From 88168035769360bed339404d89408e26a0bc0329 Mon Sep 17 00:00:00 2001 From: Rym <31435778+RymMichaut@users.noreply.github.com> Date: Fri, 9 Jun 2023 15:10:37 +0200 Subject: [PATCH 1/2] Update scenario.md Try to make the description a bit clearer and simpler --- docs/manuals/core/concepts/scenario.md | 41 ++++++++++++-------------- 1 file changed, 19 insertions(+), 22 deletions(-) diff --git a/docs/manuals/core/concepts/scenario.md b/docs/manuals/core/concepts/scenario.md index a5f7114fc..c7d26dcd9 100644 --- a/docs/manuals/core/concepts/scenario.md +++ b/docs/manuals/core/concepts/scenario.md @@ -1,17 +1,14 @@ -We introduce here the important concept of a `Scenario^`. A Taipy Scenario represents an instance of a -business problem to solve on consistent data and parameter sets. +Introducing the `Scenario`, a fundamental key concept in Taipy. -As its name implies, with Taipy scenarios, the users can instantiate different versions of a business -problem with different assumptions. This is extremely useful in a business context where impact analysis -and what-if analysis are essential in the decision process. +A Taipy scenario represents a business problem with consistent data and parameters. -After analyzing its first scenario, an end-user may be interested in modifying input -data nodes (not the intermediate nor the output data nodes), re-running the same pipelines, and -comparing the results with the previous run. +It serves as a powerful tool to create different versions of a problem by incorporating assumptions. This flexibility is particularly valuable for impact analysis and decision-making processes, enabling users to create, store, edit, and execute multiple scenarios within the same application. -For this purpose, he needs to instantiate a second scenario, execute it and compare it with the -first scenario. This process can be repeated across multiple scenarios. +Once an initial scenario has been analyzed, users can modify the input data nodes (excluding the intermediates and output data nodes), rerun his pipelines, and compare results. +This involves instantiating a second scenario, executing it, and comparing the outcomes with the first scenario. + +This iterative process can be repeated across multiple scenarios, allowing for comprehensive exploration and analysis of different problem variations. !!! example "In the example" @@ -20,9 +17,13 @@ first scenario. This process can be repeated across multiple scenarios. ![scenarios](../pic/scenarios.svg){ align=left } -A scenario represents one instance of a business problem to solve. Each new business problem instance -is represented by a new scenario. With Taipy, end-users can create, store, edit, and -execute various scenarios within the same application. + +Two use-cases arise from the utilization of Taipy scenarios: + +### - Use case 1 : +Each _scenario_ represents a distinct instance of a business problem. + +With Taipy, end-users can create, store, edit, and execute various scenarios within the same application. !!! example @@ -39,20 +40,16 @@ execute various scenarios within the same application. Then the end-user creates another scenario for February using the new information provided for the February period. And so on. -Two _scenarios_ can also represent the same instance of a business problem but with two different sets of -assumptions. +### - Use case 2: +Two _scenarios_ represent the same business problem instance, but with different sets of assumptions. !!! example - The end-user would like to simulate the impact of our capacity data on production planning for the - February use case. + The end-user wants to simulate how our capacity data affects production planning for the February situation. - The first scenario can forecast demand and compute production orders assuming a low capacity, - whereas the second assumes a higher capacity value. + In the first scenario, we forecast demand and calculate production orders based on a low capacity assumption. In the second scenario, we assume a higher capacity value. - One can note that data scientists can also use scenarios. This is often referred to as ‘experiments’. - Scenarios are in fact, a generalization of experiments in such a way that both data sceintists - and end-users can finally use the same concept: the Scenario. + It's important to note that scenarios are not only useful for end-users, but also for data scientists. They can use scenarios as experiments to test different hypotheses. Essentially, scenarios provide a common concept that both data scientists and end-users can utilize. [:material-arrow-right: The next section introduces the Cycle concept.](cycle.md) From 42582c1f546b1878057e273c5657a892bcb8c48e Mon Sep 17 00:00:00 2001 From: jrobinAV <88036007+jrobinAV@users.noreply.github.com> Date: Wed, 30 Aug 2023 14:34:14 +0200 Subject: [PATCH 2/2] apply changes --- docs/manuals/core/concepts/scenario.md | 52 ++++++++++++++++---------- 1 file changed, 33 insertions(+), 19 deletions(-) diff --git a/docs/manuals/core/concepts/scenario.md b/docs/manuals/core/concepts/scenario.md index c7d26dcd9..9d8892b39 100644 --- a/docs/manuals/core/concepts/scenario.md +++ b/docs/manuals/core/concepts/scenario.md @@ -1,29 +1,36 @@ Introducing the `Scenario`, a fundamental key concept in Taipy. -A Taipy scenario represents a business problem with consistent data and parameters. +A Taipy *Scenario* represents a business problem with consistent data and parameters. -It serves as a powerful tool to create different versions of a problem by incorporating assumptions. This flexibility is particularly valuable for impact analysis and decision-making processes, enabling users to create, store, edit, and execute multiple scenarios within the same application. +Scenarios are a powerful tool to create different versions of a business problem under different +assumptions. This is especially valuable for what-if analysis in decision-making processes, +enabling users to create, store, edit, and execute multiple scenarios with various +parameters within the same application. -Once an initial scenario has been analyzed, users can modify the input data nodes (excluding the intermediates and output data nodes), rerun his pipelines, and compare results. +Once an initial scenario has been analyzed, users can modify the input data nodes (excluding +the intermediates and output data nodes), rerun part of its tasks, and compare results. -This involves instantiating a second scenario, executing it, and comparing the outcomes with the first scenario. +This involves instantiating a second scenario, changing the input data, executing it, +and comparing the outcomes with the first scenario. -This iterative process can be repeated across multiple scenarios, allowing for comprehensive exploration and analysis of different problem variations. +This iterative process can be repeated across multiple scenarios, allowing for comprehensive +exploration and analysis of different problem variations. !!! example "In the example" - Here, our scenario consists of the two pipelines described earlier. The external light blue box in the flowchart - below represents our scenario that contains both pipelines. + Here, our scenario consists of the two pipelines described earlier. The external light + blue box in the flowchart below represents our scenario that contains both pipelines. ![scenarios](../pic/scenarios.svg){ align=left } Two use-cases arise from the utilization of Taipy scenarios: -### - Use case 1 : -Each _scenario_ represents a distinct instance of a business problem. +### - Use case 1 : +Each *scenario* represents a distinct instance of a business problem. -With Taipy, end-users can create, store, edit, and execute various scenarios within the same application. +With Taipy, end-users can create, store, edit, and execute various scenarios within the +same application. !!! example @@ -33,23 +40,30 @@ With Taipy, end-users can create, store, edit, and execute various scenarios wit - then, based on that forecast, generate the planning for the production orders. - The end-user creates the first scenario for January. It must contain everything the end-user needs - to understand the January case, access input data, compute predictions, visualize our - forecast algorithm results, make production decisions, and publish the January production orders. + The end-user creates the first scenario for January. It must contain everything the + end-user needs to understand the January case, access input data, compute predictions, + visualize our forecast algorithm results, make production decisions, and publish the + January production orders. - Then the end-user creates another scenario for February using the new information provided for the - February period. And so on. + Then the end-user creates another scenario for February using the new information provided + for the February period. And so on. ### - Use case 2: -Two _scenarios_ represent the same business problem instance, but with different sets of assumptions. +Two *scenarios* represent the same business problem instance, but with different sets of +assumptions. !!! example - The end-user wants to simulate how our capacity data affects production planning for the February situation. + The end-user wants to simulate how the capacity data affects production planning for + the February situation. - In the first scenario, we forecast demand and calculate production orders based on a low capacity assumption. In the second scenario, we assume a higher capacity value. + In the first scenario, we forecast demand and calculate production orders based on a low + capacity assumption. In the second scenario, we assume a higher capacity value. - It's important to note that scenarios are not only useful for end-users, but also for data scientists. They can use scenarios as experiments to test different hypotheses. Essentially, scenarios provide a common concept that both data scientists and end-users can utilize. + It's important to note that scenarios are not only useful for end-users, but also for + data scientists. They can use scenarios as experiments to test different hypotheses. + Essentially, scenarios provide a common concept that both data scientists and end-users + can utilize. [:material-arrow-right: The next section introduces the Cycle concept.](cycle.md)