Louis BECQUEY / RNANetLegacy

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Louis BECQUEY
f589903e 1 parent a6ac991b

Statistics over the produced data

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Showing 3 changed files with 297 additions and 276 deletions
......@@ -3,107 +3,11 @@ nohup.out
jobstats.csv
log_of_the_run.sh
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\ No newline at end of file
......
......@@ -916,7 +916,7 @@ def execute_joblist(fulljoblist, printstats=False):
if printstats:
# Write statistics in a file (header here)
f = open("jobstats.csv", "w")
f = open("data/jobstats.csv", "w")
f.write("label,comp_time,max_mem\n")
f.close()
......@@ -948,7 +948,7 @@ def execute_joblist(fulljoblist, printstats=False):
mems = [ r[1] for r in raw_results ]
# Write them to file
f = open("jobstats.csv", "a")
f = open("data/jobstats.csv", "a")
for j, t, m in zip(bunch, times, mems):
j.comp_time = t
j.max_mem = m
......@@ -1636,7 +1636,7 @@ if __name__ == "__main__":
n_pdb = [ len(rfam_acc_to_download[f]) for f in fam_stats["rfam_acc"] ]
fam_stats["n_pdb_seqs"] = n_pdb
fam_stats["total_seqs"] = fam_stats["n_seq"] + fam_stats["n_pdb_seqs"]
fam_stats.to_csv(path_to_seq_data + "realigned/statistics.csv")
fam_stats.to_csv(path_to_seq_data + "data/statistics.csv")
# print the stats
for f in fam_list:
line = fam_stats[fam_stats["rfam_acc"]==f]
......
#!/usr/bin/python3.8
import os
import os, pickle
import numpy as np
import pandas as pd
import threading as th
import scipy.stats as st
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.patches as mpatches
import scipy.cluster.hierarchy as sch
from scipy.spatial.distance import squareform
from mpl_toolkits.mplot3d import axes3d
from Bio.Phylo.TreeConstruction import DistanceCalculator
from Bio import AlignIO, SeqIO
from matplotlib import cm
from tqdm import tqdm
from functools import partial
from multiprocessing import Pool
from os import path
from collections import Counter
from RNAnet import read_cpu_number
......@@ -43,11 +45,24 @@ class DataPoint():
def load_rna_frome_file(path_to_textfile):
return DataPoint(path_to_textfile)
def reproduce_wadley_results(points, show=False, filter_helical=None, carbon=4, zone=(2.7,3.3,3.5,4.5)):
def reproduce_wadley_results(points, show=False, carbon=4, sd_range=(1,4)):
"""
Plot the joint distribution of pseudotorsion angles, in a Ramachandran-style graph.
See Wadley & Pyle (2007)
Arguments:
show: True or False, call plt.show() at this end or not
filter_helical: None, "form", "zone", or "both"
None: do not remove helical nucleotide
"form": remove nucleotides if they belong to a A, B or Z form stem
"zone": remove nucleotides falling in an arbitrary zone (see zone argument)
"both": remove nucleotides fulfilling one or both of the above conditions
carbon: 1 or 4, use C4' (eta and theta) or C1' (eta_prime and theta_prime)
sd_range: tuple, set values below avg + sd_range[0] * stdev to 0,
and values above avg + sd_range[1] * stdev to avg + sd_range[1] * stdev.
This removes noise and cuts too high peaks, to clearly see the clusters.
"""
worker_nbr = 1 + (carbon==1)
if carbon == 4:
angle = "eta"
......@@ -60,133 +75,225 @@ def reproduce_wadley_results(points, show=False, filter_helical=None, carbon=4,
else:
exit("You overestimate my capabilities !")
all_etas = []
all_thetas = []
all_forms = []
c = 0
for p in points:
all_etas += list(p.df[angle].values)
all_thetas += list(p.df['th'+angle].values)
all_forms += list(p.df['form'].values)
if (len([ x for x in p.df[angle].values if x < 0 or x > 7]) or
len([ x for x in p.df['th'+angle].values if x < 0 or x > 7])):
c += 1
if c:
print(c,"points on",len(points),"have non-radian angles !")
print("combining etas and thetas...")
warn = ""
if not filter_helical:
alldata = [ (e, t)
for e, t in zip(all_etas, all_thetas)
if ('nan' not in str((e,t))) ]
elif filter_helical == "form":
alldata = [ (e, t)
for e, t, f in zip(all_etas, all_thetas, all_forms)
if ('nan' not in str((e,t)))
and f == '.' ]
warn = "(helical nucleotides removed)"
print(len(alldata), "couples of non-helical nts found in", len(all_etas))
elif filter_helical == "zone":
alldata = [ (e, t)
for e, t in zip(all_etas, all_thetas)
if ('nan' not in str((e,t)))
and not (e>zone[0] and e<zone[1] and t>zone[2] and t<zone[3]) ]
warn = "(massive peak of helical nucleotides removed in red zone)"
print(len(alldata), "couples of non-helical nts found in", len(all_etas))
elif filter_helical == "both":
alldata = [ (e, t)
for e, t, f in zip(all_etas, all_thetas, all_forms)
if ('nan' not in str((e,t)))
and f == '.'
and not (e>zone[0] and e<zone[1] and t>zone[2] and t<zone[3]) ]
warn = "(helical nucleotide and massive peak in the red zone removed)"
print(len(alldata), "couples of non-helical nts found in", len(all_etas))
x = np.array([ p[0] for p in alldata ])
y = np.array([ p[1] for p in alldata ])
xmin, xmax = min(x), max(x)
ymin, ymax = min(y), max(y)
xx, yy = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
positions = np.vstack([xx.ravel(), yy.ravel()])
values = np.vstack([x, y])
kernel = st.gaussian_kde(values)
f = np.reshape(kernel(positions).T, xx.shape)
sign_threshold = np.mean(f) + np.std(f)
z = np.where(f < sign_threshold, 0.0, f)
z_inc = np.where(f < sign_threshold, sign_threshold, f)
# histogram :
fig, axs = plt.subplots(1,3, figsize=(18, 6))
ax = fig.add_subplot(131)
ax.cla()
plt.axhline(y=0, alpha=0.5, color='black')
plt.axvline(x=0, alpha=0.5, color='black')
plt.scatter(x, y, s=1, alpha=0.1)
plt.contourf(xx, yy, z, cmap=cm.BuPu, alpha=0.5)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
if filter_helical in ["zone","both"]:
ax.add_patch(ptch.Rectangle((zone[0],zone[2]),zone[1]-zone[0],zone[3]-zone[2], linewidth=1, edgecolor='r', facecolor='#ff000080'))
ax = fig.add_subplot(132, projection='3d')
ax.cla()
ax.plot_surface(xx, yy, z_inc, cmap=cm.coolwarm, linewidth=0, antialiased=True)
ax.set_title(f"\"Wadley plot\" of {len(alldata)} nucleotides\nJoint distribution of pseudotorsions in 3D RNA structures\n" + warn)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax = fig.add_subplot(133, projection='3d')
ax.cla()
hist, xedges, yedges = np.histogram2d(x, y, bins=200, range=[[xmin, xmax], [ymin, ymax]])
xpos, ypos = np.meshgrid(xedges[:-1], yedges[:-1], indexing="ij")
ax.bar3d(xpos.ravel(), ypos.ravel(), 0, 0.2, 0.2, hist.ravel(), zsort='average')
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
plt.savefig(f"results/wadley_{angle}_{filter_helical}.png")
if show:
plt.show()
if not path.isfile(f"data/wadley_kernel_{angle}.npz"):
c2_endo_etas = []
c3_endo_etas = []
c2_endo_thetas = []
c3_endo_thetas = []
for p in points:
df = p.df.loc[(p.df[angle].isna()==False) & (p.df["th"+angle].isna()==False), ["form","puckering", angle,"th"+angle]]
c2_endo_etas += list(df.loc[ (df.puckering=="C2'-endo"), angle ].values)
c3_endo_etas += list(df.loc[ (df.form=='.') & (df.puckering=="C3'-endo"), angle ].values)
c2_endo_thetas += list(df.loc[ (df.puckering=="C2'-endo"), "th"+angle ].values)
c3_endo_thetas += list(df.loc[ (df.form=='.') & (df.puckering=="C3'-endo"), "th"+angle ].values)
xx, yy = np.mgrid[0:2*np.pi:100j, 0:2*np.pi:100j]
positions = np.vstack([xx.ravel(), yy.ravel()])
values_c3 = np.vstack([c3_endo_etas, c3_endo_thetas])
kernel_c3 = st.gaussian_kde(values_c3)
f_c3 = np.reshape(kernel_c3(positions).T, xx.shape)
values_c2 = np.vstack([c2_endo_etas, c2_endo_thetas])
kernel_c2 = st.gaussian_kde(values_c2)
f_c2 = np.reshape(kernel_c2(positions).T, xx.shape)
# Uncomment to save the data to an archive for later use without the need to recompute
np.savez(f"data/wadley_kernel_{angle}.npz",
c3_endo_e=c3_endo_etas, c3_endo_t=c3_endo_thetas,
c2_endo_e=c2_endo_etas, c2_endo_t=c2_endo_thetas,
kernel_c3=f_c3, kernel_c2=f_c2)
else:
f = np.load(f"data/wadley_kernel_{angle}.npz")
c2_endo_etas = f["c2_endo_e"]
c3_endo_etas = f["c3_endo_e"]
c2_endo_thetas = f["c2_endo_t"]
c3_endo_thetas = f["c3_endo_t"]
f_c3 = f["kernel_c3"]
f_c2 = f["kernel_c2"]
xx, yy = np.mgrid[0:2*np.pi:100j, 0:2*np.pi:100j]
print(f"[{worker_nbr}]\tKernel computed (or loaded from file).")
# exact counts:
hist_c2, xedges, yedges = np.histogram2d(c2_endo_etas, c2_endo_thetas, bins=int(2*np.pi/0.1), range=[[0, 2*np.pi], [0, 2*np.pi]])
hist_c3, xedges, yedges = np.histogram2d(c3_endo_etas, c3_endo_thetas, bins=int(2*np.pi/0.1), range=[[0, 2*np.pi], [0, 2*np.pi]])
color_values = cm.jet(hist_c3.ravel()/hist_c3.max())
for x, y, hist, f, l in zip( (c3_endo_etas, c2_endo_etas), (c3_endo_thetas, c2_endo_thetas), (hist_c3, hist_c2), (f_c3, f_c2), ("c3","c2")):
# cut hist and kernel
hist_sup_thr = hist.mean() + sd_range[1]*hist.std()
hist_cut = np.where( hist > hist_sup_thr, hist_sup_thr, hist)
f_sup_thr = f.mean() + sd_range[1]*f.std()
f_low_thr = f.mean() + sd_range[0]*f.std()
f_cut = np.where(f > f_sup_thr, f_sup_thr, f)
f_cut = np.where(f_cut < f_low_thr, 0, f_cut)
levels = [f.mean()+f.std(), f.mean()+2*f.std(), f.mean()+4*f.std()]
# histogram:
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
xpos, ypos = np.meshgrid(xedges[:-1], yedges[:-1], indexing="ij")
ax.bar3d(xpos.ravel(), ypos.ravel(), 0.0, 0.09, 0.09, hist_cut.ravel(), color=color_values, zorder="max")
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
plt.savefig(f"results/wadley_hist_{angle}_{l}.png")
if show:
plt.show()
plt.close()
# Smoothed joint distribution
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(xx, yy, f_cut, cmap=cm.coolwarm, linewidth=0, antialiased=True)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
plt.savefig(f"results/wadley_distrib_{angle}_{l}.png")
if show:
plt.show()
plt.close()
# 2D Wadley plot
fig = plt.figure(figsize=(5,5))
ax = fig.gca()
ax.scatter(x, y, s=1, alpha=0.1)
ax.contourf(xx, yy, f_cut, alpha=0.5, cmap=cm.coolwarm, levels=levels, extend="max")
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
fig.savefig(f"results/wadley_{angle}_{l}.png")
if show:
plt.show()
print(f"[{worker_nbr}]\tComputed joint distribution of angles (C{carbon}) and saved the figures.")
def stats_len(mappings_list, points):
lengths = {}
full_lengths = {}
cols = []
lengths = []
for f in sorted(mappings_list.keys()):
lengths[f] = []
full_lengths[f] = []
if f in ["RF02540","RF02541","RF02543"]:
cols.append("red") # LSU
elif f in ["RF00177","RF01960","RF01959","RF02542"]:
cols.append("blue") # SSU
elif f in ["RF00001"]:
cols.append("green")
elif f in ["RF00002"]:
cols.append("purple")
elif f in ["RF00005"]:
cols.append("orange")
else:
cols.append("grey")
l = []
for r in points:
if r.family != f: continue
nt_codes = r.df['nt_code'].values.tolist()
lengths[f].append(len(nt_codes))
full_lengths[f].append(len([ c for c in nt_codes if c != '-']))
l.append(len(r.df['nt_code']))
lengths.append(l)
plt.figure(figsize=(10,3))
ax = plt.gca()
ax.hist(lengths, bins=100, stacked=True, log=True, color=cols, label=sorted(mappings_list.keys()))
ax.set_xlabel("Sequence length (nucleotides)")
ax.set_ylabel("Number of 3D chains")
plt.tight_layout()
handles, labels = ax.get_legend_handles_labels()
filtered_handles = [mpatches.Patch(color='red'), mpatches.Patch(color='white'),
mpatches.Patch(color='blue'), mpatches.Patch(color='white'),
mpatches.Patch(color='green'), mpatches.Patch(color='purple'),
mpatches.Patch(color='orange'), mpatches.Patch(color='grey')]
filtered_labels = ['Large Ribosomal Subunits', '(RF02540, RF02541, RF02543)','Small Ribosomal Subunits','(RF01960, RF00177)',
'5S rRNA (RF00001)', '5.8S rRNA (RF00002)', 'tRNA (RF00005)', 'Other']
ax.legend(filtered_handles, filtered_labels, loc='best', ncol=2)# bbox_to_anchor=(0.5, -0.5), ncol=4, fontsize=)
plt.savefig("results/lengths.png")
print("[3]\tComputed sequence length statistics and saved the figure.")
def format_percentage(tot, x):
if not tot:
return '0 %'
x = 100*x/tot
if x >= 0.01:
x = "%.2f" % x
else:
x = "<.01"
return x + '%'
def stats_freq(mappings_list, points):
freqs = {}
for f in mappings_list.keys():
freqs[f] = Counter()
# then for all families
lengths["all"] = []
full_lengths["all"] = []
for r in points:
nt_codes = r.df['nt_code'].values.tolist()
lengths["all"].append(len(nt_codes))
full_lengths["all"].append(len([ c for c in nt_codes if c != '-']))
dlengths = pd.DataFrame.from_dict(lengths, orient='index').transpose().drop(["all"], axis='columns').dropna(axis='columns', thresh=2)
dfulllengths = pd.DataFrame.from_dict(full_lengths, orient='index').transpose().drop(["all"], axis='columns').dropna(axis='columns', thresh=2)
print(dlengths.head())
freqs[r.family].update(dict(r.df['nt_name'].value_counts()))
df = pd.DataFrame()
for f in sorted(mappings_list.keys()):
tot = sum(freqs[f].values())
df = pd.concat([ df, pd.DataFrame([[ format_percentage(tot, x) for x in freqs[f].values() ]], columns=list(freqs[f]), index=[f]) ])
df = df.fillna(0)
df.to_csv("results/frequencies.csv")
print("[4]\tComputed nucleotide statistics and saved CSV file.")
def stats_pairs(mappings_list, points):
def line_format(family_data):
return family_data.apply(partial(format_percentage, sum(family_data)))
# Create a Counter() object by family
freqs = {}
for f in mappings_list.keys():
freqs[f] = Counter()
# Iterate over data points
for r in tqdm(points, desc="RNA points", position=0, leave=False):
# Skip if linear piece of RNA
if not sum([ x != 0 for x in r.df.paired ]):
continue
# Count each pair type within the molecule
vcnts = pd.concat(
[ pd.Series(row['pair_type_LW'].split(','))
for _, row in r.df.dropna(subset=["pair_type_LW"]).iterrows() ]
).reset_index(drop=True).value_counts()
# Add these new counts to the family's counter
freqs[r.family].update(dict(vcnts))
# Create the output dataframe
df = pd.DataFrame()
for f in sorted(mappings_list.keys()):
df = pd.concat([ df, pd.DataFrame([[ x for x in freqs[f].values() ]], columns=list(freqs[f]), index=[f]) ])
df = df.fillna(0)
# Remove not very well defined pair types (not in the 12 LW types)
col_list = [ x for x in df.columns if '.' in x ]
df['other'] = df[col_list].sum(axis=1)
df.drop(col_list, axis=1, inplace=True)
axs = dlengths.plot.hist(figsize=(10, 15), bins=range(0,650,50), sharex=True, sharey=True, subplots=True, layout=(12,6), legend=False, log=True)
# for ax, f in zip(axs, sorted(mappings_list.keys())):
# ax.text(600,150, str(len([ x for x in lengths[f] if x != np.NaN ])), fontsize=14)
plt.savefig("results/length_distribs.png")
# Compute total row
total_series = df.sum(numeric_only=True).rename("TOTAL")
df = df.append(total_series)
axs = dfulllengths.plot.hist(figsize=(10, 15), bins=range(0,650,50), sharex=True, sharey=True, subplots=True, layout=(12,6), legend=False, log=True)
# for ax, f in zip(axs, sorted(mappings_list.keys())):
# ax.text(600,150, str(len([ x for x in lengths[f] if x != np.NaN ])), fontsize=14)
plt.savefig("results/full_length_distribs.png")
# format as percentages
df = df.apply(line_format, axis=1)
# reorder columns
df.sort_values("TOTAL", axis=1, inplace=True, ascending=False)
# Save to CSV
df.to_csv("results/pairings.csv")
# Plot barplot of overall types
total_series.sort_values(ascending=False, inplace=True)
total_series.apply(lambda x: x/2.0) # each interaction was counted twice because one time per extremity
ax = total_series.plot(figsize=(5,3), kind='bar', log=True, ylim=(1e4,5000000) )
ax.set_ylabel("Number of observations")
plt.subplots_adjust(bottom=0.2, right=0.99)
plt.savefig("results/pairings.png")
print("[5]\tComputed nucleotide statistics and saved CSV and PNG file.")
def to_dist_matrix(f):
if path.isfile("data/"+f+".npy"):
return 0
print(f)
dm = DistanceCalculator('identity')
with open(path_to_seq_data+"realigned/"+f+"++.afa") as al_file:
al = AlignIO.read(al_file, "fasta")[-len(mappings_list[f]):]
......@@ -198,10 +305,18 @@ def to_dist_matrix(f):
return 0
def seq_idty(mappings_list):
famlist = sorted([ x for x in mappings_list.keys() if len(mappings_list[x]) > 1 ])
ignored = []
for x in mappings_list.keys():
if len(mappings_list[x]) == 1:
ignored.append(x)
if len(ignored):
print("Ignoring families with only one chain:", " ".join(ignored))
# compute distance matrices
p = Pool(processes=8)
pbar = tqdm(total=len(mappings_list.keys()), desc="RNA families", position=0, leave=True)
for i, _ in enumerate(p.imap_unordered(to_dist_matrix, sorted(mappings_list.keys()))):
pbar = tqdm(total=len(famlist), desc="Families idty matrices", position=1, leave=True)
for i, _ in enumerate(p.imap_unordered(to_dist_matrix, famlist)):
pbar.update(1)
pbar.close()
p.close()
......@@ -209,16 +324,16 @@ def seq_idty(mappings_list):
# load them
fam_arrays = []
for f in sorted(mappings_list.keys()):
for f in famlist:
if path.isfile("data/"+f+".npy"):
fam_arrays.append(np.load("data/"+f+".npy"))
else:
fam_arrays.append([])
fig, axs = plt.subplots(11,7, figsize=(25,25))
fig, axs = plt.subplots(5,13, figsize=(15,9))
axs = axs.ravel()
[axi.set_axis_off() for axi in axs]
for f, D, ax in zip(sorted(mappings_list.keys()), fam_arrays, axs):
for f, D, ax in zip(famlist, fam_arrays, axs):
if not len(D): continue
if D.shape[0] > 2: # Cluster only if there is more than 2 sequences to organize
D = D + D.T # Copy the lower triangle to upper, to get a symetrical matrix
......@@ -232,20 +347,16 @@ def seq_idty(mappings_list):
idx1 = Z['leaves']
D = D[idx1,:]
D = D[:,idx1[::-1]]
im = ax.matshow(D, vmin=0, vmax=1, origin='lower')
ax.set_title(f)
fig.suptitle("Distance matrices of sequences from various families\nclustered by sequence identity (Ward's method)", fontsize="18")
fig.tight_layout()
fig.subplots_adjust(top=0.92)
fig.colorbar(im, ax=axs.tolist(), shrink=0.98)
im = ax.matshow(1.0 - D, vmin=0, vmax=1, origin='lower') # convert to identity matrix 1 - D from distance matrix D
ax.set_title(f + "\n(" + str(len(mappings_list[f]))+ " chains)")
fig.tight_layout()
fig.subplots_adjust(wspace=0.1, hspace=0.3)
fig.colorbar(im, ax=axs[-1], shrink=0.8)
fig.savefig(f"results/distances.png")
print("[6]\tComputed identity matrices and saved the figure.")
if __name__ == "__main__":
#TODO: compute nt frequencies, chain lengths
#################################################################
# LOAD ALL FILES
#################################################################
......@@ -255,46 +366,52 @@ if __name__ == "__main__":
for k in mappings_list.keys():
mappings_list[k] = [ x for x in mappings_list[k] if str(x) != 'nan' ]
# print("Loading datapoints from file...")
# rna_points = []
# filelist = [path_to_3D_data+"/datapoints/"+f for f in os.listdir(path_to_3D_data+"/datapoints") if ".log" not in f and ".gz" not in f]
# p = Pool(initializer=tqdm.set_lock, initargs=(tqdm.get_lock(),), processes=read_cpu_number())
# pbar = tqdm(total=len(filelist), desc="RNA files", position=0, leave=True)
# for i, rna in enumerate(p.imap_unordered(load_rna_frome_file, filelist)):
# rna_points.append(rna)
# pbar.update(1)
# pbar.close()
# p.close()
# p.join()
# npoints = len(rna_points)
# print(npoints, "RNA files loaded.")
print("Loading datapoints from file...")
if path.isfile("data/rnapoints.dat"):
with open("data/rnapoints.dat", 'rb') as f:
rna_points = pickle.load(f)
else:
rna_points = []
filelist = [path_to_3D_data+"/datapoints/"+f for f in os.listdir(path_to_3D_data+"/datapoints") if ".log" not in f and ".gz" not in f]
p = Pool(initializer=tqdm.set_lock, initargs=(tqdm.get_lock(),), processes=read_cpu_number())
pbar = tqdm(total=len(filelist), desc="RNA files", position=0, leave=True)
for i, rna in enumerate(p.imap_unordered(load_rna_frome_file, filelist)):
rna_points.append(rna)
pbar.update(1)
pbar.close()
p.close()
p.join()
with open("data/rnapoints.dat", "wb") as f:
pickle.dump(rna_points, f)
npoints = len(rna_points)
print(npoints, "RNA files loaded.")
#################################################################
# Define threads for the tasks
#################################################################
# wadley_thr = []
# wadley_thr.append(th.Thread(target=reproduce_wadley_results, args=[rna_points], kwargs={'carbon': 1, 'filter_helical': "zone"}))
# wadley_thr.append(th.Thread(target=reproduce_wadley_results, args=[rna_points], kwargs={'carbon': 1, 'filter_helical': "form"}))
# wadley_thr.append(th.Thread(target=reproduce_wadley_results, args=[rna_points], kwargs={'carbon': 1, 'filter_helical': "both"}))
# wadley_thr.append(th.Thread(target=reproduce_wadley_results, args=[rna_points], kwargs={'carbon': 4, 'filter_helical': "form"}))
# wadley_thr.append(th.Thread(target=reproduce_wadley_results, args=[rna_points], kwargs={'carbon': 4, 'filter_helical': "form"}))
# wadley_thr.append(th.Thread(target=reproduce_wadley_results, args=[rna_points], kwargs={'carbon': 4, 'filter_helical': "both"}))
# seq_len_thr = th.Thread(target=partial(stats_len, mappings_list), args=[rna_points])
# dist_thr = th.Thread(target=seq_idty, args=[mappings_list])
# for t in wadley_thr:
# t.start()
# seq_len_thr.start()
# dist_thr.start()
# for t in wadley_thr:
# t.join()
# seq_len_thr.join()
# dist_thr.join()
# reproduce_wadley_results(rna_points)
seq_idty(mappings_list)
# stats_len(mappings_list, rna_points)
wadley_thr = []
wadley_thr.append(th.Thread(target=reproduce_wadley_results, args=[rna_points], kwargs={'carbon': 1}))
wadley_thr.append(th.Thread(target=reproduce_wadley_results, args=[rna_points], kwargs={'carbon': 4}))
seq_len_thr = th.Thread(target=partial(stats_len, mappings_list), args=[rna_points])
nt_freq_thr = th.Thread(target=partial(stats_freq, mappings_list), args=[rna_points])
pairs_freq_thr = th.Thread(target=partial(stats_pairs, mappings_list), args=[rna_points])
dist_thr = th.Thread(target=seq_idty, args=[mappings_list])
for t in wadley_thr:
t.start()
seq_len_thr.start()
nt_freq_thr.start()
pairs_freq_thr.start()
dist_thr.start()
for t in wadley_thr:
t.join()
seq_len_thr.join()
nt_freq_thr.join()
pairs_freq_thr.join()
dist_thr.join()
......