def pull_eq_structure(all_scans, eq_roo, eq_roh, mass):
from GaussianHandler import LogInterpreter
eq_crds = LogInterpreter(*all_scans, scancoord_1=(0, 1), scancoord_2=(1, 2)).\
get_crds_for_one(xdist=eq_roo, ydist=eq_roh)
eq_crds *= angtobohr
com = np.dot(mass, eq_crds) / np.sum(mass)
com_eqcrds = eq_crds - com
return com_eqcrds
def twodee_plot():
allscans = list(sorted(glob.glob(os.path.join(scan_dir, "logs", "2Dtet_par*.log"))))
ref_xyz = os.path.join(main_dir, "structures", "reference_structure_official.xyz")
data = LogInterpreter(*allscans, optimized=True, method="partrig", scancoord_1=(0, 1), scancoord_2=(1, 2))
mass = (np.array((15.999, 15.999, 1.008, 2.014, 2.014, 15.999, 2.014, 2.014, 15.999, 2.014, 2.014, 2.014, 2.014))
/ 5.485794E-4)
pdict, freq, wfns = run2d(data.energies)
dipadedodas = dipoles(data, ref_xyz, mass, pdict)
dip_spect(dipadedodas, freq, wfns)
def logData(self):
if self._logData is None:
from GaussianHandler import LogInterpreter
if self.method == "rigid":
optBool = False
else:
optBool = True
self._logData = LogInterpreter(*self.scanLogs, moleculeObj=self, optimized=optBool)
return self._logData
def make_scan_plots(log_data, grid=False, contour=True):
fig = plt.figure()
if grid:
pts = np.array(list(log_data.cartesians.keys()))
plt.plot(pts[:, 0], pts[:, 1], 'o')
# plt.close()
if contour:
pts = log_data.energies
mid_oh = (pts[:, 0] / 2) - pts[:, 1]
mid_oh *= -1
pot = pts[:, 2]
# load rigid data for difference plots
rscans = list(
sorted(glob.glob(os.path.join(scan_dir, "2Dtet_ri*.log"))))
rel = LogInterpreter(*rscans,
method='rigid',
scancoord_1=(0, 1),
scancoord_2=(1, 2),
optimized=False)
rpts = rel.energies
rpot = rpts[:, 2]
pot = np.abs((pot - rpot))
pot *= hartowave
pot[pot > 2000] = 2000
# pot *= hartowave
# pot[pot > 15000] = 15000
plt.tick_params(labelsize=22)
plt.rcParams.update({'font.size': 22})
plt.tricontourf(pts[:, 0], mid_oh, pot, cmap='Purples_r', levels=15)
plt.ylim(-0.7, 0.2)
plt.xlim(2, 4)
plt.colorbar()
plt.tricontour(pts[:, 0], mid_oh, pot, colors='k', levels=15)
# plt.close()
return fig
def lindsey(reference_xyz, all_scans, mass):
"""enumerates the code above."""
from GaussianHandler import LogInterpreter
ref = pull_ref_struct(reference_xyz, mass)
all_coords = LogInterpreter(*all_scans,
scancoord_1=(0, 1),
scancoord_2=(1, 2)).cartesians
all_coords = np.array(list(all_coords.values()))
all_coords *= angtobohr
all_dips = LogInterpreter(*all_scans).dipoles
all_dips = np.array(list(all_dips.values()))
eck_rotated_linds = np.zeros((len(all_coords), len(ref), 3))
eck_rotated_dipoles = np.zeros((len(all_dips), 3))
for i, coord, dip in zip(np.arange(len(all_coords)), all_coords, all_dips):
com = np.dot(mass, coord) / np.sum(mass)
com_coord = coord - com
com_dip = dip - com
eck = lindsey_way(ref, com_coord, mass)
eck_rotated_dipoles[i] = rotator_dipoles(eck, com_dip)
newbies = rotator(eck, com_coord)
eck_rotated_linds[i] = newbies
return eck_rotated_linds, eck_rotated_dipoles
:arg harm2d_0: PES of OH=0 for FD method
:arg harm2d_1: PES of OH=1 for FD method
:arg anharm_0: PES of OH=0 for DVR method
:arg anharm_1: PES of OH=1 for DVR method"""
Comp.plot_comparison_2d(harm2d_0, harm2d_1, anharm_0, anharm_1, mini_pot)
plt.savefig(
os.path.join(main_dir, 'figures', '2D_partrig_VAA_energycurves.png'))
plt.close()
Comp.plot_diffs(harm1d_0, harm1d_1, anharm_0, anharm_1, harm2d_0, harm2d_1)
plt.savefig(
os.path.join(main_dir, 'figures', 'E0E1_partrig_differenceplot.png'))
plt.close()
if __name__ == '__main__':
main_dir = os.path.dirname(os.path.dirname(__file__))
elect_pot = np.loadtxt(
os.path.join(main_dir, '1D Scans', '1D_partrig_electpot.dat'))
harm1d_0, harm1d_1 = run_1d_harmonic_analysis(elect_pot)
scan_dir = os.path.join(main_dir, '2D Scans')
all_scans = list(
sorted(glob.glob(os.path.join(scan_dir, "2Dtet_partrig*.log"))))
cut_dict = LogInterpreter(*all_scans).cut_dictionary(midpoint=True)
mini_pot = LogInterpreter(*all_scans).minimum_pot()
harm2d_0, harm2d_1 = run_2d_harmonic_analysis(mini_pot)
anharm_0, anharm_1 = run_2d_anharmonic_analysis(mini_pot)
run_comp_plots(mini_pot, harm1d_0, harm1d_1, harm2d_0, harm2d_1, anharm_0,
anharm_1)
plt.xlabel("OO distance")
plt.ylabel("Transition Moment")
# plt.ylim(-1, 1)
plt.legend()
plt.show()
return mus
main_dir = os.path.dirname(os.path.dirname(__file__))
scan_dir = os.path.join(main_dir, "2D Scans", "logs")
VAA_dir = os.path.join(main_dir, 'VAA')
if __name__ == '__main__':
from GaussianHandler import LogInterpreter
from Analysis2D import run_dvr, wfn_flipper
from MakeSpectra import to_eck_struct_new
ref_xyz = os.path.join(main_dir, "structures",
"reference_structure_official.xyz")
all_scans = list(
sorted(glob.glob(os.path.join(scan_dir, "2Dtet_rig*.log"))))
cut_dict = LogInterpreter(*all_scans).cut_dictionary(midpoint=True)
ohpots, wfnarray = run_dvr(cut_dict, desired_energies=4, num_pts=100)
wfnarray = wfn_flipper(wfnarray)
tet = (np.array((15.999, 15.999, 1.008, 2.014, 2.014, 15.999, 2.014, 2.014,
15.999, 2.014, 2.014, 2.014, 2.014)) / amutome)
results = to_eck_struct_new(ref_xyz, all_scans, tet)
dipadedodas = interp_dipoles(results, len(wfnarray[0]))
derivies = deriv_dipoles(dipadedodas)
mus = psi_trans(dipadedodas, wfnarray, plot=True)
lin_mus = lin_psi_trans(dipadedodas, derivies, wfnarray, plot=True)
relax_allscans = list(
sorted(glob.glob(os.path.join(scan_dir, "2Dtet_re*.log"))))
tet_h9o4 = (np.array(
(15.999, 15.999, 1.008, 1.008, 1.008, 15.999, 1.008, 1.008, 15.999,
1.008, 1.008, 1.008, 1.008)) / amutome)
tet_hd8o4 = (np.array(
(15.999, 15.999, 1.008, 2.014, 2.014, 15.999, 2.014, 2.014, 15.999,
2.014, 2.014, 2.014, 2.014)) / amutome)
tet_d9o4 = (np.array(
(15.999, 15.999, 2.014, 2.014, 2.014, 15.999, 2.014, 2.014, 15.999,
2.014, 2.014, 2.014, 2.014)) / amutome)
ref_xyz = os.path.join(main_dir, "structures",
"reference_structure_official.xyz")
rig_data = LogInterpreter(*rig_allscans,
method="rigid",
scancoord_1=(0, 1),
scancoord_2=(1, 2),
optimized=False)
partrig_data = LogInterpreter(*partrig_allscans,
method="partrig",
scancoord_1=(0, 1),
scancoord_2=(1, 2),
optimized=True)
relax_data = LogInterpreter(*relax_allscans,
method="relax",
scancoord_1=(0, 1),
scancoord_2=(1, 2),
optimized=True)
from DVR2D import twodee_plot
twodee_plot()
for j in range(len(atom_str)):
f.write(
"%s %5.8f %5.8f %5.8f \n" %
(atom_str[j], crds[i, j, 0], crds[i, j, 1], crds[i, j, 2]))
f.write("\n")
if __name__ == '__main__':
main_dir = os.path.dirname(os.path.dirname(__file__))
log_dir = os.path.join(main_dir, '2D Scans', 'logs')
rlog_list = sorted(glob.glob(os.path.join(log_dir, '2Dtet_ri*.log')))
# onedee = os.path.join(main_dir, "1D Scans", "1D_relax_ooscan.log")
reference_xyz = os.path.join(main_dir, "structures",
"reference_structure_official.xyz")
log_data = LogInterpreter(*rlog_list,
method="rigid",
optimized=False,
scancoord_1=(0, 1),
scancoord_2=(1, 2))
mass_array = (np.array(
(15.999, 15.999, 1.008, 2.014, 2.014, 15.999, 2.014, 2.014, 15.999,
2.014, 2.014, 2.014, 2.014)) / 5.485794E-4)
# all_crd = LogInterpreter(*rlog_list, method='relax', optimized=True, scancoord_1=(0, 1), scancoord_2=(1, 2))\
# .cartesians.values()
# all_crd = np.array(list(all_crd))
import eckartRotation as eckin
results = eckin.many_rotations(reference_xyz, log_data, mass_array)
zmcs = results[0]
tet = ["O", "O", "H", "D", "D", "O", "D", "D", "O", "D", "D", "D", "D"]
get_xyz('rigid_coords_posteckart.xyz', zmcs, tet)
elif i == 21:
two = wfn
elif i == 36:
three = wfn
else:
pass
wfn = [zero, one, two, three]
ref_xyz = os.path.join(main_dir, "structures",
"reference_structure_official.xyz")
slog_dir = os.path.join(main_dir, "2D Scans", "logs")
allscans = list(sorted(glob.glob(os.path.join(slog_dir, "2Dtet_rig*.log"))))
tet_hd8o4 = (np.array(
(15.999, 15.999, 1.008, 2.014, 2.014, 15.999, 2.014, 2.014, 15.999, 2.014,
2.014, 2.014, 2.014)) / 5.485794E-4)
data = LogInterpreter(*allscans, scancoord_1=(0, 1), scancoord_2=(1, 2))
results = eckin.many_rotations(ref_xyz, data, tet_hd8o4)
zmcs = results[0]
oo_pos = (0, 1)
oops = zmcs[:, oo_pos]
diffs = np.diff(oops, axis=1)
diffs = diffs.reshape((len(diffs), 3))
oodists = np.linalg.norm(diffs, axis=1)
oodists = np.around(oodists, 4)
oh_pos = (1, 2)
ohps = zmcs[:, oh_pos]
ohdiffs = np.diff(ohps, axis=1)
ohdiffs = ohdiffs.reshape((len(ohdiffs), 3))
ohdists = np.linalg.norm(ohdiffs, axis=1)
ohdists = np.around(ohdists, 4)
plt.xlim(2, 4)
if print_vals:
print("Minimum Shift:", grid[np.argmin(ground_fit)] -
grid[np.argmin(excited_fit)]) # excited shifted left = pos
print("Energy Difference:", min(excited_fit) - min(ground_fit))
return fig
if __name__ == '__main__':
main_dir = os.path.dirname(os.path.dirname(__file__))
scan_dir = os.path.join(main_dir, '2D Scans', 'logs')
all_scans = list(sorted(glob.glob(os.path.join(scan_dir,
"2Dtet_pa*.log"))))
data = LogInterpreter(*all_scans,
method='partrig',
scancoord_1=(0, 1),
scancoord_2=(1, 2),
optimized=True)
make_scan_plots(data)
# make_scan_plots(data, grid=True, contour=False)
plt.show()
# mini_pot = data.minimum_pot()
# finitedict = data.finite_dict(midpoint=True)
# cut_dict = data.cut_dictionary(midpoint=True)
#
# # for harmonic OH treatment
# # HOfreqs = do_the_freqy_science(finitedict)
# # plt.show()
# ohHARpots, HARwfnarray = run_harmonic_dvr(finitedict, desired_energies=3, num_pts=500, plots=False)
# wfn_flipper(HARwfnarray)