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')
