Preface

In [89]:

    

import networkx as nx
import figures as fg
import custom_funcs as cf
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn

from collections import defaultdict

%matplotlib inline

%load_ext autoreload
%autoreload 2


    

    




The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

In [90]:

    

seaborn.set_context("paper")
seaborn.set_style('white')


    

In [91]:

    

# Load the graphs into memory.
Gs = []

for i in range(100):
    g = nx.read_gpickle('md_final_graphs/20150902_all_ird Final Graph {0}.pkl'.format(i))
    g = cf.impute_reassortant_status(g)
    g = cf.impute_weights(g)
    g = cf.remove_zero_weighted_edges(g)  
    Gs.append(g)

len(Gs)


    

    
Out[91]:





100

In [92]:

    

# Compile the total counts of number of nodes in each graph.
# i.e. plot representation

h_subtype_data = defaultdict(list)
c_subtype_data = defaultdict(list)
s_subtype_data = defaultdict(list)
for G in Gs:
    # Human-human, chicken-chicken, and swine-swine
    hh_nodes = fg.same_host_descent(G, 'Human')
    cc_nodes = fg.same_host_descent(G, 'Chicken')
    ss_nodes = fg.same_host_descent(G, 'Swine')
    
    hh_subtypes = fg.subtype_counts(hh_nodes, G, log=True)
    ss_subtypes = fg.subtype_counts(ss_nodes, G, log=True)
    cc_subtypes = fg.subtype_counts(cc_nodes, G, log=True)
    
    def add_to_compiled(subtype_data, subtypes):
        for subtype, counts in subtypes.items():
            subtype_data[subtype].append(counts)
            
    add_to_compiled(h_subtype_data, hh_subtypes)
    add_to_compiled(s_subtype_data, ss_subtypes)
    add_to_compiled(c_subtype_data, cc_subtypes)


    

In [93]:

    

s_subtype_data


    

    
Out[93]:





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In [94]:

    

# Plot the data
def summarized_data(subtype_data):
    means = dict()
    stds = dict()
    for k, v in subtype_data.items():
        means[k] = np.mean(v)
        stds[k] = np.std(v)
    return means, stds

h_means, h_stds = summarized_data(h_subtype_data)
# c_means, c_stds = summarized_data(c_subtype_data)
s_means, s_stds = summarized_data(s_subtype_data)

fig = plt.figure(figsize=(2,6))
ax1 = fig.add_subplot(3,1,1)
ax2 = fig.add_subplot(3,1,2)
ax3 = fig.add_subplot(3,1,3)

def plot_subtype_counts(means, stds, ax, color='blue'):
    data = pd.DataFrame()
    data['means'] = pd.Series(means)
    data['stds'] = pd.Series(stds)

    data['means'].plot(kind='bar', ax=ax, yerr=data['stds'], color=color)

plot_subtype_counts(h_means, h_stds, ax1, color='blue')
# plot_subtype_counts(c_means, c_stds, ax2)
plot_subtype_counts(s_means, s_stds, ax3, color='red')


    

    




/home/ericmjl/anaconda3/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  if self._edgecolors == str('face'):






    

In [166]:

    

data_all_props = defaultdict(list)
null_all_props = defaultdict(list)


for G in Gs:
    G = cf.clean_host_species_names(G)
    data_props = cf.edge_proportion_reassortant(G, attr='host_species', exclusions=['Unknown'])
    null_props = fg.null_distribution_proportion_reassortant(G, equally=True)
    
    print(data_props, null_props)
    # Append the data to the compilation
    for k, v in data_props.items():
        data_all_props[k].append(v)
        
    for k, v in null_props.items():
        null_all_props[k].append(v)


    

    




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{'diff_attr': 0.1148036253776435, 'same_attr': 0.059900166389351084} {'diff_attr': 0.09932279909706546, 'same_attr': 0.08695652173913043}
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{'diff_attr': 0.11437565582371459, 'same_attr': 0.05580693815987934} {'diff_attr': 0.09733124018838304, 'same_attr': 0.125}
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{'diff_attr': 0.13305613305613306, 'same_attr': 0.051118210862619806} {'diff_attr': 0.10294117647058823, 'same_attr': 0.16666666666666666}
{'diff_attr': 0.1279317697228145, 'same_attr': 0.04398826979472141} {'diff_attr': 0.09375, 'same_attr': 0.09090909090909091}
{'diff_attr': 0.130879345603272, 'same_attr': 0.050955414012738856} {'diff_attr': 0.10711610486891386, 'same_attr': 0.2}
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{'diff_attr': 0.13890808569454044, 'same_attr': 0.046343975283213185} {'diff_attr': 0.11276429130775255, 'same_attr': 0.06451612903225806}
{'diff_attr': 0.1317840442114213, 'same_attr': 0.05800936291471606} {'diff_attr': 0.10555336746750689, 'same_attr': 0.12121212121212122}
{'diff_attr': 0.12979049565661727, 'same_attr': 0.04549019607843137} {'diff_attr': 0.10411985018726591, 'same_attr': 0.17647058823529413}
{'diff_attr': 0.13142554319024907, 'same_attr': 0.04654771140418929} {'diff_attr': 0.09428129829984544, 'same_attr': 0.125}
{'diff_attr': 0.13493723849372385, 'same_attr': 0.053353658536585365} {'diff_attr': 0.1155893536121673, 'same_attr': 0.0}
{'diff_attr': 0.07521265482763767, 'same_attr': 0.04907424051770627} {'diff_attr': 0.06896551724137931, 'same_attr': 0.0}
{'diff_attr': 0.13215400624349635, 'same_attr': 0.04251968503937008} {'diff_attr': 0.10479285134037368, 'same_attr': 0.04}
{'diff_attr': 0.11758793969849246, 'same_attr': 0.060556464811783964} {'diff_attr': 0.11157455683003129, 'same_attr': 0.1}

In [167]:

    

def summarize_prop_reassortant(all_props):
    means = dict()
    stds = dict()
    
    for k, v in all_props.items():
        means[k] = np.mean(v)
        stds[k] = np.std(v)

    return means, stds

data_prop_mean, data_prop_std = summarize_prop_reassortant(data_all_props)
null_prop_mean, null_prop_std = summarize_prop_reassortant(null_all_props)


    

In [168]:

    

pd.Series(null_all_props)


    

    
Out[168]:





diff_attr    [0.11370882040382572, 0.11642493139945119, 0.1...
same_attr    [0.21739130434782608, 0.09090909090909091, 0.0...
dtype: object

In [169]:

    

means = pd.DataFrame()
means['data'] = pd.Series(data_prop_mean)
means['null'] = pd.Series(null_prop_mean)

stds = pd.DataFrame()
stds['data'] = pd.Series(data_prop_std)
stds['null'] = pd.Series(null_prop_std)

means.plot(kind='bar', yerr=stds)


    

    
Out[169]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f30c3f94be0>






    




/home/ericmjl/anaconda3/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  if self._edgecolors == str('face'):






    

In [ ]: