<gh_stars>1-10
# description: scan motifs and get motif sets (co-occurring motifs) back

import os
import h5py
import logging

from tronn.interpretation.inference import run_inference
from tronn.interpretation.variants import get_differential_variants
from tronn.interpretation.variants import annotate_variants
from tronn.preprocess.variants import generate_new_fasta
from tronn.util.h5_utils import add_pwm_names_to_h5
from tronn.util.utils import DataKeys


def run(args):
    """command to analyze variants
    """
    # setup
    logger = logging.getLogger(__name__)
    logger.info("Analyzing variants")
    if args.tmp_dir is not None:
        os.system('mkdir -p {}'.format(args.tmp_dir))
    else:
        args.tmp_dir = args.out_dir

    # adjust for ataloader
    args.targets = []
    args.target_indices = []
    args.filter_targets = []
    args.singleton_filter_targets = []
    args.dataset_examples = None
    args.processed_inputs = False
    args.fifo = True
        
    # set up ref fasta
    args.ref_fasta = "{}/{}.ref.fasta".format(
        args.tmp_dir,
        os.path.basename(args.fasta.split(".fa")[0]))
    if not os.path.isfile(args.ref_fasta):
        generate_new_fasta(args.vcf_file, args.fasta, args.ref_fasta)

    # set up alt fasta
    args.alt_fasta = "{}/{}.alt.fasta".format(
        args.tmp_dir,
        os.path.basename(args.fasta.split(".fa")[0]))
    if not os.path.isfile(args.alt_fasta):
        generate_new_fasta(args.vcf_file, args.fasta, args.alt_fasta, ref=False)

    # collect a prediction sample if ensemble (for cross model quantile norm)
    # always need to do this if you're repeating backprop
    if args.model["name"] == "ensemble":
        true_sample_size = args.sample_size
        args.sample_size = 1000
        run_inference(args, warm_start=True)
        args.sample_size = true_sample_size
    
    # run inference
    inference_files = run_inference(args)

    # add in PWM names to the datasets
    for inference_file in inference_files:
        add_pwm_names_to_h5(
            inference_file,
            [pwm.name for pwm in args.pwm_list],
            other_keys=[DataKeys.FEATURES])

    # and mark which ones are differential
    get_differential_variants(inference_files[0])

    # and plot out with R
    plot_cmd = "Rscript ~/git/tronn/R/plot-h5.variants.R {} {}/{}".format(
        inference_files[0], args.out_dir, args.prefix)
    print plot_cmd
    #os.system(plot_cmd)

    # and annotate the differential
    #annotate_variants(inference_files[0], args.pwm_list)
    
    return None


# description: scan motifs and get motif sets (co-occurring motifs) back

import os
import h5py
import logging

from tronn.interpretation.inference import run_inference
from tronn.interpretation.variants import get_differential_variants
from tronn.interpretation.variants import annotate_variants
from tronn.preprocess.variants import generate_new_fasta
from tronn.util.h5_utils import add_pwm_names_to_h5
from tronn.util.utils import DataKeys


def run(args):
    """command to analyze variants
    """
    # setup
    logger = logging.getLogger(__name__)
    logger.info("Analyzing variants")
    if args.tmp_dir is not None:
        os.system('mkdir -p {}'.format(args.tmp_dir))
    else:
        args.tmp_dir = args.out_dir

    # adjust for ataloader
    args.targets = []
    args.target_indices = []
    args.filter_targets = []
    args.singleton_filter_targets = []
    args.dataset_examples = None
    args.processed_inputs = False
    args.fifo = True
        
    # set up ref fasta
    args.ref_fasta = "{}/{}.ref.fasta".format(
        args.tmp_dir,
        os.path.basename(args.fasta.split(".fa")[0]))
    if not os.path.isfile(args.ref_fasta):
        generate_new_fasta(args.vcf_file, args.fasta, args.ref_fasta)

    # set up alt fasta
    args.alt_fasta = "{}/{}.alt.fasta".format(
        args.tmp_dir,
        os.path.basename(args.fasta.split(".fa")[0]))
    if not os.path.isfile(args.alt_fasta):
        generate_new_fasta(args.vcf_file, args.fasta, args.alt_fasta, ref=False)

    # collect a prediction sample if ensemble (for cross model quantile norm)
    # always need to do this if you're repeating backprop
    if args.model["name"] == "ensemble":
        true_sample_size = args.sample_size
        args.sample_size = 1000
        run_inference(args, warm_start=True)
        args.sample_size = true_sample_size
    
    # run inference
    inference_files = run_inference(args)

    # add in PWM names to the datasets
    for inference_file in inference_files:
        add_pwm_names_to_h5(
            inference_file,
            [pwm.name for pwm in args.pwm_list],
            other_keys=[DataKeys.FEATURES])

    # and mark which ones are differential
    get_differential_variants(inference_files[0])

    # and plot out with R
    plot_cmd = "Rscript ~/git/tronn/R/plot-h5.variants.R {} {}/{}".format(
        inference_files[0], args.out_dir, args.prefix)
    print plot_cmd
    #os.system(plot_cmd)

    # and annotate the differential
    #annotate_variants(inference_files[0], args.pwm_list)
    
    return None
# description: scan motifs and get motif sets (co-occurring motifs) back command to analyze variants # setup # adjust for ataloader # set up ref fasta # set up alt fasta # collect a prediction sample if ensemble (for cross model quantile norm) # always need to do this if you're repeating backprop # run inference # add in PWM names to the datasets # and mark which ones are differential # and plot out with R #os.system(plot_cmd) # and annotate the differential #annotate_variants(inference_files[0], args.pwm_list)