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Aiguasol_exercise

Python version used 3.7.4

Recommended depency management

I have used a virtual environment with pipenv in order to run the code. To download, see https://medium.com/@mahmudahsan/how-to-use-python-pipenv-in-mac-and-windows-1c6dc87b403e.

It should also work with pip.

Install required libraries

Libraries required are stated in requirements.txt. To install use

pipenv install -r requirements.txt

Help command for arguments of main.py

pipenv run python main.py -h

usage: main.py [-h] [--apiToken APITOKEN] [--force]

Read data from ree API and compute FFT

optional arguments:
  -h, --help           show this help message and exit
  --apiToken APITOKEN  API personal_token
  --force              Force download

Code strucutre

The entrypoint is main.py. To execute in pipenv environment use

pipenv run python main.py --apiToken personal_token --force

The code is structured as follows:

Inputs

  • Config inputs

    • apiToken: valid personal token. Send an email to [email protected] to request one.
    • force: if existing, forces to download data from ree API regardless of file system cache, see Cache() class in Ree API request.

  • Hardcoded inputs

    • indicatorID: indicator ID of interest from ree API
    • start_date: beginning of the time series
    • end_date: end of the time series

Input validation

  1. start_date: check correct format ('YYYY-MM-DD')
  2. end_date: check correct format ('YYYY-MM-DD')
  3. variableID: check is integer

Ree API request

  • ReeIndicatorAPI(): first checks if there is cached data. If so, loads it. If not calls ree API, downloads and writes file.
  • Cache(): for development purposes, any call made to the API will be cached in local file system so the same call is not executed twice (consuming less resources during development time).

FFT computation

  • FFT: Computes fast Fourier transform (FFT) of a certain signal.
    • FFT().compute: Three inputs are required, x and y data plus the total time of the time-series totalDays.
    • FFT().plot: Plots time-domain data (x,y) and frequency-domain data (xf,yf).

Testing of FFT computation

A test of the FFT computation is available:

pipenv run python testFFT.ty

It computes a simple problem with manufactured data: y(x) = sin(50(2pi)x) + 0.5sin(80(2pi)x). The output is the following image

outputImage_testFFT

This script computes the FFT of y(x) and plots both, the time domain and frequency domain series. It is clear from the figure that the two excited frequencies are 50 and 80 Hz, which appear in the manufactured equation y(x). This fact suggests that the FFT implementation can be validated. Please note that this test only validates FFT().compute.

Code execution examples

The user has two options to execute the code:

  1. Execute main.py using default input parameters: pipenv run python main.py (Recommended use. Only if there is cached data.)

  2. Execute main.py only using --apiToken as input: pipenv run python main.py --apiToken 652647858608a4559e7016t3168644efb1b70880313257d4a3ac6cd93e2ad611

  3. Execute main.py using --apiToken and --force as inputs: pipenv run python main.py --apiToken 652647858608a4559e7016t3168644efb1b70880313257d4a3ac6cd93e2ad611 --force

Note that the token displayed in this section is NOT a valid token. Own personal token must be used.

Results

After executing main.py, the code generates the following output image

outputImage

  1. Plot of the raw signal obtained from ree API against the measured days.
  2. Plot of the FFT of the raw signal.
  3. Plot of the FFT of the raw signal subtracting its mean value. This helps removing the first peak at frequency = 0.
  4. Plot of the FFT of the clean signal. A clean signal is obtained by using a window function (in this particular case the Hanning function). It is supposed to remove the noise in the raw signal, although it does not suppose a great improvement.

From the figure above it can be observed that the main repeated patern in the raw signal occurs per day, see the spike in frequency = 1 day^(-1). The small frequencies with lower amplitude may be associated to the raw signal noice. In an attempt to reduce the noice, a window function has been implemented with no great success, observing similar FFT results in plots 3 and 4.