maad.util.into_bins

maad.util.into_bins(x, an, bin_step, axis=0, bin_min=None, bin_max=None, display=False)[source]

Divide a vector (1D) or a matrix (2D) into multiple bins according to a bin_step with respect of the energy

Parameters:

xarray-like: 1D or 2D array to bin
an :1d ndarray of floats: Vector containing the positions of each value. In case of 2D matrix, this vector corresponds to the horizontal (row) or vertical (columns) units
bin_stepscalar: Determine the width of each bin.
axisinteger, optional, default is 0: Determine along which axis the transformation is done. In case of matrix : axis = 0 => transformation is done on column axis = 1 => transformation is done on row
bin_minscalar, optional, default is None: This minimum value corresponds to the start of the first bin. By default, the minimum value is the first value of an.
bin_maxscalar, optional, default is None: This maximum value corresponds to end of the last bin. By default, the maximum value is the last value of an.
displayboolean, optional, defualt is False: Display the result of the tranformation : an histogram In case of matrix, the mean histogram is shown.

Returns:

xbinsarray-like: 1D or 2D array which correspond to the data after being transformed into bins
bin1d ndarray of floats: Vector containing the positions of each bin

Examples

>>> import numpy as np
>>> frequency = np.arange(0,20000,100)
>>> delta = np.random.uniform(-10,10, size=(len(frequency),))
>>> amplitude = 0.001 * frequency + 3 + delta
>>> fig_kwargs = {'figtitle':'',
              'xlabel':'Frequency [Hz]',
              'ylabel':'AU',
              'linewidth': 0.5,
              'legend' : 'before binning'
              }
>>> ax,fig = maad.util.plot1d(frequency,amplitude,**fig_kwargs)
>>> amplitude_bin,frequency_bin = maad.util.into_bins(amplitude,frequency,bin_step=1000) 
>>> fig_kwargs = {'figtitle':'',
              'xlabel':'Frequency [Hz]',
              'ylabel':'AU',
              'linewidth': 0.5,
              'color' :'red',
              'legend' : 'after binning'
              }
>>> ax,fig = maad.util.plot1d(frequency_bin, amplitude_bin, ax, **fig_kwargs)