hedonodon/Main.py
2023-03-15 16:02:47 +01:00

121 lines
4.1 KiB
Python

from CRUDManager import CRUDManager, calculateSentimentCount, calculateSentimentMean, getYesterdaysToots
from datetime import datetime, date
from DbSetup import init_db
import locale
from MastodonAccountManager import MastodonAccountManager
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from TootCrawler import TootCrawler
from SentiTooter import translateToots, createWordCountPerSentiment
locale.setlocale(locale.LC_TIME, "en_US.UTF-8")
init_db()
print('Initialize Mastodon...')
mastodonAccountManager = MastodonAccountManager()
mastodonInstance = mastodonAccountManager.instance
"""
mastodonInstance.log_in(
'USER-EMAIL',
'PW',
to_file = 'hedonodon_usercred.secret'
)
"""
print('done!')
print('Fetching recent toots...')
tootCrawler = TootCrawler(mastodonInstance)
crudManager = CRUDManager()
lastTootId = crudManager.getLastToot()
tootsDataframe = tootCrawler.buildTootsDataframe(lastTootId)
print('done!')
print('Save toots to database...')
if not tootsDataframe.empty:
crudManager.saveToDatabase(tootsDataframe, 'Toots', useIndex=False)
print('done!')
else:
print('nothing changed since last database insert!')
print('Calculate word counts...')
yesterdaysToots = getYesterdaysToots()
translatedToots = translateToots(yesterdaysToots)
wordCountsPerSentiment = createWordCountPerSentiment(translatedToots)
print('done!')
print(wordCountsPerSentiment);
print('Calculate sentiment counts...')
sentimentsYesterday = calculateSentimentCount()
print('done!')
print('Calculate sentiment mean...')
sentimentMeansYesterday = calculateSentimentMean(sentimentsYesterday)
print('done!')
print('Save calculations to database...')
if not tootsDataframe.empty:
crudManager.saveToDatabase(dataframe=sentimentsYesterday, table='SentimentCounts', useIndex=True)
crudManager.saveToDatabase(dataframe=sentimentMeansYesterday, table='SentimentMeans', useIndex=True)
print('done!')
else:
print('nothing changed since last database insert!')
print('Create figure...')
colormap = {
'negative': '#ff9999',
'neutral': '#ffcc99',
"positive": '#99ff99'
}
todaysColors = []
for sentiment in sentimentsYesterday['sentiment'].to_numpy():
todaysColors.append(colormap[sentiment])
TodayDate = datetime.strptime(sentimentsYesterday['date'][0], '%Y-%m-%d').strftime('%d.%m.%Y')
dataframe4PieChart = sentimentsYesterday.drop('date', axis=1).set_index('sentiment')
dataframe4LineChart = crudManager.loadFromDatabase('SentimentMeans', 'date').drop('index', axis=1)
fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(10, 10))
# Pie chart.
pieChartlabels = dataframe4PieChart.index.to_numpy()
pieChart = dataframe4PieChart.plot.pie(
ax=axes[0],
y='sentimentCount',
ylabel="",
labels=dataframe4PieChart['sentimentCount'],
title=f'Moods of the toots on {TodayDate} of the local timeline on fedihum.org',
colors=todaysColors,
wedgeprops=dict(linewidth=3, edgecolor='w'),
startangle=90
)
axes[0].axis('equal')
centre_circle = plt.Circle((0, 0), 0.6, fc='white')
axes[0].add_patch(centre_circle)
chartBox = axes[0].get_position()
axes[0].legend(pieChartlabels, loc='upper right', bbox_to_anchor=(0.9, 0.9))
# Line chart.
lineChart = dataframe4LineChart.plot.line(
ax=axes[1],
title='"Mean" of sentiments, calculated from nominal values, pos(1), neu (0), neg (-1)!'
)
axes[1].grid(True)
axes[1].set_xlim([date(2023, 1, 1), date(2023, 12, 31)])
axes[1].set_ylim([-1, 1])
axes[1].xaxis.set_major_locator(mdates.MonthLocator())
axes[1].xaxis.set_minor_locator(mdates.MonthLocator(bymonthday=15))
axes[1].xaxis.set_major_formatter(plt.NullFormatter())
axes[1].xaxis.set_minor_formatter(mdates.DateFormatter('%h'))
axes[1].tick_params(which='minor', length=0)
plotFileUrl = f'./plots/{TodayDate}.png'
plt.savefig(plotFileUrl)
print('done!')
media = mastodonInstance.media_post(plotFileUrl, mime_type="image/png", description=f"Sentiment analysis of local timeline on fedihum.org, showing the moods of the toots on, and the sentiment mean up to {TodayDate}.")
mastodonInstance.status_post(f'The moods of the toots on and up to {TodayDate}.\nWord counts per sentiment:\n{wordCountsPerSentiment}', media_ids=media, language='en')