def makeFigure(name='Z dim', path='./'):
fig = plt.figure()
fig.suptitle(name)
ax = HostAxes(fig, [0.08, 0.08, 0.70, 0.8])
ax.axis["right"].set_visible(False)
ax.axis["top"].set_visible(False)
ax.plot(Z_dim, FID, color=cname[0], label='FID')
ax.set_ylabel('FID')
ax.set_xlabel('Z dimension')
ax.legend()
fig.add_axes(ax)
def paraAxis(x, y, name, cnt):
ax_para = ParasiteAxes(ax, sharex=ax)
ax.parasites.append(ax_para)
ax_para.axis['right'].set_visible(True)
ax_paraD = ax_para.get_grid_helper().new_fixed_axis
ax_para.set_ylabel(name)
ax_para.axis['right'] = ax_paraD(loc='right',
offset=(40 * cnt, 0),
axes=ax_para)
ax_para.plot(x, y, label=name, color=cname[cnt + 1])
plt.legend(loc=0)
paraAxis(Z_dim, IS, "IS", 0)
paraAxis(Z_dim, nll, "nll", 1)
plt.savefig(os.path.join(path, 'z_dim.png'), dpi=100)
def show_all():
fig = plt.figure()
host = HostAxes(fig, [0.15, 0.1, 0.65, 0.8])
par1 = ParasiteAxes(host, sharex=host)
par2 = ParasiteAxes(host, sharex=host)
host.parasites.append(par1)
host.parasites.append(par2)
host.set_ylabel("Temperature")
host.set_xlabel("Datetime")
host.axis["right"].set_visible(False)
host.set_ylim(25, 35)
par1.set_ylabel("eCO2")
par1.axis["right"].set_visible(True)
par1.axis["right"].major_ticklabels.set_visible(True)
par1.axis["right"].label.set_visible(True)
par1.set_ylim(400, 2000)
par2.set_ylabel("TVOC")
new_axisline = par2.get_grid_helper().new_fixed_axis
par2.axis["right2"] = new_axisline(loc="right", axes=par2, offset=(60, 0))
par2.set_ylim(0, 1000)
fig.add_axes(host)
p1, = host.plot(temperatures, label="Temperature")
p2, = par1.plot(eCO2s, label="eCO2")
p3, = par2.plot(TVOCs, label="TVOC")
host.axis["left"].label.set_color(p1.get_color())
par1.axis["right"].label.set_color(p2.get_color())
par2.axis["right2"].label.set_color(p3.get_color())
plt.show()
def main():
wholeDir = 'E:/work/code/deepSpeech/pytorch_deepspeech/result/07-23-12-47/'
mdir = wholeDir + 'm.txt'
prefftdir = wholeDir + "fft.txt"
#gradfile = os.path.join(wholeDir,folder, 'output_now')
dimMeans, frameMeans, norm = getM_(mdir)
#drawgradgraph(gradfile)
lossdir = wholeDir + "loss.txt"
ys = np.loadtxt(lossdir)
xs = np.zeros_like(ys)
for i in range(xs.shape[0]):
xs[i] = i + 1
plt.scatter(xs, ys)
plt.savefig(os.path.dirname(wholeDir) + '/loss.png', dpi=150)
plt.clf()
myloss = np.loadtxt(wholeDir + "loss.txt")
m_loss = -np.loadtxt(wholeDir + "loss.txt")
fig = plt.figure()
host = HostAxes(fig, [0.15, 0.1, 0.65, 0.8])
par1 = ParasiteAxes(host, sharex=host)
host.parasites.append(par1)
host.axis['right'].set_visible(False)
par1.axis['right'].set_visible(True)
par1.set_ylabel('m_entropy=∑log(m_ij))')
par1.axis['right'].major_ticklabels.set_visible(True)
par1.axis['right'].label.set_visible(True)
fig.add_axes(host)
host.set_xlabel('epoch')
host.set_ylabel('|preOutput-TrueOutput|²')
p1, = host.plot(xs, myloss, label='|preOutput-TrueOutput|²')
p2, = par1.plot(xs, m_loss, label='m_entropy=∑log(m_ij)); ')
plt.title("two parts of loss")
host.legend()
# 轴名称，刻度值的颜色
host.axis['left'].label.set_color(p1.get_color())
par1.axis['right'].label.set_color(p2.get_color())
plt.savefig(os.path.join(os.path.dirname(wholeDir), 'loss_two_parts.png'),
dpi=150)
plt.clf()
Fftreader.mcolorDrawer(norm, '/m hot map', os.path.dirname(prefftdir))
#Fftreader.mcolorDrawer(norm, '/m hot map color', os.path.dirname(prefftdir))
#print('m, mean:',np.mean(norm),'max:',np.max(norm),'min:',np.min(norm),'std:',np.std(norm,ddof=1))
#print(np.max(norm))
makeVoice(prefftdir, dimMeans, frameMeans, norm)
def display(dates, cases, covid, vaccine):
fig = plt.figure()
host = HostAxes(fig, [0.15, 0.1, 0.65, 0.8])
par1 = ParasiteAxes(host, sharex=host)
par2 = ParasiteAxes(host, sharex=host)
host.parasites.append(par1)
host.parasites.append(par2)
host.set_ylabel("Cases")
host.axis["right"].set_visible(False)
par1.axis["right"].set_visible(True)
par1.set_ylabel("COVID - 19 Clicks")
par1.axis["right"].major_ticklabels.set_visible(True)
par1.axis["right"].label.set_visible(True)
par2.set_ylabel("Vaccine Clicks")
offset = (60, 0)
new_axisline = par2.get_grid_helper().new_fixed_axis
par2.axis["right2"] = new_axisline(loc="right", axes=par2, offset=offset)
fig.add_axes(host)
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m-%d'))
plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=7))
p1, = host.plot(dates, cases, label="USA 7 Day Average")
p2, = par1.plot(dates, covid, label="COVID Keyword Clicks")
p3, = par2.plot(dates, vaccine, label="Vaccine Keyword Clicks")
host.legend()
host.axis["left"].label.set_color(p1.get_color())
par1.axis["right"].label.set_color(p2.get_color())
par2.axis["right2"].label.set_color(p3.get_color())
plt.setp(host.axis["bottom"].major_ticklabels, rotation=45, ha="right")
plt.gcf().canvas.set_window_title('COVID-19 Cases vs. BI Search Queries')
plt.show()
def drawCurveDonkey(intxtpath,
outimgpath,
title,
xlabel='epoch',
par1label='loss',
par2label='accuracy(%)'):
xs = []
p1s = []
p2s = []
with open(intxtpath, 'r') as fin:
lines = [l.strip() for l in fin.readlines()]
for line in lines:
x, p1, p2 = line.split('\t')
xs.append(int(x))
p1s.append(float(p1))
p2s.append(float(p2))
fig = plt.figure()
host = HostAxes(fig, [0.15, 0.1, 0.65, 0.8])
par1 = ParasiteAxes(host, sharex=host)
host.parasites.append(par1)
host.axis['right'].set_visible(False)
par1.axis['right'].set_visible(True)
par1.set_ylabel(par2label)
par1.axis['right'].major_ticklabels.set_visible(True)
par1.axis['right'].label.set_visible(True)
fig.add_axes(host)
host.set_xlabel(xlabel)
host.set_ylabel(par1label)
p1, = host.plot(np.array(xs), np.array(p1s), label=par1label)
p2, = par1.plot(np.array(xs), np.array(p2s), label=par2label)
plt.title(title)
host.legend()
host.axis['left'].label.set_color(p1.get_color())
par1.axis['right'].label.set_color(p2.get_color())
plt.savefig(outimgpath, dpi=150)
plt.clf()
def draw_point_fcst(t2m=None,
u10m=None,
v10m=None,
rn=None,
model=None,
output_dir=None,
points=None,
extra_info=None):
plt.rcParams['font.sans-serif'] = ['SimHei'] # 步骤一（替换sans-serif字体）
plt.rcParams['axes.unicode_minus'] = False # 步骤二（解决坐标轴负数的负号显示问题）
if (sys.platform[0:3] == 'lin'):
locale.setlocale(locale.LC_CTYPE, 'zh_CN.utf8')
if (sys.platform[0:3] == 'win'):
locale.setlocale(locale.LC_CTYPE, 'chinese')
initTime = pd.to_datetime(str(
t2m['forecast_reference_time'].values)).replace(
tzinfo=None).to_pydatetime()
# draw figure
fig = plt.figure(figsize=(16, 4.5))
ax_t2m = HostAxes(fig, [0.1, 0.28, .8, .62])
ax_rn = ParasiteAxes(ax_t2m, sharex=ax_t2m)
#其他信息
#append axes
ax_t2m.parasites.append(ax_rn)
#invisible right axis of ax
ax_t2m.axis['right'].set_visible(False)
ax_t2m.axis['right'].set_visible(False)
ax_rn.axis['right'].set_visible(True)
ax_rn.axis['right'].major_ticklabels.set_visible(True)
ax_rn.axis['right'].label.set_visible(True)
#set label for axis
ax_t2m.set_ylabel('温度($^\circ$C)', fontsize=100)
ax_rn.set_ylabel('降水(mm)', fontsize=100)
fig.add_axes(ax_t2m)
# draw main figure
#2米温度——————————————————————————————————————
if (model == '中央台指导'):
model = '智能网格'
utl.add_public_title_sta(title=model + '预报 ' + extra_info['point_name'] +
' [' + str(points['lon'][0]) + ',' +
str(points['lat'][0]) + ']',
initTime=initTime,
fontsize=21)
for ifhour in t2m['forecast_period'].values:
if (ifhour == t2m['forecast_period'].values[0]):
t2m_t = (initTime + timedelta(hours=ifhour))
else:
t2m_t = np.append(t2m_t, (initTime + timedelta(hours=ifhour)))
curve_t2m = ax_t2m.plot(t2m_t,
np.squeeze(t2m['data'].values),
c='#FF6600',
linewidth=3,
label='2m温度')
ax_t2m.set_xlim(t2m_t[0], t2m_t[-1])
ax_t2m.set_ylim(
math.floor(t2m['data'].values.min() / 5) * 5 - 2,
math.ceil(t2m['data'].values.max() / 5) * 5)
#降水——————————————————————————————————————
for ifhour in rn['forecast_period'].values:
if (ifhour == rn['forecast_period'].values[0]):
rn_t = (initTime + timedelta(hours=ifhour))
else:
rn_t = np.append(rn_t, (initTime + timedelta(hours=ifhour)))
mask = (rn['data'] < 999)
rn = rn['data'].where(mask)
ax_rn.bar(rn_t,
np.squeeze(rn.values),
width=0.1,
color='#00008B',
label=str(
int(rn['forecast_period'].values[1] -
rn['forecast_period'].values[0])) + '小时降水',
alpha=0.5)
#curve_rn=ax_rn.plot(rn_t, np.squeeze(rn['data'].values), c='#40C4FF',linewidth=3)
ax_rn.set_ylim(0, np.nanmax(np.append(10, np.squeeze(rn.values))))
###
xaxis_intaval = mpl.dates.HourLocator(byhour=(8, 20)) #单位是小时
ax_t2m.xaxis.set_major_locator(xaxis_intaval)
# add legend
ax_t2m.legend(fontsize=15, loc='upper right')
ax_t2m.tick_params(length=10)
ax_t2m.tick_params(axis='y', labelsize=100)
ax_t2m.set_xticklabels([' '])
miloc = mpl.dates.HourLocator(byhour=(8, 11, 14, 17, 20, 23, 2, 5)) #单位是小时
ax_t2m.xaxis.set_minor_locator(miloc)
yminorLocator = MultipleLocator(1) #将此y轴次刻度标签设置为1的倍数
ax_t2m.yaxis.set_minor_locator(yminorLocator)
ymajorLocator = MultipleLocator(5) #将此y轴次刻度标签设置为1的倍数
ax_t2m.yaxis.set_major_locator(ymajorLocator)
ax_t2m.grid(axis='x', which='minor', ls='--')
ax_t2m.axis['left'].label.set_fontsize(15)
ax_t2m.axis['left'].major_ticklabels.set_fontsize(15)
ax_rn.axis['right'].label.set_fontsize(15)
ax_rn.axis['right'].major_ticklabels.set_fontsize(15)
#10米风——————————————————————————————————————
ax_uv = plt.axes([0.1, 0.16, .8, .12])
for ifhour in u10m['forecast_period'].values:
if (ifhour == u10m['forecast_period'].values[0]):
uv_t = (initTime + timedelta(hours=ifhour))
else:
uv_t = np.append(uv_t, (initTime + timedelta(hours=ifhour)))
wsp = (u10m**2 + v10m**2)**0.5
#curve_uv=ax_uv.plot(uv_t, np.squeeze(wsp['data'].values), c='#696969',linewidth=3,label='10m风')
ax_uv.barbs(uv_t,
np.zeros(len(uv_t)),
np.squeeze(u10m['data'].values),
np.squeeze(v10m['data'].values),
fill_empty=True,
color='gray',
barb_increments={
'half': 2,
'full': 4,
'flag': 20
},
length=5.8,
linewidth=1.5,
zorder=100)
ax_uv.set_ylim(-1, 1)
ax_uv.set_xlim(uv_t[0], uv_t[-1])
#ax_uv.axis('off')
ax_uv.set_yticklabels([' '])
#logo
utl.add_logo_extra_in_axes(pos=[0.87, 0.00, .1, .1],
which='nmc',
size='Xlarge')
#开启自适应
xaxis_intaval = mpl.dates.HourLocator(byhour=(8, 20)) #单位是小时
ax_uv.xaxis.set_major_locator(xaxis_intaval)
ax_uv.tick_params(length=5, axis='x')
ax_uv.tick_params(length=0, axis='y')
miloc = mpl.dates.HourLocator(byhour=(8, 11, 14, 17, 20, 23, 2, 5)) #单位是小时
ax_uv.xaxis.set_minor_locator(miloc)
ax_uv.grid(axis='x', which='both', ls='--')
ax_uv.set_ylabel('10m风', fontsize=15)
xstklbls = mpl.dates.DateFormatter('%m月%d日%H时')
for label in ax_uv.get_xticklabels():
label.set_rotation(30)
label.set_horizontalalignment('center')
ax_uv.tick_params(axis='x', labelsize=15)
#出图——————————————————————————————————————————————————————————
if (output_dir != None):
isExists = os.path.exists(output_dir)
if not isExists:
os.makedirs(output_dir)
output_dir2 = output_dir + model + '_起报时间_' + initTime.strftime(
"%Y年%m月%d日%H时") + '/'
if (os.path.exists(output_dir2) == False):
os.makedirs(output_dir2)
plt.savefig(output_dir2 + model + '_' + extra_info['point_name'] +
'_' + extra_info['output_head_name'] +
initTime.strftime("%Y%m%d%H") + '00' +
extra_info['output_tail_name'] + '.jpg',
dpi=200,
bbox_inches='tight')
else:
plt.show()
power_max = 0
torque_mutil_gear_max = 0
lines_set = []
for wish_id in range(0, len(wishing_list[figure_id])):
order = wishing_list[figure_id][wish_id]
vehicle_temperautre = vehicle_number[order] + '_' + str(
int(outside_at_end[order])) + 'C'
if same_vehicle_flag:
# lines for fig2
power = [
torque[order][i] * speed[order][i] / 9550
for i in range(0, len(torque[order]))
]
line_power = ax_power.plot(
time[order],
power,
linestyle=line_style[speed_at_start[order] > pre_speed_thre],
label=vehicle_temperautre,
color=line_color[temperature_target[order]])
line_gear = ax_gear.plot(
time[order],
gear[order],
linestyle=line_style[speed_at_start[order] > pre_speed_thre],
color=line_color[temperature_target[order]])
if max(gear[order]) > 10: # if it is gear ratio, plot torque*gear
torque_multi_gear = [
torque[order][i] * gear[order][i]
for i in range(0, len(torque[order]))
]
torque_mutil_gear_max = max(torque_mutil_gear_max,
max(torque_multi_gear))
line_torque_multi_gear = ax_torque_multi_gear.plot(
par1.axis["right"].label.set_visible(True)
par2.set_ylabel("Velocity")
offset = (60, 0)
new_axisline = par2._grid_helper.new_fixed_axis
par2.axis["right2"] = new_axisline(loc="right", axes=par2, offset=offset)
fig.add_axes(host)
host.set_xlim(0, 2)
host.set_ylim(0, 2)
host.set_xlabel("Distance")
host.set_ylabel("Density")
par1.set_ylabel("Temperature")
p1, = host.plot([0, 1, 2], [0, 1, 2], label="Density")
p2, = par1.plot([0, 1, 2], [0, 3, 2], label="Temperature")
p3, = par2.plot([0, 1, 2], [50, 30, 15], label="Velocity")
par1.set_ylim(0, 4)
par2.set_ylim(1, 65)
host.legend()
host.axis["left"].label.set_color(p1.get_color())
par1.axis["right"].label.set_color(p2.get_color())
par2.axis["right2"].label.set_color(p3.get_color())
plt.show()
def plot_vel_acc_rdis(vel, acc, rdis):
title_font_dict = {
'family': 'Times New Roman',
'color': 'black',
'weight': 'normal',
'fontsize': 15
}
axis_font_dict = {
'family': 'Times New Roman',
'color': 'black',
'weight': 'normal',
'fontsize': 30
}
legend_font = {
'family': 'Times New Roman',
'weight': 'normal',
'size': 10,
}
vel_x = np.array(vel)[:, 0] / 10.
vel_y = np.array(vel)[:, 1]
acc_x = np.array(acc)[:, 0] / 10.
acc_y = np.array(acc)[:, 1]
rdis_x = np.array(rdis)[:, 0] / 10.
rdis_y = np.array(rdis)[:, 1]
fig = plt.figure(figsize=(10, 3.5), dpi=200)
vel_axes = HostAxes(fig, [0.05, 0.1, 0.8, 0.8])
acc_axes = ParasiteAxes(vel_axes, sharex=vel_axes)
rdis_axes = ParasiteAxes(vel_axes, sharex=vel_axes)
vel_axes.parasites.append(acc_axes)
vel_axes.parasites.append(rdis_axes)
vel_axes.set_ylabel('Velocity (m/s)')
vel_axes.set_xlabel('Time (s)')
vel_axes.axis['right'].set_visible(False)
acc_axes.axis['right'].set_visible(True)
acc_axes.set_ylabel('Acceleration (m/s^2)')
acc_axes.axis['right'].major_ticklabels.set_visible(True)
acc_axes.axis['right'].label.set_visible(True)
rdis_axes.set_ylabel('Relative distance (m)')
offset = (60, 0)
new_axisline = rdis_axes._grid_helper.new_fixed_axis
rdis_axes.axis['right2'] = new_axisline(loc='right',
axes=rdis_axes,
offset=offset)
fig.add_axes(vel_axes)
p1, = vel_axes.plot(vel_x, vel_y, label="Vel", linewidth=2)
p2, = acc_axes.plot(acc_x, acc_y, label="Acc", linewidth=2)
p3, = rdis_axes.plot(rdis_x, rdis_y, label="RD", linewidth=2)
vel_axes.legend(prop=legend_font)
vel_axes.set_ylim(0, np.max(vel_y) + 1)
vel_axes.set_xlim(0, np.max(vel_x))
rdis_axes.set_ylim(0, np.max(rdis_y) + 1)
# plt.plot(vel_x, vel_y, color="green", label="Velocity", linewidth=2)
# plt.ylabel('Velocity (m/s)', fontdict=axis_font_dict)
#
# plt.plot(acc_x, acc_y, color="orange", label="Acceleration", linewidth=2, secondary_y=True)
# plt.ylabel('Acceleration (m/s^2)', fontdict=axis_font_dict)
#
plt.title('Kinematic information of host vehicle',
fontdict=title_font_dict)
# plt.xlabel('Time (s)', fontdict=axis_font_dict)
# plt.ylabel('Likelihood', fontdict=axis_font_dict)
# plt.tick_params(labelsize=20)
plt.legend(prop=legend_font)
# plt.ylim(0., 1.)
# left, right = plt.xlim()
# plt.xlim(0., right)
plt.show()
pass
host.set_ylabel("Density")
host.set_xlabel("Distance")
host.axis["right"].set_visible(True)
par1.axis["right"].set_visible(True)
par1.set_ylabel("Temperature")
par1.axis["right"].major_ticklabels.set_visible(True)
par1.axis["right"].label.set_visible(True)
fig.add_axes(host)
host.set_xlim(1975, 2010)
host.set_ylim(0, 100)
host.set_xlabel("GDP and Success rate")
host.set_ylabel("GDP per five years (*2*10^10")
par1.set_ylabel("Success Rate (%)")
p1, = host.plot(names, gdp_values, label="GDP")
p2, = par1.plot(names, edu_values, label="Success Rate")
par1.set_ylim(0, 100)
host.legend()
host.axis["left"].label.set_color(p1.get_color())
par1.axis["right"].label.set_color(p2.get_color())
plt.show()
par1.axis["top"].label.set_visible(True)
#offset = (0, 40)
#new_axisline = par2._grid_helper.new_fixed_axis
#par2.axis["top2"] = new_axisline(loc="top", axes=par2, offset=offset)
fig.add_axes(host)
for i in range(0, len(values)):
ratio = maxOfMax / maxPad[i]
#for j in range(0, len(values[i])) :
# values[i][j] = values[i][j] * ratio
if i == 0:
host.plot(values[i],
zcs[i],
label="PAD - all scans",
linewidth=0.8,
color=palettes["PAD"])
elif i == 1:
host.plot(values[i],
zcs[i],
linestyle='--',
label="PAD - center scan",
linewidth=0.8,
color=palettes["PAD"])
elif i == 2:
p1, = par1.plot(values[i],
zcs[i],
label="number of returns",
linewidth=0.8,
color=palettes["Point"])
def dym_quantile(n):
"""
动态分位图
当前投资已5年内历史数据计算百分位，价格合适购入
"""
# 这里的计算按一年244个交易日计算
windows = int(n * 244) # 将时间取整数
start_date = dt.datetime(2006, 1, 1)
end_date = dt.datetime(2020, 4, 1)
df = dp.load_bar_data('000300', 'XSHG', start_date=start_date, end_data=end_date)
df_finance = dp.load_finance_data('000300.XSHG', start_date=start_date, end_date=end_date)
if len(df) == len(df_finance):
print('yes!!!, len:%s' % len(df))
df['pe'] = df_finance['pe']
df['quantile'] = df_finance['pe'].rolling(windows).apply(lambda x: pd.Series(x).rank().iloc[-1] /
pd.Series(x).shape[0], raw=True)
# df['date'] = pd.to_datetime(df['date']) # 转换时间类型
# df.set_index(['date'], inplace=True)
# df.index.name = None # 去掉索引列名
df.dropna(inplace=True)
# 画出适中估值区间
# plt.figure()
# 创建第一个画板
fig = plt.figure(figsize=(16, 9))
host = HostAxes(fig, [0.15, 0.1, 0.65, 0.8])
par1 = ParasiteAxes(host, sharex=host)
par2 = ParasiteAxes(host, sharex=host)
host.parasites.append(par1)
host.parasites.append(par2)
host.set_xlabel('Date')
host.set_ylabel('Close')
host.axis['right'].set_visible(False)
par1.axis['right'].set_visible(True)
par1.set_ylabel('%sY Rolling quantile' % n)
par1.axis['right'].major_ticklabels.set_visible(True)
par1.axis['right'].label.set_visible(True)
par2.set_ylabel('PE')
new_axisline = par2.get_grid_helper().new_fixed_axis # "_grid_helper"与"get_grid_helper()"等价，可以代替
par2.axis['right2'] = new_axisline(loc='right', axes=par2, offset=(45, 0))
fig.add_axes(host)
df['date'] = pd.to_datetime(df['date'])
df['date'] = df['date'].apply(lambda x: dates.date2num(x))
p1, = host.plot(df['date'], df['close'], label="Close")
p2, = par1.plot(df['date'], df['quantile'], label="Quantile")
p3, = par2.plot(df['date'], df['pe'], label="PE")
host.legend()
# 轴名称，刻度值的颜色
host.axis['left'].label.set_color(p1.get_color())
host.xaxis.set_major_locator(ticker.MaxNLocator(math.floor(len(df) / 100)))
host.xaxis.set_major_formatter(dates.DateFormatter('%Y-%m'))
par1.axis['right'].label.set_color(p2.get_color())
par2.axis['right2'].label.set_color(p3.get_color())
par2.axis['right2'].major_ticklabels.set_color(p3.get_color()) # 刻度值颜色
par2.axis['right2'].set_axisline_style('-|>', size=1.5) # 轴的形状色
par2.axis['right2'].line.set_color(p3.get_color()) # 轴的颜色
# ax.xaxis.set_major_formatter(dates.DateFormatter('%Y-%m-%d'))
# df[['quantile', 'close']].plot(secondary_y=['quantile'], figsize=(14, 10), alpha=.8)
# plt.fill_between(df.index, y1=0.4, y2=0.6, color='blue', alpha=0.7)
# plt.fill_between(df.index, y1=0.8, y2=1, color='red', alpha=0.7)
# plt.fill_between(df.index, y1=0.0, y2=0.2, color='green', alpha=0.7)
# plt.annotate('reasonable zone', (df.index[-1], 0.5))
# 画出固定PE与收盘价的曲线
plt.show()
def draw_city(city):
select = "SELECT max, min, date FROM weather WHERE city='" + city + "'" + " AND date Between '2018年02月01日' AND '2018年02月28日'"
cur.execute(select)
all_data = cur.fetchall()
# 防止查不到数据
if len(all_data) != 0:
# 准备数据
max = []
min = []
time_point = []
# 准备标签
for data in all_data:
max_temperature = str(data[0])
max.append(int(max_temperature[0:len(max_temperature) - 1]))
min_temperature = str(data[1])
min.append(int(min_temperature[0:len(min_temperature) - 1]))
time_point.append(str(data[2]))
# 准备字体
my_font = fm.FontProperties(
fname="F:\\Project\\Environment\\web\\php\\font\\simsun.ttc")
fontcn = {'family': 'SimSun', 'size': 10}
# 绘制气温变化
fig = plt.figure(figsize=(40, 8), dpi=80)
ax_max = HostAxes(
fig,
[0, 0, 0.9, 0.9
]) # 用[left, bottom, weight, height]的方式定义axes，0 <= l,b,w,h <= 1
# parasite addtional axes, share x
ax_min = ParasiteAxes(ax_max, sharex=ax_max)
ax_max.parasites.append(ax_min)
# invisible right axis of ax_so2
ax_max.axis['right'].set_visible(False)
ax_max.axis['top'].set_visible(False)
ax_min.axis['right'].set_visible(True)
ax_min.axis['right'].major_ticklabels.set_visible(True)
ax_min.axis['right'].label.set_visible(True)
# set label for axis
ax_max.set_ylabel("摄氏度（℃）", fontdict=fontcn)
ax_max.set_xlabel("时间", fontdict=fontcn)
ax_min.set_ylabel("摄氏度（℃）", fontdict=fontcn)
fig.add_axes(ax_max)
x1 = time_point
x2 = [i for i in range(0, len(time_point))]
y1 = max
y2 = min
ax_max.plot(x1, y1, label='最高温度', color='#009966')
ax_min.plot(x2, y2, label='最低温度', color='#FFDE33')
ax_max.legend()
ax_min.axis['right'].label.set_color('#FFDE33')
ax_min.axis['right'].major_ticks.set_color('#FFDE33')
ax_min.axis['right'].major_ticklabels.set_color('#FFDE33')
ax_min.axis['right'].line.set_color('#FFDE33')
# 添加图形标题
plt.title(city + '最高温度和最低温度变化情况', loc='center', fontproperties=my_font)
# 保存图片
plt.savefig("./../../article/image/temperature/" + city + ".png",
bbox_inches='tight')
# 显示图形
# show ann img
plt.cla() # clear fig to show ann img
saved_img = plt.imread("./../../article/image/temperature/" + city +
".png")
# keep the origin image size
dpi = 80.0
height, width, depth = saved_img.shape
plt.figure(figsize=(width / dpi, height / dpi))
plt.axis('off')
plt.imshow(saved_img)
plt.show()
par1.axis["top"].set_visible(True)
par1.axis["top"].major_ticklabels.set_visible(True)
par1.axis["top"].label.set_visible(True)
#offset = (0, 40)
#new_axisline = par2._grid_helper.new_fixed_axis
#par2.axis["top2"] = new_axisline(loc="top", axes=par2, offset=offset)
fig.add_axes(host)
for i in range(0, len(values)) :
#ratio = maxOfMax / maxPad[i]
#for j in range(0, len(values[i])) :
# values[i][j] = values[i][j] * ratio
if i == 0 :
host.plot(values[i], zcs[i], label= "PAD")
#elif i == 1 :
# host.plot(values[i], zcs[i], label= "L-Architect PAD")
#host.plot(values[i], zcs[i], label= "number of returns")
#elif i == 2 :
#elif i < len(values) - 1 :
#p1, = par1.plot(values[i], zcs[i], label= os.path.splitext(os.path.basename(filenames[i + 1]))[0])
# p1, = par1.plot(values[i], zcs[i], label= "RDI")
else :
p1, = par1.plot(values[i], zcs[i], label= "number of returns")
#par1.set_ylim(0, 4)
#par2.set_ylim(1, 65)
host.legend()
par1.axis["right"].major_ticklabels.set_visible(True)
par1.axis["right"].label.set_visible(True)
fig1.add_axes(host)
host.set_xlim(np.pi / 2, np.pi)
host.set_ylim(0.29, 0.61)
host.set_xticks([1.57, 1.8, 2.0, 2.2, 2.4, 2.6, 2.8, 3.0, 3.1415])
host.set_xticklabels(
['$\\pi/2$', '1.8', '2.0', '2.2', '2.4', '2.6', '2.8', '3.0', '$\\pi$'])
host.set_xlabel("$\\theta_c$")
host.set_ylabel("$\\theta_b^*$")
par1.set_ylabel("min$_{\\theta_b} \\varepsilon$ $(\\times 10^{-3})$")
p1, = host.plot(C[0, :],
X[Z.argmin(axis=0), 0],
label="$\\theta_b^*$",
color='#1f77b4')
p2, = par1.plot(C[0, :],
Z.min(axis=0) * 1000,
label="min$_{\\theta_b} \\varepsilon$",
color='#e6550d')
par1.set_ylim(0, 2.5)
host.legend(loc='center left')
host.grid(linestyle='--')
host.axis["left"].label.set_color(p1.get_color())
par1.axis["right"].label.set_color(p2.get_color())
plt.show()
path_fig1 = join(dirname(__file__), 'result//' + 'approximation2_2.pdf')
host.parasites.append(par1)
host.set_ylabel("Density")
host.set_xlabel("Distance")
host.axis["right"].set_visible(True)
par1.axis["right"].set_visible(True)
par1.set_ylabel("Temperature")
par1.axis["right"].major_ticklabels.set_visible(True)
par1.axis["right"].label.set_visible(True)
fig.add_axes(host)
host.set_xlim(1975, 2010)
host.set_ylim(0, 100)
host.set_xlabel("Years Intervals")
host.set_ylabel("Income")
par1.set_ylabel("Pass Rate")
p1, = host.plot(names, values1, label="Income")
p2, = par1.plot(names, values2, label="Pass Rate")
par1.set_ylim(56, 65)
host.legend()
host.axis["left"].label.set_color(p1.get_color())
par1.axis["right"].label.set_color(p2.get_color())
plt.show()
def draw_city(city):
select = "SELECT so2, no2, co, time_point FROM day_data WHERE cityname='" + city + "'" + "AND time_point Between '2019-02-01' AND '2019-03-01'"
cur.execute(select)
all_data = cur.fetchall()
# 防止查不到数据
if len(all_data) != 0:
# 准备数据
so2 = []
no2 = []
co = []
time_point = []
# 准备标签
for data in all_data:
so2.append(int(data[0]))
no2.append(int(data[1]))
co.append(float(data[2]))
time_point.append(str(data[3]))
# 准备字体
my_font = fm.FontProperties(
fname="F:\\Project\\Environment\\web\\php\\font\\simsun.ttc")
fontcn = {'family': 'SimSun', 'size': 10}
# 绘制气温变化
fig = plt.figure(figsize=(40, 8), dpi=80)
ax_so2 = HostAxes(
fig,
[0, 0, 0.9, 0.9
]) # 用[left, bottom, weight, height]的方式定义axes，0 <= l,b,w,h <= 1
# parasite addtional axes, share x
ax_no2 = ParasiteAxes(ax_so2, sharex=ax_so2)
ax_co = ParasiteAxes(ax_so2, sharex=ax_so2)
ax_so2.parasites.append(ax_no2)
ax_so2.parasites.append(ax_co)
# invisible right axis of ax_so2
ax_so2.axis['right'].set_visible(False)
ax_so2.axis['top'].set_visible(False)
ax_no2.axis['right'].set_visible(True)
ax_no2.axis['right'].major_ticklabels.set_visible(True)
ax_no2.axis['right'].label.set_visible(True)
# set label for axis
ax_so2.set_ylabel("so2 μg/m3", fontdict=fontcn)
ax_so2.set_xlabel("时间", fontdict=fontcn)
ax_no2.set_ylabel("no2 μg/m3", fontdict=fontcn)
ax_co.set_ylabel("CO mg/m3", fontdict=fontcn)
co_axisline = ax_co.get_grid_helper().new_fixed_axis
ax_co.axis['right2'] = co_axisline(loc='right',
axes=ax_co,
offset=(40, 0))
fig.add_axes(ax_so2)
x1 = time_point
x2 = [i for i in range(0, len(x1))]
y1 = so2
y2 = no2
y3 = co
ax_so2.plot(x1, y1, label='so2', color='#009966')
ax_no2.plot(x2, y2, label='no2', color='#FFDE33')
ax_co.plot(x2, y3, label='co', color='#FF9933')
ax_so2.legend()
ax_no2.axis['right'].label.set_color('#FFDE33')
ax_co.axis['right2'].label.set_color('#FF9933')
ax_no2.axis['right'].major_ticks.set_color('#FFDE33')
ax_co.axis['right2'].major_ticks.set_color('#FF9933')
ax_no2.axis['right'].major_ticklabels.set_color('#FFDE33')
ax_co.axis['right2'].major_ticklabels.set_color('#FF9933')
ax_no2.axis['right'].line.set_color('#FFDE33')
ax_co.axis['right2'].line.set_color('#FF9933')
# 添加图形标题
plt.title(city + '日二氧化硫、二氧化氮、一氧化碳浓度变化情况',
loc='center',
fontproperties=my_font)
# 保存图片
plt.savefig("./../../article/image/pollution/" + city + ".png",
bbox_inches='tight')
# 显示图形
# show ann img
plt.cla() # clear fig to show ann img
saved_img = plt.imread("./../../article/image/pollution/" + city +
".png")
# keep the origin image size
dpi = 80.0
height, width, depth = saved_img.shape
plt.figure(figsize=(width / dpi, height / dpi))
plt.axis('off')
plt.imshow(saved_img)
plt.show()
new_axisline = par2._grid_helper.new_fixed_axis
par2.axis["top2"] = new_axisline(loc="top", axes=par2, offset=offset)
par2.axis["top2"].set_visible(True)
par2.axis["top2"].major_ticklabels.set_visible(True)
par2.set_xlim(0, 100)
fig.add_axes(host)
for i in range(0, len(values)):
#for j in range(0, len(values[i])) :
# values[i][j] = values[i][j] * ratio
if i < len(values) - 2:
h, = host.plot(values[i],
zcs[i],
label="PAD",
linewidth=0.8,
color=palettes["PAD"])
elif i < len(values) - 1:
#p1, = par1.plot(values[i], zcs[i], label= os.path.splitext(os.path.basename(filenames[i + 1]))[0])
p1, = par1.plot(values[i],
zcs[i],
label="Nb of returns",
linewidth=0.8,
color=palettes["Point"])
else:
p2, = par2.plot(values[i] * 100,
zcs[i],
label="Occlusion",
linewidth=0.8,
color=palettes["Occlusion"])
m_var = np.loadtxt(os.path.join(wholeDir, folder, 'm_var.txt'))
fig = plt.figure()
host = HostAxes(fig, [0.15, 0.1, 0.65, 0.8])
par1 = ParasiteAxes(host, sharex=host)
host.parasites.append(par1)
host.axis['right'].set_visible(False)
par1.axis['right'].set_visible(True)
par1.set_ylabel('m_entropy=∑log(m_ij))')
par1.axis['right'].major_ticklabels.set_visible(True)
par1.axis['right'].label.set_visible(True)
fig.add_axes(host)
host.set_xlabel('epoch')
host.set_ylabel('|preOutput-TrueOutput|²')
p1, = host.plot(np.arange(len(myloss)),
myloss / (K * T),
label='|preOutput-TrueOutput|²')
p2, = par1.plot(np.arange(len(m_loss)),
m_loss / (K * T),
label='m_entropy=∑log(m_ij)); ')
plt.title("two parts of loss normalized /KT ")
host.legend()
# 轴名称，刻度值的颜色
host.axis['left'].label.set_color(p1.get_color())
par1.axis['right'].label.set_color(p2.get_color())
plt.savefig(os.path.join(wholeDir, folder, 'loss_two_parts.png'),
dpi=150)
plt.clf()
fig = plt.figure()
host = HostAxes(fig, [0.15, 0.1, 0.65, 0.8])
#par2.set_ylabel("Velocity")
#offset = (60, 0)
#new_axisline = par2.get_grid_helper().new_fixed_axis
#par2.axis["right2"] = new_axisline(loc="right", axes=par2, offset=offset)
fig.add_axes(host)
host.set_xlim(0, 80)
host.set_ylim(0.6, 0.8)
host.set_xlabel("Number of filter size")
host.set_ylabel("F1 score")
par1.set_ylabel("Runtime")
#p1, = host.plot([1, 5, 10, 15, 20, 25, 30, 35, 40,50,60,70,80,90,100], [0.66, 0.736, 0.764, 0.778, 0.786, 0.814, 0.786, 0.783, 0.787,0.791,0.789,0.785,0.790,0.792,0.786], 'o-',label="F1 score", linewidth=2.2, color="#59604C")
#p2, = par1.plot([1, 5, 10, 15, 20, 25, 30, 35, 40,50,60,70,80,90,100], [3.52, 3.68, 3.71, 3.72, 3.84, 3.85, 3.87, 3.96, 3.86,3.876,3.83,3.83,3.89,3.97,4.04], '*--', label="Runtime", linewidth=2.2, color="#A35638")
p1, = host.plot([1, 5, 10, 15, 20, 25, 30, 35, 40, 50, 60, 70, 80], [
0.691, 0.693, 0.710, 0.715, 0.721, 0.727, 0.703, 0.704, 0.705, 0.695,
0.706, 0.687, 0.679
],
'o-',
label="F1 score",
linewidth=2.2,
color="#59604C")
p2, = par1.plot([1, 5, 10, 15, 20, 25, 30, 35, 40, 50, 60, 70, 80], [
5.13, 5.28, 5.33, 5.76, 5.99, 6.01, 6.54, 6.83, 6.99, 7.38, 7.85, 7.97,
8.42
],
'*--',
label="Runtime(second)",
linewidth=2.2,
color="#A35638")
#p1, = host.plot([1, 5, 10, 15, 20, 25, 30, 40,50,60,70,80], [0.589, 0.604, 0.614, 0.647, 0.634, 0.621, 0.612, 0.608, 0.531,0.546,0.448,0.486], 'o-',label="F1 score", linewidth=2.2, color="#59604C")
#p2, = par1.plot([1, 5, 10, 15, 20, 25, 30, 40,50,60,70,80], [19.39, 17.82, 18.902, 18.904, 19.04, 19.509, 18.68, 19.53, 19.795,20.596,21.228,21.562], '*--', label="Runtime(second)", linewidth=2.2, color="#A35638")
#p3, = par2.plot([0, 1, 2], [50, 30, 15], label="Velocity")
par1.set_ylim(0, 30)
par2.set_ylabel("Velocity")
offset = (60, 0)
new_axisline = par2._grid_helper.new_fixed_axis
par2.axis["right2"] = new_axisline(loc="right", axes=par2, offset=offset)
fig.add_axes(host)
host.set_xlim(0, 2)
host.set_ylim(0, 2)
host.set_xlabel("Distance")
host.set_ylabel("Density")
par1.set_ylabel("Temperature")
p1, = host.plot([0, 1, 2], [0, 1, 2], label="Density")
p2, = par1.plot([0, 1, 2], [0, 3, 2], label="Temperature")
p3, = par2.plot([0, 1, 2], [50, 30, 15], label="Velocity")
par1.set_ylim(0, 4)
par2.set_ylim(1, 65)
host.legend()
host.axis["left"].label.set_color(p1.get_color())
par1.axis["right"].label.set_color(p2.get_color())
par2.axis["right2"].label.set_color(p3.get_color())
plt.draw()
plt.show()
host.set_xlabel("PAD ($m^2.m^{-3}$)")
host.set_ylabel("Height (m)")
par2.set_xlabel("Points")
new_axisline = par2._grid_helper.new_fixed_axis
par2.axis["top"] = new_axisline(loc="top", axes=par2)
fig.add_axes(host)
for i in range(0, len(values)):
ratio = maxOfMax / maxPad[i]
#for j in range(0, len(values[i])) :
# values[i][j] = values[i][j] * ratio
if i < len(values) - 1:
host.plot(values[i],
zcs[i],
label=os.path.splitext(os.path.basename(filenames[i +
1]))[0])
else:
par2.plot(values[i],
zcs[i],
label=os.path.splitext(os.path.basename(filenames[i +
1]))[0])
#par1.set_ylim(0, 4)
#par2.set_ylim(1, 65)
host.legend()
#host.axis["bottom"].label.set_color(p1.get_color())
#par1.axis["top"].label.set_color(p1.get_color())
#par2.axis["top"].label.set_color(p2.get_color())
par1.axis["top"].set_visible(True)
par1.axis["top"].major_ticklabels.set_visible(True)
par1.axis["top"].label.set_visible(True)
#offset = (0, 40)
#new_axisline = par2._grid_helper.new_fixed_axis
#par2.axis["top2"] = new_axisline(loc="top", axes=par2, offset=offset)
fig.add_axes(host)
for i in range(0, len(values)):
ratio = maxOfMax / maxPad[i]
#for j in range(0, len(values[i])) :
# values[i][j] = values[i][j] * ratio
if i == 0:
host.plot(values[i], zcs[i], label="PAD", linewidth=0.8, color="black")
elif i == 1:
p1, = par1.plot(values[i],
zcs[i],
label="number of returns",
linewidth=0.8)
#host.plot(values[i], zcs[i], label= "number of returns")
#elif i == 2 :
#elif i < len(values) - 1 :
#p1, = par1.plot(values[i], zcs[i], label= os.path.splitext(os.path.basename(filenames[i + 1]))[0])
# p1, = par1.plot(values[i], zcs[i], label= "RDI")
else:
host.plot(values[i], zcs[i], label="L-Architect PAD", linewidth=0.8)
#par1.set_ylim(0, 4)
offset = (0, 40)
new_axisline = par2._grid_helper.new_fixed_axis
par2.axis["top2"] = new_axisline(loc="top", axes=par2, offset=offset)
par2.axis["top2"].set_visible(True)
par2.axis["top2"].major_ticklabels.set_visible(True)
par2.set_xlim(0,100)
fig.add_axes(host)
for i in range(0, len(values)) :
#for j in range(0, len(values[i])) :
# values[i][j] = values[i][j] * ratio
if i < len(values) - 2 :
h, = host.plot(values[i], zcs[i], label= "PAD", linewidth=0.8)
elif i < len(values) - 1 :
#p1, = par1.plot(values[i], zcs[i], label= os.path.splitext(os.path.basename(filenames[i + 1]))[0])
p1, = par1.plot(values[i], zcs[i], label= "Nb of returns", linewidth=0.8)
else :
p2, = par2.plot(values[i]*100, zcs[i], label= "Occlusion)", linewidth=0.8)
#par1.set_ylim(0, 4)
#par2.set_ylim(1, 65)
host.legend()
par1.axis["top"].major_ticklabels.set_color(p1.get_color())
par2.axis["top2"].major_ticklabels.set_color(p2.get_color())
host.axis["bottom"].major_ticklabels.set_color(h.get_color())
ax_load.axis['right2'] = load_axisline(loc='right',
axes=ax_load,
offset=(40, 0))
ax_cp.axis['right3'] = cp_axisline(loc='right', axes=ax_cp, offset=(80, 0))
ax_wear.axis['right4'] = wear_axisline(loc='right',
axes=ax_wear,
offset=(120, 0))
fig.add_axes(ax_cof)
''' #set limit of x, y
ax_cof.set_xlim(0,2)
ax_cof.set_ylim(0,3)
'''
curve_cof, = ax_cof.plot([0, 1, 2], [0, 1, 2], label="CoF", color='black')
curve_temp, = ax_temp.plot([0, 1, 2], [0, 3, 2], label="Temp", color='red')
curve_load, = ax_load.plot([0, 1, 2], [1, 2, 3], label="Load", color='green')
curve_cp, = ax_cp.plot([0, 1, 2], [0, 40, 25], label="CP", color='pink')
curve_wear, = ax_wear.plot([0, 1, 2], [25, 18, 9], label="Wear", color='blue')
ax_temp.set_ylim(0, 4)
ax_load.set_ylim(0, 4)
ax_cp.set_ylim(0, 50)
ax_wear.set_ylim(0, 30)
ax_cof.legend()
#轴名称，刻度值的颜色
#ax_cof.axis['left'].label.set_color(ax_cof.get_color())
ax_temp.axis['right'].label.set_color('red')
ax_load.axis['right2'] = load_axisline(loc='right',
axes=ax_load,
offset=(70, 0))
#ax_cp.axis['right3'] = cp_axisline(loc='right', axes=ax_cp, offset=(80,0))
#ax_wear.axis['right4'] = wear_axisline(loc='right', axes=ax_wear, offset=(120,0))
fig.add_axes(ax_cof)
#set limit of x, y
ax_cof.set_xlim(-3, 21)
ax_cof.set_ylim(0, 200)
curve_cof = ax_cof.plot(data[0],
data[1],
's-',
label="Compress Strength",
color='black')
# markerfacecolor='none 標記變成中空
curve_temp = ax_temp.plot(data[0],
data[2],
'o-.',
label='Porosity',
color='red')
curve_load = ax_load.plot(data[0],
data[3],
'^-',
label="Coef. of Permeability",
color='green')
#curve_cp = ax_cp.plot(data[0], data[4], label="CP", color='pink')
#curve_wear = ax_wear.plot(data[0], data[5], label="Wear", color='blue')
fig.add_axes(ax_cof)
x = []
y = []
# average DealPrice per month
cursor.execute(
"select SUM(DealPrice)/SUM(OutArea), FORMAT(DealDate, 'yy-MM-dd') from LJDT.dbo.DealInfo group by FORMAT(DealDate, 'yy-MM-dd') order by FORMAT(DealDate, 'yy-MM-dd')"
)
x.clear()
y.clear()
for row in cursor:
print(row)
x.append(row[1])
y.append(row[0])
points = ax_cof.plot(x, y, linewidth='2', label='unit price', color='green')
for a, b in zip(x, y):
plt.text(a,
b,
"%.2f" % b,
ha='center',
va='top',
color='green',
fontsize=10)
pass
# total deal count per month
# cursor.execute(
# "select COUNT(*) from LJDT.dbo.DealInfo group by FORMAT(DealDate, 'yy-MM-dd') order by FORMAT(DealDate, 'yy-MM-dd')"
# )
# y.clear()
def plot_multi_y(line_series_list, line_label_list, scatter_series_list,
scatter_label_list):
"""
第一条line做主轴,散点与主轴共用Y轴
:param line_series_list:
:param line_label_list:
:param scatter_series_list:
:param scatter_label_list:
:return:
"""
color_list = ['red', 'green', 'blue', 'yellow', 'pink', 'black', 'orange']
fig = plt.figure(1)
host_axes = HostAxes(fig, [0.1, 0.1, 0.6, 0.8])
fig.add_axes(host_axes)
host_axes.set_ylabel(line_label_list[0])
host_axes.axis['right'].set_visible(False)
host_axes.set_ylim(
min(line_series_list[0][1]) * 0.9,
max(line_series_list[0][1]) * 1.1)
host_axes.plot(line_series_list[0][0],
line_series_list[0][1],
label=line_label_list[0],
color=color_list[0])
label_offset = 0
# line_axes = []
for i in range(len(line_series_list) - 1):
axes = ParasiteAxes(host_axes, sharex=host_axes)
axes.set_ylabel(line_label_list[i + 1])
axis_line = axes.get_grid_helper().new_fixed_axis
axes.axis['right' + str(label_offset)] = axis_line(
loc='right', axes=axes, offset=(label_offset, 0))
axes.axis['right' + str(label_offset)].label.set_color(color_list[i +
1])
axes.axis['right' + str(label_offset)].major_ticks.set_color(
color_list[i + 1])
axes.axis['right' + str(label_offset)].major_ticklabels.set_color(
color_list[i + 1])
axes.axis['right' + str(label_offset)].line.set_color(color_list[i +
1])
label_offset += 40
axes.set_ylim(
min(line_series_list[i + 1][1]) * 0.9,
max(line_series_list[i + 1][1]) * 1.1)
axes.plot(line_series_list[i + 1][0],
line_series_list[i + 1][1],
label=line_label_list[i + 1],
color=color_list[i + 1])
# line_axes.append(axes)
host_axes.parasites.append(axes)
# scatter_axes = []
for i in range(len(scatter_series_list)): # 与主轴共用Y轴
# axes = ParasiteAxes(host_axes, sharex=host_axes)
# axes.set_ylabel(scatter_label_list[i])
# axis_line = axes.get_grid_helper().new_fixed_axis
# axes.axis['right' + str(label_offset)] = axis_line(loc='right', axes=axes, offset=(label_offset, 0))
color_item = color_list[len(line_label_list) + i + 1]
# axes.axis['right' + str(label_offset)].label.set_color(color_item)
# axes.axis['right' + str(label_offset)].major_ticks.set_color(color_item)
# axes.axis['right' + str(label_offset)].major_ticklabels.set_color(color_item)
# axes.axis['right' + str(label_offset)].line.set_color(color_item)
# label_offset += 40
# axes.set_ylim(min(scatter_series_list[i][1]), max(scatter_series_list[i][1]))
host_axes.scatter(scatter_series_list[i][0],
scatter_series_list[i][1],
label=scatter_label_list[i],
color=color_item)
# scatter_axes.append(axes)
# host_axes.parasites.append(axes)
host_axes.legend()
plt.show()
par2.set_ylabel('效率(E)(%)', color="blue")
>>>>>>> f5b55f5735c94ec9573668b5d56b78a8eef2e8f7
offset = (60, 0)
new_axisline = par2._grid_helper.new_fixed_axis
par2.axis['right2'] = new_axisline(loc='right', axes=par2, offset=offset)
fig.add_axes(host)
host.set_xlim(0, fm + 1)
<<<<<<< HEAD
host.set_ylim(0, hm + 5)
host.set_xlabel('流量')
host.set_ylabel('功率')
host.set_ylabel('效率')
x = np.linspace(0, fm, 500)
y = headparameters[0] * x ** 2 + headparameters[1] * x + headparameters[2]
p1, = host.plot(x, y, label="HQ拟合曲线", color="black")
host.scatter(flow1, head1, label="HQ离散数据")
x1 = np.linspace(0, fm, 500)
y1 = powerparameters[0] * x ** 2 + powerparameters[1] * x + powerparameters[2]
p2, = par1.plot(x, y1, label="PQ拟合曲线", color="red")
par1.scatter(flow1, power1, label="PQ离散数据")
x2 = np.linspace(0, fm, 500)
y2 = effectparameters[0] * x ** 2 + effectparameters[1] * x + effectparameters[2]
p3, = par2.plot(x, y2, label="EQ拟合曲线", color="blue")
par2.scatter(flow1, effect, label="EQ离散数据")
par1.set_ylim(0, pm * 2)
par2.set_ylim(0, nm + 5)
host.legend()
par2.axis['right2'].major_ticklabels.set_color(p3.get_color()) # 刻度值颜色
par2.axis['right2'].set_axisline_style('-|>', size=1.5) # 轴的形状色
def plot_line_(name, x, y1, y2, y3):
#fig = plt.figure(figsize=(7,5)) #figsize默认为4,4(图像尺寸),facecolor="blue"
fig = plt.figure(1) #figsize默认为4,4(图像尺寸),facecolor="blue"
ax = HostAxes(
fig, [0.15, 0.1, 0.7, 0.8
]) #用[left, bottom, weight, height]的方式定义axes，0 <= l,b,w,h <= 1
ax_ndvi = ParasiteAxes(ax, sharex=ax)
ax_t = ParasiteAxes(ax, sharex=ax)
ax_p = ParasiteAxes(ax, sharex=ax)
ax.parasites.append(ax_ndvi)
ax.parasites.append(ax_p)
ax.parasites.append(ax_t)
ax.set_ylim(0.41, 0.75)
ax.set_xlim(2004.5, 2015.5)
ax.axis['right'].set_visible(False)
ax.axis['top'].set_visible(False)
# plt.tick_params(top = 'off', right = 'off')
#ax_ndvi.axis['left1'] = new_axisline(loc='left', axes=ax_ndvi, offset=offset1)
#ax.axis['left'].set_axisline_style('->',size=2) #轴的形状色
ax.axis['left'].line.set_linewidth(3) #轴宽
ax.axis['bottom'].line.set_linewidth(3) #轴宽
offset1 = (20, 0)
offset2 = (60, 0)
ax_t.set_ylim(10, 30)
ax_p.set_ylim(500, 2500)
new_axisline = ax_t._grid_helper.new_fixed_axis # "_grid_helper"与"get_grid_helper()"等价，可以代替 #new_axisline = par2.get_grid_helper().new_fixed_axis # 用"get_grid_helper()"代替，结果一样，区别目前不清楚
new_axisline = ax_p._grid_helper.new_fixed_axis
ax_t.axis['right2'] = new_axisline(loc='right', axes=ax_t, offset=offset1)
#ax_t.axis['right2'].set_axisline_style('-|>',size=-1) #轴的形状色
ax_t.axis['right2'].line.set_linewidth(3) #轴宽
ax_t.axis['right2'].set_label(
'T') # ax_t.axis['right2'].set_axislabel_direction('-')
ax_p.axis['right3'] = new_axisline(loc='right', axes=ax_p, offset=offset2)
#ax_p.axis['right3'].set_axisline_style('->',size=-1) #轴的形状色
ax_p.axis['right3'].line.set_linewidth(3) #轴宽
ax_p.axis['right3'].set_label('P')
ax_t.yaxis.set_major_locator(MultipleLocator(2))
ax_p.yaxis.set_major_locator(MultipleLocator(500))
ax.xaxis.set_major_locator(
xmajorLocator
) #设置主刻度标签的位置,没有标签文本格式ax.xaxis.set_major_formatter(FormatStrFormatter('%5.1f'))
ax.yaxis.set_major_locator(ymajorLocator)
ax.xaxis.set_minor_locator(xminorLocator) #设置次刻度标签的位置,没有标签文本格式
ax.xaxis.set_minor_locator(xminorLocator)
#打开网格
ax_ndvi.grid(which='minor', color='black',
linestyle='--') #,lw=0.8, alpha=0.5) # grid setting) #绘制网格线
ax_t.grid(which='minor', color='black',
linestyle='--') #,lw=0.8, alpha=0.5) # grid setting) #绘制网格线
ax.tick_params(axis='both',
which='major',
direction='in',
width=2,
length=lwidth,
pad=tick_pad,
labelsize=tick_labelsz,
grid_linewidth=3)
ax.set_ylabel('NDVI', font_properties=getChineseFont(80))
#ax.set_ylabel('NDVI',fontproperties=getChineseFont(100))
ax_t.tick_params(axis='both',
which='both',
direction='in',
width=3,
length=100,
pad=18,
labelsize=100,
grid_linewidth=1)
ax_p.tick_params(axis='both',
which='both',
direction='in',
width=3,
length=100,
pad=18,
labelsize=100,
grid_linewidth=1)
fig.add_axes(ax)
ax.set_xlabel('Year')
p1, = ax.plot(x,
y1,
label="NDVI",
ls=linestyle,
lw=lwidth,
color=c1,
marker=".",
ms=16,
mfc=c1)
p2, = ax_t.plot(x,
y3,
label="Temperature",
ls=linestyle,
lw=lwidth,
color=c2,
marker="o",
ms=marksize,
mfc=c2)
p3, = ax_p.plot(x,
y2,
label="Precipitation",
ls=linestyle,
lw=lwidth,
color=c3,
marker="o",
ms=marksize,
mfc=c3)
#ax.set_xlabel('YEAR')
#ax.set_ylabel('NDVI')
plt.legend(loc=1,
bbox_to_anchor=(0.9, 1.2),
prop=getChineseFont(12),
ncol=1,
frameon=False,
mode='expend')
#plt.savefig(r'C:\Users\YeHui\Desktop\GGGGGGGGGGG.jpg')#, dpi=1000)
plt.show()
