Neighbourghood evolution
Plotting the temporal evolution of the surroundings around a grid point
draw_Neighbourghood_evol(filen, values, variable)
  filen= netCDF file name
  values= [gvarname]:[dimsval]:[neigdims]:[Nneig]:[Ncol]:[timetits]:[tkinds]:
    [timefmts]:[gtitle]:[shadxtrms]:[colobarvals]:[neighlinevals]:[gkind]:[ofile]:[close]
    [gvarname]: ':' list of names of the variables in the plot
    [dimsval]: [dimn1]|[val1]|[dimv1],...,[dimnN]|[valN]|[dimvN] dimension names, values to get
      (-1, for all; no name/value pair given full length) and variable with values of the dimension
      'WRFtime' for WRF times
      NOTE: when dimsval[X,Y] == neigdims[X,Y], valX,valY --> valX,valY-Nneig/2, valX,valY+Nneig/2
    [neigdims]: [dimnX],[dimnY] dimensions mnames along which the neigbourghood should be defined
    [Nneig]: Number of grid points of the full side of the box (odd value)
    [Ncol]: Number of columns ('auto': square final plot)
    [gvarname]: name of the variable to appear in the graph
    [timetits]: [titX],[titY] titles of the axes ('|' for spaces)
    [tkinds]: [tkindX]|[tkindY] kinds of time to appear in the graph
      'Nval': according to a given number of values as 'Nval',[Nval]
      'exct': according to an exact time unit as 'exct',[tunit];
        tunit= [Nunits],[tu]; [tu]= 'c': centuries, 'y': year, 'm': month,
          'w': week, 'd': day, 'h': hour, 'i': minute, 's': second,
          'l': milisecond
      [timefmts]: [tfmtX],[tfmtY] format of the time labels
    [gtitle]: title of the graphic ('|' for spaces)
    [shadxtrms]: minimum and maximum value for the shading or:
      'Srange': for full range
      'Saroundmean@val': for mean-xtrm,mean+xtrm where xtrm = np.min(mean-min@val,max@val-mean)
      'Saroundminmax@val': for min*val,max*val
      'Saroundpercentile@val': for median-xtrm,median+xtrm where xtrm = np.min(median-percentile_(val),
        percentile_(100-val)-median)
      'Smean@val': for -xtrm,xtrm where xtrm = np.min(mean-min*@val,max*@val-mean)
      'Smedian@val': for -xtrm,xtrm where xtrm = np.min(median-min@val,max@val-median)
      'Spercentile@val': for -xtrm,xtrm where xtrm = np.min(median-percentile_(val),
        percentile_(100-val)-median)
    [colorbarvals]=[colbarn],[fmtcolorbar],[orientation] characteristics of the colormap and colorbar
      [colorbarn]: name of the color bar
      [fmtcolorbar]: format of the numbers in the color bar 'C'-like ('auto' for %6g)
      [orientation]: orientation of the colorbar ('vertical' (default, by 'auto'), 'horizontal')
    [neighlinevals]=[linecol],[linestyle],[linewidth] characterisitcs of the lines to mark the limits of the neighborhood
      ('auto' for: ['#646464', '-', 2.])
      [linecol]: color of the line
      [linestyle]: style of the line
      [linewidth]: width of the line
    [gkind]: kind of graphical output
    [ofile]: True/False whether the netcdf with data should be created or not
    [close]: Whether figure should be finished or not
  variable= name of the variable
$ python ${pyHOME}/drawing.py -o draw_Neighbourghood_evol -S 'vas:Time|-1|WRFtime,south_north|44|XLAT,west_east|88|XLONG:south_north,west_east:5:auto:time|($[DD]^{[HH]}$),time|($[DD]^{[HH]}$):exct,1,h|exct,3,h:$%d^{%H}$,$%d^{%H}$:5|pts|neighbourghood|temporal|evolution|on|2001|Nov.|at|x=88,|y=44:0.0,20.:rainbow,auto,auto:auto:png:vas_Neigh_evol:True' -f wrfout_d01_2001-11-11_00:00:00 -v V10