astrolab.timing module

This package contains a list of functions that can be used to analyse signals for the Doppler Effect experiment.

astrolab.timing.load_signal(filename, clip=None, print_log=False, color='steelblue', fig=None, ax=None)

Load a .wav file and return both the time-series information and the sample rate.

Parameters

filename: str

The .wav file to read: a filename pointing to the location of the file.

clip: array_like, default: None

A two-element list [a,b] and clips the input signal between the times a and b (in seconds).

This can be useful if you want to remove the first a seconds or the last b seconds of your sound file. By default, no clipping is done.

print_log: bool, default: False

A boolean variable to decide whether you want to print a log of what you’ve done. In this case, whether you want to plot the resulting signal or not. By default, the signal is not plotted.

color: str, default: “steelblue”

Colour of this plot. Must be one of the matplotlib “named colors”: https://matplotlib.org/stable/gallery/color/named_colors.html.

fig: matplotlib figure object, default: None

Figure on which to plot the result. By default, a new figure is created.

ax: matplotlib axes object, default: None

Axes on which to plot the result. By default, a new axis is created.

Returns

samplerate: int

The sample rate of the signal. This is defined as the number of datapoints of the input signal per second. Typically, most recordings are taken at 44,100 Hz, meaning that there are 44,100 samples in each second.

times: array_like

An array of the time-steps at which the data is sampled, in seconds.

signal: array_like

An array containing the value of the amplitude of the signal at each time-step in times.

Raises

ValueError

In case the clip array’s entries are not in ascending order.

Usage

>>> sr, times, sig = load_signal("./filename.wav", clip=[3,20], print_log=True, color='firebrick', fig=None, ax=None)

astrolab.timing.chunk_signal(times, signal, size=2048, step=128)

Break up a signal (given in the signal array) into individual pieces (or chunks) of size size, whose left-edges are separated by a step samples.

Thus, if “chunk 0” ranges from (0, size), “chunk 1” would range from (step, size + step). Notice how both chunks are size samples wide, and their left edges are separated by step samples. The splitting is thus done with significant overlap.

For each of these chunks, it returns the average time at which the chunk was taken, and the signal’s amplitude values within that chunk.

Parameters

times: array_like

An array of times, usually one of the outputs of the load_signal function.

signal: array_like

An array of amplitudes for each of the times in times, also typically one of the outputs of the load_signal function.

size: int, default: 2048

The number of samples in a single chunk.

step: int, default: 128

The number of samples between the left-edges of successive chunks.

Returns

t: array_like

A one-dimensional array containing the average time values for each of the chunks.

chunks: array_like

A two-dimensional array. The first dimension is the chunk-number. For each chunk, it returns size number of amplitude values.

Usage

>>> average_times, chunks = chunk signal(times, sig)

astrolab.timing.power_spectrum(chunk, samplerate, lowx=1000, highx=10000, print_log=False, color='coral', alpha=1, label=None, fig=None, ax=None)

Accept an array of signal values, compute its power spectrum, and return the dominant frequency in the range [lowx, highx]. Ideally, this range should be centered around the expected “stationary” frequency, and should be wide enough contain all the variation in the frequency during the object’s motion.

Parameters

chunk: array_like

An array of amplitude values, usually a specific chunk that was returned by the chunk_signal function, although the entire signal could be used as well.

samplerate: int

The original samplerate of the signal, obtained from the load_signal function.

lowx: float, default: 1000

Lower limit (in Hz) of the frequency-range within which the dominant frequency is found and (if print_log=True) the result is plotted.

highx: float, default: 10_000

Upper limit (in Hz) of the frequency-range within which the dominant frequency is found and (if print_log=True) the result is plotted.

print_log: bool, default: False

A boolean variable to decide whether you want to print a log of what you’ve done. In this case, if print_log=True, the resulting power spectrum is plotted. By default, the power spectrum is not plotted.

color: str, default: “coral”

Set the colour of the plot that is produced if print_log=True.

alpha: float, default: 1

Set the transparency of the plot that is produced if print_log=True.

label: str or None, default: None

Set the label of the plot that is produced if print_log=True. By default, no label is set.

fig: matplotlib figure object, default: None

Figure on which to plot the result. By default, a new figure is created.

ax: matplotlib axes object, default: None

Axes on which to plot the result. By default, a new axis is created.

Returns

dom_freq: float

The frequency in the range [lowx , highx] that contributes the greatest deal to our signal in the given range of frequencies.

Usage:

>>> dominant_frequency = power_spectrum(chunks[0], samplerate, lowx=2000, highx=4000)