4 Matching Annotations
  1. Nov 2021
    1. If you don't have that information, you can determine which frequencies are important by extracting features with Fast Fourier Transform. To check the assumptions, here is the tf.signal.rfft of the temperature over time. Note the obvious peaks at frequencies near 1/year and 1/day:

      Do a fft with tensorflow

      fft = tf.signal.rfft(df['T (degC)'])
      f_per_dataset = np.arange(0, len(fft))
      
      n_samples_h = len(df['T (degC)'])
      hours_per_year = 24*365.2524
      years_per_dataset = n_samples_h/(hours_per_year)
      
      f_per_year = f_per_dataset/years_per_dataset
      plt.step(f_per_year, np.abs(fft))
      plt.xscale('log')
      plt.ylim(0, 400000)
      plt.xlim([0.1, max(plt.xlim())])
      plt.xticks([1, 365.2524], labels=['1/Year', '1/day'])
      _ = plt.xlabel('Frequency (log scale)')
      
  2. Sep 2021
  3. Nov 2019
    1. j’avais l’ambition de progresser à l’infini

      Beauvoir évoque le caractère difficilement saisissable (et potentiellement sans fin, infini) du devenir, du moi en changement.