Logic Challenge

Here at Climatempo we work with several meteorological models to consult forecast data, but the models are accurate up to a certain number of days, so we need to consult several models to compose the best result for the period sought.

The challenge is to implement a logic to assemble this composition of models with the best days of each model.

What will be evaluated?

• Logical reasoning

• Creativity

• Good coding practices

• Interpretation capacity

How to do the challenge?

1. Fork the challenge

2. Solve the challenge

3. Open the pull request

4. Answer the form

Any questions please contact our team

Rules

• The composition must be made from the current date
• The limit of each model must be respected, this means that there cannot be date conflicts between the models
• The first day of the first model and the last day of the last model must be equal to the specified period
• There can be no date gaps between models
• The all tests need to be passing

Expected model input

const models = {
  METEOROLOGIST: '0:4', // 0 -> current datetime; 4 -> current datetime + 4 days
  WRF: '5:14', // 5 -> current datetime + 5 days; 14 -> current datetime + 14 days
  CFS: '15:29', // 15 -> current datetime + 15 days; 29 -> current datetime + 29 days
}

• The object key is the model name

• The value of the object is the day limits of the model. Start and end respectively

Example

const models = {
  METEOROLOGIST: '0:4',
  WRF: '5:14',
  CFS: '15:29',
}

const period = {       
	startDate: '2023-03-08 05:00:01',
	endDate: '2023-03-20 22:00:00'
}

modelComposition(models, period)
// The result must be:
/**
[
  {
    model: 'METEOROLOGIST',
    period: {
      startDate: '2023-03-08 05:00:01',
      endDate: '2023-03-12 23:59:59'
    }
  },
  {
    model: 'WRF',
    period: {
      startDate: '2023-03-13 00:00:00',
      endDate: '2023-03-20 22:00:00'
    }
  }
]

*/

The CFS model is not included because the final period informed is within the limits of the WRF, because the current day 2023-03-08 + 29 days is 2023-04-06

Expected response format

[
  {
    model: <METEOROLOGIST|WRF|CFS>,
    period: {
      startDate: <yyyy-MM-dd HH:mm:ss>,
      endDate: <yyyy-MM-dd HH:mm:ss>,
    }
  }
]

• model: Model name

• period:

  • startDate: Start date (format: yyyy-MM-dd HH:mm:ss)

  • endDate: End date (format: yyyy-MM-dd HH:mm:ss)

---

Good luck!