Update from develop

docs/tutorials/complete_applications/scenario_management_overview/index.md

@@ -1,24 +1,18 @@
-# Getting Started with Taipy Core
-
 !!! important "Supported Python versions"
 
     Taipy requires **Python 3.8** or newer.
 
-Welcome to the **Getting Started Core** guide for Taipy Core. This tour shows you how to use Taipy Core.
-
+Welcome to the **Tutorial** guide for scenario management.
 
-# Taipy Core
+Taipy facilitates pipeline orchestration. There are a lot of reasons for using Taipy backend:
 
-Taipy Core is one of the components of Taipy to facilitate pipeline orchestration. There are a lot of reasons for using Taipy Core:
+- Taipy efficiently manages the execution of your functions/pipelines.
 
-- Taipy Core efficiently manages the execution of your functions/pipelines.
+- Taipy manages data sources and monitors KPIs.
 
-- Taipy Core manages data sources and monitors KPIs.
+- Taipy provides easy management of multiple pipelines and end-user scenarios, which comes in handy in the context of Machine Learning or Mathematical optimization.
 
-- Taipy Core provides easy management of multiple pipelines and end-user scenarios, which comes in handy in the context of Machine Learning or Mathematical optimization.
-
-Each step of the **"Getting Started Core"** will focus on basic concepts of *Taipy Core*. Note that every step is dependent on the code of the previous one. After completing the last step, you will have the skills to develop your own Taipy 
-application. 
+Each step of the **"Tutorial"** will focus on basic concepts of *Taipy Core*.
 
 ## Before we begin
 
@@ -36,34 +30,31 @@ $ pip install taipy
     [Python installation guide](http://docs.python-guide.org/en/latest/starting/installation/)
     can guide you through the process.
 
-## Using Notebooks
-
-This **Getting Started** is for Python scripts (*.py*) only. If you want to use **Jupyter Notebooks**, download this [notebook](https://docs.taipy.io/en/latest/getting_started/getting-started-core/getting_started.ipynb).
 
 ## Taipy Studio
 
-There are two ways to configure Taipy Core, either by Python code or with Taipy Studio. We strongly recommend using Taipy Studio. 
+There are two ways to configure Taipy backend (Taipy Core), either by Python code or with Taipy Studio. We strongly recommend using Taipy Studio. 
 
 Taipy Studio is a VS Code extension that provides a graphical editor to describe pipelines. The configuration of Taipy Core can be done more easily and quickly through Taipy Studio.
 
 So, without further delay, let's begin to code!
 
-## Steps
+## Concepts
 
-1. [Configuration and execution](step_01/ReadMe.md)
+1. [Configuration and execution](step_01/step_01.md)
 
-2. [Basic functions](step_02/ReadMe.md)
+2. [Basic functions](step_02/step_02.md)
 
-3. [Data Node types](step_03/ReadMe.md)
+3. [Data Node types](step_03/step_03.md)
 
-4. [Cycles](step_04/ReadMe.md)
+4. [Cycles](step_04/step_04.md)
 
-5. [Scopes](step_05/ReadMe.md)
+5. [Scopes](step_05/step_05.md)
 
-6. [Skipping tasks](step_06/ReadMe.md)
+6. [Skipping tasks](step_06/step_06.md)
 
-7. [Execution modes](step_07/ReadMe.md)
+7. [Execution modes](step_07/step_07.md)
 
-8. [Scenario comparison](step_08/ReadMe.md)
+8. [Scenario comparison](step_08/step_08.md)
 
-9. [Scenario subscription](step_09/ReadMe.md)
+9. [Scenario subscription](step_09/step_09.md)

docs/tutorials/complete_applications/scenario_management_overview/src/config_01.toml

@@ -1,22 +1,21 @@
 [TAIPY]
 
-[DATA_NODE.input]
-scope = "SCENARIO:SCOPE"
-default_data = "21:int"
+[DATA_NODE.historical_temperature]
 
-[DATA_NODE.output]
-scope = "SCENARIO:SCOPE"
+[DATA_NODE.date_to_forecast]
 
-[TASK.double]
-inputs = [ "input:SECTION",]
-function = "__main__.double:function"
-outputs = [ "output:SECTION",]
-skippable = "False:bool"
+[DATA_NODE.predictions]
 
-[PIPELINE.my_pipeline]
-tasks = [ "double:SECTION",]
+[TASK.predict]
+function = "__main__.predict:function"
+inputs = [ "historical_temperature:SECTION", "date_to_forecast:SECTION",]
+outputs = [ "predictions:SECTION",]
+skippable = "False:bool"
 
 [SCENARIO.my_scenario]
-pipelines = [ "my_pipeline:SECTION",]
+tasks = [ "predict:SECTION",]
+additional_data_nodes = []
 
 [SCENARIO.my_scenario.comparators]
+
+[SCENARIO.my_scenario.sequences]

docs/tutorials/complete_applications/scenario_management_overview/src/config_02.toml

@@ -1,22 +1,20 @@
 [TAIPY]
 
 [DATA_NODE.input]
-scope = "SCENARIO:SCOPE"
 default_data = "21:int"
 
 [DATA_NODE.output]
-scope = "SCENARIO:SCOPE"
 
 [TASK.double]
-inputs = [ "input:SECTION",]
 function = "__main__.double:function"
+inputs = [ "input:SECTION",]
 outputs = [ "output:SECTION",]
 skippable = "False:bool"
 
-[PIPELINE.my_pipeline]
-tasks = [ "double:SECTION",]
-
 [SCENARIO.my_scenario]
-pipelines = [ "my_pipeline:SECTION",]
+tasks = [ "double:SECTION",]
+additional_data_nodes = []
 
 [SCENARIO.my_scenario.comparators]
+
+[SCENARIO.my_scenario.sequences]

docs/tutorials/complete_applications/scenario_management_overview/src/config_03.toml

@@ -2,33 +2,28 @@
 
 [DATA_NODE.historical_data]
 storage_type = "csv"
-scope = "SCENARIO:SCOPE"
-default_path = "src/time_series.csv"
-has_header = "True:bool"
-exposed_type = "pandas"
+default_path = "time_series.csv"
 
 [DATA_NODE.month_data]
-scope = "SCENARIO:SCOPE"
 
 [DATA_NODE.nb_of_values]
-scope = "SCENARIO:SCOPE"
 
 [TASK.filter_current]
-inputs = [ "historical_data:SECTION",]
 function = "__main__.filter_current:function"
+inputs = [ "historical_data:SECTION",]
 outputs = [ "month_data:SECTION",]
 skippable = "False:bool"
 
 [TASK.count_values]
-inputs = [ "month_data:SECTION",]
 function = "__main__.count_values:function"
+inputs = [ "month_data:SECTION",]
 outputs = [ "nb_of_values:SECTION",]
 skippable = "False:bool"
 
-[PIPELINE.my_pipeline]
-tasks = [ "filter_current:SECTION", "count_values:SECTION",]
-
 [SCENARIO.my_scenario]
-pipelines = [ "my_pipeline:SECTION",]
+tasks = [ "filter_current:SECTION", "count_values:SECTION",]
+additional_data_nodes = []
 
 [SCENARIO.my_scenario.comparators]
+
+[SCENARIO.my_scenario.sequences]

docs/tutorials/complete_applications/scenario_management_overview/src/config_04.toml

@@ -2,37 +2,31 @@
 
 [DATA_NODE.historical_data]
 storage_type = "csv"
-scope = "SCENARIO:SCOPE"
 default_path = "time_series.csv"
-has_header = "True:bool"
-exposed_type = "pandas"
 
 [DATA_NODE.month]
-scope = "SCENARIO:SCOPE"
 
 [DATA_NODE.month_data]
-scope = "SCENARIO:SCOPE"
 
 [DATA_NODE.nb_of_values]
-scope = "SCENARIO:SCOPE"
 
 [TASK.filter_by_month]
-inputs = [ "historical_data:SECTION", "month:SECTION",]
 function = "__main__.filter_by_month:function"
+inputs = [ "historical_data:SECTION", "month:SECTION",]
 outputs = [ "month_data:SECTION",]
 skippable = "False:bool"
 
 [TASK.count_values]
-inputs = [ "month_data:SECTION",]
 function = "__main__.count_values:function"
+inputs = [ "month_data:SECTION",]
 outputs = [ "nb_of_values:SECTION",]
 skippable = "False:bool"
 
-[PIPELINE.my_pipeline]
-tasks = [ "filter_by_month:SECTION", "count_values:SECTION",]
-
 [SCENARIO.my_scenario]
-pipelines = [ "my_pipeline:SECTION",]
+tasks = [ "filter_by_month:SECTION", "count_values:SECTION",]
+additional_data_nodes = []
 frequency = "MONTHLY:FREQUENCY"
 
 [SCENARIO.my_scenario.comparators]
+
+[SCENARIO.my_scenario.sequences]

docs/tutorials/complete_applications/scenario_management_overview/src/config_05.toml

@@ -4,8 +4,6 @@
 storage_type = "csv"
 scope = "GLOBAL:SCOPE"
 default_path = "time_series.csv"
-has_header = "True:bool"
-exposed_type = "pandas"
 
 [DATA_NODE.month]
 scope = "CYCLE:SCOPE"
@@ -14,25 +12,24 @@ scope = "CYCLE:SCOPE"
 scope = "CYCLE:SCOPE"
 
 [DATA_NODE.nb_of_values]
-scope = "SCENARIO:SCOPE"
 
 [TASK.filter_by_month]
-inputs = [ "historical_data:SECTION", "month:SECTION",]
 function = "__main__.filter_by_month:function"
+inputs = [ "historical_data:SECTION", "month:SECTION",]
 outputs = [ "month_data:SECTION",]
 skippable = "False:bool"
 
 [TASK.count_values]
-inputs = [ "month_data:SECTION",]
 function = "__main__.count_values:function"
+inputs = [ "month_data:SECTION",]
 outputs = [ "nb_of_values:SECTION",]
 skippable = "False:bool"
 
-[PIPELINE.my_pipeline]
-tasks = [ "filter_by_month:SECTION", "count_values:SECTION",]
-
 [SCENARIO.my_scenario]
-pipelines = [ "my_pipeline:SECTION",]
+tasks = [ "filter_by_month:SECTION", "count_values:SECTION",]
+additional_data_nodes = []
 frequency = "MONTHLY:FREQUENCY"
 
 [SCENARIO.my_scenario.comparators]
+
+[SCENARIO.my_scenario.sequences]

docs/tutorials/complete_applications/scenario_management_overview/src/config_06.toml

@@ -4,37 +4,32 @@
 storage_type = "csv"
 scope = "GLOBAL:SCOPE"
 default_path = "time_series.csv"
-has_header = "True:bool"
-exposed_type = "pandas"
 
 [DATA_NODE.month]
 scope = "CYCLE:SCOPE"
 
 [DATA_NODE.month_data]
 scope = "CYCLE:SCOPE"
-cacheable = "True:bool"
 
 [DATA_NODE.nb_of_values]
-scope = "SCENARIO:SCOPE"
-cacheable = "True:bool"
 
 [TASK.filter_by_month]
-inputs = [ "historical_data:SECTION", "month:SECTION",]
 function = "__main__.filter_by_month:function"
+inputs = [ "historical_data:SECTION", "month:SECTION",]
 outputs = [ "month_data:SECTION",]
 skippable = "True:bool"
 
 [TASK.count_values]
-inputs = [ "month_data:SECTION",]
 function = "__main__.count_values:function"
+inputs = [ "month_data:SECTION",]
 outputs = [ "nb_of_values:SECTION",]
 skippable = "True:bool"
 
-[PIPELINE.my_pipeline]
-tasks = [ "filter_by_month:SECTION", "count_values:SECTION",]
-
 [SCENARIO.my_scenario]
-pipelines = [ "my_pipeline:SECTION",]
+tasks = [ "filter_by_month:SECTION", "count_values:SECTION",]
+additional_data_nodes = []
 frequency = "MONTHLY:FREQUENCY"
 
 [SCENARIO.my_scenario.comparators]
+
+[SCENARIO.my_scenario.sequences]

docs/tutorials/complete_applications/scenario_management_overview/src/config_07.toml

@@ -5,32 +5,29 @@ mode = "standalone"
 max_nb_of_workers = "2:int"
 
 [DATA_NODE.input]
-scope = "SCENARIO:SCOPE"
 default_data = "21:int"
 
 [DATA_NODE.intermediate]
-scope = "SCENARIO:SCOPE"
 default_data = "21:int"
 
 [DATA_NODE.output]
-scope = "SCENARIO:SCOPE"
 
 [TASK.double]
-inputs = [ "input:SECTION",]
 function = "__mp_main__.double:function"
+inputs = [ "input:SECTION",]
 outputs = [ "intermediate:SECTION",]
 skippable = "False:bool"
 
 [TASK.add]
-inputs = [ "intermediate:SECTION",]
 function = "__mp_main__.add:function"
+inputs = [ "intermediate:SECTION",]
 outputs = [ "output:SECTION",]
 skippable = "False:bool"
 
-[PIPELINE.my_pipeline]
-tasks = [ "double:SECTION", "add:SECTION",]
-
 [SCENARIO.my_scenario]
-pipelines = [ "my_pipeline:SECTION",]
+tasks = [ "double:SECTION", "add:SECTION",]
+additional_data_nodes = []
 
 [SCENARIO.my_scenario.comparators]
+
+[SCENARIO.my_scenario.sequences]

docs/tutorials/complete_applications/scenario_management_overview/src/config_08.toml

@@ -1,34 +1,30 @@
 [TAIPY]
 
 [DATA_NODE.input]
-scope = "SCENARIO:SCOPE"
 default_data = "21:int"
 
 [DATA_NODE.intermediate]
-scope = "SCENARIO:SCOPE"
 
 [DATA_NODE.output]
-scope = "SCENARIO:SCOPE"
 
 [TASK.double]
-inputs = [ "input:SECTION",]
 function = "__main__.double:function"
+inputs = [ "input:SECTION",]
 outputs = [ "intermediate:SECTION",]
 skippable = "False:bool"
 
 [TASK.add]
-inputs = [ "intermediate:SECTION",]
 function = "__main__.add:function"
+inputs = [ "intermediate:SECTION",]
 outputs = [ "output:SECTION",]
 skippable = "False:bool"
 
-[PIPELINE.my_pipeline]
-tasks = [ "double:SECTION", "add:SECTION",]
-
 [SCENARIO.multiply_scenario]
-pipelines = [ "my_pipeline:SECTION",]
-frequency = "MONTHLY:FREQUENCY"
+tasks = [ "double:SECTION", "add:SECTION",]
+additional_data_nodes = []
 name = "my_scenario"
 
 [SCENARIO.multiply_scenario.comparators]
 output = [ "__main__.compare_function:function",]
+
+[SCENARIO.multiply_scenario.sequences]