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graph_plot.py
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graph_plot.py
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import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from networkx import draw
def plotNetwork(net_graph):
"""
Plots the dominance network as a digraph.
"""
draw(net_graph, with_labels=True, font_weight='bold')
plt.show()
return
def plotHierarchy(x, y, ind_ids, intercept, slope):
"""
Plots hierarchy ranking and its steepness.
"""
n_ind = len(ind_ids)
fig, ax = plt.subplots()
ax.plot(x, y, 'o-', label='NormDS')
ax.plot(x, intercept + slope*x, '-', label='fitted line')
ax.set_yticks(np.arange(0, round(y[0])+2, 2))
ax.set_xticks(np.arange(0, n_ind, 1))
ax.set_xticklabels(ind_ids)
ax.set(xlabel='individuals id', ylabel='normalized DS',
title='hierarchy steepness')
plt.legend()
plt.show()
def plotAggrIntensity():
"""
Plots hierarchy steepnees w.r.t group sizes, for mild and fierce species.
"""
cols = ['group-size','flee-dist','aggr-intensity','steepness']
data = pd.read_csv('results.csv', usecols=cols, sep=',')
mild_data = data.query('`flee-dist` == 2 & `aggr-intensity` == "mild"')
fierce_data = data.query('`flee-dist` == 2 & `aggr-intensity` == "fierce"')
sizes = [8, 12, 18, 24, 30, 36, 42, 48]
steep_mild = np.array(mild_data['steepness'], dtype=float)
steep_fierce = np.array(fierce_data['steepness'], dtype=float)
fig, ax = plt.subplots()
ax.plot(sizes, steep_mild, 'o-', label='mild')
ax.plot(sizes, steep_fierce, 'o-', label='fierce')
ax.set_yticks(np.arange(0, 1, 0.2))
ax.set_xticks(sizes)
#ax.set_xticklabels(ind_ids)
ax.set(xlabel='group size', ylabel='hierarchy steepness',
title='Intesity of aggression')
plt.legend()
plt.show()
def plotTriadicPatterns(aggr):
"""
Plot occurences of triadic patterns for different group sizes.
- aggr : 'mild' or 'fierce'
"""
patterns = ['Null','Single-edge','Double-dominant','Double-subordinate','Pass-along','Transitive','Cycle']
cols = ['group-size','flee-dist','aggr-intensity','Null','Single-edge',
'Double-dominant','Double-subordinate','Pass-along','Transitive','Cycle']
data = pd.read_csv('results.csv', usecols=cols, sep=',')
pat_data = data.query('`flee-dist` == 2 & `aggr-intensity` == "{}"'.format(aggr))
fig, ax = plt.subplots()
for idx, row in pat_data.iterrows():
ax.plot(np.arange(0, 7, 1), row[patterns], 'o-', label=row['group-size'])
ytick = np.amax(np.array(pat_data[['Transitive']])) + 200
ax.set_yticks(np.arange(0, ytick, 400))
ax.set_xticks(np.arange(0, 8, 1))
ax.set_xticklabels(patterns)
ax.set(ylabel='pattern occurrences',
title='Triadic patterns - {} species'.format(aggr))
plt.legend()
plt.show()
def plotFleeDist(size, aggr):
"""
Plots occurences of triadic patterns for different fleeing distances.
- size : different flee distances have been tested only on 24 or 36 group size;
- aggr : 'mild' or 'fierce'.
"""
patterns = ['Null','Single-edge','Double-dominant','Double-subordinate','Pass-along','Transitive','Cycle']
cols = ['group-size','flee-dist','aggr-intensity','steepness','Null','Single-edge',
'Double-dominant','Double-subordinate','Pass-along','Transitive','Cycle']
data = pd.read_csv('results.csv', usecols=cols, sep=',')
flee_data = data.query('`group-size` == {} & `aggr-intensity` == "{}"'.format(size,aggr))
prop = {}
for p in patterns:
prop_p = []
for idx, row in flee_data.iterrows(): # for each group size
tot_mild = sum(row[patterns])
prop_p.append(round(row[p]/tot_mild, 2))
prop[p] = prop_p
fig, ax = plt.subplots()
#for idx, row in flee_data.iterrows():
# ax.plot(np.arange(0, 7, 1), row[patterns], 'o-', label=row['flee-dist'])
for fd in range(5):
plot = []
for p in patterns:
plot.append(prop[p][fd])
ax.plot(np.arange(0, 7, 1), plot, 'o-', label=str(2.0 * (fd + 1)))
#ytick = (1800 if size == 24 else 5000)
#ax.set_yticks(np.arange(0, ytick, 200))
#ax.set_xticks(np.arange(0, 8, 1))
ax.set_yticks(np.arange(0, 1.1, 0.1))
ax.set_xticks(np.arange(0, 8, 1))
ax.set_xticklabels(patterns)
ax.set(ylabel='pattern proportion',
title='Triadic patterns - fleeing distance ({})'.format(aggr))
plt.legend(title='fleeing dist')
plt.show()
def _autolabel(axes, rects):
"""
Attach a text label above each bar in *rects*, displaying its height.
"""
for rect in rects:
height = rect.get_height()
axes.annotate('{}'.format(height),
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3), # 3 points vertical offset
textcoords="offset points",
ha='center', va='bottom')
def _plotFreqPatterns():
"""
Plots occurences of triadic patterns for different group sizes.
- aggr : 'mild' or 'fierce'.
"""
patterns = ['Null','Single-edge','Double-dominant','Double-subordinate','Pass-along','Transitive','Cycle']
cols = ['group-size','flee-dist','aggr-intensity','Null','Single-edge',
'Double-dominant','Double-subordinate','Pass-along','Transitive','Cycle']
data = pd.read_csv('results.csv', usecols=cols, sep=',')
mild_data = data.query('`flee-dist` == 2 & `aggr-intensity` == "mild"')
fierce_data = data.query('`flee-dist` == 2 & `aggr-intensity` == "fierce"')
sizes = ['8', '12', '18', '24', '30', '36', '42', '48']
tot_fierce = []
for idx, row in fierce_data.iterrows():
tot_fierce.append(sum(row[patterns]))
freq_mild = {}
freq_fierce = {}
width = 0.12 # the width of the bars
for p in patterns:
freq_m = []
freq_f = []
for idx, row in mild_data.iterrows(): # for each group size
tot_mild = sum(row[patterns])
freq_m.append(round(row[p]/tot_mild, 2))
freq_f.append(round(fierce_data[p][idx+8]/tot_fierce[idx], 2))
freq_mild[p] = freq_m
freq_fierce[p] = freq_f
#rec1 = ax.bar(np.arange(0, len(sizes), 1) - width/2, freq_mild, width, label='mild')
#rec2 = ax.bar(np.arange(0, len(sizes), 1) + width/2, freq_fierce, width, label='fierce')
fig, ax = plt.subplots()
plt.grid(True, axis='y', linestyle='-.', linewidth=0.5)
for s in range(len(sizes)):
to_plot = []
for p in range(len(patterns)):
to_plot.append(freq_mild[patterns[p]][s])
step = [i + s*0.12 - 0.48 for i in range(len(patterns))]
ax.bar(step, to_plot, width, label=sizes[s])
ax.set_yticks(np.arange(0, 1.1, 0.1))
ax.set_xticks(np.arange(0, len(patterns), 1))
ax.set_xticklabels(patterns)
ax.set(ylabel='pattern proportion', title='Triadic patterns - mild species')
plt.legend(title='group size')
plt.show()
def _plotSignificance(aggr):
"""
Plots hierarchy ranking and its steepness.
"""
sizes = ['8', '12', '18', '24', '30', '36', '42', '48']
patterns = ['Null','Single-edge','Pass-along','Double-dominant','Double-subordinate','Transitive','Cycle']
sp_m = [
[-0.0944, -0.2326, -0.189, -0.3178, -0.3306, 0.8302, 0.0419], # 8
[-0.1028, -0.2673, -0.1794, -0.2757, -0.3094, 0.845, 0.0046], # 12
[-0.1249, -0.2799, -0.2341, -0.3026, -0.2995, 0.8057, 0.1444], # 18
[-0.1423, -0.3133, -0.2355, -0.2837, -0.2945, 0.7952, 0.1631], # 24
[-0.1519, -0.3301, -0.2502, -0.2832, -0.302, 0.7096, 0.3612], # 30
[-0.1528, -0.3018, -0.2889, -0.311, -0.2941, 0.6661, 0.4186], # 36
[-0.1536, -0.4038, -0.2718, -0.3095, -0.2865, 0.6476, 0.3771], # 42
[-0.1869, -0.4248, -0.2331, -0.2389, -0.2625, 0.667, 0.3993] # 48
]
sp_f = [
[-0.0949, -0.253, -0.173, -0.3001, -0.3164, 0.8384, 0.0621], # 8
[-0.112, -0.2819, -0.1952, -0.2727, -0.2813, 0.8326, 0.1521], # 12
[-0.1456, -0.2934, -0.2224, -0.2648, -0.3164, 0.7646, 0.2975], # 18
[-0.1305, -0.2387, -0.209, -0.2707, -0.2554, 0.7802, 0.3676], # 24
[-0.134, -0.3369, -0.227, -0.2839, -0.2682, 0.7402, 0.3414], # 30
[-0.1314, -0.3035, -0.2394, -0.262, -0.2777, 0.678, 0.4773], # 36
[-0.1652, -0.376, -0.2018, -0.2874, -0.2408, 0.7214, 0.3601], # 42
[-0.1727, -0.3873, -0.2315, -0.2838, -0.2749, 0.7043, 0.3382] # 48
]
sp = (sp_m if aggr == 'mild' else sp_f)
# to plot both mild and fierce in the same graph
avg_sp_m = []
avg_sp_f = []
for c in range(7):
tmp_m = []
tmp_f = []
for r in range(8):
tmp_m.append(sp_m[r][c])
tmp_f.append(sp_f[r][c])
avg_sp_m.append(np.mean(tmp_m))
avg_sp_f.append(np.mean(tmp_f))
fig, ax = plt.subplots()
ax.axhline(xmax=10, color='gray', linestyle='--', linewidth=0.5)
#for s in range(len(sizes)):
# ax.plot(np.arange(0, 7, 1), sp[s], 'o-', label=sizes[s])
ax.plot(np.arange(0, 7, 1), avg_sp_m, 'o-', label='mild')
ax.plot(np.arange(0, 7, 1), avg_sp_f, 'o-', label='fierce')
ax.set_yticks(np.arange(-1.0, 1.1, 0.25))
ax.set_xticks(np.arange(0, len(patterns), 1))
ax.set_xticklabels(patterns)
ax.set(ylabel='normalized Z-score', title='Significance Profile'.format(aggr))
plt.legend(title='aggression intensity')
plt.show()