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#!/usr/bin/env python
"""
complicated_example.py
"""
import os, sys
exe_path = os.path.split(os.path.abspath(sys.argv[0]))[0]
sys.path.append(os.path.abspath(os.path.join(exe_path,"..", "..")))
from ruffus import *
from time import sleep
import random
#88888888888888888888888888888888888888888888888888888888888888888888888888888888888888888
# options
#88888888888888888888888888888888888888888888888888888888888888888888888888888888888888888
from optparse import OptionParser
parser = OptionParser(version="%prog 1.0")
parser.add_option("-t", "--target_tasks", dest="target_tasks",
action="append",
default = ["summarise_all"],
metavar="JOBNAME",
type="string",
help="Target task(s) of pipeline.")
parser.add_option("-f", "--forced_tasks", dest="forced_tasks",
action="append",
default = list(),
metavar="JOBNAME",
type="string",
help="Pipeline task(s) which will be included even if they are up to date.")
parser.add_option("-j", "--jobs", dest="jobs",
default=5,
metavar="jobs",
type="int",
help="Specifies the number of jobs (commands) to run simultaneously.")
parser.add_option("-d", "--data_dir", dest="data_dir",
default="%s/data_for_complicated_example" % exe_path,
metavar="PATH",
type="string",
help="Directory with starting data [*.fa].")
parser.add_option("-w", "--working_dir", dest="working_dir",
default="/working_dir",
metavar="PATH",
type="string",
help="Working directory.")
parser.add_option("-v", "--verbose", dest = "verbose",
action="store_true", default=False,
help="Do not echo to shell but only print to log.")
parser.add_option("-D", "--dependency", dest="dependency_file",
metavar="FILE",
type="string",
help="Print a dependency graph of the pipeline that would be executed "
"to FILE, but do not execute it.")
parser.add_option("-F", "--dependency_graph_format", dest="dependency_graph_format",
metavar="FORMAT",
type="string",
default = 'svg',
help="format of dependency graph file. Can be 'ps' (PostScript), "+
"'svg' 'svgz' (Structured Vector Graphics), " +
"'png' 'gif' (bitmap graphics) etc ")
parser.add_option("-n", "--just_print", dest="just_print",
action="store_true", default=False,
help="Print a description of the jobs that would be executed, "
"but do not execute them.")
#88888888888888888888888888888888888888888888888888888888888888888888888888888888888888888
# imports
#88888888888888888888888888888888888888888888888888888888888888888888888888888888888888888
import StringIO
import re
import operator
import sys
from collections import defaultdict
#88888888888888888888888888888888888888888888888888888888888888888888888888888888888888888
# Functions
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#_________________________________________________________________________________________
#
# Helper function:
#
# split_gene_files
#
#_________________________________________________________________________________________
def split_gene_files ( gene_file_name,
job_completion_flag_file_name,
split_output_dir):
"""
Helper function to simulate splitting gene files into "chunks" suitable for
parallel jobs on a computational cluster
The number of output files is only known at runtime
because the number of "chunks" depend on the size
of starting the gene sets
We simulate this using a random number from 20->50
"""
#
# make output directory
#
if not os.path.exists(split_output_dir):
os.makedirs(split_output_dir)
# save number of chunks for later tasks
number_of_output_files = int(random.uniform(20, 50))
for index in range(number_of_output_files):
open("%s/%d.fa" % (split_output_dir, index), "w")
open(job_completion_flag_file_name, "w")
#_________________________________________________________________________________________
#
# get_unknown_gene_set_names
# get_species_names
#
#
# functions for getting unknown gene set names and species names
#
#_________________________________________________________________________________________
import glob, re
def get_chunked_gene_file_names (dir_name):
"""
Get list of gene file names
Helper function for getting unknown gene set names, and species names
"""
regex = re.compile(r".+/(.+).genes.fa")
gene_set_names = []
for file_name in glob.glob("%s/%s/*.genes.fa" % (d_dir, dir_name)):
m = regex.search(file_name)
gene_set_names.append(m.group(1))
return gene_set_names
def get_unknown_gene_set_names ():
return get_chunked_gene_file_names("unknown_genes")
def get_species_names ():
return get_chunked_gene_file_names("all_genes_in_each_species")
#88888888888888888888888888888888888888888888888888888888888888888888888888888888888888888
# Main logic
#88888888888888888888888888888888888888888888888888888888888888888888888888888888888888888
# get help string
f =StringIO.StringIO()
parser.print_help(f)
helpstr = f.getvalue()
(options, remaining_args) = parser.parse_args()
d_dir = options.data_dir
w_dir = options.working_dir
#_________________________________________________________________________________________
#
# Step 1:
#
# split_unknown_gene_set
#
# data_dir/unknown_genes/XXX.genes.fa
# ->working_dir/XXX/split_gene_sets.completed
# ->working_dir/XXX/NNN.fa
#
#_________________________________________________________________________________________
@follows(mkdir(w_dir))
@files_re("%s/unknown_genes/*.genes.fa" % d_dir,
r"(.*/)(.*)(\.genes.fa)",
r"\1\2\3", # unknown_gene_set file name
r"%s/\2/split_gene_sets.completed" % w_dir, # job_completion_flag
r"%s/\2" % w_dir) # split_output_dir
def split_unknown_gene_set( starting_gene_set,
job_completion_flag,
split_output_dir):
"""
Simulate splitting gene files for unknown gene set into "chunks" suitable for
parallel jobs on a computational cluster
"""
split_gene_files ( starting_gene_set,
job_completion_flag,
split_output_dir)
sleep(1)
#_________________________________________________________________________________________
#
# Step 2:
#
# split_per_species_gene_sets
# data_dir/all_genes_in_each_species/YYY.genes.fa
# ->working_dir/species_YYY/split_gene_sets.completed
# ->working_dir/species_YYY/MMM.fa
#
#_________________________________________________________________________________________
@follows(mkdir(w_dir))
@files_re("%s/all_genes_in_each_species/*.genes.fa" % d_dir,
r"(.*/)(.*)(\.genes.fa)",
r"\1\2\3", # all_genes_in_species
r"%s/species_\2/split_gene_sets.completed" % w_dir, # job_completion_flag
r"%s/species_\2" % w_dir) # split_output_dir
def split_per_species_gene_sets(all_genes_in_species,
job_completion_flag,
split_output_dir):
"""
Simulate splitting gene files for each species into "chunks" suitable for
parallel jobs on a computational cluster
"""
split_gene_files ( all_genes_in_species,
job_completion_flag,
split_output_dir)
sleep(1)
#_________________________________________________________________________________________
#
# Step 3:
#
# all_vs_all_comparisons
# working_dir/species_YYY/MMM.fa
# working_dir/XXX/NNN.fa
# -> compare/x/y.n.m.comparison_res
# -> compare/x/y.n.m.complete
#
#_________________________________________________________________________________________
#
# function for generating custom parameters
#
def generate_all_vs_all_params ():
"""
Custom function to generate
all vs. all file names for the various "chunks"
"""
chunk_index_regex = re.compile(r".+/(.+).fa")
def parse_index_from_chunk_filename (chunk_filename):
match = chunk_index_regex.search(chunk_filename)
return int(match.group(1))
species_names = get_species_names()
gene_set_names = get_unknown_gene_set_names()
for x in gene_set_names:
for y in species_names:
y = "species_" + y
m_files = glob.glob("%s/%s/*.fa" % (w_dir, x))
n_files = glob.glob("%s/%s/*.fa" % (w_dir, y))
#
# for each species chunk vs for each unknown chunk
#
for m_file in m_files:
for n_file in n_files:
input_files = [m_file, n_file]
output_dir = "%s/compare/%s" % (w_dir, x)
m = parse_index_from_chunk_filename(m_file)
n = parse_index_from_chunk_filename(n_file)
job_completion_flag = output_dir + "/%s.%d.%d.complete" % (y, m, n)
result_file = output_dir + "/%s.%d.%d.comparison_res" % (y, m, n)
name = "%s -> %d vs %d\n" % (y, m, n)
yield input_files, job_completion_flag, output_dir, result_file, name
@follows(split_unknown_gene_set, split_per_species_gene_sets)
@files(generate_all_vs_all_params)
def all_vs_all_comparisons(file_chunks,
job_completion_flag,
output_dir,
result_file,
name):
"""
Simulate comparison of gene chunks against each other
Normally runs in parallel on a computational cluster
"""
#
# make output directory
#
try:
os.makedirs(output_dir)
except OSError:
pass
open(job_completion_flag, "w")
open(result_file, "w").write(name)
#_________________________________________________________________________________________
#
# Step 4:
#
# Recombine: alignment results to make gene families
# compare/x/*.comparison_res
# -> multiple_alignment/x/x.gene_families
#
#_________________________________________________________________________________________
#
# generate_params_for_making_gene_families
#
# function for generating custom parameters
#
def generate_params_for_making_gene_families ():
"""
Custom function to combining comparison files into gene families
"""
gene_set_names = get_unknown_gene_set_names()
for x in gene_set_names:
results_files = glob.glob("%s/compare/%s/*.comparison_res" % (w_dir, x))
output_dir = "%s/multiple_alignment/%s" % (w_dir, x)
family_file = "%s/gene.families" % output_dir
yield results_files, family_file, output_dir
@follows(all_vs_all_comparisons)
@files(generate_params_for_making_gene_families)
def combine_into_gene_familes (results_files, family_file_name, output_dir):
"""
Simulate making gene families by concatenating comparison results :-)
"""
#
# make output directory
#
if not os.path.exists(output_dir):
os.makedirs(output_dir)
family_file = open(family_file_name, "w")
for f in results_files:
family_file.write(open(f).read())
sleep(1)
#_________________________________________________________________________________________
#
# Step 5:
#
# split_gene_family_for_evolutionary_analysis
# multiple_alignment/x/x.gene_families
# -> multiple_alignment/x/NNN.aln
# -> multiple_alignment/x/split.completed
#
#_________________________________________________________________________________________
@follows(combine_into_gene_familes)
@files_re("%s/multiple_alignment/*/gene.families" % w_dir,
r"(.+)/(gene.families)",
r"\1/\2",
r"\1/split.completed",
r"\1")
def split_gene_family_for_evolutionary_analysis( family_file,
job_completion_flag_file, split_output_dir):
"""
Simulate splitting family of genes into "chunks" suitable for
parallel jobs on a computational cluster
"""
# save number of chunks for later tasks
number_of_output_files = int(random.uniform(20, 50))
for index in range(number_of_output_files):
open("%s/%d.aln" % (split_output_dir, index), "w").write("chunk %d" % index)
open(job_completion_flag_file, "w")
sleep(1)
#_________________________________________________________________________________________
#
# Step 6:
#
# evolution_analysis
# multiple_alignment/x/NNN.aln
# -> multiple_alignment/x/NNN.evo_res
#
#_________________________________________________________________________________________
@follows(split_gene_family_for_evolutionary_analysis)
@files_re("%s/multiple_alignment/*/*.aln" % w_dir,
r"(.+).aln",
r"\1.evo_res")
def evolution_analysis( family_file, result_file_name):
"""
Simulate evolutionary analysis
"""
result_file = open(result_file_name, "w")
result_file.write(family_file + "\n")
sleep(1)
#_________________________________________________________________________________________
#
# Step 7:
#
# combine_evolution_analysis
# multiple_alignment/x/NNN.evo_res
# -> evolutionary_analysis/x.results
#
#_________________________________________________________________________________________
#
# generate_params_for_combining_evolutionary_analyses
#
# function for generating custom parameters
#
def generate_params_for_combining_evolutionary_analyses ():
"""
Custom function to combining evolutionary analyses per unknown gene set
"""
gene_set_names = get_unknown_gene_set_names()
for x in gene_set_names:
results_files = glob.glob("%s/multiple_alignment/%s/*.evo_res" % (w_dir, x))
combined_file = "%s/evolutionary_analysis/%s.results" % (w_dir, x)
yield results_files, combined_file
@follows(evolution_analysis, mkdir("%s/evolutionary_analysis" % w_dir))
@files(generate_params_for_combining_evolutionary_analyses)
def combine_evolution_analysis (results_files, combined_file_name):
"""
Simular combining evolutionary analyses
"""
combined_file = open(combined_file_name, "w")
for f in results_files:
combined_file.write(open(f).read())
sleep(1)
#_________________________________________________________________________________________
#
# Step 8:
#
# summarise_evolution_analysis
# evolutionary_analysis/x.results
# -> evolutionary_analysis/x.summary
#
#_________________________________________________________________________________________
@follows(combine_evolution_analysis)
@files_re("%s/evolutionary_analysis/*.results" % w_dir,
r"(.+).results",
r"\1.summary")
def summarise_evolution_analysis( results_file, summary_file_name):
"""
Simulate summary of evolutionary analysis
"""
summary_file = open(summary_file_name, "w")
summary_file.write("summary of " + open(results_file).read())
sleep(1)
#_________________________________________________________________________________________
#
# Step 9:
#
# summarise_all
# evolutionary_analysis/x.summary
# -> all.total_summary
#
#_________________________________________________________________________________________
summary_file_names = ["%s/evolutionary_analysis/%s.summary" % (w_dir, n)
for n in get_unknown_gene_set_names()]
total_summary_file_name = "%s/all.total_summary" % w_dir
@follows(summarise_evolution_analysis)
@files(summary_file_names, total_summary_file_name)
def summarise_all( summary_files, total_summary_file_name):
"""
Simulate summarize all
"""
total_summary_file = open(total_summary_file_name, "w")
total_summary_file.write("Over all Summary:\n")
for f in summary_files:
total_summary_file.write(open(f).read())
sleep(1)
#888888888888888888888888888888888888888888888888888888888888888888888888888888888888888888
#
# print pipeline or run pipeline
#
if options.just_print:
pipeline_printout(sys.stdout, options.target_tasks, options.forced_tasks, long_winded=True)
elif options.dependency_file:
graph_printout ( open(options.dependency_file, "w"),
options.dependency_graph_format,
options.target_tasks,
options.forced_tasks)
else:
pipeline_run(options.target_tasks, options.forced_tasks, multiprocess = options.jobs)
|
