Refact: Native Streams for random point clouds #793

src/mlpro/bf/examples/howto_bf_streams_006_Clouds2D4C1000Static.py

@@ -0,0 +1,142 @@
+## -------------------------------------------------------------------------------------------------
+## -- Project : MLPro - A Synoptic Framework for Standardized Machine Learning Tasks
+## -- Package : mlpro.bf.examples
+## -- Module  : howto_bf_streams_006_Clouds2D4C1000Static.py
+## -------------------------------------------------------------------------------------------------
+## -- History :
+## -- yyyy-mm-dd  Ver.      Auth.    Description
+## -- 2023-11-09  0.0.0     SP       Creation
+## -- 2023-11-09  1.0.0     SP       First implementation
+## -------------------------------------------------------------------------------------------------
+
+"""
+Ver. 1.0.0 (2022-11-22)
+
+This module demonstrates the principles of stream processing with MLPro. To this regard, stream tasks
+are added to a stream workflow. This in turn is combined with a stream of a stream provider to a
+a stream scenario. The latter one can be executed.
+
+You will learn:
+
+1) How to implement an own custom stream task.
+
+2) How to set up a stream workflow based on stream tasks.
+
+3) How to set up a stream scenario based on a stream and a processing stream workflow.
+
+4) How to run a stream scenario dark or with default visualization.
+
+"""
+
+
+from mlpro.bf.streams import *
+from mlpro.wrappers.openml import WrStreamProviderOpenML
+from mlpro.bf.streams.streams import *
+from mlpro.bf.streams.streams.clouds import StreamMLProClouds2D4C1000Static
+from mlpro.bf.streams.tasks import Window
+from mlpro.bf.streams.models import StreamTask
+
+
+from mlpro.bf.various import Log
+
+
+
+## -------------------------------------------------------------------------------------------------
+## -------------------------------------------------------------------------------------------------
+class MyTask (StreamTask):
+    """
+    Demo implementation of a stream task with custom method _run().
+    """
+
+    # needed for proper logging (see class mlpro.bf.various.Log)
+    C_NAME      = 'My stream task'
+
+
+## -------------------------------------------------------------------------------------------------
+    def _run(self, p_inst_new: list, p_inst_del: list):
+        pass
+
+
+
+## -------------------------------------------------------------------------------------------------
+## -------------------------------------------------------------------------------------------------
+class MyScenario (StreamScenario):
+    """
+    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      = ''
+
+## -------------------------------------------------------------------------------------------------
+    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('StreamMLProClouds2D4C1000Static', p_mode=p_mode, p_logging=p_logging)
+
+
+        # 2 Set up a stream workflow based on a custom stream task
+
+        # 2.1 Creation of tasks
+
+        # 2.1 Set up and add a window task
+        task_window1 = Window( p_buffer_size=30, 
+                              p_name = 't1', 
+                              p_delay = True,
+                              p_visualize = p_visualize, 
+                              p_enable_statistics = True,
+                              p_logging=logging )
+        task_empty = MyTask(p_name='t2')
+
+
+        # 2.2 Create a workflow and add the tasks
+        workflow = StreamWorkflow( p_name='wf1', 
+                                   p_range_max=StreamWorkflow.C_RANGE_NONE,    #StreamWorkflow.C_RANGE_THREAD, 
+                                   p_visualize=p_visualize,
+                                   p_logging=logging )
+
+        # 2.2.1 At first we add three tasks that build the starting points of our workflow
+        workflow.add_task( p_task=task_window1 )
+        workflow.add_task( p_task=task_empty )
+
+
+
+        # 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 = 10
+    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()
+    input('Press ENTER to start stream processing...')
+
+myscenario.run()
+
+if __name__ == '__main__':
+    input('Press ENTER to exit...')

src/mlpro/bf/streams/streams/__init__.py

@@ -6,4 +6,4 @@
 from mlpro.bf.streams.streams.csv_file import *
 from mlpro.bf.streams.streams.clouds2d_dynamic import *
 from mlpro.bf.streams.streams.clouds3d_dynamic import *
-from mlpro.bf.streams.streams.clouds import *
+#from mlpro.bf.streams.streams.clouds import *