(0.125,0, 0),
(0.375,1, 1),
(0.64,1, 1),
(0.91,0,0),
(1, 0, 0)),
'blue': ((0., 0.5, 0.5),
(0.11, 1, 1),
(0.34, 1, 1),
(0.65,0, 0),
(1, 0, 0))}
# compute the properties for the circle collection
min_count = min([min(cd.itervalues()) for cd in countdict.itervalues()])
max_count = max([max(cd.itervalues()) for cd in countdict.itervalues()])
size_scale = scale.linear(min_count,max_count).range(3,100)
color_scale = lambda c: mpl.cm.jet(scale.log(min_count,max_count).range(0,0.85)(c))
xy = []
s = []
c = []
for (i,v_gene) in enumerate(uniq_feature_values['v']):
for (j,j_gene) in enumerate(uniq_feature_values['j']):
try:
count = countdict[v_gene][j_gene]
except KeyError: # count == 0
continue
xy.append( (i,j) )
s.append(size_scale(count))
c.append(color_scale(count))
raise ValueError, "need input and output names"
data = timeseries.load_timeseries(inhandle)
matrix = data['matrix']
labels = np.asarray(data['labels'])
times = data['times']
sums = data['sums']
streams = matrix / sums
# determine colors for the streamgraph
colors = []
time_idxs = np.arange(streams.shape[1])
onset_time = lambda stream: np.min(time_idxs[stream > 0])
weight = lambda stream: np.sum(stream)
Hscale = scale.linear(range(len(times))).range(0,1-1./len(times))
Lscale = scale.root(streams.sum(axis=1)).range(0.8,0.5).power(4)
for stream in streams:
h = Hscale(onset_time(stream))
l = Lscale(weight(stream))
colors.append( colorsys.hls_to_rgb(h,l,1) + (1.,) )
colors = np.array(colors)
# sort streamgraphs appropriately
argsort_onset = streamgraph.argsort_onset(streams)
streams = streams[argsort_onset]
matrix = matrix[argsort_onset]
colors = colors[argsort_onset]
# argsort_inside_out = streamgraph.argsort_inside_out(streams)
# streams = streams[argsort_inside_out]
features,
count=options.quantify)
_jet_data = {
'red':
((0., 0, 0), (0.35, 0, 0), (0.66, 1, 1), (0.89, 1, 1), (1, 0.5, 0.5)),
'green': ((0., 0, 0), (0.125, 0, 0), (0.375, 1, 1), (0.64, 1, 1),
(0.91, 0, 0), (1, 0, 0)),
'blue':
((0., 0.5, 0.5), (0.11, 1, 1), (0.34, 1, 1), (0.65, 0, 0), (1, 0, 0))
}
# compute the properties for the circle collection
min_count = min([min(cd.itervalues()) for cd in countdict.itervalues()])
max_count = max([max(cd.itervalues()) for cd in countdict.itervalues()])
size_scale = scale.linear(min_count, max_count).range(3, 100)
color_scale = lambda c: mpl.cm.jet(
scale.log(min_count, max_count).range(0, 0.85)(c))
xy = []
s = []
c = []
for (i, v_gene) in enumerate(uniq_feature_values['v']):
for (j, j_gene) in enumerate(uniq_feature_values['j']):
try:
count = countdict[v_gene][j_gene]
except KeyError: # count == 0
continue
xy.append((i, j))
s.append(size_scale(count))
raise ValueError, "need input and output names"
data = timeseries.load_timeseries(inhandle)
matrix = data['matrix']
labels = np.asarray(data['labels'])
times = data['times']
sums = data['sums']
streams = matrix / sums
# determine colors for the streamgraph
colors = []
time_idxs = np.arange(streams.shape[1])
onset_time = lambda stream: np.min(time_idxs[stream > 0])
weight = lambda stream: np.sum(stream)
Hscale = scale.linear(range(len(times))).range(0,1-1./len(times))
Lscale = scale.root(streams.sum(axis=1)).range(0.8,0.5).power(4)
for stream in streams:
h = Hscale(onset_time(stream))
l = Lscale(weight(stream))
colors.append( colorsys.hls_to_rgb(h,l,1) + (1.,) )
colors = np.array(colors)
# sort streamgraphs appropriately
argsort_onset = streamgraph.argsort_onset(streams)
streams = streams[argsort_onset]
matrix = matrix[argsort_onset]
colors = colors[argsort_onset]
# argsort_inside_out = streamgraph.argsort_inside_out(streams)
# streams = streams[argsort_inside_out]
