1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
|
"""Read a data from ascii file and make a simple analysis
visualizing 3 of the 5 dimensions of the dataset"""
import numpy as np
from vedo import download, Points, Axes, show
from vedo.pyplot import histogram
################################### Read the csv data:
delimiter=','
fpath = download('https://vedo.embl.es/examples/data/genes.csv')
with open(fpath, "r") as f:
lines = f.readlines()
data = []
for i,lns in enumerate(lines):
if i==0:
names = lns.split(delimiter) # read header
continue
ln = lns.split(delimiter)
vals = [float(x) for x in ln]
data.append(vals)
data = np.array(data)
print("Print first 5 rows:\n", names)
print(data[:5])
print("Number of rows:", len(data))
##################################################
# extract the columns into separate vectors:
g0, g1, g2, g3, g4 = data.T # unpack genes
n0, n1, n2, n3, n4 = names
# now create and show histograms of the gene expressions
h0 = histogram(g0, xtitle=n0, c=0)
h1 = histogram(g1, xtitle=n1, c=1)
h2 = histogram(g2, xtitle=n2, c=2)
h3 = histogram(g3, xtitle=n3, c=3, logscale=True)
h4 = histogram(g4, xtitle=n4, c=4)
# this is where you choose what variables to show as 3D points
pts = np.c_[g4,g2,g3] # form an array of 3d points from the columns
pts_1 = pts[g0>0] # select only points that have g0>0
p1 = Points(pts_1).ps(4).c('red5') # create the vedo object (ps=point size)
print("after selection nr. of points is", len(pts_1))
pts_2 = pts[(g0<0) & (g1>.5)] # select excluded points that have g1>0.5
p2 = Points(pts_2).ps(8).c('green') # create the vedo object
axes = Axes(p1+p2, xtitle='gene4', ytitle='gene2', ztitle='gene3', c='k')
# Show the two clouds superposed on a new plotter window:
show([h0, h1, h2, h3, h4, (p1,p2, axes, __doc__)],
shape="1/5", # 1 spaces above and 5 below
sharecam=0, axes=0, zoom=1.4, interactive=True,
).close()
|
