yuanpeng / bin_shift_test.py

import numpy as np # +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ # First, using the method from Brian and Bob. # +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ # Generate a dummy array with a constant binning on a logarithmic scale. start_exponent = 0 stop_exponent = 3 num_bins = 10 x = np.logspace(start_exponent, stop_exponent, num=num_bins, base=10.0) # Check the binning of the array on a logarithmic scale. logged_x = np.log(x) print("1 - Bin width on a logarithmic scale before shift, ", np.diff(logged_x)) # Shift the array by half of the 'bin'. xdiff = np.append(np.diff(x), x[-1] - x[-2]) x -= xdiff / 2 # Check the binning of the array on a logarithmic scale, after the shift. print("1 - Bin width on a logarithmic scale after shift, ", np.diff(x)) # +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ # Second, using the method from Yuanpeng # +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ # Generate a dummy array with a constant binning on a logarithmic scale. start_exponent = 0 stop_exponent = 3 num_bins = 10 x = np.logspace(start_exponent, stop_exponent, num=num_bins, base=10.0) # Check the binning of the array on a logarithmic scale. logged_x = np.log(x) print("2 - Bin width on a logarithmic scale before shift, ", np.diff(logged_x)) # Shift the array by half of the 'bin'. x = np.log(x) bin_size = x[-1] - x[-2] x = np.exp(x - bin_size / 2.) # Check the binning of the array on a logarithmic scale, after the shift. logged_x = np.log(x) print("2 - Bin width on a logarithmic scale after shift, ", np.diff(logged_x))