Changeset cd3dba0 in sasmodels

Timestamp:

Nov 23, 2015 7:14:54 PM (9 years ago)

Author:

Paul Kienzle <pkienzle@…>

Branches:

master, core_shell_microgels, costrafo411, magnetic_model, release_v0.94, release_v0.95, ticket-1257-vesicle-product, ticket_1156, ticket_1265_superball, ticket_822_more_unit_tests

Children:

3a45c2c

Parents:

0e9048f

Message:

improve compare.py so that parameters can be constrained to valid values

Location:

sasmodels

Files:

• compare.py (modified) (10 diffs)
• compare_many.py (modified) (3 diffs)
• generate.py (modified) (10 diffs)
• models/HayterMSAsq.py (modified) (1 diff)
• models/bcc.py (modified) (1 diff)
• models/parallelepiped.py (modified) (1 diff)

sasmodels/compare.py

@@ -16 +16 @@
 from . import core
 from . import kerneldll
-from . import models
+from . import generate
 from .data import plot_theory, empty_data1D, empty_data2D
 from .direct_model import DirectModel
@@ -116 +116 @@
         return 2*np.random.rand()*(v if v != 0 else 1)
 
-def randomize_model(name, pars, seed=None):
+def randomize_model(pars, seed=None):
     if seed is None:
         seed = np.random.randint(1e9)
@@ -122 +122 @@
     # Note: the sort guarantees order of calls to random number generator
     pars = dict((p,randomize(p,v)) for p,v in sorted(pars.items()))
-    # The capped cylinder model has a constraint on its parameters
+
+    return pars, seed
+
+def constrain_pars(model_definition, pars):
+    name = model_definition.name
     if name == 'capped_cylinder' and pars['cap_radius'] < pars['radius']:
         pars['radius'],pars['cap_radius'] = pars['cap_radius'],pars['radius']
-    return pars, seed
+
+    # These constraints are only needed for comparison to sasview
+    if name in ('teubner_strey','broad_peak'):
+        del pars['scale']
+    if name in ('guinier',):
+        del pars['background']
+    if getattr(model_definition, 'category', None) == 'structure-factor':
+        del pars['scale'], pars['background']
+
 
 def parlist(pars):
@@ -209 +221 @@
 
 def compare(name, pars, Ncpu, Nocl, opts, set_pars):
+    model_definition = core.load_model_definition(name)
+
     view = 'linear' if '-linear' in opts else 'log' if '-log' in opts else 'q4' if '-q4' in opts else 'log'
 
@@ -231 +245 @@
     if '-random' in opts or '-random' in opt_values:
         seed = int(opt_values['-random']) if '-random' in opt_values else None
-        pars, seed = randomize_model(name, pars, seed=seed)
+        pars, seed = randomize_model(pars, seed=seed)
+        constrain_pars(model_definition, pars)
         print "Randomize using -random=%i"%seed
     pars.update(set_pars)  # set value after random to control value
@@ -241 +256 @@
         print "pars",parlist(pars)
 
-    model_definition = core.load_model_definition(name)
     # OpenCl calculation
     if Nocl > 0:
@@ -247 +261 @@
                                     dtype=dtype, cutoff=cutoff, Nevals=Nocl)
         print "opencl t=%.1f ms, intensity=%.0f"%(ocl_time, sum(ocl))
+        #print "ocl", ocl
         #print max(ocl), min(ocl)
 
@@ -261 +276 @@
             #print "ocl/sasview", (ocl-pars['background'])/(cpu-pars['background'])
             print "sasview t=%.1f ms, intensity=%.0f"%(cpu_time, sum(cpu))
+            #print "sasview",cpu
         except ImportError:
             Ncpu = 0
@@ -396 +412 @@
 
 
-def get_demo_pars(name):
-    __import__('sasmodels.models.'+name)
-    model = getattr(models, name)
-    pars = getattr(model, 'demo', None)
-    if pars is None: pars = dict((p[0],p[2]) for p in model.parameters)
+def get_demo_pars(model_definition):
+    info = generate.make_info(model_definition)
+    pars = dict((p[0],p[2]) for p in info['parameters'])
+    pars.update(info['demo'])
     return pars
 
@@ -425 +440 @@
     # if model does not define demo parameters
     name = args[0]
-    pars = get_demo_pars(name)
+    model_definition = core.load_model_definition(name)
+    pars = get_demo_pars(model_definition)
 
     Nopencl = int(args[1]) if len(args) > 1 else 5

sasmodels/compare_many.py

@@ -9 +9 @@
 from .compare import (MODELS, randomize_model, suppress_pd, eval_sasview,
                       eval_opencl, eval_ctypes, make_data, get_demo_pars,
-                      columnize)
+                      columnize, constrain_pars)
 
 def get_stats(target, value, index):
@@ -36 +36 @@
 def compare_instance(name, data, index, N=1, mono=True, cutoff=1e-5):
     model_definition = core.load_model_definition(name)
-    pars = get_demo_pars(name)
+    pars = get_demo_pars(model_definition)
     header = '\n"Model","%s","Count","%d"'%(name, N)
     if not mono: header += ',"Cutoff",%g'%(cutoff,)
     print(header)
 
-    # Stuff the failure flag into a mutable object so we can update it from
-    # within the nested function.  Note that the nested function uses "pars"
-    # which is dynamically scoped, not lexically scoped in this context.  That
-    # is, pars is replaced each time in the loop, so don't assume that it is
-    # the default values defined above.
     def trymodel(fn, *args, **kw):
         try:
-            result, _ = fn(model_definition, pars, data, *args, **kw)
+            result, _ = fn(model_definition, pars_i, data, *args, **kw)
+        except KeyboardInterrupt:
+            raise
         except:
-            result = np.NaN
-            traceback.print_exc()
+            print >>sys.stderr, traceback.format_exc()
+            print >>sys.stderr, "when comparing",name,"for seed",seed
+            if hasattr(data, 'qx_data'):
+                result = np.NaN*data.data
+            else:
+                result = np.NaN*data.x
         return result
 
     num_good = 0
     first = True
-    for _ in range(N):
-        pars, seed = randomize_model(name, pars)
-        if mono: suppress_pd(pars)
-
-        # Force parameter constraints on a per-model basis.
-        if name in ('teubner_strey','broad_peak'):
-            pars['scale'] = 1.0
-        #if name == 'parallelepiped':
-        #    pars['a_side'],pars['b_side'],pars['c_side'] = \
-        #        sorted([pars['a_side'],pars['b_side'],pars['c_side']])
-
+    for k in range(N):
+        print >>sys.stderr, name, k
+        pars_i, seed = randomize_model(pars)
+        constrain_pars(model_definition, pars_i)
+        if mono: suppress_pd(pars_i)
 
         good = True
@@ -97 +92 @@
             good = good and (stats[0] < 1e-14)
 
-        columns += [v for _,v in sorted(pars.items())]
+        columns += [v for _,v in sorted(pars_i.items())]
         if first:
-            print_column_headers(pars, labels)
+            print_column_headers(pars_i, labels)
             first = False
         if good:

sasmodels/generate.py

@@ -99 +99 @@
 The kernel module must set variables defining the kernel meta data:
 
-    *name* is the model name
+    *id* is an implicit variable formed from the filename.  It will be
+    a valid python identifier, and will be used as the reference into
+    the html documentation, with '_' replaced by '-'.
+
+    *name* is the model name as displayed to the user.  If it is missing,
+    it will be constructed from the id.
 
     *title* is a short description of the model, suitable for a tool tip,
@@ -114 +119 @@
     the kernel functions.
 
+    *category* is the default category for the model.  Models in the
+    *structure-factor* category do not have *scale* and *background*
+    added.
+
     *source* is the list of C-99 source files that must be joined to
     create the OpenCL kernel functions.  The files defining the functions
@@ -134 +143 @@
     this step is significant.
 
-An *info* dictionary is constructed from the kernel meta data and
-returned to the caller.
-
-Additional fields can be defined in the kernel definition file that
-are not needed for sas modelling.
-
     *demo* is a dictionary of parameter=value defining a set of
-    parameters to use by default when *compare* is called.
+    parameters to use by default when *compare* is called.  Any
+    parameter not set in *demo* gets the initial value from the
+    parameter list.  *demo* is mostly needed to set the default
+    polydispersity values for tests.
 
     *oldname* is the name of the model in sasview before sasmodels
@@ -149 +155 @@
     of the new model against the values of the old model before
     you are ready to add the new model to sasmodels.
+
+
+An *info* dictionary is constructed from the kernel meta data and
+returned to the caller.
 
 The model evaluator, function call sequence consists of q inputs and the return vector,
@@ -190 +200 @@
 
 import sys
-from os.path import abspath, dirname, join as joinpath, exists
+from os.path import abspath, dirname, join as joinpath, exists, basename
 import re
 
@@ -233 +243 @@
 # description and its parameter table.  The remainder of the doc comes
 # from the module docstring.
-DOC_HEADER = """.. _%(name)s:
-
-%(label)s
+DOC_HEADER = """.. _%(id)s:
+
+%(name)s
 =======================================================
 
@@ -254 +264 @@
     Generate the parameter table to include in the sphinx documentation.
     """
-    pars = COMMON_PARAMETERS + pars
     column_widths = [
         max(len(p[0]) for p in pars),
@@ -523 +532 @@
         }
 
-def make(kernel_module):
-    """
-    Build an OpenCL/ctypes function from the definition in *kernel_module*.
-
-    The module can be loaded with a normal python import statement if you
-    know which module you need, or with __import__('sasmodels.model.'+name)
-    if the name is in a string.
-    """
-    # TODO: allow Iq and Iqxy to be defined in python
+def make_info(kernel_module):
+    """
+    Interpret the model definition file, categorizing the parameters.
+    """
     #print kernelfile
+    category = getattr(kernel_module, 'category', None)
+    parameters = COMMON_PARAMETERS + kernel_module.parameters
+    # Default the demo parameters to the starting values for the individual
+    # parameters if an explicit demo parameter set has not been specified.
+    demo_parameters = getattr(kernel_module, 'demo', None)
+    if demo_parameters is None:
+        demo_parameters = dict((p[0],p[2]) for p in parameters)
+    filename = abspath(kernel_module.__file__)
+    kernel_id = basename(filename)[:-3]
+    name = getattr(kernel_module, 'name', None)
+    if name is None:
+        name = " ".join(w.capitalize() for w in kernel_id.split('_'))
     info = dict(
+        id = kernel_id,  # string used to load the kernel
         filename=abspath(kernel_module.__file__),
-        name=kernel_module.name,
+        name=name,
         title=kernel_module.title,
         description=kernel_module.description,
-        parameters=COMMON_PARAMETERS + kernel_module.parameters,
+        category=category,
+        parameters=parameters,
+        demo=demo_parameters,
         source=getattr(kernel_module, 'source', []),
         oldname=kernel_module.oldname,
@@ -550 +569 @@
     info['partype'] = categorize_parameters(info['parameters'])
     info['defaults'] = dict((p[0], p[2]) for p in info['parameters'])
-
+    return info
+
+def make(kernel_module):
+    """
+    Build an OpenCL/ctypes function from the definition in *kernel_module*.
+
+    The module can be loaded with a normal python import statement if you
+    know which module you need, or with __import__('sasmodels.model.'+name)
+    if the name is in a string.
+    """
+    info = make_info(kernel_module)
     # Assume if one part of the kernel is python then all parts are.
     source = make_model(info) if not callable(info['Iq']) else None
@@ -559 +588 @@
     Return the documentation for the model.
     """
-    subst = dict(name=kernel_module.name.replace('_', '-'),
-                 label=" ".join(kernel_module.name.split('_')).capitalize(),
-                 title=kernel_module.title,
-                 parameters=make_partable(kernel_module.parameters),
+    info = make_info(kernel_module)
+    subst = dict(id=info['id'].replace('_', '-'),
+                 name=info['name'],
+                 title=info['title'],
+                 parameters=make_partable(info['parameters']),
                  docs=kernel_module.__doc__)
     return DOC_HEADER % subst

sasmodels/models/HayterMSAsq.py

@@ -75 +75 @@
                "dielectric constant of solvent (default water), for Debye length"],
              ]
+category = "structure-factor"
 
 # No volume normalization despite having a volume parameter

sasmodels/models/bcc.py

@@ -134 +134 @@
     radius=40,
     theta=60, phi=60, psi=60,
-    radius_pd=.2, radius_pd_n=0.2,
+    radius_pd=.2, radius_pd_n=2,
     theta_pd=15, theta_pd_n=0,
     phi_pd=15, phi_pd_n=0,

sasmodels/models/parallelepiped.py

@@ -158 +158 @@
             a_side_pd=0.1, a_side_pd_n=10,
             b_side_pd=0.1, b_side_pd_n=1,
-            c_side_pd=0.1, c_side_pd_n=10,
+            c_side_pd=0.1, c_side_pd_n=1,
             theta_pd=10, theta_pd_n=1,
             phi_pd=10, phi_pd_n=1,