diff --git a/modelling/doc/monte_carlo.rst b/modelling/doc/monte_carlo.rst
index 5e63a0a6674098e4244a5e20deae8cb70c23a4c3..9a0b6f5eef70a0bbe18b2ec804d6c78aed932b36 100644
--- a/modelling/doc/monte_carlo.rst
+++ b/modelling/doc/monte_carlo.rst
@@ -21,46 +21,7 @@ Carlo sampling to the N-terminal part of crambin:
.. literalinclude:: ../../../tests/doc/scripts/modelling_monte_carlo.py
-.. method:: SampleMonteCarlo(sampler, closer, scorer, cooler, steps,\
- bb_list, initialize=True, seed=0,\
- lowest_energy_conformation=True)
-
- A convenient function to perform Monte Carlo sampling using a simulated
- annealing scheme. In every iteration, a new loop conformation gets proposed by
- the provided *sampler* and closed by the *closer*. Upon scoring, this new
- conformation gets accepted/rejected using a metropolis criterion based on the
- temperature given by the *cooler*
- => acceptance probability: exp(-delta_score/T).
- The result is stored in *bb_list* and is either the lowest energy conformation
- ever encountered or the last accepted proposal.
-
- :param sampler: Sampler object capable of initializing and altering
- conformations.
- :param closer: Closer object to adapt a new conformation to
- the environment.
- :param scorer: Scorer object to score new loop conformations.
- :param cooler: Cooler object to control the temperature of the
- Monte Carlo trajectory.
- :param steps: Number of Monte Carlo iterations to be performed.
- :param bb_list: The chosen conformation gets stored here.
- :param initialize: Whether a new bb_list should be generated as starting
- point, based on the samplers Initialize function.
- The input *bb_list* gets used otherwise.
- :param seed: Seed for internal random number generator.
- :param lowest_energy_conformation: If True, we choose the lowest scoring
- conformation of the trajectory. Otherwise,
- the last accepted proposal.
-
- :type sampler: :ref:`mc-sampler-object`
- :type closer: :ref:`mc-closer-object`
- :type scorer: :ref:`mc-scorer-object`
- :type cooler: :ref:`mc-cooler-object`
- :type steps: :class:`int`
- :type bb_list: :class:`~promod3.loop.BackboneList`
- :type initialize: :class:`bool`
- :type seed: :class:`int`
- :type lowest_energy_conformation: :class:`bool`
-
+.. autofunction:: SampleMonteCarlo
.. _mc-sampler-object:
diff --git a/modelling/pymod/CMakeLists.txt b/modelling/pymod/CMakeLists.txt
index 3f6e6f13be2079f47e8a11c1ffb979971cfb5534..5e44247d6bd03f7f80cbaee99fe973700ce4dcda 100644
--- a/modelling/pymod/CMakeLists.txt
+++ b/modelling/pymod/CMakeLists.txt
@@ -22,6 +22,7 @@ set(MODELLING_PYMOD
_denovo.py
_fragger_handle.py
_reconstruct_sidechains.py
+ _monte_carlo.py
)
pymod(NAME modelling
diff --git a/modelling/pymod/__init__.py b/modelling/pymod/__init__.py
index cdd41d485e453d2089b76a5a34c710559cd86a87..70ccfbb88eb891f54d21a19f01f99425b49d7f49 100644
--- a/modelling/pymod/__init__.py
+++ b/modelling/pymod/__init__.py
@@ -7,3 +7,4 @@ from _reconstruct_sidechains import *
from _ring_punches import *
from _denovo import *
from _fragger_handle import *
+from _monte_carlo import *
diff --git a/modelling/pymod/_monte_carlo.py b/modelling/pymod/_monte_carlo.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c284aed00a979441c76a938f8282a0484cfb4aa
--- /dev/null
+++ b/modelling/pymod/_monte_carlo.py
@@ -0,0 +1,90 @@
+import random
+import math
+
+
+def SampleMonteCarlo(sampler, closer, scorer, cooler, steps, bb_list,
+ initialize, seed = 0, lowest_energy_conformation = True):
+ ''' A convenient function to perform Monte Carlo sampling using a simulated
+ annealing scheme. In every iteration, a new loop conformation gets proposed
+ by the provided *sampler* and closed by the *closer*. Upon scoring, this new
+ conformation gets accepted/rejected using a metropolis criterion based on
+ the temperature given by the *cooler*
+ => acceptance probability: exp(-delta_score/T).
+ The result is stored in *bb_list* (passed by reference, so NO return value)
+ and is either the lowest energy conformation ever encountered or the last
+ accepted proposal.
+
+ :param sampler: Sampler object capable of initializing and altering
+ conformations.
+ :param closer: Closer object to adapt a new conformation to
+ the environment.
+ :param scorer: Scorer object to score new loop conformations.
+ :param cooler: Cooler object to control the temperature of the
+ Monte Carlo trajectory.
+ :param steps: Number of Monte Carlo iterations to be performed.
+ :param bb_list: The chosen conformation gets stored here.
+ :param initialize: Whether a new bb_list should be generated as starting
+ point, based on the samplers Initialize function.
+ The input *bb_list* gets used otherwise.
+ :param seed: Seed for internal random number generator.
+ :param lowest_energy_conformation: If True, we choose the lowest scoring
+ conformation of the trajectory.
+ Otherwise, the last accepted proposal.
+
+ :type sampler: :ref:`mc-sampler-object`
+ :type closer: :ref:`mc-closer-object`
+ :type scorer: :ref:`mc-scorer-object`
+ :type cooler: :ref:`mc-cooler-object`
+ :type steps: :class:`int`
+ :type bb_list: :class:`~promod3.loop.BackboneList`
+ :type initialize: :class:`bool`
+ :type seed: :class:`int`
+ :type lowest_energy_conformation: :class:`bool`
+ '''
+
+ # Initialize from scratch if necessary
+ if initialize:
+ initialized = False
+ for i in range(100):
+ sampler.Initialize(bb_list)
+ if closer.Close(bb_list, bb_list):
+ initialized = True
+ break
+ if not initialized:
+ raise RuntimeError("Failed to initialize monte carlo protocol!")
+
+ # setup
+ random.seed(seed)
+ score = scorer.GetScore(bb_list)
+ closed = False
+ min_score = score
+ min_score_bb_list = bb_list.Copy()
+ proposed_bb_list = bb_list.Copy()
+
+ for i in range(steps):
+
+ # try several proposals (we might not be able to close at first attempt)
+ closed = False
+ for j in range(3):
+ sampler.ProposeStep(bb_list, proposed_bb_list)
+ if closer.Close(proposed_bb_list, proposed_bb_list):
+ closed = True
+ break
+
+ # check for success, try again in next step in case of failure
+ if not closed:
+ continue
+
+ # accept / reject based on Metropolis criterion
+ temperature = cooler.GetTemperature()
+ new_score = scorer.GetScore(proposed_bb_list)
+
+ if math.exp(-(new_score-score)/temperature) > random.random():
+ positions = proposed_bb_list.Copy()
+ score = new_score
+ if lowest_energy_conformation and score < min_score:
+ min_score = score
+ min_score_bb_list = positions
+
+ if lowest_energy_conformation:
+ bb_list = min_score_bb_list
diff --git a/modelling/pymod/export_monte_carlo.cc b/modelling/pymod/export_monte_carlo.cc
index 5935c7534efd62b055bf57da7252e4f038685976..1cbf251a6f3114dc5f3c0cddd9b509b0dc1eb987 100644
--- a/modelling/pymod/export_monte_carlo.cc
+++ b/modelling/pymod/export_monte_carlo.cc
@@ -332,10 +332,4 @@ void export_monte_carlo() {
register_ptr_to_python<NTerminalCloserPtr>();
register_ptr_to_python<CTerminalCloserPtr>();
register_ptr_to_python<MonteCarloCloserPtr>();
-
- // export monte carlo sampler
- def("SampleMonteCarlo", &SampleMonteCarlo,
- (arg("sampler"), arg("closer"), arg("scorer"), arg("cooler"),
- arg("steps"), arg("backbone"), arg("initialize")=true, arg("seed")=0,
- arg("lowest_energy_conformation")=true));
}