trying to commit with github email

content/scipy/concepts/scipy-optimize/terms/curve-fit/curve-fit.md

@@ -27,7 +27,7 @@ popt, pcov = curve_fit(f, xdata, ydata, p0=None, sigma=None, absolute_sigma=Fals
 - `p0`: Inital guess of the parameters.
 - `sigma`: Defines the uncertainty in `ydata`.
 - `absolute_sigma`: If `True`, `sigma` is interpreted absolutely, and the parameter covariance pcov reflects absolute values. If `False` which is the default, `sigma` is scaled to normalize residual variance. Here, pcov(absolute_sigma=False) = pcov(absolute_sigma=True) \* chisq(popt)/(M-N).
-- `check_finite`: Ensures input arrays do not contain `NaN` or `inf`. If `True`, a `ValueError` is raised when such values are found. Defaults to `True` unless `nan_policy` is explicitly specified.
+- `check_finite`: Ensures input arrays do not contain `NaN` or `inf`. If `True`, a `ValueError` is raised when such values are found.  Defaults to `True` unless `nan_policy` is explicitly specified.
 - `bounds`: Specifies parameter bounds. Defaults to no bounds. Options include:
   - An instance of the `Bounds` class.
   - A 2-tuple of array-like objects or scalars: Scalars apply bounds uniformly and `np.inf` can disable bounds partially.