.github/workflows/examples.yml

@@ -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
-          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.
+          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.
 
       - name: Run tests
         run: pytest -vv --nbmake examples/*.ipynb

.github/workflows/pytest.yml

@@ -20,9 +20,7 @@ jobs:
         python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
         include:
           - os: macos-latest
-            python-version: "3.12"
-          - os: ubuntu-24.04-arm
-            python-version: "3.12"
+            python-version: "3.10"
 
     steps:
       - name: Checkout source
@@ -32,15 +30,17 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
-      - name: uv
-        uses: astral-sh/setup-uv@v5
 
       - name: Install
         run: |
-          uv pip install wheel --system
-          uv pip install . --system
-          uv pip install pytest pytest-cov pytest-benchmark hypothesis --system
-          uv pip freeze --system
+          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 freeze
 
       - name: Run tests
         run: pytest --cov=pyerrors -vv

.github/workflows/ruff.yml

@@ -1,15 +0,0 @@
-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"

CHANGELOG.md

@@ -2,21 +2,6 @@
 
 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

README.md

@@ -1,4 +1,4 @@
-[![](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)
+[![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-3.9+-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.
 
@@ -14,6 +14,11 @@ 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).

pyerrors/__init__.py

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

pyerrors/correlators.py

@@ -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 a is not None]  # To check if the matrices are correct for all undefined elements
+                noNull = [a for a in self.content if not (a is None)]  # To check if the matrices are correct for all undefined elements
                 self.N = noNull[0].shape[0]
                 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 item is not None:
+            if not (item is 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 ValueError("Trying to project a Corr, that already has N=1.")
+            raise Exception("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 ValueError("Length of vector list must be equal to T")
+                raise Exception("Length of vector list must be equal to T")
             vector_r = [vector_r] * self.T
         if isinstance(vector_r, list) and not isinstance(vector_l, list):
             if len(vector_r) != self.T:
-                raise ValueError("Length of vector list must be equal to T")
+                raise Exception("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 ValueError("Vectors are of wrong shape!")
+                raise Exception("Vectors are of wrong shape!")
             if normalize:
                 vector_l, vector_r = vector_l / np.sqrt((vector_l @ vector_l)), vector_r / np.sqrt(vector_r @ vector_r)
             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 ValueError("Trying to pick item from projected Corr")
+            raise Exception("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 ValueError("Can only make Corr[N=1] plottable")
-        x_list = [x for x in range(self.T) if self.content[x] is not None]
+            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]
         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 ValueError('symmetric cannot be safely applied to multi-dimensional correlators.')
+            raise Exception('symmetric cannot be safely applied to multi-dimensional correlators.')
         if self.T % 2 != 0:
-            raise ValueError("Can not symmetrize odd T")
+            raise Exception("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 ValueError("Corr could not be symmetrized: No redundant values")
+            raise Exception("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 ValueError("Can not symmetrize odd T")
+            raise Exception("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 ValueError("Corr could not be symmetrized: No redundant values")
+            raise Exception("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 ValueError("Trying to symmetrize a correlator matrix, that already has N=1.")
+            raise Exception("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 ValueError("GEVP methods only works on correlator matrices and not single correlators.")
+            raise Exception("GEVP methods only works on correlator matrices and not single correlators.")
         if ts is not None:
             if (ts <= t0):
-                raise ValueError("ts has to be larger than t0.")
+                raise Exception("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 ValueError("ts is required if sort=None.")
+                raise Exception("ts is required if sort=None.")
             if (self.content[t0] is None) or (self.content[ts] is None):
-                raise ValueError("Corr not defined at t0/ts.")
+                raise Exception("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 ValueError("ts is required for the Eigenvector sorting method.")
+                    raise Exception("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 ValueError("Unknown value for 'sort'. Choose 'Eigenvalue', 'Eigenvector' or None.")
+            raise Exception("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 NotImplementedError("Multi-operator Prony not implemented!")
+            raise Exception("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 ValueError("Only one-dimensional correlators can be safely correlated.")
+            raise Exception("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 TypeError("Can only correlate with an Obs or a Corr.")
+                    raise Exception("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 ValueError("deriv only implemented for one-dimensional correlators.")
+            raise Exception("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 ValueError('Derivative is undefined at all timeslices')
+                raise Exception('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 ValueError("Derivative is undefined at all timeslices")
+                raise Exception("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 ValueError("Derivative is undefined at all timeslices")
+                raise Exception("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 ValueError('Derivative is undefined at all timeslices')
+                raise Exception('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 ValueError("Log is undefined at all timeslices")
+                raise Exception("Log is undefined at all timeslices")
             logcorr = Corr(newcontent)
             return self * logcorr.deriv('symmetric')
         else:
-            raise ValueError("Unknown variant.")
+            raise Exception("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 ValueError("second_deriv only implemented for one-dimensional correlators.")
+            raise Exception("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 ValueError("Derivative is undefined at all timeslices")
+                raise Exception("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 ValueError("Derivative is undefined at all timeslices")
+                raise Exception("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 ValueError("Derivative is undefined at all timeslices")
+                raise Exception("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 ValueError("Log is undefined at all timeslices")
+                raise Exception("Log is undefined at all timeslices")
             logcorr = Corr(newcontent)
             return self * (logcorr.second_deriv('symmetric') + (logcorr.deriv('symmetric'))**2)
         else:
-            raise ValueError("Unknown variant.")
+            raise Exception("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 ValueError('m_eff is undefined at all timeslices')
+                raise Exception('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 ValueError('m_eff is undefined at all timeslices')
+                raise Exception('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 ValueError('m_eff is undefined at all timeslices')
+                raise Exception('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 ValueError("m_eff is undefined at all timeslices")
+                raise Exception("m_eff is undefined at all timeslices")
             return np.arccosh(Corr(newcontent, padding=[1, 1]))
 
         else:
-            raise ValueError('Unknown variant.')
+            raise Exception('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 ValueError("Correlator must be projected before fitting")
+            raise Exception("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 TypeError("fitrange has to be a list with two elements")
+                raise Exception("fitrange has to be a list with two elements")
             if len(fitrange) != 2:
-                raise ValueError("fitrange has to have exactly two elements [fit_start, fit_stop]")
+                raise Exception("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 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])
+        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])
         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 ValueError("Correlator must be projected before getting a plateau.")
+            raise Exception("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 ValueError("plateau is undefined at all timeslices in plateaurange.")
+            raise Exception("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 ValueError("Unsupported plateau method: " + method)
+            raise Exception("Unsupported plateau method: " + method)
 
     def set_prange(self, prange):
         """Sets the attribute prange of the Corr object."""
         if not len(prange) == 2:
-            raise ValueError("prange must be a list or array with two values")
+            raise Exception("prange must be a list or array with two values")
         if not ((isinstance(prange[0], int)) and (isinstance(prange[1], int))):
-            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")
+            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")
 
         self.prange = prange
         return
@@ -900,7 +900,7 @@ class Corr:
             Optional title of the figure.
         """
         if self.N != 1:
-            raise ValueError("Correlator must be projected before plotting")
+            raise Exception("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 TypeError("'comp' must be a correlator or a list of correlators.")
+                raise Exception("'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 TypeError("'plateau' must be an Obs")
+                raise Exception("'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 TypeError("'references' must be a list of floating pint values.")
+                raise Exception("'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 TypeError("'save' has to be a string.")
+                raise Exception("'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 ValueError("Correlator needs to be projected first.")
+            raise Exception("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 ValueError("Unknown datatype " + str(datatype))
+            raise Exception("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 ValueError("Addition of Corrs with different shape")
+                raise Exception("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 ValueError("Multiplication of Corr object requires N=N or N=1 and T=T")
+                raise Exception("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 ValueError("Multiplication of Corr object requires N=N or N=1 and T=T")
+                raise Exception("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 ValueError("Division returns completely undefined correlator")
+                raise Exception("Division returns completely undefined correlator")
             return Corr(newcontent)
 
         elif isinstance(y, (Obs, CObs)):
             if isinstance(y, Obs):
                 if y.value == 0:
-                    raise ValueError('Division by zero will return undefined correlator')
+                    raise Exception('Division by zero will return undefined correlator')
             if isinstance(y, CObs):
                 if y.is_zero():
-                    raise ValueError('Division by zero will return undefined correlator')
+                    raise Exception('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 ValueError('Division by zero will return undefined correlator')
+                raise Exception('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 ValueError('Operation returns undefined correlator')
+            raise Exception('Operation returns undefined correlator')
         return Corr(newcontent)
 
     def sin(self):
@@ -1392,13 +1392,13 @@ class Corr:
         '''
 
         if self.N == 1:
-            raise ValueError('Method cannot be applied to one-dimensional correlators.')
+            raise Exception('Method cannot be applied to one-dimensional correlators.')
         if basematrix is None:
             basematrix = self
         if Ntrunc >= basematrix.N:
-            raise ValueError('Cannot truncate using Ntrunc <= %d' % (basematrix.N))
+            raise Exception('Cannot truncate using Ntrunc <= %d' % (basematrix.N))
         if basematrix.N != self.N:
-            raise ValueError('basematrix and targetmatrix have to be of the same size.')
+            raise Exception('basematrix and targetmatrix have to be of the same size.')
 
         evecs = basematrix.GEVP(t0proj, tproj, sort=None)[:Ntrunc]
 

pyerrors/dirac.py

@@ -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 ValueError("Unexpected input", i, j, k)
+        raise Exception("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 ValueError("Unexpected input", i, j, k, o)
+        raise Exception("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 ValueError('Unkown gamma structure', gamma_tag)
+        raise Exception('Unkown gamma structure', gamma_tag)
     return g

pyerrors/fits.py

@@ -293,7 +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 {np.asarray(yd[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 {xd[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))
@@ -365,8 +365,6 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
             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))

pyerrors/input/__init__.py

@@ -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 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
+from . import bdio
+from . import dobs
+from . import hadrons
+from . import json
+from . import misc
+from . import openQCD
+from . import pandas
+from . import sfcf

pyerrors/input/dobs.py

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

pyerrors/input/hadrons.py

@@ -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 group + '/' + entry not in h5file:
+        if not group + '/' + entry 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 tree + '/' + key not in h5file:
+            if not tree + '/' + key in h5file:
                 raise Exception("Entry '" + key + "' not contained in the files.")
             raw_data = h5file[tree + '/' + key + '/data']
             real_data = raw_data[:].astype(np.double)

pyerrors/input/json.py

@@ -133,11 +133,10 @@ def create_json_string(ol, description='', indent=1):
         names = []
         idl = []
         for key, value in obs.idl.items():
-            samples.append(np.array([np.nan] * len(value)))
+            samples.append([np.nan] * len(value))
             names.append(key)
             idl.append(value)
-        my_obs = Obs(samples, names, idl, means=[np.nan for n in names])
-        my_obs._value = np.nan
+        my_obs = Obs(samples, names, idl)
         my_obs._covobs = obs._covobs
         for name in obs._covobs:
             my_obs.names.append(name)
@@ -332,8 +331,7 @@ def _parse_json_dict(json_dict, verbose=True, full_output=False):
         cd = _gen_covobsd_from_cdatad(o.get('cdata', {}))
 
         if od:
-            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 = Obs([[ddi[0] + values[0] for ddi in di] for di in od['deltas']], od['names'], idl=od['idl'])
             ret._value = values[0]
         else:
             ret = Obs([], [], means=[])
@@ -358,8 +356,7 @@ def _parse_json_dict(json_dict, verbose=True, full_output=False):
         taglist = o.get('tag', layout * [None])
         for i in range(layout):
             if od:
-                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.append(Obs([list(di[:, i] + values[i]) for di in od['deltas']], od['names'], idl=od['idl']))
                 ret[-1]._value = values[i]
             else:
                 ret.append(Obs([], [], means=[]))
@@ -386,8 +383,7 @@ def _parse_json_dict(json_dict, verbose=True, full_output=False):
         taglist = o.get('tag', N * [None])
         for i in range(N):
             if od:
-                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.append(Obs([di[:, i] + values[i] for di in od['deltas']], od['names'], idl=od['idl']))
                 ret[-1]._value = values[i]
             else:
                 ret.append(Obs([], [], means=[]))

pyerrors/input/openQCD.py

@@ -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 version not in known_oqcd_versions:
+    if not (version in known_oqcd_versions):
         raise Exception('Unknown openQCD version defined!')
     print("Working with openQCD version " + version)
     if 'postfix' in kwargs:

pyerrors/input/sfcf.py

@@ -127,8 +127,7 @@ 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]]
@@ -200,9 +199,9 @@ 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, rep_sep=(kwargs.get('rep_string', 'r')))
+            new_names = _get_rep_names(ls, ens_name)
         else:
-            new_names = _get_appended_rep_names(ls, prefix, name_list[0], ens_name, rep_sep=(kwargs.get('rep_string', 'r')))
+            new_names = _get_appended_rep_names(ls, prefix, name_list[0], ens_name)
         new_names = sort_names(new_names)
 
     idl = []
@@ -647,22 +646,22 @@ def _read_append_rep(filename, pattern, b2b, cfg_separator, im, single):
         return T, rep_idl, data
 
 
-def _get_rep_names(ls, ens_name=None, rep_sep='r'):
+def _get_rep_names(ls, ens_name=None):
     new_names = []
     for entry in ls:
         try:
-            idx = entry.index(rep_sep)
+            idx = entry.index('r')
         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, rep_sep='r'):
+def _get_appended_rep_names(ls, prefix, name, ens_name=None):
     new_names = []
     for exc in ls:
         if not fnmatch.fnmatch(exc, prefix + '*.' + name):
@@ -671,12 +670,12 @@ def _get_appended_rep_names(ls, prefix, name, ens_name=None, rep_sep='r'):
     for entry in ls:
         myentry = entry[:-len(name) - 1]
         try:
-            idx = myentry.index(rep_sep)
+            idx = myentry.index('r')
         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

pyerrors/obs.py

@@ -82,8 +82,6 @@ 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.')
@@ -224,7 +222,7 @@ class Obs:
                 tmp = kwargs.get(kwarg_name)
                 if isinstance(tmp, (int, float)):
                     if tmp < 0:
-                        raise ValueError(kwarg_name + ' has to be larger or equal to 0.')
+                        raise Exception(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:
@@ -293,7 +291,7 @@ class Obs:
                 texp = self.tau_exp[e_name]
                 # Critical slowing down analysis
                 if w_max // 2 <= 1:
-                    raise ValueError("Need at least 8 samples for tau_exp error analysis")
+                    raise Exception("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:
@@ -622,7 +620,7 @@ 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 ValueError('Error is 0.0')
+            raise Exception('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()
@@ -661,7 +659,7 @@ class Obs:
             with open(file_name + '.p', 'wb') as fb:
                 pickle.dump(self, fb)
         else:
-            raise TypeError("Unknown datatype " + str(datatype))
+            raise Exception("Unknown datatype " + str(datatype))
 
     def export_jackknife(self):
         """Export jackknife samples from the Obs
@@ -678,7 +676,7 @@ class Obs:
         """
 
         if len(self.names) != 1:
-            raise ValueError("'export_jackknife' is only implemented for Obs defined on one ensemble and replicum.")
+            raise Exception("'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]
@@ -713,7 +711,7 @@ class Obs:
             should agree with samples from a full bootstrap analysis up to O(1/N).
         """
         if len(self.names) != 1:
-            raise ValueError("'export_boostrap' is only implemented for Obs defined on one ensemble and replicum.")
+            raise Exception("'export_boostrap' is only implemented for Obs defined on one ensemble and replicum.")
 
         name = self.names[0]
         length = self.N
@@ -858,12 +856,15 @@ class Obs:
 
     def __pow__(self, y):
         if isinstance(y, Obs):
-            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)])
+            return derived_observable(lambda x: x[0] ** x[1], [self, y])
         else:
-            return derived_observable(lambda x, **kwargs: x[0] ** y, [self], man_grad=[y * self.value ** (y - 1)])
+            return derived_observable(lambda x: x[0] ** y, [self])
 
     def __rpow__(self, y):
-        return derived_observable(lambda x, **kwargs: y ** x[0], [self], man_grad=[y ** self.value * np.log(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])
 
     def __abs__(self):
         return derived_observable(lambda x: anp.abs(x[0]), [self])
@@ -1269,7 +1270,7 @@ 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 ValueError('Manual derivative does not have correct shape.')
+            raise Exception('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')
@@ -1335,7 +1336,7 @@ 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 ValueError('The same name has been used for deltas and covobs!')
+            raise Exception('The same name has been used for deltas and covobs!')
         new_samples = []
         new_means = []
         new_idl = []
@@ -1376,7 +1377,7 @@ def _reduce_deltas(deltas, idx_old, idx_new):
         Has to be a subset of idx_old.
     """
     if not len(deltas) == len(idx_old):
-        raise ValueError('Length of deltas and idx_old have to be the same: %d != %d' % (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)))
     if type(idx_old) is range and type(idx_new) is range:
         if idx_old == idx_new:
             return deltas
@@ -1384,7 +1385,7 @@ 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 ValueError('Error in _reduce_deltas: Config of idx_new not in idx_old')
+        raise Exception('Error in _reduce_deltas: Config of idx_new not in idx_old')
     return np.array(deltas)[indices]
 
 
@@ -1406,14 +1407,12 @@ def reweight(weight, obs, **kwargs):
     result = []
     for i in range(len(obs)):
         if len(obs[i].cov_names):
-            raise ValueError('Error: Not possible to reweight an Obs that contains covobs!')
+            raise Exception('Error: Not possible to reweight an Obs that contains covobs!')
         if not set(obs[i].names).issubset(weight.names):
-            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.')
+            raise Exception('Error: Ensembles do not fit')
         for name in obs[i].names:
             if not set(obs[i].idl[name]).issubset(weight.idl[name]):
-                raise ValueError('obs[%d] has to be defined on a subset of the configs in weight.idl[%s]!' % (i, name))
+                raise Exception('obs[%d] has to be defined on a subset of the configs in weight.idl[%s]!' % (i, name))
         new_samples = []
         w_deltas = {}
         for name in sorted(obs[i].names):
@@ -1446,21 +1445,18 @@ 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.
-    Only works if a single ensemble is present in the Obs.
-    Currently only works if ensemble content is identical (this is not strictly necessary).
+    Currently only works if ensembles are 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 ValueError(f"Ensembles do not fit {set(sorted(obs_a.names)) ^ set(sorted(obs_b.names))}")
+        raise Exception(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 ValueError('Error: Not possible to correlate Obs that contain covobs!')
+        raise Exception('Error: Not possible to correlate Obs that contain covobs!')
     for name in obs_a.names:
         if obs_a.shape[name] != obs_b.shape[name]:
-            raise ValueError('Shapes of ensemble', name, 'do not fit')
+            raise Exception('Shapes of ensemble', name, 'do not fit')
         if obs_a.idl[name] != obs_b.idl[name]:
-            raise ValueError('idl of ensemble', name, 'do not fit')
+            raise Exception('idl of ensemble', name, 'do not fit')
 
     if obs_a.reweighted is True:
         warnings.warn("The first observable is already reweighted.", RuntimeWarning)
@@ -1562,7 +1558,7 @@ def invert_corr_cov_cholesky(corr, inverrdiag):
 
     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})")
+        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)
@@ -1643,7 +1639,7 @@ def _smooth_eigenvalues(corr, E):
         Number of eigenvalues to be left substantially unchanged
     """
     if not (2 < E < 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}).")
+        raise Exception(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
@@ -1762,11 +1758,7 @@ def import_bootstrap(boots, name, random_numbers):
 
 
 def merge_obs(list_of_obs):
-    """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.
+    """Combine all observables in list_of_obs into one new observable
 
     Parameters
     ----------
@@ -1779,9 +1771,9 @@ 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 ValueError('list_of_obs contains duplicate replica: %s' % (str(replist)))
+        raise Exception('list_of_obs contains duplicate replica: %s' % (str(replist)))
     if any([len(o.cov_names) for o in list_of_obs]):
-        raise ValueError('Not possible to merge data that contains covobs!')
+        raise Exception('Not possible to merge data that contains covobs!')
     new_dict = {}
     idl_dict = {}
     for o in list_of_obs:
@@ -1832,7 +1824,7 @@ 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 ValueError('You have to provide %d mean values!' % (ol[0].N))
+        raise Exception('You have to provide %d mean values!' % (ol[0].N))
     if len(ol) == 1:
         return ol[0]
     return ol
@@ -1848,7 +1840,7 @@ def _determine_gap(o, e_content, e_name):
 
     gap = min(gaps)
     if not np.all([gi % gap == 0 for gi in gaps]):
-        raise ValueError(f"Replica for ensemble {e_name} do not have a common spacing.", gaps)
+        raise Exception(f"Replica for ensemble {e_name} do not have a common spacing.", gaps)
 
     return gap
 

pyerrors/version.py

@@ -1 +1 @@
-__version__ = "2.15.0-dev"
+__version__ = "2.13.0"

pyproject.toml

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

tests/correlators_test.py

@@ -129,7 +129,7 @@ def test_m_eff():
     with pytest.warns(RuntimeWarning):
         my_corr.m_eff('sinh')
 
-    with pytest.raises(ValueError):
+    with pytest.raises(Exception):
         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(ValueError):
+        with pytest.raises(Exception):
             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(TypeError):
+    with pytest.raises(Exception):
         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(TypeError):
+    with pytest.raises(Exception):
         my_corr.fit(f, "from 0 to 3")
-    with pytest.raises(ValueError):
+    with pytest.raises(Exception):
         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(ValueError):
+    with pytest.raises(Exception):
         corr.set_prange([2])
-    with pytest.raises(TypeError):
+    with pytest.raises(Exception):
         corr.set_prange([2, 2.3])
-    with pytest.raises(ValueError):
+    with pytest.raises(Exception):
         corr.set_prange([4, 1])
 
 

tests/dirac_test.py

@@ -30,7 +30,7 @@ def test_grid_dirac():
                   'SigmaYZ',
                   'SigmaZT']:
         pe.dirac.Grid_gamma(gamma)
-    with pytest.raises(ValueError):
+    with pytest.raises(Exception):
         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(ValueError):
+    with pytest.raises(Exception):
         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(ValueError):
+    with pytest.raises(Exception):
         pe.dirac.epsilon_tensor_rank4(0, 1, 3, 4)

tests/fits_test.py

@@ -223,9 +223,6 @@ def test_inv_cov_matrix_input_least_squares():
             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 = []

tests/json_io_test.py

@@ -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, pe.pseudo_Obs(0.5, .1, 'two|r3', samples=3221)])
+    o4 = pe.merge_obs([o2, o3])
     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), 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)
+    my_list = [pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']) for o in range(8)]
+    rw_list = pe.reweight(pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']), my_list)
 
     for obs_list in [my_list, rw_list]:
         for tag in [None, "test"]:
@@ -111,51 +111,40 @@ def test_json_corr_io():
                 for corr_tag in [None, 'my_Corr_tag']:
                     for prange in [None, [3, 6]]:
                         for gap in [False, True]:
-                            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
+                            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
 
 
 def test_json_corr_2d_io():
-    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)]
+    obs_list = [np.array([[pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test'), pe.pseudo_Obs(0.0, 0.1 * i, 'test')], [pe.pseudo_Obs(0.0, 0.1 * i, 'test'), pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test')]]) for i in range(4)]
 
     for tag in [None, "test"]:
         obs_list[3][0, 1].tag = tag
         for padding in [0, 1]:
             for prange in [None, [3, 6]]:
-                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
+                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
 
 
 def test_json_dict_io():
@@ -222,7 +211,6 @@ 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)]),
         }
     }
 
@@ -326,7 +314,7 @@ 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, pe.pseudo_Obs(0.5, .1, 'two|r3', samples=3221)])
+    o4 = pe.merge_obs([o2, o3])
     otag = 'This has been merged!'
     o4.tag = otag
     do = o - .2 * o4
@@ -340,7 +328,7 @@ def test_dobsio():
     o5 /= co2[0]
     o5.tag = 2 * otag
 
-    tt1 = pe.Obs([np.random.rand(100), np.random.rand(102)], ['t|r1', 't|r2'], idl=[range(2, 202, 2), range(22, 226, 2)])
+    tt1 = pe.Obs([np.random.rand(100), np.random.rand(100)], ['t|r1', 't|r2'], idl=[range(2, 202, 2), range(22, 222, 2)])
     tt3 = pe.Obs([np.random.rand(102)], ['qe|r1'])
 
     tt = tt1 + tt3
@@ -349,7 +337,7 @@ 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), o4.reweight(o4)]
+    ol = [o2, o3, o4, do, o5, tt, tt4, np.log(tt4 / o5**2), np.exp(o5 + np.log(co3 / tt3 + o4) / tt)]
     print(ol)
     fname = 'test_rw'
 
@@ -374,12 +362,9 @@ def test_dobsio():
 
 
 def test_reconstruct_non_linear_r_obs(tmp_path):
-    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^£$|8'[@124435%6^7&()~#"], idl=[range(1, 999, 9)])
-        )
+    to = pe.Obs([np.random.rand(500), np.random.rand(500), np.random.rand(111)],
+                ["e|r1", "e|r2", "my_new_ensemble_54^£$|8'[@124435%6^7&()~#"],
+                idl=[range(1, 501), range(0, 500), range(1, 999, 9)])
     to = 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())
@@ -387,12 +372,9 @@ 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(1200)],
-                ["e|r1", "e|r2", ],
-                idl=[range(1, 501), range(0, 1200)])
-        + pe.Obs([np.random.rand(111)], ["my_new_ensemble_54^£$|8'[@124435%6^7&()~#"], idl=[range(1, 999, 9)])
-        )
+    to = pe.Obs([np.random.rand(500), np.random.rand(500), np.random.rand(111)],
+                ["e|r1", "e|r2", "my_new_ensemble_54^£$|8'[@124435%6^7&()~#"],
+                idl=[range(1, 501), range(0, 500), range(1, 999, 9)])
     to = 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())

tests/linalg_test.py

@@ -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)], ['t1']) + pe.Obs([np.random.rand(length + 1)], ['t2']))
+                my_list.append(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']))
             my_array = const * np.array(my_list).reshape((dim, dim))
             tt = pe.linalg.matmul(my_array, my_array) - my_array @ my_array
             for t, e in np.ndenumerate(tt):
@@ -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)], ['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_list.append(pe.CObs(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']),
+                                       pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2'])))
             my_array = np.array(my_list).reshape((dim, dim)) * const
             tt = pe.linalg.matmul(my_array, my_array) - my_array @ my_array
             for t, e in np.ndenumerate(tt):
@@ -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)], ['t1']) + pe.Obs([np.random.rand(length + 1)], ['t2']))
+            my_list.append(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', '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)], ['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_list.append(pe.CObs(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']),
+                                   pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2'])))
         my_array = np.array(my_list).reshape((dim, dim)) * 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))], ['ens1'], idl=[idl]) + pe.Obs([np.random.normal(0.25, 0.1, 10)], ['ens2'], idl=[range(1, 11)]))
+            irregular_array.append(pe.Obs([np.random.normal(1.1, 0.2, len(idl)), np.random.normal(0.25, 0.1, 10)], ['ens1', 'ens2'], idl=[idl, range(1, 11)]))
         irregular_matrix = np.array(irregular_array).reshape((dim, dim)) * 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

tests/obs_test.py

@@ -61,9 +61,9 @@ def test_Obs_exceptions():
         my_obs.plot_rep_dist()
     with pytest.raises(Exception):
         my_obs.plot_piechart()
-    with pytest.raises(TypeError):
+    with pytest.raises(Exception):
         my_obs.gamma_method(S='2.3')
-    with pytest.raises(ValueError):
+    with pytest.raises(Exception):
         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(ValueError):
+    with pytest.raises(Exception):
         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)], names[:1]) + pe.Obs([np.random.rand(50), np.random.rand(50)], names[1:])
+    test_obs = pe.Obs([np.random.rand(50), np.random.rand(50), np.random.rand(50)], names)
     assert test_obs.e_names == ['A0', 'A1']
     assert test_obs.e_content['A0'] == ['A0']
     assert test_obs.e_content['A1'] == ['A1|r001', 'A1|r002']
@@ -345,9 +345,6 @@ 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']
@@ -464,18 +461,6 @@ 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
@@ -493,33 +478,26 @@ 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(ValueError):
+    with pytest.raises(Exception):
         pe.reweight(my_obs, [my_covobs])
     my_obs2 = pe.Obs([np.random.rand(1000)], ['t2'])
-    with pytest.raises(ValueError):
+    with pytest.raises(Exception):
         pe.reweight(my_obs, [my_obs + my_obs2])
-    with pytest.raises(ValueError):
+    with pytest.raises(Exception):
         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.normal(1, .1, 100)], ['t|1'])
-    my_obs2 = pe.Obs([np.random.normal(1, .1, 100)], ['t|2'], idl=[range(1, 200, 2)])
+    my_obs1 = pe.Obs([np.random.rand(100)], ['t'])
+    my_obs2 = pe.Obs([np.random.rand(100)], ['q'], idl=[range(1, 200, 2)])
     merged = pe.merge_obs([my_obs1, my_obs2])
-    diff = merged - (my_obs2 + my_obs1) / 2
-    assert np.isclose(0, diff.value, atol=1e-16)
-    with pytest.raises(ValueError):
+    diff = merged - my_obs2 - my_obs1
+    assert diff == -(my_obs1.value + my_obs2.value) / 2
+    with pytest.raises(Exception):
         pe.merge_obs([my_obs1, my_obs1])
     my_covobs = pe.cov_Obs(1.0, 0.003, 'cov')
-    with pytest.raises(ValueError):
+    with pytest.raises(Exception):
         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])
 
 
 
@@ -541,26 +519,23 @@ def test_correlate():
     assert corr1 == corr2
 
     my_obs3 = pe.Obs([np.random.rand(100)], ['t'], idl=[range(2, 102)])
-    with pytest.raises(ValueError):
+    with pytest.raises(Exception):
         pe.correlate(my_obs1, my_obs3)
 
     my_obs4 = pe.Obs([np.random.rand(99)], ['t'])
-    with pytest.raises(ValueError):
+    with pytest.raises(Exception):
         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(ValueError):
+    with pytest.raises(Exception):
         pe.correlate(my_obs1, my_new_obs)
     my_covobs = pe.cov_Obs(1.0, 0.003, 'cov')
-    with pytest.raises(ValueError):
+    with pytest.raises(Exception):
         pe.correlate(my_covobs, my_covobs)
     r_obs = pe.reweight(my_obs1, [my_obs1])[0]
     with pytest.warns(RuntimeWarning):
@@ -694,14 +669,14 @@ 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], ['a1']) + pe.Obs([arr2], ['a2'])
+    afull = pe.Obs([arr, arr2], ['a1', '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], ['a1'], idl=[idx]) + pe.Obs([zero_arr2], ['a2'], idl=[idx2])
+    a = pe.Obs([zero_arr, zero_arr2], ['a1', 'a2'], idl=[idx, idx2])
 
     afull.gamma_method()
     a.gamma_method()
@@ -787,7 +762,7 @@ 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(ValueError):
+    with pytest.raises(Exception):
         my_obs.gm()
 
     # check cases where tau is large compared to the chain length
@@ -1035,7 +1010,7 @@ 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)], ["ens|r1", "ens|r2"]) + pe.Obs([np.random.normal(1.0, 0.1, 732)], ['ens2'])
+    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.gamma_method()
     obs2 = obs1 + 1e-18
     obs2.gamma_method()
@@ -1119,9 +1094,6 @@ 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():
@@ -1138,7 +1110,7 @@ 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(ValueError):
+    with pytest.raises(Exception):
         my_new_obs.export_jackknife()
 
 

tests/sfcf_in_test.py

@@ -387,33 +387,3 @@ 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'