def _index_where(arr, value, reverse=False):
if not reverse:
idx = pl.argmin(abs(arr - value))
else:
revidx = pl.argmin(abs(arr[::-1] - value))
idx = len(arr) - (revidx + 1)
return idx
def _mean_spectra(sim, tmin=0, tmax=1000):
f = h5py.File(sim.output.spectra.path_file2D, "r")
dset_times = f["times"]
# nb_spectra = dset_times.shape[0]
times = dset_times[...]
# nt = len(times)
dset_khE = f["khE"]
kh = dset_khE[...]
dset_spectrumEK = f["spectrum2D_EK"]
dset_spectrumEA = f["spectrum2D_EA"]
imin_plot = pl.argmin(abs(times - tmin))
imax_plot = pl.argmin(abs(times - tmax))
# tmin_plot = times[imin_plot]
# tmax_plot = times[imax_plot]
machine_zero = 1e-15
EK = dset_spectrumEK[imin_plot:imax_plot + 1].mean(0)
EA = dset_spectrumEA[imin_plot:imax_plot + 1].mean(0)
EK[abs(EK) < 1e-15] = machine_zero
EA[abs(EA) < 1e-15] = machine_zero
E_tot = EK + EA
f.close()
return kh, E_tot, EK, EA
def pick_handler(event):
artist = event.artist
mouseevent = event.mouseevent
i = argmin(sum((array(artist.get_data()).T - array([mouseevent.xdata, mouseevent.ydata]))**2, axis=1))
x,y = array(artist.get_data()).T[i]
Nelements = len(artist.get_data()[0])
d = str(artist.get_url()[i]) if Nelements > 1 else str(artist.get_url())
ax = artist.axes
if not hasattr(ax, "annotation"):
ax.annotation = ax.annotate(d,
xy=(x,y), xycoords='data',
xytext=(0,30), textcoords="offset points",
size="larger", va="bottom", ha="center",
bbox=dict(boxstyle="round", fc="w", alpha=0.5),
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=-0.2"),
)
ax.annotation.xy = (x,y)
ax.annotation.set_text(d)
artist.figure.canvas.draw()
def getPreceedingNoise(self,tdData,timePreceedingSignal=-1):
#retruns the preceeding noise in X and Y channel of tdData
#it uses timePreceedingSignal to evaluate the noise, if not given,
#it autodetects a "good" time
#we should crop the data at least 2.5 ps before the main peak
nearestdistancetopeak=2.5e-12
if min(tdData[:,0])+timePreceedingSignal>-nearestdistancetopeak:
timePreceedingSignal=-min(tdData[:,0])-nearestdistancetopeak
print("Time Interval of preceeding noise to long, reset done")
#dont use user input if higher than peak
#get the first time
starttime=min(tdData[:,0])
if timePreceedingSignal<0:
#determine length automatically
ratio=2
ix_max=py.argmax(tdData[:,1])
ix_min=py.argmin(tdData[:,1])
earlier=min(ix_max,ix_min)
#take only the first nts part
endtime=tdData[int(earlier/ratio),0]
else:
endtime=starttime+timePreceedingSignal
#cut the data from starttime to endtime
noise=self.getShorterData(tdData,starttime,endtime)
#detrend the noise
noise=signal.detrend(noise[:,1:3],axis=0)
return noise
def pick_handler(event):
artist = event.artist
mouseevent = event.mouseevent
i = argmin(
sum((array(artist.get_data()).T -
array([mouseevent.xdata, mouseevent.ydata]))**2,
axis=1))
x, y = array(artist.get_data()).T[i]
Nelements = len(artist.get_data()[0])
d = str(artist.get_url()[i]) if Nelements > 1 else str(artist.get_url())
ax = artist.axes
if not hasattr(ax, "annotation"):
ax.annotation = ax.annotate(
d,
xy=(x, y),
xycoords='data',
xytext=(0, 30),
textcoords="offset points",
size="larger",
va="bottom",
ha="center",
bbox=dict(boxstyle="round", fc="w", alpha=0.5),
arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=-0.2"),
)
ax.annotation.xy = (x, y)
ax.annotation.set_text(d)
artist.figure.canvas.draw()
def extrude_mesh(self, l, z_offset):
# accepts the number of layers and the length of extrusion
mesh = self.mesh
# Extrude vertices
all_coords = []
for i in linspace(0, z_offset, l):
all_coords.append(
hstack((mesh.coordinates(), i * ones((self.n_v2, 1)))))
self.global_vertices = vstack(all_coords)
# Extrude cells (tris to tetrahedra)
for i in range(l - 1):
for c in self.mesh.cells():
# Make a prism out of 2 stacked triangles
vertices = hstack((c + i * self.n_v2, c + (i + 1) * self.n_v2))
# Determine prism orientation
smallest_vertex_index = argmin(vertices)
# Map to I-ordering of Dompierre et al.
mapping = self.indirection_table[smallest_vertex_index]
# Determine which subdivision scheme to use.
if min(vertices[mapping][[1, 5]]) < min(
vertices[mapping][[2, 4]]):
local_tets = vstack((vertices[mapping][[0,1,2,5]],\
vertices[mapping][[0,1,5,4]],\
vertices[mapping][[0,4,5,3]]))
else:
local_tets = vstack((vertices[mapping][[0,1,2,4]],\
vertices[mapping][[0,4,2,5]],\
vertices[mapping][[0,4,5,3]]))
# Concatenate local tet to cell array
self.global_tets = vstack((self.global_tets, local_tets))
# Eliminate phantom initialization tet
self.global_tets = self.global_tets[1:, :]
# Query number of vertices and tets in new mesh
self.n_verts = self.global_vertices.shape[0]
self.n_tets = self.global_tets.shape[0]
# Initialize new dolfin mesh of dimension 3
self.new_mesh = Mesh()
m = MeshEditor()
m.open(self.new_mesh, 3, 3)
m.init_vertices(self.n_verts, self.n_verts)
m.init_cells(self.n_tets, self.n_tets)
# Copy vertex data into new mesh
for i, v in enumerate(self.global_vertices):
m.add_vertex(i, Point(*v))
# Copy cell data into new mesh
for j, c in enumerate(self.global_tets):
m.add_cell(j, *c)
m.close()
def spline_interpolate(tck, r_in):
if not (type(r_in) in [list, py.ndarray]):
r_in = [r_in]
scaler_in = True
else:
scaler_in = False
out_list = []
for r in r_in:
i_intp = py.argmin(abs(r - tck[0][:, 0]))
if (r < tck[0][0, 0]):
out = tck[1][0, -1]
elif (r > tck[0][-1, -1]):
out = 0.0 #tck[1][-1,-1]
else:
for i_intp, [r_min, r_max] in enumerate(tck[0]):
if (r > r_min and r <= r_max):
break
out = 0
for ii in range(tck[2]):
out += (r - tck[0][i_intp, 0])**(tck[2] -
(ii + 1)) * tck[1][i_intp, ii]
out_list.append(out)
if scaler_in:
return out_list[0]
else:
return py.array(out_list)
def classify(self, x, k):
d = self.X - tile(x.reshape(self.n, 1), self.N)
dsq = sum(d * d, 0)
minindex = argmin(dsq)
temp = argsort(dsq)
### Custom code starting here ###
# Save the data for each class around the point in a array
score = [0, 0, 0]
# With the help of k surrounding points score each class
for x in range(0, k):
if ((self.c[temp[x]]) == 1.0):
score[0] += 1
elif ((self.c[temp[x]]) == 2.0):
score[1] += 1
elif ((self.c[temp[x]]) == 3.0):
score[2] += 1
# Check to which class the point is classified
if (score[0] > score[1] and score[0] > score[2]):
return 1.0
elif (score[1] > score[2]):
return 2.0
# If there are points with the same value, assign the class of the nearest neighbour.
elif (score[0] == score[1] and score[0] == score[2]):
return self.c[minindex]
else:
return 3.0
def extrude_mesh(self,l,z_offset):
# accepts the number of layers and the length of extrusion
# Extrude vertices
all_coords = []
for i in linspace(0,z_offset,l):
all_coords.append(hstack((mesh.coordinates(),i*ones((self.n_v2,1)))))
self.global_vertices = vstack(all_coords)
# Extrude cells (tris to tetrahedra)
for i in range(l-1):
for c in self.mesh.cells():
# Make a prism out of 2 stacked triangles
vertices = hstack((c+i*self.n_v2,c+(i+1)*self.n_v2))
# Determine prism orientation
smallest_vertex_index = argmin(vertices)
# Map to I-ordering of Dompierre et al.
mapping = self.indirection_table[smallest_vertex_index]
# Determine which subdivision scheme to use.
if min(vertices[mapping][[1,5]]) < min(vertices[mapping][[2,4]]):
local_tets = vstack((vertices[mapping][[0,1,2,5]],\
vertices[mapping][[0,1,5,4]],\
vertices[mapping][[0,4,5,3]]))
else:
local_tets = vstack((vertices[mapping][[0,1,2,4]],\
vertices[mapping][[0,4,2,5]],\
vertices[mapping][[0,4,5,3]]))
# Concatenate local tet to cell array
self.global_tets = vstack((self.global_tets,local_tets))
# Eliminate phantom initialization tet
self.global_tets = self.global_tets[1:,:]
# Query number of vertices and tets in new mesh
self.n_verts = self.global_vertices.shape[0]
self.n_tets = self.global_tets.shape[0]
# Initialize new dolfin mesh of dimension 3
self.new_mesh = Mesh()
m = MeshEditor()
m.open(self.new_mesh,3,3)
m.init_vertices(self.n_verts,self.n_verts)
m.init_cells(self.n_tets,self.n_tets)
# Copy vertex data into new mesh
for i,v in enumerate(self.global_vertices):
m.add_vertex(i,Point(*v))
# Copy cell data into new mesh
for j,c in enumerate(self.global_tets):
m.add_cell(j,*c)
m.close()
def get_kin_apex(self, phases, return_times = False):
"""
returns the kinematic state at the apices which are close to the given phases. Apex is re-calculated.
:args:
self: kin object (-> later: "self")
phases (list): list of lists of apex phases. must match with length of "kin.raw_data".
The n'th list of apex phases will be assigned to the nth "<object>.raw_data" element.
return_times (bool): if true, return only the times at which apex occurred.
:returns:
if lr_split is True:
[[r_apices], [l_apices]]
else:
[[apices], ]
where apices is the kinematic (from <object>.selection at the apices *around* the given phases.
*NOTE* The apices themselves are re-computed for higher accuracy.
"""
all_kin = []
all_kin_orig = self.get_kin()
all_apex_times = []
if len(self.raw_dat) != len(phases):
raise ValueError("length of phases list does not match number of datasets")
for raw, phase, kin_orig in zip(self.raw_dat, phases, all_kin_orig):
kin_apex = []
kin_time = arange(len(raw['phi2'].squeeze()), dtype=float) / 250.
# 1st: compute apex *times*
apex_times = []
for phi_apex in phase:
# 1a: get "rough" estimate
idx_apex = argmin(abs(raw['phi2'].squeeze() - phi_apex))
# 1b: fit quadratic function to com_y
idx_low = max(0, idx_apex - 4)
idx_high = min(raw['com'].shape[0] - 1, idx_apex + 4)
com_y_pt = raw['com'][idx_low:idx_high + 1, 2]
tp = arange(idx_high - idx_low + 1) # improve numerical accuracy: do not take absolute time
p = polyfit(tp, com_y_pt, 2) # p: polynomial, highest power coeff first
t0 = -p[1] / (2.*p[0]) # "real" index of apex (offset is 2: a value of 2
# indicates that apex is exactly at the measured frame
t_apex = kin_time[idx_apex] + (t0 - 4.) / 250.
apex_times.append(t_apex)
if return_times:
all_apex_times.append(array(apex_times))
else:
# 2nd: interpolate data
dat = vstack([interp(apex_times, kin_time, kin_orig[row, :]) for row in arange(kin_orig.shape[0])])
all_kin.append(dat)
if return_times:
return all_apex_times
return all_kin
def sort_ratios(self, ratios):
"""
::
Sort and select tuning system ratios in ascending order by
proximity to equal tempered pitch classes.
"""
ratios.append(Fraction(2,1))
res = list(set(ratios)) # eliminate duplicates
res.sort() # in-place sort
idx = [pylab.argmin( abs( pylab.array(res) - j) ) for j in self.pitch_ratios_eq]
res = [res[i] for i in idx]
return res
def sort_ratios(self, ratios):
"""
::
Sort and select tuning system ratios in ascending order by
proximity to equal tempered pitch classes.
"""
ratios.append(Fraction(2,1))
res = list(set(ratios)) # eliminate duplicates
res.sort() # in-place sort
idx = [pylab.argmin( abs( pylab.array(res) - j) ) for j in self.pitch_ratios_eq]
res = [res[i] for i in idx]
return res
def plotErrorFunction(self,l,freq):
#plots the error function
py.figure()
resolution=300
ix=py.argmin(abs(freq-self.H.getfreqs()))
n_i=py.linspace(0.0,0.05,int(resolution/2))
n_r=py.linspace(1,3,resolution)
N_R,N_I=py.meshgrid(n_r,n_i)
E_fu=py.zeros((len(n_i),len(n_r)))
for i in range(len(n_r)):
for k in range(len(n_i)):
E_fu[k,i]=self.error_func([n_r[i],n_i[k]],self.H.fdData[ix,:3],l)
# print(E_fu[:,2])
py.pcolor(N_R,N_I,py.log10(E_fu))
py.colorbar()
def classify(self, x, k):
d = self.X - tile(x.reshape(self.n,1), self.N)
# Occurrence of a class
occd = {}
for i in range(k):
dsq = sum(d*d,0)
minindex = argmin(dsq)
if self.c[minindex] in occd:
occd[self.c[minindex]] += 1
else:
occd[self.c[minindex]] = 1
# Prevent next iter giving this index again
d[:, minindex] = self.max + 1
# Return the name of the class that occurred most
return max(occd.iteritems(), key=operator.itemgetter(1))[0]
def minimize_residual_variances(self, **kwargs):
"""
Syst. residuals behave as an underpartition measures, i.e. the larger is K the lower is the residual( the more are patches the better )
stat. residuals behave as an overpartition measures, i.e. the larger is K the higher is the residuals (the lesser are the patches the worse, because you have less signal-to-noise in each patch)
"""
self.Vu = pl.zeros_like(self.Kvals).reshape(-1, 1) * 1.0
self.Vo = pl.zeros_like(self.Kvals).reshape(-1, 1) * 1.0
nvals = len(self.Kvals)
for j, K in enumerate(self.Kvals):
if self.verbose:
print(f"Running with K={K} clusters")
clusters = AgglomerativeClustering(
n_clusters=K,
affinity="precomputed",
linkage="average",
connectivity=self.connectivity,
)
clusters.fit_predict(self._Affinity)
msys, mstat = estimate_Stat_and_Sys_residuals(
clusters.labels_,
galactic_binmask=self.galactic_mask,
**kwargs)
m1 = pl.ma.masked_equal(msys[1], hp.UNSEEN).mask
m2 = pl.ma.masked_equal(mstat[1], hp.UNSEEN).mask
_, _, var_sys, var_stat = estimate_spectra(msys, mstat)
self.Vo[j], self.Vu[j] = var_stat, var_sys
# We have to match Vo and Vu, we rescale Vo so that it ranges as Vu
min_max_scaler = preprocessing.MinMaxScaler(
feature_range=(self.Vu.min(), self.Vu.max()))
self.Vu = min_max_scaler.fit_transform((self.Vu)).reshape(nvals)
self.Vo = min_max_scaler.fit_transform((self.Vo)).reshape(nvals)
Vsv = interp1d(self.Kvals,
pl.sqrt(self.Vu**2 + self.Vo**2).T,
kind="slinear")
Krange = pl.arange(self.Kvals.min(), self.Kvals.max())
minval = pl.argmin(Vsv(Krange) - Vsv(Krange).min())
Kopt = Krange[minval]
return Kopt
def minimize_partition_measures(self):
self.Vu = pl.zeros_like(self.Kvals).reshape(-1, 1) * 1.0
self.Vo = pl.zeros_like(self.Kvals).reshape(-1, 1) * 1.0
nvals = len(self.Kvals)
for j, K in enumerate(self.Kvals):
if self.verbose:
print(f"Running with K={K} clusters")
clusters = AgglomerativeClustering(
n_clusters=K,
affinity="precomputed",
linkage="average",
connectivity=self.connectivity,
)
clusters.fit_predict(self._Affinity)
mu, MD = self.intracluster_distance(K, clusters.labels_)
dmu = self._metric.pairwise(mu[:, :self._nfeat])
dmu = pl.ma.fix_invalid(dmu, fill_value=1.0).data
if self._has_angles:
dmuang = self.haversine.pairwise(mu[:, self._nfeat:])
# dmuang =pl.ma.fix_invalid(dmuang, fill_value=pl.pi).data
dmu = pl.multiply(dmu, dmuang)
pl.fill_diagonal(dmu, pl.inf)
self.Vo[j] = K / dmu.min() # overpartition meas.
self.Vu[j] = MD.sum() / K # underpartition meas.
# We have to match Vo and Vu, we rescale Vo so that it ranges as Vu
min_max_scaler = preprocessing.MinMaxScaler(
feature_range=(self.Vu.min(), self.Vu.max()))
self.Vu = min_max_scaler.fit_transform((self.Vu)).reshape(nvals)
self.Vo = min_max_scaler.fit_transform((self.Vo)).reshape(nvals)
# minimizing the squared sum
Vsv = interp1d(self.Kvals,
pl.sqrt(self.Vu**2 + self.Vo**2).T,
kind="slinear")
Krange = pl.arange(self.Kvals.min(), self.Kvals.max())
minval = pl.argmin(Vsv(Krange) - Vsv(Krange).min())
Kopt = Krange[minval]
return Kopt
def compute_registeration(P0, Yk):
'''
compute the registeration. which is transformation martix
ref: http://www.cs.virginia.edu/~mjh7v/bib/Besl92.pdf
Input:
P0: Point set
Yk: P0 corresponding Point set
Output:
qk: transform matrix
'''
# compute mu p and mu x
mup = np.mean(P0, axis=0)
Nx = len(P0)
mux = np.mean(Yk, axis=0)
# Cross-covariance martix sigma_px
sigma_px = (np.dot(P0.T,Yk) - np.dot(mup.T,mux))/Nx
Aij = sigma_px - sigma_px.T
tr = trace(sigma_px)
Sym = sigma_px + sigma_px.T - tr*identity(3)
Q_sigma_px = np.array([
[tr, Aij[1][2], Aij[2][0], Aij[0][1]],
[Aij[1][2], Sym[0][0], 0, 0],
[Aij[2][0], 0, Sym[1][1], 0],
[Aij[0][1], 0, 0, Sym[2][2]]])
# eigenvalue to get optimal R and T
w, v = np.linalg.eig(Q_sigma_px)
qR = v[:, argmin(w)]
R = get_rotation_matrix(qR)
qT = (mux.T - R.dot(mup.T)).T
return qR, qT
for e in fl[1:]:
data_add = pd.read_table(e, sep = ",")
data_add.columns = ["Time", e[3:8]]
means[k] = pd.merge(means[k], data_add, on = "Time")
means[k]["0.435"] -= 273.15
means[k]["0.455"] -= 273.15
heights = pl.array(means[k].keys())[1:].astype(float)
pl.figure()
for level in filling_levels:
index = pl.argmin(abs(means[k]["Time"] - (level / massflows[k])))
pl.plot(means[k].loc[index][1:][0:].values, heights, \
label = "filling level = " + str(level) + " kg" )
pl.xlim([58.0, 76.0])
pl.ylim([0.0, 2.4])
pl.grid(True)
pl.ylabel("Height (m)")
pl.xlabel("Temperature ($^{\circ}$C)")
pl.legend(loc="lower right")
pl.title("Mean temperature per stratum in " + sim)
pl.savefig(basedir + "fig_mean_per_sim/" + sim + ".png", bbox_inches='tight')
def main():
import optparse
from numpy import sum
# Parse command line
parser = optparse.OptionParser(usage=USAGE)
parser.add_option("-p",
"--plot",
action="store_true",
help="Generate pdf with IR-spectrum")
parser.add_option("-i",
"--info",
action="store_true",
help="Set up/ Calculate vibrations & quit")
parser.add_option("-s",
"--suffix",
action="store",
help="Call suffix for binary e.g. 'mpirun -n 4 '",
default='')
parser.add_option("-r",
"--run",
action="store",
help="path to FHI-aims binary",
default='')
parser.add_option("-x",
"--relax",
action="store_true",
help="Relax initial geometry")
parser.add_option("-m",
"--molden",
action="store_true",
help="Output in molden format")
parser.add_option("-w",
"--distort",
action="store_true",
help="Output geometry distorted along imaginary modes")
parser.add_option("-t",
"--submit",
action="store",
help="""\
Path to submission script, string <jobname>
will be replaced by name + counter, string
<outfile> will be replaced by filename""")
parser.add_option("-d",
"--delta",
action="store",
type="float",
help="Displacement",
default=0.0025)
options, args = parser.parse_args()
if options.info:
print __doc__
sys.exit(0)
if len(args) != 2:
parser.error("Need exactly two arguments")
AIMS_CALL = options.suffix + ' ' + options.run
hessian_thresh = -1
name = args[0]
mode = args[1]
delta = options.delta
run_aims = False
if options.run != '': run_aims = True
submit_script = options.submit is not None
if options.plot:
import matplotlib as mpl
mpl.use('Agg')
from pylab import figure
if options.plot or mode == '1':
from pylab import savetxt, transpose, eig, argsort, sort,\
sign, pi, dot, sum, linspace, argmin, r_, convolve
# Constant from scipy.constants
bohr = constants.value('Bohr radius') * 1.e10
hartree = constants.value('Hartree energy in eV')
at_u = constants.value('atomic mass unit-kilogram relationship')
eV = constants.value('electron volt-joule relationship')
c = constants.value('speed of light in vacuum')
Ang = 1.0e-10
hbar = constants.value('Planck constant over 2 pi')
Avo = constants.value('Avogadro constant')
kb = constants.value('Boltzmann constant in eV/K')
hessian_factor = eV / (at_u * Ang * Ang)
grad_dipole_factor = (eV / (1. / (10 * c))) / Ang #(eV/Ang -> D/Ang)
ir_factor = 1
# Asign all filenames
inputgeomerty = 'geometry.in.' + name
inputcontrol = 'control.in.' + name
atomicmasses = 'masses.' + name + '.dat'
xyzfile = name + '.xyz'
moldenname = name + '.molden'
hessianname = 'hessian.' + name + '.dat'
graddipolename = 'grad_dipole.' + name + '.dat'
irname = 'ir.' + name + '.dat'
deltas = array([-delta, delta])
coeff = array([-1, 1])
c_zero = -1. / (2. * delta)
f = open('control.in', 'r') # read control.in template
template_control = f.read()
f.close
if submit_script:
f = open(options.submit, 'r') # read submission script template
template_job = f.read()
f.close
folder = '' # Dummy
########### Central Point ##################################################
if options.relax and mode == '0':
# First relax input geometry
filename = name + '.out'
folder = name + '_relaxation'
if not os.path.exists(folder): os.mkdir(folder) # Create folder
shutil.copy('geometry.in', folder + '/geometry.in') # Copy geometry
new_control = open(folder + '/control.in', 'w')
new_control.write(template_control +
'relax_geometry trm 1E-3\n') # Relax!
new_control.close()
os.chdir(folder) # Change directoy
print 'Central Point'
if run_aims:
os.system(AIMS_CALL + ' > ' +
filename) # Run aims and pipe the output
# into a file named 'filename'
if submit_script: replace_submission(template_job, name, 0, filename)
os.chdir('..')
############################################################################
# Check for relaxed geometry
if os.path.exists(folder + '/geometry.in.next_step'):
geometry = open(folder + '/geometry.in.next_step', 'r')
else:
geometry = open('geometry.in', 'r')
# Read input geometry
n_line = 0
struc = structure()
lines = geometry.readlines()
for line in lines:
n_line = n_line + 1
if line.rfind('set_vacuum_level') != -1: # Vacuum Level
struc.vacuum_level = float(split_line(line)[-1])
if line.rfind('lattice_vector') != -1: # Lattice vectors and periodic
lat = split_line(line)[1:]
struc.lattic_vector = append(struc.lattic_vector,
float64(array(lat))[newaxis, :],
axis=0)
struc.periodic = True
if line.rfind('atom') != -1: # Set atoms
line_vals = split_line(line)
at = Atom(line_vals[-1], line_vals[1:-1])
if n_line < len(lines):
nextline = lines[n_line]
if nextline.rfind(
'constrain_relaxation') != -1: # constrained?
at = Atom(line_vals[-1], line_vals[1:-1], True)
else:
at = Atom(line_vals[-1], line_vals[1:-1])
struc.join(at)
geometry.close()
n_atoms = struc.n()
n_constrained = n_atoms - sum(struc.constrained)
# Atomic mass file
mass_file = open(atomicmasses, 'w')
mass_vector = zeros([0])
for at_unconstrained in struc.atoms[struc.constrained == False]:
mass_vector = append(mass_vector,
ones(3) * 1. / sqrt(at_unconstrained.mass()))
line = '{0:10.5f}'.format(at_unconstrained.mass())
for i in range(3):
line = line + '{0:11.4f}'.format(at_unconstrained.coord[i])
line = line + '{0:}\n'.format(at_unconstrained.kind)
mass_file.writelines(line)
mass_file.close()
# Init
dip = zeros([n_constrained * 3, 3])
hessian = zeros([n_constrained * 3, n_constrained * 3])
index = 0
counter = 1
# Set up / Read folders for displaced atoms
for atom in arange(n_atoms)[struc.constrained == False]:
for coord in arange(3):
for delta in deltas:
filename=name+'.i_atom_'+str(atom)+'.i_coord_'+str(coord)+'.displ_'+\
str(delta)+'.out'
folder=name+'.i_atom_'+str(atom)+'.i_coord_'+str(coord)+'.displ_'+\
str(delta)
if mode == '0': # Put new geometry and control.in into folder
struc_new = copy.deepcopy(struc)
struc_new.atoms[atom].coord[coord]=\
struc_new.atoms[atom].coord[coord]+delta
geoname='geometry.i_atom_'+str(atom)+'.i_coord_'+str(coord)+\
'.displ_'+str(delta)+'.in'
if not os.path.exists(folder): os.mkdir(folder)
new_geo = open(folder + '/geometry.in', 'w')
newline='#\n# temporary structure-file for finite-difference '+\
'calculation of forces\n'
newline=newline+'# displacement {0:8.4f} of \# atom '.format(delta)+\
'{0:5} direction {1:5}\n#\n'.format(atom,coord)
new_geo.writelines(newline + struc_new.to_str())
new_geo.close()
new_control = open(folder + '/control.in', 'w')
template_control = template_control.replace(
'relax_geometry', '#relax_geometry')
new_control.write(template_control+'compute_forces .true. \n'+\
'final_forces_cleaned '+\
'.true. \noutput dipole \n')
new_control.close()
os.chdir(folder) # Change directoy
print 'Processing atom: '+str(atom+1)+'/'+str(n_atoms)+', coord.: '+\
str(coord+1)+'/'+str(3)+', delta: '+str(delta)
if run_aims:
os.system(AIMS_CALL + ' > ' +
filename) # Run aims and pipe the output
# into a file named 'filename'
if submit_script:
replace_submission(template_job, name, counter,
filename)
# os.system('qsub job.sh') # Mind the environment variables
os.chdir('..')
if mode == '1': # Read output
forces_reached = False
atom_count = 0
data = open(folder + '/' + filename)
for line in data.readlines():
if line.rfind(
'Dipole correction potential jump') != -1:
dip_jump = float(split_line(line)[-2]) # Periodic
if line.rfind('| Total dipole moment [eAng]') != -1:
dip_jump = float64(
split_line(line)[-3:]) # Cluster
if forces_reached and atom_count < n_atoms: # Read Forces
struc.atoms[atom_count].force = float64(
split_line(line)[2:])
atom_count = atom_count + 1
if atom_count == n_atoms:
forces_reached = False
if line.rfind('Total atomic forces') != -1:
forces_reached = True
data.close()
if struc.periodic:
dip[index, 2] = dip[
index,
2] + dip_jump * coeff[deltas == delta] * c_zero
else:
dip[index, :] = dip[index, :] + dip_jump * coeff[
deltas == delta] * c_zero
forces = array([])
for at_unconstrained in struc.atoms[struc.constrained ==
False]:
forces = append(
forces,
coeff[deltas == delta] * at_unconstrained.force)
hessian[index, :] = hessian[index, :] + forces * c_zero
counter = counter + 1
index = index + 1
if mode == '1': # Calculate vibrations
print 'Entering hessian diagonalization'
print 'Number of atoms = ' + str(n_atoms)
print 'Name of Hessian input file = ' + hessianname
print 'Name of grad dipole input file = ' + graddipolename
print 'Name of Masses input file = ' + atomicmasses
print 'Name of XYZ output file = ' + xyzfile
print 'Threshold for Matrix elements = ' + str(hessian_thresh)
if (hessian_thresh < 0.0): print ' All matrix elements are taken'+\
' into account by default\n'
savetxt(hessianname, hessian)
savetxt(graddipolename, dip)
mass_mat = mass_vector[:, newaxis] * mass_vector[newaxis, :]
hessian[abs(hessian) < hessian_thresh] = 0.0
hessian = hessian * mass_mat * hessian_factor
hessian = (hessian + transpose(hessian)) / 2.
# Diagonalize hessian (scipy)
print 'Solving eigenvalue system for Hessian Matrix'
freq, eig_vec = eig(hessian)
print 'Done ... '
eig_vec = eig_vec[:, argsort(freq)]
freq = sort(sign(freq) * sqrt(abs(freq)))
ZPE = hbar * (freq) / (2.0 * eV)
freq = (freq) / (200. * pi * c)
grad_dipole = dip * grad_dipole_factor
eig_vec = eig_vec * mass_vector[:, newaxis] * ones(
len(mass_vector))[newaxis, :]
infrared_intensity = sum(dot(transpose(grad_dipole),eig_vec)**2,axis=0)*\
ir_factor
reduced_mass = sum(eig_vec**2, axis=0)
norm = sqrt(reduced_mass)
eig_vec = eig_vec / norm
# The rest is output, xyz, IR,...
print 'Results\n'
print 'List of all frequencies found:'
print 'Mode number Frequency [cm^(-1)] Zero point energy [eV] '+\
'IR-intensity [D^2/Ang^2]'
for i in range(len(freq)):
print '{0:11}{1:25.8f}{2:25.8f}{3:25.8f}'.format(
i + 1, freq[i], ZPE[i], infrared_intensity[i])
print '\n'
print 'Summary of zero point energy for entire system:'
print '| Cumulative ZPE = {0:15.8f} eV'.format(sum(ZPE))
print '| without first six eigenmodes = {0:15.8f} eV\n'.format(
sum(ZPE) - sum(ZPE[:6]))
print 'Stability checking - eigenvalues should all be positive for a '+\
'stable structure. '
print 'The six smallest frequencies should be (almost) zero:'
string = ''
for zz in ZPE[:6]:
string = string + '{0:25.8f}'.format(zz)
print string
print 'Compare this with the largest eigenvalue, '
print '{0:25.8f}'.format(freq[-1])
nums = arange(n_atoms)[struc.constrained == False]
nums2 = arange(n_atoms)[struc.constrained]
newline = ''
newline_ir = '[INT]\n'
if options.molden:
newline_molden = '[Molden Format]\n[GEOMETRIES] XYZ\n'
newline_molden = newline_molden + '{0:6}\n'.format(n_atoms) + '\n'
for i_atoms in range(n_constrained):
newline_molden = newline_molden + '{0:6}'.format(
struc.atoms[nums[i_atoms]].kind)
for i_coord in range(3):
newline_molden = newline_molden + '{0:10.4f}'.format(
struc.atoms[nums[i_atoms]].coord[i_coord])
newline_molden = newline_molden + '\n'
newline_molden = newline_molden + '[FREQ]\n'
for i in range(len(freq)):
newline_molden = newline_molden + '{0:10.3f}\n'.format(freq[i])
newline_molden = newline_molden + '[INT]\n'
for i in range(len(freq)):
newline_molden = newline_molden + '{0:17.6e}\n'.format(
infrared_intensity[i])
newline_molden = newline_molden + '[FR-COORD]\n'
newline_molden = newline_molden + '{0:6}\n'.format(n_atoms) + '\n'
for i_atoms in range(n_constrained):
newline_molden = newline_molden + '{0:6}'.format(
struc.atoms[nums[i_atoms]].kind)
for i_coord in range(3):
newline_molden = newline_molden + '{0:10.4f}'.format(
struc.atoms[nums[i_atoms]].coord[i_coord] / bohr)
newline_molden = newline_molden + '\n'
newline_molden = newline_molden + '[FR-NORM-COORD]\n'
for i in range(len(freq)):
newline = newline + '{0:6}\n'.format(n_atoms)
if freq[i] > 0:
newline = newline + 'stable frequency at '
elif freq[i] < 0:
newline = newline + 'unstable frequency at '
if options.distort and freq[i] < -50:
struc_new = copy.deepcopy(struc)
for i_atoms in range(n_constrained):
for i_coord in range(3):
struc_new.atoms[i_atoms].coord[i_coord]=\
struc_new.atoms[i_atoms].coord[i_coord]+\
eig_vec[(i_atoms)*3+i_coord,i]
geoname = name + '.distorted.vibration_' + str(
i + 1) + '.geometry.in'
new_geo = open(geoname, 'w')
newline_geo = '#\n# distorted structure-file for based on eigenmodes\n'
newline_geo=newline_geo+\
'# vibration {0:5} :{1:10.3f} 1/cm\n#\n'.format(i+1,freq[i])
new_geo.writelines(newline_geo + struc_new.to_str())
new_geo.close()
elif freq[i] == 0:
newline = newline + 'translation or rotation '
newline = newline + '{0:10.3f} 1/cm IR int. is '.format(freq[i])
newline = newline + '{0:10.4e} D^2/Ang^2; red. mass is '.format(
infrared_intensity[i])
newline = newline + '{0:5.3f} a.m.u.; force const. is '.format(
1.0 / reduced_mass[i])
newline = newline + '{0:5.3f} mDyne/Ang.\n'.format(
((freq[i] *
(200 * pi * c))**2) * (1.0 / reduced_mass[i]) * at_u * 1.e-2)
if options.molden: newline_molden=newline_molden+\
'vibration {0:6}\n'.format(i+1)
for i_atoms in range(n_constrained):
newline = newline + '{0:6}'.format(
struc.atoms[nums[i_atoms]].kind)
for i_coord in range(3):
newline = newline + '{0:10.4f}'.format(
struc.atoms[nums[i_atoms]].coord[i_coord])
for i_coord in range(3):
newline = newline + '{0:10.4f}'.format(
eig_vec[(i_atoms) * 3 + i_coord, i])
if options.molden:
newline_molden = newline_molden + '{0:10.4f}'.format(
eig_vec[(i_atoms) * 3 + i_coord, i] / bohr)
newline = newline + '\n'
if options.molden: newline_molden = newline_molden + '\n'
for i_atoms in range(n_atoms - n_constrained):
newline = newline + '{0:6}'.format(
struc.atoms[nums2[i_atoms]].kind)
for i_coord in range(3):
newline = newline + '{0:10.4f}'.format(
struc.atoms[nums2[i_atoms]].coord[i_coord])
for i_coord in range(3):
newline = newline + '{0:10.4f}'.format(0.0)
newline = newline + '\n'
newline_ir = newline_ir + '{0:10.4e}\n'.format(
infrared_intensity[i])
xyz = open(xyzfile, 'w')
xyz.writelines(newline)
xyz.close()
ir = open(irname, 'w')
ir.writelines(newline_ir)
ir.close()
if options.molden:
molden = open(moldenname, 'w')
molden.writelines(newline_molden)
molden.close()
if mode == '1' and options.plot:
x = linspace(freq.min() - 500, freq.max() + 500, 1000)
z = zeros(len(x))
for i in range(len(freq)):
z[argmin(abs(x - freq[i]))] = infrared_intensity[i]
window_len = 150
gauss = signal.gaussian(window_len, 10)
s = r_[z[window_len - 1:0:-1], z, z[-1:-window_len:-1]]
z_convolve = convolve(gauss / gauss.sum(), s,
mode='same')[window_len - 1:-window_len + 1]
fig = figure(0)
ax = fig.add_subplot(111)
ax.plot(x, z_convolve, 'r', lw=2)
ax.set_xlim([freq.min() - 500, freq.max() + 500])
ax.set_ylim([-0.01, ax.get_ylim()[1]])
ax.set_yticks([])
ax.set_xlabel('Frequency [1/cm]', size=20)
ax.set_ylabel('Intensity [a.u.]', size=20)
fig.savefig(name + '_IR_spectrum.pdf')
print '\n Done. '
def fig3_struct(path, fig, ax1, tmin=0, tmax=1000):
sim = fls.load_sim_for_plot(path, merge_missing_params=True)
path_file = os.path.join(path, "increments.h5")
f = h5py.File(path_file, "r")
dset_times = f["times"]
times = dset_times[...]
if tmax is None:
tmax = times.max()
rxs = f["rxs"][...]
oper = f["/info_simul/params/oper"]
nx = oper.attrs["nx"]
Lx = oper.attrs["Lx"]
deltax = Lx / nx
rxs = pl.array(rxs, dtype=pl.float64) * deltax
imin_plot = pl.argmin(abs(times - tmin))
imax_plot = pl.argmin(abs(times - tmax))
S_uL2JL = f["struc_func_uL2JL"][imin_plot:imax_plot + 1].mean(0)
S_uT2JL = f["struc_func_uT2JL"][imin_plot:imax_plot + 1].mean(0)
S_c2h2uL = f["struc_func_c2h2uL"][imin_plot:imax_plot + 1].mean(0)
S_Kolmo = f["struc_func_Kolmo"][imin_plot:imax_plot + 1].mean(0)
# S_uT2uL = f['struc_func_uT2uL'][imin_plot:imax_plot + 1].mean(0)
eps = _eps(sim, tmin)
S_Kolmo_theo = -4 * eps * rxs
Lf = pl.pi / _k_f(sim.params)
ax1.set_xscale("log")
ax1.set_yscale("linear")
# ax1.plot(rxs / Lf,
# (S_uL2JL+S_uT2JL+S_c2h2uL)/S_Kolmo_theo,
# 'y', linewidth=1, label='sum check')
def _label(x, y):
pre = ""
if y == "uu":
y = r"\mathbf{u}"
y2 = r"|\delta {}|^2".format(y)
else:
if y == "h":
pre = "c ^ 2"
y2 = r"(\delta {})^2".format(y)
return "$ -\langle {} \delta {} {} \\rangle $".format(pre, x, y2)
ax1.set_xlabel("$r/L_f$")
label1 = _label("J_L", "uu")
label2 = _label("u_L", "h")
label3 = label1[:-10] + " + " + label2[10:]
def label_norm(label):
str_norm = " / (4\epsilon r)$"
return label.rstrip(" $") + str_norm
ax1.set_ylabel(label_norm(label3))
ax1.plot(rxs / Lf,
S_Kolmo / S_Kolmo_theo,
"k",
linewidth=2,
label=(label3))
ax1.plot(
rxs / Lf,
(S_uL2JL + S_uT2JL) / S_Kolmo_theo,
"r:",
linewidth=2,
label=(label1),
)
ax1.plot(rxs / Lf,
S_c2h2uL / S_Kolmo_theo,
"b--",
linewidth=2,
label=(label2))
# ax1.plot(rxs / Lf, S_uL2JL / S_Kolmo_theo,
# 'r--', linewidth=2, label=_label('J_L', 'u_L'))
# ax1.plot(rxs / Lf, S_uT2JL / S_Kolmo_theo,
# 'r-.', linewidth=2, label=_label('J_L', 'u_T'))
# cond = rxs < 6 * deltax
# ax1.plot(rxs[cond] / Lf, 1.e0 * rxs[cond] ** 3 / S_Kolmo_theo[cond],
# 'y', linewidth=2, label='$r^3/S; r<6dx$')
ax1.axhline(1.0, color="k", ls=":")
# ax1.plot(rxs, pl.ones(rxs.shape), 'k:', linewidth=1)
ax1.set_xlim([None, 2])
ax1.set_ylim([-0.1, 1.1])
ax1.legend(loc=8)
w=eval('p.'+window+'(window_len)')
y=p.convolve(w/w.sum(),s,mode='same')
return y[window_len:-window_len+1]
for peak in set(index['peak']):
lf = [index['files'][(index['peak']==peak)*(index['beam']==i)] for i in [0,1]]
d = [[p.genfromtxt(f,skip_header=2,delimiter=',') for f in x] for x in lf]
d = [[[x[s:e,:] for s,e in [get_tri_range(x[:,4])]][0] for x in y] for y in d]
sc = 30
dsr = [smooth(x[:,3],sc) for x in d[1][1:]]
ddr = [smooth(x[:,3],sc) for x in d[0]]
p.figure()
p.title("peak:%i"%(peak))
[plot(x) for x in ddr]
dsx = [p.argmin(x[1500:]).flatten()[0] for x in dsr]
ddx = [p.argmin(x[1500:]).flatten()[0] for x in ddr]
dcut = min(dsx+ddx)
ds = [smooth(x[:,2]-x[:,3],sc)[s-dcut:] for x,s in zip(d[1][1:],dsx)]
dd = [smooth(x[:,2]-x[:,3],sc)[s-dcut:] for x,s in zip(d[0],ddx)]
dsr = [x[s-dcut:] for x,s in zip(dsr,dsx)]
ddr = [x[s-dcut:] for x,s in zip(ddr,ddx)]
avg_range = range(min(map(len,ds+dd)))
dsa = p.array([p.mean([x[i] for x in ds]) for i in avg_range])
dda = p.array([p.mean([x[i] for x in dd]) for i in avg_range])
[plot(x,'r') for x in ds]
[plot(x,'b') for x in dd]
plot(dsa)
plot(dda)
plot(dsa-dda)
def classify(self, x): d = self.X - tile(x.reshape(self.n,1), self.N) dsq = sum(d*d,0) minindex = argmin(dsq) return self.c[minindex]
means[k] = pd.merge(means[k], data_add, on = "Time")
if (sim == "m1_dt25_upper_inlet") & (e == "qt_0.095.csv"):
means[k][e[3:8]] += (273.15 + 60.0)
means[k]["0.435"] -= 273.15
means[k]["0.455"] -= 273.15
heights = pl.array(means[k].keys())[1:].astype(float)
for level in filling_levels:
# Huhn
index = pl.argmin(abs(means[k]["Time"] - (level / massflows[k])))
means_index = means[k].loc[index][1:]
Tmin = means_index.min()
Tmax = means_index.max()
Tlb = Tmin + 0.1 * (Tmax - Tmin)
Tub = Tmin + 0.9 * (Tmax - Tmin)
pTlb = pl.argmin(means_index[means_index >= Tlb])
pTub = pl.argmax(means_index[means_index <= Tub])
eTlb = means_index[pTlb]
eTub = means_index[pTub]
for e in fl[1:]:
data_add = pd.read_table(e, sep = ",")
data_add.columns = ["Time", e[3:8]]
means[k] = pd.merge(means[k], data_add, on = "Time")
means[k]["0.435"] -= 273.15
means[k]["0.455"] -= 273.15
heights = pl.array(means[k].keys())[1:].astype(float)
pl.figure()
for level in filling_levels:
index = pl.argmin(abs(means[k]["Time"] - (level / massflows[k])))
pl.plot(means[k].loc[index][1:][0:].values, heights, \
label = "filling level = " + str(level) + " kg" )
pl.xlim(58, 76)
pl.ylim([0.0, 2.4])
pl.grid(True)
pl.legend(loc="lower right")
pl.title("Mean temperature per stratum in " + sim)
pl.xlabel("Temperature ($^{\circ}$C)")
pl.ylabel("Height (m)")
pl.savefig(basedir + "fig_comp_baffle_m035_dt25/" + sim + ".png", \
bbox_inches='tight')
def main():
import optparse
from numpy import sum
# Parse command line
parser = optparse.OptionParser(usage=USAGE)
parser.add_option("-p", "--plot", action="store_true",
help="Generate pdf with IR-spectrum, broadened with Lorentzian")
parser.add_option("-i", "--info", action="store_true",
help="Set up/ Calculate vibrations & quit")
parser.add_option("-s", "--suffix", action="store",
help="Call suffix for binary e.g. 'mpirun -n 4 '",
default='')
parser.add_option("-r", "--run", action="store",
help="path to FHI-aims binary",default='')
parser.add_option("-x", "--relax", action="store_true",
help="Relax initial geometry")
parser.add_option("-m", "--molden", action="store_true",
help="Output in molden format")
parser.add_option("-w", "--distort", action="store_true",
help="Output geometry distorted along imaginary modes")
parser.add_option("-t", "--submit", action="store",
help="""\
Path to submission script, string <jobname>
will be replaced by name + counter, string
<outfile> will be replaced by filename""")
parser.add_option("-d", "--delta", action="store", type="float",
help="Displacement", default=0.0025)
parser.add_option("-b", "--broadening", action="store", type="float",
help="Broadening for IR-spectrum in cm^{-1}", default=5)
options, args = parser.parse_args()
if options.info:
print __doc__
sys.exit(0)
if len(args) != 2:
parser.error("Need exactly two arguments")
AIMS_CALL=options.suffix+' '+options.run
hessian_thresh = -1
name=args[0]
mode=args[1]
delta=options.delta
broadening=options.broadening
run_aims=False
if options.run!='': run_aims=True
submit_script = options.submit is not None
if options.plot:
import matplotlib as mpl
mpl.use('Agg')
from pylab import figure
if options.plot or mode=='1' or mode=='2':
from pylab import savetxt, transpose, eig, argsort, sort,\
sign, pi, dot, sum, linspace, argmin, r_, convolve
# Constant from scipy.constants
bohr=constants.value('Bohr radius')*1.e10
hartree=constants.value('Hartree energy in eV')
at_u=constants.value('atomic mass unit-kilogram relationship')
eV=constants.value('electron volt-joule relationship')
c=constants.value('speed of light in vacuum')
Ang=1.0e-10
hbar=constants.value('Planck constant over 2 pi')
Avo=constants.value('Avogadro constant')
kb=constants.value('Boltzmann constant in eV/K')
pi=constants.pi
hessian_factor = eV/(at_u*Ang*Ang)
grad_dipole_factor=(eV/(1./(10*c)))/Ang #(eV/Ang -> D/Ang)
ir_factor = 1
# Asign all filenames
inputgeomerty = 'geometry.in.'+name
inputcontrol = 'control.in.'+name
atomicmasses = 'masses.'+name+'.dat';
xyzfile = name+'.xyz';
moldenname =name+'.molden';
hessianname = 'hessian.'+name+'.dat';
graddipolename = 'grad_dipole.'+name+'.dat';
irname = 'ir.'+name+'.dat';
deltas=array([-delta,delta])
coeff=array([-1,1])
c_zero = - 1. / (2. * delta)
f=open('control.in','r') # read control.in template
template_control=f.read()
f.close
if submit_script:
f=open(options.submit,'r') # read submission script template
template_job=f.read()
f.close
folder='' # Dummy
########### Central Point ##################################################
if options.relax and (mode=='0' or mode=='2'):
# First relax input geometry
filename=name+'.out'
folder=name+'_relaxation'
if not os.path.exists(folder): os.mkdir(folder) # Create folder
shutil.copy('geometry.in', folder+'/geometry.in') # Copy geometry
new_control=open(folder+'/control.in','w')
new_control.write(template_control+'relax_geometry trm 1E-3\n') # Relax!
new_control.close()
os.chdir(folder) # Change directoy
print 'Central Point'
if run_aims:
os.system(AIMS_CALL+' > '+filename) # Run aims and pipe the output
# into a file named 'filename'
if submit_script: replace_submission(template_job, name, 0, filename)
os.chdir('..')
############################################################################
# Check for relaxed geometry
if os.path.exists(folder+'/geometry.in.next_step'):
geometry=open(folder+'/geometry.in.next_step','r')
else:
geometry=open('geometry.in','r')
# Read input geometry
n_line=0
struc=structure()
lines=geometry.readlines()
for line in lines:
n_line= n_line+1
if line.rfind('set_vacuum_level')!=-1: # Vacuum Level
struc.vacuum_level=float(split_line(line)[-1])
if line.rfind('lattice_vector')!=-1: # Lattice vectors and periodic
lat=split_line(line)[1:]
struc.lattice_vector=append(struc.lattice_vector,float64(array(lat))
[newaxis,:],axis=0)
struc.periodic=True
if line.rfind('atom')!=-1: # Set atoms
line_vals=split_line(line)
at=Atom(line_vals[-1],line_vals[1:-1])
if n_line<len(lines):
nextline=lines[n_line]
if nextline.rfind('constrain_relaxation')!=-1: # constrained?
at=Atom(line_vals[-1],line_vals[1:-1],True)
else:
at=Atom(line_vals[-1],line_vals[1:-1])
struc.join(at)
geometry.close()
n_atoms= struc.n()
n_constrained=n_atoms-sum(struc.constrained)
# Atomic mass file
mass_file=open(atomicmasses,'w')
mass_vector=zeros([0])
for at_unconstrained in struc.atoms[struc.constrained==False]:
mass_vector=append(mass_vector,ones(3)*1./sqrt(at_unconstrained.mass()))
line='{0:10.5f}'.format(at_unconstrained.mass())
for i in range(3):
line=line+'{0:11.4f}'.format(at_unconstrained.coord[i])
line=line+'{0:}\n'.format(at_unconstrained.kind)
mass_file.writelines(line)
mass_file.close()
# Init
dip = zeros([n_constrained*3,3])
hessian = zeros([n_constrained*3,n_constrained*3])
index=0
counter=1
# Set up / Read folders for displaced atoms
for atom in arange(n_atoms)[struc.constrained==False]:
for coord in arange(3):
for delta in deltas:
filename=name+'.i_atom_'+str(atom)+'.i_coord_'+str(coord)+'.displ_'+\
str(delta)+'.out'
folder=name+'.i_atom_'+str(atom)+'.i_coord_'+str(coord)+'.displ_'+\
str(delta)
if mode=='0' or mode=='2': # Put new geometry and control.in into folder
struc_new=copy.deepcopy(struc)
struc_new.atoms[atom].coord[coord]=\
struc_new.atoms[atom].coord[coord]+delta
geoname='geometry.i_atom_'+str(atom)+'.i_coord_'+str(coord)+\
'.displ_'+str(delta)+'.in'
if not os.path.exists(folder): os.mkdir(folder)
new_geo=open(folder+'/geometry.in','w')
newline='#\n# temporary structure-file for finite-difference '+\
'calculation of forces\n'
newline=newline+'# displacement {0:8.4f} of \# atom '.format(delta)+\
'{0:5} direction {1:5}\n#\n'.format(atom,coord)
new_geo.writelines(newline+struc_new.to_str())
new_geo.close()
new_control=open(folder+'/control.in','w')
template_control=template_control.replace('relax_geometry',
'#relax_geometry')
new_control.write(template_control+'compute_forces .true. \n'+\
'final_forces_cleaned '+\
'.true. \n')
new_control.close()
os.chdir(folder) # Change directoy
print 'Processing atom: '+str(atom+1)+'/'+str(n_atoms)+', coord.: '+\
str(coord+1)+'/'+str(3)+', delta: '+str(delta)
if run_aims:
os.system(AIMS_CALL+' > '+filename)# Run aims and pipe the output
# into a file named 'filename'
if submit_script: replace_submission(template_job, name, counter,
filename)
# os.system('qsub job.sh') # Mind the environment variables
os.chdir('..')
if mode=='1' or mode=='2': # Read output
forces_reached=False
atom_count=0
data=open(folder+'/'+filename)
for line in data.readlines():
if line.rfind('Dipole correction potential jump')!=-1:
dip_jump = float(split_line(line)[-2]) # Periodic
if line.rfind('| Total dipole moment [eAng]')!=-1:
dip_jump = float64(split_line(line)[-3:]) # Cluster
if forces_reached and atom_count<n_atoms: # Read Forces
struc.atoms[atom_count].force=float64(split_line(line)[2:])
atom_count=atom_count+1
if atom_count==n_atoms:
forces_reached=False
if line.rfind('Total atomic forces')!=-1:
forces_reached=True
data.close()
if struc.periodic:
pass
#dip[index,2]=dip[index,2]+dip_jump*coeff[deltas==delta]*c_zero
else:
dip[index,:]=dip[index,:]+dip_jump*coeff[deltas==delta]*c_zero
forces=array([])
for at_unconstrained in struc.atoms[struc.constrained==False]:
forces=append(forces,coeff[deltas==delta]*at_unconstrained.force)
hessian[index,:]=hessian[index,:]+forces*c_zero
counter=counter+1
index=index+1
if mode=='1' or mode=='2': # Calculate vibrations
print 'Entering hessian diagonalization'
print 'Number of atoms = '+str(n_atoms)
print 'Name of Hessian input file = '+hessianname
print 'Name of grad dipole input file = '+graddipolename
print 'Name of Masses input file = '+atomicmasses
print 'Name of XYZ output file = '+xyzfile
print 'Threshold for Matrix elements = '+str(hessian_thresh)
if (hessian_thresh < 0.0): print ' All matrix elements are taken'+\
' into account by default\n'
savetxt(hessianname,hessian)
savetxt(graddipolename,dip)
mass_mat=mass_vector[:,newaxis]*mass_vector[newaxis,:]
hessian[abs(hessian)<hessian_thresh]=0.0
hessian=hessian*mass_mat*hessian_factor
hessian=(hessian+transpose(hessian))/2.
# Diagonalize hessian (scipy)
print 'Solving eigenvalue system for Hessian Matrix'
freq, eig_vec = eig(hessian)
print 'Done ... '
eig_vec=eig_vec[:,argsort(freq)]
freq=sort(sign(freq)*sqrt(abs(freq)))
ZPE=hbar*(freq)/(2.0*eV)
freq = (freq)/(200.*pi*c)
grad_dipole = dip * grad_dipole_factor
eig_vec = eig_vec*mass_vector[:,newaxis]*ones(len(mass_vector))[newaxis,:]
infrared_intensity = sum(dot(transpose(grad_dipole),eig_vec)**2,axis=0)*\
ir_factor
reduced_mass=sum(eig_vec**2,axis=0)
norm = sqrt(reduced_mass)
eig_vec = eig_vec/norm
# The rest is output, xyz, IR,...
print 'Results\n'
print 'List of all frequencies found:'
print 'Mode number Frequency [cm^(-1)] Zero point energy [eV] '+\
'IR-intensity [D^2/Ang^2]'
for i in range(len(freq)):
print '{0:11}{1:25.8f}{2:25.8f}{3:25.8f}'.format(i+1,freq[i],ZPE[i],
infrared_intensity[i])
print '\n'
print 'Summary of zero point energy for entire system:'
print '| Cumulative ZPE = {0:15.8f} eV'.format(sum(ZPE))
print '| without first six eigenmodes = {0:15.8f} eV\n'.format(sum(ZPE)-
sum(ZPE[:6]))
print 'Stability checking - eigenvalues should all be positive for a '+\
'stable structure. '
print 'The six smallest frequencies should be (almost) zero:'
string=''
for zz in ZPE[:6]: string=string+'{0:25.8f}'.format(zz)
print string
print 'Compare this with the largest eigenvalue, '
print '{0:25.8f}'.format(freq[-1])
nums=arange(n_atoms)[struc.constrained==False]
nums2=arange(n_atoms)[struc.constrained]
newline=''
newline_ir='[INT]\n'
if options.molden:
newline_molden='[Molden Format]\n[GEOMETRIES] XYZ\n'
newline_molden=newline_molden+'{0:6}\n'.format(n_atoms)+'\n'
for i_atoms in range(n_constrained):
newline_molden=newline_molden+'{0:6}'.format(
struc.atoms[nums[i_atoms]].kind)
for i_coord in range(3):
newline_molden=newline_molden+'{0:10.4f}'.format(
struc.atoms[nums[i_atoms]].coord[i_coord])
newline_molden=newline_molden+'\n'
newline_molden=newline_molden+'[FREQ]\n'
for i in range(len(freq)):
newline_molden=newline_molden+'{0:10.3f}\n'.format(freq[i])
newline_molden=newline_molden+'[INT]\n'
for i in range(len(freq)):
newline_molden=newline_molden+'{0:17.6e}\n'.format(
infrared_intensity[i])
newline_molden=newline_molden+'[FR-COORD]\n'
newline_molden=newline_molden+'{0:6}\n'.format(n_atoms)+'\n'
for i_atoms in range(n_constrained):
newline_molden=newline_molden+'{0:6}'.format(
struc.atoms[nums[i_atoms]].kind)
for i_coord in range(3):
newline_molden=newline_molden+'{0:10.4f}'.format(
struc.atoms[nums[i_atoms]].coord[i_coord]/bohr)
newline_molden=newline_molden+'\n'
newline_molden=newline_molden+'[FR-NORM-COORD]\n'
for i in range(len(freq)):
newline=newline+'{0:6}\n'.format(n_atoms)
if freq[i]>0:
newline=newline+'stable frequency at '
elif freq[i]<0:
newline=newline+'unstable frequency at '
if options.distort and freq[i]<-50:
struc_new=copy.deepcopy(struc)
for i_atoms in range(n_constrained):
for i_coord in range(3):
struc_new.atoms[i_atoms].coord[i_coord]=\
struc_new.atoms[i_atoms].coord[i_coord]+\
eig_vec[(i_atoms)*3+i_coord,i]
geoname=name+'.distorted.vibration_'+str(i+1)+'.geometry.in'
new_geo=open(geoname,'w')
newline_geo='#\n# distorted structure-file for based on eigenmodes\n'
newline_geo=newline_geo+\
'# vibration {0:5} :{1:10.3f} 1/cm\n#\n'.format(i+1,freq[i])
new_geo.writelines(newline_geo+struc_new.to_str())
new_geo.close()
elif freq[i]==0:
newline=newline+'translation or rotation '
newline=newline+'{0:10.3f} 1/cm IR int. is '.format(freq[i])
newline=newline+'{0:10.4e} D^2/Ang^2; red. mass is '.format(
infrared_intensity[i])
newline=newline+'{0:5.3f} a.m.u.; force const. is '.format(
1.0/reduced_mass[i])
newline=newline+'{0:5.3f} mDyne/Ang.\n'.format(((freq[i]*(200*pi*c))**2)*
(1.0/reduced_mass[i])*at_u*1.e-2)
if options.molden: newline_molden=newline_molden+\
'vibration {0:6}\n'.format(i+1)
for i_atoms in range(n_constrained):
newline=newline+'{0:6}'.format(struc.atoms[nums[i_atoms]].kind)
for i_coord in range(3):
newline=newline+'{0:10.4f}'.format(
struc.atoms[nums[i_atoms]].coord[i_coord])
for i_coord in range(3):
newline=newline+'{0:10.4f}'.format(eig_vec[(i_atoms)*3+i_coord,i])
if options.molden: newline_molden=newline_molden+'{0:10.4f}'.format(
eig_vec[(i_atoms)*3+i_coord,i]/bohr)
newline=newline+'\n'
if options.molden: newline_molden=newline_molden+'\n'
for i_atoms in range(n_atoms-n_constrained):
newline=newline+'{0:6}'.format(struc.atoms[nums2[i_atoms]].kind)
for i_coord in range(3):
newline=newline+'{0:10.4f}'.format(
struc.atoms[nums2[i_atoms]].coord[i_coord])
for i_coord in range(3):
newline=newline+'{0:10.4f}'.format(0.0)
newline=newline+'\n'
newline_ir=newline_ir+'{0:10.4e}\n'.format(infrared_intensity[i])
xyz=open(xyzfile,'w')
xyz.writelines(newline)
xyz.close()
ir=open(irname,'w')
ir.writelines(newline_ir)
ir.close()
if options.molden:
molden=open(moldenname,'w')
molden.writelines(newline_molden)
molden.close()
if (mode=='1' or mode=='2') and options.plot:
x=linspace(freq.min()-500,freq.max()+500,1000)
z=zeros(len(x))
for i in range(len(freq)):
z[argmin(abs(x-freq[i]))]=infrared_intensity[i]
window_len=150
lorentzian=lorentz(pi,broadening,arange(250))#signal.gaussian(window_len,broadening)
s=r_[z[window_len-1:0:-1],z,z[-1:-window_len:-1]]
z_convolve=convolve(lorentzian/lorentzian.sum(),s,mode='same')[
window_len-1:-window_len+1]
fig=figure(0)
ax=fig.add_subplot(111)
ax.plot(x,z_convolve,'r',lw=2)
ax.set_xlim([freq.min()-500,freq.max()+500])
ax.set_ylim([-0.01,ax.get_ylim()[1]])
ax.set_yticks([])
ax.set_xlabel('Frequency [1/cm]',size=20)
ax.set_ylabel('Intensity [a.u.]',size=20)
fig.savefig(name+'_IR_spectrum.pdf')
print '\n Done. '
def GetMostAbundantPseudoisomer(self, pH, I, pMg, T):
v_dG0, v_dG0_tag, v_nH, v_z, v_nMg = self._Transform(pH, pMg, I, T)
i = pylab.argmin(v_dG0_tag)
return v_dG0[i], v_dG0_tag[i], v_nH[i], v_z[i], v_nMg[i]
def plotVirial(posdat, endat, bodycount, dt, eps, tot_time):
"""
makes the virial comparison plot and everything else
"""
bodycount, pos_length, postype = posdat.shape
pos_timelen = pl.linspace(0, tot_time, pos_length)
# component wise energies
kin_en_comp = posdat[:, :,
4] # (100 bodies, kin energies at timestep).shape
pot_en_comp = posdat[:, :, 5] # pot energies
kin_en_ejec_comp = pl.zeros((bodycount, pos_length))
pot_en_ejec_comp = pl.zeros((bodycount, pos_length))
masses = posdat[:, 0, 3]
# now to exclude ejected bodies
ejecta_body_array = pl.zeros(
bodycount) # identifies which bodies become ejected at which time
ejecta_time_array = pl.zeros(
pos_length) # measures no. of ejecta at time incr.
# body summed energies, for use in en. consv. and virial test
kin_en_sum = pl.zeros(pos_length)
pot_en_sum = pl.zeros(pos_length)
kin_en_ejec_sum = pl.zeros(pos_length)
pot_en_ejec_sum = pl.zeros(pos_length)
# task f) relevant
eq_time = pl.array([4.52])
eq_pos = []
eq_arg = int(pl.where(pos_timelen >= eq_time[0])[0][0])
# running through the lists
for step in pl.arange(0, pos_length):
for body in pl.arange(0, bodycount):
if kin_en_comp[body, step] + pot_en_comp[body, step] > 0:
# move to ejected lists
kin_en_ejec_comp[body, step] = kin_en_comp[body, step]
pot_en_ejec_comp[body, step] = pot_en_comp[body, step]
ejecta_body_array[body] = 1 # identification
ejecta_time_array[step] = sum(ejecta_body_array)
# stores no. of ejecta at time incr.
kin_en_comp[body, step] = 0. # necessary for elimination
pot_en_comp[body, step] = 0.
kin_en_sum[step] = sum(kin_en_comp[:, step])
pot_en_sum[step] = sum(pot_en_comp[:, step]) / 2.
# factor of 1/2 because system
kin_en_ejec_sum[step] = sum(kin_en_ejec_comp[:, step])
pot_en_ejec_sum[step] = sum(pot_en_ejec_comp[:, step]) / .2
"""print type(pos_timelen[step]), pos_timelen[step]
print type(eq_time[0]), eq_time[0]
print """
if step == eq_arg:
for i in range(len(ejecta_body_array)):
if int(ejecta_body_array[i]) == 0:
eq_pos.append(posdat[i, eq_arg, :3])
# equilibrium positions, (bodies, positions).shape
eq_pos = pl.array(eq_pos)
eq_radia = pl.zeros(len(eq_pos))
for i in range(len(eq_pos)):
eq_radia[i] = (eq_pos[i, 0]**2 + eq_pos[i, 1]**2 + eq_pos[i, 2]**2)**.5
consv_bound_ejec_sum = kin_en_sum + pot_en_sum \
+ kin_en_ejec_sum + pot_en_ejec_sum
# --- tasks b) through e) --- #
pl.figure()
pl.subplot(2, 1, 1)
pl.plot(pos_timelen, kin_en_sum, label="Kinetic")
pl.legend(loc='best')
pl.ylabel("Kinetic energy")
pl.xlim([0.0, tot_time])
pl.title(r"Bound energy over time, %dbody %gdt %g$\varepsilon$" %
(bodycount, dt, eps))
pl.grid("on")
pl.subplot(2, 1, 2)
pl.plot(pos_timelen, pot_en_sum, label="Potential")
pl.legend(loc='best')
pl.xlabel(r"Time $\tau_c$")
pl.ylabel("Potential energy")
pl.xlim([0.0, tot_time])
pl.grid("on")
pl.savefig("../figs/ClusterEnergiesComp_" + str(bodycount) + "body_dt" +
str(int(dt * 1000)) + "_eps" + str(int(eps * 100)) + "_dur" +
str(int(tot_time)) + ".png")
### --- ###
pl.figure()
pl.subplot(2, 1, 1)
pl.title(r"No. of ejecta, %dbody %gdt %g$\varepsilon$" %
(bodycount, dt, eps))
pl.ylabel(r"Ejection fraction")
pl.xlim([0.0, tot_time])
pl.plot(pos_timelen, ejecta_time_array / bodycount, label="Ejecta/Tot")
pl.legend(loc='best')
pl.grid("on")
pl.subplot(2, 1, 2)
pl.plot(pos_timelen,
kin_en_ejec_sum - pot_en_ejec_sum,
label=r"$K_e - V_e$")
pl.legend(loc='best')
pl.xlabel(r"Time $\tau_c$")
pl.ylabel("Energy")
pl.xlim([0., tot_time])
pl.title(r"Ejected bodies' energy, %dbody %gdt %g$\varepsilon$" %
(bodycount, dt, eps))
pl.grid("on")
pl.savefig("../figs/ClusterEnergiesEjecEn_" + str(bodycount) + "body_dt" +
str(int(dt * 1000)) + "_eps" + str(int(eps * 100)) + "_dur" +
str(int(tot_time)) + ".png")
### --- ###
pl.figure()
pl.subplot(2, 1, 1)
pl.plot(pos_timelen,
consv_bound_ejec_sum,
label=r"$K_b + V_b + K_e + V_e$")
pl.plot(pos_timelen,
pl.ones(pos_length) * pot_en_sum[0],
linestyle="dashed",
color="black",
label="Conserved ideal")
pl.legend(loc='best')
pl.ylabel("Energy sum")
pl.xlim([0., tot_time])
# pl.ylim([pot_en_sum[0] - 0.1*max(consv_bound_ejec_sum), max(consv_bound_ejec_sum) + 0.1*max(consv_bound_ejec_sum)])
pl.title(r"Energy conservation test, %dbody %gdt %g$\varepsilon$" %
(bodycount, dt, eps))
pl.grid("on")
pl.subplot(2, 1, 2)
pl.plot(pos_timelen,
2 * kin_en_sum / (bodycount - ejecta_time_array) + pot_en_sum /
(bodycount - ejecta_time_array),
label=r"$2K_b + V_b$")
pl.plot(pos_timelen,
pl.zeros(pos_length),
linestyle="dashed",
color="black",
label="Virial ideal")
pl.legend(loc='best')
pl.xlabel(r"Time $\tau_c$")
pl.ylabel("Virial energy comparison")
pl.xlim([0.0, tot_time])
pl.title(r"Virial comparison fit, %dbody %gdt %g$\varepsilon$" %
(bodycount, dt, eps))
pl.grid("on")
pl.savefig("../figs/ClusterEnConsvVirial_" + str(bodycount) + "body_dt" +
str(int(dt * 1000)) + "_eps" + str(int(eps * 100)) + "_dur" +
str(int(tot_time)) + ".png")
################################
# --- beginning of task f) --- #
################################
#
colorlist = []
for i in range(bodycount):
colorlist.append(random.rand(3, 1))
fig3D = pl.figure()
ax3D = fig3D.add_subplot(111, projection='3d')
for body in range(len(eq_pos)):
ax3D.plot([eq_pos[body, 0]], [eq_pos[body, 1]], [eq_pos[body, 2]],
marker="o",
color=colorlist[body])
ax3D.set_title(
r"Star cluster 3D %dbody %gdt %g$\varepsilon$, t=%g$\tau_c$" %
(bodycount, dt, eps, eq_time))
ax3D.set_xlabel("X-axis [ly]")
ax3D.set_ylabel("Y-axis [ly]")
ax3D.set_zlabel("Z-axis [ly]")
ax3D.set_xlim([-25, 25])
ax3D.set_ylim([-25, 25])
ax3D.set_zlim([-25, 25])
fig3D.savefig("../moviefigs/eps" + str(int(eps * 100)) + "/ClusterPos_" +
str(bodycount) + "body_dt" + str(int(dt * 1000)) + "_eps" +
str(int(eps * 100)) + "_dur" + str(int(tot_time)) + ".png")
print "mean eq. radius:", pl.mean(eq_radia)
print "std dev. radius:", pl.std(eq_radia)
bincount = 60
weights, edges = pl.histogram(eq_radia, bins=bincount, normed=False)
radia = edges + 0.5 * (edges[1] - edges[0])
# lsm finds correct r0
lengthnumber = 1000
alphalower = 0.01
alphaupper = 2.
alpha = pl.linspace(alphalower, alphaupper, lengthnumber)
r0lower = 0.0001
r0upper = 10.
r0 = pl.linspace(r0lower, r0upper, lengthnumber)
n0 = max(weights)
n0arg = pl.argmax(weights)
r0final = bodycount**(
1. / 3) # assuming it depends somehow on total body number in volume
nsums = pl.zeros(lengthnumber)
for alphacount in range(lengthnumber):
nset = n(edges[:-1] - edges[n0arg], alpha[alphacount] * r0final, n0)
nsums[alphacount] = sum((nset - weights)**2)
minarg = pl.argmin(nsums)
r0final *= alpha[minarg]
print "n0", n0
print "r0", r0final
"""
pl.figure()
pl.subplot(2,1,1)
pl.hist(eq_radia, label="Histogram of bodies", bins=bincount)
pl.legend(loc='best')
pl.ylabel(r"Bodies in the data")
pl.title(r"Radial density of bound bodies, %dbody %gdt %g$\varepsilon$" % (bodycount, dt, eps) )
pl.grid('on')
pl.subplot(2,1,2)
pl.plot(edges + edges[pl.argmax(weights)], n(edges, r0final, n0), label=r"$n(r)$", color='blue', linewidth=2)
pl.xlabel(r"Radius $R_0$")
pl.ylabel(r"Radial distribution model")
pl.legend(loc='best')
pl.grid('on')
pl.savefig("../figs/ClusterRadDens_"+str(bodycount)+"body_dt"+str(int(dt*1000))+"_eps"+str(int(eps*100))+"_dur"+str(int(tot_time))+".png")
"""
pl.figure()
pl.hist(eq_radia, label="Histogram of bodies", color='cyan', bins=bincount)
pl.title(r"Radial density of bound bodies, %dbody %gdt %g$\varepsilon$" %
(bodycount, dt, eps))
pl.plot(edges + edges[pl.argmax(weights)],
n(edges, r0final, n0),
label=r"$n(r)$",
color='magenta',
linewidth=2)
pl.xlabel(r"Radius $R_0$")
pl.ylabel(r"Radial distribution")
pl.legend(loc='best')
pl.grid('on')
pl.savefig("../figs/ClusterRadDens_" + str(bodycount) + "body_dt" +
str(int(dt * 1000)) + "_eps" + str(int(eps * 100)) + "_dur" +
str(int(tot_time)) + ".png")
for e in fl[1:]:
data_add = pd.read_table(e, sep = ",")
data_add.columns = ["Time", e[3:8]]
means[k] = pd.merge(means[k], data_add, on = "Time")
means[k]["0.435"] -= 273.15
means[k]["0.455"] -= 273.15
heights = pl.array(means[k].keys())[1:].astype(float)
pl.figure()
for level in filling_levels:
index = pl.argmin(abs(means[k]["Time"] - (level / massflows[k])))
pl.plot(means[k].loc[index][1:][0:].values, heights, \
label = "filling level = " + str(level) + " kg" )
pl.xlim([58.0, 86.0])
pl.ylim([0.2, 2.4])
pl.grid(True)
pl.ylabel("Height (m)")
pl.xlabel("Temperature ($^{\circ}$C)")
pl.legend(loc="lower right")
pl.title("Mean temperature per stratum in " + sim)
pl.savefig(basedir + "fig_mean_per_sim_upper_inlet/" + sim + ".png", bbox_inches='tight')
def argmindist_py(a, v):
from pylab import randn, newaxis, sum, argmin
return argmin(sum((a - v[newaxis, :])**2, axis=1))