pyqha package¶
Module contents¶
The following functions are available from pyqha
module and are the most common ones for the end user.
This submodule groups all functions relevant for computing the total energies at 0 K.
-
pyqha.fitEtot.
fitEtot
(fin, out=True, ibrav=4, fittype='quadratic', guess=None, method='BFGS', minoptions={})[source]¶ This function reads the file fin containing the energies as a function of the lattice parameters and fits them with a quartic (fittype=”quartic”) or quadratic (fittype=”quadratic”) polynomial. Then it finds the minimun energy and the corresponding lattice parameters. ibrav is the Bravais lattice, guess is an initial guess for the minimization. Depending on ibrav, a different number of lattice parameters is considered. It prints fitting results on the screen (which can be redirected to stdout) if out=True. It returns the lattice parameters and energies as in the input file fin, the fitted coefficients of the polynomial, the corresponding , the lattice parameters at the minimum and the minimun energy.
Note: for cubic systems use fitEtotV instead.
Advanced input parameters:
guess, an initial guess for the minimization. It is a 6 elements list [a,b,c,0,0,0].
method, the method to be used in the minimization procedure, as in the scipy.optimize.minimize. See its documentation for details.
minoptions, a dictionary with additional options for the minimization procedure, as in the scipy.optimize.minimize. See its documentation for details.
-
pyqha.fitEtot.
fitEtotV
(fin, fout=None)[source]¶ This function reads data from the input file fin, fits them with a Murnaghan EOS, prints the results on the stdout and write them in the file “fout”. It returns the volumes and energies read from the input file, the fitted coefficients of the EOS and the corresponding .
A collection of functions for computing harmonic quantities from phonon DOS.
-
pyqha.thermo.
compute_thermo
(E, dos, TT)[source]¶ This function computes the vibrational energy, Helmholtz energy, entropy and heat capacity in the harmonic approximation from the input numpy arrays E and dos containing the phonon DOS(E). The calculation is done over a set of temperatures given in input as a numpy array TT. It also computes the number of phonon modes obtained from the input DOS (which must be approximately equal to , with N the number of atoms per cell) and the ZPE. The input energy and dos are expected to be in 1/cm-1. It returns numpy arrays for the following quantities (in this order): temperatures, vibrational energy, Helmholtz energy, entropy, heat capacity. Plus it returns the ZPE and number of phonon modes obtained from the input DOS.
-
pyqha.thermo.
compute_thermo_geo
(fin, fout=None, ngeo=1, TT=array([1]))[source]¶ This function reads the input dos file(s) from fin+i, with i a number from 1 to ngeo + 1 and computes vibrational energy, Helmholtz energy, entropy and heat capacity in the harmonic approximation. Then writes the output on file(s) if fout!=None. Output file(s) have the following format:
T 1 ... ... ... ... and are names fout +1, fout +2,... for each geometry.
Returning values are (len(TT),ngeo) numpy matrices (T,gEvib,gFvib,gSvib,gCvib,gZPE,gmodes) containing the temperatures and the above mentioned thermodynamic functions as for example: Fvib[T,geo] -> Fvib at the temperature “T” for the geometry “geo”
-
pyqha.thermo.
dos_integral
(E, dos, m=0)[source]¶ A function to compute the integral of an input phonon DOS (dos) with the 3/8 Simpson method. m is the moment of the integral, if different moments can be calculated. For example, with (default) it returns the number of modes from the dos, with it returns the ZPE. The input energy (E) and phonon DOS (dos) are expected to be in .
-
pyqha.thermo.
gen_TT
(Tstart=1, Tend=1000, Tstep=1)[source]¶ A simple function to generate a numpy array of temperatures, starting from Tstart and ending to Tend (or the closest T<Tend accorinding to the Tstep ) with step Tstep .
-
pyqha.thermo.
rearrange_thermo
(T, Evib, Fvib, Svib, Cvib, ngeo=1)[source]¶ This function just rearranges the order of the elements in the input matrices The first index of the returning matrices X now gives all geometries at a given T, i.e. X[0] is the vector of the property X a T=T[0,0] . X[0,0] for the first geometry, X[0,1] the second geometry and so on.
This submodule groups all functions relevant for the total and vibrational energies.
-
pyqha.fitFvib.
fitFvib
(fEtot, thermodata, ibrav=4, typeEtot='quadratic', typeFvib='quadratic', defaultguess=[0.0, 0.0, 0.0, 0.0, 0.0, 0.0], method='BFGS', minoptions={}, splinesoptions=None)[source]¶ This function computes quasi-harmonic quantities from the as a function of temperature with Murnaghan’s EOS. is read from the fin file. are given in thermodata which is a list containing the number of temperatures ( nT ) for which the calculations are done and the numpy matrices for temperatures, vibrational energy, Helmholtz energy, entropy and heat capacity. All these quantities are for each (a,b,c) as in fin file. The real number of lattice parameters depends on ibrav, for example for hexagonal systems (ibrav=4) you have only (a,c) values. ibrav identifies the Bravais lattice, as in Quantum Espresso.
The function fits with a quadratic or quartic polynomial (as defined by typeEtot and typeFvib ) at each temperature in thermodata and then stores the fitted coefficients. Note that you can chose a different polynomial type for fitting and . Then it computes the minimun energy and the corresponding lattice parameters at each temperature by miniimizing the energy.
It also computes the linear thermal expansion tensor (as a numerical derivative of the minimum lattice parameters as a function of temperature (
compute_alpha()
).It returns the numpy arrays and matrices containing the temperatures (as in input), the minimun energy, minimun lattice parameters, linear thermal expansions. It also returns the fitted coefficients and the for only (at T=0 K) and the fitted coefficients and the for at each temperature.
Warning
The quantities in thermodata are usually obtained from
compute_thermo_geo()
or fromread_thermo()
andrearrange_thermo()
. It is important that the order in the total energy file fin and the order of the thermodynamic data in thermodata is the same! See also example6 and the tutorial.Advanced input parameters:
guess, an initial guess for the minimization. It is a 6 elements list [a,b,c,0,0,0].
method, the method to be used in the minimization procedure, as in the scipy.optimize.minimize. See its documentation for details. Note that the methods which usually gives better results for quasi-harmonic calculations are the “BFGS” or Newton-CG”. Default is “BFGS”.
minoptions, a dictionary with additional options for the minimization procedure, as in the scipy.optimize.minimize. See its documentation for details. Note the the options are different for different methods.
splinesoptions, determines whether to use or not splines to reduce the noise on numerical derivatives (thermal expansions). If splinesoptions*==None, use finete differences for derivatives, else use splines as implemented in scipy.interpolate (see documentation). In the latter case, *splinesoptions must be a dictionary. This dictionary must contains the keywords k0, s0, k1, s1, k2, s2 which are passed to
scipy.interpolate.splrep()
, one couple for each set of thermal expansions (alpha_xx, alpha_yy, alpha_zz). k is the order of the spline (default=3), s a smoothing condition (default=None). If splinesoptions=={} use the default options ofscipy.interpolate.splrep()
Note: use this option with care
-
pyqha.fitFvib.
fitFvibV
(fin, thermodata, verbosity='low')[source]¶ This function computes quasi-harmonic quantities from the as a function of temperature with Murnaghan’s EOS. is read from the fin file. are given in thermodata which is a list containing the number of temperatures ( nT ) for which the calculations are done and the numpy matrices for temperatures, vibrational energy, Helmholtz energy, entropy and heat capacity. All these quantities are for each volume as in fin file.
The function fits with a Murnaghan’s EOS at each temperature in thermodata and then stores the fitted coefficients. It also computes the volume thermal expansion as a numerical derivative of the minimum volume as a function of temperature (
compute_beta()
), the constant volume heat capacity at the minimum volume at each T (compute_Cv()
) and the constant pression heat capacity (compute_Cp()
).It returns the numpy 1D arrays containing the temperatures (as in input), the minimun energy, minimun volume, bulk modulus, volume thermal expansion, constant volume and constant pressure heat capacities, one matrix with all fitted coefficients at each T and finally an array with the at each T.
Warning
The quantities in thermodata are usually obtained from
compute_thermo_geo()
or fromread_thermo()
andrearrange_thermo()
. It is important that the order in the total energy file fin and the order of the thermodynamic data in thermodata is the same! See also example5 and the tutorial.
This submodule groups all functions relevant for computing elastic constants and compliances.
-
pyqha.fitC.
fS
(aS, mintemp, typeCx)[source]¶ An auxiliary function returning the elastic compliances 6x6 tensor at the set of lattice parameters given in input as mintemp. These should be the lattice parameters at a given temperature obtained from the free energy minimization, so that S(T) can be obtained. Before calling this function, the polynomial coefficients resulting from fitting the elastic compliances over a grid of lattice parameters, i.e. over different geometries, must be obtained and passed as input in aS. typeCx defines what kind of polynomial to use for fitting (“quadratic” or “quartic”)
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pyqha.fitC.
fitCT
(aC, chiC, T, minT, ibrav=4, typeC='quadratic')[source]¶ This function calculates the elastic constants tensor CT as a function of temperatature in the quasi-static approximation. It takes in input aC and chiC, the fitted coefficients of the elastic constants as a function of and the corresponding . It also takes in input an array of temperatures T and the corresponding lattice parameters minT, i.e. from a previous quasi-harmonic calculations (as in example6). It also needs in input the Bravais lattice ( ibrav ) and the type of polynomial ( typeC ) used for fitting the input aC.
The function uses the coefficients aC to compute the elastic tensor at each temperature in the array T from the corresponding lattice parameters in minT.
It returns the temperature array and the a matrix CT with all the elastic tensors at each T ( CT[i] is the elastic constants matrix for the temperature T[i])
Warning
The coefficients aC must be the result of fitting the elastic constants over the same grid used in the quasi-harmonic calculations corresponding to minT values! (See example7)
-
pyqha.fitC.
fitCxx
(celldmsx, Cxx, ibrav=4, typeC='quadratic')[source]¶ This function fits the elastic constant elements of Cxx as a function of the grid of lattice parameters . The real number of lattice parameters depends on ibrav, for example for hexagonal systems (ibrav=4) you have only (a,c) values. ibrav identifies the Bravais lattice, as in Quantum Espresso.
It returns a 6*6 matrix, each element [i,j] being the set of coefficients of the polynomial fit and another 6*6 matrix, each element [i,j] being the corresponding . If the chi squared is zero, the fitting procedure was NOT succesful
-
pyqha.fitC.
fitS
(inputfileEtot, inputpathCx, ibrav, typeSx='quadratic')[source]¶ An auxiliary function for fitting the elastic compliances elements over a grid of lattice parameters, i.e. over different geometries.
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pyqha.fitC.
rearrange_Cx
(Cx, ngeo)[source]¶ This function rearrange the input numpy matrix Cx into an equivalent matrix Cxx for fitting it. Cx is a matrix, each Cx[i] is the 6*6 C matrix for a given geometry ( i ) Cxx is a Lmath:6*6*ngeo matrix, each Cxx[i][j] is a vector with all values for different geometries of the Cij elastic constant matrix element. For example, Cxx[0,0] is the vector with ngeo values of the C11 elastic constant and so on.
Submodules¶
Additional functions are available as submodules. Please note the documentation of these functions is still ongoing and can be incomplete or wrong.
pyqha.constants module¶
Some useful standard constants for conversions and calculations.
pyqha.eos module¶
This submodule groups several functions for calculating isotropic quasi-harmonic quantities using the Murnaghan EOS.
-
pyqha.eos.
E_Murn
(V, a)[source]¶ As
E_MurnV()
but input parameters are given as a single list a=[a0,a1,a2,a3].
-
pyqha.eos.
E_MurnV
(V, a0, a1, a2, a3)[source]¶ This function implements the Murnaghan EOS (in a form which is best for fitting). Returns the energy at the volume V using the coefficients a0,a1,a2,a3 from the equation:
-
pyqha.eos.
H_Murn
(V, a)[source]¶ As
E_MurnV()
but input parameters are given as a single list a=[a0,a1,a2,a3] and it returns the enthalpy not the energy from the EOS.
-
pyqha.eos.
P_Murn
(V, a)[source]¶ As
E_MurnV()
but input parameters are given as a single list a=[a0,a1,a2,a3] and it returns the pressure not the energy from the EOS.
-
pyqha.eos.
calculate_fitted_points
(V, a)[source]¶ Calculates a denser mesh of E(V) points (1000) for plotting.
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pyqha.eos.
compute_Cp
(T, Cv, V, B0, beta)[source]¶ This function computes the isobaric heat capacity from the equation:
where Cp,Cv are the isobaric and isocoric heat capacities respectively, T is the temperature, V the unit cell volume, beta the volumetric thermal expansion and B0 the isothermal bulk modulus.
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pyqha.eos.
compute_Cv
(T, Vmin, V, Cvib)[source]¶ This function computes the isocoric heat capacity as a function of temperature. From Cvib, which is a matrix with Cvib(T,V) as from the harmonic calculations determines the Cv at each temperature by linear interpolation between the values at the two volumes closest to Vmin(T). Vmin(T) is from the minimization of F(V,T) and V is the array of volumes used for it. Returns Cv(T).
Work in progress... for now it uses all volumes in the interpolation.
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pyqha.eos.
compute_beta
(minT)[source]¶ This function computes the volumetric thermal expansion as a numerical derivative of the volume as a function of temperature V(T) given in the input array minT. This array can obtained from the free energy minimization which should be done before.
-
pyqha.eos.
fit_Murn
(V, E)[source]¶ This is the function for fitting with the Murnaghan EOS as a function of volume only.
The input variable V is an 1D array of volumes, E are the corresponding energies (or other analogous quantity to be fitted with the Murnaghan EOS.
Note: volumes must be in a.u. and energies in Rydberg.
pyqha.fitC module¶
This submodule groups all functions relevant for computing elastic constants and compliances.
-
pyqha.fitC.
fS
(aS, mintemp, typeCx)[source] An auxiliary function returning the elastic compliances 6x6 tensor at the set of lattice parameters given in input as mintemp. These should be the lattice parameters at a given temperature obtained from the free energy minimization, so that S(T) can be obtained. Before calling this function, the polynomial coefficients resulting from fitting the elastic compliances over a grid of lattice parameters, i.e. over different geometries, must be obtained and passed as input in aS. typeCx defines what kind of polynomial to use for fitting (“quadratic” or “quartic”)
-
pyqha.fitC.
fitCT
(aC, chiC, T, minT, ibrav=4, typeC='quadratic')[source] This function calculates the elastic constants tensor CT as a function of temperatature in the quasi-static approximation. It takes in input aC and chiC, the fitted coefficients of the elastic constants as a function of and the corresponding . It also takes in input an array of temperatures T and the corresponding lattice parameters minT, i.e. from a previous quasi-harmonic calculations (as in example6). It also needs in input the Bravais lattice ( ibrav ) and the type of polynomial ( typeC ) used for fitting the input aC.
The function uses the coefficients aC to compute the elastic tensor at each temperature in the array T from the corresponding lattice parameters in minT.
It returns the temperature array and the a matrix CT with all the elastic tensors at each T ( CT[i] is the elastic constants matrix for the temperature T[i])
Warning
The coefficients aC must be the result of fitting the elastic constants over the same grid used in the quasi-harmonic calculations corresponding to minT values! (See example7)
-
pyqha.fitC.
fitCxx
(celldmsx, Cxx, ibrav=4, typeC='quadratic')[source] This function fits the elastic constant elements of Cxx as a function of the grid of lattice parameters . The real number of lattice parameters depends on ibrav, for example for hexagonal systems (ibrav=4) you have only (a,c) values. ibrav identifies the Bravais lattice, as in Quantum Espresso.
It returns a 6*6 matrix, each element [i,j] being the set of coefficients of the polynomial fit and another 6*6 matrix, each element [i,j] being the corresponding . If the chi squared is zero, the fitting procedure was NOT succesful
-
pyqha.fitC.
fitS
(inputfileEtot, inputpathCx, ibrav, typeSx='quadratic')[source] An auxiliary function for fitting the elastic compliances elements over a grid of lattice parameters, i.e. over different geometries.
-
pyqha.fitC.
rearrange_Cx
(Cx, ngeo)[source] This function rearrange the input numpy matrix Cx into an equivalent matrix Cxx for fitting it. Cx is a matrix, each Cx[i] is the 6*6 C matrix for a given geometry ( i ) Cxx is a Lmath:6*6*ngeo matrix, each Cxx[i][j] is a vector with all values for different geometries of the Cij elastic constant matrix element. For example, Cxx[0,0] is the vector with ngeo values of the C11 elastic constant and so on.
pyqha.fitEtot module¶
This submodule groups all functions relevant for computing the total energies at 0 K.
-
pyqha.fitEtot.
fitEtot
(fin, out=True, ibrav=4, fittype='quadratic', guess=None, method='BFGS', minoptions={})[source] This function reads the file fin containing the energies as a function of the lattice parameters and fits them with a quartic (fittype=”quartic”) or quadratic (fittype=”quadratic”) polynomial. Then it finds the minimun energy and the corresponding lattice parameters. ibrav is the Bravais lattice, guess is an initial guess for the minimization. Depending on ibrav, a different number of lattice parameters is considered. It prints fitting results on the screen (which can be redirected to stdout) if out=True. It returns the lattice parameters and energies as in the input file fin, the fitted coefficients of the polynomial, the corresponding , the lattice parameters at the minimum and the minimun energy.
Note: for cubic systems use fitEtotV instead.
Advanced input parameters:
guess, an initial guess for the minimization. It is a 6 elements list [a,b,c,0,0,0].
method, the method to be used in the minimization procedure, as in the scipy.optimize.minimize. See its documentation for details.
minoptions, a dictionary with additional options for the minimization procedure, as in the scipy.optimize.minimize. See its documentation for details.
-
pyqha.fitEtot.
fitEtotV
(fin, fout=None)[source] This function reads data from the input file fin, fits them with a Murnaghan EOS, prints the results on the stdout and write them in the file “fout”. It returns the volumes and energies read from the input file, the fitted coefficients of the EOS and the corresponding .
pyqha.fitFvib module¶
This submodule groups all functions relevant for the total and vibrational energies.
-
pyqha.fitFvib.
fitFvib
(fEtot, thermodata, ibrav=4, typeEtot='quadratic', typeFvib='quadratic', defaultguess=[0.0, 0.0, 0.0, 0.0, 0.0, 0.0], method='BFGS', minoptions={}, splinesoptions=None)[source] This function computes quasi-harmonic quantities from the as a function of temperature with Murnaghan’s EOS. is read from the fin file. are given in thermodata which is a list containing the number of temperatures ( nT ) for which the calculations are done and the numpy matrices for temperatures, vibrational energy, Helmholtz energy, entropy and heat capacity. All these quantities are for each (a,b,c) as in fin file. The real number of lattice parameters depends on ibrav, for example for hexagonal systems (ibrav=4) you have only (a,c) values. ibrav identifies the Bravais lattice, as in Quantum Espresso.
The function fits with a quadratic or quartic polynomial (as defined by typeEtot and typeFvib ) at each temperature in thermodata and then stores the fitted coefficients. Note that you can chose a different polynomial type for fitting and . Then it computes the minimun energy and the corresponding lattice parameters at each temperature by miniimizing the energy.
It also computes the linear thermal expansion tensor (as a numerical derivative of the minimum lattice parameters as a function of temperature (
compute_alpha()
).It returns the numpy arrays and matrices containing the temperatures (as in input), the minimun energy, minimun lattice parameters, linear thermal expansions. It also returns the fitted coefficients and the for only (at T=0 K) and the fitted coefficients and the for at each temperature.
Warning
The quantities in thermodata are usually obtained from
compute_thermo_geo()
or fromread_thermo()
andrearrange_thermo()
. It is important that the order in the total energy file fin and the order of the thermodynamic data in thermodata is the same! See also example6 and the tutorial.Advanced input parameters:
guess, an initial guess for the minimization. It is a 6 elements list [a,b,c,0,0,0].
method, the method to be used in the minimization procedure, as in the scipy.optimize.minimize. See its documentation for details. Note that the methods which usually gives better results for quasi-harmonic calculations are the “BFGS” or Newton-CG”. Default is “BFGS”.
minoptions, a dictionary with additional options for the minimization procedure, as in the scipy.optimize.minimize. See its documentation for details. Note the the options are different for different methods.
splinesoptions, determines whether to use or not splines to reduce the noise on numerical derivatives (thermal expansions). If splinesoptions*==None, use finete differences for derivatives, else use splines as implemented in scipy.interpolate (see documentation). In the latter case, *splinesoptions must be a dictionary. This dictionary must contains the keywords k0, s0, k1, s1, k2, s2 which are passed to
scipy.interpolate.splrep()
, one couple for each set of thermal expansions (alpha_xx, alpha_yy, alpha_zz). k is the order of the spline (default=3), s a smoothing condition (default=None). If splinesoptions=={} use the default options ofscipy.interpolate.splrep()
Note: use this option with care
-
pyqha.fitFvib.
fitFvibV
(fin, thermodata, verbosity='low')[source] This function computes quasi-harmonic quantities from the as a function of temperature with Murnaghan’s EOS. is read from the fin file. are given in thermodata which is a list containing the number of temperatures ( nT ) for which the calculations are done and the numpy matrices for temperatures, vibrational energy, Helmholtz energy, entropy and heat capacity. All these quantities are for each volume as in fin file.
The function fits with a Murnaghan’s EOS at each temperature in thermodata and then stores the fitted coefficients. It also computes the volume thermal expansion as a numerical derivative of the minimum volume as a function of temperature (
compute_beta()
), the constant volume heat capacity at the minimum volume at each T (compute_Cv()
) and the constant pression heat capacity (compute_Cp()
).It returns the numpy 1D arrays containing the temperatures (as in input), the minimun energy, minimun volume, bulk modulus, volume thermal expansion, constant volume and constant pressure heat capacities, one matrix with all fitted coefficients at each T and finally an array with the at each T.
Warning
The quantities in thermodata are usually obtained from
compute_thermo_geo()
or fromread_thermo()
andrearrange_thermo()
. It is important that the order in the total energy file fin and the order of the thermodynamic data in thermodata is the same! See also example5 and the tutorial.
pyqha.alphagruneisenp module¶
This module is still experimental.
It contains functions for computing the thermal expansion tensor as a function of temperature from the Gruneisein parameters, the mode contributions to the heat capacity, the elastic tensor and the unit cell volume.
The mode contribution to the heat capacity are calculated in c_qv_python()
.
A faster C function is also available and used instead (if succesfully compiled.
The full calculation here can be quite time consuming. For this reason, the
compute_alpha_gruneisein()
is implemented with a simple parallelization
over the temperatures using the Python package multiprocessing
.
-
pyqha.alphagruneisenp.
c_qv
(T, omega)¶ This function calculates the mode contribution to the heat capacity at a given T and omega. A similar (faster) function should be available as C extension.
-
pyqha.alphagruneisenp.
c_qv_python
(T, omega)[source]¶ This function calculates the mode contribution to the heat capacity at a given T and omega. A similar (faster) function should be available as C extension.
-
pyqha.alphagruneisenp.
compute_alpha_grun
(T, V, S, weights, freq, grun, ibrav=4)[source]¶ This function computes the thermal expansions alpha using the Gruneisein parameters at a given temperature T. V is the unit cell volume, S is the elastic compliances matrix in Voigt notation, freq and weights are the phonon frequencies in a grid of q-point and their corresponding weights. grun are the Gruneisen parameters. ibrav identifies the Bravais lattice.
It implements the following equation:
:math:``
more comments to be added
First with min0, freq and grun T-independent
More ibrav types to be implemented
-
pyqha.alphagruneisenp.
compute_alpha_gruneisein
(TT, ibrav, celldmsx, min0=None, S=None, weights=None, freq=None, grun=None, minT=None, afreq=None, fittypefreq=None, aS=None, fittypeS=None, nproc=1)[source]¶ This function computes the thermal expansions at different temperatures from the Gruneisen parameters and other parameters.
TT is a numpy array of temperatures for which the thermal expansions are computed. ibrav identifies the Bravais lattice. celldmsx contains the lattice parameters of the computed grid as read with
read_Etot()
. Different calculation options are possible, according to the optional input parameters given in input:Input parameters Meaning min0,S,weights,freq,grun Use temperarature-independent (0 K) volume, elastic compliances, average frequencies and Gruneisen parameters. The volume is obtained from min0, the lattice parameters at the minimun 0 K energy. S is the elastic compliances tensor at 0 K calculated at min0. weights ,*freq*,*grun* are the weights, phonon frequencies and Gruneisen parameters at 0 K calculated at min0. S,weights,minT,afreq,fittypefreq Use temperarature-independent (0 K) elastic compliances but temperature-dependent volume, average frequencies and Gruneisen parameters. The volume is obtained from minT, a numpy array with the lattice parameters at the minimun free energy at each temperature . S is the elastic compliances tensor at 0 K calculated at min0. weights are the weights of the phonon frequencies. afreq, fittypefreq are the coefficients of the fitted polynomials for the phonon frequencies and the type of polynomial. They are used here to compute the average frequencies and Gruneisen parameters at each temperature. weights,afreq,fittypefreq,aS,fittypeS Use temperarature-dependent (0 K) volume, elastic compliances, average frequencies and Gruneisen parameters. The volume is obtained from minT, a numpy array with the lattice parameters at the minimun free energy at each temperature. weights are the weights of the phonon frequencies. afreq, fittypefreq are the coefficients of the fitted polynomials for the phonon frequencies and the type of polynomial. They are used here to the average frequencies and Gruneisen parameters at each temperature. aS,fittypeS are the coefficients of the fitted polynomials for the elastic compliances and the polynomial type. They are used here to compute the elastic compliances at each temperature as in the quasi- static approximation. By default, all the input parameters in the above table are ==None and if nothing is given in input the function will return None without computing anything.
nproc is the number of processes to be run in parallel.
-
pyqha.alphagruneisenp.
compute_alpha_gruneisen_loopparallel
(it)[source]¶ This function implements the parallel loop where the alpha are computed. It essentially calls the compute_alpha_grun function with the proper parameters according to the selected option. “it” is a list with all function parameters for the call in pool.map
it[0]== 0 -> use V, S, average frequencies and gruneisen parameters at 0 K it[0]== 1 -> use S at 0 K, calculate V, average frequencies and gruneisen parameters at each T it[0]== 2 -> calculate S, V, average frequencies and gruneisen parameters at each T
pyqha.fitfreqgrun module¶
This submodule groups all functions relevant for fitting phonon frequencies and computing Gruneisen parameters.
-
pyqha.fitfreqgrun.
fitfreq
(celldmsx, min0, filefreq, ibrav=4, typefreq='quadratic', compute_grun=False)[source]¶ An auxiliary function for fitting the frequencies.
celldmsx is the matrix of lattice parameters where the total energies where computed. min0 is the a set of . filefreq defines the input files (filefreq1, filefreq2, etc.) containing the frequencies for different geometries. The number of geometries is determined from the size of celldmsx. ibrav is the usual Bravais lattice. typefreq can be “quadratic” (default) or “quartic”, i.e. the kind of polynomial to be used for fitting. compute_grun defines if the Gruneisen parameters must be calculated (True) or not (False, default).
It returns a matrix of frequencies obtained for the fitted polynomial coefficients (quadratic or quartic) at the minimun point min0. It also returns the weigths of each q-point where the frequencies are available.
-
pyqha.fitfreqgrun.
fitfreqxx
(celldmsx, freqxx, ibrav, out, typefreq)[source]¶ This function fits the frequencies in freqxx as a function of the grid of lattice parameters .
It returns a matrix, whose element [i,j] is the set of coefficients of the polynomial fit and another matrix, whose element [i,j] is the corresponding . If the , the fitting procedure was NOT succesful
-
pyqha.fitfreqgrun.
freqmin
(afreq, min0, nq, modes, ibrav, typefreq)[source]¶ This function calculates the frequencies from the fitted polynomials coeffients (one for each q point and mode) at the minimun point min0 given in input. afreq is a numpy matrix containing the fitted polynomial coefficients. It can be obtained from
fitfreqxx()
.It returns a matrix, each element [i,j] being the fitted frequency
-
pyqha.fitfreqgrun.
freqmingrun
(afreq, min0, nq, modes, ibrav, typefreq)[source]¶ This function calculates the frequencies and the Gruneisen parameters from the fitted polynomials coeffients (one for each q point and mode) at the minimun point min0 given in input. afreq is a numpy matrix containing the fitted polynomial coefficients. It can be obtained from
fitfreqxx()
.It returns a matrix, each element [i,j] being the fitted frequency In addition, it returns a with the Gruneisein parameters. Each element [i,j,k] is the the Gruneisein parameter at nq=i, mode=j and direction k (for example, in hex systems k=0 is a direction, k=2 is c direction, others are zero)
Note that the Gruneisein parameters are not multiplied for the lattice parameters.
-
pyqha.fitfreqgrun.
rearrange_freqx
(freqx)[source]¶ This function rearranges the input numpy matrix freqx into an equivalent matrix freqxx for the subsequent fitting. freqx is a matrix, each freqx[i] is the frequency matrix for a given geometry i. freqxx is a matrix, each freqxx[i,j] is a vector with all values for different geometries of the frequencies at point q=i and mode=j. For example, freqxx[0,0] is the vector with ngeo values of the frequencies at the first q-point and first mode so on.
pyqha.fitutils module¶
This submodule contains all functions relevant for fitting anistotropic quantities (energies, frequencies, etc.).
-
pyqha.fitutils.
expand_quadratic_to_quartic
(a)[source]¶ This function gets a vector of coefficients from a quadratic fit and turns it into a vector of coeffients as from a quartic fit (extra coeffients are set to zero).
-
pyqha.fitutils.
fit_anis
(celldmsx, Ex, ibrav=4, out=False, type='quadratic', ylabel='Etot')[source]¶ An auxiliary function for handling fitting in the anisotropic case
-
pyqha.fitutils.
fit_quadratic
(x, y, ibrav=4, out=False, ylabel='E')[source]¶ This function fits the y values (energies) with a quadratic polynomial of up to 3 variables corresponding to the lattice parameters . In the most general form the polynomial is:
The input variable x is a matrix , where:
x[:,0] is the set of a values
x[:,1] is the set of b values
x[:,2] is the set of c values
and x[:,3], x[:,4], x[:,5] are all zeros. ibrav defines the Bravais lattice, out set the output verbosity (out=True verbose output), ylabel set a label for the quantity in y (can be or for example).
Note 1: implemented for cubic (ibrav=1,2,3), hexagonal (ibrav=4), tetragonal (ibrav=6,7), orthorombic (ibrav=8,9,10,11)
Note 2: the polynomial fit is done using
numpy.linalg.lstsq()
. Please refer to numpy documentation for further details.
-
pyqha.fitutils.
fit_quartic
(x, y, ibrav=4, out=False, ylabel='E')[source]¶ This function fits the y values (energies) with a quartic polynomial of up to 3 variables corresponding to the lattice parameters . In the most general form the polynomial is:
The input variable x is a matrix , where:
x[:,0] is the set of a values
x[:,1] is the set of b values
x[:,2] is the set of c values
and x[:,3], x[:,4], x[:,5] are all zeros. ibrav defines the Bravais lattice, out set the output verbosity (out=True verbose output), ylabel set a label for the quantity in y (can be or for example).
Note 1: implemented for cubic (ibrav=1,2,3), hexagonal (ibrav=4), tetragonal (ibrav=6,7), orthorombic (ibrav=8,9,10,11)
Note 2: the polynomial fit is done using
numpy.linalg.lstsq()
. Please refer to numpy documentation for further details.
pyqha.minutils module¶
This submodule groups all functions relevant for minimizing the energy in the anisotropic case. It also contains the functions implementing the fitting polynomials, which can be “quadratic” or “quartic” and depend on up to 3 variables.
-
pyqha.minutils.
calculate_fitted_points_anis
(celldmsx, nmesh, fittype='quadratic', ibrav=4, a=None)[source]¶ Calculates a denser mesh of Efitted(celldmsx) points for plotting. nmesh = (nx,ny,nz) gives the dimensions of the mesh.
-
pyqha.minutils.
contract_vector
(x, ibrav=4)[source]¶ Utility function: contract a vector x, len(x)=6, into a x-dim vector (x<6) according to the Bravais lattice type as in ibrav
Note: implemented for cubic (ibrav=1,2,3), hexagonal (ibrav=4), tetragonal (ibrav=6,7), orthorombic (ibrav=8,9,10,11)
-
pyqha.minutils.
expand_vector
(x, ibrav=4)[source]¶ Utility function: expands a vector x, len(x)<6, into a 6-dim vector according to the Bravais lattice type as in ibrav
Note: implemented for cubic (ibrav=1,2,3), hexagonal (ibrav=4), tetragonal (ibrav=6,7), orthorombic (ibrav=8,9,10,11)
-
pyqha.minutils.
find_min
(a, ibrav, type, guess=None, method='BFGS', minoptions={})[source]¶ An auxiliary function for handling the minimum search.
-
pyqha.minutils.
find_min_quadratic
(a, ibrav, guess, method, minoptions)[source]¶ This is the function for finding the minimum of the quadratic polynomial
-
pyqha.minutils.
find_min_quartic
(a, ibrav, guess, method, minoptions)[source]¶ This is the function for finding the minimum of the quartic polynomial
-
pyqha.minutils.
fquadratic
(x, a, ibrav=4)[source]¶ This function implements the quadratic polynomials for fitting and miminizing according to the Bravais lattice type as in ibrav. x is the vector with input coordinates , a is the vector with the polynomial coeffients. The dimension of a depends on ibrav. x has always 6 elements with zeros for those not used according to ibrav.
Note: implemented for cubic (ibrav=1,2,3), hexagonal (ibrav=4), tetragonal (ibrav=6,7), orthorombic (ibrav=8,9,10,11)
-
pyqha.minutils.
fquadratic_der
(x, a, ibrav=4)[source]¶ This function implements the first derivatives of the quadratic polynomials for fitting and miminizing according to the Bravais lattice type as in ibrav. x is the vector with input coordinates , a is the vector with the polynomial coeffients. The dimension of a depends on ibrav. x has always 6 elements with zeros for those not used according to ibrav. The derivatives are returned as a numpy vector of 6 elements, each element being a derivative with respect to .
Note: implemented for cubic (ibrav=1,2,3), hexagonal (ibrav=4), tetragonal (ibrav=6,7), orthorombic (ibrav=8,9,10,11)
-
pyqha.minutils.
fquartic
(x, a, ibrav=4)[source]¶ This function implements the quartic polynomials for fitting and miminizing according to the Bravais lattice type as in ibrav. x is the vector with input coordinates , a is the vector with the polynomial coeffients. The dimension of a depends on ibrav. x has always 6 elements with zeros for those not used according to ibrav.
Note: implemented for cubic (ibrav=1,2,3), hexagonal (ibrav=4), tetragonal (ibrav=6,7), orthorombic (ibrav=8,9,10,11)
-
pyqha.minutils.
fquartic_der
(x, a, ibrav=4)[source]¶ This function implements the first derivatives of the quadratic polynomials for fitting and miminizing according to the Bravais lattice type as in ibrav. The derivatives are returned as a numpy vector of 6 elements, each element being a derivative with respect to . x is the vector with input coordinates , a is the vector with the polynomial coeffients. The dimension of a depends on ibrav. x has always 6 elements with zeros for those not used according to ibrav.
Note: implemented for cubic (ibrav=1,2,3), hexagonal (ibrav=4), tetragonal (ibrav=6,7), orthorombic (ibrav=8,9,10,11)
pyqha.plotutils module¶
A collection of wrappers for the matplotlib functions.
Note
All functions return a matplotlib which can be modified by the user.
-
pyqha.plotutils.
multiple_plot_xy
(x, y, xlabel='', ylabel='', labels='')[source]¶ This function generates a simple xy plot with matplotlib overlapping several lines as in the matrix y. y second index refers to a line in the plot, the first index is for the array to be plotted.
-
pyqha.plotutils.
plot_EV
(V, E, a=None, labely='Etot')[source]¶ This function plots with matplotlib E(V) data and if a is given it also plot the fitted results
-
pyqha.plotutils.
plot_Etot
(celldmsx, Ex, n, nmesh=(50, 50, 50), fittype='quadratic', ibrav=4, a=None)[source]¶ This function makes a 3D plot with matplotlib Ex(celldmsx) data and if a is given it also plot the fitted results. The plot type depends on ibrav.
pyqha.properties_anis module¶
Several functions to manipulate anisotropic quantities.
-
pyqha.properties_anis.
compute_Ceps
(min0, celldmsx, T, Cvib, ibrav=4, typeCvib='quadratic')[source]¶ This function calculates the constant strain heat capacity as a function of temperature. By definition . As for the entropy, we have F(T) from the minimization of F at each T with . However, if we use finite difference for the derivatives, we can assume that is approximately constant over the temperatures where the numerical derivative is done.
To avoid the numerical derivation and the above issue, within the quasi-harmonic approximation it is better to derive it from fitting the harmonic heat capacities results on the grid at the equilibrium lattic parameters given in min0. celldms is the grid , Cvib are the harmonic heat capacity on the grid. The procedure is the same as the for the in the quasi-harmonic calculation but without the minimization step.
Note: a better way would be to do a full harmonic calculation at exactly min0. The difference with the above way is usually negligible.
Important: the above procedure relies on the quasi-harmonic approximation, i.e. on the fact that anharmonic contribution are only due to the change of phonon frequencies with the lattice parameters. In reality, this is not the case and the entropy so obtained can only be taken as an approximation of the real one.
-
pyqha.properties_anis.
compute_Csigma
(TT, Ceps, minT, alphaT, C, ibrav=4)[source]¶ This function calculates the constant strain heat capacity as a function of temperature. By definition . To avoid the numerical derivation, within the quasi-harmonic approximation it is better to derive it from fitting the harmonic heat capacities results on the grid at the equilibrium lattic parameters given in min0. celldms is the grid , Cvib are the harmonic heat capacity on the grid. The procedure is the same as the for the in the quasi-harmonic calculation but without the minimization step.
Note: a better way would be to do a full harmonic calculation at exactly min0. The difference with the above way is usually negligible.
Important: the above procedure relies on the quasi-harmonic approximation, i.e. on the fact that anharmonic contribution are only due to the change of phonon frequencies with the lattice parameters. In reality, this is not the case and the entropy so obtained can only be taken as an approximation of the real one.
Warning
Still very experimental...
-
pyqha.properties_anis.
compute_Csigma_from_alphaandC
(TT, minT, alphaT, CT, ibrav=4)[source]¶ This function calculate the difference between the constant stress heat capacity and the constant strain heat capacity from the V (obtained from the input lattice parameters minT, the thermal expansion tensor alphaT and the elastic constant tensor CT, all as a function of temperature. This is essentially the anisotropic equivalent of the equation for the isotropic case (volume only) and it avoids a further numerical derivation to obtain . It is however more complex in the anisotropic case since minT, alphaT and in particul the elastic constant tensor CT must me known in principle including their temperature dependence.
Warning
Still very experimental...
-
pyqha.properties_anis.
compute_S
(min0, celldmsx, T, Svib, ibrav=4, typeSvib='quadratic')[source]¶ This function calculates the entropy as a function of temperature. By definition . The problem is that we have F(T) from the minimization of F at each T with . However, if we use finite difference for the derivatives, we can assume that is approximately constant over the temperatures where the numerical derivative is done.
To avoid the numerical derivation and the above issue, within the quasi-harmonic approximation it is possible to derive the entropy from fitting the harmonic results on the grid at the equilibrium lattic parameters given in min0. celldms is the grid , Svib are the harmonic entropies on the grid. The procedure is the same as the for the in the quasi-harmonic calculation but without the minimization step.
Note: a better way would be to do a full harmonic calculation at exactly min0. The difference with the above way is usually negligible.
Important: the above procedure relies on the quasi-harmonic approximation, i.e. on the fact that anharmonic contribution are only due to the change of phonon frequencies with the lattice parameters. In reality, this is not the case and the entropy so obtained can only be taken as an approximation of the real one.
-
pyqha.properties_anis.
compute_alpha
(minT, ibrav)[source]¶ This function calculates the thermal expansion alphaT at different temperatures from the input minT matrix by computing the numerical derivatives with numpy. The input matrix minT has shape nT*6, where the first index is the temperature and the second the lattice parameter. For example, minT[i,0] and minT[i,2] are the lattice parameters a and c at the temperature i.
More ibrav types must be implemented
pyqha.read module¶
A collection of functions for reading different quantities from files.
-
pyqha.read.
read_Etot
(fname, ibrav=4, bc_as_a_ratio=True)[source]¶ Read cell parameters (a,b,c) and the corresponding energies from input file fname. Each set of cell parameters is stored in a numpy array of lenght 6 for (a,b,c,alpha,beta,gamma) respectively. This is done for a future possible extension but for now only the first 3 elements are used (the others are always 0). All sets are stored in celldmsx and Ex, the former is a nE*6 matrix, the latter is a nE array.
ibrav identifies the Bravais lattice as in Quantum Espresso and is needed in input (default is 4, i.e. hexagonal cell). The input file format depends on ibrav, for example in the hex case, the first two columns are for a and c and the third is for the energies.
If bc_as_a_ratio=True, the input data are assumed to be given as in the input file and hence converted into which is how they are always stored internally in
pyqha
.Units must be and
-
pyqha.read.
read_EtotV
(fname)[source]¶ Read cell volumes and the corresponding energies from input file fname (1st col, volumes, 2nd col energies). Units must be and
-
pyqha.read.
read_dos
(filename)[source]¶ Read the phonon density of states (y axis) and the corresponding energies (x axis) from the input file filename (1st col energies, 2nd col DOS) and store it in two numpy arrays which are returned.
-
pyqha.read.
read_dos_geo
(fin, ngeo)[source]¶ Read the phonon density of states and energies as in
read_dos()
from ngeo input files fin1, fin2, etc. and store it in two numpy matrices which are returned.
-
pyqha.read.
read_elastic_constants
(fname)[source]¶ This function reads and returns the elastic constants and compliances from the file fname . Elastic constants (and elastic compliances) are stored in Voigt notation They are then 6x6 matrices, stored as numpy matrices of shape [6,6] So, the elastic constant C11 is in C[0,0], C12 in C[0,1] and so on. Same for the elastic compliances.
-
pyqha.read.
read_elastic_constants_geo
(fC, ngeo)[source]¶ Read elastic constants calculated on a multidimensional grid of lattice parameters ngeo defines the total number of geometries evaluated Note: the order must be the same as for the total energies!
-
pyqha.read.
read_freq
(fname)[source]¶ Read the phonon frequencies at each q point from a frequency file fname. The format of this file is different from the one read by the
read_freq()
and contains usually more frequencies, each with a weight, but no q-point coordinates.The input frequency file is expected to have the following format: 1st line contains the number of atoms in the unit cell, the q-point grid and the total number of q-points listed nq. 2nd line not read. For the 3rd line on, the following scheme is repeated: 1st line: the weight of the q-point (they can be different because of simmetry) n modes lines, each with the corresponding phonon frequency
Returning values are a nq vector of weights, each weights[i] being the weight of given q-point and a matrix freq, each element freq[i] being the phonon frequencies (vector of modes elements) at the q-point i
-
pyqha.read.
read_freq_geo
(fname, ngeo=1)[source]¶ Read the frequencies as in
read_freq()
but for ngeo geometries. Input files are fname1, fname2, fname3, etc.
-
pyqha.read.
read_freq_geo_old
(inputfilefreq, rangegeo)[source]¶ Read the frequencies for all geometries where the gruneisen parameters must be calculated. Start, stop, step must be given accordingly. It can be used to read the frequencies only at some geometries from a larger set, if necessary, providing the proper start, stop and step values.
Notes: nq = qgeo.shape[1] -> total number of q points read modes = freqgeo.shape[2] -> number of frequency modes
-
pyqha.read.
read_freq_old
(filename)[source]¶ This function reads the phonon frequencies at each q-point from a frequency file. Input file has the following format (to be done).
Returning values are a nq*3 matrix q, each q[i] being a q point (vector of 3 elements) and a nq*modes matrix freq, each element freq[i] being the phonon frequencies (vector of modes elements)
-
pyqha.read.
read_thermo
(fname, ngeo=1)[source]¶ Read vibrational thermodynamic functions (Evib, Fvib, Svib, Cvib) as a function of temperature from the input file fname. ngeo is the number of input files to read, corresponding for example to different geometries in a quasi-harmonic calculation. If ngeo>1 reads from the files fname1, fname2, etc. up to ngeo Input file(s) have the following format:
T 1 ... ... ... ... Lines starting with “#” are not read (comments).
Returning values are numpy matrices (T,Evib,Fvib,Svib,Cvib) containing the temperatures and the above mentioned thermodynamic functions as for example: Fvib[T,geo] -> Fvib at the temperature T for the geometry geo
Units must be K for temperature, Ryd/cell for energies, Ryd/cell/K for entropy and heat capacity.
pyqha.thermo module¶
A collection of functions for computing harmonic quantities from phonon DOS.
-
pyqha.thermo.
compute_thermo
(E, dos, TT)[source] This function computes the vibrational energy, Helmholtz energy, entropy and heat capacity in the harmonic approximation from the input numpy arrays E and dos containing the phonon DOS(E). The calculation is done over a set of temperatures given in input as a numpy array TT. It also computes the number of phonon modes obtained from the input DOS (which must be approximately equal to , with N the number of atoms per cell) and the ZPE. The input energy and dos are expected to be in 1/cm-1. It returns numpy arrays for the following quantities (in this order): temperatures, vibrational energy, Helmholtz energy, entropy, heat capacity. Plus it returns the ZPE and number of phonon modes obtained from the input DOS.
-
pyqha.thermo.
compute_thermo_geo
(fin, fout=None, ngeo=1, TT=array([1]))[source] This function reads the input dos file(s) from fin+i, with i a number from 1 to ngeo + 1 and computes vibrational energy, Helmholtz energy, entropy and heat capacity in the harmonic approximation. Then writes the output on file(s) if fout!=None. Output file(s) have the following format:
T 1 ... ... ... ... and are names fout +1, fout +2,... for each geometry.
Returning values are (len(TT),ngeo) numpy matrices (T,gEvib,gFvib,gSvib,gCvib,gZPE,gmodes) containing the temperatures and the above mentioned thermodynamic functions as for example: Fvib[T,geo] -> Fvib at the temperature “T” for the geometry “geo”
-
pyqha.thermo.
dos_integral
(E, dos, m=0)[source] A function to compute the integral of an input phonon DOS (dos) with the 3/8 Simpson method. m is the moment of the integral, if different moments can be calculated. For example, with (default) it returns the number of modes from the dos, with it returns the ZPE. The input energy (E) and phonon DOS (dos) are expected to be in .
-
pyqha.thermo.
gen_TT
(Tstart=1, Tend=1000, Tstep=1)[source] A simple function to generate a numpy array of temperatures, starting from Tstart and ending to Tend (or the closest T<Tend accorinding to the Tstep ) with step Tstep .
-
pyqha.thermo.
rearrange_thermo
(T, Evib, Fvib, Svib, Cvib, ngeo=1)[source] This function just rearranges the order of the elements in the input matrices The first index of the returning matrices X now gives all geometries at a given T, i.e. X[0] is the vector of the property X a T=T[0,0] . X[0,0] for the first geometry, X[0,1] the second geometry and so on.
pyqha.write module¶
A collection of functions for writing different quantities to files or screen.
-
pyqha.write.
write_CT
(Ts, CT, fCout='')[source]¶ Write elastic constants calculated on a multidimensional grid of lattice parameters ngeo defines the total number of geometries evaluated Note: the order must be the same as for the total energies!
-
pyqha.write.
write_C_geo
(celldmsx, C, ibrav=4, fCout='')[source]¶ Write elastic constants calculated on a multidimensional grid of lattice parameters ngeo defines the total number of geometries evaluated Note: the order must be the same as for the total energies in the quasi-harmonic calculations!
-
pyqha.write.
write_Etot
(celldmsx, Ex, fname, ibrav=4)[source]¶ Read cell parameters (a,b,c,alpha,beta,gamma) and energies for a grid of cell parameters values from file output_energy1. Each celldms is a vector of lenght 6 containing a,b,c,alpha,beta,gamma respectively celldmsx and Ex contains the grid of values of celldms and E so that: celldmsx[0] = celldms0 Ex[0] = E0 celldmsx[1] = celldms1 Ex[1] = E1 celldmsx[2] = celldms2 Ex[2] = E2 ........ values are taken from the file “fname” ibrav is the Bravais lattice as in Quantum Espresso and is needed in input (default is cubic)
-
pyqha.write.
write_elastic_constants
(C, S, fname)[source]¶ Elastic constants (and elastic compliances) are stored in Voigt notation They are then 6x6 matrices, stored as numpy matrices of shape [6,6] So, the elastic constant C11 is in C[0][0], C12 in C[0][1] and so on. Same for the elastic compliances
-
pyqha.write.
write_freq
(weights, freq, filename)[source]¶ Write frequencies (or Gruneisen parameters) on an extended mesh in a file. In this format, q points coordinates are NOT written but the weight of each point yes. It can be used to write the Gruneisen mode parameters, giving them in input as freq Write the gruneisen parameters
pyqha.alphagruneisenp module¶
This module is still experimental.
It contains functions for computing the thermal expansion tensor as a function of temperature from the Gruneisein parameters, the mode contributions to the heat capacity, the elastic tensor and the unit cell volume.
The mode contribution to the heat capacity are calculated in c_qv_python()
.
A faster C function is also available and used instead (if succesfully compiled.
The full calculation here can be quite time consuming. For this reason, the
compute_alpha_gruneisein()
is implemented with a simple parallelization
over the temperatures using the Python package multiprocessing
.
-
pyqha.alphagruneisenp.
c_qv
(T, omega) This function calculates the mode contribution to the heat capacity at a given T and omega. A similar (faster) function should be available as C extension.
-
pyqha.alphagruneisenp.
c_qv2
(T, omega)[source]
-
pyqha.alphagruneisenp.
c_qv_python
(T, omega)[source] This function calculates the mode contribution to the heat capacity at a given T and omega. A similar (faster) function should be available as C extension.
-
pyqha.alphagruneisenp.
compute_alpha_grun
(T, V, S, weights, freq, grun, ibrav=4)[source] This function computes the thermal expansions alpha using the Gruneisein parameters at a given temperature T. V is the unit cell volume, S is the elastic compliances matrix in Voigt notation, freq and weights are the phonon frequencies in a grid of q-point and their corresponding weights. grun are the Gruneisen parameters. ibrav identifies the Bravais lattice.
It implements the following equation:
:math:``
more comments to be added
First with min0, freq and grun T-independent
More ibrav types to be implemented
-
pyqha.alphagruneisenp.
compute_alpha_gruneisein
(TT, ibrav, celldmsx, min0=None, S=None, weights=None, freq=None, grun=None, minT=None, afreq=None, fittypefreq=None, aS=None, fittypeS=None, nproc=1)[source] This function computes the thermal expansions at different temperatures from the Gruneisen parameters and other parameters.
TT is a numpy array of temperatures for which the thermal expansions are computed. ibrav identifies the Bravais lattice. celldmsx contains the lattice parameters of the computed grid as read with
read_Etot()
. Different calculation options are possible, according to the optional input parameters given in input:Input parameters Meaning min0,S,weights,freq,grun Use temperarature-independent (0 K) volume, elastic compliances, average frequencies and Gruneisen parameters. The volume is obtained from min0, the lattice parameters at the minimun 0 K energy. S is the elastic compliances tensor at 0 K calculated at min0. weights ,*freq*,*grun* are the weights, phonon frequencies and Gruneisen parameters at 0 K calculated at min0. S,weights,minT,afreq,fittypefreq Use temperarature-independent (0 K) elastic compliances but temperature-dependent volume, average frequencies and Gruneisen parameters. The volume is obtained from minT, a numpy array with the lattice parameters at the minimun free energy at each temperature . S is the elastic compliances tensor at 0 K calculated at min0. weights are the weights of the phonon frequencies. afreq, fittypefreq are the coefficients of the fitted polynomials for the phonon frequencies and the type of polynomial. They are used here to compute the average frequencies and Gruneisen parameters at each temperature. weights,afreq,fittypefreq,aS,fittypeS Use temperarature-dependent (0 K) volume, elastic compliances, average frequencies and Gruneisen parameters. The volume is obtained from minT, a numpy array with the lattice parameters at the minimun free energy at each temperature. weights are the weights of the phonon frequencies. afreq, fittypefreq are the coefficients of the fitted polynomials for the phonon frequencies and the type of polynomial. They are used here to the average frequencies and Gruneisen parameters at each temperature. aS,fittypeS are the coefficients of the fitted polynomials for the elastic compliances and the polynomial type. They are used here to compute the elastic compliances at each temperature as in the quasi- static approximation. By default, all the input parameters in the above table are ==None and if nothing is given in input the function will return None without computing anything.
nproc is the number of processes to be run in parallel.
-
pyqha.alphagruneisenp.
compute_alpha_gruneisen_loopparallel
(it)[source] This function implements the parallel loop where the alpha are computed. It essentially calls the compute_alpha_grun function with the proper parameters according to the selected option. “it” is a list with all function parameters for the call in pool.map
it[0]== 0 -> use V, S, average frequencies and gruneisen parameters at 0 K it[0]== 1 -> use S at 0 K, calculate V, average frequencies and gruneisen parameters at each T it[0]== 2 -> calculate S, V, average frequencies and gruneisen parameters at each T
-
pyqha.alphagruneisenp.
join
(partials)[source] This function simply puts together the different temperature ranges where alpha was calculated in parallel into a single numpy array


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Notebooks
Story: Mountain 'Bushmen'
Mountain 'Bushmen'
Title
Mountain 'Bushmen'
Collection
Summary
Mountain Bushman. By /han≠kass’o (L VIII.-22. 7982 rev.-7984 rev.). Regarding Mountain Bushmen. (L VIII.-26. 8280 rev.). A note about the 'Mountain Bushmen' (!kaugen !k'e), who live in the mountain 'kloofs' (or valleys) on the side of the Tki (a river). Another group of |xam (or branch of the 'Mountain Bushmen', called the !kaugen ss'o !k'e) lives to the south of the 'Mountain Bushmen'.
Comments
1) This story is found in Book VIII-26
Contributors
|han≠kass'o (Klein Jantje) (VIII)
Date
February 1879
Categories
Keywords
|xam groups (or 'tribes') , |xam groups (the |xam names of) , |xam groups (the 'Mountain Folk', or 'Berg Bushmen') , |xam groups (where they live) , |xam groups (!kaugen !k'e) , |xam groups (!kaugen ss'o !k'e) , Mountain Bushmen (or 'Berg', or 'Mountain Folk') , Mountain Bushmen (!kaugen !k'e) , Mountain Bushmen (where they live) , names (of groups of |xam) , names ('Mountain Folk', or 'Berg Bushmen' are !kaugen !k'e and !kaugen ss'o !k'e) , names (a river called Tki)
Story Pages
8280v


| Actions | HM Number | Topic | Student Submitted | Start Date | Due Date | Created Date | Marks Available | Grace Period |
|---|---|---|---|---|---|---|---|---|
| HM00001 | AMT 1 Instruction |
20 | 2012-12-19 03:14:07 | 2012-12-28 03:14:07 | 2012-12-18 03:14:07 | 100 | ||
| HM00002 | AMT 2 Instruction |
10 | 2012-12-19 03:14:07 | 2012-12-28 03:14:07 | 2012-12-18 03:14:07 | 100 | ||
| HM00003 | AMT 3 Instruction |
10 | 2012-12-19 03:14:07 | 2012-12-28 03:14:07 | 2012-12-18 03:14:07 | 100 | ||
| HM00004 | AMT 4 Instruction |
10 | 2012-12-19 03:14:07 | 2012-12-28 03:14:07 | 2012-12-18 03:14:07 | 100 | ||
| HM00005 | AMT 5 Instruction |
30 | 2012-12-19 03:14:07 | 2012-12-28 03:14:07 | 2012-12-18 03:14:07 | 100 |
There are three species of zebras: the plains zebra, the mountain zebra and the Grévy's zebra. The plains zebra and the mountain zebra belong to the subgenus Hippotigris, but Grévy's zebra is the sole species of subgenus Dolichohippus. The latter resembles an ass, to which it is closely related, while the former two are more horse-like. All three belong to the genus Equus, along with other living equids.
| Home Work Id | Topic | Start Date | Extension Due Date |
|---|---|---|---|
| HM00001 | AMT 1 | 2012-12-19 03:14:07 | 2012-12-19 03:14:07 |
| HM00002 | AMT 2 | 2012-12-19 03:14:07 | 2012-12-19 03:14:07 |
| HM00003 | AMT 3 | 2012-12-19 03:14:07 | 2012-12-19 03:14:07 |
|
Select All |
Student Number | First Name | Last Name |
|---|---|---|---|
| STN0001 | Johan | ANT | |
| STN0002 | Mike | Kbg | |
| STN0003 | Kumar | SDC |


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|
numspel
0.0.1
the fast number speller program
|
The Speller class It splits the number into triples, turn each triple into Number object from which the textual form of particular number could be retreived, then form a complete textual form for each triple with their corresponding units respectively. More...
#include <speller.h>
Public Types | |
| enum | SpellingLanguage { EN, ID } |
Public Member Functions | |
| Speller (const string input, const SpellingLanguage language) | |
| Speller. More... | |
| Speller (const string input) | |
| Speller. More... | |
| bool | ready () |
| ready More... | |
| string | process () |
| process More... | |
| void | setLanguage (const SpellingLanguage language) |
| setLanguage More... | |
The Speller class It splits the number into triples, turn each triple into Number object from which the textual form of particular number could be retreived, then form a complete textual form for each triple with their corresponding units respectively.
usage: Create new instance of Speller with number to spell, as in: Speller speller("1123");
The instance will be able to process() only if the number is valid i.e only contains digit character nothing else. the ability can be queried using ready() method.
|
explicit |
| input | |
| language | construct the Speller to spell 'input' in the given 'language' |
Definition at line 6 of file speller.cpp.
|
explicit |
| input | convenience constructor using EN by default |
Definition at line 15 of file speller.cpp.
| string Speller::process | ( | ) |
process
order the Speller to do its hard work converting numbers into their textual form of their language
don't forget to ask ready() before calling this function, for best result.
Definition at line 25 of file speller.cpp.
| bool Speller::ready | ( | ) |
ready
Definition at line 20 of file speller.cpp.
| void Speller::setLanguage | ( | const SpellingLanguage | language | ) |


- This app extends the NYT querying app that was provided.
- It provides fields for a start date and an end date, which are included in the query string sent to the NYT API.
- The Google App Engine backend validates this input, then passes it on to the web service.


Running the Unity Demo Scene
Integrating the Unity Plugin
The OpenKit Unity Plugin is now available for download and supports both iOS and Android.
Once you have downloaded the package, import the package into your project and select all files.
Then follow the instructions here to integrate the plugin into your game. At a high level, you will need to:
- Signup for OpenKit and create an OpenKit application
- Create a Facebook application
- Set the API keys for Facebook and OpenKit in your game
- Add the OpenKit API calls
The OpenKit plugin unity package contains a demo Scene that you can use to see sample calls and try out the service.
The OpenKit unity plugin can only be used by building and running on an actual device (iOS and Android). It will not work in the Unity simulator.
You will need to create your own Facebook application for this demo scene to work correctly, and set your Facebook application ID in the correct places.
Running the demo scene
Follow these steps to run the OpenKit demo scene.
1. Import the OpenKit Unity package
Go to Assets-->Import Package-->Custom package and choose the OpenKit Unity plugin package. Import all files
2. Follow the Unity integration guide to create a Facebook application and set your Facebook application ID in Unity
3. Open up the "DemoScene" scene in Assets/Examples/OKDemoScene
4. Click on File-->Build Settings
5. Click "Add Current" to add the DemoScene to "Scenes in Build"
It should look like this:
6. Choose iOS or Android and click "Build and Run"
Note: you must build and run your game on an actual iOS or Android Device. The OpenKit plugin will not work on the iOS Simulator or the Android Emulator.
When using the demo scene, be sure to use XCode or Eclipse/LogCat to view log statements. Some of the buttons in the demo scene don't trigger any UI actions, but you can see the results in the log statements.
Troubleshooting Facebook Login Issues
If you're having issues trying out the demo scene, make sure you have configured the following correctly:
- Your bundle identifier in Unity should match the bundle identifier in your Facebook application settings for iOS and Android
- You should have added your Android debug keyhash to your Facebook app
- Your Facebook application ID is correctly specified in both your AndroidManifest.xml file, as well as the OpenKit PostBuildScript
- In your AndroidManifest.xml file, you specified your Facebook ID and made sure to keep the SLASH , for example:
<meta-data
android:name="com.facebook.sdk.ApplicationId"
android:value="\<PHONE_NUMBER>62300"></meta-data>
Running the Android Sample App
This quickstart guide will help you set up the OpenKit SDK and try out the sample app. It assumes you have already installed the following:
- Eclipse
- Android SDK
- ADT Plugin
You can install both Eclipse and the ADT plugin from here.
Getting the SDK
Clone openkit-android. Alternatively, you can visit the link and click the 'ZIP' button on GitHub.
$ git clone https://github.com/OpenKit/openkit-android.git
Import the SDK & Sample App Into Eclipse
In Eclipse, select File > Import, then choose Existing Projects into Workspace under the General tab and click Next.
Next, browse to and select the openkit-android directory:
Eclipse should find 3 projects. Make sure OpenKitSDK,OKSampleApp, and the FacebookSDK are checked.
Click finish, and select Project > Clean. When that finishes, right click on OKSampleApp and select Run As > Android Application.
The OpenKit sample app will work on virtual devices and on Android devices that support Android 4.0.
To avoid Facebook signing issues, run the sample app on a device that does not have Facebook for Android installed (the simulator works well).
If you want to use a device that has Facebook installed, you will need to create your own Facebook application and set the sample app to use this Facebook application ID. For a walkthrough of setting up a Facebook application, see our Facebook guide here.
Using the Sample App
To get a feel for the OpenKit SDK, try the following in the sample app:
- Log into OpenKit to see user authentication
- See a demo of cloud data
- View the source of OKCloudSampleActivity.java to see the cloud data storage API in action
- View Leaderboards
- Submit a score to the Leaderboards
- View the source of "MainActivity.java" to see how to integrate the OpenKit SDK
- View the source of "SubmitScoreActivity.java" to see how to submit scores to leaderboards
- View AndroidManifest.xml to see what changes are needed and how to use your own Facebook application ID
You can also explore the OpenKitSDK project to see how it works.


Psato
Engineering
Home
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Work
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Electronics
Contacts
length of surface
Width of surface
Tile Size
338x338
350x350
400x400
450x450
600x300
500x500
510x510
220x895
240x950
600x600
450x900
230x1200
800x800
calculate material quantity for tile fixing
Number of Tiles
Cement in Kg


软件捐献向导
在您阅读这篇指南之前，请确保您已经向PMC确认了实际需要的SGA。
如果您向Apache Dubbo 捐赠了大量的代码或文档，则需要在合并代码或者文档之前签署软件授权书。
操作步骤
-
下载这篇pdf文档
-
打印下载好的文档
-
按要求填充表格（请看下边示例）
-
请您的领导在上边签字
-
扫描
-
将扫描好的文档以邮件的方式发送给secretary<EMAIL_ADDRESS>示例
下边是一个文本示例，原始文本可以在这里找到
License Agreement
This License Agreement is entered into as of the _12th_ day of
___April____, __2018__ by ___ABC Software Co., Ltd.____ ("Licensor"),
in favor of The Apache Software Foundation, a Delaware nonstock
membership corporation (the "Foundation").
WHEREAS, Licensor owns or has sufficient rights to contribute the
software source code and other related intellectual property as
itemized on Exhibit A ("Software") under the terms of this agreement
to the Foundation for use within Foundation software development
projects ("Projects").
NOW, THEREFORE, FOR GOOD AND VALUABLE CONSIDERATION, the receipt
and legal sufficiency of which are hereby acknowledged, the parties
hereto, intending to be legally bound, agree as follows:
1. Subject to the terms and conditions of this License, Licensor
hereby grants to the Foundation:
a) a non-exclusive, worldwide, royalty-free, irrevocable
copyright license to reproduce, prepare derivative works of,
publicly display, publicly perform, distribute and sublicense,
internally and externally, the Software and such derivative
works, in source code and object code form; and,
b) a non-exclusive, worldwide, royalty-free, irrevocable
patent license under Licensed Patents to make, use, sell,
offer to sell, import and otherwise transfer the Software
in source code and object code form. "Licensed Patents" mean
patent claims owned by Licensor which are necessarily
infringed by the use or sale of the Software alone.
2. Licensor represents that, to Licensor's knowledge, Licensor is
legally entitled to grant the above license. Licensor agrees to notify
the Foundation of any facts or circumstances of which Licensor becomes
aware and which makes or would make Licensor's representations in this
License Agreement inaccurate in any respect.
3. This Software is provided AS-IS, WITHOUT WARRANTIES OR CONDITIONS
OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION,
ANY WARRANTIES OR CONDITIONS OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY
OR FITNESS FOR A PARTICULAR PURPOSE.
This License Agreement is the entire agreement of the parties
with respect to its subject matter, and may only be amended by a
writing signed by each party. This License Agreement may be
executed in one or more counterparts, each of which shall be
considered an original.
IN WITNESS WHEREOF, Licensor has executed this License Agreement
as of the date first written above.
LICENSOR:
Signed By: _____________________________________ <--- Your boss's sign here
Print Name: _____Lei Li_________________________ <--- Your boss's name here
Title: ____Director_____________________________ <--- Your boss's title here
Representing: ____ABC Software Co., Ltd. _______
________________________________________________
Contact Name: ____Lei Li________________________ <--- Your boss's name here
Contact Email<EMAIL_ADDRESS><--- Your boss's email here
Exhibit A
List of software and other intellectual property covered by this agreement:
* Github address where your code is hosted
* Pull request link


{% extends "blog/base.html" %} {% load static %} {% block headerimg %} {% static '/blog/img/about-bg.jpg' %} {% endblock %} {% block headertitle%}About Me{% endblock %} {% block headersubtitle %}This is what I do.{% endblock %} {% block content %}
Hi, I'm Joseph Carpenter.
I'm a software developer living in Franklin, Tennessee. I have a degree in Computer Science from the University of Tennessee at Martin and a degree in Business Administration from Middle Tennessee State University.
This site is a place for me to share my thoughts and things I find interesting.
{% endblock %}


Learning jQuery
- Free codeschool jquery course
- Advanced codeschool jquery takes flight
- Free codecademy jquery track
Dive into Javascript
- Eloquent Javascript
- Wat #humor
Code Pens
See the Pen yofAp by Jesse Wolgamott (@jwo) on CodePen.
- Get Farmer Data
See the Pen yofAp by Jesse Wolgamott (@jwo) on CodePen.


As the heading goes by, this is a simple test to determine your understanding of JavaScript basics. this is a theory test that that involves no practical code. The test consists of five multichoice questions that will will require you to answer them all in order to be graded. Each of the five questions are valued at twenty points thus totaling to 100% for the whole test. The results are displayed immediately upon completion of the test.
As always if you choose to accept this test you are basically on your own, the moderators of this test will disavow any knowledge of your actions in event you fail.


Wingspan: 1.78 to 2.45 m (5 ft 10 in to 8 ft 0 in)
Scientific name: Haliaeetus albicilla
Carnivorous
Description
coming tomorrow
Voice
coming soon
Diet
Many studies have reflected that the primary foods of white-tailed eagles are fish and water birds. These are the
primary food as well for other sea eagle species. However, unlike most Haliaeetus, including the Bald Eagles and
Steller's sea Eagles, the water birds tend to take the primary position in the
diet.
Behaviour
Reproduction
coming soon
Habits and Lifestyle
White-tailed eagles are diurnal and spend much of their day perched on trees or crags, and may often not move for
hours. Pairs regularly roost together, often near to their nest, either on a
crag or tree or crevices, overhung ledges
or small isolated trees on a crag.
Habitat
White-tailed eagles are found along rocky coastlines, estuaries and lochs near the sea, although they will also
range
inland, especially juveniles. The species is very widely distributed, with strongholds in Russia and Norway.


Yu-Cheng Huang
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You may replace Coffee for Tea or vice versa (i.e. All Coffee or All Tea)
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WITNESS LIST House Administration Committee April 28, 1997-8:00A SB 1301 Committee Substitute (Goolsby) On: Rick Crawford (State Preservation Bd.)


Blessed is the man, who having nothing to say, abstains from giving wordy evidence of the fact.
——Paul Erdos


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Posted May 14, 2015 by James Kyle
Babel 5.4 was just released and with it comes support for a new experimental ES7 syntax proposed by Kevin Smith (@zenparsing) and implemented in Babel by Ingvar Stepanyan (@RReverser).
Warning: This syntax is highly experimental and you should not use it for anything serious (yet). If you do use this syntax, please provide feedback on GitHub.
The function bind syntax introduces a new operator ::
which performs function
binding and method extraction.
Virtual Methods
Using an iterator library implemented as a module of "virtual methods":
/* ES7 */
import { map, takeWhile, forEach } from "iterlib";
getPlayers()
::map(x => x.character())
::takeWhile(x => x.strength > 100)
::forEach(x => console.log(x));
/* ES6 */
import { map, takeWhile, forEach } from "iterlib";
let _val;
_val = getPlayers();
_val = map.call(_val, x => x.character());
_val = takeWhile.call(_val, x => x.strength > 100);
_val = forEach.call(_val, x => console.log(x));
Note: Babel's output looks different than this in order to be more concise.
Using a jquery-like library of virtual methods:
/* ES7 */
// Create bindings for just the methods that we need
let { find, html } = jake;
// Find all the divs with class="myClass", then get all of the
// "p"s and replace their content.
document.querySelectorAll("div.myClass")::find("p")::html("hahaha");
/* ES6 */
let _val;
_val = document.querySelectorAll("div.myClass");
_val = find.call(_val, "p");
_val = html.call(_val, "hahaha");
Method Extraction
Using method extraction to print the eventual value of a promise to the console:
/* ES7 */
Promise.resolve(123).then(::console.log);
/* ES6 */
// Which could be written in ES6 as:
Promise.resolve(123).then(console.log.bind(console));
Using method extraction to call an object method when a DOM event occurs:
/* ES7 */
$(".some-link").on("click", ::view.reset);
/* ES6 */
$(".some-link").on("click", view.reset.bind(view));
Note: You can read more about this syntax in the Function Bind Syntax proposal.
Usage
Enable by stage:
$ babel --stage 0
Enable by transformer:
$ babel --optional es7.functionBind
The function bind syntax will only make it into ES7 if there is enough interest. If you would like to see this syntax make it in, please give it a star on GitHub and provide any feedback you have on GitHub issues.
Special thanks to Ingvar Stepanyan (@RReverser) for the implementation in Babel.
— The Babel team


| |
Project Name | {{project.project_name}} {{project.project_name}} | Project Description | {{project.project_description}} | Created By | {{project.created_by}} | State | {{project.state}} | Source | {{project.source}} | Edit |
|
Delete |
|
Copy | Try Now |
|---|


--- title: Creates shared embeddings for categorical columns type: docs repo: https://github.com/rstudio/tfdatasets menu: main: name: Creates shared embeddings for categorical columns - tfdatasets parent: tfdatasets-reference aliases: - /reference/tfdatasets/step_shared_embeddings_column.html - /tfdatasets/reference/step_shared_embeddings_column.html - /guide/tfdatasets/reference/step_shared_embeddings_column.html - /tools/tools/tfdatasets/reference/step_shared_embeddings_column.html - /installation/tfdatasets/reference/step_shared_embeddings_column.html - /tutorials/tfdatasets/reference/step_shared_embeddings_column.html - /guide/tools/tfdatasets/reference/step_shared_embeddings_column.html - /deploy/tfdatasets/reference/step_shared_embeddings_column.html - /tools/tfdatasets/reference/step_shared_embeddings_column.html - /tutorials/tools/tfdatasets/reference/step_shared_embeddings_column.html ---
This is similar to step_embedding_column, except that it produces a list of embedding columns that share the same embedding weights.
step_shared_embeddings_column(
spec,
...,
dimension,
combiner = "mean",
initializer = NULL,
shared_embedding_collection_name = NULL,
ckpt_to_load_from = NULL,
tensor_name_in_ckpt = NULL,
max_norm = NULL,
trainable = TRUE
)
| spec | A feature specification created with |
| ... | Comma separated list of variable names to apply the step. selectors can also be used. |
| dimension | An integer specifying dimension of the embedding, must be > 0. Can also be a function of the size of the vocabulary. |
| combiner | A string specifying how to reduce if there are multiple entries in
a single row. Currently 'mean', 'sqrtn' and 'sum' are supported, with 'mean' the
default. 'sqrtn' often achieves good accuracy, in particular with bag-of-words
columns. Each of this can be thought as example level normalizations on
the column. For more information, see |
| initializer | A variable initializer function to be used in embedding
variable initialization. If not specified, defaults to
|
| shared_embedding_collection_name | Optional collective name of these columns. If not given, a reasonable name will be chosen based on the names of categorical_columns. |
| ckpt_to_load_from | String representing checkpoint name/pattern from
which to restore column weights. Required if |
| tensor_name_in_ckpt | Name of the Tensor in ckpt_to_load_from from which to
restore the column weights. Required if |
| max_norm | If not |
| trainable | Whether or not the embedding is trainable. Default is |
a FeatureSpec
object.
Does not work in the eager mode.
steps for a complete list of allowed steps.
Other Feature Spec Functions:
dataset_use_spec()
,
feature_spec()
,
fit.FeatureSpec()
,
step_bucketized_column()
,
step_categorical_column_with_hash_bucket()
,
step_categorical_column_with_identity()
,
step_categorical_column_with_vocabulary_file()
,
step_categorical_column_with_vocabulary_list()
,
step_crossed_column()
,
step_embedding_column()
,
step_indicator_column()
,
step_numeric_column()
,
step_remove_column()
,
steps


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Juan Pablo Correa Puerta
About
A last year Systems and Computing engineering student at Universidad de los Andes in Bogota, Colombia. Technology enthusiast, interested in understanding how it can withstand everything: from everyday activities to the most complex projects. Problem-solving knowledge. Programming skills with languages such as Java and Python as well basics in graphic design and video edition by using Adobe's Suit (mainly Photoshop and After Effects).
Education
Universidad de los Andes, Bogota, Colombia. Systems and Computing
Engineering.
January 2017 - Present.
Tecnológico de Monterrey, Pachuca de Soto, Mexico. Professional
Exchange.
August - December 2019.
August - December 2019.
Universidad Pontificia Bolivariana, Medellin, Colombia.
Highschool.
December 2016.
December 2016.
Work Experience
- Moodle assistant for German A1 & A2 platform. Juny 2019 - July 2020 & Juny 2020.
- Introduction to Programming in Python teaching assistant. January 2020 - May 2020.
- Algorithmic and OOP in Java teaching assistant. August 2017 - May 2019.
Skills
Java
JavaScript
React
Adobe Photoshop
Python
Adobe After Effects
Adobe Premiere Pro
SQL
Languages
Spanish - Mother tongue.
English -
Advanced (10/10 levels at University).
German -
Intermediate (5/6 levels at University).
French -
Basic.


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Your graphics card does not seem to support WebGL
Find out how to get it here
Controls:
WASD move,
R|F up | down,
Q|E roll,
up|down pitch,
left|right yaw
shift speed up space/esc toggle mouse controls T Teleport to next object
Scroll to increase/decrease your field of view.
Point your device around you to look around.
Touch the screen to move forwards. Use two fingers to go faster.


How to write the index.
1. Load the BUFR file into the IOSP/BUFR/BUFR tab
2. Press the "Write Index" button on the upper right . Accept the default name of <filename>.ncx
3. Load the index file into the IOSP/BUFR/BufrCdmIndex tab. You can make changes to the ActionS column:
4. Press the "Write Index" button on the upper right to save your changes.
5. Now the index file will be used if you open the original file in the FeatureType/PointFeature tab.
We will eventually write these files automatically.


Microsoft releases new Bird’s Eye imagery in Bing Maps spanning 70 cities in the US
by PradeepBack in July, Microsoft announced that they have added over 102,000 square kilometers of new Bird’s Eye imagery spanning 100+ cities in the US over past few months. Today, Microsoft announced that they have added another 50,000 square kilometers of new Bird’s Eye imagery spanning 70 cities in the US.
Bing Maps was one of the first mapping services to offer Bird’s Eye aerial imagery. Microsoft mentioned that they are committed to release fresh high-resolution satellite and aerial imagery in the coming months.
Cities and areas that have recently been updated with new imagery:
| Ashland, OR | Greece, NY | Olympia, WA |
| Bellevue, WA | Green Bay, WI | Outer Banks, NC |
| Benton, AR | Greenville, SC | Oxnard, CA |
| Berkeley, CA | Hayward, CA | Provo, UT |
| Billings, MT | Hartford, CT | Redmond, WA |
| Birmingham, AL | Helena, MT | Richmond, VA |
| Brick, NJ | Hollywood, CA | Rochester, NY |
| Cabot, AR | Homestead, FL | San Francisco, CA |
| Canandaigua, NY | Huntington Beach, CA | Santa Barbara, CA |
| Clearwater, FL | Lake Stevens, WA | Snoqualmie, WA |
| Cheyenne, WY | Inglewood, CA | Seattle, WA |
| Cincinnati, OH | Lake Stevens, WA | Snoqualmie, WA |
| Clearwater, FL | Leeds, AL | South Hill, WA |
| Compton, CA | Lexington, KY | Spokane, WA |
| Corcoran, MN | Lincoln, NE | Springfield, MO |
| Dayton, OH | Lithonia, GA | Staten Island, NY |
| Douglasville, GA | Little Rock, AR | Tacoma, WA |
| Enumclaw, WA | Marietta, GA | Toledo, OH |
| Eugene, OR | Milton, GA | Waterbury, CT |
| Everett, WA | Minneapolis, MN | Whittier, CA |
| Fairburn, GA | Monroe, WA | Woodstock, GA |
| Fort Lonesome, FL | New Brunswick, NJ | Yakima, WA |
| Framingham, MA | North Little Rock, AR | |
| Frankfurt, KY | Ogden, UT |
Source: Microsoft


Oscar Futures: Can Marriage Story Put Up a Fight in Best Picture?
by Nate JonesEvery week between now and January 13, when the Academy Award nominations are announced, Vulture will consult its crystal ball to determine the changing fortunes of this year’s Oscars race. In our “ Oscar Futures ” column, we’ll let you in on insider gossip, parse brand-new developments, and track industry buzz to figure out who’s up, who’s down, and who’s currently leading the race for a coveted Oscar nomination.
Best Picture
Marriage Story
As Noah Baumbach’s divorce drama begins its pre-Netflix theatrical run this weekend, critics are hailing the film as one of the year’s best. Marriage Story seems sure to score in the acting categories, but could it make waves in Best Picture as well? The film wrings a lot of knowing chuckles from industry viewers, and such broad relatability could boost it in the preferential ballot.
A Beautiful Day in the Neighborhood
For a would-be Oscar contender that comes out in two weeks, it sure seems like few pundits are talking about this movie outside of Tom Hanks’s performance. Beautiful Day was well received at TIFF, so what’s behind the lack of buzz? Is it the film’s soft and gentle vibe, which isn’t spurring the passion a Parasite or Jojo Rabbit engenders? Or, after Won’t You Be My Neighbor, are people simply Mr. Rogers-ed out?
Current Predix
Ford v Ferrari, The Irishman, Jojo Rabbit, Joker, Little Women, Marriage Story, Once Upon a Time in Hollywood, Parasite, The Two Popes
Best Director
Noah Baumbach, Marriage Story
“Noah Baumbach’s latest will win over even those who don’t love Noah Baumbach,” writes Slate’s Dana Stevens, a compliment/insult calzone that would be right at home in Marriage Story. Baumbach’s tonally dexterous efforts seem likely to land him his first directing nomination, though he’ll have tough competition from inside his own household.
Alma Har’el, Honey Boy
The Israeli filmmaker’s feature debut is filled with moving performances and arresting visual imagery, though Honey Boy is so linked to the outsize figure of writer/co-star Shia LaBeouf, who based the film on his own childhood, that voters may overlook Har’el’s contributions.
Current Predix
Noah Baumbach, Marriage Story; Bong Joon-ho, Parasite; Greta Gerwig, Little Women; Martin Scorsese, The Irishman; Quentin Tarantino, Once Upon a Time in Hollywood
Best Actor
Adam Driver, Marriage Story
Marriage Story is such a critics’ fave that pundits are starting to slate Driver as this category’s new front-runner ahead of Joaquin Phoenix. I’m not ready to go there yet — Phoenix has the track record, and the transformation, on his side — but the pair do feel like this extremely competitive category’s only two locks
Noah Jupe, Honey Boy
How darkly ironic that Honey Boy, a movie about the horrors of child acting, should launch a great new child actor. The 14-year-old Jupe has been getting raves for his precocious performance as the LaBeouf stand-in, with Peter Travers calling him “astounding.” With a small turn in Ford v Ferrari coming up, as well, the young Brit is one to watch.
Current Predix
Robert De Niro, The Irishman; Leonardo DiCaprio, Once Upon a Time in Hollywood; Adam Driver, Marriage Story; Eddie Murphy, Dolemite Is My Name; Joaquin Phoenix, Joker
Best Actress
Scarlett Johansson, Marriage Story
I’ve seen critics question whether the storytelling in Marriage Story is really as balanced between Driver and Johansson as initially appears, but that doesn’t discount how great the actress is in the scenes she does get. Stevens calls her and Driver “superb performers, attentive, generous, versatile, and able to register minute shifts of feeling and understanding on their admittedly well-proportioned features.” These raves will have to speak for themselves: After her recent PR kerfuffles, the gun-shy Johansson is letting her co-stars do the bulk of the gladhanding.
Cynthia Erivo, Harriet
With her Harriet Tubman biopic earning only polite respect from critics, Erivo needed audiences to show up to keep some wind in her sails. That’s exactly what happened, with Harriet debuting to a solid opening weekend and an A+ CinemaScore to boot. The British actress has been putting in work on the circuit, too, as voters have come away charmed by her ebullient presence.
Current Predix
Awkwafina, The Farewell; Scarlett Johansson, Marriage Story; Saoirse Ronan, Little Women; Charlize Theron, Bombshell; Renée Zellweger, Judy
Best Supporting Actor
Ray Liotta, Marriage Story
I’ve mentioned Alan Alda in this column before, but let’s also spare a minute for Liotta as Driver’s other attorney, who tears into his lines like they’re a bloody steak. Alongside Laura Dern as Johansson’s lawyer, the trio “collectively come close to stealing the movie,” A.O. Scott writes, “in part because they are playing performers fully in their element.” Individually, the two men probably lack the screen time to mount a showing in this category. It’s a pity they can’t fuse themselves into one contender, Ralan Aldotta.
Shia LaBeouf, Honey Boy
Is LaBeouf playing his own father a brilliant impersonation, a filmed therapy session, or both? Glenn Kenny in the Times hails his performance as “a bravura incarnation of resentment,” though he also sniffs, “It must be nice to have someone finance a movie of your 12-step qualification.”
Current Predix
Tom Hanks, A Beautiful Day in the Neighborhood; Anthony Hopkins, The Two Popes; Al Pacino, The Irishman; Joe Pesci, The Irishman; Brad Pitt, Once Upon a Time in Hollywood
Best Supporting Actress
Laura Dern, Marriage Story
A beloved actress putting a capstone on her career renaissance with a scene-stealing role in a Best-Picture contender. Who knows what’ll happen when actual voting begins, but at the moment, of the four acting front-runners, Dern is the one who feels closest to having their category locked up.
FKA Twigs, Honey Boy
As a neighbor who strikes up a close bond with young Jupe, Twigs does what she can with a slightly thankless role. Her new album’s apparently great, though!
Current Predix
Laura Dern, Marriage Story; Scarlett Johansson, Jojo Rabbit; Jennifer Lopez, Hustlers; Florence Pugh, Little Women; Margot Robbie, Bombshell


Birth Name: Macaulay Carson Culkin. Birth Date: August 26, 1980 Birth Place: New. York City, NY. Occupation: Actor Relationship Status:. Dating Mila Kunis.
Apr 16, 2012 X17 Online has snapped some photos of Ashton Kutcher and Mila Kunis. since splitting from longtime boyfriend Macaulay Culkin in 2010
Aug 15, 2012 All reports indicate that Macaulay. Culkin is addicted to heroin and Sadly, it was Culkin's partying that prompted Mila to dump him in 2010, sources say Celebrity Couple Alert: Ashton Kutcher and Mila Kunis Are Dating! Aug 3, 2012. Macaulay Culkin addicted to heroin 6 months to live, screams the by the break-up of his eight-year romance with live-in love Mila Kunis, aged 28 she began dating Culkin but left him in. 2010 before ballet drama Black
Jan 25, 2013. Macaulay Culkin. and Mila Kunis 11. Here we see Culkin with the young actress. Mila Kunis, who he was dating, 2009 30-year-old actor 12. Macaulay Culkin during the 82nd Academy Awards ceremony on March 7, 2010
Feb 1, 2013 He's rumored to be currently dating. Mila Kunis from That 70s Show macaulay culkin, mila kunis, young and. pretty on August 26, 2010 by Macaulay. Culkin Acting almost since infancy, Culkin stood. out as a precocious, charming child in In 2010, Culkin found himself saluting another deceased friend and mentor, when. Began career at age 4 in stage production of "Bach Babies" at New York Symphony Space, New York (date approximate) Mila Kunis. High quality Macaulay Culkin pictures and hot pictures, Macaulay Culkin news, Birth Date:. English - Dec 30, 2010 - forums. superiorpics. com Mila Kunis, 27, and former child star Macaulay Culkin have called it quits, In Touch reports "T
Playing Matchmaker: 3 Guys. Mila Kunis Should Date More from YourTango: Before Ashton, Mila's most famous love connection was with Macaulay Culkin their relationship lasted eight years until they called it quits in 2010 We aren't sure
Macaulay Culkin -- Out Alone! Macaulay Culkin 12/25/2010 5:12 AM PST BY TMZ STAFF Macaulay Culkin. Kudos to him though, he's dating Mila Kunis! Jun 20, 2013. Kunis was dating former child-star Macaulay Culkin at the time Mila Kunis and Ashton Kutcher. fall in the first category. Us Weekly snapped a Macaulay Carson Culkin (born August 26, 1980) is an American actor He became DATE: 2010/03/02 Macaulay Culkin and Mila. Kunis Wedding Video. Jan 13, 2008. Macaulay Carson Culkin is a former child star best known for his role as Kevin If he's really been dating Mila Kunis since 2002 he's not only not washed up, he's kickin' it hardcore behotch! February 06, 2010 6:25 AM Dec 19, 2012. Even his former girlfriend of eight. years, Mila Kunis, has never spoken. and dating Black Swan star, Mila Kunis, from 2002-2010, Macaulay


Plan Status: #59 was successful
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Run Plan
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Actions
Plan Summary
Current Activity
No builds are currently running.
Recent History
| Status | Reason | Completed | Test Results |
|---|---|---|---|
| #59 | Scheduled build | 12 hours ago | Testless build |
| #58 | Scheduled build | 1 day ago | Testless build |
| #57 | Scheduled build | 2 days ago | Testless build |
| #56 | Scheduled build | 3 days ago | Testless build |
| #55 | Scheduled build | 4 days ago | Testless build |
| #54 | Scheduled build | 5 days ago | Testless build |
| #53 | Scheduled build | 6 days ago | Testless build |
| #52 | Scheduled build | 1 week ago | Testless build |
| #51 | Scheduled build | 1 week ago | Testless build |
| #50 | Scheduled build | 1 week ago | Testless build |
Plan Statistics
100% Successful
- Successful Builds:
- 25 / 25
- Average Duration:
- 4 minutes


Operating System: Windows, macOS, Linux etc. Software requirement: Java Runtime Environment (available at http://java.com/) Start the Application: A) Double click FilteredPlayerApp.jar icon in the file browser or Desktop B) On the terminal java -jar FilteredPlayerApp.jar 0) Set output port for playback (default is OK)
1) Select an input file (wav or mp3)
If the characters are distorted, try full size by clicking the square icon.
2) Play and listen
You can modify the filter while listening. Rewind or fast forward: "Pause" or "Stop", then use the "Time" slider. Even if playback quality is bad. You can still use "Save as file" without problem.
3) Set filter (T: Through, M: Mute, H: Harmonic, P: Percussive)
Preset1: drumCover -- surpress percussive sound in all freq and pan range except for vocal freq range around the center
Preset2: karaokeMale -- mute male vocal freq range around the center percussive/harmonic separation is not used at all
Note: You may save/load your custom filter.
Modification: If you want more vocal, press "THROUGH" and add T higher freq and/or wider pan ranges around the center. If you want more bass guiter, add "T" at lower freq around the center. If you want more right guitar, add "T" at lower/higher freq around the right most pan ranges.
4) Save as file Volume slider affects the volume of the save file.


{% extends 'base.html' %} {% load staticfiles %} {% load crispy_forms_tags %} {% crispy postsearch_form postsearch_form.helper %} {% csrf_token %} {% block head_content %} {% endblock %} {% block content %}
These are the instructions for processing individual adsheet files:
The basic steps are as follows:
| Rename the dated xlsx file to "adsheet.xlsx" (eg. "8-AUGUST 14-2016.xlsx" becomes "adsheet.xlsx". Move the file to ADVERTISING/Inserts/MAGETRAC/csv (overwriting the existing one in this directory). |
| from the terminal (shell) on the server workstion, run adsheet.py. When prompted, enter the ad date in the DDMMYY format. | |
| Then (in CAPS), enter the ad version (PRE PRE, PRE or FINAL) |


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