Редактировать 3: Я удалил все предыдущие обновления, чтобы сохранить чистоту;Вы все еще можете проверить их в истории редактирования.
Посмотрите, делает ли этот код то, что вы хотите, изменения помечаются в комментариях:
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import pandas as pd
import numpy as np #<< a new import is required
DATA_LIMITS = [0, 15]
def datalimits(*data):
return DATA_LIMITS
fig = plt.figure(figsize=(10,18))
grid = plt.GridSpec(1, 3, wspace=0.4, hspace=0.3)
gridsize = (3, 2)
ax1 = plt.subplot2grid(gridsize, (0, 0), colspan=2, rowspan=2)
ax2 = plt.subplot2grid(gridsize, (2, 0), colspan=2, rowspan=2)
ax1.grid(False)
ax2.grid(False)
ax1.set_xlim(DATA_LIMITS)
ax1.set_ylim(DATA_LIMITS)
line_a, = ax1.plot([], [], 'o', c='red', alpha = 0.5, markersize=5,zorder=3)
line_b, = ax1.plot([], [], 'o', c='blue', alpha = 0.5, markersize=5,zorder=3)
lines=[line_a,line_b]
scat = ax1.scatter([], [], s=20, marker='o', c='white', alpha = 1,zorder=3)
scats=[scat]
line_d = ax2.plot([], [], '-', c = 'k') ##<< using '-' makes this a line plot
ax2.set_ylim(-6,6)
ax2.set_xlim(0,15)
def plots(tdf, xlim=None, ylim=None, fig=fig, ax=ax1):
df = tdf[1]
if xlim is None: xlim = datalimits(df['X'])
if ylim is None: ylim = datalimits(df['Y'])
for (group, gdf), group_line in zip(df.groupby('group'), lines+scats+line_d):
if group in ['A','B']: #<< 'D' is moved to a new if case
group_line.set_data(*gdf[['X','Y']].values.T)
elif group in ['D']:
if tdf[0]==0: #<< use this to "reset the line" when the animation restarts
## or remove the if/else part here if you want continuous (over-)plotting
group_line.set_data([0,0])
else:
_x,_y=group_line.get_data()
_x=np.append(_x,gdf['X'].values)
_y=np.append(_y,gdf['Y'].values)
group_line.set_data([_x,_y])
elif group in ['C']:
gdf['X'].values, gdf['Y'].values
scat.set_offsets(gdf[['X','Y']].values)
return [scat] + [line_a,line_b] + [line_d]
n = 9
time = range(n)
d = ({
'A1_X' : [13,14,12,13,11,12,13,12,11,10],
'A1_Y' : [6,6,7,7,7,8,8,8,9,10],
'A2_X' : [7,6,5,7,6,3,4,5,6,6],
'A2_Y' : [11,12,11,10,11,12,10,11,10,9],
'B1_X' : [8,9,8,7,6,7,5,6,7,6],
'B1_Y' : [3,4,3,2,3,4,2,1,2,3],
'B2_X' : [13,14,14,14,13,13,13,12,12,12],
'B2_Y' : [5,4,3,2,4,5,4,6,3,3],
'C1_X' : [5,6,7,5,6,5,6,5,6,5],
'C1_Y' : [10,11,10,11,12,11,10,8,7,6],
'D1_X' : [0,1,2,3,4,5,6,7,8,9],
'D1_Y' : [0,1,2,3,4,3,2,1,0,-1],
})
tuples = [((t, k.split('_')[0][0], int(k.split('_')[0][1:]), k.split('_')[1]), v[i])
for k,v in d.items() for i,t in enumerate(time) ]
df = pd.Series(dict(tuples)).unstack(-1)
df.index.names = ['time', 'group', 'id']
interval_ms = 1000
delay_ms = 2000
ani = animation.FuncAnimation(fig, plots, frames=df.groupby('time'), interval=interval_ms, repeat_delay=delay_ms,)
plt.show()