curr1 = np.array([])
curr2 = np.array([])
for WorkFunction in [WorkFunction]:
i = 0
for V in Voltages:
if (V != 0.0):
nV = int(abs(V / eta) + 1)
print("Voltage: ", V, "; number of integration steps: ", nV)
for eta in [eta]:
current0 = PS.dIdV(V,
WorkFunction,
eta,
eigEn,
tip_r2,
Ratin,
coefs,
orbs=orbs,
s=1.0,
px=0.0,
py=0.0,
pz=0.0)
WF_decay = 1 # ( tunnelling barrier changes 1:1 with applied sample bias)
current1 = PS.STM(V,
nV,
WorkFunction,
eta,
eigEn,
tip_r2,
Ratin,
coefs,
orbs=orbs,
didv_b = False
STM_b = False
states_b = False
if ((scan_type == 'didv') or (scan_type == 'dIdV')
or (scan_type == 'didv-single')):
didv = np.array([
PS.dIdV(V,
WorkFunction,
eta,
eigEn,
tip_r,
Ratin,
coefs,
orbs=sample_orbs,
s=tc[0],
px=tc[1],
py=tc[2],
pz=tc[3],
dz2=tc[4],
dxz=tc[5],
dyz=tc[6])
])
didv_b = True
WF_decay = 0.0
#print "DEBUG: didv.shape ", didv.shape
elif ((scan_type == 'states') or (scan_type == 'STATES')):
states = np.sort(eigEn)
mask = states >= V
states = states[mask]
# print "DEBUG: eigEn.shape ", eigEn.shape
# print "DEBUG: coefs.shape ", coefs.shape
# print "DEBUG: Ratin.shape ", Ratin.shape
print("energies prepared, coeffecients read")
# --- the Main calculations --- #
# 'didv'='dIdV''='didv-single' -- only dIdV for one voltage = V ; 'v-scan'='V-scan'='Voltage-scan' -- both STM & dIdV scan - V .. Vmax; 'STM'='STM-single' -- STM for one Voltage = V, use V-scan rather #
didv_b = False
STM_b = False
states_b = False
if ((scan_type == 'didv') or (scan_type == 'dIdV') or (scan_type == 'didv-single')):
didv = np.array([PS.dIdV(V, WorkFunction, eta, eigEn, tip_r, Ratin, coefs, orbs=sample_orbs,
s=tc[0], px=tc[1], py=tc[2], pz=tc[3], dz2=tc[4], dxz=tc[5], dyz=tc[6])])
didv_b = True; WF_decay = 0.0;
# print "DEBUG: didv.shape ", didv.shape
elif ((scan_type == 'states') or (scan_type == 'STATES')):
states = np.sort(eigEn); mask = states >= V; states = states[mask]; del mask;
mask = states <= V_max; states = states[mask]; del mask;
fst = True
print("DEBUG: states:", states)
for isi in states:
if fst:
didv = np.array([PS.dIdV(isi, WorkFunction, eta, eigEn, tip_r, Ratin, coefs, orbs=sample_orbs,
s=tc[0], px=tc[1], py=tc[2], pz=tc[3], dz2=tc[4], dxz=tc[5], dyz=tc[6])])
fst = False
else:
didv = np.append(didv, [PS.dIdV(isi, WorkFunction, eta, eigEn, tip_r, Ratin, coefs, orbs=sample_orbs,
s=tc[0], px=tc[1], py=tc[2], pz=tc[3], dz2=tc[4], dxz=tc[5], dyz=tc[6])], axis=0)