本文整理汇总了Python中scipy.arange方法的典型用法代码示例。如果您正苦于以下问题:Python scipy.arange方法的具体用法?Python scipy.arange怎么用?Python scipy.arange使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类scipy
的用法示例。
在下文中一共展示了scipy.arange方法的15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,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: compare_solutions
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import arange [as 别名]
def compare_solutions(A,B,m):
n = A.shape[0]
numpy.random.seed(0)
V = rand(n,m)
X = linalg.orth(V)
eigs,vecs = lobpcg(A, X, B=B, tol=1e-5, maxiter=30)
eigs.sort()
#w,v = symeig(A,B)
w,v = eig(A,b=B)
w.sort()
assert_almost_equal(w[:int(m/2)],eigs[:int(m/2)],decimal=2)
#from pylab import plot, show, legend, xlabel, ylabel
#plot(arange(0,len(w[:m])),w[:m],'bx',label='Results by symeig')
#plot(arange(0,len(eigs)),eigs,'r+',label='Results by lobpcg')
#legend()
#xlabel(r'Eigenvalue $i$')
#ylabel(r'$\lambda_i$')
#show()
示例2: test_fetch_one_column
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import arange [as 别名]
def test_fetch_one_column(tmpdata):
_urlopen_ref = datasets.mldata.urlopen
try:
dataname = 'onecol'
# create fake data set in cache
x = sp.arange(6).reshape(2, 3)
datasets.mldata.urlopen = mock_mldata_urlopen({dataname: {'x': x}})
dset = fetch_mldata(dataname, data_home=tmpdata)
for n in ["COL_NAMES", "DESCR", "data"]:
assert_in(n, dset)
assert_not_in("target", dset)
assert_equal(dset.data.shape, (2, 3))
assert_array_equal(dset.data, x)
# transposing the data array
dset = fetch_mldata(dataname, transpose_data=False, data_home=tmpdata)
assert_equal(dset.data.shape, (3, 2))
finally:
datasets.mldata.urlopen = _urlopen_ref
示例3: _plotFMeasures
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import arange [as 别名]
def _plotFMeasures(fstepsize=.1, stepsize=0.0005, start = 0.0, end = 1.0):
"""Plots 10 fmeasure Curves into the current canvas."""
p = sc.arange(start, end, stepsize)[1:]
for f in sc.arange(0., 1., fstepsize)[1:]:
points = [(x, _fmeasureCurve(f, x)) for x in p
if 0 < _fmeasureCurve(f, x) <= 1.5]
try:
xs, ys = zip(*points)
curve, = pl.plot(xs, ys, "--", color="gray", linewidth=0.8) # , label=r"$f=%.1f$"%f) # exclude labels, for legend
# bad hack:
# gets the 10th last datapoint, from that goes a bit to the left, and a bit down
datapoint_x_loc = int(len(xs)/2)
datapoint_y_loc = int(len(ys)/2)
# x_left = 0.05
# y_left = 0.035
x_left = 0.035
y_left = -0.02
pl.annotate(r"$f=%.1f$" % f, xy=(xs[datapoint_x_loc], ys[datapoint_y_loc]), xytext=(xs[datapoint_x_loc] - x_left, ys[datapoint_y_loc] - y_left), size="small", color="gray")
except Exception as e:
print e
#colors = "gcmbbbrrryk"
#colors = "yyybbbrrrckgm" # 7 is a prime, so we'll loop over all combinations of colors and markers, when zipping their cycles
示例4: regresionCurve
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import arange [as 别名]
def regresionCurve(self):
dlg = Plot(accept=True)
x = self.curvaDestilacion.column(0)
T = self.curvaDestilacion.column(1, Temperature)
dlg.addData(x, T, color="black", ls="None", marker="s", mfc="red")
parameters, r2 = curve_Predicted(x, T)
xi = arange(0, 1, 0.01)
Ti = [_Tb_Predicted(parameters, x_i) for x_i in xi]
dlg.addData(xi, Ti, color="black", lw=0.5)
# Add equation formula to plot
txt = r"$\frac{T-T_{o}}{T_{o}}=\left[\frac{A}{B}\ln\left(\frac{1}{1-x}"
txt += r"\right)\right]^{1/B}$"
To = Temperature(parameters[0])
txt2 = "\n\n\n$T_o=%s$" % To.str
txt2 += "\n$A=%0.4f$" % parameters[1]
txt2 += "\n$B=%0.4f$" % parameters[2]
txt2 += "\n$r^2=%0.6f$" % r2
dlg.plot.ax.text(0, T[-1], txt, size="14", va="top", ha="left")
dlg.plot.ax.text(0, T[-1], txt2, size="10", va="top", ha="left")
if dlg.exec_():
self.curveParameters = parameters
self.checkStatusCurve()
示例5: coupling_optim
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import arange [as 别名]
def coupling_optim(y,t):
creation=s.zeros(n_bin)
destruction=s.zeros(n_bin)
#now I try to rewrite this in a more optimized way
destruction = -s.dot(s.transpose(kernel),y)*y #much more concise way to express\
#the destruction of k-mers
kyn = kernel*y[:,s.newaxis]*y[s.newaxis,:]
for k in xrange(n_bin):
creation[k] = s.sum(kyn[s.arange(k),k-s.arange(k)-1])
creation=0.5*creation
out=creation+destruction
return out
#Now I go for the optimal optimization of the chi_{i,j,k} coefficients used by Garrick for
# dealing with a non-uniform grid.
示例6: test_SIS_compact_pairwise
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import arange [as 别名]
def test_SIS_compact_pairwise(self):
print("testing SIS_compact_pairwise")
EoN.EoNError('changing order of arguments')
Sk0 = scipy.arange(100) * 100
Ik0 = scipy.arange(100)
SI0 = Ik0.dot(scipy.arange(100))
SS0 = Sk0.dot(scipy.arange(100)) - SI0
II0 = 0
tau = 0.1
gamma = 0.3
t, S, I = EoN.SIS_compact_pairwise(Sk0, Ik0, SI0, SS0, II0, tau, gamma, tmax=5)
print("plotting SIS_compact_pairwise")
plt.clf()
plt.plot(t, S)
plt.plot(t, I)
plt.savefig('SIS_compact_pairwise')
示例7: test_SIR_compact_pairwise
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import arange [as 别名]
def test_SIR_compact_pairwise(self):
EoN.EoNError('changing order of arguments')
print("testing SIR_compact_pairwise")
Sk0 = scipy.arange(100) * 100
I0 = sum(scipy.arange(100))
R0 = 0
SI0 = 1000
SS0 = Sk0.dot(scipy.arange(100)) - SI0
tau = 0.1
gamma = 0.3
t, S, I, R = EoN.SIR_compact_pairwise(Sk0, I0, R0, SI0, SS0, tau, gamma, tmax=5)
print("plotting SIR_compact_pairwise")
plt.clf()
plt.plot(t, S)
plt.plot(t, I)
plt.plot(t, R)
plt.savefig('SIR_compact_pairwise')
示例8: up_and_out_call
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import arange [as 别名]
def up_and_out_call(s0,x,T,r,sigma,n_simulation,barrier):
n_steps=100.
dt=T/n_steps
total=0
for j in sp.arange(0, n_simulation):
sT=s0
out=False
for i in range(0,int(n_steps)):
e=sp.random.normal()
sT*=sp.exp((r-0.5*sigma*sigma)*dt+sigma*e*sp.sqrt(dt))
if sT>barrier:
out=True
if out==False:
total+=bsCall(s0,x,T,r,sigma)
return total/n_simulation
#
示例9: KMV_f
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import arange [as 别名]
def KMV_f(E,D,T,r,sigmaE):
n=10000
m=2000
diffOld=1e6 # a very big number
for i in sp.arange(1,10):
for j in sp.arange(1,m):
A=E+D/2+i*D/n
sigmaA=0.05+j*(1.0-0.001)/m
d1 = (log(A/D)+(r+sigmaA*sigmaA/2.)*T)/(sigmaA*sqrt(T))
d2 = d1-sigmaA*sqrt(T)
diff4E= (A*N(d1)-D*exp(-r*T)*N(d2)-E)/A # scale by assets
diff4A= A/E*N(d1)*sigmaA-sigmaE # a small number already
diffNew=abs(diff4E)+abs(diff4A)
if diffNew<diffOld:
diffOld=diffNew
output=(round(A,2),round(sigmaA,4),round(diffNew,5))
return output
#
示例10: extend
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import arange [as 别名]
def extend(self, extracted_features):
# This method reads the pkl files in a folder and adds them to the
# existing data for processing in the TCData class.
(data, labels, feature_string, width, height, winsize, nbins) = extracted_features
npixels = width * height
xlabel = 'Grayscale intensity'
ylabel = 'Probability'
xvals = scipy.arange(self.data.shape[0]).reshape(-1,1)
self.data = N.concatenate((self.data, data),axis=1)
self.width = N.append(self.width, width)
self.height = N.append(self.height, height)
self.xvals = N.append(self.xvals, xvals)
self.labels.extend(labels)
self.img_label_split.extend([len(self.labels)])
self.data_split.extend([self.data.shape[1]])
示例11: test_fetch_one_column
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import arange [as 别名]
def test_fetch_one_column():
_urlopen_ref = datasets.mldata.urlopen
try:
dataname = 'onecol'
# create fake data set in cache
x = sp.arange(6).reshape(2, 3)
datasets.mldata.urlopen = mock_mldata_urlopen({dataname: {'x': x}})
dset = fetch_mldata(dataname, data_home=tmpdir)
for n in ["COL_NAMES", "DESCR", "data"]:
assert_in(n, dset)
assert_not_in("target", dset)
assert_equal(dset.data.shape, (2, 3))
assert_array_equal(dset.data, x)
# transposing the data array
dset = fetch_mldata(dataname, transpose_data=False, data_home=tmpdir)
assert_equal(dset.data.shape, (3, 2))
finally:
datasets.mldata.urlopen = _urlopen_ref
示例12: xy_v_u
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import arange [as 别名]
def xy_v_u(self, v, u):
"""
give x(v,u), y(v,u)
"""
x_start = self.nomo_block.grid_box.x_left
x_stop = self.nomo_block.grid_box.x_right
if x_start > x_stop: # should no be
x_start, x_stop = x_stop, x_start
# x_init=(x_start+x_stop)/2.0
v_func = self.nomo_block.grid_box.v_func
u_func = self.nomo_block.grid_box.u_func
u_value = u_func(u) # = y
func_opt = lambda x: (v_func(x, v) - u_value) ** 2 # func to minimize
# let's try to find good starting point for optimization
x_range = arange(x_start, x_stop, (x_stop - x_start) / 30.0, dtype=complex)
# print "x_range:"
# print x_range
# use complex numbers to filter results with complex part
#values = func_opt(x_range.astype(complex))
values = x_range
values_list_complex = values.tolist()
values_list = []
for value in values_list_complex:
if value.imag == 0:
values_list.append(value.real)
else:
values_list.append(1e12) # large number
# print "values_list:"
# print values_list
min_x_idx = values_list.index(min(values_list))
x_init = x_range[min_x_idx]
# print "x_start %g"%x_start
# print "x_stop %g"%x_stop
# print "x_init %g"%x_init
# find x point where u meets v = optimization
x_opt = fmin(func_opt, [x_init], disp=0, maxiter=1e5, maxfun=1e5, ftol=1e-8, xtol=1e-8)[0]
x_transformed = self.nomo_block._give_trafo_x_(x_opt, u_value)
y_transformed = self.nomo_block._give_trafo_y_(x_opt, u_value)
return x_transformed, y_transformed, x_opt, u_value
示例13: find_log_ticks
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import arange [as 别名]
def find_log_ticks(start, stop):
"""
finds tick values for linear axis
"""
if (start < stop):
min, max = start, stop
else:
min, max = stop, start
# lists for ticks
tick_0_list = []
tick_1_list = []
tick_2_list = []
max_decade = math.ceil(math.log10(max))
min_decade = math.floor(math.log10(min))
start_ax = None
stop_ax = None
for decade in scipy.arange(min_decade, max_decade + 1, 1):
# for number in scipy.concatenate((scipy.arange(1,2,0.2),scipy.arange(2,3,0.5),scipy.arange(3,10,1))):
for number in [1, 1.2, 1.4, 1.6, 1.8, 2.0, 2.5, 3, 4, 5, 6, 7, 8, 9]:
u = number * 10.0 ** decade
if u >= min and u <= max:
if start_ax == None:
start_ax = number
stop_ax = number
if number == 1:
tick_0_list.append(u)
if number in [2, 3, 4, 5, 6, 7, 8, 9]:
tick_1_list.append(u)
if number in [1.2, 1.4, 1.6, 1.8, 2.5]:
tick_2_list.append(u)
# print tick_0_list
# print tick_1_list
# print tick_2_list
return tick_0_list, tick_1_list, tick_2_list, start_ax, stop_ax
示例14: MikotaPair
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import arange [as 别名]
def MikotaPair(n):
# Mikota pair acts as a nice test since the eigenvalues
# are the squares of the integers n, n=1,2,...
x = arange(1,n+1)
B = diag(1./x)
y = arange(n-1,0,-1)
z = arange(2*n-1,0,-2)
A = diag(z)-diag(y,-1)-diag(y,1)
return A,B
示例15: calculo
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import arange [as 别名]
def calculo(self):
ind1=self.Comp1.currentIndex()
ind2=self.Comp2.currentIndex()
if ind1!=ind2:
zi=arange(0.025, 1., 0.025)
id1=self.indices[ind1]
id2=self.indices[ind2]
x=[0]
y=[0]
for z in zi:
try:
fraccion=[0.]*len(self.indices)
fraccion[ind1]=z
fraccion[ind2]=1-z
mez=Mezcla(tipo=3, fraccionMolar=fraccion, caudalMasico=1.)
tb=mez.componente[0].Tb
corr=Corriente(T=tb, P=101325., mezcla=mez)
T=corr.eos._Dew_T()
corr=Corriente(T=T, P=101325., mezcla=mez)
while corr.Liquido.fraccion[0]==corr.Gas.fraccion[0] and corr.T<corr.mezcla.componente[1].Tb:
corr=Corriente(T=corr.T-0.1, P=101325., mezcla=mez)
x.append(corr.Liquido.fraccion[0])
y.append(corr.Gas.fraccion[0])
except:
pass
x.append(1)
y.append(1)
self.rellenar(x, y)