def setupDebug(self):
node = BulletDebugNode("Debug")
node.showWireframe(True)
self.world.setDebugNode(node)
np = self.render.attachNewNode(node)
np.show()
def pngs_equal(a, b):
if files_equal(a, b):
print a, 'and', b, 'are perfectly equal'
return True
im_a = numpy.imread(a) * 255.0
im_b = numpy.imread(b) * 255.0
if im_a.shape != im_b.shape:
print a, 'and', b, 'have different size:', im_a.shape, im_b.shape
return False
diff = im_b - im_a
alpha = im_a.shape[-1] == 4
if alpha:
diff_alpha = diff[:, :, 3]
equal = True
print a, 'and', b, 'are different, analyzing whether it is just the undefined colors...'
print 'Average difference (255=white): (R, G, B, A)'
print numpy.mean(numpy.mean(diff, 0), 0)
print 'Average difference with premultiplied alpha (255=white): (R, G, B, A)'
diff = diff[:, :, 0:3]
if alpha:
diff *= numpy.imread(a)[:, :, 3:4]
res = numpy.mean(numpy.mean(diff, 0), 0)
print res
if numpy.mean(res) > 0.01:
# dithering should make this value nearly zero...
equal = False
print 'Maximum abs difference with premultiplied alpha (255=white): (R, G, B, A)'
res = numpy.amax(numpy.amax(abs(diff), 0), 0)
print res
if max(abs(res)) > 1.1:
# this error will be visible
# - smaller errors are hidden by the weak alpha
# (but we should pay attention not to accumulate such errors at each load/save cycle...)
equal = False
if not equal:
print 'Not equal enough!'
if alpha:
numpy.figure(1)
numpy.title('Alpha')
numpy.imshow(im_b[:, :, 3], interpolation='nearest')
numpy.colorbar()
numpy.figure(2)
numpy.title('Green Error (multiplied with alpha)')
numpy.imshow(diff[:, :, 1], interpolation='nearest')
numpy.colorbar()
if alpha:
numpy.figure(3)
numpy.title('Alpha Error')
numpy.imshow(diff_alpha, interpolation='nearest')
numpy.colorbar()
numpy.show()
return equal
def pngs_equal(a, b):
if files_equal(a, b):
print a, 'and', b, 'are perfectly equal'
return True
im_a = numpy.imread(a)*255.0
im_b = numpy.imread(b)*255.0
if im_a.shape != im_b.shape:
print a, 'and', b, 'have different size:', im_a.shape, im_b.shape
return False
diff = im_b - im_a
alpha = im_a.shape[-1] == 4
if alpha:
diff_alpha = diff[:, :, 3]
equal = True
print a, 'and', b, 'are different, analyzing whether it is just the undefined colors...'
print 'Average difference (255=white): (R, G, B, A)'
print numpy.mean(numpy.mean(diff, 0), 0)
print 'Average difference with premultiplied alpha (255=white): (R, G, B, A)'
diff = diff[:, :, 0:3]
if alpha:
diff *= numpy.imread(a)[:, :, 3:4]
res = numpy.mean(numpy.mean(diff, 0), 0)
print res
if numpy.mean(res) > 0.01:
# dithering should make this value nearly zero...
equal = False
print 'Maximum abs difference with premultiplied alpha (255=white): (R, G, B, A)'
res = numpy.amax(numpy.amax(abs(diff), 0), 0)
print res
if max(abs(res)) > 1.1:
# this error will be visible
# - smaller errors are hidden by the weak alpha
# (but we should pay attention not to accumulate such errors at each load/save cycle...)
equal = False
if not equal:
print 'Not equal enough!'
if alpha:
numpy.figure(1)
numpy.title('Alpha')
numpy.imshow(im_b[:, :, 3], interpolation='nearest')
numpy.colorbar()
numpy.figure(2)
numpy.title('Green Error (multiplied with alpha)')
numpy.imshow(diff[:, :, 1], interpolation='nearest')
numpy.colorbar()
if alpha:
numpy.figure(3)
numpy.title('Alpha Error')
numpy.imshow(diff_alpha, interpolation='nearest')
numpy.colorbar()
numpy.show()
return equal
def adaptIntPlot(f,a,b):
"""
Adaptive (doubling partition) integration.
Minimizes function evaluations at the expense of some tedious
array minipulations.
"""
maxiter = 20
miniter = 5
tolerance = 0.1
maxnx = 2**maxiter
minnx = 2**miniter
x = 0.*N.zeros(maxnx)
dx = (b-a)/2.#**minsteps
nx = 2
x[0] = a
x[1] = a+dx
integral = N.sum(f(x[1:2]))*dx # 1 so we don't include the first endpt
dx /= 2.
newintegral = integral/2. + N.sum(f(x[:nx]+dx))*dx
for i in range(nx-1,-1,-1):
x[2*i] = x[i]
x[2*i+1] = x[i] + dx
nx *= 2
keepgoing = 1
while keepgoing == 1:
integral = newintegral
dx /= 2.
eff = f(x[:nx]+dx)
N.plot(x[:nx]+dx,f(x[:nx]+dx))
newintegral = integral/2. + N.sum(eff)*dx#N.sum(f(x[:nx]+dx))*dx
print newintegral*nx/(nx-1)
for i in range(nx-1,-1,-1):
x[2*i] = x[i]
x[2*i+1] = x[i] + dx
nx *= 2
keepgoing = 0
if integral*newintegral > 0.:
if ((N.fabs(N.log(integral*(nx/2)/(nx/2-1)/(newintegral*nx/(nx-1)))) >\
tolerance) and (nx < maxnx/2)) or (nx < minnx):
keepgoing = 1
elif integral*newintegral == 0.:
print "Hmmm, we have a zero integral here. Assuming convergence."
else:
keepgoing = 1
N.show()
print nx,
if nx == maxnx/2:
print 'No convergence in utils.adaptInt!'
return newintegral*nx/(nx-1)
def showRoomLayout(self,
showCeilings=True,
showWalls=True,
showFloors=True):
for np in self.scene.scene.findAllMatches(
'**/layouts/**/render-semantics/*c'):
if showCeilings:
np.show()
else:
np.hide()
for np in self.scene.scene.findAllMatches(
'**/layouts/**/render-semantics/*w'):
if showWalls:
np.show()
else:
np.hide()
for np in self.scene.scene.findAllMatches(
'**/layouts/**/render-semantics/*f'):
if showFloors:
np.show()
else:
np.hide()
def showRoomLayout(self, showCeilings=True, showWalls=True, showFloors=True):
for np in self.scene.scene.findAllMatches('**/layouts/**/render/*c'):
if showCeilings:
np.show(self.cameraMask)
else:
np.hide(BitMask32.allOn())
for np in self.scene.scene.findAllMatches('**/layouts/**/render/*w'):
if showWalls:
np.show(self.cameraMask)
else:
np.hide(BitMask32.allOn())
for np in self.scene.scene.findAllMatches('**/layouts/**/render/*f'):
if showFloors:
np.show(self.cameraMask)
else:
np.hide(BitMask32.allOn())
# plt.ylabel('Error')
# #plt.ylabel('Accuracy')
# plt.xlabel('Momentum')
# plt.show()
# #For Batch Size
# plt.xticks([1 ,2, 3, 4, 5],['1','10','100','500','1000'])
# plt.plot([1 ,2, 3, 4, 5],[1.88155 ,1.25564 ,0.43665 ,1.11511 ,3.02981 ], 'bo', label='Training')
# plt.plot([1 ,2, 3, 4, 5],[1.88067 ,1.71583 ,0.63323 ,1.07845 ,2.84858 ], 'go', label='Validation')
# plt.axis([0, 5.5, 0.0, 3.2])
# plt.legend(loc=0)
# plt.title('Cross-Entropy for different batch sizes for CNN')
# #plt.title('Accuracy for different batch sizes for CNN')
# plt.ylabel('Error')
# #plt.ylabel('Accuracy')
# plt.xlabel('Batch Size')
# plt.show()
#For 3.3
plt.xticks([1, 2, 3], ['[2 16]', '[15 16]', '[30 16]'])
plt.plot([1, 2, 3], [0.28542, 0.74748, 0.28542], 'bo', label='Training')
plt.plot([1, 2, 3], [0.27924, 0.70883, 0.27924], 'go', label='Validation')
plt.axis([0.5, 3.5, 0, 0.8])
plt.legend(loc=0)
#plt.title('Cross-Entropy for different number of filters in the first layer of CNN')
plt.title('Accuracy for different number of filters in the first layer of CNN')
#plt.ylabel('Error')
plt.ylabel('Accuracy')
plt.xlabel('Number of Units')
plt.show()
factor1 = (1.0 / (sigma * ((2 * numpy.pi)**0.5)))
factor2 = numpy.e**-(((x - mu)**2) / (2 * sigma**2))
return factor1 * factor2
xVals, yVals = [], []
mu, sigma = 0, 2.5
x = -10
while x <= 10:
xVals.append(x)
yVals.append(gaussian(x, mu, sigma))
x += 0.05
numpy.plot(xVals, yVals)
numpy.title('Normal Distribution, mu = ' + str(mu)\
+ ', sigma = ' + str(sigma))
numpy.show()
# import scipy.integrate
# def checkEmpirical(numTrials):
# for t in range(numTrials):
# mu = random.randint(-10, 10)
# sigma = random.randint(1, 10)
# print('For mu =', mu, 'and sigma =', sigma)
# for numStd in (1, 1.96, 3):
# area = scipy.integrate.quad(gaussian,
# mu-numStd*sigma,
# mu+numStd*sigma,
# (mu, sigma))[0]
# print(' Fraction within', numStd,
# 'std =', round(area, 4))
import csv
import numpy as ___
import _________ as plt
from mpl_toolkits.mplot3d import Axes3D
g = open("../galaxygas.csv","___")
gasfile = csv.reader(g)
x=[]
y=[]
z=[]
temp=[]
density =[]
for row in gasfile:
x.append(_____(row[0]))
y.append(float(row[1]))
z.______(float(row[2]))
temp.append(np.log10(float(row[3])))
density.append(np.log10(float(row[4])))
g.close()
cm = plt.get_cmap("nipy_spectral")
fig = plt.figure()
ax = fig.__________(111,projection="3d")
image = ax.scatter(x,y,z,c=temp,vmin=3,vmax=7,s=10,cmap=cm)
bar = plt.colorbar(image)
bar.set_label("Log Gas Temperature (K)")
___.show()
import numpy as np import matplotlib as mpl x = np.linspace(0, 1, 100) y = np.sin(x) np.plot(x, y) np.show()
import csv
import numpy as ___
import _________ as plt
from mpl_toolkits.mplot3d import Axes3D
g = open("../galaxygas.csv", "___")
gasfile = csv.reader(g)
x = []
y = []
z = []
temp = []
density = []
for row in gasfile:
x.append(_____(row[0]))
y.append(float(row[1]))
z.______(float(row[2]))
temp.append(np.log10(float(row[3])))
density.append(np.log10(float(row[4])))
g.close()
cm = plt.get_cmap("nipy_spectral")
fig = plt.figure()
ax = fig.__________(111, projection="3d")
image = ax.scatter(x, y, z, c=temp, vmin=3, vmax=7, s=10, cmap=cm)
bar = plt.colorbar(image)
bar.set_label("Log Gas Temperature (K)")
___.show()
import pandas as choi #melakukan import pada library pandas sebagai arjun
laptop = {
"Nama Laptop": ['Asus', 'ROG', 'Lenovo', 'Samsung']
} #membuat varibel yang bernama laptop, dan mengisi dataframe nama2 laptop
x = Arjun.DataFrame(
laptop
) #variabel x membuat DataFrame dari library pandas dan akan memanggil variabel laptop.
print(' Arjun Punya Laptop ' + x) #print hasil dari x
# In[44]: Soal2
import numpy as Arjun #melakukan import numpy sebagai arjun
matrix_x = Arjun.eye(
10) #membuat matrix dengan numpy dengan menggunakan fungsi eye
matrix_x #deklrasikan matrix_x yang telah dibuat
print(matrix_x) #print matrix_x yang telah dibuat dengan 10x10
# In[44]: Soal3
import matplotlib.pyplot as Arjun #import matploblib sebagai arjun
Arjun.plot([1, 1, 7, 4, 0, 2,
1]) #memberikan nilai plot atau grafik pada arjun
Arjun.xlabel('Arjun Yuda Firwanda') #memberikan label pada x
Arjun.ylabel('1174008') #memberikan label pada y
Arjun.show() #print hasil plot berbentuk grafik
def __mesh_colour_callback(self, dc: DynamicColour, np: NodePath) -> None:
if dc.visible:
np.show()
else:
np.hide()
np.set_color(LVecBase4f(*dc()), 1)
