Fix whitespace

hifiscan/analyzer.py

@@ -175,8 +175,8 @@ class Analyzer:
         db = a[1]
         freq = self.frequency()
         interp = np.empty_like(freq)
-        interp[1:] = np.interp(np.log(freq[1:]), logF, db)
         interp[0] = 0
+        interp[1:] = np.interp(np.log(freq[1:]), logF, db)
         return interp
 
     def X(self) -> np.ndarray:
@@ -288,12 +288,12 @@ class Analyzer:
         t = np.linspace(0, z.size / self.rate, z.size)
         return XY(t, z)
 
-    def correctionFactor(self, invResp: np.ndarray) -> XY:
+    def correctionFactor(self, h_inv: np.ndarray) -> XY:
         """
         Calculate correction factor for each frequency, given the
         inverse impulse response.
         """
-        Z = np.abs(rfft(invResp))
+        Z = np.abs(rfft(h_inv))
         Z /= Z.max()
         freq = np.linspace(0, self.rate / 2, Z.size)
         return XY(freq, Z)
@@ -392,7 +392,7 @@ def smooth(freq: np.ndarray, data: np.ndarray, smoothing: float) -> np.ndarray:
     """
     if not smoothing:
         return data
-    weight = 1 / (1 + freq * 2 ** (smoothing / 2 - 15))
+    weight = 1 / (1 + 2 ** (smoothing / 2 - 15) * freq)
     smoothed = np.empty_like(data)
     prev = data[-1]
     for i, w in enumerate(np.flip(weight), 1):
@@ -421,8 +421,8 @@ def transform_causality(x: np.ndarray, causality: float = 1) -> np.ndarray:
     the given impulse.
 
     Params:
-      causality: 0 = linear-phase, 1 = minimum-phase and
+      causality: 0 = linear-phase, 1 = minimum-phase,
-        in-between values smoothly transition between these two.
+        in-between values smoothly transition between the two.
 
     https://www.rle.mit.edu/dspg/documents/AVOHomoorphic75.pdf
     https://www.katjaas.nl/minimumphase/minimumphase.html

hifiscan/io_.py

@@ -6,12 +6,15 @@ import numpy.typing as npt
 
 
 class Sound(NamedTuple):
+    """Audio sample- and meta- data."""
+
     data: np.ndarray
     rate: int
     width: int = 4
 
 
 Correction = List[Tuple[float, float]]
+"""List of (frequency, db) tuples."""
 
 
 def write_wav(path: str, data: npt.ArrayLike, rate: int, width: int = 4):
@@ -51,9 +54,7 @@ def write_wav(path: str, data: npt.ArrayLike, rate: int, width: int = 4):
 
 
 def read_wav(path: str) -> Sound:
-    """
+    """Read WAV file and return float32 arrays between -1 and 1."""
-    Read WAV file and return float32 arrays between -1 and 1.
-    """
     with wave.open(path, 'rb') as wav:
         ch, width, rate, n, _, _ = wav.getparams()
         frames = wav.readframes(n)
@@ -79,12 +80,20 @@ def read_wav(path: str) -> Sound:
     return Sound(data, rate, width)
 
 
+def write_correction(path: str, correction: Correction):
+    """Write (frequency, db) tuples to a space-separated file."""
+    txt = '\n'.join(f'{freq} {db}' for freq, db in correction)
+    with open(path, 'w') as f:
+        f.write(txt)
+
+
 def read_correction(path: str) -> Correction:
+    """Read (frequency, db) tuples from comma- or space-separated file."""
     corr = []
     with open(path, 'r') as f:
         for line in f.readlines():
             try:
-                freq, db = line.split()
+                freq, db = line.split(',' if ',' in line else None)
                 corr.append((float(freq), float(db)))
             except ValueError:
                 pass