Added exc_energy_error.py

scripts/exc_energy_error.py

@@ -0,0 +1,186 @@
+#!/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 json
+
+
+def read_data(filename,state):
+  """ Read energies and PT2 from input file """
+  with open(filename,'r') as f:
+      lines = json.load(f)['fci']
+
+  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
+
+filename = sys.argv[1]
+print(filename)
+if len(sys.argv) > 2:
+    state = int(sys.argv[2])
+else:
+    state = 1
+data = read_data(filename,state)
+mu, err, bias, _ = compute(data)
+print(" %s: %8.3f +/- %5.3f eV\n"%(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))
+
+