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 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()
"""
from mpl_toolkits.axisartist.parasite_axes import HostAxes, ParasiteAxes
import matplotlib.pyplot as plt
if __name__ == "__main__":
fig = plt.figure(1)
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("Density")
host.set_xlabel("Distance")
host.axis["right"].set_visible(False)
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)
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)
tmpMaxZ = max(zc)
if tmpMinZ < minZ:
minZ = tmpMinZ
if tmpMaxZ > maxZ:
maxZ = tmpMaxZ
maxOfMax = max(maxPad)
host = HostAxes(fig, [0.15, 0.1, 0.65, 0.8])
par1 = ParasiteAxes(host, sharey=host)
par2 = ParasiteAxes(host, sharey=host)
host.parasites.append(par1)
host.parasites.append(par2)
host.set_xlabel("PAD ($m^2.m^{-3}$)")
host.set_ylabel("Height (m)")
#par1.set_xlabel("RDI")
par1.set_xlabel("Average number of returns per 10cm cube voxel")
host.axis["top"].set_visible(False)
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)
m_loss = np.loadtxt(os.path.join(wholeDir, folder, 'm_loss.txt'))
m_mean = np.loadtxt(os.path.join(wholeDir, folder, 'm_mean.txt'))
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()
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()
]
torque_mutil_gear_max = max(torque_mutil_gear_max,
max(torque_multi_gear))
line_torque_multi_gear = ax_torque_multi_gear.plot(
time[order],
torque_multi_gear,
linestyle=line_style[vehicle_number[order]],
color=line_color[temperature_target[order]])
gear_max = max(gear_max, max(gear[order]))
power_max = max(power_max, max(power))
lines_set = lines_set + line_power
legend_set = [line.get_label() for line in lines_set]
ax_power.legend(lines_set, legend_set, loc='upper left')
ax_power.set_xlabel('Time(s)')
ax_power.set_ylabel('Power(kw)')
ax_power.set_ylim(-20, 2 * power_max)
ax_power.set_yticks([i * 20 for i in range(0, int(power_max / 20 + 1))])
ax_gear.set_ylabel('Gear/Gear ratio')
ax_gear.set_ylim(-gear_max, gear_max + 1)
ax_gear.set_yticks([i for i in range(0, int(gear_max) + 1)])
if gear_max > 10: # if it is gear ratio, plot torque*gear
ax_torque_multi_gear.set_ylabel('Torque*gear ratio(Nm)')
ax_torque_multi_gear.set_ylim(-torque_mutil_gear_max - 50,
torque_mutil_gear_max)
ax_torque_multi_gear.set_yticks(
[i * 500 for i in range(0,
int(torque_mutil_gear_max / 500) + 2)])
ax_gear.set_ylim(0, 2 * gear_max)
ax_power.legend(lines_set, legend_set, loc='upper right')
ax_cof.parasites.append(ax_temp)
ax_cof.parasites.append(ax_load)
#ax_cof.parasites.append(ax_cp)
#ax_cof.parasites.append(ax_wear)
#invisible right axis of ax_cof
ax_cof.axis['right'].set_visible(False)
ax_cof.axis['top'].set_visible(True)
ax_temp.axis['right'].set_visible(True)
ax_temp.axis['right'].major_ticklabels.set_visible(True)
ax_temp.axis['right'].label.set_visible(True)
#set label for axis
ax_cof.set_ylabel('Compress Strength (Kgf/$\mathregular{m^2}$)',
fontsize=20)
ax_cof.set_xlabel('Addition of Waste Tire Rubber Powder (g)')
ax_temp.set_ylabel('Porosity (%)', fontsize=20)
ax_load.set_ylabel('Coef. of Permeability (cm/s)', fontsize=20)
#ax_cp.set_ylabel('CP')
#ax_wear.set_ylabel('Wear')
load_axisline = ax_load.get_grid_helper().new_fixed_axis
#cp_axisline = ax_cp.get_grid_helper().new_fixed_axis
#wear_axisline = ax_wear.get_grid_helper().new_fixed_axis
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))
#append axes
ax_data.parasites.append(ax_cpu)
ax_data.parasites.append(ax_mem)
#ax_data.parasites.append(ax_bitrates)
#ax_data.parasites.append(ax_wear)
#invisible right axis of ax_data
ax_data.axis['right'].set_visible(False)
ax_data.axis['top'].set_visible(False)
ax_cpu.axis['right'].set_visible(True)
ax_cpu.axis['right'].major_ticklabels.set_visible(True)
ax_cpu.axis['right'].label.set_visible(True)
#set label for axis
ax_data.set_ylabel('data')
ax_data.set_xlabel('time (s)')
ax_cpu.set_ylabel('cpu')
ax_mem.set_ylabel('memory')
#ax_bitrates.set_ylabel('bitrates')
#ax_wear.set_ylabel('Wear')
#new axsi line
load_axisline = ax_mem.get_grid_helper().new_fixed_axis
#cp_axisline = ax_bitrates.get_grid_helper().new_fixed_axis
#wear_axisline = ax_wear.get_grid_helper().new_fixed_axis
#axsi line padding on the right
ax_mem.axis['right2'] = load_axisline(loc='right', axes=ax_mem, offset=(40, 0))
#ax_bitrates.axis['right3'] = cp_axisline(loc='right', axes=ax_bitrates, offset=(80,0))
#ax_wear.axis['right4'] = wear_axisline(loc='right', axes=ax_wear, offset=(120,0))
cursor = conn.cursor()
#cursor.execute('SELECT * FROM dbo.DealInfo')
#cursor.execute("select SUM(DealPrice)/SUM(OutArea), DealDate from LJDT.dbo.DealInfo group by DealDate order by DealDate")
#select COUNT(*), CAST(Community as varchar(max)) from LJDT.dbo.DealInfo group by CAST(Community as varchar(max)) order by COUNT(*)
# fig, ax1 = plt.subplots()
# ax2 = ax1.twinx()
# ax3 = ax1.twinx()
fig = plt.figure(1)
# main y
ax_cof = HostAxes(
fig, [0.05, 0.05, 0.8, 0.8
]) #用[left, bottom, right, height]的方式定义axes，0 <= l,b,w,h <= 1
ax_cof.set_xlabel('month')
ax_cof.set_ylabel('unit price')
ax_cof.axis['bottom'].major_ticklabels.set_rotation(45)
ax_cof.axis['bottom'].major_ticklabels.set_fontsize(5)
# parasite addition axes, share x
ax_1 = ParasiteAxes(ax_cof, sharex=ax_cof)
ax_1.set_ylabel('deal count')
# ax_2 = ParasiteAxes(ax_cof, sharex=ax_cof)
# ax_2.set_ylabel('average area')
ax_cof.axis['right'].set_visible(False)
ax_cof.axis['top'].set_visible(False)
# ax_cof.set_ylim(2.8, 3.2)
# append axes
i = 0
f.write("year_intrval,gdp_amount,edu_att_rate \n")
while i < len(gdp_values):
f.write(
str(names[i]) + "," + str(gdp_values[i]) + "," + str(edu_values[i]) +
"\n")
i += 1
f.close()
fig = plt.figure(1)
host = HostAxes(fig, [0.1, 0.1, 0.8, 0.8])
par1 = ParasiteAxes(host, sharex=host)
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("GDP and Success rate")
host.set_ylabel("GDP per five years (*2*10^10")
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()
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()
host.parasites.append(par1)
host.axis["right"].set_visible(False)
par1.axis["right"].set_visible(True)
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='--')
f = open('output.csv', 'a+')
simplejson.dump(names, f)
f.write('\n')
simplejson.dump(values1, f)
f.write('\n')
simplejson.dump(values2, f)
f.close()
fig = plt.figure(1)
host = HostAxes(fig, [0.1, 0.1, 0.8, 0.8])
par1 = ParasiteAxes(host, sharex=host)
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")
#append axes
ax_cof.parasites.append(ax_temp)
ax_cof.parasites.append(ax_load)
ax_cof.parasites.append(ax_cp)
ax_cof.parasites.append(ax_wear)
#invisible right axis of ax_cof
ax_cof.axis['right'].set_visible(False)
ax_cof.axis['top'].set_visible(False)
ax_temp.axis['right'].set_visible(True)
ax_temp.axis['right'].major_ticklabels.set_visible(True)
ax_temp.axis['right'].label.set_visible(True)
#set label for axis
ax_cof.set_ylabel('cof')
ax_cof.set_xlabel('Distance (m)')
ax_temp.set_ylabel('Temperature')
ax_load.set_ylabel('load')
ax_cp.set_ylabel('CP')
ax_wear.set_ylabel('Wear')
load_axisline = ax_load.get_grid_helper().new_fixed_axis
cp_axisline = ax_cp.get_grid_helper().new_fixed_axis
wear_axisline = ax_wear.get_grid_helper().new_fixed_axis
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,
# host = HostAxes(fig, [0.15, 0.1, 0.65, 0.8]) #用[left, bottom, weight, height]的方式定义axes，0 <= l,b,w,h <= 1
axis_cpu = ParasiteAxes(host, sharex=host)
axis_response = ParasiteAxes(host, sharex=host)
axis_container = ParasiteAxes(host, sharex=host)
axis_throughput = ParasiteAxes(host, sharex=host)
host.parasites.append(axis_cpu)
host.parasites.append(axis_response)
host.parasites.append(axis_container)
host.parasites.append(axis_throughput)
#关闭主图的左右上轴线，打开辅轴的右边线
host.axis['left'].set_visible(False)
host.axis['right'].set_visible(False)
host.axis['top'].set_visible(False)
#设置各个轴线标签
host.set_xlabel('Time (20s)')
axis_cpu.set_ylabel('CPU Utilization')
axis_response.set_ylabel('Response Time')
axis_container.set_ylabel('Number of Instances')
axis_throughput.set_ylabel('Throughput')
#添加辅轴
cpu_axisline = axis_cpu._grid_helper.new_fixed_axis
response_axisline = axis_response._grid_helper.new_fixed_axis
container_axisline = axis_container._grid_helper.new_fixed_axis
throughput_axisline = axis_throughput._grid_helper.new_fixed_axis
axis_cpu.axis['right2'] = cpu_axisline(loc='right',
axes=axis_cpu,
offset=(0, 0))
axis_response.axis['right3'] = response_axisline(loc='right',
axes=axis_response,
offset=(40, 0))
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_xlabel("# Filter size")
host.axis["right"].set_visible(False)
#plt.xticks([])
host.axis["top"].major_ticks.set_color('white')
par1.axis["right"].set_visible(True)
#par1.set_ylabel("Runtime")
par1.axis["right"].major_ticklabels.set_visible(True)
par1.axis["right"].label.set_visible(True)
#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
the :doc:`/gallery/ticks_and_spines/multiple_yaxis_with_spines` example.
"""
from mpl_toolkits.axisartist.parasite_axes import HostAxes, ParasiteAxes
import matplotlib.pyplot as plt
fig = plt.figure(1)
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("Density")
host.set_xlabel("Distance")
host.axis["right"].set_visible(False)
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)
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)
nm = math.ceil(max(effect))
nmin = math.floor(min(effect))
<<<<<<< HEAD
fig = plt.figure(1)
=======
fig = plt.figure(figsize=(9, 5))
>>>>>>> f5b55f5735c94ec9573668b5d56b78a8eef2e8f7
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)
<<<<<<< HEAD
host.set_ylabel('扬程')
host.set_xlabel('流量')
host.axis['right'].set_visible(False)
par1.axis['right'].set_visible(True)
par1.set_ylabel('功率')
=======
host.set_ylabel('效率(E)(%)', color="blue")
host.set_xlabel('流量(Q)(m3/h)')
host.axis['right'].set_visible(False)
par1.axis['right'].set_visible(True)
par1.set_ylabel('功率(P)(kW)', color="red")
>>>>>>> f5b55f5735c94ec9573668b5d56b78a8eef2e8f7
par1.axis['right'].major_ticklabels.set_visible(True)
par1.axis['right'].label.set_visible(True)
<<<<<<< HEAD
def line_graph(self):
ords = [i.text() for i in self.temp_variables_w if i.checkState()]
if not len(ords):
return
absc = self.ui.lineComboW.currentText()
def values(s):
return self.simulation.state_fct[self.temporal_variables[s][0]]
def label(s):
if len(self.temporal_variables[s])>1:
return s + " (" + self.temporal_variables[s][1] + ")"
return s
def dimension(s):
if "energy" in s:
return "Energy (J)"
elif "Temperature" in s:
return "Temperature (K)"
else:
return label(s)
dimensions = list(set(dimension(s) for s in ords))
axis = dict()
visu.plt.ioff()
fig = visu.plt.figure()
gr_opts = dict()
for o in self._1d_options:
gr_opts[o] = self._1d_options[o].checkState()
host = HostAxes(fig, [0.15, 0.1, 0.75-(0.04*len(dimensions)), 0.8])
host.set_xlabel(label(absc))
host.set_ylabel(dimensions[0])
if gr_opts["log x"]:
host.semilogx()
if gr_opts["log y"]:
host.semilogy()
if gr_opts["grid"]:
host.grid(True, which="minor", linestyle="--")
host.grid(True, which="major")
if len(dimensions)>1:
host.axis["right"].set_visible(False)
axis[dimensions[0]] = host
for i, dim in enumerate(dimensions[1:]):
par = ParasiteAxes(host, sharex=host)
host.parasites.append(par)
par.axis["right"] = par.get_grid_helper().new_fixed_axis(loc="right", axes=par, offset=(55*i, 0))
par.axis["right"].set_visible(True)
par.set_ylabel(dim)
par.axis["right"].major_ticklabels.set_visible(True)
par.axis["right"].label.set_visible(True)
axis[dim] = par
if gr_opts["log x"]:
par.semilogx()
if gr_opts["log y"]:
par.semilogy()
for i in ords:
axis[dimension(i)].plot(values(absc), values(i), label=label(i))
fig.add_axes(host)
host.legend()
visu.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()
