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cross_ionization.py
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cross_ionization.py
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#-Begin-preamble-------------------------------------------------------
#
# CERN
#
# European Organization for Nuclear Research
#
#
# This file is part of the code:
#
# PyECLOUD Version 8.7.0
#
#
# Main author: Giovanni IADAROLA
# BE-ABP Group
# CERN
# CH-1211 GENEVA 23
# SWITZERLAND
#
# Contributors: Eleonora Belli
# Philipp Dijkstal
# Lorenzo Giacomel
# Lotta Mether
# Annalisa Romano
# Giovanni Rumolo
# Eric Wulff
#
#
# Copyright CERN, Geneva 2011 - Copyright and any other
# appropriate legal protection of this computer program and
# associated documentation reserved in all countries of the
# world.
#
# Organizations collaborating with CERN may receive this program
# and documentation freely and without charge.
#
# CERN undertakes no obligation for the maintenance of this
# program, nor responsibility for its correctness, and accepts
# no liability whatsoever resulting from its use.
#
# Program and documentation are provided solely for the use of
# the organization to which they are distributed.
#
# This program may not be copied or otherwise distributed
# without permission. This message must be retained on this and
# any other authorized copies.
#
# The material cannot be sold. CERN should be given credit in
# all references.
#
#-End-preamble---------------------------------------------------------
import scipy.io as sio
import os
import numpy as np
from numpy.random import rand
from scipy.constants import e as qe
class Ionization_Process(object):
def __init__(self, pyecl_input_folder, process_name, process_definitions, cloud_dict, target_area):
# Warn if target density doesn't correspond to density of gas ionization class?
self.name = process_name
print(('Init process %s' % self.name))
self.target_dens = process_definitions['target_density']
print(('Target density = %.2e' %(self.target_dens)))
self.last_reported_target_dens = self.target_dens
self.target_area = target_area
self.N_target = self.target_dens * target_area
self.E_eV_init = process_definitions['E_eV_init']
if 'extract_sigma' in list(process_definitions.keys()):
self.extract_sigma = process_definitions['extract_sigma']
else:
self.extract_sigma = True
if 'generate_equally' in list(process_definitions.keys()):
self.generate_equally = process_definitions['generate_equally']
else:
self.generate_equally = False
# Check that ionization product names correspond to existing clouds
product_names = process_definitions['products']
for product in product_names:
assert product in list(cloud_dict.keys()), "Product name %s does not correspond to a defined cloud name."%(product)
self.products = product_names
# Read cross section file
cross_section_file = process_definitions['cross_section']
if os.path.isfile(pyecl_input_folder + '/' + cross_section_file):
cross_section_file_path = pyecl_input_folder + '/' + cross_section_file
elif os.path.isfile(pyecl_input_folder + '/' + cross_section_file + '.mat'):
cross_section_file_path = pyecl_input_folder + '/' + cross_section_file + '.mat'
else:
cross_section_file_path = cross_section_file
print(('Cross-section from file %s' %cross_section_file_path))
cross_section = sio.loadmat(cross_section_file_path)
if self.extract_sigma:
self.extract_sigma_path = cross_section_file_path.split('.mat')[0]
self.extract_sigma_path += '_extracted.mat'
else:
self.extract_sigma_path = None
self.energy_eV = cross_section['energy_eV'].squeeze()
self.sigma_cm2 = cross_section['cross_section_cm2'].squeeze()
# Check the energy step and define helpers for interp
self.energy_eV_min = self.energy_eV.min()
self.energy_eV_max = self.energy_eV.max()
self.sigma_cm2_diff = np.append(np.diff(self.sigma_cm2), 0.)
# A 0 is appended to give the array the correct shape
flag_log = False
ndec_round_x = 8
x_interp = self.energy_eV
diff_x_interp = np.round(np.diff(x_interp), ndec_round_x)
delta_x_interp = diff_x_interp[0]
x_interp_min = self.energy_eV_min
if np.any(diff_x_interp != delta_x_interp):
# Step not linear, check if logarithmic
x_interp = np.log10(self.energy_eV)
diff_x_interp = np.round(np.diff(x_interp), ndec_round_x)
delta_x_interp = diff_x_interp[0]
x_interp_min = np.log10(self.energy_eV_min)
if np.any(diff_x_interp != delta_x_interp):
# Step neither linear nor logarithmic
raise ValueError('Energy in cross section file must be equally spaced in linear or log scale.')
else:
flag_log = True
self.delta_x_interp = delta_x_interp
self.x_interp_min = x_interp_min
self.flag_log = flag_log
def generate(self, Dt, cloud_dict, mass_proj, N_proj, nel_mp_proj,
x_proj, y_proj, z_proj, v_mp_proj, flag_generate=True):
E_eV_mp_proj = 0.5 * mass_proj / qe * v_mp_proj * v_mp_proj
# Get sigma
sigma_mp_proj = self.get_sigma(energy_eV_proj=E_eV_mp_proj)
DN_per_proj = sigma_mp_proj * self.target_dens * v_mp_proj * Dt * nel_mp_proj
N_proj = len(nel_mp_proj)
# Calculate remaining density
if flag_generate:
DN_target = np.sum(DN_per_proj)
self.N_target = np.round(self.N_target - DN_target, 3)
self.target_dens = self.N_target / self.target_area
if self.target_dens < 0.1 * self.last_reported_target_dens:
print(('Cross-ionization process %s target density = %.2e' %(self.name, self.target_dens)))
self.last_reported_target_dens = self.target_dens
new_mp_info = {}
if self.generate_equally:
# Calculate average product nel_mp_ref
nel_mp_ref_products = 0.
N_products = len(self.products)
for product in self.products:
thiscloud_gen = cloud_dict[product]
MP_e_gen = thiscloud_gen.MP_e
nel_mp_ref_products += MP_e_gen.nel_mp_ref / N_products
# Compute N_mp to add (the same for all products)
N_mp_per_proj_float = DN_per_proj / nel_mp_ref_products
N_mp_per_proj_int = np.floor(N_mp_per_proj_float)
rest = N_mp_per_proj_float - N_mp_per_proj_int
N_mp_per_proj_int = np.atleast_1d(np.int_(N_mp_per_proj_int))
N_mp_per_proj_int += np.atleast_1d(np.int_(rand(N_proj) < rest))
N_new_MPs = np.sum(N_mp_per_proj_int)
for product in self.products:
new_mp_info[product] = {}
thiscloud_gen = cloud_dict[product]
MP_e_gen = thiscloud_gen.MP_e
mass_gen = MP_e_gen.mass
# Initialize generated MPs with energy defined by user
v0_gen = np.sqrt(2 * (self.E_eV_init / 3.) * qe / mass_gen)
if self.generate_equally:
nel_mp_ref_gen = nel_mp_ref_products
else:
nel_mp_ref_gen = MP_e_gen.nel_mp_ref
# Compute N_mp to add (different for each product)
N_mp_per_proj_float = DN_per_proj / nel_mp_ref_gen
N_mp_per_proj_int = np.floor(N_mp_per_proj_float)
rest = N_mp_per_proj_float - N_mp_per_proj_int
N_mp_per_proj_int = np.atleast_1d(np.int_(N_mp_per_proj_int))
N_mp_per_proj_int += np.atleast_1d(np.int_(rand(N_proj) < rest))
N_new_MPs = np.sum(N_mp_per_proj_int)
if N_new_MPs > 0:
mask_gen = N_mp_per_proj_int > 0
N_mp_per_proj_int_masked = N_mp_per_proj_int[mask_gen]
nel_new_MPs_masked = np.ones(np.sum(mask_gen)) * nel_mp_ref_gen
nel_new_MPs = np.repeat(nel_new_MPs_masked, N_mp_per_proj_int_masked)
x_masked = x_proj[mask_gen]
y_masked = y_proj[mask_gen]
z_masked = z_proj[mask_gen]
x_new_MPs = np.repeat(x_masked, N_mp_per_proj_int_masked)
y_new_MPs = np.repeat(y_masked, N_mp_per_proj_int_masked)
z_new_MPs = np.repeat(z_masked, N_mp_per_proj_int_masked)
vx_new_MPs = np.zeros(N_new_MPs)
vy_new_MPs = np.zeros(N_new_MPs)
vz_new_MPs = np.zeros(N_new_MPs)
vx_new_MPs = v0_gen * (rand(N_new_MPs) - 0.5)
vy_new_MPs = v0_gen * (rand(N_new_MPs) - 0.5)
vz_new_MPs = v0_gen * (rand(N_new_MPs) - 0.5)
else:
nel_new_MPs = np.array([])
x_new_MPs = np.array([])
y_new_MPs = np.array([])
z_new_MPs = np.array([])
vx_new_MPs = np.array([])
vy_new_MPs = np.array([])
vz_new_MPs = np.array([])
new_mp_info[product]['N_new_MPs'] = N_new_MPs
new_mp_info[product]['nel_new_MPs'] = nel_new_MPs
new_mp_info[product]['x_new_MPs'] = x_new_MPs
new_mp_info[product]['y_new_MPs'] = y_new_MPs
new_mp_info[product]['z_new_MPs'] = z_new_MPs
new_mp_info[product]['vx_new_MPs'] = vx_new_MPs
new_mp_info[product]['vy_new_MPs'] = vy_new_MPs
new_mp_info[product]['vz_new_MPs'] = vz_new_MPs
return new_mp_info, np.sum(DN_per_proj)
def get_sigma(self, energy_eV_proj):
sigma_cm2_proj = energy_eV_proj * 0.
# For now we set sigma = 0. both below and above energies in file...
mask_below = (energy_eV_proj < self.energy_eV_min)
mask_above = (energy_eV_proj > self.energy_eV_max)
mask_interp = ~mask_below * ~mask_above
if self.flag_log:
x_interp_proj = np.log10(energy_eV_proj[mask_interp])
else:
x_interp_proj = energy_eV_proj[mask_interp]
sigma_cm2_proj[mask_interp] = self._interp(x_interp_proj=x_interp_proj)
# Return cross section in m2
return sigma_cm2_proj * 1e-4
def _interp(self, x_interp_proj):
"""
Linear interpolation of the energy - sigma curve.
"""
index_float = (x_interp_proj - self.x_interp_min) / self.delta_x_interp
index_remainder, index_int = np.modf(index_float)
index_int = index_int.astype(int)
return self.sigma_cm2[index_int] + index_remainder * self.sigma_cm2_diff[index_int]
class Cross_Ionization(object):
def __init__(self, pyecl_input_folder, cross_ion_definitions, cloud_list,
chamber_area, n_rep_test=10000, Dt_test=25e-11,
energy_eV_test=np.logspace(np.log10(1.), np.log10(25000.), num=5000)):
print('Initializing cross ionization.')
# Make cloud dict from list
cloud_dict = {}
for cloud in cloud_list:
cloud_dict.update({cloud.name : cloud})
self.projectiles_dict = {}
self.products = []
# Init projectiles and make list of products
for projectile in list(cross_ion_definitions.keys()):
print(('Projectile %s:' %(projectile)))
# Check that projectile name corresponds to existing cloud
assert projectile in list(cloud_dict.keys()), "Projectile name %s does not correspond to a defined cloud name."%(projectile)
self.projectiles_dict.update({projectile : []})
# Init processes
for process_name in list(cross_ion_definitions[projectile].keys()):
process_definitions = cross_ion_definitions[projectile][process_name]
process = Ionization_Process(pyecl_input_folder, process_name,
process_definitions, cloud_dict, chamber_area)
self.projectiles_dict[projectile].append(process)
for product in process.products:
if product not in self.products:
self.products.append(product)
# Extract sigma curves for consistency checks
self._extract_sigma(Dt=Dt_test, cloud_dict=cloud_dict,
n_rep=n_rep_test, energy_eV=energy_eV_test)
# Initialize dictionary for quantities to save
self.nel_cross_ion = {}
self.N_mp_cross_ion = {}
self.DN_proj = {}
for cloud in cloud_list:
self.nel_cross_ion[cloud.name] = 0.
self.N_mp_cross_ion[cloud.name] = 0
self.DN_proj[cloud.name] = 0.
def generate(self, Dt, cloud_list):
# Make cloud dict from list
cloud_dict = {}
for cloud in cloud_list:
cloud_dict.update({cloud.name : cloud})
new_mps_to_gen = self._init_new_mp_dict(self.products)
for projectile in list(self.projectiles_dict.keys()):
thiscloud = cloud_dict[projectile]
MP_e = thiscloud.MP_e
N_mp = MP_e.N_mp
mass = MP_e.mass
if N_mp > 0:
nel_mp = MP_e.nel_mp[:N_mp]
x_mp = MP_e.x_mp[:N_mp]
y_mp = MP_e.y_mp[:N_mp]
z_mp = MP_e.z_mp[:N_mp]
vx_mp = MP_e.vx_mp[:N_mp]
vy_mp = MP_e.vy_mp[:N_mp]
vz_mp = MP_e.vz_mp[:N_mp]
v_mp = np.sqrt(vx_mp * vx_mp +
vy_mp * vy_mp +
vz_mp * vz_mp)
for process in self.projectiles_dict[projectile]:
mp_info_from_proc, DN_proj = process.generate(Dt=Dt,
cloud_dict=cloud_dict,
mass_proj=mass,
N_proj=N_mp,
nel_mp_proj=nel_mp,
x_proj=x_mp,
y_proj=y_mp,
z_proj=z_mp,
v_mp_proj=v_mp)
for product in process.products:
self._add_to_mp_dict(new_mps_to_gen[product],
mp_info_from_proc[product])
self.DN_proj[product] += DN_proj
t_last_impact = -1
for thiscloud in cloud_list:
if thiscloud.name in self.products:
MP_e = thiscloud.MP_e
new_mps = new_mps_to_gen[thiscloud.name]
if new_mps['N_new_MPs'] > 0:
MP_e.add_new_MPs(new_mps['N_new_MPs'],
new_mps['nel_new_MPs'],
new_mps['x_new_MPs'],
new_mps['y_new_MPs'],
new_mps['z_new_MPs'],
new_mps['vx_new_MPs'],
new_mps['vy_new_MPs'],
new_mps['vz_new_MPs'],
t_last_impact)
# Add to saved data
self.nel_cross_ion[thiscloud.name] += np.sum(new_mps['nel_new_MPs'])
self.N_mp_cross_ion[thiscloud.name] += new_mps['N_new_MPs']
else:
self.nel_cross_ion[thiscloud.name] += 0.
self.N_mp_cross_ion[thiscloud.name] += 0
def save_cross_ion_data(self, cloud_name):
thiscloud_nel_cross_ion = self.nel_cross_ion[cloud_name]
thiscloud_N_mp_cross_ion = self.N_mp_cross_ion[cloud_name]
thiscloud_DN_proj = self.DN_proj[cloud_name]
self.nel_cross_ion[cloud_name] = 0.
self.N_mp_cross_ion[cloud_name] = 0.
self.DN_proj[cloud_name] = 0.
return thiscloud_nel_cross_ion, thiscloud_N_mp_cross_ion, thiscloud_DN_proj
def _extract_sigma(self, Dt, cloud_dict, n_rep, energy_eV):
v0 = 0.
N_ene = len(energy_eV)
N_mp = n_rep
x_mp = np.zeros(n_rep)
y_mp = np.zeros(n_rep)
z_mp = np.zeros(n_rep)
for projectile in list(self.projectiles_dict.keys()):
thiscloud = cloud_dict[projectile]
mass = thiscloud.MP_e.mass
nel_mp = np.ones(n_rep) * thiscloud.MP_e.nel_mp_ref
v_test = np.sqrt(2 * energy_eV * qe / mass)
for process in self.projectiles_dict[projectile]:
if process.extract_sigma:
print(('Extracting cross section for process %s' %process.name ))
save_dict = {}
save_dict['energy_eV'] = energy_eV
save_dict['sigma_cm2_interp'] = np.zeros(len(energy_eV))
for product in process.products:
this_sigma_name = 'sigma_cm2_sampled_%s' %(product)
save_dict[this_sigma_name] = np.zeros(len(energy_eV))
for i_ene, energy in enumerate(energy_eV):
if np.mod(i_ene, N_ene / 10) == 0:
print(('Extracting sigma %.0f'%(float(i_ene) / float(N_ene) * 100) + """%"""))
# Test process.get_sigma()
sigma_m2 = process.get_sigma(np.array([energy]))
save_dict['sigma_cm2_interp'][i_ene] = sigma_m2 * 1e4
# Test process.generate()
v_ene = v_test[i_ene]
v_mp = v_ene * np.ones(n_rep)
mp_info_from_proc, _ = process.generate(Dt, cloud_dict=cloud_dict,
mass_proj=mass, N_proj=N_mp,
nel_mp_proj=nel_mp, x_proj=x_mp,
y_proj=y_mp, z_proj=z_mp,
v_mp_proj=v_mp, flag_generate=False)
for product in list(mp_info_from_proc.keys()):
DN_gen = np.sum(mp_info_from_proc[product]['nel_new_MPs'])
if v_ene > 0:
sigma_m2_est = DN_gen / process.target_dens / v_ene / Dt / np.sum(nel_mp)
else:
sigma_m2_est = 0.
this_sigma_name = 'sigma_cm2_sampled_%s' %(product)
save_dict[this_sigma_name][i_ene] = sigma_m2_est * 1e4
sio.savemat(process.extract_sigma_path, save_dict, oned_as='row')
print(('Saved extracted cross section as %s' %process.extract_sigma_path))
def _init_new_mp_dict(self, products):
# Init new MP dictionary for products
new_mp_dict = {}
mp_dict_keys = ['N_new_MPs', 'nel_new_MPs', 'x_new_MPs', 'y_new_MPs',
'z_new_MPs', 'vx_new_MPs', 'vy_new_MPs', 'vz_new_MPs']
for product in products:
new_mp_dict[product] = {}
for key in mp_dict_keys:
if key == 'N_new_MPs':
new_mp_dict[product][key] = 0
else:
new_mp_dict[product][key] = np.array([])
return new_mp_dict
def _add_to_mp_dict(self, mp_dict, dict_to_add):
#sum_dict = {}
for key in list(mp_dict.keys()):
if key == 'N_new_MPs':
mp_dict[key] += dict_to_add['N_new_MPs']
else:
mp_dict[key] = np.append(mp_dict[key], dict_to_add[key])