format

data/domsdatabasen/create.py

@@ -114,7 +114,7 @@ def retry(func, *args, retries=RETRY_COUNT, delay=RETRY_DELAY, **kwargs):
         try:
             return func(*args, **kwargs)
         except Exception as e:
-            logger.warning(f"⚠️  Retry {attempt+1}/{retries} failed: {e}")
+            logger.warning(f"⚠️  Retry {attempt + 1}/{retries} failed: {e}")
             time.sleep(delay)
     raise RuntimeError(f"❌ All retries failed for {func.__name__}({args})")
 

data/lexdk/create.py

@@ -38,7 +38,7 @@ def convert_sample(example: dict) -> dict:
     #     "text": "Kullmanns Mølle er en mølle i Gudhjem, opkaldt efter Matts Kullmann, der byggede møllen i 1893 til sin søn, Christian Kullmann, se Gudhjem Mølle.",
     # }
     date = datetime.fromisoformat(example["date"])
-    text = f"{example["title"]}\n\npubliceret: {date}\n{example["text"]}"
+    text = f"{example['title']}\n\npubliceret: {date}\n{example['text']}"
 
     new_example = dict(
         text_new=text,

data/memo/create.py

@@ -96,9 +96,9 @@ def load_memo(repo_path: Path) -> pd.DataFrame:
 
     text_without_metadata = [t for t in text_df_fileames if t not in metadata_filenames]
 
-    assert (
-        len(text_without_metadata) == 0
-    ), f"Some texts in the repository do not have metadata: {text_without_metadata}"
+    assert len(text_without_metadata) == 0, (
+        f"Some texts in the repository do not have metadata: {text_without_metadata}"
+    )
 
     # merge texts with metadata
     merged_df = pd.merge(

data/ncc_books/create.py

@@ -147,7 +147,7 @@ def dynaword_format(
         "license": license,
         "domain": domain,
         "metadata": {
-            "source-pretty": f"Norwegian Colossal Corpus ({re.sub("ncc_","",source)})",
+            "source-pretty": f"Norwegian Colossal Corpus ({re.sub('ncc_', '', source)})",
             "source-type": doc_type,
         },
     }
@@ -176,9 +176,9 @@ def log_pre_filter_lang_data(
     logger.info(f"Documents of {source}:")
     logger.info(f"NO: {no_docs}, {no_perc}% ; DA: {da_docs}, {da_perc}%")
     logger.info("After language confidence filtering:")
-    logger.info(f"DA: {f_length}, lost: {100-f_perc}%")
+    logger.info(f"DA: {f_length}, lost: {100 - f_perc}%")
     logger.info("Total document change:")
-    logger.info(f"{all_docs} -> {f_length}, loss: {100-f_total_perc}%")
+    logger.info(f"{all_docs} -> {f_length}, loss: {100 - f_total_perc}%")
 
 
 def get_var_name(var):
@@ -272,7 +272,7 @@ def quality_checks(ds: Dataset) -> Dataset:
 
     long_texts = ds_f.filter(too_long_filter, num_proc=None)
     if len(long_texts["id"]) > 0:
-        logger.info(f"{len(long_texts["id"])} Long texts (>~1e5 tokens) found")
+        logger.info(f"{len(long_texts['id'])} Long texts (>~1e5 tokens) found")
         for id in long_texts["id"]:
             logger.info(f"id: {id}")
     else:

data/ncc_maalfrid/create.py

@@ -147,7 +147,7 @@ def dynaword_format(
         "license": license,
         "domain": domain,
         "metadata": {
-            "source-pretty": f"Norwegian Colossal Corpus ({re.sub("ncc_","",source)})",
+            "source-pretty": f"Norwegian Colossal Corpus ({re.sub('ncc_', '', source)})",
             "source-type": doc_type,
         },
     }
@@ -176,9 +176,9 @@ def log_pre_filter_lang_data(
     logger.info(f"Documents of {source}:")
     logger.info(f"NO: {no_docs}, {no_perc}% ; DA: {da_docs}, {da_perc}%")
     logger.info("After language confidence filtering:")
-    logger.info(f"DA: {f_length}, lost: {100-f_perc}%")
+    logger.info(f"DA: {f_length}, lost: {100 - f_perc}%")
     logger.info("Total document change:")
-    logger.info(f"{all_docs} -> {f_length}, loss: {100-f_total_perc}%")
+    logger.info(f"{all_docs} -> {f_length}, loss: {100 - f_total_perc}%")
 
 
 def get_var_name(var):
@@ -272,7 +272,7 @@ def quality_checks(ds: Dataset) -> Dataset:
 
     long_texts = ds_f.filter(too_long_filter, num_proc=num_proc)
     if len(long_texts["id"]) > 0:
-        logger.info(f"{len(long_texts["id"])} Long texts (>~1e5 tokens) found")
+        logger.info(f"{len(long_texts['id'])} Long texts (>~1e5 tokens) found")
         for id in long_texts["id"]:
             logger.info(f"id: {id}")
     else:

data/ncc_newspaper/create.py

@@ -148,7 +148,7 @@ def dynaword_format(
         "license": license,
         "domain": domain,
         "metadata": {
-            "source-pretty": f"Norwegian Colossal Corpus ({re.sub("ncc_","",source)})",
+            "source-pretty": f"Norwegian Colossal Corpus ({re.sub('ncc_', '', source)})",
             "source-type": doc_type,
         },
     }
@@ -177,9 +177,9 @@ def log_pre_filter_lang_data(
     logger.info(f"Documents of {source}:")
     logger.info(f"NO: {no_docs}, {no_perc}% ; DA: {da_docs}, {da_perc}%")
     logger.info("After language confidence filtering:")
-    logger.info(f"DA: {f_length}, lost: {100-f_perc}%")
+    logger.info(f"DA: {f_length}, lost: {100 - f_perc}%")
     logger.info("Total document change:")
-    logger.info(f"{all_docs} -> {f_length}, loss: {100-f_total_perc}%")
+    logger.info(f"{all_docs} -> {f_length}, loss: {100 - f_total_perc}%")
 
 
 def get_var_name(var):
@@ -275,7 +275,7 @@ def quality_checks(ds: Dataset) -> Dataset:
 
     long_texts = ds_f.filter(too_long_filter, num_proc=num_proc)
     if len(long_texts["id"]) > 0:
-        logger.info(f"{len(long_texts["id"])} Long texts (>~1e5 tokens) found")
+        logger.info(f"{len(long_texts['id'])} Long texts (>~1e5 tokens) found")
         for id in long_texts["id"]:
             logger.info(f"id: {id}")
     else:

data/ncc_parliament/create.py

@@ -147,7 +147,7 @@ def dynaword_format(
         "license": license,
         "domain": domain,
         "metadata": {
-            "source-pretty": f"Norwegian Colossal Corpus ({re.sub("ncc_","",source)})",
+            "source-pretty": f"Norwegian Colossal Corpus ({re.sub('ncc_', '', source)})",
             "source-type": doc_type,
         },
     }
@@ -176,9 +176,9 @@ def log_pre_filter_lang_data(
     logger.info(f"Documents of {source}:")
     logger.info(f"NO: {no_docs}, {no_perc}% ; DA: {da_docs}, {da_perc}%")
     logger.info("After language confidence filtering:")
-    logger.info(f"DA: {f_length}, lost: {100-f_perc}%")
+    logger.info(f"DA: {f_length}, lost: {100 - f_perc}%")
     logger.info("Total document change:")
-    logger.info(f"{all_docs} -> {f_length}, loss: {100-f_total_perc}%")
+    logger.info(f"{all_docs} -> {f_length}, loss: {100 - f_total_perc}%")
 
 
 def get_var_name(var):
@@ -272,7 +272,7 @@ def quality_checks(ds: Dataset) -> Dataset:
 
     long_texts = ds_f.filter(too_long_filter, num_proc=None)
     if len(long_texts["id"]) > 0:
-        logger.info(f"{len(long_texts["id"])} Long texts (>~1e5 tokens) found")
+        logger.info(f"{len(long_texts['id'])} Long texts (>~1e5 tokens) found")
         for id in long_texts["id"]:
             logger.info(f"id: {id}")
     else:

src/dynaword/datasheet.py

@@ -60,7 +60,7 @@ def human_readable_large_int(value: int) -> str:
     ]
     for threshold, label in thresholds:
         if value > threshold:
-            return f"{value/threshold:.2f}{label}"
+            return f"{value / threshold:.2f}{label}"
 
     return str(value)
 

src/dynaword/plot_tokens_over_time.py

@@ -115,13 +115,13 @@ def create_token_dataframe(filename: str = "descriptive_stats.json") -> pd.DataF
 def _format_tokens(value: float) -> str:
     """Format tokens with human-readable suffixes"""
     if value >= 1e12:
-        return f"{value/1e12:.2f}T"
+        return f"{value / 1e12:.2f}T"
     elif value >= 1e9:
-        return f"{value/1e9:.2f}G"
+        return f"{value / 1e9:.2f}G"
     elif value >= 1e6:
-        return f"{value/1e6:.2f}M"
+        return f"{value / 1e6:.2f}M"
     elif value >= 1e3:
-        return f"{value/1e3:.2f}k"
+        return f"{value / 1e3:.2f}k"
     else:
         return f"{value:.0f}"
 

src/tests/test_quality/test_short_texts.py

@@ -16,6 +16,6 @@ def test_no_one_word_documents(dataset_name: str):
 
     one_word_docs = ds.filter(lambda x: x["token_count"] <= 1)
 
-    assert (
-        len(one_word_docs) == 0
-    ), f"Found {len(one_word_docs)} one-word documents in dataset '{dataset_name}'"
+    assert len(one_word_docs) == 0, (
+        f"Found {len(one_word_docs)} one-word documents in dataset '{dataset_name}'"
+    )