Sweep generator

haiopy/__init__.py

@@ -5,3 +5,14 @@
 __author__ = """The pyfar developers"""
 __email__ = '[email protected]'
 __version__ = '0.1.0'
+
+
+from . import (
+    buffers,
+    devices,
+)
+
+__all__ = [
+    'buffers',
+    'devices',
+]

haiopy/buffers.py

@@ -4,7 +4,7 @@
 from threading import Event
 from scipy import signal
 import warnings
-
+import matplotlib.pyplot as plt
 
 class _Buffer(ABC):
     """Abstract base class for audio buffers for block-wise iteration.
@@ -538,3 +538,205 @@ def next(self):
 
     def _reset(self):
         super()._reset()
+
+class LinearSweepGenerator(_Buffer):
+    """
+    Generator to block wise calculate a linear sweep as described in [#]_.
+
+    Examples:
+    --------
+
+    >>> from haiopy.buffers import LinearSweepGenerator
+    >>> import matplotlib.pyplot as plt
+    >>> sine = LinearSweepGenerator(440, 128)
+    >>> blocks = [next(LinearSweepGenerator), next(LinearSweepGenerator), next(LinearSweepGenerator)]
+    >>> for block in blocks:
+    >>>     plt.plot((block))
+    >>> plt.show()
+
+    Source:
+    .. [#]  Farina, Angelo (2000): "Simultaneous measurement of impulse
+        response and distortion with a swept-sine technique." 108th AES
+        Convention, Paris: France.
+    """
+
+    def __init__(self,
+                 block_size,
+                 amplitude=1,
+                 sweep_duration = 10,
+                 f_1 = 0,
+                 f_2 = 22050,
+                 sampling_rate=44100) -> None:
+        """
+        Initialize a `LinearSweepGenerator`with a given block_size,
+        amplitude, sweep duration, lower frequency, upper frequency
+        and samplingrate.
+
+        Parameters
+        ----------
+        block_size : int
+            The block size in samples.
+        amplitude: double, optional
+            The amplitude of the sine. The default is ``1``.
+        sweep_duration : float, optional
+            The duration of one sweep in seconds. The default is ``10`` s
+        f_1 : int, optional
+            The starting frequency for the sweep in Hz. The default is
+            ``0`` Hz.
+        f_2: int, optional
+            The ending frequency for the sweep in Hz. The default is
+            ``22050`` Hz.
+        sampling_rate : int, optional
+            The sampling rate in Hz. The default is ``44100``.
+        """
+        super().__init__(block_size)
+        self._amplitude = amplitude
+        self._sampling_rate = sampling_rate
+        self._T = sweep_duration
+        self._f_1 = f_1
+
+        if f_2 > sampling_rate/2:
+            self.f_2 = np.floor(sampling_rate/2)
+        else:
+            self._f_2 = f_2
+
+        self._t_start = 0
+        self._in_transition = False
+
+    @property
+    def amplitude(self):
+        """Return the amplitude of the sweep."""
+        return self._amplitude
+
+    @amplitude.setter
+    def amplitude(self, amplitude):
+        self.check_if_active()
+        self._amplitude = amplitude
+        self._reset()
+
+    @property
+    def sampling_rate(self):
+        """Return the sampling rate of the generated sweep."""
+        return self._sampling_rate
+
+    @sampling_rate.setter
+    def sampling_rate(self, sampling_rate):
+        """Set the sampling rate."""
+        self.check_if_active()
+        self._sampling_rate = sampling_rate
+        self._phase = 0
+
+    @property
+    def n_channels(self):
+        """Return the number of channels. This is currently always 1."""
+        return 1
+
+    @property
+    def sweep_duration(self):
+        """Return the duration for one sweep."""
+        return self._T
+
+    @sweep_duration.setter
+    def sweep_duration(self, sweep_duration):
+        """Set the duration of one sweep."""
+        self.check_if_active()
+        self._sweep_duration = sweep_duration
+        self._T = sweep_duration
+
+    @property
+    def T(self):
+        """Return the period time T for one sweep."""
+        return self._T
+
+    @property
+    def f_1(self):
+        """Return lower freuqency limit for sweep."""
+        return self._f_1
+
+    @f_1.setter
+    def f_1(self, f_1):
+        """Set the lower frequency of the sweep."""
+        self.check_if_active()
+        self._f_1 = f_1
+        self._reset()
+
+    @property
+    def f_2(self):
+        """Return upper freuqency limit for sweep."""
+        return self._f_2
+
+    @f_2.setter
+    def f_2(self, f_2):
+        """Set the upper frequency of the sweep."""
+        self.check_if_active()
+        self._f_2 = f_2
+        self._reset()
+
+    @property
+    def t_start(self):
+        """Return current time."""
+        return self._t_start
+
+    def _set_block_size(self, block_size):
+        """Set blocksize."""
+        self.check_if_active()
+        super()._set_block_size(block_size)
+        self._reset()
+
+    def next(self):
+        """
+        Return the next audio block as numpy array and increase the current
+        time variable by one block.
+        If sweep ends inside a block, it fades out and a new sweep begins.
+        """
+
+        # if in transition to a new sweep-start (i.e. sweep ends within
+        # a signal block, not directly at the end)
+        if self._in_transition:
+            time_till_end = self._T - self._t_start
+            n_samples_1 = int(time_till_end * self._sampling_rate)
+            t_1 = np.arange(n_samples_1) + self._t_start
+            self._reset()
+            n_samples_2 = self._block_size - n_samples_1
+            t_2 = np.arange(n_samples_2) / self._sampling_rate
+            t = np.concatenate((t_1, t_2))
+        else: # usual iteration
+            n_samples = int(self._block_size)
+            t = np.arange(n_samples) / self._sampling_rate + self._t_start
+
+        # [1, page 5]
+        w_1 = 2 * np.pi * self._f_1
+        w_2 = 2 * np.pi * self._f_2
+        data = (self._amplitude *
+                np.sin(w_1 * t + (w_2-w_1) / self._T * t**2 / 2))
+
+        self._t_start += self._block_size / self._sampling_rate
+
+        # window end of first sweep to reach zero at the end:
+        # ...and set t_start new:
+        if self._in_transition:
+            win_len = 11
+            win = np.hanning(win_len)
+            win_half = win[-int(win_len/2):]
+            idx = n_samples_1-len(win_half)
+            data[idx:n_samples_1] = data[idx:n_samples_1]*win_half
+
+            # set start time based on t_2:
+            # in next iterations t_start can be increased by block/fs again as
+            # usual
+            self._t_start = t_2[-1] + 1/self._sampling_rate
+            self._in_transition = False
+
+        # if we reach desired duration of one sweep, do....:
+        if (self._t_start >=
+            (self._T - (self._block_size / self._sampling_rate))):
+            if self._t_start == self._T:
+                self._reset()
+            else:
+                self._in_transition = True
+
+        return data
+
+    def _reset(self):
+        """Reset sweep."""
+        self._t_start = 0