def make_pressure_color_arrays(vesselgraph):
edges = vesselgraph.edgelist
flags = vesselgraph.edges['flags']
data = vesselgraph.nodes['pressure']
num_nodes = len(vesselgraph.nodes['position'])
nflags = krebsutils.edge_to_node_property(num_nodes, edges, flags, 'or')
is_set = lambda flags_, flag: np.asarray(np.bitwise_and(flags_, flag), np.
bool)
gray = np.asarray((0.1, 0.1, 0.1))
lgray = np.asarray((0.5, 0.5, 0.5))
circulated = is_set(flags, krebsutils.CIRCULATED)
ncirculated = is_set(nflags, krebsutils.CIRCULATED)
capillary = is_set(flags, krebsutils.CAPILLARY)
ncapillary = is_set(nflags, krebsutils.CAPILLARY)
p0 = np.amin(data[ncirculated])
p1 = np.amax(data[ncirculated])
cm = matplotlib.cm.ScalarMappable(cmap=cm_redblue)
cm.set_clim(p0, p1)
edgedata = np.average((data[edges[:, 0]], data[edges[:, 1]]), axis=0)
edgecolors = cm.to_rgba(edgedata)[:, :3]
edgecolors[~circulated] = gray
#edgecolors[capillary] = lgray
nodecolors = cm.to_rgba(data)[:, :3]
nodecolors[~ncirculated] = gray
#nodecolors[ncapillary] = lgray
vesselgraph.edges['colors'] = edgecolors
vesselgraph.nodes['colors'] = nodecolors
def AddConductivityBoundaryConditions(vessels, vesselgroup, avgConductivity):
print '---- resistor BCs to %s -----' % str(vesselgroup)
fudgeFactor = 100.
rootNodes = vessels.roots
pressures = vessels['pressure']
numNodes = len(pressures)
nodeFlags = krebsutils.edge_to_node_property(numNodes, vessels.edgelist,
vessels['flags'], 'or')
nodeRadi = krebsutils.edge_to_node_property(numNodes, vessels.edgelist,
vessels['radius'], 'avg')
minp = np.amin(pressures)
# storage area for h5 datasets
dataArrays = []
# use radius^4 weighted average conductivity
weights = dict()
for rootNode in rootNodes:
weights[rootNode] = nodeRadi[rootNode]**4
assert nodeRadi[rootNode] > 10. or pressures[
rootNode] > minp * 1.1 or (nodeFlags[rootNode] & krebsutils.VEIN)
weightSum = np.sum(weights.values())
for rootNode in rootNodes:
weight = weights[rootNode] / weightSum
#weight = 1
conductivity = fudgeFactor * avgConductivity * weight
dataArrays.append((rootNode, krebsutils.FLOWBC_RESIST,
pressures[rootNode], conductivity))
print 'node %i -> S=%f, w=%f' % (rootNode, conductivity, weight)
# write to h5
dataArrays = zip(*dataArrays)
names = ['bc_node_index', 'bc_type', 'bc_value', 'bc_conductivity_value']
gnodes = vesselgroup['nodes']
for name, values in zip(names, dataArrays):
if name in gnodes:
gnodes[name][...] = values
else:
gnodes.create_dataset(name, data=values)
def generate_samples(graph, name, association, scale):
DATA_LINEAR = krebsutils.VesselSamplingFlags.DATA_LINEAR
DATA_CONST = krebsutils.VesselSamplingFlags.DATA_CONST
DATA_PER_NODE = krebsutils.VesselSamplingFlags.DATA_PER_NODE
if not len(graph.edgelist):
return np.asarray([], dtype=float)
if association == 'edges':
data = krebsutils.edge_to_node_property(
int(np.amax(graph.edgelist) + 1), graph.edgelist,
graph.edges[name], 'avg')
else:
data = graph.nodes[name]
# return data
return krebsutils.sample_edges(graph.nodes['position'], graph.edgelist,
data, scale, DATA_LINEAR | DATA_PER_NODE)
def make_any_color_arrays(vesselgraph, data_name):
edges = vesselgraph.edgelist
num_nodes = len(vesselgraph.nodes['position'])
flags = vesselgraph.edges['flags']
flags = np.asarray(flags, dtype='uint32')
nflags = krebsutils.edge_to_node_property(num_nodes, edges, flags, 'or')
mask = myutils.bbitwise_and(flags, krebsutils.CIRCULATED)
nmask = myutils.bbitwise_and(nflags, krebsutils.CIRCULATED)
if data_name in vesselgraph.edges:
edgedata = vesselgraph.edges[data_name]
nodedata = krebsutils.edge_to_node_property(num_nodes, edges, edgedata,
'avg')
else:
nodedata = vesselgraph.nodes[data_name]
edgedata = np.average((nodedata[edges[:, 0]], nodedata[edges[:, 1]]),
axis=0)
gray = np.asarray((0.1, 0.1, 0.1))
edgecolors = np.repeat(gray.reshape(1, -1), len(edgedata), axis=0)
nodecolors = np.repeat(gray.reshape(1, -1), len(nodedata), axis=0)
#colors = lambda arr: cm.to_rgba(arr)[:,:3]
colors = lambda arr: np.power(cm.to_rgba(arr)[:, :3], 2.4)
if data_name == 'hematocrit':
cm = matplotlib.cm.ScalarMappable(cmap=cm_hematocrit)
cm.set_clim(0, 1)
unmapped_range = (0., 1.)
edgecolors[mask] = colors(edgedata[mask])
nodecolors[nmask] = colors(nodedata[nmask])
elif data_name == 'pressure':
#this looks really ugly if there is a zero pressure node
#p0 = np.amin(nodedata)
p0 = np.min(nodedata[np.nonzero(nodedata)])
p1 = np.amax(nodedata)
unmapped_range = (p0, p1)
cm = matplotlib.cm.ScalarMappable(cmap=cm_redblue)
cm.set_clim(p0, p1)
edgecolors[mask] = colors(edgedata[mask])
nodecolors[nmask] = colors(nodedata[nmask])
elif data_name == 'shearforce':
mask = mask & (edgedata > 0)
nmask = nmask & (nodedata > 0)
edgedata = edgedata[mask]
nodedata = nodedata[nmask]
unmapped_range = edgedata.min(), edgedata.max()
edgedata = np.log10(edgedata)
nodedata = np.log10(nodedata)
p0 = -4 #np.amin(edgedata)
p1 = -1 #np.amax(edgedata)
cm = matplotlib.cm.ScalarMappable(cmap=matplotlib.cm.spectral)
cm.set_clim(p0, p1)
edgecolors[mask] = colors(edgedata)
nodecolors[nmask] = colors(nodedata)
elif data_name == 'S_tot':
#mask = mask & (edgedata>0)
#nmask = nmask & (nodedata>0)
edgedata = edgedata[mask]
nodedata = nodedata[nmask]
unmapped_range = edgedata.min(), edgedata.max()
p0 = np.amin(edgedata)
p1 = np.amax(edgedata)
#print("p0: %f, p1: %f" % (p0,p1))
#unmapped_range = (p0, p1)
cm = matplotlib.cm.ScalarMappable(cmap=matplotlib.cm.jet)
cm.set_clim(p0, p1)
#edgedata = np.log10(edgedata)
#nodedata = np.log10(nodedata)
#p0 = -4#np.amin(edgedata)
#p1 = -1#np.amax(edgedata)
#cm = matplotlib.cm.ScalarMappable(cmap=matplotlib.cm.spectral)
#cm.set_clim(p0, p1)
edgecolors[mask] = colors(edgedata)
nodecolors[nmask] = colors(nodedata)
elif data_name == 'flow':
mask = mask & (edgedata > 0)
nmask = nmask & (nodedata > 0)
edgedata = edgedata[mask]
nodedata = nodedata[nmask]
unmapped_range = edgedata.min(), edgedata.max()
edgedata = np.log10(edgedata)
nodedata = np.log10(nodedata)
p0 = np.floor(np.amin(edgedata))
p1 = np.ceil(np.amax(edgedata))
cm = matplotlib.cm.ScalarMappable(cmap=matplotlib.cm.jet)
cm.set_clim(p0, p1)
edgecolors[mask] = colors(edgedata)
nodecolors[nmask] = colors(nodedata)
elif data_name == 'conductivitySignal':
edgedata = edgedata[mask]
nodedata = nodedata[nmask]
unmapped_range = edgedata.min(), edgedata.max()
p0 = np.amin(edgedata)
p1 = np.amax(edgedata)
cm = matplotlib.cm.ScalarMappable(cmap=matplotlib.cm.jet)
cm.set_clim(p0, p1)
edgecolors[mask] = colors(edgedata)
nodecolors[nmask] = colors(nodedata)
elif data_name == 'metabolicSignal':
edgedata = edgedata[mask]
nodedata = nodedata[nmask]
unmapped_range = edgedata.min(), edgedata.max()
p0 = np.amin(edgedata)
p1 = np.amax(edgedata)
cm = matplotlib.cm.ScalarMappable(cmap=matplotlib.cm.jet)
cm.set_clim(p0, p1)
edgecolors[mask] = colors(edgedata)
nodecolors[nmask] = colors(nodedata)
elif data_name == 'flags':
edgecolors[mask
& (flags & krebsutils.ARTERY).astype(np.bool)] = np.asarray(
(1., 0., 0.))
nodecolors[nmask & (nflags
& krebsutils.ARTERY).astype(np.bool)] = np.asarray(
(1., 0., 0.))
edgecolors[mask
& (flags & krebsutils.VEIN).astype(np.bool)] = np.asarray(
(0., 0., 1.))
nodecolors[nmask
& (nflags & krebsutils.VEIN).astype(np.bool)] = np.asarray(
(0., 0., 1.))
edgecolors[mask
& (flags
& krebsutils.CAPILLARY).astype(np.bool)] = np.asarray(
(0., 1., 0.))
nodecolors[nmask
& (nflags
& krebsutils.CAPILLARY).astype(np.bool)] = np.asarray(
(0., 1., 0.))
for idx in vesselgraph.roots:
nodecolors[idx] = np.asarray((1., 1., 0.))
cm, unmapped_range = None, (None, None)
vesselgraph.edges['colors'] = edgecolors
vesselgraph.nodes['colors'] = nodecolors
return cm, unmapped_range
def InsertGraphColors(vesselgraph, po2field, data_name):
edges = vesselgraph.edgelist
num_nodes = len(vesselgraph.nodes['position'])
if data_name in vesselgraph.edges:
edgedata = data = vesselgraph.edges[data_name]
nodedata = krebsutils.edge_to_node_property(num_nodes, edges, data,
'avg')
else:
nodedata = data = vesselgraph.nodes[data_name]
edgedata = np.average((data[edges[:, 0]], data[edges[:, 1]]), axis=0)
if data_name == 'po2vessels':
try:
p1 = np.amax(data)
except ValueError:
print("p1 not found")
pass
if po2field is not None:
p1 = max(p1, np.amax(po2field))
try:
p0 = np.amin(data)
except ValueError:
print("p0 not found")
pass
if po2field is not None:
p0 = min(p0, np.amin(po2field))
#p1 = math.ceil(p1/10.0)*10.0 # round to powers of something
#p1 = 100.0
value_range = (p0, p1)
cm = matplotlib.cm.ScalarMappable(cmap=cm_po2)
elif data_name == 'saturation':
cm = matplotlib.cm.ScalarMappable(cmap=matplotlib.cm.spectral)
vesselgraph.edges['saturation']
value_range = (np.min(vesselgraph.edges['saturation']),
np.max(vesselgraph.edges['saturation']))
#value_range = (0,1.)
elif data_name == 'hboconc':
cm = matplotlib.cm.ScalarMappable(cmap=matplotlib.cm.gnuplot)
p1 = math.ceil(np.amax(data))
value_range = (0., p1)
cm.set_clim(*value_range)
colors = lambda arr: np.power(cm.to_rgba(arr)[:, :3], 2.4)
if data_name in vesselgraph.edges:
edgecolors = colors(data)
nodecolors = colors(nodedata)
else:
edgecolors = colors(edgedata)
nodecolors = colors(data)
flags = vesselgraph.edges['flags']
nflags = krebsutils.edge_to_node_property(num_nodes, edges, flags, 'or')
is_not_set = lambda flags_, flag: np.bitwise_not(
np.asarray(np.bitwise_and(flags_, flag), np.bool))
gray = np.asarray((0.3, 0.3, 0.3))
uncirculated = is_not_set(flags, krebsutils.CIRCULATED)
nuncirculated = is_not_set(nflags, krebsutils.CIRCULATED)
edgecolors[uncirculated] = gray
nodecolors[nuncirculated] = gray
print 'colormap range ', cm.get_clim()
vesselgraph.edges['colors'] = edgecolors
vesselgraph.nodes['colors'] = nodecolors
return cm
def InsertGraphColors(vesselgraph, ifffield, data_name, automatic_scale=False, max_conc=1.0):
edges = vesselgraph.edgelist
num_nodes = len(vesselgraph.nodes['position'])
# if data_name in vesselgraph.edges:
# edgedata = data = vesselgraph.edges[data_name]
# nodedata = krebsutils.edge_to_node_property(num_nodes, edges, data, 'avg')
# else:
# nodedata = data = vesselgraph.nodes[data_name]
# edgedata = np.average((data[edges[:,0]], data[edges[:,1]]), axis=0)
#note everything is uncirculated anyway
#edgedata = data = vesselgraph.edges['radius']
#nodedata = krebsutils.edge_to_node_property(num_nodes, edges, data, 'avg')
if data_name == 'auc':
#p1 = np.amax(data)
#if po2field is not None:
# p1 = max(p1, np.amax(po2field))
#p1 = math.ceil(p1/10.0)*10.0 # round to powers of something
#p1 = 100.0
#value_range = (np.amin(ifffield), np.amax(ifffield))
color_norm = matplotlib.colors.LogNorm().autoscale(ifffield)
cm = matplotlib.cm.ScalarMappable(cmap = 'Reds', norm=color_norm)
#value_range = (0, 37000)
value_range = (np.amin(ifffield), np.amax(ifffield))
if value_range[0]==0 and value_range[1] == 0:
value_range = (-1,1)
#cm = matplotlib.cm.ScalarMappable(cmap = 'Reds')
cm.set_clim(*value_range)
#cm = matplotlib.colors.LinearSegmentedColormap.from_list('my_cmap',[(0,0,0,0),'red'],256)
#cm = plt.get_cmap('Reds')
elif data_name == 'iff_pressure':
cm = matplotlib.cm.ScalarMappable(cmap = matplotlib.cm.spectral)
value_range = (np.amin(ifffield), np.amax(ifffield))
cm.set_clim(*value_range)
elif data_name == 'drug_conc':
cm = matplotlib.cm.ScalarMappable(cmap = matplotlib.cm.Spectral, norm=matplotlib.colors.LogNorm())
if automatic_scale:
value_range = (np.amin(ifffield), np.amax(ifffield))
else:
value_range= (1e-3,0.5e0)
# new global variable
if not max_conc == 1.0:
value_range = (1e-3, max_conc)
else:
value_range = (np.amin(ifffield), np.amax(ifffield))
print(value_range)
if value_range[0]==0 and value_range[1] == 0:
value_range = (1,2)
#value_range = (0, 0.42)
#color_norm = matplotlib.colors.LogNorm(*value_range)
#color_norm = matplotlib.colors.LogNorm().autoscale(ifffield)
#cm = matplotlib.cm.ScalarMappable(cmap = 'Reds', norm=color_norm)
#cm = matplotlib.cm.ScalarMappable(cmap = 'Reds', norm=color_norm)
#if np.amin(ifffield) <0:
# value_range = (0, np.amax(ifffield))
#else:
#value_range = (np.amin(ifffield), np.amax(ifffield))
#value_range = (1e-7, 0.5)
#value_range = (0, 0.42)
#value_range = (0.01, 0.3)
cm.set_clim(*value_range)
#cm.autoscale(ifffield)
#cm.LogNorm()
else:
cm = matplotlib.cm.ScalarMappable(cmap = matplotlib.cm.gnuplot)
p1 = math.ceil(np.amax(data))
value_range = (0., p1)
#colors = lambda arr: np.power(cm.to_rgba(arr)[:,:3], 2.4)
edgedata = vesselgraph.edges['radius']
nodedata = krebsutils.edge_to_node_property(num_nodes, edges, edgedata, 'avg')
if 1:
print('custom cm:')
mycm = matplotlib.colors.Colormap('Reds')
theEdgeColors = np.ones((len(edgedata),3))
theNodeColors = np.ones((len(nodedata),3))
blue=matplotlib.colors.colorConverter.to_rgba('blue')
normalizedEdgeData = matplotlib.colors.Normalize(vmin=np.amin(edgedata),vmax=np.amax(edgedata))(edgedata)
normalizedNodeData = matplotlib.colors.Normalize(vmin=np.amin(edgedata),vmax=np.amax(edgedata))(nodedata)
#normalizedEdgeData = matplotlib.colors.LogNorm(vmin=np.amin(edgedata),vmax=np.amax(edgedata))(edgedata)
#normalizedNodeData = matplotlib.colors.LogNorm(vmin=np.amin(edgedata),vmax=np.amax(edgedata))(nodedata)
#normalizedEdgeData = matplotlib.colors.LogNorm()(edgedata)
#normalizedNodeData = matplotlib.colors.LogNorm()(nodedata)
for (no, intensity) in enumerate(normalizedEdgeData):
theEdgeColors[no,:] = np.multiply(blue[0:3],intensity)
for (no, intensity) in enumerate(normalizedNodeData):
theNodeColors[no,:] = np.multiply(blue[0:3],intensity)
vesselgraph.edges['colors'] = theEdgeColors
vesselgraph.nodes['colors'] = theNodeColors
return cm
def make_any_color_arrays(vesselgraph, data_name):
edges = vesselgraph.edgelist
num_nodes = len(vesselgraph.nodes['position'])
flags = vesselgraph.edges['flags']
nflags = krebsutils.edge_to_node_property(num_nodes, edges, flags, 'or')
mask = myutils.bbitwise_and(flags, krebsutils.CIRCULATED)
nmask = myutils.bbitwise_and(nflags, krebsutils.CIRCULATED)
if data_name in vesselgraph.edges:
edgedata = vesselgraph.edges[data_name]
nodedata = krebsutils.edge_to_node_property(num_nodes, edges, edgedata,
'avg')
else:
nodedata = vesselgraph.nodes[data_name]
edgedata = np.average((nodedata[edges[:, 0]], nodedata[edges[:, 1]]),
axis=0)
gray = np.asarray((0.1, 0.1, 0.1))
edgecolors = np.repeat(gray.reshape(1, -1), len(edgedata), axis=0)
nodecolors = np.repeat(gray.reshape(1, -1), len(nodedata), axis=0)
#colors = lambda arr: cm.to_rgba(arr)[:,:3]
colors = lambda arr: np.power(cm.to_rgba(arr)[:, :3], 2.4)
if data_name == 'hematocrit':
cm = matplotlib.cm.ScalarMappable(cmap=cm_hematocrit)
cm.set_clim(0, 1)
unmapped_range = (0., 1.)
edgecolors[mask] = colors(edgedata[mask])
nodecolors[nmask] = colors(nodedata[nmask])
elif data_name == 'pressure':
p0 = np.amin(nodedata)
p1 = np.amax(nodedata)
unmapped_range = (p0, p1)
cm = matplotlib.cm.ScalarMappable(cmap=cm_redblue)
cm.set_clim(p0, p1)
edgecolors[mask] = colors(edgedata[mask])
nodecolors[nmask] = colors(nodedata[nmask])
elif data_name == 'shearforce':
mask = mask & (edgedata > 0)
nmask = nmask & (nodedata > 0)
edgedata = edgedata[mask]
nodedata = nodedata[nmask]
unmapped_range = edgedata.min(), edgedata.max()
edgedata = np.log10(edgedata)
nodedata = np.log10(nodedata)
p0 = -4 #np.amin(edgedata)
p1 = -1 #np.amax(edgedata)
cm = matplotlib.cm.ScalarMappable(cmap=matplotlib.cm.spectral)
cm.set_clim(p0, p1)
edgecolors[mask] = colors(edgedata)
nodecolors[nmask] = colors(nodedata)
elif data_name == 'flow':
mask = mask & (edgedata > 0)
nmask = nmask & (nodedata > 0)
edgedata = edgedata[mask]
nodedata = nodedata[nmask]
unmapped_range = edgedata.min(), edgedata.max()
edgedata = np.log10(edgedata)
nodedata = np.log10(nodedata)
p0 = np.floor(np.amin(edgedata))
p1 = np.ceil(np.amax(edgedata))
cm = matplotlib.cm.ScalarMappable(cmap=matplotlib.cm.jet)
cm.set_clim(p0, p1)
edgecolors[mask] = colors(edgedata)
nodecolors[nmask] = colors(nodedata)
elif data_name == 'flags':
unmapped_range = (0., 1.)
mask = mask & (edgedata > 0)
nmask = nmask & (nodedata > 0)
edgedata = edgedata[mask]
nodedata = nodedata[nmask]
edgedata = np.bitwise_and(edgedata, krebsutils.ARTERY)
cm = matplotlib.cm.ScalarMappable(cmap=matplotlib.cm.jet)
edgecolors[mask] = colors(edgedata)
nodecolors[nmask] = colors(nodedata)
vesselgraph.edges['colors'] = edgecolors
vesselgraph.nodes['colors'] = nodecolors
return cm, unmapped_range
