# Bspline #
# Represents a cubic bspline curve, supports evaluation. #
# #
#====================================================================#
import os
from numpy import loadtxt, array, ones, dot, floor, zeros
from qmcpack_input import QmcpackInput
from qmcpack_analyzer_base import QAobject, QAanalyzer
from developer import unavailable
from debug import *
try:
from matplotlib.pyplot import plot, show, figure, xlabel, ylabel, title, legend
except:
plot, show, figure, xlabel, ylabel, title, legend = unavailable(
'matplotlib.pyplot', 'plot', 'show', 'figure', 'xlabel', 'ylabel',
'title', 'legend')
#end try
class Bspline(QAobject):
A = array([
-1.0 / 6.0, 3.0 / 6.0, -3.0 / 6.0, 1.0 / 6.0, 3.0 / 6.0, -6.0 / 6.0,
0.0 / 6.0, 4.0 / 6.0, -3.0 / 6.0, 3.0 / 6.0, 3.0 / 6.0, 1.0 / 6.0,
1.0 / 6.0, 0.0 / 6.0, 0.0 / 6.0, 0.0 / 6.0
])
dA = array([
0.0, -0.5, 1.0, -0.5, 0.0, 1.5, -2.0, 0.0, 0.0, -1.5, 1.0, 0.5, 0.0,
0.5, 0.0, 0.0
])
d2A = array([
# graph.py #
# Interface with pydot to make images of directed graphs. #
# #
# Content summary: #
# Graph #
# Wrapper class for pydot functionality #
# #
#====================================================================#
from generic import obj
from developer import DevBase,unavailable
try:
from pydot import Dot,Node,Edge,Cluster as pdCluster
except ImportError:
Dot,Node,Edge,pdCluster = unavailable('Dot','Node','Edge','pdCluster')
#end try
# websites
# http://www.graphviz.org/Home.php
# http://pythonhaven.wordpress.com/tag/pydot/
# http://jseabold.net/blog/2012/02/making-graphical-models-with-pydot.html
class Graph(DevBase):
pydot_type = Dot
def __init__(self,*args,**kwargs):
# some args are
import inspect
from numpy import array, ndarray, zeros, linspace, pi, exp, sqrt, polyfit, polyval
from numpy import sum, abs, arange, empty, sin, cos, dot, atleast_2d, ogrid
from numpy import ones_like, sign, random, cross, prod
from numpy.linalg import norm
from generic import obj
from developer import unavailable, warn, error
from unit_converter import convert
from periodic_table import pt as ptable
try:
from scipy.special import betainc
from scipy.optimize import fmin
from scipy.spatial import KDTree, Delaunay, Voronoi
scipy_unavailable = False
except ImportError:
betainc = unavailable('scipy.special', 'betainc')
fmin = unavailable('scipy.optimize', 'fmin')
KDTree, Delaunay, Voronoi = unavailable('scipy.spatial', 'KDTree',
'Delaunay', 'Voronoi')
scipy_unavailable = True
#end try
def numerics_error(*args, **kwargs):
error(*args, **kwargs)
#end def numerics_error
# cost functions
least_squares = lambda p, x, y, f: ((f(p, x) - y)**2).sum()
absmin = lambda p, x, y, f: abs(f(p, x) - y).sum()
from qmcpack_quantity_analyzers \
import ScalarsDatAnalyzer,ScalarsHDFAnalyzer,DmcDatAnalyzer,\
EnergyDensityAnalyzer,TracesAnalyzer,DensityMatricesAnalyzer,\
SpinDensityAnalyzer,StructureFactorAnalyzer
from qmcpack_method_analyzers \
import OptAnalyzer,VmcAnalyzer,DmcAnalyzer
from qmcpack_result_analyzers \
import OptimizationAnalyzer,TimestepStudyAnalyzer
from simulation import SimulationAnalyzer,Simulation
from qmcpack_input import QmcpackInput
try:
import h5py
h5py_unavailable = False
except ImportError:
h5py = unavailable('h5py')
h5py_unavailable = True
#end try
class QmcpackAnalyzerCapabilities(QAobject):
def __init__(self):
self.methods=set(['opt','vmc','dmc','rmc'])
self.data_sources = set(['scalar','stat','dmc','storeconfig','opt','traces'])
self.scalars=set(['localenergy','localpotential','kinetic','elecelec','localecp','nonlocalecp','ionion','localenergy_sq','acceptratio','blockcpu','blockweight'])
self.fields=set(['energydensity','density','dm1b','spindensity','structurefactor'])
hdf_data_sources = set(['stat','storeconfig','traces'])
import sys
from numpy import *
from numpy.linalg import *
from developer import unavailable
try:
from scipy.special import betainc
from scipy.optimize import fmin
from scipy.spatial import KDTree,Delaunay
scipy_unavailable = False
except ImportError:
betainc = unavailable('scipy.special' ,'betainc')
fmin = unavailable('scipy.optimize','fmin')
KDTree = unavailable('scipy.special' ,'KDTree')
Delaunay = unavailable('scipy.special' ,'Delaunay')
scipy_unavailable = True
#end try
########################################################################
############ ndgrid
########################################################################
# retrieved from
# http://www.mailinglistarchive.com/html/[email protected]/2010-05/msg00055.html
#"""
#n-dimensional gridding like Matlab's NDGRID
#
#Typical usage:
try:
from matplotlib.pyplot import figure, plot, xlabel, ylabel, title, show, ylim, legend, xlim, rcParams, savefig, bar, xticks, subplot, grid, setp, errorbar, loglog, semilogx, semilogy
params = {
'legend.fontsize': 14,
'figure.facecolor': 'white',
'figure.subplot.hspace': 0.,
'axes.labelsize': 16,
'xtick.labelsize': 14,
'ytick.labelsize': 14
}
rcParams.update(params)
except (ImportError, RuntimeError):
figure, plot, xlabel, ylabel, title, show, ylim, legend, xlim, rcParams, savefig, bar, xticks, subplot, grid, setp, errorbar, loglog, semilogx, semilogy = unavailable(
'matplotlib.pyplot', 'figure', 'plot', 'xlabel', 'ylabel', 'title',
'show', 'ylim', 'legend', 'xlim', 'rcParams', 'savefig', 'bar',
'xticks', 'subplot', 'grid', 'setp', 'errorbar', 'loglog', 'semilogx',
'semilogy')
#end try
class Pseudopotential(DevBase):
colors = dict(s='k', p='r', d='b', f='m')
lcolors = ['k', 'r', 'b', 'm']
ldict = {0: 's', 1: 'p', 2: 'd', 3: 'f'}
format = 'generic'
conv_table = {('upf', 'fsatom'): ('--upf_pot', '--xml')}
extensions = dict(upf='upf', fsatom='xml', gamess='gamess', casino='data')
# Represents a cubic bspline curve, supports evaluation. #
# #
#====================================================================#
import os
from numpy import loadtxt,array,ones,dot,floor,zeros
from qmcpack_input import QmcpackInput
from qmcpack_analyzer_base import QAobject,QAanalyzer
from developer import unavailable
from debug import *
try:
from matplotlib.pyplot import plot,show,figure,xlabel,ylabel,title,legend
except (ImportError,RuntimeError):
plot,show,figure,xlabel,ylabel,title,legend = unavailable('matplotlib.pyplot','plot','show','figure','xlabel','ylabel','title','legend')
#end try
class Bspline(QAobject):
A = array([-1.0/6.0, 3.0/6.0, -3.0/6.0, 1.0/6.0,
3.0/6.0, -6.0/6.0, 0.0/6.0, 4.0/6.0,
-3.0/6.0, 3.0/6.0, 3.0/6.0, 1.0/6.0,
1.0/6.0, 0.0/6.0, 0.0/6.0, 0.0/6.0 ])
dA =array([0.0, -0.5, 1.0, -0.5,
0.0, 1.5, -2.0, 0.0,
0.0, -1.5, 1.0, 0.5,
0.0, 0.5, 0.0, 0.0 ])
d2A=array([0.0, 0.0, -1.0, 1.0,
0.0, 0.0, 3.0, -2.0,
# #
# pressure_fit #
# Perform a pressure vs. volume fit #
# #
# pressure eval #
# Evaluate a P(V) fit at specified volumes. #
# #
#====================================================================#
from numpy import exp
from developer import unavailable
try:
from scipy.optimize import fmin
except ImportError:
fmin = unavailable('scipy.optimize','fmin')
#end try
f = None
#minimizers
least_squares = lambda p,x,y,f: ((f(p,x)-y)**2).sum()
#fits
fits = set(['murnaghan','birch','vinet','spanu','casula'])
fstr = ''
for ft in fits:
fstr += ft+' '
#end for
em = lambda p,V: p[0] + p[2]/p[3]*V*((p[1]/V)**p[3]/(p[3]-1)+1)-p[1]*p[2]/(p[3]-1)
import inspect
from numpy import array,ndarray,zeros,linspace,pi,exp,sqrt,polyfit,polyval
from numpy import sum,abs,arange,empty,sin,cos,dot,atleast_2d,ogrid
from numpy import ones_like,sign,random,cross,prod
from numpy.linalg import norm
from generic import obj
from developer import unavailable,warn,error
from unit_converter import convert
from periodic_table import pt as ptable
try:
from scipy.special import betainc
from scipy.optimize import fmin
from scipy.spatial import KDTree,Delaunay,Voronoi
scipy_unavailable = False
except ImportError:
betainc = unavailable('scipy.special' ,'betainc')
fmin = unavailable('scipy.optimize','fmin')
KDTree,Delaunay,Voronoi = unavailable('scipy.spatial' ,'KDTree','Delaunay','Voronoi')
scipy_unavailable = True
#end try
def numerics_error(*args,**kwargs):
error(*args,**kwargs)
#end def numerics_error
# minimizers
least_squares = lambda p,x,y,f: ((f(p,x)-y)**2).sum()
absmin = lambda p,x,y,f: abs(f(p,x)-y).sum()
madmin = lambda p,x,y,f: abs(f(p,x)-y).max()
# curve fit based on fmin from scipy
import sys
from numpy import *
from numpy.linalg import *
from developer import unavailable
try:
from scipy.special import betainc
from scipy.optimize import fmin
from scipy.spatial import KDTree
scipy_unavailable = False
except ImportError:
betainc = unavailable('scipy.special' ,'betainc')
fmin = unavailable('scipy.optimize','fmin')
KDTree = unavailable('scipy.special' ,'KDTree')
scipy_unavailable = True
#end try
########################################################################
############ ndgrid
########################################################################
# retrieved from
# http://www.mailinglistarchive.com/html/[email protected]/2010-05/msg00055.html
#"""
#n-dimensional gridding like Matlab's NDGRID
#
#Typical usage:
#>>> x, y, z = [0, 1], [2, 3, 4], [5, 6, 7, 8]
# Python standard library imports
import os
import inspect
from time import process_time
from copy import deepcopy
# Non-standard Python imports
import numpy as np
from developer import unavailable # Nexus unavailable module guard
try:
import matplotlib.pyplot as plt
except:
plt = unavailable('matplotlib', 'pyplot')
#end try
try:
import h5py
except:
h5py = unavailable('h5py')
#end try
# Nexus imports
import memory
from unit_converter import convert
from generic import obj
from developer import DevBase, log, error, ci
from numerics import simstats
from grid_functions import grid_function, read_grid, StructuredGrid, grid as generate_grid
from grid_functions import SpheroidGrid
from structure import Structure, read_structure
from fileio import XsfFile
from numpy import exp
from developer import unavailable
try:
from scipy.optimize import fmin
except ImportError:
fmin = unavailable('scipy.optimize','fmin')
#end try
f = None
#minimizers
least_squares = lambda p,x,y,f: ((f(p,x)-y)**2).sum()
#fits
fits = set(['murnaghan','birch','vinet','spanu','casula'])
fstr = ''
for ft in fits:
fstr += ft+' '
#end for
em = lambda p,V: p[0] + p[2]/p[3]*V*((p[1]/V)**p[3]/(p[3]-1)+1)-p[1]*p[2]/(p[3]-1)
eb = lambda p,V: p[0] + 9*p[1]*p[2]/16*((p[1]/V)**(2./3)-1)**2*( 2 + (p[3]-4)*((p[1]/V)**(2./3)-1) )
ev = lambda p,V: p[0] + 2*p[1]*p[2]/(p[3]-1)**2*( 2 - (2+3*(p[3]-1)*((V/p[1])**(1./3)-1))*exp(-1.5*(p[3]-1)*((V/p[1])**(1./3)-1)) )
es = lambda p,V: p[0] + exp(p[1]*V)-p[2]/V**4
emo = lambda p,V: p[0]+p[2]*( exp(-2*p[3]*(V-p[1])) - 2*exp(-p[3]*(V-p[1])) )
efit = dict(murnaghan=em,birch=eb,vinet=ev,spanu=es,morse=emo)
pm = lambda p,V: p[1]/p[2]*((p[0]/V)**p[2]-1)
pb = lambda p,V: 1.5*p[1]*(p[0]/V)**(5./3)*((p[0]/V)**(2./3)-1)*(1.+.75*(p[2]-1)*((p[0]/V)**(2./3)-1))
pv = lambda p,V: 3.*p[1]*(1.-(V/p[0])**(1./3))*(p[0]/V)**(2./3)*exp(1.5*(p[2]-1)*(1.-(V/p[0])**(1./3)))
pfit = {'murnaghan':pm,'birch':pb,'vinet':pv}
from physical_system import PhysicalSystem
from simulation import Simulation
from qmcpack_input import QmcpackInput, generate_qmcpack_input
from qmcpack_input import BundledQmcpackInput, TracedQmcpackInput
from qmcpack_input import loop, linear, cslinear, vmc, dmc, collection, determinantset, hamiltonian, init, pairpot
from qmcpack_input import generate_jastrows, generate_jastrow, generate_jastrow1, generate_jastrow2, generate_jastrow3
from qmcpack_input import generate_opt, generate_opts
from qmcpack_analyzer import QmcpackAnalyzer
from converters import Pw2qmcpack, Wfconvert
from sqd import Sqd
from developer import unavailable
try:
import h5py
except ImportError:
h5py = unavailable("h5py")
# end try
class Qmcpack(Simulation):
input_type = QmcpackInput
analyzer_type = QmcpackAnalyzer
generic_identifier = "qmcpack"
infile_extension = ".in.xml"
# application = 'qmcapp'
application = "qmcapp_complex" # always use complex version until kpoint handling is fixed
application_properties = set(["serial", "omp", "mpi"])
application_results = set(["jastrow"])
preserve = Simulation.preserve | set(["should_twist_average"])
def post_init(self):
from simulation import Simulation
from qmcpack_input import QmcpackInput,generate_qmcpack_input
from qmcpack_input import TracedQmcpackInput
from qmcpack_input import loop,linear,cslinear,vmc,dmc,collection,determinantset,hamiltonian,init,pairpot,bspline_builder
from qmcpack_input import generate_jastrows,generate_jastrow,generate_jastrow1,generate_jastrow2,generate_jastrow3
from qmcpack_input import generate_opt,generate_opts
from qmcpack_analyzer import QmcpackAnalyzer
from qmcpack_converters import Pw2qmcpack,Wfconvert,Convert4qmc
from sqd import Sqd
from debug import ci,ls,gs
from developer import unavailable
from nexus_base import nexus_core
try:
import h5py
except:
h5py = unavailable('h5py')
#end try
class Qmcpack(Simulation):
input_type = QmcpackInput
analyzer_type = QmcpackAnalyzer
generic_identifier = 'qmcpack'
infile_extension = '.in.xml'
application = 'qmcpack'
application_properties = set(['serial','omp','mpi'])
application_results = set(['jastrow','cuspcorr','wavefunction'])
def post_init(self):
from subprocess import Popen
from numpy import linspace,array,zeros,append,mgrid,empty,exp
from xmlreader import readxml
from superstring import string2val,split_delims
from periodic_table import pt
from unit_converter import convert
from generic import obj
from developer import DevBase,unavailable
try:
from matplotlib.pyplot import figure,plot,xlabel,ylabel,title,show,ylim,legend,xlim,rcParams,savefig,bar,xticks,subplot,grid,setp,errorbar,loglog,semilogx,semilogy
params = {'legend.fontsize':14,'figure.facecolor':'white','figure.subplot.hspace':0.,
'axes.labelsize':16,'xtick.labelsize':14,'ytick.labelsize':14}
rcParams.update(params)
except (ImportError,RuntimeError):
figure,plot,xlabel,ylabel,title,show,ylim,legend,xlim,rcParams,savefig,bar,xticks,subplot,grid,setp,errorbar,loglog,semilogx,semilogy = unavailable('matplotlib.pyplot','figure','plot','xlabel','ylabel','title','show','ylim','legend','xlim','rcParams','savefig','bar','xticks','subplot','grid','setp','errorbar','loglog','semilogx','semilogy')
#end try
class Pseudopotential(DevBase):
colors = dict(s='k',p='r',d='b',f='m')
lcolors= ['k','r','b','m']
ldict = {0:'s',1:'p',2:'d',3:'f'}
format = 'generic'
conv_table = {('upf','fsatom'):('--upf_pot','--xml')}
extensions = dict(upf='upf',fsatom='xml',gamess='gamess',casino='data')
# graph.py #
# Interface with pydot to make images of directed graphs. #
# #
# Content summary: #
# Graph #
# Wrapper class for pydot functionality #
# #
#====================================================================#
from generic import obj
from developer import DevBase,unavailable
try:
from pydot import Dot,Node,Edge,Cluster as pdCluster
except:
Dot,Node,Edge,pdCluster = unavailable('Dot','Node','Edge','pdCluster')
#end try
# websites
# http://www.graphviz.org/Home.php
# http://pythonhaven.wordpress.com/tag/pydot/
# http://jseabold.net/blog/2012/02/making-graphical-models-with-pydot.html
class Graph(DevBase):
pydot_type = Dot
def __init__(self,*args,**kwargs):
# some args are
def test_unavailable():
from generic import generic_settings, NexusError
from developer import Void
from developer import unavailable, available
gre = generic_settings.raise_error
generic_settings.raise_error = True
try:
import keyword
except:
keyword = unavailable('keyword')
#end try
assert (not isinstance(keyword, Void))
assert (available(keyword))
try:
import an_unavailable_module
except:
an_unavailable_module = unavailable('an_unavailable_module')
#end try
assert (isinstance(an_unavailable_module, Void))
assert (not available(an_unavailable_module))
try:
from an_unavailable_module import a, b, c, d, e, f, g
except:
a, b, c, d, e, f, g = unavailable('an_unavailable_module', 'a', 'b',
'c', 'd', 'e', 'f', 'g')
#end try
void_imports = an_unavailable_module, b, c, d, e, f, g
for v in void_imports:
assert (isinstance(v, Void))
assert (not available(v))
#end for
operations = [
dir,
len,
repr,
str,
complex,
int,
float,
lambda v: v == 0,
lambda v: v != 0,
lambda v: v > 0,
lambda v: v < 0,
lambda v: v >= 0,
lambda v: v <= 0,
lambda v: v(),
lambda v: v.a,
lambda v: v['a'],
lambda v: setattr(v, 'a', 0),
lambda v: getattr(v, 'a'),
lambda v: delattr(v, 'a'),
lambda v: v + 0,
lambda v: v - 0,
lambda v: v * 0,
lambda v: v / 0,
lambda v: v % 0,
lambda v: v & 0,
lambda v: v | 0,
]
for op in operations:
for v in void_imports:
try:
op(v)
raise FailedTest
except NexusError:
None
except FailedTest:
failed()
except Exception as e:
failed(str(e))
#end try
#end for
#end for
generic_settings.raise_error = gre
