diff --git a/cli/src/xyfigure/xymodel.py b/cli/src/xyfigure/xymodel.py
index 04f8745..ded1ac1 100644
--- a/cli/src/xyfigure/xymodel.py
+++ b/cli/src/xyfigure/xymodel.py
@@ -46,12 +46,8 @@ def cross_correlation(reference, subject, verbose=False):
         print(
             f"  Reference [t_min, t_max] by dt (s): [{ref_t_min}, {ref_t_max}] by {ref_delta_t}"
         )
-        print(
-            f"  Subject [t_min, t_max] by dt (s): [{t_min}, {t_max}] by {dt}"
-        )
-        print(
-            f"  Globalized [t_min, t_max] by dt (s): [{T_MIN}, {T_MAX}] by {DT}"
-        )
+        print(f"  Subject [t_min, t_max] by dt (s): [{t_min}, {t_max}] by {dt}")
+        print(f"  Globalized [t_min, t_max] by dt (s): [{T_MIN}, {T_MAX}] by {DT}")
         print(f"  Globalized times: {t_span}")
         print(f"  Length of globalized times: {len(t_span)}")
 
@@ -59,9 +55,7 @@ def cross_correlation(reference, subject, verbose=False):
         t_span, reference[:, 0], reference[:, 1], left=0.0, right=0.0
     )
 
-    y_span = np.interp(
-        t_span, subject[:, 0], subject[:, 1], left=0.0, right=0.0
-    )
+    y_span = np.interp(t_span, subject[:, 0], subject[:, 1], left=0.0, right=0.0)
 
     cross_corr = np.correlate(ref_y_span, y_span, mode="full")
     cross_corr_max = np.max(cross_corr)
@@ -72,9 +66,7 @@ def cross_correlation(reference, subject, verbose=False):
         mode="full",
     )
 
-    ref_self_corr = np.correlate(ref_y_span, ref_y_span)[
-        0
-    ]  # self correlated reference
+    ref_self_corr = np.correlate(ref_y_span, ref_y_span)[0]  # self correlated reference
     rel_corr_error = 0.0
 
     if ref_self_corr > 0:
@@ -90,12 +82,8 @@ def cross_correlation(reference, subject, verbose=False):
     T_MIN_CORR = np.minimum(ref_t_min, t_shift[0])
     T_MAX_CORR = np.maximum(ref_t_max, t_shift[-1])
 
-    n_samples_corr = (
-        int((T_MAX_CORR - T_MIN_CORR) / DT) + 1
-    )  # DT unchanged pre-shift
-    t_span_corr = np.linspace(
-        T_MIN_CORR, T_MAX_CORR, n_samples_corr, endpoint=True
-    )
+    n_samples_corr = int((T_MAX_CORR - T_MIN_CORR) / DT) + 1  # DT unchanged pre-shift
+    t_span_corr = np.linspace(T_MIN_CORR, T_MAX_CORR, n_samples_corr, endpoint=True)
 
     ref_y_span_corr = np.interp(
         t_span_corr, reference[:, 0], reference[:, 1], left=0.0, right=0.0
@@ -111,9 +99,7 @@ def cross_correlation(reference, subject, verbose=False):
         print("\nCorrelation...")
         print(f"  Sliding dot product (cross-correlation): {cross_corr}")
         print(f"  Length of the sliding dot product: {len(cross_corr)}")
-        print(
-            f"  Max sliding dot product (cross-correlation): {cross_corr_max}"
-        )
+        print(f"  Max sliding dot product (cross-correlation): {cross_corr_max}")
         print(
             f"  Sliding dot product of normalized signals (cross-correlation): {cross_corr_unit}"
         )
@@ -316,8 +302,8 @@ def correlation(self, value):
             usecols=(ref_xcolumn, ref_ycolumn),
         )
 
-        tcorrelated, ycorrelated, cc_relative_error, L2_error = (
-            cross_correlation(ref_data, self._data, verbose=verbosity)
+        tcorrelated, ycorrelated, cc_relative_error, L2_error = cross_correlation(
+            ref_data, self._data, verbose=verbosity
         )
 
         self._data = np.transpose([tcorrelated, ycorrelated])  # overwrite
@@ -350,9 +336,7 @@ def gradient(self, value):
         # https://docs.scipy.org/doc/numpy/reference/generated/numpy.gradient.html
         # for k in range(value):
         for k in range(gradient_order):
-            ydot = np.gradient(
-                self._data[:, 1], self._data[:, 0], edge_order=2
-            )
+            ydot = np.gradient(self._data[:, 1], self._data[:, 0], edge_order=2)
             self._data[:, 1] = ydot  # overwrite
             if self._verbose:
                 print(f"  Derivative {k+1} completed.")
@@ -404,9 +388,7 @@ def integration(self, value):
             #     + ics[k]
             # )
             inty = (
-                cumulative_trapezoid(
-                    self._data[:, 1], self._data[:, 0], initial=0
-                )
+                cumulative_trapezoid(self._data[:, 1], self._data[:, 0], initial=0)
                 + ics[k]
             )
 
@@ -522,18 +504,10 @@ def __init__(self, guid, **kwargs):
         }
         self._plot_kwargs = kwargs.get("plot_kwargs", _default)
         self._alpha = self._plot_kwargs.get("alpha", _default["alpha"])
-        self._edgecolor = self._plot_kwargs.get(
-            "edgecolor", _default["edgecolor"]
-        )
-        self._facecolor = self._plot_kwargs.get(
-            "facecolor", _default["facecolor"]
-        )
-        self._linestyle = self._plot_kwargs.get(
-            "linestyle", _default["linestyle"]
-        )
-        self._linewidth = self._plot_kwargs.get(
-            "linewidth", _default["linewidth"]
-        )
+        self._edgecolor = self._plot_kwargs.get("edgecolor", _default["edgecolor"])
+        self._facecolor = self._plot_kwargs.get("facecolor", _default["facecolor"])
+        self._linestyle = self._plot_kwargs.get("linestyle", _default["linestyle"])
+        self._linewidth = self._plot_kwargs.get("linewidth", _default["linewidth"])
 
 
 """