fix: renamed covdiag to inverrdiag needed for the cholesky decomposit…

…ion and corrected its documentation

pyerrors/fits.py

@@ -156,8 +156,8 @@ def func_b(a, x):
         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 erros, dy_f included!) of your own choosing for a correlated fit.
-        The matrix must be a lower triangular matrix constructed from a Cholesky decomposition: The function invert_corr_cov_cholesky(corr, covdiag) can be
-        used to construct it from a correlation matrix (corr) and the errors dy_f of the data points (covdiag = np.diag(1 / np.asarray(dy_f))).
+        The matrix must be a lower triangular matrix constructed from a Cholesky decomposition: The function invert_corr_cov_cholesky(corr, inverrdiag) can be
+        used to construct it from a correlation matrix (corr) and the errors dy_f of the data points (inverrdiag = np.diag(1 / np.asarray(dy_f))).
     expected_chisquare : bool
         If True estimates the expected chisquare which is
         corrected by effects caused by correlated input data (default False).
@@ -311,12 +311,12 @@ def chisqfunc_uncorr(p):
             if (chol_inv[0].shape[0] != chol_inv[0].shape[1]):
                 raise TypeError('The inverse covariance matrix handed over needs to have the same number of rows as columns.')
             if (chol_inv[1] != key_ls):
-                raise ValueError('The keys of the corr matrix are not same or do not appear in the same order as the x and y values.')
+                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]
         else:
             corr = covariance(y_all, correlation=True, **kwargs)
-            covdiag = np.diag(1 / np.asarray(dy_f))
-            chol_inv = invert_corr_cov_cholesky(corr, covdiag)
+            inverrdiag = np.diag(1 / np.asarray(dy_f))
+            chol_inv = invert_corr_cov_cholesky(corr, inverrdiag)
 
         def general_chisqfunc(p, ivars, pr):
             model = anp.concatenate([anp.array(funcd[key](p, xd[key])).reshape(-1) for key in key_ls])

pyerrors/obs.py

@@ -1544,14 +1544,14 @@ def covariance(obs, visualize=False, correlation=False, smooth=None, **kwargs):
     return cov
 
 
-def invert_corr_cov_cholesky(corr, covdiag):
+def invert_corr_cov_cholesky(corr, inverrdiag):
     """Constructs a lower triangular matrix, chol, via the Cholesky decomposition of the correlation matrix, corr,
-       and then returns the inverse covariance matrix, chol_inv, as a lower triangular matrix by solving chol * x = covdiag.
+       and then returns the inverse covariance matrix, chol_inv, as a lower triangular matrix by solving chol * x = inverrdiag.
 
     corr : np.ndarray
            correlation matrix
-    covdiag : np.ndarray
-              diagonal matrix, the entries are the errors of the data points considered
+    inverrdiag : np.ndarray
+              diagonal matrix, the entries are the inverse errors of the data points considered
     """
 
     condn = np.linalg.cond(corr)
@@ -1560,7 +1560,7 @@ def invert_corr_cov_cholesky(corr, covdiag):
     if condn > 1e13:
         warnings.warn("Correlation matrix may be ill-conditioned, condition number: {%1.2e}" % (condn), RuntimeWarning)
     chol = np.linalg.cholesky(corr)
-    chol_inv = scipy.linalg.solve_triangular(chol, covdiag, lower=True)
+    chol_inv = scipy.linalg.solve_triangular(chol, inverrdiag, lower=True)
 
     return chol_inv