def __call__(self, inputs):
from pylab import text
from matplotlib.font_manager import FontProperties as FP
kwds = {}
#kwds['text'] = self.get_input('text')
kwds['fontsize'] = self.get_input('fontsize')
kwds['alpha'] = self.get_input('alpha')
kwds['color'] = self.get_input('color')
kwds['backgroundcolor'] = self.get_input('backgroundcolor')
kwds['rotation'] = self.get_input('rotation')
kwds['fontproperties'] = FP(**self.get_input('fontproperties'))
for key, value in self.get_input('kwargs').iteritems():
try:
kwds[key] = value
except:
print 'key already defined. skip it'
pass
#res = text(0,0, self.get_input('text'), **kwds)
return (kwds, )
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 14 13:14:08 2020
@author: cdragon-ljl
"""
from matplotlib import pyplot as plt
from matplotlib.font_manager import FontProperties as FP
#添加中文字体
font = FP(fname=r"c:\windows\fonts\simsun.ttc", size=15)
plt.figure(figsize=(30, 10), dpi=80)
x_3 = range(1, 32)
y_3 = [
11, 17, 16, 11, 12, 11, 12, 6, 6, 7, 8, 9, 12, 15, 14, 17, 18, 21, 16, 17,
20, 14, 15, 15, 15, 19, 21, 22, 22, 22, 23
]
x_10 = range(41, 72)
y_10 = [
26, 26, 28, 19, 21, 17, 16, 19, 18, 20, 20, 19, 22, 23, 17, 20, 21, 20, 22,
15, 11, 15, 5, 13, 17, 10, 11, 13, 12, 13, 6
]
#绘制散点图
plt.scatter(x_3, y_3, label="3月份")
plt.scatter(x_10, y_10, label="10月份")
#调整x轴的刻度
data = dataset.Appliances
#print(data.head())
#print(data.describe())
# 统计各列缺失值总数
#missing = dataset.isnull().sum()
#print(missing)
# 绘图查看
font1 = {
'family': 'Times New Roman',
'color': 'k',
'weight': 'normal',
'size': 15,
}
font2 = FP('Times New Roman', size=15)
'''
plt.figure(figsize=(10,4))
plt.hist(data,bins=50)
plt.xlabel('Wh',fontdict=font1)
plt.ylabel('Frequency',fontdict=font1)
plt.xticks([0,200,400,600,800,1000],['0','200','400','600','800','1000'],fontproperties=font2)
plt.yticks([0,1000,2000,3000,4000,5000,6000,],['0','1000','2000','3000','4000','5000','6000'],fontproperties=font2)
plt.savefig('图3直方图.svg',bbox_inches='tight')
'''
'''
plt.figure(figsize=(2,10))
sns.boxplot(data=dataset.iloc[:,1:2])
#plt.xlabel('load',fontdict=font1)
plt.xticks([])
plt.xlabel('Appliances',fontdict=font1)
"""
Using python to draw tracking results compared with groundtruth
"""
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties as FP
import numpy as np
efp = FP('Times New Roman', size=13)
#### load groundtruth txt file
gt = np.loadtxt("groundtruth.txt")
print(type(gt), gt.shape)
timestamp = gt[:, 0]
gt_pos_x = gt[:, 1]
gt_pos_y = gt[:, 2]
gt_pos_z = gt[:, 3]
gt_heading = gt[:, 4]
### load track results
track = np.loadtxt("track_results.txt")
tr_pos_x = track[:, 1]
tr_pos_y = track[:, 2]
tr_v = track[:, 3]
tr_heading = track[:, 4]
tr_yaw_rate = track[:, 5]
### ------Begin plot-------
plt.figure('position')
plt.grid(linestyle="-.")
def plot_ti_dhdl(dhdl_data, labels=None, colors=None, units='kT', ax=None):
'''Plot the dhdl of TI.
Parameters
----------
dhdl_data : :class:`~alchemlyb.estimators.TI` or list
One or more :class:`~alchemlyb.estimators.TI` estimator, where the
dhdl value will be taken from.
labels : List
list of labels for labelling all the alchemical transformations.
colors : List
list of colors for plotting all the alchemical transformations.
Default: ['r', 'g', '#7F38EC', '#9F000F', 'b', 'y']
units : str
The label for the unit of the estimate. Default: "kT"
ax : matplotlib.axes.Axes
Matplotlib axes object where the plot will be drawn on. If ``ax=None``,
a new axes will be generated.
Returns
-------
matplotlib.axes.Axes
An axes with the TI dhdl drawn.
Note
----
The code is taken and modified from
`Alchemical Analysis <https://github.com/MobleyLab/alchemical-analysis>`_.
.. versionchanged:: 0.5.0
The `units` will be used to change the underlying data instead of only
changing the figure legend.
.. versionadded:: 0.4.0
'''
# Make it into a list
# separate_dhdl method is used so that the input for the actual plotting
# Function are a uniformed list of series object which only contains one
# lambda.
if not isinstance(dhdl_data, list):
dhdl_list = dhdl_data.separate_dhdl()
else:
dhdl_list = []
for dhdl in dhdl_data:
dhdl_list.extend(dhdl.separate_dhdl())
# Convert unit
new_unit = []
convert = get_unit_converter(units)
for dhdl in dhdl_list:
new_unit.append(convert(dhdl))
dhdl_list = new_unit
if ax is None:
fig, ax = plt.subplots(figsize=(8, 6))
ax.spines['bottom'].set_position('zero')
ax.spines['top'].set_color('none')
ax.spines['right'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
for k, spine in ax.spines.items():
spine.set_zorder(12.2)
# Make level names
if labels is None:
lv_names2 = []
for dhdl in dhdl_list:
# Assume that the dhdl has only one columns
lv_names2.append(dhdl.name.capitalize())
else:
if len(labels) == len(dhdl_list):
lv_names2 = labels
else: # pragma: no cover
raise ValueError(
'Length of labels ({}) should be the same as the number of data ({})'
.format(len(labels), len(dhdl_list)))
if colors is None:
colors = ['r', 'g', '#7F38EC', '#9F000F', 'b', 'y']
else:
if len(colors) >= len(dhdl_list):
pass
else: # pragma: no cover
raise ValueError(
'Number of colors ({}) should be larger than the number of data ({})'
.format(len(labels), len(dhdl_list)))
# Get the real data out
xs, ndx, dx = [0], 0, 0.001
min_y, max_y = 0, 0
for dhdl in dhdl_list:
x = dhdl.index.values
y = dhdl.values.ravel()
min_y = min(y.min(), min_y)
max_y = max(y.max(), max_y)
for i in range(len(x) - 1):
if i % 2 == 0:
ax.fill_between(x[i:i + 2] + ndx,
0,
y[i:i + 2],
color=colors[ndx],
alpha=1.0)
else:
ax.fill_between(x[i:i + 2] + ndx,
0,
y[i:i + 2],
color=colors[ndx],
alpha=0.5)
xlegend = [-100 * wnum for wnum in range(len(lv_names2))]
ax.plot(xlegend, [0 * wnum for wnum in xlegend],
ls='-',
color=colors[ndx],
label=lv_names2[ndx])
xs += (x + ndx).tolist()[1:]
ndx += 1
# Make sure the tick labels are not overcrowded.
xs = np.array(xs)
dl_mat = np.array([xs - i for i in xs])
ri = range(len(xs))
def getInd(r=ri, z=[0]):
primo = r[0]
min_dl = ndx * 0.02 * 2**(primo > 10)
if dl_mat[primo].max() < min_dl:
return z
for i in r: # pragma: no cover
for j in range(len(xs)):
if dl_mat[i, j] > min_dl:
z.append(j)
return getInd(ri[j:], z)
xt = []
for i in range(len(xs)):
if i in getInd():
xt.append(i)
else:
xt.append('')
plt.xticks(xs[1:], xt[1:], fontsize=10)
ax.yaxis.label.set_size(10)
# Remove the abscissa ticks and set up the axes limits.
for tick in ax.get_xticklines():
tick.set_visible(False)
ax.set_xlim(0, ndx)
min_y *= 1.01
max_y *= 1.01
# Modified so that the x label won't conflict with the lambda label
min_y -= (max_y - min_y) * 0.1
ax.set_ylim(min_y, max_y)
for i, j in zip(xs[1:], xt[1:]):
ax.annotate(
('%.2f' % (i - 1.0 if i > 1.0 else i) if not j == '' else ''),
xy=(i, 0),
size=10,
rotation=90,
va='bottom',
ha='center',
color='#151B54')
if ndx > 1:
lenticks = len(ax.get_ymajorticklabels()) - 1
if min_y < 0: lenticks -= 1
if lenticks < 5: # pragma: no cover
from matplotlib.ticker import AutoMinorLocator as AML
ax.yaxis.set_minor_locator(AML())
ax.grid(which='both', color='w', lw=0.25, axis='y', zorder=12)
ax.set_ylabel(
r'$\langle{\frac{\partial U}{\partial\lambda}}\rangle_{\lambda}$' +
'({})'.format(units),
fontsize=20,
color='#151B54')
ax.annotate(r'$\mathit{\lambda}$',
xy=(0, 0),
xytext=(0.5, -0.05),
size=18,
textcoords='axes fraction',
va='top',
ha='center',
color='#151B54')
lege = ax.legend(prop=FP(size=14), frameon=False, loc=1)
for l in lege.legendHandles:
l.set_linewidth(10)
return ax
import numpy as np
import csv
import math
from scipy import interpolate
from matplotlib import pyplot as plt
from matplotlib.ticker import LinearLocator, MultipleLocator, FormatStrFormatter
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
import pandas as pd
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
from matplotlib.font_manager import FontProperties as FP
plt.rc('font', family='Times New Roman')
font_TNM = FP(fname=r"C:\Windows\Fonts\times.ttf", size=10.5)
Altitude_scalar = 12.44
Latitude_scalar = 52.13
Longitude_scalar = 39.78
Altitude_xishu = 1.32
Latitude_xishu = 1.08
Longitude_xishu = 1.08
# pred_true_values line
"""
data_LSTM = pd.read_csv(r"D:\轨迹预测\prediction\LSTM\pred_y.csv")
data_GRU = pd.read_csv(r"D:\轨迹预测\prediction\GRU\pred_y.csv")
data_BP = pd.read_csv(r"D:\轨迹预测\prediction\BP\pred_y10.csv")
data_SVR = pd.read_csv(r"D:\轨迹预测\prediction\SVR\pred_y.csv")
import numpy as np
import pandas as pd
import pymc as pm
import os
import matplotlib.pyplot as plt
from pylab import *
from scipy.stats.mstats import mquantiles
from matplotlib.font_manager import FontManager as FM
from matplotlib.font_manager import FontProperties as FP
myfont = FP(fname='D://Lib//site-packages//matplotlib//'
'mpl-data//fonts//ttf//vera.ttf')
mpl.rcParams['font.sans-serif'] = ['SimHei']
# 逻辑斯蒂函数
def logistic(x, beta, alpha=0):
return 1.0 / (1.0 + np.exp((beta * x) + alpha))
# 散点图
def scatter_figure(data, index):
plt.scatter(data[:, 0], data[:, 2], color='black', alpha=0.5)
plt.yticks([0, 1])
plt.ylabel(u"销售点", fontproperties=myfont)
plt.xlabel("属性距离", fontproperties=myfont)
plt.title(u'销售点与每个县城相关属性距离散点图', fontproperties=myfont)
plt.xticks(data[:, 0],
index,
size='small',
fontproperties=myfont,
"""
Learn how to use subplot to draw multiple plots in one figure
References: https://matplotlib.org/gallery/subplots_axes_and_figures/subplot.html
"""
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties as FP
import numpy as np
mpl.rcParams['font.family'] = 'sans-serif'
mpl.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
efp = FP('Times New Roman')
x1 = np.linspace(0.0, 5.0)
x2 = np.linspace(0.0, 2.0)
y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)
y2 = np.cos(2 * np.pi * x2)
plt.subplot(2, 1, 1)
plt.plot(x1, y1, 'o-')
plt.title('A tale of 2 subplots', fontweight='bold', fontproperties=efp)
plt.ylabel('Damped oscillation', fontproperties=efp)
plt.subplot(2, 1, 2)
plt.plot(x2, y2, '.-')
plt.xlabel('time [s]')
plt.ylabel('Undamped', fontproperties=efp)
plt.show()
d = y - gt_pos_y[idx]
delta_gt_pos_y.append(d)
vx = [x / t for x, t in zip(delta_gt_pos_x, delta_t)]
vy = [y / t for y, t in zip(delta_gt_pos_y, delta_t)]
v = [cmath.sqrt(a**2 + b**2) for a, b in zip(vx, vy)]
v.insert(0, 0.0)
### load track results
track = np.loadtxt("track_results.txt")
tr_pos_x = track[:, 1]
tr_pos_y = track[:, 2]
tr_v = track[:, 3]
tr_heading = track[:, 4]
tr_yaw_rate = track[:, 5]
frame = np.arange(len(v))
mpl.rcParams['font.family'] = 'sans-serif'
mpl.rcParams['font.sans-serif'] = 'SimHei,Times New Roman'
cfp = FP('SimHei', size=15)
plt.figure("v")
plt.grid(linestyle="-.", color='gray')
plt.plot(frame, tr_v * 3.6, '-', color='blue', label="UKF输出")
plt.plot(frame, np.array(v) * 3.6, color='red', label="直接计算")
plt.xlabel("Frames", fontsize=15)
plt.ylabel("Velocity [km/h]", fontsize=15)
plt.legend(prop=cfp, ncol=2)
# plt.savefig("v.svg", format="svg")
plt.show()
def plot_dF_state(estimators,
labels=None,
colors=None,
units='kT',
orientation='portrait',
nb=10):
'''Plot the dhdl of TI.
Parameters
----------
estimators : :class:`~alchemlyb.estimators` or list
One or more :class:`~alchemlyb.estimators`, where the
dhdl value will be taken from. For more than one estimators
with more than one alchemical transformation, a list of list format
is used.
labels : List
list of labels for labelling different estimators.
colors : List
list of colors for plotting different estimators.
units : str
The unit of the estimate. Default: "kT"
orientation : string
The orientation of the figure. Can be `portrait` or `landscape`
nb : int
Maximum number of dF states in one row in the `portrait` mode
Returns
-------
matplotlib.figure.Figure
An Figure with the dF states drawn.
Note
----
The code is taken and modified from
`Alchemical Analysis <https://github.com/MobleyLab/alchemical-analysis>`_.
.. versionchanged:: 0.5.0
The `units` will be used to change the underlying data instead of only
changing the figure legend.
.. versionadded:: 0.4.0
'''
try:
len(estimators)
except TypeError:
estimators = [
estimators,
]
formatted_data = []
for dhdl in estimators:
try:
len(dhdl)
formatted_data.append(dhdl)
except TypeError:
formatted_data.append([
dhdl,
])
estimators = formatted_data
# Get the dF
dF_list = []
error_list = []
max_length = 0
convert = get_unit_converter(units)
for dhdl_list in estimators:
len_dF = sum([len(dhdl.delta_f_) - 1 for dhdl in dhdl_list])
if len_dF > max_length:
max_length = len_dF
dF = []
error = []
for dhdl in dhdl_list:
for i in range(len(dhdl.delta_f_) - 1):
dF.append(convert(dhdl.delta_f_).iloc[i, i + 1])
error.append(convert(dhdl.d_delta_f_).iloc[i, i + 1])
dF_list.append(dF)
error_list.append(error)
# Get the determine orientation
if orientation == 'landscape':
if max_length < 8:
fig, ax = plt.subplots(figsize=(8, 6))
else:
fig, ax = plt.subplots(figsize=(max_length, 6))
axs = [
ax,
]
xs = [
np.arange(max_length),
]
elif orientation == 'portrait':
if max_length < nb:
xs = [
np.arange(max_length),
]
fig, ax = plt.subplots(figsize=(8, 6))
axs = [
ax,
]
else:
xs = np.array_split(np.arange(max_length), max_length / nb + 1)
fig, axs = plt.subplots(nrows=len(xs), figsize=(8, 6))
mnb = max([len(i) for i in xs])
else:
raise ValueError(
"Not recognising {}, only supports 'landscape' or 'portrait'.".
format(orientation))
# Sort out the colors
if colors is None:
colors_dict = {
'TI': '#C45AEC',
'TI-CUBIC': '#33CC33',
'DEXP': '#F87431',
'IEXP': '#FF3030',
'GINS': '#EAC117',
'GDEL': '#347235',
'BAR': '#6698FF',
'UBAR': '#817339',
'RBAR': '#C11B17',
'MBAR': '#F9B7FF'
}
colors = []
for dhdl in estimators:
dhdl = dhdl[0]
if isinstance(dhdl, TI):
colors.append(colors_dict['TI'])
elif isinstance(dhdl, BAR):
colors.append(colors_dict['BAR'])
elif isinstance(dhdl, MBAR):
colors.append(colors_dict['MBAR'])
else:
if len(colors) >= len(estimators):
pass
else:
raise ValueError(
'Number of colors ({}) should be larger than the number of data ({})'
.format(len(colors), len(estimators)))
# Sort out the labels
if labels is None:
labels = []
for dhdl in estimators:
dhdl = dhdl[0]
if isinstance(dhdl, TI):
labels.append('TI')
elif isinstance(dhdl, BAR):
labels.append('BAR')
elif isinstance(dhdl, MBAR):
labels.append('MBAR')
else:
if len(labels) == len(estimators):
pass
else:
raise ValueError(
'Length of labels ({}) should be the same as the number of data ({})'
.format(len(labels), len(estimators)))
# Plot the figure
width = 1. / (len(estimators) + 1)
elw = 30 * width
ndx = 1
for x, ax in zip(xs, axs):
lines = []
for i, (dF, error) in enumerate(zip(dF_list, error_list)):
y = [dF[j] for j in x]
ye = [error[j] for j in x]
if orientation == 'landscape':
lw = 0.1 * elw
elif orientation == 'portrait':
lw = 0.05 * elw
line = ax.bar(x + len(lines) * width,
y,
width,
color=colors[i],
yerr=ye,
lw=lw,
error_kw=dict(elinewidth=elw,
ecolor='black',
capsize=0.5 * elw))
lines += (line[0], )
for dir in ['left', 'right', 'top', 'bottom']:
if dir == 'left':
ax.yaxis.set_ticks_position(dir)
else:
ax.spines[dir].set_color('none')
if orientation == 'landscape':
plt.yticks(fontsize=8)
ax.set_xlim(x[0] - width, x[-1] + len(lines) * width)
plt.xticks(x + 0.5 * width * len(estimators),
tuple(['%d--%d' % (i, i + 1) for i in x]),
fontsize=8)
elif orientation == 'portrait':
plt.yticks(fontsize=10)
ax.xaxis.set_ticks([])
for i in x + 0.5 * width * len(estimators):
ax.annotate(r'$\mathrm{%d-%d}$' % (i, i + 1),
xy=(i, 0),
xycoords=('data', 'axes fraction'),
xytext=(0, -2),
size=10,
textcoords='offset points',
va='top',
ha='center')
ax.set_xlim(x[0] - width,
x[-1] + len(lines) * width + (mnb - len(x)))
ndx += 1
x = np.arange(max_length)
ax = plt.gca()
for tick in ax.get_xticklines():
tick.set_visible(False)
if orientation == 'landscape':
leg = plt.legend(lines,
labels,
loc=3,
ncol=2,
prop=FP(size=10),
fancybox=True)
plt.title('The free energy change breakdown', fontsize=12)
plt.xlabel('States', fontsize=12, color='#151B54')
plt.ylabel(r'$\Delta G$ ({})'.format(units),
fontsize=12,
color='#151B54')
elif orientation == 'portrait':
leg = ax.legend(lines,
labels,
loc=0,
ncol=2,
prop=FP(size=8),
title=r'$\Delta G$ ({})'.format(units) +
r'$\mathit{vs.}$ lambda pair',
fancybox=True)
leg.get_frame().set_alpha(0.5)
return fig
def plot_convergence(*data, units='kT', ax=None):
"""Plot the forward and backward convergence.
The input could be the result from
:func:`~alchemlyb.convergence.forward_backward_convergence` or it could
be given explicitly as `forward`, `forward_error`, `backward`,
`backward_error`.
`forward`: A list of free energy estimate from the first X% of data,
where `forward_error` is the corresponding error.
`backward`: A list of free energy estimate from the last X% of data.,
where `backward_error` is the corresponding error.
These four array_like objects should have the same
shape and can be used as input for the
:func:`matplotlib.pyplot.errorbar`.
Parameters
----------
data : Dataframe or 4 array_like objects
Output Dataframe from
:func:`~alchemlyb.convergence.forward_backward_convergence`.
Or given explicitly as `forward`, `forward_error`, `backward`,
`backward_error` see :ref:`plot_convergence <plot_convergence>`.
units : str
The label for the unit of the estimate. Default: "kT"
ax : matplotlib.axes.Axes
Matplotlib axes object where the plot will be drawn on. If ``ax=None``,
a new axes will be generated.
Returns
-------
matplotlib.axes.Axes
An axes with the forward and backward convergence drawn.
Note
----
The code is taken and modified from
`Alchemical Analysis <https://github.com/MobleyLab/alchemical-analysis>`_.
If `data` is not an :class:pandas.Dataframe` produced by
:func:`~alchemlyb.convergence.forward_backward_convergence`,
the unit will be adjusted according to the units
variable. Otherwise, the units variable is for labelling only.
Changing it doesn't change the unit of the underlying variable.
.. versionchanged:: 0.6.0
data now takes in dataframe
.. versionadded:: 0.4.0
"""
if len(data) == 1 and isinstance(data[0], pd.DataFrame):
dataframe = get_unit_converter(units)(data[0])
forward = dataframe['Forward'].to_numpy()
forward_error = dataframe['Forward_Error'].to_numpy()
backward = dataframe['Backward'].to_numpy()
backward_error = dataframe['Backward_Error'].to_numpy()
else:
try:
forward, forward_error, backward, backward_error = data
except ValueError: # pragma: no cover
raise ValueError('Ensure all four of forward, forward_error, '
'backward, backward_error are supplied.')
if ax is None: # pragma: no cover
fig, ax = plt.subplots(figsize=(8, 6))
plt.setp(ax.spines['bottom'], color='#D2B9D3', lw=3, zorder=-2)
plt.setp(ax.spines['left'], color='#D2B9D3', lw=3, zorder=-2)
for dire in ['top', 'right']:
ax.spines[dire].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
f_ts = np.linspace(0, 1, len(forward) + 1)[1:]
r_ts = np.linspace(0, 1, len(backward) + 1)[1:]
line0 = ax.fill_between([0, 1],
backward[-1] - backward_error[-1],
backward[-1] + backward_error[-1],
color='#D2B9D3',
zorder=1)
line1 = ax.errorbar(
f_ts,
forward,
yerr=forward_error,
color='#736AFF',
lw=3,
zorder=2,
marker='o',
mfc='w',
mew=2.5,
mec='#736AFF',
ms=12,
)
line2 = ax.errorbar(
r_ts,
backward,
yerr=backward_error,
color='#C11B17',
lw=3,
zorder=3,
marker='o',
mfc='w',
mew=2.5,
mec='#C11B17',
ms=12,
)
plt.xticks(r_ts[::2], fontsize=10)
plt.yticks(fontsize=10)
ax.legend((line1[0], line2[0]), ('Forward', 'Reverse'),
loc=9,
prop=FP(size=18),
frameon=False)
ax.set_xlabel(r'Fraction of the simulation time',
fontsize=16,
color='#151B54')
ax.set_ylabel(r'$\Delta G$ ({})'.format(units),
fontsize=16,
color='#151B54')
plt.xticks(f_ts, ['%.2f' % i for i in f_ts])
plt.tick_params(axis='x', color='#D2B9D3')
plt.tick_params(axis='y', color='#D2B9D3')
return ax
del a_limit['banci12']
'''
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl
from matplotlib.font_manager import FontProperties as FP
mpl.rcParams['font.family'] = 'sans-serif'
mpl.rcParams[
'font.sans-serif'] = 'NSimSun,Times New Roman' #//中文除外的设置成New Roman，中文设置成宋体
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
fig = plt.figure()
cfp = FP('NSimSun', size=12)
efp = FP('Times New Roman', size=12)
ax1 = fig.add_subplot(2, 2, 1)
ax2 = fig.add_subplot(2, 2, 2)
ax3 = fig.add_subplot(2, 2, 3)
ax4 = fig.add_subplot(2, 2, 4)
ax1.plot(a, color='black')
ax1.set_ylim([200, 1200])
ax1.set_yticklabels(['250', '250', '500', '750', '1 000'], fontproperties=efp)
ax1.set_xticks(range(0, 12))
ax1.set_xticklabels(range(1, 12))
ax1.set_xlabel(u'站点', fontsize=13.5)
ax1.set_ylabel(u'车头时距/s', fontsize=13.5)
