def plot_heatmap_at_iteration(self, iteration, **options):
'''I line up the actual grid with interpolating grid so there is no interpolation, actually.
Options:
min_max_scale: [min, max] of the scale
colorbar_decimals: number of decimals used in the colorbar
'''
ax = plt.subplot()
ternary.plot_heatmap(lambda x: self.interp_histogram_at_iteration(iteration, x),
steps = self.num_individuals, boundary=True, **options)
scale = self.num_individuals
ternary.draw_boundary(scale=scale, ax=ax)
plt.gca().set_aspect('equal', adjustable='box')
plt.xlim([-.1*scale,1.1*scale])
plt.ylim([-0.13*scale, scale])
plt.text(1.01 * scale, -.05*scale, r'$f_1$', fontsize=35 )
plt.text(-.08 * scale, -.05*scale, r'$f_3$', fontsize=35 )
plt.text(.46*scale, .9*scale, r'$f_2$', fontsize=35)
plt.gca().yaxis.set_visible(False)
plt.gca().xaxis.set_visible(False)
plt.grid(False)
# Plot the fractional generation
generation_formatted = '%.2f' % self.frac_gen[iteration]
textbox = r'$N=' + str(self.num_individuals) + r'$' '\n'
textbox += 'Num Simulations: ' + r'$10^' + str(int(np.log10(self.num_simulations))) + r'$' + '\n'
textbox += 'Generation: ' + generation_formatted
plt.text(.71*scale, .85*scale, textbox, fontsize=15, linespacing=1.75)
def plot_crps(cls, data, show_3d=False):
""" Plot performance graph for all CRPs (Constant Rebalanced Portfolios).
:param data: Stock prices.
:param show_3d: Show CRPs on a 3-simplex, works only for 3 assets.
简易performance绘图模型，仅用于3种特定资产回报情况。
"""
def _crp(data):
B = list(tools.simplex_mesh(2, 100))
crps = CRP.run_combination(data, b=B)
x = [b[0] for b in B]
y = [c.total_wealth for c in crps]
return x, y
# init
import ternary
data = data.dropna(how='any')
data = data / data.ix[0]
dim = data.shape[1]
# plot prices
if dim == 2 and not show_3d:
fig, axes = plt.subplots(ncols=2, sharey=True)
data.plot(ax=axes[0], logy=True)
else:
data.plot(logy=False)
if show_3d:
assert dim == 3, '3D plot works for exactly 3 assets.'
plt.figure()
fun = lambda b: CRP(b).run(data).total_wealth
ternary.plot_heatmap(fun, steps=20, boundary=True)
elif dim == 2:
x, y = _crp(data)
s = pd.Series(y, index=x)
s.plot(ax=axes[1], logy=True)
plt.title('CRP performance')
plt.xlabel('weight of {}'.format(data.columns[0]))
elif dim > 2:
fig, axes = plt.subplots(ncols=dim - 1, nrows=dim - 1)
for i in range(dim - 1):
for j in range(i + 1, dim):
x, y = _crp(data[[i, j]])
ax = axes[i][j - 1]
ax.plot(x, y)
ax.set_title('{} & {}'.format(data.columns[i],
data.columns[j]))
ax.set_xlabel('weights of {}'.format(data.columns[i]))
def plot_crps(cls, data, show_3d=False):
""" Plot performance graph for all CRPs (Constant Rebalanced Portfolios).
:param data: Stock prices.
:param show_3d: Show CRPs on a 3-simplex, works only for 3 assets.
"""
def _crp(data):
B = list(tools.simplex_mesh(2, 100))
crps = CRP.run_combination(data, b=B)
x = [b[0] for b in B]
y = [c.total_wealth for c in crps]
return x, y
# init
import ternary
data = data.dropna(how='any')
data = data / data.ix[0]
dim = data.shape[1]
# plot prices
if dim == 2 and not show_3d:
fig, axes = plt.subplots(ncols=2, sharey=True)
data.plot(ax=axes[0], logy=True)
else:
data.plot(logy=False)
if show_3d:
assert dim == 3, '3D plot works for exactly 3 assets.'
plt.figure()
fun = lambda b: CRP(b).run(data).total_wealth
ternary.plot_heatmap(fun, steps=20, boundary=True)
elif dim == 2:
x,y = _crp(data)
s = pd.Series(y, index=x)
s.plot(ax=axes[1], logy=True)
plt.title('CRP performance')
plt.xlabel('weight of {}'.format(data.columns[0]))
elif dim > 2:
fig, axes = plt.subplots(ncols=dim-1, nrows=dim-1)
for i in range(dim-1):
for j in range(i + 1, dim):
x,y = _crp(data[[i,j]])
ax = axes[i][j-1]
ax.plot(x,y)
ax.set_title('{} & {}'.format(data.columns[i], data.columns[j]))
ax.set_xlabel('weights of {}'.format(data.columns[i]))
def plot_heatmap_at_iteration(self, iteration, **options):
'''I line up the actual grid with interpolating grid so there is no interpolation, actually.
Options:
min_max_scale: [min, max] of the scale
colorbar_decimals: number of decimals used in the colorbar
'''
ax = plt.subplot()
ternary.plot_heatmap(
lambda x: self.interp_histogram_at_iteration(iteration, x),
steps=self.num_individuals,
boundary=True,
**options)
scale = self.num_individuals
ternary.draw_boundary(scale=scale, ax=ax)
plt.gca().set_aspect('equal', adjustable='box')
plt.xlim([-.1 * scale, 1.1 * scale])
plt.ylim([-0.13 * scale, scale])
plt.text(1.01 * scale, -.05 * scale, r'$f_1$', fontsize=35)
plt.text(-.08 * scale, -.05 * scale, r'$f_3$', fontsize=35)
plt.text(.46 * scale, .9 * scale, r'$f_2$', fontsize=35)
plt.gca().yaxis.set_visible(False)
plt.gca().xaxis.set_visible(False)
plt.grid(False)
# Plot the fractional generation
generation_formatted = '%.2f' % self.frac_gen[iteration]
textbox = r'$N=' + str(self.num_individuals) + r'$' '\n'
textbox += 'Num Simulations: ' + r'$10^' + str(
int(np.log10(self.num_simulations))) + r'$' + '\n'
textbox += 'Generation: ' + generation_formatted
plt.text(.71 * scale,
.85 * scale,
textbox,
fontsize=15,
linespacing=1.75)
def heatmap_example(func, steps=100, boundary=True):
ternary.plot_heatmap(func, steps=steps, boundary=boundary)
ternary.draw_boundary(scale=steps)
