def test(self):
w = util.Color(255, 255, 255, 255)
b = util.Color(0, 0, 0, 255)
op = numeric.BinarySegment(b, w, 5)
aggs = np.array([[0, 0, 0, 0, 0], [1, 1, 1, 1, 1], [5, 5, 5, 5, 5],
[6, 6, 6, 6, 6], [9, 9, 9, 9, 9], [0, 1, 5, 6, -1]])
out = op.shade(aggs)
expected = np.array([[b, b, b, b, b], [b, b, b, b, b], [w, w, w, w, w],
[w, w, w, w, w], [w, w, w, w, w], [b, b, w, w,
b]])
self.assertTrue(np.array_equal(out, expected))
def __init__(self,
cutoff,
cat=0,
above=util.Color(228, 26, 28, 255),
below=util.Color(55, 126, 184, 255),
background=util.Color(255, 255, 255, 0)):
self.cutoff = cutoff
self.cat = cat
self.above = np.array(above, dtype=np.uint8)
self.below = np.array(below, dtype=np.uint8)
self.background = np.array(background, dtype=np.uint8)
def test_linear(self):
red = util.Color(255, 0, 0, 255)
white = util.Color(255, 255, 255, 255)
op = numeric.InterpolateColors(white, red)
aggs = np.arange(0, 256).reshape((32, 8))
out = op.shade(aggs)
var = np.arange(0, 256)[::-1].reshape((32, 8))
const = np.empty((32, 8), dtype=np.int)
const.fill(255)
expected = np.dstack((const, var, var, const))
self.assertTrue(np.array_equal(out, expected))
def __init__(self,
colors,
background=util.Color(255, 255, 255, 255),
alphamin=0,
log=False,
logbase=10):
"""
colors -- a list of colors in cateogry-order.
alphamin -- minimum alpha value when (default is 0)
log -- Log-based interpolate? (default is false)
logbase -- Base to use if log is true (default is 10)
background -- Color for empty category list (default is white)
If C categories and N colors are presented:
* N == C: No problems
* N > C: Only the first C colors are used
* N < C: The last color is replicated to make N == C
TODO: Change 'colors' to a dictionary of category-to-color mapping
TODO: mask out zero-sum regions in alpha and opaque blend
"""
self.catcolors = np.array(
[np.array(v, dtype=np.uint8) for v in colors])
self.background = np.array(background, dtype=np.uint8)
self.alphamin = alphamin
self.log = log
self.logbase = logbase
def __init__(self,
low,
high,
log=False,
reserve=util.Color(255, 255, 255, 255),
empty=np.nan):
self.low = low
self.high = high
self.reserve = reserve
self.log = log
self.empty = empty
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)
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 _test(self, r, g, b, a):
c = util.Color(r, g, b, a)
self.assertEqual(util.Color(r, g, b, a), c)
self.assertEqual(c, [r, g, b, a])