Mercurial > repos > proteore > proteore_filter_keywords_values
view filter_kw_val.py @ 2:52a7afd01c6d draft
planemo upload commit 9af2cf12c26c94e7206751ccf101a3368f92d0ba
author | proteore |
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date | Tue, 18 Dec 2018 09:25:11 -0500 |
parents | a55e8b137c6b |
children | 2080e2a4f209 |
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import argparse, re, csv def options(): """ Parse options: -i, --input Input filename and boolean value if the file contains header ["filename,true/false"] --kw Keyword to be filtered, the column number where this filter applies, boolean value if the keyword should be filtered in exact ["keyword,ncol,true/false"]. This option can be repeated: --kw "kw1,c1,true" --kw "kw2,c1,false" --kw "kw3,c2,true" --kwfile A file that contains keywords to be filter, the column where this filter applies and boolean value if the keyword should be filtered in exact ["filename,ncol,true/false"] --value The value to be filtered, the column number where this filter applies and the operation symbol ["value,ncol,=/>/>=/</<=/!="] --values_range range of values to be keep, example : --values_range 5 20 c1 true --operator The operator used to filter with several keywords/values : AND or OR --o --output The output filename --filtered_file The file contains removed lines -s --sort_col Used column to sort the file, ",true" for reverse sorting, ",false" otherwise example : c1,false """ parser = argparse.ArgumentParser() parser.add_argument("-i", "--input", help="Input file", required=True) parser.add_argument("--kw", nargs="+", action="append", help="") parser.add_argument("--kw_file", nargs="+", action="append", help="") parser.add_argument("--value", nargs="+", action="append", help="") parser.add_argument("--values_range", nargs="+", action="append", help="") parser.add_argument("--operator", default="OR", type=str, choices=['AND','OR'],help='') parser.add_argument("-o", "--output", default="output.txt") parser.add_argument("--filtered_file", default="filtered_output.txt") parser.add_argument("-s","--sort_col", help="") args = parser.parse_args() filters(args) def str_to_bool(v): if v.lower() in ('yes', 'true', 't', 'y', '1'): return True 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]+$") int_format = re.compile(r"^[-]?[0-9][0-9]*$") test = "" if number_format == "int": test = re.match(int_format, n) elif number_format == "float": test = re.match(float_format, n) if test: return True #Filter the document def filters(args): filename = args.input.split(",")[0] header = str_to_bool(args.input.split(",")[1]) csv_file = blank_to_NA(read_file(filename)) results_dict = {} if args.kw: keywords = args.kw for k in keywords: results_dict=filter_keyword(csv_file, header, results_dict, k[0], k[1], k[2]) 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(",",".") if is_number("float", v[0]): csv_file = comma_number_to_float(csv_file,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,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=[] if header is True : remaining_lines.append(csv_file[0]) filtered_lines.append(csv_file[0]) if results_dict == {} : #no filter used remaining_lines.extend(csv_file[1:]) else : for id_line,line in enumerate(csv_file) : if id_line in results_dict : #skip header and empty lines if args.operator == 'OR' : if any(results_dict[id_line]) : filtered_lines.append(line) else : remaining_lines.append(line) elif args.operator == "AND" : if all(results_dict[id_line]) : filtered_lines.append(line) else : 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) # Write results to output with open(args.output,"w") as output : writer = csv.writer(output,delimiter="\t") writer.writerows(remaining_lines) # Write filtered lines to filtered_output with open(args.filtered_file,"w") as filtered_output : writer = csv.writer(filtered_output,delimiter="\t") writer.writerows(filtered_lines) #function to sort the csv_file by value in a specific column def sort_by_column(tab,sort_col,reverse,header): if len(tab) > 1 : #if there's more than just a header or 1 row if header : head=tab[0] tab=tab[1:] #list of empty cells in the column to sort unsortable_lines = [i for i,line in enumerate(tab) if (line[sort_col]=='' or line[sort_col] == 'NA')] unsorted_tab=[ tab[i] for i in unsortable_lines] tab= [line for i,line in enumerate(tab) if i not in unsortable_lines] if only_number(tab,sort_col) and any_float(tab,sort_col) : tab = sorted(tab, key=lambda row: float(row[sort_col]), reverse=reverse) elif only_number(tab,sort_col): tab = sorted(tab, key=lambda row: int(row[sort_col]), reverse=reverse) else : tab = sorted(tab, key=lambda row: row[sort_col], reverse=reverse) tab.extend(unsorted_tab) if header is True : tab = [head]+tab return tab #replace all blank cells to NA def blank_to_NA(csv_file) : tmp=[] for line in csv_file : line = ["NA" if cell=="" or cell==" " or cell=="NaN" else cell for cell in line ] tmp.append(line) return tmp #turn into float a column def comma_number_to_float(csv_file,ncol,header) : ncol = int(ncol.replace("c","")) - 1 if header : tmp=[csv_file[0]] csv_file=csv_file[1:] else : tmp=[] for line in csv_file : line[ncol]=line[ncol].replace(",",".") tmp.append(line) return (tmp) #return True is there is at least one float in the column def any_float(tab,col) : for line in tab : if is_number("float",line[col].replace(",",".")) : return True return False def only_number(tab,col) : for line in tab : if not (is_number("float",line[col].replace(",",".")) or is_number("int",line[col].replace(",","."))) : return False return True #Read the keywords file to extract the list of keywords def read_keywords_file(filename,header,ncol): with open(filename, "r") as csv_file : lines= csv.reader(csv_file, delimiter='\t') lines = blank_to_NA(lines) if (len(lines[0])) > 1 : keywords = [line[ncol] for line in lines] else : keywords= ["".join(key) for key in lines] if header : keywords = keywords[1:] keywords = list(set(keywords)) return keywords # Read input file def read_file(filename): with open(filename,"r") as f : reader=csv.reader(f,delimiter="\t") tab=list(reader) # Remove empty lines (contain only space or new line or "") #[tab.remove(blank) for blank in tab if blank.isspace() or blank == ""] tab=[line for line in tab if len("".join(line).replace(" ","")) !=0 ] 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(";") #Perfect match or not if match is True : found_in_line = any(pid.upper() in keywords for pid in keyword_inline) else: found_in_line = any(ft in pid.upper() for pid in keyword_inline for ft in keywords) #if the keyword is found in line if id_line in results_dict : results_dict[id_line].append(found_in_line) else : results_dict[id_line]=[found_in_line] return results_dict #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() if value.replace(".", "", 1).isdigit(): to_filter=value_compare(value,filter_value,opt) #adding the result to the dictionary if id_line in results_dict : results_dict[id_line].append(to_filter) else : results_dict[id_line]=[to_filter] #impossible to treat (ex : "" instead of a number), we keep the line by default else : nb_string+=1 if id_line in results_dict : results_dict[id_line].append(False) else : results_dict[id_line]=[False] #number of lines in the csv file if header : nb_lines = len(csv_file) -1 else : nb_lines = len(csv_file) #if there's no numeric value in the column if nb_string == nb_lines : print ('No numeric values found in the column '+str(ncol+1)) print ('The filter "'+str(opt)+' '+str(filter_value)+'" can not be applied on the column '+str(ncol+1)) return results_dict #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 value.replace(".", "", 1).isdigit(): value=float(value) if inclusive is True: in_range = not (bottom_value <= value <= top_value) else : in_range = not (bottom_value < value < top_value) #adding the result to the dictionary if id_line in results_dict : results_dict[id_line].append(in_range) else : results_dict[id_line]=[in_range] #impossible to treat (ex : "" instead of a number), we keep the line by default else : nb_string+=1 if id_line in results_dict : results_dict[id_line].append(False) else : results_dict[id_line]=[False] #number of lines in the csv file if header : nb_lines = len(csv_file) -1 else : nb_lines = len(csv_file) #if there's no numeric value in the column if nb_string == nb_lines : print ('No numeric values found in the column '+str(ncol+1)) 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 if opt == "<": if float(value) < filter_value: test_value = True elif opt == "<=": if float(value) <= filter_value: test_value = True elif opt == ">": if float(value) > filter_value: test_value = True elif opt == ">=": if float(value) >= filter_value: test_value = True elif opt == "=": if float(value) == filter_value: test_value = True elif opt == "!=": if float(value) != filter_value: test_value = True return test_value if __name__ == "__main__": options()