Changeset 7609046 in sasmodels for sasmodels/compare.py

Timestamp:

Jul 19, 2018 10:47:44 AM (6 years ago)

Author:

GitHub <noreply@…>

Branches:

master

Children:

f41027b

Parents:

9ce5bcb (diff), 1d9998c (diff)
Note: this is a merge changeset, the changes displayed below correspond to the merge itself.
Use the (diff) links above to see all the changes relative to each parent.

git-author:

Paul Kienzle <pkienzle@…> (07/19/18 10:47:44)

git-committer:

GitHub <noreply@…> (07/19/18 10:47:44)

Message:

Merge branch 'master' into ticket-608-user-defined-weights

File:

• sasmodels/compare.py (modified) (20 diffs)

sasmodels/compare.py

@@ -107 +107 @@
     -title="note" adds note to the plot title, after the model name
     -weights shows weights plots for the polydisperse parameters
+    -profile shows the sld profile if the model has a plottable sld profile
 
     === output options ===
     -edit starts the parameter explorer
     -help/-html shows the model docs instead of running the model
+
+    === environment variables ===
+    -DSAS_MODELPATH=path sets directory containing custom models
+    -DSAS_OPENCL=vendor:device|none sets the target OpenCL device
+    -DXDG_CACHE_HOME=~/.cache sets the pyopencl cache root (linux only)
+    -DSAS_COMPILER=tinycc|msvc|mingw|unix sets the DLL compiler
+    -DSAS_OPENMP=1 turns on OpenMP for the DLLs
+    -DSAS_DLL_PATH=path sets the path to the compiled modules
 
 The interpretation of quad precision depends on architecture, and may
@@ -645 +654 @@
 
 def make_data(opts):
-    # type: (Dict[str, Any]) -> Tuple[Data, np.ndarray]
+    # type: (Dict[str, Any], float) -> Tuple[Data, np.ndarray]
     """
     Generate an empty dataset, used with the model to set Q points
@@ -667 +676 @@
         if opts['zero']:
             q = np.hstack((0, q))
-        data = empty_data1D(q, resolution=res)
+        # TODO: provide command line control of lambda and Delta lambda/lambda
+        #L, dLoL = 5, 0.14/np.sqrt(6)  # wavelength and 14% triangular FWHM
+        L, dLoL = 0, 0
+        data = empty_data1D(q, resolution=res, L=L, dL=L*dLoL)
         index = slice(None, None)
     return data, index
@@ -772 +784 @@
             dim = base._kernel.dim
             weights.plot_weights(model_info, get_mesh(model_info, base_pars, dim=dim))
+        if opts['show_profile']:
+            import pylab
+            base, comp = opts['engines']
+            base_pars, comp_pars = opts['pars']
+            have_base = base._kernel.info.profile is not None
+            have_comp = (
+                comp is not None
+                and comp._kernel.info.profile is not None
+                and base_pars != comp_pars
+            )
+            if have_base or have_comp:
+                pylab.figure()
+            if have_base:
+                plot_profile(base._kernel.info, **base_pars)
+            if have_comp:
+                plot_profile(comp._kernel.info, label='comp', **comp_pars)
+                pylab.legend()
     if opts['plot']:
         import matplotlib.pyplot as plt
@@ -777 +806 @@
     return limits
 
+def plot_profile(model_info, label='base', **args):
+    # type: (ModelInfo, List[Tuple[float, np.ndarray, np.ndarray]]) -> None
+    """
+    Plot the profile returned by the model profile method.
+
+    *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
+
+    args = dict((k, v) for k, v in args.items()
+                if "_pd" not in k
+                   and ":" not in k
+                   and k not in ("background", "scale", "theta", "phi", "psi"))
+    args = args.copy()
+
+    args.pop('scale', 1.)
+    args.pop('background', 0.)
+    z, rho = model_info.profile(**args)
+    #pylab.interactive(True)
+    pylab.plot(z, rho, '-', label=label)
+    pylab.grid(True)
+    #pylab.show()
+
+
+
 def run_models(opts, verbose=False):
     # type: (Dict[str, Any]) -> Dict[str, Any]
@@ -786 +844 @@
     base_n, comp_n = opts['count']
     base_pars, comp_pars = opts['pars']
-    data = opts['data']
+    base_data, comp_data = opts['data']
 
     comparison = comp is not None
@@ -800 +858 @@
             print("%s t=%.2f ms, intensity=%.0f"
                   % (base.engine, base_time, base_value.sum()))
-        _show_invalid(data, base_value)
+        _show_invalid(base_data, base_value)
     except ImportError:
         traceback.print_exc()
@@ -812 +870 @@
                 print("%s t=%.2f ms, intensity=%.0f"
                       % (comp.engine, comp_time, comp_value.sum()))
-            _show_invalid(data, comp_value)
+            _show_invalid(base_data, comp_value)
         except ImportError:
             traceback.print_exc()
@@ -866 +924 @@
     have_base, have_comp = (base_value is not None), (comp_value is not None)
     base, comp = opts['engines']
-    data = opts['data']
+    base_data, comp_data = opts['data']
     use_data = (opts['datafile'] is not None) and (have_base ^ have_comp)
 
     # Plot if requested
     view = opts['view']
+    #view = 'log'
     if limits is None:
         vmin, vmax = np.inf, -np.inf
@@ -884 +943 @@
         if have_comp:
             plt.subplot(131)
-        plot_theory(data, base_value, view=view, use_data=use_data, limits=limits)
+        plot_theory(base_data, base_value, view=view, use_data=use_data, limits=limits)
         plt.title("%s t=%.2f ms"%(base.engine, base_time))
         #cbar_title = "log I"
@@ -891 +950 @@
             plt.subplot(132)
         if not opts['is2d'] and have_base:
-            plot_theory(data, base_value, view=view, use_data=use_data, limits=limits)
-        plot_theory(data, comp_value, view=view, use_data=use_data, limits=limits)
+            plot_theory(comp_data, base_value, view=view, use_data=use_data, limits=limits)
+        plot_theory(comp_data, comp_value, view=view, use_data=use_data, limits=limits)
         plt.title("%s t=%.2f ms"%(comp.engine, comp_time))
         #cbar_title = "log I"
@@ -908 +967 @@
             err[err > cutoff] = cutoff
         #err,errstr = base/comp,"ratio"
-        plot_theory(data, None, resid=err, view=errview, use_data=use_data)
+        # Note: base_data only since base and comp have same q values (though
+        # perhaps different resolution), and we are plotting the difference
+        # at each q
+        plot_theory(base_data, None, resid=err, view=errview, use_data=use_data)
         plt.xscale('log' if view == 'log' and not opts['is2d'] else 'linear')
         plt.legend(['P%d'%(k+1) for k in range(setnum+1)], loc='best')
@@ -942 +1004 @@
 OPTIONS = [
     # Plotting
-    'plot', 'noplot', 'weights',
+    'plot', 'noplot',
+    'weights', 'profile',
     'linear', 'log', 'q4',
     'rel', 'abs',
@@ -1058 +1121 @@
 
     invalid = [o[1:] for o in flags
-               if o[1:] not in NAME_OPTIONS
-               and not any(o.startswith('-%s='%t) for t in VALUE_OPTIONS)]
+               if not (o[1:] in NAME_OPTIONS
+                       or any(o.startswith('-%s='%t) for t in VALUE_OPTIONS)
+                       or o.startswith('-D'))]
     if invalid:
         print("Invalid options: %s"%(", ".join(invalid)))
@@ -1075 +1139 @@
         'qmax'      : 0.05,
         'nq'        : 128,
-        'res'       : 0.0,
+        'res'       : '0.0',
         'noise'     : 0.0,
         'accuracy'  : 'Low',
@@ -1096 +1160 @@
         'count'     : '1',
         'show_weights' : False,
+        'show_profile' : False,
         'sphere'    : 0,
         'ngauss'    : '0',
@@ -1115 +1180 @@
         elif arg.startswith('-q='):
             opts['qmin'], opts['qmax'] = [float(v) for v in arg[3:].split(':')]
-        elif arg.startswith('-res='):      opts['res'] = float(arg[5:])
+        elif arg.startswith('-res='):      opts['res'] = arg[5:]
         elif arg.startswith('-noise='):    opts['noise'] = float(arg[7:])
         elif arg.startswith('-sets='):     opts['sets'] = int(arg[6:])
@@ -1156 +1221 @@
         elif arg == '-default': opts['use_demo'] = False
         elif arg == '-weights': opts['show_weights'] = True
+        elif arg == '-profile': opts['show_profile'] = True
         elif arg == '-html':    opts['html'] = True
         elif arg == '-help':    opts['html'] = True
+        elif arg.startswith('-D'):
+            var, val = arg[2:].split('=')
+            os.environ[var] = val
     # pylint: enable=bad-whitespace,C0321
 
@@ -1173 +1242 @@
     if opts['qmin'] is None:
         opts['qmin'] = 0.001*opts['qmax']
-    if opts['datafile'] is not None:
-        data = load_data(os.path.expanduser(opts['datafile']))
-    else:
-        data, _ = make_data(opts)
 
     comparison = any(PAR_SPLIT in v for v in values)
@@ -1216 +1281 @@
         opts['cutoff'] = [float(opts['cutoff'])]*2
 
-    base = make_engine(model_info[0], data, opts['engine'][0],
+    if PAR_SPLIT in opts['res']:
+        opts['res'] = [float(k) for k in opts['res'].split(PAR_SPLIT, 2)]
+        comparison = True
+    else:
+        opts['res'] = [float(opts['res'])]*2
+
+    if opts['datafile'] is not None:
+        data = load_data(os.path.expanduser(opts['datafile']))
+    else:
+        # Hack around the fact that make_data doesn't take a pair of resolutions
+        res = opts['res']
+        opts['res'] = res[0]
+        data0, _ = make_data(opts)
+        if res[0] != res[1]:
+            opts['res'] = res[1]
+            data1, _ = make_data(opts)
+        else:
+            data1 = data0
+        opts['res'] = res
+        data = data0, data1
+
+    base = make_engine(model_info[0], data[0], opts['engine'][0],
                        opts['cutoff'][0], opts['ngauss'][0])
     if comparison:
-        comp = make_engine(model_info[1], data, opts['engine'][1],
+        comp = make_engine(model_info[1], data[1], opts['engine'][1],
                            opts['cutoff'][1], opts['ngauss'][1])
     else: