def run_gap_nogap_comparison():
splopter_gap = spl.Splopter('bin/data/',
'benchmarking_sam/',
'prebprobe_fullgap/',
prepare=False)
splopter_nogap = spl.Splopter('bin/data/',
'benchmarking_sam/',
'prebprobe_fullnogap/',
prepare=False)
splopter_gap.prepare(denormaliser_fl=False)
splopter_nogap.prepare(denormaliser_fl=False)
ivdata_g = splopter_gap.trim()
ivdata_ng = splopter_nogap.trim()
ivdata_g2 = splopter_gap.trim(trim_beg=0.05, trim_end=0.45)
ivdata_ng2 = splopter_nogap.trim(trim_beg=0.05, trim_end=0.45)
# ifit_g = splopter_gap.fit(ivdata_g, f.IonCurrentSEFitter(), print_fl=True)
# ifit_ng = splopter_nogap.fit(ivdata_ng, f.IonCurrentSEFitter(), print_fl=True)
ffit_g2 = splopter_gap.fit(ivdata_g2, print_fl=True)
ffit_ng2 = splopter_nogap.fit(ivdata_ng2, print_fl=True)
fig1 = plt.figure()
splopter_gap.plot_iv(fig=fig1, plot_tot=True, label='Gap')
splopter_nogap.plot_iv(fig=fig1,
plot_vf=True,
plot_tot=True,
label='No Gap')
fig2 = plt.figure()
splopter_gap.plot_f_fit(
ffit_g2,
fig=fig2,
plot_vf=False,
label=r'Gap - $T_e$ = {:.2g}, $I_{}$ = {:.3g}'.format(
ffit_g2.get_temp(errors_fl=False), r'{sat}',
ffit_g2.get_isat(errors_fl=False)))
splopter_nogap.plot_f_fit(
ffit_ng2,
fig=fig2,
label=r'No Gap - $T_e$ = {:.2g}, $I_{}$ = {:.3g}'.format(
ffit_ng2.get_temp(errors_fl=False), '{sat}',
ffit_ng2.get_isat(errors_fl=False)))
# fig3 = plt.figure()
# splopter_gap.plot_i_fit(ifit_g, fig=fig3, label='Gap ')
# splopter_nogap.plot_i_fit(ifit_ng, fig=fig3, label='No Gap ')
plt.legend()
plt.show()
def run_maxwellian_comparison():
splopter_sheath = spl.Splopter('bin/data/',
'benchmarking_sam/',
'disttest_fullnogap/',
prepare=False)
splopter_whole = spl.Splopter('bin/data/',
'benchmarking_sam/',
'prebprobe_fullnogap/',
prepare=False)
fig = plt.figure()
run_spice_df_analysis(splopter_sheath, fig=fig)
run_spice_df_analysis(splopter_whole, fig=fig)
plt.show()
def test():
flpt = spl.Splopter('bin/data_local/', 'benchmarking/', 'nogap/')
vf = flpt.get_vf()
phi = flpt.get_plasma_potential()
mu = flpt.denormaliser.mu
const = np.log(0.6 * np.sqrt((2 * np.pi) / mu))
temperature = (vf - phi) / const
print(temperature)
def draw_potential(splopter=None, t_dict_label=sd.POT, plot_obj_fl=False):
if not splopter:
splopter = spl.Splopter('bin/data_local/',
'benchmarking/',
'disttest_fullnogap/',
prepare=False)
# splopter = spl.Flopter('bin/data/', 'benchmarking_sam/', 'prebprobe_fullgap/', prepare=False)
plasma_parameter = np.flip(splopter.tdata.t_dict[t_dict_label], 0)
objects_raw = np.flip(splopter.tdata.objectsenum, 0)
probe_obj_indices = splopter.parser.get_probe_obj_indices()
objects = np.zeros(np.shape(plasma_parameter))
wall_indices = np.where(plasma_parameter == 0)
probe_objs = [
np.where(objects_raw == index + 1) for index in probe_obj_indices
]
if plot_obj_fl:
plt.figure()
plt.imshow(objects_raw, cmap='Greys')
plt.colorbar()
plasma_parameter[wall_indices] = np.NaN
for probe_obj in probe_objs:
plasma_parameter[probe_obj] = np.NaN
objects[wall_indices] = 3.0
for probe_obj in probe_objs:
objects[probe_obj] = 1.5
plt.figure()
plt.imshow(
objects,
cmap='Greys',
extent=[0,
len(plasma_parameter[0]) / 2, 0,
len(plasma_parameter) / 2])
ax = plt.gca()
im = ax.imshow(
plasma_parameter,
extent=[0,
len(plasma_parameter[0]) / 2, 0,
len(plasma_parameter) / 2],
interpolation=None)
plt.xlabel(r'y / $\lambda_D$', fontsize=15)
plt.ylabel(r'z / $\lambda_D$', fontsize=15)
# plt.title('Electrostatic potential for a flush mounted probe', fontsize=20)
plt.quiver([200], [200], splopter.tdata.by, splopter.tdata.bz, scale=5)
plt.colorbar(im, fraction=0.035, pad=0.04)
plt.show()
def test2():
flpt = spl.Splopter('bin/data_local/', 'benchmarking/', 'nogap/')
# plt.figure()
print(flpt.tdata.diagnostics.keys())
e_hist_before = flpt.tdata.diagnostics['eHistx1']
i_hist_before = flpt.tdata.diagnostics['iHistx1']
plt.plot(e_hist_before, label='eHistBefore')
plt.plot(i_hist_before, label='iHistBefore')
flpt.denormalise()
e_hist_after = flpt.tdata.diagnostics['eHistx1']
i_hist_after = flpt.tdata.diagnostics['iHistx1']
# plt.plot(e_hist_after, label='eHistAfter')
# plt.plot(i_hist_after, label='iHistAfter')
plt.legend()
plt.show()
from flopter.spice import splopter as spl
import matplotlib.pyplot as plt
if __name__ == '__main__':
spl_t0_old = spl.Splopter('bin/data_local_m/',
'magnum/',
'fetail_T0_S1/',
reduce_fl=True)
spl_t0 = spl.Splopter('bin/data_local_m/',
'magnum/',
'fetail_T0_S/',
reduce_fl=True)
spl_t2 = spl.Splopter('bin/data_local_m/',
'magnum/',
'fetail_T2-5_S1/',
reduce_fl=True)
spl_t10 = spl.Splopter('bin/data_local_m/',
'magnum/',
'fetail_T10_S1/',
reduce_fl=True)
splopters = [spl_t0_old, spl_t0, spl_t2, spl_t10]
for splopter in splopters:
splopter.prepare(homogenise_fl=True, denormaliser_fl=False)
plt.figure()
splopter.plot_raw()
plt.show()
def homogenise_run(path):
splopter = spl.Splopter(path, reduce=DESIRED_VARIABLES, ignore_tzero_fl=True, version=2.14,
store_dataframe_fl=True, ignore_a_file=True)
splopter.parse_input()
iv_data, raw_iv_data = splopter.homogenise(reduced_bin_fract=0.1)
return splopter.iv_df, iv_data
def compare_larmor_radii(files,
mount='bin/data/',
folder='benchmarking_mass/'):
import gc
splopters = {}
for file in files:
sp = spl.Splopter(mount, folder, file, prepare=True)
print(sp.tdata.diagnostics.keys())
print(len(sp.tdata.diagnostics['eTrackery']),
np.size(sp.tdata.diagnostics['iTrackery']))
print('Tracking Ions')
i_splits = np.where(
np.abs(np.diff(sp.tdata.diagnostics['iTrackery'], axis=0)) > 0.5
)[0] + 1
iy_tracks = np.split(sp.tdata.diagnostics['iTrackery'], i_splits)
iz_tracks = np.split(sp.tdata.diagnostics['iTrackerz'], i_splits)
iy_tracks.sort(key=len, reverse=True)
iz_tracks.sort(key=len, reverse=True)
# print(i_splits.shape, len(iy_tracks), iy_tracks[200])
# for i, t in enumerate(iy_tracks):
# print('{}) {}'.format(i, len(t)))
i_tracks = zip(iy_tracks[:200], iz_tracks[:200])
print('Tracking Electrons')
# Find positions within tracking array where target seems to change by selecting elements with a large jump
e_splits = np.where(
np.abs(np.diff(sp.tdata.diagnostics['eTrackery'], axis=0)) > 0.5
)[0] + 1
# Split tracking diagnostic array into different tracks using points found above
ey_tracks = np.split(sp.tdata.diagnostics['eTrackery'], e_splits)
ez_tracks = np.split(sp.tdata.diagnostics['eTrackerz'], e_splits)
# Sort the tracks in descending order by length
ey_tracks.sort(key=len, reverse=True)
ez_tracks.sort(key=len, reverse=True)
# Create a zip of the tracks individual components
e_tracks = zip(ey_tracks[:200], ez_tracks[:200])
# 'iTrackery', 'iTrackerz', 'iTrackerux', 'iTrackeruy', 'iTrackeruz',
# 'eTrackery', 'eTrackerz', 'eTrackerux', 'eTrackeruy', 'eTrackeruz'
print('Lengths. ion: {}, electron: {}'.format(len(i_splits),
len(e_splits)))
print('Plotting 1')
plt.figure()
for y_track, z_track in i_tracks:
plt.plot(y_track, z_track)
plt.title(file + ' - Ions')
plt.xlabel('Timestep')
plt.ylabel(r'Z Position ($\lambda_D$)')
plt.figure()
print('Plotting 2')
for y_track, z_track in e_tracks:
plt.plot(y_track, z_track)
plt.title(file + ' - Electrons')
plt.xlabel('Timestep')
plt.ylabel(r'Z Position ($\lambda_D$)')
print('...finished plotting')
# plt.figure()
# # plt.plot(sp.tdata.diagnostics['iTrackery'], sp.tdata.diagnostics['iTrackerz'], 'x', label='Ion track')
# plt.plot(sp.tdata.diagnostics['eTrackery'], sp.tdata.diagnostics['eTrackerz'], 'x', label='Electron track')
# plt.legend()
# plt.title(file)
# plt.xlabel(r'Y Position ($\lambda_D$)')
# plt.ylabel(r'Z Position ($\lambda_D$)')
# deallocate the splopter object
del sp
gc.collect()
plt.show()
def injection_dist_function(gauss_fl=True, show_fl=True):
splopter = spl.Splopter('bin/data_local/', 'tests/',
'injtestperp_halfnogap2/')
# splopter = spl.Flopter('bin/data_local/', 'tests/', 'injtest_halfnogap1/')
splopter.prepare(homogenise_fl=False)
v_scale = 1000
i_inj_path = splopter.afile_path.replace('.mat', '.i_inj')
e_inj_path = splopter.afile_path.replace('.mat', '.e_inj')
i_inj = -splopter.denormaliser(np.loadtxt(i_inj_path),
c.CONV_VELOCITY) / v_scale
e_inj = -splopter.denormaliser(np.loadtxt(e_inj_path),
c.CONV_VELOCITY) / v_scale
# runpath = 'data/'
# i_inj_path = runpath + 'injtest_halfnogap.i_inj'
# e_inj_path = runpath + 'injtest_halfnogap.e_inj'
# i_inj = np.loadtxt(i_inj_path)
# e_inj = np.loadtxt(e_inj_path)
i_hist, i_gaps = np.histogram(i_inj, density=True, bins='auto')
i_bins = (i_gaps[:-1] + i_gaps[1:]) / 2
e_hist, e_gaps = np.histogram(e_inj, density=True, bins='auto')
e_bins = (e_gaps[:-1] + e_gaps[1:]) / 2
t_e_guess = splopter.denormaliser.temperature / (v_scale**2)
guess = [t_e_guess, 1]
if gauss_fl:
i_fitter = f.GaussianVelIonEvFitter(mu=splopter.denormaliser.mu)
i_fdata = get_histogram_fit(splopter,
i_hist,
i_bins,
i_fitter,
v_scale=v_scale)
else:
i_fitter = f.MaxwellianVelFitter(mu=splopter.denormaliser.mu)
i_fdata = i_fitter.fit(i_bins, i_hist, initial_vals=guess)
i_fdata.print_fit_params()
if gauss_fl:
e_fitter = f.GaussianVelElecEvFitter()
e_fdata = get_histogram_fit(splopter,
e_hist,
e_bins,
e_fitter,
v_scale=v_scale)
else:
e_fitter = f.MaxwellianVelFitter(mu=1)
e_fdata = e_fitter.fit(e_bins, e_hist, initial_vals=guess)
e_fdata.print_fit_params()
plt.figure()
plt.plot(i_bins, i_hist)
plt.plot(i_bins, i_fdata.fit_y)
plt.title('Ion injection DF')
plt.figure()
plt.plot(e_bins, e_hist)
plt.plot(e_bins, e_fdata.fit_y)
plt.title('Electron injection DF')
if show_fl:
plt.show()
def run_current_comparison():
splopter_prep = spl.Splopter('bin/data/',
'benchmarking_sam/',
'disttest_fullnogap/',
prepare=False)
splopter_top = spl.Splopter('bin/data/',
'bms_distruns/',
'disttest_fullnogap_top/',
prepare=False)
splopter_bottom = spl.Splopter('bin/data/',
'bms_distruns/',
'disttest_fullnogap_bottom/',
prepare=False)
splopters = {
'prep': splopter_prep,
'top': splopter_top,
'bottom': splopter_bottom
}
currents = {}
times = {}
diagnostics = {}
for name, sp in splopters.items():
currents[name] = sp.tdata.objectscurrente[0]
times[name] = sp.tdata.t[1:]
diagnostics[name] = [
diag for diag_name, diag in sp.tdata.diagnostics.items()
if 'eHist' in diag_name
]
print(np.shape(diagnostics.items()))
for name in splopters.keys():
plt.figure(1)
plt.plot(times[name], currents[name], label=name)
plt.legend()
plt.figure(2)
for i, diag in enumerate(diagnostics[name]):
plt.plot(diag, label=name.join(str(i)))
plt.legend()
# current_prep = splopter_prep.tdata.objectscurrente
# time_prep = splopter_prep.tdata.t[1:]
# print(np.shape(time_prep), np.shape(current_prep))
# current_top = splopter_top.tdata.objectscurrente
# time_top = splopter_top.tdata.t[1:]
# for i in range(len(current_prep)):
# plt.figure()
# # current_combo = np.concatenate((current_prep[i], current_run0[i]))
# plt.plot(time_prep, current_prep[i], label='prep')
# plt.plot(time_top, current_top[i], label='top')
# # plt.plot(current_combo)
# plt.legend()
plt.figure()
plt.imshow(splopter_prep.tdata.pot)
plt.colorbar()
plt.figure()
plt.imshow(splopter_top.tdata.pot)
plt.colorbar()
plt.figure()
plt.imshow(splopter_bottom.tdata.pot)
plt.colorbar()
print(splopter_prep.tdata.diagnostics.keys())
print(splopter_top.tdata.diagnostics.keys())
plt.show()
def run_multihistogram_extr(z_high=370.0,
z_low=70.0,
num_samples=11,
fig=None,
show=None):
splopter = spl.Splopter('bin/data_local/',
'benchmarking/',
'disttest_fullnogap/',
prepare=False)
# path = 'bin/data_local/benchmarking/disttest_fullnogap/'
nproc = int(np.squeeze(splopter.tdata.nproc))
u_pars = {}
assert isinstance(num_samples, int)
if num_samples <= 1:
num_samples = 2
z_vals = np.linspace(z_low, z_high, num_samples)
for z in z_vals[:-1]:
u_pars[z] = np.array([], dtype=np.float64)
ralpha = (-splopter.tdata.alphayz / 180.0) * np.pi
rbeta = ((90.0 - splopter.tdata.alphaxz) / 180) * np.pi
for i in range(nproc):
num = str(i).zfill(2)
filename = splopter.tfile_path.replace('.mat', '{}.mat'.format(num))
p_file = loadmat(filename)
# [print(key+': ', str(np.shape(array))) for key, array in p_file.items() if '__' not in key]
for j in range(len(z_vals) - 1):
print('int(j)', int(j))
print('z_vals[int(j)]', z_vals[int(j)])
print('z_vals[int(j+1)]', z_vals[int(j + 1)])
indices = np.where((p_file['z'] > z_vals[int(j)])
& (p_file['z'] <= z_vals[int(j + 1)])
& (p_file['stype'] == 2))
u_pars[z_vals[int(j)]] = np.append(
u_pars[z_vals[int(j)]],
(p_file['uy'][indices] * np.cos(ralpha) * np.cos(rbeta)) -
(p_file['uz'][indices] * np.sin(ralpha)))
# u_par = (u_pars['uy'] * np.cos(ralpha) * np.cos(rbeta)) - (u_pars['uz'] * np.sin(ralpha))
# u_par = (u_pars['ux'] * np.sin(rbeta) * np.cos(ralpha)) + \
# (u_pars['uy'] * np.cos(ralpha) * np.cos(rbeta)) - \
# (u_pars['uz'] * np.sin(ralpha))
# sp.io.savemat('test.mat', u_par)
print('Finished compiling ')
if not fig:
fig = plt.figure()
# plt.hist(u_pars['u_par'], bins=500)
# plt.title('z_high = {}, z_low = {}'.format(z_high, z_low))
# run_spice_df_analysis(splopter, fig=fig)
for z_val, u_par in u_pars.items():
print(u_par)
hist, gaps = np.histogram(u_par, bins='auto', density=True)
plt.plot(gaps[:-1], hist, label='z_low = {}'.format(z_val))
plt.legend()
if show:
plt.show()
# splopter = spl.Flopter('bin/data/', 'benchmarking_sam/', 'prebprobe2_fullgap/', prepare=True)
# splopter = spl.Flopter('bin/data/', 'benchmarking_sam/', 'prebprobe2_fullnogap/', prepare=True)
# splopter = spl.Flopter('bin/data/', 'angledtip/', 'angledtiptest/', prepare=False)
# splopter = spl.Flopter('bin/data/', 'angledtip/', 'angledtiptest1/', prepare=False)
# splopter = spl.Flopter('bin/data/', 'test/', 'floatingpottest/', prepare=False)
# Radii comparison files
# comparison_files = ['benchmark_ms_g_hg/', 'massrationtest_ms_g_hg/', 'ionmasstest_ms_g_hg/',
# 'speciesmasstest_ms_g_hg/']
# compare_larmor_radii(comparison_files)
# splopter = spl.Splopter('bin/data/', 'benchmarking_mass/', 'benchmark_ms_g_hg/', prepare=True)
# splopter = spl.Splopter('bin/data/', 'benchmarking_mass/', 'massrationtest_ms_g_hg/', prepare=True)
# splopter = spl.Splopter('bin/data/', 'benchmarking_mass/', 'ionmasstest_ms_g_hg/', prepare=True)
# splopter = spl.Splopter('bin/data/', 'benchmarking_mass/', 'speciesmasstest_ms_g_hg/', prepare=True)
splopter = spl.Splopter('bin/data/', 'magnum/', 'sprobe_1e19_nopadding1/')
# splopter = spl.Splopter('bin/data/', 'magnum/', 'sprobe_1e19_nopadding1/')
# splopter.plot_2d_variable(show_fl=False)
# splopter.analyse_iv(show_fl=True)
# splopter.plot_1d_variable(variable_label=c.DIAG_WALL_POT, time_dep_fl=True, diagnostic_fl=True)
# splopter.plot_1d_variable(variable_label=sd.NZ)
# extract_density(splopter)
splopter.prepare(homogenise_fl=True, denormaliser_fl=False)
regions = splopter.parser.get_hist_diag_regions(2)
# run_multi_hist_analysis(regions=regions, splopter=splopter, show_fl=True, species=2)
# run_gap_nogap_comparison()
# run_multi_hist_analysis(splopter=splopter, fitter=f.GaussianVelElecEvFitter(), show_fl=False)
# angles_search_str = '/*[!.{yml, inp}]/backup*'
angles_search_str = '/*[!.{yml, inp}]'
desired_variables = td.DEFAULT_REDUCED_DATASET | {'ProbePot'}
all_run_dirs = {}
scans = glob.glob(scans_searchstr)
scans = list(set(scans) - skippable_scans)
for scan in scans:
if scan in skippable_scans:
print(f'Skipping {scan}...\n\n')
continue
all_run_dirs[scan] = glob.glob(scan + angles_search_str)
print(f'{scan}: {all_run_dirs[scan]}\n')
# For adding a new run to the list of splopters
angle_dir = all_run_dirs[scans[6]][0]
print(angle_dir)
splopters = {}
if angle_dir in splopters:
print(f'Removing {angle_dir} from existing list of splopters')
splopters.pop(angle_dir)
splopter = spl.Splopter(lowdens_dir / angle_dir, reduce=desired_variables)
splopter.prepare(denormaliser_fl=True, homogenise_fl=True, find_se_temp_fl=False)
splopter.denormalise()
splopters[angle_dir] = splopter
print('Created splopter')