Merge branch 'develop' into feat/typehints

.github/workflows/docs.yml

@@ -13,7 +13,7 @@ jobs:
       - name: Set up Python environment
         uses: actions/setup-python@v5
         with:
-          python-version: "3.10"
+          python-version: "3.12"
       - uses: actions/checkout@v4
       - name: Updated documentation
         run: |

.github/workflows/flake8.yml

@@ -17,7 +17,7 @@ jobs:
       - name: Set up Python environment
         uses: actions/setup-python@v5
         with:
-          python-version: "3.10"
+          python-version: "3.12"
       - name: flake8 Lint
         uses: py-actions/flake8@v2
         with:

.github/workflows/pytest.yml

@@ -17,7 +17,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest]
-        python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
+        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
         include:
           - os: macos-latest
             python-version: "3.12"
@@ -42,5 +42,10 @@ jobs:
           uv pip install pytest pytest-cov pytest-benchmark hypothesis typing_extensions --system
           uv pip freeze --system
 
-      - name: Run tests
+      - name: Run tests with -Werror
+        if: matrix.python-version != '3.14'
         run: pytest --cov=pyerrors -vv -Werror
+
+      - name: Run tests without -Werror for python 3.14
+        if: matrix.python-version == '3.14'
+        run: pytest --cov=pyerrors -vv

CHANGELOG.md

@@ -2,6 +2,27 @@
 
 All notable changes to this project will be documented in this file.
 
+## [2.16.0] - 2025-10-30
+
+### Added
+- Support for custom configuration number extraction in the sfcf input module.
+
+### Fixed
+- Calculation of expected chisquare in connection with priors.
+
+### Changed
+- Support for python<3.10 was dropped.
+
+## [2.15.1] - 2025-10-19
+
+### Fixed
+- Fixed handling of padding in Correlator prune method.
+
+## [2.15.0] - 2025-10-10
+
+### Added
+- Option to explicitly specify the number of fit parameters added.
+
 ## [2.14.0] - 2025-03-09
 
 ### 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)
+[![](https://img.shields.io/badge/python-3.10+-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.
 

pyerrors/correlators.py

@@ -1399,13 +1399,15 @@ class Corr:
         tmpmat = np.empty((Ntrunc, Ntrunc), dtype=object)
         rmat = []
         for t in range(basematrix.T):
-            for i in range(Ntrunc):
+            if self.content[t] is None:
-                for j in range(Ntrunc):
+                rmat.append(None)
-                    tmpmat[i][j] = evecs[i].T @ self[t] @ evecs[j]
+            else:
-            rmat.append(np.copy(tmpmat))
+                for i in range(Ntrunc):
+                    for j in range(Ntrunc):
+                        tmpmat[i][j] = evecs[i].T @ self[t] @ evecs[j]
+                rmat.append(np.copy(tmpmat))
 
-        newcontent = [None if (self.content[t] is None) else rmat[t] for t in range(self.T)]
+        return Corr(rmat)
-        return Corr(newcontent)
 
 
 def _sort_vectors(vec_set_in: list[Optional[ndarray]], ts: int) -> list[Union[None, ndarray, list[ndarray]]]:

pyerrors/fits.py

@@ -154,7 +154,7 @@ def least_squares(x: Any, y: Union[dict[str, ndarray], list[Obs], ndarray, dict[
         Obs (e.g. results from a previous fit) or strings containing a value and an error formatted like
         0.548(23), 500(40) or 0.5(0.4)
     silent : bool, optional
-        If true all output to the console is omitted (default False).
+        If True all output to the console is omitted (default False).
     initial_guess : list
         can provide an initial guess for the input parameters. Relevant for
         non-linear fits with many parameters. In case of correlated fits the guess is used to perform
@@ -162,10 +162,10 @@ def least_squares(x: Any, y: Union[dict[str, ndarray], list[Obs], ndarray, dict[
     method : str, optional
         can be used to choose an alternative method for the minimization of chisquare.
         The possible methods are the ones which can be used for scipy.optimize.minimize and
-        migrad of iminuit. If no method is specified, Levenberg-Marquard is used.
+        migrad of iminuit. If no method is specified, Levenberg–Marquardt is used.
         Reliable alternatives are migrad, Powell and Nelder-Mead.
     tol: float, optional
-        can be used (only for combined fits and methods other than Levenberg-Marquard) to set the tolerance for convergence
+        can be used (only for combined fits and methods other than Levenberg–Marquardt) to set the tolerance for convergence
         to a different value to either speed up convergence at the cost of a larger error on the fitted parameters (and possibly
         invalid estimates for parameter uncertainties) or smaller values to get more accurate parameter values
         The stopping criterion depends on the method, e.g. migrad: edm_max = 0.002 * tol * errordef (EDM criterion: edm < edm_max)
@@ -175,7 +175,7 @@ def least_squares(x: Any, y: Union[dict[str, ndarray], list[Obs], ndarray, dict[
         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)
+        array: shape = (number of y values) X (number 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.
@@ -191,6 +191,9 @@ def least_squares(x: Any, y: Union[dict[str, ndarray], list[Obs], ndarray, dict[
         If True, a quantile-quantile plot of the fit result is generated (default False).
     num_grad : bool
         Use numerical differentation instead of automatic differentiation to perform the error propagation (default False).
+    n_parms : int, optional
+        Number of fit parameters. Overrides automatic detection of parameter count.
+        Useful when autodetection fails. Must match the length of initial_guess or priors (if provided).
 
     Returns
     -------
@@ -292,26 +295,38 @@ def least_squares(x: Any, y: Union[dict[str, ndarray], list[Obs], ndarray, dict[
         raise Exception("No y errors available, run the gamma method first.")
 
     # number of fit parameters
-    n_parms_ls = []
+    if 'n_parms' in kwargs:
-    for key in key_ls:
+        n_parms = kwargs.get('n_parms')
-        if not callable(funcd[key]):
+        if not isinstance(n_parms, int):
-            raise TypeError('func (key=' + key + ') is not a function.')
+            raise TypeError(
-        if np.asarray(xd[key]).shape[-1] != len(yd[key]):
+                f"'n_parms' must be an integer, got {n_parms!r} "
-            raise ValueError('x and y input (key=' + key + ') do not have the same length')
+                f"of type {type(n_parms).__name__}."
-        for n_loc in range(100):
+            )
-            try:
+        if n_parms <= 0:
-                funcd[key](np.arange(n_loc), x_all.T[0])
+            raise ValueError(
-            except TypeError:
+                f"'n_parms' must be a positive integer, got {n_parms}."
-                continue
+            )
-            except IndexError:
+    else:
-                continue
+        n_parms_ls = []
+        for key in key_ls:
+            if not callable(funcd[key]):
+                raise TypeError('func (key=' + key + ') is not a function.')
+            if np.asarray(xd[key]).shape[-1] != len(yd[key]):
+                raise ValueError('x and y input (key=' + key + ') do not have the same length')
+            for n_loc in range(100):
+                try:
+                    funcd[key](np.arange(n_loc), x_all.T[0])
+                except TypeError:
+                    continue
+                except IndexError:
+                    continue
+                else:
+                    break
             else:
-                break
+                raise RuntimeError("Fit function (key=" + key + ") is not valid.")
-        else:
+            n_parms_ls.append(n_loc)
-            raise RuntimeError("Fit function (key=" + key + ") is not valid.")
-        n_parms_ls.append(n_loc)
 
-    n_parms = max(n_parms_ls)
+        n_parms = max(n_parms_ls)
 
     if len(key_ls) > 1:
         for key in key_ls:
@@ -479,7 +494,7 @@ def least_squares(x: Any, y: Union[dict[str, ndarray], list[Obs], ndarray, dict[
             hat_vector = prepare_hat_matrix()
             A = W @ hat_vector
             P_phi = A @ np.linalg.pinv(A.T @ A) @ A.T
-            expected_chisquare = np.trace((np.identity(y_all.shape[-1]) - P_phi) @ W @ cov @ W)
+            expected_chisquare = np.trace((np.identity(y_all.shape[-1]) - P_phi) @ W @ cov @ W) + len(loc_priors)
             output.chisquare_by_expected_chisquare = output.chisquare / expected_chisquare
             if not silent:
                 print('chisquare/expected_chisquare:', output.chisquare_by_expected_chisquare)
@@ -557,17 +572,20 @@ def total_least_squares(x: list[Obs], y: list[Obs], func: Callable, silent: bool
         It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation
         will not work.
     silent : bool, optional
-        If true all output to the console is omitted (default False).
+        If True all output to the console is omitted (default False).
     initial_guess : list
         can provide an initial guess for the input parameters. Relevant for non-linear
         fits with many parameters.
     expected_chisquare : bool
-        If true prints the expected chisquare which is
+        If True prints the expected chisquare which is
         corrected by effects caused by correlated input data.
         This can take a while as the full correlation matrix
         has to be calculated (default False).
     num_grad : bool
-        Use numerical differentation instead of automatic differentiation to perform the error propagation (default False).
+        Use numerical differentiation instead of automatic differentiation to perform the error propagation (default False).
+    n_parms : int, optional
+        Number of fit parameters. Overrides automatic detection of parameter count.
+        Useful when autodetection fails. Must match the length of initial_guess (if provided).
 
     Notes
     -----
@@ -597,19 +615,32 @@ def total_least_squares(x: list[Obs], y: list[Obs], func: Callable, silent: bool
     if not callable(func):
         raise TypeError('func has to be a function.')
 
-    for i in range(42):
+    if 'n_parms' in kwargs:
-        try:
+        n_parms = kwargs.get('n_parms')
-            func(np.arange(i), x.T[0])
+        if not isinstance(n_parms, int):
-        except TypeError:
+            raise TypeError(
-            continue
+                f"'n_parms' must be an integer, got {n_parms!r} "
-        except IndexError:
+                f"of type {type(n_parms).__name__}."
-            continue
+            )
-        else:
+        if n_parms <= 0:
-            break
+            raise ValueError(
+                f"'n_parms' must be a positive integer, got {n_parms}."
+            )
     else:
-        raise RuntimeError("Fit function is not valid.")
+        for i in range(100):
+            try:
+                func(np.arange(i), x.T[0])
+            except TypeError:
+                continue
+            except IndexError:
+                continue
+            else:
+                break
+        else:
+            raise RuntimeError("Fit function is not valid.")
+
+        n_parms = i
 
-    n_parms = i
     if not silent:
         print('Fit with', n_parms, 'parameter' + 's' * (n_parms > 1))
 

pyerrors/input/sfcf.py

@@ -8,12 +8,13 @@ from .utils import sort_names, check_idl
 import itertools
 from numpy import ndarray
 from typing import Any, Union, Optional, Literal
+from Collections.abc import Callable
 
 
 sep = "/"
 
 
-def read_sfcf(path: str, prefix: str, name: str, quarks: str='.*', corr_type: str="bi", noffset: int=0, wf: int=0, wf2: int=0, version: str="1.0c", cfg_separator: str="n", silent: bool=False, **kwargs) -> list[Obs]:
+def read_sfcf(path: str, prefix: str, name: str, quarks: str='.*', corr_type: str="bi", noffset: int=0, wf: int=0, wf2: int=0, version: str="1.0c", cfg_separator: str="n", cfg_func: Callable | None = None, silent: bool=False, **kwargs) -> list[Obs]:
     """Read sfcf files from given folder structure.
 
     Parameters
@@ -74,11 +75,11 @@ def read_sfcf(path: str, prefix: str, name: str, quarks: str='.*', corr_type: st
     """
     ret = read_sfcf_multi(path, prefix, [name], quarks_list=[quarks], corr_type_list=[corr_type],
                           noffset_list=[noffset], wf_list=[wf], wf2_list=[wf2], version=version,
-                          cfg_separator=cfg_separator, silent=silent, **kwargs)
+                          cfg_separator=cfg_separator, cfg_func=cfg_func, silent=silent, **kwargs)
     return ret[name][quarks][str(noffset)][str(wf)][str(wf2)]
 
 
-def read_sfcf_multi(path: str, prefix: str, name_list: list[str], quarks_list: list[str]=['.*'], corr_type_list: list[str]=['bi'], noffset_list: list[int]=[0], wf_list: list[int]=[0], wf2_list: list[int]=[0], version: str="1.0c", cfg_separator: str="n", silent: bool=False, keyed_out: bool=False, **kwargs) -> dict:
+def read_sfcf_multi(path: str, prefix: str, name_list: list[str], quarks_list: list[str]=['.*'], corr_type_list: list[str]=['bi'], noffset_list: list[int]=[0], wf_list: list[int]=[0], wf2_list: list[int]=[0], version: str="1.0c", cfg_separator: str="n", cfg_func: Callable | None = None, silent: bool=False, keyed_out: bool=False, **kwargs) -> dict:
     """Read sfcf files from given folder structure.
 
     Parameters
@@ -246,7 +247,16 @@ def read_sfcf_multi(path: str, prefix: str, name_list: list[str], quarks_list: l
     for key in needed_keys:
         internal_ret_dict[key] = []
 
-    rep_idl: list = []
+    def _default_idl_func(cfg_string, cfg_sep):
+        return int(cfg_string.split(cfg_sep)[-1])
+
+    if cfg_func is None:
+        print("Default idl function in use.")
+        cfg_func = _default_idl_func
+        cfg_func_args = [cfg_separator]
+    else:
+        cfg_func_args = kwargs.get("cfg_func_args", [])
+
     if not appended:
         for i, item in enumerate(ls):
             rep_path = path + '/' + item
@@ -270,7 +280,7 @@ def read_sfcf_multi(path: str, prefix: str, name_list: list[str], quarks_list: l
             for cfg in sub_ls:
                 try:
                     if compact:
-                        rep_idl.append(int(cfg.split(cfg_separator)[-1]))
+                        rep_idl.append(cfg_func(cfg, *cfg_func_args))
                     else:
                         rep_idl.append(int(cfg[3:]))
                 except Exception:
@@ -353,7 +363,7 @@ def read_sfcf_multi(path: str, prefix: str, name_list: list[str], quarks_list: l
                 rep_idl = []
                 rep_data = []
                 filename = path + '/' + file
-                T, rep_idl, rep_data = _read_append_rep(filename, pattern, intern[name]['b2b'], cfg_separator, im, intern[name]['single'])
+                T, rep_idl, rep_data = _read_append_rep(filename, pattern, intern[name]['b2b'], im, intern[name]['single'], cfg_func, cfg_func_args)
                 if rep == 0:
                     intern[name]['T'] = T
                     for t in range(intern[name]['T']):
@@ -586,12 +596,7 @@ def _read_compact_rep(path: str, rep: str, sub_ls: list[str], intern: dict[str,
     return return_vals
 
 
-def _read_chunk(chunk: list[str], gauge_line: int, cfg_sep: str, start_read: int, T: int, corr_line: int, b2b: bool, pattern: str, im: int, single: bool) -> tuple[int, list[float]]:
+def _read_chunk_data(chunk: list[str], start_read: int, T: int, corr_line: int, b2b: bool, pattern: str, im: int, single: bool) -> tuple[int, list[float]]:
-    try:
-        idl = int(chunk[gauge_line].split(cfg_sep)[-1])
-    except Exception:
-        raise Exception("Couldn't parse idl from directory, problem with chunk around line ", gauge_line)
-
     found_pat = ""
     data = []
     for li in chunk[corr_line + 1:corr_line + 6 + b2b]:
@@ -600,10 +605,10 @@ def _read_chunk(chunk: list[str], gauge_line: int, cfg_sep: str, start_read: int
         for t, line in enumerate(chunk[start_read:start_read + T]):
             floats = list(map(float, line.split()))
             data.append(floats[im + 1 - single])
-    return idl, data
+    return data
 
 
-def _read_append_rep(filename: str, pattern: str, b2b: bool, cfg_separator: str, im: int, single: bool) -> tuple[int, list[int], list[list[float]]]:
+def _read_append_rep(filename: str, pattern: str, b2b: bool, im: int, single: bool, idl_func: Callable[str, list], cfg_func_args: list) -> tuple[int, list[int], list[list[float]]]:
     with open(filename, 'r') as fp:
         content = fp.readlines()
         data_starts = []
@@ -639,7 +644,11 @@ def _read_append_rep(filename: str, pattern: str, b2b: bool, cfg_separator: str,
             start = data_starts[cnfg]
             stop = start + data_starts[1]
             chunk = content[start:stop]
-            idl, data = _read_chunk(chunk, gauge_line, cfg_separator, start_read, T, corr_line, b2b, pattern, im, single)
+            try:
+                idl = idl_func(chunk[gauge_line], *cfg_func_args)
+            except Exception:
+                raise Exception("Couldn't parse idl from file", filename, ", problem with chunk of lines", start + 1, "to", stop + 1)
+            data = _read_chunk_data(chunk, start_read, T, corr_line, b2b, pattern, im, single)
             rep_idl.append(idl)
             rep_data.append(data)
 

pyerrors/version.py

@@ -1 +1 @@
-__version__ = "2.15.0-dev"
+__version__ = "2.17.0-dev"

setup.py

@@ -24,18 +24,18 @@ setup(name='pyerrors',
       author_email='fabian.joswig@ed.ac.uk',
       license="MIT",
       packages=find_packages(),
-      python_requires='>=3.9.0',
+      python_requires='>=3.10.0',
       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',
           'Programming Language :: Python :: 3',
-          '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',
+          'Programming Language :: Python :: 3.14',
           'Topic :: Scientific/Engineering :: Physics'
       ],
      )

tests/correlators_test.py

@@ -783,3 +783,26 @@ def test_complex_add_and_mul():
         cc += 2j
         cc = cc * 4j
         cc.real + cc.imag
+
+
+def test_prune_with_Nones():
+    N = 3
+    T = 10
+
+    front_padding = 1
+    back_padding = T // 2
+
+    Ntrunc = N - 1
+    t0proj = 2
+    tproj = 3
+
+    corr_content = np.array([[[pe.pseudo_Obs((i+j+1)**(-t), .01, "None_prune_test") for i in range(N)] for j in range(N)] for t in range(T // 2 - front_padding)])
+    unpadded_corr = pe.Corr(corr_content)
+    padded_corr = pe.Corr(corr_content, padding=[front_padding, back_padding])
+
+    tmp_corr = unpadded_corr.prune(Ntrunc, t0proj=t0proj-front_padding, tproj=tproj-front_padding)
+    pruned_then_padded = pe.Corr(tmp_corr.content, padding=[front_padding, back_padding])
+    padded_then_pruned = padded_corr.prune(Ntrunc, t0proj=t0proj, tproj=tproj)
+
+    for t in range(T):
+        assert np.all(pruned_then_padded.content[t] == padded_then_pruned.content[t])

tests/data/sfcf_test/data_apf/data_apf_r0.f_1

@@ -0,0 +1,970 @@
+[run]
+
+version     2.1
+date        2022-01-19 11:04:03 +0100
+host        r04n07.palma.wwu
+dir         /scratch/tmp/j_kuhl19
+user        j_kuhl19
+gauge_name  /data_a_r0_n1.lex
+gauge_md5   1ea28326e4090996111a320b8372811d
+param_name  sfcf_unity_test.in
+param_md5   d881e90d41188a33b8b0f1bd0bc53ea5
+param_hash  686af5e712ee2902180f5428af94c6e7
+data_name   ./output_10519905/data_af_1
+
+[correlator]
+
+name      f_1
+quarks    lquark lquark
+offset    0
+wf        0
+wf_2      0
+corr
++3.5119415254545021e+02 +6.7620978057264750e-15
+
+[correlator]
+
+name      f_1
+quarks    lquark lquark
+offset    0
+wf        0
+wf_2      1
+corr
++3.5120703575855339e+02 +6.5026340956203663e-15
+
+[correlator]
+
+name      f_1
+quarks    lquark lquark
+offset    0
+wf        0
+wf_2      2
+corr
++3.5120808902177868e+02 +6.5443496235264788e-15
+
+[correlator]
+
+name      f_1
+quarks    lquark lquark
+offset    0
+wf        1
+wf_2      0
+corr
++3.5120703575855515e+02 +6.9706500417651470e-15
+
+[correlator]
+
+name      f_1
+quarks    lquark lquark
+offset    0
+wf        1
+wf_2      1
+corr
++3.5122001235609065e+02 +6.9516150897757419e-15
+
+[correlator]
+
+name      f_1
+quarks    lquark lquark
+offset    0
+wf        1
+wf_2      2
+corr
++3.5122104108046199e+02 +6.9232860455434941e-15
+
+[correlator]
+
+name      f_1
+quarks    lquark lquark
+offset    0
+wf        2
+wf_2      0
+corr
++3.5120808902177447e+02 +1.0849949614595719e-14
+
+[correlator]
+
+name      f_1
+quarks    lquark lquark
+offset    0
+wf        2
+wf_2      1
+corr
++3.5122104108046182e+02 +1.0866063643253473e-14
+
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+  1 +6.5551520722862705e+01 +2.0963356863957609e-13
+  2 +1.0542820240851569e+00 +2.3989756974599379e-15
+  3 -4.1024441815729936e+01 -5.7107484666182308e-15
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    0
+wf        2
+corr_t
+  1 +6.5529951269442847e+01 -6.6512260271334321e-14
+  2 +1.0516822345055969e+00 -2.2935262162529075e-15
+  3 -4.1025142768037746e+01 +3.7566377680004518e-16
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    1
+wf        0
+corr_t
+  1 +6.5471188727965909e+01 -1.6112786177915427e-11
+  2 +1.0447210337411881e+00 -7.0387528705692678e-13
+  3 -4.1025094911167137e+01 +4.6509152745618223e-13
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    1
+wf        1
+corr_t
+  1 +6.5551520722842213e+01 -8.1976426690345305e-13
+  2 +1.0542820240843382e+00 +2.1626370477046812e-13
+  3 -4.1024441815730086e+01 -2.4147931196409923e-14
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    1
+wf        2
+corr_t
+  1 +6.5529951269443117e+01 +7.9192560386479701e-14
+  2 +1.0516822345055870e+00 -1.2443038782429568e-14
+  3 -4.1025142768037739e+01 +5.9315333178954509e-17
+
+[run]
+
+version     2.1
+date        2022-01-19 11:04:05 +0100
+host        r04n07.palma.wwu
+dir         /scratch/tmp/j_kuhl19
+user        j_kuhl19
+gauge_name  /data_a_r0_n2.lex
+gauge_md5   1ea28326e4090996111a320b8372811d
+param_name  sfcf_unity_test.in
+param_md5   d881e90d41188a33b8b0f1bd0bc53ea5
+param_hash  686af5e712ee2902180f5428af94c6e7
+data_name   ./output_10519905/data_af_A
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    0
+wf        0
+corr_t
+  1 +6.5471188727972304e+01 -6.1214214711790100e-12
+  2 +1.0447210336915187e+00 +8.9219487930753188e-13
+  3 -4.1025094911185178e+01 -4.8315634170546161e-14
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    0
+wf        1
+corr_t
+  1 +6.5551520722862705e+01 +2.0963356863957609e-13
+  2 +1.0542820240851569e+00 +2.3989756974599379e-15
+  3 -4.1024441815729936e+01 -5.7107484666182308e-15
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    0
+wf        2
+corr_t
+  1 +6.5529951269442847e+01 -6.6512260271334321e-14
+  2 +1.0516822345055969e+00 -2.2935262162529075e-15
+  3 -4.1025142768037746e+01 +3.7566377680004518e-16
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    1
+wf        0
+corr_t
+  1 +6.5471188727965909e+01 -1.6112786177915427e-11
+  2 +1.0447210337411881e+00 -7.0387528705692678e-13
+  3 -4.1025094911167137e+01 +4.6509152745618223e-13
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    1
+wf        1
+corr_t
+  1 +6.5551520722842213e+01 -8.1976426690345305e-13
+  2 +1.0542820240843382e+00 +2.1626370477046812e-13
+  3 -4.1024441815730086e+01 -2.4147931196409923e-14
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    1
+wf        2
+corr_t
+  1 +6.5529951269443117e+01 +7.9192560386479701e-14
+  2 +1.0516822345055870e+00 -1.2443038782429568e-14
+  3 -4.1025142768037739e+01 +5.9315333178954509e-17
+
+[run]
+
+version     2.1
+date        2022-01-19 11:04:07 +0100
+host        r04n07.palma.wwu
+dir         /scratch/tmp/j_kuhl19
+user        j_kuhl19
+gauge_name  /data_a_r0_n3.lex
+gauge_md5   1ea28326e4090996111a320b8372811d
+param_name  sfcf_unity_test.in
+param_md5   d881e90d41188a33b8b0f1bd0bc53ea5
+param_hash  686af5e712ee2902180f5428af94c6e7
+data_name   ./output_10519905/data_af_A
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    0
+wf        0
+corr_t
+  1 +6.5471188727972304e+01 -6.1214214711790100e-12
+  2 +1.0447210336915187e+00 +8.9219487930753188e-13
+  3 -4.1025094911185178e+01 -4.8315634170546161e-14
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    0
+wf        1
+corr_t
+  1 +6.5551520722862705e+01 +2.0963356863957609e-13
+  2 +1.0542820240851569e+00 +2.3989756974599379e-15
+  3 -4.1024441815729936e+01 -5.7107484666182308e-15
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    0
+wf        2
+corr_t
+  1 +6.5529951269442847e+01 -6.6512260271334321e-14
+  2 +1.0516822345055969e+00 -2.2935262162529075e-15
+  3 -4.1025142768037746e+01 +3.7566377680004518e-16
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    1
+wf        0
+corr_t
+  1 +6.5471188727965909e+01 -1.6112786177915427e-11
+  2 +1.0447210337411881e+00 -7.0387528705692678e-13
+  3 -4.1025094911167137e+01 +4.6509152745618223e-13
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    1
+wf        1
+corr_t
+  1 +6.5551520722842213e+01 -8.1976426690345305e-13
+  2 +1.0542820240843382e+00 +2.1626370477046812e-13
+  3 -4.1024441815730086e+01 -2.4147931196409923e-14
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    1
+wf        2
+corr_t
+  1 +6.5529951269443117e+01 +7.9192560386479701e-14
+  2 +1.0516822345055870e+00 -1.2443038782429568e-14
+  3 -4.1025142768037739e+01 +5.9315333178954509e-17
+
+[run]
+
+version     2.1
+date        2022-01-19 11:04:09 +0100
+host        r04n07.palma.wwu
+dir         /scratch/tmp/j_kuhl19
+user        j_kuhl19
+gauge_name  /data_a_r0_n4.lex
+gauge_md5   1ea28326e4090996111a320b8372811d
+param_name  sfcf_unity_test.in
+param_md5   d881e90d41188a33b8b0f1bd0bc53ea5
+param_hash  686af5e712ee2902180f5428af94c6e7
+data_name   ./output_10519905/data_af_A
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    0
+wf        0
+corr_t
+  1 +6.5471188727972304e+01 -6.1214214711790100e-12
+  2 +1.0447210336915187e+00 +8.9219487930753188e-13
+  3 -4.1025094911185178e+01 -4.8315634170546161e-14
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    0
+wf        1
+corr_t
+  1 +6.5551520722862705e+01 +2.0963356863957609e-13
+  2 +1.0542820240851569e+00 +2.3989756974599379e-15
+  3 -4.1024441815729936e+01 -5.7107484666182308e-15
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    0
+wf        2
+corr_t
+  1 +6.5529951269442847e+01 -6.6512260271334321e-14
+  2 +1.0516822345055969e+00 -2.2935262162529075e-15
+  3 -4.1025142768037746e+01 +3.7566377680004518e-16
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    1
+wf        0
+corr_t
+  1 +6.5471188727965909e+01 -1.6112786177915427e-11
+  2 +1.0447210337411881e+00 -7.0387528705692678e-13
+  3 -4.1025094911167137e+01 +4.6509152745618223e-13
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    1
+wf        1
+corr_t
+  1 +6.5551520722842213e+01 -8.1976426690345305e-13
+  2 +1.0542820240843382e+00 +2.1626370477046812e-13
+  3 -4.1024441815730086e+01 -2.4147931196409923e-14
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    1
+wf        2
+corr_t
+  1 +6.5529951269443117e+01 +7.9192560386479701e-14
+  2 +1.0516822345055870e+00 -1.2443038782429568e-14
+  3 -4.1025142768037739e+01 +5.9315333178954509e-17
+
+[run]
+
+version     2.1
+date        2022-01-19 11:04:11 +0100
+host        r04n07.palma.wwu
+dir         /scratch/tmp/j_kuhl19
+user        j_kuhl19
+gauge_name  /data_a_r0_n5.lex
+gauge_md5   1ea28326e4090996111a320b8372811d
+param_name  sfcf_unity_test.in
+param_md5   d881e90d41188a33b8b0f1bd0bc53ea5
+param_hash  686af5e712ee2902180f5428af94c6e7
+data_name   ./output_10519905/data_af_A
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    0
+wf        0
+corr_t
+  1 +6.5471188727972304e+01 -6.1214214711790100e-12
+  2 +1.0447210336915187e+00 +8.9219487930753188e-13
+  3 -4.1025094911185178e+01 -4.8315634170546161e-14
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    0
+wf        1
+corr_t
+  1 +6.5551520722862705e+01 +2.0963356863957609e-13
+  2 +1.0542820240851569e+00 +2.3989756974599379e-15
+  3 -4.1024441815729936e+01 -5.7107484666182308e-15
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    0
+wf        2
+corr_t
+  1 +6.5529951269442847e+01 -6.6512260271334321e-14
+  2 +1.0516822345055969e+00 -2.2935262162529075e-15
+  3 -4.1025142768037746e+01 +3.7566377680004518e-16
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    1
+wf        0
+corr_t
+  1 +6.5471188727965909e+01 -1.6112786177915427e-11
+  2 +1.0447210337411881e+00 -7.0387528705692678e-13
+  3 -4.1025094911167137e+01 +4.6509152745618223e-13
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    1
+wf        1
+corr_t
+  1 +6.5551520722842213e+01 -8.1976426690345305e-13
+  2 +1.0542820240843382e+00 +2.1626370477046812e-13
+  3 -4.1024441815730086e+01 -2.4147931196409923e-14
+
+[correlator]
+
+name      f_A
+quarks    lquark lquark
+offset    1
+wf        2
+corr_t
+  1 +6.5529951269443117e+01 +7.9192560386479701e-14
+  2 +1.0516822345055870e+00 -1.2443038782429568e-14
+  3 -4.1025142768037739e+01 +5.9315333178954509e-17
+

tests/fits_test.py

@@ -1098,6 +1098,7 @@ def test_combined_fit_xerr():
     }
     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)
+    pe.fits.least_squares(xd, yd, fitd, n_parms=4)
 
 
 def test_x_multidim_fit():
@@ -1340,6 +1341,54 @@ def test_combined_fit_constant_shape():
     funcs = {"a": lambda a, x: a[0] + a[1] * x,
              "": lambda a, x: a[1] + x * 0}
     pe.fits.least_squares(x, y, funcs, method='migrad')
+    pe.fits.least_squares(x, y, funcs, method='migrad', n_parms=2)
+
+def test_fit_n_parms():
+    # Function that fails if the number of parameters is not specified:
+    def fcn(p, x):                                                          
+        # Assumes first half of terms are A second half are E 
+        NTerms = int(len(p)/2)
+        A = anp.array(p[0:NTerms])[:, np.newaxis]   # shape (n, 1)                                                  
+        E_P = anp.array(p[NTerms:])[:, np.newaxis]    # shape (n, 1)
+        # This if statement handles the case where x is a single value rather than an array                                        
+        if isinstance(x, anp.float64) or isinstance(x, anp.int64) or isinstance(x, float)  or isinstance(x, int):
+            x = anp.array([x])[np.newaxis, :]             # shape (1, m)                                            
+        else:
+            x = anp.array(x)[np.newaxis, :]             # shape (1, m)                                              
+        exp_term = anp.exp(-E_P * x)                      
+        weighted_sum = A * exp_term                            # shape (n, m)                                      
+        return anp.mean(weighted_sum, axis=0)       # shape(m)
+
+    c = pe.Corr([pe.pseudo_Obs(2. * np.exp(-.2 * t) + .4 * np.exp(+.4 * t) + .4 * np.exp(-.6 * t), .1, 'corr') for t in range(12)])
+
+    c.fit(fcn, n_parms=2)
+    c.fit(fcn, n_parms=4)
+
+    xf = [pe.pseudo_Obs(t, .05, 'corr') for t in range(c.T)]
+    yf = [c[t] for t in range(c.T)]
+    pe.fits.total_least_squares(xf, yf, fcn, n_parms=2)
+    pe.fits.total_least_squares(xf, yf, fcn, n_parms=4)
+
+    # Is expected to fail, this is what is fixed with n_parms
+    with pytest.raises(RuntimeError):
+        c.fit(fcn, )
+    with pytest.raises(RuntimeError):
+        pe.fits.total_least_squares(xf, yf, fcn, )
+    # Test for positivity
+    with pytest.raises(ValueError):
+        c.fit(fcn, n_parms=-2)
+    with pytest.raises(ValueError):
+        pe.fits.total_least_squares(xf, yf, fcn, n_parms=-4)
+    # Have to pass an interger
+    with pytest.raises(TypeError):
+        c.fit(fcn, n_parms=2.)
+    with pytest.raises(TypeError):
+        pe.fits.total_least_squares(xf, yf, fcn, n_parms=1.2343)
+    # Improper number of parameters (function should fail)
+    with pytest.raises(ValueError):
+        c.fit(fcn, n_parms=7)
+    with pytest.raises(ValueError):
+        pe.fits.total_least_squares(xf, yf, fcn, n_parms=5)
 
 
 def fit_general(x, y, func, silent=False, **kwargs):
@@ -1562,3 +1611,81 @@ def old_prior_fit(x, y, func, priors, silent=False, **kwargs):
         qqplot(x, y, func, result)
 
     return output
+
+def test_dof_prior_fit():
+    """Performs an uncorrelated fit with a prior to uncorrelated data then
+    the expected chisquare and the usual dof need to agree"""
+    N = 5
+
+    def fitf(a, x):
+        return a[0] + 0 * x
+
+    x = [1. for i in range(N)]
+    y = [pe.cov_Obs(i, .1, '%d' % (i)) for i in range(N)]
+    [o.gm() for o in y]
+    res = pe.fits.least_squares(x, y, fitf, expected_chisquare=True, priors=[pe.cov_Obs(3, 1, 'p')])
+    assert res.chisquare_by_expected_chisquare == res.chisquare_by_dof
+    
+    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):
+            if(i==j):
+                r[i, j] = 1.0 # 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):
+            if(i==j):
+                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'+str(i)]))
+            else:
+                data.append(pe.Obs([[np.exp(-(i + 1)) + np.exp(-(i + 1)) * o for o in y[i]]], ['ens'+str(i)]))
+
+        [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
+
+        fit_exp = pe.least_squares(x, data, fitf, expected_chisquare=True, priors = {0:pe.cov_Obs(1.0, 1, 'p')})
+        fit_cov = pe.least_squares(x, data, fitf,  correlated_fit = True, inv_chol_cov_matrix = [chol_inv,chol_inv_keys],  priors = {0:pe.cov_Obs(1.0, 1, 'p')})
+        assert np.isclose(fit_exp.chisquare_by_expected_chisquare,fit_exp.chisquare_by_dof,atol=1e-8)
+        assert np.isclose(fit_exp.chisquare_by_expected_chisquare,fit_cov.chisquare_by_dof,atol=1e-8)

tests/obs_test.py

@@ -152,7 +152,7 @@ def test_function_overloading():
     np.arccos(1 / b)
     np.arctan(1 / b)
     np.arctanh(1 / b)
-    np.sinc(1 / b)
+    #np.sinc(1 / b)  # Commented out for now
 
     b ** b
     0.5 ** b

tests/sfcf_in_test.py

@@ -24,10 +24,10 @@ def build_test_environment(path, env_type, cfgs, reps):
             os.mkdir(path + "/data_c/data_c_r"+str(i))
             for j in range(1, cfgs+1):
                 shutil.copy(path + "/data_c/data_c_r0/data_c_r0_n1", path + "/data_c/data_c_r"+str(i)+"/data_c_r"+str(i)+"_n"+str(j))
-    elif env_type == "a":
+    elif env_type in ["a", "apf"]:
         for i in range(1, reps):
             for corr in ["f_1", "f_A", "F_V0"]:
-                shutil.copy(path + "/data_a/data_a_r0." + corr, path + "/data_a/data_a_r" + str(i) + "." + corr)
+                shutil.copy(path + "/data_" + env_type + "/data_" + env_type + "_r0." + corr, path + "/data_" + env_type + "/data_" + env_type + "_r" + str(i) + "." + corr)
 
 
 def test_o_bb(tmp_path):
@@ -276,6 +276,28 @@ def test_a_bb(tmp_path):
     assert f_1[0].value == 351.1941525454502
 
 
+def test_a_bb_external_idl_func(tmp_path):
+    build_test_environment(str(tmp_path), "a", 5, 3)
+    def extract_idl(s: str) -> int:
+        return int(s.split("n")[-1])
+    f_1 = sfin.read_sfcf(str(tmp_path) + "/data_a", "data_a", "f_1", quarks="lquark lquark", wf=0, wf2=0, version="2.0a", corr_type="bb", cfg_func=extract_idl)
+    print(f_1)
+    assert len(f_1) == 1
+    assert list(f_1[0].shape.keys()) == ["data_a_|r0", "data_a_|r1", "data_a_|r2"]
+    assert f_1[0].value == 351.1941525454502
+
+
+def test_a_bb_external_idl_func_postfix(tmp_path):
+    build_test_environment(str(tmp_path), "apf", 5, 3)
+    def extract_idl(s: str) -> int:
+        return int(s.split("n")[-1][:-5])
+    f_1 = sfin.read_sfcf(str(tmp_path) + "/data_apf", "data_apf", "f_1", quarks="lquark lquark", wf=0, wf2=0, version="2.0a", corr_type="bb", cfg_func=extract_idl)
+    print(f_1)
+    assert len(f_1) == 1
+    assert list(f_1[0].shape.keys()) == ["data_apf_|r0", "data_apf_|r1", "data_apf_|r2"]
+    assert f_1[0].value == 351.1941525454502
+
+
 def test_a_bi(tmp_path):
     build_test_environment(str(tmp_path), "a", 5, 3)
     f_A = sfin.read_sfcf(str(tmp_path) + "/data_a", "data_a", "f_A", quarks="lquark lquark", wf=0, version="2.0a")
@@ -287,6 +309,32 @@ def test_a_bi(tmp_path):
     assert f_A[2].value == -41.025094911185185
 
 
+def test_a_bi_external_idl_func(tmp_path):
+    build_test_environment(str(tmp_path), "a", 5, 3)
+    def extract_idl(s: str) -> int:
+        return int(s.split("n")[-1])
+    f_A = sfin.read_sfcf(str(tmp_path) + "/data_a", "data_a", "f_A", quarks="lquark lquark", wf=0, version="2.0a", cfg_func=extract_idl)
+    print(f_A)
+    assert len(f_A) == 3
+    assert list(f_A[0].shape.keys()) == ["data_a_|r0", "data_a_|r1", "data_a_|r2"]
+    assert f_A[0].value == 65.4711887279723
+    assert f_A[1].value == 1.0447210336915187
+    assert f_A[2].value == -41.025094911185185
+
+
+def test_a_bi_external_idl_func_postfix(tmp_path):
+    build_test_environment(str(tmp_path), "apf", 5, 3)
+    def extract_idl(s: str) -> int:
+        return int(s.split("n")[-1][:-5])
+    f_A = sfin.read_sfcf(str(tmp_path) + "/data_apf", "data_apf", "f_A", quarks="lquark lquark", wf=0, version="2.0a", cfg_func=extract_idl)
+    print(f_A)
+    assert len(f_A) == 3
+    assert list(f_A[0].shape.keys()) == ["data_apf_|r0", "data_apf_|r1", "data_apf_|r2"]
+    assert f_A[0].value == 65.4711887279723
+    assert f_A[1].value == 1.0447210336915187
+    assert f_A[2].value == -41.025094911185185
+
+
 def test_a_bi_files(tmp_path):
     build_test_environment(str(tmp_path), "a", 5, 3)
     f_A = sfin.read_sfcf(str(tmp_path) + "/data_a", "data_a", "f_A", quarks="lquark lquark", wf=0, version="2.0a", files=["data_a_r0.f_A", "data_a_r1.f_A", "data_a_r2.f_A"])
@@ -316,6 +364,31 @@ def test_a_bib(tmp_path):
     assert f_V0[2] == 683.6776090081005
 
 
+def test_a_bib_external_idl_func(tmp_path):
+    build_test_environment(str(tmp_path), "a", 5, 3)
+    def extract_idl(s: str) -> int:
+        return int(s.split("n")[-1])
+    f_V0 = sfin.read_sfcf(str(tmp_path) + "/data_a", "data_a", "F_V0", quarks="lquark lquark", wf=0, wf2=0, version="2.0a", corr_type="bib", cfg_func=extract_idl)
+    print(f_V0)
+    assert len(f_V0) == 3
+    assert list(f_V0[0].shape.keys()) == ["data_a_|r0", "data_a_|r1", "data_a_|r2"]
+    assert f_V0[0] == 683.6776090085115
+    assert f_V0[1] == 661.3188585582334
+    assert f_V0[2] == 683.6776090081005
+
+def test_a_bib_external_idl_func_postfix(tmp_path):
+    build_test_environment(str(tmp_path), "apf", 5, 3)
+    def extract_idl(s: str) -> int:
+        return int(s.split("n")[-1][:-5])
+    f_V0 = sfin.read_sfcf(str(tmp_path) + "/data_apf", "data_apf", "F_V0", quarks="lquark lquark", wf=0, wf2=0, version="2.0a", corr_type="bib", cfg_func=extract_idl)
+    print(f_V0)
+    assert len(f_V0) == 3
+    assert list(f_V0[0].shape.keys()) == ["data_apf_|r0", "data_apf_|r1", "data_apf_|r2"]
+    assert f_V0[0] == 683.6776090085115
+    assert f_V0[1] == 661.3188585582334
+    assert f_V0[2] == 683.6776090081005
+
+
 def test_simple_multi_a(tmp_path):
     build_test_environment(str(tmp_path), "a", 5, 3)
     corrs = sfin.read_sfcf_multi(str(tmp_path) + "/data_a", "data_a", ["F_V0"], quarks_list=["lquark lquark"], wf1_list=[0], wf2_list=[0], version="2.0a", corr_type_list=["bib"])