def read_emotions(mode, filter_value): databases = ['ms_emotions_db'] df_list = [] for db_name in databases: client = MongoClient() db = client[db_name] collections = db.collection_names(include_system_collections=False) if mode == 'Fight': collections = [x for x in collections if x[-10] != '0'] else: collections = [x for x in collections if x[-10] == '0'] for collection_name in collections: collection = db[collection_name] df_list.append(pd.DataFrame(list(collection.find()))) inst_df = read_instagram_posts(mode) inst_df = Filtering.filtering_by_users_on_photo(inst_df, filter_value) df = pd.concat(df_list, ignore_index=True) df = df[df['post_id'].isin(inst_df['id'].get_values())] # df.drop(['_id', 'post_id' ], 1, inplace=True) return df
def distrib_graphs(mode, filter_value): df = LoadingData.read_instagram_posts_to_dataframe(mode) emo_df = LoadingData.read_microsoft_emotions_to_dataframe(mode, include_non_emotions_column=True) df = Filtering.filtering_by_users_on_photo(df, filter_value) emo_df = emo_df[emo_df['post_id'].isin(df['id'].get_values())] data = emo_df.drop(['_id', 'post_id'], axis=1) signs = list(data.columns) for sign in signs: if sign == 'anger': distr_data = data[sign].get_values() distr_data *= 100 f, axis = plt.subplots(1, 1, sharex=True) plt.title(sign) if mode == 'Fight': axis.hist(distr_data, color='#DC143C') f.savefig( '/media/vasiliy/66E473BDE4738DD5/StadiumProject/Graphs/Microsoft/Emotions_distribution/Filtered_by_users/gh' + str( filter_value) + '/' + mode + '_' + sign + '.png', format='png', orientation='landscape') else: axis.hist(distr_data, color='#000080') f.savefig( '/media/vasiliy/66E473BDE4738DD5/StadiumProject/Graphs/Microsoft/Emotions_distribution/Filtered_by_users/gh' + str( filter_value) + '/' + mode + '_' + sign + '.png', format='png', orientation='landscape') elif sign == 'contempt': distr_data = data[sign].get_values() distr_data *= 100 f, axis = plt.subplots(1, 1, sharex=True) plt.title(sign) if mode == 'Fight': axis.hist(distr_data, color='#DC143C') f.savefig( '/media/vasiliy/66E473BDE4738DD5/StadiumProject/Graphs/Microsoft/Emotions_distribution/Filtered_by_users/gh' + str( filter_value) + '/' + mode + '_' + sign + '.png', format='png', orientation='landscape') else: axis.hist(distr_data, color='#000080') f.savefig( '/media/vasiliy/66E473BDE4738DD5/StadiumProject/Graphs/Microsoft/Emotions_distribution/Filtered_by_users/gh' + str( filter_value) + '/' + mode + '_' + sign + '.png', format='png', orientation='landscape') elif sign == 'disgust': distr_data = data[sign].get_values() distr_data *= 100 f, axis = plt.subplots(1, 1, sharex=True) plt.title(sign) if mode == 'Fight': axis.hist(distr_data, color='#DC143C') f.savefig( '/media/vasiliy/66E473BDE4738DD5/StadiumProject/Graphs/Microsoft/Emotions_distribution/Filtered_by_users/gh' + str( filter_value) + '/' + mode + '_' + sign + '.png', format='png', orientation='landscape') else: axis.hist(distr_data, color='#000080') f.savefig( '/media/vasiliy/66E473BDE4738DD5/StadiumProject/Graphs/Microsoft/Emotions_distribution/Filtered_by_users/gh' + str( filter_value) + '/' + mode + '_' + sign + '.png', format='png', orientation='landscape') elif sign == 'fear': distr_data = data[sign].get_values() distr_data *= 100 f, axis = plt.subplots(1, 1, sharex=True) plt.title(sign) if mode == 'Fight': axis.hist(distr_data, color='#DC143C') f.savefig( '/media/vasiliy/66E473BDE4738DD5/StadiumProject/Graphs/Microsoft/Emotions_distribution/Filtered_by_users/gh' + str( filter_value) + '/' + mode + '_' + sign + '.png', format='png', orientation='landscape') else: axis.hist(distr_data, color='#000080') f.savefig( '/media/vasiliy/66E473BDE4738DD5/StadiumProject/Graphs/Microsoft/Emotions_distribution/Filtered_by_users/gh' + str( filter_value) + '/' + mode + '_' + sign + '.png', format='png', orientation='landscape') elif sign == 'neutral': distr_data = data[sign].get_values() distr_data *= 100 f, axis = plt.subplots(1, 1, sharex=True) plt.title(sign) if mode == 'Fight': axis.hist(distr_data, color='#DC143C') f.savefig( '/media/vasiliy/66E473BDE4738DD5/StadiumProject/Graphs/Microsoft/Emotions_distribution/Filtered_by_users/gh' + str( filter_value) + '/' + mode + '_' + sign + '.png', format='png', orientation='landscape') else: axis.hist(distr_data, color='#000080') f.savefig( '/media/vasiliy/66E473BDE4738DD5/StadiumProject/Graphs/Microsoft/Emotions_distribution/Filtered_by_users/gh' + str( filter_value) + '/' + mode + '_' + sign + '.png', format='png', orientation='landscape') elif sign == 'sadness': distr_data = data[sign].get_values() distr_data *= 100 f, axis = plt.subplots(1, 1, sharex=True) plt.title(sign) if mode == 'Fight': axis.hist(distr_data, color='#DC143C') f.savefig( '/media/vasiliy/66E473BDE4738DD5/StadiumProject/Graphs/Microsoft/Emotions_distribution/Filtered_by_users/gh' + str( filter_value) + '/' + mode + '_' + sign + '.png', format='png', orientation='landscape') else: axis.hist(distr_data, color='#000080') f.savefig( '/media/vasiliy/66E473BDE4738DD5/StadiumProject/Graphs/Microsoft/Emotions_distribution/Filtered_by_users/gh' + str( filter_value) + '/' + mode + '_' + sign + '.png', format='png', orientation='landscape') elif sign == 'surprise': distr_data = data[sign].get_values() distr_data *= 100 f, axis = plt.subplots(1, 1, sharex=True) plt.title(sign) if mode == 'Fight': axis.hist(distr_data, color='#DC143C') f.savefig( '/media/vasiliy/66E473BDE4738DD5/StadiumProject/Graphs/Microsoft/Emotions_distribution/Filtered_by_users/gh' + str( filter_value) + '/' + mode + '_' + sign + '.png', format='png', orientation='landscape') else: axis.hist(distr_data, color='#000080') f.savefig( '/media/vasiliy/66E473BDE4738DD5/StadiumProject/Graphs/Microsoft/Emotions_distribution/Filtered_by_users/gh' + str( filter_value) + '/' + mode + '_' + sign + '.png', format='png', orientation='landscape') else: f, axis = plt.subplots(1, 1, sharex=True) distr_data = data[sign].get_values() distr_data *= 100 plt.title(sign) if mode == 'Fight': axis.hist(distr_data, color='#DC143C') f.savefig( '/media/vasiliy/66E473BDE4738DD5/StadiumProject/Graphs/Microsoft/Emotions_distribution/Filtered_by_users/gh' + str( filter_value) + '/' + mode + '_' + sign + '.png', format='png', orientation='landscape') else: axis.hist(distr_data, color='#000080') f.savefig( '/media/vasiliy/66E473BDE4738DD5/StadiumProject/Graphs/Microsoft/Emotions_distribution/Filtered_by_users/gh' + str( filter_value) + '/' + mode + '_' + sign + '.png', format='png', orientation='landscape')