Instruction:
How would you code a function in Python 3 to
return the derivatives of y at the points xs ys

Response:
def get_derivatives(xs, ys, fd=False):
    """
    return the derivatives of y(x) at the points x
    if scipy is available a spline is generated to calculate the derivatives
    if scipy is not available the left and right slopes are calculated, if both exist the average is returned
    putting fd to zero always returns the finite difference slopes
    """
    try:
        if fd:
            raise SplineInputError('no spline wanted')
        if len(xs) < 4:
            er = SplineInputError('too few data points')
            raise er
        from scipy.interpolate import UnivariateSpline
        spline = UnivariateSpline(xs, ys)
        d = spline.derivative(1)(xs)
    except (ImportError, SplineInputError):
        d = []
        m, left, right = 0, 0, 0
        for n in range(0, len(xs), 1):
            try:
                left = (ys[n] - ys[n-1]) / (xs[n] - xs[n-1])
                m += 1
            except IndexError:
                pass
            try:
                right = (ys[n+1] - ys[n]) / (xs[n+1] - xs[n])
                m += 1
            except IndexError:
                pass
            d.append(left + right / m)
    return d


Instruction:
Here you have a function in Python 3, explain what it does
def reciprocal(x, a, b, n):
    if n < 1:
        n = 1
    elif n > 5:
        n = 5
    if isinstance(x, list):
        y_l = []
        for x_v in x:
            y_l.append(a + b / x_v ** n)
        y = np.array(y_l)
    else:
        y = a + b / x ** n
    return y

Response: reciprocal function to fit convergence data


Instruction:
Given the following Python 3 function, write the documentation
def p0_reciprocal(xs, ys):
    a0 = ys[len(ys) - 1]
    b0 = ys[0]*xs[0] - a0*xs[0]
    return [a0, b0, 1]

Response: predictor for first guess for reciprocal


Instruction:
Can you generate a brief explanation for the following Python 3 code
def single_reciprocal(x, a, b, c):
    if isinstance(x, list):
        y_l = []
        for x_v in x:
            y_l.append(a + b / (x_v - c))
        y = np.array(y_l)
    else:
        y = a + b / (x - c)
    return y

Response: reciprocal function to fit convergence data


Instruction:
Here you have a function in Python 3, explain what it does
def simple_reciprocal(x, a, b):
    if isinstance(x, list):
        y_l = []
        for x_v in x:
            y_l.append(a + b / x_v)
        y = np.array(y_l)
    else:
        y = a + b / x
    return y

Response: simple reciprocal function to fit convergence data


Instruction:
Explain what the following Python 3 code does
def extrapolate_reciprocal(xs, ys, n, noise):
    if len(xs) > 4 and noise:
        y1 = (ys[-3] + ys[-4]) / 2
        y2 = (ys[-1] + ys[-2]) / 2
        x1 = (xs[-3] + xs[-4]) / 2
        x2 = (xs[-1] + xs[-2]) / 2
        try:
            b = (y1 - y2) / (1/x1**n - 1/x2**n)
            a = y2 - b / x2**n
        except IndexError:
            print_and_raise_error(xs, ys, 'extrapolate_reciprocal')
    else:
        try:
            b = (ys[-2] - ys[-1]) / (1/(xs[-2])**n - 1/(xs[-1])**n)
            a = ys[-1] - b / (xs[-1])**n
        except IndexError:
            print_and_raise_error(xs, ys, 'extrapolate_reciprocal')
    return [a, b, n]

Response:
extrapolate_reciprocal returns the parameters such that a + b / x^n hits the last two data points
            a + b n hits the last two data points
            b = n - 1


Instruction:
Implement a function in Python 3 to
measure the quality of a fit

Response:
def measure(function, xs, ys, popt, weights):
    """
    measure the quality of a fit
    """
    m = 0
    n = 0
    for x in xs:
        try:
            if len(popt) == 2:
                m += (ys[n] - function(x, popt[0], popt[1]))**2 * weights[n]
            elif len(popt) == 3:
                m += (ys[n] - function(x, popt[0], popt[1], popt[2]))**2 * weights[n]
            else:
                raise NotImplementedError
            n += 1
        except IndexError:
            raise RuntimeError('y does not exist for x = ', x, ' this should not happen')

    return m


Instruction:
Can you tell what is the following Python 3 function doing
def multi_curve_fit(xs, ys, verbose):
    #functions = {exponential: p0_exponential, reciprocal: p0_reciprocal, single_reciprocal: p0_single_reciprocal}
    functions = {
        exponential: p0_exponential,
        reciprocal: p0_reciprocal,
        #single_reciprocal: p0_single_reciprocal,
        simple_reciprocal: p0_simple_reciprocal,
        simple_2reciprocal: p0_simple_2reciprocal,
        simple_4reciprocal: p0_simple_4reciprocal,
        simple_5reciprocal: p0_simple_5reciprocal
    }
    from scipy.optimize import curve_fit
    fit_results = {}
    best = ['', np.inf]
    for function in functions:
        try:
            weights = get_weights(xs, ys)
            popt, pcov = curve_fit(function, xs, ys, functions[function](xs, ys), maxfev=8000, sigma=weights)
            pcov = []
            m = measure(function, xs, ys, popt, weights)
            fit_results.update({function: {'measure': m, 'popt': popt, 'pcov': pcov}})
            for f in fit_results:
                if fit_results[f]['measure'] <= best[1]:
                    best = f, fit_results[f]['measure']
            if verbose:
                print(str(function), m)
        except RuntimeError:
            print('no fit found for ', function)

    return fit_results[best[0]]['popt'], fit_results[best[0]]['pcov'], best

Response: fit multiple functions to the x y data return the best fit


Instruction:
How would you explain what the following Python 3 function does
def multi_reciprocal_extra(xs, ys, noise=False):
    ns = np.linspace(0.5, 6.0, num=56)
    best = ['', np.inf]
    fit_results = {}
    weights = get_weights(xs, ys)
    for n in ns:
        popt = extrapolate_reciprocal(xs, ys, n, noise)
        m = measure(reciprocal, xs, ys, popt, weights)
        pcov = []
        fit_results.update({n: {'measure': m, 'popt': popt, 'pcov': pcov}})
    for n in fit_results:
        if fit_results[n]['measure'] <= best[1]:
            best = reciprocal, fit_results[n]['measure'], n
    return fit_results[best[2]]['popt'], fit_results[best[2]]['pcov'], best

Response: Calculates for a series of powers ns the parameters for which the last two points are at the curve. Returns the best fit.


Instruction:
How would you explain what the following Python 3 function does
def print_plot_line(function, popt, xs, ys, name, tol=0.05, extra=''):
    idp = id_generator()
    f = open('convdat.'+str(idp), mode='w')
    for n in range(0, len(ys), 1):
        f.write(str(xs[n]) + ' ' + str(ys[n]) + '\n')
    f.close()
    tol = abs(tol)
    line = "plot 'convdat.%s' pointsize 4 lt 0, " % idp
    line += '%s lt 3, %s lt 4, %s lt 4, ' % (popt[0], popt[0] - tol, popt[0] + tol)
    if function is exponential:
        line += "%s + %s * %s ** -x" % (popt[0], popt[1], min(max(1.00001, popt[2]), 1.2))
    elif function is reciprocal:
        line += "%s + %s / x**%s" % (popt[0], popt[1], min(max(0.5, popt[2]), 6))
    elif function is single_reciprocal:
        line += "%s + %s / (x - %s)" % (popt[0], popt[1], popt[2])
    elif function is simple_reciprocal:
        line += "%s + %s / x" % (popt[0], popt[1])
    elif function is simple_2reciprocal:
        line += "%s + %s / x**2" % (popt[0], popt[1])
    elif function is simple_4reciprocal:
        line += "%s + %s / x**4" % (popt[0], popt[1])
    elif function is simple_5reciprocal:
        line += "%s + %s / x**0.5" % (popt[0], popt[1])
    else:
        print(function, ' no plot ')

    with open('plot-fits', mode='a') as f:
        f.write('set title "' + name + ' - ' + extra + '"\n')
        f.write("set output '" + name + '-' + idp + ".gif'" + '\n')
        f.write("set yrange [" + str(popt[0] - 5 * tol) + ':' + str(popt[0] + 5 * tol)+']\n')
        f.write(line + '\n')
        f.write('pause -1 \n')

Response: print the line to plot the x y data with the fitted function


Instruction:
Create a Python 3 function to
test it and at which dy ( x ) / dx < tol for all x < x_value dy ( x ) < tol for all x < x_value dy < tol for all x < x_value dy < tol for all x < x_value dy < tol for all x < x_value dy < tol for all x < x_value dy < tol for all x < x_value dy < tol for all x < x_value dy < tol for all x < x_value

Response:
def determine_convergence(xs, ys, name, tol=0.0001, extra='', verbose=False, mode='extra', plots=True):
    """
    test it and at which x_value dy(x)/dx < tol for all x >= x_value, conv is true is such a x_value exists.
    """
    if len(xs) != len(ys):
        raise RuntimeError('the range of x and y are not equal')
    conv = False
    x_value = float('inf')
    y_value = None
    n_value = None
    popt = [None, None, None]
    if len(xs) > 2:
        ds = get_derivatives(xs[0:len(ys)], ys)
        try:
            if None not in ys:
                if mode == 'fit':
                    popt, pcov, func = multi_curve_fit(xs, ys, verbose)
                elif mode == 'extra':
                    res = multi_reciprocal_extra(xs, ys)
                    if res is not None:
                        popt, pcov, func = multi_reciprocal_extra(xs, ys)
                    else:
                        print(xs, ys)
                        popt, pcov = None, None
                elif mode == 'extra_noise':
                    popt, pcov, func = multi_reciprocal_extra(xs, ys, noise=True)
                else:
                    raise NotImplementedError('unknown mode for test conv')
                if func[1] > abs(tol):
                    print('warning function ', func[0], ' as the best fit but not a good fit: ', func[1])
                # todo print this to file via a method in helper, as dict
                if plots:
                    with open(name+'.fitdat', mode='a') as f:
                        f.write('{')
                        f.write('"popt": ' + str(popt) + ', ')
                        f.write('"pcov": ' + str(pcov) + ', ')
                        f.write('"data": [')
                        for n in range(0, len(ys), 1):
                            f.write('[' + str(xs[n]) + ' ' + str(ys[n]) + ']')
                        f.write(']}\n')

                    print_plot_line(func[0], popt, xs, ys, name, tol=tol, extra=extra)

        except ImportError:
            popt, pcov = None, None
        for n in range(0, len(ds), 1):
            if verbose:
                print(n, ys[n])
                print(ys)
            if tol < 0:
                if popt[0] is not None:
                    test = abs(popt[0] - ys[n])
                else:
                    test = float('inf')
            else:
                test = abs(ds[n])
            if verbose:
                print(test)
            if test < abs(tol):
                if verbose:
                    print('converged')
                conv = True
                if xs[n] < x_value:
                    x_value = xs[n]
                    y_value = ys[n]
                    n_value = n
            else:
                if verbose:
                    print('not converged')
                conv = False
                x_value = float('inf')
        if n_value is None:
            return [conv, x_value, y_value, n_value, popt[0], None]
        else:
            return [conv, x_value, y_value, n_value, popt[0], ds[n_value]]
    else:
        return [conv, x_value, y_value, n_value, popt[0], None]


Instruction:
Implement a Python 3 function for
listing all prime factors of a given natural integer from greatest to smallest

Response:
def prime_factors(n):
    """Lists prime factors of a given natural integer, from greatest to smallest
    :param n: Natural integer
    :rtype : list of all prime factors of the given natural n
    """
    i = 2
    while i <= sqrt(n):
        if n % i == 0:
            l = prime_factors(n/i)
            l.append(i)
            return l
        i += 1
    return [n]


Instruction:
Make a summary of the following Python 3 code
def _factor_generator(n):
    p = prime_factors(n)
    factors = {}
    for p1 in p:
        try:
            factors[p1] += 1
        except KeyError:
            factors[p1] = 1
    return factors

Response: Returns the prime factors and their multiplicity of a natural integer.


Instruction:
Write a Python 3 script to
return the list of divisors of n in ascending order

Response:
def divisors(n):
    """
    From a given natural integer, returns the list of divisors in ascending order
    :param n: Natural integer
    :return: List of divisors of n in ascending order
    """
    factors = _factor_generator(n)
    _divisors = []
    listexponents = [[k**x for x in range(0, factors[k]+1)] for k in list(factors.keys())]
    listfactors = _cartesian_product(listexponents)
    for f in listfactors:
        _divisors.append(reduce(lambda x, y: x*y, f, 1))
    _divisors.sort()
    return _divisors


Instruction:
Write a Python 3 function that can
give a tuple of tuples generate a delimited string form.

Response:
def str_delimited(results, header=None, delimiter="\t"):
    """
    Given a tuple of tuples, generate a delimited string form.
    >>> results = [["a","b","c"],["d","e","f"],[1,2,3]]
    >>> print(str_delimited(results,delimiter=","))
    a,b,c
    d,e,f
    1,2,3

    Args:
        result: 2d sequence of arbitrary types.
        header: optional header

    Returns:
        Aligned string output in a table-like format.
    """
    returnstr = ""
    if header is not None:
        returnstr += delimiter.join(header) + "\n"
    return returnstr + "\n".join([delimiter.join([str(m) for m in result])
                                  for result in results])


Instruction:
Can you generate a brief explanation for the following Python 3 code
def formula_double_format(afloat, ignore_ones=True, tol=1e-8):
    if ignore_ones and afloat == 1:
        return ""
    elif abs(afloat - int(afloat)) < tol:
        return str(int(afloat))
    else:
        return str(round(afloat, 8))

Response: This function is used to format the amount of a single formula.


Instruction:
How would you implement a function in Python 3 that
converts a formula with unicode subscripts to Fe2O3.

Response:
def unicodeify(formula):
    """
    Generates a formula with unicode subscripts, e.g. Fe2O3 is transformed
    to Fe₂O₃. Does not support formulae with decimal points.

    :param formula:
    :return:
    """

    if '.' in formula:
        raise ValueError('No unicode character exists for subscript period.')

    subscript_unicode_map = {0: '₀', 1: '₁', 2: '₂', 3: '₃', 4: '₄',
                             5: '₅', 6: '₆', 7: '₇', 8: '₈', 9: '₉'}

    for original_subscript, subscript_unicode in subscript_unicode_map.items():
        formula = formula.replace(str(original_subscript), subscript_unicode)

    return formula


Instruction:
Make a summary of the following Python 3 code
def stream_has_colours(stream):
    if not hasattr(stream, "isatty"):
        return False

    if not stream.isatty():
        return False  # auto color only on TTYs
    try:
        import curses
        curses.setupterm()
        return curses.tigetnum("colors") > 2
    except:
        return False

Response: Returns True if the given stream supports colours.


Instruction:
Can you implement a function in Python 3 that
returns a disordered formula for a single structure.

Response:
def disordered_formula(disordered_struct, symbols=('x', 'y', 'z'), fmt='plain'):
    """
    Returns a formula of a form like AxB1-x (x=0.5)
    for disordered structures. Will only return a
    formula for disordered structures with one
    kind of disordered site at present.

    Args:
        disordered_struct: a disordered structure
        symbols: a tuple of characters to use for
        subscripts, by default this is ('x', 'y', 'z')
        but if you have more than three disordered
        species more symbols will need to be added
        fmt (str): 'plain', 'HTML' or 'LaTeX'

    Returns (str): a disordered formula string
    """

    # this is in string utils and not in
    # Composition because we need to have access
    # to site occupancies to calculate this, so
    # have to pass the full structure as an argument
    # (alternatively this could be made a method on
    # Structure)
    from pymatgen.core.composition import Composition
    from pymatgen.core.periodic_table import get_el_sp

    if disordered_struct.is_ordered:
        raise ValueError("Structure is not disordered, "
                         "so disordered formula not defined.")

    disordered_site_compositions = {site.species
                                    for site in disordered_struct if not site.is_ordered}

    if len(disordered_site_compositions) > 1:
        # this probably won't happen too often
        raise ValueError("Ambiguous how to define disordered "
                         "formula when more than one type of disordered "
                         "site is present.")
    disordered_site_composition = disordered_site_compositions.pop()

    disordered_species = {str(sp) for sp, occu in disordered_site_composition.items()}

    if len(disordered_species) > len(symbols):
        # this probably won't happen too often either
        raise ValueError("Not enough symbols to describe disordered composition: "
                         "{}".format(symbols))
    symbols = list(symbols)[0:len(disordered_species) - 1]

    comp = disordered_struct.composition.get_el_amt_dict().items()
    # sort by electronegativity, as per composition
    comp = sorted(comp, key=lambda x: get_el_sp(x[0]).X)

    disordered_comp = []
    variable_map = {}

    total_disordered_occu = sum([occu for sp, occu in comp
                                 if str(sp) in disordered_species])

    # composition to get common factor
    factor_comp = disordered_struct.composition.as_dict()
    factor_comp['X'] = total_disordered_occu
    for sp in disordered_species:
        del factor_comp[str(sp)]
    factor_comp = Composition.from_dict(factor_comp)
    factor = factor_comp.get_reduced_formula_and_factor()[1]

    total_disordered_occu /= factor
    remainder = "{}-{}".format(formula_double_format(total_disordered_occu, ignore_ones=False),
                               '-'.join(symbols))

    for sp, occu in comp:
        sp = str(sp)
        if sp not in disordered_species:
            disordered_comp.append((sp, formula_double_format(occu/factor)))
        else:
            if len(symbols) > 0:
                symbol = symbols.pop(0)
                disordered_comp.append((sp, symbol))
                variable_map[symbol] = occu / total_disordered_occu / factor
            else:
                disordered_comp.append((sp, remainder))

    if fmt == 'LaTeX':
        sub_start = "_{"
        sub_end = "}"
    elif fmt == 'HTML':
        sub_start = "<sub>"
        sub_end = "</sub>"
    elif fmt != 'plain':
        raise ValueError("Unsupported output format, "
                         "choose from: LaTeX, HTML, plain")

    disordered_formula = []
    for sp, occu in disordered_comp:
        disordered_formula.append(sp)
        if occu:  # can be empty string if 1
            if fmt != 'plain':
                disordered_formula.append(sub_start)
            disordered_formula.append(occu)
            if fmt != 'plain':
                disordered_formula.append(sub_end)
    disordered_formula.append(" ")
    disordered_formula += ["{}={} ".format(k, formula_double_format(v))
                           for k, v in variable_map.items()]

    comp = disordered_struct.composition

    return "".join(map(str, disordered_formula))[0:-1]


Instruction:
Create a Python 3 function for
comparing the bond table of the two molecules.

Response:
def are_equal(self, mol1, mol2):
        """
        Compare the bond table of the two molecules.

        Args:
            mol1: first molecule. pymatgen Molecule object.
            mol2: second moleculs. pymatgen Molecule objec.
        """
        b1 = set(self._get_bonds(mol1))
        b2 = set(self._get_bonds(mol2))
        return b1 == b2


Instruction:
How would you explain what the following Python 3 function does
def _get_bonds(self, mol):
        num_atoms = len(mol)
        # index starting from 0
        if self.ignore_ionic_bond:
            covalent_atoms = [i for i in range(num_atoms) if mol.species[i].symbol not in self.ionic_element_list]
        else:
            covalent_atoms = list(range(num_atoms))
        all_pairs = list(itertools.combinations(covalent_atoms, 2))
        pair_dists = [mol.get_distance(*p) for p in all_pairs]
        elements = mol.composition.as_dict().keys()
        unavailable_elements = list(set(elements) -
                                    set(self.covalent_radius.keys()))
        if len(unavailable_elements) > 0:
            raise ValueError("The covalent radius for element {} is not "
                             "available".format(unavailable_elements))
        bond_13 = self.get_13_bonds(self.priority_bonds)
        max_length = [(self.covalent_radius[mol.sites[p[0]].specie.symbol] +
                       self.covalent_radius[mol.sites[p[1]].specie.symbol]) *
                      (1 + (self.priority_cap if p in self.priority_bonds
                            else (self.bond_length_cap if p not in bond_13
                                  else self.bond_13_cap))) *
                      (0.1 if (self.ignore_halogen_self_bond and p not in self.priority_bonds and
                               mol.sites[p[0]].specie.symbol in self.halogen_list and
                               mol.sites[p[1]].specie.symbol in self.halogen_list)
                       else 1.0)
                      for p in all_pairs]

        bonds = [bond
                 for bond, dist, cap in zip(all_pairs, pair_dists, max_length)
                 if dist <= cap]
        return bonds

Response: Find all the bonds in a molecule.


Instruction:
Make a summary of the following Python 3 code
def compare_sym_bands(bands_obj, bands_ref_obj, nb=None):

    nkpt = len(bands_obj.kpoints)
    if bands_ref_obj.is_spin_polarized:
        nbands = min(bands_obj.nb_bands, 2 * bands_ref_obj.nb_bands)
    else:
        # TODO: why is this needed? Shouldn't pmg take care of nb_bands?
        nbands = min(len(bands_obj.bands[Spin.up]),
                     len(bands_ref_obj.bands[Spin.up]))
    # print(nbands)
    arr_bands = np.array(bands_obj.bands[Spin.up][:nbands])
    # arr_bands_lavg = (arr_bands-np.mean(arr_bands,axis=1).reshape(nbands,1))

    if bands_ref_obj.is_spin_polarized:
        arr_bands_ref_up = np.array(bands_ref_obj.bands[Spin.up])
        arr_bands_ref_dw = np.array(bands_ref_obj.bands[Spin.down])
        # print(arr_bands_ref_up.shape)
        arr_bands_ref = np.vstack((arr_bands_ref_up, arr_bands_ref_dw))
        arr_bands_ref = np.sort(arr_bands_ref, axis=0)[:nbands]
        # print(arr_bands_ref.shape)
    else:
        arr_bands_ref = np.array(bands_ref_obj.bands[Spin.up][:nbands])

    # arr_bands_ref_lavg =
    # (arr_bands_ref-np.mean(arr_bands_ref,axis=1).reshape(nbands,1))

    # err = np.sum((arr_bands_lavg-arr_bands_ref_lavg)**2,axis=1)/nkpt
    corr = np.array(
        [distance.correlation(arr_bands[idx], arr_bands_ref[idx]) for idx in
         range(nbands)])

    if type(nb) == int: nb = [nb]

    bcheck = {}

    if max(nb) < nbands:
        branches = [[s['start_index'], s['end_index'], s['name']] for s in
                    bands_ref_obj.branches]

        if not bands_obj.is_metal() and not bands_ref_obj.is_metal():
            zero_ref = bands_ref_obj.get_vbm()['energy']
            zero = bands_obj.get_vbm()['energy']
            if not zero:
                vbm = bands_ref_obj.get_vbm()['band_index'][Spin.up][-1]
                zero = max(arr_bands[vbm])
        else:
            zero_ref = 0  # bands_ref_obj.efermi
            zero = 0  # bands_obj.efermi
            print(zero, zero_ref)

        for nbi in nb:
            bcheck[nbi] = {}

            bcheck[nbi]['Dist'] = np.mean(abs(arr_bands[nbi] - zero
                                              - arr_bands_ref[nbi] + zero_ref))
            bcheck[nbi]['Corr'] = corr[nbi]

            for start, end, name in branches:
                # werr.append((sum((arr_bands_corr[nb][start:end+1] -
                # arr_bands_ref_corr[nb][start:end+1])**2)/(end+1-start)*100,name))
                bcheck[nbi][name] = np.mean(abs(arr_bands[nbi][start:end + 1]
                                                - zero
                                                - arr_bands_ref[nbi][
                                                  start:end + 1] + zero_ref))
    else:
        bcheck = "No nb given"

    return bcheck

Response: Compare two bands and return the mean of correlation between bzt and vasp bandstructure on the symmetric band structure.


Instruction:
Create a Python 3 function for
returning the seebeck analytic formula in the single parabolic model

Response:
def seebeck_spb(eta,Lambda=0.5):
    """
        Seebeck analytic formula in the single parabolic model
    """
    from fdint import fdk

    return constants.k/constants.e * ((2. + Lambda) * fdk( 1.+ Lambda, eta)/
                 ((1.+Lambda)*fdk(Lambda, eta))- eta) * 1e+6


Instruction:
Can you generate a brief explanation for the following Python 3 code
def eta_from_seebeck(seeb,Lambda):
    from scipy.optimize import fsolve
    out = fsolve(lambda x: (seebeck_spb(x,Lambda) - abs(seeb)) ** 2, 1.,full_output=True)
    return out[0][0]

Response: This function takes a value of seebeck and adjusts the analytic seebeck until it s equal


Instruction:
Make a summary of the following Python 3 code
def seebeck_eff_mass_from_carr(eta, n, T, Lambda):
    from fdint import fdk

    return (2 * np.pi**2 * abs(n) * 10 ** 6 / (fdk(0.5,eta))) ** (2. / 3)\
            / (2 * constants.m_e * constants.k * T / (constants.h/2/np.pi) ** 2)

Response: Calculate the seebeck effective mass at a certain carrier concentration


Instruction:
Can you write a function in Python 3 where it
calculates the seebeck effective mass at a certain carrier concentration

Response:
def seebeck_eff_mass_from_seebeck_carr(seeb, n, T, Lambda):
    """
        Find the chemical potential where analytic and calculated seebeck are identical
        and then calculate the seebeck effective mass at that chemical potential and
        a certain carrier concentration n
    """
    try:
        from fdint import fdk
    except ImportError:
        raise BoltztrapError("fdint module not found. Please, install it.\n"+
                            "It is needed to calculate Fermi integral quickly.")

    eta = eta_from_seebeck(seeb,Lambda)
    mass = seebeck_eff_mass_from_carr(eta, n, T, Lambda)
    return mass


Instruction:
Given the following Python 3 function, write the documentation
def _auto_set_energy_range(self):
        emins = [min([e_k[0] for e_k in self._bs.bands[Spin.up]])]
        emaxs = [max([e_k[0] for e_k in self._bs.bands[Spin.up]])]

        if self._bs.is_spin_polarized:
            emins.append(min([e_k[0] for e_k in
                              self._bs.bands[Spin.down]]))

            emaxs.append(max([e_k[0] for e_k in
                              self._bs.bands[Spin.down]]))

        min_eigenval = Energy(min(emins) - self._bs.efermi, "eV"). \
            to("Ry")
        max_eigenval = Energy(max(emaxs) - self._bs.efermi, "eV"). \
            to("Ry")

        # set energy range to buffer around min/max EV
        # buffer does not increase CPU time but will help get equal
        # energies for spin up/down for band structure
        const = Energy(2, "eV").to("Ry")
        self._ll = min_eigenval - const
        self._hl = max_eigenval + const

        en_range = Energy(max((abs(self._ll), abs(self._hl))),
                          "Ry").to("eV")

        self.energy_span_around_fermi = en_range * 1.01
        print("energy_span_around_fermi = ",
              self.energy_span_around_fermi)

Response: Auto - determine the energy range for the current entry.


Instruction:
Implement a function in Python 3 to
run the BoltzTraP and ensure that the current state of the current node is consistent with the current state of the current node.

Response:
def run(self, path_dir=None, convergence=True, write_input=True,
            clear_dir=False, max_lpfac=150, min_egrid=0.00005):
        """
        Write inputs (optional), run BoltzTraP, and ensure
        convergence (optional)
        Args:
            path_dir (str): directory in which to run BoltzTraP
            convergence (bool): whether to check convergence and make
                corrections if needed
            write_input: (bool) whether to write input files before the run
                (required for convergence mode)
            clear_dir: (bool) whether to remove all files in the path_dir
                before starting
            max_lpfac: (float) maximum lpfac value to try before reducing egrid
                in convergence mode
            min_egrid: (float) minimum egrid value to try before giving up in
                convergence mode

        Returns:

        """

        # TODO: consider making this a part of custodian rather than pymatgen
        # A lot of this functionality (scratch dirs, handlers, monitors)
        # is built into custodian framework

        if convergence and not write_input:
            raise ValueError("Convergence mode requires write_input to be "
                             "true")

        if self.run_type in ("BANDS", "DOS", "FERMI"):
            convergence = False
            if self.lpfac > max_lpfac:
                max_lpfac = self.lpfac

        if self.run_type == "BANDS" and self.bs.is_spin_polarized:
            print("Reminder: for run_type " + str(
                self.run_type) + ", spin component are not separated! "
                                 "(you have a spin polarized band structure)")

        if self.run_type in ("FERMI", "DOS") and self.spin is None:
            if self.bs.is_spin_polarized:
                raise BoltztrapError(
                    "Spin parameter must be specified for spin polarized "
                    "band structures!")
            else:
                self.spin = 1

        dir_bz_name = "boltztrap"
        if path_dir is None:
            temp_dir = tempfile.mkdtemp()
            path_dir = os.path.join(temp_dir, dir_bz_name)
        else:
            path_dir = os.path.abspath(
                os.path.join(path_dir, dir_bz_name))

        if not os.path.exists(path_dir):
            os.mkdir(path_dir)
        elif clear_dir:
            for c in os.listdir(path_dir):
                os.remove(os.path.join(path_dir, c))

        FORMAT = "%(message)s"
        logging.basicConfig(level=logging.INFO, format=FORMAT,
                            filename=os.path.join(path_dir, "../boltztrap.out"))

        with cd(path_dir):
            lpfac_start = self.lpfac
            converged = False

            while self.energy_grid >= min_egrid and not converged:
                self.lpfac = lpfac_start
                if time.time() - self.start_time > self.timeout:
                    raise BoltztrapError("no doping convergence after timeout "
                                         "of {} s".format(self.timeout))

                logging.info("lpfac, energy_grid: {} {}".format(self.lpfac, self.energy_grid))

                while self.lpfac <= max_lpfac and not converged:
                    if time.time() - self.start_time > self.timeout:
                        raise BoltztrapError("no doping convergence after "
                                        "timeout of {} s".format(self.timeout))

                    if write_input:
                        self.write_input(path_dir)

                    bt_exe = ["x_trans", "BoltzTraP"]
                    if self._bs.is_spin_polarized or self.soc:
                        bt_exe.append("-so")

                    p = subprocess.Popen(bt_exe, stdout=subprocess.PIPE,
                                         stdin=subprocess.PIPE,
                                         stderr=subprocess.PIPE)
                    p.wait()

                    for c in p.communicate():
                        logging.info(c.decode())
                        if "error in factorization" in c.decode():
                            raise BoltztrapError("error in factorization")

                    warning = ""

                    with open(os.path.join(path_dir,
                                           dir_bz_name + ".outputtrans")) as f:
                        for l in f:
                            if "Option unknown" in l:
                                raise BoltztrapError(
                                    "DOS mode needs a custom version of "
                                    "BoltzTraP code is needed")
                            if "WARNING" in l:
                                warning = l
                                break
                            if "Error - Fermi level was not found" in l:
                                warning = l
                                break

                    if not warning and convergence:
                        # check convergence for warning
                        analyzer = BoltztrapAnalyzer.from_files(path_dir)
                        for doping in ['n', 'p']:
                            for c in analyzer.mu_doping[doping]:
                                if len(analyzer.mu_doping[doping][c]) != len(
                                        analyzer.doping[doping]):
                                    warning = "length of mu_doping array is " \
                                              "incorrect"
                                    break

                                if doping == 'p' and \
                                                sorted(
                                                    analyzer.mu_doping[doping][
                                                        c], reverse=True) != \
                                                analyzer.mu_doping[doping][c]:
                                    warning = "sorting of mu_doping array " \
                                              "incorrect for p-type"
                                    break

                                # ensure n-type doping sorted correctly
                                if doping == 'n' and sorted(
                                        analyzer.mu_doping[doping][c]) != \
                                        analyzer.mu_doping[doping][c]:
                                    warning = "sorting of mu_doping array " \
                                              "incorrect for n-type"
                                    break

                    if warning:
                        self.lpfac += 10
                        logging.warn("Warning detected: {}! Increase lpfac to "
                                     "{}".format(warning, self.lpfac))

                    else:
                        converged = True

                if not converged:
                    self.energy_grid /= 10
                    logging.info("Could not converge with max lpfac; "
                                 "Decrease egrid to {}".format(self.energy_grid))

            if not converged:
                raise BoltztrapError(
                    "Doping convergence not reached with lpfac=" + str(
                        self.lpfac) + ", energy_grid=" + str(self.energy_grid))

            return path_dir


Instruction:
Write a Python 3 function that can
function used to read bands from Boltztrap output and get them comparable with that one from a DFT calculation.

Response:
def get_symm_bands(self, structure, efermi, kpt_line=None,
                       labels_dict=None):
        """
            Function useful to read bands from Boltztrap output and get a
            BandStructureSymmLine object comparable with that one from a DFT
            calculation (if the same kpt_line is provided). Default kpt_line
            and labels_dict is the standard path of high symmetry k-point for
            the specified structure. They could be extracted from the
            BandStructureSymmLine object that you want to compare with. efermi
            variable must be specified to create the BandStructureSymmLine
            object (usually it comes from DFT or Boltztrap calc)
        """
        try:
            if kpt_line is None:
                kpath = HighSymmKpath(structure)
                kpt_line = [Kpoint(k, structure.lattice.reciprocal_lattice) for
                            k in
                            kpath.get_kpoints(coords_are_cartesian=False)[0]]
                labels_dict = {l: k for k, l in zip(
                    *kpath.get_kpoints(coords_are_cartesian=False)) if l}
                kpt_line = [kp.frac_coords for kp in kpt_line]
            elif type(kpt_line[0]) == Kpoint:
                kpt_line = [kp.frac_coords for kp in kpt_line]
                labels_dict = {k: labels_dict[k].frac_coords for k in
                               labels_dict}

            idx_list = []
            #       kpt_dense=np.array([kp for kp in self._bz_kpoints])
            for i, kp in enumerate(kpt_line):
                w = []
                prec = 1e-05
                while len(w) == 0:
                    w = np.where(np.all(
                        np.abs(kp - self._bz_kpoints) < [prec] * 3,
                        axis=1))[0]
                    prec *= 10

                # print( prec )
                idx_list.append([i, w[0]])

                # if len(w)>0:
                #     idx_list.append([i,w[0]])
                # else:
                #     w=np.where(np.all(np.abs(kp.frac_coords-self._bz_kpoints)
                # <[1e-04,1e-04,1e-04],axis=1))[0]
                #     idx_list.append([i,w[0]])

            idx_list = np.array(idx_list)
            # print( idx_list.shape )

            bands_dict = {Spin.up: (self._bz_bands * Energy(1, "Ry").to(
                "eV") + efermi).T[:, idx_list[:, 1]].tolist()}
            # bz_kpoints = bz_kpoints[idx_list[:,1]].tolist()

            sbs = BandStructureSymmLine(kpt_line, bands_dict,
                                        structure.lattice.reciprocal_lattice,
                                        efermi,
                                        labels_dict=labels_dict)

            return sbs

        except:
            raise BoltztrapError(
                "Bands are not in output of BoltzTraP.\nBolztrapRunner must "
                "be run with run_type=BANDS")


Instruction:
Write a Python 3 script to
compare the two - dimensional BandStructureSymmLine with the two - dimensional BandStructureSymmLine as reference and return a dictionary with these keys as keys and values as values.

Response:
def check_acc_bzt_bands(sbs_bz, sbs_ref, warn_thr=(0.03, 0.03)):
        """
            Compare sbs_bz BandStructureSymmLine calculated with boltztrap with
            the sbs_ref BandStructureSymmLine as reference (from MP for
            instance), computing correlation and energy difference for eight bands
            around the gap (semiconductors) or fermi level (metals).
            warn_thr is a threshold to get a warning in the accuracy of Boltztap
            interpolated bands.
            Return a dictionary with these keys:
            - "N": the index of the band compared; inside each there are:
                - "Corr": correlation coefficient for the 8 compared bands
                - "Dist": energy distance for the 8 compared bands
                - "branch_name": energy distance for that branch
            - "avg_corr": average of correlation coefficient over the 8 bands
            - "avg_dist": average of energy distance over the 8 bands
            - "nb_list": list of indexes of the 8 compared bands
            - "acc_thr": list of two float corresponing to the two warning
                         thresholds in input
            - "acc_err": list of two bools:
                         True if the avg_corr > warn_thr[0], and
                         True if the avg_dist > warn_thr[1]
            See also compare_sym_bands function doc
        """
        if not sbs_ref.is_metal() and not sbs_bz.is_metal():
            vbm_idx = sbs_bz.get_vbm()['band_index'][Spin.up][-1]
            cbm_idx = sbs_bz.get_cbm()['band_index'][Spin.up][0]
            nb_list = range(vbm_idx - 3, cbm_idx + 4)

        else:
            bnd_around_efermi = []
            delta = 0
            spin = list(sbs_bz.bands.keys())[0]
            while len(bnd_around_efermi) < 8 and delta < 100:
                delta += 0.1
                bnd_around_efermi = []
                for nb in range(len(sbs_bz.bands[spin])):
                    for kp in range(len(sbs_bz.bands[spin][nb])):
                        if abs(sbs_bz.bands[spin][nb][
                                   kp] - sbs_bz.efermi) < delta:
                            bnd_around_efermi.append(nb)
                            break
            if len(bnd_around_efermi) < 8:
                print("Warning! check performed on " + str(
                    len(bnd_around_efermi)))
                nb_list = bnd_around_efermi
            else:
                nb_list = bnd_around_efermi[:8]

        # print(nb_list)
        bcheck = compare_sym_bands(sbs_bz, sbs_ref, nb_list)
        # print(bcheck)
        acc_err = [False, False]
        avg_corr = sum([item[1]['Corr'] for item in bcheck.iteritems()]) / 8
        avg_distance = sum([item[1]['Dist'] for item in bcheck.iteritems()]) / 8

        if avg_corr > warn_thr[0]: acc_err[0] = True
        if avg_distance > warn_thr[0]: acc_err[1] = True

        bcheck['avg_corr'] = avg_corr
        bcheck['avg_distance'] = avg_distance
        bcheck['acc_err'] = acc_err
        bcheck['acc_thr'] = warn_thr
        bcheck['nb_list'] = nb_list

        if True in acc_err:
            print("Warning! some bands around gap are not accurate")

        return bcheck


Instruction:
Here you have a function in Python 3, explain what it does
def get_seebeck(self, output='eigs', doping_levels=True):
        return BoltztrapAnalyzer._format_to_output(self._seebeck,
                                                   self._seebeck_doping,
                                                   output,
                                                   doping_levels, 1e6)

Response:
Returns the Seebeck coefficient of the given base - level a
           .


Instruction:
Make a summary of the following Python 3 code
def get_conductivity(self, output='eigs', doping_levels=True,
                         relaxation_time=1e-14):
        """
            Gives the conductivity (1/Ohm*m) in either a full 3x3 tensor
            form, as 3 eigenvalues, or as the average value
            (trace/3.0) If doping_levels=True, the results are given at
            different p and n doping
            levels (given by self.doping), otherwise it is given as a series
            of electron chemical potential values

            Args:
                output (string): the type of output. 'tensor' give the full
                3x3 tensor, 'eigs' its 3 eigenvalues and
                'average' the average of the three eigenvalues
                doping_levels (boolean): True for the results to be given at
                different doping levels, False for results
                at different electron chemical potentials
                relaxation_time (float): constant relaxation time in secs

            Returns:
                If doping_levels=True, a dictionary {temp:{'p':[],'n':[]}}.
                The 'p' links to conductivity
                at p-type doping and 'n' to the conductivity at n-type
                doping. Otherwise,
                returns a {temp:[]} dictionary. The result contains either
                the sorted three eigenvalues of the symmetric
                conductivity tensor (format='eigs') or a full tensor (3x3
                array) (output='tensor') or as an average
                (output='average').
                The result includes a given constant relaxation time

                units are 1/Ohm*m
        """
        return BoltztrapAnalyzer._format_to_output(self._cond,
                                                   self._cond_doping, output,
                                                   doping_levels,
                                                   relaxation_time)

Response: Returns the conductivity of the current object.


Instruction:
Create a Python 3 function for
returning the power factor of the current state of the entry in the system.

Response:
def get_power_factor(self, output='eigs', doping_levels=True,
                         relaxation_time=1e-14):
        """
        Gives the power factor (Seebeck^2 * conductivity) in units
        microW/(m*K^2) in either a full 3x3 tensor form,
        as 3 eigenvalues, or as the average value (trace/3.0) If
        doping_levels=True, the results are given at
        different p and n doping levels (given by self.doping), otherwise it
        is given as a series of
        electron chemical potential values

        Args:
            output (string): the type of output. 'tensor' give the full 3x3
            tensor, 'eigs' its 3 eigenvalues and
            'average' the average of the three eigenvalues
            doping_levels (boolean): True for the results to be given at
            different doping levels, False for results
            at different electron chemical potentials
            relaxation_time (float): constant relaxation time in secs

        Returns:
            If doping_levels=True, a dictionnary {temp:{'p':[],'n':[]}}. The
            'p' links to power factor
            at p-type doping and 'n' to the conductivity at n-type doping.
            Otherwise,
            returns a {temp:[]} dictionary. The result contains either the
            sorted three eigenvalues of the symmetric
            power factor tensor (format='eigs') or a full tensor (3x3 array) (
            output='tensor') or as an average
            (output='average').
            The result includes a given constant relaxation time

            units are microW/(m K^2)
        """
        result = None
        result_doping = None
        if doping_levels:
            result_doping = {doping: {t: [] for t in
                                      self._seebeck_doping[doping]} for
                             doping in self._seebeck_doping}

            for doping in result_doping:
                for t in result_doping[doping]:
                    for i in range(len(self.doping[doping])):
                        full_tensor = np.dot(self._cond_doping[doping][t][i],
                                             np.dot(
                                                 self._seebeck_doping[doping][
                                                     t][i],
                                                 self._seebeck_doping[doping][
                                                     t][i]))
                        result_doping[doping][t].append(full_tensor)

        else:
            result = {t: [] for t in self._seebeck}
            for t in result:
                for i in range(len(self.mu_steps)):
                    full_tensor = np.dot(self._cond[t][i],
                                         np.dot(self._seebeck[t][i],
                                                self._seebeck[t][i]))
                    result[t].append(full_tensor)

        return BoltztrapAnalyzer._format_to_output(result, result_doping,
                                                   output, doping_levels,
                                                   multi=1e6 * relaxation_time)


Instruction:
Can you generate the documentation for the following Python 3 function
def get_thermal_conductivity(self, output='eigs', doping_levels=True,
                                 k_el=True, relaxation_time=1e-14):
        """
        Gives the electronic part of the thermal conductivity in either a
        full 3x3 tensor form,
        as 3 eigenvalues, or as the average value (trace/3.0) If
        doping_levels=True, the results are given at
        different p and n doping levels (given by self.doping), otherwise it
        is given as a series of
        electron chemical potential values

        Args:
            output (string): the type of output. 'tensor' give the full 3x3
            tensor, 'eigs' its 3 eigenvalues and
            'average' the average of the three eigenvalues
            doping_levels (boolean): True for the results to be given at
            different doping levels, False for results
            at different electron chemical potentials
            k_el (boolean): True for k_0-PF*T, False for k_0
            relaxation_time (float): constant relaxation time in secs

        Returns:
            If doping_levels=True, a dictionary {temp:{'p':[],'n':[]}}. The
            'p' links to thermal conductivity
            at p-type doping and 'n' to the thermal conductivity at n-type
            doping. Otherwise,
            returns a {temp:[]} dictionary. The result contains either the
            sorted three eigenvalues of the symmetric
            conductivity tensor (format='eigs') or a full tensor (3x3 array) (
            output='tensor') or as an average
            (output='average').
            The result includes a given constant relaxation time

            units are W/mK
        """
        result = None
        result_doping = None
        if doping_levels:
            result_doping = {doping: {t: [] for t in
                                      self._seebeck_doping[doping]} for
                             doping in self._seebeck_doping}
            for doping in result_doping:
                for t in result_doping[doping]:
                    for i in range(len(self.doping[doping])):
                        if k_el:
                            pf_tensor = np.dot(self._cond_doping[doping][t][i],
                                               np.dot(
                                                   self._seebeck_doping[doping][
                                                       t][i],
                                                   self._seebeck_doping[doping][
                                                       t][i]))
                            result_doping[doping][t].append((
                                self._kappa_doping[doping][t][
                                    i] - pf_tensor * t))
                        else:
                            result_doping[doping][t].append((
                                self._kappa_doping[doping][t][i]))
        else:
            result = {t: [] for t in self._seebeck}
            for t in result:
                for i in range(len(self.mu_steps)):
                    if k_el:
                        pf_tensor = np.dot(self._cond[t][i],
                                           np.dot(self._seebeck[t][i],
                                                  self._seebeck[t][i]))
                        result[t].append((self._kappa[t][i] - pf_tensor * t))
                    else:
                        result[t].append((self._kappa[t][i]))

        return BoltztrapAnalyzer._format_to_output(result, result_doping,
                                                   output, doping_levels,
                                                   multi=relaxation_time)

Response: Returns the thermal conductivity of the entry in the system.


Instruction:
Explain what the following Python 3 code does
def get_zt(self, output='eigs', doping_levels=True, relaxation_time=1e-14,
               kl=1.0):
        """
        Gives the ZT coefficient (S^2*cond*T/thermal cond) in either a full
        3x3 tensor form,
        as 3 eigenvalues, or as the average value (trace/3.0) If
        doping_levels=True, the results are given at
        different p and n doping levels (given by self.doping), otherwise it
        is given as a series of
        electron chemical potential values. We assume a constant relaxation
        time and a constant
        lattice thermal conductivity

        Args:
            output (string): the type of output. 'tensor' give the full 3x3
            tensor, 'eigs' its 3 eigenvalues and
            'average' the average of the three eigenvalues
            doping_levels (boolean): True for the results to be given at
            different doping levels, False for results
            at different electron chemical potentials
            relaxation_time (float): constant relaxation time in secs
            k_l (float): lattice thermal cond in W/(m*K)

        Returns:
            If doping_levels=True, a dictionary {temp:{'p':[],'n':[]}}. The
            'p' links to ZT
            at p-type doping and 'n' to the ZT at n-type doping. Otherwise,
            returns a {temp:[]} dictionary. The result contains either the
            sorted three eigenvalues of the symmetric
            ZT tensor (format='eigs') or a full tensor (3x3 array) (
            output='tensor') or as an average
            (output='average').
            The result includes a given constant relaxation time and lattice
            thermal conductivity
        """
        result = None
        result_doping = None
        if doping_levels:
            result_doping = {doping: {t: [] for t in
                                      self._seebeck_doping[doping]} for
                             doping in self._seebeck_doping}

            for doping in result_doping:
                for t in result_doping[doping]:
                    for i in range(len(self.doping[doping])):
                        pf_tensor = np.dot(self._cond_doping[doping][t][i],
                                           np.dot(
                                               self._seebeck_doping[doping][t][
                                                   i],
                                               self._seebeck_doping[doping][t][
                                                   i]))
                        thermal_conduct = (self._kappa_doping[doping][t][i]
                                           - pf_tensor * t) * relaxation_time
                        result_doping[doping][t].append(
                            np.dot(pf_tensor * relaxation_time * t,
                                   np.linalg.inv(
                                       thermal_conduct + kl * np.eye(3, 3))))
        else:
            result = {t: [] for t in self._seebeck}
            for t in result:
                for i in range(len(self.mu_steps)):
                    pf_tensor = np.dot(self._cond[t][i],
                                       np.dot(self._seebeck[t][i],
                                              self._seebeck[t][i]))
                    thermal_conduct = (self._kappa[t][i]
                                       - pf_tensor * t) * relaxation_time
                    result[t].append(np.dot(pf_tensor * relaxation_time * t,
                                            np.linalg.inv(
                                                thermal_conduct + kl *
                                                np.eye(3, 3))))

        return BoltztrapAnalyzer._format_to_output(result, result_doping,
                                                   output, doping_levels)

Response: Returns the ZT coefficient of the symmetric entry of the current object.


Instruction:
Write a Python 3 function that can
give the average effective mass tensor. We call it average because it takes into account all the bands and regions in the Brillouin zone. This is different than the standard textbook effective mass which relates often to only one (parabolic) band. The average effective mass tensor is defined as the integrated average of the second derivative of E(k) This effective mass tensor takes into account: -non-parabolicity -multiple extrema -multiple bands For more information about it. See: Hautier, G., Miglio, A., Waroquiers, D., Rignanese, G., & Gonze, X. (2014). How Does Chemistry Influence Electron Effective Mass in Oxides? A High-Throughput Computational Analysis. Chemistry of Materials, 26(19), 5447–5458. doi:10.1021/cm404079a or Hautier, G., Miglio, A., Ceder, G., Rignanese, G.-M., & Gonze, X. (2013). Identification and design principles of low hole effective mass p-type transparent conducting oxides. Nature Communications, 4, 2292. doi:10.1038/ncomms3292 Depending on the value of output, we have either the full 3x3 effective mass tensor, its 3 eigenvalues or an average Args: output (string): 'eigs' for eigenvalues, 'tensor' for the full tensor and 'average' for an average (trace/3) doping_levels (boolean): True for the results to be given at different doping levels, False for results at different electron chemical potentials Returns: If doping_levels=True,a dictionary {'p':{temp:[]},'n':{temp:[]}} with an array of effective mass tensor, eigenvalues of average value (depending on output) for each temperature and for each doping level. The 'p' links to hole effective mass tensor and 'n' to electron effective mass tensor.

Response:
def get_average_eff_mass(self, output='eigs', doping_levels=True):
        """
        Gives the average effective mass tensor. We call it average because
        it takes into account all the bands
        and regions in the Brillouin zone. This is different than the standard
        textbook effective mass which relates
        often to only one (parabolic) band.
        The average effective mass tensor is defined as the integrated
        average of the second derivative of E(k)
        This effective mass tensor takes into account:
        -non-parabolicity
        -multiple extrema
        -multiple bands

        For more information about it. See:

        Hautier, G., Miglio, A., Waroquiers, D., Rignanese, G., & Gonze,
        X. (2014).
        How Does Chemistry Influence Electron Effective Mass in Oxides?
        A High-Throughput Computational Analysis. Chemistry of Materials,
        26(19), 5447–5458. doi:10.1021/cm404079a

        or

        Hautier, G., Miglio, A., Ceder, G., Rignanese, G.-M., & Gonze,
        X. (2013).
        Identification and design principles of low hole effective mass
        p-type transparent conducting oxides.
        Nature Communications, 4, 2292. doi:10.1038/ncomms3292

        Depending on the value of output, we have either the full 3x3
        effective mass tensor,
        its 3 eigenvalues or an average

        Args:
            output (string): 'eigs' for eigenvalues, 'tensor' for the full
            tensor and 'average' for an average (trace/3)
            doping_levels (boolean): True for the results to be given at
            different doping levels, False for results
            at different electron chemical potentials
        Returns:
            If doping_levels=True,a dictionary {'p':{temp:[]},'n':{temp:[]}}
            with an array of effective mass tensor, eigenvalues of average
            value (depending on output) for each temperature and for each
            doping level.
            The 'p' links to hole effective mass tensor and 'n' to electron
            effective mass tensor.
        """
        result = None
        result_doping = None
        conc = self.get_carrier_concentration()
        if doping_levels:
            result_doping = {doping: {t: [] for t in self._cond_doping[doping]}
                             for
                             doping in self.doping}
            for doping in result_doping:
                for temp in result_doping[doping]:
                    for i in range(len(self.doping[doping])):
                        try:
                            result_doping[doping][temp].append(np.linalg.inv(
                                np.array(self._cond_doping[doping][temp][i])) * \
                                                               self.doping[doping][
                                                                   i] * 10 ** 6 *
                                                                    constants.e ** 2 /
                                                                    constants.m_e)
                        except np.linalg.LinAlgError:
                            pass
        else:
            result = {t: [] for t in self._seebeck}
            for temp in result:
                for i in range(len(self.mu_steps)):
                    try:
                        cond_inv = np.linalg.inv(np.array(self._cond[temp][i]))
                    except np.linalg.LinAlgError:
                        pass
                    result[temp].append(cond_inv * \
                                        conc[temp][i] * 10 ** 6 *
                                                constants.e ** 2 /
                                                constants.m_e)

        return BoltztrapAnalyzer._format_to_output(result, result_doping,
                                                   output, doping_levels)


Instruction:
Make a summary of the following Python 3 code
def get_seebeck_eff_mass(self, output='average', temp=300, doping_levels=False,
                                Lambda=0.5):
        """
        Seebeck effective mass calculated as explained in Ref.
        Gibbs, Z. M. et al., Effective mass and fermi surface complexity factor
        from ab initio band structure calculations.
        npj Computational Materials 3, 8 (2017).

        Args:
            output: 'average' returns the seebeck effective mass calculated using
                    the average of the three diagonal components of the seebeck tensor.
                    'tensor' returns the seebeck effective mass respect to the three
                    diagonal components of the seebeck tensor.
            doping_levels: False means that the seebeck effective mass is calculated
                           for every value of the chemical potential
                           True means that the seebeck effective mass is calculated
                           for every value of the doping levels for both n and p types
            temp:   temperature of calculated seebeck.
            Lambda: fitting parameter used to model the scattering (0.5 means constant
                    relaxation time).
        Returns:
            a list of values for the seebeck effective mass w.r.t the chemical potential,
            if doping_levels is set at False;
            a dict with n an p keys that contain a list of values for the seebeck effective
            mass w.r.t the doping levels, if doping_levels is set at True;
            if 'tensor' is selected, each element of the lists is a list containing
            the three components of the seebeck effective mass.
        """

        if doping_levels:
            sbk_mass = {}
            for dt in ('n','p'):
                conc = self.doping[dt]
                seebeck = self.get_seebeck(output=output, doping_levels=True)[dt][temp]
                sbk_mass[dt] = []
                for i in range(len(conc)):
                    if output == 'average':
                        sbk_mass[dt].append(
                            seebeck_eff_mass_from_seebeck_carr(abs(seebeck[i]),
                                                                conc[i], temp, Lambda))
                    elif output == 'tensor':
                        sbk_mass[dt].append([])
                        for j in range(3):
                            sbk_mass[dt][-1].append(
                                seebeck_eff_mass_from_seebeck_carr(abs(seebeck[i][j][j]),
                                                                    conc[i], temp, Lambda))

        else:
            seebeck = self.get_seebeck(output=output, doping_levels=False)[temp]
            conc = self.get_carrier_concentration()[temp]
            sbk_mass = []
            for i in range(len(conc)):
                if output == 'average':
                    sbk_mass.append(
                        seebeck_eff_mass_from_seebeck_carr(abs(seebeck[i]),
                                                            conc[i], temp, Lambda))
                elif output == 'tensor':
                    sbk_mass.append([])
                    for j in range(3):
                        sbk_mass[-1].append(
                            seebeck_eff_mass_from_seebeck_carr(abs(seebeck[i][j][j]),
                                                                conc[i], temp, Lambda))
        return sbk_mass

Response: Returns the seebeck effective mass for a given temperature and doping level.


Instruction:
Can you create a Python 3 function that
returns the complexity factor for a given set of parameters.

Response:
def get_complexity_factor(self, output='average', temp=300, doping_levels=False,
                              Lambda=0.5):
        """
        Fermi surface complexity factor respect to calculated as explained in Ref.
        Gibbs, Z. M. et al., Effective mass and fermi surface complexity factor
        from ab initio band structure calculations.
        npj Computational Materials 3, 8 (2017).

        Args:
            output: 'average' returns the complexity factor calculated using the average
                    of the three diagonal components of the seebeck and conductivity tensors.
                    'tensor' returns the complexity factor respect to the three
                    diagonal components of seebeck and conductivity tensors.
            doping_levels: False means that the complexity factor is calculated
                           for every value of the chemical potential
                           True means that the complexity factor is calculated
                           for every value of the doping levels for both n and p types
            temp:   temperature of calculated seebeck and conductivity.
            Lambda: fitting parameter used to model the scattering (0.5 means constant
                    relaxation time).
        Returns:
            a list of values for the complexity factor w.r.t the chemical potential,
            if doping_levels is set at False;
            a dict with n an p keys that contain a list of values for the complexity factor
            w.r.t the doping levels, if doping_levels is set at True;
            if 'tensor' is selected, each element of the lists is a list containing
            the three components of the complexity factor.
        """

        if doping_levels:
            cmplx_fact = {}
            for dt in ('n','p'):
                sbk_mass = self.get_seebeck_eff_mass(output, temp, True, Lambda)[dt]
                cond_mass = self.get_average_eff_mass(output=output, doping_levels=True)[dt][temp]

                if output == 'average':
                    cmplx_fact[dt] = [ (m_s/abs(m_c))**1.5 for m_s,m_c in zip(sbk_mass,cond_mass)]
                elif output == 'tensor':
                    cmplx_fact[dt] = []
                    for i in range(len(sbk_mass)):
                        cmplx_fact[dt].append([])
                        for j in range(3):
                            cmplx_fact[dt][-1].append((sbk_mass[i][j]/abs(cond_mass[i][j][j]))**1.5)

        else:
            sbk_mass = self.get_seebeck_eff_mass(output, temp, False, Lambda)
            cond_mass = self.get_average_eff_mass(output=output, doping_levels=False)[temp]

            if output == 'average':
                cmplx_fact = [ (m_s/abs(m_c))**1.5 for m_s,m_c in zip(sbk_mass,cond_mass)]
            elif output == 'tensor':
                cmplx_fact = []
                for i in range(len(sbk_mass)):
                    cmplx_fact.append([])
                    for j in range(3):
                        cmplx_fact[-1].append((sbk_mass[i][j]/abs(cond_mass[i][j][j]))**1.5)

        return cmplx_fact


Instruction:
How would you code a function in Python 3 to
return a CompleteDos object with the DOS from the interpolated structure and spin components.

Response:
def get_complete_dos(self, structure, analyzer_for_second_spin=None):
        """
            Gives a CompleteDos object with the DOS from the interpolated
            projected band structure
            Args:
                the structure (necessary to identify sites for projection)
                analyzer_for_second_spin must be specified to have a
                CompleteDos with both Spin components
            Returns:
                a CompleteDos object
            Example of use in case of spin polarized case:

                BoltztrapRunner(bs=bs,nelec=10,run_type="DOS",spin=1).run(path_dir='dos_up/')
                an_up=BoltztrapAnalyzer.from_files("dos_up/boltztrap/",dos_spin=1)

                BoltztrapRunner(bs=bs,nelec=10,run_type="DOS",spin=-1).run(path_dir='dos_dw/')
                an_dw=BoltztrapAnalyzer.from_files("dos_dw/boltztrap/",dos_spin=-1)

                cdos=an_up.get_complete_dos(bs.structure,an_dw)

        """
        pdoss = {}
        spin_1 = list(self.dos.densities.keys())[0]

        if analyzer_for_second_spin:
            if not np.all(self.dos.energies ==
                                  analyzer_for_second_spin.dos.energies):
                raise BoltztrapError(
                    "Dos merging error: energies of the two dos are different")

            spin_2 = list(analyzer_for_second_spin.dos.densities.keys())[0]
            if spin_1 == spin_2:
                raise BoltztrapError(
                    "Dos merging error: spin component are the same")

        for s in self._dos_partial:
            if structure.sites[int(s)] not in pdoss:
                pdoss[structure.sites[int(s)]] = {}
            for o in self._dos_partial[s]:
                if Orbital[o] not in pdoss[structure.sites[int(s)]]:
                    pdoss[structure.sites[int(s)]][Orbital[o]] = {}
                pdoss[structure.sites[int(s)]][Orbital[o]][
                    spin_1] = self._dos_partial[s][o]
                if analyzer_for_second_spin:
                    pdoss[structure.sites[int(s)]][Orbital[o]][
                        spin_2] = analyzer_for_second_spin._dos_partial[s][o]
        if analyzer_for_second_spin:
            tdos = Dos(self.dos.efermi, self.dos.energies,
                       {spin_1: self.dos.densities[spin_1],
                        spin_2: analyzer_for_second_spin.dos.densities[
                            spin_2]})
        else:
            tdos = self.dos

        return CompleteDos(structure, total_dos=tdos, pdoss=pdoss)


Instruction:
Write a Python 3 function that can
return the carrier concentration at each temperature

Response:
def get_carrier_concentration(self):
        """
        gives the carrier concentration (in cm^-3)

        Returns
            a dictionary {temp:[]} with an array of carrier concentration
            (in cm^-3) at each temperature
            The array relates to each step of electron chemical potential
        """

        return {temp: [1e24 * i / self.vol for i in self._carrier_conc[temp]]
                for temp in self._carrier_conc}


Instruction:
Can you generate a brief explanation for the following Python 3 code
def get_hall_carrier_concentration(self):
        result = {temp: [] for temp in self._hall}
        for temp in self._hall:
            for i in self._hall[temp]:
                trace = (i[1][2][0] + i[2][0][1] + i[0][1][2]) / 3.0
                if trace != 0.0:
                    result[temp].append(1e-6 / (trace * constants.e))
                else:
                    result[temp].append(0.0)
        return result

Response: Returns the Hall carrier concentration for the current temperature


Instruction:
Make a summary of the following Python 3 code
def parse_outputtrans(path_dir):
        run_type = None
        warning = None
        efermi = None
        gap = None
        doping_levels = []

        with open(os.path.join(path_dir, "boltztrap.outputtrans"), 'r') \
                as f:
            for line in f:
                if "WARNING" in line:
                    warning = line
                elif "Calc type:" in line:
                    run_type = line.split()[-1]
                elif line.startswith("VBM"):
                    efermi = Energy(line.split()[1], "Ry").to("eV")
                elif line.startswith("Egap:"):
                    gap = Energy(float(line.split()[1]), "Ry").to("eV")
                elif line.startswith("Doping level number"):
                    doping_levels.append(float(line.split()[6]))

        return run_type, warning, efermi, gap, doping_levels

Response: Parses boltztrap. outputtrans file and returns tuple of run type warning efermi gap and doping levels


Instruction:
Here you have a function in Python 3, explain what it does
def parse_transdos(path_dir, efermi, dos_spin=1, trim_dos=False):

        """
        Parses .transdos (total DOS) and .transdos_x_y (partial DOS) files
        Args:
            path_dir: (str) dir containing DOS files
            efermi: (float) Fermi energy
            dos_spin: (int) -1 for spin down, +1 for spin up
            trim_dos: (bool) whether to post-process / trim DOS

        Returns:
            tuple - (DOS, dict of partial DOS)
        """

        data_dos = {'total': [], 'partial': {}}
        # parse the total DOS data
        ## format is energy, DOS, integrated DOS
        with open(os.path.join(path_dir, "boltztrap.transdos"), 'r') as f:
            count_series = 0  # TODO: why is count_series needed?
            for line in f:
                if line.lstrip().startswith("#"):
                    count_series += 1
                    if count_series > 1:
                        break
                else:
                    data_dos['total'].append(
                        [Energy(float(line.split()[0]), "Ry").to("eV"),
                         float(line.split()[1])])
                    total_elec = float(line.split()[2])

        lw_l = 0
        hg_l = -len(data_dos['total'])
        if trim_dos:
            # Francesco knows what this does
            # It has something to do with a trick of adding fake energies
            # at the endpoints of the DOS, and then re-trimming it. This is
            # to get the same energy scale for up and down spin DOS.
            tmp_data = np.array(data_dos['total'])
            tmp_den = np.trim_zeros(tmp_data[:, 1], 'f')[1:]
            lw_l = len(tmp_data[:, 1]) - len(tmp_den)
            tmp_ene = tmp_data[lw_l:, 0]
            tmp_den = np.trim_zeros(tmp_den, 'b')[:-1]
            hg_l = len(tmp_ene) - len(tmp_den)
            tmp_ene = tmp_ene[:-hg_l]
            tmp_data = np.vstack((tmp_ene, tmp_den)).T
            data_dos['total'] = tmp_data.tolist()

        # parse partial DOS data
        for file_name in os.listdir(path_dir):
            if file_name.endswith(
                    "transdos") and file_name != 'boltztrap.transdos':
                tokens = file_name.split(".")[1].split("_")
                site = tokens[1]
                orb = '_'.join(tokens[2:])
                with open(os.path.join(path_dir, file_name), 'r') as f:
                    for line in f:
                        if not line.lstrip().startswith(" #"):
                            if site not in data_dos['partial']:
                                data_dos['partial'][site] = {}
                            if orb not in data_dos['partial'][site]:
                                data_dos['partial'][site][orb] = []
                            data_dos['partial'][site][orb].append(
                                float(line.split()[1]))
                data_dos['partial'][site][orb] = data_dos['partial'][site][
                                                     orb][lw_l:-hg_l]

        dos_full = {'energy': [], 'density': []}

        for t in data_dos['total']:
            dos_full['energy'].append(t[0])
            dos_full['density'].append(t[1])

        dos = Dos(efermi, dos_full['energy'],
                  {Spin(dos_spin): dos_full['density']})
        dos_partial = data_dos['partial']  # TODO: make this real DOS object?

        return dos, dos_partial

Response: Parses the. transdos file and returns a dictionary of DOS and partial DOS.


Instruction:
Create a Python 3 function for
parsing boltztrap. intrans file and returns a dictionary containing the value of scissor applied to bands or other inputs that had been used in the Boltztrap run.

Response:
def parse_intrans(path_dir):
        """
        Parses boltztrap.intrans mainly to extract the value of scissor applied to the bands or some other inputs
        Args:
            path_dir: (str) dir containing the boltztrap.intrans file
        Returns:
            intrans (dict): a dictionary containing various inputs that had been used in the Boltztrap run.
        """
        intrans = {}
        with open(os.path.join(path_dir, "boltztrap.intrans"), 'r') as f:
            for line in f:
                if "iskip" in line:
                    intrans["scissor"] = Energy(float(line.split(" ")[3]),
                                                "Ry").to("eV")
                if "HISTO" in line or "TETRA" in line:
                    intrans["dos_type"] = line[:-1]
        return intrans


Instruction:
Make a summary of the following Python 3 code
def parse_struct(path_dir):
        with open(os.path.join(path_dir, "boltztrap.struct"), 'r') as f:
            tokens = f.readlines()
            return Lattice([[Length(float(tokens[i].split()[j]), "bohr").
                           to("ang") for j in range(3)] for i in
                            range(1, 4)]).volume

Response: Parses boltztrap. struct file and returns a volume


Instruction:
How would you implement a function in Python 3 that
parses the conductivity and Hall tensors and returns the set of mu_steps and mu_doping and hall_doping for the current base level and carrier.

Response:
def parse_cond_and_hall(path_dir, doping_levels=None):
        """
        Parses the conductivity and Hall tensors
        Args:
            path_dir: Path containing .condtens / .halltens files
            doping_levels: ([float]) - doping lvls, parse outtrans to get this

        Returns:
            mu_steps, cond, seebeck, kappa, hall, pn_doping_levels,
            mu_doping, seebeck_doping, cond_doping, kappa_doping,
            hall_doping, carrier_conc
        """

        # Step 1: parse raw data but do not convert to final format
        t_steps = set()
        mu_steps = set()
        data_full = []
        data_hall = []
        data_doping_full = []
        data_doping_hall = []
        doping_levels = doping_levels or []

        # parse the full conductivity/Seebeck/kappa0/etc data
        ## also initialize t_steps and mu_steps
        with open(os.path.join(path_dir, "boltztrap.condtens"), 'r') as f:
            for line in f:
                if not line.startswith("#"):
                    mu_steps.add(float(line.split()[0]))
                    t_steps.add(int(float(line.split()[1])))
                    data_full.append([float(c) for c in line.split()])

        # parse the full Hall tensor
        with open(os.path.join(path_dir, "boltztrap.halltens"), 'r') as f:
            for line in f:
                if not line.startswith("#"):
                    data_hall.append([float(c) for c in line.split()])

        if len(doping_levels) != 0:
            # parse doping levels version of full cond. tensor, etc.
            with open(
                    os.path.join(path_dir, "boltztrap.condtens_fixdoping"),
                    'r') as f:
                for line in f:
                    if not line.startswith("#") and len(line) > 2:
                        data_doping_full.append([float(c)
                                                 for c in line.split()])

            # parse doping levels version of full hall tensor
            with open(
                    os.path.join(path_dir, "boltztrap.halltens_fixdoping"),
                    'r') as f:
                for line in f:
                    if not line.startswith("#") and len(line) > 2:
                        data_doping_hall.append(
                            [float(c) for c in line.split()])

        # Step 2: convert raw data to final format

        # sort t and mu_steps (b/c they are sets not lists)
        # and convert to correct energy
        t_steps = sorted([t for t in t_steps])
        mu_steps = sorted([Energy(m, "Ry").to("eV") for m in mu_steps])

        # initialize output variables - could use defaultdict instead
        # I am leaving things like this for clarity
        cond = {t: [] for t in t_steps}
        seebeck = {t: [] for t in t_steps}
        kappa = {t: [] for t in t_steps}
        hall = {t: [] for t in t_steps}
        carrier_conc = {t: [] for t in t_steps}
        dos_full = {'energy': [], 'density': []}

        mu_doping = {'p': {t: [] for t in t_steps},
                     'n': {t: [] for t in t_steps}}
        seebeck_doping = {'p': {t: [] for t in t_steps},
                          'n': {t: [] for t in t_steps}}
        cond_doping = {'p': {t: [] for t in t_steps},
                       'n': {t: [] for t in t_steps}}
        kappa_doping = {'p': {t: [] for t in t_steps},
                        'n': {t: [] for t in t_steps}}
        hall_doping = {'p': {t: [] for t in t_steps},
                       'n': {t: [] for t in t_steps}}

        # process doping levels
        pn_doping_levels = {'p': [], 'n': []}
        for d in doping_levels:
            if d > 0:
                pn_doping_levels['p'].append(d)
            else:
                pn_doping_levels['n'].append(-d)

        # process raw conductivity data, etc.
        for d in data_full:
            temp, doping = d[1], d[2]
            carrier_conc[temp].append(doping)

            cond[temp].append(np.reshape(d[3:12], (3, 3)).tolist())
            seebeck[temp].append(np.reshape(d[12:21], (3, 3)).tolist())
            kappa[temp].append(np.reshape(d[21:30], (3, 3)).tolist())

        # process raw Hall data
        for d in data_hall:
            temp, doping = d[1], d[2]
            hall_tens = [np.reshape(d[3:12], (3, 3)).tolist(),
                         np.reshape(d[12:21], (3, 3)).tolist(),
                         np.reshape(d[21:30], (3, 3)).tolist()]
            hall[temp].append(hall_tens)

        # process doping conductivity data, etc.
        for d in data_doping_full:
            temp, doping, mu = d[0], d[1], d[-1]
            pn = 'p' if doping > 0 else 'n'
            mu_doping[pn][temp].append(Energy(mu, "Ry").to("eV"))
            cond_doping[pn][temp].append(
                np.reshape(d[2:11], (3, 3)).tolist())
            seebeck_doping[pn][temp].append(
                np.reshape(d[11:20], (3, 3)).tolist())
            kappa_doping[pn][temp].append(
                np.reshape(d[20:29], (3, 3)).tolist())

        # process doping Hall data
        for d in data_doping_hall:
            temp, doping, mu = d[0], d[1], d[-1]
            pn = 'p' if doping > 0 else 'n'
            hall_tens = [np.reshape(d[2:11], (3, 3)).tolist(),
                         np.reshape(d[11:20], (3, 3)).tolist(),
                         np.reshape(d[20:29], (3, 3)).tolist()]
            hall_doping[pn][temp].append(hall_tens)

        return mu_steps, cond, seebeck, kappa, hall, pn_doping_levels, \
               mu_doping, seebeck_doping, cond_doping, kappa_doping, \
               hall_doping, carrier_conc


Instruction:
Here you have a function in Python 3, explain what it does
def from_files(path_dir, dos_spin=1):
        run_type, warning, efermi, gap, doping_levels = \
            BoltztrapAnalyzer.parse_outputtrans(path_dir)

        vol = BoltztrapAnalyzer.parse_struct(path_dir)

        intrans = BoltztrapAnalyzer.parse_intrans(path_dir)

        if run_type == "BOLTZ":
            dos, pdos = BoltztrapAnalyzer.parse_transdos(
                path_dir, efermi, dos_spin=dos_spin, trim_dos=False)

            mu_steps, cond, seebeck, kappa, hall, pn_doping_levels, mu_doping, \
            seebeck_doping, cond_doping, kappa_doping, hall_doping, \
            carrier_conc = BoltztrapAnalyzer. \
                parse_cond_and_hall(path_dir, doping_levels)

            return BoltztrapAnalyzer(
                gap, mu_steps, cond, seebeck, kappa, hall, pn_doping_levels,
                mu_doping, seebeck_doping, cond_doping, kappa_doping,
                hall_doping, intrans, dos, pdos, carrier_conc, vol, warning)

        elif run_type == "DOS":
            trim = True if intrans["dos_type"] == "HISTO" else False
            dos, pdos = BoltztrapAnalyzer.parse_transdos(
                path_dir, efermi, dos_spin=dos_spin, trim_dos=trim)

            return BoltztrapAnalyzer(gap=gap, dos=dos, dos_partial=pdos,
                                     warning=warning, vol=vol)

        elif run_type == "BANDS":
            bz_kpoints = np.loadtxt(
                os.path.join(path_dir, "boltztrap_band.dat"))[:, -3:]
            bz_bands = np.loadtxt(
                os.path.join(path_dir, "boltztrap_band.dat"))[:, 1:-6]
            return BoltztrapAnalyzer(bz_bands=bz_bands, bz_kpoints=bz_kpoints,
                                     warning=warning, vol=vol)

        elif run_type == "FERMI":
            """
            """

            if os.path.exists(os.path.join(path_dir, 'boltztrap_BZ.cube')):
                fs_data = read_cube_file(
                    os.path.join(path_dir, 'boltztrap_BZ.cube'))
            elif os.path.exists(os.path.join(path_dir, 'fort.30')):
                fs_data = read_cube_file(os.path.join(path_dir, 'fort.30'))
            else:
                raise BoltztrapError("No data file found for fermi surface")
            return BoltztrapAnalyzer(fermi_surface_data=fs_data)

        else:
            raise ValueError("Run type: {} not recognized!".format(run_type))

Response: This function takes a set of files and returns a BoltztrapAnalyzer object from the set of files.


Instruction:
How would you implement a function in Python 3 that
checks for the defined errors and put all found errors in self. errors

Response:
def check(self):
        """
        Check for the defined errors, put all found errors in self.errors, return True if any were found False if no
        errors were found
        """
        parser = get_parser(self.scheduler_adapter.name, err_file=self.err_file, out_file=self.out_file,
                            run_err_file=self.run_err_file, batch_err_file=self.batch_err_file)
        parser.parse()
        self.errors = parser.errors
        if len(self.errors) == 0:
            return False
        else:
            return True


Instruction:
Here you have a function in Python 3, explain what it does
def apply_corrections(self):
        for error in self.errors:
            for solution in error.scheduler_adapter_solutions:
                if self.scheduler_adapter is not None:
                    if self.scheduler_adapter.__getattribut__(solution[0].__name__)(solution[1]):
                        return True
            for solution in error.application_adapter_solutions:
                if self.application_adapter is not None:
                    if self.application_adapter.__getattribut__(solution[0].__name__)(solution[1]):
                        return True
        return False

Response: Method to directly apply the corrections.


Instruction:
Create a Python 3 function for
parsing LAMMPS log file with focus on thermo data.

Response:
def parse_lammps_log(filename="log.lammps"):
    """
    Parses log file with focus on thermo data. Both one and multi line
    formats are supported. Any incomplete runs (no "Loop time" marker)
    will not be parsed.

    Notes:
        SHAKE stats printed with thermo data are not supported yet.
        They are ignored in multi line format, while they may cause
        issues with dataframe parsing in one line format.

    Args:
        filename (str): Filename to parse.

    Returns:
        [pd.DataFrame] containing thermo data for each completed run.

    """
    with open(filename) as f:
        lines = f.readlines()
    begin_flag = ("Memory usage per processor =",
                  "Per MPI rank memory allocation (min/avg/max) =")
    end_flag = "Loop time of"
    begins, ends = [], []
    for i, l in enumerate(lines):
        if l.startswith(begin_flag):
            begins.append(i)
        elif l.startswith(end_flag):
            ends.append(i)

    def _parse_thermo(lines):
        multi_pattern = r"-+\s+Step\s+([0-9]+)\s+-+"
        # multi line thermo data
        if re.match(multi_pattern, lines[0]):
            timestep_marks = [i for i, l in enumerate(lines)
                              if re.match(multi_pattern, l)]
            timesteps = np.split(lines, timestep_marks)[1:]
            dicts = []
            kv_pattern = r"([0-9A-Za-z_\[\]]+)\s+=\s+([0-9eE\.+-]+)"
            for ts in timesteps:
                data = {}
                data["Step"] = int(re.match(multi_pattern, ts[0]).group(1))
                data.update({k: float(v) for k, v
                             in re.findall(kv_pattern, "".join(ts[1:]))})
                dicts.append(data)
            df = pd.DataFrame(dicts)
            # rearrange the sequence of columns
            columns = ["Step"] + [k for k, v in
                                  re.findall(kv_pattern,
                                             "".join(timesteps[0][1:]))]
            df = df[columns]
        # one line thermo data
        else:
            df = pd.read_csv(StringIO("".join(lines)), delim_whitespace=True)
        return df

    runs = []
    for b, e in zip(begins, ends):
        runs.append(_parse_thermo(lines[b + 1:e]))
    return runs


Instruction:
Make a summary of the following Python 3 code
def from_string(cls, string):
        lines = string.split("\n")
        timestep = int(lines[1])
        natoms = int(lines[3])
        box_arr = np.loadtxt(StringIO("\n".join(lines[5:8])))
        bounds = box_arr[:, :2]
        tilt = None
        if "xy xz yz" in lines[4]:
            tilt = box_arr[:, 2]
            x = (0, tilt[0], tilt[1], tilt[0] + tilt[1])
            y = (0, tilt[2])
            bounds -= np.array([[min(x), max(x)], [min(y), max(y)], [0, 0]])
        box = LammpsBox(bounds, tilt)
        data_head = lines[8].replace("ITEM: ATOMS", "").split()
        data = pd.read_csv(StringIO("\n".join(lines[9:])), names=data_head,
                           delim_whitespace=True)
        return cls(timestep, natoms, box, data)

Response: Constructor from string parsing.


Instruction:
How would you implement a function in Python 3 that
gets the number of k - point divisions for a given k - point density per reciprocal - atom and a given structure

Response:
def get_num_division_kpoints(structure, kppa):
    """
    Get kpoint divisions for a given k-point density (per reciprocal-atom):
    kppa and a given structure
    """
    output = run_aconvasp_command(["aconvasp", "--kpoints", str(kppa)],
                                  structure)
    tmp = output[0].rsplit("\n")[6].rsplit(" ")
    return [int(tmp[5]), int(tmp[6]), int(tmp[7])]


Instruction:
How would you explain what the following Python 3 function does
def get_minkowski_red(structure):
    output = run_aconvasp_command(["aconvasp", "--kpath"], structure)
    started = False
    poscar_string = ""
    if "ERROR" in output[1]:
        raise AconvaspError(output[1])
    for line in output[0].split("\n"):
        if started or line.find("KPOINTS TO RUN") != -1:
            poscar_string = poscar_string + line + "\n"
        if line.find("STRUCTURE TO RUN") != -1:
            started = True
        if line.find("KPOINTS TO RUN") != -1:
            started = False
    return Poscar.from_string(poscar_string).structure

Response: Get a minkowski reduced structure


Instruction:
Implement a function in Python 3 to
get a minkowski reduced structure

Response:
def get_conv_struct(structure):
    """
    Get a minkowski reduced structure
    """
    output = run_aconvasp_command(["aconvasp", "--std_conv"], structure)
    if "ERROR" in output[1]:
        raise AconvaspError(output[1])
    tmp = Poscar.from_string(output[0])
    return {'struct': tmp.structure, 'comm': tmp.comment}


Instruction:
Write a Python 3 script for
getting a kpoint file ready to be ran in VASP along the symmetry lines of the Brillouin Zone

Response:
def get_vasp_kpoint_file_sym(structure):
    """
    get a kpoint file ready to be ran in VASP along the symmetry lines of the
    Brillouin Zone
    """
    output = run_aconvasp_command(["aconvasp", "--kpath"], structure)
    if "ERROR" in output[1]:
        raise AconvaspError(output[1])
    started = False
    kpoints_string = ""
    for line in output[0].split("\n"):
        #print line
        if started or line.find("END") != -1:
            kpoints_string = kpoints_string + line + "\n"
        if line.find("KPOINTS TO RUN") != -1:
            started = True
        if line.find("END") != -1:
            started = False
    return kpoints_string


Instruction:
Here you have a function in Python 3, explain what it does
def _my_partial(func, *args, **kwargs):
    newobj = partial(func, *args, **kwargs)
    # monkey patch
    newobj.from_string = FloatWithUnit.from_string
    return newobj

Response: Partial returns a partial object and therefore we cannot inherit it from FloatWithUnit


Instruction:
Write a Python 3 script to
return a FloatWithUnit instance if obj is scalar a dictionary of objects with units if obj is a dict else an array of objects with units if obj is a dict else an instance of .

Response:
def obj_with_unit(obj, unit):
    """
    Returns a `FloatWithUnit` instance if obj is scalar, a dictionary of
    objects with units if obj is a dict, else an instance of
    `ArrayWithFloatWithUnit`.

    Args:
        unit: Specific units (eV, Ha, m, ang, etc.).
    """
    unit_type = _UNAME2UTYPE[unit]

    if isinstance(obj, numbers.Number):
        return FloatWithUnit(obj, unit=unit, unit_type=unit_type)
    elif isinstance(obj, collections.Mapping):
        return {k: obj_with_unit(v, unit) for k,v in obj.items()}
    else:
        return ArrayWithUnit(obj, unit=unit, unit_type=unit_type)


Instruction:
Explain what the following Python 3 code does
def unitized(unit):
    def wrap(f):
        def wrapped_f(*args, **kwargs):
            val = f(*args, **kwargs)
            unit_type = _UNAME2UTYPE[unit]

            if isinstance(val, FloatWithUnit) or isinstance(val, ArrayWithUnit):
                return val.to(unit)

            elif isinstance(val, collections.abc.Sequence):
                # TODO: why don't we return a ArrayWithUnit?
                # This complicated way is to ensure the sequence type is
                # preserved (list or tuple).
                return val.__class__([FloatWithUnit(i, unit_type=unit_type,
                                                    unit=unit) for i in val])
            elif isinstance(val, collections.Mapping):
                for k, v in val.items():
                    val[k] = FloatWithUnit(v, unit_type=unit_type, unit=unit)
            elif isinstance(val, numbers.Number):
                return FloatWithUnit(val, unit_type=unit_type, unit=unit)
            elif val is None:
                pass
            else:
                raise TypeError("Don't know how to assign units to %s" % str(val))
            return val
        return wrapped_f
    return wrap

Response: Decorator to assign units to the output of a function.


Instruction:
Write a Python 3 function that can
convert all units to base SI units including derived units.

Response:
def as_base_units(self):
        """
        Converts all units to base SI units, including derived units.

        Returns:
            (base_units_dict, scaling factor). base_units_dict will not
            contain any constants, which are gathered in the scaling factor.
        """
        b = collections.defaultdict(int)
        factor = 1
        for k, v in self.items():
            derived = False
            for d in DERIVED_UNITS.values():
                if k in d:
                    for k2, v2 in d[k].items():
                        if isinstance(k2, Number):
                            factor *= k2 ** (v2 * v)
                        else:
                            b[k2] += v2 * v
                    derived = True
                    break
            if not derived:
                si, f = _get_si_unit(k)
                b[si] += v
                factor *= f ** v
        return {k: v for k, v in b.items() if v != 0}, factor


Instruction:
Explain what the following Python 3 code does
def get_conversion_factor(self, new_unit):
        uo_base, ofactor = self.as_base_units
        un_base, nfactor = Unit(new_unit).as_base_units
        units_new = sorted(un_base.items(),
                           key=lambda d: _UNAME2UTYPE[d[0]])
        units_old = sorted(uo_base.items(),
                           key=lambda d: _UNAME2UTYPE[d[0]])
        factor = ofactor / nfactor
        for uo, un in zip(units_old, units_new):
            if uo[1] != un[1]:
                raise UnitError("Units %s and %s are not compatible!" % (uo, un))
            c = ALL_UNITS[_UNAME2UTYPE[uo[0]]]
            factor *= (c[uo[0]] / c[un[0]]) ** uo[1]
        return factor

Response: Returns a conversion factor between this unit and a new unit.


Instruction:
Can you generate the documentation for the following Python 3 function
def from_string(cls, s):
        # Extract num and unit string.
        s = s.strip()
        for i, char in enumerate(s):
            if char.isalpha() or char.isspace():
                break
        else:
            raise Exception("Unit is missing in string %s" % s)
        num, unit = float(s[:i]), s[i:]

        # Find unit type (set it to None if it cannot be detected)
        for unit_type, d in BASE_UNITS.items():
            if unit in d:
                break
        else:
            unit_type = None

        return cls(num, unit, unit_type=unit_type)

Response: Initialize a FloatWithUnit from a string. Example Memory. from_string


Instruction:
Can you tell what is the following Python 3 function doing
def to(self, new_unit):
        return FloatWithUnit(
            self * self.unit.get_conversion_factor(new_unit),
            unit_type=self._unit_type,
            unit=new_unit)

Response: Returns a new object with the current value converted to the given new unit.


Instruction:
Explain what the following Python 3 code does
def to(self, new_unit):
        return self.__class__(
            np.array(self) * self.unit.get_conversion_factor(new_unit),
            unit_type=self.unit_type, unit=new_unit)

Response: Returns a new object with the same units as this one.


Instruction:
Create a Python 3 function for
returning a string showing the available conversions. Useful tool in interactive mode.

Response:
def conversions(self):
        """
        Returns a string showing the available conversions.
        Useful tool in interactive mode.
        """
        return "\n".join(str(self.to(unit)) for unit in self.supported_units)