def rankings():
rankfig, (ra1, ra2) = plt.subplots(2, figsize=(10.24, 7.68))
dframe1 = pd.read_sql_query("select * from domains order by hits desc",
engine)
title1 = dframe1['title']
hitRank1 = []
for i in range(0, len(title1)):
hitRank1.append(i)
hits1 = dframe1['hits']
dframe2 = pd.read_sql_query("select * from sources order by hits desc",
engine)
hits2 = dframe2['hits']
hitRank2 = []
for i in range(0, len(hits2)):
hitRank2.append(i)
ra1.plot(hitRank1, hits1, color='orange')
ra1.set_title('Hits by Rank, .onions')
ra2.plot(hitRank2, hits2, color='orange')
ra2.set_title('Hits by Rank, Sources')
for ax in (ra1, ra2):
ax.set(xlabel='Rank', ylabel='Hits')
plt.tight_layout(w_pad=1)
canvasD = fg(rankfig)
img3 = BytesIO()
rankfig.savefig(img3)
img3.seek(0)
return send_file(img3, mimetype='image/png')
def figure2():
fig2, ax2 = plt.subplots(figsize=(10.24, 7.68))
dframeS = pd.read_sql_query(
"select * from sources order by hits desc limit 12",
engine).drop([2, 8])
domainS = dframeS['domain']
hitsS = dframeS['hits']
plt.barh(domainS, hitsS, align='center', color='orange')
plt.xlabel('Hits')
plt.ylabel('Domain')
ax2.invert_yaxis()
plt.title('Hits / Top 10 .onion Sources')
plt.tight_layout(w_pad=1)
canvasS = fg(fig2)
img2 = BytesIO()
fig2.savefig(img2)
img2.seek(0)
return send_file(img2, mimetype='image/png')
def figure1():
fig, ax = plt.subplots(figsize=(10.24, 7.68))
dframeD = pd.read_sql_query(
"select * from domains order by hits desc limit 10", engine)
titleD = dframeD['title']
domainD = dframeD['domain']
hitsD = dframeD['hits']
plt.barh(domainD, hitsD, align='center', color='orange')
plt.xlabel('Hits')
plt.ylabel('Domain')
ax.invert_yaxis()
plt.title('Hits / Top 10 .onion Domains')
plt.tight_layout(w_pad=1)
canvasD = fg(fig)
img = BytesIO()
fig.savefig(img)
img.seek(0)
return send_file(img, mimetype='image/png')
def status_pie():
fig, ax = plt.subplots(figsize=(10.24, 7.68))
status = pd.read_sql_query(
"select count(*) from timestamps where timestamp like '%04/%' and status='200'",
engine)
status = status.rename(index={0: 1})
status = status.append(
pd.read_sql_query(
"select count(*) from timestamps where timestamp like '%04/%' and status like '%30%'",
engine))
status = status.rename(index={0: 2})
status = status.append(
pd.read_sql_query(
"select count(*) from timestamps where timestamp like '%04/%' and status like '%40%'",
engine))
status = status.rename(index={0: 3})
status = status.append(
pd.read_sql_query(
"select count(*) from timestamps where timestamp like '%04/%' and status like '%50%'",
engine))
status = status.rename(index={0: 4})
status = status.append(
pd.read_sql_query(
"select count(*) from timestamps where timestamp like '%04/%' and status='ConnectTimeout'",
engine))
status = status.rename(index={0: 5})
status = status.append(
pd.read_sql_query(
"select count(*) from timestamps where timestamp like '%04/%' and status='SSLError'",
engine))
status = status.rename(index={0: 6})
status = status.append(
pd.read_sql_query(
"select count(*) from timestamps where timestamp like '%04/%' and status like '%Attribute%'",
engine))
status = status.rename(index={0: 7})
status = status.append(
pd.read_sql_query(
"select count(*) from timestamps where timestamp like '%04/%' and status like '%Read%'",
engine))
status = status.rename(index={0: 8})
status = status.append(
pd.read_sql_query(
"select count(*) from timestamps where timestamp like '%04/%' and status like '%Connection%'",
engine))
status = status.rename(columns={'count(*)': 'Hits'}, index={0: 9})
status = status.rename(index={
1: 0,
2: 1,
3: 2,
4: 3,
5: 4,
6: 5,
7: 6,
8: 7,
9: 8
})
newCol = pd.DataFrame([
'200 Status', '300 Series Status', '400 Series Status',
'500 Series Status', 'Connect Timeout', 'SSLError', 'Attribute Error',
'Read Timeout', 'Connection Error'
],
columns=['Status'])
dframe = pd.DataFrame({
'Status': newCol['Status'],
'Hits': status['Hits']
}).sort_values('Hits', ascending=False)
labels = dframe['Status'].drop([0, 4, 1])
sizes = dframe['Hits'].drop([0, 4, 1])
ax.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=45)
ax.axis('equal')
canvasD = fg(fig)
img5 = BytesIO()
fig.savefig(img5)
img5.seek(0)
return send_file(img5, mimetype='image/png')
def statuses():
sfig, (ax1, ax2) = plt.subplots(2, figsize=(11.36, 13.37))
status = pd.read_sql_query(
"select count(*) from timestamps where timestamp like '%04/%' and status='200'",
engine)
status = status.rename(index={0: 1})
status = status.append(
pd.read_sql_query(
"select count(*) from timestamps where timestamp like '%04/%' and status like '%30%'",
engine))
status = status.rename(index={0: 2})
status = status.append(
pd.read_sql_query(
"select count(*) from timestamps where timestamp like '%04/%' and status like '%40%'",
engine))
status = status.rename(index={0: 3})
status = status.append(
pd.read_sql_query(
"select count(*) from timestamps where timestamp like '%04/%' and status like '%50%'",
engine))
status = status.rename(index={0: 4})
status = status.append(
pd.read_sql_query(
"select count(*) from timestamps where timestamp like '%04/%' and status='ConnectTimeout'",
engine))
status = status.rename(index={0: 5})
status = status.append(
pd.read_sql_query(
"select count(*) from timestamps where timestamp like '%04/%' and status='SSLError'",
engine))
status = status.rename(index={0: 6})
status = status.append(
pd.read_sql_query(
"select count(*) from timestamps where timestamp like '%04/%' and status like '%Attribute%'",
engine))
status = status.rename(index={0: 7})
status = status.append(
pd.read_sql_query(
"select count(*) from timestamps where timestamp like '%04/%' and status like '%Read%'",
engine))
status = status.rename(index={0: 8})
status = status.append(
pd.read_sql_query(
"select count(*) from timestamps where timestamp like '%04/%' and status like '%Connection%'",
engine))
status = status.rename(columns={'count(*)': 'Hits'}, index={0: 9})
status = status.rename(index={
1: 0,
2: 1,
3: 2,
4: 3,
5: 4,
6: 5,
7: 6,
8: 7,
9: 8
})
newCol = pd.DataFrame([
'200 Status', '300 Series Status', '400 Series Status',
'500 Series Status', 'Connect Timeout', 'SSLError', 'Attribute Error',
'Read Timeout', 'Connection Error'
],
columns=['Status'])
dframe = pd.DataFrame({
'Status': newCol['Status'],
'Hits': status['Hits']
}).sort_values('Hits', ascending=False)
ax1.set_title('Hits by Status')
ax2.set_title('Hits by Status')
bar1 = ax1.barh(dframe['Status'],
dframe['Hits'],
align='center',
color='orange')
ax1.invert_yaxis()
bar2 = ax2.barh(dframe['Status'].drop([4]),
dframe['Hits'].drop([4]),
align='center',
color='orange')
ax2.invert_yaxis()
for index, value in enumerate(dframe['Hits']):
ax1.text(value, index, value)
for index, value in enumerate(dframe['Hits'].drop([4])):
ax2.text(value, index, value)
for ax in (ax1, ax2):
ax.set(xlabel='Hits', ylabel='Status')
plt.tight_layout(w_pad=1)
canvasD = fg(sfig)
img4 = BytesIO()
sfig.savefig(img4)
img4.seek(0)
return send_file(img4, mimetype='image/png')