tests/testfiles/ref/P501_D_AM_fm_FB_48k_FFT8K_Hann_75_1-12Oct.txt

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tests/testfiles/ref/P501_D_AM_fm_FB_48k_FFT8K_Hann_75_1-12Oct_A.txt

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tests/testfiles/ref/P501_D_AM_fm_FB_48k_FFT8K_Hann_75_1-3Oct.txt

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tests/testfiles/ref/P501_D_AM_fm_FB_48k_FFT8K_Hann_75_1-3Oct_A.txt

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tests/testfiles/ref/P501_D_AM_fm_FB_48k_FFT8K_Hann_75_A.txt

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tests/testfiles/ref/P501_D_AM_fm_FB_48k_LvT_SlRect_43ms.txt

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tests/testfiles/ref/P501_D_AM_fm_FB_48k_LvT_SlRect_43ms_A.txt

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tests/testfiles/ref/a.txt

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tests/testfiles/ref/b.txt

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tests/testfiles/ref/c.txt

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tests/testfiles/ref/d.txt

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+22687.5	-2.76
+22710.9375	-2.76
+22734.375	-2.76
+22757.8125	-2.76
+22781.25	-2.76
+22804.6875	-2.76
+22828.125	-2.76
+22851.5625	-2.76
+22875	-2.76
+22898.4375	-2.76
+22921.875	-2.76
+22945.3125	-2.76
+22968.75	-2.76
+22992.1875	-2.76
+23015.625	-2.76
+23039.0625	-2.76
+23062.5	-2.76
+23085.9375	-2.76
+23109.375	-2.76
+23132.8125	-2.76
+23156.25	-2.76
+23179.6875	-2.76
+23203.125	-2.76
+23226.5625	-2.76
+23250	-2.76
+23273.4375	-2.76
+23296.875	-2.76
+23320.3125	-2.76
+23343.75	-2.76
+23367.1875	-2.76
+23390.625	-2.76
+23414.0625	-2.76
+23437.5	-2.76
+23460.9375	-2.76
+23484.375	-2.76
+23507.8125	-2.76
+23531.25	-2.76
+23554.6875	-2.76
+23578.125	-2.76
+23601.5625	-2.76
+23625	-2.76
+23648.4375	-2.76
+23671.875	-2.76
+23695.3125	-2.76
+23718.75	-2.76
+23742.1875	-2.76
+23765.625	-2.76
+23789.0625	-2.76
+23812.5	-2.76
+23835.9375	-2.76
+23859.375	-2.76
+23882.8125	-2.76
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+23976.5625	-2.76
+24000	-2.76

tools/downloader.py

@@ -5,24 +5,33 @@ Created on Jan 16 2025 21:31
 @author: Jan.Reimes
 """
 
+import numpy as np
 from pathlib import Path
-from typing import Any
+from typing import Any, Self, Optional
 import shutil
 from enum import Enum
 import urllib.request
 
 
 class Downloads(Enum):
-    P501 = r'https://www.itu.int/rec/dologin_pub.asp?lang=e&id=T-REC-P.501-202005-I!!SOFT-ZST-E&type=items'
+    P501_B = (P501_ANNEX_B := r'https://www.itu.int/wftp3/public/t/testsignal/GenAudio/P501/v2025_04/Speech_Signals_AnnexB.zip')
+    P501_C = (P501_ANNEX_C :=r'https://www.itu.int/wftp3/public/t/testsignal/GenAudio/P501/v2025_04/Speech_Signals_AnnexC.zip')
+    P501_D = (P501_ANNEX_D :=r'https://www.itu.int/wftp3/public/t/testsignal/GenAudio/P501/v2025_04/Speech_Signals_AnnexD.zip')
+    P501_E = (P501_ANNEX_E :=r'https://www.itu.int/wftp3/public/t/testsignal/GenAudio/P501/v2025_04/Speech_Signals_AnnexE.zip')
+    P501_7 = (P501_CL_7 :=r'https://www.itu.int/wftp3/public/t/testsignal/GenAudio/P501/v2025_04/Test_Signals_Clause_7.zip')
+    P501 = [P501_ANNEX_B, P501_ANNEX_C, P501_ANNEX_D, P501_ANNEX_E, P501_CL_7]
     G191 = r'https://www.itu.int/rec/dologin_pub.asp?lang=e&id=T-REC-G.191-202405-I!!SOFT-ZST-E&type=items'
-    evs = 'https://www.3gpp.org/ftp/Specs/archive/26_series/26.442/26442-i00.zip'
-    amr_wb = 'https://www.3gpp.org/ftp/tsg_sa/WG4_CODEC/EVS_Permanent_Documents/EVS-7a_S4-130155.zip'
-    amr = 'https://www.3gpp.org/ftp/tsg_sa/WG4_CODEC/EVS_Permanent_Documents/EVS-7a_S4-130155.zip'
+    EVS = r'https://www.3gpp.org/ftp/Specs/archive/26_series/26.442/26442-i00.zip'
+    AMR_WB = r'https://www.3gpp.org/ftp/tsg_sa/WG4_CODEC/EVS_Permanent_Documents/EVS-7a_S4-130155.zip'
+    AMR = r'https://www.3gpp.org/ftp/tsg_sa/WG4_CODEC/EVS_Permanent_Documents/EVS-7a_S4-130155.zip'
 
     @classmethod
-    def get(cls, x: Any) -> 'Downloads':
+    def get(cls, x: Any) -> Self:
+        if isinstance(x, cls):
+            return x
+
         try:
-            return cls[str(x).lower()]
+            return cls[str(x).upper()]
         except:  # noqa: E722
             return cls(x)
 
@@ -33,7 +42,7 @@ def download(url: str, dl_path: Path) -> Path:
     return Path(x[0])
 
 class FileFetcher:
-    def __init__(self, doc_download: Downloads, dl_dir: Path, extract_dir: Path = None):
+    def __init__(self, doc_download: Downloads, dl_dir: Path, extract_dir: Optional[Path] = None):
         self.doc_download = Downloads.get(doc_download)
 
         self.dl_dir = Path(dl_dir).absolute()
@@ -41,12 +50,12 @@ class FileFetcher:
 
         self.extract_dir = Path(dl_dir if extract_dir is None else extract_dir).absolute() / self.doc_download.name
 
-    def get_file(self, name: str) -> Path:
+    def get_file(self, name: str) -> Path|None:
 
         self.dl_dir.mkdir(parents=True, exist_ok=True)
         self.extract_dir.mkdir(parents=True, exist_ok=True)
 
-        def _search_file(name: str, raise_if_not_found: bool = True) -> Path:
+        def _search_file(name: str, raise_if_not_found: bool = True) -> Path|None:
             candidates = list(self.extract_dir.rglob(name + '*'))
             if len(candidates) == 0:
                 if raise_if_not_found:
@@ -61,15 +70,23 @@ class FileFetcher:
             self.zip_dl_file.unlink(missing_ok=True)
             shutil.rmtree(self.extract_dir, ignore_errors=True)
 
-            download((url := self.doc_download.value), self.zip_dl_file)
+            # download value might contain multiple URLs
+            urls = np.atleast_1d(self.doc_download.value)
+            for url in urls:
+                if not isinstance(url, str):
+                    raise ValueError(f"Invalid URL: {url}")
+
+                download(url, self.zip_dl_file)
                 shutil.unpack_archive(self.zip_dl_file, self.extract_dir)
 
-            # extract all zip files (inside the downloaded zip) recursively
+                # Search for zip files in the extract directory and unpack them.
+                # This is useful if the archive contains nested zip files.
                 while len(zip_files := list(self.extract_dir.rglob('*.zip'))) > 0:
                     for zip_file in zip_files:
                         shutil.unpack_archive(zip_file, zip_file.parent)
                         zip_file.unlink(missing_ok=True)
 
+            # final search for the file
             return _search_file(name)
         else:
             return file

tools/harmonics.py

@@ -6,123 +6,135 @@ Created on Sep 26 2024 09:40
 """
 
 import numpy as np
-from typing import Union, Iterable, Optional
-from scipy.signal import stft, istft
-#from scipy.fft import rfft, irfft, rfftfreq  # real-valued FFT is more performant in scipy than in numpy
-from numpy.typing import NDArray
+from typing import Union, Iterable, Optional, Dict, Any
+from numpy.typing import NDArray, ArrayLike
 from ensure import Check
 
+from nonlinearity.spectrum import FftSpectrumAvg
 
-def create_harmonics(signal: NDArray, fs: int, harmonics: Union[int, Iterable[int]],
+NDIntArray = NDArray[np.int_]
+
+def create_harmonics(
+    signal: ArrayLike,
+    fs: int,
+    harmonics: Union[int, Iterable[int]],
     harmonic_level_offsets_db: Union[float, Iterable[float]] = 0.0,
-                     max_harmonic_freq: Optional[float] = None, min_harmonic_freq: Optional[float] = None,
-                     alias_margin: float = 0.85, overall_level_harmonics_db: Optional[float] = None,
-                     nperseg: int = 16384, noverlap: Union[float, int] = 0.75, nfft: Optional[int] = None,
-                     harmonics_only: bool = True) -> NDArray:
+    max_harmonic_freq: Optional[float] = None,
+    min_harmonic_freq: Optional[float] = None,
+    alias_margin: float = 0.85,
+    overall_level_harmonics_db: Optional[float] = None,
+    harmonics_only: bool = True,
+    **spec_args
+) -> NDArray:
     """
-    Generates harmonics for a given signal.
-
-    Parameters:
-    signal (NDArray): Input signal array.
-    fs (int): Sampling frequency of the input signal.
-    harmonics (Union[int, Iterable[int]]): Number of harmonics or an array of harmonic indices.
-    harmonic_level_offsets_db (Union[float, Iterable[float]], optional): Level offsets for each harmonic in dB. Default is 0.0.
-    alias_margin (float, optional): Margin to avoid aliasing. Default is 0.85.
-    overall_level_harmonics_db (Optional[float], optional): Overall level of harmonics in dB. Default is None.
-    nperseg (int, optional): Length of each segment for STFT. Default is 16384.
-    noverlap (Union[float, int], optional): Number of points to overlap between segments. Default is 0.75.
-    nfft (Optional[int], optional): Number of FFT points. Default is None.
-    harmonics_only (bool, optional): If True, return only the harmonics. If False, add harmonics to the original signal. Default is True.
+    Adds harmonic distortion to a signal using Chebyshev polynomials of the first kind.
+
+    Harmonic levels are specified as relative dB offsets from the original signal's RMS level.
+    The function generates harmonics, optionally limits their frequency range, and scales their levels.
+
+    Args:
+        signal (ArrayLike): Input 1D signal array.
+        fs (int): Sampling frequency in Hz.
+        harmonics (Union[int, Iterable[int]]): Harmonic orders to generate (e.g., 2 for 2nd harmonic).
+        harmonic_level_offsets_db (Union[float, Iterable[float]], optional): Relative dB offsets for each harmonic.
+            Defaults to 0.0.
+        max_harmonic_freq (Optional[float], optional): Maximum frequency for each harmonic in Hz. Defaults to None.
+        min_harmonic_freq (Optional[float], optional): Minimum frequency for each harmonic in Hz. Defaults to None.
+        alias_margin (float, optional): Margin factor to avoid aliasing (0 < alias_margin <= 1). Defaults to 0.85.
+        overall_level_harmonics_db (Optional[float], optional): Target overall RMS level for harmonics in dB.
+            If None, no scaling is applied. Defaults to None.
+        harmonics_only (bool, optional): If True, output contains only harmonics; if False, adds original signal.
+            Defaults to True.
+        **spec_args: Additional keyword arguments for spectrum analysis.
 
     Returns:
-    NDArray: Output signal with harmonics.
+        NDArray: Output signal with added harmonics, same shape as input.
+
+    Raises:
+        ValueError: If input signal is not 1D.
+        ValueError: If harmonic orders are less than 2.
+        ValueError: If lengths of harmonics and harmonic_level_offsets_db do not match.
+
     """
 
     # Convert the input signal to a numpy array if it's not already
-    signal = np.array(signal)
+    signal: NDArray = np.asarray(signal, dtype=np.float64).squeeze()
+    if len(signal.shape) != 1:
+        raise ValueError("Input signal must be 1D.")
+
+    # Normalize input to [-1, 1]
+    signal /= (norm_fac := np.maximum(np.max(np.abs(signal)), 1e-8))
 
     # check array of harmonic
     if isinstance(harmonics, int):
-        harmonics = np.arange(harmonics) + 2
+        harmonics: NDIntArray = np.arange(harmonics) + 2
     else:
-        harmonics = np.array(harmonics).astype(int)
+        harmonics: NDIntArray = np.array(harmonics).astype(int)
 
     harmonics.sort()
     Check(harmonics.min()).is_greater_than_or_equal_to(2).or_raise(ValueError,
                                                                    f"min(harmonics) must be >= 2, but got: {harmonics}"
                                                                    )
 
-    def _check_harmonic_param(p: Optional[float|int], default_value: Union[float, int]) -> NDArray:
+    def _check_harmonic_param(p: Optional[float|int|Any], default_value: Union[float, int]) -> NDArray:
         if isinstance(p, (float, int)):
             p = np.ones_like(harmonics) * p
         elif p is None:
             p = np.ones_like(harmonics) * default_value # fs / 2
 
-        return np.array(p)
+        return np.array(p).astype(float)
 
     # check min/max of harmonic frequencies
-    max_harmonic_freq = _check_harmonic_param(max_harmonic_freq, fs/2)
-    max_harmonic_freq = np.minimum(max_harmonic_freq, alias_margin * fs / 2)  # shift up to Nyquist limit (minus margin)
+    max_harmonic_freq: NDArray = _check_harmonic_param(max_harmonic_freq, fs/2)
+    max_harmonic_freq: NDArray = np.minimum(max_harmonic_freq, alias_margin * fs / 2)  # shift up to Nyquist limit (minus margin)
 
-    min_harmonic_freq = _check_harmonic_param(min_harmonic_freq, 20.0)
+    min_harmonic_freq: NDArray = _check_harmonic_param(min_harmonic_freq, 20.0)
 
-    harmonic_level_offsets_db = _check_harmonic_param(harmonic_level_offsets_db, 0.0)
-    harmonic_level_offsets_db = np.power(10, harmonic_level_offsets_db / 20)
+    # determine absolute levels of harmonics from level offsets
+    harmonic_level_offsets_db: NDArray = _check_harmonic_param(harmonic_level_offsets_db, 0.0)
+    harmonic_level_offsets_lin: NDArray = np.power(10, harmonic_level_offsets_db / 20)
 
     # check shape of max_harmonic_freq and harmonics
-    for x, name in zip([max_harmonic_freq, min_harmonic_freq, harmonic_level_offsets_db],
-                       ['max_harmonic_freq', 'min_harmonic_freq', 'harmonic_level_offsets_db']):
+    for x, name in zip([max_harmonic_freq, min_harmonic_freq, harmonic_level_offsets_lin],
+                       ['max_harmonic_freq', 'min_harmonic_freq', 'harmonic_level_offsets_lin']):
         Check(x.shape[0]).equals(x.shape[0]).or_raise(
             ValueError,
             f"Shape of {name} and harmonics must be equal, but got: "
             f"{x.shape[0]} vs {x.shape[0]}"
         )
 
-    # Fourier Transform of the original signal
-    N = len(signal)
-    noverlap = round(noverlap * nperseg) if isinstance(noverlap, float) else noverlap
-    freqs, t, signal_fft = stft(signal, fs, nperseg=nperseg, noverlap=noverlap, nfft=(nfft := nfft or nperseg),
-                                scaling='psd')
+    # Handle default case: no level offsets → 0 dB for all
+    if harmonic_level_offsets_db is None:
+        harmonic_level_offsets_db = [0.0] * len(harmonics)
 
-    # Initialize array for the (total) harmonic spectrum
-    harmonics_spectrum = np.zeros_like(signal_fft)
+    if len(harmonics) != len(harmonic_level_offsets_db):
+        raise ValueError("Length of 'harmonics' and 'harmonic_level_offsets_db' must match.")
 
-    # Add harmonics by modulating the frequency domain
-    harmonic_freq: int
-    for i, harmonic_freq in enumerate(harmonics):
-        harmonic_fft = np.zeros_like(signal_fft)
+    # Initialize output
+    y = np.zeros_like(signal)
 
-        # Apply frequency shift by modulating with a complex exponential
-        valid_idx = np.flatnonzero(
-            (min_harmonic_freq[i] <= np.abs(freqs * harmonic_freq)) & (np.abs(freqs * harmonic_freq) <= max_harmonic_freq[i]))  # Only shift within Nyquist limit, incl. margin
-        harmonic_fft[harmonic_freq * valid_idx, :] = signal_fft[valid_idx, :] * harmonic_level_offsets_db[i]
+    # Precompute Chebyshev polynomials up to max harmonic
+    max_order = max(harmonics)
+    T: Dict[int, NDArray] = {0: np.ones_like(signal), 1: signal.copy() } # T[0] = T_0(x), T[1] = T_1(x)
+    for n in range(2, max_order + 1):
+        T[n] = 2.0 * signal * T[n - 1] - T[n - 2]
 
-        harmonics_spectrum += harmonic_fft
+    # Add specified harmonics
+    for h, db_offset in zip(harmonics, harmonic_level_offsets_db):
+        if h < 1:
+            raise ValueError("Harmonic orders must be >= 1.")
+        y += (10**(db_offset/20.0)) * T[h]
 
     # Apply overall scaling if specified
     if overall_level_harmonics_db is not None:
-        # Compute the current harmonic power level in the frequency domain
-        current_harmonic_magnitude = np.abs(harmonics_spectrum).mean(axis=1)
-        current_harmonic_magnitude[1:-1] *= np.sqrt(2)  # Double for non-DC and non-Nyquist components
-        current_harmonic_power = np.sum(current_harmonic_magnitude ** 2)
-
-        current_harmonic_db = 10 * np.log10(current_harmonic_power)
-
-        # Compute scaling factor to match desired level
-        desired_harmonic_power_db = overall_level_harmonics_db
-        scaling_factor_db = desired_harmonic_power_db - current_harmonic_db
-        scaling_factor = np.power(10, scaling_factor_db / 20)
-
-        # Scale the harmonic spectrum
-        harmonics_spectrum *= scaling_factor
+        overall_level = FftSpectrumAvg.from_signals(y, fs, **spec_args).get_level('dB', include_window_correction=True)
+        y *= np.power(10, (float(overall_level_harmonics_db) - overall_level) / 20)
 
-    # Inverse Fourier Transform to get the signal back in time domain
-    _, output = istft(harmonics_spectrum, fs, nperseg=nperseg, noverlap=noverlap, nfft=nfft, scaling='psd')
-    output = output[:N]
     if not harmonics_only:
-        output += signal
+        y += signal
 
-    return output
+    # De-normalize output
+    return y * norm_fac # scale back to original amplitude
 
 
 if __name__ == "__main__":

tools/metrics.py

@@ -12,7 +12,7 @@ from typing import Optional, Union, Iterable
 from collections import namedtuple
 from ensure import check
 
-from nonlinearity.spectrum import calculate_spectrum_vs_time
+from nonlinearity.spectrum import FftSpectrumAvg
 
 THDResult = namedtuple('THDResult',
                        ['thdn', 'thd', 'freqs_harmonics', 'levels_harmonics', 'level_harmonics_total',
@@ -25,13 +25,16 @@ def thd_plus_n(signal: NDArray, fs: int, f0: float, harmonics: Optional[Union[in
                fmin: float = 20.0, fmax: float = 20_000.0, delta_f_factor: Optional[float] = None,
                always_2d: bool = False,
                ) -> THDResult:
+
+    n_channels = 1
     if len(signal.shape) == 1:
         signal = signal[:, np.newaxis]
         single_channel = True
+    elif len(signal.shape) == 2:
+        single_channel = False
+        n_channels = signal.shape[1]
     elif len(signal.shape) > 2:
         raise ValueError(f"Invalid signal shape: {signal.shape}")
-    else:
-        single_channel = False
 
     # parse harmonics
     all_harmonics = np.arange(2 * f0, fmax, step=f0)
@@ -53,8 +56,9 @@ def thd_plus_n(signal: NDArray, fs: int, f0: float, harmonics: Optional[Union[in
         raise ValueError(f"Parameter harmonics must be either int or List[int], but got {type(harmonics)}")
 
     # average FFT spectrum (full frequency range) in power domain
-    freq, time, spec, wcf = calculate_spectrum_vs_time(signal, fs, window, nperseg, noverlap, nfft, fmin=0, fmax=fs / 2)
-    spec_avg = spec.mean(axis=1) * np.power(10, wcf / 10)
+    spectrum = FftSpectrumAvg.from_signals(signal, fs, always_2d=True, window=window, nperseg=nperseg, noverlap=noverlap, nfft=nfft, fmin=20.0, fmax=fs / 2)
+    spec_avg = spectrum.get_values('pow', include_window_correction=True)
+    freq = spectrum.get_freqs()
 
     # find indices for fmin, fmax, f0 +/- margin
     idx_fmin = np.argmin(np.abs(freq - fmin))
@@ -80,16 +84,16 @@ def thd_plus_n(signal: NDArray, fs: int, f0: float, harmonics: Optional[Union[in
 
     # energy of fundamental frequency
     s = slice(idx0_lower, idx0_upper + 1)
-    level_fund = spec_avg[s, :].sum(axis=0)
+    level_fund = spec_avg[s].sum(axis=0)
 
     ### Calculation of THD+N ###
 
     # levels below and above f0
     s = slice(idx_fmin, idx0_lower)
-    level_below_f0 = spec_avg[s, :].sum(axis=0)
+    level_below_f0 = spec_avg[s].sum(axis=0)
 
     s = slice(idx0_upper + 1, idx_fmax + 1)
-    level_above_f0 = spec_avg[s, :].sum(axis=0)
+    level_above_f0 = spec_avg[s].sum(axis=0)
 
     thdn = 10 * np.log10(level_fund) - 10 * np.log10(level_below_f0 + level_above_f0)
 
@@ -101,16 +105,16 @@ def thd_plus_n(signal: NDArray, fs: int, f0: float, harmonics: Optional[Union[in
 
     # find harmonics, calculate levels
     harmonics_idx = [np.argmin(np.abs(freq - fh)) for fh in harmonics]
-    levels_harmonics = np.zeros_like(harmonics_idx, dtype=spec_avg.dtype)
+    levels_harmonics = np.zeros((len(harmonics), n_channels), dtype=spec_avg.dtype)
     for j, h_ii in enumerate(harmonics_idx):
         s = slice(max(idx_fmin, h_ii - nbr_margin_lower), min(idx_fmax, h_ii + nbr_margin_upper) + 1)
-        levels_harmonics[j] = spec_avg[s, :].sum(axis=0)
+        levels_harmonics[j, :] = spec_avg[s].sum(axis=0)
 
-    level_harmonics_total = levels_harmonics.sum()
+    level_harmonics_total = levels_harmonics.sum(axis=0)
     thd = 10 * np.log10(level_fund) - 10 * np.log10(level_harmonics_total)
 
     # additional overall level for information
-    level_overall = spec_avg[idx_fmin:idx_fmax + 1, :].sum(axis=0)
+    level_overall = spec_avg[idx_fmin:idx_fmax + 1].sum(axis=0)
 
     # result dict (w/o harmonics)
     result = dict(thdn=thdn, thd=thd, level_harmonics_total=level_harmonics_total,

tools/plot_spectra.py

@@ -7,12 +7,15 @@ Created on Dez 18 2024 09:22
 from typing import Dict, Any, Optional
 from pathlib import Path
 import matplotlib.pyplot as plt
+from functools import partial
 from matplotlib.ticker import LogLocator, FuncFormatter
 import pandas as pd
 import numpy as np
 from scipy.signal import freqz
 
-from nonlinearity import NLMResult, MIN_LEVEL_LIN
+from nonlinearity import NLMResult, to_db as _to_db
+
+to_db = partial(_to_db, is_rms=True)
 
 FREQ_RANGE_CURVE = {'color': 'k', 'linewidth': 2.0}
 FREQ_RANGE_FILL = {'alpha': 0.1, 'facecolor': 'k', 'hatch': '', 'edgecolor': 'k', 'linewidth': 0.0}
@@ -71,50 +74,48 @@ class NLMPlot:
 
             idx = 1
             axes = pd.Series(dtype=object)
+            ax = None
             if plot_components:
                 axes['components'] = ax = fig.add_subplot(nbr_axes, 1, idx, sharex=None)
                 idx += 1
 
                 for name, spectrum in spectra.items():
-                    spectrum = np.maximum(spectrum[:, i], MIN_LEVEL_LIN)
                     label = f"{name} [{levels[name][0]:.1f} dB]"
                     label = f"{label} - {ch_name}" if self.always_use_channel_titles else label
-                    ax.plot(self.result.freq, 20 * np.log10(spectrum), label=label)
+                    ax.plot(self.result.freq, to_db(spectrum), label=label)
 
                 ax.set_title(f"Signal Components")
                 self._apply_ylim_range(ax, 'components', 90)
 
             if plot_nlm:
-                axes['nlm'] = ax = fig.add_subplot(nbr_axes, 1, idx, sharex=axes.iloc[-1])
+                axes['nlm'] = ax = fig.add_subplot(nbr_axes, 1, idx, sharex=ax)
                 idx += 1
-                lin = np.maximum(self.result.spectra.linear[:, i], MIN_LEVEL_LIN)
-                nl = np.maximum(self.result.spectra.nonlinear[:, i], MIN_LEVEL_LIN)
+                lin = to_db(self.result.spectra.linear[:, i])
+                nl = to_db(self.result.spectra.nonlinear[:, i])
 
                 label = "NLM=LIN-NL"
                 label = f"{label} - {ch_name}" if self.always_use_channel_titles else label
-                ax.plot(self.result.freq, 20 * np.log10(lin/nl), label=label)
+                ax.plot(self.result.freq, lin-nl, label=label)
 
                 ax.set_title(f"NLM vs Frequency")
                 self._apply_ylim_range(ax, 'nlm', None)
 
             if plot_fr:
-                axes['fr'] = ax = fig.add_subplot(nbr_axes, 1, idx, sharex=axes.iloc[-1])
+                axes['fr'] = ax = fig.add_subplot(nbr_axes, 1, idx, sharex=ax)
                 idx += 1
 
                 label = "Coherent Frequencies"
                 label = f"{label} - {ch_name}" if self.always_use_channel_titles else label
                 ii = fr_res.idx_coherent[:, i]
-                ax.plot(fr_res.freq[ii], 20 * np.log10(np.maximum(np.abs(fr_res.freq_resp[:, i][ii]), MIN_LEVEL_LIN)),
-                        'ro', alpha=0.1, label=label)
+                ax.plot(fr_res.freq[ii], to_db(np.abs(fr_res.freq_resp[:, i][ii])), 'ro', alpha=0.1, label=label)
 
                 label = f"Freq. Resp. [{fr_res.freq_resp.shape[0]}]"
                 label = f"{label} - {ch_name}" if self.always_use_channel_titles else label
-                ax.plot(fr_res.freq, 20 * np.log10(np.maximum(np.abs(fr_res.freq_resp[:, i]), MIN_LEVEL_LIN)),
-                        label=label, alpha=0.65)
+                ax.plot(fr_res.freq, to_db(np.abs(fr_res.freq_resp[:, i])), label=label, alpha=0.65)
 
                 # IR -> FR
                 w, fr = freqz(fr_res.imp_resp[:, i], 1, worN=fr_res.freq, fs=self.result.fs)
-                fr_db = 20 * np.log10(np.maximum(np.abs(fr), MIN_LEVEL_LIN))
+                fr_db = to_db(np.abs(fr))
 
                 label = f"Freq. Resp. (IR) [{fr_res.imp_resp.shape[0]}]"
                 label = f"{label} - {ch_name}" if self.always_use_channel_titles else label
@@ -124,7 +125,7 @@ class NLMPlot:
                 self._apply_ylim_range(ax, 'fr', 100)
 
             if plot_cxy:
-                axes['cxy'] = ax = fig.add_subplot(nbr_axes, 1, idx, sharex=axes.iloc[-1])
+                axes['cxy'] = ax = fig.add_subplot(nbr_axes, 1, idx, sharex=ax)
                 idx += 1
 
                 label = "Coherence"
@@ -156,7 +157,7 @@ class NLMPlot:
 
                 ax.set_xlim([self.fmin, self.fmax])
 
-        fig.suptitle(f"{self.title}\nNLM = {self.result.nlm.squeeze():.1f} dB", wrap=True)
+        fig.suptitle(f"{self.title}\nNLM = {self.result.nlm[0]:.1f} dB", wrap=True)
         fig.tight_layout()
         if save_path:
             (save_path := Path(save_path)).mkdir(parents=True, exist_ok=True)
@@ -165,5 +166,7 @@ class NLMPlot:
         if show_blocking:
             plt.show(block=1)
 
+        plt.close(fig)
+
 if __name__ == "__main__":
     pass
\ No newline at end of file