add param input and udpate docstring

neurodsp/aperiodic/autocorr.py

@@ -88,7 +88,7 @@ def compute_decay_time(timepoints, autocorrs, fs, level=0):
     return result
 
 
-def fit_autocorr(timepoints, autocorrs, fit_function='single_exp'):
+def fit_autocorr(timepoints, autocorrs, fit_function='single_exp', bounds=None):
     """Fit autocorrelation function, returning timescale estimate.
 
     Parameters
@@ -102,22 +102,30 @@ def fit_autocorr(timepoints, autocorrs, fit_function='single_exp'):
         If provided, timepoints are converted to time values.
     fit_func : {'single_exp', 'double_exp'}
         Which fitting function to use to fit the autocorrelation results.
+    bounds : tuple of list
+        Parameter bounds for fitting.
+        Organized as ([min_p1, min_p1, ...], [max_p1, max_p2, ...]).
 
     Returns
     -------
     popts
-        Fit parameters.
+        Fit parameters. Parameters depend on the fitting function.
+        If `fit_func` is 'single_exp', fit parameters are: tau, scale, offset
+        If `fit_func` is 'douple_exp', fit parameters are: tau1, tau2, scale1, scale2, offset
+        See fit function for more details.
 
     Notes
     -----
     The values / units of the returned parameters are dependent on the units of samples.
     For example, if the timepoints input is in samples, the fit tau value is too.
+    If providing parameter bounds, these also need to match the unit of timepoints.
     """
 
-    if fit_function == 'single_exp':
-        p_bounds = ([0, 0, 0], [np.inf, np.inf, np.inf])
-    elif fit_function == 'double_exp':
-        p_bounds = ([0, 0, 0, 0, 0], [np.inf, np.inf, np.inf, np.inf, np.inf])
+    if not bounds:
+        if fit_function == 'single_exp':
+            p_bounds = ([0, 0, 0], [np.inf, np.inf, np.inf])
+        elif fit_function == 'double_exp':
+            p_bounds = ([0, 0, 0, 0, 0], [np.inf, np.inf, np.inf, np.inf, np.inf])
 
     popts, _ = curve_fit(AC_FIT_FUNCS[fit_function], timepoints, autocorrs, bounds=p_bounds)