comparison filter_kw_val.py @ 6:b4641c0f8a82 draft

planemo upload commit 77279e994f5751c6cd9aa165aa0604db3d241271-dirty

5:33ca9ba2495a

import argparse, re, csv

def options():
    """
    Parse options:
        -i, --input     Input filename and boolean value if the file contains header ["filename,true/false"]

    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
        return False
    else:
        raise argparse.ArgumentTypeError('Boolean value expected.')

#Check if a variable is a float or an integer
def is_number(number_format, n):
    float_format = re.compile(r"^[-]?[0-9][0-9]*.?[0-9]+$")
    int_format = re.compile(r"^[-]?[0-9][0-9]*$")
    scientific_number = re.compile(r"^[-+]?[\d]+\.?[\d]*[Ee](?:[-+]?[\d]+)?$")

    if args.kw_file:
        key_files = args.kw_file
        for kf in key_files:
            header = str_to_bool(kf[1])
            ncol = column_from_txt(kf[2]) 
            keywords = read_keywords_file(kf[0],header,ncol)
            results_dict=filter_keyword(csv_file, header, results_dict, keywords, kf[3], kf[4])

    if args.value:
        for v in args.value:
            v[0] = v[0].replace(",",".")
            v[2] = operator_dict[v[2]]
            if is_number("float", v[0]):
                csv_file = comma_number_to_float(csv_file,column_from_txt(v[1]),header)
                results_dict = filter_value(csv_file, header, results_dict, v[0], v[1], v[2])
            else:
                raise ValueError("Please enter a number in filter by value")

    if args.values_range:
        for vr in args.values_range:
            vr[:2] = [value.replace(",",".") for value in vr[:2]]
            csv_file = comma_number_to_float(csv_file,column_from_txt(vr[2]),header)
            if (is_number("float", vr[0]) or is_number("int", vr[0])) and (is_number("float",vr[1]) or is_number("int",vr[1])):
                results_dict = filter_values_range(csv_file, header, results_dict, vr[0], vr[1], vr[2], vr[3])

    remaining_lines=[]
    filtered_lines=[]

                        remaining_lines.append(line)

    #sort of results by column
    if args.sort_col :
        sort_col=args.sort_col.split(",")[0]
        sort_col=column_from_txt(sort_col)
        reverse=str_to_bool(args.sort_col.split(",")[1])
        remaining_lines= sort_by_column(remaining_lines,sort_col,reverse,header)
        filtered_lines = sort_by_column(filtered_lines,sort_col,reverse,header)

    #swap lists of lines (files) if 'keep' option selected

    return tab

#seek for keywords in rows of csvfile, return a dictionary of boolean (true if keyword found, false otherwise) 
def filter_keyword(csv_file, header, results_dict, keywords, ncol, match):
    match=str_to_bool(match)
    ncol=column_from_txt(ncol)
    if type(keywords) != list : keywords = keywords.upper().split()            # Split list of filter keyword

    for id_line,line in enumerate(csv_file):
        if header is True and id_line == 0 : continue
        keyword_inline = line[ncol].replace('"', "").split(";")

#filter ba determined value in rows of csvfile, return a dictionary of boolean (true if value filtered, false otherwise)
def filter_value(csv_file, header, results_dict, filter_value, ncol, opt):

    filter_value = float(filter_value)
    ncol=column_from_txt(ncol)
    nb_string=0

    for id_line,line in enumerate(csv_file):
        if header is True and id_line == 0 : continue
        value = line[ncol].replace('"', "").replace(",",".").strip()

#filter ba determined value in rows of csvfile, return a dictionary of boolean (true if value filtered, false otherwise)
def filter_values_range(csv_file, header, results_dict, bottom_value, top_value, ncol, inclusive):
    inclusive=str_to_bool(inclusive)
    bottom_value = float(bottom_value)
    top_value=float(top_value)
    ncol=column_from_txt(ncol)
    nb_string=0

    for id_line, line in enumerate(csv_file):
        if header is True and id_line == 0 : continue
        value = line[ncol].replace('"', "").replace(",",".").strip()

        if inclusive : print ('The filter "'+str(bottom_value)+' <= x <= '+str(top_value)+'" can not be applied on the column '+str(ncol+1))
        else : print ('The filter "'+str(bottom_value)+' < x < '+str(top_value)+'" can not be applied on the column '+str(ncol+1))

    return results_dict 

def column_from_txt(ncol):
    if is_number("int", ncol.replace("c", "")): 
        ncol = int(ncol.replace("c", "")) - 1 
    else:
        raise ValueError("Please specify the column where "
                         "you would like to apply the filter "
                         "with valid format")
    return ncol

#return True if value is in the determined values, false otherwise
def value_compare(value,filter_value,opt):
    test_value=False

6:b4641c0f8a82

import argparse, re, csv, sys

def options():
    """
    Parse options:
        -i, --input     Input filename and boolean value if the file contains header ["filename,true/false"]

    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
        return False
    else:
        raise argparse.ArgumentTypeError('Boolean value expected.')

def proper_ncol (ncol,file):
    if ncol not in range(len(file[0])):
        print("Column "+str(ncol+1)+" not found in input file")
        #traceback.print_exc(file=sys.stdout)
        sys.exit(1)

#Check if a variable is a float or an integer
def is_number(number_format, n):
    float_format = re.compile(r"^[-]?[0-9][0-9]*.?[0-9]+$")
    int_format = re.compile(r"^[-]?[0-9][0-9]*$")
    scientific_number = re.compile(r"^[-+]?[\d]+\.?[\d]*[Ee](?:[-+]?[\d]+)?$")

    if args.kw_file:
        key_files = args.kw_file
        for kf in key_files:
            header = str_to_bool(kf[1])
            ncol = column_from_txt(kf[2],csv_file) 
            keywords = read_keywords_file(kf[0],header,ncol)
            results_dict=filter_keyword(csv_file, header, results_dict, keywords, kf[3], kf[4])

    if args.value:
        for v in args.value:
            v[0] = v[0].replace(",",".")
            v[2] = operator_dict[v[2]]
            if is_number("float", v[0]):
                csv_file = comma_number_to_float(csv_file,column_from_txt(v[1],csv_file),header)
                results_dict = filter_value(csv_file, header, results_dict, v[0], v[1], v[2])
            else:
                raise ValueError("Please enter a number in filter by value")

    if args.values_range:
        for vr in args.values_range:
            vr[:2] = [value.replace(",",".") for value in vr[:2]]
            csv_file = comma_number_to_float(csv_file,column_from_txt(vr[2],csv_file),header)
            if (is_number("float", vr[0]) or is_number("int", vr[0])) and (is_number("float",vr[1]) or is_number("int",vr[1])):
                results_dict = filter_values_range(csv_file, header, results_dict, vr[0], vr[1], vr[2], vr[3])

    remaining_lines=[]
    filtered_lines=[]

                        remaining_lines.append(line)

    #sort of results by column
    if args.sort_col :
        sort_col=args.sort_col.split(",")[0]
        sort_col=column_from_txt(sort_col,csv_file)
        reverse=str_to_bool(args.sort_col.split(",")[1])
        remaining_lines= sort_by_column(remaining_lines,sort_col,reverse,header)
        filtered_lines = sort_by_column(filtered_lines,sort_col,reverse,header)

    #swap lists of lines (files) if 'keep' option selected

    return tab

#seek for keywords in rows of csvfile, return a dictionary of boolean (true if keyword found, false otherwise) 
def filter_keyword(csv_file, header, results_dict, keywords, ncol, match):
    match=str_to_bool(match)
    ncol=column_from_txt(ncol,csv_file)
    if type(keywords) != list : keywords = keywords.upper().split()            # Split list of filter keyword

    for id_line,line in enumerate(csv_file):
        if header is True and id_line == 0 : continue
        keyword_inline = line[ncol].replace('"', "").split(";")

#filter ba determined value in rows of csvfile, return a dictionary of boolean (true if value filtered, false otherwise)
def filter_value(csv_file, header, results_dict, filter_value, ncol, opt):

    filter_value = float(filter_value)
    ncol=column_from_txt(ncol,csv_file)
    nb_string=0

    for id_line,line in enumerate(csv_file):
        if header is True and id_line == 0 : continue
        value = line[ncol].replace('"', "").replace(",",".").strip()

#filter ba determined value in rows of csvfile, return a dictionary of boolean (true if value filtered, false otherwise)
def filter_values_range(csv_file, header, results_dict, bottom_value, top_value, ncol, inclusive):
    inclusive=str_to_bool(inclusive)
    bottom_value = float(bottom_value)
    top_value=float(top_value)
    ncol=column_from_txt(ncol,csv_file)
    nb_string=0

    for id_line, line in enumerate(csv_file):
        if header is True and id_line == 0 : continue
        value = line[ncol].replace('"', "").replace(",",".").strip()

        if inclusive : print ('The filter "'+str(bottom_value)+' <= x <= '+str(top_value)+'" can not be applied on the column '+str(ncol+1))
        else : print ('The filter "'+str(bottom_value)+' < x < '+str(top_value)+'" can not be applied on the column '+str(ncol+1))

    return results_dict 

def column_from_txt(ncol,file):
    if is_number("int", ncol.replace("c", "")): 
        ncol = int(ncol.replace("c", "")) - 1 
    else:
        raise ValueError("Please specify the column where "
                         "you would like to apply the filter "
                         "with valid format")

    proper_ncol (ncol,file)
    
    return ncol

#return True if value is in the determined values, false otherwise
def value_compare(value,filter_value,opt):
    test_value=False