add more plotting functions

3 files changed, 51 insertions, 14 deletions

diff --git a/bin/analyze-archive.py b/bin/analyze-archive.py
index 14cc67e..f203038 100755
--- a/bin/analyze-archive.py
+++ b/bin/analyze-archive.py
@@ -156,8 +156,9 @@ if __name__ == '__main__':
 
     if 'plot-unparam' in opts:
         for kv in opts['plot-unparam'].split(';'):
-            state_or_trans, attribute = kv.split(' ')
-            plotter.plot_y(model.by_name[state_or_trans][attribute])
+            state_or_trans, attribute, ylabel = kv.split(':')
+            fname = 'param_y_{}_{}.pdf'.format(state_or_trans,attribute)
+            plotter.plot_y(model.by_name[state_or_trans][attribute], xlabel = 'measurement #', ylabel = ylabel, output = fname)
 
     if len(show_models):
         print('--- simple static model ---')
@@ -191,15 +192,40 @@ if __name__ == '__main__':
 
     if len(show_models):
         print('--- param model ---')
+
     param_model, param_info = model.get_fitted(safe_functions_enabled = safe_functions_enabled)
+
+    if 'paramdetection' in show_models or 'all' in show_models:
+        for state in model.states_and_transitions():
+            for attribute in model.attributes(state):
+                info = param_info(state, attribute)
+                print('{:10s} {:10s} non-param stddev {:f}'.format(
+                    state, attribute, model.stats[state][attribute]['std_static']
+                ))
+                print('{:10s} {:10s} param-lut stddev {:f}'.format(
+                    state, attribute, model.stats[state][attribute]['std_param_lut']
+                ))
+                for param in sorted(model.stats[state][attribute]['std_by_param'].keys()):
+                    print('{:10s} {:10s} {:10s} stddev {:f}'.format(
+                        state, attribute, param, model.stats[state][attribute]['std_by_param'][param]
+                    ))
+                if info != None:
+                    for param_name in sorted(info['fit_result'].keys(), key=str):
+                        param_fit = info['fit_result'][param_name]['results']
+                        for function_type in sorted(param_fit.keys()):
+                            function_rmsd = param_fit[function_type]['rmsd']
+                            print('{:10s} {:10s} {:10s} mean {:10s} RMSD {:.0f}'.format(
+                                state, attribute, str(param_name), function_type, function_rmsd
+                            ))
+
     if 'param' in show_models or 'all' in show_models:
         for state in model.states():
             for attribute in model.attributes(state):
                 if param_info(state, attribute):
                     print('{:10s}: {}'.format(state, param_info(state, attribute)['function']._model_str))
                     print('{:10s}  {}'.format('', param_info(state, attribute)['function']._regression_args))
-        for trans in model.attributes(trans):
-            for attribute in ['energy', 'rel_energy_prev', 'rel_energy_next', 'duration', 'timeout']:
+        for trans in model.transitions():
+            for attribute in model.attributes(trans):
                 if param_info(trans, attribute):
                     print('{:10s}: {:10s}: {}'.format(trans, attribute, param_info(trans, attribute)['function']._model_str))
                     print('{:10s}  {:10s}  {}'.format('', '', param_info(trans, attribute)['function']._regression_args))
diff --git a/lib/dfatool.py b/lib/dfatool.py
index b23d02f..a19ba13 100755
--- a/lib/dfatool.py
+++ b/lib/dfatool.py
@@ -14,6 +14,7 @@ import tarfile
 from multiprocessing import Pool
 from automata import PTA
 from functions import analytic
+from functions import AnalyticFunction
 from utils import is_numeric
 
 arg_support_enabled = True
diff --git a/lib/plotter.py b/lib/plotter.py
index 2ec635f..24e2197 100755
--- a/lib/plotter.py
+++ b/lib/plotter.py
@@ -80,10 +80,10 @@ def plot_substate_thresholds_p(model, aggregate):
     data = [aggregate[key]['sub_thresholds'] for key in keys]
     boxplot(keys, None, None, data, 'Zustand', '% Clipping')
 
-def plot_y(Y, ylabel = None, title = None):
-    plot_xy(np.arange(len(Y)), Y, ylabel = ylabel, title = title)
+def plot_y(Y, **kwargs):
+    plot_xy(np.arange(len(Y)), Y, **kwargs)
 
-def plot_xy(X, Y, xlabel = None, ylabel = None, title = None):
+def plot_xy(X, Y, xlabel = None, ylabel = None, title = None, output = None):
     fig, ax1 = plt.subplots(figsize=(10,6))
     if title != None:
         fig.canvas.set_window_title(title)
@@ -91,14 +91,21 @@ def plot_xy(X, Y, xlabel = None, ylabel = None, title = None):
         ax1.set_xlabel(xlabel)
     if ylabel != None:
         ax1.set_ylabel(ylabel)
-    plt.subplots_adjust(left = 0.05, bottom = 0.05, right = 0.99, top = 0.99)
-    plt.plot(X, Y, "rx")
-    plt.show()
+    plt.subplots_adjust(left = 0.1, bottom = 0.1, right = 0.99, top = 0.99)
+    plt.plot(X, Y, "bo", markersize=2)
+    if output:
+        plt.savefig(output)
+        with open('{}.txt'.format(output), 'w') as f:
+            print('X Y', file=f)
+            for i in range(len(X)):
+                print('{} {}'.format(X[i], Y[i]), file=f)
+    else:
+        plt.show()
 
 def _param_slice_eq(a, b, index):
     return (*a[1][:index], *a[1][index+1:]) == (*b[1][:index], *b[1][index+1:]) and a[0] == b[0]
 
-def plot_param(model, state_or_trans, attribute, param_idx, xlabel = None, ylabel = None, title = None, extra_function = None):
+def plot_param(model, state_or_trans, attribute, param_idx, xlabel = None, ylabel = None, title = None, extra_function = None, output = None):
     fig, ax1 = plt.subplots(figsize=(10,6))
     if title != None:
         fig.canvas.set_window_title(title)
@@ -106,7 +113,7 @@ def plot_param(model, state_or_trans, attribute, param_idx, xlabel = None, ylabe
         ax1.set_xlabel(xlabel)
     if ylabel != None:
         ax1.set_ylabel(ylabel)
-    plt.subplots_adjust(left = 0.05, bottom = 0.05, right = 0.99, top = 0.99)
+    plt.subplots_adjust(left = 0.1, bottom = 0.1, right = 0.99, top = 0.99)
 
     param_name = model.param_name(param_idx)
 
@@ -143,7 +150,7 @@ def plot_param(model, state_or_trans, attribute, param_idx, xlabel = None, ylabe
         v = by_other_param[k]
         v['X'] = np.array(v['X'])
         v['Y'] = np.array(v['Y'])
-        plt.plot(v['X'], v['Y'], "rx", color=cm(i))
+        plt.plot(v['X'], v['Y'], "ro", color=cm(i), markersize=3)
         YY2_legend.append(legend_sanitizer.sub('_', 'X_{}'.format(k)))
         YY2.append(v['X'])
         YY2_legend.append(legend_sanitizer.sub('_', 'Y_{}'.format(k)))
@@ -181,7 +188,10 @@ def plot_param(model, state_or_trans, attribute, param_idx, xlabel = None, ylabe
             print(' '.join(map(str, elem)), file=f)
 
     print(data_filename_base, function_filename)
-    plt.show()
+    if output:
+        plt.savefig(output)
+    else:
+        plt.show()
 
 
 def plot_param_fit(function, name, fitfunc, funp, parameters, datatype, index, X, Y, xaxis=None, yaxis=None):