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main_gl.py
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main_gl.py
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__author__ = 'hervemn'
# coding: utf-8
from OpenGL.GL import *
from OpenGL.GLU import *
from OpenGL.GLUT import *
import pycuda.driver as cuda
from pycuda import gpuarray as ga
import pycuda.gl as cudagl
import glutil
from vector import Vec
import numpy as np
import matplotlib.pyplot as plt
import sys
from OpenGL.arrays import vbo
from simulation_loader import simulation
class window(object):
def __init__(self, pyramid, name, level, n_iterations, is_simu, scrambled, perform_em, sample_param, output_folder,
fasta_file, candidates_blacklist, n_neighbours, allow_repeats, id_selected_gpu,
main_thread):
#mouse handling for transforming scene
id_exp = 0
self.main_thread = main_thread
self.n_neighbours = n_neighbours
self.id_selected_gpu = id_selected_gpu
self.use_rippe = True
self.mouse_down = False
self.size_points = 6
self.mouse_old = Vec([0., 0.])
self.rotate = Vec([0., 0., 0.])
self.translate = Vec([0., 0., 0.])
self.initrans = Vec([0., 0., -2.])
self.scrambled = scrambled
self.sample_param = sample_param
self.allow_repeats = allow_repeats
self.width = 800
self.height = 600
self.n_iterations_em = n_iterations
self.n_iterations_mcmc = n_iterations
self.dt = np.float32(0.01)
self.white = 1
self.collect_4_graph3d = dict()
self.output_folder = output_folder
self.fasta_file = fasta_file
glutInit(sys.argv)
glutInitDisplayMode(GLUT_RGBA | GLUT_DOUBLE | GLUT_DEPTH)
glutInitWindowSize(self.width, self.height)
glutInitWindowPosition(0, 0)
if perform_em:
name_window = "Expectation Maximization : " + name
else:
name_window = "MCMC MTM : " + name
name_window = "GRAAL: structure visualization"
self.win = glutCreateWindow(name_window)
#gets called by GLUT every frame
glutDisplayFunc(self.draw)
#handle user input
glutKeyboardFunc(self.on_key)
glutMouseFunc(self.on_click)
glutMotionFunc(self.on_mouse_motion)
#this will call draw every 30 ms
glutTimerFunc(30, self.timer, 30)
#setup OpenGL scene
self.glinit()
#setup CUDA
self.cuda_gl_init()
#set up initial conditions
self.use_rippe = True
self.simulation = simulation(pyramid, name, level, n_iterations, is_simu, self, self.output_folder,
self.fasta_file, candidates_blacklist, self.allow_repeats)
self.texid= self.simulation.texid
self.init_n_frags = self.simulation.init_n_frags
print "n init frags = ", self.init_n_frags
self.pbo_im_buffer = self.simulation.pbo_im_buffer
self.pos_vbo = self.simulation.pos_vbo
self.col_vbo = self.simulation.col_vbo
self.n_frags = self.simulation.sampler.n_new_frags
print "start sampling... "
self.str_likelihood = "likelihood = " + str(0)
self.str_n_contigs = "n contigs = " + str(0)
self.str_curr_id_frag = "current id frag = " + str(0)
self.str_curr_cycle = "current cycle = "+ str(0)
self.str_curr_temp = "current temperature = "+ str(0)
self.str_curr_dist = "current dist = "+ str(0)
self.collect_likelihood = []
self.collect_n_contigs = []
self.collect_mean_len = []
self.collect_op_sampled = []
self.collect_id_fA_sampled = []
self.collect_id_fB_sampled = []
self.collect_full_likelihood = []
self.collect_dist_from_init_genome = []
self.collect_fact = []
self.collect_slope = []
self.collect_d = []
self.collect_d_nuc = []
self.collect_d_max = []
self.collect_likelihood_nuisance = []
self.collect_success = []
self.collect_all = []
self.file_mean_len = os.path.join(self.simulation.output_folder, str(id_exp) + 'behaviour_mean_len.pdf')
self.file_n_contigs = os.path.join(self.simulation.output_folder, str(id_exp) + 'behaviour_n_contigs.pdf')
self.file_fact = os.path.join(self.simulation.output_folder, str(id_exp) + 'behaviour_fact.pdf')
self.file_slope = os.path.join(self.simulation.output_folder, str(id_exp) + 'behaviour_slope.pdf')
self.file_d_nuc = os.path.join(self.simulation.output_folder, str(id_exp) + 'behaviour_d_nuc.pdf')
self.file_d = os.path.join(self.simulation.output_folder, str(id_exp) + 'behaviour_d.pdf')
self.file_d_max = os.path.join(self.simulation.output_folder, str(id_exp) + 'behaviour_d_max.pdf')
self.file_dist_init_genome = os.path.join(self.simulation.output_folder, str(id_exp) + 'behaviour_dist_init_genome.pdf')
self.txt_file_mean_len = os.path.join(self.simulation.output_folder, str(id_exp) + 'list_mean_len.txt')
self.txt_file_n_contigs = os.path.join(self.simulation.output_folder, str(id_exp) + 'list_n_contigs.txt')
self.txt_file_dist_init_genome = os.path.join(self.simulation.output_folder, str(id_exp) + 'list_dist_init_genome.txt')
self.txt_file_likelihood = os.path.join(self.simulation.output_folder, str(id_exp) + 'list_likelihood.txt')
self.txt_file_fact = os.path.join(self.simulation.output_folder, str(id_exp) + 'list_fact.txt')
self.txt_file_slope = os.path.join(self.simulation.output_folder, str(id_exp) + 'list_slope.txt')
self.txt_file_d_nuc = os.path.join(self.simulation.output_folder, str(id_exp) + 'list_d_nuc.txt')
self.txt_file_d = os.path.join(self.simulation.output_folder, str(id_exp) + 'list_d.txt')
self.txt_file_d_max = os.path.join(self.simulation.output_folder, str(id_exp) + 'list_d_max.txt')
self.txt_file_success = os.path.join(self.simulation.output_folder, str(id_exp) + 'list_success.txt')
self.txt_file_list_mutations = os.path.join(self.simulation.output_folder, str(id_exp) + 'list_mutations.txt')
self.file_all_data = os.path.join(self.simulation.output_folder, str(id_exp) + 'behaviour_all.txt')
def replay_simu(self, folder_res,):
file_simu = os.path.join(folder_res, 'list_mutations.txt')
file_likelihood = os.path.join(folder_res, 'list_likelihood.txt')
file_n_contigs = os.path.join(folder_res, 'list_n_contigs.txt')
file_distances = os.path.join(folder_res, 'list_dist_init_genome.txt')
h = open(file_simu, 'r')
all_lines = h.readlines()
list_id_fA = []
list_id_fB = []
list_op_sampled = []
for i in xrange(1, len(all_lines)):
line = all_lines[i]
data = line.split('\t')
id_fA = int(data[0])
id_fB = int(data[1])
id_op = int(data[2])
list_id_fA.append(id_fA)
list_id_fB.append(id_fB)
list_op_sampled.append(id_op)
h.close()
h_likeli = open(file_likelihood, 'r')
all_lines_likeli = h_likeli.readlines()
list_likelihood = []
for i in xrange(0, len(all_lines_likeli)):
line = all_lines_likeli[i]
likeli = np.float32(line)
list_likelihood.append(likeli)
h_likeli.close()
h_n_contigs = open(file_n_contigs, 'r')
list_n_contigs = []
all_lines_contigs = h_n_contigs.readlines()
for i in xrange(0, len(all_lines_contigs)):
line = all_lines_contigs[i]
n_contigs = np.float32(line)
list_n_contigs.append(n_contigs)
h_n_contigs.close()
h_distances = open(file_distances, 'r')
list_distances = []
all_lines_distances = h_distances.readlines()
for i in xrange(0, len(all_lines_distances)):
line = all_lines_distances[i]
distance = np.float32(line)
list_distances.append(distance)
h_distances.close()
for i in xrange(0, 1000):
self.simulation.sampler.modify_gl_cuda_buffer(0, self.dt)
self.remote_update()
self.str_n_contigs = "n contigs = " + str(self.simulation.sampler.gpu_vect_frags.id_c.max())
self.simulation.sampler.explode_genome(self.dt)
# print list_likelihood
for i in xrange(0, len(list_id_fA)):
self.simulation.sampler.apply_replay_simu(list_id_fA[i], list_id_fB[i], list_op_sampled[i], self.dt)
self.str_curr_cycle = "cycle = " + str(int(i))
self.str_likelihood = "likelihood = " + str(list_likelihood[i])
self.str_n_contigs = "n contigs = " + str(list_n_contigs[i])
self.str_curr_id_frag = "current frag = "+ str(list_id_fA[i])
self.str_curr_dist = "current dist = "+ str(list_distances[i])
self.str_curr_temp = "current temperature = "+str(0)
self.simulation.export_new_fasta()
def start_EM(self,):
print "start expectation maximization ... "
delta = np.ones((self.n_iterations_em,),dtype=np.int32) * self.n_neighbours
o, d, d_high = self.simulation.sampler.display_current_matrix(self.simulation.input_matrix)
self.simulation.sampler.init_likelihood()
self.simulation.sampler.modify_gl_cuda_buffer(0, self.dt)
ready = 0
if self.scrambled:
self.simulation.sampler.explode_genome(self.dt)
o, d, d_high = self.simulation.sampler.display_current_matrix(self.simulation.scrambled_input_matrix)
list_frags = np.arange(0, self.simulation.sampler.n_new_frags, dtype=np.int32)
self.iter = 0
n_iter = np.float32(self.n_iterations_em)
self.bins_rippe = self.simulation.sampler.bins
for j in xrange(0, self.n_iterations_em):
print "cycle = ", j
self.str_curr_cycle = "current cycle = "+ str(j)
np.random.shuffle(list_frags)
# d = self.simulation.sampler.step_nuisance_parameters(0, 0, 0)
for i in list_frags:
# print "id_frag =", i
self.curr_frag = i
if bool(glutMainLoopEvent):
glutMainLoopEvent()
else:
glutCheckLoop()
o, n_contigs, min_len, mean_len, max_len, op_sampled, id_f_sampled, dist, temp = self.simulation.sampler.step_max_likelihood(i,
delta[j],
512, self.dt,
np.float32(j), n_iter)
self.str_likelihood = "likelihood = " + str(o)
self.str_n_contigs = "n contigs = " + str(n_contigs)
self.str_curr_id_frag = "current frag = "+ str(i)
self.str_curr_dist = "current dist = "+ str(dist)
self.str_curr_temp = "current temperature = "+str(temp)
self.str_curr_d = "current d = " + str(d)
self.collect_full_likelihood.append(self.simulation.sampler. likelihood_t)
self.collect_likelihood.append(o)
self.collect_n_contigs.append(n_contigs)
self.collect_mean_len.append(mean_len)
self.collect_op_sampled.append(op_sampled)
self.collect_id_fB_sampled.append(id_f_sampled)
self.collect_id_fA_sampled.append(i)
self.collect_dist_from_init_genome.append(dist)
self.iter += 1
# sampling nuisance parameters
if self.sample_param :
fact, d, d_max, d_nuc, slope, likeli, success, y_eval = self.simulation.sampler.step_nuisance_parameters(self.dt, np.float32(j),
n_iter)
self.y_eval = y_eval
else:
success = 1
curr_param = np.copy(self.simulation.sampler.param_simu)
kuhn, lm, c1, slope, d, d_max, fact, d_nuc = curr_param[0]
likeli = o
mean_len_kb = mean_len / 1000.
self.main_thread.update_gui(o, n_contigs, mean_len_kb, dist, slope, d_max)
self.collect_fact.append(fact)
self.collect_d.append(d)
self.collect_d_max.append(d_max)
self.collect_d_nuc.append(d_nuc)
self.collect_slope.append(slope)
self.collect_likelihood_nuisance.append(likeli)
self.collect_success.append(success)
o, d, d_high = self.simulation.sampler.display_current_matrix(self.simulation.output_matrix_em)
self.simulation.export_new_fasta()
self.save_behaviour_to_txt()
def init_4_sub_sampling(self, fact, id_exp):
self.simulation.output_folder = os.path.join(self.simulation.folder_sub_sampling, str(fact))
if not os.path.exists(self.simulation.output_folder):
os.mkdir(self.simulation.output_folder)
self.file_mean_len = os.path.join(self.simulation.output_folder, str(id_exp) + 'behaviour_mean_len.pdf')
self.file_n_contigs = os.path.join(self.simulation.output_folder, str(id_exp) + 'behaviour_n_contigs.pdf')
self.file_fact = os.path.join(self.simulation.output_folder, str(id_exp) + 'behaviour_fact.pdf')
self.file_slope = os.path.join(self.simulation.output_folder, str(id_exp) + 'behaviour_slope.pdf')
self.file_d_nuc = os.path.join(self.simulation.output_folder, str(id_exp) + 'behaviour_d_nuc.pdf')
self.file_d = os.path.join(self.simulation.output_folder, str(id_exp) + 'behaviour_d.pdf')
self.file_d_max = os.path.join(self.simulation.output_folder, str(id_exp) + 'behaviour_d_max.pdf')
self.file_dist_init_genome = os.path.join(self.simulation.output_folder, str(id_exp) + 'behaviour_dist_init_genome.pdf')
self.txt_file_mean_len = os.path.join(self.simulation.output_folder, str(id_exp) + 'list_mean_len.txt')
self.txt_file_n_contigs = os.path.join(self.simulation.output_folder, str(id_exp) + 'list_n_contigs.txt')
self.txt_file_dist_init_genome = os.path.join(self.simulation.output_folder, str(id_exp) + 'list_dist_init_genome.txt')
self.txt_file_likelihood = os.path.join(self.simulation.output_folder, str(id_exp) + 'list_likelihood.txt')
self.txt_file_fact = os.path.join(self.simulation.output_folder, str(id_exp) + 'list_fact.txt')
self.txt_file_slope = os.path.join(self.simulation.output_folder, str(id_exp) + 'list_slope.txt')
self.txt_file_d_nuc = os.path.join(self.simulation.output_folder, str(id_exp) + 'list_d_nuc.txt')
self.txt_file_d = os.path.join(self.simulation.output_folder, str(id_exp) + 'list_d.txt')
self.txt_file_d_max = os.path.join(self.simulation.output_folder, str(id_exp) + 'list_d_max.txt')
self.txt_file_success = os.path.join(self.simulation.output_folder, str(id_exp) + 'list_success.txt')
self.txt_file_list_mutations = os.path.join(self.simulation.output_folder, str(id_exp) + 'list_mutations.txt')
self.file_all_data = os.path.join(self.simulation.output_folder, str(id_exp) + 'behaviour_all.txt')
self.simulation.sampler.update_texture_4_sub(fact)
def save_behaviour_to_txt(self):
list_file = [self.txt_file_mean_len, self.txt_file_n_contigs, self.txt_file_dist_init_genome, self.txt_file_likelihood,
self.txt_file_fact, self.txt_file_slope, self.txt_file_d_max, self.txt_file_d_nuc,
self.txt_file_success]
list_data = [self.collect_mean_len, self.collect_n_contigs, self.collect_dist_from_init_genome, self.collect_likelihood,
self.collect_fact, self.collect_slope, self.collect_d_max, self.collect_d_nuc,
self.collect_success]
for d in range(0, len(list_file)):
thefile = list_file[d]
h = open(thefile, 'w')
data = list_data[d]
for item in data:
h.write("%s\n" % item)
h.close()
f_mutations = open(self.txt_file_list_mutations, 'w')
f_mutations.write("%s\t%s\t%s\n"%("id_fA", "id_fB", "id_mutation"))
for i in xrange(0, len(self.collect_id_fA_sampled)):
id_fA = self.collect_id_fA_sampled[i]
id_fB = self.collect_id_fB_sampled[i]
id_mut = self.collect_op_sampled[i]
f_mutations.write("%s\t%s\t%s\n"%(id_fA, id_fB, id_mut))
f_mutations.close()
def start_MTM(self,):
print "set jumping distribution..."
delta = 5
self.simulation.sampler.set_jumping_distributions_parameters(delta)
self.simulation.sampler.init_likelihood()
print "start sampling launched ... "
print self.simulation.n_iterations
delta = range(5, 5 + self.simulation.n_iterations * 2, 2)
print delta
# if self.scrambled:
# self.simulation.sampler.modify_genome(500)
# o, d, d_high = self.simulation.sampler.display_current_matrix(self.simulation.input_matrix)
if self.scrambled:
# self.simulation.sampler.modify_genome(500)
self.simulation.sampler.explode_genome(self.dt)
n_iter = np.float32(self.simulation.n_iterations)
list_frags = np.arange(0, self.n_frags, dtype=np.int32)
for j in xrange(0, self.n_iterations_mcmc):
print "cycle = ", j
self.str_curr_cycle = "current cycle = "+ str(j)
np.random.shuffle(list_frags)
for i in list_frags:
# print "id_frag =", i
if bool(glutMainLoopEvent):
glutMainLoopEvent()
else:
glutCheckLoop()
o, n_contigs, min_len, mean_len, max_len, temp, dist = self.simulation.sampler.step_mtm(i, np.float32(j), n_iter, self.dt)
self.str_likelihood = "likelihood = " + str(o)
self.str_n_contigs = "n contigs = " + str(n_contigs)
self.str_curr_id_frag = "current frag = "+ str(i)
self.str_curr_temp = "current temperature = "+ str(temp)
self.collect_likelihood.append(o)
self.collect_n_contigs.append(n_contigs)
self.collect_mean_len.append(mean_len)
self.collect_dist_from_init_genome.append(dist)
# self.simulation.export_new_fasta()
o, d, d_high = self.simulation.sampler.display_current_matrix(self.simulation.output_matrix_mcmc)
self.simulation.plot_info_simu(self.collect_likelihood, self.collect_n_contigs, self.file_n_contigs,
"n_contigs")
self.simulation.plot_info_simu(self.collect_likelihood, self.collect_mean_len, self.file_mean_len,
"mean length contigs")
self.simulation.plot_info_simu(self.collect_likelihood, self.collect_dist_from_init_genome, self.file_dist_init_genome,
"distance from init genome")
self.simulation.plot_info_simu(self.collect_likelihood_nuisance, self.collect_slope, self.file_slope,
"slope")
self.simulation.plot_info_simu(self.collect_likelihood_nuisance, self.collect_fact, self.file_fact,
"scale factor")
self.simulation.plot_info_simu(self.collect_likelihood_nuisance, self.collect_d_nuc, self.file_d_nuc,
"val trans")
self.save_behaviour_to_txt()
def remote_update(self):
if bool(glutMainLoopEvent):
glutMainLoopEvent()
else:
glutCheckLoop()
def setup_simu(self, id_f_ins):
self.simulation.sampler.insert_repeats(id_f_ins)
self.simulation.sampler.simulate_rippe_contacts()
plt.imshow(self.simulation.sampler.hic_matrix, interpolation='nearest', vmin=0, vmax=100)
plt.show()
def test_model(self, id_fi, delta):
id_fi = np.int32(id_fi)
id_neighbors = np.copy(self.simulation.sampler.old_return_neighbours(id_fi, delta))
id_neighbors.sort()
np.sort(id_neighbors)
print "physic model = ", self.simulation.sampler.param_simu
j = 0
n_iter = self.n_iterations_em
self.simulation.sampler.step_max_likelihood_4_visu(id_fi, delta, 512, self.dt, np.float32(j), n_iter)
nscore = np.copy(self.simulation.sampler.score)
plt.figure()
plt.plot(id_neighbors, nscore[range(0, len(nscore), self.simulation.sampler.n_tmp_struct)], '-', markersize=10)
plt.plot(id_neighbors, nscore[range(1, len(nscore), self.simulation.sampler.n_tmp_struct)], '-', markersize=10)
plt.plot(id_neighbors, nscore[range(2, len(nscore), self.simulation.sampler.n_tmp_struct)], '-', markersize=10)
plt.plot(id_neighbors, nscore[range(3, len(nscore), self.simulation.sampler.n_tmp_struct)], '-', markersize=10)
plt.plot(id_neighbors, nscore[range(4, len(nscore), self.simulation.sampler.n_tmp_struct)], '-', markersize=10)
plt.plot(id_neighbors, nscore[range(5, len(nscore), self.simulation.sampler.n_tmp_struct)], '-', markersize=10)
plt.plot(id_neighbors, nscore[range(6, len(nscore), self.simulation.sampler.n_tmp_struct)], '-', markersize=10)
plt.plot(id_neighbors, nscore[range(7, len(nscore), self.simulation.sampler.n_tmp_struct)], '-', markersize=10)
# plt.plot(id_neighbors, nscore[range(12, len(nscore), self.simulation.sampler.n_tmp_struct)], '-*', markersize=10)
plt.legend([self.simulation.sampler.modification_str[i] for i in range(0, 8)])
plt.ylabel('log likelihood')
plt.xlabel('fragments id')
# plt.show()
plt.figure()
for i in range(0, self.simulation.sampler.n_tmp_struct):
print i
plt.plot(id_neighbors, nscore[range(i, len(nscore), self.simulation.sampler.n_tmp_struct)], '-', markersize=10)
plt.legend(self.simulation.sampler.modification_str)
plt.show()
def discrete_spiral(self, n_points):
# (di, dj) is a vector - direction in which we move right now
di = 1
dj = 0
# length of current segment
segment_length = 1
# current position (i, j) and how much of current segment we passed
i = 0
j = 0
segment_passed = 0
output_x = [0]
output_y = [0]
for k in range(0, n_points - 1):
# make a step, add 'direction' vector (di, dj) to current position (i, j)
i += di
j += dj
output_x.append(i)
output_y.append(j)
segment_passed += 1
if (segment_passed == segment_length):
# done with current segment
segment_passed = 0
# 'rotate' directions
buffer = di
di = -dj
dj = buffer
# increase segment length if necessary
if (dj == 0):
segment_length += 1
return output_x, output_y
def new_test_model(self, id_fi, vmin, vmax):
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.mlab import griddata
delta = self.simulation.sampler.n_frags - 1
id_fi = np.int32(id_fi)
id_neighbors = np.copy(self.simulation.sampler.old_return_neighbours(id_fi, delta))
# id_neighbors.revert()
id_neighbors_sorted = np.sort(id_neighbors)
arg_index = np.argsort(id_neighbors)
list_arg_index = list(arg_index)
# np.sort(id_neighbors)
print "physic model = ", self.simulation.sampler.param_simu
j = 0
n_iter = self.n_iterations_em
self.simulation.sampler.step_max_likelihood_4_visu(id_fi, delta, 512, self.dt, np.float32(j), n_iter)
ori_nscore = np.copy(self.simulation.sampler.score)
n_activ_frags = len(ori_nscore) / self.simulation.sampler.n_tmp_struct
n_tmp_struct = self.simulation.sampler.n_tmp_struct
nscore = np.zeros_like(ori_nscore, dtype=np.float64)
id = 0
max_id = n_activ_frags - 1
for i in list_arg_index:
id_start = id * n_tmp_struct
id_end = (id + 1) * n_tmp_struct
loc_index = range((max_id - i) * n_tmp_struct, (max_id - i + 1) * n_tmp_struct)
# print loc_index
nscore[loc_index] = ori_nscore[id_start: id_end]
id += 1
nscore = np.array(nscore, dtype=np.float64)
# print nscore
m = np.min(nscore)
print "val min score = ", m
print "val max score = ", np.max(nscore)
id_start = 0
new_index = self.simulation.sampler.return_neighbours(id_fi, delta)
new_index.reverse()
init_ordered_score = nscore[range(7, len(nscore), self.simulation.sampler.n_tmp_struct)] - m
out_x, out_y = self.discrete_spiral(len(init_ordered_score))
# big_x, big_y = self.discrete_spiral(self.simulation.sampler.n_tmp_struct - id_start)
big_x = [0, 0, 1, 1, 2, 2, 3, 3, 4, 4]
big_y = [0, 1, 0, 1, 0, 1, 0, 1, 0, 1]
x = []
y = []
z = []
dt = 0.1
out_x = np.array(out_x, dtype=np.float32)
out_y = np.array(out_y, dtype=np.float32)
dx_span = 2 * out_x.max()
dy_span = 2 * out_y.max()
# ord_mut = range(id_start, self.simulation.sampler.n_tmp_struct)
ord_mut = [6, 7, 2, 3, 4, 5, 8, 9, 10, 11]
ord_mut = ord_mut.reverse()
# ord_mut = [6]
# for i in range(0, len(ord_mut)):
# x.extend(list(out_x + big_x[i] * dx_span))
# y.extend(list(out_y + big_x[i] * dy_span))
# s = nscore[range(ord_mut[i], len(nscore), self.simulation.sampler.n_tmp_struct)] - m
# z.extend(list(s))
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
# print "len out x", len(out_x)
# ax.plot_trisurf(x, y, z, cmap=plt.cm.jet)
# plt.show()
print "ord mut =", ord_mut
print "len nscore= ", nscore.shape
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
t = 0
if vmin == '':
Tvmin = (nscore.max()-m)*0.001
Tvmax = (nscore.max()-m)
else:
m = vmin
Tvmin = 0
Tvmax = vmax - vmin
for i in ord_mut:
# x.extend(list(out_x + big_x[i - id_start] * dx_span))
# y.extend(list(out_y + big_y[i - id_start] * dy_span))
x.extend(list(out_x + big_x[t] * dx_span))
y.extend(list(out_y + big_y[t] * dy_span))
s = nscore[range(i, len(nscore), self.simulation.sampler.n_tmp_struct)] - m
# print "mutation group = ", i
# print "local min = ", s.min()
z.extend(list(s + m))
loc_x = out_x + big_x[t] * dx_span
loc_y = out_y + big_y[t] * dy_span
# xi = np.linspace(loc_x.min(), loc_x.max(), 800)
# yi = np.linspace(loc_y.min(), loc_y.max(), 800)
# zi = griddata(loc_x, loc_y, s, xi, yi)
# tri = ax.plot_trisurf(loc_x, loc_y, s, cmap=plt.cm.gist_rainbow_r, vmin=Tvmin, vmax=Tvmax)
# xv, yv = np.meshgrid(xi, yi)
# cset = ax.contourf(xv, yv, zi, zdir='z', offset = -50, cmap=plt.cm.gist_rainbow_r, vmin=0, vmax=nscore.max() - m)
t += 1
# plt.colorbar(tri)
x = np.array(x)
y = np.array(y)
z = np.array(z)
xi = np.linspace(x.min(), x.max(), 400)
yi = np.linspace(y.min(), y.max(), 400)
zi = griddata(x, y, z, xi, yi)
xv, yv = np.meshgrid(xi, yi)
# ax.plot_wireframe(xv, yv, zi)
# cset = ax.contourf(xv, yv, zi, zdir='y', offset = 50, cmap=plt.cm.gist_rainbow_r)
# ax.set_zlabel('Log likelihood')
# ax.set_axis_off()
# ax.set_zlim(bottom=Tvmin, top=Tvmax, auto=False)
# plt.show()
#
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
# ax.scatter(x, y, z, c=z, cmap=plt.cm.gist_rainbow_r, vmin=vmin, vmax=vmax)
# print "vmin = ", (nscore.max()-m)*0.5
# print "vmax = ", (nscore.max()-m)
# plt.show()
#
# im_out = np.ones((max(x) - min(x) + 1, max(y) - min(y) + 1), dtype=np.float32) * m
# x = np.array(x)
# y = np.array(y)
# im_out[list(x - x.min()), list(y - y.min())] = z
# plt.imshow(im_out, cmap = plt.cm.gist_rainbow_r, interpolation='nearest', vmin=(nscore.max()-m)*0.5, vmax=nscore.max()-m)
# plt.colorbar()
# plt.show()
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
# ax.bar3d(x, y, np.zeros(len(x)), 1, 1, z, cmap=plt.cm.jet)
# plt.show()
#
# plt.figure()
# plt.plot(id_neighbors, nscore[range(0, len(nscore), self.simulation.sampler.n_tmp_struct)], '-', markersize=10)
# plt.plot(id_neighbors, nscore[range(1, len(nscore), self.simulation.sampler.n_tmp_struct)], '-', markersize=10)
# plt.plot(id_neighbors, nscore[range(2, len(nscore), self.simulation.sampler.n_tmp_struct)], '-', markersize=10)
# plt.plot(id_neighbors, nscore[range(3, len(nscore), self.simulation.sampler.n_tmp_struct)], '-', markersize=10)
# plt.plot(id_neighbors, nscore[range(4, len(nscore), self.simulation.sampler.n_tmp_struct)], '-', markersize=10)
# plt.plot(id_neighbors, nscore[range(5, len(nscore), self.simulation.sampler.n_tmp_struct)], '-', markersize=10)
# plt.plot(id_neighbors, nscore[range(6, len(nscore), self.simulation.sampler.n_tmp_struct)], '-', markersize=10)
# plt.plot(id_neighbors, nscore[range(7, len(nscore), self.simulation.sampler.n_tmp_struct)], '-', markersize=10)
# # plt.plot(id_neighbors, nscore[range(12, len(nscore), self.simulation.sampler.n_tmp_struct)], '-*', markersize=10)
#
# plt.legend([self.simulation.sampler.modification_str[i] for i in range(0, 8)])
# plt.ylabel('log likelihood')
# plt.xlabel('fragments id')
# # plt.show()
# plt.figure()
# for i in range(0, self.simulation.sampler.n_tmp_struct):
# print i
# plt.plot(id_neighbors, nscore[range(i, len(nscore), self.simulation.sampler.n_tmp_struct)], '-', markersize=10)
# plt.legend(self.simulation.sampler.modification_str)
# plt.show()
return id_neighbors, nscore, x, y, z
def debug_test_model(self, id_fi, delta):
id_fi = np.int32(id_fi)
id_neighbors = np.copy(self.simulation.sampler.return_neighbours(id_fi, delta))
id_neighbors.sort()
np.sort(id_neighbors)
print "physic model = ", self.simulation.sampler.param_simu
self.simulation.sampler.debug_step_max_likelihood(id_fi, delta, 512, self.dt)
nscore = np.copy(self.simulation.sampler.score)
plt.figure()
plt.plot(id_neighbors, nscore[range(0, len(nscore), self.simulation.sampler.n_tmp_struct)], '-', markersize=10)
plt.plot(id_neighbors, nscore[range(1, len(nscore), self.simulation.sampler.n_tmp_struct)], '-*', markersize=10)
plt.plot(id_neighbors, nscore[range(2, len(nscore), self.simulation.sampler.n_tmp_struct)], '-*', markersize=10)
plt.plot(id_neighbors, nscore[range(3, len(nscore), self.simulation.sampler.n_tmp_struct)], '-*', markersize=10)
plt.plot(id_neighbors, nscore[range(4, len(nscore), self.simulation.sampler.n_tmp_struct)], '-*', markersize=10)
plt.plot(id_neighbors, nscore[range(5, len(nscore), self.simulation.sampler.n_tmp_struct)], '-*', markersize=10)
plt.plot(id_neighbors, nscore[range(6, len(nscore), self.simulation.sampler.n_tmp_struct)], '-*', markersize=10)
plt.legend([self.simulation.sampler.modification_str[i] for i in range(0, 7)])
# plt.show()
plt.figure()
for i in range(0, self.simulation.sampler.n_tmp_struct):
print i
plt.plot(id_neighbors, nscore[range(i, len(nscore), self.simulation.sampler.n_tmp_struct)], '-*', markersize=10)
plt.legend(self.simulation.sampler.modification_str)
plt.show()
def cuda_gl_init(self,):
cuda.init()
if bool(glutMainLoopEvent):
# print "----SELECT COMPUTING DEVICE (NVIDIA GPU)----"
# num_gpu = cuda.Device.count()
# for i in range(0,num_gpu):
# tmp_dev = cuda.Device(i)
# print "device_id = ",i,tmp_dev.name()
# id_gpu = raw_input("Select GPU: ")
# id_gpu = int(id_gpu)
curr_gpu = cuda.Device(self.id_selected_gpu)
print "you have selected ", curr_gpu.name()
self.ctx_gl = cudagl.make_context(curr_gpu, flags=cudagl.graphics_map_flags.NONE)
else:
import pycuda.gl.autoinit
curr_gpu = cudagl.autoinit.device
self.ctx_gl = cudagl.make_context(curr_gpu, flags=cudagl.graphics_map_flags.NONE)
def glut_print(self, x, y, font, text, r, g , b , a):
blending = False
if glIsEnabled(GL_BLEND) :
blending = True
glEnable(GL_BLEND)
glColor3f(r, g, b)
glRasterPos2f(x, y)
for ch in text :
glutBitmapCharacter( font , ctypes.c_int( ord(ch) ) )
if not blending :
glDisable(GL_BLEND)
def glinit(self):
glViewport(0, 0, self.width, self.height)
glMatrixMode(GL_PROJECTION)
glLoadIdentity()
gluPerspective(60., self.width / float(self.height), .1, 1000.)
glMatrixMode(GL_MODELVIEW)
###GL CALLBACKS
def timer(self, t):
glutTimerFunc(t, self.timer, t)
glutPostRedisplay()
def on_key(self, *args):
ESCAPE = '\033'
if args[0] == ESCAPE or args[0] == 'q':
self.simulation.release()
sys.exit()
elif args[0] == 'p':
self.size_points += 1
elif args[0] == 'm':
self.size_points -= 1
elif args[0] == 's':
self.start_EM()
elif args[0] == 'w':
self.white *= -1
# elif args[0] == 'e':
# # idn0, nscore0, x0, y0, z0 = self.new_test_model(self.curr_frag, '', '')
# # self.collect_4_graph3d[self.iter] = dict()
# # self.collect_4_graph3d[self.iter]['x0'] = x0
# # self.collect_4_graph3d[self.iter]['y0'] = y0
# # self.collect_4_graph3d[self.iter]['z0'] = z0
#
# self.simulation.plot_info_simu(self.collect_likelihood, self.collect_n_contigs, self.file_n_contigs,
# "n_contigs")
# self.simulation.plot_info_simu(self.collect_likelihood, self.collect_mean_len, self.file_mean_len,
# "mean length contigs")
# self.simulation.plot_info_simu(self.collect_likelihood, self.collect_dist_from_init_genome, self.file_dist_init_genome,
# "distance from init genome")
# self.simulation.plot_info_simu(self.collect_likelihood_nuisance, self.collect_slope, self.file_slope,
# "slope")
# self.simulation.plot_info_simu(self.collect_likelihood_nuisance, self.collect_fact, self.file_fact,
# "scale factor")
# self.simulation.plot_info_simu(self.collect_likelihood_nuisance, self.collect_d_nuc, self.file_d_nuc,
# "val trans")
# self.simulation.plot_info_simu(self.collect_likelihood_nuisance, self.collect_d, self.file_d,
# "d")
# self.simulation.plot_info_simu(self.collect_likelihood_nuisance, self.collect_d_max, self.file_d_max,
# "dist max intra")
#
# # if self.sample_param:
# if 1 == 1:
# plt.figure()
# plt.loglog(self.bins_rippe, self.y_eval, '-b')
# plt.loglog(self.bins_rippe, self.simulation.sampler.mean_contacts, '-*r')
# plt.xlabel("genomic separation ( kb)")
# plt.ylabel("n contacts")
# plt.title("rippe curve")
# plt.legend(["fit", "obs"])
# plt.show()
# self.simulation.sampler.display_modif_vect(0, 0, -1, 100)
# self.save_behaviour_to_txt()
# self.simulation.export_new_fasta()
# o, d, d_high = self.simulation.sampler.display_current_matrix(self.simulation.output_matrix_em)
def on_click(self, button, state, x, y):
if state == GLUT_DOWN:
self.mouse_down = True
self.button = button
else:
self.mouse_down = False
self.mouse_old.x = x
self.mouse_old.y = y
def on_mouse_motion(self, x, y):
dx = x - self.mouse_old.x
dy = y - self.mouse_old.y
if self.mouse_down and self.button == 0: #left button
self.rotate.x += dy * .2
self.rotate.y += dx * .2
elif self.mouse_down and self.button == 2: #right button
self.translate.z -= dy * .01
self.mouse_old.x = x
self.mouse_old.y = y
###END GL CALLBACKS
def draw(self):
"""Render the particles"""
glFlush()
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)
if self.white == -1:
glClearColor(1, 1, 1, 1)
else:
glClearColor(0, 0, 0, 0)
glMatrixMode(GL_MODELVIEW)
glLoadIdentity()
#handle mouse transformations
glTranslatef(self.initrans.x, self.initrans.y, self.initrans.z)
glRotatef(self.rotate.x, 1, 0, 0)
glRotatef(self.rotate.y, 0, 1, 0) #we switched around the axis so make this rotate_z
glTranslatef(self.translate.x, self.translate.y, self.translate.z)
#render the particles
self.render_image()
self.render()
#draw the x, y and z axis as lines
glutil.draw_axes()
############## enable 2d display #######################
glMatrixMode(GL_PROJECTION)
glPushMatrix()
glLoadIdentity()
glOrtho(0.0, self.width, self.height, 0.0, -1.0, 10.0)
glMatrixMode(GL_MODELVIEW)
glLoadIdentity()
glDisable(GL_CULL_FACE)
glClear(GL_DEPTH_BUFFER_BIT)
self.glut_print( 10 , 15 , GLUT_BITMAP_9_BY_15 , self.str_curr_cycle , 0.0 , 1.0 , 0.0 , 1.0)
self.glut_print( 10 , 30 , GLUT_BITMAP_9_BY_15 , self.str_curr_temp , 0.0 , 1.0 , 0.0 , 1.0)
self.glut_print( 10 , 45 , GLUT_BITMAP_9_BY_15 , self.str_curr_id_frag , 0.0 , 1.0 , 0.0 , 1.0)
self.glut_print( 10 , 60 , GLUT_BITMAP_9_BY_15 , self.str_likelihood , 0.0 , 1.0 , 0.0 , 1.0)
self.glut_print( 10 , 75 , GLUT_BITMAP_9_BY_15 , self.str_n_contigs , 0.0 , 1.0 , 0.0 , 1.0)
self.glut_print( 10 , 90 , GLUT_BITMAP_9_BY_15 , self.str_curr_dist , 0.0 , 1.0 , 0.0 , 1.0)
# self.glut_print( 10 , 15 , GLUT_BITMAP_9_BY_15 , self.str_curr_cycle , 1.0 , 1.0 , 1.0 , 1.0)
# self.glut_print( 10 , 30 , GLUT_BITMAP_9_BY_15 , self.str_curr_temp , 1.0 , 1.0 , 1.0 , 1.0)
# self.glut_print( 10 , 45 , GLUT_BITMAP_9_BY_15 , self.str_curr_id_frag , 1.0 , 1.0 , 1.0 , 1.0)
# self.glut_print( 10 , 60 , GLUT_BITMAP_9_BY_15 , self.str_likelihood , 1.0 , 1.0 , 1.0 , 1.0)
# self.glut_print( 10.1 , 60.1 , GLUT_BITMAP_9_BY_15 , self.str_likelihood , 1.0 , 1.0 , 1.0 , 1.0)
# self.glut_print( 9.9 , 59.9, GLUT_BITMAP_9_BY_15 , self.str_likelihood , 1.0 , 1.0 , 1.0 , 1.0)
# self.glut_print( 10 , 75 , GLUT_BITMAP_9_BY_15 , self.str_n_contigs , 1.0 , 1.0 , 1.0 , 1.0)
# self.glut_print( 10 , 90 , GLUT_BITMAP_9_BY_15 , self.str_curr_dist , 1.0 , 1.0 , 1.0 , 1.0)
## Making sure we can render 3d again
glMatrixMode(GL_PROJECTION)
glPopMatrix()
glMatrixMode(GL_MODELVIEW)
#############
glutSwapBuffers()
def render(self):
glEnable(GL_POINT_SMOOTH)
glEnable(GL_BLEND)
glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA)
# glBegin(GL_POINTS)
# glEnable(GL_POINT_SMOOTH)
glHint(GL_POINT_SMOOTH_HINT, GL_NICEST) # a reactiver
# glHint(GL_POINT_SMOOTH_HINT, GL_NICEST);
# glHint(GL_LINE_SMOOTH_HINT, GL_NICEST);
# glHint(GL_POLYGON_SMOOTH_HINT, GL_NICEST);
#
glPointSize(self.size_points)
# glEnable(GL_BLEND)
# glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA)
#setup the VBOs
self.col_vbo.bind()
glColorPointer(4, GL_FLOAT, 0, self.col_vbo)
self.pos_vbo.bind()
glVertexPointer(4, GL_FLOAT, 0, self.pos_vbo)
glEnableClientState(GL_VERTEX_ARRAY)
glEnableClientState(GL_COLOR_ARRAY)
#draw the VBOs
glDrawArrays(GL_POINTS, 0, int(self.simulation.sampler.n_new_frags))
glDisableClientState(GL_COLOR_ARRAY)
glDisableClientState(GL_VERTEX_ARRAY)
# glEnd(GL_POINTS)
glDisable(GL_BLEND)
def render_image(self):
blending = False
if glIsEnabled(GL_BLEND):
blending = True
else:
glEnable(GL_BLEND)
glColor4f(1, 1, 1, 1)
# glDisable(GL_DEPTH_TEST)
glEnable(GL_TEXTURE_2D)
glBindTexture(GL_TEXTURE_2D, self.texid)
glBindBuffer(GL_PIXEL_UNPACK_BUFFER, self.pbo_im_buffer)
#Copyng from buffer to texture
glTexSubImage2D(GL_TEXTURE_2D, 0, 0, 0, self.init_n_frags, self.init_n_frags, GL_LUMINANCE, GL_UNSIGNED_BYTE, None)
glBindBuffer(GL_PIXEL_UNPACK_BUFFER, 0) # Unbind
#glDisable(GL_DEPTH_TEST)
glEnable(GL_TEXTURE_2D)
glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_LINEAR)
glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR)
glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_REPEAT)
glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_REPEAT)
#
glBegin(GL_QUADS)
glVertex2f(-1, 0)
glTexCoord2f(-1, 0)
glVertex2f(0, 0)
glTexCoord2f(0, 0)
glVertex2f(0, 1)
glTexCoord2f(0, 1)
glVertex2f(-1, 1)
glTexCoord2f(-1, 1)
glEnd()
glDisable(GL_TEXTURE_2D)
if not blending :
glDisable(GL_BLEND)