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  ####
  ####   MIT License
  ####
  ####   ParaMonte: plain powerful parallel Monte Carlo library.
  ####
  ####   Copyright (C) 2012-present, The Computational Data Science Lab
  ####
  ####   This file is part of the ParaMonte library.
  ####
  ####   Permission is hereby granted, free of charge, to any person obtaining a 
  ####   copy of this software and associated documentation files (the "Software"), 
  ####   to deal in the Software without restriction, including without limitation 
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  ####   Software is furnished to do so, subject to the following conditions:
  ####
  ####   The above copyright notice and this permission notice shall be 
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  ####
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  ####   EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF 
  ####   MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 
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  ####   OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE 
  ####   OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
  ####
  ####   ACKNOWLEDGMENT
  ####
  ####   ParaMonte is an honor-ware and its currency is acknowledgment and citations.
  ####   As per the ParaMonte library license agreement terms, if you use any parts of 
  ####   this library for any purposes, kindly acknowledge the use of ParaMonte in your 
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  ####   library as described on this page:
  ####
  ####       https://github.com/cdslaborg/paramonte/blob/master/ACKNOWLEDGMENT.md
  ####
  ####################################################################################################################################
  ####################################################################################################################################
  
  import numpy as np
  import _paramonte as pm
  import _CorCovMat as ccm
  from _OutputFileContents import OutputFileContents
  from paramonte.vis.LineScatterPlot import LineScatterPlot
  from paramonte.vis.EllipsoidPlot import EllipsoidPlot
  
  Struct = pm.Struct
  newline = pm.newline
  
  # amazingly strange. If Struct is taken from outside this module,
  # it will automatically add: isParaDRAM, isParaNest, isParaTemp attributes
  # print("\nself.contents.isParaDRAM: {}\n".format(self.contents.isParaDRAM))
  # issue resolved: self._method = pm.Struct in _ParaMonteSampler (without parentheses, it does not instantiate).
  # class Struct: pass
  
  ####################################################################################################################################
  #### RestartFileContents
  ####################################################################################################################################
  
  class RestartFileContents(OutputFileContents):
      """
  
      This is the **RestartFileContents** class for generating instances
      of the ParaMonte restart output contents. This class is NOT meant to
      be directly accessed by the ParaMonte library users. It is internally
      used by the ParaMonte library to parse the contents of the output restart
      files generated by the ParaMonte sampler routines. For example, the ParaDRAM
      sampler class makes calls to this class via its ``readRestart()`` method to
      return a list of objects of class ``RestartFileContents``.
  
          **Parameters**
  
              file
  
                  The full path to the file containing the sample/chain.
  
              methodName
  
                  A string representing the name of the ParaMonte sampler used
                  to call the constructor of the ``RestartFileContents`` class.
  
              reportEnabled
  
                  A logical input parameter indicating whether the ParaMonte
                  automatic guidelines to the standard output should be provided 
                  or not. The default value is ``True``.
  
          **Attributes**
  
              A dynamic set of attributes that are directly parsed from the file.
  
          **Returns**
  
              restartFileContents
  
                  An object of class ``RestartFileContents``.
  
      ----------------------------------------------------------------------
      """
  
      ################################################################################################################################
      #### __init__
      ################################################################################################################################
  
      def __init__( self
                  , file
                  , methodName
                  , reportEnabled
                  ):
  
          super().__init__(file, methodName, reportEnabled)
          # superclass sets the following
          # self.file = file
          # self._methodName = methodName
          # self._reportEnabled = reportEnabled
  
          self.ndim = None
          self.count = None
          self.contents = None
          self._fileType = "restart"
          self._contents = []
          self._lineList = []
          self._lineListLen = []
          self.propNameList = []
  
          if self._methodName == pm.names.paradram:
              self._readRestartParaDRAM()
          else:
              pm.abort( msg = "Internal error occurred. Unrecognized methodName in the class \n"
                              "constructor of RestartFileContents: " + self.methodName
                      , marginTop = 1
                      , marginBot = 1
                      , methodName = "ParaMonte"
                      )
  
      ################################################################################################################################
      #### _readRestartParaDRAM
      ################################################################################################################################
  
      def _readRestartParaDRAM(self):
  
          # ParaDRAM field names
  
          self.propNameList = [ "meanAcceptanceRateSinceStart"
                              , "sampleSize"
                              , "logSqrtDeterminant"
                              , "adaptiveScaleFactorSquared"
                              , "meanVec"
                              , "covMat"
                              , "corMat"
                              ]
  
          ############################################################################################################################
          #### data
          ############################################################################################################################
  
          with open(self.file,"r") as fid: self._contents = fid.read().replace(pm.creturn,"")
          self._lineList = self._contents.split(pm.newline)
          self._lineListLen = len(self._lineList)
  
          # find count of updates
  
          self.count = self._contents.count(self.propNameList[0])
  
          # find ndim via meanVec entry: self.propNameList[4]
  
          rowOffset = 0;
          while self.propNameList[4] not in self._lineList[rowOffset]:
              rowOffset = rowOffset + 1
              if rowOffset > self._lineListLen: self._reportCorruptFile()
          rowOffset = rowOffset + 1 # the first numeric value of the meanVec
  
          self.ndim = 0
          while pm.utils.isNumericString( self._lineList[rowOffset+self.ndim] ): self.ndim += 1
          if self.ndim==0: self._reportCorruptFile()
  
          # parse the restart file contents
  
          fieldNamesDict =    { self.propNameList[0] : np.zeros(self.count)
                              , self.propNameList[1] : np.zeros(self.count)
                              , self.propNameList[2] : np.zeros(self.count)
                              , self.propNameList[3] : np.zeros(self.count)
                              , self.propNameList[4] : np.zeros((self.count,self.ndim))
                              , self.propNameList[5] : np.zeros((self.count,self.ndim,self.ndim))
                              , self.propNameList[6] : np.zeros((self.count,self.ndim,self.ndim))
                              }
  
          skip = 10 + (self.ndim * (self.ndim + 3)) // 2
          progressFraction = np.floor( self.count / 20 )
          for icount in range(self.count):
  
              if icount%progressFraction==0: self._progress.updateBar(icount/self.count-1)
  
              istart = icount * skip + 1
  
              rowOffset = 0
              fieldNamesDict[self.propNameList[0]][icount] = np.double( self._lineList[istart+rowOffset] )
  
              rowOffset = 2
              fieldNamesDict[self.propNameList[1]][icount] = np.double( self._lineList[istart+rowOffset] )
  
              rowOffset = 4
              fieldNamesDict[self.propNameList[2]][icount] = np.double( self._lineList[istart+rowOffset] )
  
              rowOffset = 6
              fieldNamesDict[self.propNameList[3]][icount] = np.double( self._lineList[istart+rowOffset] )
  
              rowOffset = 8
              iend = istart + rowOffset + self.ndim
              fieldNamesDict[self.propNameList[4]][icount,:] = np.double( self._lineList[istart+rowOffset:iend] )
  
              iend += 1 # the first numeric element of the covariance matrix
              for i in range(self.ndim): # covmat
                  iPlusOne = i + 1
                  istart = iend
                  iend = istart + iPlusOne
                  #print("\n{}".format(np.double( self._lineList[istart:iend]) ))
                  fieldNamesDict[self.propNameList[5]][icount,i,0:iPlusOne] = np.double( self._lineList[istart:iend] )
                  fieldNamesDict[self.propNameList[5]][icount,0:iPlusOne,i] = fieldNamesDict[self.propNameList[5]][icount,i,0:iPlusOne]
  
              fieldNamesDict[self.propNameList[6]][icount,:,:] = ccm.getCorFromCov( fieldNamesDict[self.propNameList[5]][icount,:,:] )
  
              #print("\n") # xxx
              #print(self.ndim) # xxx
              #print(self.count) # xxx
              #print(self.propNameList[0]+"\n"+str(fieldNamesDict[self.propNameList[0]][icount])) # xxx
              #print(self.propNameList[1]+"\n"+str(fieldNamesDict[self.propNameList[1]][icount])) # xxx
              #print(self.propNameList[2]+"\n"+str(fieldNamesDict[self.propNameList[2]][icount])) # xxx
              #print(self.propNameList[3]+"\n"+str(fieldNamesDict[self.propNameList[3]][icount])) # xxx
              #print(self.propNameList[4]+"\n"+str(fieldNamesDict[self.propNameList[4]][icount])) # xxx
              #print(self.propNameList[5]+"\n"+str(fieldNamesDict[self.propNameList[5]][icount,:,:])) # xxx
              #print(ccm.getCorFromCov( fieldNamesDict[self.propNameList[6]][icount,:,:] )) # xxx
              #break # xxx
  
          self._progress.updateBar(1)
  
          self.contents = Struct()
          for fieldName in self.propNameList: setattr(self.contents, fieldName, fieldNamesDict[fieldName])
          _ = fieldNamesDict.pop(self.propNameList[4])
          _ = fieldNamesDict.pop(self.propNameList[5])
          _ = fieldNamesDict.pop(self.propNameList[6]) # get rid of meanVec, covMat, corMat
          import pandas as pd
          self.df = pd.DataFrame.from_dict(fieldNamesDict)
  
          self._progress.note()
  
          ############################################################################################################################
          #### graphics
          ############################################################################################################################
  
          self._plotTypeList =    [ "line"
                                  , "scatter"
                                  , "lineScatter"
                                  ]
  
          if self.ndim>1: self._plotTypeList +=   [ "covmat2"
                                                  , "covmat3"
                                                  , "cormat2"
                                                  , "cormat3"
                                                  ]
  
          self._progress.note( msg = "adding the graphics tools... ", end = newline, pre = True )
          self.plot = Struct()
          self._resetPlot(resetType="hard")
          self._progress.note()
  
          self.plot.reset = self._resetPlot
  
      ################################################################################################################################
      #### _reportCorruptFile
      ################################################################################################################################
  
      def _reportCorruptFile(self):
          pm.abort( msg = "The structure of the file: " + newline
                          + newline
                          + "    \"" + self.file + "\"" + newline
                          + newline
                          + "does not match a " + self._methodName + " " + self._fileType + " file." + newline
                          + "The contents of the file may have been compromised." + newline
                          + "Verify the integrity of the contents of this file before attempting to reread it."
                  , marginTop = 2
                  , marginBot = 1
                  , methodName = self._methodName
                  )
  
      ################################################################################################################################
      #### _resetPlot
      ################################################################################################################################
  
      def _resetPlot  ( self
                      , resetType = "soft"
                      , plotNames = "all"
                      ):
          """
  
          Reset the properties of the plot to the original default settings.
          Use this method when you change many attributes of the plot and
          you want to clean up and go back to the default settings.
  
              **Parameters**
  
                  resetType (optional)
  
                      An optional string with possible value of ``"hard"``.
                      If provided, the plot object will be regenerated from scratch.
                      This includes reading the original data frame again and resetting
                      everything. If not provided, then only the plot settings will be
                      reset without reseting the dataFrame.
  
                  plotNames (optional)
  
                      An optional string value or list of string values representing 
                      the names of plots to reset. If no value is provided, 
                      then all plots will be reset.
  
              **Returns**
  
                  None
  
              **Example**
  
                  .. code-block:: python
  
                      reset("hard")                       # regenerate all plots from scratch
                      reset("hard","covmat2")             # regenerate covmat2 plot from scratch
                      reset("hard",["covmat2","covmat3"]) # regenerate covmat2 & covmat3 plots
  
          """
  
          requestedPlotTypeList = []
          if isinstance(plotNames, str):
              plotTypeLower = plotNames.lower()
              if plotTypeLower=="all":
                  requestedPlotTypeList = self._plotTypeList
              elif plotNames in self._plotTypeList:
                  requestedPlotTypeList = [plotNames]
              else:
                  self._reportWrongPlotName(plotNames)
          elif isinstance(plotNames, list):
              for plotName in plotNames:
                  if plotName not in self._plotTypeList: self._reportWrongPlotName(plotName)
          else:
              self._reportWrongPlotName("a none-string none-list object.")
  
          resetTypeIsHard = None
          if isinstance(resetType, str):
              resetTypeIsHard = resetType.lower()=="hard"
          else:
              resetTypeIsHard = None
              pm.abort( msg   = "The input argument resetType must be a string representing" + newline
                              + "the type of the reset to be performed on the plots." + newline
                              + "A list of possible plots includes: \"hard\", \"soft\"" + newline
                              + "Here is the help for the ``reset()`` method: " + newline
                              + newline
                              + self._resetPlot.__doc__
                      , marginTop = 1
                      , marginBot = 1
                      , methodName = self._methodName
                      )
  
          ############################################################################################################################
          #### reset plots
          ############################################################################################################################
  
          for requestedPlotType in requestedPlotTypeList:
  
              plotObject = None
              requestedPlotTypeLower = requestedPlotType.lower()
  
             #is3d        = "3"           in requestedPlotTypeLower
              isLine      = "line"        in requestedPlotTypeLower
              isScatter   = "scatter"     in requestedPlotTypeLower
              isCovMat    = "covmat"      in requestedPlotTypeLower
              isCorMat    = "cormat"      in requestedPlotTypeLower
  
              if not resetTypeIsHard:
                  plotComponent = getattr(self, "plot")
                  plotObject = getattr(plotComponent, requestedPlotType)
                  plotObject._reset()
  
              ########################################################################################################################
              #### reset line / scatter
              ########################################################################################################################
  
              if isLine or isScatter:
  
                  if resetTypeIsHard:
                      plotObject = LineScatterPlot( plotType = requestedPlotType
                                                  , dataFrame = self.df
                                                  , methodName = self._methodName
                                                  , reportEnabled = self._reportEnabled
                                                  , resetPlot = self._resetPlot
                                                  )
  
                  if self._methodName == pm.names.paradram: plotObject.ycolumns = self.propNameList[0]
                  plotObject.ccolumns = []
                  plotObject.colorbar.kws.extend = "neither"
                  plotObject.colorbar.kws.orientation = "vertical"
                  plotObject.colorbar.kws.spacing = "uniform"
  
                  if isLine:
                      if isScatter:
                          plotObject.lineCollection.enabled = False
                          plotObject.plot.enabled = True
                          plotObject.plot.kws.alpha = 0.2
                          plotObject.plot.kws.color = "grey"
                          plotObject.plot.kws.linewidth = 0.75
                      else:
                          plotObject.lineCollection.enabled = True
                          plotObject.plot.enabled = False
  
                  setattr(self.plot, requestedPlotType, plotObject)
  
              ########################################################################################################################
              #### reset covmat / cormat
              ########################################################################################################################
  
              if isCovMat or isCorMat:
  
                  if self._methodName == pm.names.paradram:
  
                      matrix = None
                      if isCovMat: matrix = self.contents.covMat
                      if isCorMat: matrix = self.contents.corMat
  
                      if resetTypeIsHard:
                          plotObject = EllipsoidPlot  ( matrix = matrix
                                                      , plotType = requestedPlotType
                                                      , methodName = self._methodName
                                                      , reportEnabled = self._reportEnabled
                                                      , resetPlot = self._resetPlot
                                                      )
  
                      plotObject.rows = plotObject.getLogLinSpace()
                      plotObject.center = self.contents.meanVec
                      plotObject.title.enabled = True
                      matrixType = "covariance" if isCovMat else "correlation"
                      plotObject.title.label = "Evolution of the " + matrixType + " matrices of the proposal distribution"
  
                      plotObject.colorbar.kws.extend = "neither"
                      plotObject.colorbar.kws.orientation = "vertical"
                      plotObject.colorbar.kws.spacing = "uniform"
  
                      setattr(self.plot, requestedPlotType, plotObject)
  
      ################################################################################################################################
  