def generate(self, input=None):
dsi = input
###### BINNING USING CONFI
# Generate bin values for range start_scale to end_scale
# Calculate the number of bins at binsize across range
dso = DataSet()
dso.import_data(dsi)
r = dsi.scales_r[1]
self._bin_size, self._bin_offset = self.config.get('bin_size'), self.config.get('bin_offset')
bins = np.arange(r[0] + self._bin_offset, r[1] + self._bin_offset, self._bin_size)
number_of_bins = len(bins) - 1
# Can't increase the size of data, if bins > current size return the original
if number_of_bins >= len(dso.scales[1]):
return {'dso': dso}
# Resize (lossy) to the new shape
old_shape, new_shape = list(dsi.data.shape), list(dso.data.shape)
new_shape[1] = number_of_bins
dso.crop(new_shape) # Lossy crop, but we'll be within the boundary below
for n, d in enumerate(dsi.data):
binned_data = np.histogram(dsi.scales[1], bins=bins, weights=d)
binned_num = np.histogram(dsi.scales[1], bins=bins) # Number of data points that ended up contributing to each bin
dso.data[n, :] = binned_data[0] / binned_num[0] # Mean
dso.scales[1] = [float(x) for x in binned_data[1][:-1]]
#dso.labels[1] = [str(x) for x in binned_data[1][:-1]]
# Remove any NaNs that have crept in (due to the histogram)
dso.remove_invalid_data()
return {'output': dso, 'input': input} # Pass back input for difference plot
