-
Notifications
You must be signed in to change notification settings - Fork 1
/
spaceless.CSC.py
133 lines (110 loc) · 4.99 KB
/
spaceless.CSC.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
print """Cancer Stem Cell Simulation
Spaceless Model
"""
from spaceless.Toys import build_CSC_reg, regular_processor
from spaceless import Post
import time
import matplotlib.pyplot as plt
import numpy as np
import csv
sim = build_CSC_reg(mean_mutations=1, init_steps=500, update_mean_mutations=25, post_steps=500)
# from cc3dtools.Genome import save_genomes2, get2_to_dict
# from cc3dtools.GenomeCompare import GenomeCompare
file_name = './spaceless_data/CSC_reg.'+time.ctime()
# # # genomes = sim.get_genomes()
# # types = sim.get_types()
# # sim.sort_genomes()
# # genomes = sim.sorted_genomes
# print sim.cell_stats()
# map(lambda ix, cell: (cell.cell_type, ix), sim.cells.items())
# vals = regular_processor(sim)
# print vals
# with open(file_name+'stats.csv', 'w') as f:
# writer = csv.writer(f)
# for val in vals:
# writer.writerow(val)
vals = regular_processor(sim, thresholds=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1])
statistics = np.array(vals[1:])
plt.figure('DvsN')
to_plot = 5
threshold = np.where(statistics[:,1] == 0.1)
plt.scatter(statistics[threshold,0], statistics[threshold,to_plot], label='0.1', c='r')
threshold = np.where(statistics[:,1] == 0.5)
plt.scatter(statistics[threshold,0], statistics[threshold,to_plot], label='0.5', c='g')
threshold = np.where(statistics[:,1] == 0.9)
plt.scatter(statistics[threshold,0], statistics[threshold,to_plot], label='0.9', c='b')
threshold = np.where(statistics[:,1] == 1)
plt.scatter(statistics[threshold,0], statistics[threshold,to_plot], label='1', c='y')
plt.title('D vs sample size (for different thresholds')
plt.legend()
plt.show()
plt.figure('EpivsN')
to_plot = 4
threshold = np.where(statistics[:,1] == 0.1)
plt.scatter(statistics[threshold,0], statistics[threshold,to_plot], label='0.1', c='r')
threshold = np.where(statistics[:,1] == 0.5)
plt.scatter(statistics[threshold,0], statistics[threshold,to_plot], label='0.5', c='g')
threshold = np.where(statistics[:,1] == 0.9)
plt.scatter(statistics[threshold,0], statistics[threshold,to_plot], label='0.9', c='b')
threshold = np.where(statistics[:,1] == 1)
plt.scatter(statistics[threshold,0], statistics[threshold,to_plot], label='1', c='y')
plt.title('Epi vs sample size (for different thresholds')
plt.legend()
plt.show()
plt.figure('SHvsN')
to_plot = 3
threshold = np.where(statistics[:,1] == 0.1)
plt.scatter(statistics[threshold,0], statistics[threshold,to_plot], label='0.1', c='r')
threshold = np.where(statistics[:,1] == 0.5)
plt.scatter(statistics[threshold,0], statistics[threshold,to_plot], label='0.5', c='g')
threshold = np.where(statistics[:,1] == 0.9)
plt.scatter(statistics[threshold,0], statistics[threshold,to_plot], label='0.9', c='b')
threshold = np.where(statistics[:,1] == 1)
plt.scatter(statistics[threshold,0], statistics[threshold,to_plot], label='1', c='y')
plt.title('SH vs sample size (for different thresholds')
plt.legend()
plt.show()
plt.figure('Dvst')
to_plot = 5
threshold = np.where(statistics[:,0] == 525)
plt.scatter(statistics[threshold,1], statistics[threshold,to_plot], label='N=525', c='r')
threshold = np.where(statistics[:,0] == 775)
plt.scatter(statistics[threshold,1], statistics[threshold,to_plot], label='N=775', c='g')
threshold = np.where(statistics[:,0] == 1025)
plt.scatter(statistics[threshold,1], statistics[threshold,to_plot], label='N=1025', c='b')
threshold = np.where(statistics[:,0] == 1900)
plt.scatter(statistics[threshold,1], statistics[threshold,to_plot], label='N=1900', c='y')
plt.title('D vs t (for different N)')
plt.legend()
plt.show()
plt.figure('Svst')
to_plot = 3
threshold = np.where(statistics[:,0] == 525)
plt.scatter(statistics[threshold,1], statistics[threshold,to_plot], label='N=525', c='r')
threshold = np.where(statistics[:,0] == 775)
plt.scatter(statistics[threshold,1], statistics[threshold,to_plot], label='N=775', c='g')
threshold = np.where(statistics[:,0] == 1025)
plt.scatter(statistics[threshold,1], statistics[threshold,to_plot], label='N=1025', c='b')
threshold = np.where(statistics[:,0] == 1900)
plt.scatter(statistics[threshold,1], statistics[threshold,to_plot], label='N=1900', c='y')
plt.title('SH vs t (for different N)')
plt.legend()
plt.show()
plt.figure('epivst')
to_plot = 4
threshold = np.where(statistics[:,0] == 525)
plt.scatter(statistics[threshold,1], statistics[threshold,to_plot], label='N=525', c='r')
threshold = np.where(statistics[:,0] == 775)
plt.scatter(statistics[threshold,1], statistics[threshold,to_plot], label='N=775', c='g')
threshold = np.where(statistics[:,0] == 1025)
plt.scatter(statistics[threshold,1], statistics[threshold,to_plot], label='N=1025', c='b')
threshold = np.where(statistics[:,0] == 1900)
plt.scatter(statistics[threshold,1], statistics[threshold,to_plot], label='N=1900', c='y')
plt.title('Pi vs t (for different N)')
plt.legend()
plt.show()
# for testing purposes
# this should show that any cell with max division of 4
# should not have the middle element larger than 4
# from collections import Counter
# print Counter(map(lambda cell: (cell.max_divisions, cell.number_of_divisions, cell.cell_type), sim.cells.values()))