grond check: can now plot synthetics of reference solution against data

apps/grond

@@ -201,7 +201,14 @@ def command_check(args):
 
         parser.add_option(
             '--plot', dest='show_plot', action='store_true',
-            help='plot sample synthetics and data')
+            help='plot sample synthetics and data.')
+
+        parser.add_option(
+            '--nrandom', dest='n_random_synthetics', metavar='N', type='int',
+            default=10,
+            help='set number of random synthetics to forward model (default: '
+                 '10). If set to zero, create synthetics for the reference '
+                 'solution.')
 
     parser, options, args = cl_parse('check', args, setup)
     if len(args) < 2:
@@ -220,7 +227,8 @@ def command_check(args):
         config,
         event_names=event_names,
         target_string_ids=target_string_ids,
-        show_plot=options.show_plot)
+        show_plot=options.show_plot,
+        n_random_synthetics=options.n_random_synthetics)
 
 
 def command_go(args):

src/core.py

@@ -1599,7 +1599,13 @@ def check_problem(problem):
         raise GrondError('no targets available')
 
 
-def check(config, event_names=None, target_string_ids=None, show_plot=False):
+def check(
+        config,
+        event_names=None,
+        target_string_ids=None,
+        show_plot=False,
+        n_random_synthetics=10):
+
     from matplotlib import pyplot as plt
     from grond.plot import colors
 
@@ -1626,11 +1632,19 @@ def check(config, event_names=None, target_string_ids=None, show_plot=False):
             xbounds = num.array(problem.bounds(), dtype=num.float)
 
             results_list = []
-            for i in xrange(10):
-                x = problem.random_uniform(xbounds)
+
+            if n_random_synthetics == 0:
+                x = problem.pack(problem.base_source)
                 ms, ns, results = problem.evaluate(x, result_mode='full')
                 results_list.append(results)
 
+            else:
+                for i in xrange(n_random_synthetics):
+                    x = problem.random_uniform(xbounds)
+                    ms, ns, results = problem.evaluate(x, result_mode='full')
+                    results_list.append(results)
+
+
             if show_plot:
                 for itarget, target in enumerate(problem.targets):
                     yabsmaxs = []