docs: def build_kcorrection_array(
          log,
          redshiftArray,
          snTypesArray,
          snLightCurves,
          pathToOutputDirectory,
          plot=True):
      """
      *Given the random redshiftArray and snTypeArray, generate a dictionary of k-correction polynomials (one for each filter) for every object.*
  
      **Key Arguments:**
          - ``log`` -- logger
          - ``redshiftArray`` -- the pre-generated redshift array
          - ``snTypesArray`` -- the pre-generated array of random sn types
          - ``snLightCurves`` -- yaml style dictionary of SN lightcurve info
          - ``pathToOutputDirectory`` -- path to the output directory (provided by the user)
          - ``plot`` -- generate plot?
  
      **Return:**
          - None
      """
      ################ > IMPORTS ################
      ## STANDARD LIB ##
      ## THIRD PARTY ##
      import yaml
      import numpy as np
      ## LOCAL APPLICATION ##
  
      ################ >ACTION(S) ################
      dataDir = pathToOutputDirectory + "/k_corrections/"
      filters = ['g', 'r', 'i', 'z']
      fileName = pathToOutputDirectory + "/transient_light_curves.yaml"
      stream = file(fileName, 'r')
      generatedLCs = yaml.load(stream)
      models = generatedLCs.keys()
      kCorList = []
  
      for i in range(len(redshiftArray)):
          redshift = redshiftArray[i]
          kCorDict = {}
          for model in models:
              for ffilter in filters:
                  filterDir = dataDir + model + "/" + ffilter
                  strRed = "%0.3f" % (redshift,)
                  fileName = filterDir + "/z" + \
                      str(strRed).replace(".", "pt") + "_poly.yaml"
                  try:
                      stream = file(fileName, 'r')
                      yamlContent = yaml.load(stream)
                      # log.info('yamlContent %s' % (yamlContent,))
                      stream.close()
                      flatPoly = np.poly1d(yamlContent['polyCoeffs'])
                  except:
                      flatPoly = None
                  kCorDict[ffilter] = flatPoly
                  kCorList.append(kCorDict)
  
      kCorArray = np.array(kCorList)
      return kCorArray