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])
# 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")
ySize = 500
else:
xSize = 800
ySize = 400
pth = os.path.join(os.path.dirname(__file__), "..")
sys.path.append(pth)
f = cdms2.open(vcs.sample_data + "/" + testConfig[plot][0])
s = f(testConfig[plot][1])
x = regression.init(bg=bg, geometry=(xSize, ySize))
# graphics method
if (plot.find('boxfill') != -1):
gm = x.getboxfill(plot)
elif (plot.find('meshfill') != -1):
gm = x.getmeshfill(plot)
elif (plot.find('isofill') != -1):
gm = x.getisofill(plot)
elif (plot.find('isoline') != -1):
gm = x.getisoline(plot)
else:
print "Invalid plot"
sys.exit(13)
x.setantialiasing(0)
x.drawlogooff()
x.plot(s, gm, ratio="autot")
name = "test_vcs_autot_axis_titles_" + plot[2:] + "_" + x_over_y + "_" + str(
bg) + ".png"
regression.run(x, name, sys.argv[1])
import vcs, numpy, os, sys, vcs.testing.regression as regression s = numpy.sin(numpy.arange(100)) s = numpy.reshape(s,(10,10)) x = regression.init() x.plot(s, bg=1) regression.run(x, "test_vcs_boxfill_10x10_numpy.png")
import vcs, numpy, cdms2, MV2, os, sys, vcs.testing.regression as regression
f=cdms2.open(os.path.join(vcs.sample_data,"clt.nc"))
T=f('clt')
v = regression.init()
v.plot(T,bg=1)
regression.run(v, 'first_png_blank.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 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 vcs, numpy, cdms2, MV2, os, sys, vcs.testing.regression as regression
f = cdms2.open(os.path.join(vcs.sample_data, "clt.nc"))
T = f('clt')
v = regression.init()
v.plot(T, bg=1)
regression.run(v, 'first_png_blank.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 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)
regression.run(canvas, "test_matplotlib_colormap.png")
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 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")
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 vcs.testing.regression as regression
x = regression.init()
yx = x.createyxvsx()
data = """-11.14902417 -9.17390922 -7.29515002
-7.51774549 -8.63608171
-10.4827395 -9.93859485 -7.3394366 -5.39241468 -5.74825567
-6.74967902 -7.09622319 -5.93836983 -4.04592997 -2.65591499
-1.68180032 -0.86935245 -0.40114047 -0.54273785 -1.36178957
-2.67488251 -3.87524401 -4.84708491 -5.49186142 -5.28618944
-4.30557389 -2.89804038 -1.53825408 -1.84771029 -2.74948361
-2.23517037 -1.73306118 -0.71200646 0.76416785 1.51511193
-0.04018418 -1.54564706 -1.88664877 -0.43751604 0.89988184
0.33437949 -1.70341844 -3.79880014 -4.03570169 -4.7740073
-5.04626101 -3.77609961 -3.18667176 -2.21038272 -1.3666902
-0.54267951 -0.16472441 -0.52871418 -0.83520848 -0.90315403
-0.21747426 0.01922666 0.89621996 1.75691927 3.12657503
4.55749531 6.04921304 7.20744489 7.65294958""".split(
)
data = numpy.array(data, dtype=numpy.float)
data = MV2.array(data)
yx.datawc_x1 = 0
yx.datawc_x2 = 80
yx.datawc_y1 = -12
yx.datawc_y2 = 12
x.plot(data, yx, bg=1)
regression.run(x, "test_vcs_1D_datawc.png", src)
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)
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 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.createisoline()
t = x.createtext()
t.color = 243
t.height = 25
to = x.createtextorientation()
to.height = 55
tt = x.createtexttable()
tt.color = 245
iso.textcolors = [None, None, None, 242, 244]
iso.text = [t, tt, to]
iso.label = "y"
x.plot(s, iso, bg=1)
regression.run(x, "test_vcs_isoline_labels_multi_label_input_types.png")
import os, sys, numpy, vcs, vcs.testing.regression as regression x = regression.init() data = numpy.array([1, 2, 3, 4]) blon = numpy.array([-1, 1, 1, 0, -1]) blat = numpy.array([0, 0, 1, 2, 1]) acell = numpy.array([blat, blon]) bcell = numpy.array([blat, blon + 2.5]) ccell = numpy.array([blat + 2.5, blon + 2.5]) dcell = numpy.array([blat + 2.5, blon]) mesh = numpy.array([acell, bcell, ccell, dcell]) m = x.createmeshfill() x.plot(data, mesh, m, bg=1) regression.run(x, "test_vcs_gen_meshfill.png")
import os, sys, cdms2, vcs, vcs.testing.regression as regression
x = regression.init()
f = cdms2.open(os.path.join(vcs.sample_data, "clt.nc"))
s = f("clt")
x.meshfill(s, bg=1)
regression.run(x, "test_vcs_meshfill_regular_grid.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
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)
'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");
import vcs, cdms2, sys, os, vcs.testing.regression as regression
zoom = sys.argv[2]
f = cdms2.open(os.path.join(vcs.sample_data, 'clt.nc'))
s = f("clt", slice(0, 1), squeeze=1)
x = regression.init()
i = x.createisofill()
p = x.getprojection("polar")
i.projection = p
if (zoom == 'none'):
x.plot(s, i, bg=1)
elif (zoom == 'subset'):
x.plot(s(latitude=(-50, 90), longitude=(30, -30)), i, bg=1)
else:
i.datawc_x1 = 30
i.datawc_x2 = -30
i.datawc_y1 = -50
i.datawc_y2 = 90
if (zoom == 'datawc1'):
i.datawc_x1, i.datawc_x2 = i.datawc_x2, i.datawc_x1
if (zoom == 'datawc2'):
i.datawc_y1, i.datawc_y2 = i.datawc_y2, i.datawc_y1
x.plot(s, i, bg=1)
file = "test_vcs_polar_zoom_" + zoom + ".png"
regression.run(x, file)
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 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")
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
import vcs.testing.regression as regression
# 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()
# Create and plot quick boxfill with default settings:
boxfill=canvas.createboxfill()
# Change the type
boxfill.boxfill_type = 'custom'
levels = range(20,81,10)
boxfill.levels=levels
boxfill.ext_2="y"
boxfill.fillareacolors=vcs.getcolors(boxfill.levels)
canvas.plot(clt, boxfill, bg=1)
regression.run(canvas, "test_boxfill_custom_ext2.png")
import os, sys, cdms2, vcs, vcs.testing.regression as regression
x = regression.init()
f = cdms2.open(vcs.sample_data + "/clt.nc")
s = f("clt", slice(0, 1), squeeze=1)
b = x.createboxfill()
b.level_1 = 20
b.level_2 = 80
b.ext_1 = "y"
x.plot(s, b, bg=1)
regression.run(x, "test_boxfill_lev1_lev2_ext1.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)
regression.run(x, "test_box_custom_as_def_vistrails_exts.png", sys.argv[1])
import os, sys, numpy, cdms2, MV2, vcs, vcs.testing.regression as regression
x = regression.init()
x.setcolormap("classic")
m = x.createmarker()
m.x = [[
0.,
], [
5,
], [
10.,
], [15.]]
m.y = [[
0.,
], [
5,
], [
10.,
], [15.]]
m.worldcoordinate = [-5, 20, -5, 20]
#m.worldcoordinate=[-10,10,0,10]
m.type = ['plus', 'diamond', 'square_fill', "hurricane"]
m.color = [242, 243, 244, 242]
m.size = [20, 20, 20, 5]
x.plot(m, bg=1)
regression.run(x, "test_markers.png")
import cdms2, os, sys, vcs, cdtime, vcs.testing.regression as regression
# Test that we can restrict the plot using datawc along a time axis
dataFile = cdms2.open(os.path.join(vcs.sample_data, "clt.nc"))
clt = dataFile("clt")
clt = clt(latitude=(-90.0, 90.0),
longitude=(0.),
squeeze=1,
time=('1979-1-1 0:0:0.0', '1988-12-1 0:0:0.0'))
# Initialize canvas:
canvas = regression.init()
# Create and plot quick boxfill with default settings:
boxfill = canvas.createboxfill()
# Change the type
boxfill.boxfill_type = 'custom'
boxfill.datawc_y1 = 12
canvas.plot(clt, boxfill, bg=1)
# Load the image testing module:
# Create the test image and compare:
regression.run(canvas, "test_vcs_boxfill_datawc_time.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 vcs, numpy, cdms2, MV2, os, sys, vcs.testing.regression as regression x = regression.init() d = numpy.sin(numpy.arange(100)) d = numpy.reshape(d,(10,10)) one = x.create1d() x.plot(d,one,bg=1) regression.run(x, "test_vcs_1D_with_manyDs.png", sys.argv[1])
import vcs, cdms2, os, sys, vcs.testing.regression as regression
f = cdms2.open(os.path.join(vcs.sample_data, "clt.nc"))
s = f("clt", longitude=slice(34, 35), squeeze=1)
x = regression.init()
x.plot(s, bg=1)
regression.run(x, "test_vcs_auto_time_labels.png", sys.argv[1])
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)
regression.run(canvas, "test_matplotlib_colormap.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 os
import sys
import vcs
import vcs.testing.regression as regression
# 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()
# Create and plot quick boxfill with default settings:
boxfill = canvas.createboxfill()
# Change the type
boxfill.boxfill_type = 'custom'
levels = range(20, 81, 10)
boxfill.levels = levels
boxfill.ext_2 = "y"
boxfill.fillareacolors = vcs.getcolors(boxfill.levels)
canvas.plot(clt, boxfill, bg=1)
fnm = os.path.split(__file__)[1][:-3] + ".png"
regression.run(canvas, fnm)
import os, sys, numpy, cdms2, MV2, vcs, vcs.testing.regression as regression
x = regression.init()
x.setcolormap("classic")
m = x.createmarker()
m.x = [[0.0], [5], [10.0], [15.0]]
m.y = [[0.0], [5], [10.0], [15.0]]
m.worldcoordinate = [-5, 20, -5, 20]
# m.worldcoordinate=[-10,10,0,10]
m.type = ["plus", "diamond", "square_fill", "hurricane"]
m.color = [242, 243, 244, 242]
m.size = [20, 20, 20, 5]
x.plot(m, bg=1)
regression.run(x, "test_markers.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 os, sys, cdms2, vcs, vcs.testing.regression as regression
f = cdms2.open(os.path.join(vcs.sample_data,"clt.nc"))
s = f("clt")
s3 = f("clt",longitude=(0,360))
x = regression.init()
x.plot(s,bg=1)
x.clear()
x.plot(s3,bg=1)
regression.run(x, "test_lon_axes_freak_out.png")
import os, sys, cdms2, vcs, vcs.testing.regression as regression
x = regression.init()
f = cdms2.open(vcs.sample_data+"/clt.nc")
s = f("clt", slice(0,1), squeeze=1)
b = x.createboxfill()
b.level_1 = 20
b.level_2 = 80
b.ext_1 = "y"
b.ext_2 = "y"
x.plot(s, b, bg=1)
regression.run(x, "test_boxfill_lev1_lev2_ext1_ext2.png")
import cdms2, os, sys, vcs, cdtime, vcs.testing.regression as regression
# Test that we can restrict the plot using datawc along a time axis
dataFile = cdms2.open(os.path.join(vcs.sample_data, "clt.nc"))
clt = dataFile("clt")
clt = clt(latitude=(-90.0, 90.0), longitude=(0.), squeeze=1,
time=('1979-1-1 0:0:0.0', '1988-12-1 0:0:0.0'))
# Initialize canvas:
canvas = regression.init()
# Create and plot quick boxfill with default settings:
boxfill=canvas.createboxfill()
# Change the type
boxfill.boxfill_type = 'custom'
boxfill.datawc_y1 = 12
canvas.plot(clt, boxfill, bg=1)
# Load the image testing module:
# Create the test image and compare:
regression.run(canvas, "test_vcs_boxfill_datawc_time.png")
import sys, os, vcs, MV2, vcs.testing.regression as regression
data = MV2.array([[-0.50428531,-0.8505522 ,],
[ 0.70056821,-0.27235352,],
[ 0.05106154, 0.23012322,],
[-0.26478429, 0.11950427,],
[ 0.85760801,-0.08336641,],
[ 1.14083397,-0.78326507,]])
x = regression.init()
td = x.createtaylordiagram('new')
td.quadrans = 2
x.plot(data, td, skill = td.defaultSkillFunction, bg=1)
regression.run(x, "test_vcs_taylor_2quads.png")
import vcs, numpy, cdms2, MV2, os, sys, vcs.testing.regression as regression x = regression.init() d = numpy.sin(numpy.arange(100)) d = numpy.reshape(d, (10, 10)) one = x.create1d() x.plot(d, one, bg=1) regression.run(x, "test_1D_with_manyDs.png", sys.argv[1])
import os, sys, cdms2, vcs, vcs.testing.regression as regression
flip = False
if (len(sys.argv) == 3):
flip = True
fileName = os.path.basename(__file__)
fileName = os.path.splitext(fileName)[0]
if (flip):
fileName = fileName + '_flip'
fileName = fileName + '.png'
f = cdms2.open(os.path.join(vcs.sample_data, "sampleCurveGrid4.nc"))
s = f("sample")
x = regression.init()
m = x.createmeshfill()
# m.mesh = True
m.datawc_x1 = -20
m.datawc_x2 = 20
if (flip):
m.datawc_x1, m.datawc_x2 = m.datawc_x2, m.datawc_x1
m.datawc_y1 = -20
m.datawc_y2 = 20
x.plot(s, m, bg=1)
regression.run(x, fileName)
import os, sys, MV2, numpy, vcs, cdms2, vcs.testing.regression as regression
src = sys.argv[1]
pth0 = os.path.dirname(__file__)
f = cdms2.open(os.path.join(pth0, "celine.nc"))
s = f("data")
x = regression.init()
x.setantialiasing(0)
x.setcolormap("classic")
x.scriptrun(os.path.join(pth0, "celine.json"))
i = x.getisofill("celine")
x.plot(s, i, bg=1)
fnm = "test_celine_iso.png"
regression.run(x, fnm)
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")
import vcs, os, sys, cdms2, vcs.testing.regression as regression
f = cdms2.open(os.path.join(vcs.sample_data, "sampleCurveGrid4.nc"))
s = f("sample")
x = regression.init()
x.plot(s, bg=1)
regression.run(x, "test_plot_unstructured_via_boxfill.png")
import cdms2, os, sys, vcs, vcs.testing.regression as regression
# 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()
# Create and plot quick boxfill with default settings:
boxfill=canvas.createboxfill()
# Change the type
boxfill.boxfill_type = 'custom'
levels = range(20,81,10)
boxfill.levels=levels
boxfill.fillareacolors=vcs.getcolors(levels)
canvas.plot(clt, boxfill, bg=1)
regression.run(canvas, "test_boxfill_custom_no_default_levels.png")
