def test_pan_scale(self):
self.assertEqual(util.zoom_fit((10, 10), (0, 0, 20, 20)),
[0., 0., .5, .5])
self.assertEqual(util.zoom_fit((10, 10), (5, 5, 20, 20)),
[-2.5, -2.5, .5, .5])
self.assertEqual(util.zoom_fit((10, 10), (-4, -7, 20, 20)),
[2., 3.5, .5, .5])
def test_pan_scale(self):
self.assertEqual(util.zoom_fit((10, 10), (0, 0, 20, 20)),
[0., 0., .5, .5])
self.assertEqual(util.zoom_fit((10, 10), (5, 5, 20, 20)),
[-2.5, -2.5, .5, .5])
self.assertEqual(util.zoom_fit((10, 10), (-4, -7, 20, 20)),
[2., 3.5, .5, .5])
def test_pan(self):
self.assertEqual(util.zoom_fit((10, 10), (0, 0, 10, 10)),
[0., 0., 1., 1.])
self.assertEqual(util.zoom_fit((10, 10), (5, 5, 10, 10)),
[-5., -5., 1., 1.])
self.assertEqual(util.zoom_fit((10, 10), (-4, -7, 10, 10)),
[4., 7., 1., 1.])
def test_pan(self):
self.assertEqual(util.zoom_fit((10, 10), (0, 0, 10, 10)),
[0., 0., 1., 1.])
self.assertEqual(util.zoom_fit((10, 10), (5, 5, 10, 10)),
[-5., -5., 1., 1.])
self.assertEqual(util.zoom_fit((10, 10), (-4, -7, 10, 10)),
[4., 7., 1., 1.])
def test_scale(self):
self.assertEqual(util.zoom_fit((10, 10), (0, 0, 10, 10)),
[0., 0., 1., 1.])
self.assertEqual(util.zoom_fit((10, 10), (0, 0, 20, 20)),
[0., 0., .5, .5])
self.assertEqual(util.zoom_fit((10, 10), (0, 0, 5, 5)),
[0., 0., 2., 2.])
self.assertEqual(util.zoom_fit((10, 10), (0, 0, 10, 20)),
[0., 0., .5, .5])
self.assertEqual(util.zoom_fit((10, 10), (0, 0, 10, 20), False),
[0., 0., 1., .5])
def test_scale(self):
self.assertEqual(util.zoom_fit((10, 10), (0, 0, 10, 10)),
[0., 0., 1., 1.])
self.assertEqual(util.zoom_fit((10, 10), (0, 0, 20, 20)),
[0., 0., .5, .5])
self.assertEqual(util.zoom_fit((10, 10), (0, 0, 5, 5)),
[0., 0., 2., 2.])
self.assertEqual(util.zoom_fit((10, 10), (0, 0, 10, 20)),
[0., 0., .5, .5])
self.assertEqual(util.zoom_fit((10, 10), (0, 0, 10, 20), False),
[0., 0., 1., .5])
def _create_plot_component():
red = util.Color(255, 0, 0, 255)
green = util.Color(0, 255, 0, 255)
blue = util.Color(0, 0, 255, 255)
purple = util.Color(125, 0, 255, 255)
white = util.Color(255, 255, 255, 255)
black = util.Color(0, 0, 0, 255)
clear = util.Color(0, 0, 0, 0)
with Timer("Loeading") as arTimer:
#glyphs = npg.load_csv("../data/circlepoints.csv", 1, 2, 3, 4)
#glyphs = npg.load_hdf("../data/CensusTracts.hdf5", "__data__", "LAT", "LON")
glyphs = npg.load_hdf("../data/tweets-subset.hdf",
"test",
"longitude",
"latitude",
vc="lang_primary")
screen = (800, 600)
ivt = util.zoom_fit(screen, glyphs.bounds())
with Timer("Abstract-Render") as arTimer:
image = core.render(
glyphs, infos.encode(["Arabic", "English", "Turkish", "Russian"]),
npg.PointCountCategories(),
npg.Spread(2) + categories.HDAlpha(
[red, blue, green, purple, black], alphamin=.3, log=True),
screen, ivt)
# image = core.render(glyphs,
# infos.valAt(4,0),
# npg.PointCount(),
# npg.Spread(1) + numeric.BinarySegment(white, black, 1),
# screen,
# ivt)
# Create a plot data object and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", image)
# Create the plot
plot = Plot(pd)
img_plot = plot.img_plot("imagedata")[0]
# Tweak some of the plot properties
plot.title = "Abstract Rendering"
plot.padding = 50
return plot
def _create_plot_component():
red = util.Color(255,0,0,255)
green = util.Color(0,255,0,255)
blue = util.Color(0,0,255,255)
purple = util.Color(125,0,255,255)
white = util.Color(255,255,255,255)
black = util.Color(0,0,0,255)
clear = util.Color(0,0,0,0)
with Timer("Loeading") as arTimer:
#glyphs = npg.load_csv("../data/circlepoints.csv", 1, 2, 3, 4)
glyphs = npg.load_hdf("../data/CensusTracts.hdf5", "__data__", "LAT", "LON")
#glyphs = npg.load_hdf("../data/tweets-subset.hdf", "test",
# "longitude", "latitude", vc="lang_primary")
screen = (800,600)
ivt = util.zoom_fit(screen,glyphs.bounds())
with Timer("Abstract-Render") as arTimer:
image = core.render(glyphs,
infos.encode(["Arabic","English","Turkish","Russian"]),
npg.PointCountCategories(),
npg.Spread(2) + categories.HDAlpha([red, blue, green, purple, black], alphamin=.3, log=True),
screen,
ivt)
# image = core.render(glyphs,
# infos.valAt(4,0),
# npg.PointCount(),
# npg.Spread(1) + numeric.BinarySegment(white, black, 1),
# screen,
# ivt)
# Create a plot data object and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", image)
# Create the plot
plot = Plot(pd)
img_plot = plot.img_plot("imagedata")[0]
# Tweak some of the plot properties
plot.title = "Abstract Rendering"
plot.padding = 50
return plot
def _create_plot_component():
red = util.Color(255,0,0,255)
green = util.Color(0,255,0,255)
blue = util.Color(0,0,255,255)
white = util.Color(255,255,255,255)
black = util.Color(0,0,0,255)
shape = glyphset.ShapeCodes.POINT
#glyphs = glyphset.load_csv("../data/checkerboard.csv", 2, 0, 1, 3,1,1, shape)
#glyphs = glyphset.load_csv("../data/circlepoints.csv", 1, 2, 3, 4,.1,.1, shape)
#glyphs = glyphset.load_csv("../data/sourceforge.csv", 1, 1, 2, -1,.1,.1, shape)
glyphs = glyphset.load_hdf("../data/CensusTracts.hdf5", "__data__", "LON", "LAT", None, .1, .1, shape)
#glyphs = glyphset.load_hdf("../data/tweets-subset.hdf", "test", "longitude", "latitude", None, .1, .1, shape)
screen = (800,600)
ivt = util.zoom_fit(screen,glyphs.bounds())
with Timer("Abstract-Render") as arTimer:
# image = core.render(glyphs,
# infos.val(),
# categories.CountCategories(),
# categories.HDAlpha([red, blue]),
# screen,
# ivt)
image = core.render(glyphs,
infos.valAt(4,0),
numeric.Count(),
numeric.BinarySegment(white, black, 1),
screen,
ivt)
print("screen x image -- {0} x {1}".format(screen, image.shape))
# Create a plot data object and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", image)
# Create the plot
plot = Plot(pd)
img_plot = plot.img_plot("imagedata")[0]
# Tweak some of the plot properties
plot.title = "Abstract Rendering"
plot.padding = 50
return plot
def _create_plot_component():
red = util.Color(255, 0, 0, 255)
green = util.Color(0, 255, 0, 255)
blue = util.Color(0, 0, 255, 255)
purple = util.Color(125, 0, 255, 255)
white = util.Color(255, 255, 255, 255)
black = util.Color(0, 0, 0, 255)
shape = glyphset.ShapeCodes.RECT
glyphs = blzg.load_csv(
"../data/circlepoints.csv",
"x",
"y",
"series",
schema="{r:float32, theta:float32, x:float32, y:float32, series:int32}"
)
screen = (800, 600)
ivt = util.zoom_fit(screen, glyphs.bounds())
with Timer("Abstract-Render") as arTimer:
image = core.render(
glyphs, infos.val(), blzg.CountCategories("int32"),
categories.HDAlpha([red, blue, green, purple, black]), screen, ivt)
# image = core.render(glyphs,
# infos.valAt(4,0),
# blzg.Count(),
# numeric.BinarySegment(white, black, 1),
# screen,
# ivt)
# Create a plot data object and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", image)
# Create the plot
plot = Plot(pd)
img_plot = plot.img_plot("imagedata")[0]
# Tweak some of the plot properties
plot.title = "Abstract Rendering"
plot.padding = 50
return plot
def _create_plot_component():
red = util.Color(255,0,0,255)
green = util.Color(0,255,0,255)
blue = util.Color(0,0,255,255)
purple = util.Color(125,0,255,255)
white = util.Color(255,255,255,255)
black = util.Color(0,0,0,255)
shape = glyphset.ShapeCodes.RECT
glyphs = blzg.load_csv("../data/circlepoints.csv", "x", "y", "series",
schema="{r:float32, theta:float32, x:float32, y:float32, series:int32}")
screen = (800,600)
ivt = util.zoom_fit(screen,glyphs.bounds())
with Timer("Abstract-Render") as arTimer:
image = core.render(glyphs,
infos.val(),
blzg.CountCategories("int32"),
categories.HDAlpha([red, blue, green, purple, black]),
screen,
ivt)
# image = core.render(glyphs,
# infos.valAt(4,0),
# blzg.Count(),
# numeric.BinarySegment(white, black, 1),
# screen,
# ivt)
# Create a plot data object and give it this data
pd = ArrayPlotData()
pd.set_data("imagedata", image)
# Create the plot
plot = Plot(pd)
img_plot = plot.img_plot("imagedata")[0]
# Tweak some of the plot properties
plot.title = "Abstract Rendering"
plot.padding = 50
return plot
