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spaceless.SD.py
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spaceless.SD.py
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print """Single Driver Simulation
Spaceless Model
"""
from spaceless.Toys import build_SD, regular_processor
from spaceless import Post
import time
import matplotlib.pyplot as plt
import numpy as np
import csv
sim = build_SD(mean_mutations=1, init_steps=500, update_mean_mutations=10, post_steps=500)
# from cc3dtools.Genome import save_genomes2, get2_to_dict
# from cc3dtools.GenomeCompare import GenomeCompare
file_name = './spaceless_data/SD.'+time.ctime()
# # genomes = sim.get_genomes()
# types = sim.get_types()
# sim.sort_genomes()
# genomes = sim.sorted_genomes
# fa = Post.frequency_analyze(sim.get_genomes(), subsample=100)[0]
# plt.figure()
# plt.bar(*zip(*fa.items()))
# plt.show()
# fa = Post.frequency_analyze(sim.sorted_genomes['cancer'], subsample=100)[0]
# fa2 = Post.frequency_analyze(sim.sorted_genomes['normal'], subsample=100)[0]
# plt.figure()
# plt.bar(*zip(*fa.items()), color=(0,1,0,0.4), label='cancer')
# plt.bar(*zip(*fa2.items()), color=(0,0,1,0.4), label='non-cancer')
# plt.legend()
# plt.show()
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()
print(vals)
# with open(file_name+'stats.csv', 'w') as f:
# writer = csv.writer(f)
# for val in vals:
# writer.writerow(val)