Merge branch 'develop'

.github/workflows/pytest.yml

@@ -43,4 +43,4 @@ jobs:
           uv pip freeze --system
 
       - name: Run tests
-        run: pytest --cov=pyerrors -vv
+        run: pytest --cov=pyerrors -vv -Werror

CHANGELOG.md

@@ -2,6 +2,11 @@
 
 All notable changes to this project will be documented in this file.
 
+## [2.15.0] - 2025-10-10
+
+### Added
+- Option to explicitly specify the number of fit parameters added.
+
 ## [2.14.0] - 2025-03-09
 
 ### Added

examples/06_gevp.ipynb

@@ -151,7 +151,7 @@
     "\n",
     "$$C_{\\textrm{projected}}(t)=v_1^T \\underline{C}(t) v_2$$\n",
     "\n",
-    "If we choose the vectors to be $v_1=v_2=(0,1,0,0)$, we should get the same correlator as in the cell above. \n",
+    "If we choose the vectors to be $v_1=v_2=(1,0,0,0)$, we should get the same correlator as in the cell above. \n",
     "\n",
     "Thinking about it this way is usefull in the Context of the generalized eigenvalue problem (GEVP), used to find the source-sink combination, which best describes a certain energy eigenstate.\n",
     "A good introduction is found in https://arxiv.org/abs/0902.1265."

pyerrors/fits.py

@@ -131,7 +131,7 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
         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
@@ -139,10 +139,10 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
     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)
@@ -152,7 +152,7 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
         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.
@@ -168,6 +168,9 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
         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
     -------
@@ -269,26 +272,38 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
         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:
@@ -535,17 +550,20 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
         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
     -----
@@ -575,19 +593,32 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
     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/json.py

@@ -571,7 +571,6 @@ def _ol_from_dict(ind, reps='DICTOBS'):
     counter = 0
 
     def dict_replace_obs(d):
-        nonlocal ol
         nonlocal counter
         x = {}
         for k, v in d.items():
@@ -592,7 +591,6 @@ def _ol_from_dict(ind, reps='DICTOBS'):
         return x
 
     def list_replace_obs(li):
-        nonlocal ol
         nonlocal counter
         x = []
         for e in li:
@@ -613,7 +611,6 @@ def _ol_from_dict(ind, reps='DICTOBS'):
         return x
 
     def obslist_replace_obs(li):
-        nonlocal ol
         nonlocal counter
         il = []
         for e in li:
@@ -694,7 +691,6 @@ def _od_from_list_and_dict(ol, ind, reps='DICTOBS'):
 
     def dict_replace_string(d):
         nonlocal counter
-        nonlocal ol
         x = {}
         for k, v in d.items():
             if isinstance(v, dict):
@@ -710,7 +706,6 @@ def _od_from_list_and_dict(ol, ind, reps='DICTOBS'):
 
     def list_replace_string(li):
         nonlocal counter
-        nonlocal ol
         x = []
         for e in li:
             if isinstance(e, list):

pyerrors/input/pandas.py

@@ -1,6 +1,7 @@
 import warnings
 import gzip
 import sqlite3
+from contextlib import closing
 import pandas as pd
 from ..obs import Obs
 from ..correlators import Corr
@@ -29,9 +30,8 @@ def to_sql(df, table_name, db, if_exists='fail', gz=True, **kwargs):
     None
     """
     se_df = _serialize_df(df, gz=gz)
-    con = sqlite3.connect(db)
+    with closing(sqlite3.connect(db)) as con:
-    se_df.to_sql(table_name, con, if_exists=if_exists, index=False, **kwargs)
+        se_df.to_sql(table_name, con=con, if_exists=if_exists, index=False, **kwargs)
-    con.close()
 
 
 def read_sql(sql, db, auto_gamma=False, **kwargs):
@@ -52,9 +52,8 @@ def read_sql(sql, db, auto_gamma=False, **kwargs):
     data : pandas.DataFrame
         Dataframe with the content of the sqlite database.
     """
-    con = sqlite3.connect(db)
+    with closing(sqlite3.connect(db)) as con:
-    extract_df = pd.read_sql(sql, con, **kwargs)
+        extract_df = pd.read_sql(sql, con=con, **kwargs)
-    con.close()
     return _deserialize_df(extract_df, auto_gamma=auto_gamma)
 
 

pyerrors/version.py

@@ -1 +1 @@
-__version__ = "2.14.0"
+__version__ = "2.15.0"

setup.py

@@ -30,7 +30,6 @@ setup(name='pyerrors',
       classifiers=[
           'Development Status :: 5 - Production/Stable',
           'Intended Audience :: Science/Research',
-          'License :: OSI Approved :: MIT License',
           'Programming Language :: Python :: 3',
           'Programming Language :: Python :: 3.9',
           'Programming Language :: Python :: 3.10',

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):