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11b697145b
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972f514e6b
@ -50,52 +50,9 @@ def cqi_corpora_corpus_update_db(cqi_client: cqi.CQiClient, corpus_name: str):
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@cqi_over_socketio
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def cqi_corpora_corpus_get_visualization_data(cqi_client: cqi.CQiClient, corpus_name: str):
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corpus = cqi_client.corpora.get(corpus_name)
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# s_attrs = [x for x in corpus.structural_attributes.list() if not x.has_values]
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# p_attrs = corpus.positional_attributes.list()
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# payload = {
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# 's_attrs': {},
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# 'p_attrs': {},
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# 'values': {
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# 's_attrs': {},
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# 'p_attrs': {}
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# }
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# }
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# for s_attr in s_attrs:
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# s_attr_lbound, s_attr_rbound = s_attr.cpos_by_id(text_id)
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# s_attr_cpos_range = range(s_attr_lbound, s_attr_rbound + 1)
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# payload['text']['lexicon'][text_id] = {
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# 's_attrs': [s_attr_lbound, s_attr_rbound],
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# 'counts': {
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# 'token': s_attr_rbound - s_attr_lbound + 1
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# },
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# 'freqs': {
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# p_attr.name: dict(Counter(p_attr.ids_by_cpos(list(s_attr_cpos_range))))
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# for p_attr in p_attrs
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# }
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# }
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# for p_attr in p_attrs:
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# payload['p_attrs'] = dict(
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# )
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# payload['values']['p_attrs'] = dict(
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# zip(
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# range(0, p_attr.lexicon_size),
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# p_attr.values_by_ids(list(range(0, p_attr.lexicon_size)))
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# )
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# )
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text = corpus.structural_attributes.get('text')
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text_value_names = []
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text_values = []
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for text_sub_attr in corpus.structural_attributes.list(filters={'part_of': text}):
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text_value_names.append(text_sub_attr.name[(len(text.name) + 1):])
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text_values.append(text_sub_attr.values_by_ids(list(range(0, text.size))))
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s = corpus.structural_attributes.get('s')
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ent = corpus.structural_attributes.get('ent')
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ent_value_names = []
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ent_values = []
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for ent_sub_attr in corpus.structural_attributes.list(filters={'part_of': ent}):
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ent_value_names.append(ent_sub_attr.name[(len(ent.name) + 1):])
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ent_values.append(ent_sub_attr.values_by_ids(list(range(0, ent.size))))
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word = corpus.positional_attributes.get('word')
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lemma = corpus.positional_attributes.get('lemma')
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pos = corpus.positional_attributes.get('pos')
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@ -165,7 +122,10 @@ def cqi_corpora_corpus_get_visualization_data(cqi_client: cqi.CQiClient, corpus_
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)
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}
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}
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payload['text']['values'] = text_value_names
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payload['text']['values'] = [
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sub_attr.name[(len(text.name) + 1):]
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for sub_attr in corpus.structural_attributes.list(filters={'part_of': text})
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]
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payload['s'] = {'lexicon': {}, 'values': None}
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for s_id in range(0, s.size):
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payload['s']['lexicon'][s_id] = {
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@ -180,22 +140,15 @@ def cqi_corpora_corpus_get_visualization_data(cqi_client: cqi.CQiClient, corpus_
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payload['ent']['lexicon'][ent_id] = {
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# 'bounds': ent.cpos_by_id(ent_id)
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}
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payload['ent']['values'] = ent_value_names
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payload['ent']['values'] = [
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sub_attr.name[(len(ent.name) + 1):]
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for sub_attr in corpus.structural_attributes.list(filters={'part_of': ent})
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]
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payload['lookups'] = {
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'corpus': {},
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'text': {
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text_id: {
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text_value_name: text_values[text_value_name_idx][text_id_idx]
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for text_value_name_idx, text_value_name in enumerate(text_value_names)
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} for text_id_idx, text_id in enumerate(range(0, text.size))
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},
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'text': {},
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's': {},
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'ent': {
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ent_id: {
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ent_value_name: ent_values[ent_value_name_idx][ent_id_idx]
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for ent_value_name_idx, ent_value_name in enumerate(ent_value_names)
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} for ent_id_idx, ent_id in enumerate(range(0, ent.size))
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},
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'ent': {},
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'word': dict(
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zip(
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range(0, word.lexicon_size),
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@ -115,188 +115,164 @@ class CQiCorpus {
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getCorpusData() {
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return new Promise((resolve, reject) => {
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const dummyData = {
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"corpus": {
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"bounds": [1, 689],
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"counts": {
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"token": 743,
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"ent": 321,
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"s": 234
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"num_tokens": 2000, // number of tokens in the corpus
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"num_unique_words": 500, // number of unique words in the corpus
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"num_unique_lemmas": 200, // number of unique lemmas in the corpus
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"num_sentences": 90, // number of sentences in the corpus
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"average_sentence_length": 11, // average number of tokens per sentence in the corpus
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"num_ent_types": 30, // number of entities in the corpus
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"num_unique_ent_types":10,
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"ent_type_freqs": {
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"str": 10, // number of ent_types with ent_type "str"
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// ...
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},
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"freqs": {
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"word": {
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"1": 876,
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"2": 234,
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"3": 657
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"texts": [
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{
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"num_tokens": 11, // number of tokens in the text
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"num_unique_words": 12, // number of unique words in the text
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"word_freqs": { // frequency of unique words in the text (sorted by frequency)
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"str": "int", // number of tokens with word "str"
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// ...
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},
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"lemma": {
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"1": 543,
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"2": 876,
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"3": 321
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"num_unique_lemmas": 15, // number of unique lemmas in the text
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"lemma_freqs": { // frequency of unique lemmas in the text (sorted by frequency)
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"str": "int", // number of tokens with lemma "str"
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// ...
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},
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"pos": {
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"1": 456,
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"2": 789,
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"3": 234
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"num_sentences": 4, // number of sentences in the text
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"average_sentence_length": 3, // average number of tokens per sentence in the text
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"num_ent_types": 12, // number of ent_types in the text
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"num_unique_ent_types": 28, // number of unique ent_types in the text
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"num_entities_by_id": {
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"1": "int", // number of entities with id 1
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// ...
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},
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"simple_pos": {
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"1": 987,
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"2": 876,
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"3": 543
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"author": "Author Name",
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"title": "Titel",
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"publishing_year": 1950
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},
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"ent": {
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"1": 654,
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"2": 321,
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"3": 987
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{
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"num_tokens": 15, // number of tokens in the text
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"num_unique_words": 4, // number of unique words in the text
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"word_freqs": { // frequency of unique words in the text (sorted by frequency)
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"str": "int", // number of tokens with word "str"
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// ...
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},
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"num_unique_lemmas": 90, // number of unique lemmas in the text
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"lemma_freqs": { // frequency of unique lemmas in the text (sorted by frequency)
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"str": "int", // number of tokens with lemma "str"
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// ...
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},
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"num_sentences": 11, // number of sentences in the text
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"average_sentence_length": 3, // average number of tokens per sentence in the text
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"num_ent_types": 4, // number of ent_types in the text
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"num_unique_ent_types": 300, // number of unique ent_types in the text
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"num_entities_by_id": {
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"1": "int", // number of entities with id 1
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// ...
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},
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"author": "Author Name",
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"title": "Titel 1",
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"publishing_year": 1962
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},
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{
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"num_tokens": 11, // number of tokens in the text
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"num_unique_words": 12, // number of unique words in the text
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"word_freqs": { // frequency of unique words in the text (sorted by frequency)
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"str": "int", // number of tokens with word "str"
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// ...
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},
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"num_unique_lemmas": 64, // number of unique lemmas in the text
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"lemma_freqs": { // frequency of unique lemmas in the text (sorted by frequency)
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"str": "int", // number of tokens with lemma "str"
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// ...
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},
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"num_sentences": 52, // number of sentences in the text
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"average_sentence_length": 3, // average number of tokens per sentence in the text
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"num_ent_types": 45, // number of ent_types in the text
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"num_unique_ent_types": 68, // number of unique ent_types in the text
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"num_entities_by_id": {
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"1": "int", // number of entities with id 1
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// ...
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},
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"author": "Author Name",
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"title": "Titel 2",
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"publishing_year": 1850
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},
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{
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"num_tokens": 56, // number of tokens in the text
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"num_unique_words": 13, // number of unique words in the text
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"word_freqs": { // frequency of unique words in the text (sorted by frequency)
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"str": "int", // number of tokens with word "str"
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// ...
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},
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"num_unique_lemmas": 43, // number of unique lemmas in the text
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"lemma_freqs": { // frequency of unique lemmas in the text (sorted by frequency)
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"str": "int", // number of tokens with lemma "str"
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// ...
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},
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"num_sentences": 45, // number of sentences in the text
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"average_sentence_length": 56, // average number of tokens per sentence in the text
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"num_ent_types": 8792, // number of ent_types in the text
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"num_unique_ent_types": 56758, // number of unique ent_types in the text
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"num_entities_by_id": {
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"1": "int", // number of entities with id 1
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// ...
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},
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"author": "Author Name",
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"title": "Titel 3",
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"publishing_year": 1504
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},
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{
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"num_tokens": 54345, // number of tokens in the text
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"num_unique_words": 561, // number of unique words in the text
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"word_freqs": { // frequency of unique words in the text (sorted by frequency)
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"str": "int", // number of tokens with word "str"
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||||
// ...
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},
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"num_unique_lemmas": 546, // number of unique lemmas in the text
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"lemma_freqs": { // frequency of unique lemmas in the text (sorted by frequency)
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"str": "int", // number of tokens with lemma "str"
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// ...
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},
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"num_sentences": 5427, // number of sentences in the text
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"average_sentence_length": 657, // average number of tokens per sentence in the text
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"num_ent_types": 3465, // number of ent_types in the text
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"num_unique_ent_types": 45, // number of unique ent_types in the text
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"num_entities_by_id": {
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||||
"1": "int", // number of entities with id 1
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// ...
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},
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"author": "Author Name",
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"title": "Titel 4",
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"publishing_year": 1712
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},
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{
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"num_tokens": 4354, // number of tokens in the text
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"num_unique_words": 45234, // number of unique words in the text
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"word_freqs": { // frequency of unique words in the text (sorted by frequency)
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"testwort": 50, // number of tokens with word "str"
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"testwort2": 1
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},
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"num_unique_lemmas": 15, // number of unique lemmas in the text
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"lemma_freqs": { // frequency of unique lemmas in the text (sorted by frequency)
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"testlemma": 11, // number of tokens with lemma "str"
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"testlemma2": 1
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},
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"num_sentences": 90, // number of sentences in the text
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"average_sentence_length": 7, // average number of tokens per sentence in the text
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"num_ent_types": 19,
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||||
"num_unique_ent_types": 5, // number of unique ent_types in the text
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||||
"num_entities_by_id": {
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||||
"1": "int", // number of entities with id 1
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||||
// ...
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},
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"author": "Author Name 2",
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"title": "Titel 5",
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"publishing_year": 1951
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}
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}
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},
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"text": {
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"1": {
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"bounds": [0, 435],
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"counts": {
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"token": 345,
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"ent_type": 123,
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"s": 89
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},
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"freqs": {
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||||
"word": {
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||||
"1": 25,
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"2": 90,
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"3": 200
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||||
},
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||||
"lemma": {
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||||
"1": 654,
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||||
"2": 321,
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"3": 987
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||||
},
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"pos": {
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||||
"1": 543,
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||||
"2": 876,
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||||
"3": 234
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||||
},
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||||
"simple_pos": {
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||||
"1": 987,
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||||
"2": 654,
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||||
"3": 321
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||||
},
|
||||
"ent_type": {
|
||||
"1": 234,
|
||||
"2": 789,
|
||||
"3": 543
|
||||
}
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||||
},
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||||
"values": {
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||||
"author": 1,
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||||
"publishing_year":1950,
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"title": 1
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||||
}
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||||
},
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||||
"2": {
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||||
"bounds": [435, 689],
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"counts": {
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||||
"token": 389,
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||||
"ent_type": 198,
|
||||
"s": 145
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||||
},
|
||||
"freqs": {
|
||||
"word": {
|
||||
"1": 60,
|
||||
"2": 70,
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||||
"3": 100
|
||||
},
|
||||
"lemma": {
|
||||
"1": 654,
|
||||
"2": 321,
|
||||
"3": 987
|
||||
},
|
||||
"pos": {
|
||||
"1": 543,
|
||||
"2": 876,
|
||||
"3": 234
|
||||
},
|
||||
"simple_pos": {
|
||||
"1": 987,
|
||||
"2": 654,
|
||||
"3": 321
|
||||
},
|
||||
"ent_type": {
|
||||
"1": 234,
|
||||
"2": 789,
|
||||
"3": 543
|
||||
}
|
||||
},
|
||||
"values": {
|
||||
"author": 2,
|
||||
"publishing_year":1951,
|
||||
"title": 2
|
||||
}
|
||||
}
|
||||
},
|
||||
"s": {
|
||||
"1": {
|
||||
"bounds": [345, 678]
|
||||
}
|
||||
},
|
||||
"ent": {
|
||||
"1": {
|
||||
"bounds": [567, 890],
|
||||
"values": {
|
||||
"type": 789
|
||||
}
|
||||
}
|
||||
},
|
||||
"token": {
|
||||
"310": {
|
||||
"values": {
|
||||
"word": 1,
|
||||
"lemma": 2,
|
||||
"pos": 1,
|
||||
"simple_pos": 1
|
||||
}
|
||||
}
|
||||
},
|
||||
"value_lookups": {
|
||||
"text": {
|
||||
"author": {
|
||||
"1": "John Doe",
|
||||
"2": "Jane Smith"
|
||||
},
|
||||
"title": {
|
||||
"1": "Test Title 1",
|
||||
"2": "Test Title 2"
|
||||
}
|
||||
},
|
||||
"ent": {
|
||||
"type": {
|
||||
"1": "Person",
|
||||
"2": "Organization"
|
||||
}
|
||||
},
|
||||
"token": {
|
||||
"word": {
|
||||
"1": "apple",
|
||||
"2": "banana",
|
||||
"3": "orange"
|
||||
},
|
||||
"lemma": {
|
||||
"1": "run",
|
||||
"2": "walk",
|
||||
"3": "jump"
|
||||
},
|
||||
"pos": {
|
||||
"1": "noun",
|
||||
"2": "verb",
|
||||
"3": "adjective"
|
||||
},
|
||||
"simple_pos": {
|
||||
"1": "subject",
|
||||
"2": "object",
|
||||
"3": "predicate"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
]
|
||||
};
|
||||
|
||||
resolve(dummyData);
|
||||
/*
|
||||
|
||||
@ -34,7 +34,6 @@ class CorpusAnalysisApp {
|
||||
.then(
|
||||
cQiCorpus => {
|
||||
this.data.corpus = {o: cQiCorpus};
|
||||
this.data.corpus.o.getVisualizationData().then(data => console.log(data));
|
||||
// this.data.corpus.o.getVisualizationData()
|
||||
// .then(
|
||||
// (visualizationData) => {
|
||||
@ -49,8 +48,6 @@ class CorpusAnalysisApp {
|
||||
this.renderGeneralCorpusInfo(corpusData);
|
||||
this.renderTextInfoList(corpusData);
|
||||
this.renderTextProportionsGraphic(corpusData);
|
||||
this.renderWordFrequenciesGraphic(corpusData);
|
||||
this.renderWordDistributionsGraphic(corpusData);
|
||||
});
|
||||
// TODO: Don't do this hgere
|
||||
cQiCorpus.updateDb();
|
||||
@ -115,85 +112,38 @@ class CorpusAnalysisApp {
|
||||
}
|
||||
|
||||
renderGeneralCorpusInfo(corpusData) {
|
||||
document.querySelector('.corpus-num-tokens').innerHTML = corpusData.corpus.counts.token;
|
||||
document.querySelector('.corpus-num-s').innerHTML = corpusData.corpus.counts.s;
|
||||
// corpusGeneralInfoListElement.querySelector('.corpus-text-count').innerHTML = <b>Corpus text count:</b> ${Object.entries(corpusData.text).length;
|
||||
document.querySelector('.corpus-num-unique-words').innerHTML = Object.entries(corpusData.corpus.freqs.word).length;
|
||||
document.querySelector('.corpus-num-unique-lemmas').innerHTML = Object.entries(corpusData.corpus.freqs.lemma).length;
|
||||
document.querySelector('.corpus-num-unique-pos').innerHTML = Object.entries(corpusData.corpus.freqs.pos).length;
|
||||
document.querySelector('.corpus-num-unique-simple-pos').innerHTML = Object.entries(corpusData.corpus.freqs.simple_pos).length;
|
||||
let corpusGeneralInfoListElement = document.querySelector('.corpus-general-info-list');
|
||||
corpusGeneralInfoListElement.querySelector('.corpus-num-tokens').innerHTML = `<b>Number of tokens:</b> ${this.data.corpus.o.size}`;
|
||||
corpusGeneralInfoListElement.querySelector('.corpus-text-count').innerHTML = `<b>Corpus text count:</b> ${corpusData.texts.length}`;
|
||||
corpusGeneralInfoListElement.querySelector('.corpus-num-unique-words').innerHTML = `<b>Corpus unique word count:</b> ${corpusData.num_unique_words}`;
|
||||
corpusGeneralInfoListElement.querySelector('.corpus-num-unique-lemmas').innerHTML = `<b>Corpus unique lemma count:</b> ${corpusData.num_unique_lemmas}`;
|
||||
// corpusGeneralInfoListElement.querySelector('.corpus-most-frequent-words').innerHTML = `<b>Corpus most frequent words:</b> ${corpusData.most_frequent_words.join(', ');
|
||||
corpusGeneralInfoListElement.querySelector('.corpus-num-sentences').innerHTML = `<b>Corpus sentence count:</b> ${corpusData.num_sentences}`;
|
||||
corpusGeneralInfoListElement.querySelector('.corpus-average-sentence-length').innerHTML = `<b>Corpus average sentence length:</b> ${corpusData.average_sentence_length}`;
|
||||
corpusGeneralInfoListElement.querySelector('.corpus-num-ent-types').innerHTML = `<b>Corpus entity count:</b> ${corpusData.num_ent_types}`;
|
||||
corpusGeneralInfoListElement.querySelector('.corpus-num-unique-ent-types').innerHTML = `<b>Corpus unique entity count:</b> ${corpusData.num_unique_ent_types}`;
|
||||
}
|
||||
|
||||
renderTextInfoList(corpusData) {
|
||||
// let corpusTextInfoListElement = document.querySelector('.corpus-text-info-list');
|
||||
// let corpusTextInfoList = new CorpusTextInfoList(corpusTextInfoListElement);
|
||||
// for (let text of Object.values(corpusData.text)) {
|
||||
// text.values.title = corpusData.value_lookups.text.title[text.values.title];
|
||||
// }
|
||||
// corpusTextInfoList.add(Object.values(corpusData.text));
|
||||
let corpusTextInfoListElement = document.querySelector('.corpus-text-info-list');
|
||||
let corpusTextInfoList = new CorpusTextInfoList(corpusTextInfoListElement);
|
||||
corpusTextInfoList.add(corpusData.texts);
|
||||
|
||||
// let textCountChipElement = document.querySelector('.text-count-chip');
|
||||
// textCountChipElement.innerHTML = `Text count: ${Object.values(corpusData.text).length}`;
|
||||
}
|
||||
|
||||
renderTextProportionsGraphic(corpusData) {
|
||||
// let textProportionsGraphicElement = document.querySelector('#text-proportions-graphic');
|
||||
// let texts = Object.values(corpusData.text);
|
||||
// let graphData = [
|
||||
// {
|
||||
// values: texts.map(text => text.counts.token),
|
||||
// labels: texts.map(text => `${text.values.title} (${text.values.publishing_year})`),
|
||||
// type: 'pie'
|
||||
// }
|
||||
// ];
|
||||
// let graphLayout = {
|
||||
// height: 400,
|
||||
// width: 500
|
||||
// };
|
||||
// Plotly.newPlot(textProportionsGraphicElement, graphData, graphLayout);
|
||||
let textProportionsGraphicElement = document.querySelector('#text-proportions-graphic');
|
||||
let graphData = [
|
||||
{
|
||||
values: corpusData.texts.map(text => text.num_tokens),
|
||||
labels: corpusData.texts.map(text => `${text.title} (${text.publishing_year})`),
|
||||
type: 'pie'
|
||||
}
|
||||
|
||||
renderWordFrequenciesGraphic(corpusData) {
|
||||
// let wordFrequenciesGraphicElement = document.querySelector('#word-frequencies-graphic');
|
||||
// let words = Object.entries(corpusData.value_lookups.token.word);
|
||||
// let texts = Object.values(corpusData.text);
|
||||
// let graphData = [];
|
||||
// for (let word of words) {
|
||||
// let data = {
|
||||
// x: texts.map(text => `${text.values.title} (${text.values.publishing_year})`),
|
||||
// y: texts.map(text => text.freqs.word[word[0]]),
|
||||
// name: word[1],
|
||||
// type: 'bar'
|
||||
// };
|
||||
// graphData.push(data);
|
||||
// }
|
||||
|
||||
// let graphLayout = {
|
||||
// height: 400,
|
||||
// width: 500,
|
||||
// barmode: 'stack',
|
||||
// type: 'bar'
|
||||
// };
|
||||
// Plotly.newPlot(wordFrequenciesGraphicElement, graphData, graphLayout);
|
||||
}
|
||||
|
||||
renderWordDistributionsGraphic(corpusData) {
|
||||
// let wordDistributionGraphicElement = document.querySelector('#word-distributions-graphic');
|
||||
// var trace1 = {
|
||||
// x: [1, 2, 3, 4],
|
||||
// y: [10, 11, 12, 13],
|
||||
// mode: 'markers',
|
||||
// marker: {
|
||||
// size: [40, 60, 80, 100]
|
||||
// }
|
||||
// };
|
||||
// var data = [trace1];
|
||||
// var layout = {
|
||||
// title: 'Marker Size',
|
||||
// showlegend: false,
|
||||
// height: 600,
|
||||
// width: 600
|
||||
// };
|
||||
// Plotly.newPlot(wordDistributionGraphicElement, data, layout);
|
||||
];
|
||||
let graphLayout = {
|
||||
height: 400,
|
||||
width: 500
|
||||
};
|
||||
Plotly.newPlot(textProportionsGraphicElement, graphData, graphLayout);
|
||||
}
|
||||
}
|
||||
|
||||
@ -29,11 +29,11 @@ class CorpusTextInfoList extends ResourceList {
|
||||
<tr class="list-item clickable hoverable">
|
||||
<td><span class="title"></span> (<span class="publishing_year"></span>)</td>
|
||||
<td><span class="num_tokens"></span></td>
|
||||
<td><span class="num_sentences"></span></td>
|
||||
<td><span class="num_unique_words"></span></td>
|
||||
<td><span class="num_unique_lemmas"></span></td>
|
||||
<td><span class="num_unique_pos"></span></td>
|
||||
<td><span class="num_unique_simple_pos"></span></td>
|
||||
<td><span class="num_sentences"></span></td>
|
||||
<td><span class="average_sentence_length"></span></td>
|
||||
<td><span class="num_unique_ent_types"></span></td>
|
||||
</tr>
|
||||
`.trim();
|
||||
}
|
||||
@ -44,11 +44,11 @@ class CorpusTextInfoList extends ResourceList {
|
||||
'title',
|
||||
'publishing_year',
|
||||
'num_tokens',
|
||||
'num_sentences',
|
||||
'num_unique_words',
|
||||
'num_unique_lemmas',
|
||||
'num_unique_pos',
|
||||
'num_unique_simple_pos'
|
||||
'num_sentences',
|
||||
'average_sentence_length',
|
||||
'num_unique_ent_types'
|
||||
];
|
||||
}
|
||||
|
||||
@ -68,11 +68,11 @@ class CorpusTextInfoList extends ResourceList {
|
||||
<tr>
|
||||
<th>Text<span class="sort right material-icons" data-sort="title" style="cursor:pointer; color:#aa9cc9">arrow_drop_down</span></th>
|
||||
<th>Number of tokens<span class="sort right material-icons" data-sort="num_tokens" style="cursor:pointer">arrow_drop_down</span></th>
|
||||
<th>Number of sentences<span class="sort right material-icons" data-sort="num_sentences" style="cursor:pointer">arrow_drop_down</span></th>
|
||||
<th>Number of unique words<span class="sort right material-icons" data-sort="num_unique_words" style="cursor:pointer">arrow_drop_down</span></th>
|
||||
<th>Number of unique lemmas<span class="sort right material-icons" data-sort="num_unique_lemmas" style="cursor:pointer">arrow_drop_down</span></th>
|
||||
<th>Number of unique pos<span class="sort right material-icons" data-sort="num_unique_pos" style="cursor:pointer">arrow_drop_down</span></th>
|
||||
<th>Number of unique simple pos<span class="sort right material-icons" data-sort="num_unique_simple_pos" style="cursor:pointer">arrow_drop_down</span></th>
|
||||
<th>Number of sentences<span class="sort right material-icons" data-sort="num_sentences" style="cursor:pointer">arrow_drop_down</span></th>
|
||||
<th>Average sentence length<span class="sort right material-icons" data-sort="average_sentence_length" style="cursor:pointer">arrow_drop_down</span></th>
|
||||
<th>Number of unique entity types<span class="sort right material-icons" data-sort="num_unique_ent_types" style="cursor:pointer">arrow_drop_down</span></th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody class="list"></tbody>
|
||||
@ -83,14 +83,14 @@ class CorpusTextInfoList extends ResourceList {
|
||||
|
||||
mapResourceToValue(corpusTextData) {
|
||||
return {
|
||||
title: corpusTextData.values.title,
|
||||
publishing_year: corpusTextData.values.publishing_year,
|
||||
num_tokens: corpusTextData.counts.token,
|
||||
num_sentences: corpusTextData.counts.s,
|
||||
num_unique_words: Object.entries(corpusTextData.freqs.word).length,
|
||||
num_unique_lemmas: Object.entries(corpusTextData.freqs.lemma).length,
|
||||
num_unique_pos: Object.entries(corpusTextData.freqs.pos).length,
|
||||
num_unique_simple_pos: Object.entries(corpusTextData.freqs.simple_pos).length
|
||||
title: corpusTextData.title,
|
||||
publishing_year: corpusTextData.publishing_year,
|
||||
num_tokens: corpusTextData.num_tokens,
|
||||
num_unique_words: corpusTextData.num_unique_words,
|
||||
num_unique_lemmas: corpusTextData.num_unique_lemmas,
|
||||
num_sentences: corpusTextData.num_sentences,
|
||||
average_sentence_length: corpusTextData.average_sentence_length,
|
||||
num_unique_ent_types: corpusTextData.num_unique_ent_types
|
||||
};
|
||||
}
|
||||
|
||||
|
||||
@ -19,6 +19,7 @@
|
||||
<div class="row" id="corpus-analysis-app-overview">
|
||||
<div class="col s12">
|
||||
<h1>{{ title }}</h1>
|
||||
</div>
|
||||
|
||||
{% for extension in extensions %}
|
||||
<div class="col s3">
|
||||
@ -30,75 +31,50 @@
|
||||
</div>
|
||||
</div>
|
||||
{% endfor %}
|
||||
</div>
|
||||
|
||||
<div class="row">
|
||||
<div class="row">
|
||||
<div class="col s12">
|
||||
<h4><i class="material-icons left">query_stats</i>Visualizations</h4>
|
||||
</div>
|
||||
</div>
|
||||
<div class="row">
|
||||
<div class="col s2">
|
||||
<div class="card hoverable" style="border-radius: 10px !important; background-color:#6b3f89; color:white">
|
||||
<div class="card-content" style="padding:10px !important; text-align:center;">
|
||||
<p>Number of tokens</p>
|
||||
<span class="card-title corpus-num-tokens"></span>
|
||||
<div class="col s4" >
|
||||
<div class="card hoverable">
|
||||
<div class="card-content">
|
||||
<span class="card-title">General information about the Corpus</span>
|
||||
<p></p>
|
||||
<br>
|
||||
<ul class="corpus-general-info-list">
|
||||
<li class="corpus-num-tokens"></li>
|
||||
<br>
|
||||
<li class="corpus-text-count"></li>
|
||||
<br>
|
||||
<li class="corpus-num-unique-words"></li>
|
||||
<br>
|
||||
<li class="corpus-num-unique-lemmas"></li>
|
||||
<br>
|
||||
<li class="corpus-num-sentences"></li>
|
||||
<br>
|
||||
<li class="corpus-average-sentence-length"></li>
|
||||
<br>
|
||||
<li class="corpus-num-ent-types"></li>
|
||||
<br>
|
||||
<li class="corpus-num-unique-ent-types"></li>
|
||||
<br>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="col s2">
|
||||
<div class="card hoverable" style="border-radius: 10px !important; background-color:#6b3f89; color:white">
|
||||
<div class="card-content" style="padding:10px !important; text-align:center">
|
||||
<p>Number of sentences</p>
|
||||
<span class="card-title corpus-num-s"></span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="col s2">
|
||||
<div class="card hoverable" style="border-radius: 10px !important; background-color:#6b3f89; color:white">
|
||||
<div class="card-content" style="padding:10px !important; text-align:center">
|
||||
<p>Number of unique words</p>
|
||||
<span class="card-title corpus-num-unique-words"></span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="col s2">
|
||||
<div class="card hoverable" style="border-radius: 10px !important; background-color:#6b3f89; color:white">
|
||||
<div class="card-content" style="padding:10px !important; text-align:center">
|
||||
<p>Number of unique lemmas</p>
|
||||
<span class="card-title corpus-num-unique-lemmas"></span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="col s2">
|
||||
<div class="card hoverable" style="border-radius: 10px !important; background-color:#6b3f89; color:white">
|
||||
<div class="card-content" style="padding:10px !important; text-align:center">
|
||||
<p>Number of unique pos</p>
|
||||
<span class="card-title corpus-num-unique-pos"></span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="col s2">
|
||||
<div class="card hoverable" style="border-radius: 10px !important; background-color:#6b3f89; color:white">
|
||||
<div class="card-content" style="padding:10px !important; text-align:center">
|
||||
<p>Number of unique simple_pos</p>
|
||||
<span class="card-title corpus-num-unique-simple-pos"></span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="row">
|
||||
<div class="col s12">
|
||||
<div class="col s8">
|
||||
<div class="card hoverable">
|
||||
<div class="card-content">
|
||||
<span class="card-title">Text information</span>
|
||||
<div class="chip text-count-chip" style="background-color:#6b3f89; color:white""></div>
|
||||
<div class="corpus-text-info-list no-autoinit"></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="row">
|
||||
<div class="col s3">
|
||||
</div>
|
||||
<div class="row">
|
||||
<div class="col 12">
|
||||
<div class="card hoverable">
|
||||
<div class="card-content">
|
||||
<span class="card-title">Text proportions within the corpus</span>
|
||||
@ -106,25 +82,6 @@
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="col s3">
|
||||
<div class="card hoverable">
|
||||
<div class="card-content">
|
||||
<span class="card-title">Word frequencies</span>
|
||||
<div id="word-frequencies-graphic"></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="col s6">
|
||||
<div class="card hoverable">
|
||||
<div class="card-content">
|
||||
<span class="card-title">Word distributions</span>
|
||||
<div id="word-distributions-graphic"></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user