### Local Development Setup Commands

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/README.md

Commands to set up the development environment, install dependencies, run tests, check formatting, and build the project.

```bash
npm ci
npm test
npm run check:format
npm run build
```

--------------------------------

### Install Dependencies

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs_theme/contributing.md

Installs all necessary project dependencies.

```bash
npm ci
```

--------------------------------

### Install Project Dependencies

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs_theme/contributing.md

Install all necessary dependencies for the project before running tests or other development commands.

```bash
npm install
```

--------------------------------

### Install Dependencies and Run Tests

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Install project dependencies using npm and run all tests using Jest. This step is crucial before committing changes.

```bash
npm install
npm run test
```

--------------------------------

### Install jsthermalcomfort with npm

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs_theme/installation.md

Use npm to install the jsthermalcomfort package locally.

```bash
npm install jsthermalcomfort
```

--------------------------------

### Example Usage of adaptive_en

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Demonstrates how to use the adaptive_en function to calculate thermal comfort results. It shows how to access specific comfort categories from the returned object.

```javascript
const results = adaptive_en([25,25], [25,25], [20,9], [0.1,0.1]);
console.log(results);
console.log(results.acceptability_cat_i);
```

--------------------------------

### Common Commands for jsthermalcomfort

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs_theme/overview.md

Lists common npm commands for managing the jsthermalcomfort project, including dependency installation, testing, documentation generation, and linting.

```bash
npm install
npm test
npm run docs
npm run lint
```

--------------------------------

### Use Fans Heatwave Prediction Example

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Demonstrates how to use the use_fans_heatwaves function to predict thermal comfort results. The function takes environmental and personal parameters and returns an object with various thermal comfort metrics.

```javascript
const results = use_fans_heatwaves(25, 25, 0.1, 50, 1.2, 0.5);
console.log(results);
```

--------------------------------

### Quick Start: Calculate PMV and PPD

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs_theme/overview.md

Demonstrates a basic usage of the pmv_ppd function to calculate the Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD) indices. Ensure the 'jsthermalcomfort' library is imported.

```javascript
import { pmv_ppd } from 'jsthermalcomfort';

// Calculate PMV and PPD
const results = pmv_ppd(25, 25, 0.1, 50, 1.2, 0.5);
console.log(`PMV: ${results.pmv}, PPD: ${results.ppd}%`);
```

--------------------------------

### Calculate Adaptive ASHRAE Comfort (Imperial)

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Calculates thermal comfort acceptability using the adaptive ASHRAE model with imperial units. This example demonstrates using the 'IP' system argument.

```javascript
import { adaptive_ashrae_array } from "jsthermalcomfort/models";
// For users who want to use the IP system
const results = adaptive_ashrae_array([77], [77], [68], [0.3], 'IP');
console.log(results);
// {tmp_cmf: [75.2], tmp_cmf_80_low: [68.9], tmp_cmf_80_up: [81.5],
//  tmp_cmf_90_low: [70.7], tmp_cmf_90_up: [79.7], acceptability_80: [true],
//  acceptability_90: [true]}
```

--------------------------------

### JOS3 Simple Simulation

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/examples/calc_jos3.html

Demonstrates a basic simulation using the JOS3 model, setting initial environmental conditions and running a simulation. It also shows how to access BMR and body name list properties.

```javascript
import { JOS3 } from "https://cdn.jsdelivr.net/npm/jsthermalcomfort/lib/esm/models/JOS3.js";
import { local_clo_typical_ensembles } from "https://cdn.jsdelivr.net/npm/jsthermalcomfort/lib/esm/jos3_functions/utilities.js";

let model = new JOS3(1.7, 60, undefined, 30);
// Set the first phase model.
model.to = 28; // Operative temperature [°C]
model.rh = 40; // Relative humidity [%]
model.v = 0.2; // Air velocity [m/s]
model.par = 1.2; // Physical activity ratio [-]
model.simulate(60); // Exposure time = 60 [min]

// Set the next condition (You only need to change the parameters that you want to change)
model.to = 20; // Change only operative temperature
model.simulate(60); // Additional exposure time = 60 [min]

let t_skin_mean_trace = {
    x: model.cycle_time,
    y: model.t_skin_mean,
    mode: "lines",
};

// Plot time series of mean skin temperature.
let layout = {
    xaxis: {
        title: "Time [min]",
    },
    yaxis: {
        title: "Mean skin temperature [°C]",
    },
};
Plotly.newPlot("graph1", [t_skin_mean_trace], layout);

// Print the BMR value using the getter
document.getElementById("example1 bmr").innerHTML = model.bmr;
document.getElementById("example1 Body name list").innerHTML = model.body_names;
```

--------------------------------

### Run Tests

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs_theme/contributing.md

Executes the project's test suite.

```bash
npm test
```

--------------------------------

### Implement and Document New Thermal Comfort Model

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs_theme/contributing.md

Create a new JavaScript file for your model in `src/models/` and document it using JSDoc. Ensure the JSDoc includes `@public`, `@memberof models`, and clear `@param` and `@returns` descriptions.

```javascript
/**
 * @public
 * @memberof models
 * @name my_new_model
 * @description Calculates a new thermal comfort index.
 *
 * @param {number} tdb Dry bulb air temperature [°C]
 * @param {number} rh Relative humidity [%]
 * @returns {number} The calculated index value.
 */
export function my_new_model(tdb, rh) {
  // your implementation here
}
```

--------------------------------

### Bump Version and Publish

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs_theme/contributing.md

Commands to bump the package version and push commits and tags to trigger the publish workflow.

```bash
npm version patch
npm version minor
npm version major
npm version 1.2.3
git push && git push --tags
```

--------------------------------

### Get MET Value for Typical Tasks

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Import and use the `met_typical_tasks` object to retrieve the MET (Metabolic Equivalent of Task) value for various activities. This is useful for estimating the metabolic rate of individuals during different tasks.

```javascript
import { met_typical_tasks } from "jsthermalcomfort/utilities"; //The path to utilities
console.log(met_typical_tasks['Seated_Cquiet']);
// output 1.0
```

--------------------------------

### Update Project Documentation

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs_theme/contributing.md

Generate or update project documentation. This command should be run after making code changes that affect the documentation.

```bash
npm run docs
```

--------------------------------

### Adaptive ASHRAE Comfort with Low Temperature Input

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Demonstrates the adaptive ASHRAE model with a low indoor temperature input, resulting in NaN values. The model requires a running mean temperature above 10°C.

```javascript
import { adaptive_ashrae_array } from "jsthermalcomfort/models";
const results = adaptive_ashrae_array([25], [25], [9], [0.1]);
console.log(results);
// {tmp_cmf: [NaN], tmp_cmf_80_low: [NaN], ...}
// The adaptive thermal comfort model can only be used
// if the running mean temperature is higher than 10°C
```

--------------------------------

### Format Code with Prettier

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs_theme/contributing.md

Run Prettier to automatically format your code according to project standards.

```bash
npm run format
```

--------------------------------

### Run Subset of Tests

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Execute a specific subset of tests by providing a path to the test file and a test pattern or name. This is useful for faster feedback during development.

```bash
npm run test -- '<path_to_test_file>' -t '<test_pattern/name>'
```

--------------------------------

### Run Project Tests

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs_theme/contributing.md

Execute all project tests using Jest to ensure code quality and functionality.

```bash
npm run test
```

--------------------------------

### Initialize JOS3 Model

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Initializes the JOS3 thermal physiology model. Optional parameters can be set for body characteristics and environmental conditions.

```javascript
const X = new JOS3();
X.tdb = 20;
X.tr = 20;
X.v = 0.1;
X.rh = 50;
X.pr = 1.37;
X.wc = 0.5;
console.log(X.t_skin_mean); // 33.0
```

--------------------------------

### Clone the jsthermalcomfort Repository

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs_theme/contributing.md

Clone your forked repository locally to begin development. Ensure you fetch and pull updates from the main branch.

```bash
git clone git@github.com:FedericoTartarini/jsthermalcomfort.git
```

--------------------------------

### Update Documentation

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Generate updated documentation files after making changes to the code or Markdown files. Commit these documentation changes separately.

```bash
npm run docs
git add .
git commit -m "Your detailed description of your changes."
```

--------------------------------

### Push Branch to GitHub

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs_theme/contributing.md

Push your local development branch to your forked GitHub repository.

```bash
git push origin name-of-your-bugfix-or-feature
```

--------------------------------

### Import and Access Garment Clo Values

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Demonstrates how to import the clo_individual_garments utility and access the clo value for a specific garment like 'Metal_chair'. This is useful for retrieving predefined thermal comfort data.

```javascript
import { clo_individual_garments } from "jsthermalcomfort/utilities"; //The path to utilities
console.log(clo_individual_garments['Metal_chair']);
// output 0.0
```

--------------------------------

### Calculate Adaptive EN Comfort (SI Units)

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Calculates adaptive thermal comfort using the EN model with SI units. Logs the results and specific acceptability category.

```javascript
const results = adaptive_en(25, 25, 20, 0.1);
console.log(results);
console.log(results.acceptability_cat_i);
```

--------------------------------

### Calculate Thermal Comfort Results

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Demonstrates how to use the `two_nodes_array` function to calculate various thermal comfort metrics for multiple nodes. The output includes skin temperature, heat loss, core temperature, and more.

```javascript
const results = two_nodes_array([25,30], [25,35], [0.3,0.5], [50,60], [1.2,1.5], [0.5, 0.3], [0,0], [1.8258,1.8258], [101325,101325], ["standing","standing"], [90,90])
console.log(results);
```

--------------------------------

### Register New Thermal Comfort Model

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs_theme/contributing.md

Export your newly created model function in `src/models/index.js` to make it available in the library.

```javascript
import { my_new_model } from "./my_new_model.js";

export default {
  // ... existing models
  my_new_model,
};
```

--------------------------------

### Adaptive EN Comfort with Out-of-Range Input

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Demonstrates the adaptive EN model's behavior when the running mean temperature is outside the standard applicability limits (10-30°C).

```javascript
const results = adaptive_en(25, 25, 9, 0.1);
console.log(results);
```

--------------------------------

### Calculate Adaptive EN Comfort (IP Units)

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Calculates adaptive thermal comfort using the EN model with IP units. This is useful for systems operating in Fahrenheit and feet per second.

```javascript
const results = adaptive_en(77, 77, 68, 0.3, 'IP');
console.log(results);
```

--------------------------------

### Import jsthermalcomfort in website (ESM)

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs_theme/installation.md

Include the jsthermalcomfort library in your website by importing it within a script tag marked as type="module".

```html
<script type="module">
  import { models, utilities, pschymetrics } from "https://cdn.jsdelivr.net/npm/jsthermalcomfort/lib/esm/index.js"
</script>
```

--------------------------------

### p_sat

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Calculates the saturation vapor pressure of water at a given dry bulb temperature in Pascals.

```APIDOC
## p_sat

### Description
Calculates the saturation vapor pressure of water at a given dry bulb temperature in Pascals.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Method
Not applicable (JavaScript function)

### Endpoint
Not applicable (JavaScript function)

### Parameters
#### Parameters
- **tdb** (number) - Required - Air temperature, [°C]

### Returns
- **number**: vapour pressure of water, [Pa]
```

--------------------------------

### Calculate Adaptive EN Comfort (Array Version)

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Calculates adaptive thermal comfort using the EN model with array inputs for multiple data points. Supports both SI and IP units.

```javascript
adaptive_en_array(tdb, tr, t_running_mean, v, units, limit_inputs)
```

--------------------------------

### Commit Code Changes

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs_theme/contributing.md

Stage all changes and commit them with a descriptive message detailing your modifications.

```bash
git add .
git commit -m "Your detailed description of your changes."
```

--------------------------------

### Create a New Development Branch

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs_theme/contributing.md

Create a new branch for your bug fix or feature development. Follow the specified naming conventions for new branches.

```bash
git checkout -b name-of-your-bugfix-or-feature
```

--------------------------------

### Adaptive Thermal Heat Balance (athb_array)

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Calculates the PMV value using the Adaptive Thermal Heat Balance Framework. This version supports arrays for all input parameters.

```APIDOC
## athb_array

### Description
Calculates the PMV value using the Adaptive Thermal Heat Balance Framework [27]. The adaptive thermal heat balance (ATHB) framework introduced a method to account for the three adaptive principals, namely physiological, behavioral, and psychological adaptation, individually within existing heat balance models. The objective is a predictive model of thermal sensation applicable during the design stage or in international standards without knowing characteristics of future occupants. This is a version that supports arrays.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
- **tdb** (Array<number>): Dry bulb air temperature, in [°C].
- **tr** (Array<number>): Mean radiant temperature, in [°C].
- **vr** (Array<number>): Relative air speed, in [m/s]. Note: vr is the relative air speed caused by body movement and not the air speed measured by the air speed sensor. The relative air speed is the sum of the average air speed measured by the sensor plus the activity-generated air speed (Vag). Where Vag is the activity-generated air speed caused by motion of individual body parts. vr can be calculated using the function jsthermalcomfort.utilities.v_relative.
- **rh** (Array<number>): Relative humidity, [%].
- **met** (Array<number>): Metabolic rate, [met].
- **t_running_mean** (Array<number>): Running mean outdoor temperature, in [°C].

### Returns
- **Array<number>**: An array of Predicted Mean Vote values calculated with the Adaptive Thermal Heat Balance framework.

### Request Example
```javascript
const tdb = [25, 26];
const tr = [25, 26];
const vr = [0.1, 0.1];
const rh = [50, 50];
const met = [1.1, 1.1];
const t_running_mean = [20, 20];

const athb_results = athb_array(tdb, tr, vr, rh, met, t_running_mean);
console.log(athb_results); // Output: [0.2, 0.2]
```

### Response
#### Success Response (200)
- **Array<number>**: An array of Predicted Mean Vote values calculated with the Adaptive Thermal Heat Balance framework.

#### Response Example
```json
[
  0.2,
  0.2
]
```
```

--------------------------------

### Calculate Cooling Effect (IP Units)

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Calculates the Cooling Effect using Imperial Units (IP). The 'units' parameter must be set to 'IP'.

```javascript
// For users who want to use the IP system
const CE_IP = cooling_effect(77, 77, 1.64, 50, 1, 0.6, "IP");
console.log(CE_IP); // Output: {ce: 3.74}
```

--------------------------------

### Update Documentation Display Name for New Model

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs_theme/contributing.md

Add a human-readable title for your new model to the `displayNameMap` in `docs_theme/index.js` for display in the generated documentation.

```javascript
const displayNameMap = {
  // ... existing mappings
  my_new_model: "My New Premium Model (MNPM)",
};
```

--------------------------------

### at(rh, v, q, kwargs)

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Calculates the Apparent Temperature (AT) based on relative humidity, wind speed, and net radiation. The `kwargs` parameter allows for optional settings like rounding the output.

```APIDOC
## at(rh, v, q, kwargs)

### Description
Calculates the Apparent Temperature (AT) based on relative humidity, wind speed, and net radiation. The `kwargs` parameter allows for optional settings like rounding the output.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
* **rh** (number) - Required - Relative humidity, in [%].
* **v** (number) - Required - Wind speed 10m above ground level, in [m/s].
* **q** (number | undefined) - Optional - Net radiation absorbed per unit area of body surface [W/m2].
* **kwargs** (object) - Optional - Other parameters. Defaults to `{round:true}`.
  * **kwargs.round** (boolean) - Optional - If True rounds output value, if False it does not round it. Defaults to `true`.

### Request Example
```javascript
const result = at(25, 30, 0.1);
console.log(result); // {at: 24.1}
```

### Response
#### Success Response (200)
* **at** (number) : Apparent Temperature, [°C]

#### Response Example
```json
{
  "at": 24.1
}
```
```

--------------------------------

### Calculate Adaptive Predicted Mean Vote (aPMV)

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Calculates the Adaptive Predicted Mean Vote (aPMV) for a single set of thermal comfort parameters. Ensure all input values are within the standard applicability limits of the model, or set `limit_inputs` to false.

```javascript
const tdb = 24,
const tr = 30,
const vr = 0.22,
const rh = 50,
const met = 1.4,
const clo = 0.5,
const a_coefficient = 0.293,
const wme = undefined,

const result = a_pmv(tdb, tr, vr, rh, met, clo, a_coefficient, wme);
console.log(result) //output { a_pmv: 0.48 }
```

--------------------------------

### Calculate Adaptive ASHRAE Comfort (SI Units)

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Calculates adaptive thermal comfort using SI units. Ensure inputs are within the standard applicability limits for valid results.

```javascript
import { adaptive_ashrae } from "jsthermalcomfort/models";
const results = adaptive_ashrae(25, 25, 20, 0.1);
console.log(results);
// {tmp_cmf: 24.0, tmp_cmf_80_low: 20.5, tmp_cmf_80_up: 27.5,
//   tmp_cmf_90_low: 21.5, tmp_cmf_90_up: 26.5, acceptability_80: true,
//   acceptability_90: true}
console.log(results.acceptability_80);
// true
```

--------------------------------

### Calculate Adaptive ASHRAE Comfort (IP Units)

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Calculates adaptive thermal comfort using Imperial Units (IP). This is useful for regions that prefer imperial measurements.

```javascript
import { adaptive_ashrae } from "jsthermalcomfort/models";
// For users who want to use the IP system
const results = adaptive_ashrae(77, 77, 68, 0.3, 'IP');
console.log(results);
// {tmp_cmf: 75.2, tmp_cmf_80_low: 68.9, tmp_cmf_80_up: 81.5,
//  tmp_cmf_90_low: 70.7, tmp_cmf_90_up: 79.7, acceptability_80: true,
//  acceptability_90: true}
```

--------------------------------

### t_wb

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Calculates the wet-bulb temperature using the Stull equation.

```APIDOC
## t_wb

### Description
Calculates the wet-bulb temperature using the Stull equation.

### Parameters
#### Path Parameters
- **tdb** (number) - air temperature, [°C]
- **rh** (number) - relative humidity, [%]

### Returns
- **number** - wet-bulb temperature, [°C]
```

--------------------------------

### JOS3 Detailed Simulation

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/examples/calc_jos3.html

Performs a detailed simulation with JOS3, allowing for specific body parameters, environmental conditions, and clothing insulation for different body parts. It demonstrates running multiple simulations with changing conditions and plotting various skin temperatures.

```javascript
import { JOS3 } from "https://cdn.jsdelivr.net/npm/jsthermalcomfort/lib/esm/models/JOS3.js";
import { local_clo_typical_ensembles } from "https://cdn.jsdelivr.net/npm/jsthermalcomfort/lib/esm/jos3_functions/utilities.js";

// Build a model and set a body built
// Create an instance of the JOS3 class with optional body parameters such as body height, weight, age, sex, etc.
// Define a JOS3 object
model = new JOS3(
    1.7,
    60,
    20,
    30,
    "male",
    undefined,
    "japanese",
    "fujimoto",
    "all",
);

// Set environmental conditions
model.tdb = 28; // Air temperature [°C]
model.tr = 30; // Mean radiant temperature [°C]
model.rh = 40; // Relative humidity [%]
model.v = [
    0.2, // head
    0.4, // neck
    0.4, // chest
    0.1, // back
    0.1, // pelvis
    0.4, // left shoulder
    0.4, // left arm
    0.4, // left hand
    0.4, // right shoulder
    0.4, // right arm
    0.4, // right hand
    0.1, // left thigh
    0.1, // left leg
    0.1, // left foot
    0.1, // right thigh
    0.1, // right leg
    0.1, // right foot
];
model.clo = local_clo_typical_ensembles[ "briefs, socks, undershirt, work jacket, work pants, safety shoes" ]["local_body_part"];
model.par = 1.2; // Physical activity ratio [-]
model.posture = "sitting"; // Posture

// Run JOS-3 model
model.simulate(
    30, // Number of loops of a simulation
    60, // Time delta [sec]
);
// Exposure time = 30 [loops] * 60 [sec] = 30 [min]

// Set the next condition
model.to = 20; // Change operative temperature
model.v = {
    head: 0.2,
    neck: 0.4,
    chest: 0.4,
    back: 0.1,
    pelvis: 0.1,
    left_shoulder: 0.4,
    left_arm: 0.4,
    left_hand: 0.4,
    right_shoulder: 0.4,
    right_arm: 0.4,
    right_hand: 0.4,
    left_thigh: 0.1,
    left_leg: 0.1,
    left_foot: 0.1,
    right_thigh: 0.1,
    right_leg: 0.1,
    right_foot: 0.1,
};

// Run JOS-3 model again
model.simulate(
    60, // Number of loops of a simulation
    60, // Time delta [sec]
);
// Additional exposure time = 60 [loops] * 60 [sec] = 60 [min]

// Set the next condition
model.tdb = 30; // Change air temperature [°C]
model.tr = 35; // Change mean radiant temperature [°C]

// Run JOS-3 model once more
model.simulate(
    30, // Number of loops of a simulation
    60, // Time delta [sec]
);
// Additional exposure time = 30 [loops] * 60 [sec] = 30 [min]

// Show the results
let t_skin_head_trace = {
    x: model.cycle_time,
    y: model.t_skin_head,
    mode: "lines",
};
let t_skin_chest_trace = {
    x: model.cycle_time,
    y: model.t_skin_chest,
    mode: "lines",
};
let t_skin_left_hand_trace = {
    x: model.cycle_time,
    y: model.t_skin_left_hand,
    mode: "lines",
};

layout = {
    xaxis: {
        title: "Time [min]",
    },
    yaxis: {
        title: "Mean skin temperature [°C]",
    },
};

Plotly.newPlot(
    "graph2",
    [
        t_skin_mean_trace,
        t_skin_head_trace,
        t_skin_chest_trace,
        t_skin_left_hand_trace,
    ],
    layout,
);
```

--------------------------------

### Calculate Cooling Effect (SI Units)

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Calculates the Cooling Effect using SI units. Ensure all input parameters are provided in the correct format.

```javascript
const CE = cooling_effect(25, 25, 0.3, 50, 1.2, 0.5);
console.log(CE); // Output: {ce: 1.64}
```

--------------------------------

### p_sat_torr_array

Source: https://github.com/federicotartarini/jsthermalcomfort/blob/main/docs/index.html

Estimates the saturation vapor pressure in torr for an array of dry bulb air temperatures.

```APIDOC
## p_sat_torr_array

### Description
Estimates the saturation vapor pressure in torr for an array of dry bulb air temperatures.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Method
Not applicable (JavaScript function)

### Endpoint
Not applicable (JavaScript function)

### Parameters
#### Parameters
- **tdb** (Array<number>) - Required - Dry bulb air temperature [C]

### Returns
- **Array<number>**: saturation vapour pressure [torr]
```