async def curation(self, ctx, *, authorperm: str):
show_all_voter = False
all_posts = False
t = PrettyTable([
"Voter", "Voting time", "Vote", "Early vote loss", "Curation",
"Performance"
])
t.align = "l"
index = 0
index += 1
comment = Comment(authorperm, steem_instance=self.stm)
payout = None
curation_rewards_SBD = comment.get_curation_rewards(
pending_payout_SBD=True, pending_payout_value=payout)
curation_rewards_SP = comment.get_curation_rewards(
pending_payout_SBD=False, pending_payout_value=payout)
rows = []
sum_curation = [0, 0, 0, 0]
max_curation = [0, 0, 0, 0, 0, 0]
highest_vote = [0, 0, 0, 0, 0, 0]
for vote in comment["active_votes"]:
vote_SBD = self.stm.rshares_to_sbd(int(vote["rshares"]))
curation_SBD = curation_rewards_SBD["active_votes"][vote["voter"]]
curation_SP = curation_rewards_SP["active_votes"][vote["voter"]]
if vote_SBD > 0:
penalty = (
(comment.get_curation_penalty(vote_time=vote["time"])) *
vote_SBD)
performance = (float(curation_SBD) / vote_SBD * 100)
else:
performance = 0
penalty = 0
vote_befor_min = ((
(vote["time"]) - comment["created"]).total_seconds() / 60)
sum_curation[0] += vote_SBD
sum_curation[1] += penalty
sum_curation[2] += float(curation_SP)
sum_curation[3] += float(curation_SBD)
row = [
vote["voter"], vote_befor_min, vote_SBD, penalty,
float(curation_SP), performance
]
if row[-1] > max_curation[-1]:
max_curation = row
if row[2] > highest_vote[2]:
highest_vote = row
rows.append(row)
sortedList = sorted(rows, key=lambda row: (row[1]), reverse=False)
new_row = []
new_row2 = []
voter = []
voter2 = []
voter = [""]
voter2 = [""]
for row in sortedList:
if show_all_voter:
if not all_posts:
voter = [row[0]]
if all_posts:
new_row[0] = "%d. %s" % (index, comment.author)
t.add_row(new_row + voter + [
"%.1f min" % row[1],
"%.3f SBD" % float(row[2]),
"%.3f SBD" % float(row[3]),
"%.3f SP" % (row[4]),
"%.1f %%" % (row[5])
])
new_row = new_row2
t.add_row(new_row2 + voter2 + ["", "", "", "", ""])
if sum_curation[0] > 0:
curation_sum_percentage = sum_curation[3] / sum_curation[0] * 100
else:
curation_sum_percentage = 0
sum_line = new_row2 + voter2
sum_line[-1] = "High. vote"
t.add_row(sum_line + [
"%.1f min" % highest_vote[1],
"%.3f SBD" % float(highest_vote[2]),
"%.3f SBD" % float(highest_vote[3]),
"%.3f SP" % (highest_vote[4]),
"%.1f %%" % (highest_vote[5])
])
sum_line[-1] = "High. Cur."
t.add_row(sum_line + [
"%.1f min" % max_curation[1],
"%.3f SBD" % float(max_curation[2]),
"%.3f SBD" % float(max_curation[3]),
"%.3f SP" % (max_curation[4]),
"%.1f %%" % (max_curation[5])
])
sum_line[-1] = "Sum"
t.add_row(sum_line + [
"-",
"%.3f SBD" % (sum_curation[0]),
"%.3f SBD" % (sum_curation[1]),
"%.3f SP" % (sum_curation[2]),
"%.2f %%" % curation_sum_percentage
])
response = "curation for %s\n" % (authorperm)
response += t.get_string()
await ctx.channel.send("```" + response + "```")
continue
total_rshares_sum = 0
for v in activeVotes:
if v["rshares"] > 0:
rshares_sum += int(v["rshares"])
total_rshares_sum += int(v["rshares"])
if v["voter"] == vote_log["voter"]:
rshares = int(v["rshares"])
curation_rshares = 0.25 * total_rshares_sum
for vote in activeVotes:
voter_rshares = int(vote["rshares"])
rshares_after = rshares_sum - rshares_before - voter_rshares
if voter_rshares > 0:
y = curation_performance(rshares_before, voter_rshares, rshares_after)
if voter_rshares > 0 and vote["voter"] == vote_log["voter"]:
performance = (1 - c.get_curation_penalty(vote_time=vote["time"])) * (curation_rshares * y) / voter_rshares * 100
if voter_rshares > 0 and voter_rshares > rshares / rshares_divider:
vote_SBD = stm.rshares_to_sbd(voter_rshares)
p = float(curation_rewards_SBD["active_votes"][vote["voter"]]) / vote_SBD * 100
if p > best_performance:
best_performance = p
best_vote_delay_min = ((vote["time"]) - c["created"]).total_seconds() / 60
if voter_rshares > 0:
rshares_before += voter_rshares
vote_log["is_pending"] = False
if acc_data is not None and acc_data["optimize_vote_delay"] and abs(vote_delay_diff) < 1.0 and not vote_log["trail_vote"]:
optimize_threshold = 1 + (acc_data["optimize_threshold"] / 100)
optimize_ma_length = acc_data["optimize_ma_length"]
