant == 'log':
newcontent = []
@ -695,11 +695,11 @@ class Corr:
else:
newcontent.append(np.log(self.content[t]))
if (all([x is None for x in newcontent])):
raise Exception("Log is undefined at all timeslices")
raise ValueError("Log is undefined at all timeslices")
logcorr = Corr(newcontent)
return self * (logcorr.second_deriv('symmetric') + (logcorr.deriv('symmetric'))**2)
else:
raise Exception("Unknown variant.")
raise ValueError("Unknown variant.")
def m_eff(self, variant='log', guess=1.0):
"""Returns the effective mass of the correlator as correlator object
@ -728,7 +728,7 @@ class Corr:
else:
newcontent.append(self.content[t] / self.content[t + 1])
if (all([x is None for x in newcontent])):
raise Exception('m_eff is undefined at all timeslices')
raise ValueError('m_eff is undefined at all timeslices')
return np.log(Corr(newcontent, padding=[0, 1]))
@ -742,7 +742,7 @@ class Corr:
else:
newcontent.append(self.content[t - 1] / self.content[t + 1])
if (all([x is None for x in newcontent])):
raise Exception('m_eff is undefined at all timeslices')
raise ValueError('m_eff is undefined at all timeslices')
return np.log(Corr(newcontent, padding=[1, 1])) / 2
@ -767,7 +767,7 @@ class Corr:
else:
newcontent.append(np.abs(find_root(self.content[t][0] / self.content[t + 1][0], root_function, guess=guess)))
if (all([x is None for x in newcontent])):
raise Exception('m_eff is undefined at all timeslices')
raise ValueError('m_eff is undefined at all timeslices')
return Corr(newcontent, padding=[0, 1])
@ -779,11 +779,11 @@ class Corr:
else:
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")
raise ValueError("m_eff is undefined at all timeslices")
return np.arccosh(Corr(newcontent, padding=[1, 1]))
else:
raise Exception('Unknown variant.')
raise ValueError('Unknown variant.')
def fit(self, function, fitrange=None, silent=False, **kwargs):
r'''Fits function to the data
@ -801,7 +801,7 @@ class Corr:
Decides whether output is printed to the standard output.
'''
if self.N != 1:
raise Exception("Correlator must be projected before fitting")
raise ValueError("Correlator must be projected before fitting")
if fitrange is None:
if self.prange:
@ -810,12 +810,12 @@ class Corr:
fitrange = [0, self.T - 1]
else:
if not isinstance(fitrange, list):
raise Exception("fitrange has to be a list with two elements")
raise TypeError("fitrange has to be a list with two elements")
if len(fitrange) != 2:
raise Exception("fitrange has to have exactly two elements [fit_start, fit_stop]")
raise ValueError("fitrange has to have exactly two elements [fit_start, fit_stop]")
xs = np.array([x for x in range(fitrange[0], fitrange[1] + 1) if not self.content[x] is None])
ys = np.array([self.content[x][0] for x in range(fitrange[0], fitrange[1] + 1) if not self.content[x] is None])
xs = np.array([x for x in range(fitrange[0], fitrange[1] + 1) if self.content[x] is not None])
ys = np.array([self.content[x][0] for x in range(fitrange[0], fitrange[1] + 1) if self.content[x] is not None])
result = least_squares(xs, ys, function, silent=silent, **kwargs)
return result
@ -840,9 +840,9 @@ class Corr:
else:
raise Exception("no plateau range provided")
if self.N != 1:
raise Exception("Correlator must be projected before getting a plateau.")
raise ValueError("Correlator must be projected before getting a plateau.")
if (all([self.content[t] is None for t in range(plateau_range[0], plateau_range[1] + 1)])):
raise Exception("plateau is undefined at all timeslices in plateaurange.")
raise ValueError("plateau is undefined at all timeslices in plateaurange.")
if auto_gamma:
self.gamma_method()
if method == "fit":
@ -854,16 +854,16 @@ class Corr:
return returnvalue
else:
raise Exception("Unsupported plateau method: " + method)
raise ValueError("Unsupported plateau method: " + method)
def set_prange(self, prange):
"""Sets the attribute prange of the Corr object."""
if not len(prange) == 2:
raise Exception("prange must be a list or array with two values")
raise ValueError("prange must be a list or array with two values")
if not ((isinstance(prange[0], int)) and (isinstance(prange[1], int))):
raise Exception("Start and end point must be integers")
if not (0 <= prange[0] <= self.T and 0 <= prange[1] <= self.T and prange[0] < prange[1]):
raise Exception("Start and end point must define a range in the interval 0,T")
raise TypeError("Start and end point must be integers")
if not (0 <= prange[0] <= self.T and 0 <= prange[1] <= self.T and prange[0] <= prange[1]):
raise ValueError("Start and end point must define a range in the interval 0,T")
self.prange = prange
return
@ -900,7 +900,7 @@ class Corr:
Optional title of the figure.
"""
if self.N != 1:
raise Exception("Correlator must be projected before plotting")
raise ValueError("Correlator must be projected before plotting")
if auto_gamma:
self.gamma_method()
@ -941,7 +941,7 @@ class Corr:
hide_from = None
ax1.errorbar(x[:hide_from], y[:hide_from], y_err[:hide_from], label=corr.tag, mfc=plt.rcParams['axes.facecolor'])
else:
raise Exception("'comp' must be a correlator or a list of correlators.")
raise TypeError("'comp' must be a correlator or a list of correlators.")
if plateau:
if isinstance(plateau, Obs):
@ -950,14 +950,14 @@ class Corr:
ax1.axhline(y=plateau.value, linewidth=2, color=plt.rcParams['text.color'], alpha=0.6, marker=',', ls='--', label=str(plateau))
ax1.axhspan(plateau.value - plateau.dvalue, plateau.value + plateau.dvalue, alpha=0.25, color=plt.rcParams['text.color'], ls='-')
else:
raise Exception("'plateau' must be an Obs")
raise TypeError("'plateau' must be an Obs")
if references:
if isinstance(references, list):
for ref in references:
ax1.axhline(y=ref, linewidth=1, color=plt.rcParams['text.color'], alpha=0.6, marker=',', ls='--')
else:
raise Exception("'references' must be a list of floating pint values.")
raise TypeError("'references' must be a list of floating pint values.")
if self.prange:
ax1.axvline(self.prange[0], 0, 1, ls='-', marker=',', color="black", zorder=0)
@ -991,7 +991,7 @@ class Corr:
if isinstance(save, str):
fig.savefig(save, bbox_inches='tight')
else:
raise Exception("'save' has to be a string.")
raise TypeError("'save' has to be a string.")
def spaghetti_plot(self, logscale=True):
"""Produces a spaghetti plot of the correlator suited to monitor exceptional configurations.
@ -1002,7 +1002,7 @@ class Corr:
Determines whether the scale of the y-axis is logarithmic or standard.
"""
if self.N != 1:
raise Exception("Correlator needs to be projected first.")
raise ValueError("Correlator needs to be projected first.")
mc_names = list(set([item for sublist in [sum(map(o[0].e_content.get, o[0].mc_names), []) for o in self.content if o is not None] for item in sublist]))
x0_vals = [n for (n, o) in zip(np.arange(self.T), self.content) if o is not None]
@ -1044,7 +1044,7 @@ class Corr:
elif datatype == "pickle":
dump_object(self, filename, **kwargs)
else:
raise Exception("Unknown datatype " + str(datatype))
raise ValueError("Unknown datatype " + str(datatype))
def print(self, print_range=None):
print(self.__repr__(print_range))
@ -1094,7 +1094,7 @@ class Corr:
def __add__(self, y):
if isinstance(y, Corr):
if ((self.N != y.N) or (self.T != y.T)):
raise Exception("Addition of Corrs with different shape")
raise ValueError("Addition of Corrs with different shape")
newcontent = []
for t in range(self.T):
if _check_for_none(self, self.content[t]) or _check_for_none(y, y.content[t]):
@ -1122,7 +1122,7 @@ class Corr:
def __mul__(self, y):
if isinstance(y, Corr):
if not ((self.N == 1 or y.N == 1 or self.N == y.N) and self.T == y.T):
raise Exception("Multiplication of Corr object requires N=N or N=1 and T=T")
raise ValueError("Multiplication of Corr object requires N=N or N=1 and T=T")
newcontent = []
for t in range(self.T):
if _check_for_none(self, self.content[t]) or _check_for_none(y, y.content[t]):
@ -1193,7 +1193,7 @@ class Corr:
def __truediv__(self, y):
if isinstance(y, Corr):
if not ((self.N == 1 or y.N == 1 or self.N == y.N) and self.T == y.T):
raise Exception("Multiplication of Corr object requires N=N or N=1 and T=T")
raise ValueError("Multiplication of Corr object requires N=N or N=1 and T=T")
newcontent = []
for t in range(self.T):
if _check_for_none(self, self.content[t]) or _check_for_none(y, y.content[t]):
@ -1207,16 +1207,16 @@ class Corr:
newcontent[t] = None
if all([item is None for item in newcontent]):
raise Exception("Division returns completely undefined correlator")
raise ValueError("Division returns completely undefined correlator")
return Corr(newcontent)
elif isinstance(y, (Obs, CObs)):
if isinstance(y, Obs):
if y.value == 0:
raise Exception('Division by zero will return undefined correlator')
raise ValueError('Division by zero will return undefined correlator')
if isinstance(y, CObs):
if y.is_zero():
raise Exception('Division by zero will return undefined correlator')
raise ValueError('Division by zero will return undefined correlator')
newcontent = []
for t in range(self.T):
@ -1228,7 +1228,7 @@ class Corr:
elif isinstance(y, (int, float)):
if y == 0:
raise Exception('Division by zero will return undefined correlator')
raise ValueError('Division by zero will return undefined correlator')
newcontent = []
for t in range(self.T):
if _check_for_none(self, self.content[t]):
@ -1284,7 +1284,7 @@ class Corr:
if np.isnan(tmp_sum.value):
newcontent[t] = None
if all([item is None for item in newcontent]):
raise Exception('Operation returns undefined correlator')
raise ValueError('Operation returns undefined correlator')
return Corr(newcontent)
def sin(self):
@ -1392,13 +1392,13 @@ class Corr:
'''
if self.N == 1:
raise Exception('Method cannot be applied to one-dimensional correlators.')
raise ValueError('Method cannot be applied to one-dimensional correlators.')
if basematrix is None:
basematrix = self
if Ntrunc >= basematrix.N:
raise Exception('Cannot truncate using Ntrunc <= %d' % (basematrix.N))
raise ValueError('Cannot truncate using Ntrunc <= %d' % (basematrix.N))
if basematrix.N != self.N:
raise Exception('basematrix and targetmatrix have to be of the same size.')
raise ValueError('basematrix and targetmatrix have to be of the same size.')
evecs = basematrix.GEVP(t0proj, tproj, sort=None)[:Ntrunc]

View file

@ -34,7 +34,7 @@ def epsilon_tensor(i, j, k):
"""
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)
raise ValueError("Unexpected input", i, j, k)
return (i - j) * (j - k) * (k - i) / 2
@ -52,7 +52,7 @@ def epsilon_tensor_rank4(i, j, k, o):
"""
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)
raise ValueError("Unexpected input", i, j, k, o)
return (i - j) * (j - k) * (k - i) * (i - o) * (j - o) * (o - k) / 12
@ -92,5 +92,5 @@ def Grid_gamma(gamma_tag):
elif gamma_tag == 'SigmaZT':
g = 0.5 * (gamma[2] @ gamma[3] - gamma[3] @ gamma[2])
else:
raise Exception('Unkown gamma structure', gamma_tag)
raise ValueError('Unkown gamma structure', gamma_tag)
return g

View file

@ -14,7 +14,7 @@ from autograd import hessian as auto_hessian
from autograd import elementwise_grad as egrad
from numdifftools import Jacobian as num_jacobian
from numdifftools import Hessian as num_hessian
from .obs import Obs, derived_observable, covariance, cov_Obs
from .obs import Obs, derived_observable, covariance, cov_Obs, invert_corr_cov_cholesky
class Fit_result(Sequence):
@ -151,6 +151,14 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
For details about how the covariance matrix is estimated see `pyerrors.obs.covariance`.
In practice the correlation matrix is Cholesky decomposed and inverted (instead of the covariance matrix).
This procedure should be numerically more stable as the correlation matrix is typically better conditioned (Jacobi preconditioning).
inv_chol_cov_matrix [array,list], optional
array: shape = (no of y values) X (no of y values)
list: for an uncombined fit: [""]
for a combined fit: list of keys belonging to the corr_matrix saved in the array, must be the same as the keys of the y dict in alphabetical order
If correlated_fit=True is set as well, can provide an inverse covariance matrix (y errors, dy_f included!) of your own choosing for a correlated fit.
The matrix must be a lower triangular matrix constructed from a Cholesky decomposition: The function invert_corr_cov_cholesky(corr, inverrdiag) can be
used to construct it from a correlation matrix (corr) and the errors dy_f of the data points (inverrdiag = np.diag(1 / np.asarray(dy_f))). For the correct
ordering the correlation matrix (corr) can be sorted via the function sort_corr(corr, kl, yd) where kl is the list of keys and yd the y dict.
expected_chisquare : bool
If True estimates the expected chisquare which is
corrected by effects caused by correlated input data (default False).
@ -165,6 +173,57 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
-------
output : Fit_result
Parameters and information on the fitted result.
Examples
------
>>> # Example of a correlated (correlated_fit = True, inv_chol_cov_matrix handed over) combined fit, based on a randomly generated data set
>>> import numpy as np
>>> from scipy.stats import norm
>>> from scipy.linalg import cholesky
>>> import pyerrors as pe
>>> # generating the random data set
>>> num_samples = 400
>>> N = 3
>>> x = np.arange(N)
>>> x1 = norm.rvs(size=(N, num_samples)) # generate random numbers
>>> x2 = norm.rvs(size=(N, num_samples)) # generate random numbers
>>> r = r1 = r2 = np.zeros((N, N))
>>> y = {}
>>> for i in range(N):
>>> for j in range(N):
>>> r[i, j] = np.exp(-0.8 * np.fabs(i - j)) # element in correlation matrix
>>> errl = np.sqrt([3.4, 2.5, 3.6]) # set y errors
>>> for i in range(N):
>>> for j in range(N):
>>> r[i, j] *= errl[i] * errl[j] # element in covariance matrix
>>> c = cholesky(r, lower=True)
>>> y = {'a': np.dot(c, x1), 'b': np.dot(c, x2)} # generate y data with the covariance matrix defined
>>> # random data set has been generated, now the dictionaries and the inverse covariance matrix to be handed over are built
>>> x_dict = {}
>>> y_dict = {}
>>> chol_inv_dict = {}
>>> data = []
>>> for key in y.keys():
>>> x_dict[key] = x
>>> for i in range(N):
>>> data.append(pe.Obs([[i + 1 + o for o in y[key][i]]], ['ens'])) # generate y Obs from the y data
>>> [o.gamma_method() for o in data]
>>> corr = pe.covariance(data, correlation=True)
>>> inverrdiag = np.diag(1 / np.asarray([o.dvalue for o in data]))
>>> chol_inv = pe.obs.invert_corr_cov_cholesky(corr, inverrdiag) # gives form of the inverse covariance matrix needed for the combined correlated fit below
>>> y_dict = {'a': data[:3], 'b': data[3:]}
>>> # common fit parameter p[0] in combined fit
>>> def fit1(p, x):
>>> return p[0] + p[1] * x
>>> def fit2(p, x):
>>> return p[0] + p[2] * x
>>> fitf_dict = {'a': fit1, 'b':fit2}
>>> fitp_inv_cov_combined_fit = pe.least_squares(x_dict,y_dict, fitf_dict, correlated_fit = True, inv_chol_cov_matrix = [chol_inv,['a','b']])
Fit with 3 parameters
Method: Levenberg-Marquardt
`ftol` termination condition is satisfied.
chisquare/d.o.f.: 0.5388013574561786 # random
fit parameters [1.11897846 0.96361162 0.92325319] # random
'''
output = Fit_result()
@ -197,7 +256,7 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
if sorted(list(funcd.keys())) != key_ls:
raise ValueError('x and func dictionaries do not contain the same keys.')
x_all = np.concatenate([np.array(xd[key]) for key in key_ls])
x_all = np.concatenate([np.array(xd[key]).transpose() for key in key_ls]).transpose()
y_all = np.concatenate([np.array(yd[key]) for key in key_ls])
y_f = [o.value for o in y_all]
@ -234,7 +293,7 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
if len(key_ls) > 1:
for key in key_ls:
if np.asarray(yd[key]).shape != funcd[key](np.arange(n_parms), xd[key]).shape:
raise ValueError(f"Fit function {key} returns the wrong shape ({funcd[key](np.arange(n_parms), xd[key]).shape} instead of {xd[key].shape})\nIf the fit function is just a constant you could try adding x*0 to get the correct shape.")
raise ValueError(f"Fit function {key} returns the wrong shape ({funcd[key](np.arange(n_parms), xd[key]).shape} instead of {np.asarray(yd[key]).shape})\nIf the fit function is just a constant you could try adding x*0 to get the correct shape.")
if not silent:
print('Fit with', n_parms, 'parameter' + 's' * (n_parms > 1))
@ -297,15 +356,21 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
return anp.sum(general_chisqfunc_uncorr(p, y_f, p_f) ** 2)
if kwargs.get('correlated_fit') is True:
corr = covariance(y_all, correlation=True, **kwargs)
covdiag = np.diag(1 / np.asarray(dy_f))
condn = np.linalg.cond(corr)
if condn > 0.1 / np.finfo(float).eps:
raise Exception(f"Cannot invert correlation matrix as its condition number exceeds machine precision ({condn:1.2e})")
if condn > 1e13:
warnings.warn("Correlation matrix may be ill-conditioned, condition number: {%1.2e}" % (condn), RuntimeWarning)
chol = np.linalg.cholesky(corr)
chol_inv = scipy.linalg.solve_triangular(chol, covdiag, lower=True)
if 'inv_chol_cov_matrix' in kwargs:
chol_inv = kwargs.get('inv_chol_cov_matrix')
if (chol_inv[0].shape[0] != len(dy_f)):
raise TypeError('The number of columns of the inverse covariance matrix handed over needs to be equal to the number of y errors.')
if (chol_inv[0].shape[0] != chol_inv[0].shape[1]):
raise TypeError('The inverse covariance matrix handed over needs to have the same number of rows as columns.')
if (chol_inv[1] != key_ls):
raise ValueError('The keys of inverse covariance matrix are not the same or do not appear in the same order as the x and y values.')
chol_inv = chol_inv[0]
if np.any(np.diag(chol_inv) <= 0) or (not np.all(chol_inv == np.tril(chol_inv))):
raise ValueError('The inverse covariance matrix inv_chol_cov_matrix[0] has to be a lower triangular matrix constructed from a Cholesky decomposition.')
else:
corr = covariance(y_all, correlation=True, **kwargs)
inverrdiag = np.diag(1 / np.asarray(dy_f))
chol_inv = invert_corr_cov_cholesky(corr, inverrdiag)
def general_chisqfunc(p, ivars, pr):
model = anp.concatenate([anp.array(funcd[key](p, xd[key])).reshape(-1) for key in key_ls])
@ -350,7 +415,6 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
fit_result = scipy.optimize.least_squares(chisqfunc_residuals_uncorr, x0, method='lm', ftol=1e-15, gtol=1e-15, xtol=1e-15)
if kwargs.get('correlated_fit') is True:
def chisqfunc_residuals(p):
return general_chisqfunc(p, y_f, p_f)

View file

@ -5,11 +5,11 @@ r'''
For comparison with other analysis workflows `pyerrors` can also 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.
'''
from . import bdio
from . import dobs
from . import hadrons
from . import json
from . import misc
from . import openQCD
from . import pandas
from . import sfcf
from . import bdio as bdio
from . import dobs as dobs
from . import hadrons as hadrons
from . import json as json
from . import misc as misc
from . import openQCD as openQCD
from . import pandas as pandas
from . import sfcf as sfcf

View file

@ -79,7 +79,7 @@ def _dict_to_xmlstring_spaces(d, space=' '):
o += space
o += li + '\n'
if li.startswith('<') and not cm:
if not '<%s' % ('/') in li:
if '<%s' % ('/') not in li:
c += 1
cm = False
return o
@ -529,7 +529,8 @@ def import_dobs_string(content, full_output=False, separator_insertion=True):
deltas.append(repdeltas)
idl.append(repidl)
res.append(Obs(deltas, obs_names, idl=idl))
obsmeans = [np.average(deltas[j]) for j in range(len(deltas))]
res.append(Obs([np.array(deltas[j]) - obsmeans[j] for j in range(len(obsmeans))], obs_names, idl=idl, means=obsmeans))
res[-1]._value = mean[i]
_check(len(e_names) == ne)
@ -671,7 +672,7 @@ def _dobsdict_to_xmlstring_spaces(d, space=' '):
o += space
o += li + '\n'
if li.startswith('<') and not cm:
if not '<%s' % ('/') in li:
if '<%s' % ('/') not in li:
c += 1
cm = False
return o

View file

@ -113,7 +113,7 @@ def read_hd5(filestem, ens_id, group, attrs=None, idl=None, part="real"):
infos = []
for hd5_file in files:
h5file = h5py.File(path + '/' + hd5_file, "r")
if not group + '/' + entry in h5file:
if group + '/' + entry not in h5file:
raise Exception("Entry '" + entry + "' not contained in the files.")
raw_data = h5file[group + '/' + entry + '/corr']
real_data = raw_data[:].view("complex")
@ -186,7 +186,7 @@ def _extract_real_arrays(path, files, tree, keys):
for hd5_file in files:
h5file = h5py.File(path + '/' + hd5_file, "r")
for key in keys:
if not tree + '/' + key in h5file:
if tree + '/' + key not in h5file:
raise Exception("Entry '" + key + "' not contained in the files.")
raw_data = h5file[tree + '/' + key + '/data']
real_data = raw_data[:].astype(np.double)

View file

@ -133,10 +133,11 @@ def create_json_string(ol, description='', indent=1):
names = []
idl = []
for key, value in obs.idl.items():
samples.append([np.nan] * len(value))
samples.append(np.array([np.nan] * len(value)))
names.append(key)
idl.append(value)
my_obs = Obs(samples, names, idl)
my_obs = Obs(samples, names, idl, means=[np.nan for n in names])
my_obs._value = np.nan
my_obs._covobs = obs._covobs
for name in obs._covobs:
my_obs.names.append(name)
@ -331,7 +332,8 @@ def _parse_json_dict(json_dict, verbose=True, full_output=False):
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'])
r_offsets = [np.average([ddi[0] for ddi in di]) for di in od['deltas']]
ret = Obs([np.array([ddi[0] for ddi in od['deltas'][i]]) - r_offsets[i] for i in range(len(od['deltas']))], od['names'], idl=od['idl'], means=[ro + values[0] for ro in r_offsets])
ret._value = values[0]
else:
ret = Obs([], [], means=[])
@ -356,7 +358,8 @@ def _parse_json_dict(json_dict, verbose=True, full_output=False):
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']))
r_offsets = np.array([np.average(di[:, i]) for di in od['deltas']])
ret.append(Obs([od['deltas'][j][:, i] - r_offsets[j] for j in range(len(od['deltas']))], od['names'], idl=od['idl'], means=[ro + values[i] for ro in r_offsets]))
ret[-1]._value = values[i]
else:
ret.append(Obs([], [], means=[]))
@ -383,7 +386,8 @@ def _parse_json_dict(json_dict, verbose=True, full_output=False):
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']))
r_offsets = np.array([np.average(di[:, i]) for di in od['deltas']])
ret.append(Obs([od['deltas'][j][:, i] - r_offsets[j] for j in range(len(od['deltas']))], od['names'], idl=od['idl'], means=[ro + values[i] for ro in r_offsets]))
ret[-1]._value = values[i]
else:
ret.append(Obs([], [], means=[]))

View file

@ -47,7 +47,7 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
Reweighting factors read
"""
known_oqcd_versions = ['1.4', '1.6', '2.0']
if not (version in known_oqcd_versions):
if version not in known_oqcd_versions:
raise Exception('Unknown openQCD version defined!')
print("Working with openQCD version " + version)
if 'postfix' in kwargs:

View file

@ -127,7 +127,8 @@ def read_sfcf_multi(path, prefix, name_list, quarks_list=['.*'], corr_type_list=
check_configs: list[list[int]]
list of list of supposed configs, eg. [range(1,1000)]
for one replicum with 1000 configs
rep_string: str
Separator of ensemble name and replicum. Example: In "ensAr0", "r" would be the separator string.
Returns
-------
result: dict[list[Obs]]
@ -199,9 +200,9 @@ def read_sfcf_multi(path, prefix, name_list, quarks_list=['.*'], corr_type_list=
else:
ens_name = kwargs.get("ens_name")
if not appended:
new_names = _get_rep_names(ls, ens_name)
new_names = _get_rep_names(ls, ens_name, rep_sep=(kwargs.get('rep_string', 'r')))
else:
new_names = _get_appended_rep_names(ls, prefix, name_list[0], ens_name)
new_names = _get_appended_rep_names(ls, prefix, name_list[0], ens_name, rep_sep=(kwargs.get('rep_string', 'r')))
new_names = sort_names(new_names)
idl = []
@ -224,12 +225,23 @@ def read_sfcf_multi(path, prefix, name_list, quarks_list=['.*'], corr_type_list=
intern[name]["spec"][quarks][off] = {}
for w in wf_list:
intern[name]["spec"][quarks][off][w] = {}
for w2 in wf2_list:
intern[name]["spec"][quarks][off][w][w2] = {}
intern[name]["spec"][quarks][off][w][w2]["pattern"] = _make_pattern(version, name, off, w, w2, intern[name]['b2b'], quarks)
if b2b:
for w2 in wf2_list:
intern[name]["spec"][quarks][off][w][w2] = {}
intern[name]["spec"][quarks][off][w][w2]["pattern"] = _make_pattern(version, name, off, w, w2, intern[name]['b2b'], quarks)
else:
intern[name]["spec"][quarks][off][w]["0"] = {}
intern[name]["spec"][quarks][off][w]["0"]["pattern"] = _make_pattern(version, name, off, w, 0, intern[name]['b2b'], quarks)
internal_ret_dict = {}
needed_keys = _lists2key(name_list, quarks_list, noffset_list, wf_list, wf2_list)
needed_keys = []
for name, corr_type in zip(name_list, corr_type_list):
b2b, single = _extract_corr_type(corr_type)
if b2b:
needed_keys.extend(_lists2key([name], quarks_list, noffset_list, wf_list, wf2_list))
else:
needed_keys.extend(_lists2key([name], quarks_list, noffset_list, wf_list, ["0"]))
for key in needed_keys:
internal_ret_dict[key] = []
@ -270,10 +282,14 @@ def read_sfcf_multi(path, prefix, name_list, quarks_list=['.*'], corr_type_list=
if i == 0:
if version != "0.0" and compact:
file = path + '/' + item + '/' + sub_ls[0]
for name in name_list:
for name_index, name in enumerate(name_list):
if version == "0.0" or not compact:
file = path + '/' + item + '/' + sub_ls[0] + '/' + name
for key in _lists2key(quarks_list, noffset_list, wf_list, wf2_list):
if corr_type_list[name_index] == 'bi':
name_keys = _lists2key(quarks_list, noffset_list, wf_list, ["0"])
else:
name_keys = _lists2key(quarks_list, noffset_list, wf_list, wf2_list)
for key in name_keys:
specs = _key2specs(key)
quarks = specs[0]
off = specs[1]
@ -307,7 +323,6 @@ def read_sfcf_multi(path, prefix, name_list, quarks_list=['.*'], corr_type_list=
cfg_path = path + '/' + item + '/' + subitem
file_data = _read_o_file(cfg_path, name, needed_keys, intern, version, im)
rep_data.append(file_data)
print(rep_data)
for t in range(intern[name]["T"]):
internal_ret_dict[key][t].append([])
for cfg in range(no_cfg):
@ -363,12 +378,13 @@ def read_sfcf_multi(path, prefix, name_list, quarks_list=['.*'], corr_type_list=
result_dict = {}
if keyed_out:
for key in needed_keys:
name = _key2specs(key)[0]
result = []
for t in range(intern[name]["T"]):
result.append(Obs(internal_ret_dict[key][t], new_names, idl=idl))
result_dict[key] = result
else:
for name in name_list:
for name, corr_type in zip(name_list, corr_type_list):
result_dict[name] = {}
for quarks in quarks_list:
result_dict[name][quarks] = {}
@ -376,12 +392,19 @@ def read_sfcf_multi(path, prefix, name_list, quarks_list=['.*'], corr_type_list=
result_dict[name][quarks][off] = {}
for w in wf_list:
result_dict[name][quarks][off][w] = {}
for w2 in wf2_list:
key = _specs2key(name, quarks, off, w, w2)
if corr_type != 'bi':
for w2 in wf2_list:
key = _specs2key(name, quarks, off, w, w2)
result = []
for t in range(intern[name]["T"]):
result.append(Obs(internal_ret_dict[key][t], new_names, idl=idl))
result_dict[name][quarks][str(off)][str(w)][str(w2)] = result
else:
key = _specs2key(name, quarks, off, w, "0")
result = []
for t in range(intern[name]["T"]):
result.append(Obs(internal_ret_dict[key][t], new_names, idl=idl))
result_dict[name][quarks][str(off)][str(w)][str(w2)] = result
result_dict[name][quarks][str(off)][str(w)][str(0)] = result
return result_dict
@ -624,22 +647,22 @@ def _read_append_rep(filename, pattern, b2b, cfg_separator, im, single):
return T, rep_idl, data
def _get_rep_names(ls, ens_name=None):
def _get_rep_names(ls, ens_name=None, rep_sep='r'):
new_names = []
for entry in ls:
try:
idx = entry.index('r')
idx = entry.index(rep_sep)
except Exception:
raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.")
if ens_name:
new_names.append('ens_name' + '|' + entry[idx:])
new_names.append(ens_name + '|' + entry[idx:])
else:
new_names.append(entry[:idx] + '|' + entry[idx:])
return new_names
def _get_appended_rep_names(ls, prefix, name, ens_name=None):
def _get_appended_rep_names(ls, prefix, name, ens_name=None, rep_sep='r'):
new_names = []
for exc in ls:
if not fnmatch.fnmatch(exc, prefix + '*.' + name):
@ -648,12 +671,12 @@ def _get_appended_rep_names(ls, prefix, name, ens_name=None):
for entry in ls:
myentry = entry[:-len(name) - 1]
try:
idx = myentry.index('r')
idx = myentry.index(rep_sep)
except Exception:
raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.")
if ens_name:
new_names.append('ens_name' + '|' + entry[idx:])
new_names.append(ens_name + '|' + entry[idx:])
else:
new_names.append(myentry[:idx] + '|' + myentry[idx:])
return new_names

View file

@ -82,6 +82,8 @@ class Obs:
raise ValueError('Names are not unique.')
if not all(isinstance(x, str) for x in names):
raise TypeError('All names have to be strings.')
if len(set([o.split('|')[0] for o in names])) > 1:
raise ValueError('Cannot initialize Obs based on multiple ensembles. Please average separate Obs from each ensemble.')
else:
if not isinstance(names[0], str):
raise TypeError('All names have to be strings.')
@ -222,7 +224,7 @@ class Obs:
tmp = kwargs.get(kwarg_name)
if isinstance(tmp, (int, float)):
if tmp < 0:
raise Exception(kwarg_name + ' has to be larger or equal to 0.')
raise ValueError(kwarg_name + ' has to be larger or equal to 0.')
for e, e_name in enumerate(self.e_names):
getattr(self, kwarg_name)[e_name] = tmp
else:
@ -291,7 +293,7 @@ class Obs:
texp = self.tau_exp[e_name]
# Critical slowing down analysis
if w_max // 2 <= 1:
raise Exception("Need at least 8 samples for tau_exp error analysis")
raise ValueError("Need at least 8 samples for tau_exp error analysis")
for n in range(1, w_max // 2):
_compute_drho(n + 1)
if (self.e_rho[e_name][n] - self.N_sigma[e_name] * self.e_drho[e_name][n]) < 0 or n >= w_max // 2 - 2:
@ -620,7 +622,7 @@ class Obs:
if not hasattr(self, 'e_dvalue'):
raise Exception('Run the gamma method first.')
if np.isclose(0.0, self._dvalue, atol=1e-15):
raise Exception('Error is 0.0')
raise ValueError('Error is 0.0')
labels = self.e_names
sizes = [self.e_dvalue[name] ** 2 for name in labels] / self._dvalue ** 2
fig1, ax1 = plt.subplots()
@ -659,7 +661,7 @@ class Obs:
with open(file_name + '.p', 'wb') as fb:
pickle.dump(self, fb)
else:
raise Exception("Unknown datatype " + str(datatype))
raise TypeError("Unknown datatype " + str(datatype))
def export_jackknife(self):
"""Export jackknife samples from the Obs
@ -676,7 +678,7 @@ class Obs:
"""
if len(self.names) != 1:
raise Exception("'export_jackknife' is only implemented for Obs defined on one ensemble and replicum.")
raise ValueError("'export_jackknife' is only implemented for Obs defined on one ensemble and replicum.")
name = self.names[0]
full_data = self.deltas[name] + self.r_values[name]
@ -711,7 +713,7 @@ class Obs:
should agree with samples from a full bootstrap analysis up to O(1/N).
"""
if len(self.names) != 1:
raise Exception("'export_boostrap' is only implemented for Obs defined on one ensemble and replicum.")
raise ValueError("'export_boostrap' is only implemented for Obs defined on one ensemble and replicum.")
name = self.names[0]
length = self.N
@ -856,15 +858,12 @@ class Obs:
def __pow__(self, y):
if isinstance(y, Obs):
return derived_observable(lambda x: x[0] ** x[1], [self, y])
return derived_observable(lambda x, **kwargs: x[0] ** x[1], [self, y], man_grad=[y.value * self.value ** (y.value - 1), self.value ** y.value * np.log(self.value)])
else:
return derived_observable(lambda x: x[0] ** y, [self])
return derived_observable(lambda x, **kwargs: x[0] ** y, [self], man_grad=[y * self.value ** (y - 1)])
def __rpow__(self, y):
if isinstance(y, Obs):
return derived_observable(lambda x: x[0] ** x[1], [y, self])
else:
return derived_observable(lambda x: y ** x[0], [self])
return derived_observable(lambda x, **kwargs: y ** x[0], [self], man_grad=[y ** self.value * np.log(y)])
def __abs__(self):
return derived_observable(lambda x: anp.abs(x[0]), [self])
@ -1270,7 +1269,7 @@ def derived_observable(func, data, array_mode=False, **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.')
raise ValueError('Manual derivative does not have correct shape.')
elif kwargs.get('num_grad') is True:
if multi > 0:
raise Exception('Multi mode currently not supported for numerical derivative')
@ -1336,7 +1335,7 @@ def derived_observable(func, data, array_mode=False, **kwargs):
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!')
raise ValueError('The same name has been used for deltas and covobs!')
new_samples = []
new_means = []
new_idl = []
@ -1377,7 +1376,7 @@ def _reduce_deltas(deltas, idx_old, idx_new):
Has to be a subset of idx_old.
"""
if not len(deltas) == len(idx_old):
raise Exception('Length of deltas and idx_old have to be the same: %d != %d' % (len(deltas), len(idx_old)))
raise ValueError('Length of deltas and idx_old have to be the same: %d != %d' % (len(deltas), len(idx_old)))
if type(idx_old) is range and type(idx_new) is range:
if idx_old == idx_new:
return deltas
@ -1385,7 +1384,7 @@ def _reduce_deltas(deltas, idx_old, idx_new):
return deltas
indices = np.intersect1d(idx_old, idx_new, assume_unique=True, return_indices=True)[1]
if len(indices) < len(idx_new):
raise Exception('Error in _reduce_deltas: Config of idx_new not in idx_old')
raise ValueError('Error in _reduce_deltas: Config of idx_new not in idx_old')
return np.array(deltas)[indices]
@ -1407,12 +1406,14 @@ def reweight(weight, obs, **kwargs):
result = []
for i in range(len(obs)):
if len(obs[i].cov_names):
raise Exception('Error: Not possible to reweight an Obs that contains covobs!')
raise ValueError('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')
raise ValueError('Error: Ensembles do not fit')
if len(obs[i].mc_names) > 1 or len(weight.mc_names) > 1:
raise ValueError('Error: Cannot reweight an Obs that contains multiple ensembles.')
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))
raise ValueError('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(obs[i].names):
@ -1445,18 +1446,21 @@ def correlate(obs_a, obs_b):
-----
Keep in mind to only correlate primary observables which have not been reweighted
yet. The reweighting has to be applied after correlating the observables.
Currently only works if ensembles are identical (this is not strictly necessary).
Only works if a single ensemble is present in the Obs.
Currently only works if ensemble content is identical (this is not strictly necessary).
"""
if len(obs_a.mc_names) > 1 or len(obs_b.mc_names) > 1:
raise ValueError('Error: Cannot correlate Obs that contain multiple ensembles.')
if sorted(obs_a.names) != sorted(obs_b.names):
raise Exception(f"Ensembles do not fit {set(sorted(obs_a.names)) ^ set(sorted(obs_b.names))}")
raise ValueError(f"Ensembles do not fit {set(sorted(obs_a.names)) ^ set(sorted(obs_b.names))}")
if len(obs_a.cov_names) or len(obs_b.cov_names):
raise Exception('Error: Not possible to correlate Obs that contain covobs!')
raise ValueError('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')
raise ValueError('Shapes of ensemble', name, 'do not fit')
if obs_a.idl[name] != obs_b.idl[name]:
raise Exception('idl of ensemble', name, 'do not fit')
raise ValueError('idl of ensemble', name, 'do not fit')
if obs_a.reweighted is True:
warnings.warn("The first observable is already reweighted.", RuntimeWarning)
@ -1544,6 +1548,92 @@ def covariance(obs, visualize=False, correlation=False, smooth=None, **kwargs):
return cov
def invert_corr_cov_cholesky(corr, inverrdiag):
"""Constructs a lower triangular matrix `chol` via the Cholesky decomposition of the correlation matrix `corr`
and then returns the inverse covariance matrix `chol_inv` as a lower triangular matrix by solving `chol * x = inverrdiag`.
Parameters
----------
corr : np.ndarray
correlation matrix
inverrdiag : np.ndarray
diagonal matrix, the entries are the inverse errors of the data points considered
"""
condn = np.linalg.cond(corr)
if condn > 0.1 / np.finfo(float).eps:
raise ValueError(f"Cannot invert correlation matrix as its condition number exceeds machine precision ({condn:1.2e})")
if condn > 1e13:
warnings.warn("Correlation matrix may be ill-conditioned, condition number: {%1.2e}" % (condn), RuntimeWarning)
chol = np.linalg.cholesky(corr)
chol_inv = scipy.linalg.solve_triangular(chol, inverrdiag, lower=True)
return chol_inv
def sort_corr(corr, kl, yd):
""" Reorders a correlation matrix to match the alphabetical order of its underlying y data.
The ordering of the input correlation matrix `corr` is given by the list of keys `kl`.
The input dictionary `yd` (with the same keys `kl`) must contain the corresponding y data
that the correlation matrix is based on.
This function sorts the list of keys `kl` alphabetically and sorts the matrix `corr`
according to this alphabetical order such that the sorted matrix `corr_sorted` corresponds
to the y data `yd` when arranged in an alphabetical order by its keys.
Parameters
----------
corr : np.ndarray
A square correlation matrix constructed using the order of the y data specified by `kl`.
The dimensions of `corr` should match the total number of y data points in `yd` combined.
kl : list of str
A list of keys that denotes the order in which the y data from `yd` was used to build the
input correlation matrix `corr`.
yd : dict of list
A dictionary where each key corresponds to a unique identifier, and its value is a list of
y data points. The total number of y data points across all keys must match the dimensions
of `corr`. The lists in the dictionary can be lists of Obs.
Returns
-------
np.ndarray
A new, sorted correlation matrix that corresponds to the y data from `yd` when arranged alphabetically by its keys.
Example
-------
>>> import numpy as np
>>> import pyerrors as pe
>>> corr = np.array([[1, 0.2, 0.3], [0.2, 1, 0.4], [0.3, 0.4, 1]])
>>> kl = ['b', 'a']
>>> yd = {'a': [1, 2], 'b': [3]}
>>> sorted_corr = pe.obs.sort_corr(corr, kl, yd)
>>> print(sorted_corr)
array([[1. , 0.3, 0.4],
[0.3, 1. , 0.2],
[0.4, 0.2, 1. ]])
"""
kl_sorted = sorted(kl)
posd = {}
ofs = 0
for ki, k in enumerate(kl):
posd[k] = [i + ofs for i in range(len(yd[k]))]
ofs += len(posd[k])
mapping = []
for k in kl_sorted:
for i in range(len(yd[k])):
mapping.append(posd[k][i])
corr_sorted = np.zeros_like(corr)
for i in range(corr.shape[0]):
for j in range(corr.shape[0]):
corr_sorted[i][j] = corr[mapping[i]][mapping[j]]
return corr_sorted
def _smooth_eigenvalues(corr, E):
"""Eigenvalue smoothing as described in hep-lat/9412087
@ -1553,7 +1643,7 @@ def _smooth_eigenvalues(corr, E):
Number of eigenvalues to be left substantially unchanged
"""
if not (2 < E < corr.shape[0] - 1):
raise Exception(f"'E' has to be between 2 and the dimension of the correlation matrix minus 1 ({corr.shape[0] - 1}).")
raise ValueError(f"'E' has to be between 2 and the dimension of the correlation matrix minus 1 ({corr.shape[0] - 1}).")
vals, vec = np.linalg.eigh(corr)
lambda_min = np.mean(vals[:-E])
vals[vals < lambda_min] = lambda_min
@ -1672,7 +1762,11 @@ def import_bootstrap(boots, name, random_numbers):
def merge_obs(list_of_obs):
"""Combine all observables in list_of_obs into one new observable
"""Combine all observables in list_of_obs into one new observable.
This allows to merge Obs that have been computed on multiple replica
of the same ensemble.
If you like to merge Obs that are based on several ensembles, please
average them yourself.
Parameters
----------
@ -1685,9 +1779,9 @@ 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)))
raise ValueError('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!')
raise ValueError('Not possible to merge data that contains covobs!')
new_dict = {}
idl_dict = {}
for o in list_of_obs:
@ -1738,7 +1832,7 @@ def cov_Obs(means, cov, name, grad=None):
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))
raise ValueError('You have to provide %d mean values!' % (ol[0].N))
if len(ol) == 1:
return ol[0]
return ol
@ -1754,7 +1848,7 @@ def _determine_gap(o, e_content, e_name):
gap = min(gaps)
if not np.all([gi % gap == 0 for gi in gaps]):
raise Exception(f"Replica for ensemble {e_name} do not have a common spacing.", gaps)
raise ValueError(f"Replica for ensemble {e_name} do not have a common spacing.", gaps)
return gap

View file

@ -1 +1 @@
__version__ = "2.11.1"
__version__ = "2.15.0-dev"

View file

@ -1,3 +1,6 @@
[build-system]
requires = ["setuptools >= 63.0.0", "wheel"]
build-backend = "setuptools.build_meta"
[tool.ruff.lint]
ignore = ["F403"]

View file

@ -24,19 +24,19 @@ setup(name='pyerrors',
author_email='fabian.joswig@ed.ac.uk',
license="MIT",
packages=find_packages(),
python_requires='>=3.8.0',
install_requires=['numpy>=1.24,<2', 'autograd>=1.6.2', 'numdifftools>=0.9.41', 'matplotlib>=3.7', 'scipy>=1.10', 'iminuit>=2.21', 'h5py>=3.8', 'lxml>=4.9', 'python-rapidjson>=1.10', 'pandas>=2.0'],
python_requires='>=3.9.0',
install_requires=['numpy>=2.0', 'autograd>=1.7.0', 'numdifftools>=0.9.41', 'matplotlib>=3.9', 'scipy>=1.13', 'iminuit>=2.28', 'h5py>=3.11', 'lxml>=5.0', 'python-rapidjson>=1.20', 'pandas>=2.2'],
extras_require={'test': ['pytest', 'pytest-cov', 'pytest-benchmark', 'hypothesis', 'nbmake', 'flake8']},
classifiers=[
'Development Status :: 5 - Production/Stable',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9',
'Programming Language :: Python :: 3.10',
'Programming Language :: Python :: 3.11',
'Programming Language :: Python :: 3.12',
'Programming Language :: Python :: 3.13',
'Topic :: Scientific/Engineering :: Physics'
],
)

View file

@ -129,7 +129,7 @@ def test_m_eff():
with pytest.warns(RuntimeWarning):
my_corr.m_eff('sinh')
with pytest.raises(Exception):
with pytest.raises(ValueError):
my_corr.m_eff('unkown_variant')
@ -140,7 +140,7 @@ def test_m_eff_negative_values():
assert m_eff_log[padding + 1] is None
m_eff_cosh = my_corr.m_eff('cosh')
assert m_eff_cosh[padding + 1] is None
with pytest.raises(Exception):
with pytest.raises(ValueError):
my_corr.m_eff('logsym')
@ -155,7 +155,7 @@ def test_correlate():
my_corr = pe.correlators.Corr([pe.pseudo_Obs(10, 0.1, 't'), pe.pseudo_Obs(0, 0.05, 't')])
corr1 = my_corr.correlate(my_corr)
corr2 = my_corr.correlate(my_corr[0])
with pytest.raises(Exception):
with pytest.raises(TypeError):
corr3 = my_corr.correlate(7.3)
@ -176,9 +176,9 @@ def test_fit_correlator():
assert fit_res[0] == my_corr[0]
assert fit_res[1] == my_corr[1] - my_corr[0]
with pytest.raises(Exception):
with pytest.raises(TypeError):
my_corr.fit(f, "from 0 to 3")
with pytest.raises(Exception):
with pytest.raises(ValueError):
my_corr.fit(f, [0, 2, 3])
@ -256,11 +256,11 @@ def test_prange():
corr = pe.correlators.Corr(corr_content)
corr.set_prange([2, 4])
with pytest.raises(Exception):
with pytest.raises(ValueError):
corr.set_prange([2])
with pytest.raises(Exception):
with pytest.raises(TypeError):
corr.set_prange([2, 2.3])
with pytest.raises(Exception):
with pytest.raises(ValueError):
corr.set_prange([4, 1])

View file

@ -30,7 +30,7 @@ def test_grid_dirac():
'SigmaYZ',
'SigmaZT']:
pe.dirac.Grid_gamma(gamma)
with pytest.raises(Exception):
with pytest.raises(ValueError):
pe.dirac.Grid_gamma('Not a gamma matrix')
@ -44,7 +44,7 @@ def test_epsilon_tensor():
(1, 1, 3) : 0.0}
for key, value in check.items():
assert pe.dirac.epsilon_tensor(*key) == value
with pytest.raises(Exception):
with pytest.raises(ValueError):
pe.dirac.epsilon_tensor(0, 1, 3)
@ -59,5 +59,5 @@ def test_epsilon_tensor_rank4():
(1, 2, 3, 1) : 0.0}
for key, value in check.items():
assert pe.dirac.epsilon_tensor_rank4(*key) == value
with pytest.raises(Exception):
with pytest.raises(ValueError):
pe.dirac.epsilon_tensor_rank4(0, 1, 3, 4)

View file

@ -152,6 +152,127 @@ def test_alternative_solvers():
chisquare_values = np.array(chisquare_values)
assert np.all(np.isclose(chisquare_values, chisquare_values[0]))
def test_inv_cov_matrix_input_least_squares():
num_samples = 400
N = 10
x = norm.rvs(size=(N, num_samples)) # generate random numbers
r = np.zeros((N, N))
for i in range(N):
for j in range(N):
r[i, j] = np.exp(-0.8 * np.fabs(i - j)) # element in correlation matrix
errl = np.sqrt([3.4, 2.5, 3.6, 2.8, 4.2, 4.7, 4.9, 5.1, 3.2, 4.2]) # set y errors
for i in range(N):
for j in range(N):
r[i, j] *= errl[i] * errl[j] # element in covariance matrix
c = cholesky(r, lower=True)
y = np.dot(c, x)
x = np.arange(N)
x_dict = {}
y_dict = {}
for i,item in enumerate(x):
x_dict[str(item)] = [x[i]]
for linear in [True, False]:
data = []
for i in range(N):
if linear:
data.append(pe.Obs([[i + 1 + o for o in y[i]]], ['ens']))
else:
data.append(pe.Obs([[np.exp(-(i + 1)) + np.exp(-(i + 1)) * o for o in y[i]]], ['ens']))
[o.gamma_method() for o in data]
data_dict = {}
for i,item in enumerate(x):
data_dict[str(item)] = [data[i]]
corr = pe.covariance(data, correlation=True)
chol = np.linalg.cholesky(corr)
covdiag = np.diag(1 / np.asarray([o.dvalue for o in data]))
chol_inv = scipy.linalg.solve_triangular(chol, covdiag, lower=True)
chol_inv_keys = [""]
chol_inv_keys_combined_fit = [str(item) for i,item in enumerate(x)]
if linear:
def fitf(p, x):
return p[1] + p[0] * x
fitf_dict = {}
for i,item in enumerate(x):
fitf_dict[str(item)] = fitf
else:
def fitf(p, x):
return p[1] * anp.exp(-p[0] * x)
fitf_dict = {}
for i,item in enumerate(x):
fitf_dict[str(item)] = fitf
fitpc = pe.least_squares(x, data, fitf, correlated_fit=True)
fitp_inv_cov = pe.least_squares(x, data, fitf, correlated_fit = True, inv_chol_cov_matrix = [chol_inv,chol_inv_keys])
fitp_inv_cov_combined_fit = pe.least_squares(x_dict, data_dict, fitf_dict, correlated_fit = True, inv_chol_cov_matrix = [chol_inv,chol_inv_keys_combined_fit])
for i in range(2):
diff_inv_cov = fitp_inv_cov[i] - fitpc[i]
diff_inv_cov.gamma_method()
assert(diff_inv_cov.is_zero(atol=0.0))
diff_inv_cov_combined_fit = fitp_inv_cov_combined_fit[i] - fitpc[i]
diff_inv_cov_combined_fit.gamma_method()
assert(diff_inv_cov_combined_fit.is_zero(atol=1e-12))
with pytest.raises(ValueError):
pe.least_squares(x_dict, data_dict, fitf_dict, correlated_fit = True, inv_chol_cov_matrix = [corr,chol_inv_keys_combined_fit])
def test_least_squares_invalid_inv_cov_matrix_input():
xvals = []
yvals = []
err = 0.1
def func_valid(a,x):
return a[0] + a[1] * x
for x in range(1, 8, 2):
xvals.append(x)
yvals.append(pe.pseudo_Obs(x + np.random.normal(0.0, err), err, 'test1') + pe.pseudo_Obs(0, err / 100, 'test2', samples=87))
[o.gamma_method() for o in yvals]
#dictionaries for a combined fit
xvals_dict = { }
yvals_dict = { }
for i,item in enumerate(np.arange(1, 8, 2)):
xvals_dict[str(item)] = [xvals[i]]
yvals_dict[str(item)] = [yvals[i]]
chol_inv_keys_combined_fit = ['1', '3', '5', '7']
chol_inv_keys_combined_fit_invalid = ['2', '7', '100', '8']
func_dict_valid = {"1": func_valid,"3": func_valid,"5": func_valid,"7": func_valid}
corr_valid = pe.covariance(yvals, correlation = True)
chol = np.linalg.cholesky(corr_valid)
covdiag = np.diag(1 / np.asarray([o.dvalue for o in yvals]))
chol_inv_valid = scipy.linalg.solve_triangular(chol, covdiag, lower=True)
chol_inv_keys = [""]
pe.least_squares(xvals, yvals,func_valid, correlated_fit = True, inv_chol_cov_matrix = [chol_inv_valid,chol_inv_keys])
pe.least_squares(xvals_dict, yvals_dict,func_dict_valid, correlated_fit = True, inv_chol_cov_matrix = [chol_inv_valid,chol_inv_keys_combined_fit])
chol_inv_invalid_shape1 = np.zeros((len(yvals),len(yvals)-1))
chol_inv_invalid_shape2 = np.zeros((len(yvals)+2,len(yvals)))
# for an uncombined fit
with pytest.raises(TypeError):
pe.least_squares(xvals, yvals, func_valid, correlated_fit = True, inv_chol_cov_matrix = [chol_inv_invalid_shape1,chol_inv_keys])
with pytest.raises(TypeError):
pe.least_squares(xvals, yvals, func_valid,correlated_fit = True, inv_chol_cov_matrix = [chol_inv_invalid_shape2,chol_inv_keys])
with pytest.raises(ValueError):
pe.least_squares(xvals, yvals, func_valid,correlated_fit = True, inv_chol_cov_matrix = [chol_inv_valid,chol_inv_keys_combined_fit_invalid])
#repeat for a combined fit
with pytest.raises(TypeError):
pe.least_squares(xvals_dict, yvals_dict,func_dict_valid, correlated_fit = True, inv_chol_cov_matrix = [chol_inv_invalid_shape1,chol_inv_keys_combined_fit])
with pytest.raises(TypeError):
pe.least_squares(xvals_dict, yvals_dict,func_dict_valid, correlated_fit = True, inv_chol_cov_matrix = [chol_inv_invalid_shape2,chol_inv_keys_combined_fit])
with pytest.raises(ValueError):
pe.least_squares(xvals_dict, yvals_dict,func_dict_valid, correlated_fit = True, inv_chol_cov_matrix = [chol_inv_valid,chol_inv_keys_combined_fit_invalid])
def test_correlated_fit():
num_samples = 400
@ -964,6 +1085,20 @@ def test_combined_resplot_qqplot():
fr = pe.least_squares(xd, yd, fd, resplot=True, qqplot=True)
plt.close('all')
def test_combined_fit_xerr():
fitd = {
'a' : lambda p, x: p[0] * x[0] + p[1] * x[1],
'b' : lambda p, x: p[0] * x[0] + p[2] * x[1],
'c' : lambda p, x: p[0] * x[0] + p[3] * x[1],
}
yd = {
'a': [pe.cov_Obs(3 + .1 * np.random.uniform(), .1**2, 'a' + str(i)) for i in range(5)],
'b': [pe.cov_Obs(1 + .1 * np.random.uniform(), .1**2, 'b' + str(i)) for i in range(6)],
'c': [pe.cov_Obs(3 + .1 * np.random.uniform(), .1**2, 'c' + str(i)) for i in range(3)],
}
xd = {k: np.transpose([[1 + .01 * np.random.uniform(), 2] for i in range(len(yd[k]))]) for k in fitd}
pe.fits.least_squares(xd, yd, fitd)
def test_x_multidim_fit():
x1 = np.arange(1, 10)

View file

@ -12,7 +12,7 @@ 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])
o4 = pe.merge_obs([o2, o3, pe.pseudo_Obs(0.5, .1, 'two|r3', samples=3221)])
otag = 'This has been merged!'
o4.tag = otag
do = o - .2 * o4
@ -101,8 +101,8 @@ def test_json_string_reconstruction():
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)
my_list = [pe.Obs([np.random.normal(1.0, 0.1, 100), np.random.normal(1.0, 0.1, 321)], ['ens1|r1', 'ens1|r2'], idl=[range(1, 201, 2), range(321)]) for o in range(8)]
rw_list = pe.reweight(pe.Obs([np.random.normal(1.0, 0.1, 100), np.random.normal(1.0, 0.1, 321)], ['ens1|r1', 'ens1|r2'], idl=[range(1, 201, 2), range(321)]), my_list)
for obs_list in [my_list, rw_list]:
for tag in [None, "test"]:
@ -111,40 +111,51 @@ def test_json_corr_io():
for corr_tag in [None, 'my_Corr_tag']:
for prange in [None, [3, 6]]:
for gap in [False, True]:
my_corr = pe.Corr(obs_list, padding=[pad, pad], prange=prange)
my_corr.tag = corr_tag
if gap:
my_corr.content[4] = None
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]])
for index, entry in enumerate(my_corr):
if entry is None:
assert recover[index] is None
assert my_corr.tag == recover.tag
assert my_corr.prange == recover.prange
assert my_corr.reweighted == recover.reweighted
for mult in [1., pe.cov_Obs([12.22, 1.21], [.212**2, .11**2], 'renorm')[0]]:
my_corr = mult * pe.Corr(obs_list, padding=[pad, pad], prange=prange)
my_corr.tag = corr_tag
if gap:
my_corr.content[4] = None
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]])
for index, entry in enumerate(my_corr):
if entry is None:
assert recover[index] is None
assert my_corr.tag == recover.tag
assert my_corr.prange == recover.prange
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)]
obs_list = [np.array([
[
pe.merge_obs([pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test|r2'), pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test|r1', samples=321)]),
pe.merge_obs([pe.pseudo_Obs(0.0, 0.1 * i, 'test|r2'), pe.pseudo_Obs(0.0, 0.1 * i, 'test|r1', samples=321)]),
],
[
pe.merge_obs([pe.pseudo_Obs(0.0, 0.1 * i, 'test|r2'), pe.pseudo_Obs(0.0, 0.1 * i, 'test|r1', samples=321),]),
pe.merge_obs([pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test|r2'), pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test|r1', samples=321)]),
],
]) for i in range(4)]
for tag in [None, "test"]:
obs_list[3][0, 1].tag = tag
for padding in [0, 1]:
for prange in [None, [3, 6]]:
my_corr = pe.Corr(obs_list, padding=[padding, padding], prange=prange)
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]])
for index, entry in enumerate(my_corr):
if entry is None:
assert recover[index] is None
assert my_corr.tag == recover.tag
assert my_corr.prange == recover.prange
for mult in [1., pe.cov_Obs([12.22, 1.21], [.212**2, .11**2], 'renorm')[0]]:
my_corr = mult * pe.Corr(obs_list, padding=[padding, padding], prange=prange)
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]])
for index, entry in enumerate(my_corr):
if entry is None:
assert recover[index] is None
assert my_corr.tag == recover.tag
assert my_corr.prange == recover.prange
def test_json_dict_io():
@ -211,6 +222,7 @@ def test_json_dict_io():
'd': pe.pseudo_Obs(.01, .001, 'testd', samples=10) * pe.cov_Obs(1, .01, 'cov1'),
'se': None,
'sf': 1.2,
'k': pe.cov_Obs(.1, .001**2, 'cov') * pe.merge_obs([pe.pseudo_Obs(1.0, 0.1, 'test|r2'), pe.pseudo_Obs(1.0, 0.1, 'test|r1', samples=321)]),
}
}
@ -314,7 +326,7 @@ def test_dobsio():
o2 = pe.pseudo_Obs(0.5, .1, 'two|r1')
o3 = pe.pseudo_Obs(0.5, .1, 'two|r2')
o4 = pe.merge_obs([o2, o3])
o4 = pe.merge_obs([o2, o3, pe.pseudo_Obs(0.5, .1, 'two|r3', samples=3221)])
otag = 'This has been merged!'
o4.tag = otag
do = o - .2 * o4
@ -328,7 +340,7 @@ def test_dobsio():
o5 /= co2[0]
o5.tag = 2 * otag
tt1 = pe.Obs([np.random.rand(100), np.random.rand(100)], ['t|r1', 't|r2'], idl=[range(2, 202, 2), range(22, 222, 2)])
tt1 = pe.Obs([np.random.rand(100), np.random.rand(102)], ['t|r1', 't|r2'], idl=[range(2, 202, 2), range(22, 226, 2)])
tt3 = pe.Obs([np.random.rand(102)], ['qe|r1'])
tt = tt1 + tt3
@ -337,7 +349,7 @@ def test_dobsio():
tt4 = pe.Obs([np.random.rand(100), np.random.rand(100)], ['t|r1', 't|r2'], idl=[range(1, 101, 1), range(2, 202, 2)])
ol = [o2, o3, o4, do, o5, tt, tt4, np.log(tt4 / o5**2), np.exp(o5 + np.log(co3 / tt3 + o4) / tt)]
ol = [o2, o3, o4, do, o5, tt, tt4, np.log(tt4 / o5**2), np.exp(o5 + np.log(co3 / tt3 + o4) / tt), o4.reweight(o4)]
print(ol)
fname = 'test_rw'
@ -362,9 +374,12 @@ def test_dobsio():
def test_reconstruct_non_linear_r_obs(tmp_path):
to = pe.Obs([np.random.rand(500), np.random.rand(500), np.random.rand(111)],
["e|r1", "e|r2", "my_new_ensemble_54^£$|8'[@124435%6^7&()~#"],
idl=[range(1, 501), range(0, 500), range(1, 999, 9)])
to = (
pe.Obs([np.random.rand(500), np.random.rand(1200)],
["e|r1", "e|r2", ],
idl=[range(1, 501), range(0, 1200)])
+ pe.Obs([np.random.rand(111)], ["my_new_ensemble_54^£$|8'[@124435%6^7&()~#"], idl=[range(1, 999, 9)])
)
to = np.log(to ** 2) / to
to.dump((tmp_path / "test_equality").as_posix())
ro = pe.input.json.load_json((tmp_path / "test_equality").as_posix())
@ -372,9 +387,12 @@ def test_reconstruct_non_linear_r_obs(tmp_path):
def test_reconstruct_non_linear_r_obs_list(tmp_path):
to = pe.Obs([np.random.rand(500), np.random.rand(500), np.random.rand(111)],
["e|r1", "e|r2", "my_new_ensemble_54^£$|8'[@124435%6^7&()~#"],
idl=[range(1, 501), range(0, 500), range(1, 999, 9)])
to = (
pe.Obs([np.random.rand(500), np.random.rand(1200)],
["e|r1", "e|r2", ],
idl=[range(1, 501), range(0, 1200)])
+ pe.Obs([np.random.rand(111)], ["my_new_ensemble_54^£$|8'[@124435%6^7&()~#"], idl=[range(1, 999, 9)])
)
to = np.log(to ** 2) / to
for to_list in [[to, to, to], np.array([to, to, to])]:
pe.input.json.dump_to_json(to_list, (tmp_path / "test_equality_list").as_posix())

View file

@ -34,7 +34,7 @@ def test_matmul():
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_list.append(pe.Obs([np.random.rand(length)], ['t1']) + pe.Obs([np.random.rand(length + 1)], ['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):
@ -43,8 +43,8 @@ def test_matmul():
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_list.append(pe.CObs(pe.Obs([np.random.rand(length)], ['t1']) + pe.Obs([np.random.rand(length + 1)], ['t2']),
pe.Obs([np.random.rand(length)], ['t1']) + pe.Obs([np.random.rand(length + 1)], ['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):
@ -151,7 +151,7 @@ def test_multi_dot():
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_list.append(pe.Obs([np.random.rand(length)], ['t1']) + pe.Obs([np.random.rand(length + 1)], ['t2']))
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):
@ -160,8 +160,8 @@ def test_multi_dot():
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_list.append(pe.CObs(pe.Obs([np.random.rand(length)], ['t1']) + pe.Obs([np.random.rand(length + 1)], ['t2']),
pe.Obs([np.random.rand(length)], ['t1']) + pe.Obs([np.random.rand(length + 1)], ['t2'])))
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):
@ -209,7 +209,7 @@ def test_irregular_matrix_inverse():
for idl in [range(8, 508, 10), range(250, 273), [2, 8, 19, 20, 78, 99, 828, 10548979]]:
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_array.append(pe.Obs([np.random.normal(1.1, 0.2, len(idl))], ['ens1'], idl=[idl]) + pe.Obs([np.random.normal(0.25, 0.1, 10)], ['ens2'], idl=[range(1, 11)]))
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
@ -276,10 +276,10 @@ def test_matrix_functions():
for (i, j), entry in np.ndenumerate(check_inv):
entry.gamma_method()
if(i == j):
assert math.isclose(entry.value, 1.0, abs_tol=1e-9), 'value ' + str(i) + ',' + str(j) + ' ' + str(entry.value)
assert math.isclose(entry.value, 1.0, abs_tol=2e-9), 'value ' + str(i) + ',' + str(j) + ' ' + str(entry.value)
else:
assert math.isclose(entry.value, 0.0, abs_tol=1e-9), 'value ' + str(i) + ',' + str(j) + ' ' + str(entry.value)
assert math.isclose(entry.dvalue, 0.0, abs_tol=1e-9), 'dvalue ' + str(i) + ',' + str(j) + ' ' + str(entry.dvalue)
assert math.isclose(entry.value, 0.0, abs_tol=2e-9), 'value ' + str(i) + ',' + str(j) + ' ' + str(entry.value)
assert math.isclose(entry.dvalue, 0.0, abs_tol=2e-9), 'dvalue ' + str(i) + ',' + str(j) + ' ' + str(entry.dvalue)
# Check Cholesky decomposition
sym = np.dot(matrix, matrix.T)

View file

@ -61,9 +61,9 @@ def test_Obs_exceptions():
my_obs.plot_rep_dist()
with pytest.raises(Exception):
my_obs.plot_piechart()
with pytest.raises(Exception):
with pytest.raises(TypeError):
my_obs.gamma_method(S='2.3')
with pytest.raises(Exception):
with pytest.raises(ValueError):
my_obs.gamma_method(tau_exp=2.3)
my_obs.gamma_method()
my_obs.details()
@ -199,7 +199,7 @@ def test_gamma_method_no_windowing():
assert np.isclose(np.sqrt(np.var(obs.deltas['ens'], ddof=1) / obs.shape['ens']), obs.dvalue)
obs.gamma_method(S=1.1)
assert obs.e_tauint['ens'] > 0.5
with pytest.raises(Exception):
with pytest.raises(ValueError):
obs.gamma_method(S=-0.2)
@ -333,7 +333,7 @@ def test_derived_observables():
def test_multi_ens():
names = ['A0', 'A1|r001', 'A1|r002']
test_obs = pe.Obs([np.random.rand(50), np.random.rand(50), np.random.rand(50)], names)
test_obs = pe.Obs([np.random.rand(50)], names[:1]) + pe.Obs([np.random.rand(50), np.random.rand(50)], names[1:])
assert test_obs.e_names == ['A0', 'A1']
assert test_obs.e_content['A0'] == ['A0']
assert test_obs.e_content['A1'] == ['A1|r001', 'A1|r002']
@ -345,6 +345,9 @@ def test_multi_ens():
ensembles.append(str(i))
assert my_sum.e_names == sorted(ensembles)
with pytest.raises(ValueError):
test_obs = pe.Obs([np.random.rand(50), np.random.rand(50), np.random.rand(50)], names)
def test_multi_ens2():
names = ['ens', 'e', 'en', 'e|r010', 'E|er', 'ens|', 'Ens|34', 'ens|r548984654ez4e3t34terh']
@ -461,6 +464,18 @@ def test_cobs_overloading():
obs / cobs
def test_pow():
data = [1, 2.341, pe.pseudo_Obs(4.8, 0.48, "test_obs"), pe.cov_Obs(1.1, 0.3 ** 2, "test_cov_obs")]
for d in data:
assert d * d == d ** 2
assert d * d * d == d ** 3
for d2 in data:
assert np.log(d ** d2) == d2 * np.log(d)
assert (d ** d2) ** (1 / d2) == d
def test_reweighting():
my_obs = pe.Obs([np.random.rand(1000)], ['t'])
assert not my_obs.reweighted
@ -478,26 +493,33 @@ def test_reweighting():
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):
with pytest.raises(ValueError):
pe.reweight(my_obs, [my_covobs])
my_obs2 = pe.Obs([np.random.rand(1000)], ['t2'])
with pytest.raises(Exception):
with pytest.raises(ValueError):
pe.reweight(my_obs, [my_obs + my_obs2])
with pytest.raises(Exception):
with pytest.raises(ValueError):
pe.reweight(my_irregular_obs, [my_obs])
my_merged_obs = my_obs + pe.Obs([np.random.rand(1000)], ['q'])
with pytest.raises(ValueError):
pe.reweight(my_merged_obs, [my_merged_obs])
def test_merge_obs():
my_obs1 = pe.Obs([np.random.rand(100)], ['t'])
my_obs2 = pe.Obs([np.random.rand(100)], ['q'], idl=[range(1, 200, 2)])
my_obs1 = pe.Obs([np.random.normal(1, .1, 100)], ['t|1'])
my_obs2 = pe.Obs([np.random.normal(1, .1, 100)], ['t|2'], idl=[range(1, 200, 2)])
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):
diff = merged - (my_obs2 + my_obs1) / 2
assert np.isclose(0, diff.value, atol=1e-16)
with pytest.raises(ValueError):
pe.merge_obs([my_obs1, my_obs1])
my_covobs = pe.cov_Obs(1.0, 0.003, 'cov')
with pytest.raises(Exception):
with pytest.raises(ValueError):
pe.merge_obs([my_obs1, my_covobs])
my_obs3 = pe.Obs([np.random.rand(100)], ['q|2'], idl=[range(1, 200, 2)])
with pytest.raises(ValueError):
pe.merge_obs([my_obs1, my_obs3])
@ -519,23 +541,26 @@ def test_correlate():
assert corr1 == corr2
my_obs3 = pe.Obs([np.random.rand(100)], ['t'], idl=[range(2, 102)])
with pytest.raises(Exception):
with pytest.raises(ValueError):
pe.correlate(my_obs1, my_obs3)
my_obs4 = pe.Obs([np.random.rand(99)], ['t'])
with pytest.raises(Exception):
with pytest.raises(ValueError):
pe.correlate(my_obs1, my_obs4)
my_obs5 = pe.Obs([np.random.rand(100)], ['t'], idl=[range(5, 505, 5)])
my_obs6 = pe.Obs([np.random.rand(100)], ['t'], idl=[range(5, 505, 5)])
corr3 = pe.correlate(my_obs5, my_obs6)
assert my_obs5.idl == corr3.idl
my_obs7 = pe.Obs([np.random.rand(99)], ['q'])
with pytest.raises(ValueError):
pe.correlate(my_obs1, my_obs7)
my_new_obs = pe.Obs([np.random.rand(100)], ['q3'])
with pytest.raises(Exception):
with pytest.raises(ValueError):
pe.correlate(my_obs1, my_new_obs)
my_covobs = pe.cov_Obs(1.0, 0.003, 'cov')
with pytest.raises(Exception):
with pytest.raises(ValueError):
pe.correlate(my_covobs, my_covobs)
r_obs = pe.reweight(my_obs1, [my_obs1])[0]
with pytest.warns(RuntimeWarning):
@ -554,11 +579,11 @@ def test_merge_idx():
for j in range(5):
idll = [range(1, int(round(np.random.uniform(300, 700))), int(round(np.random.uniform(1, 14)))) for i in range(10)]
assert pe.obs._merge_idx(idll) == sorted(set().union(*idll))
assert list(pe.obs._merge_idx(idll)) == sorted(set().union(*idll))
for j in range(5):
idll = [range(int(round(np.random.uniform(1, 28))), int(round(np.random.uniform(300, 700))), int(round(np.random.uniform(1, 14)))) for i in range(10)]
assert pe.obs._merge_idx(idll) == sorted(set().union(*idll))
assert list(pe.obs._merge_idx(idll)) == sorted(set().union(*idll))
idl = [list(np.arange(1, 14)) + list(range(16, 100, 4)), range(4, 604, 4), [2, 4, 5, 6, 8, 9, 12, 24], range(1, 20, 1), range(50, 789, 7)]
new_idx = pe.obs._merge_idx(idl)
@ -669,14 +694,14 @@ def test_gamma_method_irregular():
assert (a.dvalue - 5 * a.ddvalue < expe and expe < a.dvalue + 5 * a.ddvalue)
arr2 = np.random.normal(1, .2, size=N)
afull = pe.Obs([arr, arr2], ['a1', 'a2'])
afull = pe.Obs([arr], ['a1']) + pe.Obs([arr2], ['a2'])
configs = np.ones_like(arr2)
for i in np.random.uniform(0, len(arr2), size=int(.8*N)):
configs[int(i)] = 0
zero_arr2 = [arr2[i] for i in range(len(arr2)) if not configs[i] == 0]
idx2 = [i + 1 for i in range(len(configs)) if configs[i] == 1]
a = pe.Obs([zero_arr, zero_arr2], ['a1', 'a2'], idl=[idx, idx2])
a = pe.Obs([zero_arr], ['a1'], idl=[idx]) + pe.Obs([zero_arr2], ['a2'], idl=[idx2])
afull.gamma_method()
a.gamma_method()
@ -762,7 +787,7 @@ def test_gamma_method_irregular():
my_obs.gm()
idl += [range(1, 400, 4)]
my_obs = pe.Obs([dat for i in range(len(idl))], ['%s|%d' % ('A', i) for i in range(len(idl))], idl=idl)
with pytest.raises(Exception):
with pytest.raises(ValueError):
my_obs.gm()
# check cases where tau is large compared to the chain length
@ -1010,7 +1035,7 @@ def test_correlation_intersection_of_idls():
def test_covariance_non_identical_objects():
obs1 = pe.Obs([np.random.normal(1.0, 0.1, 1000), np.random.normal(1.0, 0.1, 1000), np.random.normal(1.0, 0.1, 732)], ["ens|r1", "ens|r2", "ens2"])
obs1 = pe.Obs([np.random.normal(1.0, 0.1, 1000), np.random.normal(1.0, 0.1, 1000)], ["ens|r1", "ens|r2"]) + pe.Obs([np.random.normal(1.0, 0.1, 732)], ['ens2'])
obs1.gamma_method()
obs2 = obs1 + 1e-18
obs2.gamma_method()
@ -1063,6 +1088,27 @@ def test_covariance_reorder_non_overlapping_data():
assert np.isclose(corr1[0, 1], corr2[0, 1], atol=1e-14)
def test_sort_corr():
xd = {
'b': [1, 2, 3],
'a': [2.2, 4.4],
'c': [3.7, 5.1]
}
yd = {k : pe.cov_Obs(xd[k], [.2 * o for o in xd[k]], k) for k in xd}
key_orig = list(yd.keys())
y_all = np.concatenate([np.array(yd[key]) for key in key_orig])
[o.gm() for o in y_all]
cov = pe.covariance(y_all)
key_ls = key_sorted = sorted(key_orig)
y_sorted = np.concatenate([np.array(yd[key]) for key in key_sorted])
[o.gm() for o in y_sorted]
cov_sorted = pe.covariance(y_sorted)
retcov = pe.obs.sort_corr(cov, key_orig, yd)
assert np.sum(retcov - cov_sorted) == 0
def test_empty_obs():
o = pe.Obs([np.random.rand(100)], ['test'])
q = o + pe.Obs([], [], means=[])
@ -1073,6 +1119,9 @@ def test_reweight_method():
obs1 = pe.pseudo_Obs(0.2, 0.01, 'test')
rw = pe.pseudo_Obs(0.999, 0.001, 'test')
assert obs1.reweight(rw) == pe.reweight(rw, [obs1])[0]
rw2 = pe.pseudo_Obs(0.999, 0.001, 'test2')
with pytest.raises(ValueError):
obs1.reweight(rw2)
def test_jackknife():
@ -1089,7 +1138,7 @@ def test_jackknife():
assert np.allclose(tmp_jacks, my_obs.export_jackknife())
my_new_obs = my_obs + pe.Obs([full_data], ['test2'])
with pytest.raises(Exception):
with pytest.raises(ValueError):
my_new_obs.export_jackknife()
@ -1436,4 +1485,4 @@ def test_missing_replica():
for op in [[O1O2, O1O2b], [O1O2O3, O1O2O3b]]:
assert np.isclose(op[1].value, op[0].value)
assert np.isclose(op[1].dvalue, op[0].dvalue, atol=0, rtol=5e-2)
assert np.isclose(op[1].dvalue, op[0].dvalue, atol=0, rtol=5e-2)

View file

@ -387,3 +387,33 @@ def test_find_correlator():
found_start, found_T = sfin._find_correlator(file, "2.0", "name f_A\nquarks lquark lquark\noffset 0\nwf 0", False, False)
assert found_start == 21
assert found_T == 3
def test_get_rep_name():
names = ['data_r0', 'data_r1', 'data_r2']
new_names = sfin._get_rep_names(names)
assert len(new_names) == 3
assert new_names[0] == 'data_|r0'
assert new_names[1] == 'data_|r1'
assert new_names[2] == 'data_|r2'
names = ['data_q0', 'data_q1', 'data_q2']
new_names = sfin._get_rep_names(names, rep_sep='q')
assert len(new_names) == 3
assert new_names[0] == 'data_|q0'
assert new_names[1] == 'data_|q1'
assert new_names[2] == 'data_|q2'
def test_get_appended_rep_name():
names = ['data_r0.f_1', 'data_r1.f_1', 'data_r2.f_1']
new_names = sfin._get_appended_rep_names(names, 'data', 'f_1')
assert len(new_names) == 3
assert new_names[0] == 'data_|r0'
assert new_names[1] == 'data_|r1'
assert new_names[2] == 'data_|r2'
names = ['data_q0.f_1', 'data_q1.f_1', 'data_q2.f_1']
new_names = sfin._get_appended_rep_names(names, 'data', 'f_1', rep_sep='q')
assert len(new_names) == 3
assert new_names[0] == 'data_|q0'
assert new_names[1] == 'data_|q1'
assert new_names[2] == 'data_|q2'