petites modifs de syntaxe ailleurs

DNS

@@ -0,0 +1 @@
+https://www.youtube.com/watch?v=mC9yute2k_Q 3000

million/analyze/count_participations.py

@@ -1,23 +1,30 @@
-
-from typing import List
+from collections import Counter
+from typing import Dict, List
 from million.model.message import Message
 from million.model.participant import Participant
 
 
-def count_participations(messages: List[Message], participants: List[Participant]):
+def count_participations(
+        messages: List[Message],
+        participants: List[Participant] | None = [],
+        threshold: int | None = 0
+        ) -> Dict[str, int]:
     """
-    Count the number of messages sent by each participant
+    Count the number of messages sent by each participant,\n
+    you can specify a threshold to return only people having reached that many counts
     """
-    participations = {}
-    for participant in participants:
-        participations[participant.name] = 0
-
-    for message in messages:
-        if message.sender_name not in participations:
-            participations[message.sender_name] = 1
-        else:
-            participations[message.sender_name] += 1
+    participations = dict.fromkeys([p.name for p in participants], 0)
+    participations.update(Counter([m.sender_name for m in messages]))
+    
+    return {k: v for k,v in sorted(participations.items(), key=lambda x: -x[1]) if v >= threshold}
 
-    ordered_participations = sorted(
-        participations.items(), key=lambda x: x[1], reverse=True)
-    return [{"name": v[0], "participations": v[1]} for v in ordered_participations]
+def podium(
+        messages: List[Message],
+        top: int,
+        participants: List[Participant] | None = [],
+        ) -> Dict[str, int]:
+    """
+    Returns the N biggest counters
+    """
+    cp = count_participations(messages, participants)
+    return {k: cp[k] for idx, k in enumerate(cp) if idx < top}

million/analyze/dns_solver.py

@@ -0,0 +1,36 @@
+from typing import Dict
+
+from pydantic import BaseModel, PrivateAttr
+from million.model.message import Message
+
+_default_file_path = './DNS'
+
+class DNS_solver(BaseModel):
+    file_path:str = _default_file_path
+
+    _bank: Dict[str, int] | None = PrivateAttr(None)
+
+    def solve(self, msg: Message) -> int:
+        if self._bank == None: 
+            self._bank = self.load(_default_file_path)
+
+        k = self._get_key(msg)
+        if k and k in self._bank: return self._bank[k]
+
+        return None
+
+    def load(self, file_name: str) -> Dict[str, int]:
+        result = {}
+        with open(file_name, 'r') as f:
+            for line in f:
+                a,b = line.split()
+                result[a] = int(b)
+
+        return result
+
+    def _get_key(self, msg: Message) -> str:
+
+        # look into msg attributes
+        # find uri
+        return (msg.share or None) and msg.share.link or \
+            (msg.gifs or None) and msg.gifs[0].uri

million/analyze/find_holes.py

@@ -1,5 +1,3 @@
-
-
 from typing import List
 from million.model.message import Message
 from million.model.sequence import Sequence
@@ -23,7 +21,6 @@ def compute_sequences(messages: List[Message], accepted_max: int = 1_000_000) ->
 
     return sequences            
 
-
 def merge_duplicates(sequences: List[Sequence]) -> List[Sequence]:
     """ 
     Take sequences as an input and returns a list with every
@@ -43,8 +40,6 @@ def merge_duplicates(sequences: List[Sequence]) -> List[Sequence]:
 
     return result
 
-
-
 def invert_sequences(sequences: List[Sequence]) -> List[Sequence]:
     """ 
     Returns the sequences representing the spaces between

million/analyze/message_evaluation.py

@@ -1,38 +1,47 @@
-from math import floor
 import re
 from typing import Dict
 from million.model.message import Message
+import million.analyze.dns_solver as dns
 
-memoization: Dict[Message, int] = {}
 
-# TODO WIP
-# - DNS to resolve audio, gif, pictures with counts
-def __compute__(msg: Message) -> int:
-    value = __computeContent(msg)
+_memoization: Dict[Message, int] = {}
+_dns_solver: dns.DNS_solver = dns.DNS_solver()
 
-    memoization[msg] = value
+def get(msg: Message) -> int:
+    """
+    Returns the estimated value counted in this message
+    """
+    return _memoization.get(msg, _compute(msg))
+
+def reset(msg: Message) -> None:
+    """
+    Drop memorized value of this Message
+    """
+    if msg in _memoization:
+        _memoization.pop(msg)
+
+def reset() -> None:
+    """
+    Drop every memorized message value
+    """
+    _memoization.clear()
+
+
+def _compute(msg: Message) -> int:
+    value = _dns_solver.solve(msg) or \
+        _computeContent(msg) or \
+        None
+
+    _memoization[msg] = value
     return value
 
-def __computeContent(msg: Message) -> int:
+def _computeContent(msg: Message) -> int:
     # TODO parse potential math expressions in content
-    match = re.search(r"\d+", msg.content)
+    match = msg.content and re.search(r"\d+", msg.content)
     
     if match:
-        value = int(match[0])
+        value = int(match.group())
     else:
         value = None
     
-    return value
-
-def reset(msg: Message) -> None:
-    if msg in memoization:
-        memoization.pop(msg)
-
-def reset() -> None:
-    memoization.clear()
-
-def get(msg: Message) -> int:
-    """
-    Returns the estimated value counted in this message
-    """
-    return memoization.get(msg, __compute__(msg))
+    return value

million/analyze/retain_counts.py

@@ -1,15 +1,10 @@
-
-import re
 from typing import List
 from million.model.message import Message
+import million.analyze.message_evaluation as msg_val
 
 
 def retain_counts(messages : List[Message])-> List[Message]:
     """
-    Retain only the messages that have a content
+    Retain only the messages that have a counted value
     """
-    return [
-        m for m in messages 
-        if m.content and
-        re.search('(\d{2,}|^\d$)', m.content)
-        ]
+    return [msg for msg in messages if msg_val.get(msg)]

million/analyze/word_finder.py

@@ -4,7 +4,7 @@ from million.model.message import Message
 
 
 def _wordFilter(msg: Message, words: List[str]) -> bool:
-    rgx = r"(\b"+ r'\b|\b'.join(words) + r"\b)"
+    rgx = r"(\b"+ r"\b|\b".join(words) + r"\b)"
     return msg.content and re.search(rgx, msg.content, re.I)
 
 def findWords(messages: List[Message], words: List[str]) -> List[Message]:

million/model/fb_export.py

@@ -1,11 +1,10 @@
 from __future__ import annotations
-
 from typing import Any, List, Set
 from pydantic import BaseModel
-
 from million.model.message import Message
 from million.model.participant import Participant
 
+
 class Image(BaseModel):
     creation_timestamp: int
     uri: str
@@ -28,6 +27,7 @@ class FacebookExport(BaseModel):
     image: Image
     joinable_mode: JoinableMode
 
+
     def merge(self, other: FacebookExport) -> None:
         if self == other:
             self.messages.extend(other.messages)
@@ -37,7 +37,8 @@ class FacebookExport(BaseModel):
     def sort(self) -> None:
         self.messages.sort(key = lambda m: m.timestamp_ms)
 
-    # NOTE Toughen equality conditions ?
+
     def __eq__(self, other: FacebookExport) -> bool:
+        # NOTE Toughen equality conditions ?
         return self.title == other.title \
             and self.image == other.image

million/model/message.py

@@ -1,7 +1,7 @@
 from datetime import datetime
-from math import floor
 from typing import Any, List
-from pydantic import BaseModel
+from uuid import uuid4
+from pydantic import BaseModel, PrivateAttr, computed_field
 
 class Reaction(BaseModel):
     reaction: str
@@ -45,10 +45,18 @@ class Message(BaseModel):
     is_unsent: bool | None = None
     is_geoblocked_for_viewer: bool
 
+    _id: str = PrivateAttr(default_factory=lambda: str(uuid4()))
+
+
     def __str__(self) -> str:
         dt = datetime.fromtimestamp(self.timestamp_ms / 1000)
         dt_str = dt.strftime("%d/%m/%Y, %H:%M:%S")
         return f"{self.sender_name}({dt_str}) : {self.content}"
 
     def __hash__(self) -> int:
-        return hash(self.sender_name + str(self.timestamp_ms))
+        return hash(self.item_id)
+    
+    @computed_field
+    @property
+    def item_id(self) -> str:
+        return self._id

million/model/sequence.py

@@ -1,8 +1,5 @@
 from __future__ import annotations
-
-from pydantic import BaseModel
-import pydantic
-
+from pydantic import validator, BaseModel
 from million.model.message import Message
 import million.analyze.message_evaluation as msg_val
 
@@ -11,7 +8,7 @@ class Sequence(BaseModel):
     start_message: Message
     end_message: Message | None = None
 
-    @pydantic.validator('end_message', pre=True, always=True)
+    @validator('end_message', pre=True, always=True)
     def default_end_message(cls, v, *, values):
         return v or values['start_message'] 
 

million/parse/fb_exports.py

@@ -48,6 +48,7 @@ def parse_dirfiles(file_dir: str) -> FacebookExport:
     result.sort()
     return result
 
+
 def __read_broken_fb_json(binary_data):
     # https://stackoverflow.com/questions/50008296/facebook-json-badly-encoded
     repaired = re.sub(

scripts/find_gromots.py

@@ -1,14 +1,9 @@
 from datetime import datetime
 from million.analyze.word_finder import findWords
-from million.parse.fb_exports import FacebookExportParser
+import million.parse.fb_exports as fb
 
 
 DATA_PATH = './data/'
-
-parser = FacebookExportParser()
-
-export = parser.parse(DATA_PATH)
-
 gros_mots = [
     '.*merde.*',
     'sexe',
@@ -27,6 +22,7 @@ gros_mots = [
     'bais.*'
     ]
 
+export = fb.parse_dirfiles(DATA_PATH)
 msg_gros_mots = findWords(export.messages, gros_mots)
 
 msg_gros_mots_grp = {}

scripts/find_holes.py

@@ -1,5 +1,5 @@
 from datetime import datetime
-from million.analyze.find_holes import compute_sequences, find_holes
+import million.analyze.find_holes as fh
 from million.analyze.retain_counts import retain_counts
 import million.parse.fb_exports as fb
 
@@ -10,13 +10,15 @@ export = fb.parse_dirfiles(DATA_PATH)
 
 filtered = retain_counts(export.messages)
 
-sequences = compute_sequences(filtered)
+sequences = fh.compute_sequences(filtered)
 
 actual_counted = sum([s.length() for s in sequences])
 
 print(f"Actual counted: {actual_counted}")
 
-holes = find_holes(filtered)
+merged = fh.merge_duplicates(sequences)
+merged = [s for s in merged if s.length() > 1]
+holes = fh.find_holes(filtered)
 
 print(len(holes))
 

scripts/read_top.py

@@ -1,4 +1,3 @@
-from million.view.bar_chart import plot as bar_chart
 from million.analyze.count_participations import count_participations
 from million.analyze.retain_counts import retain_counts
 import million.parse.fb_exports as fb
@@ -7,15 +6,11 @@ import million.parse.fb_exports as fb
 DATA_PATH = './data/'
 
 export = fb.parse_dirfiles(DATA_PATH)
-
 filtered = retain_counts(export.messages)
 
 print(len(filtered))
 
-counted_participations = count_participations(filtered, export.participants)
-
-kept_participations = [
-    p for p in counted_participations if p['participations'] > 100]
+participations = count_participations(filtered, export.participants, 100)
 
-print("\n".join(
-    [f"{p['name']}: {p['participations']}" for p in kept_participations]))
+for name, count in participations.items():
+    print(f"{name}: {count}")