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import networkx as nx
import pandas as pd
from matplotlib import pyplot as plt
import glob

%matplotlib inline


    

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def get_data(filename):
    return pd.read_csv(filename)


    

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def clean_dataframe(dataframe):
    dataframe['agent_name'] = 'A' + dataframe['V2'].astype(str)
    dataframe['diff_avg'] = dataframe['avg_pos'] - dataframe['avg_neg']
    return dataframe


    

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def get_val_map(dataframe):
    """Get dictionary of Agent ID and difference of means"""
    return dict(zip(dataframe.agent_name, dataframe.diff_avg))


    

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def create_graph_from_edge_list(filename):
    return nx.read_edgelist(filename)


    

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def get_values(G, val_map):
    return [val_map.get(node) for node in G.nodes()]


    

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def plot_network(G):
    nx.draw_networkx(G,
                     cmap=plt.get_cmap('jet'),
                     node_color=values,
                     pos=nx.circular_layout(G))
    plt.show()


    

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def get_time_from_filename(filename):
    num = int(filename.split('/')[-1].replace('.csv', '').replace('t_', ''))
    num_format_4 = '{0:04d}'.format(num)
    return "{}.png".format(num_format_4)


    

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def plot_graph(filename, save=False):
    data = get_data(filename)
    data = clean_dataframe(data)

    val_map = get_val_map(data)

    G = create_graph_from_edge_list(edge_list_file)

    values = get_values(G, val_map)
    plt.figure(figsize=(11,11))
    nx.draw_networkx(G,
                     cmap=plt.get_cmap('bwr'),
                     node_color=values,
                     node_size=700,
                     vmin=-1,vmax=1,
                     width=0.5,
                     pos=nx.circular_layout(G))
    plt.axis('off')
    print(filename)
    if save is True:
        plt.savefig(get_time_from_filename(filename))
    # plt.show()
    plt.close()
    return None


    

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base_dir = '../../results/simulations/02-lens_single_2015-04-07_21:25:50//'
edge_list_file = base_dir + '/output/edge_list.gz'
time_files = glob.glob(base_dir + '/output/t_*.csv')
print(len(time_files))
sorted(time_files)[2:52]


    

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for filename in sorted(time_files)[2:52]:
    plot_graph(filename, True)


    

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# nx.degree_centrality(G)


    

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# nx.betweenness_centrality(G)


    

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# sorted(nx.betweenness_centrality(G), key=nx.betweenness_centrality(G).get)


    

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import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import axes3d
import os, sys
import numpy as np

def make_gif(files,output,delay=100, repeat=True,**kwargs):
    """
    Uses imageMagick to produce an animated .gif from a list of
    picture files.
    """
     
    loop = -1 if repeat else 0
    os.system('convert -delay %d -loop %d %s %s'
              %(delay,loop," ".join(files),output))


    

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import glob

files = sorted(glob.glob('*.png'))
print(files)
make_gif(files, 'network-0-50.gif', delay=50)


    

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def make_movie(files,output, fps=10,bitrate=1800,**kwargs):
    """
    Uses mencoder, produces a .mp4/.ogv/... movie from a list of
    picture files.
    """
     
    output_name, output_ext = os.path.splitext(output)
    command = { '.mp4' : 'mencoder "mf://%s" -mf fps=%d -o %s.mp4 -ovc lavc\
                         -lavcopts vcodec=msmpeg4v2:vbitrate=%d'
                         %(",".join(files),fps,output_name,bitrate)}
                          
    command['.ogv'] = command['.mp4'] + '; ffmpeg -i %s.mp4 -r %d %s'%(output_name,fps,output)
     
    print(command[output_ext])
    output_ext = os.path.splitext(output)[1]
    os.system(command[output_ext])


    

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import glob

files = sorted(glob.glob('*.png'))
print(files)


    

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# make_movie(files, 'network-0-50.mp4', delay=50)


    

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%%bash
rm out.mp4
ffmpeg -framerate 1/0.5 -i %04d.png -c:v libx264 -vf fps=25 -pix_fmt yuv420p out.mp4