format all code

src/editors/amplitude/editor.py

@@ -4,6 +4,7 @@ from ..common import find_moving_average_highlights
 import numpy as np
 import structlog
 
+
 class AmplitudeEditor:
     def __init__(self, video_path, audio_path, params):
         self.logger = structlog.get_logger("amplitude")
@@ -21,8 +22,13 @@ class AmplitudeEditor:
 
     def edit(self, large_window, small_window, params):
         window_factor = self.bitrate / self.factor
-        long_ma = np_moving_average(self.squared_subsample, large_window * window_factor)
-        short_ma = np_moving_average(self.squared_subsample, small_window * window_factor)
-        highlights = find_moving_average_highlights(short_ma, long_ma, self.factor / self.bitrate)
+        long_ma = np_moving_average(
+            self.squared_subsample, large_window * window_factor
+        )
+        short_ma = np_moving_average(
+            self.squared_subsample, small_window * window_factor
+        )
+        highlights = find_moving_average_highlights(
+            short_ma, long_ma, self.factor / self.bitrate
+        )
         return highlights, large_window, small_window
-

src/editors/common.py

@@ -1,5 +1,6 @@
 from ..models.moment import Moment
 
+
 def find_moving_average_highlights(short_ma, long_ma, scaling_factor=1):
     in_a_clip = False
     m = None

src/editors/sentiment/editor.py

@@ -12,12 +12,14 @@ from flair.data import Sentence
 from ...math.average import np_moving_average
 from ..common import find_moving_average_highlights
 
+
 @dataclass
 class TextGlob:
-    start:float
-    stop:float
-    text:str
-    sentiment:float
+    start: float
+    stop: float
+    text: str
+    sentiment: float
+
 
 class SentimentEditor:
     def __init__(self, video_path, audio_path, params):
@@ -30,20 +32,24 @@ class SentimentEditor:
             self.logger.info("transcribing audio", path=audio_path)
             self.result = self.model.transcribe(audio_path)
 
-            with open(dest_location, 'w') as fp:
+            with open(dest_location, "w") as fp:
                 json.dump(self.result, fp)
         else:
             self.logger.info("cached transcription found", path=dest_location)
 
-        with open(dest_location, 'r') as f:
-          self.result = json.load(f)
+        with open(dest_location, "r") as f:
+            self.result = json.load(f)
 
         self.segments = []
-        for segment in self.result['segments']:
-            self.segments.append(TextGlob(segment['start'], segment['end'], segment['text'], 0))
-        classifier = TextClassifier.load('en-sentiment')
+        for segment in self.result["segments"]:
+            self.segments.append(
+                TextGlob(segment["start"], segment["end"], segment["text"], 0)
+            )
+        classifier = TextClassifier.load("en-sentiment")
         self.sentiments = []
-        self.logger.info("calculating sentiment on segments", segments=len(self.segments))
+        self.logger.info(
+            "calculating sentiment on segments", segments=len(self.segments)
+        )
         for segment in self.segments:
             sentence = Sentence(segment.text)
             classifier.predict(sentence)
@@ -53,11 +59,13 @@ class SentimentEditor:
             segment.sentiment = sentsum
             self.sentiments.append(sentsum)
         self.sentiments = np.array(self.sentiments)
-   
+
     def edit(self, large_window, small_window, params):
         end_time = self.segments[-1].stop
         window_factor = len(self.sentiments) / end_time
         long_ma = np_moving_average(self.sentiments, large_window)
         short_ma = np_moving_average(self.sentiments, small_window)
-        highlights = find_moving_average_highlights(short_ma, long_ma, 1.0 / window_factor)
+        highlights = find_moving_average_highlights(
+            short_ma, long_ma, 1.0 / window_factor
+        )
         return highlights, large_window, small_window

src/math/average.py

@@ -1,4 +1,5 @@
 import numpy as np
 
+
 def np_moving_average(data: int, window: int) -> np.ndarray:
     return np.convolve(data, np.ones(int(window)), "valid") / window

src/math/cost.py

@@ -3,5 +3,6 @@ Functions in this file should always target for 0 to be the
 lowest possible error -> smaller values win
 """
 
+
 def quadratic_loss(target, result):
-    return (target - result)**2.0
+    return (target - result) ** 2.0

src/math/distribution.py

@@ -1,5 +1,6 @@
 import numpy as np
 
+
 def create_distribution(center, spread, count):
     high = center * (1.0 + spread)
     low = center - (center * spread)

src/mediautils/audio.py

@@ -4,10 +4,8 @@ from pathlib import Path
 import numpy as np
 import scipy.io.wavfile as wav
 
-def extract_audio_from_video(
-    video_path: str,
-    filename: str
-):
+
+def extract_audio_from_video(video_path: str, filename: str):
     tempdir = tempfile.gettempdir()
 
     dest_location = f"{tempdir}/{filename}.wav"
@@ -19,6 +17,7 @@ def extract_audio_from_video(
     vid.close()
     return dest_location, False
 
+
 def process_audio(source_audio_path):
     rate, data_raw = wav.read(source_audio_path)
     data_raw = data_raw.astype(np.int32)
@@ -26,5 +25,6 @@ def process_audio(source_audio_path):
     duration = len(mono) / rate
     return mono, duration, rate
 
+
 def resample(data: np.ndarray, factor: int) -> np.ndarray:
     return data[::factor].copy()

src/mediautils/video.py

@@ -9,16 +9,27 @@ def get_subclips(source_video_path, moments):
     return clips, vid
 
 
-def render_moments(moments, input_video_path, output_path, intro_path=None, outro_path=None, parallelism=1):
+def render_moments(
+    moments,
+    input_video_path,
+    output_path,
+    intro_path=None,
+    outro_path=None,
+    parallelism=1,
+):
     clips, _ = get_subclips(input_video_path, moments)
     if intro_path is not None:
         size = clips[0].size
         iclip = mp.VideoFileClip(intro_path)
         iclip.resize(height=size[1])
         clips.insert(0, iclip)
-    composite = mp.concatenate_videoclips(clips, method='compose')
+    composite = mp.concatenate_videoclips(clips, method="compose")
     composite.write_videofile(output_path, logger=None, threads=parallelism)
 
 
 def filter_moments(moments, min_length, max_length):
-    return [m for m in moments if m.get_duration() > min_length and m.get_duration() < max_length]
+    return [
+        m
+        for m in moments
+        if m.get_duration() > min_length and m.get_duration() < max_length
+    ]

src/models/moment.py

@@ -1,6 +1,4 @@
-
 class Moment:
-
     def __init__(self, start, stop):
         self.start = start
         self.stop = stop

src/utils/prereq.py

@@ -9,12 +9,18 @@ log = structlog.get_logger()
 
 
 def install_ffmpeg():
-    if not click.confirm('Do you want to install ffmpeg? It is required for ALE.', default=False):
-        log.warn("ffmpeg not installed. Please install it manually or restart ALE. Exiting...")
+    if not click.confirm(
+        "Do you want to install ffmpeg? It is required for ALE.", default=False
+    ):
+        log.warn(
+            "ffmpeg not installed. Please install it manually or restart ALE. Exiting..."
+        )
         sys.exit(0)
     system = platform.system().lower()
     if system == "linux":
-        package_manager = "apt-get" if os.path.exists("/etc/apt/sources.list") else "yum"
+        package_manager = (
+            "apt-get" if os.path.exists("/etc/apt/sources.list") else "yum"
+        )
         command = f"sudo {package_manager} install -y ffmpeg"
     elif system == "darwin":
         command = "brew install ffmpeg"