added test data for HB1A

scripts/HoV6Sn6_contour.py

@@ -0,0 +1,24 @@
+import matplotlib.pyplot as plt
+from tavi.data.tavi import TAVI
+
+tavi = TAVI()
+
+tavi_file_name = "./test_data/tavi_test_exp1031.h5"
+tavi.new_tavi_file(tavi_file_name)
+
+nexus_file_name = "./test_data/nexus_exp1031.h5"
+tavi.load_nexus_data_from_disk(nexus_file_name)
+
+
+dataset = tavi.data["IPTS32912_HB1A_exp1031"]
+
+print(len(dataset))
+# 001 s1 scans
+scan_list = [dataset[f"scan{i:04}"] for i in range(90, 121, 5)]
+
+sg1 = tavi.generate_scan_group(signals=scan_list, signal_axes=("persistent_field", "s1", "detector"))
+contour1 = sg1.generate_contour(rebin_steps=(None, None))
+sg1.plot_contour(contour1, cmap="turbo")
+
+
+plt.show()

scripts/test_data_conversion.py

@@ -29,6 +29,7 @@ def test_open_tavi_file(tavi):
     tavi = TAVI()
     # test_conversion(424)
     # test_conversion(710)
+    test_conversion(1031)
     test_load_nexus_to_new_tavi(tavi)
 
     # test_open_exsiting_tavi()

scripts/test_fit.py

@@ -13,9 +13,18 @@ def test_fit_scan(tavi):
 
     x, y, xerr, yerr, xlabel, ylabel, title, label = curve1
     f1 = Fit(x=x, y=y, fit_range=(0.0, 4))
-    f1.add_background()
-    f1.add_signal()
-    f1.add_signal(model="Gaussian", values=(0, None, None), vary=(False, True, True))
+    f1.add_background(values=(0.7,))
+    f1.add_signal(
+        values=(None, 3.5, None),
+        vary=(True, True, True),
+    )
+    f1.add_signal(
+        model="Gaussian",
+        values=(None, 0, None),
+        vary=(True, False, True),
+        mins=(0, 0, 0.1),
+        maxs=(None, 0.1, 0.3),
+    )
     f1.perform_fit()
 
     p1.plot_curve(*curve1)

src/tavi/data/fit.py

@@ -34,6 +34,7 @@ def __init__(self, x, y, err=None, fit_range=None):
             y (list)
             err (list | None)
             fit_range (tuple)
+            NUM_PTS (int): number of points for the fit curve
         """
         self.NUM_PTS = 100
         self.range = fit_range
@@ -55,17 +56,16 @@ def __init__(self, x, y, err=None, fit_range=None):
             self.x.max(),
             num=self.NUM_PTS,
         )
+        self.y_plot = None
 
         self.background_models = []
         self.signal_models = []
         self.pars = Parameters()
         self.num_backgrounds = 0
         self.num_signals = 0
-        self.FIT_STATUS = None
-        self.chisqr = 0
+        self.fit_result = None
+
         self.PLOT_SEPARATELY = False
-        self.y_plot = None
-        self.best_values = None
 
     def add_background(
         self,
@@ -95,31 +95,37 @@ def add_background(
         else:
             prefix = ""
         model = Fit.models[model](prefix=prefix, nan_policy="propagate")
-        num_params = len(model.param_names)
-
+        param_names = model.param_names
+        # guess initials
         pars = model.guess(self.y, x=self.x)
-        if values is None:
-            values = []
-            for idx, param in enumerate(model.param_names):
-                values.append(pars[param].value)
-        if vary is None:
-            vary = [True] * num_params
-        if mins is None:
-            mins = [None] * num_params
-        if maxs is None:
-            maxs = [None] * num_params
-        if exprs is None:
-            exprs = [None] * num_params
-
-        for idx, param in enumerate(model.param_names):
-            self.pars.add(
-                param,
-                value=pars[param].value,
-                vary=vary[idx],
-                min=mins[idx],
-                max=maxs[idx],
-                expr=exprs[idx],
-            )
+
+        # overwrite with user input
+        if values is not None:
+            for idx, v in enumerate(values):
+                if v is not None:
+                    pars[param_names[idx]].set(value=v)
+
+        if vary is not None:
+            for idx, v in enumerate(vary):
+                if v is not None:
+                    pars[param_names[idx]].set(vary=v)
+
+        if mins is not None:
+            for idx, v in enumerate(mins):
+                if v is not None:
+                    pars[param_names[idx]].set(min=v)
+        if maxs is not None:
+            for idx, v in enumerate(maxs):
+                if v is not None:
+                    pars[param_names[idx]].set(max=v)
+
+        if exprs is not None:
+            for idx, v in enumerate(exprs):
+                if v is not None:
+                    pars[param_names[idx]].set(expr=v)
+
+        for param_name in param_names:
+            self.pars.add(pars[param_name])
 
         self.background_models.append(model)
 
@@ -132,7 +138,7 @@ def add_signal(
         maxs=None,
         exprs=None,
     ):
-        """Set the model for background
+        """Set the model for signal
 
         Args:
             model (str): Constant, Linear, Quadratic, Polynomial,
@@ -145,34 +151,37 @@ def add_signal(
         self.num_signals += 1
         prefix = f"s{self.num_signals}_"
         model = Fit.models[model](prefix=prefix, nan_policy="propagate")
-
+        param_names = model.param_names
+        # guess initials
         pars = model.guess(self.y, x=self.x)
 
-        num_params = len(model.param_names)
-        if values is None:
-            values = []
-            for idx, param in enumerate(model.param_names):
-                values.append(pars[param].value)
-        if vary is None:
-            vary = [True] * num_params
-        if mins is None:
-            mins = [None] * num_params
-        if maxs is None:
-            maxs = [None] * num_params
-        if exprs is None:
-            exprs = [None] * num_params
-
-        # pars = model.guess(self.y, x=self.x)
-        for idx, param in enumerate(model.param_names):
-            self.pars.add(
-                param,
-                value=values[idx],
-                vary=vary[idx],
-                min=mins[idx],
-                max=maxs[idx],
-                expr=exprs[idx],
-            )
-
+        # overwrite with user input
+        if values is not None:
+            for idx, v in enumerate(values):
+                if v is not None:
+                    pars[param_names[idx]].set(value=v)
+
+        if vary is not None:
+            for idx, v in enumerate(vary):
+                if v is not None:
+                    pars[param_names[idx]].set(vary=v)
+
+        if mins is not None:
+            for idx, v in enumerate(mins):
+                if v is not None:
+                    pars[param_names[idx]].set(min=v)
+        if maxs is not None:
+            for idx, v in enumerate(maxs):
+                if v is not None:
+                    pars[param_names[idx]].set(max=v)
+
+        if exprs is not None:
+            for idx, v in enumerate(exprs):
+                if v is not None:
+                    pars[param_names[idx]].set(expr=v)
+
+        for param_name in param_names:
+            self.pars.add(pars[param_name])
         self.signal_models.append(model)
 
     def perform_fit(self):
@@ -186,10 +195,8 @@ def perform_fit(self):
         else:
             out = model.fit(self.y, self.pars, x=self.x, weights=self.err)
 
-        self.chisqr = out.chisqr
-        self.FIT_STATUS = out.success
+        self.result = out
 
         # self.y_plot = model.eval(self.pars, x=self.x_plot)
         self.y_plot = model.eval(out.params, x=self.x_plot)
-        self.best_values = out.best_values
         print(out.fit_report(min_correl=0.25))

src/tavi/data/scan_group.py

@@ -31,7 +31,6 @@ def __init__(
         signal_axes=(None, None, None),
         background_axes=(None, None, None),
     ):
-
         self.signals = signals
         self.backgrounds = backgrounds
         self.signal_axes = list(signal_axes)
@@ -165,7 +164,6 @@ def plot_waterfall(self, contour_plot, shifts=None, ylim=None, xlim=None, fmt="o
         fig, ax = plt.subplots()
         shift = 0
         for i in range(num):
-
             if np.isnan(z[:, i]).all():  # all nan
                 continue
             else: