sportowe_wizualizacja/11tegen11gif.ipynb
2019-10-12 13:36:27 +05:30

86 KiB
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!git clone https://github.com/statsbomb/open-data.git
Cloning into 'open-data'...
remote: Enumerating objects: 600, done.
remote: Counting objects: 100% (600/600), done.
remote: Compressing objects: 100% (271/271), done.
remote: Total 6950 (delta 519), reused 407 (delta 326), pack-reused 6350
Receiving objects: 100% (6950/6950), 729.86 MiB | 24.95 MiB/s, done.
Resolving deltas: 100% (6161/6161), done.
Checking out files: 100% (1273/1273), done.
#edit only this tab
#give the folder path of the match
path = "/content/open-data/data/events/"
home_team = 'Espanyol'
away_team = 'Barcelona'
import PIL
import imageio
from IPython import display
import json
import os
import codecs
from pandas.io.json import json_normalize
import numpy as np
#import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.patches import Arc, Rectangle, ConnectionPatch
from matplotlib.offsetbox import  OffsetImage
#import squarify
from functools import reduce

Xg_req = pd.DataFrame(data=None)
for filename in (os.listdir(path)):
   #print(filename)
  filename = '69275.json' # remove the comment line to work for this match
with codecs.open("%s" % path + filename,encoding='utf-8') as data_file:    
    data = json.load(data_file)
    df = pd.DataFrame(data=None)
        
    df = json_normalize(data, sep = "_")
        
        #df =  df[(df['type_name'] == "Shot")]
        #df = df.loc[:,['location','shot_body_part_id','shot_end_location','shot_one_on_one','shot_technique_id','shot_type_id','under_pressure','shot_outcome_id']]
        #print(df.shape)
Xg_req = Xg_req.append(df,ignore_index=True,sort=False)
        #df.drop(df.index, inplace=True)
        
print("done")
df = Xg_req
done
#type_id=30 is pass event AND type_id=19 is substitution event

pass_m = df.query('type_id == 30')
substitution = df.query('type_id == 19')
#pass_m = df.query('type_name == pass')
#this cell is WIP
substitution_home = set()
substitution_home = substitution[substitution.team_name == home_team]
substitution_home = substitution_home['substitution_replacement_name'].unique()
substitution_away = set()
substitution_away = substitution[substitution.team_name == away_team]
substitution_away = substitution_away['substitution_replacement_name'].unique()
print(substitution_away)
print(substitution_home)
['Javier Alejandro Mascherano' 'Seydou Kéita' 'Bojan Krkíc Pérez']
['Jesús Alberto Dátolo' 'Jordi Amat Maas' 'David García De La Cruz']
# to get 

home_player = pass_m[(pass_m.team_name == home_team)] 
home_team_list = set()
home_team_list = home_player['player_name'].unique()
#print(substitution_belgium)
#belgium_list = [player for player in belgium_list if player not in substitution_belgium]
#belgium_list.remove([x for x in substitution_belgium])#belgium_list - substitution_belgium
home_player =pass_m['player_name'].isin(home_team_list)
pass_home = pass_m[home_player] #contains 11 players of home team

away_player = pass_m[(pass_m.team_name == away_team)] 
away_list = away_player['player_name'].unique()
#away_list = away_list - substitution_brazil
away_player = pass_m['player_name'].isin(away_list)
pass_away = pass_m[away_player]
pass_home.head()
50_50_outcome_id 50_50_outcome_name bad_behaviour_card_id bad_behaviour_card_name ball_receipt_outcome_id ball_receipt_outcome_name ball_recovery_recovery_failure block_deflection block_offensive carry_end_location clearance_aerial_won clearance_body_part_id clearance_body_part_name clearance_head clearance_left_foot clearance_right_foot counterpress dribble_nutmeg dribble_outcome_id dribble_outcome_name dribble_overrun duel_outcome_id duel_outcome_name duel_type_id duel_type_name duration foul_committed_advantage foul_committed_card_id foul_committed_card_name foul_committed_offensive foul_committed_type_id foul_committed_type_name foul_won_advantage foul_won_defensive goalkeeper_body_part_id goalkeeper_body_part_name goalkeeper_end_location goalkeeper_outcome_id goalkeeper_outcome_name goalkeeper_position_id ... period play_pattern_id play_pattern_name player_id player_name position_id position_name possession possession_team_id possession_team_name related_events second shot_aerial_won shot_body_part_id shot_body_part_name shot_end_location shot_first_time shot_freeze_frame shot_key_pass_id shot_one_on_one shot_outcome_id shot_outcome_name shot_redirect shot_statsbomb_xg shot_technique_id shot_technique_name shot_type_id shot_type_name substitution_outcome_id substitution_outcome_name substitution_replacement_id substitution_replacement_name tactics_formation tactics_lineup team_id team_name timestamp type_id type_name under_pressure
4 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.606100 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... 1 9 From Kick Off 26609.0 Pablo Daniel Osvaldo 23.0 Center Forward 2 214 Espanyol [ca57ebcb-f65d-4d0c-b54d-c2ea0ccb8553] 0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 214 Espanyol 00:00:00.738 30 Pass NaN
8 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.776163 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... 1 9 From Kick Off 26211.0 Joan Verdú Fernández 21.0 Left Wing 2 214 Espanyol [cb6b2c06-a935-4588-b7cb-e1e952c9ddbe] 2 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 214 Espanyol 00:00:02.511 30 Pass NaN
16 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.242900 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... 1 9 From Kick Off 7029.0 José María Callejón Bueno 17.0 Right Wing 2 214 Espanyol [34e65d1a-4eca-4b55-8340-d125a8a32808, a6aedc7... 9 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 214 Espanyol 00:00:09.581 30 Pass True
20 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.298500 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... 1 4 From Throw In 6886.0 Dídac Vilá Rosselló 6.0 Left Back 3 214 Espanyol [98eb8c82-78c5-4c62-8c1b-02f5194eec55] 18 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 214 Espanyol 00:00:18.784 30 Pass NaN
24 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.950262 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... 1 4 From Throw In 24783.0 Javier Márquez Moreno 13.0 Right Center Midfield 3 214 Espanyol [1323492f-0fdc-4df7-bfd2-1a3f8c7b63e5, e27e148... 20 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 214 Espanyol 00:00:20.122 30 Pass True

5 rows × 122 columns

def draw_pitch(ax):
    # focus on only half of the pitch
    #Pitch Outline & Centre Line
    Pitch = Rectangle([0,0], width = 120, height = 80, fill = False)
    #Left, Right Penalty Area and midline
    LeftPenalty = Rectangle([0,22.3], width = 14.6, height = 35.3, fill = False)
    RightPenalty = Rectangle([105.4,22.3], width = 14.6, height = 35.3, fill = False)
    midline = ConnectionPatch([60,0], [60,80], "data", "data")

    #Left, Right 6-yard Box
    LeftSixYard = Rectangle([0,32], width = 4.9, height = 16, fill = False)
    RightSixYard = Rectangle([115.1,32], width = 4.9, height = 16, fill = False)


    #Prepare Circles
    centreCircle = plt.Circle((60,40),8.1,color="white", fill = False)
    centreSpot = plt.Circle((60,40),0.71,color="white")
    #Penalty spots and Arcs around penalty boxes
    leftPenSpot = plt.Circle((9.7,40),0.71,color="white")
    rightPenSpot = plt.Circle((110.3,40),0.71,color="white")
    leftArc = Arc((9.7,40),height=16.2,width=16.2,angle=0,theta1=310,theta2=50,color="white")
    rightArc = Arc((110.3,40),height=16.2,width=16.2,angle=0,theta1=130,theta2=230,color="white")
    
    element = [Pitch, LeftPenalty, RightPenalty, midline, LeftSixYard, RightSixYard, centreCircle, 
               centreSpot, rightPenSpot, leftPenSpot, leftArc, rightArc]
    for i in element:
        ax.add_patch(i)
filenames_home = []
players = home_team_list #['Alisson Ramsés Becker','Fágner Conserva Lemos','Fernando Luiz Rosa','Gabriel Fernando de Jesus','João Miranda de Souza Filho','José Paulo Bezzera Maciel Júnior','Marcelo Vieira da Silva Júnior','Neymar da Silva Santos Junior','Philippe Coutinho Correia','Thiago Emiliano da Silva','Willian Borges da Silva']
fig=plt.figure() #set up the figures
from matplotlib import rcParams
plt.style.use('dark_background')
fig.set_size_inches(16,9)
ax=fig.add_subplot(1,1,1)
draw_pitch(ax) #overlay our different objects on the pitch
plt.ylim(-2, 82)
plt.xlim(-2, 122)
plt.axis('off')
y_loc =0
timestamp = ['00:10:00:000','00:15:00:000','00:20:00:000','00:25:00:000','00:30:00:000','00:35:00:000','00:40:00:000','00:45:00:000','00:50:00:000']
time = 0
half =1
while (time < len(timestamp) ):
  for player in players:
      x_avg = 0
      y_avg = 0
      touches = 0
      if player  in players:
    #print(players[player])
          if time != 0:
            #print('YES.',timestamp[time])
            play_temp = pass_home[ (pass_home.timestamp > timestamp[time-1]) &  (pass_home.timestamp <= timestamp[time])]
          else:
            play_temp = pass_home[(pass_home.timestamp <= timestamp[time])]  
            
          play_temp = play_temp[(play_temp.player_name == player) & (play_temp.period == half)]
          
          if len(play_temp) != 0:
            for i in range(len(play_temp)):
                touches+=1
                #https://math.stackexchange.com/questions/1013230/how-to-find-coordinates-of-reflected-point
                #y_loc = (play_temp.iloc[i]['location'][1] + 40 ) %80
                y_loc = -2*(play_temp.iloc[i]['location'][1] - 40) + play_temp.iloc[i]['location'][1] 
                x_avg = x_avg + play_temp.iloc[i]['location'][0]
                y_avg = y_avg + y_loc
          if len(play_temp) != 0:      
            x_avg = x_avg/len(play_temp)
            y_avg = y_avg/len(play_temp)
      
            ax.scatter(x_avg, y_avg, s= (5 *touches ))  
            ax.annotate(player, (x_avg, y_avg))
  plt.text(50,-4,str(half)+' Half '+str(timestamp[time]),fontsize=20,color='#cb4154')        
  plt.savefig('home_image'+ str(half) + str(timestamp[time]) + str('.png'))
  filenames_home.append('home_image'+ str(half) + str(timestamp[time]) + str('.png'))
  plt.cla()
  time +=1
  if time == 8 and half ==1:
    time = 0
    half = 2
  #fig=plt.figure()
  plt.style.use('dark_background')
  fig.set_size_inches(16,9)
  ax=fig.add_subplot(1,1,1)
  draw_pitch(ax) #overlay our different objects on the pitch
  plt.ylim(-2, 82)
  plt.xlim(-2, 122)
  plt.axis('off')
  kargs = { 'duration': 2 }

import imageio
images = []
for filename in filenames_home:
  frame = 2*(i**0.5)  
  images.append(imageio.imread(filename))
imageio.mimsave('home.gif', images,**kargs)
#plt.show()
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
filenames_away = []
players = away_list #['Alisson Ramsés Becker','Fágner Conserva Lemos','Fernando Luiz Rosa','Gabriel Fernando de Jesus','João Miranda de Souza Filho','José Paulo Bezzera Maciel Júnior','Marcelo Vieira da Silva Júnior','Neymar da Silva Santos Junior','Philippe Coutinho Correia','Thiago Emiliano da Silva','Willian Borges da Silva']
fig=plt.figure() #set up the figures
from matplotlib import rcParams
plt.style.use('dark_background')
fig.set_size_inches(16,9)
ax=fig.add_subplot(1,1,1)
draw_pitch(ax) #overlay our different objects on the pitch
plt.ylim(-2, 82)
plt.xlim(-2, 122)
plt.axis('off')
y_loc =0
timestamp = ['00:10:00:000','00:15:00:000','00:20:00:000','00:25:00:000','00:30:00:000','00:35:00:000','00:40:00:000','00:45:00:000','00:50:00:000']
time = 0
half =1
while (time < len(timestamp) ):
  for player in players:
      x_avg = 0
      y_avg = 0
      touches = 0
      if player  in players:
    #print(players[player])
          if time != 0:
            #print('YES.',timestamp[time])
            play_temp = pass_away[ (pass_away.timestamp > timestamp[time-1]) &  (pass_away.timestamp <= timestamp[time])]
          else:
            play_temp = pass_away[(pass_away.timestamp <= timestamp[time])]  
            
          play_temp = play_temp[(play_temp.player_name == player) & (play_temp.period == half)]
          #print(play_temp.player_name ,play_temp.timestamp)
          #print('\n')
          if len(play_temp) != 0:
            for i in range(len(play_temp)):
                touches+=1
                #y_loc = (play_temp.iloc[i]['location'][1] + 40 ) %80
                y_loc = -2*(play_temp.iloc[i]['location'][1] - 40) + play_temp.iloc[i]['location'][1] 
                x_avg = x_avg + play_temp.iloc[i]['location'][0]
                y_avg = y_avg + y_loc
          if len(play_temp) != 0:      
            x_avg = x_avg/len(play_temp)
            y_avg = y_avg/len(play_temp)
      #print(x_avg,y_avg,players[player])
            ax.scatter(x_avg, y_avg, s= (5 *touches ))  
            ax.annotate(player, (x_avg, y_avg))
  plt.text(50,-4,str(half)+' Half '+str(timestamp[time]),fontsize=20,color='#cb4154')        
  plt.savefig('away_image'+ str(half) + str(timestamp[time]) + str('.png'))
  filenames_away.append('away_image'+ str(half) + str(timestamp[time]) + str('.png'))
  plt.cla()
  time +=1
  if time == 8 and half ==1:
    time = 0
    half = 2
  #fig=plt.figure()
  plt.style.use('dark_background')
  fig.set_size_inches(16,9)
  ax=fig.add_subplot(1,1,1)
  draw_pitch(ax) #overlay our different objects on the pitch
  plt.ylim(-2, 82)
  plt.xlim(-2, 122)
  plt.axis('off')
  
kargs = { 'duration': 2 }
import imageio
images = []
for filename in filenames_away:
  frame = 2*(i**0.5)  
  images.append(imageio.imread(filename))
imageio.mimsave('away.gif', images,**kargs)
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "
kargs = { 'duration': 2 }
import imageio
images = []
for filename in filenames:
  frame = 2*(i**0.5)  
  images.append(imageio.imread(filename))
imageio.mimsave('movie.gif', images,**kargs)