pyrad.proc.process_centroids

pyrad.proc.process_centroids(procstatus, dscfg, radar_list=None)

Computes centroids for the semi-supervised hydrometeor classification

Parameters:

• procstatus (int) – Processing status: 0 initializing, 1 processing volume, 2 post-processing

• dscfg (dictionary of dictionaries) –

  data set configuration. Accepted Configuration Keywords:

  datatype : list of string. Dataset keyword
      The input data types
  samples_per_vol : int. Dataset keyword
      Maximum number of samples per volume kept for further analysis.
      Default 20000
  nbins : int.
      Number of bins of the histogram used to make the data platykurtic.
      Default 110
  pdf_zh_max : int
      Multiplicative factor to the Guassian function used to make the
      distribution of the reflectivity platykurtic that determines the
      number of samples for each bin. Default 10000
  pdf_relh_max : int
      Multiplicative factor to the Guassian function used to make the
      distribution of the height relative to the iso-0 platykurtic that
      determines the number of samples for each bin. Default 20000
  sigma_zh, sigma_relh : float
      sigma of the respective Gaussian functions. Defaults 0.75 and 1.5
  randomize : bool
      If True the data is randomized to avoid the effects of the
      quantization. Default True
  platykurtic_dBZ : bool
      If True makes the reflectivity distribution platykurtic. Default
      True
  platykurtic_H_ISO0 : bool
      If True makes the height respect to the iso-0 distribution
      platykurtic. Default True
  relh_slope : float. Dataset keyword
      The slope used to transform the height relative to the iso0 into
      a sigmoid function. Default 0.001
  external_iterations : int. Dataset keywords
      Number of iterations of the external loop. This number will
      determine how many medoids are computed for each hydrometeor
      class. Default 30
  internal_iterations : int. Dataset keyword
      Maximum number of iterations of the internal loop. Default 10
  sample_data : Bool.
      If True the data is going to be sampled prior to each external
      iteration. Default False
  nsamples_iter : int.
      Number of samples per iteration. Default 20000
  alpha : float
      Minimum value to accept the cluster according to p. Default 0.01
  cv_approach : Bool
      If true it is used a critical value approach to reject or accept
      similarity between observations and reference. If false it is used
      a p-value approach. Default True
  n_samples_syn : int
      Number of samples drawn from reference to compare it with
      observations in the KS test. Default 50
  num_samples_arr : array of int
      Number of observation samples used in the KS test to choose from.
      Default (30, 35, 40)
  acceptance_threshold : float. Dataset keyword
      Threshold on the inter-quantile coefficient of dispersion of the
      medoids above which the medoid of the class is not acceptable.
      Default 0.5
  nmedoids_min : int
      Minimum number of intermediate medoids to compute the final
      result. Default 1
  var_names : tupple
      The names of the features. Default ('dBZ', 'ZDR', 'KDP', 'RhoHV',
      'H_ISO0')
  hydro_names: tupple
      The name of the hydrometeor types. Default ('AG', 'CR', 'LR',
      'RP', 'RN', 'VI', 'WS', 'MH', 'IH/HDG')
  weight : tupple
      The weight given to each feature when comparing to the reference.
      It is in the same order as var_names. Default (1., 1., 1., 1.,
      0.75)
  parallelized : bool
      If True the centroids search is going to be parallelized. Default
      False
  kmax_iter : int
      Maximum number of iterations of the k-medoids algorithm. Default
      100
  nsamples_small : int
      Maximum number before using the k-medoids CLARA algorithm. If this
      number is exceeded the CLARA algorithm will be used. Default 40000
  sampling_size_clara : int
      Number of samples used in each iteration of the k-medoids CLARA
      algorithm. Default 10000
  niter_clara : int
      Number of iterations performed by the k-medoids CLARA algorithm.
      Default 5
  keep_labeled_data : bool
      If True the labeled data is going to be kept for storage. Default
      True
  use_median : bool
      If True the intermediate centroids are computed as the median
      of the observation variables and the final centroids are computed
      as the median of the intermediate centroids. If false they are
      computed using the kmedoids algorithm. Default false
  allow_label_duplicates : bool
      If True allow to label multiple clusters with the same label.
      Default True
  

• radar_list (list of Radar objects) – Optional. list of radar objects

Returns:

• new_dataset (dict) – dictionary containing the output

• ind_rad (int) – radar index