Changeset 3c24ccd in sasmodels

Timestamp:

Oct 18, 2017 12:03:43 PM (7 years ago)

Author:

Paul Kienzle <pkienzle@…>

Branches:

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

Children:

ef8e68c

Parents:

fbb9397

Message:

add -weights option to sascomp to show dispersity distribution

Location:

sasmodels

Files:

• compare.py (modified) (6 diffs)
• details.py (modified) (1 diff)
• direct_model.py (modified) (2 diffs)
• weights.py (modified) (1 diff)

sasmodels/compare.py

@@ -42 +42 @@
 from . import exception
 from .data import plot_theory, empty_data1D, empty_data2D, load_data
-from .direct_model import DirectModel
+from .direct_model import DirectModel, get_mesh
 from .convert import revert_name, revert_pars, constrain_new_to_old
 from .generate import FLOAT_RE
+from .weights import plot_weights
 
 try:
@@ -102 +103 @@
     -abs/-rel* plot relative or absolute error
     -title="note" adds note to the plot title, after the model name
+    -weights shows weights plots for the polydisperse parameters
 
     === output options ===
@@ -834 +836 @@
         if opts['plot']:
             limits = plot_models(opts, result, limits=limits, setnum=k)
+        if opts['show_weights']:
+            base, _ = opts['engines']
+            base_pars, _ = opts['pars']
+            model_info = base._kernel.info
+            dim = base._kernel.dim
+            plot_weights(model_info, get_mesh(model_info, base_pars, dim=dim))
     if opts['plot']:
         import matplotlib.pyplot as plt
@@ -996 +1004 @@
 OPTIONS = [
     # Plotting
-    'plot', 'noplot',
+    'plot', 'noplot', 'weights',
     'linear', 'log', 'q4',
     'rel', 'abs',
@@ -1146 +1154 @@
         'engine'    : 'default',
         'evals'     : '1',
+        'show_weights' : False,
     }
     for arg in flags:
@@ -1194 +1203 @@
         elif arg == '-demo':    opts['use_demo'] = True
         elif arg == '-default': opts['use_demo'] = False
+        elif arg == '-weights': opts['show_weights'] = True
         elif arg == '-html':    opts['html'] = True
         elif arg == '-help':    opts['html'] = True

sasmodels/details.py

@@ -234 +234 @@
     # skipping scale and background when building values and weights
     values, dispersity, weights = zip(*mesh[2:npars+2]) if npars else ((), (), ())
-    #weights = correct_theta_weights(kernel.info.parameters, values, weights)
+    weights = correct_theta_weights(kernel.info.parameters, values, weights)
     length = np.array([len(w) for w in weights])
     offset = np.cumsum(np.hstack((0, length)))

sasmodels/direct_model.py

@@ -55 +55 @@
     *mono* is True if polydispersity should be set to none on all parameters.
     """
-    parameters = calculator.info.parameters
-    if mono:
-        active = lambda name: False
-    elif calculator.dim == '1d':
-        active = lambda name: name in parameters.pd_1d
-    elif calculator.dim == '2d':
-        active = lambda name: name in parameters.pd_2d
-    else:
-        active = lambda name: True
-
-    #print("pars",[p.id for p in parameters.call_parameters])
-    mesh = [get_weights(p, pars, active(p.name))
-            for p in parameters.call_parameters]
-
+    mesh = get_mesh(calculator.info, pars, dim=calculator.dim, mono=mono)
     call_details, values, is_magnetic = make_kernel_args(calculator, mesh)
     #print("values:", values)
     return calculator(call_details, values, cutoff, is_magnetic)
-
 
 def call_ER(model_info, pars):
@@ -128 +114 @@
     return x, y, model_info.profile_axes
 
-
-def get_weights(parameter, values, active=True):
-    # type: (Parameter, Dict[str, float]) -> Tuple[np.ndarray, np.ndarray]
+def get_mesh(model_info, values, dim='1d', mono=False):
+    # type: (ModelInfo, Dict[str, float], str, bool) -> List[Tuple[float, np.ndarray, np.ndarry]]
+    """
+    Retrieve the dispersity mesh described by the parameter set.
+
+    Returns a list of *(value, dispersity, weights)* with one tuple for each
+    parameter in the model call parameters.  Inactive parameters return the
+    default value with a weight of 1.0.
+    """
+    parameters = model_info.parameters
+    if mono:
+        active = lambda name: False
+    elif dim == '1d':
+        active = lambda name: name in parameters.pd_1d
+    elif dim == '2d':
+        active = lambda name: name in parameters.pd_2d
+    else:
+        active = lambda name: True
+
+    #print("pars",[p.id for p in parameters.call_parameters])
+    mesh = [_get_par_weights(p, values, active(p.name))
+            for p in parameters.call_parameters]
+    return mesh
+
+
+def _get_par_weights(parameter, values, active=True):
+    # type: (Parameter, Dict[str, float]) -> Tuple[float, np.ndarray, np.ndarray]
     """
     Generate the distribution for parameter *name* given the parameter values

sasmodels/weights.py

@@ -232 +232 @@
 
 
-def plot_weights(model_info, pairs):
-    # type: (ModelInfo, List[Tuple[np.ndarray, np.ndarray]]) -> None
+def plot_weights(model_info, mesh):
+    # type: (ModelInfo, List[Tuple[float, np.ndarray, np.ndarray]]) -> None
     """
     Plot the weights returned by :func:`get_weights`.
 
-    *model_info* is
-    :param model_info:
-    :param pairs:
-    :return:
+    *model_info* defines model parameters, etc.
+
+    *mesh* is a list of tuples containing (*value*, *dispersity*, *weights*)
+    for each parameter, where (*dispersity*, *weights*) pairs are the
+    distributions to be plotted.
     """
     import pylab
 
-    if any(len(values)>1 for values, weights in pairs):
+    if any(len(dispersity)>1 for value, dispersity, weights in mesh):
         labels = [p.name for p in model_info.parameters.call_parameters]
-        pylab.interactive(True)
+        #pylab.interactive(True)
         pylab.figure()
-        for (v,w), s in zip(pairs, labels):
-            if len(v) > 1:
-                #print("weights for", s, v, w)
-                pylab.plot(v, w, '-o', label=s)
+        for (v,x,w), s in zip(mesh, labels):
+            if len(x) > 1:
+                pylab.plot(x, w, '-o', label=s)
         pylab.grid(True)
         pylab.legend()