def add_particles(self,
particle_position,
radius,
radius_scale=1.0,
color=(1, 1, 1),
color_by='',
alpha=1.0,
alpha_by='',
kernel=1.0):
if len(color_by) == len(particle_position):
if np.max(color_by) > 1.0:
color_by /= np.max(color_by)
import matplotlib.cm as cm
#color = cm.winter_r(color_by)
color = cm.GnBu(color_by)
color = color[:, 0:3]
if len(alpha_by) == len(particle_position):
if np.max(alpha_by) > 1.0:
alpha_by /= np.max(alpha_by)
alpha = alpha_by
self.particles.append({
'pos': particle_position,
'color': color,
'alpha': alpha,
'radius': radius,
'radius_scale': radius_scale,
'kernel': kernel
})
def get_cmaps_biasCNN():
"""
Create color maps
"""
nsteps = 8
colors_all_1 = np.moveaxis(
np.expand_dims(cm.Greys(np.linspace(0, 1, nsteps)), axis=2), [0, 1, 2],
[0, 2, 1])
colors_all_2 = np.moveaxis(
np.expand_dims(cm.GnBu(np.linspace(0, 1, nsteps)), axis=2), [0, 1, 2],
[0, 2, 1])
colors_all_3 = np.moveaxis(
np.expand_dims(cm.YlGn(np.linspace(0, 1, nsteps)), axis=2), [0, 1, 2],
[0, 2, 1])
colors_all_4 = np.moveaxis(
np.expand_dims(cm.OrRd(np.linspace(0, 1, nsteps)), axis=2), [0, 1, 2],
[0, 2, 1])
colors_all = np.concatenate((colors_all_1[np.arange(2, nsteps, 1), :, :],
colors_all_2[np.arange(2, nsteps, 1), :, :],
colors_all_3[np.arange(2, nsteps, 1), :, :],
colors_all_4[np.arange(2, nsteps, 1), :, :],
colors_all_2[np.arange(2, nsteps, 1), :, :]),
axis=1)
int_inds = [3, 3, 3, 3]
colors_main = np.asarray(
[colors_all[int_inds[ii], ii, :] for ii in range(np.size(int_inds))])
colors_main = np.concatenate((colors_main, colors_all[5, 1:2, :]), axis=0)
# plot the color map
#plt.figure();plt.imshow(np.expand_dims(colors_main,axis=0))
colors_sf = np.moveaxis(
np.expand_dims(cm.GnBu(np.linspace(0, 1, 8)), axis=2), [0, 1, 2],
[0, 2, 1])
colors_sf = colors_sf[np.arange(2, 8, 1), :, :]
return colors_main, colors_sf
def plot_cube(cube, angle=320):
'''cube must be 3d
'''
cube = normalize(cube)
cube = np.expand_dims(cube, axis=-1) #28x28x1
facecolors = cm.GnBu(cube) #28x28x4
filled = np.ones(cube.shape) #28x28x1
x, y, z = np.indices(np.array(cube.shape) + 1) #29x29x2 for each axis
fig = plt.figure(figsize=[6, 6])
ax = fig.gca(projection='3d')
ax.view_init(30, angle)
ax.set_axis_off()
ax.set_box_aspect((cube.shape[0], cube.shape[1], 20))
ax.voxels(x, y, z, filled=filled, facecolors=facecolors, linewidth=0.0001)
plt.show()
def Visual3D(self, draw_edge=True):
fig = plt.figure(figsize=[10, 10])
ax = fig.gca(projection='3d')
pos = self.pos
x = pos.T[0]
y = pos.T[1]
z = pos.T[2]
ax.scatter(x, y, z, c='r')
edge = set()
for i in range(self.n):
for j in range(self.n):
if (i != j and self.adjacence[i, j] > 1.e-5):
x1, x2 = pos[i][0], pos[j][0]
y1, y2 = pos[i][1], pos[j][1]
z1, z2 = pos[i][2], pos[j][2]
if (x1 > x2):
x1, x2 = x2, x1
y1, y2 = y2, y1
z1, z2 = z2, z1
edge.add(((x1, x2), (y1, y2), (z1, z2)))
edge = list(edge)
if (draw_edge):
for i in range(len(edge)):
ax.plot(*edge[i])
plt.show()
#Projection2D
edge = set()
fig = plt.figure(figsize=[10, 10])
for i in range(self.n):
for j in range(self.n):
if (i != j and self.adjacence[i, j] > 1.e-5):
x1, x2 = pos[i][0], pos[j][0]
y1, y2 = pos[i][1], pos[j][1]
if (x1 > x2):
x1, x2 = x2, x1
y1, y2 = y2, y1
edge.add(((x1, x2), (y1, y2)))
edge = list(edge)
if (draw_edge):
for i in range(len(edge)):
plt.plot(*edge[i], color='black', linewidth=0.3)
point_c = pos[:, 2] / np.max(pos[:, 2])
colors = cm.rainbow(point_c)
plt.scatter(x, y, color=colors)
plt.show()
#PCA Verion
fig = plt.figure(figsize=[10, 10])
edge = set()
pca = PCA(2)
pca.fit(self.pos)
pos = pca.transform(self.pos)
for i in range(self.n):
for j in range(self.n):
if (i != j and self.adjacence[i, j] > 1.e-5):
x1, x2 = pos[i][0], pos[j][0]
y1, y2 = pos[i][1], pos[j][1]
if (x1 > x2):
x1, x2 = x2, x1
y1, y2 = y2, y1
edge.add(((x1, x2), (y1, y2)))
edge = list(edge)
for i in range(len(edge)):
plt.plot(*edge[i], color='black', linewidth=0.3)
colors = cm.GnBu(point_c)
plt.scatter(pos.T[0], pos.T[1], color=colors)
plt.show()
cmap = cm.GnBu
bounds = np.linspace(0, 40, 41)
norm = colors.BoundaryNorm(bounds, cmap.N)
#bounds = np.linspace(0,120,21)
#norm = colors.BoundaryNorm(bounds, ncolors=256)
#print norm(100)
#sys.exit()
for key, value in patches.iteritems():
print key + " " + str(color[key])
ax.add_collection(
PatchCollection(value,
facecolor=cm.GnBu(norm(color[key])),
cmap=cm.GnBu,
edgecolor='k',
linewidth=1.,
zorder=2))
# ax.add_collection(PatchCollection(value, facecolors=color[key], cmap=cm.GnBu, edgecolor='k',
# linewidth=1., zorder=2, norm=norm))
### Plot colorbar
#set room for colorbar
fig.subplots_adjust(right=0.85)
axcb = fig.add_axes([0.87, 0.15, 0.03, 0.7])
cb = mpl.colorbar.ColorbarBase(axcb,
import csv
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdate
import time
import matplotlib.colors as colors_fun
import matplotlib.cm as cm
import plotly.plotly as py
import plotly.graph_objs as go
import pdb
file = open('homeless_by_county.csv', 'rt')
reader = csv.reader(file)
data = [x for x in reader]
county = [x[0] for x in data]
number = [x[1] for x in data]
colors = cm.GnBu(np.linspace(1, 0, len(number)) )
colors = [colors_fun.rgb2hex(color) for color in colors]
trace = go.Pie(labels=county, values=number,
hoverinfo='label+percent', textinfo='value',
textfont=dict(size=20),
marker=dict(colors=colors,
line=dict(color='#000000', width=0.5)))
py.iplot([trace], filename='Homeless-by-county')