In [1]:

    

import bacteriopop_utils
import feature_selection_utils
import load_data
import dynamic_mode_decomposition as dmd
import network_construction as net
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline


    

In [2]:

    

import os
import matplotlib as mpl


    

In [3]:

    

mpl.rcParams.update({
    'font.size': 18, 'axes.titlesize': 20, 'axes.labelsize': 15,
    'xtick.labelsize': 15, 'ytick.labelsize': 15,
    #'font.family': 'Lato',  # I don't have this font family
        'font.weight': 600,
    'axes.labelweight': 600, 'axes.titleweight': 600,
    #'figure.autolayout': True  # screws up layout for seaborn facetgrid objects. 
    })


    

In [4]:

    

#Calculation parameters:
p = 1  # percent abundance to consider significant. 
adjacency_magnitude = 1.5 # signal to trim by before plotting


    

In [5]:

    

# Only look for bacteria who are p% of the population in at least 1 sample. 
mappings, nodes = dmd.find_fixed_adjacency_matrix(p/100,'order',True)


    

    




all groups of oxygen/week/replicate have abundances that sum to 1
columns after aggregating on phylo level: Index([u'oxygen', u'replicate', u'week', u'kingdom', u'phylum', u'class',
       u'order', u'abundance'],
      dtype='object')

In [6]:

    

# Only pull out species that have interactions with another member with magnitude greater than 0.5
mappings, nodes = net.reduce_all_adjacency_matrixes_in_dict(mappings, nodes, 
                                                            adjacency_magnitude)


    

    




network_construction.py:106: ComplexWarning: Casting complex values to real discards the imaginary part
  new_adj[i][j] = adj[node1][node2]

In [7]:

    

# Convert all of the dataframes to Pandas
mappings = dmd.DMD_results_dict_from_numpy_to_pandas(mappings,nodes)
# Depreciated: aggregate_adjacency_matrix_over_replicates  
# std_mappings, avg_mappings, snr_mappings = dmd.aggregate_adjacency_matrix_over_replicates(mappings)


    

In [8]:

    

plot_dir = './plots/poster'
if not os.path.exists(plot_dir):
    os.makedirs(plot_dir)


    

In [9]:

    

mappings.keys()


    

    
Out[9]:





[('High', 4),
 ('Low', 1),
 ('High', 3),
 ('Low', 2),
 ('High', 2),
 ('Low', 3),
 ('High', 1),
 ('Low', 4)]

In [10]:

    

# mappings, nodes
low_agg = net.aggregate_adjacency_matrices([
        mappings[('Low', 1)],
        mappings[('Low', 2)],
        mappings[('Low', 3)],
        mappings[('Low', 4)]
    ])


    

In [11]:

    

low_agg.keys()


    

    
Out[11]:





['standard deviation', 'mean']

In [12]:

    

p_low = net.plot_heatmap(low_agg['mean'], 
                         'Low Oxygen: Most Significant Interactions', 
                         './plots/poster/low_avg--FROM_PANELS', 
                         file_type='.png',
                         width=14, height=10)


    

In [13]:

    

# mappings, nodes
high_agg = net.aggregate_adjacency_matrices([
        mappings[('High', 1)],
        mappings[('High', 2)],
        mappings[('High', 3)],
        mappings[('High', 4)]
    ])


    

In [14]:

    

p_high = net.plot_heatmap(high_agg['mean'], 
                          'High Oxygen: Most Significant Interactions', 
                          './plots/poster/high_avg--FROM_PANELS', 
                         file_type='.png',
                width=14, height=10)


    

In [15]:

    

aggregated_results_Panels = \
    net.summarize_replicate_adjacency_matrices(mappings)


    

In [16]:

    

aggregated_results_Panels.keys()


    

    
Out[16]:





['high', 'Low']

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