Instruction:
Can you create a Python 3 function that
performs Electrostatic Freysoldt Correction on the species.

Response:
def perform_es_corr(self, lattice, q, step=1e-4):
        """
        Peform Electrostatic Freysoldt Correction
        """
        logger.info("Running Freysoldt 2011 PC calculation (should be " "equivalent to sxdefectalign)")
        logger.debug("defect lattice constants are (in angstroms)" + str(lattice.abc))

        [a1, a2, a3] = ang_to_bohr * np.array(lattice.get_cartesian_coords(1))
        logging.debug("In atomic units, lat consts are (in bohr):" + str([a1, a2, a3]))
        vol = np.dot(a1, np.cross(a2, a3))  # vol in bohr^3

        def e_iso(encut):
            gcut = eV_to_k(encut)  # gcut is in units of 1/A
            return scipy.integrate.quad(lambda g: self.q_model.rho_rec(g * g)**2, step, gcut)[0] * (q**2) / np.pi

        def e_per(encut):
            eper = 0
            for g2 in generate_reciprocal_vectors_squared(a1, a2, a3, encut):
                eper += (self.q_model.rho_rec(g2)**2) / g2
            eper *= (q**2) * 2 * round(np.pi, 6) / vol
            eper += (q**2) * 4 * round(np.pi, 6) \
                * self.q_model.rho_rec_limit0 / vol
            return eper

        eiso = converge(e_iso, 5, self.madetol, self.energy_cutoff)
        logger.debug("Eisolated : %f", round(eiso, 5))

        eper = converge(e_per, 5, self.madetol, self.energy_cutoff)

        logger.info("Eperiodic : %f hartree", round(eper, 5))
        logger.info("difference (periodic-iso) is %f hartree", round(eper - eiso, 6))
        logger.info("difference in (eV) is %f", round((eper - eiso) * hart_to_ev, 4))

        es_corr = round((eiso - eper) / self.dielectric * hart_to_ev, 6)
        logger.info("Defect Correction without alignment %f (eV): ", es_corr)
        return es_corr


Instruction:
Create a Python 3 function for
performing potential alignment correction for a given axis.

Response:
def perform_pot_corr(self,
                         axis_grid,
                         pureavg,
                         defavg,
                         lattice,
                         q,
                         defect_position,
                         axis,
                         madetol=0.0001,
                         widthsample=1.0):
        """
        For performing planar averaging potential alignment

        title is for name of plot, if you dont want a plot then leave it as None
        widthsample is the width (in Angstroms) of the region in between defects where the potential alignment correction is averaged
        """
        logging.debug("run Freysoldt potential alignment method for axis " + str(axis))
        nx = len(axis_grid)

        # shift these planar averages to have defect at origin
        axfracval = lattice.get_fractional_coords(defect_position)[axis]
        axbulkval = axfracval * lattice.abc[axis]
        if axbulkval < 0:
            axbulkval += lattice.abc[axis]
        elif axbulkval > lattice.abc[axis]:
            axbulkval -= lattice.abc[axis]

        if axbulkval:
            for i in range(nx):
                if axbulkval < axis_grid[i]:
                    break
            rollind = len(axis_grid) - i
            pureavg = np.roll(pureavg, rollind)
            defavg = np.roll(defavg, rollind)

        # if not self._silence:
        logger.debug("calculating lr part along planar avg axis")
        reci_latt = lattice.reciprocal_lattice
        dg = reci_latt.abc[axis]
        dg /= ang_to_bohr  # convert to bohr to do calculation in atomic units

        # Build background charge potential with defect at origin
        v_G = np.empty(len(axis_grid), np.dtype("c16"))
        v_G[0] = 4 * np.pi * -q / self.dielectric * self.q_model.rho_rec_limit0
        g = np.roll(np.arange(-nx / 2, nx / 2, 1, dtype=int), int(nx / 2)) * dg
        g2 = np.multiply(g, g)[1:]
        v_G[1:] = 4 * np.pi / (self.dielectric * g2) * -q * self.q_model.rho_rec(g2)
        v_G[nx // 2] = 0 if not (nx % 2) else v_G[nx // 2]

        # Get the real space potential by peforming a  fft and grabbing the imaginary portion
        v_R = np.fft.fft(v_G)

        if abs(np.imag(v_R).max()) > self.madetol:
            raise Exception("imaginary part found to be %s", repr(np.imag(v_R).max()))
        v_R /= (lattice.volume * ang_to_bohr**3)
        v_R = np.real(v_R) * hart_to_ev

        # get correction
        short = (defavg - pureavg - v_R)
        checkdis = int((widthsample / 2) / (axis_grid[1] - axis_grid[0]))
        mid = int(len(short) / 2)

        tmppot = [short[i] for i in range(mid - checkdis, mid + checkdis + 1)]
        logger.debug("shifted defect position on axis (%s) to origin", repr(axbulkval))
        logger.debug("means sampling region is (%f,%f)", axis_grid[mid - checkdis], axis_grid[mid + checkdis])

        C = -np.mean(tmppot)
        logger.debug("C = %f", C)
        final_shift = [short[j] + C for j in range(len(v_R))]
        v_R = [elmnt - C for elmnt in v_R]

        logger.info("C value is averaged to be %f eV ", C)
        logger.info("Potentital alignment energy correction (-q*delta V):  %f (eV)", -q * C)
        self.pot_corr = -q * C

        # log plotting data:
        self.metadata["pot_plot_data"][axis] = {
            "Vr": v_R,
            "x": axis_grid,
            "dft_diff": defavg - pureavg,
            "final_shift": final_shift,
            "check": [mid - checkdis, mid + checkdis + 1]
        }

        # log uncertainty:
        self.metadata["pot_corr_uncertainty_md"][axis] = {"stats": stats.describe(tmppot)._asdict(), "potcorr": -q * C}

        return self.pot_corr


Instruction:
Can you generate a brief explanation for the following Python 3 code
def plot(self, axis, title=None, saved=False):

        x = self.metadata['pot_plot_data'][axis]['x']
        v_R = self.metadata['pot_plot_data'][axis]['Vr']
        dft_diff = self.metadata['pot_plot_data'][axis]['dft_diff']
        final_shift = self.metadata['pot_plot_data'][axis]['final_shift']
        check = self.metadata['pot_plot_data'][axis]['check']

        plt.figure()
        plt.clf()
        plt.plot(x, v_R, c="green", zorder=1, label="long range from model")
        plt.plot(x, dft_diff, c="red", label="DFT locpot diff")
        plt.plot(x, final_shift, c="blue", label="short range (aligned)")

        tmpx = [x[i] for i in range(check[0], check[1])]
        plt.fill_between(tmpx, -100, 100, facecolor="red", alpha=0.15, label="sampling region")

        plt.xlim(round(x[0]), round(x[-1]))
        ymin = min(min(v_R), min(dft_diff), min(final_shift))
        ymax = max(max(v_R), max(dft_diff), max(final_shift))
        plt.ylim(-0.2 + ymin, 0.2 + ymax)
        plt.xlabel("distance along axis ($\AA$)", fontsize=15)
        plt.ylabel("Potential (V)", fontsize=15)
        plt.legend(loc=9)
        plt.axhline(y=0, linewidth=0.2, color="black")
        plt.title(str(title) + " defect potential", fontsize=18)
        plt.xlim(0, max(x))
        if saved:
            plt.savefig(str(title) + "FreyplnravgPlot.pdf")
        else:
            return plt

Response: Plots the planar average electrostatic potential against the Long range and short range models from Freysoldt


Instruction:
Write a Python 3 script to
get the BandFilling correction for a defect entry

Response:
def get_correction(self, entry):
        """
        Gets the BandFilling correction for a defect entry
        """
        eigenvalues = entry.parameters["eigenvalues"]
        kpoint_weights = entry.parameters["kpoint_weights"]
        potalign = entry.parameters["potalign"]
        vbm = entry.parameters["vbm"]
        cbm = entry.parameters["cbm"]

        bf_corr = self.perform_bandfill_corr(eigenvalues, kpoint_weights, potalign, vbm, cbm)

        entry.parameters["bandfilling_meta"] = dict(self.metadata)

        return {"bandfilling": bf_corr}


Instruction:
How would you explain what the following Python 3 function does
def perform_bandfill_corr(self, eigenvalues, kpoint_weights, potalign, vbm, cbm):
        bf_corr = 0.

        self.metadata["potalign"] = potalign
        self.metadata["num_hole_vbm"] = 0.
        self.metadata["num_elec_cbm"] = 0.

        if len(eigenvalues.keys()) == 1:  # needed because occupation of non-spin calcs is still 1... should be 2
            spinfctr = 2.
        elif len(eigenvalues.keys()) == 2:
            spinfctr = 1.
        else:
            raise ValueError("Eigenvalue keys greater than 2")

        # for tracking mid gap states...
        resolution = self.resolution
        shifted_cbm = potalign + cbm  # shift cbm with potential alignment
        shifted_vbm = potalign + vbm  # shift vbm with potential alignment

        occupied_midgap = {en: [] for en in np.arange(shifted_vbm, shifted_cbm + resolution, resolution)}
        occupation = {en: 0. for en in np.arange(shifted_vbm, shifted_cbm + resolution, resolution)}
        unoccupied_midgap = {en: [] for en in np.arange(shifted_vbm, shifted_cbm + resolution, resolution)}

        for spinset in eigenvalues.values():
            for kptset, weight in zip(spinset, kpoint_weights):
                for eig in kptset:  # eig[0] is eigenvalue and eig[1] is occupation
                    if (eig[1] and (eig[0] > shifted_cbm)):  # donor MB correction
                        bf_corr += weight * spinfctr * eig[1] * (eig[0] - shifted_cbm)  # "move the electrons down"
                        self.metadata["num_elec_cbm"] += weight * spinfctr * eig[1]
                    elif (eig[1] != 1.) and (eig[0] <= shifted_vbm):  # acceptor MB correction
                        bf_corr += weight * spinfctr * (1. - eig[1]) * (shifted_vbm - eig[0])  # "move the holes up"
                        self.metadata["num_hole_vbm"] += weight * spinfctr * (1. - eig[1])
                    elif (eig[0] > shifted_vbm) and (eig[0] < shifted_cbm):
                        for en in np.arange(shifted_vbm, shifted_cbm + resolution, resolution):
                            if (eig[0] < en + resolution) and (eig[0] > en):
                                if eig[1]:
                                    occupied_midgap[en].append(eig[0])
                                    occupation[en] += eig[1] * weight * spinfctr
                                else:
                                    unoccupied_midgap[en].append(eig[0])
                                continue

        bf_corr *= -1  # need to take negative of this shift for energetic correction

        # summarize defect level results
        self.metadata["total_occupation_defect_levels"] = 0.
        self.metadata["occupied_def_levels"] = []
        self.metadata["unoccupied_def_levels"] = []
        for en in occupied_midgap.keys():
            if len(occupied_midgap[en]):
                self.metadata["occupied_def_levels"].append([np.mean(occupied_midgap[en]), occupation[en]])
                self.metadata["total_occupation_defect_levels"] += occupation[en]
            elif len(unoccupied_midgap[en]):
                self.metadata["unoccupied_def_levels"].append(np.mean(unoccupied_midgap[en]))

        return bf_corr

Response: This function calculates the band filling correction based on the given eigenvalues and kpoint weights.


Instruction:
Implement a function in Python 3 to
get the BandEdge correction for a defect entry.

Response:
def get_correction(self, entry):
        """
        Gets the BandEdge correction for a defect entry
        """
        # TODO: add smarter defect level shifting based on defect level projection onto host bands
        hybrid_cbm = entry.parameters["hybrid_cbm"]
        hybrid_vbm = entry.parameters["hybrid_vbm"]
        vbm = entry.parameters["vbm"]
        cbm = entry.parameters["cbm"]
        num_hole_vbm = entry.parameters["num_hole_vbm"]
        num_elec_cbm = entry.parameters["num_elec_cbm"]

        self.metadata["vbmshift"] = hybrid_vbm - vbm  # note vbmshift has UPWARD as positive convention
        self.metadata["cbmshift"] = hybrid_cbm - cbm  # note cbmshift has UPWARD as positive convention

        charge = entry.charge
        vbm_shift_correction = charge * self.metadata["vbmshift"]
        # negative sign has to do with fact that these are holes
        hole_vbm_shift_correction = -1. * num_hole_vbm * self.metadata["vbmshift"]
        elec_cbm_shift_correction = num_elec_cbm * self.metadata["cbmshift"]

        entry.parameters["bandshift_meta"] = dict(self.metadata)

        return {
            "vbm_shift_correction": vbm_shift_correction,
            "hole_vbm_shift_correction": hole_vbm_shift_correction,
            "elec_cbm_shift_correction": elec_cbm_shift_correction
        }


Instruction:
Make a summary of the following Python 3 code
def from_string(cls, s):
        for num, text in cls._STATUS_TABLE.items():
            if text == s: return cls(num)
        else:
            #raise ValueError("Wrong string %s" % s)
            logger.warning("Got unknown status: %s" % s)
            return cls.from_string("UNKNOWN")

Response: Return a : class : JobStatus instance from its string representation.


Instruction:
Can you create a Python 3 function that
returns a new istance of the appropriate subclass of QueueJob.

Response:
def from_qtype_and_id(qtype, queue_id, qname=None):
        """
        Return a new istance of the appropriate subclass.

        Args:
            qtype: String specifying the Resource manager type.
            queue_id: Job identifier.
            qname: Name of the queue (optional).
        """
        for cls in all_subclasses(QueueJob):
            if cls.QTYPE == qtype: break
        else:
            logger.critical("Cannot find QueueJob subclass registered for qtype %s" % qtype)
            cls = QueueJob

        return cls(queue_id, qname=qname)


Instruction:
Can you generate a brief explanation for the following Python 3 code
def get_stable_entries(self, charge_to_discharge=True):
        list_copy = list(self._stable_entries)
        return list_copy if charge_to_discharge else list_copy.reverse()

Response: Get the stable entries in the electrode.


Instruction:
Write a Python 3 script to
return the unstable entries for the electrode.

Response:
def get_unstable_entries(self, charge_to_discharge=True):
        """
        Returns the unstable entries for the electrode.

        Args:
            charge_to_discharge: Order from most charge to most discharged
                state? Defaults to True.

        Returns:
            A list of unstable entries in the electrode, ordered by amount of
            the working ion.
        """
        list_copy = list(self._unstable_entries)
        return list_copy if charge_to_discharge else list_copy.reverse()


Instruction:
Can you generate a brief explanation for the following Python 3 code
def get_all_entries(self, charge_to_discharge=True):
        all_entries = list(self.get_stable_entries())
        all_entries.extend(self.get_unstable_entries())
        # sort all entries by amount of working ion ASC
        fsrt = lambda e: e.composition.get_atomic_fraction(self.working_ion)
        all_entries = sorted([e for e in all_entries],
                             key=fsrt)
        return all_entries if charge_to_discharge else all_entries.reverse()

Response: Returns a list of all entries in the electrode ordered by amount of working ion.


Instruction:
Make a summary of the following Python 3 code
def get_max_instability(self, min_voltage=None, max_voltage=None):
        data = []
        for pair in self._select_in_voltage_range(min_voltage, max_voltage):
            if pair.decomp_e_charge is not None:
                data.append(pair.decomp_e_charge)
            if pair.decomp_e_discharge is not None:
                data.append(pair.decomp_e_discharge)
        return max(data) if len(data) > 0 else None

Response: Returns the maximum instability of all compounds along a path for a specific voltage range.


Instruction:
Here you have a function in Python 3, explain what it does
def get_min_instability(self, min_voltage=None, max_voltage=None):
        data = []
        for pair in self._select_in_voltage_range(min_voltage, max_voltage):
            if pair.decomp_e_charge is not None:
                data.append(pair.decomp_e_charge)
            if pair.decomp_e_discharge is not None:
                data.append(pair.decomp_e_discharge)
        return min(data) if len(data) > 0 else None

Response: Returns the minimum instability along a path for a specific voltage range.


Instruction:
Here you have a function in Python 3, explain what it does
def get_max_muO2(self, min_voltage=None, max_voltage=None):
        data = []
        for pair in self._select_in_voltage_range(min_voltage, max_voltage):
            if pair.muO2_discharge is not None:
                data.extend([d['chempot'] for d in pair.muO2_discharge])
            if pair.muO2_charge is not None:
                data.extend([d['chempot'] for d in pair.muO2_discharge])
        return max(data) if len(data) > 0 else None

Response: Returns the maximum critical oxygen chemical potential along path.


Instruction:
Explain what the following Python 3 code does
def get_min_muO2(self, min_voltage=None, max_voltage=None):
        data = []
        for pair in self._select_in_voltage_range(min_voltage, max_voltage):
            if pair.muO2_discharge is not None:
                data.extend([d['chempot'] for d in pair.muO2_discharge])
            if pair.muO2_charge is not None:
                data.extend([d['chempot'] for d in pair.muO2_discharge])
        return min(data) if len(data) > 0 else None

Response: Returns the minimum critical oxygen chemical potential along path.


Instruction:
Make a summary of the following Python 3 code
def get_sub_electrodes(self, adjacent_only=True, include_myself=True):
        battery_list = []
        pair_it = self._vpairs if adjacent_only \
            else itertools.combinations_with_replacement(self._vpairs, 2)

        ion = self._working_ion

        for pair in pair_it:
            entry_charge = pair.entry_charge if adjacent_only \
                else pair[0].entry_charge
            entry_discharge = pair.entry_discharge if adjacent_only \
                else pair[1].entry_discharge

            chg_frac = entry_charge.composition.get_atomic_fraction(ion)
            dischg_frac = entry_discharge.composition.get_atomic_fraction(ion)

            def in_range(entry):
                frac = entry.composition.get_atomic_fraction(ion)
                return chg_frac <= frac <= dischg_frac

            if include_myself or entry_charge != self.fully_charged_entry \
                    or entry_discharge != self.fully_discharged_entry:
                unstable_entries = filter(in_range,
                                          self.get_unstable_entries())
                stable_entries = filter(in_range, self.get_stable_entries())
                all_entries = list(stable_entries)
                all_entries.extend(unstable_entries)
                battery_list.append(self.__class__(all_entries,
                                                   self.working_ion_entry))
        return battery_list

Response: Returns a list of InsertionElectrode objects for all subelectrodes in the current region.


Instruction:
Write a Python 3 script for
generating a dict representation of the current object.

Response:
def as_dict_summary(self, print_subelectrodes=True):
        """
        Generate a summary dict.

        Args:
            print_subelectrodes: Also print data on all the possible
                subelectrodes.

        Returns:
            A summary of this electrode"s properties in dict format.
        """
        chg_comp = self.fully_charged_entry.composition
        dischg_comp = self.fully_discharged_entry.composition

        ion = self.working_ion
        d = {"average_voltage": self.get_average_voltage(),
             "max_voltage": self.max_voltage,
             "min_voltage": self.min_voltage,
             "max_delta_volume": self.max_delta_volume,
             "max_voltage_step": self.max_voltage_step,
             "capacity_grav": self.get_capacity_grav(),
             "capacity_vol": self.get_capacity_vol(),
             "energy_grav": self.get_specific_energy(),
             "energy_vol": self.get_energy_density(),
             "working_ion": self._working_ion.symbol,
             "nsteps": self.num_steps,
             "framework": self._vpairs[0].framework.to_data_dict,
             "formula_charge": chg_comp.reduced_formula,
             "id_charge": self.fully_charged_entry.entry_id,
             "formula_discharge": dischg_comp.reduced_formula,
             "id_discharge": self.fully_discharged_entry.entry_id,
             "fracA_charge": chg_comp.get_atomic_fraction(ion),
             "fracA_discharge": dischg_comp.get_atomic_fraction(ion),
             "max_instability": self.get_max_instability(),
             "min_instability": self.get_min_instability(),
             "material_ids" : [itr_ent.entry_id for itr_ent in self._entries],
             "stable_material_ids" : [itr_ent.entry_id for itr_ent in self.get_stable_entries()],
             "unstable_material_ids": [itr_ent.entry_id for itr_ent in self.get_unstable_entries()],
             }

        if all(['decomposition_energy' in itr_ent.data for itr_ent in self._entries]):
            d.update({"stability_charge": self.fully_charged_entry.data['decomposition_energy'],
                 "stability_discharge": self.fully_discharged_entry.data['decomposition_energy'],
                 "stability_data":{itr_ent.entry_id: itr_ent.data['decomposition_energy'] for itr_ent in self._entries},
                 })

        if all(['muO2' in itr_ent.data for itr_ent in self._entries]):
            d.update({"muO2_data" : {itr_ent.entry_id: itr_ent.data['muO2'] for itr_ent in self._entries}})

        if print_subelectrodes:
            f_dict = lambda c: c.as_dict_summary(print_subelectrodes=False)
            d["adj_pairs"] = list(map(f_dict,
                                 self.get_sub_electrodes(adjacent_only=True)))
            d["all_pairs"] = list(map(f_dict,
                                 self.get_sub_electrodes(adjacent_only=False)))
        return d


Instruction:
Make a summary of the following Python 3 code
def zval_dict_from_potcar(potcar):
    zval_dict = {}
    for p in potcar:
        zval_dict.update({p.element: p.ZVAL})
    return zval_dict

Response: Creates zval_dict for calculating the ionic polarization from the ionic polarization from the object


Instruction:
Given the following Python 3 function, write the documentation
def calc_ionic(site, structure, zval):
    norms = structure.lattice.lengths_and_angles[0]
    return np.multiply(norms, -site.frac_coords * zval)

Response: Calculates the ionic dipole moment using ZVAL from pseudopotential structure Returns polarization in electron Angstroms.


Instruction:
Write a Python 3 script to
get the total ionic dipole moment for a structure.

Response:
def get_total_ionic_dipole(structure, zval_dict):
    """
    Get the total ionic dipole moment for a structure.

    structure: pymatgen Structure
    zval_dict: specie, zval dictionary pairs
    center (np.array with shape [3,1]) : dipole center used by VASP
    tiny (float) : tolerance for determining boundary of calculation.
    """

    tot_ionic = []
    for site in structure:
        zval = zval_dict[str(site.specie)]
        tot_ionic.append(calc_ionic(site, structure, zval))
    return np.sum(tot_ionic, axis=0)


Instruction:
Implement a function in Python 3 to
give coords and a site find closest site to coords.

Response:
def get_nearest_site(self, coords, site, r=None):
        """
        Given coords and a site, find closet site to coords.
        Args:
            coords (3x1 array): cartesian coords of center of sphere
            site: site to find closest to coords
            r: radius of sphere. Defaults to diagonal of unit cell

        Returns:
            Closest site and distance.
        """
        index = self.index(site)
        if r is None:
            r = np.linalg.norm(np.sum(self.lattice.matrix, axis=0))
        ns = self.get_sites_in_sphere(coords, r, include_index=True)
        # Get sites with identical index to site
        ns = [n for n in ns if n[2] == index]
        # Sort by distance to coords
        ns.sort(key=lambda x: x[1])
        # Return PeriodicSite and distance of closest image
        return ns[0][0:2]


Instruction:
Given the following Python 3 function, write the documentation
def from_outcars_and_structures(cls, outcars, structures,
                                    calc_ionic_from_zval=False):
        """
        Create Polarization object from list of Outcars and Structures in order
        of nonpolar to polar.

        Note, we recommend calculating the ionic dipole moment using calc_ionic
        than using the values in Outcar (see module comments). To do this set
        calc_ionic_from_zval = True
        """
        p_elecs = []
        p_ions = []

        for i, o in enumerate(outcars):
            p_elecs.append(o.p_elec)
            if calc_ionic_from_zval:
                p_ions.append(
                    get_total_ionic_dipole(structures[i], o.zval_dict))
            else:
                p_ions.append(o.p_ion)
        return cls(p_elecs, p_ions, structures)

Response: Create Polarization object from list of Outcars and Structures.


Instruction:
Can you create a Python 3 function that
gets the electronic and ionic dipole moments and polarizations.

Response:
def get_pelecs_and_pions(self, convert_to_muC_per_cm2=False):
        """
        Get the electronic and ionic dipole moments / polarizations.

        convert_to_muC_per_cm2: Convert from electron * Angstroms to microCoulomb
            per centimeter**2
        """

        if not convert_to_muC_per_cm2:
            return self.p_elecs, self.p_ions

        if convert_to_muC_per_cm2:
            p_elecs = self.p_elecs.T
            p_ions = self.p_ions.T

            volumes = [s.lattice.volume for s in self.structures]
            e_to_muC = -1.6021766e-13
            cm2_to_A2 = 1e16
            units = 1.0 / np.array(volumes)
            units *= e_to_muC * cm2_to_A2

            p_elecs = np.matmul(units, p_elecs)
            p_ions = np.matmul(units, p_ions)

            p_elecs, p_ions = p_elecs.T, p_ions.T

            return p_elecs, p_ions


Instruction:
Can you generate the documentation for the following Python 3 function
def get_same_branch_polarization_data(self, convert_to_muC_per_cm2=True, all_in_polar=True):

        p_elec, p_ion = self.get_pelecs_and_pions()
        p_tot = p_elec + p_ion
        p_tot = np.array(p_tot)

        lattices = [s.lattice for s in self.structures]
        volumes = np.array([s.lattice.volume for s in self.structures])

        L = len(p_elec)

        e_to_muC = -1.6021766e-13
        cm2_to_A2 = 1e16
        units = 1.0 / np.array(volumes)
        units *= e_to_muC * cm2_to_A2

        # convert polarizations and lattice lengths prior to adjustment
        if convert_to_muC_per_cm2 and not all_in_polar:
            # Convert the total polarization
            p_tot = np.multiply(units.T[:, np.newaxis], p_tot)
            # adjust lattices
            for i in range(L):
                lattice = lattices[i]
                l, a = lattice.lengths_and_angles
                lattices[i] = Lattice.from_lengths_and_angles(
                    np.array(l) * units.ravel()[i], a)
        #  convert polarizations to polar lattice
        elif convert_to_muC_per_cm2 and all_in_polar:
            abc = [lattice.abc for lattice in lattices]
            abc = np.array(abc)  # [N, 3]
            p_tot /= abc  # e * Angstroms to e
            p_tot *= abc[-1] / volumes[-1] * e_to_muC * cm2_to_A2  # to muC / cm^2
            for i in range(L):
                lattice = lattices[-1]  # Use polar lattice
                l, a = lattice.lengths_and_angles
                lattices[i] = Lattice.from_lengths_and_angles(
                    np.array(l) * units.ravel()[-1], a)  # Use polar units (volume)

        d_structs = []
        sites = []
        for i in range(L):
            l = lattices[i]
            frac_coord = np.divide(np.array([p_tot[i]]),
                                   np.array([l.a, l.b, l.c]))
            d = PolarizationLattice(l, ["C"], [np.array(frac_coord).ravel()])
            d_structs.append(d)
            site = d[0]
            if i == 0:
                # Adjust nonpolar polarization to be closest to zero.
                # This is compatible with both a polarization of zero or a half quantum.
                prev_site = [0, 0, 0]
            else:
                prev_site = sites[-1].coords
            new_site = d.get_nearest_site(prev_site, site)
            sites.append(new_site[0])

        adjust_pol = []
        for s, d in zip(sites, d_structs):
            l = d.lattice
            adjust_pol.append(
                np.multiply(s.frac_coords, np.array([l.a, l.b, l.c])).ravel())
        adjust_pol = np.array(adjust_pol)

        return adjust_pol

Response: This method returns the same branch dipole moment or polarization for given polarization data.


Instruction:
Implement a function in Python 3 to
return the dipole / polarization quanta along a b and c for all structures.

Response:
def get_lattice_quanta(self, convert_to_muC_per_cm2=True, all_in_polar=True):
        """
        Returns the dipole / polarization quanta along a, b, and c for
        all structures.
        """
        lattices = [s.lattice for s in self.structures]
        volumes = np.array([s.lattice.volume for s in self.structures])

        L = len(self.structures)

        e_to_muC = -1.6021766e-13
        cm2_to_A2 = 1e16
        units = 1.0 / np.array(volumes)
        units *= e_to_muC * cm2_to_A2

        # convert polarizations and lattice lengths prior to adjustment
        if convert_to_muC_per_cm2 and not all_in_polar:
            # adjust lattices
            for i in range(L):
                lattice = lattices[i]
                l, a = lattice.lengths_and_angles
                lattices[i] = Lattice.from_lengths_and_angles(
                    np.array(l) * units.ravel()[i], a)
        elif convert_to_muC_per_cm2 and all_in_polar:
            for i in range(L):
                lattice = lattices[-1]
                l, a = lattice.lengths_and_angles
                lattices[i] = Lattice.from_lengths_and_angles(
                    np.array(l) * units.ravel()[-1], a)

        quanta = np.array(
            [np.array(l.lengths_and_angles[0]) for l in lattices])

        return quanta


Instruction:
Implement a Python 3 function for
getting difference between nonpolar and polar same branch polarization.

Response:
def get_polarization_change(self, convert_to_muC_per_cm2=True, all_in_polar=True):
        """
        Get difference between nonpolar and polar same branch polarization.
        """
        tot = self.get_same_branch_polarization_data(
            convert_to_muC_per_cm2=convert_to_muC_per_cm2, all_in_polar=all_in_polar)
        # reshape to preserve backwards compatibility due to changes
        # when switching from np.matrix to np.array
        return (tot[-1] - tot[0]).reshape((1,  3))


Instruction:
Implement a Python 3 function for
getting magnitude of difference between nonpolar and polar same branch.

Response:
def get_polarization_change_norm(self, convert_to_muC_per_cm2=True, all_in_polar=True):
        """
        Get magnitude of difference between nonpolar and polar same branch
        polarization.
        """
        polar = self.structures[-1]
        a, b, c = polar.lattice.matrix
        a, b, c = a / np.linalg.norm(a), b / np.linalg.norm(
            b), c / np.linalg.norm(c)
        P = self.get_polarization_change(convert_to_muC_per_cm2=convert_to_muC_per_cm2,
                                         all_in_polar=all_in_polar).ravel()
        P_norm = np.linalg.norm(a * P[0] + b * P[1] + c * P[2])
        return P_norm


Instruction:
Write a Python 3 function that can
fit splines to same branch polarization.

Response:
def same_branch_splines(self, convert_to_muC_per_cm2=True, all_in_polar=True):
        """
        Fit splines to same branch polarization. This is used to assess any jumps
        in the same branch polarizaiton.
        """
        from scipy.interpolate import UnivariateSpline
        tot = self.get_same_branch_polarization_data(
            convert_to_muC_per_cm2=convert_to_muC_per_cm2, all_in_polar=all_in_polar)
        L = tot.shape[0]
        try:
            sp_a = UnivariateSpline(range(L), tot[:, 0].ravel())
        except:
            sp_a = None
        try:
            sp_b = UnivariateSpline(range(L), tot[:, 1].ravel())
        except:
            sp_b = None
        try:
            sp_c = UnivariateSpline(range(L), tot[:, 2].ravel())
        except:
            sp_c = None
        return sp_a, sp_b, sp_c


Instruction:
Given the following Python 3 function, write the documentation
def max_spline_jumps(self, convert_to_muC_per_cm2=True, all_in_polar=True):
        tot = self.get_same_branch_polarization_data(
            convert_to_muC_per_cm2=convert_to_muC_per_cm2, all_in_polar=all_in_polar)
        sps = self.same_branch_splines(convert_to_muC_per_cm2=convert_to_muC_per_cm2,
                                       all_in_polar=all_in_polar)
        max_jumps = [None, None, None]
        for i, sp in enumerate(sps):
            if sp != None:
                max_jumps[i] = max(tot[:, i].ravel() - sp(range(len(tot[:, i].ravel()))))
        return max_jumps

Response: Get maximum difference between spline and same branch polarization data.


Instruction:
Can you write a function in Python 3 where it
gets the average difference between spline and same branch polarization data.

Response:
def smoothness(self, convert_to_muC_per_cm2=True, all_in_polar=True):
        """
        Get rms average difference between spline and same branch polarization data.
        """
        tot = self.get_same_branch_polarization_data(
            convert_to_muC_per_cm2=convert_to_muC_per_cm2, all_in_polar=all_in_polar)
        L = tot.shape[0]
        try:
            sp = self.same_branch_splines(convert_to_muC_per_cm2=convert_to_muC_per_cm2,
                                          all_in_polar=all_in_polar)
        except:
            print("Something went wrong.")
            return None
        sp_latt = [sp[i](range(L)) for i in range(3)]
        diff = [sp_latt[i] - tot[:, i].ravel() for i in range(3)]
        rms = [np.sqrt(np.sum(np.square(diff[i])) / L) for i in range(3)]
        return rms


Instruction:
Make a summary of the following Python 3 code
def spline(self):
        from scipy.interpolate import UnivariateSpline
        sp = UnivariateSpline(range(len(self.energies)), self.energies, k=4)
        return sp

Response: Fit spline to energy trend data.


Instruction:
How would you explain what the following Python 3 function does
def smoothness(self):
        energies = self.energies
        try:
            sp = self.spline()
        except:
            print("Energy spline failed.")
            return None
        spline_energies = sp(range(len(energies)))
        diff = spline_energies - energies
        rms = np.sqrt(np.sum(np.square(diff)) / len(energies))
        return rms

Response: Get the average difference between spline and energy trend.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def max_spline_jump(self):
        sp = self.spline()
        return max(self.energies - sp(range(len(self.energies))))

Response: Get maximum difference between spline and energy trend.


Instruction:
Can you tell what is the following Python 3 function doing
def endpoints_minima(self, slope_cutoff=5e-3):
        energies = self.energies
        try:
            sp = self.spline()
        except:
            print("Energy spline failed.")
            return None
        der = sp.derivative()
        der_energies = der(range(len(energies)))
        return {"polar": abs(der_energies[-1]) <= slope_cutoff,
                "nonpolar": abs(der_energies[0]) <= slope_cutoff}

Response: Test if spline endpoints are at minima for a given slope cutoff.


Instruction:
Can you implement a function in Python 3 that
processes a single entry with the chosen Corrections.

Response:
def process_entry(self, entry):
        """
        Process a single entry with the chosen Corrections.

        Args:
            entry: A ComputedEntry object.

        Returns:
            An adjusted entry if entry is compatible, otherwise None is
            returned.
        """
        try:
            corrections = self.get_corrections_dict(entry)
        except CompatibilityError:
            return None
        entry.correction = sum(corrections.values())
        return entry


Instruction:
Can you generate the documentation for the following Python 3 function
def get_corrections_dict(self, entry):
        corrections = {}
        for c in self.corrections:
            val = c.get_correction(entry)
            if val != 0:
                corrections[str(c)] = val
        return corrections

Response: Returns the corrections applied to a particular entry.


Instruction:
Can you generate the documentation for the following Python 3 function
def get_explanation_dict(self, entry):
        centry = self.process_entry(entry)
        if centry is None:
            uncorrected_energy = entry.uncorrected_energy
            corrected_energy = None
        else:
            uncorrected_energy = centry.uncorrected_energy
            corrected_energy = centry.energy
        d = {"compatibility": self.__class__.__name__,
             "uncorrected_energy": uncorrected_energy,
             "corrected_energy": corrected_energy}
        corrections = []
        corr_dict = self.get_corrections_dict(entry)
        for c in self.corrections:
            cd = {"name": str(c),
                  "description": c.__doc__.split("Args")[0].strip(),
                  "value": corr_dict.get(str(c), 0)}
            corrections.append(cd)
        d["corrections"] = corrections
        return d

Response: Returns an explanation dict of the corrections that are being applied to a given compute entry.


Instruction:
How would you explain what the following Python 3 function does
def explain(self, entry):
        d = self.get_explanation_dict(entry)
        print("The uncorrected value of the energy of %s is %f eV" %
              (entry.composition, d["uncorrected_energy"]))
        print("The following corrections / screening are applied for %s:\n" %
              d["compatibility"])
        for c in d["corrections"]:
            print("%s correction: %s\n" % (c["name"],
                                           c["description"]))
            print("For the entry, this correction has the value %f eV." % c[
                "value"])
            print("-" * 30)

        print("The final energy after corrections is %f" % d[
            "corrected_energy"])

Response: Prints an explanation of the corrections that are being applied for a specific entry.


Instruction:
Write a Python 3 function for
reading a string representation of a CSSR object.

Response:
def from_string(string):
        """
        Reads a string representation to a Cssr object.

        Args:
            string (str): A string representation of a CSSR.

        Returns:
            Cssr object.
        """
        lines = string.split("\n")
        toks = lines[0].split()
        lengths = [float(i) for i in toks]
        toks = lines[1].split()
        angles = [float(i) for i in toks[0:3]]
        latt = Lattice.from_lengths_and_angles(lengths, angles)
        sp = []
        coords = []
        for l in lines[4:]:
            m = re.match(
                r"\d+\s+(\w+)\s+([0-9\-\.]+)\s+([0-9\-\.]+)\s+([0-9\-\.]+)",
                l.strip())
            if m:
                sp.append(m.group(1))
                coords.append([float(m.group(i)) for i in range(2, 5)])
        return Cssr(Structure(latt, sp, coords))


Instruction:
Can you generate a brief explanation for the following Python 3 code
def freq_units(units):

    d = {"thz": FreqUnits(1, "THz"),
         "ev": FreqUnits(const.value("hertz-electron volt relationship") * const.tera, "eV"),
         "mev": FreqUnits(const.value("hertz-electron volt relationship") * const.tera / const.milli, "meV"),
         "ha": FreqUnits(const.value("hertz-hartree relationship") * const.tera, "Ha"),
         "cm-1": FreqUnits(const.value("hertz-inverse meter relationship") * const.tera * const.centi, "cm^{-1}"),
         'cm^-1': FreqUnits(const.value("hertz-inverse meter relationship") * const.tera * const.centi, "cm^{-1}")
         }
    try:
        return d[units.lower().strip()]
    except KeyError:
        raise KeyError('Value for units `{}` unknown\nPossible values are:\n {}'.format(units, list(d.keys())))

Response: Returns conversion factor from THz to the requred units and label in the form of a namedtuple


Instruction:
Write a Python 3 script for
adding a dos for plotting.

Response:
def add_dos(self, label, dos):
        """
        Adds a dos for plotting.

        Args:
            label:
                label for the DOS. Must be unique.
            dos:
                PhononDos object
        """

        densities = dos.get_smeared_densities(self.sigma) if self.sigma \
            else dos.densities
        self._doses[label] = {'frequencies': dos.frequencies, 'densities': densities}


Instruction:
Can you generate the documentation for the following Python 3 function
def add_dos_dict(self, dos_dict, key_sort_func=None):
        if key_sort_func:
            keys = sorted(dos_dict.keys(), key=key_sort_func)
        else:
            keys = dos_dict.keys()
        for label in keys:
            self.add_dos(label, dos_dict[label])

Response: Adds a dictionary of Dos to the set of Doses.


Instruction:
Here you have a function in Python 3, explain what it does
def get_plot(self, xlim=None, ylim=None, units="thz"):

        u = freq_units(units)

        ncolors = max(3, len(self._doses))
        ncolors = min(9, ncolors)

        import palettable

        colors = palettable.colorbrewer.qualitative.Set1_9.mpl_colors

        y = None
        alldensities = []
        allfrequencies = []
        plt = pretty_plot(12, 8)

        # Note that this complicated processing of frequencies is to allow for
        # stacked plots in matplotlib.
        for key, dos in self._doses.items():
            frequencies = dos['frequencies'] * u.factor
            densities = dos['densities']
            if y is None:
                y = np.zeros(frequencies.shape)
            if self.stack:
                y += densities
                newdens = y.copy()
            else:
                newdens = densities
            allfrequencies.append(frequencies)
            alldensities.append(newdens)

        keys = list(self._doses.keys())
        keys.reverse()
        alldensities.reverse()
        allfrequencies.reverse()
        allpts = []
        for i, (key, frequencies, densities) in enumerate(zip(keys, allfrequencies, alldensities)):
            allpts.extend(list(zip(frequencies, densities)))
            if self.stack:
                plt.fill(frequencies, densities, color=colors[i % ncolors],
                         label=str(key))
            else:
                plt.plot(frequencies, densities, color=colors[i % ncolors],
                         label=str(key), linewidth=3)

        if xlim:
            plt.xlim(xlim)
        if ylim:
            plt.ylim(ylim)
        else:
            xlim = plt.xlim()
            relevanty = [p[1] for p in allpts
                         if xlim[0] < p[0] < xlim[1]]
            plt.ylim((min(relevanty), max(relevanty)))

        ylim = plt.ylim()
        plt.plot([0, 0], ylim, 'k--', linewidth=2)

        plt.xlabel(r'$\mathrm{{Frequencies\ ({})}}$'.format(u.label))
        plt.ylabel(r'$\mathrm{Density\ of\ states}$')

        plt.legend()
        leg = plt.gca().get_legend()
        ltext = leg.get_texts()  # all the text.Text instance in the legend
        plt.setp(ltext, fontsize=30)
        plt.tight_layout()
        return plt

Response: Returns a matplotlib plot showing the DOS.


Instruction:
Can you tell what is the following Python 3 function doing
def show(self, xlim=None, ylim=None, units="thz"):
        plt = self.get_plot(xlim, ylim, units=units)
        plt.show()

Response: Show the plot using matplotlib.


Instruction:
Create a Python 3 function to
get the data nicely formatted for a plot

Response:
def bs_plot_data(self):

        """
        Get the data nicely formatted for a plot

        Returns:
            A dict of the following format:
            ticks: A dict with the 'distances' at which there is a qpoint (the
            x axis) and the labels (None if no label)
            frequencies: A list (one element for each branch) of frequencies for
            each qpoint: [branch][qpoint][mode]. The data is
            stored by branch to facilitate the plotting
            lattice: The reciprocal lattice.
        """
        distance = []
        frequency = []

        ticks = self.get_ticks()

        for b in self._bs.branches:

            frequency.append([])
            distance.append([self._bs.distance[j]
                             for j in range(b['start_index'],
                                            b['end_index'] + 1)])

            for i in range(self._nb_bands):
                frequency[-1].append(
                    [self._bs.bands[i][j]
                     for j in range(b['start_index'], b['end_index'] + 1)])

        return {'ticks': ticks, 'distances': distance, 'frequency': frequency,
                'lattice': self._bs.lattice_rec.as_dict()}


Instruction:
Given the following Python 3 function, write the documentation
def get_plot(self, ylim=None, units="thz"):

        u = freq_units(units)

        plt = pretty_plot(12, 8)

        band_linewidth = 1

        data = self.bs_plot_data()
        for d in range(len(data['distances'])):
            for i in range(self._nb_bands):
                plt.plot(data['distances'][d],
                         [data['frequency'][d][i][j] * u.factor
                          for j in range(len(data['distances'][d]))], 'b-',
                         linewidth=band_linewidth)

        self._maketicks(plt)

        # plot y=0 line
        plt.axhline(0, linewidth=1, color='k')

        # Main X and Y Labels
        plt.xlabel(r'$\mathrm{Wave\ Vector}$', fontsize=30)
        ylabel = r'$\mathrm{{Frequencies\ ({})}}$'.format(u.label)
        plt.ylabel(ylabel, fontsize=30)

        # X range (K)
        # last distance point
        x_max = data['distances'][-1][-1]
        plt.xlim(0, x_max)

        if ylim is not None:
            plt.ylim(ylim)

        plt.tight_layout()

        return plt

Response: Returns a matplotlib object for the bandstructure plot.


Instruction:
Can you tell what is the following Python 3 function doing
def save_plot(self, filename, img_format="eps", ylim=None, units="thz"):
        plt = self.get_plot(ylim=ylim, units=units)
        plt.savefig(filename, format=img_format)
        plt.close()

Response: Save matplotlib plot to a file.


Instruction:
Implement a Python 3 function for
getting all ticks and labels for a band structure plot.

Response:
def get_ticks(self):
        """
        Get all ticks and labels for a band structure plot.

        Returns:
            A dict with 'distance': a list of distance at which ticks should
            be set and 'label': a list of label for each of those ticks.
        """
        tick_distance = []
        tick_labels = []
        previous_label = self._bs.qpoints[0].label
        previous_branch = self._bs.branches[0]['name']
        for i, c in enumerate(self._bs.qpoints):
            if c.label is not None:
                tick_distance.append(self._bs.distance[i])
                this_branch = None
                for b in self._bs.branches:
                    if b['start_index'] <= i <= b['end_index']:
                        this_branch = b['name']
                        break
                if c.label != previous_label \
                        and previous_branch != this_branch:
                    label1 = c.label
                    if label1.startswith("\\") or label1.find("_") != -1:
                        label1 = "$" + label1 + "$"
                    label0 = previous_label
                    if label0.startswith("\\") or label0.find("_") != -1:
                        label0 = "$" + label0 + "$"
                    tick_labels.pop()
                    tick_distance.pop()
                    tick_labels.append(label0 + "$\\mid$" + label1)
                else:
                    if c.label.startswith("\\") or c.label.find("_") != -1:
                        tick_labels.append("$" + c.label + "$")
                    else:
                        tick_labels.append(c.label)
                previous_label = c.label
                previous_branch = this_branch
        return {'distance': tick_distance, 'label': tick_labels}


Instruction:
How would you implement a function in Python 3 that
plots two band structure for comparison. One is in red the other in blue. The two band structures need to be defined on the same symmetry lines! and the distance between symmetry lines is the one of the band structure used to build the PhononBSPlotter Args: another PhononBSPlotter object defined along the same symmetry lines Returns: a matplotlib object with both band structures

Response:
def plot_compare(self, other_plotter):
        """
        plot two band structure for comparison. One is in red the other in blue.
        The two band structures need to be defined on the same symmetry lines!
        and the distance between symmetry lines is
        the one of the band structure used to build the PhononBSPlotter

        Args:
            another PhononBSPlotter object defined along the same symmetry lines

        Returns:
            a matplotlib object with both band structures

        """

        data_orig = self.bs_plot_data()
        data = other_plotter.bs_plot_data()

        if len(data_orig['distances']) != len(data['distances']):
            raise ValueError('The two objects are not compatible.')

        plt = self.get_plot()
        band_linewidth = 1
        for i in range(other_plotter._nb_bands):
            for d in range(len(data_orig['distances'])):
                plt.plot(data_orig['distances'][d],
                         [e[i] for e in data['frequency']][d],
                         'r-', linewidth=band_linewidth)

        return plt


Instruction:
Can you generate a brief explanation for the following Python 3 code
def plot_brillouin(self):

        # get labels and lines
        labels = {}
        for q in self._bs.qpoints:
            if q.label:
                labels[q.label] = q.frac_coords

        lines = []
        for b in self._bs.branches:
            lines.append([self._bs.qpoints[b['start_index']].frac_coords,
                          self._bs.qpoints[b['end_index']].frac_coords])

        plot_brillouin_zone(self._bs.lattice_rec, lines=lines, labels=labels)

Response: Plot the Brillouin zone.


Instruction:
Can you generate the documentation for the following Python 3 function
def _plot_thermo(self, func, temperatures, factor=1, ax=None, ylabel=None, label=None, ylim=None, **kwargs):

        ax, fig, plt = get_ax_fig_plt(ax)

        values = []

        for t in temperatures:
            values.append(func(t, structure=self.structure) * factor)

        ax.plot(temperatures, values, label=label, **kwargs)

        if ylim:
            ax.set_ylim(ylim)

        ax.set_xlim((np.min(temperatures), np.max(temperatures)))
        ylim = plt.ylim()
        if ylim[0] < 0 < ylim[1]:
            plt.plot(plt.xlim(), [0, 0], 'k-', linewidth=1)

        ax.set_xlabel(r"$T$ (K)")
        if ylabel:
            ax.set_ylabel(ylabel)

        return fig

Response: Plots a thermodynamic property for a generic function from a PhononDos instance.


Instruction:
Can you generate the documentation for the following Python 3 function
def plot_cv(self, tmin, tmax, ntemp, ylim=None, **kwargs):
        temperatures = np.linspace(tmin, tmax, ntemp)

        if self.structure:
            ylabel = r"$C_v$ (J/K/mol)"
        else:
            ylabel = r"$C_v$ (J/K/mol-c)"

        fig = self._plot_thermo(self.dos.cv, temperatures, ylabel=ylabel, ylim=ylim, **kwargs)

        return fig

Response: Plots the constant volume specific heat C_v in a temperature range.


Instruction:
Can you write a function in Python 3 where it
plots the vibrational entrpy in a temperature range.

Response:
def plot_entropy(self, tmin, tmax, ntemp, ylim=None, **kwargs):
        """
        Plots the vibrational entrpy in a temperature range.

        Args:
            tmin: minimum temperature
            tmax: maximum temperature
            ntemp: number of steps
            ylim: tuple specifying the y-axis limits.
            kwargs: kwargs passed to the matplotlib function 'plot'.
        Returns:
            matplotlib figure
        """
        temperatures = np.linspace(tmin, tmax, ntemp)

        if self.structure:
            ylabel = r"$S$ (J/K/mol)"
        else:
            ylabel = r"$S$ (J/K/mol-c)"

        fig = self._plot_thermo(self.dos.entropy, temperatures, ylabel=ylabel, ylim=ylim, **kwargs)

        return fig


Instruction:
Can you create a Python 3 function that
plots the internal energy of the vibrational in a temperature range.

Response:
def plot_internal_energy(self, tmin, tmax, ntemp, ylim=None, **kwargs):
        """
        Plots the vibrational internal energy in a temperature range.

        Args:
            tmin: minimum temperature
            tmax: maximum temperature
            ntemp: number of steps
            ylim: tuple specifying the y-axis limits.
            kwargs: kwargs passed to the matplotlib function 'plot'.
        Returns:
            matplotlib figure
        """
        temperatures = np.linspace(tmin, tmax, ntemp)

        if self.structure:
            ylabel = r"$\Delta E$ (kJ/mol)"
        else:
            ylabel = r"$\Delta E$ (kJ/mol-c)"

        fig = self._plot_thermo(self.dos.internal_energy, temperatures, ylabel=ylabel, ylim=ylim,
                                factor=1e-3, **kwargs)

        return fig


Instruction:
Can you write a function in Python 3 where it
plots the vibrational contribution to the Helmoltz free energy in a temperature range.

Response:
def plot_helmholtz_free_energy(self, tmin, tmax, ntemp, ylim=None, **kwargs):
        """
        Plots the vibrational contribution to the Helmoltz free energy in a temperature range.

        Args:
            tmin: minimum temperature
            tmax: maximum temperature
            ntemp: number of steps
            ylim: tuple specifying the y-axis limits.
            kwargs: kwargs passed to the matplotlib function 'plot'.
        Returns:
            matplotlib figure
        """
        temperatures = np.linspace(tmin, tmax, ntemp)

        if self.structure:
            ylabel = r"$\Delta F$ (kJ/mol)"
        else:
            ylabel = r"$\Delta F$ (kJ/mol-c)"

        fig = self._plot_thermo(self.dos.helmholtz_free_energy, temperatures, ylabel=ylabel, ylim=ylim,
                                factor=1e-3, **kwargs)

        return fig


Instruction:
How would you code a function in Python 3 to
plot all the thermodynamic properties in a temperature range.

Response:
def plot_thermodynamic_properties(self, tmin, tmax, ntemp, ylim=None, **kwargs):
        """
        Plots all the thermodynamic properties in a temperature range.

        Args:
            tmin: minimum temperature
            tmax: maximum temperature
            ntemp: number of steps
            ylim: tuple specifying the y-axis limits.
            kwargs: kwargs passed to the matplotlib function 'plot'.
        Returns:
            matplotlib figure
        """
        temperatures = np.linspace(tmin, tmax, ntemp)

        mol = "" if self.structure else "-c"

        fig = self._plot_thermo(self.dos.cv, temperatures, ylabel="Thermodynamic properties", ylim=ylim,
                                label=r"$C_v$ (J/K/mol{})".format(mol), **kwargs)
        self._plot_thermo(self.dos.entropy, temperatures, ylim=ylim, ax=fig.axes[0],
                          label=r"$S$ (J/K/mol{})".format(mol), **kwargs)
        self._plot_thermo(self.dos.internal_energy, temperatures, ylim=ylim, ax=fig.axes[0], factor=1e-3,
                          label=r"$\Delta E$ (kJ/K/mol{})".format(mol), **kwargs)
        self._plot_thermo(self.dos.helmholtz_free_energy, temperatures, ylim=ylim, ax=fig.axes[0], factor=1e-3,
                          label=r"$\Delta F$ (kJ/K/mol{})".format(mol), **kwargs)

        fig.axes[0].legend(loc="best")

        return fig


Instruction:
How would you explain what the following Python 3 function does
def as_dict(self):
        pwinput_dict = {'structure': self.structure.as_dict(),
                        'pseudo': self.pseudo,
                        'sections': self.sections,
                        'kpoints_mode': self.kpoints_mode,
                        'kpoints_grid': self.kpoints_grid,
                        'kpoints_shift': self.kpoints_shift}
        return pwinput_dict

Response: Create a dictionary representation of a PWInput object


Instruction:
Here you have a function in Python 3, explain what it does
def from_dict(cls, pwinput_dict):
        pwinput = cls(structure=Structure.from_dict(pwinput_dict['structure']),
                          pseudo=pwinput_dict['pseudo'],
                          control=pwinput_dict['sections']['control'],
                          system=pwinput_dict['sections']['system'],
                          electrons=pwinput_dict['sections']['electrons'],
                          ions=pwinput_dict['sections']['ions'],
                          cell=pwinput_dict['sections']['cell'],
                          kpoints_mode=pwinput_dict['kpoints_mode'],
                          kpoints_grid=pwinput_dict['kpoints_grid'],
                          kpoints_shift=pwinput_dict['kpoints_shift'])
        return pwinput

Response: Load a PWInput object from a dictionary with PWInput data.


Instruction:
Write a Python 3 function that can
write the PWSCF input file to a file.

Response:
def write_file(self, filename):
        """
        Write the PWSCF input file.

        Args:
            filename (str): The string filename to output to.
        """
        with open(filename, "w") as f:
            f.write(self.__str__())


Instruction:
How would you explain what the following Python 3 function does
def from_string(string):
        lines = list(clean_lines(string.splitlines()))

        def input_mode(line):
            if line[0] == "&":
                return ("sections", line[1:].lower())
            elif "ATOMIC_SPECIES" in line:
                return ("pseudo", )
            elif "K_POINTS" in line:
                return ("kpoints", line.split("{")[1][:-1])
            elif "CELL_PARAMETERS" in line or "ATOMIC_POSITIONS" in line:
                return ("structure", line.split("{")[1][:-1])
            elif line == "/":
                return None
            else:
                return mode

        sections = {"control": {}, "system": {}, "electrons": {},
                    "ions": {}, "cell":{}}
        pseudo = {}
        pseudo_index = 0
        lattice = []
        species = []
        coords = []
        structure = None
        site_properties = {"pseudo":[]}
        mode = None
        for line in lines:
            mode = input_mode(line)
            if mode == None:
                pass
            elif mode[0] == "sections":
                section = mode[1]
                m = re.match(r'(\w+)\(?(\d*?)\)?\s*=\s*(.*)', line)
                if m:
                    key = m.group(1).strip()
                    key_ = m.group(2).strip()
                    val = m.group(3).strip()
                    if key_ != "":
                        if sections[section].get(key, None) == None:
                            val_ = [0.0]*20 # MAX NTYP DEFINITION
                            val_[int(key_)-1] = PWInput.proc_val(key, val)
                            sections[section][key] = val_

                            site_properties[key] = []
                        else:
                            sections[section][key][int(key_)-1] = PWInput.proc_val(key, val)
                    else:
                        sections[section][key] = PWInput.proc_val(key, val)

            elif mode[0] == "pseudo":
                m = re.match(r'(\w+)\s+(\d*.\d*)\s+(.*)', line)
                if m:
                    pseudo[m.group(1).strip()] = {}
                    pseudo[m.group(1).strip()]["index"] = pseudo_index
                    pseudo[m.group(1).strip()]["pseudopot"] = m.group(3).strip()
                    pseudo_index += 1
            elif mode[0] == "kpoints":
                m = re.match(r'(\d+)\s+(\d+)\s+(\d+)\s+(\d+)\s+(\d+)\s+(\d+)', line)
                if m:
                    kpoints_grid = (int(m.group(1)), int(m.group(2)), int(m.group(3)))
                    kpoints_shift = (int(m.group(4)), int(m.group(5)), int(m.group(6)))
                else:
                    kpoints_mode = mode[1]
            elif mode[0] == "structure":
                m_l = re.match(r'(-?\d+\.?\d*)\s+(-?\d+\.?\d*)\s+(-?\d+\.?\d*)', line)
                m_p = re.match(r'(\w+)\s+(-?\d+\.\d*)\s+(-?\d+\.?\d*)\s+(-?\d+\.?\d*)', line)
                if m_l:
                    lattice += [ float(m_l.group(1)), float(m_l.group(2)), float(m_l.group(3)) ]
                elif m_p:
                    site_properties["pseudo"].append(pseudo[m_p.group(1)]["pseudopot"])
                    species += [pseudo[m_p.group(1)]["pseudopot"].split(".")[0]]
                    coords += [[float(m_p.group(2)), float(m_p.group(3)), float(m_p.group(4))]]

                    for k, v in site_properties.items():
                        if k != "pseudo":
                            site_properties[k].append(sections['system'][k][pseudo[m_p.group(1)]["index"]])
                if mode[1] == "angstrom":
                    coords_are_cartesian = True
                elif mode[1] == "crystal":
                    coords_are_cartesian = False

        structure = Structure(Lattice(lattice), species, coords,
                              coords_are_cartesian=coords_are_cartesian,
                              site_properties=site_properties)
        return PWInput(structure=structure, control=sections["control"],
                       system=sections["system"], electrons=sections["electrons"],
                       ions=sections["ions"], cell=sections["cell"], kpoints_mode=kpoints_mode,
                       kpoints_grid=kpoints_grid, kpoints_shift=kpoints_shift)

Response: Reads an object from a string.


Instruction:
Make a summary of the following Python 3 code
def proc_val(key, val):
        float_keys = ('etot_conv_thr','forc_conv_thr','conv_thr','Hubbard_U','Hubbard_J0','defauss',
                      'starting_magnetization',)

        int_keys = ('nstep','iprint','nberrycyc','gdir','nppstr','ibrav','nat','ntyp','nbnd','nr1',
                    'nr2','nr3','nr1s','nr2s','nr3s','nspin','nqx1','nqx2','nqx3','lda_plus_u_kind',
                    'edir','report','esm_nfit','space_group','origin_choice','electron_maxstep',
                    'mixing_ndim','mixing_fixed_ns','ortho_para','diago_cg_maxiter','diago_david_ndim',
                    'nraise','bfgs_ndim','if_pos','nks','nk1','nk2','nk3','sk1','sk2','sk3','nconstr')

        bool_keys = ('wf_collect','tstress','tprnfor','lkpoint_dir','tefield','dipfield','lelfield',
                     'lorbm','lberry','lfcpopt','monopole','nosym','nosym_evc','noinv','no_t_rev',
                     'force_symmorphic','use_all_frac','one_atom_occupations','starting_spin_angle',
                     'noncolin','x_gamma_extrapolation','lda_plus_u','lspinorb','london',
                     'ts_vdw_isolated','xdm','uniqueb','rhombohedral','realxz','block',
                     'scf_must_converge','adaptive_thr','diago_full_acc','tqr','remove_rigid_rot',
                     'refold_pos')

        def smart_int_or_float(numstr):
            if numstr.find(".") != -1 or numstr.lower().find("e") != -1:
                return float(numstr)
            else:
                return int(numstr)

        try:
            if key in bool_keys:
                if val.lower() == ".true.":
                    return True
                elif val.lower() == ".false.":
                    return False
                else:
                    raise ValueError(key + " should be a boolean type!")

            if key in float_keys:
                return float(re.search(r"^-?\d*\.?\d*d?-?\d*", val.lower()).group(0).replace("d", "e"))

            if key in int_keys:
                return int(re.match(r"^-?[0-9]+", val).group(0))

        except ValueError:
            pass

        try:
            val = val.replace("d","e")
            return smart_int_or_float(val)
        except ValueError:
            pass

        if "true" in val.lower():
            return True
        if "false" in val.lower():
            return False

        m = re.match(r"^[\"|'](.+)[\"|']$", val)
        if m:
            return m.group(1)

Response: This method is used to convert the values of the PWINPUT parameters to proper types.


Instruction:
Create a Python 3 function for
printing to the given stream the template of the class of type qtype.

Response:
def show_qparams(qtype, stream=sys.stdout):
    """Print to the given stream the template of the :class:`QueueAdapter` of type `qtype`."""
    for cls in all_subclasses(QueueAdapter):
        if cls.QTYPE == qtype: return stream.write(cls.QTEMPLATE)

    raise ValueError("Cannot find class associated to qtype %s" % qtype)


Instruction:
How would you explain what the following Python 3 function does
def make_qadapter(**kwargs):
    # Get all known subclasses of QueueAdapter.
    d = {c.QTYPE: c for c in all_subclasses(QueueAdapter)}

    # Preventive copy before pop
    kwargs = copy.deepcopy(kwargs)
    qtype = kwargs["queue"].pop("qtype")

    return d[qtype](**kwargs)

Response: Return the concrete class of QueueAdapter class from a string.


Instruction:
Can you write a function in Python 3 where it
builds and returns a string with the command required to execute the qadapter with the qadapter.

Response:
def string_to_run(self, qad, executable, stdin=None, stdout=None, stderr=None, exec_args=None):
        """
        Build and return a string with the command required to launch `executable` with the qadapter `qad`.

        Args
            qad: Qadapter instance.
            executable (str): Executable name or path
            stdin (str): Name of the file to be used as standard input. None means no redirection.
            stdout (str): Name of the file to be used as standard output. None means no redirection.
            stderr (str): Name of the file to be used as standard error. None means no redirection.
            exec_args: Optional list of strings with options passed to `executable`.

        Return:
            String with command to execute.
        """
        stdin = "< " + stdin if stdin is not None else ""
        stdout = "> " + stdout if stdout is not None else ""
        stderr = "2> " + stderr if stderr is not None else ""

        if exec_args:
            executable = executable + " " + " ".join(list_strings(exec_args))

        basename = os.path.basename(self.name)
        if basename in ["mpirun", "mpiexec", "srun"]:
            if self.type is None:
                # $MPIRUN -n $MPI_PROCS $EXECUTABLE < $STDIN > $STDOUT 2> $STDERR
                num_opt = "-n " + str(qad.mpi_procs)
                cmd = " ".join([self.name, self.options, num_opt, executable, stdin, stdout, stderr])
            else:
                raise NotImplementedError("type %s is not supported!" % self.type)

        elif basename == "runjob":
            #runjob --ranks-per-node 2 --exp-env OMP_NUM_THREADS --exe $ABINIT < $STDIN > $STDOUT 2> $STDERR
            #runjob -n 2 --exp-env=OMP_NUM_THREADS --exe $ABINIT < $STDIN > $STDOUT 2> $STDERR
            # exe must be absolute path or relative to cwd.
            bg_size, rpn = qad.bgsize_rankspernode()
            #num_opt = "-n " + str(qad.mpi_procs)
            num_opt = "--ranks-per-node " + str(rpn)
            cmd = " ".join([self.name, self.options, num_opt, "--exp-env OMP_NUM_THREADS",
                           "--exe `which " + executable + "` ", stdin, stdout, stderr])
        else:
            if qad.mpi_procs != 1:
                raise ValueError("Cannot use mpi_procs > when mpi_runner basename=%s" % basename)
            cmd = " ".join([executable, stdin, stdout, stderr])

        return cmd


Instruction:
Explain what the following Python 3 code does
def as_ompenv(cls, obj):
        if isinstance(obj, cls): return obj
        if obj is None: return cls()
        return cls(**obj)

Response: Convert an object into an OmpEnv object


Instruction:
Can you create a Python 3 function that
returns a dictionary representation of the object.

Response:
def as_dict(self):
        """
        Provides a simple though not complete dict serialization of the object (OMP missing, not all limits are
        kept in the dictionary, ... other things to be checked)

        Raise:
            `ValueError` if errors.
        """
        if self.has_omp:
            raise NotImplementedError('as_dict method of QueueAdapter not yet implemented when OpenMP is activated')
        return {'@module': self.__class__.__module__,
                '@class': self.__class__.__name__,
                'priority': self.priority,
                'hardware': self.hw.as_dict(),
                'queue': {'qtype': self.QTYPE,
                          'qname': self._qname,
                          'qnodes': self.qnodes,
                          'qparams': self._qparams},
                'limits': {'timelimit_hard': self._timelimit_hard,
                           'timelimit': self._timelimit,
                           'min_cores': self.min_cores,
                           'max_cores': self.max_cores,
                           'min_mem_per_proc': self.min_mem_per_proc,
                           'max_mem_per_proc': self.max_mem_per_proc,
                           'memory_policy': self.memory_policy
                           },
                'job': {},
                'mpi_procs': self._mpi_procs,
                'mem_per_proc': self._mem_per_proc,
                'master_mem_overhead': self._master_mem_overhead
                }


Instruction:
Write a Python 3 script to
validate the list of supported parameters for the current class.

Response:
def validate_qparams(self):
        """
        Check if the keys specified by the user in qparams are supported.

        Raise:
            `ValueError` if errors.
        """
        # No validation for ShellAdapter.
        if isinstance(self, ShellAdapter): return

        # Parse the template so that we know the list of supported options.
        err_msg = ""
        for param in self.qparams:
            if param not in self.supported_qparams:
                err_msg += "Unsupported QUEUE parameter name %s\n" % param
                err_msg += "Supported parameters:\n"
                for param_sup in self.supported_qparams:
                    err_msg += "    %s \n" % param_sup

        if err_msg:
            raise ValueError(err_msg)


Instruction:
Write a Python 3 script to
validate the parameters of the run. Raises self. Error if invalid parameters.

Response:
def validate(self):
        """Validate the parameters of the run. Raises self.Error if invalid parameters."""
        errors = []
        app = errors.append

        if not self.hint_cores >= self.mpi_procs * self.omp_threads >= self.min_cores:
            app("self.hint_cores >= mpi_procs * omp_threads >= self.min_cores not satisfied")

        if self.omp_threads > self.hw.cores_per_node:
            app("omp_threads > hw.cores_per_node")

        if self.mem_per_proc > self.hw.mem_per_node:
            app("mem_mb >= self.hw.mem_per_node")

        if not self.max_mem_per_proc >= self.mem_per_proc >= self.min_mem_per_proc:
            app("self.max_mem_per_proc >= mem_mb >= self.min_mem_per_proc not satisfied")

        if self.priority <= 0:
            app("priority must be > 0")

        if not (1 <= self.min_cores <= self.hw.num_cores >= self.hint_cores):
            app("1 <= min_cores <= hardware num_cores >= hint_cores not satisfied")

        if errors:
            raise self.Error(str(self) + "\n".join(errors))


Instruction:
Can you implement a function in Python 3 that
trues if the qadapter can run the ParalConf pconf

Response:
def can_run_pconf(self, pconf):
        """True if the qadapter in principle is able to run the :class:`ParalConf` pconf"""
        if not self.hint_cores >= pconf.num_cores >= self.min_cores: return False
        if not self.hw.can_use_omp_threads(self.omp_threads): return False
        if pconf.mem_per_proc > self.hw.mem_per_node: return False
        if self.allocation == "force_nodes" and pconf.num_cores % self.hw.cores_per_node != 0:
            return False

        return self.condition(pconf)


Instruction:
How would you code a function in Python 3 to
distribute the current set of processes and threads to the other processes.

Response:
def distribute(self, mpi_procs, omp_threads, mem_per_proc):
        """
        Returns (num_nodes, mpi_per_node)

        Aggressive: When Open MPI thinks that it is in an exactly- or under-subscribed mode
        (i.e., the number of running processes is equal to or less than the number of available processors),
        MPI processes will automatically run in aggressive mode, meaning that they will never voluntarily give
        up the processor to other processes. With some network transports, this means that Open MPI will spin
        in tight loops attempting to make message passing progress, effectively causing other processes to not get
        any CPU cycles (and therefore never make any progress)
        """
        class Distrib(namedtuple("Distrib", "num_nodes mpi_per_node exact")):
            pass
            #@property
            #def mem_per_node
            #    return self.mpi_per_node * mem_per_proc
            #def set_nodes(self, nodes):

        hw = self.hw

        # TODO: Add check on user-memory
        if mem_per_proc <= 0:
            logger.warning("mem_per_proc <= 0")
            mem_per_proc = hw.mem_per_core

        if mem_per_proc > hw.mem_per_node:
            raise self.Error(
                "mem_per_proc > mem_per_node.\n Cannot distribute mpi_procs %d, omp_threads %d, mem_per_proc %s" %
                             (mpi_procs, omp_threads, mem_per_proc))

        # Try to use all then cores in the node.
        num_nodes, rest_cores = hw.divmod_node(mpi_procs, omp_threads)

        if num_nodes == 0 and mpi_procs * mem_per_proc <= hw.mem_per_node:
            # One node is enough
            return Distrib(num_nodes=1, mpi_per_node=mpi_procs, exact=True)

        if num_nodes == 0: num_nodes = 2
        mpi_per_node = mpi_procs // num_nodes
        if mpi_per_node * mem_per_proc <= hw.mem_per_node and rest_cores == 0:
            # Commensurate with nodes.
            return Distrib(num_nodes=num_nodes, mpi_per_node=mpi_per_node, exact=True)

        #if mode == "block", "cyclic"

        # Try first to pack MPI processors in a node as much as possible
        mpi_per_node = int(hw.mem_per_node / mem_per_proc)
        assert mpi_per_node != 0
        num_nodes = (mpi_procs * omp_threads) // mpi_per_node
        print("exact --> false", num_nodes, mpi_per_node)

        if mpi_per_node * omp_threads <= hw.cores_per_node and mem_per_proc <= hw.mem_per_node:
            return Distrib(num_nodes=num_nodes, mpi_per_node=mpi_per_node, exact=False)

        if (mpi_procs * omp_threads) % mpi_per_node != 0:
            # Have to reduce the number of MPI procs per node
            for mpi_per_node in reversed(range(1, mpi_per_node)):
                if mpi_per_node > hw.cores_per_node: continue
                num_nodes = (mpi_procs * omp_threads) // mpi_per_node
                if (mpi_procs * omp_threads) % mpi_per_node == 0 and mpi_per_node * mem_per_proc <= hw.mem_per_node:
                    return Distrib(num_nodes=num_nodes, mpi_per_node=mpi_per_node, exact=False)
        else:
            raise self.Error("Cannot distribute mpi_procs %d, omp_threads %d, mem_per_proc %s" %
                            (mpi_procs, omp_threads, mem_per_proc))


Instruction:
How would you implement a function in Python 3 that
returns substitution dict for replacements into the template

Response:
def get_subs_dict(self, qnodes=None):
        """
        Return substitution dict for replacements into the template
        Subclasses may want to customize this method.
        """
        #d = self.qparams.copy()
        d = self.qparams
        d.update(self.optimize_params(qnodes=qnodes))
        # clean null values
        subs_dict = {k: v for k, v in d.items() if v is not None}
        #print("subs_dict:", subs_dict)
        return subs_dict


Instruction:
Write a Python 3 function that can
return a string with the options that are passed to the resource manager.

Response:
def _make_qheader(self, job_name, qout_path, qerr_path):
        """Return a string with the options that are passed to the resource manager."""
        # get substitution dict for replacements into the template
        subs_dict = self.get_subs_dict()

        # Set job_name and the names for the stderr and stdout of the
        # queue manager (note the use of the extensions .qout and .qerr
        # so that we can easily locate this file.
        subs_dict['job_name'] = job_name.replace('/', '_')
        subs_dict['_qout_path'] = qout_path
        subs_dict['_qerr_path'] = qerr_path

        qtemplate = QScriptTemplate(self.QTEMPLATE)
        # might contain unused parameters as leftover $$.
        unclean_template = qtemplate.safe_substitute(subs_dict)

        # Remove lines with leftover $$.
        clean_template = []
        for line in unclean_template.split('\n'):
            if '$$' not in line:
                clean_template.append(line)

        return '\n'.join(clean_template)


Instruction:
Write a Python 3 script to
return a string representing the queue manager script.

Response:
def get_script_str(self, job_name, launch_dir, executable, qout_path, qerr_path,
                       stdin=None, stdout=None, stderr=None, exec_args=None):
        """
        Returns a (multi-line) String representing the queue script, e.g. PBS script.
        Uses the template_file along with internal parameters to create the script.

        Args:
            job_name: Name of the job.
            launch_dir: (str) The directory the job will be launched in.
            executable: String with the name of the executable to be executed or list of commands
            qout_path Path of the Queue manager output file.
            qerr_path: Path of the Queue manager error file.
            exec_args: List of arguments passed to executable (used only if executable is a string, default: empty)
        """
        # PbsPro does not accept job_names longer than 15 chars.
        if len(job_name) > 14 and isinstance(self, PbsProAdapter):
            job_name = job_name[:14]

        # Construct the header for the Queue Manager.
        qheader = self._make_qheader(job_name, qout_path, qerr_path)

        # Add the bash section.
        se = ScriptEditor()

        # Cd to launch_dir immediately.
        se.add_line("cd " + os.path.abspath(launch_dir))

        if self.setup:
            se.add_comment("Setup section")
            se.add_lines(self.setup)
            se.add_emptyline()

        if self.modules:
            # stderr is redirected to mods.err file.
            # module load 2>> mods.err
            se.add_comment("Load Modules")
            se.add_line("module purge")
            se.load_modules(self.modules)
            se.add_emptyline()

        se.add_comment("OpenMp Environment")
        if self.has_omp:
            se.declare_vars(self.omp_env)
            se.add_emptyline()
        else:
            se.declare_vars({"OMP_NUM_THREADS": 1})

        if self.shell_env:
            se.add_comment("Shell Environment")
            se.declare_vars(self.shell_env)
            se.add_emptyline()

        if self.pre_run:
            se.add_comment("Commands before execution")
            se.add_lines(self.pre_run)
            se.add_emptyline()

        # Construct the string to run the executable with MPI and mpi_procs.
        if is_string(executable):
            line = self.mpi_runner.string_to_run(self, executable,
                                                 stdin=stdin, stdout=stdout, stderr=stderr, exec_args=exec_args)
            se.add_line(line)
        else:
            assert isinstance(executable, (list, tuple))
            se.add_lines(executable)

        if self.post_run:
            se.add_emptyline()
            se.add_comment("Commands after execution")
            se.add_lines(self.post_run)

        return qheader + se.get_script_str() + "\n"


Instruction:
Can you generate the documentation for the following Python 3 function
def submit_to_queue(self, script_file):
        if not os.path.exists(script_file):
            raise self.Error('Cannot find script file located at: {}'.format(script_file))

        if self.num_launches == self.max_num_launches:
            raise self.MaxNumLaunchesError("num_launches %s == max_num_launches %s" % (self.num_launches, self.max_num_launches))

        # Call the concrete implementation.
        s = self._submit_to_queue(script_file)
        self.record_launch(s.qid)

        if s.qid is None:
            raise self.Error("Error in job submission with %s. file %s \n" %
                            (self.__class__.__name__, script_file) +
                             "The error response reads:\n %s \n " % s.err +
                             "The out response reads:\n %s \n" % s.out)

        # Here we create a concrete instance of QueueJob
        return QueueJob.from_qtype_and_id(self.QTYPE, s.qid, self.qname), s.process

Response: Submits the job to the queue.


Instruction:
Implement a function in Python 3 to
return the number of jobs in the queue.

Response:
def get_njobs_in_queue(self, username=None):
        """
        returns the number of jobs in the queue, probably using subprocess or shutil to
        call a command like 'qstat'. returns None when the number of jobs cannot be determined.

        Args:
            username: (str) the username of the jobs to count (default is to autodetect)
        """
        if username is None: username = getpass.getuser()
        njobs, process = self._get_njobs_in_queue(username=username)

        if process is not None and process.returncode != 0:
            # there's a problem talking to squeue server?
            err_msg = ('Error trying to get the number of jobs in the queue' +
                       'The error response reads:\n {}'.format(process.stderr.read()))
            logger.critical(err_msg)

        if not isinstance(self, ShellAdapter):
            logger.info('The number of jobs currently in the queue is: {}'.format(njobs))

        return njobs


Instruction:
Can you tell what is the following Python 3 function doing
def more_master_mem_overhead(self, mem_increase_mb=1000):
        old_master_mem_overhead = self.master_mem_overhead
        new_master_mem_overhead = old_master_mem_overhead + mem_increase_mb
        if new_master_mem_overhead + self.mem_per_proc < self.hw.mem_per_node:
            self.set_master_mem_overhead(new_master_mem_overhead)
            return new_master_mem_overhead

        raise self.Error('could not increase master_mem_overhead further')

Response: Method to increase the amount of memory overheaded asked for the master node.


Instruction:
Explain what the following Python 3 code does
def more_time(self, factor=1):
        base_increase = int(self.timelimit_hard / 10)

        new_time = self.timelimit + base_increase*factor
        print('qadapter: trying to increase time')
        if new_time < self.timelimit_hard:
            self.set_timelimit(new_time)
            print('new time set: ', new_time)
            return new_time

        self.priority = -1

        raise self.Error("increasing time is not possible, the hard limit has been reached")

Response: Method to increase the wall time based on the hard limit


Instruction:
Make a summary of the following Python 3 code
def set_mem_per_proc(self, mem_mb):
        super().set_mem_per_proc(mem_mb)
        self.qparams["mem_per_cpu"] = self.mem_per_proc

Response: Set the memory per process in megabytes


Instruction:
Given the following Python 3 function, write the documentation
def get_select(self, ret_dict=False, qnodes=None, memory_policy=None):
        hw, mem_per_proc = self.hw, int(self.mem_per_proc)
        #dist = self.distribute(self.mpi_procs, self.omp_threads, mem_per_proc)
        """
        if self.pure_mpi:
            num_nodes, rest_cores = hw.divmod_node(self.mpi_procs, self.omp_threads)

            if num_nodes == 0:
                logger.info("IN_CORE PURE MPI: %s" % self.run_info)
                chunks = 1
                ncpus = rest_cores
                mpiprocs = rest_cores
                mem = mem_per_proc * ncpus
                ompthreads = 1

            elif rest_cores == 0:
                # Can allocate entire nodes because self.mpi_procs is divisible by cores_per_node.
                logger.info("PURE MPI run commensurate with cores_per_node %s" % self.run_info)
                chunks = num_nodes
                ncpus = hw.cores_per_node
                mpiprocs = hw.cores_per_node
                mem = ncpus * mem_per_proc
                ompthreads = 1

            else:
                logger.info("OUT-OF-CORE PURE MPI (not commensurate with cores_per_node): %s" % self.run_info)
                chunks = self.mpi_procs
                ncpus = 1
                mpiprocs = 1
                mem = mem_per_proc
                ompthreads = 1

        elif self.pure_omp:
            # Pure OMP run.
            logger.info("PURE OPENMP run: %s" % self.run_info)
            assert hw.can_use_omp_threads(self.omp_threads)
            chunks = 1
            ncpus = self.omp_threads
            mpiprocs = 1
            mem = mem_per_proc
            ompthreads = self.omp_threads

        elif self.hybrid_mpi_omp:
            assert hw.can_use_omp_threads(self.omp_threads)
            num_nodes, rest_cores = hw.divmod_node(self.mpi_procs, self.omp_threads)
            #print(num_nodes, rest_cores)
            # TODO: test this

            if rest_cores == 0 or num_nodes == 0:
                logger.info("HYBRID MPI-OPENMP run, perfectly divisible among nodes: %s" % self.run_info)
                chunks = max(num_nodes, 1)
                mpiprocs = self.mpi_procs // chunks

                chunks = chunks
                ncpus = mpiprocs * self.omp_threads
                mpiprocs = mpiprocs
                mem = mpiprocs * mem_per_proc
                ompthreads = self.omp_threads

            else:
                logger.info("HYBRID MPI-OPENMP, NOT commensurate with nodes: %s" % self.run_info)
                chunks=self.mpi_procs
                ncpus=self.omp_threads
                mpiprocs=1
                mem= mem_per_proc
                ompthreads=self.omp_threads

        else:
            raise RuntimeError("You should not be here")
        """
        if memory_policy is None:
            memory_policy = self.memory_policy
        if qnodes is None:
            qnodes = self.qnodes
        else:
            if qnodes not in ["standard", "shared", "exclusive"]:
                raise ValueError("Nodes must be either in standard, shared or exclusive mode "
                                 "while qnodes parameter was {}".format(self.qnodes))
        if qnodes == "standard":
            return self._get_select_standard(ret_dict=ret_dict, memory_policy=memory_policy)
        else:
            return self._get_select_with_master_mem_overhead(ret_dict=ret_dict, qnodes=qnodes,
                                                             memory_policy=memory_policy)

Response: Get select from the current state of the current state of the current object.


Instruction:
Can you implement a function in Python 3 that
submits a job script to the queue.

Response:
def _submit_to_queue(self, script_file):
        """Submit a job script to the queue."""
        if sys.version_info[0] < 3:
            process = Popen(['qsub', script_file], stdout=PIPE, stderr=PIPE)
        else:
            # need string not bytes so must use universal_newlines
            process = Popen(['qsub', script_file], stdout=PIPE, stderr=PIPE, universal_newlines=True)

        out, err = process.communicate()
        # grab the return code. PBS returns 0 if the job was successful
        queue_id = None
        if process.returncode == 0:
            try:
                # output should of the form '2561553.sdb' or '352353.jessup' - just grab the first part for job id
                queue_id = int(out.split('.')[0])
            except:
                # probably error parsing job code
                logger.critical("Could not parse job id following qsub...")
        return SubmitResults(qid=queue_id, out=out, err=err, process=process)


Instruction:
Given the following Python 3 function, write the documentation
def set_mpi_procs(self, mpi_procs):
        QueueAdapter.set_mpi_procs(self, mpi_procs)

        num_nodes, rest_cores = self.hw.divmod_node(mpi_procs, omp_threads=1)
        if num_nodes == 0:
            self.qparams["nodes"] = 1
            self.qparams["ppn"] = mpi_procs
        else:
            if rest_cores != 0:
                # Pack cores as much as possible.
                num_nodes += 1
            self.qparams["nodes"] = num_nodes
            self.qparams["ppn"] = self.hw.cores_per_node

Response: Set the number of CPUs used for MPI.


Instruction:
Implement a function in Python 3 to
set the memory per process in megabytes

Response:
def set_mem_per_proc(self, mem_mb):
        """Set the memory per process in megabytes"""
        super().set_mem_per_proc(mem_mb)
        self.qparams["mem_per_slot"] = str(int(self.mem_per_proc)) + "M"


Instruction:
Can you generate the documentation for the following Python 3 function
def set_timelimit(self, timelimit):
        super().set_timelimit(timelimit)
        self.qparams["wall_clock_limit"] = qu.time2loadlever(timelimit)

Response: Sets the time limit for the loadlever of the current record.


Instruction:
How would you implement a function in Python 3 that
returns ( bg_size ranks_per_node ) from mpi_procs and omp_threads.

Response:
def bgsize_rankspernode(self):
	    """Return (bg_size, ranks_per_node) from mpi_procs and omp_threads."""
	    bg_size = int(math.ceil((self.mpi_procs * self.omp_threads)/ self.hw.cores_per_node))
	    bg_size = max(bg_size, 32) # TODO hardcoded
	    ranks_per_node = int(math.ceil(self.mpi_procs / bg_size))

	    return bg_size, ranks_per_node


Instruction:
Given the following Python 3 function, write the documentation
def alternate(*iterables):
    items = []
    for tup in zip(*iterables):
        items.extend([item for item in tup])
    return items

Response: A generator that returns all the items in the iterable.


Instruction:
Explain what the following Python 3 code does
def walk(cls, top=".", ext=".abo"):
        paths = []
        for root, dirs, files in os.walk(top):
            for f in files:
                if f.endswith(ext):
                    paths.append(os.path.join(root, f))

        parser = cls()
        okfiles = parser.parse(paths)
        return parser, paths, okfiles

Response: Walk the directory tree starting from top and parse timing data.


Instruction:
Given the following Python 3 function, write the documentation
def parse(self, filenames):
        filenames = list_strings(filenames)

        read_ok = []
        for fname in filenames:
            try:
                fh = open(fname)
            except IOError:
                logger.warning("Cannot open file %s" % fname)
                continue

            try:
                self._read(fh, fname)
                read_ok.append(fname)

            except self.Error as e:
                logger.warning("exception while parsing file %s:\n%s" % (fname, str(e)))
                continue

            finally:
                fh.close()

        # Add read_ok to the list of files that have been parsed.
        self._filenames.extend(read_ok)
        return read_ok

Response: Read and parse a filename or a list of filenames.


Instruction:
Make a summary of the following Python 3 code
def _read(self, fh, fname):
        if fname in self._timers:
            raise self.Error("Cannot overwrite timer associated to: %s " % fname)

        def parse_line(line):
            """Parse single line."""
            name, vals = line[:25], line[25:].split()
            try:
                ctime, cfract, wtime, wfract, ncalls, gflops = vals
            except ValueError:
                # v8.3 Added two columns at the end [Speedup, Efficacity]
                ctime, cfract, wtime, wfract, ncalls, gflops, speedup, eff = vals

            return AbinitTimerSection(name, ctime, cfract, wtime, wfract, ncalls, gflops)

        data = {}
        inside, has_timer = 0, False
        for line in fh:
            #print(line.strip())
            if line.startswith(self.BEGIN_TAG):
                has_timer = True
                sections = []
                info = {}
                inside = 1
                line = line[len(self.BEGIN_TAG):].strip()[:-1]

                info["fname"] = fname
                for tok in line.split(","):
                    key, val = [s.strip() for s in tok.split("=")]
                    info[key] = val

            elif line.startswith(self.END_TAG):
                inside = 0
                timer = AbinitTimer(sections, info, cpu_time, wall_time)
                mpi_rank = info["mpi_rank"]
                data[mpi_rank] = timer

            elif inside:
                inside += 1
                line = line[1:].strip()

                if inside == 2:
                    d = dict()
                    for tok in line.split(","):
                        key, val = [s.strip() for s in tok.split("=")]
                        d[key] = float(val)
                    cpu_time, wall_time = d["cpu_time"], d["wall_time"]

                elif inside > 5:
                    sections.append(parse_line(line))

                else:
                    try:
                        parse_line(line)
                    except:
                        parser_failed = True

                    if not parser_failed:
                        raise self.Error("line should be empty: " + str(inside) + line)

        if not has_timer:
            raise self.Error("%s: No timer section found" % fname)

        # Add it to the dict
        self._timers[fname] = data

Response: Read the contents of the file and return a dictionary of the data.


Instruction:
Can you tell what is the following Python 3 function doing
def timers(self, filename=None, mpi_rank="0"):
        if filename is not None:
            return [self._timers[filename][mpi_rank]]
        else:
            return [self._timers[filename][mpi_rank] for filename in self._filenames]

Response: Return the list of timers associated to the given filename and MPI rank mpi_rank.


Instruction:
Write a Python 3 script to
return the names of sections ordered by ordkey.

Response:
def section_names(self, ordkey="wall_time"):
        """
        Return the names of sections ordered by ordkey.
        For the time being, the values are taken from the first timer.
        """
        section_names = []

        # FIXME this is not trivial
        for idx, timer in enumerate(self.timers()):
            if idx == 0:
                section_names = [s.name for s in timer.order_sections(ordkey)]
                #check = section_names
                #else:
                #  new_set = set( [s.name for s in timer.order_sections(ordkey)])
                #  section_names.intersection_update(new_set)
                #  check = check.union(new_set)

        #if check != section_names:
        #  print("sections", section_names)
        #  print("check",check)

        return section_names


Instruction:
Can you tell what is the following Python 3 function doing
def get_sections(self, section_name):
        sections = []
        for timer in self.timers():
            for sect in timer.sections:
                if sect.name == section_name:
                    sections.append(sect)
                    break
            else:
                sections.append(AbinitTimerSection.fake())

        return sections

Response: Return the list of sections stored in self. timers() given section_name.