def _visualize(map):
try:
# from alife.simulators.mayavi.plot3D import plot3d
from mayavi import mlab
except:
print "Can't show you nice picture; couldn't import mayavi"
return
# plot3d(map)
mlab.barchart(map, scale_factor=0.6)
mlab.show()
def test_barchart(self):
s = np.random.random((10, 10))
x, y = np.indices(s.shape)
bar1 = mlab.barchart(x, y, s)
bar2 = mlab.barchart(s)
bar3 = mlab.barchart(s, auto_scale=False)
# Check that auto scaling worked well in the different
# configurations
for bar in bar1, bar2, bar3:
self.assertEqual(bar.glyph.glyph_source.glyph_source.y_length, 0.9)
def test_barchart(self):
s = np.random.random((10, 10))
x, y = np.indices(s.shape)
bar1 = mlab.barchart(x, y, s)
bar2 = mlab.barchart(s)
bar3 = mlab.barchart(s, auto_scale=False)
# Check that auto scaling worked well in the different
# configurations
for bar in bar1, bar2, bar3:
self.assertEqual(bar.glyph.glyph_source.glyph_source.y_length, 0.9)
def analyze():
mcs = []
import mayavi.mlab as mlab
for gamma_b in linspace(2*pi*1e3, 2*pi*17e3, 5):
mc = MonteCarlo()
mc.run_stats(35.5e-6, 0.1e-6, 300, gamma_b=gamma_b)
mcs.append(mc)
mlab.figure()
mlab.barchart(mc.cov)
mlab.axes()
mlab.title("gamma_b/2pi = "+str(gamma_b/(2*pi)))
return mcs
def plot_3d_barchart(self, data_dict, Type, Freq):
''' Using the mayavi library, plot a 3D barchart of the data of requested type, and freq.'''
extent_dim = self._get_extent(3)
Xlocs,Xlabels,Ylocs,Ylabels = self._get_ticks(5,5,extent_dim)
data = self.get_data_type(data_dict, Type)
v_min,v_max = self.get_data_scale(data_dict, Type)
freq_array = data_dict['freq']
freq_ind = self.get_nearest_freq(freq_array,Freq)
from mayavi import mlab
mlab.figure( bgcolor=(0.5,0.5,0.5) )# grey bg
mlab.barchart(data[freq_ind,:,:],vmin=v_min,vmax=v_max,auto_scale=False,colormap='jet',extent = extent_dim)
mlab.title('Freq %.3e' %freq_array[freq_ind],size=5,height=0.1)
mlab.show()
def build_from_bin(mp):
boundary = mp.boundary
start_index = mp.start_index
_build_surface(start_index, boundary)
bld_cells = filter(lambda cell: cell.tag == str2tag["Building"], mp.cells)
bld_vecs = _build_buildings(bld_cells)
return mb.barchart(*bld_vecs, colormap="summer")
def mayavi_barchart():
"""
绘制barchart图
:return:
"""
mlab.figure(fgcolor=(0, 0, 0), bgcolor=(1, 1, 1)) # 更改背景色
# s = np.random.rand(3, 3)
# print(s)
# mlab.barchart(s)
# mlab.vectorbar()
# mlab.show()
x, y = np.mgrid[-5:5:20j, -5:5:20j]
# print(x, y)
s = peaks(x, y)
mlab.barchart(x, y, s)
mlab.vectorbar()
mlab.show()
def test_barchart(self):
"""Test the barchart function."""
s = np.abs(np.random.random((3,3)))
b = mlab.barchart(s)
self.assertEqual(b.glyph.glyph.scale_mode,
'scale_by_vector_components')
s += 1
b.mlab_source.update()
self.assertEqual(b.glyph.glyph.scale_mode,
'scale_by_vector_components')
def test_barchart(self):
"""Test the barchart function."""
s = np.abs(np.random.random((3, 3)))
b = mlab.barchart(s)
self.assertEqual(b.glyph.glyph.scale_mode,
'scale_by_vector_components')
s += 1
b.mlab_source.update()
self.assertEqual(b.glyph.glyph.scale_mode,
'scale_by_vector_components')
def build_from_json(m_dict):
boundary = m_dict[strBoundary]
start_index = m_dict.get(strLocation, [0, 0, 0])
fig = _build_surface(start_index, boundary)
if m_dict.get(strBuilding) is None:
return fig
all_list = [], [], [], []
for bld_dict in m_dict[strBuilding]:
location = bld_dict[strLocation]
boundary = bld_dict[strBoundary]
ret = _build_building(location, boundary)
map(lambda item, item_list: item_list.extend(item), ret, all_list)
return mb.barchart(*all_list, colormap="summer")
from pylab import *
from mayavi import mlab
if __name__ == '__main__':
fig = mlab.figure(size = (3000, 3000), bgcolor = (1, 1, 1))
mlab.barchart(np.random.random((3, 3)))
mlab.savefig(sys.argv[1], size = (3000, 3000))
def creator(name, curDir):
saveName = name.split('.')[0]
data = np.load(curDir+'/'+name)
plotter = np.array([data[0]]).reshape(160,160)*255
mlab.barchart(plotter, auto_scale=False, reset_zoom=False)
mlab.savefig('../../mayaHist/'+saveName+'.png')
import fileinput
class odor():
def __init__(self, index, name, glom_weights):
self.index = index
self.name = name
self.glom_weights = glom_weights
odors = {} # by name
for line in fileinput.input('input-odors.txt'):
data = line.split('\t')
odors.update({data[0]: odor(fileinput.lineno(), data[0], [float(i) for i in data[1:]])})
if __name__ == '__main__':
for name in odors:
print name, odors[name].index
from mayavi.mlab import barchart, show
barchart([odors[name].glom_weights for name in odors])
show()
def test_barchart():
""" Demo the bar chart plot with a 2D array."""
s = np.abs(np.random.random((3, 3)))
return mlab.barchart(s)
# Coloque o bloco recem criado na lista auxiliar
blocosaux.append(new_bloco)
vetx.append(new_bloco[0])
vety.append(new_bloco[1])
vetz.append(new_bloco[2])
f.scene.disable_render = False
for cont1 in range(len(vetx)):
if cont1 < 100:
mlab.barchart(vetx[(cont1 - 1):cont1],
vety[(cont1 - 1):cont1],
vetz[(cont1 - 1):cont1],
resolution=20)
camera.yaw(ang)
else:
if cont1 < 1000:
limite = 10
if cont1 % limite == 0:
camera.yaw(ang)
mlab.barchart(vetx[(cont1 - limite):cont1],
vety[(cont1 - limite):cont1],
vetz[(cont1 - limite):cont1],
resolution=20)
#print cont1
else:
import numpy as np import matplotlib.pyplot as plt from scipy.special import gamma # plot complex function N = 300 # make scale x = np.linspace(-5, 5, N) y = np.linspace(-5, 5, N) xx, yy = np.meshgrid(x, y) z = xx + yy * 1.0j # complex function w = np.abs(gamma(z)) w[np.where(w > 10)] = 10 from mayavi import mlab mlab.barchart(xx, yy, w) mlab.show() # color mapping plt.pcolor(x, y, np.abs(w)) # countor line # plt.contour(x, y, np.abs(w)) plt.colorbar() plt.show()
def anim():
f = mlab.gcf()
print("showing map")
mlab.barchart(A1)
mlab.barchart(A2)
mlab.barchart(A3)
mlab.barchart(A4)
s = mlab.gcf()
s.scene.background = (1, 1, 1)
source = s.children[0]
colors = source.children[0]
# colors = manager.children[0]
# print(colors)
colors.scalar_lut_manager.lut_mode = "Blues"
colors = s.children[1].children[0]
colors.scalar_lut_manager.lut_mode = "Wistia"
colors = s.children[2].children[0]
colors.scalar_lut_manager.lut_mode = "RdYlBu"
colors = s.children[3].children[0]
colors.scalar_lut_manager.lut_mode = "OrRd"
mlab.points3d(0,
0,
0,
mode='cube',
color=(1, 1, 0),
scale_mode='none',
scale_factor='0.5')
mlab.points3d(9,
9,
0,
mode='cube',
color=(1, 1, 0),
scale_mode='none',
scale_factor='0.5')
for scene_cnt in range(0, 27):
print('Updating scene... the number is ', scene_cnt)
# mlab.clf(figure=None)
num = scene_cnt
plan_path_Hybrid1 = "plan_path_Hybrid_temp" + str(num) + ".npy"
plan_path_Hybrid = np.load(file=plan_path_Hybrid1)
# print("综合规划路径数组类型输出：",plan_path_Hybrid.shape)
# print("输出Hybird路径：\n")
# print(plan_path_Hybrid)
# print("x=0时：")
# print(plan_path_Hybrid[0])
# print("测试...取第一维高度：")
# print(plan_path_Hybrid[:,0])
x1 = plan_path_Hybrid[:, 1]
x1 = np.insert(x1, 0, 0)
x1 = np.append(x1, 9)
y1 = plan_path_Hybrid[:, 2]
y1 = np.insert(y1, 0, 0)
y1 = np.append(y1, 9)
z1 = plan_path_Hybrid[:, 0] + 0.5
z1 = np.insert(z1, 0, 0)
z1 = np.append(z1, 0)
# camera状态改变可视化过程
camera_path = "plan_path_Hybrid_temp" + str(num) + ".npy"
camera_path = np.load(file=camera_path)
# print("测试...取摄像机开关状态：")
c = plan_path_Hybrid[:, 3]
# print(c)
# print(len(c))
# print(c[0])
cc = [] # cc表示改变摄像机状态的点的位置集合
for i in range(len(c)):
if c[i] != c[i - 1]:
# print(i-1,c[i-1])
cc.append(i - 1)
# print("改变摄像机状态的点的位置集合：",cc)
ccc = []
n = 2
for i in range(0, len(cc), n):
# print(cc[i:i + n])
ccc.append(cc[i:i + n])
# print("将状态的位置集合拆分成两两的线段：",ccc)
# print("改变camera状态的线段的条数：",len(ccc))
# print("####",np.shape(ccc)[0])
# mlab.plot3d(x, y, z, color=(0.23, 0.6, 1), colormap='Spectral')
mlab.plot3d(x1,
y1,
z1,
color=(0, 0, 1),
opacity=1,
tube_radius=None,
colormap='Spectral')
mlab.plot3d(x,
y,
z,
color=(0, 0, 0),
opacity=1,
tube_radius=None,
colormap='Spectral')
for i in range(np.shape(ccc)[0]):
c1 = plan_path_Hybrid[ccc[i][0]:(ccc[i][1] + 1), 0:3]
# print("******",c1)
np.savetxt("c" + str(i) + ".txt", c1, fmt='%d', delimiter=' ')
camera_path_1 = np.loadtxt("c" + str(i) + ".txt",
delimiter=' ',
dtype=int)
# print("@@@@@@@@@",camera_path_1)
X = camera_path_1[:, 1]
Y = camera_path_1[:, 2]
Z = camera_path_1[:, 0] + 0.5
for i in range(np.shape(ccc)[0]):
mlab.plot3d(X,
Y,
Z,
color=(0, 1, 0),
opacity=1,
tube_radius=None,
colormap='Spectral')
a = plan_path_Hybrid[num][0]
b = plan_path_Hybrid[num][1]
c = plan_path_Hybrid[num][2]
# print(c, a, b)
mlab.points3d(b,
c,
a + 0.5,
mode='sphere',
color=(1, 1, 0),
scale_mode='none',
scale_factor='0.5')
# mlab.view(focalpoint='auto')
yield
reference_path = "reference_path2" + ".npy"
reference = np.load(file=reference_path)
# print(reference)
x = reference[:, 1]
x = np.insert(x, 0, 0)
x = np.append(x, 18)
# print("x的内容",x)
y = reference[:, 2]
y = np.insert(y, 9, 9)
y = np.append(y, 9)
z = reference[:, 0] + 0.5
z = np.insert(z, 0, 0)
z = np.append(z, 0)
mlab.barchart(A1)
# mlab.barchart(A2)
# mlab.barchart(A3)
mlab.barchart(A4)
s = mlab.gcf()
s.scene.background = (1, 1, 1)
source = s.children[0]
colors = source.children[0]
colors.scalar_lut_manager.lut_mode = "Blues"
colors = s.children[1].children[0]
colors.scalar_lut_manager.lut_mode = "OrRd"
# colors = s.children[2].children[0]
# colors.scalar_lut_manager.lut_mode = "RdYlBu"
# colors = s.children[3].children[0]
# colors.scalar_lut_manager.lut_mode = "Wistia"
# integer toward 0 min, max, inc where min is 0, if not in interval
#return False
if (x < d[0] or x > d[1]):
return False
return int((x - d[0])/d[2])
def compute():
for i in range(nhost):
pc.submit(f, i)
den = numpy.zeros(matrank)
while(pc.working()):
den += pc.pyret()
return den
if __name__ == '__main__':
pc.runworker()
density = compute()
pc.done()
print "density max = ", density.max()
density = density * (20/density.max())
import pickle
pickle.dump(density, open('density.dat', 'w'))
#following works on linux if using openmpi
from mayavi.mlab import barchart,show
barchart(density)
show()
import numpy as np
from mayavi import mlab
ZP = np.load("orderbook_shorter.npy")
X, Y = np.mgrid[0:ZP.shape[0]:1, 0:ZP.shape[1]:1]
print(X.shape, Y.shape, ZP.shape)
s = mlab.barchart(X, Y, ZP)
mlab.show()
cNorm = colors.Normalize(vmin=0, vmax=vv[-1]) scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm) colorVals = [scalarMap.to_rgba(i) for i in range(ly)] # generate plot data xpos = np.arange(0,lx,1) ypos = np.arange(0,ly,1) xpos, ypos = np.meshgrid(xpos+0.25, ypos+0.25) xpos = xpos.flatten() ypos = ypos.flatten() zpos = np.zeros(n) dx = 0.5*np.ones_like(zpos) dy = dx.copy() dz = dat.flatten() cc = np.tile(range(lx), (ly,1)) cc = cc.T.flatten() # generate plot fig = plt.figure() ax = fig.add_subplot(111, projection='3d') opacity = 1 mlab.barchart(xpos,ypos,dz*100) # for i in range(n): # ax.bar3d(xpos[i], ypos[i], zpos[i], dx[i], dy[i], dz[i], # color=colorVals[cc[i]], alpha=opacity, zsort='max') # # plt.autoscale(enable=True, axis='both', tight=True) # plt.grid() # plt.show()
#!/usr/bin/env python # -*- coding: utf-8 -*- import numpy as np import mayavi.mlab as mlab mlab.figure(fgcolor=(0, 0, 0), bgcolor=(1, 1, 1)) #更改背景色 p = np.random.normal(size=(20, 20)) mlab.barchart(p) #x,y为矩阵p的坐标,可以省略 mlab.vectorbar() #颜色bar mlab.show()
regions = lulu.decompose(img)
value_maxes = []
height = 0
for area in sorted(regions.keys()):
pulses = regions[area]
if len(pulses) == 0 or area < 280 or area > 300:
continue
values = [crh.get_value(p) for p in pulses]
height_diff = max(values) - min(values)
value_maxes.append(height_diff)
centre = height + height_diff / 2.0
pulse_values = np.zeros_like(img)
for p in pulses:
crh.set_array(pulse_values, p, crh.get_value(p))
y, x = np.where(pulse_values)
s = pulse_values[y, x]
mlab.barchart(x, y, [height + centre] * len(s), s,
opacity=1.0, scale_factor=1.5)
height += height_diff + 0.5
scene = mlab.get_engine().scenes[0]
scene.scene.parallel_projection = True
mlab.show()
# if check_on(events,i,j,k,ioct,t2):
# s[i][j][k] = ioct+1.0
#
#
# mlab.barchart(s,vmin=0,vmax=8,scale_mode='none',line_width=5.0,lateral_scale=1.0,transparent=True)
# return mlab.screenshot(antialiased=True)
frames = []
for t in np.linspace(0., (duration), int(fps * duration)):
data.append(get_data(t))
temp_view = data[0]
bar = mlab.barchart(temp_view,
vmin=0.0,
vmax=9.0,
scale_mode='none',
line_width=2.0,
lateral_scale=0.99,
auto_scale=False)
lut = bar.module_manager.scalar_lut_manager.lut.table.to_array()
#for i in range(8):
# lut[i+1] = lut[255*i/9]
lut[0] = [0, 0, 0, 10]
bar.module_manager.scalar_lut_manager.lut.table = lut
data2 = []
for view in data:
data2.append(np.reshape(view, np.product(view.shape)))
#for view in data:
for node in G.nodes(): #Function call Kwalk(G,node,k_walk_length,numOfRandomWalks) Kwalk(G,node,k_walk_length,numOfRandomWalks) # Flag_Node_Pair = [ ( Number of Flags,Node ) , ( , ) ..............] Flag_Node_Pair = Flagger.items() total=sum(Flagger.values()) s =[] for i in range(m): x = [] for j in range(n): x.append(Flagger[(i,j)]*600/float(total)) s.append(x) m = numpy.array([i for i in s ]) mlab.barchart(m) mlab.show() """ for i in range(len(Flag_Node_Pair)): Flag_Node_Pair[i] = (Flag_Node_Pair[i][1],Flag_Node_Pair[i][0]) #Sorting the Flag_Node_Pair, to find the nodes which have most frequently visited ! Flag_Node_Pair.sort() Flag_Node_Pair.reverse() for length_set in range(1,len(Flag_Node_Pair)): DominatingSet = [i[1] for i in Flag_Node_Pair[:length_set]] if Correctness_Of_DominatingSet(G,k_walk_length,numOfRandomWalks,DominatingSet) : break
import fileinput
class odor():
def __init__(self, index, name, glom_weights):
self.index = index
self.name = name
self.glom_weights = glom_weights
odors = {} # by name
for line in fileinput.input('input-odors.txt'):
data = line.split('\t')
odors.update({
data[0]:
odor(fileinput.lineno(), data[0], [float(i) for i in data[1:]])
})
if __name__ == '__main__':
for name in odors:
print name, odors[name].index
from mayavi.mlab import barchart, show
barchart([odors[name].glom_weights for name in odors])
show()
def main_plot4():
__import__("matplotlib").rcParams.update({'axes.labelsize': 20,
'axes.titlesize': 20})
from mayavi import mlab
names = ['res5.json', 'res24.json', 'res26.json', 'res29.json']
ps = _load_jsons(names, 'ps')
ns = _load_jsons(names, 'ns')
size = 20
t_size = 50
fig = mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
T_fine = r_[0:len(ps) - 1:(t_size + 1) * 1j].astype(int)
N_fine = r_[:size + 1]
T_fine, N_fine = np.meshgrid(T_fine, N_fine)
P_all = array([(array(p[:len(p) // 2]) + p[len(p) // 2:]) for p in ps])
P_fine = P_all[T_fine, N_fine]
# No idea what's wrong with the extent here ....
surf = mlab.barchart(P_fine * size, colormap='blue-red',
extent=[0, size, 0, size, 0, 1 + 2. / size],
figure=fig)
# surf.module_manager.scalar_lut_manager.reverse_lut = True
ax0 = new_axis(fig)
ax0.axes.x_label = "State (n)"
ax0.axes.y_label = "Time"
ax0.axes.z_label = "Population"
ax0.axes.x_axis_visibility = False
ax0.axes.y_axis_visibility = False
ax0.axes.z_axis_visibility = True
ax0.axes.number_of_labels = 3
ax0.axes.use_data_bounds = False
ax0.axes.bounds = [0, size, 0, size, 0, size]
ax0.axes.ranges = [0, len(N_fine) - 1, 0, 1, 0, 1]
ax0.axes.use_ranges = True
ax0.axes.label_format = '%.1f'
ax1 = new_axis(fig)
ax1.axes.x_label = "State (n)"
ax1.axes.y_label = "Time"
ax1.axes.z_label = "Population"
ax1.axes.x_axis_visibility = False
ax1.axes.y_axis_visibility = True
ax1.axes.z_axis_visibility = False
ax1.axes.number_of_labels = 6
ax1.axes.use_data_bounds = False
ax1.axes.bounds = [0, size, 0, size, 0, size]
ax1.axes.ranges = [0, len(N_fine) - 1, 0, 1, 0, 1]
ax1.axes.use_ranges = True
ax1.axes.label_format = '%.1f'
ax2 = new_axis(fig)
ax2.axes.x_label = "State (n)"
ax2.axes.y_label = "Time"
ax2.axes.z_label = "Population"
ax2.axes.x_axis_visibility = True
ax2.axes.y_axis_visibility = False
ax2.axes.z_axis_visibility = False
ax2.axes.number_of_labels = 6
ax2.axes.use_data_bounds = False
ax2.axes.bounds = [0, size, 0, size, 0, size]
ax2.axes.ranges = [0, len(N_fine) - 1, 0, 1, 0, 1]
ax2.axes.use_ranges = True
ax2.axes.label_format = '%.0f'
ax2.title_text_property.vertical_justification = 'center'
ax2.label_text_property.vertical_justification = 'center'
ax2.label_text_property.justification = 'right'
mlab.outline(surf, color=(0., 0., 0.),
extent=[0, size, 0, size, 0, size])
# mlab.show_pipeline()
mlab.view(290, 90)
mlab.show()
# for k in range(nz): # for j in range(ny): # for i in range(nx): # if check_on(events,i,j,k,ioct,t2): # s[i][j][k] = ioct+1.0 # # # mlab.barchart(s,vmin=0,vmax=8,scale_mode='none',line_width=5.0,lateral_scale=1.0,transparent=True) # return mlab.screenshot(antialiased=True) frames = [] for t in np.linspace(0.,(duration),int(fps*duration)): data.append(get_data(t)) temp_view = data[0]; bar = mlab.barchart(temp_view,vmin=0.0,vmax=9.0,scale_mode='none',line_width=2.0,lateral_scale=0.99,auto_scale=False) lut = bar.module_manager.scalar_lut_manager.lut.table.to_array() #for i in range(8): # lut[i+1] = lut[255*i/9] lut[0] = [0,0,0,10] bar.module_manager.scalar_lut_manager.lut.table = lut data2 = [] for view in data: data2.append(np.reshape(view,np.product(view.shape))) #for view in data: # bar.mlab_source.set(scalars=np.reshape(view,np.product(view.shape)))
# print used_ip
tmp = sorted([int(re.search("\d+$", each).group()) for each in used_ip])
print tmp
tmp = np.zeros(16 * 16)
for each in get_ip_last_num(used_ip):
tmp[each] = 1
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
# print tmp
# ax.bar3d(range(16)*16, get_y(), np.zeros(16*16), 1, 1, tmp, 'b', alpha=0.8)
#
# fig.show()
mlab.barchart(range(16) * 16, get_y(), tmp + 0.01)
last_time = last_time - 1
#必须加入show()才能正常显示。。
mlab.show()
# sleep(30)
# ip_nums_file.close()
# while (1):
# g = IP2MAC()
# for each in used_ip:
# print each, g.getMac(each)
# # used_ip.sort()
# for each in used_ip:
'''
import numpy as np
import mayavi.mlab as mlab
sig = np.array([[1.75, 1.88, 2.79],
[1.56, 1.81, 2.13],
[1.18, 4.11, 3.77]], dtype='f')
#sig_max = np.max(sig)
#sig /= sig_max
kappa = np.array([[1.4519, 2.5000, 6.7886],
[5.2363, 13.6182, 16.9232],
[14.4421, 22.9346, 39.0977]], dtype='f')
#kmax = np.max(kappa)
#kappa /= kmax
thickness = np.array([8, 14, 20], dtype='f')[:, None] * np.ones_like(sig)
angle = np.array([0, 10, 20], dtype='f')[None, :] * np.ones_like(sig)
mlab.figure(bgcolor=(1, 1, 1))
#mlab.barchart(thickness, angle, kappa, lateral_scale=0.4)
mlab.barchart(thickness, angle, sig, lateral_scale=0.4)
mlab.axes(xlabel='thickness',
ylabel='angle',
zlabel='crack initiation stress', nb_labels=3)
mlab.show()
import mayavi.mlab as mlab
import xml_loader as loader
CHAIN = loader.xml_dataloader("../build/chain_nor.xml", "Nor")
mlab.figure(size=(1920, 1080))
barc = mlab.barchart(CHAIN,
lateral_scale=1,
colormap='viridis',
scale_factor=0.5) #colormap='viridis'
mlab.colorbar(object=barc, orientation='vertical', nb_labels=10)
mlab.view(azimuth=270, elevation=0)
mlab.gcf().scene.parallel_projection = True
cam = barc.scene.camera
cam.zoom(1.2)
mlab.draw()
# mlab.savefig(filename='color_'+file_name)
mlab.show()
blocos.append(new_bloco);
# Coloque o bloco recem criado na lista auxiliar
blocosaux.append(new_bloco)
vetx.append(new_bloco[0]);
vety.append(new_bloco[1]);
vetz.append(new_bloco[2]);
f.scene.disable_render = False
for cont1 in range(len(vetx)):
if cont1 < 100:
mlab.barchart(vetx[(cont1-1):cont1], vety[(cont1-1):cont1], vetz[(cont1-1):cont1], resolution = 20)
camera.yaw(ang)
else:
if cont1 < 1000:
limite = 10
if cont1%limite == 0:
camera.yaw(ang)
mlab.barchart(vetx[(cont1-limite):cont1], vety[(cont1-limite):cont1], vetz[(cont1-limite):cont1], resolution = 20)
#print cont1
else:
limite = 100
if cont1%limite == 0:
camera.yaw(-ang)
mlab.barchart(vetx[(cont1-limite):cont1], vety[(cont1-limite):cont1], vetz[(cont1-limite):cont1], resolution = 20)
def make_frame(t):
# x = volumes_data[:, :, :, int(t * 100/5/2)]
# vol.mlab_source.set(scalars=x)
mlab.clf(figure=None)
print(int(t * 100 / 5 / 2))
mlab.barchart(A1)
mlab.barchart(A2)
mlab.barchart(A3)
mlab.barchart(A4)
mlab.points3d(0,
0,
0,
mode='cube',
color=(1, 1, 0),
scale_mode='none',
scale_factor='0.5')
mlab.points3d(A.shape[0] - 1,
A.shape[1] - 1,
0,
mode='cube',
color=(1, 1, 0),
scale_mode='none',
scale_factor='0.5')
num = int(t)
# plan_path_Hybrid1 = "plan_path_Hybrid_temp" + str(num) + ".npy"
plan_path_Hybrid1 = "plan_path_PP_temp" + str(num) + ".npy"
plan_path_Hybrid = np.load(file=plan_path_Hybrid1)
# print("综合规划路径数组类型输出：",plan_path_Hybrid.shape)
# print("输出Hybird路径：\n")
# print(plan_path_Hybrid)
# print("x=0时：")
# print(plan_path_Hybrid[0])
# print("测试...取第一维高度：")
# print(plan_path_Hybrid[:,0])
x1 = plan_path_Hybrid[:, 1]
x1 = np.insert(x1, 0, 0)
x1 = np.append(x1, A.shape[0] - 1)
y1 = plan_path_Hybrid[:, 2]
y1 = np.insert(y1, 0, 0)
y1 = np.append(y1, A.shape[1] - 1)
z1 = plan_path_Hybrid[:, 0] + 0.5
z1 = np.insert(z1, 0, 0)
z1 = np.append(z1, 0)
# camera状态改变可视化过程
# camera_path = "plan_path_Hybrid_temp" + str(num) + ".npy"
camera_path = "plan_path_PP_temp" + str(num) + ".npy"
camera_path = np.load(file=camera_path)
# print("测试...取摄像机开关状态：")
c = plan_path_Hybrid[:, 3]
# print(c)
# print(len(c))
# print(c[0])
cc = [] # cc表示改变摄像机状态的点的位置集合
for i in range(len(c)):
if c[i] != c[i - 1]:
# print(i-1,c[i-1])
cc.append(i - 1)
# print("改变摄像机状态的点的位置集合：",cc)
ccc = []
n = 2
for i in range(0, len(cc), n):
# print(cc[i:i + n])
ccc.append(cc[i:i + n])
# print("将状态的位置集合拆分成两两的线段：",ccc)
# print("改变camera状态的线段的条数：",len(ccc))
# print("####",np.shape(ccc)[0])
# mlab.plot3d(x, y, z, color=(0.23, 0.6, 1), colormap='Spectral')
mlab.plot3d(x1,
y1,
z1,
color=(0, 0, 1),
opacity=1,
tube_radius=None,
colormap='Spectral')
mlab.plot3d(x,
y,
z,
color=(0, 0, 0),
opacity=1,
tube_radius=None,
colormap='Spectral')
for i in range(np.shape(ccc)[0]):
c1 = plan_path_Hybrid[ccc[i][0]:(ccc[i][1] + 1), 0:3]
# print("******",c1)
np.savetxt("c" + str(i) + ".txt", c1, fmt='%d', delimiter=' ')
camera_path_1 = np.loadtxt("c" + str(i) + ".txt",
delimiter=' ',
dtype=int)
# print("@@@@@@@@@",camera_path_1)
X = camera_path_1[:, 1]
Y = camera_path_1[:, 2]
Z = camera_path_1[:, 0] + 0.5
for i in range(np.shape(ccc)[0]):
mlab.plot3d(X,
Y,
Z,
color=(0, 1, 0),
opacity=1,
tube_radius=None,
colormap='Spectral')
a = plan_path_Hybrid[num][0]
b = plan_path_Hybrid[num][1]
c = plan_path_Hybrid[num][2]
# print(c, a, b)
mlab.points3d(b,
c,
a + 0.5,
mode='cube',
color=(1, 1, 0),
scale_mode='none',
scale_factor='0.5')
s = mlab.gcf()
s.scene.background = (1, 1, 1)
source = s.children[0]
colors = source.children[0]
# colors = manager.children[0]
# print(colors)
colors.scalar_lut_manager.lut_mode = "Blues"
colors = s.children[1].children[0]
colors.scalar_lut_manager.lut_mode = "Wistia"
colors = s.children[2].children[0]
colors.scalar_lut_manager.lut_mode = "RdYlBu"
colors = s.children[3].children[0]
colors.scalar_lut_manager.lut_mode = "OrRd"
mlab.move(forward=-4, right=-4)
mlab.savefig('abc.png', figure=mlab.gcf(), magnification=2)
# mlab.show()
# return mlab.screenshot(antialiased=True)
print(mlab.screenshot(figure=None, mode='rgb', antialiased=False))
return mlab.screenshot(figure=None, mode='rgb', antialiased=False)
def plot_cov(self):
import mayavi.mlab as mlab
mlab.figure()
mlab.barchart(self.cov)
mlab.axes()
n = len(p1)
n_side = int(math.sqrt(n))
# zigzag_idx = zigzag(n_side) # make zigzag index
# prime_digits = p1[zigzag_idx] # put primes sequence in zigzag
# uncomment one; this or zizzag(above) for differnet arrangement
spiral_idx = spiral(n_side) - 1 # make spiral index
prime_digits = p1[spiral_idx] # put primes sequence in spiral
x = np.arange(0, n_side, 1)
xs = np.tile(x, n_side)
y = np.arange(1, n_side + 1, 1)
ys = np.repeat(y, n_side)
dz = np.ravel(prime_digits)
fig = mlab.figure(bgcolor=(0, 0, 0), size=(1280, 1024))
s = mlab.barchart(xs,
ys,
dz,
opacity=1,
colormap='spectral',
extent=(0, 2, 0, 2, 0, 0.001))
s.glyph.color_mode = 'color_by_vector'
mlab.savefig('babylon_1.obj')
mlab.show()
height = 0
for area in sorted(regions.keys()):
pulses = regions[area]
if len(pulses) == 0 or area < 280 or area > 300:
continue
values = [crh.get_value(p) for p in pulses]
height_diff = max(values) - min(values)
value_maxes.append(height_diff)
centre = height + height_diff / 2.0
pulse_values = np.zeros_like(img)
for p in pulses:
crh.set_array(pulse_values, p, crh.get_value(p))
y, x = np.where(pulse_values)
s = pulse_values[y, x]
mlab.barchart(x,
y, [height + centre] * len(s),
s,
opacity=1.0,
scale_factor=1.5)
height += height_diff + 0.5
scene = mlab.get_engine().scenes[0]
scene.scene.parallel_projection = True
mlab.show()
