Fix ValueError when refitting after load(fitted_as_initial=True)

impedance/models/circuits/circuits.py

@@ -96,7 +96,7 @@ def fit(self, frequencies, impedance, bounds=None,
         if len(frequencies) != len(impedance):
             raise TypeError('length of frequencies and impedance do not match')
 
-        if self.initial_guess != []:
+        if len(self.initial_guess) != 0:
             parameters, conf = circuit_fit(frequencies, impedance,
                                            self.circuit, self.initial_guess,
                                            constants=self.constants,

impedance/tests/test_model_io.py

@@ -31,3 +31,29 @@ def test_model_io():
 
     fitted_template = CustomCircuit()
     fitted_template.load('test_io.json', fitted_as_initial=True)
+
+
+def test_refit_after_load_fitted_as_initial():
+    # Regression test for #310: loading a fitted model with
+    # fitted_as_initial=True stores initial_guess as a numpy array, which
+    # broke the subsequent ``fit`` call because the emptiness check compared
+    # the array against ``[]`` (raising a broadcasting ValueError).
+    data = np.genfromtxt(os.path.join(".", "data",
+                                      "exampleData.csv"), delimiter=',')
+    frequencies = data[:, 0]
+    Z = data[:, 1] + 1j * data[:, 2]
+
+    randles = CustomCircuit(initial_guess=[.01, .005, .1, .005, .1, .9,
+                                           .001, 200],
+                            circuit='R0-p(R1,C1)-p(R1,CPE1)-Wo1')
+    randles.fit(frequencies, Z)
+    randles.save('./test_io.json')
+
+    reloaded = CustomCircuit()
+    reloaded.load('./test_io.json', fitted_as_initial=True)
+    assert isinstance(reloaded.initial_guess, np.ndarray)
+
+    # Refitting must not raise; it previously failed with
+    # "operands could not be broadcast together with shapes (n,) (0,)"
+    reloaded.fit(frequencies, Z)
+    assert reloaded.parameters_ is not None