def __init__(self, name='AP4'):
""" Initialize EOS instance with interp1d from scipy.interpolate
"""
if not name in EOS_NAMES:
print(' <<< EOS %s not found >>> ' % name)
self.name = name
self.mB = 1.660538921e-24 # g
dat = np.genfromtxt('/Users/yonggao/Desktop/scalarized_ns/code/EOS/' +
name + '.txt')
self.rho, self.p, self.e = dat[:, 0], dat[:, 1], dat[:, 2]
self.min_rho = np.min(self.rho)
self.max_rho = np.max(self.rho)
self.min_p = np.min(self.p)
self.max_p = np.max(self.p)
self.min_e = np.min(self.e)
self.max_e = np.max(self.e)
self.min_n = self.rho2n(self.min_rho)
self.max_n = self.rho2n(self.max_rho)
lgrho, lgp, lge = lg(self.rho), lg(self.p), lg(self.e)
self.lgrho2lgp = sp_interp1d(lgrho, lgp)
self.lgrho2lge = sp_interp1d(lgrho, lge)
self.lgp2lgrho = sp_interp1d(lgp, lgrho)
self.lgp2lge = sp_interp1d(lgp, lge)
self.lge2lgp = sp_interp1d(lge, lgp)
self.lge2lgrho = sp_interp1d(lge, lgrho)
def calculating_h(T, M1, M2, d1, d2, Ecv, p):
Ea = 1.5 * k * T
avg_share_transmit_energy = (4 * M1 * M2) / ((M1 + M2)**2)
E02 = Ecv / 2
Lambda = (4 * k1 * T) / ((pi * (d1 + d2)**2) * p * (1 + M2 / M1)**0.5)
Nk = lg(Ea / E02) / lg(1 - avg_share_transmit_energy)
Lk = Lambda * Nk
# h = round(Lk * 100, 1) #cm
return Lambda, Lk
def main():
points=np.array([[0,0],[2,0],[3,0],[0,2],[0,-3],[7,0],[7,3],[7,1],[7,-2],[9,-1]],dtype=float)
print 'Input point cloud:\n'
print points
print '\nDiameter of point cloud:\t'+str(max(dmatrix))+'\n'
link=linkage(points) #linkage can also take the output of pdist(points) -- a flattened distance matrix -- as it input
root=linkage_to_dendrogram(link)
maxbits=lg(root.card())
print 'Minimal spanning tree edges from given list of points:\n'
print minimum_spanning_tree(csr_matrix(squareform(dmatrix))).todok().keys()
print '\n'
print 'Minimal spanning tree midpoints from given list of points:\n'
print bridges(points,'elltwo')
print '\n\n'
print 'Producing dendrogram from linkage matrix:\n'
print root
print root.heights()
print 'The entropy of this dendrogram is: '+str(root.entropy())+', which is about'+str(np.floor(100*root.entropy()/maxbits))+'\% of max entropy\n\n'
print '\n\nNow combing the dendrogram:\n'
root.comb()
print root
print root.heights()
print 'The entropy of the combed dendrogram is: '+str(root.entropy())+', which is about'+str(np.floor(100*root.entropy()/maxbits))+'\% of max entropy\n\n'
print 'Here are some slices of the dendrogram:\n'
print 'At height 5: '
print root.slice(4)
print double(root.slice_incidence_matrix(4))
print double(root.clusters_incidence_matrix())
print 'At height 3.5: '
print root.slice(2.5)
print double(root.slice_incidence_matrix(2.5))
print double(root.clusters_incidence_matrix())
print 'At height 1.5: '
print root.slice(.5)
print double(root.slice_incidence_matrix(.5))
print double(root.clusters_incidence_matrix())
res=[1,3,4,5,6,7,9]
print '\nRestriction to '+str(res)+' yields the dendrogram:\n'
new_root=root.restricted(res)
print new_root
print new_root.heights()
delta=1.8
print '\nTruncation at delta='+str(delta)+' yields the dendrogram:\n'
trunc_root=root.truncated(delta)
print trunc_root
print trunc_root.heights()
print trunc_root.slice(0)
print double(trunc_root.slice_incidence_matrix(0))
print double(trunc_root.clusters_incidence_matrix())
x=np.array([1,0,2,3,0,0,4,0,0,5,6])
print x
choose_half(x)
print x
def entropy(self):
return (1./self.card()) * sum( (child.card() * child.entropy() - (child.card()-1.) * (lg(child.card())-lg(self.card()))) for child in self._children )
def p2e(self, p):
if p < self.min_p:
return 1.0e-9
return 10.0**self.lgp2lge(lg(p))
def p2rho(self, p):
return 10.0**self.lgp2lgrho(lg(p))
def rho2e(self, rho):
return 10.0**self.lgrho2lge(lg(rho))
def rho2p(self, rho):
return 10.0**self.lgrho2lgp(lg(rho))
def e2rho(self, e):
if e < self.min_e:
return 1.0e-9
return 10.0**self.lge2lgrho(lg(e))
def e2p(self, e):
if e < self.min_e:
return 1.0e-9
return 10.0**self.lge2lgp(lg(e))