mirror of
https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
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236 lines
6.5 KiB
Python
236 lines
6.5 KiB
Python
"""
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Functions to simplify plotting of fourier spectra
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"""
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# vim: fdm=indent ts=4
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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import numpy as np
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from .fft import ft_spectrum
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def plot_spectrum(
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spectrum, freqs,
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plot_complex=False, plot_power=False, plot_amplitude=None,
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freq_unit="Hz", freq_scaler=1,
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title='Spectrum', xlabel='Frequency', ax=None,
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**plot_kwargs
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):
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""" Plot a signal's spectrum on an Axis object"""
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plot_amplitude = plot_amplitude or (not plot_power and not plot_complex)
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alpha = 1
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if ax is None:
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ax = plt.gca()
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ax.set_title(title)
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ax.set_xlabel(xlabel + (" ["+freq_unit+"]" if freq_unit else "" ))
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ylabel = ""
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if plot_amplitude or plot_complex:
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ylabel = "Amplitude"
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if plot_power:
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if ylabel:
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ylabel += "|"
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ylabel += "Power"
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ax.set_ylabel(ylabel)
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if plot_complex:
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alpha = 0.5
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ax.plot(freqs/freq_scaler, np.real(spectrum), '.-', label='Real', alpha=alpha, **plot_kwargs)
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ax.plot(freqs/freq_scaler, np.imag(spectrum), '.-', label='Imag', alpha=alpha, **plot_kwargs)
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if plot_power:
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ax.plot(freqs/freq_scaler, np.abs(spectrum)**2, '.-', label='Power', alpha=alpha, **plot_kwargs)
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if plot_amplitude:
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ax.plot(freqs/freq_scaler, np.abs(spectrum), '.-', label='Abs', alpha=alpha, **plot_kwargs)
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ax.legend()
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return ax
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def plot_phase(
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spectrum, freqs,
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ylim_epsilon=0.5, ax=None, grid=True,
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freq_unit="Hz", freq_scaler=1, xlabel='Frequency',
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major_divider=2, minor_divider=12,
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**plot_kwargs
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):
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"""
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Plot the phase of spectrum
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"""
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if ax is None:
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ax = plt.gca()
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ax.set_ylabel("Phase")
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ax.set_xlabel(xlabel + (" ["+freq_unit+"]" if freq_unit else "" ))
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ax.grid(grid)
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ax.plot(freqs/freq_scaler, np.angle(spectrum), '.-', **plot_kwargs)
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ax.set_ylim(-1*np.pi - ylim_epsilon, np.pi + ylim_epsilon)
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axis_pi_ticker(ax.yaxis, major_divider=major_divider, minor_divider=minor_divider)
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return ax
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def axis_pi_ticker(axis, major_divider=2, minor_divider=12):
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major_tick = Multiple(major_divider, np.pi, '\pi')
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minor_tick = Multiple(minor_divider, np.pi, '\pi')
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axis.set_major_locator(major_tick.locator())
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axis.set_major_formatter(major_tick.formatter())
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axis.set_minor_locator(minor_tick.locator())
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return axis
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def plot_signal(
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signal, sample_rate = 1,
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time=None, ax=None,
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title='Signal', ylabel='Amplitude',
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time_unit="s", xlabel='Time',
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**kwargs):
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"""
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Plot the signal in the time domain
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"""
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if ax is None:
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ax = plt.gca()
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if time is None:
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time = np.arange(len(signal))/sample_rate
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ax.set_title(title)
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ax.set_xlabel(xlabel + (" ["+time_unit+"]" if time_unit else "" ))
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ax.set_ylabel(ylabel)
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ax.plot(time, signal, **kwargs)
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return ax
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def plot_combined_spectrum(
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spectrum, freqs,
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fig=None, gs=None,
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spectrum_ax=None, phase_ax=None,
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spectrum_kwargs={}, phase_kwargs={},
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**shared_kwargs
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):
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"""
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Plot both the frequencies and phase in one figure.
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"""
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ax1, ax2 = None, None
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if spectrum_ax is not None or phase_ax is not None:
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if spectrum_ax is not None:
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ax1 = spectrum_ax
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if phase_ax is not None:
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ax2 = phase_ax
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else:
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# configure plotting layout
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if fig is None:
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fig = plt.figure(figsize=(8, 16))
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if gs is None:
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gs = gridspec.GridSpec(2, 1, figure=fig, height_ratios=[3,1], hspace=0)
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ax1 = fig.add_subplot(gs[:-1, -1])
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ax2 = fig.add_subplot(gs[-1, -1], sharex=ax1)
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axes = np.array([ax1, ax2])
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# plot the spectrum
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plot_spectrum(spectrum, freqs, ax=ax1, **{ **shared_kwargs, **spectrum_kwargs})
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# plot the phase
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plot_phase(spectrum, freqs, ax=ax2, **{ **shared_kwargs, **phase_kwargs})
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ax1.xaxis.tick_top()
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[label.set_visible(False) for label in ax1.get_xticklabels()]
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return fig, axes
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def plot_signal_and_spectrum(
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signal, sample_rate=1, title=None,
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signal_kwargs={}, ft_kwargs={},
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spectrum_kwargs={}, phase_kwargs={'major_divider':1, 'minor_divider': 6},
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**phase_spectrum_kwargs
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):
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"""
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Create a figure showing both the signal and the combined spectrum.
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"""
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fig = plt.figure(figsize=(16, 4))
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if title:
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fig.suptitle(title)
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# setup plot layout
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gs0 = gridspec.GridSpec(1, 2, figure=fig)
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gs00 = gs0[0].subgridspec(1, 1)
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gs01 = gs0[1].subgridspec(2, 1, height_ratios=[3,1], hspace=0)
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# plot the signal
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ax1 = fig.add_subplot(gs00[0, 0])
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plot_signal(signal, sample_rate, ax=ax1, **signal_kwargs)
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# plot spectrum
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signal_fft, freqs = ft_spectrum(signal, sample_rate, **ft_kwargs)
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_, (ax2, ax3) = plot_combined_spectrum(
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signal_fft, freqs,
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fig=fig, gs=gs01,
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spectrum_kwargs=spectrum_kwargs, phase_kwargs=phase_kwargs,
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**phase_spectrum_kwargs
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)
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# return the axes
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axes = np.array([ax1, ax2, ax3])
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return fig, axes
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def multiple_formatter(denominator=2, number=np.pi, latex='\pi'):
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"""
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From https://stackoverflow.com/a/53586826
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"""
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def gcd(a, b):
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while b:
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a, b = b, a%b
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return a
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def _multiple_formatter(x, pos):
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den = denominator
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num = np.int(np.rint(den*x/number))
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com = gcd(num,den)
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(num,den) = (int(num/com),int(den/com))
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if den==1:
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if num==0:
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return r'$0$'
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if num==1:
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return r'$%s$'%latex
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elif num==-1:
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return r'$-%s$'%latex
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else:
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return r'$%s%s$'%(num,latex)
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else:
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if num==1:
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return r'$\frac{%s}{%s}$'%(latex,den)
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elif num==-1:
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return r'$\frac{-%s}{%s}$'%(latex,den)
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else:
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return r'$\frac{%s%s}{%s}$'%(num,latex,den)
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return _multiple_formatter
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class Multiple:
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"""
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From https://stackoverflow.com/a/53586826
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"""
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def __init__(self, denominator=2, number=np.pi, latex='\pi'):
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self.denominator = denominator
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self.number = number
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self.latex = latex
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def locator(self):
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return plt.MultipleLocator(self.number / self.denominator)
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def formatter(self):
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return plt.FuncFormatter(multiple_formatter(self.denominator, self.number, self.latex))
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