def make_network_nice(folder, projects, cols=None, fname="fig.svg"):
prob_cut = 0.001
lnk_genes = []
anl_genes = []
for project in projects:
lnk_genes.append([])
anl_genes.append(set())
dist_file = op.join(folder, project + "_tetra_dists.txt")
for l in open(dist_file, "r"):
d = l.split(",")
prob = float(d[2]) # statistical control
# prob = float(d[3]) # biological control
anl_genes[-1].update([d[0], d[1]])
if prob < prob_cut:
lnk_genes[-1].append([d[0], d[1], prob])
if cols is None:
cols = int(np.round(np.sqrt(len(projects))))
# all genes compared
tot_genes = set(i for j in anl_genes for i in j)
# only genes that have links somewhere
# tot_genes = set()
# for l_genes in lnk_genes:
# for g1, g2, p in l_genes:
# tot_genes.update([g1, g2])
# alternative A:
# sorted_genes = sorted(tot_genes)
# alternative B: manually order the genes to prevent confusion
sorted_genes = [
i.strip()
for i in """
rpoA,fdhA,soxA-allo,soxA-rhodo,dsrA,
rpoB,frhB,soxY-allo,soxY-rhodo,dsrB,
cdsA-allo,cdsA-geo,cdsA-rhodo,cdsA-synn,
rpoC,soxZ-allo,soxZ-rhodo,ligE
""".split(
","
)
]
stot_genes = [g for g in sorted_genes if g in tot_genes]
tot_genes = stot_genes + sorted(tot_genes.difference(stot_genes))
adj_f = 2 * np.pi / len(tot_genes)
g_pos = {g: (np.sin(adj_f * i), np.cos(adj_f * i)) for i, g in enumerate(tot_genes)}
g_ang = {g: 90 - 360 * i / len(tot_genes) for i, g in enumerate(tot_genes)}
colormap.set_under(colormap(0))
colormap.set_over(colormap(colormap.N))
norm = mpl.colors.LogNorm(vmin=10e-10, vmax=prob_cut)
p_val_to_c = lambda p: colormap(norm(p))
fig = plt.figure(figsize=(2, 6))
for proj, grey_labels, l_genes in zip(projects, anl_genes, lnk_genes):
# ax = plt.subplot(gs[projects.index(proj)])
ax = fig.add_subplot(int(np.ceil(len(projects) / cols)), cols, projects.index(proj) + 1)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.set_xlim(-1.1, 1.1)
ax.set_ylim(-1.2, 1.2)
ax.set_frame_on(False)
black_labels = set()
for g1, g2, p in l_genes:
black_labels.update([g1, g2])
for g in grey_labels.intersection(tot_genes):
ha, va, ang = text_params_from_ang(g_ang[g])
ax.text(*g_pos[g], s=g, size=8, color="grey", va=va, ha=ha, rotation=ang)
for g in black_labels:
ha, va, ang = text_params_from_ang(g_ang[g])
ax.text(*g_pos[g], s=g, size=8, color="black", va=va, ha=ha, rotation=ang)
for g1, g2, p in l_genes:
clr = p_val_to_c(p)
arrow = FancyArrowPatch(
g_pos[g1], g_pos[g2], color=clr, lw=0.3, arrowstyle="simple,head_length=8,head_width=4"
)
ax.add_patch(arrow)
# TODO: turn off
# ax.text(0.5, 0.5, proj, ha='center', va='center',
# transform=plt.gca().transAxes)
cax = fig.add_axes([0.33, 0.05, 0.33, 0.010])
mpl.colorbar.ColorbarBase(cax, cmap=colormap, norm=norm, orientation="horizontal")
plt.savefig(fname, bbox_inches="tight")
plt.show()
def get_mesh(self, attribute='color', cmap='nyu40'):
""" Extract a mesh from the TSDF using marching cubes
If TSDF also has atribute_vols, these are extracted as
vertex_attributes. The mesh is also colored using the cmap
Args:
attribute: which tsdf attribute is used to color the mesh
cmap: colormap for converting the attribute to a color
Returns:
trimesh.Trimesh
"""
tsdf_vol = self.tsdf_vol.detach().clone()
# measure.marching_cubes() likes positive
# values in front of surface
tsdf_vol = -tsdf_vol
# don't close surfaces using unknown-empty boundry
tsdf_vol[tsdf_vol == -1] = 1
tsdf_vol = tsdf_vol.clamp(-1, 1).cpu().numpy()
if tsdf_vol.min() >= 0 or tsdf_vol.max() <= 0:
return trimesh.Trimesh(vertices=np.zeros((0, 3)))
verts, faces, _, _ = measure.marching_cubes(tsdf_vol, level=0)
verts_ind = np.round(verts).astype(int)
verts = verts * self.voxel_size + self.origin.cpu().numpy()
vertex_attributes = {}
# get vertex attributes
if 'semseg' in self.attribute_vols:
semseg_vol = self.attribute_vols['semseg'].detach().cpu().numpy()
semseg = semseg_vol[verts_ind[:, 0], verts_ind[:, 1], verts_ind[:,
2]]
vertex_attributes['semseg'] = semseg
if 'instance' in self.attribute_vols:
instance_vol = self.attribute_vols['instance']
instance_vol = instance_vol.detach().cpu().numpy()
instance = instance_vol[verts_ind[:, 0], verts_ind[:, 1],
verts_ind[:, 2]]
vertex_attributes['instance'] = instance
# color mesh
if attribute == 'color' and 'color' in self.attribute_vols:
color_vol = self.attribute_vols['color']
color_vol = color_vol.detach().clamp(0, 255).byte().cpu().numpy()
colors = color_vol[:, verts_ind[:, 0], verts_ind[:, 1],
verts_ind[:, 2]].T
elif attribute == 'instance':
label_viz = instance + 1
n = label_viz.max()
cmap = (colormap('jet')(np.linspace(0, 1, n))[:, :3] * 255).astype(
np.uint8)
cmap = cmap[np.random.permutation(n), :]
cmap = np.insert(cmap, 0, [0, 0, 0], 0)
colors = cmap[label_viz, :]
elif attribute == 'semseg':
if cmap == 'nyu40':
cmap = np.array(
NYU40_COLORMAP) # FIXME: support more general colormaps
else:
raise NotImplementedError('colormap %s' % cmap)
label_viz = semseg.copy()
label_viz[(label_viz < 0) | (label_viz >= len(cmap))] = 0
colors = cmap[label_viz, :]
else:
colors = None
mesh = trimesh.Trimesh(vertices=verts,
faces=faces,
vertex_colors=colors,
vertex_attributes=vertex_attributes,
process=False)
return mesh
#!/usr/bin/env python3
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.cm import jet as colormap
from matplotlib.ticker import NullFormatter, MultipleLocator
t, w, r = zip((0.1, 0.4, 1), (0.9, 0.3, 5), (1.7, 0.5, 7), (2.7, 0.6, 6), (3.5, 0.3, 3), (4.5, 0.4, 4), (5.3, 0.3, 7))
fig, ax = plt.subplots(subplot_kw = {'polar':True})
bars = ax.bar(t, r, width = w, bottom = 0.0, lw = 2, edgecolor = 'Black', zorder = 2)
for r, bar in zip(r, bars):
bar.set_facecolor(colormap(r/9.0))
bar.set_alpha(0.7)
ax.yaxis.set_major_locator(MultipleLocator(2))
for axis in (ax.xaxis, ax.yaxis):
axis.set_major_formatter(NullFormatter()) #no tick labels
ax.set_ylim([0, 8])
ax.grid(True)
plt.show()