Loading sample_simulation_mcvine/post_processing/neutrons2lambda.py 0 → 100644 +56 −0 Original line number Diff line number Diff line import numpy as np from mcni.utils import conversion from mcni.neutron_storage import readneutrons_asnpyarr as rdn def process(neutronspath): r""" calculating d spacing from the scattering events ---------- neutronspath : str the path where the scattered neutron events are saved. Returns ------- tuple Three float numbers: ``(lambda in angstrom, d spacing in angstrom, probability of the scattered neutrons)`` """ narr = rdn(neutronspath) # neutron events vx = narr[:,3]; vy = narr[:,4]; vz = narr[:,5] p = narr[:,9] v = (vx*vx + vy*vy +vz*vz)**.5 ## calculating velocity vector cos2theta = vz/v # calculating the scattering angle lamda = 2 * np.pi / conversion.V2K / v # calculating the wavelength sintheta = np.sqrt((1 - cos2theta) / 2) d = lamda / (2 * sintheta) # calculating the d-spacing return lamda, d, p, cos2theta def convert2histogram(x_values, y_values, bins, range_): r""" Converting the data to histogram ---------- x_values : array_like Input data. The histogram is computed over the flattened array. y_values: array_like, An array of the same shape as a. Each value in a only contributes its associated weight towards the bin count (instead of 1). bins : int or sequence of scalars or str, optional. If bins is an int, it defines the number of equal-width bins in the given range (10, by default). If bins is a sequence, it defines a monotonically increasing array of bin edges, including the rightmost edge, allowing for non-uniform bin widths. Returns ------- tuple Three float numbers: ``(x in bins, y in bins, associated error)`` """ I_d, dbb = np.histogram(x_values, bins=bins, range=range_, weights=y_values) errorbar_sqr_I_d, edgesS = np.histogram(x_values, bins=bins, range= range_, weights=y_values * y_values) error = np.sqrt(errorbar_sqr_I_d ) dcs = (dbb[1:] + dbb[:-1]) / 2 return(dcs, I_d, error) Loading
sample_simulation_mcvine/post_processing/neutrons2lambda.py 0 → 100644 +56 −0 Original line number Diff line number Diff line import numpy as np from mcni.utils import conversion from mcni.neutron_storage import readneutrons_asnpyarr as rdn def process(neutronspath): r""" calculating d spacing from the scattering events ---------- neutronspath : str the path where the scattered neutron events are saved. Returns ------- tuple Three float numbers: ``(lambda in angstrom, d spacing in angstrom, probability of the scattered neutrons)`` """ narr = rdn(neutronspath) # neutron events vx = narr[:,3]; vy = narr[:,4]; vz = narr[:,5] p = narr[:,9] v = (vx*vx + vy*vy +vz*vz)**.5 ## calculating velocity vector cos2theta = vz/v # calculating the scattering angle lamda = 2 * np.pi / conversion.V2K / v # calculating the wavelength sintheta = np.sqrt((1 - cos2theta) / 2) d = lamda / (2 * sintheta) # calculating the d-spacing return lamda, d, p, cos2theta def convert2histogram(x_values, y_values, bins, range_): r""" Converting the data to histogram ---------- x_values : array_like Input data. The histogram is computed over the flattened array. y_values: array_like, An array of the same shape as a. Each value in a only contributes its associated weight towards the bin count (instead of 1). bins : int or sequence of scalars or str, optional. If bins is an int, it defines the number of equal-width bins in the given range (10, by default). If bins is a sequence, it defines a monotonically increasing array of bin edges, including the rightmost edge, allowing for non-uniform bin widths. Returns ------- tuple Three float numbers: ``(x in bins, y in bins, associated error)`` """ I_d, dbb = np.histogram(x_values, bins=bins, range=range_, weights=y_values) errorbar_sqr_I_d, edgesS = np.histogram(x_values, bins=bins, range= range_, weights=y_values * y_values) error = np.sqrt(errorbar_sqr_I_d ) dcs = (dbb[1:] + dbb[:-1]) / 2 return(dcs, I_d, error)
