def label_mode(self, v):
if v == "Auto":
self.labels = None
elif v == "None":
self.labels = {}
else:
if vcs.isboxfill(self._gm):
self.labels = self._gm.autolabels(self._var)
def label_mode(self, v):
if v == "Auto":
self.labels = None
elif v == "None":
self.labels = {}
else:
if vcs.isboxfill(self._gm):
self.labels = self._gm.autolabels(self._var)
def levels(self):
"""Used internally, don't worry about it."""
levs = list(self._gm.levels)
# Check if they're autolevels
if numpy.allclose(levs, 1e20):
if vcs.isboxfill(self._gm) == 1:
nlevs = self.color_2 - self.color_1 + 1
minval, maxval = vcs.minmax(self._var)
levs = vcs.mkscale(minval, maxval)
if len(levs) == 1:
levs.append(levs[0] + .00001)
delta = (levs[-1] - levs[0]) / nlevs
levs = list(numpy.arange(levs[0], levs[-1] + delta, delta))
else:
levs = vcs.mkscale(*vcs.minmax(self._var))
# Now adjust for ext_1 nad ext_2
if self.ext_left:
levs.insert(0, -1e20)
if self.ext_right:
levs.append(1e20)
return levs
def levels(self):
"""Used internally, don't worry about it."""
levs = list(self._gm.levels)
# Check if they're autolevels
if numpy.allclose(levs, 1e20):
if vcs.isboxfill(self._gm) == 1:
nlevs = self.color_2 - self.color_1 + 1
minval, maxval = vcs.minmax(self._var)
levs = vcs.mkscale(minval, maxval)
if len(levs) == 1:
levs.append(levs[0] + .00001)
delta = (levs[-1] - levs[0]) / nlevs
levs = list(numpy.arange(levs[0], levs[-1] + delta, delta))
else:
levs = vcs.mkscale(*vcs.minmax(self._var))
# Now adjust for ext_1 nad ext_2
if self.ext_left:
levs.insert(0, -1e20)
if self.ext_right:
levs.append(1e20)
return levs
def finalize( self, flip_x=False, flip_y=False ):
"""By the time this is called, all synchronize operations should have been done. But even
so, each variable has a min and max and a min and max for each of its axes. We need to
simplify further for the plot package.
The options flip_x and flip_y may be set to True to flip the axis. That is, in x right
to left and left to right, and in y top to bottom and bottom to top."""
# old test:
#if self.presentation.__class__.__name__=="GYx" or\
# self.presentation.__class__.__name__=="Gfi":
# interim test here and below. Once all the is* functions work, I should
# drop the tests on self.presentation.__class__.__name__ :
if vcs.isscatter(self.presentation):
ylabel, xlabel = string.split(self.title, ' vs ')
#pdb.set_trace()
#in the case of scatter plots there are 2 variables packed together
var = self.vars[0]
[xMIN, xMAX], [yMIN, yMAX] = self.make_ranges(var)
#print xMIN, xMAX, yMIN, yMAX
#print vcs.mkscale(xMIN, xMAX)
#print vcs.mkscale(yMIN, yMAX)
self.presentation.xticlabels1 = vcs.mklabels(vcs.mkscale(xMIN, xMAX))
self.presentation.datawc_x1 = xMIN
self.presentation.datawc_x2 = xMAX
self.presentation.xticlabels2 = {(xMIN+xMAX)/2.: xlabel}
if flip_y:
self.presentation.datawc_y2 = yMIN
self.presentation.datawc_y1 = yMAX
self.presentation.flip = True
else:
self.presentation.datawc_y1 = yMIN
self.presentation.datawc_y2 = yMAX
self.presentation.yticlabels1 = vcs.mklabels(vcs.mkscale(yMIN, yMAX))
self.presentation.yticlabels2 = {(yMIN+yMAX)/2.: ylabel}
self.presentation.linewidth = 0
self.presentation.markercolor = 1
self.presentation.markersize = 5
#add overplotline is a total kludge
self.presentation.overplotline = self.overplotline
if flip_y:
self.presentation.flip = True
#self.presentation.list()
elif vcs.isyxvsx(self.presentation) or\
vcs.isisofill(self.presentation) or\
vcs.isboxfill(self.presentation) or\
self.presentation.__class__.__name__=="GYx" or\
self.presentation.__class__.__name__=="G1d" or\
self.presentation.__class__.__name__=="Gv":
#pdb.set_trace()
if flip_y:
self.presentation.flip = True
var = self.vars[0]
axmax = self.axmax[seqgetattr(var,'id','')]
axmin = self.axmin[seqgetattr(var,'id','')]
varmax = self.varmax[seqgetattr(var,'id','')]
varmin = self.varmin[seqgetattr(var,'id','')]
for v in self.vars[1:]:
for ax in axmax.keys():
axmax[ax] = max(axmax[ax],self.axmax[seqgetattr(v,'id','')][ax])
axmin[ax] = min(axmin[ax],self.axmin[seqgetattr(v,'id','')][ax])
varmax = max(varmax,self.varmax[v.id])
varmin = min(varmin,self.varmin[v.id])
if vcs.isyxvsx(self.presentation) or\
self.presentation.__class__.__name__=="GYx" or\
self.presentation.__class__.__name__=="G1d":
if len(axmax.keys())<=0:
return None
# VCS Yxvsx
ax = axmax.keys()[0]
if flip_x:
self.presentation.datawc_x2 = axmin[ax]
self.presentation.datawc_x1 = axmax[ax]
else:
self.presentation.datawc_x1 = axmin[ax]
self.presentation.datawc_x2 = axmax[ax]
if flip_y:
self.presentation.datawc_y2 = varmin
self.presentation.datawc_y1 = varmax
else:
self.presentation.datawc_y1 = varmin
self.presentation.datawc_y2 = varmax
#print "DEBUG, in finalize for line plot, datawc_{x1,x2,y1,y2}=",\
# self.presentation.datawc_x1, self.presentation.datawc_x2,\
# self.presentation.datawc_y1, self.presentation.datawc_y2
if vcs.isisofill(self.presentation) or self.presentation.__class__.__name__=="Gfi"\
or vcs.isboxfill(self.presentation):
# VCS Isofill or Boxfill
# First we have to identify which axes will be plotted as X and Y.
# If the axes each had an 'axis' attribute, axaxi will look something like
# {'X':'axis1id', 'Y':'axis2id'}. If one misses the attribute, 'axis0id':'axis0id'.
axaxi = {ax:id for id,ax in self.axax[seqgetattr(var,'id','')].items()}
if 'X' in axaxi.keys() and 'Y' in axaxi.keys():
axx = axaxi['X']
axy = axaxi['Y']
elif 'Y' in axaxi.keys() and 'Z' in axaxi.keys():
axx = axaxi['Y']
axy = axaxi['Z']
#added case of time vs variable
elif 'T' in axaxi.keys() and 'Y' in axaxi.keys():
axx = axaxi['T']
axy = axaxi['Y']
if axx == 'time':
t=var.getTime()
if 'units' in dir(t) and t.units == "months since 1800":
time_lables = {}
months_names = get_month_strings(length=3)
tc=t.asComponentTime()
for i, v in enumerate(t):
time_lables[v] = months_names[tc[i].month-1]
self.presentation.xticlabels1 = time_lables
self.presentation.datawc_timeunits = t.units
#self.presentation.list()
elif len(axaxi.keys())==2:
# It's not clear what should be the X variable and what the Y variable,
# but it's worth trying to do something
axx = None
axy = None
for axetc in var.getDomain()[:]:
ax = axetc[0]
if getattr(ax,'units',None)=='mbar':
# probably pressure levels, a vertical axis
axy = ax.id
else:
axx = ax.id
if axx is None or axy is None:
# last resort
axy = axaxi[axaxi.keys()[0]]
axx = axaxi[axaxi.keys()[1]]
else:
return None
# Now send the plotted min,max for the X,Y axes to the graphics:
# and if it is not a polar projection
if vcs.getprojection(self.presentation.projection)._type!=-3:
if flip_x:
self.presentation.datawc_x2 = axmin[axx]
self.presentation.datawc_x1 = axmax[axx]
else:
self.presentation.datawc_x1 = axmin[axx]
self.presentation.datawc_x2 = axmax[axx]
if flip_y:
self.presentation.datawc_y2 = axmin[axy]
self.presentation.datawc_y1 = axmax[axy]
else:
self.presentation.datawc_y1 = axmin[axy]
self.presentation.datawc_y2 = axmax[axy]
# The variable min and max, varmin and varmax, should be passed on to the graphics
# for setting the contours. But apparently you can't tell VCS just the min and max;
# you have to give it all the contour levels. So...
if vcs.isboxfill(self.presentation):
self.presentation.boxfill_type = 'custom' # without this, can't set levels
nlevels = 16
try:
levels = [float(v) for v in vcs.mkscale( varmin, varmax, nlevels )]
# Exceptions occur because mkscale doesn't always work. E.g. vcs.mkscale(0,1.e35,16)
except RuntimeWarning:
levels = []
if levels==[]:
## Here's how to do it with percentiles (clip out large values first).
#pc05 = numpy.percentile(self.vars[0],0.05)
#pc95 = numpy.percentile(self.vars[0],0.95)
#levels = [float(v) for v in vcs.mkscale( pc05, pc95, nlevels-2 )]
#levels = [varmin]+levels+[varmax]
# Evenly distributed levels, after clipping out large values:
# This cannot be expected to work always, but it's better than doing nothing.
amed = numpy.median(self.vars[0]._data)
vclip = amed * 1.0e6
print "WARNING graphics problems, clipping some data at",vclip
self.vars[0]._data[ self.vars[0]._data > vclip ] = vclip
a = numpy.sort(self.vars[0]._data.flatten())
asp = numpy.array_split(a,nlevels)
afirsts = [c[0] for c in asp]+[asp[-1][-1]]
alasts = [asp[0][0]]+[c[-1] for c in asp]
levels = [0.5*(afirsts[i]+alasts[i]) for i in range(len(afirsts))]
levf = levels[0]
levl = levels[-1]
levels = [ round(lv,2) for lv in levels ]
levels[0] = round(1.1*levels[0]-0.1*levels[1],2)
levels[-1] = round(1.1*levels[-1]-0.1*levels[-2],2)
# ... mkscale returns numpy.float64, which behaves unexpectedly in _setlevels when
# passed a tuple value
if levels is not None and len(levels)>0:
self.presentation.levels = levels
#nlevels = max(1, len(levels) - 1)
#nlrange = range(nlevels+1)
#nlrange.reverse()
#self.presentation.legend = vcs.mklabels( self.presentation.levels )
## Once you set the levels, the VCS default color choice looks bad. So you really
## have to set contour fill colors (integers from 0 through 255) too:
#cmin = 32./nlevels
#cmax = 255./nlevels
## A more flexible way to do what's going on here, thanks to Charles Doutriaux:
## r=10
## g=16
## b=20
## X.setcolorcell(16,r,g,b)
## colors = [16,17,18,...] etc.
## vcs.getcolors is useful, more complicated - see its doc string
#colors = [int(round(a*cmin+(nlevels-a)*cmax)) for a in nlrange]
#self.presentation.fillareacolors = colors
##self.presentation.fillareacolors=[32,48,64,80,96,112,128,144,160,176,240]
elif vcs.isvector(self.presentation) or self.presentation.__class__.__name__=="Gv":
# axis min,max copied from isofill
axaxi = {ax:id for id,ax in self.axax[seqgetattr(var,'id','')].items()}
if 'X' in axaxi.keys() and 'Y' in axaxi.keys():
axx = axaxi['X']
axy = axaxi['Y']
elif 'Y' in axaxi.keys() and 'Z' in axaxi.keys():
axx = axaxi['Y']
axy = axaxi['Z']
self.presentation.datawc_x1 = axmin[axx]
self.presentation.datawc_x2 = axmax[axx]
self.presentation.datawc_y1 = axmin[axy]
self.presentation.datawc_y2 = axmax[axy]
vec = self.presentation
vec.scale = min(vcsx.bgX,vcsx.bgY)/ 10.
if hasattr(self.vars[0],'__getitem__') and not hasattr( self.vars[0], '__cdms_internals__'):
# generally a tuple of variables - we need 2 variables to describe a vector
v = self.vars[0][0]
w = self.vars[0][1]
else: # We shouldn't get here, but may as well try to make it work if possible:
print "WARNING trying to make a vector plot without tuples! Variables involved are:"
v = self.vars[0]
print "variable",v.id
v = self.vars[1]
print "variable",v.id
nlats = latAxis(v).shape[0]
nlons = lonAxis(w).shape[0]
nlatvs = vcsx.bgY/16 # how many vectors we want in lat direction
nlonvs = vcsx.bgX/16
#self.strideX = int( 0.9* vcsx.bgX/nlons )
#self.strideY = int( 0.6* vcsx.bgY/nlats )
self.strideX = max(1, int( nlons/nlonvs )) # stride values must be at least 1
self.strideY = max(1, int( nlats/nlatvs ))
else:
print "ERROR cannot identify graphics method",self.presentation.__class__.__name__
def test():
import vcs,cdms2 as cdms,time,os,sys,support # import vcs and cdms
bg=support.bg
f=cdms.open(os.path.join(cdms.__path__[0],'..','..','..','..','sample_data','clt.nc'))
s=f('clt') # get slab clt
x=vcs.init() # construct vcs canvas
x.plot(s,'default','boxfill','quick',bg=bg)# plot slab the old way
#support.check_plot(x)
if bg==0:
x.geometry(450,337) # change the geometry
x.flush()
#support.check_plot(x)
a=x.getboxfill('quick') # get 'quick' boxfill graphics method
if not vcs.isgraphicsmethod(a): # test object 'a' for graphics method
raise Exception, "Error did not retrieve the gm"
else:
if not vcs.isboxfill(a): # check for boxfill
raise Exception, "Error gm is not right type"
# Change boxfill's legend
a.legend={0:'Great',20:'Outstanding',40:'Wonderful',60:'Happy',80:'Exciting!',100:'Best'}
support.check_plot(x)
a.legend={0:'100',20:'80',40:'60',60:'40',80:'20',100:'0'}
support.check_plot(x)
a.legend={0:'Blue',20:'Green',40:'Yellow',60:'Red',80:'Burgundy',100:'Magenta'}
support.check_plot(x)
a.legend=(90)
support.check_plot(x)
a.legend=[10,90]
support.check_plot(x)
a.legend=(1.5,45.8,89.7)
support.check_plot(x)
a.legend=None
support.check_plot(x)
if not '--extended' in sys.argv:
print '\n************* PARTIAL TEST *****************'
print 'FOR COMPLETE TEST OF THIS MODULE USE '
print ' -F (--full) or -E (--extended) option'
print '************* PARTIAL TEST *****************\n'
sys.exit()
a.color_1=50 # change color_1 index attribute
support.check_plot(x)
a.xticlabels('lon30','lon30') # change xlabels attribute
support.check_plot(x)
a.xticlabels('','') # change remove xlables from plot
support.check_plot(x)
a.datawc(-45.0, 45.0, -90.0, 90.0) # change region
support.check_plot(x)
a.datawc(1e20,1e20,1e20,1e20) # change region back
support.check_plot(x)
a.xticlabels('*') # change attribute labels back
support.check_plot(x)
x.mode=0 # turn atomatic update off
a.color_1=100 # change color_1 attribute
a.color_2=200 # change color_2 index value
a.xticlabels('lon30','lon30') # change attribute
a.yticlabels('','') # change y-labels off attribute
a.datawc(-45.0, 45.0, -90.0, 90.0) # change region
support.check_plot(x)
x.update() # view changes now
support.check_plot(x)
# Test log10 boxfill plot
x.clear() # clear the VCS Canvas
a.boxfill_type = 'log10' # Change the Boxfill type to log10
a.datawc(1e20,1e20,1e20,1e20) # change region
a.level_1=1e20 # change level_1
a.level_2=1e20 # change level_2
a.color_1=16 # change color_1 attribute
a.color_2=239 # change color_2 index value
x.boxfill(s,a,bg=bg) # plot using default template
support.check_plot(x)
# Test custom boxfill plot
x.clear() # clear the VCS Canvas
a.boxfill_type = 'custom' # Change the Boxfill type to custom
a.levels=(0,20,35,40,75,100) # Set the custom ranges
a.fillareacolors=(16,25,38,55,100,166) # Set the color indices
x.boxfill(s,a,bg=bg) # plot using default template
support.check_plot(x)
# Test legend with fillareacolors boxfill plot
x.clear() # clear the VCS Canvas
a.boxfill_type = 'linear' # Change the Boxfill type back to linear
a.fillareacolors=(254,) # Set the color indices
a.level_1=0 # change level_1
a.level_2=0 # change level_2
x.boxfill(s,a,bg=bg) # plot using default template
support.check_plot(x)
a.script('test','w') # save 'quick' boxfill as a Python script
x.mode=1 # turn atomatic update mode back on
a.color_1=16 # change color_1 attribute
support.check_plot(x)
a.color_2=239 # change color_2 index value
support.check_plot(x)
a.level_1=20 # change level_1
support.check_plot(x)
a.level_2=80 # change level_2
support.check_plot(x)
a.datawc(1e20,1e20,1e20,1e20) # change region back
support.check_plot(x)
a.yticlabels('*') # change y-labels attribute
support.check_plot(x)
x.scriptobject(a,'test', 'a') # append 'quick' to the existing file
a.script('test.scr','w') # save 'quick' as a VCS script file
objs =x.listelements('template') # get the list of templates
t=x.createtemplate('test') # create template 'test' from 'default' template
if not vcs.istemplate(t): # test whether 't' is a template or not
raise Exception,"Error template not created"
else:
a2 =x.listelements('template') # get the list of templates
if objs==a2:
raise Exception,"Error template not created or added to list"
x.clear() # clear the VCS Canvas
x.boxfill(s,a,'default',bg=bg) # plot using default template
support.check_plot(x)
x.clear() # clear the VCS Canvas
x.boxfill(a,'default',s,bg=bg) # plot using default template, but, reverse the order
support.check_plot(x)
x.clear() # clear the VCS Canvas
x.boxfill(s,a,t,bg=bg) # plot using template 'test'
support.check_plot(x)
x.clear() # clear the VCS Canvas
x.boxfill(a,s,t,bg=bg) # plot using template 'test', but reverse the objects
support.check_plot(x)
x.clear() # clear the VCS Canvas
x.boxfill(t,a,s,bg=bg) # plot using template 'test', but reverse the objects
support.check_plot(x)
x.clear() # clear the VCS Canvas
x.plot(t,a,s,bg=bg) # plot using the new way
support.check_plot(x)
x.clear() # clear the VCS Canvas
x.plot(a,t,s,bg=bg) # plot using the new way
support.check_plot(x)
x.clear() # clear the VCS Canvas
x.plot(s,t,a,bg=bg) # plot using the new way
support.check_plot(x)
x.clear() # clear the VCS Canvas
x.plot('default',a,s,bg=bg) # plot using the new way
support.check_plot(x)
x.clear() # clear the VCS Canvas
x.plot('default',s,bg=bg) # plot using the new way
support.check_plot(x)
a = x.listelements('boxfill') # show list of xyvsy
r=x.createboxfill('test2','quick') # create xyvsy 'test2'
a2 = x.listelements('boxfill') # show list of xyvsy
if a2==a:
raise "error gm not created or not added to list"
x.removeobject(r) # remove xyvsy 'test2'
a3 = x.listelements('boxfill') # show list of xyvsy
if a3!=a:
raise "error gm not removed"
a=x.getboxfill('quick') # get 'quick' boxfill graphics method
x.clear()
x.plot(s,a,bg=bg)
support.check_plot(x)
if support.dogui:
x.graphicsmethodgui('boxfill','quick') # display the boxfill graphics method GUI
raw_input("Press enter when done with gui testing")
print '*************************************************************************************'
print '****** ******'
print '****** B O X F I L L T E S T C O M P L E T E D S U C E S S F U L L Y ******'
print '****** ******'
print '*************************************************************************************'
def color_2(self, c):
if vcs.isboxfill(self._gm):
if self._gm.fillareacolors:
self._gm.fillareacolors = None
self._gm.color_2 = c
def color_2(self):
"""Used for boxfills only."""
if vcs.isboxfill(self._gm):
return self._gm.color_2
else:
return None
def labels(self):
if self._gm.legend is None:
if vcs.isboxfill(self._gm):
return self._gm.autolabels(self._var)
return self._gm.legend
def test():
import vcs
import cdms2 as cdms
import os
import support # import vcs and cdms
bg = support.bg
f = cdms.open(os.path.join(vcs.sample_data, 'clt.nc'))
s = f('clt') # get slab clt
x = vcs.init() # construct vcs canvas
x.plot(s, 'default', 'boxfill', 'quick', bg=bg) # plot slab the old way
# support.check_plot(x)
if bg == 0:
x.geometry(450, 337) # change the geometry
x.flush()
# support.check_plot(x)
a = x.getboxfill('quick') # get 'quick' boxfill graphics method
# test object 'a' for graphics method
if not vcs.isgraphicsmethod(a):
raise Exception("Error did not retrieve the gm")
else:
if not vcs.isboxfill(a): # check for boxfill
raise Exception("Error gm is not right type")
# Change boxfill's legend
a.legend = {
0: 'Great',
20: 'Outstanding',
40: 'Wonderful',
60: 'Happy',
80: 'Exciting!',
100: 'Best'
}
support.check_plot(x)
a.legend = {0: '100', 20: '80', 40: '60', 60: '40', 80: '20', 100: '0'}
support.check_plot(x)
a.legend = {
0: 'Blue',
20: 'Green',
40: 'Yellow',
60: 'Red',
80: 'Burgundy',
100: 'Magenta'
}
support.check_plot(x)
a.legend = (90)
support.check_plot(x)
a.legend = [10, 90]
support.check_plot(x)
a.legend = (1.5, 45.8, 89.7)
support.check_plot(x)
a.legend = None
support.check_plot(x)
if '--extended' not in sys.argv:
print '\n************* PARTIAL TEST *****************'
print 'FOR COMPLETE TEST OF THIS MODULE USE '
print ' -F (--full) or -E (--extended) option'
print '************* PARTIAL TEST *****************\n'
sys.exit()
a.color_1 = 50 # change color_1 index attribute
support.check_plot(x)
a.xticlabels('lon30', 'lon30') # change xlabels attribute
support.check_plot(x)
a.xticlabels('', '') # change remove xlables from plot
support.check_plot(x)
a.datawc(-45.0, 45.0, -90.0, 90.0) # change region
support.check_plot(x)
a.datawc(1e20, 1e20, 1e20, 1e20) # change region back
support.check_plot(x)
a.xticlabels('*') # change attribute labels back
support.check_plot(x)
x.mode = 0 # turn atomatic update off
a.color_1 = 100 # change color_1 attribute
a.color_2 = 200 # change color_2 index value
a.xticlabels('lon30', 'lon30') # change attribute
a.yticlabels('', '') # change y-labels off attribute
a.datawc(-45.0, 45.0, -90.0, 90.0) # change region
support.check_plot(x)
x.update() # view changes now
support.check_plot(x)
# Test log10 boxfill plot
x.clear() # clear the VCS Canvas
a.boxfill_type = 'log10' # Change the Boxfill type to log10
a.datawc(1e20, 1e20, 1e20, 1e20) # change region
a.level_1 = 1e20 # change level_1
a.level_2 = 1e20 # change level_2
a.color_1 = 16 # change color_1 attribute
a.color_2 = 239 # change color_2 index value
x.boxfill(s, a, bg=bg) # plot using default template
support.check_plot(x)
# Test custom boxfill plot
x.clear() # clear the VCS Canvas
a.boxfill_type = 'custom' # Change the Boxfill type to custom
a.levels = (0, 20, 35, 40, 75, 100) # Set the custom ranges
a.fillareacolors = (16, 25, 38, 55, 100, 166) # Set the color indices
x.boxfill(s, a, bg=bg) # plot using default template
support.check_plot(x)
# Test legend with fillareacolors boxfill plot
x.clear() # clear the VCS Canvas
# Change the Boxfill type back to linear
a.boxfill_type = 'linear'
a.fillareacolors = (254, ) # Set the color indices
a.level_1 = 0 # change level_1
a.level_2 = 0 # change level_2
x.boxfill(s, a, bg=bg) # plot using default template
support.check_plot(x)
a.script('test', 'w') # save 'quick' boxfill as a Python script
x.mode = 1 # turn atomatic update mode back on
a.color_1 = 16 # change color_1 attribute
support.check_plot(x)
a.color_2 = 239 # change color_2 index value
support.check_plot(x)
a.level_1 = 20 # change level_1
support.check_plot(x)
a.level_2 = 80 # change level_2
support.check_plot(x)
a.datawc(1e20, 1e20, 1e20, 1e20) # change region back
support.check_plot(x)
a.yticlabels('*') # change y-labels attribute
support.check_plot(x)
x.scriptobject(a, 'test', 'a') # append 'quick' to the existing file
a.script('test.scr', 'w') # save 'quick' as a VCS script file
# get the list of templates
objs = x.listelements('template')
# create template 'test' from 'default' template
t = x.createtemplate('test')
# test whether 't' is a template or not
if not vcs.istemplate(t):
raise Exception("Error template not created")
else:
# get the list of templates
a2 = x.listelements('template')
if objs == a2:
raise Exception("Error template not created or added to list")
x.clear() # clear the VCS Canvas
x.boxfill(s, a, 'default', bg=bg) # plot using default template
support.check_plot(x)
x.clear() # clear the VCS Canvas
# plot using default template, but, reverse the order
x.boxfill(a, 'default', s, bg=bg)
support.check_plot(x)
x.clear() # clear the VCS Canvas
x.boxfill(s, a, t, bg=bg) # plot using template 'test'
support.check_plot(x)
x.clear() # clear the VCS Canvas
# plot using template 'test', but reverse the objects
x.boxfill(a, s, t, bg=bg)
support.check_plot(x)
x.clear() # clear the VCS Canvas
# plot using template 'test', but reverse the objects
x.boxfill(t, a, s, bg=bg)
support.check_plot(x)
x.clear() # clear the VCS Canvas
x.plot(t, a, s, bg=bg) # plot using the new way
support.check_plot(x)
x.clear() # clear the VCS Canvas
x.plot(a, t, s, bg=bg) # plot using the new way
support.check_plot(x)
x.clear() # clear the VCS Canvas
x.plot(s, t, a, bg=bg) # plot using the new way
support.check_plot(x)
x.clear() # clear the VCS Canvas
x.plot('default', a, s, bg=bg) # plot using the new way
support.check_plot(x)
x.clear() # clear the VCS Canvas
x.plot('default', s, bg=bg) # plot using the new way
support.check_plot(x)
a = x.listelements('boxfill') # show list of xyvsy
r = x.createboxfill('test2', 'quick') # create xyvsy 'test2'
a2 = x.listelements('boxfill') # show list of xyvsy
if a2 == a:
raise "error gm not created or not added to list"
x.removeobject(r) # remove xyvsy 'test2'
a3 = x.listelements('boxfill') # show list of xyvsy
if a3 != a:
raise "error gm not removed"
# get 'quick' boxfill graphics method
a = x.getboxfill('quick')
x.clear()
x.plot(s, a, bg=bg)
support.check_plot(x)
if support.dogui:
x.graphicsmethodgui('boxfill',
'quick') # display the boxfill graphics method GUI
raw_input("Press enter when done with gui testing")
print '*************************************************************************************'
print '****** ******'
print '****** B O X F I L L T E S T C O M P L E T E D S U C E S S F U L L Y ******'
print '****** ******'
print '*************************************************************************************'
def color_2(self, c):
if vcs.isboxfill(self._gm):
if self._gm.fillareacolors:
self._gm.fillareacolors = None
self._gm.color_2 = c
def color_2(self):
"""Used for boxfills only."""
if vcs.isboxfill(self._gm):
return self._gm.color_2
else:
return None
def labels(self):
if self._gm.legend is None:
if vcs.isboxfill(self._gm):
return self._gm.autolabels(self._var)
return self._gm.legend