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How to use looks_like method in Sure

Best Python code snippet using sure_python

detect_format.py

Source:detect_format.py

1#!/usr/bin/python2# Filename: detect_format.py3import csv4import re5import os6import sys7from optparse import OptionParser8# A bit of path manipulation to import autozip.py from ../utils/9GETEV_MAIN_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))10if not GETEV_MAIN_PATH in sys.path:11    sys.path.insert(1, GETEV_MAIN_PATH)12del GETEV_MAIN_PATH13from utils import autozip14MAX_LINES_CHECKED = 10015def detect_format(file_input):16    """Detect the genetic data format of a file.17    Takes a path to a file, or a string generator (e.g. a filehandle).18    Tries to match one of the following:19      23ANDME: 23andme (microarray genotyping)20      CGIVAR: Complete Genomics var file21      deCODEme: deCODEme (microarray genotyping)22      GFF: General Feature Format23      VCF: Variant Call Format (only tested for 23andme exome data)24    """25    looks_like = dict()26    if isinstance(file_input, str):27        try:28            f_in = autozip.file_open(file_input, 'r')29        except AssertionError:30            f_in = autozip.file_open(file_input, 'r', 'deCODEme_scan.csv')31            print "deCODEme archive (deCODEme) detected"32            looks_like['deCODEme'] = True33    else:34        f_in = file_input35    line_count = 036    for line in f_in:37        line_count += 138        if any([looks_like[x] for x in looks_like.keys()]):39            break    40        if line_count > MAX_LINES_CHECKED:41            break42        # Check comment lines, if they exist, for information on file type.43        if re.match('#', line):44            if re.match(r'#TYPE.*VAR-ANNOTATION', line):45                print "Complete Genomics var file format (CGIVAR) detected"46                looks_like['CGIVAR'] = True47            if re.match(r'##gff-version', line):48                print "General Feature Format (GFF) detected"49                looks_like['GFF'] = True50            if re.match(r'# This data file generated by 23andMe', line):51                print "23andme microarray genotyping data (23ANDME) detected"52                looks_like['23ANDME'] = True53            if re.match(r'##fileformat=VCFv4', line):54                print "Variant Call Format (VCF) detected"55                looks_like['VCF'] = True56        # Look at other lines and decide based on their format.57        tsv_data = line.split('\t')58        csv_data = list(csv.reader([line]))[0]59        if ( len(csv_data) > 5 and60             re.match(r'rs', csv_data[0]) and61             re.match(r'[ACGT]', csv_data[1]) and62             re.match(r'[0-9]', csv_data[3]) and63             re.match(r'[+-]', csv_data[4]) and64             re.match(r'[ACGT]', csv_data[5]) ):65            print "deCODEme microarray genotyping data (deCODEme) guessed"66            looks_like['deCODEme'] = True67        if ( len(csv_data) > 3 and68             re.match(r'rs', csv_data[0]) and69             re.match(r'[0-9]', csv_data[2]) and70             re.match(r'[ACGT]', csv_data[3]) ):71            print "Family Tree DNA genotyping data (FTDNA) guessed"72            looks_like['FTDNA'] = True73        if ( len(tsv_data) > 3 and74             re.match(r'rs', tsv_data[0]) and 75             re.match(r'[0-9]', tsv_data[2]) and76             re.match(r'[ACGT][ACGT]', tsv_data[3]) ):77            print "23andme microarray genotyping data (23ANDME) guessed"78            looks_like['23ANDME'] = True79        if ( len(tsv_data) > 6 and80             re.match(r'chr', tsv_data[3]) and 81             re.match(r'[0-9]', tsv_data[4]) and 82             re.match(r'[0-9]', tsv_data[5]) and83             (tsv_data[6] == "no-call" or tsv_data[6] == "ref") ):84            print "Complete Genomics var file format (CGIvar) guessed"85            looks_like['CGIVAR'] = True86        if ( len(tsv_data) > 6 and87             re.match(r'[0-9]', tsv_data[3]) and88             re.match(r'[0-9]', tsv_data[4]) and89             tsv_data[6] == "+" ):90            print "General Feature Format (GFF) guessed"91            looks_like['GFF'] = True92        if ( len(tsv_data) > 7 and93             re.match(r'[0-9]', tsv_data[1]) and94             re.match(r'[ACGT]', tsv_data[3]) and95             re.match(r'[ACGT]', tsv_data[4]) and96             len(tsv_data[7].split(';')) > 2 ):97            print "Variant Call Format (VCF) guessed"98            looks_like['VCF'] = True99    100    if isinstance(file_input, str):101        f_in.close()102    if any([looks_like[x] for x in looks_like.keys()]):103        return [x for x in looks_like.keys() if looks_like[x]][0]104    else:105        return 'UNKNOWN'106def main():107    # Parse options108    usage = ("\n%prog -i inputfile\n"109             "%prog < inputfile")110    parser = OptionParser(usage=usage)111    parser.add_option("-i", "--input", dest="inputfile",112                      help="Read genetic data from INFILE", metavar="INFILE")113    options, args = parser.parse_args()114    # Handle input115    if sys.stdin.isatty():  # false if data is piped in116        var_input = options.inputfile117    else:118        var_input = sys.stdin119    # Handle output120    format_type = detect_format(var_input)121    print format_type122if __name__ == "__main__":...

test_standardcomparator.py

Source:test_standardcomparator.py

...14    comp1 = InteratomicDistanceComparator(n_top=3,15                                          pair_cor_cum_diff=0.03,16                                          pair_cor_max=0.7,17                                          dE=0.3)18    assert comp1.looks_like(a1, a2)19    comp2 = InteratomicDistanceComparator(n_top=3,20                                          pair_cor_cum_diff=0.03,21                                          pair_cor_max=0.7,22                                          dE=0.15)23    assert not comp2.looks_like(a1, a2)24    comp3 = InteratomicDistanceComparator(n_top=3,25                                          pair_cor_cum_diff=0.02,26                                          pair_cor_max=0.7,27                                          dE=0.3)28    assert not comp3.looks_like(a1, a2)29    hard_E_comp = EnergyComparator(dE=1.0)30    assert hard_E_comp.looks_like(a1, a2)31    soft_E_comp = EnergyComparator(dE=.01)32    assert not soft_E_comp.looks_like(a1, a2)33    set_raw_score(a1, .1)34    set_raw_score(a2, .27)35    rs_comp = RawScoreComparator(0.15)36    assert not rs_comp.looks_like(a1, a2)37    comp1 = SequentialComparator([hard_E_comp, rs_comp], [0, 0])38    assert not comp1.looks_like(a1, a2)39    comp2 = SequentialComparator([hard_E_comp, rs_comp], [0, 1])...

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