import numpy as np
from fos import SimpleWindow, run
from fos.actor.network import AttributeNetwork
wi = SimpleWindow()
# node positions
pos = np.array( [ [0,0,0], [10,10,10] ], dtype = np.float32)
siz = np.array( [ [1.0, 1.0 ]], dtype = np.float32 )
col = np.array( [ [255,0,0,255],
[0,255,0,255]], dtype = np.ubyte)
edg = np.array( [ [0,1]], dtype = np.uint32 )
aff = np.eye(4, dtype = np.float32)
aff[:3,3] = [0,0,0]
nlabs = {0 : { 'label' : 'Node 1',
'size' : 20,
'font' : 'Times New Roman',
'color' : ( 255, 0, 0, 255 ) },
1 : { 'label' : 'Node 2'}
}
cu = AttributeNetwork(affine = aff,
node_position = pos,
node_size = siz,
node_color = col,
node_label = nlabs,
edge_connectivity = edg)
print parents
print labeling
print colors
mycpt = "Treedemo - Fos.me"
try:
# Try and create a window with multisampling (antialiasing)
config = Config(
sample_buffers=1,
samples=4,
depth_size=16,
double_buffer=True,
)
window = Window(resizable=True,
config=config,
vsync=False,
width=1000,
height=800,
caption=mycpt) # "vsync=False" to check the framerate
except fos.lib.pyglet.window.NoSuchConfigException:
# Fall back to no multisampling for old hardware
print "fallback"
window = Window(resizable=True, caption=mycpt)
ac = []
# spread factor
s = 500
# duplicator
d = 30
# tune it up
# this is very inefficient, because it copies the position arrays
pos = f['neurons/position'].value
parents = f['neurons/localtopology'].value
labeling = f['neurons/labeling'].value
colors = f['neurons/segmentcolors'].value
f.close()
print pos
print parents
print labeling
print colors
mycpt = "Treedemo - Fos.me"
try:
# Try and create a window with multisampling (antialiasing)
config = Config(sample_buffers=1, samples=4,depth_size=16, double_buffer=True,)
window = Window(resizable=True, config=config, vsync=False, width=1000, height=800, caption = mycpt) # "vsync=False" to check the framerate
except pyglet.window.NoSuchConfigException:
# Fall back to no multisampling for old hardware
print "fallback"
window = Window(resizable=True, caption = mycpt)
ac=[]
# spread factor
s=500
# duplicator
d = 30
# tune it up
# this is very inefficient, because it copies the position arrays
bigpos = np.zeros( (d*len(pos), 3), dtype = np.float32 )
bigpar = np.zeros( (d*len(parents)), dtype = np.float32 )
