1 | """ |
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2 | A set of functions which extend the capabilities of the Python csv |
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3 | module. |
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4 | |
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5 | CSV files have the extension .csv, which stands for Comma Separated Value |
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6 | file. There is no standardised form for this format, so the user is provided |
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7 | with a variety of options for parsing different styles of csv files. |
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8 | |
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9 | These have been left as functions to aviod confusion with the standard |
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10 | csv module. |
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11 | """ |
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12 | |
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13 | import csv |
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14 | import numpy as num |
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15 | |
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16 | |
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17 | def load_csv_as_dict(file_name, title_check_list=None, delimiter=','): |
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18 | """ |
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19 | Load in the csv as a dictionary, title as key and column info as value. |
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20 | Also, create a dictionary, title as key and column index as value, |
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21 | to keep track of the column order. |
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22 | |
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23 | file_name The path to the file to read. |
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24 | |
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25 | title_check_list List of titles that *must* be columns in the file. |
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26 | |
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27 | delimiter is the delimiter used to separate the fields |
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28 | |
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29 | return 2 dictionaries: ({key:column}, {title:index}). |
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30 | |
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31 | WARNING: Values are returned as strings. |
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32 | Do this to change a list of strings to a list of floats |
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33 | time = [float(x) for x in time] |
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34 | """ |
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35 | |
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36 | # FIXME(Ole): Consider dealing with files without headers |
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37 | # FIXME(Ole): Consider a wrapper automatically converting text fields |
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38 | # to the right type by trying for: int, float, string |
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39 | |
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40 | attribute_dic = {} |
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41 | title_index_dic = {} |
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42 | titles_stripped = [] # List of titles |
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43 | |
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44 | reader = csv.reader(file(file_name), delimiter=delimiter) |
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45 | |
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46 | # Read in and manipulate the title info |
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47 | titles = reader.next() |
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48 | for i, title in enumerate(titles): |
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49 | header = title.strip() |
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50 | titles_stripped.append(header) |
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51 | title_index_dic[header] = i |
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52 | title_count = len(titles_stripped) |
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53 | |
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54 | # Check required columns |
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55 | if title_check_list is not None: |
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56 | for title_check in title_check_list: |
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57 | if not title_index_dic.has_key(title_check): |
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58 | msg = 'Reading error. This row is not present %s' % title_check |
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59 | raise IOError, msg |
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60 | |
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61 | |
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62 | # Create a dictionary of column values, indexed by column title |
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63 | for line in reader: |
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64 | n = len(line) # Number of entries |
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65 | if n < title_count: |
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66 | msg = 'Entry in file %s had %d columns ' % (file_name, n) |
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67 | msg += 'although there were %d headers' % title_count |
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68 | raise IOError, msg |
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69 | for i, value in enumerate(line[:title_count]): # skip trailing data |
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70 | attribute_dic.setdefault(titles_stripped[i], []).append(value) |
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71 | |
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72 | return attribute_dic, title_index_dic |
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73 | |
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74 | |
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75 | |
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76 | def load_csv_as_array(file_name, delimiter = ','): |
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77 | """ |
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78 | Convert CSV files of the form: |
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79 | |
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80 | time, discharge, velocity |
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81 | 0.0, 1.2, 0.0 |
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82 | 0.1, 3.2, 1.1 |
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83 | ... |
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84 | |
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85 | to a dictionary of numeric arrays. |
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86 | |
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87 | file_name The path to the file to read. |
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88 | delimiter is the delimiter used to separate the fields |
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89 | |
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90 | See underlying function load_csv_as_dict for more details. |
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91 | """ |
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92 | |
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93 | X, _ = load_csv_as_dict(file_name, delimiter=delimiter) |
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94 | |
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95 | |
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96 | # Return result as a dict of arrays |
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97 | ret = {} |
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98 | for key in X.keys(): |
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99 | ret[key] = num.array([float(x) for x in X[key]]) |
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100 | |
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101 | return ret |
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102 | |
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103 | |
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104 | def load_csv_as_matrix(file_name, delimiter = ','): |
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105 | """ |
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106 | Convert CSV files of the form: |
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107 | |
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108 | time, discharge, velocity |
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109 | 0.0, 1.2, 0.0 |
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110 | 0.1, 3.2, 1.1 |
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111 | ... |
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112 | |
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113 | to a numeric matrix. |
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114 | |
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115 | file_name The path to the file to read. |
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116 | delimiter is the delimiter used to separate the fields |
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117 | |
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118 | See underlying function load_csv_as_dict for more details. |
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119 | """ |
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120 | |
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121 | X, title_indices = load_csv_as_dict(file_name, delimiter=delimiter) |
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122 | |
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123 | col_titles = title_indices.keys() |
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124 | |
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125 | # Return result as a 2D array |
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126 | ret = num.zeros((len(X[col_titles[0]]), len(title_indices)), float) |
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127 | |
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128 | header = [] |
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129 | for col_title in col_titles: |
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130 | index = title_indices[col_title] |
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131 | header.append(col_title) |
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132 | for i, x in enumerate(X[col_title]): |
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133 | ret[i, index] = float(x) |
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134 | |
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135 | return header, ret |
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136 | |
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137 | |
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138 | |
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139 | ## |
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140 | # @brief Store keyword params into a CSV file. |
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141 | # @param verbose True if this function is to be verbose. |
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142 | # @param kwargs Dictionary of keyword args to store. |
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143 | # @note If kwargs dict contains 'file_name' key, that has the output filename. |
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144 | # If not, make up a filename in the output directory. |
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145 | def store_parameters(verbose=False, **kwargs): |
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146 | """ |
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147 | Store "kwargs" into a temp csv file, if "completed" is in kwargs, |
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148 | csv file is kwargs[file_name] else it is kwargs[output_dir]+details_temp.csv |
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149 | |
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150 | Must have a file_name keyword arg, this is what is writing to. |
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151 | might be a better way to do this using CSV module Writer and writeDict. |
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152 | |
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153 | writes file to "output_dir" unless "completed" is in kwargs, then |
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154 | it writes to "file_name" kwargs |
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155 | """ |
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156 | |
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157 | import types |
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158 | |
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159 | # Check that kwargs is a dictionary |
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160 | if type(kwargs) != types.DictType: |
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161 | raise TypeError |
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162 | |
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163 | # is 'completed' in kwargs? |
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164 | completed = kwargs.has_key('completed') |
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165 | |
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166 | # get file name and removes from dict and assert that a file_name exists |
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167 | if completed: |
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168 | try: |
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169 | file = str(kwargs['file_name']) |
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170 | except: |
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171 | raise 'kwargs must have file_name' |
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172 | else: |
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173 | # write temp file in output directory |
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174 | try: |
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175 | file = str(kwargs['output_dir']) + 'detail_temp.csv' |
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176 | except: |
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177 | raise 'kwargs must have output_dir' |
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178 | |
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179 | # extracts the header info and the new line info |
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180 | line = '' |
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181 | header = '' |
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182 | count = 0 |
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183 | keys = kwargs.keys() |
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184 | keys.sort() |
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185 | |
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186 | # used the sorted keys to create the header and line data |
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187 | for k in keys: |
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188 | header += str(k) |
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189 | line += str(kwargs[k]) |
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190 | count += 1 |
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191 | if count < len(kwargs): |
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192 | header += ',' |
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193 | line += ',' |
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194 | header += '\n' |
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195 | line += '\n' |
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196 | |
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197 | # checks the header info, if the same, then write, if not create a new file |
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198 | # try to open! |
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199 | try: |
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200 | fid = open(file, 'r') |
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201 | file_header = fid.readline() |
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202 | fid.close() |
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203 | if verbose: log.critical('read file header %s' % file_header) |
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204 | except: |
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205 | msg = 'try to create new file: %s' % file |
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206 | if verbose: log.critical(msg) |
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207 | #tries to open file, maybe directory is bad |
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208 | try: |
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209 | fid = open(file, 'w') |
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210 | fid.write(header) |
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211 | fid.close() |
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212 | file_header=header |
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213 | except: |
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214 | msg = 'cannot create new file: %s' % file |
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215 | raise Exception, msg |
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216 | |
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217 | # if header is same or this is a new file |
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218 | if file_header == str(header): |
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219 | fid = open(file, 'a') |
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220 | fid.write(line) |
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221 | fid.close() |
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222 | else: |
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223 | # backup plan, |
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224 | # if header is different and has completed will append info to |
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225 | # end of details_temp.cvs file in output directory |
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226 | file = str(kwargs['output_dir']) + 'detail_temp.csv' |
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227 | fid = open(file, 'a') |
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228 | fid.write(header) |
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229 | fid.write(line) |
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230 | fid.close() |
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231 | |
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232 | if verbose: |
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233 | log.critical('file %s', file_header.strip('\n')) |
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234 | log.critical('head %s', header.strip('\n')) |
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235 | if file_header.strip('\n') == str(header): |
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236 | log.critical('they equal') |
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237 | |
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238 | msg = 'WARNING: File header does not match input info, ' \ |
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239 | 'the input variables have changed, suggest you change file name' |
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240 | log.critical(msg) |
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241 | |
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242 | |
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243 | |
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244 | def csv2building_polygons(file_name, |
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245 | floor_height=3, |
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246 | clipping_polygons=None): |
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247 | """ |
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248 | Convert CSV files of the form: |
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249 | |
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250 | easting,northing,id,floors |
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251 | 422664.22,870785.46,2,0 |
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252 | 422672.48,870780.14,2,0 |
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253 | 422668.17,870772.62,2,0 |
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254 | 422660.35,870777.17,2,0 |
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255 | 422664.22,870785.46,2,0 |
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256 | 422661.30,871215.06,3,1 |
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257 | 422667.50,871215.70,3,1 |
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258 | 422668.30,871204.86,3,1 |
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259 | 422662.21,871204.33,3,1 |
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260 | 422661.30,871215.06,3,1 |
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261 | |
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262 | to a dictionary of polygons with id as key. |
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263 | The associated number of floors are converted to m above MSL and |
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264 | returned as a separate dictionary also keyed by id. |
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265 | |
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266 | Optional parameter floor_height is the height of each building story. |
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267 | Optional parameter clipping_olygons is a list of polygons selecting |
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268 | buildings. Any building not in these polygons will be omitted. |
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269 | |
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270 | See csv2polygons for more details |
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271 | """ |
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272 | |
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273 | polygons, values = csv2polygons(file_name, |
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274 | value_name='floors', |
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275 | clipping_polygons=None) |
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276 | |
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277 | |
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278 | heights = {} |
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279 | for key in values.keys(): |
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280 | v = float(values[key]) |
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281 | heights[key] = v*floor_height |
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282 | |
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283 | return polygons, heights |
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284 | |
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285 | |
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286 | ## |
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287 | # @brief Convert CSV file into a dictionary of polygons and associated values. |
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288 | # @param filename The path to the file to read, value_name name for the 4th column |
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289 | def csv2polygons(file_name, |
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290 | value_name='value', |
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291 | clipping_polygons=None): |
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292 | """ |
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293 | Convert CSV files of the form: |
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294 | |
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295 | easting,northing,id,value |
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296 | 422664.22,870785.46,2,0 |
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297 | 422672.48,870780.14,2,0 |
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298 | 422668.17,870772.62,2,0 |
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299 | 422660.35,870777.17,2,0 |
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300 | 422664.22,870785.46,2,0 |
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301 | 422661.30,871215.06,3,1 |
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302 | 422667.50,871215.70,3,1 |
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303 | 422668.30,871204.86,3,1 |
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304 | 422662.21,871204.33,3,1 |
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305 | 422661.30,871215.06,3,1 |
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306 | |
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307 | to a dictionary of polygons with id as key. |
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308 | The associated values are returned as a separate dictionary also keyed by id. |
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309 | |
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310 | |
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311 | easting: x coordinate relative to zone implied by the model |
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312 | northing: y coordinate relative to zone implied by the model |
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313 | id: tag for polygon comprising points with this tag |
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314 | value: numeral associated with each polygon. These must be the same for all points in each polygon. |
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315 | |
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316 | The last header, value, can take on other names such as roughness, floors, etc - or it can be omitted |
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317 | in which case the returned values will be None |
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318 | |
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319 | Eastings and Northings will be returned as floating point values while |
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320 | id and values will be returned as strings. |
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321 | |
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322 | Optional argument: clipping_polygons will select only those polygons that are |
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323 | fully within one or more of the clipping_polygons. In other words any polygon from |
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324 | the csv file which has at least one point not inside one of the clipping polygons |
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325 | will be excluded |
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326 | |
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327 | See underlying function load_csv_as_dict for more details. |
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328 | """ |
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329 | |
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330 | X, _ = load_csv_as_dict(file_name) |
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331 | |
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332 | msg = 'Polygon csv file must have 3 or 4 columns' |
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333 | assert len(X.keys()) in [3, 4], msg |
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334 | |
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335 | msg = 'Did not find expected column header: easting' |
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336 | assert 'easting' in X.keys(), msg |
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337 | |
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338 | msg = 'Did not find expected column header: northing' |
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339 | assert 'northing' in X.keys(), northing |
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340 | |
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341 | msg = 'Did not find expected column header: northing' |
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342 | assert 'id' in X.keys(), msg |
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343 | |
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344 | if value_name is not None: |
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345 | msg = 'Did not find expected column header: %s' % value_name |
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346 | assert value_name in X.keys(), msg |
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347 | |
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348 | polygons = {} |
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349 | if len(X.keys()) == 4: |
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350 | values = {} |
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351 | else: |
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352 | values = None |
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353 | |
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354 | # Loop through entries and compose polygons |
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355 | excluded_polygons={} |
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356 | past_ids = {} |
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357 | last_id = None |
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358 | for i, id in enumerate(X['id']): |
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359 | |
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360 | # Check for duplicate polygons |
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361 | if id in past_ids: |
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362 | msg = 'Polygon %s was duplicated in line %d' % (id, i) |
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363 | raise Exception, msg |
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364 | |
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365 | if id not in polygons: |
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366 | # Start new polygon |
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367 | polygons[id] = [] |
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368 | if values is not None: |
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369 | values[id] = X[value_name][i] |
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370 | |
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371 | # Keep track of previous polygon ids |
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372 | if last_id is not None: |
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373 | past_ids[last_id] = i |
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374 | |
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375 | # Append this point to current polygon |
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376 | point = [float(X['easting'][i]), float(X['northing'][i])] |
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377 | |
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378 | if clipping_polygons is not None: |
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379 | exclude=True |
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380 | for clipping_polygon in clipping_polygons: |
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381 | if inside_polygon(point, clipping_polygon): |
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382 | exclude=False |
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383 | break |
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384 | |
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385 | if exclude is True: |
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386 | excluded_polygons[id]=True |
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387 | |
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388 | polygons[id].append(point) |
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389 | |
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390 | # Check that value is the same across each polygon |
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391 | msg = 'Values must be the same across each polygon.' |
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392 | msg += 'I got %s in line %d but it should have been %s' % (X[value_name][i], i, values[id]) |
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393 | assert values[id] == X[value_name][i], msg |
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394 | |
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395 | last_id = id |
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396 | |
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397 | # Weed out polygons that were not wholly inside clipping polygons |
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398 | for id in excluded_polygons: |
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399 | del polygons[id] |
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400 | |
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401 | return polygons, values |
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402 | |
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403 | |
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404 | |
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405 | |
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406 | |
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