本文整理汇总了Python中matplotlib.mlab.bivariate_normal函数的典型用法代码示例。如果您正苦于以下问题：Python bivariate_normal函数的具体用法？Python bivariate_normal怎么用？Python bivariate_normal使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了bivariate_normal函数的20个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: function
def function(position):
xg = 3.0 * position[0]
yg = 3.0 * position[1]
h1 = bivariate_normal(xg, yg, 1.0, 1.0, 0.0, 0.0)
h2 = bivariate_normal(xg, yg, 1.5, 0.5, 1, 1)
h = 10.0 * (h1 - h2)
return h
开发者ID:FluidityProject，项目名称:fluidity，代码行数:7，代码来源:height.py
示例2: calcAtomGaussians
def calcAtomGaussians(mol,a=0.03,step=0.02,weights=None):
"""
useful things to do with these:
fig.axes[0].imshow(z,cmap=cm.gray,interpolation='bilinear',origin='lower',extent=(0,1,0,1))
fig.axes[0].contour(x,y,z,20,colors='k')
fig=Draw.MolToMPL(m);
contribs=Crippen.rdMolDescriptors._CalcCrippenContribs(m)
logps,mrs=zip(*contribs)
x,y,z=Draw.calcAtomGaussians(m,0.03,step=0.01,weights=logps)
fig.axes[0].imshow(z,cmap=cm.jet,interpolation='bilinear',origin='lower',extent=(0,1,0,1))
fig.axes[0].contour(x,y,z,20,colors='k',alpha=0.5)
fig.savefig('coumlogps.colored.png',bbox_inches='tight')
"""
import numpy
from matplotlib import mlab
x = numpy.arange(0,1,step)
y = numpy.arange(0,1,step)
X,Y = numpy.meshgrid(x,y)
if weights is None:
weights=[1.]*mol.GetNumAtoms()
Z = mlab.bivariate_normal(X,Y,a,a,mol._atomPs[0][0], mol._atomPs[0][1])*weights[0]
for i in range(1,mol.GetNumAtoms()):
Zp = mlab.bivariate_normal(X,Y,a,a,mol._atomPs[i][0], mol._atomPs[i][1])
Z += Zp*weights[i]
return X,Y,Z
开发者ID:ashwin，项目名称:rdkit，代码行数:28，代码来源:__init__.py
示例3: main
def main(args):
x = np.arange(-3.0, 3.0, 0.025)
y = np.arange(-3.0, 3.0, 0.025)
X, Y = np.meshgrid(x, y)
Z0 = mlab.bivariate_normal(X, Y, 1.0, 4.0, 0.0, 0.0)
Z1 = mlab.bivariate_normal(X, Y, 4.0, 1.0, 0.0, 0.0)
Z = Z1 - Z0
cdict = {
"red": ((0.0, 0.0, 0.98), (1.0, 1.0, 1.0)),
"green": ((0.0, 0.0, 0.74), (1.0, 1.0, 1.0)),
"blue": ((0.0, 0.0, 0.0), (1.0, 1.0, 1.0)),
}
_cmap = LinearSegmentedColormap("tmp", cdict)
plt.imshow(Z >= 0, interpolation="nearest", cmap=_cmap, extent=(-3, 3, -3, 3))
CS = plt.contour(Z, [0], colors="g", extent=(-3, 3, -3, 3))
plt.clabel(CS, inline=1, manual=[(-2, 2), (2, 2)], fmt="boundaries")
CS0 = plt.contour(X, Y, Z0, 4, colors="r")
plt.clabel(CS0, inline=1, manual=[(0, 0)], fmt="p(x | y = 0)")
plt.text(0, -2, "f(x) = 0")
plt.text(0, 2, "f(x) = 0")
CS1 = plt.contour(X, Y, Z1, 4, colors="b")
plt.clabel(CS1, inline=1, manual=[(0, 0)], fmt="p(x | y = 1)")
plt.text(-2, 0, "f(x) = 1")
plt.text(2, 0, "f(x) = 1")
plt.show()
开发者ID:misaka-10032，项目名称:ML，代码行数:30，代码来源:1-plot.py
示例4: heatmap_with_hexagon_cell
def heatmap_with_hexagon_cell(x,y,timestamp):
from matplotlib import cm
from matplotlib import mlab as ml
n = 1e5
#x = y = NP.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z1 = ml.bivariate_normal(X, Y, 2, 2, 0, 0)
Z2 = ml.bivariate_normal(X, Y, 4, 1, 1, 1)
ZD = Z2 - Z1
x = X.ravel()
y = Y.ravel()
z = ZD.ravel()
gridsize=300
plt.subplot(111)
# if 'bins=None', then color of each hexagon corresponds directly to its count
# 'C' is optional--it maps values to x-y coordinates; if 'C' is None (default) then
# the result is a pure 2D histogram
plt.hexbin(x, y, C=z, gridsize=gridsize, cmap=cm.jet, bins=None)
plt.axis([x.min(), x.max(), y.min(), y.max()])
plt.text(18.9300,72.8200,"ChurchGate",bbox=dict(facecolor='green', alpha=0.5))
plt.text(19.1833,72.8333,"Malad",bbox=dict(facecolor='green', alpha=0.5))
plt.text(19.0587,72.8997,"Chembur",bbox=dict(facecolor='green', alpha=0.5))
plt.text(19.2045,72.8376,"Kandivili",bbox=dict(facecolor='green', alpha=0.5))
cb = plt.colorbar()
cb.set_label('mean value')
plt.show()
开发者ID:shashanksingh，项目名称:prediction_python，代码行数:29，代码来源:plot_heatmap.py
示例5: test_mask_image_over_under
def test_mask_image_over_under():
delta = 0.025
x = y = np.arange(-3.0, 3.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
Z = 10*(Z2 - Z1) # difference of Gaussians
palette = copy(plt.cm.gray)
palette.set_over('r', 1.0)
palette.set_under('g', 1.0)
palette.set_bad('b', 1.0)
Zm = ma.masked_where(Z > 1.2, Z)
fig, (ax1, ax2) = plt.subplots(1, 2)
im = ax1.imshow(Zm, interpolation='bilinear',
cmap=palette,
norm=colors.Normalize(vmin=-1.0, vmax=1.0, clip=False),
origin='lower', extent=[-3, 3, -3, 3])
ax1.set_title('Green=low, Red=high, Blue=bad')
fig.colorbar(im, extend='both', orientation='horizontal',
ax=ax1, aspect=10)
im = ax2.imshow(Zm, interpolation='nearest',
cmap=palette,
norm=colors.BoundaryNorm([-1, -0.5, -0.2, 0, 0.2, 0.5, 1],
ncolors=256, clip=False),
origin='lower', extent=[-3, 3, -3, 3])
ax2.set_title('With BoundaryNorm')
fig.colorbar(im, extend='both', spacing='proportional',
orientation='horizontal', ax=ax2, aspect=10)
开发者ID:4over7，项目名称:matplotlib，代码行数:30，代码来源:test_image.py
示例6: get_image
def get_image():
delta = 0.25
x = y = np.arange(-3.0, 3.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
Z = Z2 - Z1 # difference of Gaussians
return Z
开发者ID:AlexandreAbraham，项目名称:matplotlib，代码行数:8，代码来源:affine_image.py
示例7: get_gauss_kernel
def get_gauss_kernel(sigma, size, res):
"""
return a two dimesional gausian kernel of shape (size*(1/resolution),size*(1/resolution))
with a std deviation of std
"""
x,y = ny.mgrid[-size/2:size/2:res,-size/2:size/2:res]
b=bivariate_normal(x,y,sigma,sigma)
A=(1/ny.max(b))
#A=1
return x,y,A*bivariate_normal(x, y, sigma, sigma)
开发者ID:ohaas，项目名称:cmn，代码行数:10，代码来源:model.py
示例8: __call__
def __call__(self, inputs):
from matplotlib.mlab import bivariate_normal
X = self.get_input('X')
Y = self.get_input('Y')
Z1 = bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
# difference of Gaussians
Z = 10.0 * (Z2 - Z1)
return Z
开发者ID:MarieLatutu，项目名称:openalea-components，代码行数:10，代码来源:py_pylab.py
示例9: create_plot
def create_plot():
x = np.linspace(-3.0, 3.0, 30)
y = np.linspace(-2.0, 2.0, 30)
X, Y = np.meshgrid(x, y)
Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
Z = 10.0 * (Z2 - Z1)
fig, ax = plt.subplots()
CS = ax.contourf(X, Y, Z, 30)
return fig
开发者ID:Bantalik，项目名称:mpld3，代码行数:11，代码来源:test_contourf.py
示例10: test_image_plot
def test_image_plot(self):
delta = 0.025
x = y = np.arange(-3.0, 3.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
Z = Z2 - Z1 # difference of Gaussians
im = plt.imshow(Z, interpolation='bilinear', cmap=cm.RdYlGn,
origin='lower', extent=[-3, 3, -3, 3],
vmax=abs(Z).max(), vmin=-abs(Z).max())
plt.show()
开发者ID:rosswhitfield，项目名称:mantid，代码行数:12，代码来源:MantidPlotMatplotlibTest.py
示例11: test3
def test3():
N = 1000
x = (np.linspace(-2.0, 3.0, N))
y = (np.linspace(-2.0, 2.0, N))
X, Y = np.meshgrid(x, y)
X1 = 0.5*(X[:-1,:-1] + X[1:,1:])
Y1 = 0.5*(Y[:-1,:-1] + Y[1:,1:])
from matplotlib.mlab import bivariate_normal
Z1 = bivariate_normal(X1, Y1, 0.1, 0.2, 1.27, 1.11) + 100.*bivariate_normal(X1, Y1, 1.0, 1.0, 0.23, 0.72)
Z1[Z1>0.9*np.max(Z1)] = +np.inf
cdict = {'red': [(0.0, 1.0, 1.0),
(1.0, 1.0, 1.0)],
'green': [(0.0, 0.0, 0.0),
(1.0, 0.0, 0.0)],
'blue': [(0.0, 0.0, 0.0),
(1.0, 0.0, 0.0)],
'alpha': [(0.0, 0.0, 0.0),
(1.0, 1.0, 1.0)],
}
# this is a color maps showing how the resukt should look like
cdictx = {'red': [(0.0, 1.0, 1.0),
(1.0, 1.0, 1.0)],
'green': [(0.0, 1.0, 1.0),
(1.0, 0.0, 0.0)],
'blue': [(0.0, 1.0, 1.0),
(1.0, 0.0, 0.0)],
}
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
col = mpl_colors.LinearSegmentedColormap('test', cdict)
i = ax.pcolorfast(x, y, Z1, cmap = col)
plt.colorbar(i)
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
col = mpl_colors.LinearSegmentedColormap('testx', cdictx)
i = ax.pcolorfast(x, y, Z1, cmap = col)
plt.colorbar(i)
plt.show()
开发者ID:earnric，项目名称:modules，代码行数:53，代码来源:test.py
示例12: testcontour
def testcontour(self):
#contour plot test data:
delta = 0.025
x = np.arange(-3.0, 3.0, delta)
y = np.arange(-2.0, 2.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
# difference of Gaussians
Z = 10.0 * (Z2 - Z1)
plt.contour(X, Y, Z, 20)
开发者ID:QBI-Software，项目名称:Tracking，代码行数:12，代码来源:test_plot.py
示例13: main
def main():
x = np.linspace(-3.0, 3.0, 30)
y = np.linspace(-2.0, 2.0, 30)
X, Y = np.meshgrid(x, y)
Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
Z = 10.0 * (Z2 - Z1)
fig, ax = plt.subplots()
CS = ax.contour(X, Y, Z)
ax.clabel(CS, inline=True, fontsize=10)
return fig
开发者ID:Ahmed，项目名称:mpld3，代码行数:12，代码来源:test_contour.py
示例14: calcAtomGaussians
def calcAtomGaussians(mol,a=0.03,step=0.02,weights=None):
import numpy
from matplotlib import mlab
x = numpy.arange(0,1,step)
y = numpy.arange(0,1,step)
X,Y = numpy.meshgrid(x,y)
if weights is None:
weights=[1.]*mol.GetNumAtoms()
Z = mlab.bivariate_normal(X,Y,a,a,mol._atomPs[0][0], mol._atomPs[0][1])*weights[0]
for i in range(1,mol.GetNumAtoms()):
Zp = mlab.bivariate_normal(X,Y,a,a,mol._atomPs[i][0], mol._atomPs[i][1])
Z += Zp*weights[i]
return X,Y,Z
开发者ID:nclopezo，项目名称:chembl_beaker，代码行数:13，代码来源:__init__.py
示例15: get_test_data
def get_test_data(delta=0.05):
from matplotlib.mlab import meshgrid, bivariate_normal
x = y = npy.arange(-3.0, 3.0, delta)
X, Y = meshgrid(x,y)
Z1 = bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
Z = Z2-Z1
X = X * 10
Y = Y * 10
Z = Z * 500
return X,Y,Z
开发者ID:mcvine，项目名称:sansmodels，代码行数:13，代码来源:test3d.py
示例16: main
def main():
# Part of the example at
# http://matplotlib.sourceforge.net/plot_directive/mpl_examples/pylab_examples/contour_demo.py
delta = 0.025
x = numpy.arange(-3.0, 3.0, delta)
y = numpy.arange(-2.0, 2.0, delta)
X, Y = numpy.meshgrid(x, y)
Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
Z = 10.0 * (Z2 - Z1)
pyplot.figure()
CS = pyplot.contour(X, Y, Z)
pyplot.show()
开发者ID:ChaiZQ，项目名称:pyinstaller，代码行数:13，代码来源:test_matplotlib.py
示例17: image_demo
def image_demo(fig, ax):
delta = 0.025
x = y = np.arange(-3.0, 3.0, delta)
xx, yy = np.meshgrid(x, y)
z1 = mlab.bivariate_normal(xx, yy, 1.0, 1.0, 0.0, 0.0)
z2 = mlab.bivariate_normal(xx, yy, 1.5, 0.5, 1, 1)
image = z2-z1 # Difference of Gaussians
img_plot = ax.imshow(image)
ax.set_title('image')
fig.tight_layout()
# `colorbar` should be called after `tight_layout`.
fig.colorbar(img_plot, ax=ax)
开发者ID:thuyen，项目名称:matplotlib-style-gallery，代码行数:13，代码来源:artist-demo.py
示例18: calcAtomGaussians
def calcAtomGaussians(mol,a=0.03,step=0.02,weights=None):
import numpy
from matplotlib import mlab
x = numpy.arange(0,1,step)
y = numpy.arange(0,1,step)
X,Y = numpy.meshgrid(x,y)
if weights is None:
weights=[1.]*mol.GetNumAtoms()
Z = mlab.bivariate_normal(X,Y,a,a,mol._atomPs[0][0], mol._atomPs[0][1])*weights[0] # this is not bivariate case ... only univariate no mixtures #matplotlib.mlab.bivariate_normal(X, Y, sigmax=1.0, sigmay=1.0, mux=0.0, muy=0.0, sigmaxy=0.0)
for i in range(1,mol.GetNumAtoms()):
Zp = mlab.bivariate_normal(X,Y,a,a,mol._atomPs[i][0], mol._atomPs[i][1])
Z += Zp*weights[i]
return X,Y,Z
开发者ID:cheminfo，项目名称:RDKitjs，代码行数:13，代码来源:similarityMap_basic_functions.py
示例19: check_abnormal_with_density
def check_abnormal_with_density(meanx, meany, stdx, stdy, target_sz):
normal_sz = 264
target_sz = target_sz
X = np.arange(plot_lim_min, plot_lim_max, 0.1)
Y = np.arange(plot_lim_min, plot_lim_max, 0.1)
mX, mY = np.meshgrid(X, Y)
n1 = mlab.bivariate_normal(mX, mY, 2.15, 0.89, 15.31, -6.5)
n2 = mlab.bivariate_normal(mX, mY, 3.16, 3.21, 18, -17.5)
n3 = mlab.bivariate_normal(mX, mY, 1.79, 1, 17.5, -11.3)
n4 = mlab.bivariate_normal(mX, mY, 3.6, 2.5, 16, -11.4)
n5 = mlab.bivariate_normal(mX, mY, 3.4, 2.6, 14, -18)
n6 = mlab.bivariate_normal(mX, mY, 3.65, 4.85, 11, 4.7)
n7 = mlab.bivariate_normal(mX, mY, 1.85, 1.45, 15.8, -13)
normal_dist = n1 + n2 + n3 + n4 + n5 + n6 + n7
target_dist = mlab.bivariate_normal(mX, mY, stdx, stdy, meanx, meany)
normal_dist = normal_dist*(normal_sz/float(normal_sz+target_sz))
target_dist = target_dist*(target_sz/float(target_sz+target_sz))
s = 0
for x in range(len(X)) :
for y in range(len(Y)):
det = target_dist[x,y] - normal_dist[x,y]
if det > 0:
s = s + det
return s
开发者ID:zedoul，项目名称:AnomalyDetection，代码行数:27，代码来源:main.py
示例20: slit_image
def slit_image(self, y_strength):
x = numpy.arange(0, self.length*self.length_mult+1, 1.0)
y = numpy.arange(0, self.width*self.width_mult+1, 1.0)
X, Y = numpy.meshgrid(x, y)
ptsource = mlab.bivariate_normal(X, Y, self.FWHM, self.FWHM, len(x)/2.0+(self.object_location-0.5)*self.length, len(y)/2.0)
sky = numpy.zeros([len(y), len(x)])
for i in numpy.arange(len(x)/2.0-self.length/2.0, len(x)/2.0+self.length/2.0, 0.1):
for j in numpy.arange(len(y)/2.0-self.width/2.0, len(y)/2.0+self.width/2.0, 0.1):
sky += ((numpy.random.randn(1))**2.0)*mlab.bivariate_normal(X, Y, self.FWHM, self.FWHM, i, j)
#ptsource = mlab.bivariate_normal
composite = numpy.round(sky) + numpy.round(ptsource*500.0*y_strength)
return composite
开发者ID:soylentdeen，项目名称:FG_Widget，代码行数:14，代码来源:make_fake_dark.py
注：本文中的matplotlib.mlab.bivariate_normal函数示例由纯净天空整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。 |
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