Mercurial > repos > miller-lab > genome_diversity
annotate make_gd_file.py @ 32:03c22b722882
remove BeautifulSoup dependency
author | Richard Burhans <burhans@bx.psu.edu> |
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date | Fri, 20 Sep 2013 13:54:23 -0400 |
parents | 8997f2ca8c7a |
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rev | line source |
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1 #!/usr/bin/env python |
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2 |
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3 import base64 |
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4 import json |
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5 import math |
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6 import re |
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7 import sys |
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8 |
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9 identifier_regex = re.compile('[0-9A-Z_a-z]+$') |
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10 |
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11 def unwrap_column_names(string): |
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12 column_names = [] |
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13 string = unwrap_string(string) |
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14 for line in string.split('\n'): |
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15 line = line.strip() |
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16 if is_identifier(line): |
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17 column_names.append(line) |
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18 else: |
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19 die('invalid column name format: {}'.format(line)) |
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20 return column_names |
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21 |
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22 def unwrap_string(string): |
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23 try: |
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24 decoded = base64.b64decode(string) |
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25 except: |
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26 die('invalid base64 string: {}'.format(string)) |
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27 return decoded |
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28 |
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29 def is_identifier(string): |
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30 match = identifier_regex.match(string) |
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31 if match: |
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32 return True |
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33 else: |
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34 return False |
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35 |
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36 def read_individual_names(filename): |
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37 tokens = [] |
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38 names = [] |
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39 with open(filename) as fh: |
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40 for line in fh: |
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41 line = line.rstrip('\r\n') |
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42 elems = line.split() |
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43 |
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44 columns = len(elems) |
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45 if columns == 0: |
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46 continue |
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47 |
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48 first_token = elems[0] |
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49 |
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50 if columns == 1: |
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51 name = first_token |
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52 else: |
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53 keywords = ' '.join(elems[1:]) |
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54 name = ' '.join([first_token, keywords]) |
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55 |
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56 if first_token not in tokens: |
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57 tokens.append(first_token) |
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58 names.append(name) |
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59 else: |
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60 die('duplicate first column entry in Names dataset: {}'.format(first_token)) |
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61 return names |
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62 |
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63 def fold_line(line, maxlen, prefix): |
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64 prefix_len = len(prefix) |
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65 |
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66 lines = [] |
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67 |
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68 while len(line) > maxlen: |
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69 split_points = [] |
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70 state = 0 |
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71 for i in range(maxlen - prefix_len): |
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72 c = line[i] |
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73 if state == 0: |
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74 if c == '"': |
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75 state = 1 |
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76 elif c in [ '{', ':', ',', '}', '[', ']' ]: |
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77 split_points.append(i) |
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78 elif state == 1: |
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79 if c == '"': |
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80 state = 0 |
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81 elif c == '\\': |
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82 state = 2 |
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83 elif state == 2: |
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84 state = 1 |
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85 idx = split_points[-1] |
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86 lines.append('{0}{1}'.format(prefix, line[:idx+1])) |
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87 line = line[idx+1:] |
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88 |
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89 lines.append('{0}{1}'.format(prefix, line)) |
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90 |
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91 return lines |
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92 |
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93 def die(message): |
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94 print >> sys.stderr, message |
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95 sys.exit(1) |
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96 |
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97 ################################################################################ |
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98 |
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99 type_to_columns = { |
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100 'gd_snp':4, |
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101 'gd_genotype':1 |
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102 } |
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103 |
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104 if len(sys.argv) != 12: |
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105 print >> sys.stderr, 'Usage' |
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106 sys.exit(1) |
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107 |
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108 input, scaffold_col, pos_col, ref_col, rPos_col, preamble_arg, names, species_arg, dbkey, output_type, output = sys.argv[1:12] |
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109 |
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110 preamble_column_names = unwrap_column_names(preamble_arg) |
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111 first_individual_column = len(preamble_column_names) + 1 |
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112 |
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113 individual_names = read_individual_names(names) |
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114 |
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115 species = unwrap_string(species_arg) |
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116 if not is_identifier(species): |
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117 die('invalid species format: {}'.format(species)) |
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118 |
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119 if not output_type in type_to_columns: |
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120 die('unknown output type: {}'.format(output_type)) |
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121 columns_per_individual = type_to_columns[output_type] |
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122 |
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123 jdict = {} |
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124 |
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125 column_names = preamble_column_names[:] |
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126 for i in range(1, len(individual_names) + 1): |
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127 if output_type == 'gd_snp': |
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128 column_names.append('{}A'.format(i)) |
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129 column_names.append('{}B'.format(i)) |
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130 column_names.append('{}G'.format(i)) |
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131 column_names.append('{}Q'.format(i)) |
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132 elif output_type == 'gd_genotype': |
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133 column_names.append('{}G'.format(i)) |
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134 else: |
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135 die('unknown output type: {}'.format(output_type)) |
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136 |
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137 jdict['column_names'] = column_names |
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138 |
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139 individuals = [] |
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140 |
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141 for pos, individual in enumerate(individual_names): |
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142 col = first_individual_column + pos * columns_per_individual |
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143 individuals.append([individual, col]) |
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144 |
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145 jdict['individuals'] = individuals |
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146 |
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147 jdict['scaffold'] = int(scaffold_col) |
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148 jdict['pos'] = int(pos_col) |
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149 jdict['ref'] = int(ref_col) |
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150 jdict['rPos'] = int(rPos_col) |
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151 |
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152 jdict['species'] = species |
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153 jdict['dbkey'] = dbkey |
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154 |
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155 json_string = json.dumps(jdict, separators=(',',':'), sort_keys=True) |
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156 |
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157 min_cols = len(column_names) |
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158 pos_col = int(pos_col) - 1 |
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159 rPos_col = int(rPos_col) - 1 |
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160 |
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161 def is_int(string): |
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162 try: |
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163 int(string) |
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164 return True |
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165 except ValueError: |
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166 return False |
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167 |
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168 with open(output, 'w') as ofh: |
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169 lines = fold_line(json_string, 200, '#') |
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170 for line in lines: |
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171 print >> ofh, line |
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172 |
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173 with open(input) as fh: |
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174 line_number = 0 |
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175 for line in fh: |
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176 line_number += 1 |
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177 if line[0] == '#': |
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178 continue |
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179 line = line.rstrip('\r\n') |
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180 elems = line.split('\t') |
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181 if len(elems) < min_cols: |
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182 die('Too few columns on line {0} of input file. Expecting {1}, saw {2}.'.format(line_number, min_cols, len(elems))) |
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183 if not is_int(elems[pos_col]): |
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184 die('bad pos on line {0} column {1} of input file: {2}'.format(line_number, pos_col+1, elems[pos_col])) |
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185 if not is_int(elems[rPos_col]): |
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186 die('bad rPos on line {0} column {1} of input file: {2}'.format(line_number, rPos_col+1, elems[rPos_col])) |
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187 print >> ofh, line |