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----
-sidebar_position: 2
-title: M3SA Integration
-hide_title: true
-sidebar_label: M3SA Integration
-description: M3SA Integration
----
-
-# M3SA integration tutorial
-
-M3SA is a tool able to perform "Multi-Meta-Model Simulation Analysis". The tool is designed to analyze the output of
-simulations, by leveraging predictions, generate Multi-Model graphs, novel models, and more. M3SA can integrate with any
-simulation infrastructure, as long as integration steps are followed.
-
-We build our tool towards performance, scalability, and **universality**. In this document, we present the steps to
-integrate our tool into your simulation infrastructure.
-
-If you are using OpenDC, none of adaptation steps are necessary, yet they can be useful to understand the structure
-of the tool. Step 3 is still necessary.
-
-## Step 1: Adapt the simulator output folder structure
-
-The first step is to adapt the I/O of your simulation to the format of our tool. The output folder structure should have
-the
-following format:
-
-```
-[1] ── {simulation-folder-name} 📁 🔧
-[2]    ├── inputs 📁 🔒
-[3]    │   └── {m3sa-config-file}.json 📄 🔧
-[4]    │   └── {other input files / folders} 🔧
-[5]    ├── outputs 📁 🔒
-[6]    │   ├── raw-output 📁 🔒
-[7]    │   │   ├── 0 📁 🔒
-[8]    │   │   │   └── seed={your_seed}🔒
-[9]    │   │   │       └── {simulation_data_file}.parquet 📄 🔧
-[10]   │   │   │       └── {any other files / folders} ⚪
-[11]   │   │   ├── 1 📁 ⚪ 🔒
-[12]   │   │   │   └── seed={your_seed} 📁 ⚪ 🔒
-[13]   │   │   │       └── {simulation_data_file}.parquet 📄 ⚪ 🔧
-[14]   │   │   │       └── {any other files / folders} ⚪󠁪
-[15]   │   │   ├── metamodel 📁 ⚪
-[16]   │   │      └── seed={your_seed} 📁 ⚪
-[17]   │   │           └── {your_metric_name}.parquet 📄 ⚪
-[18]   │   │           └── {any other files / folders} ⚪
-[19]   │   └── {any other files / folders} 📁 ⚪
-[20]|  └──{any other files / folders} 📁 ⚪
-```
-
-📄 = file <br />
-📁 = folder <br />
-🔒 = fixed, the name of the folder/file must be the same.<br />
-🔧 = flexible, the name of the folder/file can differ. However, the item must be present.<br />
-⚪ = optional and flexible. The item can be absent. <br />
-
-- [1] = the name of the analyzed folder.
-- [2] = the _inputs_ folder, containing various inputs / configuration files.
-- [3] = the configuration file for M3SA, flexible naming, but needs to be a JSON file
-- [4],[10],[14],[18],[19],[20] = any other input files or folders.
-- [5] = the _outputs_ folder, containing the raw-output. can contain any other files or folders, besides the raw-output
-folder.
-After running a simulation, also a "simulation-analysis" folder will be generated in this folder.
-- [6] = raw-output folder, containing the raw output of the simulation.
-- [7],[11] = the IDs of the models. Must always start from zero. Possible values are 0, 1, 2 ... n, and "metamodel". The
-id
-of "metamodel" is reserved for the Meta-Model. Any simulation data in the respective folder will be treated as
-Meta-Model data.
-- [8],[12] = the seed of the simulation. the seed must be the same for both [8], [12], and other equivalent, further
-files.
-- [9],[13] = the file in which the simulation data is stored. The name of the file can differ, but it must be a parquet
-file.
-- [15] = the Meta-Model folder, optional. If the folder is present, its data will be treated as Meta-Model data.
-- [16] = the Meta-Model seed folder. The seed must be the same as the seed of the simulation.
-- [17] = the Meta-Model output. The name of the file is of the type ```{your_metric_name}.parquet```. For example, if
-you analyze CO2 emissions, the file will be named ```co2_emissions.parquet```.
-
----
-
-## Step 2: Adapt the simulation file format
-
-The simulator data file must be a 🪵 _parquet_ 🪵 file.
-
-The file must contain (at least) the columns:
-
-- timestamp: the timestamp, in miliseconds, of the data point (e.g., 30000, 60000, 90000) - the time unit is flexible.
-- {metric_name}: the value of the metric at the given timestamp. This is the metric analyzed (e.g., CO2_emissions,
-energy_usage).
-
-e.g., if you are analyzing the CO2 emissions of a datacenter, for a timeperiod of 5 minutes, and the data is sampled
-every 30 seconds, the file will look like this:
-
-| timestamp | co2_emissions |
-|-----------|---------------|
-| 30000     | 31.2          |
-| 60000     | 31.4          |
-| 90000     | 28.5          |
-| 120000    | 31.8          |
-| 150000    | 51.5          |
-| 180000    | 51.2          |
-| 210000    | 51.4          |
-| 240000    | 21.5          |
-| 270000    | 21.8          |
-| 300000    | 21.2          |
-
----
-
-## Step 3: Running M3SA
-
-### 3.1 Setup the Simulator Specifics
-
-Update the simulation folder name ([9], [13], [17] from Step 1), in the
-file ```simulator_specifics.py```, from ```opendc/src/python/simulator_specifics.py```.
-
-### 3.2 Setup the python program arguments
-
-### Arguments for Main.py Setup
-Main.py takes two arguments:
-
-1. Argument 1 is the path to the output directory where M3SA output files will be stored.
-2. Argument 2 is the path to the input file that contains the configuration of M3SA.
-
-e.g.,
-
-```json
-"simulation-123/outputs/" "simulation-123/inputs/m3sa-configurator.json"
-```
-
-### 3.3 Working directory Main.py Setup
-
-Make sure to set the working directory to the directory where the main.py file is located.
-
-e.g.,
-
-```
-/your/path/to-analyzer/src/main/python
-```
-
-If you are using OpenDC, you can set the working directory to the following path:
-
-```
-/your/path/opendc/opendc-analyze/src/main/python
-```
-
----
-
-## Optional: Step 4: Simulate and analyze, with one click
-
-The simulation and analysis can be executed as a single command; if no errors are encountered, from the user
-perspective,
-this operation is atomic. We integrated M3SA into OpenDC to facilitate this process.
-
-To further integrate M3SA into any simulation infrastructure, M3SA needs to called from
-the simulation infrastructure, and provided the following running setup:
-
-1. script language: Python
-2. argument 1: the path of the output directory, in which M3SA output files will be stored
-3. argument 2: the path of the input file, containing the configuration of M3SA
-4. other language-specific setup
-
-For example, the integration of the M3SA into OpenDC can be found
-in ```Analyzr.kt``` from ```opendc-analyze/src/main/kotlin/Analyzr.kt```.
-Below, we provide a snippet of the code:
-
-```kotlin
-val ANALYSIS_SCRIPTS_DIRECTORY: String = "./opendc-analyze/src/main/python"
-val ABSOLUTE_SCRIPT_PATH: String =
-    Path("$ANALYSIS_SCRIPTS_DIRECTORY/main.py").toAbsolutePath().normalize().toString()
-val SCRIPT_LANGUAGE: String = "python3"
-
-fun analyzeResults(outputFolderPath: String, analyzerSetupPath: String) {
-    val process = ProcessBuilder(
-        SCRIPT_LANGUAGE,
-        ABSOLUTE_SCRIPT_PATH,
-        outputFolderPath, // argument 1
-        analyzerSetupPath // argument 2
-    )
-        .directory(Path(ANALYSIS_SCRIPTS_DIRECTORY).toFile())
-        .start()
-
-    val exitCode = process.waitFor()
-    if (exitCode == 0) {
-        println("[Analyzr.kt says] Analysis completed successfully.")
-    } else {
-        val errors = process.errorStream.bufferedReader().readText()
-        println("[Analyzr.kt says] Exit code ${exitCode}; Error(s): $errors")
-    }
-}
-```
diff --git a/site/docs/tutorials/_category_.json b/site/docs/tutorials/_category_.json
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-{
-  "label": "Tutorials",
-  "position": 3,
-  "link": {
-    "type": "generated-index",
-    "description": "Tutorials demonstrating how to conduct experiments with OpenDC."
-
-  }
-}
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