def calculate_bandwidth_dist(sample_):
import kern_density_est
optimum = {}
if domain: print "positive_interactions"
if likelihood_cross_validation:
if domain:
print "negative_interactions"
optimum[
"positive_interactions"] = kern_density_est.chrom_cross_validation_distance(
prior_elements,
thresholds=np.linspace(0.01, .4, 200),
classification_of_interactions="positive_interactions",
weights=weights)
optimum[
"negative_interactions"] = kern_density_est.chrom_cross_validation_distance(
prior_elements,
thresholds=np.linspace(0.01, .4, 200),
classification_of_interactions="negative_interactions",
weights=weights)
else:
optimum[
"positive_interactions"] = kern_density_est.chrom_cross_validation_distance(
prior_elements,
thresholds=np.linspace(0.01, .4, 200),
classification_of_interactions="positive_interactions")
optimum["negative_interactions"] = {}
for positive_or_negative_side in [
"positive_side", "negative_side"
]:
optimum["negative_interactions"][
positive_or_negative_side] = "scott"
else:
optimum["positive_interactions"] = {}
optimum["negative_interactions"] = {}
for positive_or_negative_side in [
"positive_side", "negative_side"
]:
optimum["positive_interactions"][
positive_or_negative_side] = kern_density_est.cross_validation(
sample_[positive_or_negative_side]
["positive_interactions"])
optimum["negative_interactions"][
positive_or_negative_side] = "scott"
return optimum
def calculate_bandwidth_dist(sample_):
import kern_density_est
optimum = {}
if domain: print "positive_interactions"
if likelihood_cross_validation:
if domain:
print "negative_interactions"
optimum["positive_interactions"] = kern_density_est.chrom_cross_validation_distance(prior_elements, thresholds = np.linspace(0.01, .4, 200), classification_of_interactions = "positive_interactions", weights = weights)
optimum["negative_interactions"] = kern_density_est.chrom_cross_validation_distance(prior_elements, thresholds = np.linspace(0.01, .4, 200), classification_of_interactions = "negative_interactions", weights = weights)
else:
optimum["positive_interactions"] = kern_density_est.chrom_cross_validation_distance(prior_elements, thresholds = np.linspace(0.01, .4, 200), classification_of_interactions = "positive_interactions")
optimum["negative_interactions"] = {}
for positive_or_negative_side in ["positive_side", "negative_side"]:
optimum["negative_interactions"][positive_or_negative_side] = "scott"
else:
optimum["positive_interactions"] = {}
optimum["negative_interactions"] = {}
for positive_or_negative_side in ["positive_side", "negative_side"]:
optimum["positive_interactions"][positive_or_negative_side] = kern_density_est.cross_validation(sample_[positive_or_negative_side]["positive_interactions"])
optimum["negative_interactions"][positive_or_negative_side] = "scott"
return optimum
def prior_bins_prob_and_plotter(prior_elements, low_dist, up_dist, use_smooth_prior_for_estimation, plot_atr, plot_atr_kernel):
from matplotlib.backends.backend_pdf import PdfPages
import config_variables
domain = config_variables.domain
mode = config_variables.mode
chroms_in_prior = config_variables.chroms_in_prior
np = config_variables.np
dataset_names_option = config_variables.dataset_names_option
low_cor, up_cor = -1., 1.
one_sided_or_two_sided = config_variables.one_sided_or_two_sided
log_distances = config_variables.log_distances
likelihood_cross_validation = config_variables.likelihood_cross_validation
distant_enh_only = config_variables.distant_enh_only
interacting_enhancers_only = config_variables.interacting_enhancers_only
def bins_prep_adaptive(array, l_limit, u_limit, how_many_in_bin):
array_sorted=sorted(np.concatenate((array, [l_limit], [u_limit])))
bins=[]
bins = [array_sorted[n] for n in range(len(array_sorted)) if (n % how_many_in_bin)==0]
if bins[-1] <> array_sorted[-1]: bins.append(array_sorted[-1])
return bins
def profile_histogram_adaptive_domains(array, l_limit, u_limit, how_many_in_bin, possible_distances_counts):#, f, colour):
"""
gives the empirical probabilities of the interactions based on the mean interactions in "IHH015M_ipet.tsv"
"""
bins = bins_prep_adaptive(array, l_limit, u_limit, how_many_in_bin)
distance_allocations = np.digitize(array, bins=bins)
distance_allocations = distance_allocations - 1
probabilities = np.zeros(len(bins)-1)
norm = np.sum(1/possible_distances_counts.astype(float))
for index, el in enumerate(np.unique(distance_allocations)): probabilities[index] = np.sum(1/possible_distances_counts[el == distance_allocations].astype(float))
differences = np.diff(bins)
prob = probabilities/norm/differences
#if one_sided_or_two_sided == "single_sided":
# prob, bins = two_sided_to_one_sided_domain(prob, bins, number_of_samples = 800000, number_of_new_bins = 600)
return prob, bins
def profile_histogram_adaptive(array, l_limit, u_limit, how_many_in_bin):
"""
gives the empirical probabilities of the interactions based on the mean interactions in "IHH015M_ipet.tsv"
"""
bins = bins_prep_adaptive(array, l_limit, u_limit, how_many_in_bin)
n, bins = np.histogram(array, bins=bins, density = True)
return n, bins
import itertools
def create_priors_domains():
if not(domain):
if mode == 'promoter_enhancer_interactions':
if interacting_enhancers_only: initiatie_number_of_bins = iter([25, 20, 2000, 2000])
else: initiatie_number_of_bins = iter([25, 20, 10000, 10000])
else:
if interacting_enhancers_only: initiatie_number_of_bins = iter([80, 70, 20000, 20000])
else: initiatie_number_of_bins = iter([80, 70, 200000, 200000])
else:
if mode == 'promoter_enhancer_interactions': initiatie_number_of_bins = iter([25, 20, 40, 50])
else: initiatie_number_of_bins = iter([70, 70, 200, 200])
for classification_of_interactions in ["positive_interactions", "negative_interactions"]:
for attribute_of_interaction in ["distance", "correlation"]:
number_in_bin = initiatie_number_of_bins.next()
prior_elements[mode][classification_of_interactions][attribute_of_interaction]["number_in_bin_of_histogram"] = number_in_bin
if attribute_of_interaction == "distance":
total_array = [prior_elements[mode][classification_of_interactions][attribute_of_interaction]["attribute_values"][chrom_] for chrom_ in chroms_in_prior]
total_array = np.array(list(itertools.chain.from_iterable(total_array)))
if not(domain):
if one_sided_or_two_sided == "double_sided":
for sign, positive_or_negative_side in zip([1, -1], ["positive_side", "negative_side"]):
prior_elements[mode][classification_of_interactions][attribute_of_interaction][positive_or_negative_side] = {}
[prior_elements[mode][classification_of_interactions][attribute_of_interaction][positive_or_negative_side]["prior_frequencies"],
prior_elements[mode][classification_of_interactions][attribute_of_interaction][positive_or_negative_side]["prior_bins"]] = profile_histogram_adaptive(total_array[sign*total_array > 0], l_limit = low_dist[positive_or_negative_side], u_limit = up_dist[positive_or_negative_side], how_many_in_bin = int(number_in_bin/2.))
else:
[prior_elements[mode][classification_of_interactions][attribute_of_interaction]["prior_frequencies"],
prior_elements[mode][classification_of_interactions][attribute_of_interaction]["prior_bins"]] = profile_histogram_adaptive(total_array, low_dist, up_dist, number_in_bin)
else:
possible_distances_counts = prior_elements[mode][classification_of_interactions][attribute_of_interaction]["possible_distances_counts"]
possible_distances_counts = np.array(possible_distances_counts)[possible_distances_counts <> 0]
total_array = np.array(total_array)[possible_distances_counts <> 0]
if one_sided_or_two_sided == "double_sided":
for sign, positive_or_negative_side in zip([1, -1], ["positive_side", "negative_side"]):
prior_elements[mode][classification_of_interactions][attribute_of_interaction][positive_or_negative_side] = {}
[prior_elements[mode][classification_of_interactions][attribute_of_interaction][positive_or_negative_side]["prior_frequencies"],
prior_elements[mode][classification_of_interactions][attribute_of_interaction][positive_or_negative_side]["prior_bins"]] = profile_histogram_adaptive_domains(total_array[sign*total_array > 0], low_dist[positive_or_negative_side], up_dist[positive_or_negative_side], int(number_in_bin/2.), possible_distances_counts[sign*total_array > 0])
elif one_sided_or_two_sided == "single_sided":
[prior_elements[mode][classification_of_interactions][attribute_of_interaction]["prior_frequencies"],
prior_elements[mode][classification_of_interactions][attribute_of_interaction]["prior_bins"]] = profile_histogram_adaptive_domains(total_array, low_dist, up_dist, number_in_bin, possible_distances_counts)
if attribute_of_interaction == "correlation":
for data_set_name in dataset_names_option:
total_array = [prior_elements[mode][classification_of_interactions][attribute_of_interaction][data_set_name]["attribute_values"][chrom_] for chrom_ in chroms_in_prior]
total_array = list(itertools.chain.from_iterable(total_array))
[prior_elements[mode][classification_of_interactions][attribute_of_interaction][data_set_name]["prior_frequencies"],
prior_elements[mode][classification_of_interactions][attribute_of_interaction][data_set_name]["prior_bins"]] = profile_histogram_adaptive(total_array, low_cor, up_cor, number_in_bin)
#if one_sided_or_two_sided == "single_sided" and domain:
#new_boundries_after_folding = prior_elements[mode]["positive_interactions"]["distance"]["prior_bins"][[0, -1]].tolist() + prior_elements[mode]["negative_interactions"]["distance"]["prior_bins"][[0, -1]].tolist()
#config_variables.low_dist, config_variables.up_dist = min(new_boundries_after_folding), max(new_boundries_after_folding)
create_priors_domains()
def join_two_sides_of_prior_together(classification_of_interactions):
prob = {}
bins = {}
for positive_or_negative_side in ["positive_side", "negative_side"]:
prob[positive_or_negative_side], bins[positive_or_negative_side] = [prior_elements[mode][classification_of_interactions]["distance"][positive_or_negative_side]["prior_frequencies"],
prior_elements[mode][classification_of_interactions]["distance"][positive_or_negative_side]["prior_bins"]]
bins_ = np.r_[bins["negative_side"], bins["positive_side"]]
if log_distances:
prob_ = np.r_[prob["negative_side"], [0], prob["positive_side"]]
else:
prob_ = np.r_[prob["negative_side"], [(prob["negative_side"][-1] + prob["positive_side"][0])/2.], prob["positive_side"]]
prob_ /= sum(prob_*np.diff(bins_))
return prob_, bins_
def plot_histogram_priors(bins_, prob_, colour = ("g", "y")):
plt.bar(bins_["positive_interactions"][:-1], prob_["positive_interactions"], np.diff(bins_["positive_interactions"]), alpha=0.2, color=colour[0])
plt.bar(bins_["negative_interactions"][:-1], prob_["negative_interactions"], np.diff(bins_["negative_interactions"]), alpha=0.2, color=colour[1])
def calculate_or_plot_kern(attribute_of_interaction_, sample_, l_limit, up_limit, number_of_bins, colour = ("r", "b"), weights_ = None, bandwidth_pos = None, bandwidth_neg = None):
prob_ = {}
bins_ = {}
import kern_density_est
kern_density_est.plot_atr = plot_atr_kernel
xgrid = [[],[]]
xgrid[0] = np.linspace(l_limit, up_limit, number_of_bins[0])
xgrid[1] = np.linspace(l_limit, up_limit, number_of_bins[1])
if domain:
if attribute_of_interaction_ == "distance":
#prob_["positive_interactions"], bins_["positive_interactions"] = kern_density_est.kern_scipy_gaus_weighted(sample_["positive_interactions"], colour[0], xgrid[0], weights = weights_["positive_interactions"], bandwidth = "scott", factor = None)#bandwidth_pos)
#prob_["negative_interactions"], bins_["negative_interactions"] = kern_density_est.kern_scipy_gaus_weighted(sample_["negative_interactions"], colour[1], xgrid[1], weights = weights_["negative_interactions"], bandwidth = "scott", factor = None)#bandwidth_neg)
prob_["positive_interactions"], bins_["positive_interactions"] = kern_density_est.kern_scipy_gaus_weighted(sample_["positive_interactions"], colour[0], xgrid[0], weights = weights_["positive_interactions"], bandwidth = bandwidth_pos, plot_atr = True)#bandwidth_pos)
prob_["negative_interactions"], bins_["negative_interactions"] = kern_density_est.kern_scipy_gaus_weighted(sample_["negative_interactions"], colour[1], xgrid[1], weights = weights_["negative_interactions"], bandwidth = bandwidth_neg, plot_atr = True)#bandwidth_neg)
#bandwidth_pos = kern_density_est.cross_validation(sample_["positive_interactions"])# * sample_["positive_interactions"].std(ddof=1)
#bandwidth_neg = kern_density_est.cross_validation(sample_["negative_interactions"])# * sample_["negative_interactions"].std(ddof=1)
#prob_["positive_interactions"], bins_["positive_interactions"] = kern_density_est.kernel_weighted_samples(sample_["positive_interactions"], colour[0], xgrid[0], weights = weights_["positive_interactions"], fft = False, bw=bandwidth_pos)
#prob_["negative_interactions"], bins_["negative_interactions"] = kern_density_est.kernel_weighted_samples(sample_["negative_interactions"], colour[1], xgrid[1], weights = weights_["negative_interactions"], fft = False, bw=bandwidth_neg)
else:
#kernel_ = "gaussian"
#bandwidth_pos = kern_density_est.cross_validation(sample_["positive_interactions"], kernel = kernel_) # kernel =
#bandwidth_neg = kern_density_est.cross_validation(sample_["negative_interactions"], kernel = kernel_)
#prob_["positive_interactions"], bins_["positive_interactions"] = kern_density_est.kern_sklearn_expon(sample_["positive_interactions"], colour[0], xgrid[0], bandwidth = bandwidth_pos, kernel_ = kernel_)
#prob_["negative_interactions"], bins_["negative_interactions"] = kern_density_est.kern_sklearn_expon(sample_["negative_interactions"], colour[1], xgrid[1], bandwidth = bandwidth_neg, kernel_ = kernel_)
bandwidth_pos = kern_density_est.chrom_cross_validation_correlation(prior_elements, data_set_name, thresholds = np.linspace(0.01, .4, 200), classification_of_interactions = "positive_interactions", plot_likelihood_function = False)
bandwidth_neg = kern_density_est.chrom_cross_validation_correlation(prior_elements, data_set_name, thresholds = np.linspace(0.01, .4, 200), classification_of_interactions = "negative_interactions", plot_likelihood_function = False)
prob_["positive_interactions"], bins_["positive_interactions"] = kern_density_est.kern_scipy_gaus(sample_["positive_interactions"], colour[0], xgrid[0], bandwidth = bandwidth_pos)
prob_["negative_interactions"], bins_["negative_interactions"] = kern_density_est.kern_scipy_gaus(sample_["negative_interactions"], colour[1], xgrid[1], bandwidth = bandwidth_neg)
else:
#if attribute_of_interaction_ == "distance": bandwidth_pos = optimum["distance"][ite]
#else: bandwidth_pos = optimum[data_set_name]
if attribute_of_interaction_ == "distance" and positive_or_negative_side == "negative_side": label_1, label_2 = None, None
else: label_1, label_2 = "positive interactions", "negative interactions"
if likelihood_cross_validation:
if attribute_of_interaction_ == "correlation":
bandwidth_pos = kern_density_est.chrom_cross_validation_correlation(prior_elements, data_set_name, thresholds = np.linspace(0.01, .4, 200), classification_of_interactions = "positive_interactions", plot_likelihood_function = False)
print bandwidth_pos
prob_["positive_interactions"], bins_["positive_interactions"] = kern_density_est.kern_scipy_gaus(sample_["positive_interactions"], colour[0], xgrid[0], bandwidth = bandwidth_pos, label = label_1)
prob_["negative_interactions"], bins_["negative_interactions"] = kern_density_est.kern_scipy_gaus(sample_["negative_interactions"], colour[1], xgrid[1], bandwidth = "scott", label = label_2)
else:
bandwidth_pos = kern_density_est.cross_validation(sample_["positive_interactions"])# * sample_["positive_interactions"].std(ddof=1)
print bandwidth_pos
prob_["positive_interactions"], bins_["positive_interactions"] = kern_density_est.kern_scipy_gaus(sample_["positive_interactions"], colour[0], xgrid[0], bandwidth=bandwidth_pos, label = label_1)
prob_["negative_interactions"], bins_["negative_interactions"] = kern_density_est.kern_scipy_gaus(sample_["negative_interactions"], colour[1], xgrid[1], bandwidth="scott", label = label_2)
if use_smooth_prior_for_estimation: return prob_, bins_
else: return [[], []], [[], []]
import matplotlib.pyplot as plt
plt.rcParams['xtick.labelsize'] = 20.
prob={}
bins={}
optimum = {}
number_of_bins = [2000,2000]
#number_of_samples = [800000, 800000]
if one_sided_or_two_sided == "double_sided":
if plot_atr or plot_atr_kernel:
plt.figure(1, figsize=(8, 6), dpi=200)
plt.title("Distance prior", fontsize=20)
plt.ylabel('density', fontsize=20)
plt.xlabel("distance [B]", fontsize=20)
tick_labels = [8, 4, 0 , 4, 8]
string_labels = [r"$10^{%2d}$" % (i) for i in tick_labels]
plt.xticks([-8., -5., 0., 5., 8.], string_labels, fontsize=20)#["a", "b", "c", "d", "e"])#
plt.xlim([-8.5, 8.5])
prob_smooth={}
bins_smooth={}
attribute_ = {}
weights = {}
for sign, positive_or_negative_side in zip([1, -1], ["positive_side", "negative_side"]):
prob[positive_or_negative_side] = {}
bins[positive_or_negative_side] = {}
attribute_[positive_or_negative_side] = {}
weights[positive_or_negative_side] = {}
prob_smooth[positive_or_negative_side] = {}
bins_smooth[positive_or_negative_side] = {}
for classification_of_interactions in ["positive_interactions", "negative_interactions"]:
prob[positive_or_negative_side][classification_of_interactions] = prior_elements[mode][classification_of_interactions]["distance"][positive_or_negative_side]["prior_frequencies"]
bins[positive_or_negative_side][classification_of_interactions] = prior_elements[mode][classification_of_interactions]["distance"][positive_or_negative_side]["prior_bins"]
total_array = [prior_elements[mode][classification_of_interactions]["distance"]["attribute_values"][chrom_] for chrom_ in chroms_in_prior]
total_array = np.array(list(itertools.chain.from_iterable(total_array)))
attribute_[positive_or_negative_side][classification_of_interactions] = total_array[sign*total_array > 0]
if domain:
possible_distances_counts = prior_elements[mode][classification_of_interactions]["distance"]["possible_distances_counts"]
possible_distances_counts = np.array(possible_distances_counts)[possible_distances_counts <> 0]
total_array = np.array(total_array)[possible_distances_counts <> 0]
weights[positive_or_negative_side][classification_of_interactions] = (1./possible_distances_counts[sign*total_array > 0])/np.sum(1./possible_distances_counts[sign*total_array > 0])
attribute_[positive_or_negative_side][classification_of_interactions] = total_array[sign*total_array > 0]
if plot_atr: plot_histogram_priors(bins[positive_or_negative_side], prob[positive_or_negative_side])
if use_smooth_prior_for_estimation or plot_atr_kernel:
import kern_density_est
optimum["distance"] = {}
if domain: print "positive_interactions"
if likelihood_cross_validation:
if domain:
print "negative_interactions"
optimum["distance"]["positive_interactions"] = kern_density_est.chrom_cross_validation_distance(prior_elements, thresholds = np.linspace(0.01, .4, 200), classification_of_interactions = "positive_interactions", plot_likelihood_function = False, weights = weights)
optimum["distance"]["negative_interactions"] = kern_density_est.chrom_cross_validation_distance(prior_elements, thresholds = np.linspace(0.01, .4, 200), classification_of_interactions = "negative_interactions", plot_likelihood_function = False, weights = weights)
else:
optimum["distance"]["positive_interactions"] = kern_density_est.chrom_cross_validation_distance(prior_elements, thresholds = np.linspace(0.01, .4, 200), classification_of_interactions = "positive_interactions", plot_likelihood_function = False)
optimum["distance"]["negative_interactions"] = {}
for positive_or_negative_side in ["positive_side", "negative_side"]: optimum["distance"]["negative_interactions"][positive_or_negative_side] = None
else:
optimum["distance"]["positive_interactions"] = {}
for positive_or_negative_side in ["positive_side", "negative_side"]: optimum["distance"]["positive_interactions"][positive_or_negative_side] = None
optimum["distance"]["negative_interactions"] = {}
for positive_or_negative_side in ["positive_side", "negative_side"]: optimum["distance"]["negative_interactions"][positive_or_negative_side] = None
for sign, positive_or_negative_side in zip([1, -1], ["positive_side", "negative_side"]):
print positive_or_negative_side
prob_smooth[positive_or_negative_side], bins_smooth[positive_or_negative_side] = calculate_or_plot_kern("distance", attribute_[positive_or_negative_side], low_dist[positive_or_negative_side], up_dist[positive_or_negative_side], number_of_bins, colour = ("g", "y"), weights_ = weights[positive_or_negative_side], bandwidth_pos = optimum["distance"]["positive_interactions"][positive_or_negative_side], bandwidth_neg = optimum["distance"]["negative_interactions"][positive_or_negative_side])
if use_smooth_prior_for_estimation:
for positive_or_negative_side in ["positive_side", "negative_side"]:
for classification_of_interactions in ["positive_interactions", "negative_interactions"]:
prob_smooth_ = prob_smooth[positive_or_negative_side][classification_of_interactions][:-1] + np.diff(prob_smooth[positive_or_negative_side][classification_of_interactions])/2.
prob_smooth_ /= sum(prob_smooth_*np.diff(bins_smooth[positive_or_negative_side][classification_of_interactions]))
[prior_elements[mode][classification_of_interactions]["distance"][positive_or_negative_side]["prior_frequencies"],
prior_elements[mode][classification_of_interactions]["distance"][positive_or_negative_side]["prior_bins"]] = prob_smooth_, bins_smooth[positive_or_negative_side][classification_of_interactions]
for classification_of_interactions in ["positive_interactions", "negative_interactions"]:
[prior_elements[mode][classification_of_interactions]["distance"]["prior_frequencies"],
prior_elements[mode][classification_of_interactions]["distance"]["prior_bins"]] = join_two_sides_of_prior_together(classification_of_interactions)
else:
for classification_of_interactions in ["positive_interactions", "negative_interactions"]:
prob[classification_of_interactions] = prior_elements[mode][classification_of_interactions]["distance"]["prior_frequencies"]
bins[classification_of_interactions] = prior_elements[mode][classification_of_interactions]["distance"]["prior_bins"]
total_array = [prior_elements[mode][classification_of_interactions]["distance"]["attribute_values"][chrom_] for chrom_ in chroms_in_prior]
total_array = np.array(list(itertools.chain.from_iterable(total_array)))
if plot_atr or plot_atr_kernel:
plt.figure(1, figsize=(8, 8), dpi=200)
plt.title("Distance prior", fontsize=20)
plt.ylabel('density', fontsize=20)
plt.xlabel('distance', fontsize=20)
if plot_atr: plot_histogram_priors(bins, prob)
if use_smooth_prior_for_estimation or plot_atr_kernel:
prob_smooth, bins_smooth = calculate_or_plot_kern(total_array, low_dist, up_dist, number_of_bins, colour = ("g", "y"))
if use_smooth_prior_for_estimation:
for classification_of_interactions in ["positive_interactions", "negative_interactions"]:
prob_smooth_ = prob_smooth[classification_of_interactions][:-1] + np.diff(prob_smooth[classification_of_interactions])/2.
prob_smooth_ /= sum(prob_smooth_*np.diff(bins_smooth[classification_of_interactions]))
[prior_elements[mode][classification_of_interactions]["distance"]["prior_frequencies"],
prior_elements[mode][classification_of_interactions]["distance"]["prior_bins"]] = prob_smooth_, bins_smooth[classification_of_interactions]
if plot_atr or plot_atr_kernel: x1,x2,y1,y2 = plt.axis(); plt.axis((x1,x2,0.,y2*1.2)); plt.legend(); pdf = PdfPages('multipage_priors_average{0}.pdf'.format(one_sided_or_two_sided)); pdf.savefig()
prob={}
bins={}
attribute_={}
number_of_bins = [2000,2000]
#number_of_samples = [800000, 800000]
for i, data_set_name in enumerate(dataset_names_option):
print data_set_name
for classification_of_interactions in ["positive_interactions", "negative_interactions"]:
prob[classification_of_interactions] = prior_elements[mode][classification_of_interactions]["correlation"][data_set_name]["prior_frequencies"]
bins[classification_of_interactions] = prior_elements[mode][classification_of_interactions]["correlation"][data_set_name]["prior_bins"]
total_array = [prior_elements[mode][classification_of_interactions]["correlation"][data_set_name]["attribute_values"][chrom_] for chrom_ in chroms_in_prior]
total_array = np.array(list(itertools.chain.from_iterable(total_array)))
attribute_[classification_of_interactions] = total_array
if plot_atr or plot_atr_kernel:
plt.figure(i+2, figsize=(8, 6), dpi=200)
if data_set_name == "ER": plt.title(u'ER-\u03B1', fontsize=20)
else: plt.title(data_set_name, fontsize=20)
plt.ylabel('density', fontsize=20)
plt.xlabel('correlation', fontsize=20)
#x1,x2,y1,y2 = plt.axis()
#plt.axis((x1,x2,0,y2*1.2))
if plot_atr: plot_histogram_priors(bins, prob)
if use_smooth_prior_for_estimation or plot_atr_kernel:
import kern_density_est
prob_smooth, bins_smooth = calculate_or_plot_kern("correlation", attribute_, -1., 1., number_of_bins, colour = ("g", "y"))
if use_smooth_prior_for_estimation:
for classification_of_interactions in ["positive_interactions", "negative_interactions"]:
prob_smooth_ = prob_smooth[classification_of_interactions][:-1] + np.diff(prob_smooth[classification_of_interactions])/2.
prob_smooth_ /= sum(prob_smooth_*np.diff(bins_smooth[classification_of_interactions]))
[prior_elements[mode][classification_of_interactions]["correlation"][data_set_name]["prior_frequencies"],
prior_elements[mode][classification_of_interactions]["correlation"][data_set_name]["prior_bins"]] = prob_smooth_, bins_smooth[classification_of_interactions]
if plot_atr or plot_atr_kernel: x1,x2,y1,y2 = plt.axis(); plt.axis((x1,x2,0,y2*1.2)); plt.legend(); pdf.savefig() #plt.ylim(0, plt.ylim()[0]);
if plot_atr_kernel or plot_atr: pdf.close(); plt.show()
return prior_elements