| 1 | #!/usr/bin/env python
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| 2 |
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| 3 | from __future__ import print_function
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| 4 |
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| 5 | import os
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| 6 | import re
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| 7 | import sys
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| 8 | import json
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| 9 |
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| 10 | import argparse
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| 11 | import openpyxl
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| 12 |
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| 13 | import util
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| 14 | import xlsxutil
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| 15 |
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| 16 | jury_from_countries = {}
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| 17 | tele_from_countries = {}
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| 18 |
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| 19 |
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| 20 | def create_to_country_voting_groups(data_hashmap_array):
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| 21 |
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| 22 | # Example values for header-names
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| 23 | # (semi-) final: f, sf
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| 24 | # Jury or Televoting: J, T
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| 25 | # Year: 1975, ...
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| 26 | # To country: Belgium
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| 27 | # From country: Belgium
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| 28 | # Edition: 1975f, 1975sf
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| 29 |
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| 30 |
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| 31 | # Sort so array entries are grouped by the country receiving the votes in a given year
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| 32 | data_hashmap_array.sort(key=xlsxutil.sortkey_year_to_country)
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| 33 |
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| 34 |
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| 35 | # Debug output
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| 36 | #
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| 37 | # for data_hashmap in data_hashmap_array:
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| 38 | # print(data_hashmap.get('To country'), data_hashmap.get('Year'), data_hashmap.get('Points'), "(Points from " + data_hashmap.get('From country') + ")")
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| 39 |
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| 40 |
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| 41 | # Build array of country groups
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| 42 | # A country grouping includes all the votes that country receive that year
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| 43 | country_groups = []
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| 44 |
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| 45 | prev_data_hashmap = data_hashmap_array[0]
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| 46 | country_group = [ ]
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| 47 |
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| 48 | i = 1
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| 49 | num_rows = len(data_hashmap_array)
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| 50 |
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| 51 | while (i < num_rows):
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| 52 | country_group.append(prev_data_hashmap)
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| 53 | data_hashmap = data_hashmap_array[i]
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| 54 |
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| 55 | if (data_hashmap.get('To country') != prev_data_hashmap.get('To country')):
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| 56 | # moving on to a new country group
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| 57 | country_groups.append(country_group)
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| 58 | country_group = [ ]
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| 59 |
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| 60 | prev_data_hashmap = data_hashmap
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| 61 | i = i + 1
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| 62 |
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| 63 | country_group.append(prev_data_hashmap)
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| 64 | country_groups.append(country_group)
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| 65 |
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| 66 | return country_groups
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| 67 |
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| 68 |
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| 69 | def fileset_voting_for_esc_country_in_year(to_country_year_votes):
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| 70 |
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| 71 | # Looking to build data-structure (for output as JSON) in the form
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| 72 | # { "FileSet":
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| 73 | # [
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| 74 | # { "FileName": "France1991\.nul" },
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| 75 | # { "Description":
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| 76 | # {
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| 77 | # "Metadata":
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| 78 | # [
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| 79 | # { "name": "Germany-J", "content": 12 }, # J = Jury Vote
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| 80 | # { "name": "Germany-T", "content": 6 }, # T = Televote (if present)
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| 81 | # ...
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| 82 | # ]
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| 83 | # }
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| 84 | # }
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| 85 | # ]
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| 86 | # }
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| 87 |
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| 88 | metadata_array = []
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| 89 |
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| 90 | jury_metadata_vals = []
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| 91 | tele_metadata_vals = []
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| 92 |
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| 93 | to_country_jury_total = 0
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| 94 | to_country_tele_total = 0
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| 95 |
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| 96 | # Use the first record to be a representative for 'top level' (tl)
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| 97 | # metadata about the votes cast 'To country'
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| 98 | tl_rec = to_country_year_votes[0]
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| 99 | tl_to_country = tl_rec.get('To country')
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| 100 | tl_year = tl_rec.get('Year')
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| 101 | # tl_vote_type = tl_rec.get('Jury or Televoting')
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| 102 | tl_to_country_id = re.sub(r'\s+', '', tl_to_country) + str(tl_year)
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| 103 |
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| 104 | for to_country_year_vote in to_country_year_votes:
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| 105 | to_country = to_country_year_vote.get('To country')
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| 106 | year = to_country_year_vote.get('Year')
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| 107 | from_country = to_country_year_vote.get('From country')
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| 108 | vote_type = to_country_year_vote.get('Jury or Televoting')
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| 109 | points = to_country_year_vote.get('Points')
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| 110 |
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| 111 | id_from_country = re.sub(r'\s+', '', from_country)
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| 112 |
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| 113 | voting_rec = { "name": id_from_country+"-"+vote_type, "content": points }
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| 114 |
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| 115 | metadata_array.append(voting_rec)
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| 116 |
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| 117 | if (vote_type == "J"):
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| 118 | jury_metadata_vals.append(id_from_country+"-J")
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| 119 | jury_from_countries[id_from_country] = 1
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| 120 | to_country_jury_total = to_country_jury_total + points
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| 121 |
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| 122 | elif (vote_type == "T"):
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| 123 | tele_metadata_vals.append(id_from_country+"-T")
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| 124 | tele_from_countries[id_from_country] = 1
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| 125 | to_country_tele_total = to_country_tele_total + points
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| 126 | else:
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| 127 | util.eprint("Warning: Unrecognized voting type: " + vote_type)
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| 128 |
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| 129 | if (len(jury_metadata_vals)>0):
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| 130 | metadata_array.append({ "name": "JuryVotesJSON", "content": json.dumps(jury_metadata_vals) })
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| 131 | metadata_array.append({ "name": "JuryVotesTotal", "content": to_country_jury_total})
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| 132 | if (len(tele_metadata_vals)>0):
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| 133 | metadata_array.append({ "name": "TeleVotesJSON", "content": json.dumps(tele_metadata_vals) })
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| 134 | metadata_array.append({ "name": "TeleVotesTotal", "content": to_country_tele_total})
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| 135 |
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| 136 | # id_to_country = to_country_year_votes[0].get('To country')
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| 137 | # id_to_country = re.sub(r'\s+', '', id_to_country)
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| 138 | #id_year = to_country_year_votes[0].get('Year');
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| 139 | #id = id_to_country + str(id_year);
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| 140 |
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| 141 | filename_id = tl_to_country_id + "\\.nul"
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| 142 |
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| 143 | fileset = {
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| 144 | "FileSet" : [
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| 145 | { "FileName": filename_id, },
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| 146 | { "Description" : { "Metadata" : metadata_array } }
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| 147 | ]
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| 148 | }
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| 149 |
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| 150 | return fileset
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| 151 |
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| 152 |
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| 153 | def gs_directory_metadata(to_country_year_voting_groups):
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| 154 | # Next step is to express the grouped to-country voting data
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| 155 | # in the Greenstone JSON metadata format:
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| 156 |
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| 157 | # { "DirectoryMetadata":
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| 158 | # [
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| 159 | # { "FileSet":
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| 160 | # [
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| 161 | # { "FileName": "France1991\.nul" },
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| 162 | # { "Description":
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| 163 | # {
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| 164 | # "Metadata":
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| 165 | # [
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| 166 | # { "name": "Germany-J", "content": "12" }, # J = Jury Vote
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| 167 | # ...
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| 168 | # ]
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| 169 | # }
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| 170 | # }
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| 171 | # ]
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| 172 | # }
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| 173 | # ...
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| 174 | # ]
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| 175 | # }
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| 176 |
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| 177 | directory_metadata = []
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| 178 |
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| 179 | for to_country_year_votes in to_country_year_voting_groups:
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| 180 |
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| 181 | fileset = fileset_voting_for_esc_country_in_year(to_country_year_votes)
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| 182 | directory_metadata.append(fileset)
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| 183 |
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| 184 | filename_id = fileset.get('FileSet')[0].get('FileName')
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| 185 | num_countries_voting_data = len(fileset.get('FileSet')[1].get('Description').get('Metadata'))
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| 186 |
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| 187 | print(" " + filename_id.ljust(28) + ": " + str(num_countries_voting_data) + " votes")
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| 188 |
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| 189 |
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| 190 | greenstone_metadata_json = { "DirectoryMetadata": directory_metadata }
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| 191 |
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| 192 | return greenstone_metadata_json
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| 193 |
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| 194 |
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| 195 | def display_gs_head_metadata_tags():
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| 196 |
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| 197 | print()
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| 198 | print()
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| 199 | print("For, e.g., '<display><format>' section of collectionConfig.xml:")
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| 200 | print()
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| 201 | print(" <gsf:headMetaTags>")
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| 202 |
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| 203 | for from_country in sorted(jury_from_countries.keys()):
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| 204 | print(" <gsf:metadata name=\""+from_country+"-J\" />")
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| 205 |
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| 206 | print()
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| 207 | for from_country in sorted(tele_from_countries.keys()):
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| 208 | print(" <gsf:metadata name=\""+from_country+"-T\" />")
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| 209 |
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| 210 | print(" </gsf:headMetaTags>")
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| 211 |
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| 212 |
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| 213 | if __name__ == "__main__":
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| 214 |
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| 215 | parser = argparse.ArgumentParser()
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| 216 | parser.add_argument('--sheetname', help="The name of the sheet within the Excel file to extractc data from")
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| 217 | parser.add_argument('--votingtype', choices=['J','T'], help="Filter to only J=Jury or T=Tele cast votes")
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| 218 | parser.add_argument('input-file.xlsx')
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| 219 | parser.add_argument('output-file.json', nargs='?')
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| 220 |
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| 221 |
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| 222 | args = parser.parse_args()
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| 223 |
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| 224 | sheetname = getattr(args,'sheetname');
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| 225 | voting_type = getattr(args,'votingtype');
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| 226 |
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| 227 | excel_input_filename = getattr(args,'input-file.xlsx');
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| 228 | json_output_filename = getattr(args,'output-file.json');
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| 229 |
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| 230 | if (json_output_filename == None):
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| 231 | json_output_filename = os.path.splitext(excel_input_filename)[0]+'.json'
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| 232 |
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| 233 | worksheet = xlsxutil.load_xslx_sheet(excel_input_filename,sheetname)
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| 234 |
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| 235 | data_hashmap_array = xlsxutil.convert_worksheet_to_hashmaps(worksheet)
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| 236 |
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| 237 | print("Number of data rows in Excel file: " + str(len(data_hashmap_array)))
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| 238 |
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| 239 | # Filter down to just the voting results concerning finals
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| 240 | data_hashmap_array_filtered = list(filter(xlsxutil.filter_finalist_votes, data_hashmap_array))
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| 241 |
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| 242 |
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| 243 | print("Number of finalist voting data rows: " + str(len(data_hashmap_array_filtered)))
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| 244 |
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| 245 | if voting_type != None:
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| 246 | # Further filter down by the type of voting results cast
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| 247 | if (voting_type == "J"):
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| 248 | data_hashmap_array_filtered = list(filter(xlsxutil.filter_jury_votes, data_hashmap_array_filtered))
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| 249 | print("Number Jury cast data rows: " + str(len(data_hashmap_array_filtered)))
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| 250 | else:
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| 251 | # Must be "T"
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| 252 | data_hashmap_array_filtered = list(filter(xlsxutil.filter_tele_votes, data_hashmap_array_filtered))
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| 253 | print("Number Televoting cast data rows: " + str(len(data_hashmap_array_filtered)))
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| 254 |
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| 255 |
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| 256 | to_country_year_voting_groups = create_to_country_voting_groups(data_hashmap_array_filtered)
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| 257 |
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| 258 | # Debug output
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| 259 | #
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| 260 | # print(to_country_year_voting_groups)
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| 261 |
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| 262 | print()
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| 263 | print("Generating Greenstone JSON to-country voting metadata received by:")
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| 264 | greenstone_metadata_json = gs_directory_metadata(to_country_year_voting_groups)
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| 265 |
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| 266 | print("Saving output as: " + json_output_filename)
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| 267 | xlsxutil.save_greenstone_json_metadata(greenstone_metadata_json,json_output_filename)
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| 268 |
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| 269 | display_gs_head_metadata_tags()
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| 270 |
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| 271 | print()
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| 272 |
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