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 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)
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_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()
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()
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()
ax12.set_ylabel('Velocity(kph) & Torque(Nm)')
ax12.set_ylim(-torque_max / 2,
torque_max) # occupy two thirds of the figure, upper part
ax12.set_yticks([i * 50 for i in range(0, int(torque_max / 50) + 1)])
plt.savefig(fig1_name + '.png', transparent=True)
# plt.show()
# plt.close()
for figure_id in range(0, len(wishing_list)):
fig2 = plt.figure(
) # power, gear(gear ratio) and torque*gear ratio(if available)
fig2_name = 'fig2 '
ax_power = HostAxes(fig2, [0.1, 0.08, 0.7, 0.9]) # generate a main axes
ax_gear = ParasiteAxes(
ax_power, sharex=ax_power
) # generate a parasite axes of the main axes, sharing the x axis
ax_torque_multi_gear = ParasiteAxes(ax_power, sharex=ax_power)
ax_power.parasites.append(
ax_gear) # claiming the relation of the main and parasite axes
ax_power.parasites.append(ax_torque_multi_gear)
ax_power.axis['right'].set_visible(False)
ax_gear.axis['right'].set_visible(True)
ax_gear.axis['right'].major_ticklabels.set_visible(True)
ax_gear.axis['right'].label.set_visible(True)
if max(gear[wishing_list[figure_id][0]]) > 10:
torque_mutil_gear_axisline = ax_torque_multi_gear.get_grid_helper(
).new_fixed_axis # ax_gear is the twin axes, no need to specify like this
ax_torque_multi_gear.axis['right2'] = torque_mutil_gear_axisline(
loc='right', axes=ax_torque_multi_gear, offset=(40, 0))
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()
cpu = [float(re.findall(r"\d+\.?\d*", x[7])[0]) for x in linesList] #print(cpu) mem = [int(re.findall(r"\d+\.?\d*", x[8])[0]) for x in linesList] #print(mem) title = title + [x[9] for x in linesList][0] #print(title) file.close() #create HostAxes(data) and ParasiteAxes(cpu, memory)########## #figure define, what is 1? fig = plt.figure(1) #use [left, bottom, weight, height]to define axes，0 <= l,b,w,h <= 1 ax_data = HostAxes(fig, [0, 0, 0.9, 0.9]) #parasite addtional axes, share x ax_cpu = ParasiteAxes(ax_data, sharex=ax_data) ax_mem = ParasiteAxes(ax_data, sharex=ax_data) #ax_bitrates = ParasiteAxes(ax_data, sharex=ax_data) #ax_wear = ParasiteAxes(ax_data, sharex=ax_data) #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)
# 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
ax_cof.parasites.append(ax_1)
# ax_cof.parasites.append(ax_2)
ax1_axisline = ax_1.get_grid_helper().new_fixed_axis
# ax2_axisline = ax_2.get_grid_helper().new_fixed_axis
"""
==================
Demo Parasite Axes
==================
"""
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
edu_values = list(edu_list.values())
f = open(results_path, 'w')
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)
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()
os.path.join(wholeDir, folder, x)
for x in os.listdir(os.path.join(wholeDir, folder))
if 'music_mask_' in x and 'txt' in x
]
makeVoicefor_mask(music_mask_dirs, True, fftangle)
myloss = np.loadtxt(os.path.join(wholeDir, folder, 'myloss.txt'))
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)); ')
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()
if tmpMaxZ > maxZ:
maxZ = tmpMaxZ
for i in range (0, len(zcs)):
zcs[i] -= minZ
maxZ = maxZ - minZ + 2
print (zcs)
minZ = minEffZ
#print(values)
#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("Mean of the number of returns per 10 x 10 x 10 cm voxel")
par1.set_xlabel("Number of returns per 10 x 10 x 10 cm 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)
zc, value = np.loadtxt(filenames[i], dtype='float, float', delimiter='\t', usecols=(4, 5), unpack=True, skiprows=11)
zcs.append(zc)
values.append(value)
maxPad.append(max(value))
tmpMinZ = min(zc)
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")
par2.set_xlabel("Points")
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)
ax.w_xaxis.set_pane_color((1, 1, 1, 1))
ax.w_yaxis.set_pane_color((1, 1, 1, 1))
ax.w_zaxis.set_pane_color((1, 1, 1, 1))
fig.text(0.7, 0.08, "$\\theta_b$")
fig.text(0.2, 0.18, "$\\theta_c$")
fig.text(0.1, 0.5, "$\\varepsilon$")
plt.show()
path_fig = join(dirname(__file__), 'result//' + 'approximation2_1.pdf')
fig.savefig(path_fig, dpi=300, transparent=True, bbox_inches='tight')
fig1 = plt.figure(1, figsize=(2.5, 1.2))
host = HostAxes(fig1, [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.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', '$\\pi$'])
values1.append(gdp_list[i][0])
values2.append(gdp_list[i][1])
i += 5
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)
anscpuList.append(list(map(floatTranslator, cpuList)))
ansresponseList.append(list(map(floatTranslator, responseList)))
ansthroughputList.append(list(map(floatTranslator, throughputList)))
anssupplypodList.append(list(map(intTranslator, supplypodList)))
from mpl_toolkits.axisartist.parasite_axes import HostAxes, ParasiteAxes
import matplotlib.pyplot as plt
import numpy as np
for botton in range(len(dirName)):
#设置三个轴，并添加它们
# fig = plt.figure(figsize = (20,10))
fig = plt.figure(figsize=(20, 10))
host = HostAxes(fig, [0.15, 0.1, 0.65, 0.8])
# 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')
from mpl_toolkits.axisartist.parasite_axes import HostAxes, ParasiteAxes
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
host = HostAxes(fig, [0.15, 0.1, 0.7, 0.8])
par1 = ParasiteAxes(host, sharex=host)
#par2 = ParasiteAxes(host, sharex=host)
host.parasites.append(par1)
#host.parasites.append(par2)
#host.set_ylabel("F1 score")
#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")
values.append(value / nbVox)
else:
values.append(value / nbVox)
maxPad.append(max(value))
tmpMinZ = min(zc)
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)
skiprows=11)
zcs.append(zc)
values.append(value)
maxPad.append(max(value))
tmpMinZ = min(zc)
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])
par2 = ParasiteAxes(host, sharey=host)
host.parasites.append(par2)
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:
from mpl_toolkits.axisartist.parasite_axes import HostAxes, ParasiteAxes
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure(1) #定义figure，（1）中的1是什么
ax_cof = 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_temp = ParasiteAxes(ax_cof, sharex=ax_cof)
ax_load = ParasiteAxes(ax_cof, sharex=ax_cof)
ax_cp = ParasiteAxes(ax_cof, sharex=ax_cof)
ax_wear = ParasiteAxes(ax_cof, sharex=ax_cof)
#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)')
An alternative approach using standard Matplotlib subplots is shown in the :doc:`/gallery/ticks_and_spines/multiple_yaxis_with_spines` example. An alternative approach using :mod:`mpl_toolkits.axes_grid1` and :mod:`mpl_toolkits.axisartist` is found in the :doc:`/gallery/axisartist/demo_parasite_axes2` example. """ from mpl_toolkits.axisartist.parasite_axes import HostAxes, ParasiteAxes import matplotlib.pyplot as plt 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.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) par2.axis["right2"] = par2.new_fixed_axis(loc="right", offset=(60, 0)) fig.add_axes(host) p1, = host.plot([0, 1, 2], [0, 1, 2], label="Density")
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
minZ = tmpMinZ
if tmpMaxZ > maxZ:
maxZ = tmpMaxZ
for i in range (0, len(zcs)):
zcs[i] -= minZ
maxZ = maxZ - minZ + 2
#minZ = minEffZ
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)")
host.set_xlim(0, MAX_PAD)
par1.set_xlabel("Average number of returns per 10cm cube voxel")
#par1.set_xlabel("RDI")
par2.set_xlabel("Occusion (%)")
host.axis["top"].set_visible(False)
par1.axis["top"].set_visible(True)
par1.axis["top"].major_ticklabels.set_visible(True)
`~.mpl_toolkits.axes_grid1.parasite_axes.HostAxes` and
`~.mpl_toolkits.axes_grid1.parasite_axes.ParasiteAxes`. An alternative
approach using the :ref:`toolkit_axesgrid1-index` and
:ref:`toolkit_axisartist-index`
is found in the :doc:`/gallery/axisartist/demo_parasite_axes2` example.
An alternative approach using the usual matplotlib subplots is shown in
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")
def draw(flow1, head1, headparameters, power1, powerparameters, effect, effectparameters, params):
fm = math.ceil(max(flow1))
hm = math.ceil(max(head1))
hmin = math.floor(min(head1))
pm = math.ceil(max(power1))
pmin = math.floor(min(power1))
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")
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 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()