def plot_spectral_points(self, spectra, fmt='r+', labels=None, lpos=None):
'''
Plots spectral profiles in feature space.
'''
m, n, c = self.__dims__
assert type(spectra) in (
list, tuple,
np.ndarray), 'Expected spectra to be a list, tuple, or numpy array'
if labels is not None and lpos is None:
lpos = [(30, -30)] * len(labels)
for i, spec in enumerate(spectra):
plt.plot(spec[m], spec[n], fmt, ms=20, mew=2)
if labels is not None:
xy = (spec[m], spec[n])
plt.annotate(labels[i],
xy=xy,
xytext=lpos[i],
textcoords='offset points',
ha='right',
va='bottom',
fontsize=14,
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3,rad=0'))
def plot_xy_points(self,
coords,
fmt='r+',
labels=None,
lpos=None,
dd=True):
'''
Add point(s) to the plot, given by their longitude-latitude (XY)
coordinates, which will be displayed at the appropriate feature space
coordinates.
'''
assert not self.__raveled__, 'Cannot do this when the input array is raveled'
m, n, c = self.__dims__
pcoords = xy_to_pixel(coords, gt=self.__gt__, wkt=self.__wkt__, dd=dd)
if labels is not None and lpos is None:
lpos = [(10, -10)] * len(labels)
# http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot
for i, (x, y) in enumerate(pcoords):
spec = self.features[x, y, :]
plt.plot(spec[m], spec[n], fmt, ms=20, mew=2)
if labels is not None:
plt.annotate(labels[i],
xy=(spec[m], spec[n]),
xytext=lpos[i],
textcoords='offset points',
ha='right',
va='bottom',
fontsize=14,
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3,rad=0'))
def plot_profits(self, player, period):
profits = self.results["profits"][-period:]
plt.title("Profits")
time_window = 100
x = np.arange(len(profits[:, player]))
y = []
for i in x:
if i < time_window:
y_value = np.mean(profits[:i + 1, player])
else:
y_value = np.mean(profits[i - time_window:i + 1, player])
y.append(y_value)
plt.plot(x, y, color="black")
maximum_profit = \
self.parameters["n_positions"] * \
self.parameters["n_prices"]
plt.ylim(0, maximum_profit)
plt.annotate("Time window: {}".format(time_window),
xy=(0.8, 0.1),
xycoords='axes fraction',
fontsize=6)
# plt.annotate(self.string_parameters, xy=(-0.05, -0.1), xycoords='axes fraction', fontsize=6)
plt.savefig(self.format_fig_name("profits_player{}".format(player)))
plt.close()
def biaozhukedu(dfc, weibiao):
if weibiao == dfc.index.max():
kedus = [dfc.loc[weibiao]]
else:
kedus = [dfc.loc[weibiao], dfc.loc[dfc.index.max()]]
# print(type(kedus[0]))
for ii in range(len(kedus)):
kedu = kedus[ii]
if (len(dfc.index)) > 12:
idx = kedu.name
else:
idx = list(dfc.index).index(kedu.name)
if not np.isnan(kedu.iloc[0]):
plt.plot([idx, idx], [0, kedu.iloc[0]], 'c--')
plt.annotate(str(kedu.name),
xy=(idx, 0),
xycoords='data',
xytext=(-20, -20),
textcoords='offset points',
color='r',
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=0"))
for i in range(len(kedu)):
if np.isnan(kedu.iloc[i]):
# print(kedu.iloc[i])
# print(type(kedu.iloc[i]))
continue
plt.scatter([
idx,
], [kedu.iloc[i]], 50, color='Wheat')
# global ywananchor
if kedu.map(lambda x: abs(x)).max() >= ywananchor:
kedubiaozhi = "%.1f万" % (kedu.iloc[i] / 10000)
plt.gca().yaxis.set_major_formatter(
FuncFormatter(lambda x, pos: "%d万" % int(x / 10000))
) # 纵轴主刻度文本用y_formatter函数计算
else:
kedubiaozhi = "%d" % kedu.iloc[i]
fontsize = 8
if (i % 2) == 0:
zhengfu = -1
else:
zhengfu = 0.4
plt.annotate(
kedubiaozhi,
xy=(idx, kedu.iloc[i]),
xycoords='data',
xytext=(len(kedubiaozhi) * fontsize * zhengfu,
int(len(kedubiaozhi) * fontsize * (-1) * zhengfu / 2)),
textcoords='offset points',
fontsize=fontsize,
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=.2",
color='Purple'))
def add_data(self,
income,
grade,
color='yellow',
markersize=25,
markertext=None):
"""Add a value and plot it"""
self.incomes.append(income)
self.grades.append(grade)
plt.plot([income], [grade],
color=color,
marker='o',
markersize=markersize)
if markertext:
plt.annotate(markertext,
xy=(income, grade),
horizontalalignment='center',
verticalalignment='center')
plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
plt.axis([40, 160, 0, 0.03])
plt.grid(True)
plt.show()
'==========================================='
ax = plt.subplot(111)
t = np.arange(0.0, 5.0, 0.01)
s = np.cos(2 * np.pi * t)
line, = plt.plot(t, s, lw=2)
plt.annotate(
'local max',
xy=(2, 1),
xytext=(3, 1.5),
arrowprops=dict(facecolor='black', shrink=0.05),
)
plt.ylim(-2, 2)
plt.show()
'==========================================='
# 导入 matplotlib 的所有内容（nympy 可以用 np 这个名字来使用）
# 创建一个 8 * 6 点（point）的图，并设置分辨率为 80
figure(figsize=(8, 6), dpi=80)
# 创建一个新的 1 * 1 的子图，接下来的图样绘制在其中的第 1 块（也是唯一的一块）
subplot(1, 1, 1)
del expr
S_T = S_BM[ -1 ]
mu_3s = np.mean( S_T ) + 3 * np.std( S_T )
mu_5s = np.mean( S_T ) + 5 * np.std( S_T )
#............................................................................
# Histogram of final values
#............................................................................
plt.hist( S_T, bins = 100, edgecolor = 'darkgray', color = 'darkblue' )
plt.axvline( S0, linestyle = 'dashed', alpha = 0.8, color = 'darkred' )
plt.axvline( np.mean( S_T ) , linestyle = 'dashed', alpha = 0.8, color = 'red' )
plt.axvline( mu_3s, linestyle = 'dashed', alpha = 0.8, color = 'red' )
plt.annotate( s = '$\mu + 3\sigma = $' + str( round( mu_3s ,1) ), xy=(mu_3s, 500 ) )
plt.axvline( mu_5s, linestyle = 'dashed', alpha = 0.8, color = 'red' )
plt.annotate( s = '$\mu + 5\sigma = $' + str( round( mu_5s ,1) ), xy=(mu_5s, 500 ) )
plt.xlabel('index level' )
plt.ylabel('frequency' )
plt.title( 'Geometric Brownian Motion: distribution of $S_T$' )
#............................................................................
# Sample of paths
#............................................................................
plt.plot( S_BM[:, :10], lw = 0.85, linestyle = '-.' )
plt.xlabel('time')
plt.xlabel('index level')
def getcutpoint(inputdatapath):
"""
根据时间间隔找到分割点，生成最近一次的图像和全部综合图像并返回
"""
totalmen = gettotalmem(getdatapath(inputdatapath))
inputdf = getdatadf(getdatapath(inputdatapath))
ds = inputdf['time'] - inputdf['time'].shift()
deltads = ds[ds > getdelta()]
outlst = list()
for ix in deltads.index:
ipos = list(inputdf.index).index(ix)
# 处理内存占满（但未重启）的特殊情况
if inputdf.iloc[ipos - 1]['freeper'] == 0:
continue
print()
print(ipos, ix, deltads[ix])
outlst.append(ipos)
# bfdf = inputdf[inputdf.index >= (ix - deltads[ix] + pd.Timedelta(minutes=-5))]
# tmpdf = bfdf[bfdf.index < (ix + pd.Timedelta(minutes=5))]
# print(tmpdf)
outlst.insert(0, 0)
outlst.append(inputdf.shape[0])
plt.rcParams['font.sans-serif'] = 'SimHei'
olen = len(outlst)
if olen == 2:
picheight = 1
elif olen == 3:
picheight = 2
else:
picheight = 3
plt.figure(figsize=(10, 10 * picheight))
imgpath = getdirmain() / 'img' / 'freemen.png'
touchfilepath2depth(imgpath)
# fig, ax1 = plt.subplots()
# 针对数据集周期次数进行处理，主要是处理小于三次的情况
if len(outlst) > 3:
plt.subplot(311)
elif len(outlst) == 3:
plt.subplot(211)
# 最近数据集图形化输出
plt.ylabel(f'空闲内存百分比({totalmen}G)')
cutdf = inputdf.iloc[outlst[-2]:outlst[-1]]
plt.plot(cutdf.index, cutdf['freeper'])
plt.ylim(0, 100)
plt.title('最近一次运行')
plt.annotate(cutdf.index[0].strftime("%y-%m-%d %H:%M"),
xy=[cutdf.index[0], cutdf.iloc[0, 1]])
plt.annotate(cutdf.index[-1].strftime("%y-%m-%d %H:%M"),
xy=[cutdf.index[-1], cutdf.iloc[-1, 1]])
# 针对单次（一般也是首次运行）数据集进行处理
if len(outlst) == 2:
plt.savefig(imgpath)
return str(imgpath)
# 针对数据集周期次数进行处理，主要是处理小于三次的情况
if len(outlst) == 3:
plt.subplot(212)
elif len(outlst) > 3:
plt.subplot(312)
plt.ylabel(f'空闲内存百分比({totalmen}G)')
plt.ylim(0, 100)
plt.title('最近两次运行')
twolst = outlst[-3:]
for i in range(len(twolst) - 1):
cutdf = inputdf.iloc[twolst[i]:twolst[i + 1]]
plt.plot(cutdf.index, cutdf['freeper'])
plt.annotate(cutdf.index[0].strftime("%y-%m-%d %H:%M"),
xy=[cutdf.index[0], cutdf.iloc[0, 1]])
plt.annotate(cutdf.index[-1].strftime("%y-%m-%d %H:%M"),
xy=[cutdf.index[-1], cutdf.iloc[-1, 1]])
# 综合（全部）数据集图形输出
# 针对仅有两次数据集进行处理
if len(outlst) == 3:
plt.savefig(imgpath)
return str(imgpath)
else:
plt.subplot(313)
plt.ylabel(f'空闲内存百分比({totalmen}G)')
plt.ylim(0, 100)
plt.title('历次运行')
for i in range(len(outlst) - 1):
cutdf = inputdf.iloc[outlst[i]:outlst[i + 1]]
plt.plot(cutdf.index, cutdf['freeper'])
plt.annotate(cutdf.index[0].strftime("%y-%m-%d %H:%M"),
xy=[cutdf.index[0], cutdf.iloc[0, 1]])
plt.annotate(cutdf.index[-1].strftime("%y-%m-%d %H:%M"),
xy=[cutdf.index[-1], cutdf.iloc[-1, 1]])
plt.savefig(imgpath)
return str(imgpath)