Mercurial > repos > proteore > proteore_filter_keywords_values

annotate filter_kw_val.py @ 5:33ca9ba2495a draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
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author proteore
date Tue, 05 Mar 2019 07:37:10 -0500
parents 2080e2a4f209
children b4641c0f8a82
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
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1 import argparse, re, csv
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2
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3 def options():
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4 """
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5 Parse options:
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6 -i, --input Input filename and boolean value if the file contains header ["filename,true/false"]
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7 --kw Keyword to be filtered, the column number where this filter applies,
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8 boolean value if the keyword should be filtered in exact ["keyword,ncol,true/false"].
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9 This option can be repeated: --kw "kw1,c1,true" --kw "kw2,c1,false" --kw "kw3,c2,true"
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10 --kwfile A file that contains keywords to be filter, the column where this filter applies and
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11 boolean value if the keyword should be filtered in exact ["filename,ncol,true/false"]
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12 --value The value to be filtered, the column number where this filter applies and the
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13 operation symbol ["value,ncol,=/>/>=/</<=/!="]
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14 --values_range range of values to be keep, example : --values_range 5 20 c1 true
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15 --operation 'keep' or 'discard' lines concerned by filter(s)
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16 --operator The operator used to filter with several keywords/values : AND or OR
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17 --o --output The output filename
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18 --discarded_lines The file contains removed lines
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19 -s --sort_col Used column to sort the file, ",true" for reverse sorting, ",false" otherwise example : c1,false
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20 """
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21 parser = argparse.ArgumentParser()
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22 parser.add_argument("-i", "--input", help="Input file", required=True)
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23 parser.add_argument("--kw", nargs="+", action="append", help="")
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24 parser.add_argument("--kw_file", nargs="+", action="append", help="")
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25 parser.add_argument("--value", nargs="+", action="append", help="")
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26 parser.add_argument("--values_range", nargs="+", action="append", help="")
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27 parser.add_argument("--operation", default="keep", type=str, choices=['keep','discard'],help='')
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28 parser.add_argument("--operator", default="OR", type=str, choices=['AND','OR'],help='')
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29 parser.add_argument("-o", "--output", default="output.txt")
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30 parser.add_argument("--discarded_lines", default="filtered_output.txt")
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31 parser.add_argument("-s","--sort_col", help="")
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32
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33 args = parser.parse_args()
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34
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35 filters(args)
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36
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37 def str_to_bool(v):
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38 if v.lower() in ('yes', 'true', 't', 'y', '1'):
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39 return True
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40 elif v.lower() in ('no', 'false', 'f', 'n', '0'):
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41 return False
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42 else:
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43 raise argparse.ArgumentTypeError('Boolean value expected.')
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44
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45 #Check if a variable is a float or an integer
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46 def is_number(number_format, n):
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47 float_format = re.compile(r"^[-]?[0-9][0-9]*.?[0-9]+$")
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48 int_format = re.compile(r"^[-]?[0-9][0-9]*$")
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49 scientific_number = re.compile(r"^[-+]?[\d]+\.?[\d]*[Ee](?:[-+]?[\d]+)?$")
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50 test = ""
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51 if number_format == "int":
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52 test = re.match(int_format, n)
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53 elif number_format == "float":
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54 test = re.match(float_format, n)
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55 if test is None : test = re.match(scientific_number,n)
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56
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57 if test:
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58 return True
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59 else :
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60 return False
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61
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62 #Filter the document
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63 def filters(args):
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64 filename = args.input.split(",")[0]
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65 header = str_to_bool(args.input.split(",")[1])
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66 csv_file = blank_to_NA(read_file(filename))
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67 results_dict = {}
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68 operator_dict = { "Equal" : "=" , "Higher" : ">" , "Equal-or-higher" : ">=" , "Lower" : "<" , "Equal-or-lower" : "<=" , "Different" : "!=" }
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69
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70 if args.kw:
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71 keywords = args.kw
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72 for k in keywords:
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73 results_dict=filter_keyword(csv_file, header, results_dict, k[0], k[1], k[2])
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74
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75 if args.kw_file:
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76 key_files = args.kw_file
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77 for kf in key_files:
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78 header = str_to_bool(kf[1])
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79 ncol = column_from_txt(kf[2])
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80 keywords = read_keywords_file(kf[0],header,ncol)
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81 results_dict=filter_keyword(csv_file, header, results_dict, keywords, kf[3], kf[4])
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82
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83 if args.value:
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84 for v in args.value:
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85 v[0] = v[0].replace(",",".")
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86 v[2] = operator_dict[v[2]]
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87 if is_number("float", v[0]):
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88 csv_file = comma_number_to_float(csv_file,column_from_txt(v[1]),header)
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89 results_dict = filter_value(csv_file, header, results_dict, v[0], v[1], v[2])
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90 else:
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91 raise ValueError("Please enter a number in filter by value")
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92
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93 if args.values_range:
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94 for vr in args.values_range:
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95 vr[:2] = [value.replace(",",".") for value in vr[:2]]
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96 csv_file = comma_number_to_float(csv_file,column_from_txt(vr[2]),header)
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97 if (is_number("float", vr[0]) or is_number("int", vr[0])) and (is_number("float",vr[1]) or is_number("int",vr[1])):
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98 results_dict = filter_values_range(csv_file, header, results_dict, vr[0], vr[1], vr[2], vr[3])
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99
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100 remaining_lines=[]
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101 filtered_lines=[]
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102
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103 if header is True :
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104 remaining_lines.append(csv_file[0])
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105 filtered_lines.append(csv_file[0])
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106
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107 if results_dict == {} : #no filter used
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108 remaining_lines.extend(csv_file[1:])
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109 else :
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110 for id_line,line in enumerate(csv_file) :
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111 if id_line in results_dict : #skip header and empty lines
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112 if args.operator == 'OR' :
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113 if any(results_dict[id_line]) :
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114 filtered_lines.append(line)
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115 else :
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116 remaining_lines.append(line)
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117
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118 elif args.operator == "AND" :
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119 if all(results_dict[id_line]) :
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120 filtered_lines.append(line)
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121 else :
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122 remaining_lines.append(line)
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123
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124 #sort of results by column
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125 if args.sort_col :
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126 sort_col=args.sort_col.split(",")[0]
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127 sort_col=column_from_txt(sort_col)
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128 reverse=str_to_bool(args.sort_col.split(",")[1])
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129 remaining_lines= sort_by_column(remaining_lines,sort_col,reverse,header)
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130 filtered_lines = sort_by_column(filtered_lines,sort_col,reverse,header)
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131
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132 #swap lists of lines (files) if 'keep' option selected
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133 if args.operation == "keep" :
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134 swap = remaining_lines, filtered_lines
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135 remaining_lines = swap[1]
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136 filtered_lines = swap[0]
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137
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138 # Write results to output
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139 with open(args.output,"w") as output :
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140 writer = csv.writer(output,delimiter="\t")
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141 writer.writerows(remaining_lines)
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142
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143 # Write filtered lines to filtered_output
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144 with open(args.discarded_lines,"w") as filtered_output :
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145 writer = csv.writer(filtered_output,delimiter="\t")
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146 writer.writerows(filtered_lines)
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147
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148 #function to sort the csv_file by value in a specific column
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149 def sort_by_column(tab,sort_col,reverse,header):
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150
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151 if len(tab) > 1 : #if there's more than just a header or 1 row
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152 if header :
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153 head=tab[0]
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154 tab=tab[1:]
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155
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156 #list of empty cells in the column to sort
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157 unsortable_lines = [i for i,line in enumerate(tab) if (line[sort_col]=='' or line[sort_col] == 'NA')]
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158 unsorted_tab=[ tab[i] for i in unsortable_lines]
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159 tab= [line for i,line in enumerate(tab) if i not in unsortable_lines]
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160
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161 if only_number(tab,sort_col) and any_float(tab,sort_col) :
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162 tab = comma_number_to_float(tab,sort_col,False)
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163 tab = sorted(tab, key=lambda row: float(row[sort_col]), reverse=reverse)
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164 elif only_number(tab,sort_col):
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165 tab = sorted(tab, key=lambda row: int(row[sort_col]), reverse=reverse)
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166 else :
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167 tab = sorted(tab, key=lambda row: row[sort_col], reverse=reverse)
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168
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169 tab.extend(unsorted_tab)
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170 if header is True : tab = [head]+tab
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171
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172 return tab
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173
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174
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175 #replace all blank cells to NA
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176 def blank_to_NA(csv_file) :
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177
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178 tmp=[]
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179 for line in csv_file :
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180 line = ["NA" if cell=="" or cell==" " or cell=="NaN" else cell for cell in line ]
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181 tmp.append(line)
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182
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183 return tmp
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184
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185 #turn into float a column
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186 def comma_number_to_float(csv_file,ncol,header) :
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187 if header :
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188 tmp=[csv_file[0]]
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189 csv_file=csv_file[1:]
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190 else :
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191 tmp=[]
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192
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193 for line in csv_file :
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194 line[ncol]=line[ncol].replace(",",".")
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195 tmp.append(line)
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196
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197 return (tmp)
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198
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199 #return True is there is at least one float in the column
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200 def any_float(tab,col) :
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201
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202 for line in tab :
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203 if is_number("float",line[col].replace(",",".")) :
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204 return True
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205
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206 return False
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207
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208 def only_number(tab,col) :
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209 for line in tab :
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210 if not (is_number("float",line[col].replace(",",".")) or is_number("int",line[col].replace(",","."))) :
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211 return False
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212 return True
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213
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214 #Read the keywords file to extract the list of keywords
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215 def read_keywords_file(filename,header,ncol):
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216 with open(filename, "r") as csv_file :
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217 lines= csv.reader(csv_file, delimiter='\t')
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218 lines = blank_to_NA(lines)
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219 if (len(lines[0])) > 1 : keywords = [line[ncol] for line in lines]
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220 else :
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221 keywords= ["".join(key) for key in lines]
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222 if header : keywords = keywords[1:]
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223 keywords = list(set(keywords))
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224
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225 return keywords
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226
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227 # Read input file
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228 def read_file(filename):
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229 with open(filename,"r") as f :
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230 reader=csv.reader(f,delimiter="\t")
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231 tab=list(reader)
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232
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233 # Remove empty lines (contain only space or new line or "")
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234 #[tab.remove(blank) for blank in tab if blank.isspace() or blank == ""]
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235 tab=[line for line in tab if len("".join(line).replace(" ","")) !=0 ]
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236
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237 return tab
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238
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239 #seek for keywords in rows of csvfile, return a dictionary of boolean (true if keyword found, false otherwise)
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240 def filter_keyword(csv_file, header, results_dict, keywords, ncol, match):
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241 match=str_to_bool(match)
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242 ncol=column_from_txt(ncol)
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243 if type(keywords) != list : keywords = keywords.upper().split() # Split list of filter keyword
0
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244
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245 for id_line,line in enumerate(csv_file):
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246 if header is True and id_line == 0 : continue
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247 keyword_inline = line[ncol].replace('"', "").split(";")
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248
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249 #Perfect match or not
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250 if match is True :
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251 found_in_line = any(pid.upper() in keywords for pid in keyword_inline)
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252 else:
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253 found_in_line = any(ft in pid.upper() for pid in keyword_inline for ft in keywords)
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254
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255 #if the keyword is found in line
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256 if id_line in results_dict : results_dict[id_line].append(found_in_line)
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257 else : results_dict[id_line]=[found_in_line]
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258
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259 return results_dict
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260
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261 #filter ba determined value in rows of csvfile, return a dictionary of boolean (true if value filtered, false otherwise)
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262 def filter_value(csv_file, header, results_dict, filter_value, ncol, opt):
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263
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264 filter_value = float(filter_value)
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265 ncol=column_from_txt(ncol)
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266 nb_string=0
0
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267
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268 for id_line,line in enumerate(csv_file):
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269 if header is True and id_line == 0 : continue
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270 value = line[ncol].replace('"', "").replace(",",".").strip()
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271 if value.replace(".", "", 1).isdigit():
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272 to_filter=value_compare(value,filter_value,opt)
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273
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274 #adding the result to the dictionary
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275 if id_line in results_dict : results_dict[id_line].append(to_filter)
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276 else : results_dict[id_line]=[to_filter]
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277
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278 #impossible to treat (ex : "" instead of a number), we keep the line by default
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279 else :
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280 nb_string+=1
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281 if id_line in results_dict : results_dict[id_line].append(False)
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282 else : results_dict[id_line]=[False]
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283
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284 #number of lines in the csv file
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285 if header : nb_lines = len(csv_file) -1
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286 else : nb_lines = len(csv_file)
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287
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288 #if there's no numeric value in the column
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289 if nb_string == nb_lines :
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290 print ('No numeric values found in the column '+str(ncol+1))
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291 print ('The filter "'+str(opt)+' '+str(filter_value)+'" can not be applied on the column '+str(ncol+1))
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292
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293 return results_dict
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294
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295 #filter ba determined value in rows of csvfile, return a dictionary of boolean (true if value filtered, false otherwise)
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296 def filter_values_range(csv_file, header, results_dict, bottom_value, top_value, ncol, inclusive):
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297 inclusive=str_to_bool(inclusive)
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298 bottom_value = float(bottom_value)
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299 top_value=float(top_value)
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300 ncol=column_from_txt(ncol)
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301 nb_string=0
0
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302
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303 for id_line, line in enumerate(csv_file):
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304 if header is True and id_line == 0 : continue
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305 value = line[ncol].replace('"', "").replace(",",".").strip()
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306 if value.replace(".", "", 1).isdigit():
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307 value=float(value)
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308 if inclusive is True:
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309 in_range = not (bottom_value <= value <= top_value)
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310 else :
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311 in_range = not (bottom_value < value < top_value)
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312
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313 #adding the result to the dictionary
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314 if id_line in results_dict : results_dict[id_line].append(in_range)
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315 else : results_dict[id_line]=[in_range]
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316
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317 #impossible to treat (ex : "" instead of a number), we keep the line by default
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318 else :
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319 nb_string+=1
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320 if id_line in results_dict : results_dict[id_line].append(False)
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321 else : results_dict[id_line]=[False]
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322
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323 #number of lines in the csv file
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324 if header : nb_lines = len(csv_file) -1
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325 else : nb_lines = len(csv_file)
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326
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327 #if there's no numeric value in the column
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328 if nb_string == nb_lines :
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329 print ('No numeric values found in the column '+str(ncol+1))
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330 if inclusive : print ('The filter "'+str(bottom_value)+' <= x <= '+str(top_value)+'" can not be applied on the column '+str(ncol+1))
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331 else : print ('The filter "'+str(bottom_value)+' < x < '+str(top_value)+'" can not be applied on the column '+str(ncol+1))
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332
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333 return results_dict
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334
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335 def column_from_txt(ncol):
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336 if is_number("int", ncol.replace("c", "")):
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337 ncol = int(ncol.replace("c", "")) - 1
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338 else:
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339 raise ValueError("Please specify the column where "
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340 "you would like to apply the filter "
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341 "with valid format")
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342 return ncol
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343
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344 #return True if value is in the determined values, false otherwise
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345 def value_compare(value,filter_value,opt):
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346 test_value=False
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347
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348 if opt == "<":
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349 if float(value) < filter_value:
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350 test_value = True
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351 elif opt == "<=":
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352 if float(value) <= filter_value:
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353 test_value = True
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354 elif opt == ">":
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355 if float(value) > filter_value:
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356 test_value = True
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357 elif opt == ">=":
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358 if float(value) >= filter_value:
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359 test_value = True
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360 elif opt == "=":
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361 if float(value) == filter_value:
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362 test_value = True
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363 elif opt == "!=":
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364 if float(value) != filter_value:
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365 test_value = True
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366
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367 return test_value
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368
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369 if __name__ == "__main__":
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370 options()