add discord bridge field to the channel model

will be used in a future change to clean up markov chains

ircbot/migrations/0019_ircchannel_discord_bridge.py

@@ -0,0 +1,18 @@
+# Generated by Django 3.2.18 on 2023-02-16 22:38
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('ircbot', '0018_ircserver_replace_irc_control_with_markdown'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='ircchannel',
+            name='discord_bridge',
+            field=models.CharField(blank=True, default='', max_length=32),
+        ),
+    ]

ircbot/models.py

@@ -104,6 +104,8 @@ class IrcChannel(models.Model):
 
     markov_learn_from_channel = models.BooleanField(default=True)
 
+    discord_bridge = models.CharField(default='', max_length=32, blank=True)
+
     class Meta:
         """Settings for the model."""
 

markov/lib.py

@@ -1,17 +1,16 @@
+"""Provide methods for manipulating markov chain processing."""
 import logging
-import random
+from random import SystemRandom as sysrand
 
 from django.db.models import Sum
 
 from markov.models import MarkovContext, MarkovState, MarkovTarget
 
-
-log = logging.getLogger('markov.lib')
+log = logging.getLogger(__name__)
 
 
 def generate_line(context, topics=None, min_words=15, max_words=30, sentence_bias=2, max_tries=5):
-    """String multiple sentences together into a coherent sentence."""
-
+    """Combine multiple sentences together into a coherent sentence."""
     tries = 0
     line = []
     min_words_per_sentence = min_words / sentence_bias
@@ -23,7 +22,7 @@ def generate_line(context, topics=None, min_words=15, max_words=30, sentence_bia
         else:
             if len(line) > 0:
                 if line[-1][-1] not in [',', '.', '!', '?', ':']:
-                    line[-1] += random.choice(['?', '.', '!'])
+                    line[-1] += sysrand.choice(['?', '.', '!'])
 
         tries += 1
 
@@ -33,7 +32,6 @@ def generate_line(context, topics=None, min_words=15, max_words=30, sentence_bia
 
 def generate_longish_sentence(context, topics=None, min_words=15, max_words=30, max_tries=100):
     """Generate a Markov chain, but throw away the short ones unless we get desperate."""
-
     sent = ""
     tries = 0
     while tries < max_tries:
@@ -52,20 +50,19 @@ def generate_longish_sentence(context, topics=None, min_words=15, max_words=30,
 
 def generate_sentence(context, topics=None, min_words=15, max_words=30):
     """Generate a Markov chain."""
-
     words = []
     # if we have topics, try to work from it and work backwards
     if topics:
-        topic_word = random.choice(topics)
+        topic_word = sysrand.choice(topics)
         topics.remove(topic_word)
-        log.debug("looking for topic '{0:s}'".format(topic_word))
+        log.debug("looking for topic '%s'", topic_word)
         new_states = MarkovState.objects.filter(context=context, v=topic_word)
 
         if len(new_states) > 0:
-            log.debug("found '{0:s}', starting backwards".format(topic_word))
+            log.debug("found '%s', starting backwards", topic_word)
             words.insert(0, topic_word)
             while len(words) <= max_words and words[0] != MarkovState._start2:
-                log.debug("looking backwards for '{0:s}'".format(words[0]))
+                log.debug("looking backwards for '%s'", words[0])
                 new_states = MarkovState.objects.filter(context=context, v=words[0])
                 # if we find a start, use it
                 if MarkovState._start2 in new_states:
@@ -87,7 +84,7 @@ def generate_sentence(context, topics=None, min_words=15, max_words=30):
 
     i = len(words)
     while words[-1] != MarkovState._stop:
-        log.debug("looking for '{0:s}','{1:s}'".format(words[i-2], words[i-1]))
+        log.debug("looking for '%s','%s'", words[i-2], words[i-1])
         new_states = MarkovState.objects.filter(context=context, k1=words[i-2], k2=words[i-1])
         log.debug("states retrieved")
 
@@ -103,7 +100,7 @@ def generate_sentence(context, topics=None, min_words=15, max_words=30):
             words.append(MarkovState._stop)
         elif len(target_hits) > 0:
             # if there's a target word in the states, pick it
-            target_hit = random.choice(target_hits)
+            target_hit = sysrand.choice(target_hits)
             log.debug("found a topic hit %s, using it", target_hit)
             topics.remove(target_hit)
             words.append(target_hit)
@@ -129,7 +126,6 @@ def generate_sentence(context, topics=None, min_words=15, max_words=30):
 
 def get_or_create_target_context(target_name):
     """Return the context for a provided nick/channel, creating missing ones."""
-
     target_name = target_name.lower()
 
     # find the stuff, or create it
@@ -156,7 +152,6 @@ def get_or_create_target_context(target_name):
 
 def get_word_out_of_states(states, backwards=False):
     """Pick one random word out of the given states."""
-
     # work around possible broken data, where a k1,k2 should have a value but doesn't
     if len(states) == 0:
         states = MarkovState.objects.filter(v=MarkovState._stop)
@@ -168,9 +163,9 @@ def get_word_out_of_states(states, backwards=False):
         # this being None probably means there's no data for this context
         raise ValueError("no markov states to generate from")
 
-    hit = random.randint(0, count_sum)
+    hit = sysrand.randint(0, count_sum)
 
-    log.debug("sum: {0:d} hit: {1:d}".format(count_sum, hit))
+    log.debug("sum: %s hit: %s", count_sum, hit)
 
     states_itr = states.iterator()
     for state in states_itr:
@@ -183,13 +178,12 @@ def get_word_out_of_states(states, backwards=False):
 
             break
 
-    log.debug("found '{0:s}'".format(new_word))
+    log.debug("found '%s'", new_word)
     return new_word
 
 
 def learn_line(line, context):
     """Create a bunch of MarkovStates for a given line of text."""
-
     log.debug("learning %s...", line[:40])
 
     words = line.split()
@@ -200,7 +194,7 @@ def learn_line(line, context):
             return
 
     for i, word in enumerate(words):
-        log.debug("'{0:s}','{1:s}' -> '{2:s}'".format(words[i], words[i+1], words[i+2]))
+        log.debug("'%s','%s' -> '%s'", words[i], words[i+1], words[i+2])
         state, created = MarkovState.objects.get_or_create(context=context,
                                                            k1=words[i],
                                                            k2=words[i+1],