pymatgen.apps.battery package

Submodules

pymatgen.apps.battery.analyzer module

Analysis classes for batteries.

class BatteryAnalyzer(struc_oxid, working_ion='Li', oxi_override=None)[source]

Bases: object

A suite of methods for starting with an oxidized structure and determining its potential as a battery.

Pass in a structure for analysis.

Parameters:

• struc_oxid – a Structure object; oxidation states must be assigned for this structure; disordered structures should be OK

• working_ion – a String symbol or Element for the working ion.

• oxi_override – a dict of String element symbol, Integer oxidation state pairs. by default, H, C, N, O, F, S, Cl, Se, Br, Te, I are considered anions.

_get_int_removals_helper(spec_amts_oxi, redox_el, redox_els, numa)[source]

This is a helper method for get_removals_int_oxid!

Parameters:

• spec_amts_oxi – a dict of species to their amounts in the structure

• redox_el – the element to oxidize or reduce

• redox_els – the full list of elements that might be oxidized or reduced

• numa – a running set of numbers of A ion at integer oxidation steps

Returns:

a set of numbers A; steps for oxidizing oxid_el first, then the other oxid_els in this list

_get_max_cap_ah(remove, insert)[source]

Give max capacity in mAh for inserting and removing a charged ion This method does not normalize the capacity and intended as a helper method.

get_max_capgrav(remove=True, insert=True)[source]

Give max capacity in mAh/g for inserting and removing a charged ion Note that the weight is normalized to the most ion-packed state, thus removal of 1 Li from LiFePO4 gives the same capacity as insertion of 1 Li into FePO4.

Parameters:

• remove – (bool) whether to allow ion removal

• insert – (bool) whether to allow ion insertion

Returns:

max grav capacity in mAh/g

get_max_capvol(remove=True, insert=True, volume=None)[source]

Give max capacity in mAh/cc for inserting and removing a charged ion into base structure.

Parameters:

• remove – (bool) whether to allow ion removal

• insert – (bool) whether to allow ion insertion

• volume – (float) volume to use for normalization (default=volume of initial structure)

Returns:

max vol capacity in mAh/cc

get_removals_int_oxid()[source]

Returns a set of ion removal steps, e.g. set([1 2 4]) etc. in order to produce integer oxidation states of the redox metals. If multiple redox metals are present, all combinations of reduction/oxidation are tested. Note that having more than 3 redox metals will likely slow down the algorithm.

Examples

LiFePO4 will return [1] Li4Fe3Mn1(PO4)4 will return [1, 2, 3, 4]) Li6V4(PO4)6 will return [4, 6]) note that this example is not normalized

Returns:

array of integer ion removals. If you double the unit cell, your answers will be twice as large!

property max_ion_insertion[source]

Maximum number of ion A that can be inserted while maintaining charge-balance. No consideration is given to whether there (geometrically speaking) are ion sites to actually accommodate the extra ions.

Returns:

integer amount of ion. Depends on cell size (this is an ‘extrinsic’ function!)

property max_ion_removal[source]

Maximum number of ion A that can be removed while maintaining charge-balance.

Returns:

integer amount of ion. Depends on cell size (this is an ‘extrinsic’ function!)

is_redox_active_intercalation(element) → bool[source]

True if element is redox active and interesting for intercalation materials.

Parameters:

element – Element object

pymatgen.apps.battery.battery_abc module

This module defines the abstract base classes for battery-related classes. Regardless of the kind of electrode, conversion or insertion, there are many common definitions and properties, e.g., average voltage, capacity, etc. which can be defined in a general way. The Abc for battery classes implements some of these common definitions to allow sharing of common logic between them.

class AbstractElectrode(voltage_pairs: tuple[AbstractVoltagePair, ...], working_ion_entry: ComputedEntry, framework_formula: str)[source]

Bases: Sequence, MSONable

An Abstract Base Class representing an Electrode. It is essentially a sequence of VoltagePairs. Generally, subclasses only need to implement three abstract properties: voltage_pairs, working_ion and working_ion_entry.

The general concept is that all other battery properties such as capacity, etc. are derived from voltage pairs.

One of the major challenges with representing battery materials is keeping track of the normalization between different entries. For example, one entry might be TiO2 with one unit cell whereas another is LiTi2O4 with two unit cells. When computing battery properties, it is needed to always use a universal reference state otherwise you have normalization errors (e.g., the energy of LiTi2O4 must be divided by two to be compared with TiO2).

For properties such as volume, mass, or mAh transferred within the voltage pair, a universal convention is necessary. AbstractElectrode can query for extrinsic properties of several different AbstractVoltagePairs belonging to a single charge/discharge path and be confident that the normalization is being carried out properly throughout, even if more AbstractVoltagePairs are added later.

The universal normalization is defined by the reduced structural framework of the entries, which is common along the entire charge/discharge path. For example, LiTi2O4 has a reduced structural framework of TiO2. Another example is Li9V6P16O58 which would have a reduced structural framework of V3P8O29. Note that reduced structural frameworks need not be charge-balanced or physical, e.g. V3P8O29 is not charge-balanced, they are just a tool for normalization.

Example: for a LiTi2O4 -> TiO2 AbstractVoltagePair, extrinsic quantities like mAh or cell volumes are given per TiO2 formula unit.

Developers implementing a new battery (other than the two general ones already implemented) need to implement a VoltagePair and an Electrode.

voltage_pairs[source]

Objects that represent each voltage step

Type:

tuple[AbstractVoltagePair, …]

working_ion[source]

Representation of the working ion that only contains element type

working_ion_entry[source]

Representation of the working_ion that contains the energy

Type:

ComputedEntry

framework_formula[source]

The compositions of one formula unit of the host material

Type:

str

_abc_impl = <_abc._abc_data object>[source]

_select_in_voltage_range(min_voltage=None, max_voltage=None)[source]

Selects VoltagePairs within a certain voltage range.

Parameters:

• min_voltage (float) – The minimum allowable voltage for a given step.

• max_voltage (float) – The maximum allowable voltage allowable for a given step.

Returns:

A list of VoltagePair objects

property framework[source]

The composition object representing the framework.

framework_formula: str[source]

get_average_voltage(min_voltage=None, max_voltage=None)[source]

Average voltage for path satisfying between a min and max voltage.

Parameters:

• min_voltage (float) – The minimum allowable voltage for a given step.

• max_voltage (float) – The maximum allowable voltage allowable for a given step.

Returns:

Average voltage in V across the insertion path (a subset of the path can be chosen by the optional arguments)

get_capacity_grav(min_voltage=None, max_voltage=None, use_overall_normalization=True)[source]

Get the gravimetric capacity of the electrode.

Parameters:

• min_voltage (float) – The minimum allowable voltage for a given step.

• max_voltage (float) – The maximum allowable voltage allowable for a given step.

• use_overall_normalization (booL) – If False, normalize by the discharged state of only the voltage pairs matching the voltage criteria. if True, use default normalization of the full electrode path.

Returns:

Gravimetric capacity in mAh/g across the insertion path (a subset of the path can be chosen by the optional arguments).

get_capacity_vol(min_voltage=None, max_voltage=None, use_overall_normalization=True)[source]

Get the volumetric capacity of the electrode.

Parameters:

• min_voltage (float) – The minimum allowable voltage for a given step.

• max_voltage (float) – The maximum allowable voltage allowable for a given step.

• use_overall_normalization (booL) – If False, normalize by the discharged state of only the voltage pairs matching the voltage criteria. if True, use default normalization of the full electrode path.

Returns:

Volumetric capacity in mAh/cc across the insertion path (a subset of the path can be chosen by the optional arguments)

get_energy_density(min_voltage=None, max_voltage=None, use_overall_normalization=True)[source]

Parameters:

• min_voltage (float) – The minimum allowable voltage for a given step.

• max_voltage (float) – The maximum allowable voltage allowable for a given step.

• use_overall_normalization (booL) – If False, normalize by the discharged state of only the voltage pairs matching the voltage criteria. if True, use default normalization of the full electrode path.

Returns:

Energy density in Wh/L across the insertion path (a subset of the path can be chosen by the optional arguments).

get_specific_energy(min_voltage=None, max_voltage=None, use_overall_normalization=True)[source]

Returns the specific energy of the battery in mAh/g.

Parameters:

• min_voltage (float) – The minimum allowable voltage for a given step.

• max_voltage (float) – The maximum allowable voltage allowable for a given step.

• use_overall_normalization (booL) – If False, normalize by the discharged state of only the voltage pairs matching the voltage criteria. if True, use default normalization of the full electrode path.

Returns:

Specific energy in Wh/kg across the insertion path (a subset of the path can be chosen by the optional arguments)

get_sub_electrodes(adjacent_only=True)[source]

If this electrode contains multiple voltage steps, then it is possible to use only a subset of the voltage steps to define other electrodes. Must be implemented for each electrode object.

Parameters:

adjacent_only – Only return electrodes from compounds that are adjacent on the convex hull, i.e. no electrodes returned will have multiple voltage steps if this is set true

Returns:

A list of Electrode objects

get_summary_dict(print_subelectrodes=True) → dict[source]

Generate a summary dict.

Parameters:

print_subelectrodes – Also print data on all the possible subelectrodes.

Returns:

A summary of this electrode’s properties in dict format.

property max_delta_volume[source]

Maximum volume change along insertion.

property max_voltage[source]

Highest voltage along insertion.

property max_voltage_step[source]

Maximum absolute difference in adjacent voltage steps.

property min_voltage[source]

Lowest voltage along insertion.

property normalization_mass[source]

Mass used for normalization. This is the mass of the discharged electrode of the last voltage pair.

Type:

Returns

property normalization_volume[source]

Mass used for normalization. This is the vol of the discharged electrode of the last voltage pair.

Type:

Returns

property num_steps[source]

The number of distinct voltage steps in from fully charge to discharge based on the stable intermediate states.

voltage_pairs: tuple[AbstractVoltagePair, ...][source]

property working_ion[source]

Working ion as pymatgen Element object.

working_ion_entry: ComputedEntry[source]

property x_charge: float[source]

The number of working ions per formula unit of host in the charged state.

property x_discharge: float[source]

The number of working ions per formula unit of host in the discharged state.

class AbstractVoltagePair(voltage: float, mAh: float, mass_charge: float, mass_discharge: float, vol_charge: float, vol_discharge: float, frac_charge: float, frac_discharge: float, working_ion_entry: ComputedEntry, framework_formula: str)[source]

Bases: MSONable

An Abstract Base Class for a Voltage Pair.

voltage[source]

Voltage of voltage pair.

Type:

float

mAh[source]

Energy in mAh.

Type:

float

mass_charge[source]

Mass of charged pair.

Type:

float

mass_discharge[source]

Mass of discharged pair.

Type:

float

vol_charge[source]

Vol of charged pair.

Type:

float

vol_discharge[source]

Vol of discharged pair.

Type:

float

frac_charge[source]

Frac of working ion in charged pair.

Type:

float

frac_discharge[source]

Frac of working ion in discharged pair.

Type:

float

working_ion_entry[source]

Working ion as an entry.

Type:

ComputedEntry

framework_formula[source]

The compositions of one formula unit of the host material

Type:

str

frac_charge: float[source]

frac_discharge: float[source]

property framework: Composition[source]

The composition object representing the framework.

framework_formula: str[source]

mAh: float[source]

mass_charge: float[source]

mass_discharge: float[source]

vol_charge: float[source]

vol_discharge: float[source]

voltage: float[source]

property working_ion: Element[source]

Working ion as pymatgen Element object.

working_ion_entry: ComputedEntry[source]

property x_charge: float[source]

The number of working ions per formula unit of host in the charged state.

property x_discharge: float[source]

The number of working ions per formula unit of host in the discharged state.

pymatgen.apps.battery.conversion_battery module

This module contains the classes to build a ConversionElectrode.

class ConversionElectrode(voltage_pairs: tuple[AbstractVoltagePair, ...], working_ion_entry: ComputedEntry, framework_formula: str, initial_comp_formula: str)[source]

Bases: AbstractElectrode

Class representing a ConversionElectrode, since it is dataclass this object can be constructed for the attributes. However, it is usually easier to construct a ConversionElectrode using one of the classmethod constructors provided.

Attribute:

voltage_pairs: The voltage pairs making up the Conversion Electrode. working_ion_entry: A single ComputedEntry or PDEntry

representing the element that carries charge across the battery, e.g. Li.

initial_comp_formula: Starting composition for ConversionElectrode represented

as a string/formula.

_abc_impl = <_abc._abc_data object>[source]

classmethod from_composition_and_entries(comp, entries_in_chemsys, working_ion_symbol='Li', allow_unstable=False)[source]

Convenience constructor to make a ConversionElectrode from a composition and all entries in a chemical system.

Parameters:

• comp – Starting composition for ConversionElectrode, e.g., Composition(“FeF3”)

• entries_in_chemsys – Sequence containing all entries in a chemical system. E.g., all Li-Fe-F containing entries.

• working_ion_symbol – Element symbol of working ion. Defaults to Li.

• allow_unstable – If True, allow any composition to be used as the starting point of a conversion voltage curve, this is useful for comparing with insertion electrodes

classmethod from_composition_and_pd(comp, pd, working_ion_symbol='Li', allow_unstable=False)[source]

Convenience constructor to make a ConversionElectrode from a composition and a phase diagram.

Parameters:

• comp – Starting composition for ConversionElectrode, e.g., Composition(“FeF3”)

• pd – A PhaseDiagram of the relevant system (e.g., Li-Fe-F)

• working_ion_symbol – Element symbol of working ion. Defaults to Li.

• allow_unstable – Allow compositions that are unstable

get_sub_electrodes(adjacent_only=True)[source]

If this electrode contains multiple voltage steps, then it is possible to use only a subset of the voltage steps to define other electrodes. For example, an LiTiO2 electrode might contain three subelectrodes: [LiTiO2 –> TiO2, LiTiO2 –> Li0.5TiO2, Li0.5TiO2 –> TiO2] This method can be used to return all the subelectrodes with some options.

Parameters:

adjacent_only – Only return electrodes from compounds that are adjacent on the convex hull, i.e. no electrodes returned will have multiple voltage steps if this is set true

Returns:

A list of ConversionElectrode objects

get_summary_dict(print_subelectrodes=True) → dict[source]

Generate a summary dict. Populates the summary dict with the basic information from the parent method then populates more information. Since the parent method calls self.get_summary_dict(print_subelectrodes=True) for the subelectrodes. The current method will be called from within super().get_summary_dict.

Parameters:

print_subelectrodes – Also print data on all the possible subelectrodes.

Returns:

A summary of this electrode’s properties in dict format.

property initial_comp: Composition[source]

The pymatgen Composition representation of the initial composition.

initial_comp_formula: str[source]

is_super_electrode(conversion_electrode) → bool[source]

Checks if a particular conversion electrode is a sub electrode of the current electrode. Starting from a more lithiated state may result in a subelectrode that is essentially on the same path. For example, a ConversionElectrode formed by starting from an FePO4 composition would be a super_electrode of a ConversionElectrode formed from an LiFePO4 composition.

class ConversionVoltagePair(voltage: float, mAh: float, mass_charge: float, mass_discharge: float, vol_charge: float, vol_discharge: float, frac_charge: float, frac_discharge: float, working_ion_entry: ComputedEntry, framework_formula: str, rxn: BalancedReaction, entries_charge: Iterable[ComputedEntry], entries_discharge: Iterable[ComputedEntry])[source]

Bases: AbstractVoltagePair

A VoltagePair representing a Conversion Reaction with a defined voltage. Typically not initialized directly but rather used by ConversionElectrode.

rxn[source]

BalancedReaction for the step

Type:

BalancedReaction

voltage[source]

Voltage for the step

Type:

float

mAh[source]

Capacity of the step

Type:

float

vol_charge[source]

Volume of charged state

Type:

float

vol_discharge[source]

Volume of discharged state

Type:

float

mass_charge[source]

Mass of charged state

Type:

float

mass_discharge[source]

Mass of discharged state

Type:

float

frac_charge[source]

Fraction of working ion in the charged state

Type:

float

frac_discharge[source]

Fraction of working ion in the discharged state

Type:

float

entries_charge[source]

Entries representing decompositions products in the charged state. Enumerates the decompositions products at the tieline, so the number of entries will be one fewer than the dimensions of the phase diagram

Type:

[ComputedEntry]

entries_discharge[source]

Entries representing decompositions products in the discharged state. Enumerates the decompositions products at the tieline, so the number of entries will be one fewer than the dimensions of the phase diagram

Type:

[ComputedEntry]

working_ion_entry[source]

Entry of the working ion.

Type:

ComputedEntry

entries_charge: Iterable[ComputedEntry][source]

entries_discharge: Iterable[ComputedEntry][source]

classmethod from_steps(step1, step2, normalization_els, framework_formula)[source]

Creates a ConversionVoltagePair from two steps in the element profile from a PD analysis.

Parameters:

• step1 – Starting step

• step2 – Ending step

• normalization_els – Elements to normalize the reaction by. To ensure correct capacities.

• framework_formula – Formula of the framework.

rxn: BalancedReaction[source]

pymatgen.apps.battery.insertion_battery module

This module is used for analysis of materials with potential application as intercalation batteries.

class InsertionElectrode(voltage_pairs: tuple[pymatgen.apps.battery.battery_abc.AbstractVoltagePair, ...], working_ion_entry: ComputedEntry, framework_formula: str, stable_entries: Iterable[ComputedEntry], unstable_entries: Iterable[ComputedEntry])[source]

Bases: AbstractElectrode

A set of topotactically related compounds, with different amounts of a single element, e.g. TiO2 and LiTiO2, that can be used to define an insertion battery electrode.

_abc_impl = <_abc._abc_data object>[source]

as_dict_legacy()[source]

Returns: MSONable dict.

classmethod from_dict_legacy(d)[source]

Parameters:

d (dict) – Dict representation.

Returns:

InsertionElectrode

classmethod from_entries(entries: Iterable[ComputedEntry | ComputedStructureEntry], working_ion_entry: ComputedEntry | ComputedStructureEntry | PDEntry, strip_structures: bool = False)[source]

Create a new InsertionElectrode.

Parameters:

• entries – A list of ComputedEntries, ComputedStructureEntries, or subclasses representing the different topotactic states of the battery, e.g. TiO2 and LiTiO2.

• working_ion_entry – A single ComputedEntry or PDEntry representing the element that carries charge across the battery, e.g. Li.

• strip_structures – Since the electrode document only uses volume we can make the electrode object significantly leaner by dropping the structure data. If this parameter is set to True, the ComputedStructureEntry will be replaced with a ComputedEntry and the volume will be stored in ComputedEntry.data[‘volume’]. If entries provided are ComputedEntries, must set strip_structures=False.

property fully_charged_entry[source]

The most charged entry along the topotactic path.

property fully_discharged_entry[source]

The most discharged entry along the topotactic path.

get_all_entries(charge_to_discharge=True)[source]

Return all entries input for the electrode.

Parameters:

charge_to_discharge – order from most charge to most discharged state? Defaults to True.

Returns:

A list of all entries in the electrode (both stable and unstable), ordered by amount of the working ion.

get_max_instability(min_voltage=None, max_voltage=None)[source]

The maximum instability along a path for a specific voltage range.

Parameters:

• min_voltage – The minimum allowable voltage.

• max_voltage – The maximum allowable voltage.

Returns:

Maximum decomposition energy of all compounds along the insertion path (a subset of the path can be chosen by the optional arguments)

get_max_muO2(min_voltage=None, max_voltage=None)[source]

Maximum critical oxygen chemical potential along path.

Parameters:

• min_voltage – The minimum allowable voltage.

• max_voltage – The maximum allowable voltage.

Returns:

Maximum critical oxygen chemical of all compounds along the insertion path (a subset of the path can be chosen by the optional arguments).

get_min_instability(min_voltage=None, max_voltage=None)[source]

The minimum instability along a path for a specific voltage range.

Parameters:

• min_voltage – The minimum allowable voltage.

• max_voltage – The maximum allowable voltage.

Returns:

Minimum decomposition energy of all compounds along the insertion path (a subset of the path can be chosen by the optional arguments)

get_min_muO2(min_voltage=None, max_voltage=None)[source]

Minimum critical oxygen chemical potential along path.

Parameters:

• min_voltage – The minimum allowable voltage for a given step

• max_voltage – The maximum allowable voltage allowable for a given step

Returns:

Minimum critical oxygen chemical of all compounds along the insertion path (a subset of the path can be chosen by the optional arguments).

get_stable_entries(charge_to_discharge=True)[source]

Get the stable entries.

Parameters:

charge_to_discharge – order from most charge to most discharged state? Default to True.

Returns:

A list of stable entries in the electrode, ordered by amount of the working ion.

get_sub_electrodes(adjacent_only=True, include_myself=True)[source]

If this electrode contains multiple voltage steps, then it is possible to use only a subset of the voltage steps to define other electrodes. For example, an LiTiO2 electrode might contain three subelectrodes: [LiTiO2 –> TiO2, LiTiO2 –> Li0.5TiO2, Li0.5TiO2 –> TiO2] This method can be used to return all the subelectrodes with some options.

Parameters:

• adjacent_only – Only return electrodes from compounds that are adjacent on the convex hull, i.e. no electrodes returned will have multiple voltage steps if this is set True.

• include_myself – Include this identical electrode in the list of results.

Returns:

A list of InsertionElectrode objects

get_summary_dict(print_subelectrodes=True) → dict[source]

Generate a summary dict. Populates the summary dict with the basic information from the parent method then populates more information. Since the parent method calls self.get_summary_dict(print_subelectrodes=True) for the subelectrodes. The current method will be called from within super().get_summary_dict.

Parameters:

print_subelectrodes – Also print data on all the possible subelectrodes.

Returns:

A summary of this electrode’s properties in dict format.

get_unstable_entries(charge_to_discharge=True)[source]

Returns the unstable entries for the electrode.

Parameters:

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.

stable_entries: Iterable[ComputedEntry][source]

unstable_entries: Iterable[ComputedEntry][source]

class InsertionVoltagePair(voltage: float, mAh: float, mass_charge: float, mass_discharge: float, vol_charge: float, vol_discharge: float, frac_charge: float, frac_discharge: float, working_ion_entry: ComputedEntry, framework_formula: str, entry_charge: ComputedEntry, entry_discharge: ComputedEntry)[source]

Bases: AbstractVoltagePair

Defines an Insertion Voltage Pair.

entry_charge: ComputedEntry[source]

entry_discharge: ComputedEntry[source]

classmethod from_entries(entry1, entry2, working_ion_entry)[source]

Parameters:

• entry1 – Entry corresponding to one of the entries in the voltage step.

• entry2 – Entry corresponding to the other entry in the voltage step.

• working_ion_entry – A single ComputedEntry or PDEntry representing the element that carries charge across the battery, e.g. Li.

pymatgen.apps.battery.plotter module

This module provides plotting capabilities for battery related applications.

class VoltageProfilePlotter(xaxis='capacity', hide_negative=False)[source]

Bases: object

A plotter to make voltage profile plots for batteries.

Parameters:

• xaxis – The quantity to use as the xaxis. Can be either

• capacity_grav (-) – the graviometric capcity

• capacity_vol (-) – the volumetric capacity

• x_form (-) – the number of working ions per formula unit of the host

• frac_x (-) – the atomic fraction of the working ion

• hide_negative – If True only plot the voltage steps above zero.

_choose_best_x_lable(formula, wion_symbol)[source]

add_electrode(electrode, label=None)[source]

Add an electrode to the plot.

Parameters:

• electrode – An electrode. All electrodes satisfying the AbstractElectrode interface should work.

• label – A label for the electrode. If None, defaults to a counting system, i.e. ‘Electrode 1’, ‘Electrode 2’, …

get_plot(width=8, height=8, term_zero=True)[source]

Returns a plot object.

Parameters:

• width – Width of the plot. Defaults to 8 in.

• height – Height of the plot. Defaults to 6 in.

• term_zero – If True append zero voltage point at the end

Returns:

A matplotlib plot object.

get_plot_data(electrode, term_zero=True)[source]

Parameters:

• electrode – Electrode object

• term_zero – If True append zero voltage point at the end.

Returns:

Plot data in x, y.

get_plotly_figure(width=800, height=600, font_dict=None, term_zero=True, **kwargs)[source]

Return plotly Figure object.

Parameters:

• width – Width of the plot. Defaults to 800 px.

• height – Height of the plot. Defaults to 600 px.

• font_dict – define the font. Defaults to {“family”: “Arial”, “size”: 24, “color”: “#000000”}

• term_zero – If True append zero voltage point at the end

• **kwargs – passed to plotly.graph_objects.Layout

save(filename, image_format='eps', width=8, height=6)[source]

Save the plot to an image file.

Parameters:

• filename – Filename to save to.

• image_format – Format to save to. Defaults to eps.

• width – Width of the plot. Defaults to 8 in.

• height – Height of the plot. Defaults to 6 in.

show(width=8, height=6)[source]

Show the voltage profile plot.

Parameters:

• width – Width of the plot. Defaults to 8 in.

• height – Height of the plot. Defaults to 6 in.