abics.mc module

class abics.mc.CanonicalMonteCarlo(model, kT, config)

Bases: object

Parameters

• model (dft_latgas object) – DFT lattice gas mapping model

• kT (float) – Temperature

• config (config object) – Configuration

MCstep()

__init__(model, kT, config)

Parameters

• model (dft_latgas object) – DFT lattice gas mapping model

• kT (float) – Temperature

• config (config object) – Configuration

run(nsteps, sample_frequency=9223372036854775807, print_frequency=9223372036854775807, observer=<abics.mc.observer_base object>, save_obs=False)

Parameters

• nsteps (int) – The number of Monte Carlo steps for running.

• sample_frequency (int) – The number of Monte Carlo steps for observation of physical quantities.

• print_frequency (int) – The number of Monte Carlo steps for saving physical quantities.

• observer (observer object) –

• save_obs (boolean) –

Returns

obs_decode

Return type

list

abics.mc.args_info(*args)

Parameters

args (list) –

Returns

args_info

Return type

numpy array

abics.mc.binning(x, nlevels)

Parameters

• x (list) – Coordinates

• nlevels (int) – Number to determine the number of measurements

Returns

error_estimate – error estimation

Return type

list

class abics.mc.grid_1D(dx, minx, maxx)

Bases: object

Parameters

• dx (float) – interval

• minx (float) – minimum value of x

• maxx (float) – maximum value of x

__init__(dx, minx, maxx)

Parameters

• dx (float) – interval

• minx (float) – minimum value of x

• maxx (float) – maximum value of x

class abics.mc.model

Bases: object

This class defines a model whose energy equals 0 no matter the configuration, and the configuration never changes. This is a base template for building useful models.

energy(config)

Calculate energy of configuration: input: config

Parameters

config (config object) – configuration

model_name = None

newconfig(config, dconfig)

Update config by using the trial step, dconfig

Parameters

• config (config object) – Original configuration This may be mutated through this function

• dconfig (config object) – Difference of configuration

Returns

config – updated configuration

Return type

config object

trialstep(config, energy)

Define a trial step on config. Returns dconfig, which can contain the minimal information for constructing the trial configuration from config to be used in newconfig(). Make sure that config is unchanged.

Parameters

• config (config object) – configuration

• energy (float) – energy

Returns

• dconfig (config object) – The minimal information for constructing the trial configuration from config to be used in newconfig()

• dE (float) – Energy diffence

abics.mc.obs_decode(args_info, obs_array)

Parameters

• args_info (numpy array) –

• obs_array (numpy array) –

Returns

args

Return type

list

abics.mc.obs_encode(*args)

Parameters

args (list) –

Returns

obs_array

Return type

numpy array

class abics.mc.observer_base

Bases: object

logfunc(calc_state)

Parameters

calc_state (MonteCarlo algorithm object) – MonteCarlo algorithm

Returns

calc_state.energy – (Energy)

Return type

tuple

obs_info(calc_state)

Parameters

calc_state (MonteCarlo algorithm object) – MonteCarlo algorithm

Returns

args_info

Return type

numpy array

observe(calc_state, outputfi, lprint=True)

Parameters

• calc_state (MonteCarlo algorithm object) – MonteCarlo algorithm

• outputfi (_io.TextIOWrapper) – TextIOWrapper for output

• lprint (boolean) – if true, log info is outputted to TextIOWrapper

Returns

obs_log – log information about observation

Return type

numpy array

savefuncs(calc_state)

Parameters

calc_state (MonteCarlo algorithm object) – MonteCarlo algorithm

writefile(calc_state)

Parameters

calc_state (MonteCarlo algorithm object) – MonteCarlo algorithm

abics.mc.verylargeint = 9223372036854775807

Defines base classes for Monte Carlo simulations