'w20', 'w21', 'w22', 'w23', 'w24', 'w25', 'w26', 'w27', 'w28', 'w29',
       'w30', 'w31', 'w32', 'w33', 'w34', 'w35', 'w36', 'w37', 'w38', 'w39',
       'w40', 'w41', 'w42', 'w43', 'w44', 'w45', 'w46', 'w47', 'w48', 'w49',
       'w50', 'w51', 'w52', 'w53', 'w54', 'w55', 'w56', 'w57', 'w58', 'w59',
       'w60', 'w61', 'w62', 'w63', 'w64', 'w65', 'w66', 'w67', 'w68', 'w69',
       'w70', 'w71', 'w72', 'w73', 'w74', 'w75', 'w76', 'w77', 'w78', 'w79',
       'w80', 'w81', 'w82', 'w83', 'w84', 'w85', 'w86', 'w87', 'w88', 'w89',
       'w90', 'w91', 'w92', 'w93', 'w94', 'w95', 'w96', 'w97', 'w98', 'w99',
       'w100', 'w101', 'w102']

x = regression.init()

m = x.createmarker()
M=7
m.worldcoordinate=[0,M,0,M]
m.type = wmo
m.color=[242,]
m.size=[10.,]
xs = []
ys=[]
for Y in range(7):
  for X in range(15):
    ys.append([M-M*(Y+1)/8.,])
    xs.append([M*(X+1)/16.,])
m.x = xs
m.y = ys
m.list()
x.plot(m, bg=1)
regression.run(x, "wmo_markers.png");

for i in range(12):
    cont_index = i % 6 + 1
    cont_line = vcs.createline()
    cont_line.width = i % 3 + 1
    cont_line.type = line_styles[i % 5]
    print "Cont_line_rtype:",line_styles[i % 5]
    cont_line.color = i + 200
    template = multitemplate.get(i)
    if cont_index != 3 and i != 4 and i != 11:
        canvas.plot(clt, template, boxfill, continents=cont_index, continents_line=cont_line, bg=1)
    elif cont_index == 3:
        canvas.setcontinentsline(cont_line)
        canvas.setcontinentstype(3)
        canvas.plot(clt, template, boxfill, bg=1)
    elif i == 4:
        canvas.setcontinentstype(0)
        # Make sure absolute path works
        path = os.path.join(vcs.prefix, "share", "vcs", "data_continent_political")
        canvas.plot(clt, template, boxfill, continents=path, continents_line=cont_line, bg=1)
    elif i == 11:
        # Make sure the dotdirectory other* works
        dotdir = vcs.getdotdirectory()
        current_dotdir = os.environ.get(dotdir[1], dotdir[0])
        os.environ["UVCDAT_DIR"] = os.path.join(vcs.prefix, "share", "vcs")
        # Should pick up the other7 continents
        canvas.plot(clt, template, boxfill, continents=7, continents_line=cont_line, bg=1)
        os.environ["UVCDAT_DIR"] = current_dotdir

regression.run(canvas, "test_continents.png")

import os, sys, numpy, cdms2, MV2, vcs, vcs.testing.regression as regression

x = regression.init()
t = cdms2.createAxis(numpy.arange(120))
t.designateTime()
t.id = "time"
t.units = "months since 2014"
data = MV2.arange(120,0,-1)
data.id = "data"
data.setAxis(0,t)
x = regression.init()
x.plot(data,bg=1)
fnm = 'test_vcs_monotonic_decreasing_yxvsx_default.png'
regression.run(x, fnm)

import os, sys, cdms2, vcs, vcs.testing.regression as regression

dataset = cdms2.open(os.path.join(vcs.sample_data, "clt.nc"))
data = dataset("clt")
canvas = regression.init()

isoline = canvas.createisoline()
isoline.label = "y"
isoline.labelskipdistance = 15.0
texts = []
colors = []
for i in range(10):
    text = canvas.createtext()
    text.color = 20 * i
    text.height = 12
    colors.append(255 - text.color)
    if i % 2 == 0:
        texts.append(text.name)
    else:
        texts.append(text)
isoline.text = texts
isoline.linecolors = colors

# Next plot the isolines with labels
canvas.plot(data, isoline, bg=1)
regression.run(canvas, "test_vcs_isoline_labelskipdistance.png")

import os, sys, cdms2, vcs, vcs.testing.regression as regression

f = cdms2.open(os.path.join(vcs.sample_data, "clt.nc"))
s = f("clt")
x = regression.init()
iso = x.createisofill()
p = x.createprojection()
p.type = "lambert"
iso.projection = p
x.plot(s(latitude=(20, 60), longitude=(-140, -20)), iso, bg=True)
regression.run(x, "test_vcs_lambert.png")

import vcs, numpy, cdms2, MV2, os, sys, vcs.testing.regression as regression

x = regression.init()
f=cdms2.open(os.path.join(vcs.sample_data,"ta_ncep_87-6-88-4.nc"))
vr = "ta"
s=f(vr,slice(0,1),longitude=slice(90,91),squeeze=1)
x.plot(s,bg=1)
regression.run(x, 'test_vcs_flipY.png')

import os, sys, numpy, cdms2, MV2, vcs, vcs.testing.regression as regression

x = regression.init()
f = cdms2.open(os.path.join(vcs.sample_data, "clt.nc"))
s = f("clt")
box = x.createboxfill()

# Should ignore the string here
box.xmtics1 = {i:"Test" for i in range(-180, 180, 15) if i % 30 != 0}
box.ymtics1 = {i:"Test" for i in range(-90, 90, 5) if i % 10 != 0}
box.xmtics2 = "lon15"
box.ymtics2 = "lat5"
template = x.createtemplate()
template.xmintic1.priority = 1
template.ymintic1.priority = 1
template.xmintic2.priority = 1
template.xmintic2.y2 += template.xmintic1.y1 - template.xmintic1.y2
template.ymintic2.priority = 1
x.plot(s, template, box, bg=1)
regression.run(x, "test_vcs_mintics.png")

import cdms2
import os
import sys
import vcs.testing.regression as regression
import vcs
import numpy

data = sys.argv[2]
level = sys.argv[3]
levels = {'0': range(-5,36,5),
          '1': [-1000, -15, 35],
          '2': [-300, -15, 0, 15, 25],
          '3': range(190, 320, 10)}

x=regression.init(bg=1)
f=cdms2.open(data)
if (level == '3'):
    s=f("test")
else:
    s=f("sst")
iso=x.createisofill()
iso.levels=levels[level]
x.plot(s,iso)
regression.run(x, "test_vcs_isofill_level%s.png"%level)

import os, sys, cdms2, vcs, vcs.testing.regression as regression

x = regression.init()
x.backgroundcolor = (255, 255, 255)
x.open()
regression.run(x, "test_backgroundcolor_white.png")

import vcs, numpy, cdms2, MV2, os, sys, vcs.testing.regression as regression

x = regression.init()
d = numpy.sin(numpy.arange(100))
b = x.createboxfill()
x.plot(d,b,bg=1)
regression.run(x, "test_1d_in_boxfill.png", sys.argv[1])

import vcs, numpy, os, sys, cdms2, vcs.testing.regression as regression

x=regression.init()
f=cdms2.open(os.path.join(vcs.sample_data,"clt.nc"))
data=f("clt",slice(0,1,))
gm = x.createisoline()
gm.levels = range(0,110,10)
gm.linecolors = ["green","red","blue","bisque","yellow","grey",
        [100,0,0,50], [0,100,0],"salmon",[0,0,100,75]]
x.plot(data,gm,bg=True)
regression.run(x, 'test_vcs_settings_color_name_rgba_isoline.png')

import vcs, numpy, os, sys, cdms2, vcs.testing.regression as regression

x = regression.init()
f=cdms2.open(os.path.join(vcs.sample_data,"clt.nc"))
data=f("clt",slice(0,1,))
gm = x.createboxfill()
gm.boxfill_type = "custom"
gm.levels = range(0,110,10)
gm.fillareacolors = ["green","red","blue","bisque","yellow","grey",
        [100,0,0,50], [0,100,0],"salmon",[0,0,100,75]]
x.plot(data,gm,bg=True)
regression.run(x, 'test_vcs_settings_color_name_rgba_boxfill.png')

import vcs,numpy,cdms2,MV2,os,sys


import vcs.testing.regression as regression
x = regression.init()


m = x.createmarker()
M=1
m.worldcoordinate=[0,M,0,M]
m.type = "w07"
m.color=[242,]
m.size=[1.,2.,5.]
m.x = [[.25,],[.5,],[.75]]
m.y = [.5,]
x.plot(m,bg=1)
fnm = 'wmo_marker.png'
x.png(fnm)

regression.run(x, "wmo_marker.png")

isoline = canvas.createisoline()
isoline.label = "y"
texts = []
colors = []
bcolors = []
bopacities = []
for i in range(10):
    text = canvas.createtext()
    random.seed(i*200)
    text.color = random.randint(1, 255)
    text.height = 12
    random.jumpahead(i * 100)
    colors.append(random.randint(1, 255))
    random.jumpahead(i * 20)
    bcolors.append(random.randint(1, 255))
    bopacities.append(random.randint(0, 100))
    if i % 2 == 0:
        texts.append(text.name)
    else:
        texts.append(text)
isoline.text = texts
isoline.labelbackgroundcolors = bcolors
isoline.labelbackgroundopacities = bopacities
isoline.labelskipdistance = 15.0

# First test using isoline.text[...].color
canvas.plot(data, isoline, bg=1)
baseline = os.path.splitext(sys.argv[1])
baselineImage = "%s%s" % baseline
regression.run(canvas, baselineImage)

import cdms2, os, sys, vcs, vcs.testing.regression as regression

cdmsfile = cdms2.open(os.path.join(vcs.sample_data,"clt.nc"))
data = cdmsfile('clt')
x = regression.init()
t=x.gettemplate('default')
x.plot(data, t, bg=True)

# This should force the image to update
x.setcolormap('blue2darkorange')
regression.run(x, "test_vcs_setcolormap.png")

0.059503571833625334
0.059503571833625334 0.05664014775641405 0.05193557222118004
0.04777129850801233 0.0407139313814465 0.029382624830271705
0.018469399844287374 0.0162382275289592 0.02646680241827459
0.04792041732949079 0.0689138797030203 0.08167038620212037
0.09273558459066569 0.11266293431057901 0.13663018925347364
0.15229174546388072 0.15284435880966177 0.13423845476113883
0.09945904378274077 0.07032267160267985 0.05551039827020481
0.045537187647785464 0.040532491867244946 0.03577527125478327
-999. -999. -999.
-0.058062458673116 -0.08764922509099882 -0.11697036914487152
-0.14836133615864944 -0.17956528904564023 -0.21109198032585794
-0.23846429237248942 -0.2598536549218765 -0.27795672866320387
-0.2939939095159731 -0.30541031366330024 -0.307643559333884
-0.30078421139811795 -0.2841339526883441 -0.26485737397202497
-0.24287299694779327 -0.22379014890999907 -0.20121548204699846
-0.1746486732156772 -0.14585019344118372 -0.12070675757803526
-0.0997891159111037 -0.08229393660994214 -0.06779720501287469
-0.057213385470859794 -0.04875768191096844 -0.0402377347189964
-0.030169328367807245 -0.017560662894847895 -0.006968922654137132
0.0009773980274431048 0.007054306637034288 0.010472286514133042
0.010702384151997032 0.009231553701801242 0.007544033101056543
0.004639797857203645 -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999. -999.
-999. -999. -999.
""".split()
data = numpy.array(data, dtype=numpy.float)
data = MV2.masked_less(data, -900)
x.plot(data, yx, bg=1)
regression.run(x, "test_vcs_1D_datawc_missing.png")

import os, sys, cdms2, vcs, vcs.testing.regression as regression

f = cdms2.open(os.path.join(vcs.sample_data,"clt.nc"))
s = f("clt",slice(0,1),squeeze=1)
x = regression.init()
gm = x.createboxfill()
gm.boxfill_type = "custom"
gm.levels = [1.e20,1.e20]
gm.ext_1 = "y"
gm.ext_2 = "y"
x.plot(s, gm, bg=1)
fnm = os.path.split(__file__)[1][:-3] + ".png"
regression.run(x, fnm, sys.argv[1])

elif args.src == "canvas":
  ## Still setting vcs to make sure it is not used
  vcs._colorMap = "blue2green"
  x.setcolormap("blue2grey")
else:
  ## Still setting vcs and canvas to make sure it is not used
  vcs._colorMap = "blue2green"
  x.setcolormap("blue2grey")
  gm.colormap = "blue2orange"

if args.gm != "meshfill":
  f=cdms2.open(os.path.join(vcs.sample_data,"clt.nc"))
  if args.gm == "vector":
    u = f("u")[...,::2,::2]
    v = f("v")[...,::2,::2]
    gm.scale = 8.
  else:
    s=f("clt",slice(0,1))
else:
  f=cdms2.open(os.path.join(vcs.sample_data,'sampleCurveGrid4.nc'))
  s=f("sample")
if args.gm == "vector":
  x.plot(u,v,gm,bg=True)
else:
  x.plot(s,gm,bg=True)

fnm = "test_vcs_colormaps_source_%s_%s.png" % (args.gm,args.src)
x.png(fnm)
baselineImage = args.baseline
ret = regression.run(x, fnm, baselineImage)

# Create the fill area and the text annotations
fill_test = x.createfillarea('fill_hatches_patterns')
fill_test.style = style_list
fill_test.index = index_list
fill_test.color = color_list
fill_test.x = x_list
fill_test.y = y_list
fill_test.pixelspacing = [15, 15]
fill_test.pixelscale = 15.0

fill_info = x.createtext('fill_info')
fill_info.angle = 45
fill_info.height = 12
fill_info.color = 241  # Black
fill_info.string = txt_str_list
fill_info.x = txt_x_list
fill_info.y = txt_y_list

# Create a title
plot_title = x.createtext('plot_title')
plot_title.height = 40
plot_title.string = ['Testing hatches and patterns in VCS/CDAT']
plot_title.x = [.01]
plot_title.y = [.9]

# Initialize and use a second graphics canvas
x.plot(plot_title, bg=1)
x.plot(fill_test, bg=1)
x.plot(fill_info, bg=1)
regression.run(x, "test_vcs_hatches_patterns.png")

import os, sys, cdms2, vcs, vcs.testing.regression as regression
import matplotlib
sp = matplotlib.__version__.split(".")
if int(sp[0])*10+int(sp[1])<15:
    # This only works with matplotlib 1.5 and greater
    sys.exit()

# Load the clt data:
dataFile = cdms2.open(os.path.join(vcs.sample_data, "clt.nc"))
clt = dataFile("clt")
clt = clt(latitude=(-90.0, 90.0), longitude=(-180., 175.), squeeze=1,
          time=('1979-1-1 0:0:0.0', '1988-12-1 0:0:0.0'))

# Initialize canvas:
canvas = regression.init()
canvas.setcolormap(vcs.matplotlib2vcs("viridis"))
canvas.plot(clt, bg=1)
fnm = os.path.split(__file__)[1][:-3] + ".png"
regression.run(canvas, fnm)