#=======================================================================
# A complex example of a CDAT contour plot.
#
# Copyright (c) 2003-2004 by Johnny Lin.  For licensing, distribution
# conditions, contact information, and additional documentation see
# the URL http://www.johnny-lin.com/pylib.html#license.
#=======================================================================


#- Import modules:

import MA
import Numeric as N
import cdms, vcs


#- Data:  "Distorted" bullseyes over each pole, with missing data in
#  the mid-eastern Pacific Ocean:

lat = N.arange(13) * 15.0 - 90.
lon = N.arange(25) * 15.0 - 180.0
data = N.outerproduct(N.sin(lat*N.pi/360.), N.cos(lon*N.pi/360.)) 

missing = 1e20
data[6:8,2:3] = missing
data[4:5,3:4] = missing


#- Open VCS canvas:

v = vcs.init()


#- Create graphics method object for isoline and fill plots and 
#  template for isofill:

my_fill_gm = v.createisofill('my_fill_gm', 'default')
my_line_gm = v.createisoline('my_line_gm', 'default')
my_fill_tpl = v.createtemplate('my_fill_tpl', 'default')


#- Create a blue/red-scale colormap and make it the active color 
#  map:

my_cmap = v.createcolormap('my_cmap', 'default')
for icell in range(16,128):
    rvalue = int( float(icell-16)/(127.-16.) * 75. )
    gvalue = rvalue
    bvalue = 100
    my_cmap.index[icell] = [rvalue, gvalue, bvalue]
for icell in range(128,240):
    rvalue = 100
    gvalue = 75 - int( float(icell-128)/(239.-128.) * 75. )
    bvalue = gvalue
    my_cmap.index[icell] = [rvalue, gvalue, bvalue]
v.setcolormap('my_cmap')


#- Set coordinates (normalized units) for lower-left and upper-
#  right bounding box (bbll*, bbur*); set height of font for 
#  the overall title (title_fontht), [xy]titles (xytitle_fontht),
#  and for the tick labels (ticklabel_fontht); set length of tick 
#  marks (in normalized units):

bbllx = 0.2
bblly = 0.3
bburx = 0.9
bbury = 0.9

title_fontht = 40
xytitle_fontht = 30
ticklabel_fontht = 30
ticklength = 0.01


#- Set font to use for the ticklabels (ticklabel_font) and the
#  titles (titles_font):

ticklabel_font = 1
titles_font = 1


#- Set conversion factor to multiply font height coordinates by
#  to obtain the value in normalized coordinates.  This is set
#  by trial-and-error:

fontht2norm = 0.0007


#- Create text-table and text-orientation objects for x- and 
#  y-axis tick labels and using them set font and font height.
#  Also set y-axis tick labels to be vertically aligned at the
#  mid-way point:

ttab_xticklabel = \
    v.createtexttable( 'xticklabel_ttab', 'default' )
ttab_yticklabel = \
    v.createtexttable( 'yticklabel_ttab', 'default' )
tori_xticklabel = \
    v.createtextorientation( 'xticklabel_tori', 'defcenter' )
tori_yticklabel = \
    v.createtextorientation( 'yticklabel_tori', 'default' )

ttab_xticklabel.font = ticklabel_font
ttab_yticklabel.font = ticklabel_font
ttab_xticklabel.color = 241
ttab_yticklabel.color = 241
tori_xticklabel.height = ticklabel_fontht
tori_yticklabel.height = ticklabel_fontht
tori_yticklabel.valign = 'half'


#- Create/set text-table and text-orientation objects for overall 
#  title and axis title settings.  Turn on these fields in the
#  template and set the new text-table/orientation objects to
#  those fields in the template:

ttab_title = \
    v.createtexttable( 'title_ttab', 'default' )
tori_title = \
    v.createtextorientation( 'title_tori', 'defcenter' )
ttab_title.font = titles_font
ttab_title.color = 241
tori_title.height = title_fontht

ttab_xytitle = \
    v.createtexttable( 'xytitle_ttab', 'default' )
ttab_xytitle.font = titles_font
ttab_xytitle.color = 241

tori_xtitle = \
    v.createtextorientation( 'xtitle_tori', 'defcenter' )
tori_ytitle = \
    v.createtextorientation( 'ytitle_tori', 'defcentup' )
tori_xtitle.height = xytitle_fontht
tori_ytitle.height = xytitle_fontht

my_fill_tpl.dataname.texttable = ttab_title
my_fill_tpl.dataname.textorientation = tori_title
my_fill_tpl.xname.texttable = ttab_xytitle
my_fill_tpl.xname.textorientation = tori_xtitle
my_fill_tpl.yname.texttable = ttab_xytitle
my_fill_tpl.yname.textorientation = tori_ytitle

my_fill_tpl.dataname.priority = 1
my_fill_tpl.xname.priority = 1
my_fill_tpl.yname.priority = 1


#- Turn off some default titling fields:

my_fill_tpl.mean.priority = 0
my_fill_tpl.max.priority = 0
my_fill_tpl.min.priority = 0


#- Set position of data field, plot box, and x-axis bottom ticks 
#  and labels; set text-table and text-orientation of x-axis bottom 
#  labels; turn off upper-right set of tick labels:

my_fill_tpl.data.x1 = my_fill_tpl.box1.x1 = bbllx
my_fill_tpl.data.y1 = my_fill_tpl.box1.y1 = bblly
my_fill_tpl.data.x2 = my_fill_tpl.box1.x2 = bburx
my_fill_tpl.data.y2 = my_fill_tpl.box1.y2 = bbury

my_fill_tpl.xtic1.y1 = bblly - ticklength
my_fill_tpl.xtic1.y2 = bblly
my_fill_tpl.xlabel1.y = bblly - (3.0 * ticklength)
my_fill_tpl.xlabel1.texttable = ttab_xticklabel
my_fill_tpl.xlabel1.textorientation = tori_xticklabel

my_fill_tpl.xtic2.priority = 0
my_fill_tpl.ytic2.priority = 0


#- Create "nice" x-axis labels and set them into the graphics
#  method:

my_fill_gm.xticlabels1 = {   0.0:"0"
                         ,  45.0:"45E",   -45.0:"45W"
                         ,  90.0:"90E",   -90.0:"90W" 
                         , 135.0:"135E", -135.0:"135W" 
                         , 180.0:"180E", -180.0:"180W" }


#- Create "nice" y-axis labels, set them in the graphics method,
#  calculate y-axis title location based on the longest label 
#  length, set y-axis ticks and tick label locations, and set
#  text-table and text-orientation for y-axis tick labels:

ylabels = {  0.0:"EQ"
          , 30.0:"30N", -30.0:"30S"
          , 60.0:"60N", -60.0:"60S"
          , 90.0:"90N", -90.0:"90S" }
my_fill_gm.yticlabels1 = ylabels
longest_ylabels = max([ len(ylabels.values()[i]) \
                        for i in range(len(ylabels)) ])

my_fill_tpl.ytic1.x1 = bbllx - ticklength
my_fill_tpl.ytic1.x2 = bbllx
my_fill_tpl.ylabel1.x = bbllx - ticklength \
                 - ( longest_ylabels \
                   * ticklabel_fontht * fontht2norm )
my_fill_tpl.ylabel1.texttable = ttab_yticklabel
my_fill_tpl.ylabel1.textorientation = tori_yticklabel


#- Position overall title, x-axis title, and y-axis title:

my_fill_tpl.dataname.x = (bburx-bbllx)/2.0 + bbllx 
my_fill_tpl.dataname.y = bbury + ( 1.7 * title_fontht \
                              * fontht2norm )

my_fill_tpl.xname.x = (bburx-bbllx)/2.0 + bbllx
my_fill_tpl.xname.y = my_fill_tpl.xlabel1.y \
                    - (1.7 * title_fontht * fontht2norm)

my_fill_tpl.yname.x = my_fill_tpl.ylabel1.x \
                    - ( 1.6 * title_fontht * fontht2norm )
my_fill_tpl.yname.y = (bbury-bblly)/2.0 + bblly


#- Calculate and set filled-contour levels based upon the max and
#  min of the non-missing data in the array:

my_min = MA.minimum(MA.masked_values(data, missing))
my_max = MA.maximum(MA.masked_values(data, missing))
con_levels = vcs.mkscale(my_min, my_max, 16)
my_fill_gm.levels = con_levels
my_fill_gm.fillareacolors = vcs.getcolors(con_levels, split=1)


#- Create text-table object that has color set to black to use in
#  making sure the legend (color bar) has black lettering and set
#  my isofill template legend text-table to that object:

ttab_black = v.createtexttable('ttab_black', 'default')
ttab_black.color = 241
my_fill_tpl.legend.texttable = ttab_black


#- Now that the good template settings are all done, copy it and
#  turn off a bunch of things for the isoline overlay.  Turn things
#  off on the isoline template to make sure nothing is over-written:

my_line_tpl = v.createtemplate('my_line_tpl', 'my_fill_tpl')
my_line_tpl.dataname.priority = 0
my_line_tpl.xname.priority = 0
my_line_tpl.yname.priority = 0
my_line_tpl.xlabel1.priority = 0
my_line_tpl.xlabel2.priority = 0
my_line_tpl.ylabel1.priority = 0
my_line_tpl.ylabel2.priority = 0
my_line_tpl.xtic1.priority = 0
my_line_tpl.xtic2.priority = 0
my_line_tpl.ytic1.priority = 0
my_line_tpl.ytic2.priority = 0
my_line_tpl.box1.priority = 0


#- Make contour lines to be at the same place as the filled contours
#  and turn the contour labels on.  Make the linestyle for contour 
#  lines to be dashed if contour are negative and solid if they are 
#  positive or 0:

my_line_gm.level = con_levels
my_line_gm.label = 'y'

line_pos_con = v.createline('pos_con_line', 'default')
line_neg_con = v.createline('neg_con_line', 'default')
line_pos_con.type = 'solid'
line_neg_con.type = 'dash'
my_line_gm.line = N.where( N.array(con_levels) >= 0.0 \
                         , line_pos_con, line_neg_con ).tolist()


#- Create latitude and longitude axis:

lonAxis = cdms.createAxis(lon)
latAxis = cdms.createAxis(lat)


#- Render plot, plot overall title, x-axis title, and y-axis title:

v.plot( data, my_fill_gm, my_fill_tpl, xaxis=lonAxis, yaxis=latAxis \
      , continents=1 \
      , xname='Longitude [deg]', yname='Latitude [deg]' \
      , name='A Complex Contour Plot' )
v.plot( data, my_line_gm, my_line_tpl, xaxis=lonAxis, yaxis=latAxis )


#- Postscript output of the plot:

v.postscript('latlon_plot.ps')
v.gif('latlon_plot.gif', 'r')




#====== end of file ======