mirror of
https://github.com/QuantumPackage/qp2.git
synced 2024-11-15 02:23:51 +01:00
190 lines
5.0 KiB
Python
Executable File
190 lines
5.0 KiB
Python
Executable File
#!/usr/bin/env python3
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# Computes the error on the excitation energy of a CIPSI run.
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def student(p,df):
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import scipy
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from scipy.stats import t
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return t.ppf(p, df)
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def chi2cdf(x,k):
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import scipy
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import scipy.stats
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return scipy.stats.chi2.cdf(x,k)
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def jarque_bera(data):
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n = max(len(data), 2)
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norm = 1./ sum( [ w for (_,w) in data ] )
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mu = sum( [ w* x for (x,w) in data ] ) * norm
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sigma2 = sum( [ w*(x-mu)**2 for (x,w) in data ] ) * norm
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if sigma2 > 0.:
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S = ( sum( [ w*(x-mu)**3 for (x,w) in data ] ) * norm ) / sigma2**(3./2.)
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K = ( sum( [ w*(x-mu)**4 for (x,w) in data ] ) * norm ) / sigma2**2
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else:
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S = 0.
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K = 0.
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# Value of the Jarque-Bera test
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JB = n/6. * (S**2 + 1./4. * (K-3.)**2)
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# Probability that the data comes from a Gaussian distribution
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p = 1. - chi2cdf(JB,2)
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return JB, mu, sqrt(sigma2/(n-1)), p
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to_eV = 27.2107362681
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import sys, os
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import scipy
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import scipy.stats
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from math import sqrt, gamma, exp
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import qp_json
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def read_data(ezfio_filename,state):
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""" Read energies and PT2 from input file """
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data = qp_json.load_last(ezfio_filename)
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for method in data.keys():
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x = data[method]
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lines = x
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print(f"State: {state}")
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gs = []
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es = []
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for l in lines:
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try:
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pt2_0 = l['states'][0]['pt2']
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e_0 = l['states'][0]['energy']
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pt2_1 = l['states'][state]['pt2']
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e_1 = l['states'][state]['energy']
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gs.append( (e_0, pt2_0) )
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es.append( (e_1, pt2_1) )
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except: pass
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def f(p_1, p0, p1):
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e, pt2 = p0
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y0, x0 = p_1
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y1, x1 = p1
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try:
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alpha = (y1-y0)/(x0-x1)
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except ZeroDivisionError:
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alpha = 1.
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return [e, pt2, alpha]
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for l in (gs, es):
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p_1, p0, p1 = l[0], l[0], l[1]
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l[0] = f(p_1, p0, p1)
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for i in range(1,len(l)-1):
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p_1 = (l[i-1][0], l[i-1][1])
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p0 = l[i]
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p1 = l[i+1]
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l[i] = f(p_1, p0, p1)
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i = len(l)-1
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p_1 = (l[i-1][0], l[i-1][1])
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p0 = l[i]
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p1 = l[-1]
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l[i] = f(p_1, p0, p1)
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return [ x+y for x,y in zip(gs,es) ]
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def compute(data):
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d = []
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for e0, p0, a0, e1, p1, a1 in data:
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x = (e1+p1)-(e0+p0)
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w = 1./sqrt(p0**2 + p1**2)
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bias = (a1-1.)*p1 - (a0-1.)*p0
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d.append( (x,w,bias) )
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x = []
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target = (scipy.stats.norm.cdf(1.)-0.5)*2 # = 0.6827
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print("| %2s | %8s | %8s | %8s | %8s | %8s |"%( "N", "DE", "+/-", "bias", "P(G)", "J"))
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print("|----+----------+----------+----------+----------+----------|")
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xmax = (0.,0.,0.,0.,0.,0,0.)
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for i in range(len(data)-1):
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jb, mu, sigma, p = jarque_bera( [ (x,w) for (x,w,bias) in d[i:] ] )
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bias = sum ( [ w * e for (_,w,e) in d[i:] ] ) / sum ( [ w for (_,w,_) in d[i:] ] )
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mu = (mu+0.5*bias) * to_eV
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sigma = sigma * to_eV
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bias = bias * to_eV
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n = len(data[i:])
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beta = student(0.5*(1.+target/p) ,n)
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err = sigma * beta + 0.5*abs(bias)
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print("| %2d | %8.3f | %8.3f | %8.3f | %8.3f | %8.3f |"%( n, mu, err, bias, p, jb))
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if n < 3 :
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continue
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y = (err, p, mu, err, jb,n,bias)
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if p > xmax[1]: xmax = y
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if p < 0.8:
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continue
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x.append(y)
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x = sorted(x)
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print("|----+----------+----------+----------+----------+----------|")
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if x != []:
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xmax = x[0]
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_, p, mu, err, jb, n, bias = xmax
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beta = student(0.5*(1.+target/p),n)
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print("| %2d | %8.3f | %8.3f | %8.3f | %8.3f | %8.3f |\n"%(n, mu, err, bias, p, jb))
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return mu, err, bias, p
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ezfio_filename = sys.argv[1]
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print(ezfio_filename)
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if len(sys.argv) > 2:
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state = int(sys.argv[2])
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else:
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state = 1
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data = read_data(ezfio_filename,state)
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mu, err, bias, _ = compute(data)
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print(" %s: %8.3f +/- %5.3f eV\n"%(ezfio_filename, mu, err))
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import numpy as np
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A = np.array( [ [ data[-1][1], 1. ],
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[ data[-2][1], 1. ] ] )
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B = np.array( [ [ data[-1][0] ],
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[ data[-2][0] ] ] )
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E0 = np.linalg.solve(A,B)[1]
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A = np.array( [ [ data[-1][4], 1. ],
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[ data[-2][4], 1. ] ] )
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B = np.array( [ [ data[-1][3] ],
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[ data[-2][3] ] ] )
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E1 = np.linalg.solve(A,B)[1]
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average_2 = (E1-E0)*to_eV
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A = np.array( [ [ data[-1][1], 1. ],
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[ data[-2][1], 1. ],
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[ data[-3][1], 1. ] ] )
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B = np.array( [ [ data[-1][0] ],
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[ data[-2][0] ],
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[ data[-3][0] ] ] )
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E0 = np.linalg.lstsq(A,B,rcond=None)[0][1]
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A = np.array( [ [ data[-1][4], 1. ],
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[ data[-2][4], 1. ],
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[ data[-3][4], 1. ] ] )
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B = np.array( [ [ data[-1][3] ],
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[ data[-2][3] ],
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[ data[-3][3] ] ] )
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E1 = np.linalg.lstsq(A,B,rcond=None)[0][1]
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average_3 = (E1-E0)*to_eV
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exc = ((data[-1][3] + data[-1][4]) - (data[-1][0] + data[-1][1])) * to_eV
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error_2 = abs(average_2 - average_3)
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error_3 = abs(average_3 - exc)
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print(" 2-3 points: %.3f +/- %.3f "% (average_3, error_2))
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print(" largest wf: %.3f +/- %.3f "%(average_3, error_3))
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