本文整理匯總了Python中matplotlib.pyplot.cm.rainbow方法的典型用法代碼示例。如果您正苦於以下問題：Python cm.rainbow方法的具體用法？Python cm.rainbow怎麽用？Python cm.rainbow使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類matplotlib.pyplot.cm的用法示例。
在下文中一共展示了cm.rainbow方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: getcolors
# 需要導入模塊: from matplotlib.pyplot import cm [as 別名]
# 或者: from matplotlib.pyplot.cm import rainbow [as 別名]
def getcolors(number):
return list(cm.rainbow(linspace(0, 1, number))) 示例2: plotdisptraj
# 需要導入模塊: from matplotlib.pyplot import cm [as 別名]
# 或者: from matplotlib.pyplot.cm import rainbow [as 別名]
def plotdisptraj(s, P_UCS, E, E0, UCS, UC, diagnostics):
# measured energy dependant offset at FOMS normalized to 0 for EbE0=1
xf1t = lambda EbE0: -.078269*EbE0 + .078269 # + .059449
xf2t = lambda EbE0: -.241473*EbE0 + .241473 # + .229314
xf6t = lambda EbE0: 1.174523*EbE0 - 1.174523 # - 1.196090
xf7t = lambda EbE0: .998679*EbE0 - .998679 # - 1.018895
xf8t = lambda EbE0: .769875*EbE0 - .769875 # - .787049
steps = 6
X = [empty([6, P_UCS+1]) for i in range(steps)]
dEbE = linspace(-0.005, 0.005, steps)
for deltaE, i in zip(dEbE, range(steps)):
# R calculated for every energy (not necessary)
gamma = (E+deltaE*E)/E0+1
R = UCS2R(P_UCS, UCS, gamma)
X[i][:, 0] = array([0, 0, 0, 0, 0, deltaE])
X[i] = trackpart(X[i], R, P_UCS, P_UCS)*1e3
fig = Figure()
ax = fig.add_subplot(1, 1, 1)
drawlattice(ax, UC, diagnostics, X, 0)
ax.set_xlabel(r'orbit position s / (m)')
ax.set_ylabel(r'radial displacement / (mm)')
x = [s[UCS[0, :] == 7][i] for i in [0, 1, 5, 6, 7]]
color = iter(cm.rainbow(linspace(0, 1, steps)))
for i in range(steps):
c = next(color)
EE0 = 1 + dEbE[i]
y = array([xf1t(EE0), xf2t(EE0), xf6t(EE0), xf7t(EE0), xf8t(EE0)])*1e3
ax.plot(x, y, 'o', c=c)
ax.plot(s, X[i][0, :], c=c, label=r'$\delta={:g}$\textperthousand'.format(dEbE[i]*1e3))
ax.plot([], [], 'ok', label=r'measured')
#ax.get_xaxis().set_visible(False)
#leg = ax.legend(fancybox=True, loc=0)
#leg.get_frame().set_alpha(0.5)
ax.set_xlim([0, nanmax(s)])
return fig 示例3: plot_stuff
# 需要導入模塊: from matplotlib.pyplot import cm [as 別名]
# 或者: from matplotlib.pyplot.cm import rainbow [as 別名]
def plot_stuff(save=None):
colors=cm.rainbow(np.linspace(0,1,10))
valid_set, valid_labels = data.valid_batch(2000)
class_embeddings = np.array(embedding_fn(valid_set))
plt.figure(figsize=(18,9))
for cls, color in zip(range(10),colors):
current_points = class_embeddings[valid_labels==cls]
if save is not None:
plt.title('Iteration {}'.format(save))
plt.subplot(121).scatter(current_points[:,0], current_points[:,1],
c=color, #valid_labels[valid_labels==cls],
marker='${}$'.format(cls), s=100, linewidths=0.1, edgecolor='black')
plt.subplot(121).set_xlim([-1, 1])
plt.subplot(121).set_ylim([-1, 1])
current_points /= np.sqrt((current_points * current_points).sum(axis=1)).reshape(current_points.shape[0], 1)
plt.subplot(122).scatter(current_points[:1000,0], current_points[:1000,1],
c=color, #valid_labels[valid_labels==cls],
marker='${}$'.format(cls), s=100, linewidths=0.1, edgecolor='black')
plt.legend()
if save is not None:
plt.savefig('pics/{}.png'.format(save))
plt.close()
else:
plt.show() 示例4: _get_color_dict
# 需要導入模塊: from matplotlib.pyplot import cm [as 別名]
# 或者: from matplotlib.pyplot.cm import rainbow [as 別名]
def _get_color_dict(baseline_tagger, cFraction):
from matplotlib.pyplot import cm
color_dict = {}
color=iter(cm.rainbow(np.linspace(0,1,len(cFraction))))
for c_fraction in cFraction:
c=next(color)
color_dict.update({"DL1c"+str(int(c_fraction*100.)): c,})
color_dict.update({baseline_tagger: "black"})
return color_dict