pyplot diagram generate

240112_diagram.py

import matplotlib.pyplot as plt
import matplotlib.dates
import numpy as np
from random import random
from random import gauss
from random import uniform

from datetime import datetime
from datetime import timedelta

#  GEO-08


val_base = 1.15
val_dir = 1.0

val_dt = 0.02

predict_num = 720
monitor_num = 2880

times = []
#  data = [0.3, 0.5, 0.8, 1.4]
data = []

break_gap = [[180,280], [400,550], [2660,2730]]

DPI=96
#  W=1220
W=950
#  W=1141
#  H=300
H=550

matplotlib.rc("font",family='DengXian')

fig=plt.figure(figsize=(W/DPI, H/DPI), dpi=DPI)
fig.patch.set_edgecolor('orange')

time_start = datetime(2020,1,19,0,0,0)
predict_start = datetime(2020,1,20,0,0,0)

t = time_start
data = []
data_predict = []

falloff = 0.999

# noise
mu = 0.0
sigma = 0.2

# data prepare
dp = []
count = 1440

for i in range(count):
    val_to_push = np.nan
    if random() < 0.2:
        val_dir = val_dir * -1
        if val_base < 0.1:
            val_dir = 1
        elif val_base > 2.1:
            val_dir = -1
    val_to_push = val_base + random()*val_dt*val_dir
    val_base = val_to_push
    dp.append(val_base)

offset = 0.0

idx = 0

t = time_start

for i in range(predict_num + monitor_num):
    data.append((dp[idx] + gauss(mu, sigma)) + offset if i <= 2880 else np.nan)
    data_predict.append(np.nan if (i <= 2880 or i > 3600)  else (dp[idx] + gauss(mu, sigma) + offset))
    idx=idx+1
    if idx >= 1440:
        idx = 0
        offset = uniform(0.05, 0.08) * (1 if random() < 0.5 else -1)
    t = t + timedelta(seconds=(30))
    times.append(t)

# apply data gaps
for gap in break_gap:
    for i in range(gap[0], gap[1]):
        data[i] = np.nan


dates = matplotlib.dates.date2num(times)

mask = dates >= matplotlib.dates.date2num(predict_start)


plt.ticklabel_format(axis='y', style='plain', useOffset=False)
plt.plot_date(dates, data, c='#205b84', linewidth=1, linestyle='solid', marker=None, label="UERE监测数据")
plt.plot_date(dates, data_predict, c='#ffa500', linewidth=1, linestyle='solid', marker=None, label="UERE预测数据")
#  plt.plot_date(dates, data, linewidth=2, linestyle='solid', markersize=3)
plt.legend(loc="upper right")
plt.gcf().autofmt_xdate()
plt.title('C37 MEO-18 2020/01/19-2020/01/20')

ax=plt.gca()
#  ax.spines['bottom'].set_color('white')
#  ax.spines['top'].set_color('white')
#  ax.spines['left'].set_color('white')
#  ax.spines['right'].set_color('white')

#  ax.tick_params(axis='x', color='white', labelcolor='white')
#  ax.tick_params(axis='y', color='white', labelcolor='white')
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%H:%M:%S'))


#  fig.set_size_inch()
#  plt.figure(figsize=(W/DPI, H/DPI))
plt.ylabel('UERE(m)')

#  plt.subplots_adjust(top=0.4)

  
plt.show()
#  plt.savefig('tmp.png', transparent = True, dpi=DPI)

diagram.py

import matplotlib.pyplot as plt
import matplotlib.dates
from random import random
from random import gauss

import numpy as np

from datetime import datetime
from datetime import timedelta

times = []
#  data = [0.3, 0.5, 0.8, 1.4]
data = []

DPI=96
#  W=1220
W=950
#  W=1141
#  H=300
H=550

num = 190

t_from = 11

#  mu = 1.07e-5
#  sigma = 0.8e-6
#  sigma = 0.5  # standard deviation
mu = 3.45    # mean
sigma = 0.35

fig=plt.figure(figsize=(W/DPI, H/DPI), dpi=DPI)
fig.patch.set_edgecolor('orange')

except_idx = -1
#  except_idx = num - int(random() * 15 + 110)

step = 240 / 190.0

val_raise = 0.2e-6
raise_falloff = 0.99

data_filtered = []

for i in range(num):
    t = datetime(2023, 10, 31, 9, 0, 0)
    t = t + timedelta(minutes=(i*step))
    times.append(t)
    #  show_except = 0.0
    #  drop = 0.0
    val = gauss(mu, sigma)
    #  if i == except_idx:
        #  val_raise += random() * 1.2
        #  val_raise += random() * 1.2e-7
    #  #  show_except = (random()*4.0 + 5.0) if i == except_idx else 0.0

    val = val + val_raise

    #  if val > 8.4:
    data_filtered.append(val)
    #  else:
        #  data_filtered.append(np.nan)
    data.append(val)

    #  data.append(max(0.25, gauss(mu, sigma)))

dates = matplotlib.dates.date2num(times)
plt.plot_date(dates, data, c='orange', linewidth=2, linestyle='solid', markersize=3)
#  plt.plot_date(dates, data_filtered, c='orange', linewidth=2, linestyle='solid', markersize=6, markerfacecolor='red')
#  plt.plot_date(dates, data, linewidth=2, linestyle='solid', markersize=3)
plt.gcf().autofmt_xdate()

ax=plt.gca()
ax.spines['bottom'].set_color('white')
ax.spines['top'].set_color('white')
ax.spines['left'].set_color('white')
ax.spines['right'].set_color('white')

ax.tick_params(axis='x', color='white', labelcolor='white')
ax.tick_params(axis='y', color='white', labelcolor='white')


#  fig.set_size_inch()
#  plt.figure(figsize=(W/DPI, H/DPI))
#  plt.ylabel('some numbers')

#  plt.subplots_adjust(top=0.4)

  
#  plt.show()
plt.savefig('tmp.png', transparent = True, dpi=DPI)

sim_diagram.py

import matplotlib.pyplot as plt
import matplotlib.dates
import numpy as np
from random import random
from random import gauss

from datetime import datetime
from datetime import timedelta

#  GEO-08

sim_data = [
[884.570836,884.569775,884.56853,884.56733,884.566245,884.564984,884.563797,884.562559,884.561525,884.56031,884.559162,884.558044,884.556892,884.555824,884.554648,884.553681,884.552596,884.55162,884.550716,884.549779,884.548738,884.547762,884.546776,884.546126],
[884.5692361593246, 884.5681689977646, 884.5671019554138, 884.5660347938538, 884.5649675130844, 884.5639003515244, 884.5628333091736, 884.5617661476135, 884.5606988668442, 884.5596318244934, 884.5585646629333, 884.5574975013733, 884.5564304590225, 884.5553631782532, 884.5542960166931, 884.5532289743423, 884.552161693573, 884.5510946512222, 884.5500274896622, 884.5489604473114, 884.547893166542, 884.5468261241913, 884.5457589626312, 884.5446919202805],
[884.56766,884.566634, 884.565507,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan]
#  [884.56766,884.566634,884.565507,884.564484,884.563454,884.562423,884.561388,884.560346,884.559307,884.558277,884.557246,884.556226,884.555229,884.554226,884.553243,884.552256,884.551281,884.550312,884.549356,884.548388,884.547429,884.546476,884.545521,884.544601],
]


times = []
#  data = [0.3, 0.5, 0.8, 1.4]
data = []

DPI=96
#  W=1220
W=950
#  W=1141
#  H=300
H=550

num = 190


t_from = 11

mu = 1.07e-5
sigma = 0.8e-7
#  sigma = 0.5  # standard deviation
#  mu = 3.45    # mean
#  sigma = 0.5
matplotlib.rc("font",family='DengXian')

fig=plt.figure(figsize=(W/DPI, H/DPI), dpi=DPI)
fig.patch.set_edgecolor('orange')

except_idx = int(random() * 15 + 70)

time_start = datetime(2023,7,14,0,0,0)

count = len(sim_data[0])
#  days = 15

step = 16.0 / count

for i in range(count):
    #  t = datetime(2023, 10, 31, 9, 0, 0)
    t = time_start
    t = t + timedelta(hours=(i * round(24 * step)))
    times.append(t)
    #  show_except = 0.0
    #  drop = 0.0
    #  if i == except_idx:
    #  #  show_except = (random()*4.0 + 5.0) if i == except_idx else 0.0
    #  data.append(1.22e-5)
    #  else:
        #  data.append(gauss(mu, sigma))
    #  data.append(max(0.25, gauss(mu, sigma)))

dates = matplotlib.dates.date2num(times)
plt.ticklabel_format(axis='y', style='plain', useOffset=False)
plt.plot_date(dates, sim_data[0], c='blue', linewidth=2, linestyle='solid', marker=None, label="真值")
plt.plot_date(dates, sim_data[2], c='green', linewidth=2, linestyle='solid', marker=None, label="igmas")
plt.plot_date(dates, sim_data[1], c='orange', linewidth=2, linestyle='solid', marker=None, label="预测")
#  plt.plot_date(dates, data, linewidth=2, linestyle='solid', markersize=3)
plt.legend(loc="upper right")
plt.gcf().autofmt_xdate()
plt.title('C01 GEO-08 2023/7/14-2023/7/29')

#  ax=plt.gca()
#  ax.spines['bottom'].set_color('white')
#  ax.spines['top'].set_color('white')
#  ax.spines['left'].set_color('white')
#  ax.spines['right'].set_color('white')

#  ax.tick_params(axis='x', color='white', labelcolor='white')
#  ax.tick_params(axis='y', color='white', labelcolor='white')


#  fig.set_size_inch()
#  plt.figure(figsize=(W/DPI, H/DPI))
#  plt.ylabel('some numbers')

#  plt.subplots_adjust(top=0.4)

  
plt.show()
#  plt.savefig('tmp.png', transparent = True, dpi=DPI)

tiaobian_diagram.py

import matplotlib.pyplot as plt
import matplotlib.dates
from random import random
from random import gauss

import numpy as np

from datetime import datetime
from datetime import timedelta

times = []
#  data = [0.3, 0.5, 0.8, 1.4]
data = []

DPI=96
#  W=1220
#  W=950
W=821
#  W=1141
#  H=300
H=370

num = 200

#  mu = 1.07e-5
#  sigma = 0.8e-6
#  sigma = 0.5  # standard deviation
mu = 8.1e-3    # mean
sigma = 2.15e-3

matplotlib.rc("font",family='Microsoft YaHei')

fig=plt.figure(figsize=(W/DPI, H/DPI), dpi=DPI)
fig.patch.set_edgecolor('orange')

#  except_idx = -1
except_idx = int(num * 0.5) - int(random() * 5)

step = 2


for i in range(num):
    t = datetime(2023, 11, 21, 9, 31, 0)
    t = t + timedelta(seconds=(i*step))
    times.append(t)
    #  show_except = 0.0
    #  drop = 0.0
    if i > except_idx:
        val = gauss(-mu, sigma)
    else:
        val = gauss(mu, sigma)
        #  val_raise += random() * 1.2
        #  val_raise += random() * 1.2e-7
    #  #  show_except = (random()*4.0 + 5.0) if i == except_idx else 0.0

    data.append(val)

    #  data.append(max(0.25, gauss(mu, sigma)))

dates = matplotlib.dates.date2num(times)
plt.title("钟差趋势", color='white')
plt.plot_date(dates, data, linewidth=1, linestyle='solid', markersize=0)
#  plt.plot_date(dates, data_filtered, c='orange', linewidth=2, linestyle='solid', markersize=6, markerfacecolor='red')
#  plt.plot_date(dates, data, linewidth=2, linestyle='solid', markersize=3)
plt.gcf().autofmt_xdate()

ax=plt.gca()
plt.ticklabel_format(axis='y', style='sci', scilimits=(0,0))
ax.spines['bottom'].set_color('white')
ax.spines['top'].set_color('white')
ax.spines['left'].set_color('white')
ax.spines['right'].set_color('white')

ax.tick_params(axis='x', color='white', labelcolor='white')
ax.tick_params(axis='y', color='white', labelcolor='white')


#  fig.set_size_inch()
#  plt.figure(figsize=(W/DPI, H/DPI))
plt.ylabel('钟差 (ms)', color='white')

#  plt.subplots_adjust(top=0.4)

  
#  plt.show()
plt.savefig('tmp.png', transparent = True, dpi=DPI)