#622

fhswf · Feb 12, 2023 · 52b23c1 · 52b23c1
1 parent aeed02b
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diff --git a/src/mlpro/bf/examples/howto_bf_streams_114_stream_task_deriver.py b/src/mlpro/bf/examples/howto_bf_streams_114_stream_task_deriver.py
@@ -8,10 +8,11 @@
 ## -- 2023-02-02  0.0.0     SY       Creation
 ## -- 2023-02-05  1.0.0     SY       First version release
 ## -- 2023-02-07  1.1.0     SY       Change the dataset to doublespiral2d
+## -- 2023-02-12  1.2.0     DA       New plot parameter p_view_autoselect
 ## -------------------------------------------------------------------------------------------------
 
 """
-Ver. 1.2.0 (2023-02-07)
+Ver. 1.2.0 (2023-02-12)
 
 This module demonstrates the principles of stream processing with MLPro. To this regard, a stream of
 a stream provider is combined with a stream workflow to a stream scenario. The workflow consists of 
@@ -94,14 +95,14 @@ def _setup(self, p_mode, p_visualize: bool, p_logging):
         derived_feature = features[0]
 
         task_deriver_1 = Deriver( p_name='t2',
-                                 p_range_max=Task.C_RANGE_THREAD,
-                                 p_visualize=p_visualize,
-                                 p_logging=p_logging,
-                                 p_features=features,
-                                 p_label=None,
-                                 p_derived_feature=derived_feature,
-                                 p_derived_label=None,
-                                 p_order_derivative=1 )
+                                  p_range_max=Task.C_RANGE_THREAD,
+                                  p_visualize=p_visualize,
+                                  p_logging=p_logging,
+                                  p_features=features,
+                                  p_label=None,
+                                  p_derived_feature=derived_feature,
+                                  p_derived_label=None,
+                                  p_order_derivative=1 )
 
         workflow.add_task( p_task=task_deriver_1, p_pred_tasks=[task_rearranger] )
 
@@ -110,14 +111,14 @@ def _setup(self, p_mode, p_visualize: bool, p_logging):
         derived_feature = features[0]
 
         task_deriver_2 = Deriver( p_name='t3',
-                                 p_range_max=Task.C_RANGE_THREAD,
-                                 p_visualize=p_visualize,
-                                 p_logging=p_logging,
-                                 p_features=features,
-                                 p_label=None,
-                                 p_derived_feature=derived_feature,
-                                 p_derived_label=None,
-                                 p_order_derivative=2 )
+                                  p_range_max=Task.C_RANGE_THREAD,
+                                  p_visualize=p_visualize,
+                                  p_logging=p_logging,
+                                  p_features=features,
+                                  p_label=None,
+                                  p_derived_feature=derived_feature,
+                                  p_derived_label=None,
+                                  p_order_derivative=2 )
 
         workflow.add_task( p_task=task_deriver_2, p_pred_tasks=[task_rearranger, task_deriver_1] )
 
@@ -153,6 +154,7 @@ def _setup(self, p_mode, p_visualize: bool, p_logging):
 
 if __name__ == '__main__':
     myscenario.init_plot( p_plot_settings=PlotSettings( p_view = PlotSettings.C_VIEW_ND,
+                                                        p_view_autoselect = False,
                                                         p_step_rate = 2 ) )
     input('Press ENTER to start stream processing...')
 

diff --git a/src/mlpro/bf/mt.py b/src/mlpro/bf/mt.py
@@ -30,10 +30,11 @@
 ## -- 2022-12-29  1.7.0     DA       Refactoring of plot settings
 ## -- 2022-12-30  1.7.1     DA       Bugfix in method Task._get_plot_host_tag()
 ## -- 2023-01-01  1.8.0     DA       Refactoring of plot settings
+## -- 2023-02-12  1.9.0     DA       New plot parameter p_view_autoselect
 ## -------------------------------------------------------------------------------------------------
 
 """
-Ver. 1.8.0 (2023-01-01)
+Ver. 1.9.0 (2023-02-12)
 
 This module provides classes for multitasking with optional interprocess communication (IPC) based
 on shared objects. Multitasking in MLPro combines multrithreading and multiprocessing and simplifies
@@ -963,7 +964,8 @@ def init_plot( self,
                                                    p_plot_depth = ps.plot_depth,
                                                    p_detail_level = ps.detail_level,
                                                    p_force_fg = ps.force_fg,
-                                                   p_id=task_ax_id )
+                                                   p_id=task_ax_id,
+                                                   p_view_autoselect = ps.view_autoselect )
 
             else:
                 # Task plots embedded in the predecessor/workflow figure/subplot

diff --git a/src/mlpro/bf/plot.py b/src/mlpro/bf/plot.py
@@ -25,10 +25,11 @@
 ## -- 2023-01-01  2.7.0     DA       Class Plottable: introduction of update step rate
 ## -- 2023-01-04  2.8.0     DA       Class PlotSettings: new parameters p_horizon, p_force_fg
 ## -- 2023-02-02  2.8.1     MRD      Disable Tkinter backend for macos https://bugs.python.org/issue46573
+## -- 2023-02-12  2.9.0     DA       Class PlotSettings: new parameter p_view_autoselect
 ## -------------------------------------------------------------------------------------------------
 
 """
-Ver. 2.8.1 (2023-02-02)
+Ver. 2.9.0 (2023-02-12)
 
 This module provides various classes related to data plotting.
 """
@@ -95,6 +96,8 @@ class PlotSettings:
         Optional boolean flag. If True, the releated window is kept in foreground. Default = True.
     p_id : int
         Optional unique id of the subplot within the figure. Default = 1.
+    p_view_autoselect : bool
+        If True, the final view is automatically selected during runtime. Default = True.
     p_kwargs : dict
         Further optional named parameters.
     """
@@ -119,24 +122,26 @@ def __init__( self,
                   p_detail_level : int = 0,
                   p_force_fg : bool = True,
                   p_id : int = 1,
+                  p_view_autoselect : bool = True,
                   **p_kwargs ):
 
         if p_view not in self.C_VALID_VIEWS:
             raise ParamError('Wrong value for parameter p_view. See class mlpro.bf.plot.SubPlotSettings for more details.')
 
-        self.view           = p_view
-        self.axes           = p_axes
-        self.pos_x          = p_pos_x
-        self.pos_y          = p_pos_y
-        self.size_x         = p_size_x
-        self.size_y         = p_size_y
-        self.step_rate      = p_step_rate
-        self.horizon        = p_horizon
-        self.plot_depth     = p_plot_depth
-        self.detail_level   = p_detail_level
-        self.force_fg       = p_force_fg
-        self.id             = p_id
-        self.kwargs         = p_kwargs.copy()
+        self.view            = p_view
+        self.axes            = p_axes
+        self.pos_x           = p_pos_x
+        self.pos_y           = p_pos_y
+        self.size_x          = p_size_x
+        self.size_y          = p_size_y
+        self.step_rate       = p_step_rate
+        self.horizon         = p_horizon
+        self.plot_depth      = p_plot_depth
+        self.detail_level    = p_detail_level
+        self.force_fg        = p_force_fg
+        self.id              = p_id
+        self.view_autoselect = p_view_autoselect
+        self.kwargs          = p_kwargs.copy()
 
 
 

diff --git a/src/mlpro/bf/streams/models.py b/src/mlpro/bf/streams/models.py
@@ -46,10 +46,11 @@
 ## --                                - Refactoring of plotting
 ## --                                - incorporation of new plot parameter p_horizon
 ## -- 2023-01-05  1.0.1     DA       Refactoring of method StreamShared.get_instances()
+## -- 2023-02-12  1.1.0     DA       Class StreamTask: implementation of plot parameter view_autoselect
 ## -------------------------------------------------------------------------------------------------
 
 """
-Ver. 1.0.1 (2023-01-05)
+Ver. 1.1.0 (2023-02-12)
 
 This module provides classes for standardized stream processing. 
 """
@@ -975,7 +976,9 @@ def update_plot( self,
         try:
             self._plot_view_finalized
         except:
-            self._finalize_plot_view(p_inst_ref=inst_ref)
+            if self._plot_settings.view_autoselect:
+                self._finalize_plot_view(p_inst_ref=inst_ref)
+
             self._plot_view_finalized = True
 
         Task.update_plot(self, p_inst_new=inst_new, p_inst_del=inst_del, **p_kwargs)

diff --git a/src/mlpro/oa/examples/howto_oa_pp_005_rearranger_window_bd_normminmax_nd.py b/src/mlpro/oa/examples/howto_oa_pp_005_rearranger_window_bd_normminmax_nd.py
@@ -35,6 +35,7 @@
 from mlpro.bf.streams.streams import *
 from mlpro.bf.streams.tasks import Window, Rearranger
 from mlpro.oa.streams import *
+from mlpro.oa.streams.tasks import *
 
 
 

diff --git a/src/mlpro/oa/examples/howto_oa_pp_008_rearranger_deriver_normalizer.py b/src/mlpro/oa/examples/howto_oa_pp_008_rearranger_deriver_normalizer.py
@@ -0,0 +1,164 @@
+## -------------------------------------------------------------------------------------------------
+## -- Project : MLPro - A Synoptic Framework for Standardized Machine Learning Tasks
+## -- Package : mlpro.oa.examples
+## -- Module  : howto_oa_pp_008_rearranger_deriver_normalizer.py
+## -------------------------------------------------------------------------------------------------
+## -- History :
+## -- yyyy-mm-dd  Ver.      Auth.    Description
+## -- 2023-02-12  1.0.0     DA       Adapted from howto_bf_stream_task_deriver
+## -------------------------------------------------------------------------------------------------
+
+"""
+Ver. 1.0.0 (2023-02-12)
+
+This module was adapted from howto_bf_streams_114_stream_task_deriver. It adds an online adaptive
+boundary detector and min/max normalizer task to the workflow.
+
+You will learn:
+
+1) How to set up an online adaptive stream workflow based on stream tasks.
+
+2) How to set up a stream scenario based on a stream and a processing stream workflow.
+
+3) How to add a task Deriver and how to extend the features.
+
+4) How to normalize even derived data based on a boundary detector
+        
+5) How to run a stream scenario dark or with visualization.
+
+"""
+
+
+from mlpro.bf.streams.streams import *
+from mlpro.bf.streams.tasks import Rearranger, Deriver
+
+from mlpro.oa.streams import *
+from mlpro.oa.streams.tasks import BoundaryDetector, NormalizerMinMax
+
+
+
+## -------------------------------------------------------------------------------------------------
+## -------------------------------------------------------------------------------------------------
+class MyScenario (OAScenario):
+    """
+    Example of a custom stream scenario including a stream and a stream workflow. See class 
+    mlpro.bf.streams.models.StreamScenario for further details and explanations.
+    """
+
+    C_NAME      = 'Demo Deriver'
+
+## -------------------------------------------------------------------------------------------------
+    def _setup(self, p_mode, p_visualize: bool, p_logging):
+
+        # 1 Import a native stream from MLPro
+        provider_mlpro = StreamProviderMLPro(p_logging=p_logging)
+        stream = provider_mlpro.get_stream('DoubleSpiral2D', p_mode=p_mode, p_logging=p_logging)
+
+        # 2 Set up a stream workflow 
+        workflow = StreamWorkflow( p_name='wf1', 
+                                   p_range_max=Task.C_RANGE_NONE, 
+                                   p_visualize=p_visualize,
+                                   p_logging=logging )
+
+        # 2.1 Set up and add a rearranger task to reduce the feature and label space
+        features = stream.get_feature_space().get_dims()
+        features_new = [ ( 'F', features[0:1] ) ]
+
+        task_rearranger = Rearranger( p_name='t1',
+                                      p_range_max=Task.C_RANGE_THREAD,
+                                      p_visualize=p_visualize,
+                                      p_logging=p_logging,
+                                      p_features_new=features_new )
+
+        workflow.add_task( p_task=task_rearranger )
+
+        # 2.2 Set up and add a deriver task to extend the feature and label space (1st derivative)
+        features = task_rearranger._feature_space.get_dims()
+        derived_feature = features[0]
+
+        task_deriver_1 = Deriver( p_name='t2',
+                                  p_range_max=Task.C_RANGE_THREAD,
+                                  p_visualize=p_visualize,
+                                  p_logging=p_logging,
+                                  p_features=features,
+                                  p_label=None,
+                                  p_derived_feature=derived_feature,
+                                  p_derived_label=None,
+                                  p_order_derivative=1 )
+
+        workflow.add_task( p_task=task_deriver_1, p_pred_tasks=[task_rearranger] )
+
+        # 2.3 Set up and add a deriver task to extend the feature and label space (2nd derivative)
+        features = task_deriver_1._feature_space.get_dims()
+        derived_feature = features[0]
+
+        task_deriver_2 = Deriver( p_name='t3',
+                                  p_range_max=Task.C_RANGE_THREAD,
+                                  p_visualize=p_visualize,
+                                  p_logging=p_logging,
+                                  p_features=features,
+                                  p_label=None,
+                                  p_derived_feature=derived_feature,
+                                  p_derived_label=None,
+                                  p_order_derivative=2 )
+
+        workflow.add_task( p_task=task_deriver_2, p_pred_tasks=[task_rearranger, task_deriver_1] )
+
+        # 2.4 Boundary detector 
+        task_bd = BoundaryDetector( p_name='t4', 
+                                    p_ada=True, 
+                                    p_visualize=True,   
+                                    p_logging=p_logging )
+
+        workflow.add_task( p_task = task_bd, p_pred_tasks=[task_deriver_2])
+
+        # # 2.5 MinMax-Normalizer
+        task_norm_minmax = NormalizerMinMax( p_name='t5', 
+                                             p_ada=True, 
+                                             p_visualize=p_visualize, 
+                                             p_logging=p_logging )
+
+        task_bd.register_event_handler( p_event_id=BoundaryDetector.C_EVENT_ADAPTED, p_event_handler=task_norm_minmax.adapt_on_event )
+
+        workflow.add_task(p_task = task_norm_minmax, p_pred_tasks=[task_bd])
+
+        # 3 Return stream and workflow
+        return stream, workflow
+
+
+
+
+# 1 Preparation of demo/unit test mode
+if __name__ == '__main__':
+    # 1.1 Parameters for demo mode
+    cycle_limit = 800
+    logging     = Log.C_LOG_ALL
+    visualize   = True
+
+else:
+    # 1.2 Parameters for internal unit test
+    cycle_limit = 2
+    logging     = Log.C_LOG_NOTHING
+    visualize   = False
+
+
+# 2 Instantiate the stream scenario
+myscenario = MyScenario( p_mode=Mode.C_MODE_SIM,
+                         p_cycle_limit=cycle_limit,
+                         p_visualize=visualize,
+                         p_logging=logging )
+
+
+# 3 Reset and run own stream scenario
+myscenario.reset()
+
+if __name__ == '__main__':
+    myscenario.init_plot( p_plot_settings=PlotSettings( p_view = PlotSettings.C_VIEW_ND,
+                                                        p_view_autoselect = False,
+                                                        p_step_rate = 2 ) )
+    input('Press ENTER to start stream processing...')
+
+myscenario.run()
+
+if __name__ == '__main__':
+    input('Press ENTER to exit...')