flattened results in data_type
This commit is contained in:
Klemek
@@ -23,49 +23,45 @@ class Composition:
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self.spoilers = 0
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def to_string(self, msg_count: int) -> List[str]:
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ret = []
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ret += [
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f"- **avg. characters / message**: {self.total_characters/msg_count:.2f}"
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]
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if self.plain_text > 0:
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ret += [
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f"- **plain text messages**: {self.plain_text:,} ({percent(self.plain_text/msg_count)})"
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]
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if self.edited > 0:
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ret += [
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f"- **edited messages**: {self.edited:,} ({percent(self.edited/msg_count)})"
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]
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if self.everyone > 0:
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ret += [
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f"- **@\u200beveryone**: {self.everyone:,} ({percent(self.everyone/msg_count)})"
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]
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if self.mentions > 0:
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ret += [
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f"- **mentions**: {self.mentions:,} (in {percent(self.mention_msg/msg_count)} of msg, avg. {precise(self.mentions/msg_count)}/msg)",
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]
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if self.answers > 0:
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ret += [
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f"- **answers**: {self.answers:,} ({percent(self.answers/msg_count)})"
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]
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total_emotes = val_sum(self.emotes)
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if total_emotes > 0:
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top_emote = top_key(self.emotes)
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ret += [
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f"- **emojis**: {total_emotes:,} (in {percent(self.emote_msg/msg_count)} of msg, avg. {precise(total_emotes/msg_count)}/msg)",
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f"- **most used emoji**: {top_emote} ({plural(self.emotes[top_emote], 'time')}, {percent(self.emotes[top_emote]/total_emotes)})",
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]
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if self.emote_only > 0:
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ret += [
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f"- **emoji-only messages**: {self.emote_only:,} ({percent(self.emote_only/msg_count)})"
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]
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if self.images > 0:
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ret += [f"- **images**: {self.images:,} ({percent(self.images/msg_count)})"]
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if self.links > 0:
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ret += [f"- **links**: {self.links:,} ({percent(self.link_msg/msg_count)})"]
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if self.spoilers > 0:
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ret += [
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f"- **spoilers**: {self.spoilers:,} ({percent(self.spoilers/msg_count)})"
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]
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if self.tts > 0:
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ret += [f"- **tts messages**: {self.tts:,} ({percent(self.tts/msg_count)})"]
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top_emote = top_key(self.emotes)
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ret = [
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f"- **avg. characters / message**: {self.total_characters/msg_count:.2f}",
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f"- **plain text messages**: {self.plain_text:,} ({percent(self.plain_text/msg_count)})"
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if self.plain_text > 0
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else "",
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f"- **edited messages**: {self.edited:,} ({percent(self.edited/msg_count)})"
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if self.edited > 0
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else "",
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f"- **@\u200beveryone**: {self.everyone:,} ({percent(self.everyone/msg_count)})"
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if self.everyone > 0
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else "",
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f"- **mentions**: {self.mentions:,} (in {percent(self.mention_msg/msg_count)} of msg, avg. {precise(self.mentions/msg_count)}/msg)"
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if self.mentions > 0
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else "",
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f"- **answers**: {self.answers:,} ({percent(self.answers/msg_count)})"
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if self.answers > 0
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else "",
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f"- **emojis**: {total_emotes:,} (in {percent(self.emote_msg/msg_count)} of msg, avg. {precise(total_emotes/msg_count)}/msg)"
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if total_emotes > 0
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else "",
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f"- **most used emoji**: {top_emote} ({plural(self.emotes[top_emote], 'time')}, {percent(self.emotes[top_emote]/total_emotes)})"
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if total_emotes > 0
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else "",
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f"- **emoji-only messages**: {self.emote_only:,} ({percent(self.emote_only/msg_count)})"
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if self.emote_only > 0
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else "",
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f"- **images**: {self.images:,} ({percent(self.images/msg_count)})"
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if self.images > 0
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else "",
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f"- **links**: {self.links:,} ({percent(self.link_msg/msg_count)})"
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if self.links > 0
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else "",
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f"- **spoilers**: {self.spoilers:,} ({percent(self.spoilers/msg_count)})"
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if self.spoilers > 0
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else "",
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f"- **tts messages**: {self.tts:,} ({percent(self.tts/msg_count)})"
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if self.tts > 0
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else "",
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]
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return ret
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@@ -67,13 +67,8 @@ class Frequency:
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f"- **busiest hour ever**: {str_datetime(self.busiest_hour)} ({from_now(self.busiest_hour)}, {self.busiest_hour_count} msg)",
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f"- **longest break**: {plural(round(self.longest_break.total_seconds()/3600), 'hour')} ({plural(self.longest_break.days,'day')}) from {str_datetime(self.longest_break_start)} ({from_now(self.longest_break_start)})",
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f"- **avg. streak**: {precise(sum(self.streaks)/len(self.streaks), precision=3)} msg",
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f"- **longest streak**: {self.longest_streak:,} msg from {str_datetime(self.longest_streak_start)} ({from_now(self.longest_streak_start)})"
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if member_specific
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else f"- **longest streak**: {mention(self.longest_streak_author)} ({self.longest_streak:,} msg from {str_datetime(self.longest_streak_start)}, {from_now(self.longest_streak_start)})",
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]
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if member_specific:
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ret += [
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f"- **longest streak**: {self.longest_streak:,} msg from {str_datetime(self.longest_streak_start)} ({from_now(self.longest_streak_start)})"
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]
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else:
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ret += [
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f"- **longest streak**: {mention(self.longest_streak_author)} ({self.longest_streak:,} msg from {str_datetime(self.longest_streak_start)}, {from_now(self.longest_streak_start)})"
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]
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return ret
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+60
-64
@@ -25,74 +25,70 @@ class Presence:
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show_top_channel: bool,
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member_specific: bool,
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) -> List[str]:
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ret = []
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if chan_count is None:
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type = "server's"
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elif chan_count == 1:
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type = "channel's"
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else:
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type = "channels'"
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if member_specific:
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ret += [
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f"- **messages**: {msg_count:,} ({percent(msg_count/total_msg)} of {type})"
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]
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else:
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top_member = top_key(self.messages)
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ret += [
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f"- **top messages**: {mention(top_member)} ({self.messages[top_member]:,} msg, {percent(self.messages[top_member]/val_sum(self.messages))})"
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]
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if show_top_channel:
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top_channel = top_key(self.channel_usage)
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channel_sum = val_sum(self.channel_usage)
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found_in = sorted(
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self.channel_usage,
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key=lambda k: self.channel_usage[k] / self.channel_total[k],
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)[-1]
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ret += [
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f"- **most visited channel**: {channel_mention(top_channel)} ({self.channel_usage[top_channel]:,} msg, {percent(self.channel_usage[top_channel]/channel_sum)})",
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]
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if member_specific:
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ret += [
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f"- **most contributed channel**: {channel_mention(found_in)} ({self.channel_usage[found_in]:,} msg, {percent(self.channel_usage[found_in]/self.channel_total[found_in])} of {type})"
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]
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if member_specific:
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if len(self.mentions) > 0:
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top_mention = top_key(self.mentions)
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mention_sum = val_sum(self.mentions)
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ret += [
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f"- **was mentioned**: {plural(mention_sum, 'time')} ({percent(mention_sum/val_sum(self.mention_count))} of {type})",
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f"- **mostly mentioned by**: {mention(top_mention)} ({plural(self.mentions[top_mention], 'time')}, {percent(self.mentions[top_mention]/mention_sum)})",
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]
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if len(self.mention_others) > 0:
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top_mention = top_key(self.mention_others)
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mention_sum = val_sum(self.mention_others)
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if member_specific:
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ret += [
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f"- **mentioned others**: {plural(mention_sum, 'time')} ({percent(mention_sum/val_sum(self.mention_count))} of {type})",
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f"- **mostly mentioned**: {mention(top_mention)} ({plural(self.mention_others[top_mention], 'time')}, {percent(self.mention_others[top_mention]/mention_sum)})",
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]
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else:
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top_member = top_key(self.mention_count)
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ret += [
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f"- **mentioned**: {plural(mention_sum, 'time')} ({mention(top_member)}, {percent(self.mention_count[top_member]/val_sum(self.mention_count))})",
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f"- **top mentions**: {mention(top_member)} ({plural(self.mention_count[top_member], 'time')}, {percent(self.mention_count[top_member]/val_sum(self.mention_count))})",
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f"- **most mentioned**: {mention(top_mention)} ({plural(self.mention_others[top_mention], 'time')}, {percent(self.mention_others[top_mention]/mention_sum)})",
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]
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if len(self.reactions) > 0:
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total_used = val_sum(self.reactions)
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top_reaction = top_key(self.reactions)
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ret += [
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f"- **reactions**: {plural(total_used, 'time')}",
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f"- **most used reaction**: {top_reaction} ({plural(self.reactions[top_reaction], 'time')}, {percent(self.reactions[top_reaction]/total_used)})",
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]
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if member_specific:
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ret[
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-2
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] += f" ({percent(total_used/val_sum(self.used_reaction))} of {type})"
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else:
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top_member = top_key(self.used_reaction)
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ret.insert(
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-1,
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f"- **top reactions**: {mention(top_member)} ({plural(self.used_reaction[top_member], 'time')}, {percent(self.used_reaction[top_member]/val_sum(self.used_reaction))})",
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)
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top_member = top_key(self.messages)
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top_channel = top_key(self.channel_usage)
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channel_sum = val_sum(self.channel_usage)
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found_in = top_key(
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self.channel_usage,
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key=lambda k: self.channel_usage[k] / self.channel_total[k],
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)
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top_mention = top_key(self.mentions)
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mention_sum = val_sum(self.mentions)
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top_mention_others = top_key(self.mention_others)
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mention_others_sum = val_sum(self.mention_others)
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top_member_mentioned = top_key(self.mention_count)
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total_reaction_used = val_sum(self.reactions)
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top_reaction = top_key(self.reactions)
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top_reaction_member = top_key(self.used_reaction)
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ret = [
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f"- **messages**: {msg_count:,} ({percent(msg_count/total_msg)} of {type})"
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if member_specific
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else f"- **top messages**: {mention(top_member)} ({self.messages[top_member]:,} msg, {percent(self.messages[top_member]/val_sum(self.messages))})",
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f"- **most visited channel**: {channel_mention(top_channel)} ({self.channel_usage[top_channel]:,} msg, {percent(self.channel_usage[top_channel]/channel_sum)})"
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if show_top_channel
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else "",
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f"- **most contributed channel**: {channel_mention(found_in)} ({self.channel_usage[found_in]:,} msg, {percent(self.channel_usage[found_in]/self.channel_total[found_in])} of {type})"
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if show_top_channel and member_specific
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else "",
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f"- **was mentioned**: {plural(mention_sum, 'time')} ({percent(mention_sum/val_sum(self.mention_count))} of {type})"
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if member_specific and len(self.mentions) > 0
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else "",
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f"- **mostly mentioned by**: {mention(top_mention)} ({plural(self.mentions[top_mention], 'time')}, {percent(self.mentions[top_mention]/mention_sum)})"
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if member_specific and len(self.mentions) > 0
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else "",
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f"- **mentioned others**: {plural(mention_others_sum, 'time')} ({percent(mention_others_sum/val_sum(self.mention_count))} of {type})"
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if len(self.mention_others) > 0 and member_specific
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else "",
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f"- **mostly mentioned**: {mention(top_mention_others)} ({plural(self.mention_others[top_mention_others], 'time')}, {percent(self.mention_others[top_mention_others]/mention_others_sum)})"
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if len(self.mention_others) > 0 and member_specific
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else "",
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f"- **mentioned**: {plural(mention_others_sum, 'time')} ({mention(top_member_mentioned)}, {percent(self.mention_count[top_member_mentioned]/val_sum(self.mention_count))})"
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if len(self.mention_others) > 0 and not member_specific
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else "",
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f"- **top mentions**: {mention(top_member_mentioned)} ({plural(self.mention_count[top_member_mentioned], 'time')}, {percent(self.mention_count[top_member_mentioned]/val_sum(self.mention_count))})"
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if len(self.mention_others) > 0 and not member_specific
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else "",
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f"- **most mentioned**: {mention(top_mention_others)} ({plural(self.mention_others[top_mention_others], 'time')}, {percent(self.mention_others[top_mention_others]/mention_others_sum)})"
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if len(self.mention_others) > 0 and not member_specific
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else "",
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f"- **reactions**: {plural(total_reaction_used, 'time')}"
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if len(self.reactions) > 0 and not member_specific
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else "",
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f"- **reactions**: {plural(total_reaction_used, 'time')} ({percent(total_reaction_used/val_sum(self.used_reaction))} of {type})"
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if len(self.reactions) > 0 and member_specific
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else "",
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f"- **top reactions**: {mention(top_reaction_member)} ({plural(self.used_reaction[top_reaction_member], 'time')}, {percent(self.used_reaction[top_reaction_member]/val_sum(self.used_reaction))})"
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if len(self.reactions) > 0 and not member_specific
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else "",
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f"- **most used reaction**: {top_reaction} ({plural(self.reactions[top_reaction], 'time')}, {percent(self.reactions[top_reaction]/total_reaction_used)})"
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if len(self.reactions) > 0
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else "",
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]
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return ret
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@@ -94,5 +94,6 @@ class ChannelLogs:
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channel = dict(self.__dict__)
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channel.pop("channel", None)
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channel.pop("guild", None)
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channel.pop("start_date", None)
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channel["messages"] = [message.dict() for message in self.messages]
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return channel
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+11
-3
@@ -1,5 +1,5 @@
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from calendar import month
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from typing import List, Dict, Union, Optional, Any
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from typing import Callable, List, Dict, Union, Optional, Any
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import os
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import logging
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import discord
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@@ -117,11 +117,19 @@ def no_duplicate(seq: list) -> list:
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# DICTS
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def top_key(d: Dict[Union[str, int], int]) -> Union[str, int]:
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return sorted(d, key=lambda k: d[k])[-1]
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def top_key(
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d: Dict[Union[str, int], int], key: Optional[Callable] = None
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) -> Union[str, int]:
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if len(d) == 0:
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return None
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if key is None:
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key = lambda k: d[k]
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return sorted(d, key=key)[-1]
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def val_sum(d: Dict[Any, int]) -> int:
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if len(d) == 0:
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return 0
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return sum(d.values())
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