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mirror of https://github.com/QuantumPackage/qp2.git synced 2024-06-22 20:42:06 +02:00
qp2/scripts/qp_exc_energy.py

190 lines
5.0 KiB
Python
Executable File

#!/usr/bin/env python3
# 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))