def index(self,
genome_dir,
genome_fasta,
annotation_gtf=None,
feature_from_gtf_to_use_as_exon=None,
junction_tab_file=None,
sjdboverhang=None,
genomeSAindexNbases=None,
genomeChrBinNbits=None,
genome_size=None):
FileRoutines.safe_mkdir(genome_dir)
options = "--runMode genomeGenerate"
options += " --genomeDir %s" % os.path.abspath(genome_dir)
options += " --runThreadN %i" % self.threads
options += " --genomeFastaFiles %s" % (
os.path.abspath(genome_fasta) if isinstance(genome_fasta, str) else
" ".join(map(os.path.abspath, genome_fasta)))
options += " --sjdbGTFfile %s" % annotation_gtf if annotation_gtf else ""
options += " --sjdbFileChrStartEnd %s" % junction_tab_file if junction_tab_file else ""
options += " --sjdbOverhang %i" % sjdboverhang if sjdboverhang else "" # number of bases taken from both sides of splice junction. 100 by default
if genome_size:
options += " --genomeSAindexNbases %i" % min(
[14, (floor(log(genome_size, 2) / 2)) - 1])
else:
options += " --genomeSAindexNbases %i" % genomeSAindexNbases if genomeSAindexNbases else "" # size of k-mers used for preindexing of suffix array
options += " --genomeChrBinNbits %i" % genomeChrBinNbits if genomeChrBinNbits else "" # padding size (log2) of reference sequences. 18 by default
# recommended value min(18, log2(GenomeLength/NumberOfScaffolds))
options += " --sjdbGTFfeatureExon %s" % feature_from_gtf_to_use_as_exon if feature_from_gtf_to_use_as_exon else ""
self.execute(options)
def parallel_align(self, list_of_files, output_dir, msa_tool='prank',
seq_type=None, bootstrap_number=100, genetic_code=1, threads=None,
msa_tool_options=None, seq_cutoff=None, col_cutoff=None, mafft_bin=None,
prank_bin=None, muscle_bin=None, pagan_bin=None, ruby_bin=None, program=None,
cmd_log_file=None,
cpus_per_task=1,
handling_mode="local",
job_name=None,
log_prefix=None,
error_log_prefix=None,
max_jobs=None,
max_running_time=None,
max_memory_per_node=None,
max_memmory_per_cpu=None,
modules_list=None,
environment_variables_dict=None):
common_options = self.parse_common_options(output_dir=output_dir, msa_tool=msa_tool,
seq_type=seq_type, bootstrap_number=bootstrap_number,
genetic_code=genetic_code, threads=threads,
msa_tool_options=msa_tool_options, seq_cutoff=seq_cutoff,
col_cutoff=col_cutoff, mafft_bin=mafft_bin,
prank_bin=prank_bin, muscle_bin=muscle_bin,
pagan_bin=pagan_bin, ruby_bin=ruby_bin, program=program)
FileRoutines.safe_mkdir(output_dir)
options_list = []
for filename in list_of_files:
basename = FileRoutines.split_filename(filename)[1]
op = common_options
op += " --seqFile %s" % filename
op += " --dataset %s" % basename
options_list.append(op)
if handling_mode == "local":
self.parallel_execute(options_list)
elif handling_mode == "slurm":
cmd_list = ["%s%s %s" % ((self.path + "/") if self.path else "", self.cmd, options) for options in options_list]
self.slurm_run_multiple_jobs_in_wrap_mode(cmd_list,
cmd_log_file,
max_jobs=max_jobs,
job_name=job_name,
log_prefix=log_prefix,
error_log_prefix=error_log_prefix,
cpus_per_node=None,
max_running_jobs=None,
max_running_time=max_running_time,
cpus_per_task=cpus_per_task,
max_memory_per_node=max_memory_per_node,
max_memmory_per_cpu=max_memmory_per_cpu,
modules_list=modules_list,
environment_variables_dict=environment_variables_dict)
def parallel_align(self, list_of_files, output_directory, output_suffix=None, tree_file=None, output_format=None, show_xml=None,
show_tree=None, show_ancestral_sequences=None, show_evolutionary_events=None,
showall=None, compute_posterior_support=None, njtree=None, skip_insertions=False,
codon_alignment=None, translated_alignment=None,
cmd_log_file=None,
cpus_per_task=1,
handling_mode="local",
job_name=None,
log_prefix=None,
error_log_prefix=None,
max_jobs=None,
max_running_time=None,
max_memory_per_node=None,
max_memmory_per_cpu=None,
modules_list=None,
environment_variables_dict=None):
common_options = self.parse_common_options(tree_file=tree_file, output_format=output_format, show_xml=show_xml,
show_tree=show_tree, show_ancestral_sequences=show_ancestral_sequences,
show_evolutionary_events=show_evolutionary_events, showall=showall,
compute_posterior_support=compute_posterior_support, njtree=njtree,
skip_insertions=skip_insertions, codon_alignment=codon_alignment,
translated_alignment=translated_alignment)
FileRoutines.safe_mkdir(output_directory)
options_list = []
for filename in list_of_files:
basename = FileRoutines.split_filename(filename)[1]
op = common_options
op += " -d=%s" % filename
op += " -o=%s/%s.fasta" % (output_directory,
("%s_%s" % (basename, output_suffix)) if output_suffix else basename)
options_list.append(op)
if handling_mode == "local":
self.parallel_execute(options_list)
elif handling_mode == "slurm":
cmd_list = ["%s%s %s" % ((self.path + "/") if self.path else "", self.cmd, options) for options in options_list]
self.slurm_run_multiple_jobs_in_wrap_mode(cmd_list,
cmd_log_file,
max_jobs=max_jobs,
job_name=job_name,
log_prefix=log_prefix,
error_log_prefix=error_log_prefix,
cpus_per_node=None,
max_running_jobs=None,
max_running_time=max_running_time,
cpus_per_task=cpus_per_task,
max_memory_per_node=max_memory_per_node,
max_memmory_per_cpu=max_memmory_per_cpu,
modules_list=modules_list,
environment_variables_dict=environment_variables_dict)
def extract_proteins_from_selected_families(
families_id_file,
fam_file,
pep_file,
output_dir="./",
pep_format="fasta",
out_prefix=None,
create_dir_for_each_family=False):
from RouToolPa.Routines import SequenceRoutines
fam_id_list = IdList()
fam_dict = SynDict()
#print(pep_file)
FileRoutines.safe_mkdir(output_dir)
out_dir = FileRoutines.check_path(output_dir)
create_directory_for_each_family = True if out_prefix else create_dir_for_each_family
if families_id_file:
fam_id_list.read(families_id_file)
fam_dict.read(fam_file, split_values=True, values_separator=",")
protein_dict = SeqIO.index_db("tmp.idx", pep_file, format=pep_format)
for fam_id in fam_id_list if families_id_file else fam_dict:
if fam_id in fam_dict:
if create_directory_for_each_family:
fam_dir = "%s%s/" % (out_dir, fam_id)
FileRoutines.safe_mkdir(fam_dir)
out_file = "%s%s.pep" % (fam_dir, out_prefix
if out_prefix else fam_id)
else:
out_file = "%s/%s.pep" % (out_dir, out_prefix
if out_prefix else fam_id)
SeqIO.write(SequenceRoutines.record_by_id_generator(
protein_dict, fam_dict[fam_id], verbose=True),
out_file,
format=pep_format)
else:
print("%s was not found" % fam_id)
os.remove("tmp.idx")
def parallel_positive_selection_test(self,
in_dir,
tree_file,
out_dir,
results_file,
seq_type="codons",
codon_frequency="F3X4",
noisy=3,
verbose="concise",
runmode=0,
clock=0,
aminoacid_distance=None,
genetic_code=0,
fix_kappa=False,
kappa=5,
getSE=0,
RateAncestor=0,
small_difference=0.000001,
clean_data=True,
method=0):
"""
This function implements positive selection test (branch-site model)
for branch labeled in tree file using model_A vs model_A_null(omega fixed to 1) comparison
"""
FileRoutines.safe_mkdir(out_dir)
alignment_files_list = FileRoutines.make_list_of_path_to_files(in_dir)
tree_file_abs_path = os.path.abspath(tree_file)
options_list = []
dir_list = []
basename_dir_list = []
model_list = ["Model_A", "Model_A_null"]
fix_omega_dict = {"Model_A": False, "Model_A_null": True}
for filename in alignment_files_list:
directory, basename, extension = FileRoutines.split_filename(
filename)
filename_out_dir = os.path.abspath("%s/%s/" % (out_dir, basename))
basename_dir_list.append(basename)
FileRoutines.safe_mkdir(filename_out_dir)
for model in model_list:
model_dir = "%s/%s/" % (filename_out_dir, model)
FileRoutines.safe_mkdir(model_dir)
out_file = "%s/%s/%s.out" % (filename_out_dir, model, basename)
ctl_file = "%s/%s/%s.ctl" % (filename_out_dir, model, basename)
options_list.append("%s.ctl" % basename)
dir_list.append(model_dir)
self.generate_ctl_file(os.path.abspath(filename),
tree_file_abs_path,
out_file,
ctl_file,
seq_type=seq_type,
codon_frequency=codon_frequency,
noisy=noisy,
verbose=verbose,
runmode=runmode,
clock=clock,
aminoacid_distance=aminoacid_distance,
model=2,
nssites=2,
genetic_code=genetic_code,
fix_kappa=fix_kappa,
kappa=kappa,
fix_omega=fix_omega_dict[model],
omega=1,
getSE=getSE,
RateAncestor=RateAncestor,
Mgene=0,
small_difference=small_difference,
clean_data=clean_data,
method=method)
self.parallel_execute(options_list, dir_list=dir_list)
results_dict = OrderedDict()
double_delta_dict = OrderedDict()
raw_pvalues_dict = OrderedDict()
raw_pvalues_list = []
for basename in basename_dir_list:
results_dict[basename] = OrderedDict()
for model in model_list:
output_file = "%s/%s/%s/%s.out" % (out_dir, basename, model,
basename)
codeml_report = CodeMLReport(output_file)
results_dict[basename][model] = codeml_report.LnL
skipped_genes_set = set()
for basename in basename_dir_list:
for model in model_list:
if results_dict[basename][model] is None:
print("LnL was not calculated for %s" % basename)
skipped_genes_set.add(basename)
break
else:
doubled_delta = 2 * (results_dict[basename]["Model_A"] -
results_dict[basename]["Model_A_null"])
p_value = chisqprob(doubled_delta, 1) # degrees of freedom = 1
double_delta_dict[basename] = doubled_delta
raw_pvalues_dict[basename] = p_value
raw_pvalues_list.append(p_value)
adjusted_pvalues_list = fdrcorrection0(raw_pvalues_list)[1]
#print adjusted_pvalues_list
i = 0
with open(results_file, "w") as out_fd:
out_fd.write(
"id\tmodel_a_null,LnL\tmodel_a,LnL\t2*delta\traw p-value\tadjusted p-value\n"
)
for basename in basename_dir_list:
for model in model_list:
if results_dict[basename][model] is None:
print("LnL was not calculated for %s" % basename)
break
else:
#doubled_delta = 2 * (results_dict[basename]["Model_A"] - results_dict[basename]["Model_A_null"])
#p_value = chisqprob(doubled_delta, 1) # degrees of freedom = 1
#print basename, results_dict[basename]["Model_A_null"],results_dict[basename]["Model_A"], double_delta_dict[basename], raw_pvalues_dict[basename], adjusted_pvalues_list[i]
out_fd.write(
"%s\t%f\t%f\t%f\t%f\t%f\n" %
(basename, results_dict[basename]["Model_A_null"],
results_dict[basename]["Model_A"],
double_delta_dict[basename],
raw_pvalues_dict[basename], adjusted_pvalues_list[i]))
i += 1
def parallel_codeml(self,
in_dir,
tree_file,
out_dir,
seq_type="codons",
codon_frequency="F3X4",
noisy=0,
verbose="concise",
runmode=0,
clock=0,
aminoacid_distance=None,
model=1,
nssites=0,
genetic_code=0,
fix_kappa=False,
kappa=5,
fix_omega=False,
omega=0.2,
getSE=0,
RateAncestor=0,
small_difference=0.000001,
clean_data=True,
method=0,
Mgene=None):
FileRoutines.safe_mkdir(out_dir)
alignment_files_list = FileRoutines.make_list_of_path_to_files(in_dir)
tree_file_abs_path = os.path.abspath(tree_file)
options_list = []
dir_list = []
for filename in alignment_files_list:
directory, basename, extension = FileRoutines.split_filename(
filename)
filename_out_dir = os.path.abspath("%s/%s/" % (out_dir, basename))
out_file = "%s/%s.out" % (filename_out_dir, basename)
ctl_file = "%s/%s.ctl" % (filename_out_dir, basename)
options_list.append(ctl_file)
dir_list.append(filename_out_dir)
FileRoutines.safe_mkdir(filename_out_dir)
self.generate_ctl_file(os.path.abspath(filename),
tree_file_abs_path,
out_file,
ctl_file,
seq_type=seq_type,
codon_frequency=codon_frequency,
noisy=noisy,
verbose=verbose,
runmode=runmode,
clock=clock,
aminoacid_distance=aminoacid_distance,
model=model,
nssites=nssites,
genetic_code=genetic_code,
fix_kappa=fix_kappa,
kappa=kappa,
fix_omega=fix_omega,
omega=omega,
getSE=getSE,
RateAncestor=RateAncestor,
Mgene=Mgene,
small_difference=small_difference,
clean_data=clean_data,
method=method)
self.parallel_execute(options_list, dir_list=dir_list)
def align_samples(self,
samples_dir,
output_dir,
genome_dir,
genome_fasta=None,
samples=None,
annotation_gtf=None,
sjdboverhang=None,
genomeSAindexNbases=None,
genomeChrBinNbits=None,
genome_size=None,
feature_from_gtf_to_use_as_exon=None,
exon_tag_to_use_as_transcript_id=None,
exon_tag_to_use_as_gene_id=None,
length_of_sequences_flanking_junction=None,
junction_tab_file_list=None,
three_prime_trim=None,
five_prime_trim=None,
adapter_seq_for_three_prime_clip=None,
max_mismatch_percent_for_adapter_trimming=None,
three_prime_trim_after_adapter_clip=None,
output_type="BAM",
sort_bam=True,
max_memory_per_thread_for_bam_sorting="4G",
include_unmapped_reads_in_bam=True,
output_unmapped_reads=True,
two_pass_mode=True,
max_intron_length=None,
input_is_se=None,
filename_fragment_to_mark_se_reads=".se."):
#STAR.threads = threads
#STAR.path = star_dir
if genome_fasta:
STAR.index(genome_dir,
genome_fasta,
annotation_gtf=annotation_gtf,
junction_tab_file=junction_tab_file_list,
sjdboverhang=sjdboverhang,
genomeSAindexNbases=genomeSAindexNbases,
genomeChrBinNbits=genomeChrBinNbits,
genome_size=genome_size)
sample_list = samples if samples else self.get_sample_list(samples_dir)
FileRoutines.safe_mkdir(output_dir)
for sample in sample_list:
print("Handling %s" % sample)
sample_dir = "%s/%s/" % (samples_dir, sample)
alignment_sample_dir = "%s/%s/" % (output_dir, sample)
FileRoutines.safe_mkdir(alignment_sample_dir)
filetypes, forward_files, reverse_files, se_files = FileRoutines.make_lists_forward_and_reverse_files(
sample_dir,
filename_fragment_to_mark_se_reads=
filename_fragment_to_mark_se_reads,
input_is_se=input_is_se)
#print (se_files)
print("\tAligning reads...")
self.align(
genome_dir,
forward_files if forward_files else se_files,
reverse_read_list=reverse_files,
annotation_gtf=annotation_gtf if not genome_fasta else None,
feature_from_gtf_to_use_as_exon=feature_from_gtf_to_use_as_exon,
exon_tag_to_use_as_transcript_id=
exon_tag_to_use_as_transcript_id,
exon_tag_to_use_as_gene_id=exon_tag_to_use_as_gene_id,
length_of_sequences_flanking_junction=
length_of_sequences_flanking_junction,
junction_tab_file_list=junction_tab_file_list,
three_prime_trim=three_prime_trim,
five_prime_trim=five_prime_trim,
adapter_seq_for_three_prime_clip=
adapter_seq_for_three_prime_clip,
max_mismatch_percent_for_adapter_trimming=
max_mismatch_percent_for_adapter_trimming,
three_prime_trim_after_adapter_clip=
three_prime_trim_after_adapter_clip,
output_type=output_type,
sort_bam=sort_bam,
max_memory_per_thread_for_bam_sorting=
max_memory_per_thread_for_bam_sorting,
include_unmapped_reads_in_bam=include_unmapped_reads_in_bam,
output_unmapped_reads=output_unmapped_reads,
output_dir=alignment_sample_dir,
two_pass_mode=two_pass_mode,
max_intron_length=max_intron_length)
def parallel_search_tandem_repeat(self, query_file, output_prefix, matching_weight=2, mismatching_penalty=7,
indel_penalty=7,
match_probability=80, indel_probability=10, min_alignment_score=50, max_period=500,
report_flanking_sequences=False, splited_fasta_dir="splited_fasta_dir",
splited_result_dir="splited_output", converted_output_dir="converted_output",
max_len_per_file=100000, store_intermediate_files=False, max_repeat_length=None):
work_dir = os.getcwd()
splited_filename = FileRoutines.split_filename(query_file)
self.split_fasta_by_seq_len(query_file, splited_fasta_dir, max_len_per_file=max_len_per_file,
output_prefix=splited_filename[1])
common_options = self.parse_common_options(matching_weight=matching_weight,
mismatching_penalty=mismatching_penalty,
indel_penalty=indel_penalty, match_probability=match_probability,
indel_probability=indel_probability,
min_alignment_score=min_alignment_score,
max_period=max_period,
report_flanking_sequences=report_flanking_sequences,
make_dat_file=True, max_repeat_length=max_repeat_length)
common_options += " -h" # suppress html output
options_list = []
splited_files = os.listdir(splited_fasta_dir)
FileRoutines.safe_mkdir(splited_result_dir)
FileRoutines.safe_mkdir(converted_output_dir)
os.chdir(splited_result_dir)
input_dir = splited_fasta_dir if (splited_fasta_dir[0] == "/") or (splited_fasta_dir[0] == "~") \
else "../%s" % splited_fasta_dir
for filename in splited_files:
file_options = "%s/%s" % (input_dir, filename)
file_options += common_options
options_list.append(file_options)
self.parallel_execute(options_list)
os.chdir(work_dir)
trf_output_file_list = []
for filename in splited_files:
trf_output_file = "%s/%s.%i.%i.%i.%i.%i.%i.%i.dat" % (splited_result_dir, filename,
matching_weight, mismatching_penalty,
indel_penalty, match_probability,
indel_probability,
min_alignment_score, max_period)
trf_output_file_list.append(trf_output_file)
trf_report = self.convert_trf_report(trf_output_file_list, output_prefix)
"""
for suffix in (".rep", ".gff", ".simple.gff", ".short.tab", ".wide.tab", ".with_rep_seqs.gff", ".fasta"):
file_str = ""
merged_file = "%s%s" % (output_prefix, suffix)
for filename in splited_files:
file_str += " %s/%s%s" % (converted_output_dir, filename, suffix)
CGAS.cat(file_str, merged_file)
"""
compress_splited_out_string = "tar czf %s.splited_output.tar.gz %s" % (output_prefix, splited_result_dir)
os.system(compress_splited_out_string)
if not store_intermediate_files:
shutil.rmtree(splited_fasta_dir)
shutil.rmtree(splited_result_dir)
shutil.rmtree(converted_output_dir)
return trf_report
def draw_window_density_distribution(self, count_dict, window_size, output_prefix=None, suptitle="SNP density distribution",
density_multiplicator=1000,
number_of_bins=None, width_of_bins=None,
max_threshold=None, min_threshold=None,
scaffold_black_list=[], scaffold_white_list=[],
sort_scaffolds=False, scaffold_ordered_list=None, subplot_size=4,
per_scaffold_histo_dir="per_scaffold_histo_dir/",
subplot_tuple=None, share_x_axis=True, share_y_axis=True,
extensions=("png",), show_mean_and_median=True):
"""
scaffold_threshold: if number of scaffolds is higher draw only separated_histograms
"""
samples_list = count_dict.keys()
final_scaffold_list = self.get_filtered_scaffold_list(count_dict,
scaffold_black_list=scaffold_black_list,
sort_scaffolds=sort_scaffolds,
scaffold_ordered_list=scaffold_ordered_list,
scaffold_white_list=scaffold_white_list)
scaffold_number = len(final_scaffold_list)
FileRoutines.safe_mkdir(per_scaffold_histo_dir)
xlabel = "Number of SNPs"
ylabel = "Number of windows"
scaled_count_dict = OrderedDict()
empty_windows_scaffold_dict = OrderedDict()
for scaffold in final_scaffold_list:
for sample in count_dict:
if scaffold not in count_dict[sample]:
continue
empty_windows_scaffold_dict[scaffold] = np.zeros(len(count_dict[sample][scaffold]))
break
for sample in samples_list:
scaled_count_dict[sample] = OrderedDict()
for scaffold in final_scaffold_list:
if scaffold not in count_dict[sample]:
scaled_count_dict[sample][scaffold] = empty_windows_scaffold_dict[scaffold]
scaled_count_dict[sample][scaffold] = np.array(map(float, count_dict[sample][scaffold])) * density_multiplicator / window_size
print("Drawing separated histograms for each scaffold...")
extended_label_dict = OrderedDict()
for scaffold in final_scaffold_list:
print("Drawing histogram for scaffold %s" % scaffold)
#scaffold_data = [scaled_count_dict[sample][scaffold] if scaffold in scaled_count_dict[sample] else empty_windows_scaffold_dict[scaffold] for sample in samples_list]
scaffold_data = [scaled_count_dict[sample][scaffold] for sample in samples_list]
out_prefix = "%s/%s.%s" % (per_scaffold_histo_dir, output_prefix, scaffold) if output_prefix else "%s/%s" % (per_scaffold_histo_dir, scaffold)
for sample in samples_list:
median = np.median(scaled_count_dict[sample][scaffold])
mean = np.mean(scaled_count_dict[sample][scaffold])
extended_label = "%s: Med. %.2f, Avg: %.2f" % (sample, float(median), float(mean))
print(extended_label)
if scaffold in extended_label_dict:
extended_label_dict[scaffold].append(extended_label)
else:
extended_label_dict[scaffold] = [extended_label]
#print scaffold_data
self.draw_histogram(scaffold_data, output_prefix=out_prefix, number_of_bins=number_of_bins,
width_of_bins=width_of_bins,
max_threshold=max_threshold, min_threshold=min_threshold,
xlabel=xlabel, ylabel=ylabel,
title=scaffold, extensions=extensions, ylogbase=None, subplot=None, suptitle=None,
close_figure=True,
data_label_list=extended_label_dict[scaffold] if show_mean_and_median else samples_list)
#print scaled_count_dict
print("Drawing histograms for all scaffolds on same figure...")
data = list(recursive_generator(scaled_count_dict))
min_value = min(data) if data else 0
max_value = max(data) if data else 0
#print len(scaled_count_dict)
#print data
bin_array = self.generate_bin_array(data, y_list=None, bin_number=number_of_bins,
bin_width=width_of_bins, bin_array=None,
min_x_value=min_threshold, max_x_value=max_threshold, min_y_value=None,
max_y_value=None, add_max_value=True)
plt.suptitle(suptitle)
if subplot_tuple is None:
side = math.sqrt(scaffold_number)
rounded_side = int(side)
side = rounded_side + 1 if side % rounded_side else rounded_side
subplot_tupleee = (side, side)
#print subplot_tupleee
else:
subplot_tupleee = subplot_tuple
if len(subplot_tupleee) != 2:
raise ValueError("Subplot tuple should contain exactly two values, not %i!" % len(subplot_tuple))
if not (isinstance(subplot_tuple[0], int) and isinstance(subplot_tuple[1], int)):
raise ValueError("Subplot tuple should contain two values, not (%s, %s)!" % (str(type(subplot_tuple[0])),
str(type(subplot_tuple[1]))))
figure = plt.figure(256, figsize=(subplot_size * subplot_tupleee[0],
subplot_size * subplot_tupleee[1]),
dpi=200)
print (subplot_size * subplot_tupleee[0], subplot_size * subplot_tupleee[1])
number_of_subplots = subplot_tupleee[0] * subplot_tupleee[1]
subplot_list = []
for dataset_index in range(0, len(final_scaffold_list)):
scaffold = final_scaffold_list[dataset_index]
if dataset_index > 0:
if share_x_axis and share_y_axis:
subplot_list.append(figure.add_subplot(subplot_tupleee[0],
subplot_tupleee[1],
dataset_index + 1,
sharex=subplot_list[0],
sharey=subplot_list[0]))
elif share_x_axis:
subplot_list.append(figure.add_subplot(subplot_tupleee[0],
subplot_tupleee[1],
dataset_index + 1,
sharex=subplot_list[0]))
elif share_y_axis:
subplot_list.append(figure.add_subplot(subplot_tupleee[0],
subplot_tupleee[1],
dataset_index + 1,
sharex=subplot_list[0],
sharey=subplot_list[0]))
else:
subplot_list.append(figure.add_subplot(subplot_tupleee[0],
subplot_tupleee[1],
dataset_index + 1))
else:
subplot_list.append(figure.add_subplot(subplot_tupleee[0],
subplot_tupleee[1],
dataset_index + 1))
#print dataset_index + 1
#print subplot_tupleee[0] * (subplot_tupleee[1] - 1)
#print ((dataset_index + 1) > (subplot_tupleee[0] * (subplot_tupleee[1] - 1)))
histo = self.draw_histogram([scaled_count_dict[sample][scaffold] for sample in samples_list],
number_of_bins=None,
width_of_bins=None, max_threshold=None,
min_threshold=None,
xlabel=xlabel if ((dataset_index + 1) > (subplot_tupleee[0] * (subplot_tupleee[1] - 1))) else None,
ylabel=ylabel if ((dataset_index + 1) % subplot_tupleee[0]) == 1 else None,
title=scaffold, extensions=("png",), ylogbase=None,
subplot=subplot_list[dataset_index],
suptitle=None,
data_label_list=extended_label_dict[scaffold] if show_mean_and_median else samples_list,
bin_array=bin_array)
plt.xlim(xmin=min_threshold if min_threshold and (min_threshold >= min_value) else min_value,
xmax=max_threshold if max_threshold and (max_threshold <= max_value) else max_value)
#print histo
"""
if output_prefix:
output_histo_file = "%s.%s.%shisto" % (output_prefix,
dataset_index if parameters[8] is None else parameters[10],
("log%i." % parameters[7]) if parameters[7] else "")
np.savetxt(output_histo_file, histo, fmt="%i\t%i")
"""
plt.tight_layout()
if output_prefix:
for ext in extensions:
plt.savefig("%s.%s" % (output_prefix, ext))
plt.close(figure)
print("Drawing combined histogram for all scaffolds...")
combined_count_dict = OrderedDict()
extended_combined_label_list = []
for sample in samples_list:
combined_count_dict[sample] = []
for scaffold in count_dict[sample]:
combined_count_dict[sample] = combined_count_dict[sample] + count_dict[sample][scaffold]
combined_count_dict[sample] = np.array(map(float, combined_count_dict[sample]))* density_multiplicator / window_size
median = np.median(combined_count_dict[sample])
mean = np.mean(combined_count_dict[sample])
extended_label = "%s: Med. %.2f, Avg: %.2f" % (sample, float(median), float(mean))
print(extended_label)
extended_combined_label_list.append(extended_label)
#print combined_count_dict
figure = plt.figure(384, figsize=(8,8))
self.draw_histogram([combined_count_dict[sample] for sample in combined_count_dict],
output_prefix="%s.combined" % output_prefix if output_prefix else "combined",
number_of_bins=number_of_bins,
width_of_bins=width_of_bins,
max_threshold=max_threshold, min_threshold=min_threshold,
xlabel=xlabel, ylabel=ylabel,
title="SNP density distribution(all scaffolds)",
extensions=extensions, ylogbase=None, subplot=None, suptitle=None,
close_figure=True, data_label_list=extended_combined_label_list if show_mean_and_median else samples_list)
def parallel_predict(self,
species,
genome_file,
output,
strand="both",
gene_model=None,
output_gff3=True,
other_options="",
split_dir="splited_input",
splited_output_dir="splited_output_dir",
config_dir=None,
combine_output_to_single_file=True,
use_softmasking=None,
hints_file=None,
extrinsicCfgFile=None,
predict_UTR=None,
external_process_pool=None,
async_run=False,
min_intron_len=None,
parsing_mode="parse"):
common_options = self.parse_options(species,
genome_file="",
strand=strand,
gene_model=gene_model,
output_gff3=output_gff3,
other_options=other_options,
config_dir=config_dir,
use_softmasking=use_softmasking,
hints_file=hints_file,
extrinsicCfgFile=extrinsicCfgFile,
predict_UTR=predict_UTR,
min_intron_len=min_intron_len)
splited_dir = FileRoutines.check_path(split_dir)
splited_out_dir = FileRoutines.check_path(splited_output_dir)
FileRoutines.safe_mkdir(splited_dir)
FileRoutines.safe_mkdir(splited_out_dir)
self.split_fasta_by_seq_len(genome_file,
splited_dir,
parsing_mode=parsing_mode)
input_list_of_files = sorted(os.listdir(splited_dir))
list_of_output_files = []
options_list = []
for filename in input_list_of_files:
input_file = "%s%s" % (splited_dir, filename)
output_file = "%s%s.gff" % (splited_out_dir, filename)
list_of_output_files.append(output_file)
options = common_options
options += " %s" % input_file
options += " > %s" % output_file
options_list.append(options)
self.parallel_execute(options_list,
external_process_pool=external_process_pool,
async_run=async_run)
if combine_output_to_single_file:
CGAS.cat(list_of_output_files, output=output)