Fix tutorial code

docs/manuals/characterization/t1.rst

@@ -98,11 +98,10 @@ that is close to a logical value '0'.
     mu = 1e-6
 
     # qubit properties
-    t1 = [45 * mu, 45 * mu]
-    t2 = [value/2 for value in t1]
+    t1 = 45 * mu
 
     # we will guess that our guess is 10% off the exact value of t1 for qubit 0.
-    t1_estimated_shift = t1[0]/10
+    t1_estimated_shift = t1/10
 
     # We use log space for the delays because of the noise properties
     delays = np.logspace(1, 11, num=23, base=np.exp(1))
@@ -111,25 +110,28 @@ that is close to a logical value '0'.
     # Adding circuits with delay=0 and long delays so the centers in the IQ plane won't be misplaced.
     # Without this, the fitting can provide wrong results.
     delays = np.insert(delays, 0, 0)
-    delays = np.append(delays, [t1[0]*3])
+    delays = np.append(delays, [t1*3])
 
     num_qubits = 2
     num_shots = 2048
 
     backend = MockIQBackend(
-        MockIQT1Helper(t1=t1, iq_cluster_centers=[((-5.0, -4.0), (-5.0, 4.0)), ((3.0, 1.0), (5.0, -3.0))]
-                           , iq_cluster_width=[1.0, 2.0])
+        MockIQT1Helper(
+            t1=t1,
+            iq_cluster_centers=[((-5.0, -4.0), (-5.0, 4.0)), ((3.0, 1.0), (5.0, -3.0))],
+            iq_cluster_width=[1.0, 2.0],
+        )
     )
 
     # Creating a T1 experiment
     expT1_kerneled = T1((0,), delays)
     expT1_kerneled.analysis = T1KerneledAnalysis()
-    expT1_kerneled.analysis.set_options(p0={"amp": 1, "tau": t1[0] + t1_estimated_shift, "base": 0})
+    expT1_kerneled.analysis.set_options(p0={"amp": 1, "tau": t1 + t1_estimated_shift, "base": 0})
 
     # Running the experiment
-    expdataT1_kerneled = expT1_kerneled.run(backend=backend, meas_return="avg",
-                                            meas_level=MeasLevel.KERNELED,
-                                            shots=num_shots).block_for_results()
+    expdataT1_kerneled = expT1_kerneled.run(
+        backend=backend, meas_return="avg", meas_level=MeasLevel.KERNELED, shots=num_shots
+    ).block_for_results()
 
     # Displaying results
     display(expdataT1_kerneled.figure(0))