ml4eft.analyse.animate.Animate

class ml4eft.analyse.animate.Animate(c, frames)

Bases: object

Post-training animator that animates the evolution of models during training

__init__(c, frames)

Animate constructor

Parameters

• c (dict) – Of the form {‘c1’: value, ‘c2’: value}

• frames (int) – Number of frames (epochs) to animatie

Methods

__init__(c, frames)	Animate constructor
make_animation_1d(analyser, df)	Returns a 1-dimensional animation of the EFT ratio functions, comparing the analytical result and the ML model
make_animation_2d(analyser, df, c_name, ...)	Returns a 2-dimensional animation of the EFT ratio functions, comparing the analytical result and the ML model

make_animation_1d(analyser, df)

Returns a 1-dimensional animation of the EFT ratio functions, comparing the analytical result and the ML model

Parameters

• analyser (ml4eft.analyse.analyse.Analyse) – Analyser object

• df (pandas.DataFrame) – phase space 1-d grid stored as a DataFrame

Returns

anim – matplotlib.animation.FuncAnimation object

Return type

matplotlib.animation.FuncAnimation

Examples

>>> anim = Animate(c={'ctgre': 2, 'cut': 0}, frames=200)
>>> anim_tt = anim.make_animation_1d(analyser, df)
>>> anim_tt

make_animation_2d(analyser, df, c_name, order, process, shape)

Returns a 2-dimensional animation of the EFT ratio functions, comparing the analytical result and the ML model

Parameters

• analyser (ml4eft.analyse.analyse.Analyse) – Analyser object

• df (pandas.DataFrame) – phase space grid stored as a DataFrame

• c_name (str) – Name of EFT coefficient

• order (str) – Order of the EFT expansion, choose between ‘lin’ and ‘quad’

• process (str) – Supported options are ‘tt’ or ‘ZH’

• shape (array_like) – dimension of phase space grid passed as tuple

Returns

anim – matplotlib.animation.FuncAnimation object

Return type

matplotlib.animation.FuncAnimation

Examples

>>> import ml4eft.preproc.constants as constants
>>> mt = constants.mt

We create a grid in the \((m_{tt}, Y)\) phase-space

>>> s = 14 ** 2
>>> epsilon = 1e-2
>>> mtt_min, mtt_max = 0.5, 2
>>> y_min, y_max = - np.log(np.sqrt(s) / mtt_min), np.log(np.sqrt(s) / mtt_min)

>>> x_spacing, y_spacing = 1e-2, 0.01
>>> mtt_span = np.arange(mtt_min, mtt_max, x_spacing)
>>> y_span = np.arange(y_min, y_max, y_spacing)

>>> mtt_grid, y_grid = np.meshgrid(mtt_span, y_span)
>>> grid = np.c_[y_grid.ravel(), mtt_grid.ravel()]

Convert the numpy grid to a DataFrame

>>> df = pd.DataFrame(grid, columns=['y', 'm_tt'])

Create an Animate object and start the animation

>>> anim = Animate(c={'ctgre': 2, 'cut': 0}, frames=200)

>>> anim_tt = anim.make_animation_2d(analyser, df, 'ctgre_ctgre', 'quad', 'tt', mtt_grid.shape)
>>> anim_tt