mirror of
https://github.com/QuantumPackage/qp2.git
synced 2024-12-23 04:43:45 +01:00
189 lines
5.0 KiB
Python
Executable File
189 lines
5.0 KiB
Python
Executable File
#!/usr/bin/env python
|
|
# Computes the error on the excitation energy of a CIPSI run.
|
|
|
|
def student(p,df):
|
|
import scipy
|
|
from scipy.stats import t
|
|
return t.ppf(p, df)
|
|
|
|
|
|
def chi2cdf(x,k):
|
|
import scipy
|
|
import scipy.stats
|
|
return scipy.stats.chi2.cdf(x,k)
|
|
|
|
|
|
def jarque_bera(data):
|
|
|
|
n = max(len(data), 2)
|
|
norm = 1./ sum( [ w for (_,w) in data ] )
|
|
|
|
mu = sum( [ w* x for (x,w) in data ] ) * norm
|
|
sigma2 = sum( [ w*(x-mu)**2 for (x,w) in data ] ) * norm
|
|
if sigma2 > 0.:
|
|
S = ( sum( [ w*(x-mu)**3 for (x,w) in data ] ) * norm ) / sigma2**(3./2.)
|
|
K = ( sum( [ w*(x-mu)**4 for (x,w) in data ] ) * norm ) / sigma2**2
|
|
else:
|
|
S = 0.
|
|
K = 0.
|
|
|
|
# Value of the Jarque-Bera test
|
|
JB = n/6. * (S**2 + 1./4. * (K-3.)**2)
|
|
|
|
# Probability that the data comes from a Gaussian distribution
|
|
p = 1. - chi2cdf(JB,2)
|
|
|
|
return JB, mu, sqrt(sigma2/(n-1)), p
|
|
|
|
|
|
|
|
to_eV = 27.2107362681
|
|
import sys, os
|
|
import scipy
|
|
import scipy.stats
|
|
from math import sqrt, gamma, exp
|
|
import qp_json
|
|
|
|
|
|
def read_data(ezfio_filename,state):
|
|
""" Read energies and PT2 from input file """
|
|
data = qp_json.load_last(ezfio_filename)
|
|
for method in data.keys():
|
|
x = data[method]
|
|
lines = x
|
|
|
|
print(f"State: {state}")
|
|
|
|
gs = []
|
|
es = []
|
|
|
|
for l in lines:
|
|
try:
|
|
pt2_0 = l['states'][0]['pt2']
|
|
e_0 = l['states'][0]['energy']
|
|
pt2_1 = l['states'][state]['pt2']
|
|
e_1 = l['states'][state]['energy']
|
|
gs.append( (e_0, pt2_0) )
|
|
es.append( (e_1, pt2_1) )
|
|
except: pass
|
|
|
|
def f(p_1, p0, p1):
|
|
e, pt2 = p0
|
|
y0, x0 = p_1
|
|
y1, x1 = p1
|
|
try:
|
|
alpha = (y1-y0)/(x0-x1)
|
|
except ZeroDivisionError:
|
|
alpha = 1.
|
|
return [e, pt2, alpha]
|
|
|
|
for l in (gs, es):
|
|
p_1, p0, p1 = l[0], l[0], l[1]
|
|
l[0] = f(p_1, p0, p1)
|
|
|
|
for i in range(1,len(l)-1):
|
|
p_1 = (l[i-1][0], l[i-1][1])
|
|
p0 = l[i]
|
|
p1 = l[i+1]
|
|
l[i] = f(p_1, p0, p1)
|
|
|
|
i = len(l)-1
|
|
p_1 = (l[i-1][0], l[i-1][1])
|
|
p0 = l[i]
|
|
p1 = l[-1]
|
|
l[i] = f(p_1, p0, p1)
|
|
|
|
return [ x+y for x,y in zip(gs,es) ]
|
|
|
|
|
|
def compute(data):
|
|
|
|
d = []
|
|
for e0, p0, a0, e1, p1, a1 in data:
|
|
x = (e1+p1)-(e0+p0)
|
|
w = 1./sqrt(p0**2 + p1**2)
|
|
bias = (a1-1.)*p1 - (a0-1.)*p0
|
|
d.append( (x,w,bias) )
|
|
|
|
x = []
|
|
target = (scipy.stats.norm.cdf(1.)-0.5)*2 # = 0.6827
|
|
|
|
print("| %2s | %8s | %8s | %8s | %8s | %8s |"%( "N", "DE", "+/-", "bias", "P(G)", "J"))
|
|
print("|----+----------+----------+----------+----------+----------|")
|
|
xmax = (0.,0.,0.,0.,0.,0,0.)
|
|
for i in range(len(data)-1):
|
|
jb, mu, sigma, p = jarque_bera( [ (x,w) for (x,w,bias) in d[i:] ] )
|
|
bias = sum ( [ w * e for (_,w,e) in d[i:] ] ) / sum ( [ w for (_,w,_) in d[i:] ] )
|
|
mu = (mu+0.5*bias) * to_eV
|
|
sigma = sigma * to_eV
|
|
bias = bias * to_eV
|
|
n = len(data[i:])
|
|
beta = student(0.5*(1.+target/p) ,n)
|
|
err = sigma * beta + 0.5*abs(bias)
|
|
print("| %2d | %8.3f | %8.3f | %8.3f | %8.3f | %8.3f |"%( n, mu, err, bias, p, jb))
|
|
if n < 3 :
|
|
continue
|
|
y = (err, p, mu, err, jb,n,bias)
|
|
if p > xmax[1]: xmax = y
|
|
if p < 0.8:
|
|
continue
|
|
x.append(y)
|
|
|
|
x = sorted(x)
|
|
|
|
print("|----+----------+----------+----------+----------+----------|")
|
|
if x != []:
|
|
xmax = x[0]
|
|
_, p, mu, err, jb, n, bias = xmax
|
|
beta = student(0.5*(1.+target/p),n)
|
|
print("| %2d | %8.3f | %8.3f | %8.3f | %8.3f | %8.3f |\n"%(n, mu, err, bias, p, jb))
|
|
|
|
return mu, err, bias, p
|
|
|
|
ezfio_filename = sys.argv[1]
|
|
print(ezfio_filename)
|
|
if len(sys.argv) > 2:
|
|
state = int(sys.argv[2])
|
|
else:
|
|
state = 1
|
|
data = read_data(ezfio_filename,state)
|
|
mu, err, bias, _ = compute(data)
|
|
print(" %s: %8.3f +/- %5.3f eV\n"%(ezfio_filename, mu, err))
|
|
|
|
import numpy as np
|
|
A = np.array( [ [ data[-1][1], 1. ],
|
|
[ data[-2][1], 1. ] ] )
|
|
B = np.array( [ [ data[-1][0] ],
|
|
[ data[-2][0] ] ] )
|
|
E0 = np.linalg.solve(A,B)[1]
|
|
A = np.array( [ [ data[-1][4], 1. ],
|
|
[ data[-2][4], 1. ] ] )
|
|
B = np.array( [ [ data[-1][3] ],
|
|
[ data[-2][3] ] ] )
|
|
E1 = np.linalg.solve(A,B)[1]
|
|
average_2 = (E1-E0)*to_eV
|
|
|
|
A = np.array( [ [ data[-1][1], 1. ],
|
|
[ data[-2][1], 1. ],
|
|
[ data[-3][1], 1. ] ] )
|
|
B = np.array( [ [ data[-1][0] ],
|
|
[ data[-2][0] ],
|
|
[ data[-3][0] ] ] )
|
|
E0 = np.linalg.lstsq(A,B,rcond=None)[0][1]
|
|
A = np.array( [ [ data[-1][4], 1. ],
|
|
[ data[-2][4], 1. ],
|
|
[ data[-3][4], 1. ] ] )
|
|
B = np.array( [ [ data[-1][3] ],
|
|
[ data[-2][3] ],
|
|
[ data[-3][3] ] ] )
|
|
E1 = np.linalg.lstsq(A,B,rcond=None)[0][1]
|
|
average_3 = (E1-E0)*to_eV
|
|
|
|
exc = ((data[-1][3] + data[-1][4]) - (data[-1][0] + data[-1][1])) * to_eV
|
|
error_2 = abs(average_2 - average_3)
|
|
error_3 = abs(average_3 - exc)
|
|
print(" 2-3 points: %.3f +/- %.3f "% (average_3, error_2))
|
|
print(" largest wf: %.3f +/- %.3f "%(average_3, error_3))
|
|
|
|
|