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Update notebook and pep8 #33

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2 changes: 1 addition & 1 deletion Makefile
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
all:
python setup.py build_ext
/cmmc/ptmp/pyironhb/mambaforge/envs/pyiron_mpie_cmti_2025-07-14/bin/python setup.py build_ext

clean:
rm -rf mamonca/*.so mamonca/mc.cpp build
127 changes: 75 additions & 52 deletions mamonca/mc.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,7 @@ cdef class MC:
>>> first_shell_tensor = neighbors.get_shell_matrix()[0]
>>>
>>> mc = MC(len(structure))
>>> mc.set_heisenberg_coeff(J*first_shell_tensor)
>>> mc.set_heisenberg_coeff(J * first_shell_tensor)
>>>
>>> mc.run(temperature=300, number_of_iterations=1000)

Expand All @@ -43,19 +43,20 @@ cdef class MC:

def __cinit__(self, number_of_atoms):
"""
args:
Args:
number_of_atoms (int): number of atoms
"""
self.c_mc.create_atoms(number_of_atoms)

def set_landau_coeff(self, coeff, deg, index=0):
"""
Args:
coeff (float/list/ndarray): Coefficient to the Landau term. If a single number is
given, the same parameter is applied to all the atoms.
coeff (float/list/ndarray): Coefficient to the Landau term. If a
single number is given, the same parameter is applied to all
the atoms.
deg (int): Polynomial degree (usually an even number)
index (int): Potential index for thermodynamic integration (0 or 1; choose 0 if
not thermodynamic integration)
index (int): Potential index for thermodynamic integration
(0 or 1; choose 0 if not thermodynamic integration)

Comment:
Landau term is given by: sum_i coeff_i*m_i^deg
Expand Down Expand Up @@ -120,8 +121,8 @@ cdef class MC:
def clear_heisenberg_coeff(self, index=0):
"""
Args:
index (int): potential index for thermodynamic integration (0 or 1; choose 0 if
not thermodynamic integration)
index (int): potential index for thermodynamic integration
(0 or 1; choose 0 if not thermodynamic integration)

This function erases all the Heisenberg coefficients defined before.
"""
Expand All @@ -130,17 +131,15 @@ cdef class MC:
def clear_landau_coeff(self, index=0):
"""
Args:
index (int): potential index for thermodynamic integration (0 or 1; choose 0 if
not thermodynamic integration)
index (int): potential index for thermodynamic integration
(0 or 1; choose 0 if not thermodynamic integration)

This function erases all the Landau coefficients defined before.
"""
self.c_mc.clear_landau_coeff(index)

def clear_all_coeff(self):
"""
This function erases all the coefficients defined before.
"""
"""This function erases all the coefficients defined before."""
self.clear_heisenberg_coeff()
self.clear_heisenberg_coeff(1)
self.clear_landau_coeff()
Expand Down Expand Up @@ -184,20 +183,22 @@ cdef class MC:
Returns:
dict of output values
"""
return {'energy': self.get_energy(index=index),
'mean_energy': self.get_mean_energy(index=index),
'magnetization': np.mean(self.get_magnetization()),
'energy_variance': self.get_energy_variance(index=index),
'acceptance_ratio': self.get_acceptance_ratio(),
'steps_per_second': self.get_steps_per_second()}
return {
'energy': self.get_energy(index=index),
'mean_energy': self.get_mean_energy(index=index),
'magnetization': np.mean(self.get_magnetization()),
'energy_variance': self.get_energy_variance(index=index),
'acceptance_ratio': self.get_acceptance_ratio(),
'steps_per_second': self.get_steps_per_second()
}


def run(self, temperature, number_of_iterations=1, reset=True):
"""
Args:
temperature (float): Temperature in K
number_of_iterations (int): Number of MC steps (internally multiplied
by the number of atoms)
number_of_iterations (int): Number of MC steps (internally
multiplied by the number of atoms)
reset (bool): Resets statistics (s. get_mean_energy() etc.)
"""
if reset:
Expand All @@ -207,7 +208,8 @@ cdef class MC:
def get_acceptance_ratio(self, individual=False):
"""
Args:
individual (bool): If true, an array containing each acceptance ratio is returned
individual (bool): If true, an array containing each
acceptance ratio is returned
Returns:
Acceptance ratio since the last reset (s. reset())
"""
Expand Down Expand Up @@ -288,11 +290,11 @@ cdef class MC:
dphi = np.array([dphi]).flatten()
flip = np.array([flip]).flatten()
if len(dm)==1:
dm = np.array(self.c_mc.get_number_of_atoms()*dm.tolist())
dm = np.array(self.c_mc.get_number_of_atoms() * dm.tolist())
if len(dphi)==1:
dphi = np.array(self.c_mc.get_number_of_atoms()*dphi.tolist())
dphi = np.array(self.c_mc.get_number_of_atoms() * dphi.tolist())
if len(flip)==1:
flip = np.array(self.c_mc.get_number_of_atoms()*flip.tolist())
flip = np.array(self.c_mc.get_number_of_atoms() * flip.tolist())
self.c_mc.set_magnitude(dm, dphi, flip)

def run_gradient_descent(self, max_iter=None, step_size=1, decrement=0.001, diff=1.0e-8):
Expand All @@ -316,48 +318,66 @@ cdef class MC:
use_derivative=True,
):
"""
Set metadynamics calculation. Currently only the average magnetization can be chosen as
the collective variable.
Set metadynamics calculation. Currently only the average magnetization
can be chosen as the collective variable.

Args:
max_range (float): Maximum magnetization value (larger: less accurate; smaller:
potentially misses free energy minimum)
energy_increment (float): Energy increment (or prefactor) for the Gaussian histogram
(larger: less accurate; smaller: slower convergence)
length_scale (float): Magnetization length scale (or Gaussian smearing) (larger:
potentially smears out free energy minimum; smaller: MC could become unstable)
bins (int): Number of bins for histogram (larger: slower; smaller: less accurate)
cutoff (float): Cutoff value (in length_scale unit) above which the Gaussian smearing
is considered to be 0
use_derivative (bool): Use derivative information of metadynamics. This should be set
to True if the number of atoms is sufficiently high (>1000) and the MC step
magnitude is not too large (around 0.1). In other words, except for code testing,
it is unlikely that this should be set to False. If set to False, make sure that
the number of bins is large enough for the Metadynamics to make sense (it has to
be at least 10 x max_range x n_atoms / displacement_magnitude).
max_range (float): Maximum magnetization value (larger: less
accurate; smaller: potentially misses free energy minimum)
energy_increment (float): Energy increment (or prefactor) for the
Gaussian histogram (larger: less accurate; smaller: slower
convergence)
length_scale (float): Magnetization length scale (or Gaussian
smearing) (larger: potentially smears out free energy minimum;
smaller: MC could become unstable)
bins (int): Number of bins for histogram (larger: slower; smaller:
less accurate)
cutoff (float): Cutoff value (in length_scale unit) above which the
Gaussian smearing is considered to be 0
use_derivative (bool): Use derivative information of metadynamics.
This should be set to True if the number of atoms is
sufficiently high (>1000) and the MC step magnitude is not too
large (around 0.1). In other words, except for code testing, it
is unlikely that this should be set to False. If set to False,
make sure that the number of bins is large enough for the
Metadynamics to make sense (it has to be at least
10 x max_range x n_atoms / displacement_magnitude).
"""
if bins < max_range/length_scale:
raise ValueError('Number of bins too small for this length_scale and max_range')
raise ValueError(
"Number of bins too small for this length_scale and max_range"
)
self.c_mc.set_metadynamics(
max_range, energy_increment, length_scale, bins, cutoff, use_derivative*1
max_range,
energy_increment,
length_scale,
bins,
cutoff,
int(use_derivative),
)

def get_metadynamics_free_energy(self, derivative=False):
"""
Get free energy of the metadynamics simulation

Args:
derivative (bool): Whether to return the derivative values (only available if
derivative is used for metadynamics).
derivative (bool): Whether to return the derivative values (only
available if derivative is used for metadynamics).

Returns:
(dict) Containing ndarray of 'magnetization' and 'free_energy'
"""
data = np.array(self.c_mc.get_histogram(derivative*1)).reshape(2, -1, order='C')
data = np.array(
self.c_mc.get_histogram(derivative*1)
).reshape(2, -1, order='C')
return {'magnetization': data[0], 'free_energy': -data[1]}

def switch_spin_dynamics(
self, turn_on=True, damping_parameter=8.0e-3, delta_t=1.0e-3, rescale_mag=True
self,
turn_on=True,
damping_parameter=8.0e-3,
delta_t=1.0e-3,
rescale_mag=True,
):
"""
Turn on (or off) spin dynamics calculation
Expand All @@ -366,17 +386,20 @@ cdef class MC:
turn_on (bool): Turn on or off (turning it off means mc)
damping_parameter (float): damping parameter (default: 8.0e-3)
delta_t (float): time discretization in fs (default: 1.0e-3)
rescaler_mag (bool): Whether or not rescale the displacement magnitude. Not setting
it to True when spin dynamics on might rescale the time scale. This argument is
probably going to be removed in the near future.
rescaler_mag (bool): Whether or not rescale the displacement
magnitude. Not setting it to True when spin dynamics on might
rescale the time scale. This argument is probably going to be
removed in the near future.

The equations are based on this paper:
Ma, Pui-Wai, and S. L. Dudarev.
"Longitudinal magnetic fluctuations in Langevin spin dynamics."
Physical Review B 86.5 (2012): 054416.
https://journals.aps.org/prb/pdf/10.1103/PhysRevB.86.054416
"""
self.c_mc.switch_spin_dynamics(1*turn_on, damping_parameter, delta_t, rescale_mag*1)
self.c_mc.switch_spin_dynamics(
int(turn_on), damping_parameter, delta_t, int(rescale_mag)
)

def activate_debug(self):
"""
Expand Down
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