maad.util.crop_image

maad.util.crop_image(im, tn, fn, fcrop=None, tcrop=None)[source]

Crop a spectrogram (or an image) in time (horizontal X axis) and frequency (vertical y axis)

Parameters:

im2d ndarray: image to be cropped
tn, fn1d ndarray: tn is the time vector. fn is the frequency vector. They are required in order to know the correspondance between pixels and (time,frequency)
fcrop, tcroplist of 2 scalars [min, max], optional, default is None: fcrop corresponds to the min and max boundary frequency values tcrop corresponds to the min and max boundary time values

Returns:

im2d ndarray: image cropped
tn, fn, 1d ndarray: new time and frequency vectors

Examples

>>> w, fs = maad.sound.load('../data/cold_forest_daylight.wav') 
>>> Sxx_power,tn,fn,_ = maad.sound.spectrogram(w,fs)
>>> Lxx = maad.spl.power2dBSPL(Sxx_power, gain=42) # convert into dB SPL
>>> fig_kwargs = {'vmax': Lxx.max(),
                'vmin':0,
                'extent':(tn[0], tn[-1], fn[0], fn[-1]),
                'title':'Power spectrogram density (PSD)',
                'xlabel':'Time [s]',
                'ylabel':'Frequency [Hz]',
                }
>>> fig, ax = maad.util.plot2d(Lxx,**fig_kwargs)      
>>> Lxx_crop, tn_crop, fn_crop = maad.util.crop_image(Lxx, tn, fn, fcrop=(2000,10000), tcrop=(0,30))
>>> fig_kwargs = {'vmax': Lxx.max(),
                'vmin':0,
                'extent':(tn_crop[0], tn_crop[-1], fn_crop[0], fn_crop[-1]),
                'title':'Crop of the power spectrogram density (PSD)',
                'xlabel':'Time [s]',
                'ylabel':'Frequency [Hz]',
                }
>>> fig, ax = maad.util.plot2d(Lxx_crop,**fig_kwargs)  