Changeset 630156b in sasmodels

Timestamp:

Apr 20, 2017 3:52:02 PM (8 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:

3a45c2c, 8ae8532, ba62072, 93d0ea7

Parents:

f2f5413

Message:

sascomp: improve data file handling; add -help as alias to -html; default to -mono

Location:

sasmodels

Files:

• compare.py (modified) (9 diffs)
• data.py (modified) (1 diff)

sasmodels/compare.py

@@ -73 +73 @@
     -1d*/-2d computes 1d or 2d data
     -preset*/-random[=seed] preset or random parameters
-    -mono/-poly* force monodisperse/polydisperse
+    -mono*/-poly force monodisperse or allow polydisperse demo parameters
     -magnetic/-nonmagnetic* suppress magnetism
     -cutoff=1e-5* cutoff value for including a point in polydispersity
@@ -84 +84 @@
     -edit starts the parameter explorer
     -default/-demo* use demo vs default parameters
-    -html shows the model docs instead of running the model
+    -help/-html shows the model docs instead of running the model
     -title="note" adds note to the plot title, after the model name
     -data="path" uses q, dq from the data file
@@ -753 +753 @@
     comp = opts['engines'][1] if have_comp else None
     data = opts['data']
-    use_data = have_base ^ have_comp
+    use_data = (opts['datafile'] is not None) and (have_base ^ have_comp)
 
     # Plot if requested
     view = opts['view']
     import matplotlib.pyplot as plt
-    if limits is None:
+    if limits is None and not use_data:
         vmin, vmax = np.Inf, -np.Inf
         if have_base:
@@ -836 +836 @@
     'linear', 'log', 'q4',
     'hist', 'nohist',
-    'edit', 'html',
+    'edit', 'html', 'help',
     'demo', 'default',
     ])
@@ -947 +947 @@
         'cutoff'    : 0.0,
         'seed'      : -1,  # default to preset
-        'mono'      : False,
+        'mono'      : True,
         # Default to magnetic a magnetic moment is set on the command line
         'magnetic'  : False,
@@ -958 +958 @@
         'html'      : False,
         'title'     : None,
-        'data'      : None,
+        'datafile'  : None,
     }
     engines = []
@@ -980 +980 @@
         elif arg.startswith('-random='):   opts['seed'] = int(arg[8:])
         elif arg.startswith('-title='):    opts['title'] = arg[7:]
-        elif arg.startswith('-data='):     opts['data'] = arg[6:]
+        elif arg.startswith('-data='):     opts['datafile'] = arg[6:]
         elif arg == '-random':  opts['seed'] = np.random.randint(1000000)
         elif arg == '-preset':  opts['seed'] = -1
@@ -1005 +1005 @@
         elif arg == '-default':    opts['use_demo'] = False
         elif arg == '-html':    opts['html'] = True
+        elif arg == '-help':    opts['html'] = True
     # pylint: enable=bad-whitespace
 
@@ -1121 +1122 @@
 
     # Create the computational engines
-    if opts['data'] is not None:
-        data = load_data(os.path.expanduser(opts['data']))
+    if opts['datafile'] is not None:
+        data = load_data(os.path.expanduser(opts['datafile']))
     else:
         data, _ = make_data(opts)

sasmodels/data.py

@@ -51 +51 @@
     from sas.sascalc.dataloader.loader import Loader  # type: ignore
     loader = Loader()
-    data = loader.load(filename)
-    if data is None:
+    # Allow for one part in multipart file
+    if '[' in filename:
+        filename, indexstr = filename[:-1].split('[')
+        index = int(indexstr)
+    else:
+        index = None
+    datasets = loader.load(filename)
+    if datasets is None:
         raise IOError("Data %r could not be loaded" % filename)
+    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