def plot_historical_relevance(self, word, period, method, smooth=None):
time_series = TimeSeries(word)
series = time_series.get_series()
original_series = time_series.get_modified_series(series)
if smooth != None:
series = time_series.smoothify_series(original_series, smooth)
else:
series = original_series
x0 = [i + self.year_range[0] for i in range(self.max_range)]
y0 = self.compute_relevance(original_series)
if 'level' in method:
level_peak = LevelPeakDetector(series)
if method == 'level 1':
y = self.compute_relevance(level_peak.get_levels(1))
else:
y = self.compute_relevance(level_peak.get_levels(2))
elif 'window' in method:
window_peak = WindowPeakDetector(series)
if method == 'window 1':
y = self.compute_relevance(window_peak.compute_peaks(1))
elif method == 'window 2':
y = self.compute_relevance(window_peak.compute_peaks(2))
else:
y = self.compute_relevance(window_peak.compute_peaks(3))
elif 'double' in method:
double_peak = DoubleChangePeakDetector(series)
y = double_peak.compute_relevance(0.1)
plotter = Plotter(x0, series, period)
plotter.plot_peaks(word, y, method + ' function')
