Fix probems for fitting

src/qcvv/calibrations/qubit_spectroscopy.py

@@ -100,7 +100,7 @@ def qubit_spectroscopy(
             if count % points == 0 and count > 0:
                 yield prec_data
                 yield lorentzian_fit(
-                    prec_data,
+                    data + prec_data,
                     x="frequency[GHz]",
                     y="MSR[uV]",
                     qubit=qubit,

src/qcvv/calibrations/resonator_spectroscopy.py

@@ -104,7 +104,7 @@ def resonator_spectroscopy(
             if count % points == 0 and count > 0:
                 yield precision_sweep__data
                 yield lorentzian_fit(
-                    precision_sweep__data,
+                    fast_sweep_data + precision_sweep__data,
                     x="frequency[GHz]",
                     y="MSR[uV]",
                     qubit=qubit,

src/qcvv/data.py

@@ -19,9 +19,9 @@ def __init__(self, name=None):
 
         self.df = pd.DataFrame()
 
-    # def __add__(self, data):
-    #     self.df = pd.concat([self.df, data.df)
-    #     return self
+    def __add__(self, data):
+        self.df = pd.concat([self.df, data.df], ignore_index=True)
+        return self
 
     @abstractmethod
     def add(self, data):
@@ -165,7 +165,7 @@ def __init__(self, name=None, quantities=None):
         if quantities is not None:
             self.quantities = quantities
             for name in quantities:
-                self.df.insert(0, name, pd.Series())
+                self.df.insert(0, name, pd.Series(dtype=object))
 
     def add(self, data):
         """Add a row to dataset.

src/qcvv/fitting/methods.py

@@ -2,23 +2,37 @@
 """Routine-specific method for post-processing data acquired."""
 import lmfit
 import numpy as np
-import yaml
 
+from qcvv.config import log
 from qcvv.data import Data
 from qcvv.fitting.utils import lorenzian, parse
 
 
 def lorentzian_fit(data, x, y, qubit, nqubits, labels):
     """Fitting routine for resonator spectroscopy"""
 
+    data_fit = Data(
+        name=f"fit_q{qubit}",
+        quantities=[
+            "fit_amplitude",
+            "fit_center",
+            "fit_sigma",
+            "fit_offset",
+            labels[1],
+            labels[0],
+        ],
+    )
+
     frequencies = data.get_values(*parse(x))
     voltages = data.get_values(*parse(y))
 
     # Create a lmfit model for fitting equation defined in resonator_peak
     model_Q = lmfit.Model(lorenzian)
 
     # Guess parameters for Lorentzian max or min
-    if nqubits == 1 and labels[0] == "resonator_freq":
+    if (nqubits == 1 and labels[0] == "resonator_freq") or (
+        nqubits != 1 and labels[0] == "qubit_freq"
+    ):
         guess_center = frequencies[
             np.argmax(voltages)
         ]  # Argmax = Returns the indices of the maximum values along an axis.
@@ -46,7 +60,13 @@ def lorentzian_fit(data, x, y, qubit, nqubits, labels):
     guess_parameters = model_Q.make_params()
 
     # fit the model with the data and guessed parameters
-    fit_res = model_Q.fit(data=voltages, frequency=frequencies, params=guess_parameters)
+    try:
+        fit_res = model_Q.fit(
+            data=voltages, frequency=frequencies, params=guess_parameters
+        )
+    except:
+        log.warning("The fitting was not successful")
+        return data_fit
 
     # get the values for postprocessing and for legend.
     f0 = fit_res.best_values["center"]
@@ -57,18 +77,6 @@ def lorentzian_fit(data, x, y, qubit, nqubits, labels):
         + fit_res.best_values["offset"]
     )
 
-    data_fit = Data(
-        name=f"fit_q{qubit}",
-        quantities=[
-            "fit_amplitude",
-            "fit_center",
-            "fit_sigma",
-            "fit_offset",
-            labels[1],
-            labels[0],
-        ],
-    )
-
     freq = f0 * 1e6
 
     data_fit.add(

src/qcvv/plots/scatters.py

@@ -21,7 +21,16 @@ def frequency_msr_phase__fast_precision(folder, routine, qubit, format):
     try:
         data_fit = Data.load_data(folder, routine, format, f"fit_q{qubit}")
     except:
-        data_fit = Data()
+        data_fit = Data(
+            quantities=[
+                "fit_amplitude",
+                "fit_center",
+                "fit_sigma",
+                "fit_offset",
+                "label1",
+                "label2",
+            ]
+        )
 
     fig = make_subplots(
         rows=1,
@@ -70,7 +79,7 @@ def frequency_msr_phase__fast_precision(folder, routine, qubit, format):
         row=1,
         col=2,
     )
-    if len(data_fast) > 0:
+    if len(data_fast) > 0 and len(data_fit) > 0:
         freqrange = np.linspace(
             min(data_fast.get_values("frequency", "GHz")),
             max(data_fast.get_values("frequency", "GHz")),