def check_image(self, compare_against):
"""
Checks the current render window's output against the image specified in the argument,
returns the result of regression.check_result_image
"""
generate_png(self.win, self.test_file)
pth = os.path.join(os.path.dirname(__file__), "../..")
sys.path.append(pth)
import vcs.testing.regression as regression
return regression.check_result_image(self.test_file, compare_against)
def check_image(self, compare_against):
"""
Checks the current render window's output against the image specified in the argument,
returns the result of regression.check_result_image
"""
generate_png(self.win, self.test_file)
pth = os.path.join(os.path.dirname(__file__), "../..")
sys.path.append(pth)
import vcs.testing.regression as regression
return regression.check_result_image(self.test_file, compare_against)
def test( self, interactive=False ):
self.build()
# test_image = os.path.join( self.test_dir, 'images', '.'.join( [ self.name, 'png' ] ) )
test_image = '.'.join( [ self.name, 'test', 'png' ] )
self.canvas.png( test_image, width = 900, height = 600 )
ret = regression.check_result_image( test_image, self.image_name,\
regression.defaultThreshold+3. )
if interactive:
print "Type <Enter> to continue and update ref image ( type 'n' to skip update )."
sys.stdout.flush()
line = sys.stdin.readline()
if line[0] <> 'n':
self.update_image()
sys.exit(ret)
def plot_a_ratio(s,gm,ratio):
ret = 0
x = regression.init()
x.drawlogooff()
x.open()
x.geometry(400,800)
y = regression.init()
y.open()
y.geometry(800,400)
for X in [x,y]:
X.plot(s,gm,ratio=ratio)
if X.islandscape():
orient = "ldscp"
else:
orient = "port"
fnm = "aspect_ratio_%s_%s.png" % (orient,ratio)
X.png(fnm)
src = os.path.join(pth0,fnm)
ret += regression.check_result_image(fnm, src)
return ret
def plot_a_ratio(s, gm, ratio):
ret = 0
x = regression.init()
x.drawlogooff()
x.open()
x.geometry(400, 800)
y = regression.init()
y.open()
y.geometry(800, 400)
for X in [x, y]:
X.plot(s, gm, ratio=ratio)
if X.islandscape():
orient = "ldscp"
else:
orient = "port"
fnm = "aspect_ratio_%s_%s.png" % (orient, ratio)
X.png(fnm)
src = os.path.join(pth0, fnm)
ret += regression.check_result_image(fnm, src)
return ret
import os, sys, cdms2, vcs, vcs.testing.regression as regression
src1 = sys.argv[1]
src2 = sys.argv[2]
x = regression.init()
f=cdms2.open(os.path.join(vcs.sample_data,"clt.nc"))
s=f("clt",time=slice(0,1),latitude=(-7,5),squeeze=1)
x.plot(s,bg=1)
fnm = "test_vcs_issue_960_labels_1.png"
x.png(fnm)
ret = regression.check_result_image(fnm, src1)
b=x.createboxfill()
b.datawc_y1=-7
b.datawc_y2=5
x.plot(s,b,bg=1)
fnm = "test_vcs_issue_960_labels_2.png"
x.png(fnm)
ret += regression.check_result_image(fnm, src2)
sys.exit(ret)
import vcs, numpy, cdms2, MV2, os, sys, vcs.testing.regression as regression x = vcs.init() a=numpy.arange(100) a.shape=(10,10) x.plot(a,bg=1) fnm = "test_vcs_draw_logo_on.png" x.png(fnm) regression.check_result_image(fnm, sys.argv[1])
import vcs
import sys
import argparse
import vcs.testing.regression as regression
import os
p =argparse.ArgumentParser()
p.add_argument("-H","--fitToHeight",default=True,action="store_false")
p.add_argument("-u","--units",default="percent")
p.add_argument("-x","--xoffset",default=0,type=float)
p.add_argument("-y","--yoffset",default=0,type=float)
p.add_argument("-z","--zoom",default=1.,type=float)
p.add_argument("-s","--source",default="./somefile.png")
args = p.parse_args(sys.argv[1:])
print args
bg=True
x=vcs.init(bg=bg,geometry=(1200,800))
x.open()
png = os.path.join(sys.prefix,"share","uvcdat","sample_data","BlueMarble.ppm")
x.put_png_on_canvas(png,args.zoom,args.xoffset,args.yoffset,args.units,args.fitToHeight)
fnm = "test_vcs_put_png_on_canvas_%s_%s_%s_%s_%s" % (args.zoom,args.xoffset,args.yoffset,args.units,args.fitToHeight)
x.png(fnm)
ret = regression.check_result_image(fnm+'.png',args.source,20.)
gm.scale = 4.
x.plot(u, v, gm, bg=bg)
elif gm_type in ["scatter", "xvsy"]:
x.plot(s, s2, gm, bg=bg)
else:
x.plot(s, gm, bg=bg)
fnm = "test_vcs_basic_%s" % gm_type.lower()
if args.mask:
fnm += "_masked"
elif args.bigvalues:
fnm += "_bigvalues"
if args.projtype != "default":
fnm += "_%s_proj" % args.projtype
if args.zero:
fnm += "_zero"
if args.transparent:
fnm += "_transparent"
fnm += nm_xtra
x.png(fnm)
print "fnm:", fnm
print "src:", src
if args.show:
raw_input("Press Enter")
ret = regression.check_result_image(fnm + '.png',
src,
20.,
cleanup=not args.keep)
if args.show:
raw_input("Press Enter")
sys.exit(ret)
assert histo2.fillareastyles == histo.fillareastyles assert histo2.fillareacolors == histo.fillareacolors assert histo2.fillareaindices == histo.fillareaindices print "Inherited all values." histo2.levels = [10, 20, 10, 100, 110, 50, 20] histo3 = vcsaddons.createhistogram(source=histo2.name, x=x) print "Checking name-based inheritance" assert histo3.bins == histo2.bins assert histo3.line == histo2.line assert histo3.linewidth == histo2.linewidth assert histo3.linecolors == histo2.linecolors assert histo3.fillareastyles == histo2.fillareastyles assert histo3.fillareacolors == histo2.fillareacolors assert histo3.fillareaindices == histo2.fillareaindices print "Inherited all values." histo3.datawc_y1 = -1 histo3.datawc_y2 = 200000 histo3.datawc_x1 = 0 histo3.datawc_x2 = 100 histo3.bins = None histo3.plot(clt, template="default", bg=True) fnm = "test_vcs_addons_histogram_inherit.png" x.png(fnm) ret = regression.check_result_image(fnm, src) sys.exit(ret)
import os, sys, vcs.testing.regression as regression
import vcs
from vcsaddons import EzTemplate
import cdms2, vcs, sys
## 12 plots 1 legend per row on the right
## Initialize VCS
x = vcs.init()
x.drawlogooff()
bg = True
M = EzTemplate.Multi(rows=4, columns=3)
M.legend.direction = 'vertical'
for i in range(12):
t = M.get(legend='local')
if i % 3 != 2:
t.legend.priority = 0 # Turn off legend
fnm = "test_12_plot_one_leg_per_row_right.png"
M.preview(fnm, bg=bg)
ret = regression.check_result_image(fnm, sys.argv[1])
if not bg:
raw_input("Press Enter")
sys.exit(ret)
#Now plots the amplitude underneath the data
b = x.createboxfill()
b.xticlabels1 = vcs.elements["list"]["Lon30"]
b.yticlabels1 = vcs.elements["list"]["Lat20"]
x.plot(A, b, bg=bg)
u.setAxis(0, lats)
u.setAxis(1, lons)
v.setAxis(0, lats)
v.setAxis(1, lons)
x.plot(u, v, gm, bg=bg)
ret = 0
if args.show:
x.interact()
else:
fnm = "test_vcs_basic_vectors_%i" % args.angle
if args.mask:
fnm += "_masked"
if args.scale != 1.:
fnm += "_%.1g" % args.scale
fnm += nm_xtra
x.png(fnm)
print "fnm:", fnm
print "src:", src
ret = regression.check_result_image(fnm + '.png',
src,
regression.defaultThreshold,
cleanup=not args.keep)
if args.show:
raw_input("Press Enter")
sys.exit(ret)
s = f("clt", slice(0, 12)) # read only 12 times steps to speed up things
gm = vcs.creategraphicsmethod(gm_type, "default")
if args.projtype != "default":
p = vcs.createprojection()
try:
ptype = int(args.projtype)
except:
ptype = args.projtype
p.type = ptype
gm.projection = p
x.plot(s, gm, bg=1)
x.animate.create()
prefix = "test_vcs_animate_%s_%s" % (gm_type.lower(), args.projtype.lower())
x.animate.save("%s.mp4" % prefix)
pngs = x.animate.close(preserve_pngs=True) # so we can look at them again
ret = 0
pdir = os.path.split(pngs[0])[0]
p = pdir + os.sep + "anim_0.png"
ret = regression.check_result_image(p, args.src, args.threshold)
if ret == 0 and not args.keep:
for f in pngs:
if os.path.isfile(f):
os.remove(f)
os.removedirs(pdir)
os.remove("%s.mp4" % prefix)
sys.exit(ret)
gm.markercolor = [100, 0, 0, 50.]
if gm_type=="vector":
gm.scale = 4.
x.plot(u,v,gm,bg=bg)
elif gm_type in ["scatter","xvsy"]:
x.plot(s,s2,gm,bg=bg)
else:
x.plot(s,gm,bg=bg)
fnm = "test_vcs_basic_%s" % gm_type.lower()
if args.mask:
fnm+="_masked"
elif args.bigvalues:
fnm+="_bigvalues"
if args.projtype!="default":
fnm+="_%s_proj" % args.projtype
if args.zero:
fnm+="_zero"
if args.transparent:
fnm+="_transparent"
fnm+=nm_xtra
x.png(fnm)
print "fnm:",fnm
print "src:",src
if args.show:
raw_input("Press Enter")
ret = regression.check_result_image(fnm+'.png',src,20., cleanup=not args.keep)
if args.show:
raw_input("Press Enter")
sys.exit(ret)
import sys, cdms2, vcs, vcs.testing.regression as regression data = cdms2.open(vcs.sample_data+"/clt.nc") v = data['v'][...,::10,::10] u = data['u'][...,::10,::10] canvas = regression.init() gv = vcs.createvector() gv.scaletype = 'off' canvas.plot(u, v, gv) outFilename = 'test_vcs_vectors_scale_options_off.png' canvas.png(outFilename) ret = regression.check_result_image(outFilename, sys.argv[1]) canvas.clear() v = data['v'][...,::4,::4] u = data['u'][...,::4,::4] gv.scaletype = 'constant' gv.scale = 0.1 canvas.plot(u, v, gv) outFilename = 'test_vcs_vectors_scale_options_constant.png' canvas.png(outFilename) ret += regression.check_result_image(outFilename, sys.argv[2]) canvas.clear() v = data['v'] u = data['u'] gv.scale = 1.0 gv.scaletype = 'linear'
import os, sys, cdms2, vcs, vcs.testing.regression as regression x = regression.init() x.backgroundcolor = (255, 255, 255) x.open() x.backgroundcolor = (255, 255, 0) x.update() regression.check_result_image(x, "test_backgroundcolor_yellow.png")
# Initialize the canvas
canvas = regression.init()
# Plot the dataset
canvas.plot(clt, bg=bg)
# Mask values
masked = cdms2.MV2.masked_greater(clt, 50.)
gm = vcs.creategraphicsmethod(gm_type, "default")
# Set the missing color and opacity
gm.missing = [50., 50., 50., 50.]
# Plot the masked values
canvas.plot(masked, gm, bg=bg)
# Write to png file
fnm = "test_vcs_missing_opacity_%s" % args.gm.lower()
canvas.png(fnm)
# Test the output
ret = regression.check_result_image(fnm + '.png',
args.src,
args.threshold,
cleanup=not args.keep)
if args.show:
raw_input("Press Enter")
sys.exit(ret)
s = f("clt", slice(0, 12)) # read only 12 times steps to speed up things
gm = vcs.creategraphicsmethod(gm_type, "default")
if args.projtype != "default":
p = vcs.createprojection()
try:
ptype = int(args.projtype)
except:
ptype = args.projtype
p.type = ptype
gm.projection = p
x.plot(s, gm, bg=1)
x.animate.create()
prefix = "test_vcs_animate_%s_%s" % (gm_type.lower(), args.projtype.lower())
x.animate.save("%s.mp4" % prefix)
pngs = x.animate.close(preserve_pngs=True) # so we can look at them again
ret = 0
pdir = os.path.split(pngs[0])[0]
p = pdir + os.sep + "anim_0.png"
ret = regression.check_result_image(p, args.src, args.threshold)
if ret == 0 and not args.keep:
for f in pngs:
if os.path.isfile(f):
os.remove(f)
os.removedirs(pdir)
os.remove("%s.mp4" % prefix)
sys.exit(ret)
nm_xtra += "_SH"
else:
nm_xtra += "_NH"
if args.lon1 != args.lon2:
xtra["longitude"] = (args.lon1, args.lon2)
nm_xtra += "_%i_%i" % (args.lon1, args.lon2)
if not args.contig:
nm_xtra += "_non-contig"
xtra["time"] = slice(0, 1)
xtra["squeeze"] = 1
f = cdms2.open(os.path.join(vcs.sample_data, 'tas_ccsr-95a_1979.01-1979.12.nc'))
s = f("tas", **xtra)
f.close()
x.plot(s, gm, bg=bg)
fnm = "test_vcs_%s_%s" % (args.gm.lower(), args.fill_style.lower())
if args.projtype != "default":
fnm += "_%s_proj" % args.projtype
fnm += nm_xtra
x.png(fnm)
print "fnm:", fnm
print "src:", src
ret = regression.check_result_image(fnm + '.png', src,
args.threshold,
cleanup=not args.keep)
if args.show:
raw_input("Press Enter")
sys.exit(ret)
x.setantialiasing(0)
x.drawlogooff()
x.setbgoutputdimensions(1200,1091,units="pixels")
iso=x.createisoline()
iso.label='y'
levs = range(0,101,10)
iso.level=levs
# add dummy values to levs to get the correct number of cols
cols=vcs.getcolors(levs+[56,])
print levs
print cols
iso.textcolors = cols
iso.linecolors = cols
x.plot(s,iso,bg=1)
x.animate.create()
print "Saving now"
prefix= os.path.split(__file__)[1][:-3]
x.animate.save("%s.mp4"%prefix)
pngs = x.animate.close(preserve_pngs = True) # so we can look at them again
src_pth = sys.argv[1]
pth = os.path.join(src_pth,prefix)
ret = 0
for p in pngs:
print "Checking:",p
ret += regression.check_result_image(p,os.path.join(pth,os.path.split(p)[1]))
if ret == 0:
os.removedirs(os.path.split(p)[0])
os.remove("%s.mp4" % prefix)
sys.exit(ret)
import vcs, numpy, cdms2, MV2, os, sys, time, vcs.testing.regression as regression
pth = os.path.join(os.path.dirname(__file__),"..")
sys.path.append(pth)
f = cdms2.open(os.path.join(vcs.sample_data,"clt.nc"))
s = f("clt",slice(0,12)) # read only 12 times steps to speed up things
x = regression.init()
gm=x.createisofill()
x.plot(s,gm,bg=1)
x.animate.create()
print "Saving now"
prefix= os.path.split(__file__)[1][:-3]
x.animate.save("%s.mp4"%prefix)
pngs = x.animate.close(preserve_pngs = True) # so we can look at them again
src_pth = sys.argv[1]
pth = os.path.join(src_pth,prefix)
ret = 0
for p in pngs:
ret += regression.check_result_image(p,os.path.join(pth,os.path.split(p)[1]),
regression.defaultThreshold)
if ret == 0:
os.removedirs(os.path.split(p)[0])
os.remove("%s.mp4" % prefix)
sys.exit(ret)
x.setcolormap("rainbow")
gm = vcs.createvector()
gm.scale = args.scale
nm_xtra = ""
xtra = {}
import cdms2
import os
f = cdms2.open(os.path.join(vcs.sample_data, "clt.nc"))
u = f("u")
v = f("v")
u = MV2.masked_greater(u, 35.0)[..., ::2, ::2]
v = MV2.masked_greater(v, 888.0)[..., ::2, ::2]
print u.max(), v.max(), u.min(), v.min()
# x.plot(U)
x.plot(u, v, gm, bg=bg)
ret = 0
if args.show:
pass
# x.interact()
else:
fnm = "test_vcs_vectors_missing"
if args.scale != 5.0:
fnm += "_%.1g" % args.scale
fnm += nm_xtra
x.png(fnm)
ret = regression.check_result_image(fnm + ".png", src, regression.defaultThreshold, cleanup=not args.keep)
if args.show:
raw_input("Press Enter")
sys.exit(ret)
import sys, cdms2, vcs, vcs.testing.regression as regression data = cdms2.open(vcs.sample_data + "/clt.nc") v = data['v'][..., ::10, ::10] u = data['u'][..., ::10, ::10] canvas = regression.init() gv = vcs.createvector() gv.scaletype = 'off' canvas.plot(u, v, gv) outFilename = 'test_vcs_vectors_scale_options_off.png' canvas.png(outFilename) ret = regression.check_result_image(outFilename, sys.argv[1]) canvas.clear() v = data['v'][..., ::4, ::4] u = data['u'][..., ::4, ::4] gv.scaletype = 'constant' gv.scale = 0.1 canvas.plot(u, v, gv) outFilename = 'test_vcs_vectors_scale_options_constant.png' canvas.png(outFilename) ret += regression.check_result_image(outFilename, sys.argv[2]) canvas.clear() v = data['v'] u = data['u'] gv.scale = 1.0 gv.scaletype = 'linear'
x = regression.init()
polar = vcsaddons.getpolar("annual_cycle")
polar.markers = ["dot"]
polar.markersizes = [3]
polar.magnitude_tick_angle = numpy.pi / 8
import cdms2, cdutil
f = cdms2.open(os.path.join(vcs.sample_data, "clt.nc"))
clt = f("clt")
cdutil.setAxisTimeBoundsMonthly(clt.getTime())
averaged_time = cdutil.averager(clt, axis="t")
averaged_time = averaged_time.reshape(
(1, averaged_time.shape[0], averaged_time.shape[1]))
averaged_time_for_departures = numpy.repeat(averaged_time, len(clt), axis=0)
clt_departures = clt - averaged_time_for_departures
clt_departures.setAxisList(clt.getAxisList())
avg_departures = cdutil.averager(clt_departures, axis="xy")
theta = range(1, len(clt) + 1)
magnitude = avg_departures
polar.plot(magnitude, theta, bg=True, x=x)
fnm = "test_vcs_addons_polar_annual.png"
x.png(fnm)
ret = regression.check_result_image(fnm, src)
sys.exit(ret)
import os, sys, cdms2, vcs, vcs.testing.regression as regression
src1 = sys.argv[1]
src2 = sys.argv[2]
x = regression.init()
f = cdms2.open(os.path.join(vcs.sample_data, "clt.nc"))
s = f("clt", time=slice(0, 1), latitude=(-7, 5), squeeze=1)
x.plot(s, bg=1)
fnm = "test_vcs_issue_960_labels_1.png"
x.png(fnm)
ret = regression.check_result_image(fnm, src1)
b = x.createboxfill()
b.datawc_y1 = -7
b.datawc_y2 = 5
x.plot(s, b, bg=1)
fnm = "test_vcs_issue_960_labels_2.png"
x.png(fnm)
ret += regression.check_result_image(fnm, src2)
sys.exit(ret)