def waveform_trio_features_and_points(audio,
feature,
point_list,
rms=None,
peak_envelope=None,
figsize=None):
# configuring figure and subplots
if not figsize:
figsize = DEFAULT_FIG_SIZE
f = plt.figure(figsize=figsize)
axes_list = f.subplots(2, sharex=True)
# add waveform trio to first axes
_add_waveform_trio_to_axes(axes_list[0], audio, rms, peak_envelope)
# add feature
_add_curve_to_axes(axes_list[1], feature, label=feature.label)
axes_list[1].legend(loc='lower right', fontsize='x-small')
_add_points_to_axes(axes_list[1], point_list.time,
point_list.get_values(feature))
MultiCursor(f.canvas, axes_list, color='gray', lw=1)
f.show()
def waveform_trio_and_features(audio,
rms=None,
peak_envelope=None,
features=(),
figsize=None):
"""
Plot a graph showing curves for the ``audio`` waveform, the ``rms`` and the
``peak_envelope``; followed by a series of graphs, one for each time-series
in the tuple `features`.
"""
if not features:
raise ValueError("the features to be plotted were not specified")
# configuring figure and subplots
if not figsize:
figsize = DEFAULT_FIG_SIZE
f = plt.figure(figsize=figsize)
axes_list = f.subplots(len(features) + 1, sharex=True)
plt.subplots_adjust(hspace=0.05)
# add audio to first axes
_add_waveform_trio_to_axes(axes_list[0], audio, rms, peak_envelope)
# add features to the other axes
for i, feature in enumerate(features, start=1):
_add_curve_to_axes(axes_list[i], feature, label=feature.label)
axes_list[i].legend(loc='lower right', fontsize='x-small')
MultiCursor(f.canvas, axes_list, color='gray', lw=1)
plt.show()
return f
def smooth(x, isosbestic, calcium, **kwargs):
"""Function that smooths the raw data and displays it in a plot.
Args : x (arr) = The time data in X
isosbestic (arr) = The adjusted isosbestic signal (time fitted to the video)
calcium (arr) = The adjusted calcium signal (time fitted to the video)
kwargs (dict) = Dictionnary with the parameters
Returns : x (arr) = The new time data in X
isosbestic_smoothed (arr) = The smoothed isosbestic signal
calcium_smoothed (arr) = The smoothed calcium signal
"""
print("\nStarting smoothing for Isosbestic and Calcium signals !")
isosbestic_smoothed = smooth_signal(isosbestic, window_len=kwargs["smoothing_window"])[:-kwargs["smoothing_window"]+1]
calcium_smoothed = smooth_signal(calcium, window_len=kwargs["smoothing_window"])[:-kwargs["smoothing_window"]+1]
x_max = x[-1]
# print("Data length : {0}".format(ut.h_m_s(x_max, add_tags=True)))
if kwargs["photometry_pp"]["plots_to_display"]["smoothing"]:
xticks, xticklabels, unit = utils.generate_xticks_and_labels(x_max)
fig = plt.figure(figsize=(10, 5), dpi=200.)
ax0 = plt.subplot(211)
p, = ax0.plot(x, isosbestic_smoothed, alpha=0.8, c=kwargs["photometry_pp"]["purple_laser"], lw=kwargs["lw"])
ax0.set_xticks(xticks)
ax0.set_xticklabels(xticklabels, fontsize=kwargs["fsl"])
ax0.set_xlim(0, x_max)
y_min, y_max, round_factor = utils.generate_yticks(isosbestic_smoothed, 0.1)
ax0.set_yticks(np.arange(y_min, y_max+round_factor, round_factor))
ax0.set_yticklabels(["{:.0f}".format(i) for i in np.arange(y_min, y_max+round_factor, round_factor)*1000], fontsize=kwargs["fsl"])
ax0.set_ylim(y_min, y_max)
ax0.set_ylabel("mV", fontsize=kwargs["fsl"])
ax0.legend(handles=[p], labels=["isosbestic"], loc=2, fontsize=kwargs["fsl"])
ax0.set_title("Smoothed Isosbestic and Calcium signals", fontsize=kwargs["fst"])
ax0.tick_params(axis='both', which='major', labelsize=kwargs["fsl"])
ax1 = plt.subplot(212, sharex=ax0)
b, = ax1.plot(x, calcium_smoothed, alpha=0.8, c=kwargs["photometry_pp"]["blue_laser"], lw=kwargs["lw"])
ax1.set_xticks(xticks)
ax1.set_xticklabels(xticklabels, fontsize=kwargs["fsl"])
ax1.set_xlim(0, x_max)
y_min, y_max, round_factor = utils.generate_yticks(calcium_smoothed, 0.1)
ax1.set_yticks(np.arange(y_min, y_max+round_factor, round_factor))
ax1.set_yticklabels(["{:.0f}".format(i) for i in np.arange(y_min, y_max+round_factor, round_factor)*1000], fontsize=kwargs["fsl"])
ax1.set_ylim(y_min, y_max)
ax1.set_ylabel("mV", fontsize=kwargs["fsl"])
ax1.legend(handles=[b], labels=["calcium"], loc=2, fontsize=kwargs["fsl"])
ax1.set_xlabel("Time ({0})".format(unit), fontsize=kwargs["fsl"])
ax1.tick_params(axis='both', which='major', labelsize=kwargs["fsl"])
plt.tight_layout()
if kwargs["photometry_pp"]["multicursor"]:
multi = MultiCursor(fig.canvas, [ax0, ax1], color='r', lw=1, vertOn=[ax0, ax1]) # FIXME: unused
if kwargs["save"]:
plt.savefig(os.path.join(kwargs["save_dir"], "Smoothed_Signals.{0}".format(kwargs["extension"])), dpi=200.)
return x, isosbestic_smoothed, calcium_smoothed
def show(Close, Earns):
Xl = range(len(Close))
name, rate = 0, 1
fig = plt.figure('定投策略收益率')
ax1 = plt.subplot(211)
plt.plot(Xl, Close, label='收盘价')
ax1.axhline(y=Close[0],
color='#d46061',
linewidth=1,
label='建仓价位%d' % Close[0])
plt.legend(loc='upper left')
low, high = 0, 0
ax2 = plt.subplot(212)
for Earn in Earns:
plt.plot(Xl, Earn[rate], label=Earn[name])
low = min(low, min(Earn[rate]))
high = max(high, max(Earn[rate]))
plt.ylim(int(low - 5) / 5 * 5, int(high + 5) / 5 * 5) #不同参数比较时固定合理的y坐标范围
ax2.axhline(y=0, color='#d46061', linewidth=1, label='0')
# low, high = min(Earn), max(Earn)
# mean = sum(Earn) / len(Earn)
# ax2.axhline(y=low, color='yellow', linewidth=1, label='min=%0.2f'%low)
# ax2.axhline(y=high, color='gray', linewidth=1, label='high=%0.2f'%high)
# ax2.axhline(y=mean, color='green', linewidth=1, label='mean=%0.2f'%mean)
plt.legend(loc='upper left')
# plt.ylim(-25, 15)
multi = MultiCursor(fig.canvas, (ax1, ax2), color='r', lw=1)
plt.show()
def plot_A_share(self, ticker):
self.setting()
fig = plt.figure(tight_layout=True)
ax1 = fig.add_subplot(5, 2, 1)
ax2 = fig.add_subplot(5, 2, 2)
ax3 = fig.add_subplot(5, 2, 3)
ax4 = fig.add_subplot(5, 2, 4)
ax5 = fig.add_subplot(5, 2, 5)
ax6 = fig.add_subplot(5, 2, 6)
ax7 = fig.add_subplot(5, 2, 7)
ax8 = fig.add_subplot(5, 2, 8)
ax9 = fig.add_subplot(5, 2, 9)
ax10 = fig.add_subplot(5, 2, 10)
# 左边
self.close_df(ticker).plot(ax=ax1, sharex=ax5)
self.balance_df.plot(ax=ax3)
self.cash_df.plot(ax=ax5, sharex=ax5)
analysis.get_drawdown_df(self.balance_df).plot(ax=ax9, sharex=ax5)
holding_pnl = self.env.recorder.holding_pnl.single_dataframe()
holding_pnl.rename(columns=dict(value=f'holding_pnl'), inplace=True)
holding_pnl.plot(ax=ax7, sharex=ax5)
# for i in self.holding_pnl_df:
# i.plot(ax=ax7, sharex=ax5)
# 右边
market_value = self.env.recorder.market_value.single_dataframe()
market_value.rename(columns=dict(value=f'market_value'), inplace=True)
market_value.plot(ax=ax2, sharex=ax5)
margin = self.env.recorder.margin.single_dataframe()
margin.rename(columns=dict(value=f'margin'), inplace=True)
margin.plot(ax=ax4, sharex=ax5)
# for i in self.positions_df:
# i.plot(ax=ax2, sharex=ax5)
# for i in self.margin_df:
# i.plot(ax=ax4, sharex=ax5)
self.realized_pnl_df.plot(ax=ax6, sharex=ax5, kind='bar')
sm.qqplot(self.returns_df['returns'],
dist='norm',
line='s',
ax=ax8,
marker='.')
self.returns_df[self.returns_df != 0].hist(bins=100, ax=ax10)
MultiCursor(fig.canvas, (ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax9),
color='r',
lw=1)
plt.show()
def __init__(self):
super(ApplicationWindow, self).__init__()
# connect to the UI made by QtDesigner
self.ui = myMainWindow.Ui_MainWindow()
self.ui.setupUi(self)
# parameters for computing
self.targetDir = ''
self.showindicator = True
self.mycanvas = MyCanvas(
self) # mainAppWindow.ui.mainWidget)#, width=5, height=4, dpi=100)
self.mycanvas.mpl_connect('button_press_event',
self.on_mousebutton_press)
self.mycanvas.mpl_connect('button_release_event',
self.on_mousebutton_release)
self.mycanvas.mpl_connect('motion_notify_event', self.on_cursor_motion)
self.ui.verticalLayout.addWidget(self.mycanvas)
self.ui.loadstButton.setDisabled(True)
self.dataframe = pd.DataFrame()
self.pdfile = 'config\\policyselections'
self.multicursor = MultiCursor(
self.mycanvas, (self.mycanvas.axes, self.mycanvas.axes1),
horizOn=True,
color='r',
lw=0.5)
self.ui.buttonCursor.setText("-")
def add_widget(self, ith_subwidget, widget, ymain=False, connect_slider=False):
""" 添加一个能接收消息事件的控件。
Args:
ith_subwidget (int.): 子窗口序号。
widget (AxesWidget): 控件。
Returns:
AxesWidget. widget
"""
# 对新创建的Axes做相应的处理
# 并且调整Cursor
for plotter in widget.plotters.values():
if plotter.twinx:
plotter.ax.format_coord = self._format_coord
self.axes.append(plotter.ax)
#self._cursor_axes[ith_subwidget] = plotter.ax
self._cursor = MultiCursor(self._fig.canvas,
self._cursor_axes.values(),
color='r', lw=2, horizOn=False,
vertOn=True)
self._subwidgets[ith_subwidget] = widget
if connect_slider:
self._slider.add_observer(widget)
return widget
def ShowGraph():# Show Graph to U and I
global Um, Im, Fi, P, T, Z
t = arange(0, 100, T)
fig = figure(0)
axU = fig.add_subplot(211)
axU.spines['right'].set_color('none')
axU.spines['top'].set_color('none')
axU.xaxis.set_ticks_position('bottom')
axU.spines['bottom'].set_position(('data', 0))
axU.yaxis.set_ticks_position('left')
axU.spines['left'].set_position(('data', 0))
axU.set_title('Show Graph to Imax, Umax', fontsize = 25)
axU.plot(t, Um*sin(P*t+Fi), color = "blue", linewidth = 2.5, linestyle = "-", Label = "U(t)")# U(t)
axU.grid(True)
axU.legend(["Umax"])
axU.set_xlim(0, 40)
axI = fig.add_subplot(212, sharex = axU)
axI.spines['right'].set_color('none')
axI.spines['top'].set_color('none')
axI.xaxis.set_ticks_position('bottom')
axI.spines['bottom'].set_position(('data', 0))
axI.yaxis.set_ticks_position('left')
axI.spines['left'].set_position(('data', 0))
axI.plot(t, Im*sin(P*t), color = "green", linewidth = 2.5, linestyle = "-", Label = "I(t)")# I(t)
axI.grid(True)
axI.legend(["Imax"])
axI.set_xlim(0, 40)
multi = MultiCursor(fig.canvas, (axU, axI), color = 'r', lw = 1)
show()
def __init__(self, code, index_code = '000001'):
self.code = code
self.index_code = index_code
self.base_color = '#e6daa6'
self.k_data, self.d_data, self.i_data = self.read_data()
self.date_tickers = self.k_data.time.values
self.k_data.time = self.k_data.index
self.i_data.time = self.i_data.index
self.volumeMin = 0
self.volumeMax = 0
self.priceMin = 0
self.priceMax = 0
self.dateMin = 0
self.dateMax = 0
self.fig = plt.figure(facecolor = self.base_color, figsize = (24, 24))
self.price_ax = plt.subplot2grid((12,12), (0,0), rowspan = 7, colspan = 8, facecolor = self.base_color, fig = self.fig)
self.index_ax = plt.subplot2grid((12,12), (7,0), rowspan = 4, colspan = 8, facecolor = self.base_color, sharex = self.price_ax, fig = self.fig)
self.volume_ax = plt.subplot2grid((12,12), (11,0), rowspan = 1, colspan = 8, facecolor = self.base_color, sharex = self.price_ax, fig = self.fig)
self.dist_ax = plt.subplot2grid((12,12), (0,8), rowspan = 7, colspan = 4, facecolor = self.base_color, sharey = self.price_ax, fig = self.fig)
self.press = None
self.release = None
self.keypress = self.fig.canvas.mpl_connect('key_press_event', self.on_key_press)
self.cidpress = self.fig.canvas.mpl_connect('button_press_event', self.on_press)
self.cidrelease = self.fig.canvas.mpl_connect('button_release_event', self.on_release)
self.multi = MultiCursor(self.fig.canvas, (self.price_ax, self.volume_ax, self.index_ax), color='b', lw=1, horizOn = True, vertOn = True)
def ma_kdj(self):
'''对比均线与KDJ绿线，看看能否搞一个新的策略'''
show_period = 120
#global lt,wan,qian,bai,shi,ge,zbname,tf
logging.info('开始：', time.asctime())
t0 = time.time()
series = self.get_series()
close = np.cumsum(series).astype(float)
fig, axes = plt.subplots(5, 1, sharex=True)
#fig.set_tight_layout(True)
# 布林线
upperband, middleband, lowerband = ta.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)
axes[0].plot(close[-show_period:], 'rd-', markersize = 5)
axes[0].plot(upperband[-show_period:], 'y-')
axes[0].plot(middleband[-show_period:], 'b-')
axes[0].plot(lowerband[-show_period:], 'y-')
#策略：金叉死叉
order, earn, ma_fast, ma_slow = strategy.strategy_gold_die(series)
axes[1].plot(ma_fast[-show_period:], 'g-')
axes[1].plot(ma_slow[-show_period:], 'r-')
# 策略：KDJ绿线
order2, earn2, fastk, fastd = strategy.strategy_kdj(series)
axes[2].plot(fastk[-show_period:], 'r-')
axes[2].plot(fastd[-show_period:], 'g-')
# 策略：通道宽度
order, earn, bandwidth, mean = strategy.strategy_bandwidth(series)
axes[3].plot(bandwidth[-show_period:], 'r-')
axes[3].plot([0,show_period],[mean, mean],'k',alpha=.4)
# 策略：正弦变换
order, earn, sine, leadsine = strategy.strategy_ht_sine(series)
axes[4].plot(sine[-show_period:], 'r-')
axes[4].plot(leadsine[-show_period:], 'g-')
title1 = '指标：' + self.wei + '位 ' + self.zbname.get() + ' 同反：' + str(self.tf.get())
axes[0].set_title(title1, fontproperties="SimHei")
#axes[1].set_title('金叉死叉', fontproperties="SimHei")
#axes[2].set_title('均线', fontproperties="SimHei")
#axes[3].set_title('KDJ绿线', fontproperties="SimHei")
#axes[4].set_xticks(range(120)) # 120个数据
#axes[4].set_xticklabels(self.get_labels(self.lt.last_period), rotation=35) # 设置x轴标签（13个）
#axes[4].xaxis.set_major_locator(MultipleLocator(10)) # 主坐标：间隔10
t = time.time() - t0
logging.info('结束', time.asctime())
logging.info('耗时：{}分{}秒'.format(int(t)//60, int(t)%60))
multi = MultiCursor(fig.canvas, (axes[0], axes[1], axes[2], axes[3], axes[4]), color='b', lw=2)
plt.show()
def trend(df):
signals = pd.DataFrame(index=df.index)
signals['MACDTrendSignal'] = 0
signals['MACDTrendSignal'][26:] = np.where(
df['MACD'][26:] >= df['Signal'][26:], 1, 0)
signals['action'] = signals['MACDTrendSignal'].diff()
signals['MACD'] = df['MACD']
print('MACD is: \n', signals.MACD)
fignew = plt.figure()
ax1 = fignew.add_subplot(211)
df['MACD'].plot(ax=ax1, color='m', marker='o')
df['Signal'].plot(ax=ax1, color='y')
ax1.plot(signals.loc[signals.action == 1.0].index,
signals.MACD[signals.action == 1.0],
'^',
markersize=10,
color='g')
ax1.plot(signals.loc[signals.action == -1.0].index,
signals.MACD[signals.action == -1.0],
'v',
markersize=10,
color='r')
ax2 = fignew.add_subplot(212, sharex=ax1)
df['Close'].plot(ax=ax2)
multiC = MultiCursor(fignew.canvas, (ax1, ax2), lw=1)
plt.show()
return signals
def show(Xl, Close, Earn):
global title
fig = plt.figure(title)
ax1 = plt.subplot(211)
plt.plot(Xl, Close, label='收盘价')
ax1.axhline(y=Close[0],
color='#d46061',
linewidth=1,
label='建仓价位%d' % Close[0])
plt.legend(loc='upper left')
ax2 = plt.subplot(212)
plt.plot(Xl, Earn, label='收益率')
low, high = min(Earn), max(Earn)
mean = sum(Earn) / len(Earn)
ax2.axhline(y=0, color='#d46061', linewidth=1, label='0')
ax2.axhline(y=low, color='yellow', linewidth=1, label='min=%0.2f' % low)
ax2.axhline(y=high, color='gray', linewidth=1, label='high=%0.2f' % high)
ax2.axhline(y=mean, color='green', linewidth=1, label='mean=%0.2f' % mean)
plt.legend(loc='upper left')
# plt.ylim(-25, 15)
multi = MultiCursor(fig.canvas, (ax1, ax2), color='r', lw=1)
print Earn[-1]
plt.show()
def drawStats2(prices, period):
ps2 = [p['close'] for p in prices][-140:-115]
ps = [p['close'] for p in prices][-140:-120]
phs = [p['high'] for p in prices][-140:-120]
pls = [p['low'] for p in prices][-140:-120]
l = len(prices)
ts = np.arange(20)
pz = np.poly1d(np.polyfit(ts, ps, 1))
phz = np.poly1d(np.polyfit(ts, phs, 1))
plz = np.poly1d(np.polyfit(ts, pls, 1))
fig = plt.figure()
ax1 = fig.add_subplot(311)
ax1.plot(ts, ps, '-', ts, pz(ts), '-', ts, phz(ts), '--', ts, plz(ts),
'--')
#plt.plot(ts, ps, 'o', label='Original data', linestyle='-', markersize=2)
#plt.plot(ts, m * ts + c, 'r', label='Fitted line')
#plt.legend()
ax2 = fig.add_subplot(312)
ax2.plot(ts, (pz(ts) - plz(ts)) - (phz(ts) - pz(ts)), '-')
ax2.grid()
ts2 = np.arange(len(ps2))
ax3 = fig.add_subplot(313)
ax3.plot(ts2, ps2, '-')
multi = MultiCursor(fig.canvas, (ax1, ax2),
color='r',
lw=1,
horizOn=False,
vertOn=True)
plt.show()
return
def simple_visualization(df, name, result):
from matplotlib.widgets import Cursor
fig = plt.figure()
fig.suptitle(name, fontsize=18)
ax1 = fig.add_subplot(311)
ax1.plot(df.index,
df['Close'],
color='b',
linewidth=2,
marker='o',
markersize=5)
ax1.plot(df.index, df['Bollinger avg'], color='black', linewidth=0.5)
ax1.plot(df.index, df['Bollinger Upper'], color='r', linewidth=0.5)
ax1.plot(df.index, df['Bollinger Lower'], color='g', linewidth=0.5)
#cursor = Cursor(ax1, useblit=True,color='blue', linewidth=2)
ax1.fill_between(df.index,
df['Bollinger Upper'],
df['Bollinger Lower'],
color='yellow')
ax2 = fig.add_subplot(312, sharex=ax1, facecolor='grey', title='MACD')
ax2.plot(df.index, df['MACD'], color='m')
ax2.plot(df.index, df['Signal'], color='y')
handles, label = ax2.get_legend_handles_labels()
ax2.legend(handles, label, loc='lower right', fancybox=True, shadow=True)
ax2.bar(df.index, df['MACD Histogram'], color='b')
ax3 = fig.add_subplot(313, sharex=ax1, title='RSI')
ax3.plot(df.index, df['RSI'])
thresh = pd.DataFrame(index=df.index)
thresh['Overbought'] = 70
thresh['Oversold'] = 30
ax3.plot(df.index, thresh['Overbought'], '--', color='red')
ax3.plot(df.index, thresh['Oversold'], '--', color='green')
multiC = MultiCursor(fig.canvas, (ax1, ax2, ax3), lw=1)
plt.show()
def to_XYZ_slice(self, toSlice):
from matplotlib.widgets import MultiCursor
'''
1) set MatPLotlibCursor correctly
2) collect the next click
3) (Perhaps reset the cursor)
4) set the variables
5) Update the plot
'''
self.MatPlotMultiCursor = None
currType = self.varType.get()
if currType == toSlice:
self.varStatus.set('Already on {}-plane.'.format(toSlice))
return
hor, ver = self.toXYZ_horOrVer(toSlice, currType)
#print('Hor:',hor,' Ver:',ver)
self.MatPlotMultiCursor = MultiCursor(self.MatPlotFig.canvas,
(self.MatPlotAx, ),
color='k',
lw=1,
horizOn=hor,
vertOn=ver)
def update_figure(self, measurements, state_means, segment):
# Draw plots
self.top_axis, tmp_ele = bombo.PlotElevation(self.top_axis,
measurements, state_means)
self.bottom_axis, tmp_speed = bombo.PlotSpeed(self.bottom_axis,
segment)
# Add cursor
def onclick(event):
cursor_anchored.mouse_move(event)
self.draw()
if platform.system() == "Darwin":
# Cursor on both plots but not linked to the trace
self.multi = MultiCursor(self.fig.canvas,
(self.top_axis, self.bottom_axis),
color='r',
lw=1,
vertOn=True,
horizOn=True)
elif platform.system() == 'Windows':
cursor_anchored = MultiCursorLinkedToTrace(self.top_axis,
tmp_ele[0], tmp_ele[1],
self.bottom_axis,
tmp_speed[0],
tmp_speed[1])
self.mpl_connect('motion_notify_event', onclick)
# Draw
self.fig.set_tight_layout(True)
self.draw()
def graph_data(self, dpi=100, figsize=(11, 7)):
fig, axes = plt.subplots(2, 2, dpi=dpi, figsize=figsize)
# Top left
axes[0][0].set_title('Sensor & Sensor with α')
# Top Right
axes[0][1].set_title('Temperature')
# Bottom Left
# axes[1][0].set_title(str('Final ') + str(self.alpha_divisions) + str(' calculations | α = ') + str(self.final_alpha_number))
axes[1][0].set_title('Final {} calculations | α = {}'.format(
self.alpha_divisions, self.final_alpha_number))
# Bottom Right
axes[1][1].set_title('{} calculations'.format(len(self.stdev_df)))
# Raw sensor data and sensor data with alpha applied (Top left)
self.df.plot(kind='line',
use_index=True,
x=self.time_header,
y=[self.target_header, self.final_alpha_number],
ax=axes[0][0])
# Raw temperature data (Top right)
self.df.plot(kind='line',
use_index=True,
x=self.time_header,
y=self.temp_header,
ax=axes[0][1])
guide_line = MultiCursor(fig.canvas, (axes[0][0], axes[0][1]),
lw=1,
horizOn=False,
vertOn=True)
# Final alpha calculations with final alpha number highlighted red (Bottom Left)
self.stdev_df[:self.alpha_divisions + self.alpha_divisions +
20].plot(kind='scatter',
x='ALPHA',
y='STDEV',
ax=axes[1][0])
self.stdev_df[:1].plot(kind='scatter',
x='ALPHA',
y='STDEV',
color='red',
ax=axes[1][0])
# All alpha calculations with final alpha number highlighted red (Bottom Right)
self.stdev_df.plot(kind='scatter',
x='ALPHA',
y='STDEV',
ax=axes[1][1])
self.stdev_df[:1].plot(kind='scatter',
x='ALPHA',
y='STDEV',
color='red',
ax=axes[1][1])
plt.subplots_adjust(hspace=0.5)
plt.show()
def plot(self, Ks=None, Ps=None, callput=None):
import matplotlib.pyplot as plt
from matplotlib.widgets import MultiCursor
fig, ax = plt.subplots(1, 2)
k_x = np.linspace(self.spot * 0.5, self.spot * 1.5, 200)
callput_x = np.vectorize(lambda x: 1 if x > self.forward else -1)(k_x)
ax[0].plot(k_x, self.cal_price(k_x, callput_x))
if Ks is not None:
KC = list(zip(Ks, callput, Ps))
KC.sort(key=lambda x: x[0])
Ks, Callput, Prices = tuple(zip(*KC))
ax[0].plot(Ks, Prices, 'r.')
ax[0].set_title('Fit Market Quote')
ax[1].plot(k_x, self.solve_bsm_vol(k_x), label='Python solver')
ax[1].set_title('Volatility Curve Comparison')
ax[1].legend()
cursor = MultiCursor(fig.canvas, (ax[0], ax[1]),
horizOn=True,
vertOn=True,
useblit=True,
color='black',
lw=1,
ls=':')
plt.show()
def __call__(self, fig=1, Xdata_are_time=True):
"""
IMPORTANT : cursor objects (i.e. multi & cid)
must be stored as global variables when you call the method
like this :
multi , cid = PnC(fig=1)
"""
if type(fig) is int:
figobj = plt.figure(fig)
elif type(fig) is matplotlib.figure.Figure:
figobj = fig
print(
"INFO : press SPACE to record a X-value, \n press R to Remove the previously recorded one"
)
def onclick_discont(event):
if event.key == ' ':
if Xdata_are_time:
ix, iy = matplotlib.dates.num2date(
event.xdata).replace(tzinfo=None), event.ydata
else:
ix, iy = event.xdata, event.ydata
print("INFO : X value recorded : ", ix)
for ax in figobj.axes:
out_bar_list = stats.plot_vertical_bar_ax([ix],
ax,
"b",
linewidth=1)
self.ver_bar_stk.append(out_bar_list[0])
self.selectedX.append(ix)
plt.draw()
elif event.key == 'r' and len(self.selectedX) > 0:
last = self.selectedX[-1]
self.selectedX.remove(last)
#for ax in figobj.axes:
# stats.plot_vertical_bar_ax([last],ax,"r")
last_bar = self.ver_bar_stk[-1]
self.ver_bar_stk.remove(last_bar)
last_bar.remove()
print("INFO : value removed : ", last)
plt.draw()
return None
multi = MultiCursor(figobj.canvas, figobj.axes, color='k', lw=1)
cid = figobj.canvas.mpl_connect('key_press_event', onclick_discont)
return multi, cid
def on_leave_axes(self, event):
if event.inaxes is self._slider_ax:
# 进入后会创建_slider_cursor,离开后复原
axes = [self.axes[i] for i in self._cursor_axes_index.values()]
#axes = list(reversed(axes)) # 很奇怪，如果没有按顺序给出，显示会有问题。
self._cursor = MultiCursor(self._fig.canvas, axes, color='r', lw=2, horizOn=False, vertOn=True)
event.canvas.draw()
log.debug("on_leave_axes")
def clicd3(evt):
print(evt.y)#recupere la coordonée selon y
print(l3.nearest(evt.y))#reecupere le bouton le plus proche
print(evt)
fr=l3.nearest(evt.y)
#vou[fr][i][0]
#s1 = [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
s2 = []
s1 = []
t=[]
fig = plt.figure()
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212, sharex=ax1)
for i in range(len(vou[fr]) - 11550):
s1.append(vou[fr][i][0])
s2.append((vou[fr][i+1][0] -vou[fr][i][0] )/15)
t.append(i)
print('att')
ax1.plot(t, s1)
#ax2.plot(t, s2)
if i==11550:
del s1[i]
ax2.plot(t, s2)
#t=np.arange(59)
print (t)
print (s1)
#t = range(i+1)
print('bientot')
#ax2 = fig.add_subplot(212, sharex=ax1)
#ax2.plot(t, s2)
multi = MultiCursor(fig.canvas, (ax1, ax2), color='r', lw=1)
plt.show()
pts = ginput(2)
print(pts)
x = math.floor(pts[0][0])
x1 = math.floor(pts[1][0])
y = str(s1[x])
v = str(s2[x])
y1 = str(s1[x1])
v1 = str(s2[x1])
ax1.text(0, 50, r'y(t)=' + y, fontsize=15, bbox={'facecolor': 'blue', 'alpha': 0.5, 'pad': 10})
ax2.text(0, 13, r'v(y)=' + v, fontsize=15)
ax1.annotate('y(t)=' + y, xy=(x, s1[x]), xytext=(x + 1, s1[x] + 5),
arrowprops=dict(facecolor='black', shrink=0.05))
ax2.annotate('v(y)=' + v, xy=(x, s2[x]), xytext=(x + 1, s2[x] + 5),
arrowprops=dict(facecolor='black', shrink=0.05))
ax1.text(0, 50, r'y(t)=' + y1, fontsize=15, bbox={'facecolor': 'blue', 'alpha': 0.5, 'pad': 10})
ax2.text(0, 13, r'v(y)=' + v1, fontsize=15)
ax1.annotate('y(t)=' + y1, xy=(x1, s1[x1]), xytext=(x1 + 1, s1[x1] + 5),
arrowprops=dict(facecolor='black', shrink=0.05))
ax2.annotate('v(y)=' + v1, xy=(x1, s2[x1]), xytext=(x1 + 1, s2[x1] + 5),
arrowprops=dict(facecolor='black', shrink=0.05))
plt.show()
def disp(self, new=False):
if new:
self.proj2.display(figure=self.top_ax, title=self.title)
xb = self.top_ax.get_xbound()
sidedisplay(self.proj1, self.side_ax)
yb = self.side_ax.get_ybound()
else:
yb = self.side_ax.get_ybound()
xb = self.top_ax.get_xbound()
self.spec_ax.clear()
if self.cursors.value:
self.multitop = MultiCursor(self.fig.canvas,
(self.spec_ax, self.top_ax),
color='r',
lw=1,
horizOn=False,
vertOn=True)
self.multiside = MultiCursor(self.fig.canvas,
(self.spec_ax, self.side_ax),
color='r',
lw=1,
horizOn=True,
vertOn=False)
else:
self.multitop = None
self.multiside = None
if self.posview.value:
self.data.display(scale=self.scale.value,
new_fig=False,
figure=self.spec_ax,
mpldic={'cmap': 'winter'})
if self.negview.value:
self.data.display(scale=-self.scale.value,
new_fig=False,
figure=self.spec_ax,
mpldic={'cmap': 'YlOrRd'})
self.spec_ax.set_xbound(xb)
self.spec_ax.set_ybound(yb)
self.fig.canvas.header_visible = False
for s in ["left", "top", "right"]:
self.top_ax.spines[s].set_visible(False)
self.top_ax.yaxis.set_visible(False)
for s in ["top", "right", "bottom"]:
self.side_ax.spines[s].set_visible(False)
self.side_ax.xaxis.set_visible(False)
def discont_manu_click(self, fig=1):
"""
manual discontinuities are both recorded in the "main" discont list
and in a new discont_manu list,
thus the manual discontinuites can be identified
IMPORTANT : cursor objects (multi , cid)
must be stored as global variables like this :
multi , cid = tsout.discont_manu_click()
NOTE : This method was created before point_n_click_plot():
this other one is more complete
both has to be merged ASAP !!!!!
"""
if type(fig) is int:
figobj = plt.figure(fig)
elif type(fig) is plt.Figure:
figobj = fig
if not self.bool_discont:
self.discont = []
if not self.bool_discont_manu:
self.discont_manu = []
print("INFO : press SPACE to record a manual discontinuity")
def onclick_discont(event):
ix, iy = matplotlib.dates.num2date(
event.xdata).replace(tzinfo=None), event.ydata
print("INFO : discontinuity recorded : ", ix)
for ax in figobj.axes:
stats.plot_vertical_bar_ax([ix], ax, "g")
#
self.bool_discont = True
self.bool_discont_manu = True
self.discont.append(ix)
self.discont = sorted(self.discont)
self.discont_manu.append(ix)
self.discont_manu = sorted(self.discont_manu)
#figobj.show()
plt.draw()
return None
multi = MultiCursor(figobj.canvas, figobj.axes, color='k', lw=1)
cid = figobj.canvas.mpl_connect('key_press_event', onclick_discont)
return multi, cid
def plot_exit(fig, exit_profit, exit_nbar_best, exit_nbar_worst, profits_more, risks, nbar, binwidth=1):
fig.canvas.set_window_title(u'exit info')
axescolor = '#f6f6f6' # the axes background color
left, width = 0.1, 0.8
rect2 = [left, 0.4, width, 0.4]
rect3 = [left, 0.1, width, 0.3]
ax1 = fig.add_axes(rect3, axisbg=axescolor)
ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1)
if nbar > 0:
# plot ax1
profits_more.plot(ax=ax1, kind='bar', grid = False, use_index = False, label=u"%s bar more profits"%nbar)
risks.plot(ax=ax1, kind='bar', grid = False, use_index = False, color = 'y', label=u"%s bar risks"%nbar)
temp = risks[risks<0]
ax1.plot(range(len(temp)), [temp.mean()]*len(temp), 'y--',
label=u"av risk: %s"%temp.mean())
temp = profits_more[profits_more>0]
ax1.plot(range(len(temp)), [temp.mean()]*len(temp), 'r--',
label=u"av profits more: %s"%temp.mean())
ax1.legend(loc='upper left').get_frame().set_alpha(0.5)
ax1.annotate(str(np.mean(risks)), xy=(len(exit_profit)/2, np.mean(risks)), xycoords='data',
xytext=(-30, -30), textcoords='offset points', color='b',
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=.2")
)
# plot ax2
for i in xrange(len(exit_profit)):
if(exit_nbar_best[i]>exit_profit[i] and exit_profit[i]>0):
px21 = ax2.bar(i, exit_profit[i], width=binwidth, color='blue')
px22 = ax2.bar(i, exit_nbar_best[i]-exit_profit[i], width=binwidth, color='red', bottom = exit_profit[i])
elif(exit_nbar_best[i]<exit_profit[i] and exit_profit[i]>0 and exit_nbar_best[i]>0):
ax2.bar(i, exit_nbar_best[i], width=binwidth, color='red')
ax2.bar(i, exit_profit[i]-exit_nbar_best[i], width=binwidth, color='blue', bottom = exit_nbar_best[i])
elif(exit_nbar_best[i]<exit_profit[i] and exit_profit[i]<0):
ax2.bar(i, exit_profit[i], width=binwidth, color='red')
ax2.bar(i, exit_nbar_best[i]-exit_profit[i], width=binwidth, color='blue', bottom = exit_profit[i])
elif(exit_nbar_best[i]>exit_profit[i] and exit_profit[i]<0 and exit_nbar_best[i]<0):
ax2.bar(i, exit_nbar_best[i], width=binwidth, color='red')
ax2.bar(i, exit_profit[i]-exit_nbar_best[i], width=binwidth, color='blue', bottom = exit_nbar_best[i])
else:
ax2.bar(i, exit_nbar_best[i], width=binwidth, color='red')
ax2.bar(i, exit_profit[i], width=binwidth, color='blue')
ax2.legend((px21[0], px22[0]), (u'actual profit', u'more profits'),loc='upper left').get_frame().set_alpha(0.5)
for ax in ax1, ax2:
ax.axhline(color='k')
ax.set_xticklabels([])
ax1.set_xlabel("")
#ax2.set_title(u"出场相关信息", fontproperties=font_big)
multi = MultiCursor(fig.canvas, fig.axes, color='r', lw=1, horizOn=False, vertOn=True)
return [ax1, ax2], [multi]
else:
return [], []
def plot_strength_data(path):
"""
Plots the torque and velocity signals versus time in one subplot and the
anatomic position signal versus time in the other subplot.
Args:
path: A character string with the path to the file containing
isokinetic strength test data.
Returns:
A figure with two subplots
"""
# Read data
if "corrected" in path:
data = np.loadtxt(path)
else:
data = np.loadtxt(path, skiprows=6)
time = data[:, 0]
torque = data[:, 1]
velocity = data[:, 4]
angle = abs(data[:, 3])
# Detect selected isokinetic velocity
if ("60gs" in path) is True:
iso_vel = 60
elif ("120gs" in path) is True:
iso_vel = 120
elif ("180gs" in path) is True:
iso_vel = 180
# Plot
fig1 = plt.figure(figsize=(18, 9))
ax1 = fig1.add_subplot(2, 1, 1)
ax2 = fig1.add_subplot(2, 1, 2)
# Add a multicursor to all subplotsg
multi = MultiCursor(fig1.canvas, (ax1, ax2), color="k", linewidth=1)
multi
ax1.plot(time, torque, color="tab:blue", label="Torque (Nm)")
ax1.plot(time, velocity, color="tab:orange", label="Velocity (°/s)")
ax2.plot(time, angle, color="tab:green", label="Anatomical position (°)")
ax1.legend(loc="upper right")
ax2.legend(loc="upper right")
# Add a horizontal line at torque and velocity = 0
ax1.axhline(y=0, color="tab:blue", linestyle="dotted")
# Add horizontal lines at the selected isokinetic velocity
ax1.axhline(y=iso_vel, color="tab:orange", linestyle="dotted")
ax1.axhline(y=-iso_vel, color="tab:orange", linestyle="dotted")
title = set_plot_title(path)
ax1.set_title(title)
plt.tight_layout()
plt.show()
def init_layout(self, w_width, *args):
# 布局参数
self._w_width_min = 50
self._w_width = w_width
self._init_widgets(*args)
self._connect()
self._cursor = MultiCursor(self._fig.canvas, self.axes,
color='r', lw=2, horizOn=False,
vertOn=True)
return self.axes
def plot_exit(fig, exit_profit, exit_nbar_best, exit_nbar_worst,
profits_more, risks, nbar, binwidth=1):
# fig.canvas.set_window_title('出场信息')
axescolor = '#f6f6f6' # the axes background color
left, width = 0.1, 0.8
rect2 = [left, 0.4, width, 0.4]
rect3 = [left, 0.1, width, 0.3]
ax1 = fig.add_axes(rect3, facecolor=axescolor)
ax2 = fig.add_axes(rect2, facecolor=axescolor, sharex=ax1)
if nbar > 0:
six.print_("**66666")
# plot ax1
profits_more.plot(ax=ax1, kind='bar', grid = False, use_index = False, label="%s根最优"%nbar)
risks.plot(ax=ax1, kind='bar', grid = False, use_index = False, color = 'y', label="%s根最差"%nbar)
temp = risks[risks<0]
ax1.plot(range(len(temp)), [temp.mean()]*len(temp), 'y--', label="平均风险: %s"%temp.mean())
temp = profits_more[profits_more>0]
ax1.plot(range(len(temp)), [temp.mean()]*len(temp), 'r--', label="平均更优: %s"%temp.mean())
ax1.legend(prop=font, loc='upper left').get_frame().set_alpha(0.5)
#ax1.annotate(str(np.mean(risks)), xy=(len(records)/2, np.mean(risks)), xycoords='data',
#xytext=(-30, -30), textcoords='offset points', color='b',
#arrowprops=dict(arrowstyle="->",
#connectionstyle="arc3,rad=.2")
#)
# plot ax2
for i in xrange(len(exit_profit)):
if(exit_nbar_best[i]>exit_profit[i] and exit_profit[i]>0):
px21 = ax2.bar(i, exit_profit[i], width=binwidth, color='blue')
px22 = ax2.bar(i, exit_nbar_best[i]-exit_profit[i], width=binwidth, color='red', bottom = exit_profit[i])
elif(exit_nbar_best[i]<exit_profit[i] and exit_profit[i]>0 and exit_nbar_best[i]>0):
ax2.bar(i, exit_nbar_best[i], width=binwidth, color='red')
ax2.bar(i, exit_profit[i]-exit_nbar_best[i], width=binwidth, color='blue', bottom = exit_nbar_best[i])
elif(exit_nbar_best[i]<exit_profit[i] and exit_profit[i]<0):
ax2.bar(i, exit_profit[i], width=binwidth, color='red')
ax2.bar(i, exit_nbar_best[i]-exit_profit[i], width=binwidth, color='blue', bottom = exit_profit[i])
elif(exit_nbar_best[i]>exit_profit[i] and exit_profit[i]<0 and exit_nbar_best[i]<0):
ax2.bar(i, exit_nbar_best[i], width=binwidth, color='red')
ax2.bar(i, exit_profit[i]-exit_nbar_best[i], width=binwidth, color='blue', bottom = exit_nbar_best[i])
else:
ax2.bar(i, exit_nbar_best[i], width=binwidth, color='red')
ax2.bar(i, exit_profit[i], width=binwidth, color='blue')
ax2.legend((px21[0], px22[0]), ('实际盈利', '延出最优盈利'),loc='upper left', prop=font).get_frame().set_alpha(0.5)
ax2.set_ylabel("交易区间内的盈利", fontproperties = font)
for ax in ax1, ax2:
#if ax!=ax1:
ax.set_xticklabels([])
ax1.set_xlabel("")
ax2.set_title("出场相关信息", fontproperties=font_big)
multi = MultiCursor(fig.canvas, fig.axes, color='r', lw=1, horizOn=False, vertOn=True)
return [ax1, ax2], [multi]
else:
return [], []
def __init__(self, data, X=None, Y=None):
self.data = data
self.X = np.arange(self.data.shape[1]) if X is None else X
self.Y = np.arange(self.data.shape[0]) if Y is None else Y
vmin = np.min(self.data)
vmax = np.max(self.data)
# Create and displays the figure object.
self.fig = plt.figure(figsize=(8,8), frameon=True, tight_layout=True)
# Create grid for layout
grid = GridSpec(4,4)
self.ax_main = self.fig.add_subplot(grid[0:3,0:3])
#self.ax_main.autoscale(enable=True, tight=True)
self.ax_main.autoscale(enable=False)
self.ax_main.set_xlim(np.min(self.X), np.max(self.X))
self.ax_main.set_ylim(np.min(self.Y), np.max(self.Y))
# Use 'auto' to adjust the aspect ratio to fill the figure window, 'equal' to fix it.
self.ax_main.set_aspect('auto', adjustable='box-forced')
self.ax_h = self.fig.add_subplot(grid[3,0:3], sharex=self.ax_main)
self.ax_h.set_axis_bgcolor('0.8')
self.ax_h.autoscale(False)
self.ax_h.set_ylim(vmin, vmax)
self.ax_v = self.fig.add_subplot(grid[0:3,3], sharey=self.ax_main)
self.ax_v.set_axis_bgcolor('0.8')
self.ax_v.autoscale(False)
self.ax_v.set_xlim(vmax, vmin)
self.prev_pt = None
self.ax_cb = None
self.cursor = MultiCursor(self.fig.canvas, (self.ax_main, self.ax_h, self.ax_v),
horizOn=True, vertOn=True, color='white', ls='--', lw=1)
self.fig.canvas.mpl_connect('button_press_event', self._plot_clicked)
# Setup control buttons
btn_grid = GridSpecFromSubplotSpec(4, 1, subplot_spec=grid[3,3])
self.btn_colorbar = Button(self.fig.add_subplot(btn_grid[2,0]), 'Colorbar')
self.btn_colorbar.on_clicked(self._plot_colorbar)
self.btn_reset = Button(self.fig.add_subplot(btn_grid[3,0]), 'Reset')
self.btn_reset.on_clicked(self._plot_reset)
# Setup color range sliders
self.slider_vmin = Slider(self.fig.add_subplot(btn_grid[0,0]), "vmin", vmin, vmax, valinit=vmin)
self.slider_vmin.on_changed(self._plot_rangechanged)
self.slider_vmax = Slider(self.fig.add_subplot(btn_grid[1,0]), "vmax", vmin, vmax, valinit=vmax, slidermin=self.slider_vmin)
self.slider_vmax.on_changed(self._plot_rangechanged)
self.slider_vmin.slidermax = self.slider_vmax
self.fig.canvas.draw()
def analyseRelative(year, portfolio):
fig1, (ax0, ax1, ax2) = setUpPlotRelative()
dataList, pandaFrame = readCsvFiles(portfolio, year)
dfNormalized = normalize(dataList)
myNormalized = dfNormalized.plot(ax=ax0)
dataRelative = relativeData(dfNormalized)
myRelativePlot = dataRelative.plot(ax=ax1)
multi = MultiCursor(fig1.canvas, (ax0, ax1), color='r', lw=1, horizOn=False, vertOn=True)
def plot_ticker(quandl_dict, ticker, start_date, risk, momentum, pattern,
volume, bottom_indicators):
"""This function plots with the selected ticker"""
risk = risk.lower()
momentum = momentum.lower()
pattern = pattern.lower()
volume = volume.lower()
bottom_indicators = [indicator.lower() for indicator in bottom_indicators]
if start_date is None:
start_date = "2000-01-01"
last_trading_date = get_date(ticker, quandl_dict)
data = get_data(ticker, start_date, last_trading_date)
if data is None:
return False
fig, ax0, ax1, ax2, ax3, ax4, axVol = build_plots(data)
plot_functions[risk](ax0, data)
plot_candlestick(ax1, data)
# momentum
plot_functions[momentum](ax1, data)
# volume
plot_functions[volume](axVol, data)
# combos...
plot_functions[bottom_indicators[0]](ax2, data)
plot_functions[bottom_indicators[1]](ax3, data)
plot_functions[bottom_indicators[2]](ax4, data)
# pattern
nonzero = plot_functions[pattern](data)
if nonzero is not None:
ax1.scatter(data['Date'][nonzero],
data['Adj_High'][nonzero],
s=50,
c='none',
edgecolors='#cc9900',
marker='o')
title = '{}: {} - {}'.format(quandl_dict[ticker][0], start_date,
last_trading_date)
fig.canvas.set_window_title(title)
multi = MultiCursor(fig.canvas, (ax0, ax1, ax2, ax3, ax4, axVol),
useblit=True,
horizOn=False,
vertOn=True,
color='wheat',
lw=1)
plt.show()
return True