Changeset 74b0495 in sasmodels for sasmodels

Timestamp:

Oct 10, 2017 1:39:39 PM (7 years ago)

Author:

Paul Kienzle <pkienzle@…>

Branches:

master, core_shell_microgels, costrafo411, magnetic_model, ticket-1257-vesicle-product, ticket_1156, ticket_1265_superball, ticket_822_more_unit_tests

Children:

09141ff

Parents:

b76191e

Message:

update oriented sesans and simultaneous fit examples

Location:

sasmodels

Files:

• bumps_model.py (modified) (2 diffs)
• data.py (modified) (3 diffs)

sasmodels/bumps_model.py

@@ -133 +133 @@
     """
     _cache = None # type: Dict[str, np.ndarray]
-    def __init__(self, data, model, cutoff=1e-5):
+    def __init__(self, data, model, cutoff=1e-5, name=None):
         # type: (Data, Model, float) -> None
         # remember inputs so we can inspect from outside
+        self.name = data.filename if name is None else name
         self.model = model
         self.cutoff = cutoff
@@ -204 +205 @@
         """
         data, theory, resid = self._data, self.theory(), self.residuals()
-        plot_theory(data, theory, resid, view, Iq_calc=self.Iq_calc)
+        # TODO: hack to display oriented usans 2-D pattern
+        Iq_calc = self.Iq_calc if isinstance(self.Iq_calc, tuple) else None
+        plot_theory(data, theory, resid, view, Iq_calc=Iq_calc)
 
     def simulate_data(self, noise=None):

sasmodels/data.py

@@ -44 +44 @@
     Data = Union["Data1D", "Data2D", "SesansData"]
 
-def load_data(filename):
+def load_data(filename, index=0):
     # type: (str) -> Data
     """
@@ -55 +55 @@
         filename, indexstr = filename[:-1].split('[')
         index = int(indexstr)
-    else:
-        index = None
     datasets = loader.load(filename)
     if datasets is None:
@@ -62 +60 @@
     if not isinstance(datasets, list):
         datasets = [datasets]
-    if index is None and len(datasets) > 1:
-        raise ValueError("Need to specify filename[index] for multipart data")
-    data = datasets[index if index is not None else 0]
-    if hasattr(data, 'x'):
-        data.qmin, data.qmax = data.x.min(), data.x.max()
-        data.mask = (np.isnan(data.y) if data.y is not None
-                     else np.zeros_like(data.x, dtype='bool'))
-    elif hasattr(data, 'qx_data'):
-        data.mask = ~data.mask
-    return data
+    for data in datasets:
+        if hasattr(data, 'x'):
+            data.qmin, data.qmax = data.x.min(), data.x.max()
+            data.mask = (np.isnan(data.y) if data.y is not None
+                        else np.zeros_like(data.x, dtype='bool'))
+        elif hasattr(data, 'qx_data'):
+            data.mask = ~data.mask
+    return datasets[index] if index != 'all' else datasets