pygromos.files.otherfiles package

Submodules

pygromos.files.otherfiles.new_repdat module

FUNCTIONLIB: Repdat File Description:

From a Replica-Exchange simulation a repdat file will be created, that gives insight on the replica exchanges of the simulation

Author: Benjamin Schroeder

class pygromos.files.otherfiles.new_repdat.Repdat(input_path: str)[source]

Bases: pandas.core.frame.DataFrame

Replica exchange statistic file This class is a representation for all transition information during a replica exchange run. it adds some useful functionality.

DATA: pandas.core.frame.DataFrame
SYSTEM: pygromos.files.blocks.replica_exchange_blocks.repex_system
__init__(input_path: str)[source]

Repdat_Constructor

Parameters

input_path (str) – path to gromos repdadat file

_caculate_transition_traces() → None[source]

TODO: refactor code! ..autofunction: _caculate_transition_traces

calculates the transition traces for all replicas from raw data and stores them in self.transition_traces. In the end you recieve the trace a replica coord system moved through s dist format: {replicaID: {[trials…], [position…], [PotE…]}}

Returns

None

Return type

None

_calculate_ndowns_nups_for_each_state(time_stride: int = - 1, min_state_potential_treshold: Optional[float] = None, verbose: bool = False)[source]

calculates the visit counts for each replicaID position (Temperature or s_value).

It splits into substates depending on the state potentials, to destinguish which state passed by. Up and Downs are counted from top to bottom. Additionally also a time dependend series is generated. This can be binned by the argument time_window. If the min_state_potential_treshold is given, than a minimal state is also dependent on the other states, if they are below the threshold, the state is undefined.

In the end you recieve the position state visit counts in a dict: format: {{replicaposition:{“tot_nup”:[], “tot_ndown”:[], “dt”:float, “dt_nup”:[], “dt_ndown”:[]}}

Parameters

  • time_stride (int, optional) – determines the window bin size of flow trajectory for each replicaID. This there are total_transitions/time_window bins containing time_window many flow values. default -1 counts all frames

  • min_state_potential_treshold (float, optional) – a threshold, defining if a state is governing a system at a time point t

  • verbose (bool, optional)

Return type

None

_calculate_replica_roundtrips()[source]
..autofunction: _calculate_replica_roundtrips

This function is calculating the roundtrips over all replica positions for each replica.

Returns

None

Return type

None

_clean_replica_round_trips(replica_round_trips: Dict[int, int]) → Dict[int, int][source]

_clean_replica_round_trips - privat

This function cleans up so that the minimal rountrip number in a roundtrip dict is 0

Parameters

replica_round_trips (Dict[int:int]) – a dictionary containing all replica roundtrip counts

Returns

Dict[int – a dictionary containing all replica roundtrip counts with lowest value 0

Return type

int]

added_property = 1
append(repdat: Union[List[pygromos.utils.typing.Repdat_Type], pygromos.utils.typing.Repdat_Type])[source]

This function concatenates two repdat files into the executing obj.

Parameters

repdat (List[Repdat] or Repdat) – one or multiple Repdat files.

Return type

None

clean_file_runs()[source]

clean_file DEAPRECEATED - ABOUT TO BE REMOVED!

Updates the run numbers to be continous sequential. (for example needed for concatenation)

count_state_per_position: Dict[int, Dict[str, Union[List[int], int]]] = None
get_replicaPosition_dependend_nup_ndown(time_window_size: int = - 1, potential_treshold: Optional[float] = None, recalculate: bool = False) → Dict[int, Dict[str, Union[List, pandas.core.frame.DataFrame, dict, float]]][source]
..autofunction: get_replicaPosition_dependend_nup_ndown

This function is returning the replica position visit counts by all simulation state.

Parameters

  • time_window_size (int) – how many timesteps shall be binned

  • potential_treshold (float) – if defined, and there is a time window, in which multiple states are below this threshold, the count is in an undefined state.

  • recalculate (bool) – shall the dict be recalculated?

Returns

returns a dict for all replica positions and the visit counts.

Return type

Dict[int, Dict[str, Union[List or float]]]

get_replicaPosition_dependend_nup_ndown_for_each_state(time_window_size: int = - 1, potential_treshold: Optional[float] = None, recalculate: bool = False) → Dict[int, Dict[str, List]][source]
..autofunction: get_replicaPosition_dependend_nup_ndown_for_each_state

This function is returning the replica position visit counts by each simulation state, per state.

Parameters

  • time_window_size (int) – how many timesteps shall be binned

  • potential_treshold (float) – if defined, and there is a time window, in which multiple states are below this threshold, the count is in an undefined state.

  • recalculate (bool) – shall the dict be recalculated?

Returns

returns a dict for all replica positions and their state visit counts.

Return type

Dict[int, Dict[str, Union[List or float]]]

get_replica_roundtrips(recalculate: bool = False) → Dict[int, int][source]
..autofunction: get_replica_roundtrips

This function is returning the count of rountrips (RT) for each replica.

Parameters

recalculate (bool) – shall the dict be recalculated?

Returns

returns a dict for all replica and their rountrip counts.

Return type

Dict[int,int]

get_replica_traces(recalculate: bool = False) → pandas.core.frame.DataFrame[source]

returns a replica_traces dictionary. :Parameters: recalculate (bool, optional) – shall the dict be recalculated, if already present?

Returns

dictionary containing all individual replica_traces

Return type

Dict[int, Dict[str,List[float]]]

replica_round_trips: Dict[int, int] = None
transition_traces: Dict[int, Dict[str, List[float]]] = None
write(out_path: str) → str[source]
..autofunction:

Write out a repdat file to the outpath.

Parameters

out_path (str) – determines the output path

Returns

out_path

:rtype:str

pygromos.files.otherfiles.noe_output module

FUNCTIONLIB: gromos++ input file functions Description:

in this lib, gromosXX input file mainpulating functions are gathered

Author: Benjamin Schroeder

class pygromos.files.otherfiles.noe_output.JVAL(in_value: str)[source]

Bases: pygromos.files._basics._general_gromos_file._general_gromos_file

content: pandas.core.frame.DataFrame
read_file()[source]

give back the content. WARNING DEAPRECEATED.

Warning

DEAPRECEATED

Returns

key is the block name of the gromos file, any is the content of a block

Return type

Dict[str, any]

write(out_path: str) → str[source]

writes a general Gromos File out

Parameters

out_path (str) – out path where the file should be.

Returns

out_path

Return type

str

class pygromos.files.otherfiles.noe_output.NOE(in_value: str)[source]

Bases: pygromos.files._basics._general_gromos_file._general_gromos_file

AVERAGE_NOE: pandas.core.frame.DataFrame
NOE_VIOLATIONS: pandas.core.frame.DataFrame
RESTRAINT_LEGEND: pandas.core.frame.DataFrame
TITLE: pygromos.files.blocks._general_blocks.TITLE
read_file()[source]

give back the content. WARNING DEAPRECEATED.

Warning

DEAPRECEATED

Returns

key is the block name of the gromos file, any is the content of a block

Return type

Dict[str, any]

pygromos.files.otherfiles.repdat module

FUNCTIONLIB: Repdat File Description:

From a Replica-Exchange simulation a repdat file will be created, that gives insight on the replica exchanges of the simulation

Author: Benjamin Schroeder

class pygromos.files.otherfiles.repdat.Repdat(input_path: str)[source]

Bases: pygromos.files._basics._general_gromos_file._general_gromos_file

Replica exchange statistic file This class is a representation for all transition information during a replica exchange run. it adds some useful functionality.

DATA: pandas.core.frame.DataFrame
SYSTEM: pygromos.files.blocks.replica_exchange_blocks.repex_system
__init__(input_path: str)[source]

Repdat_Constructor

Parameters

input_path (str) – path to gromos repdadat file

_caculate_transition_traces()[source]
calculates the transition traces for all replicas from raw data and stores them in self.transition_traces.

In the end you recieve the trace a replica coord system moved through s dist format: {replicaID: {[trials…], [position…], [PotE…]}}

TODO: refactor code!

Return type

None

_calculate_ndowns_nups_for_each_state(time_stride: int = - 1, min_state_potential_treshold: Optional[float] = None, verbose: bool = False)[source]

calculates the visit counts for each replicaID position (Temperature or s_value).

It splits into substates depending on the state potentials, to destinguish which state passed by. Up and Downs are counted from top to bottom. Additionally also a time dependend series is generated. This can be binned by the argument time_window. If the min_state_potential_treshold is given, than a minimal state is also dependent on the other states, if they are below the threshold, the state is undefined.

In the end you recieve the position state visit counts in a dict: format: {{replicaposition:{“tot_nup”:[], “tot_ndown”:[], “dt”:float, “dt_nup”:[], “dt_ndown”:[]}}

Parameters

  • time_stride (int, optional) – determines the window bin size of flow trajectory for each replicaID. This there are total_transitions/time_window bins containing time_window many flow values. default -1 counts all frames

  • min_state_potential_treshold (float, optional) – a threshold, defining if a state is governing a system at a time point t

  • verbose (bool, optional)

Return type

None

_calculate_replica_roundtrips()[source]

This function is calculating the roundtrips over all replica positions for each replica.

Return type

None

_clean_replica_round_trips(replica_round_trips: Dict[int, int]) → Dict[int, int][source]

_clean_replica_round_trips - privat

This function cleans up so that the minimal rountrip number in a roundtrip dict is 0

Parameters

replica_round_trips (Dict[int:int]) – a dictionary containing all replica roundtrip counts

Returns

Dict[int – a dictionary containing all replica roundtrip counts with lowest value 0

Return type

int]

append(repdat: Union[List[pygromos.utils.typing.Repdat_Type], pygromos.utils.typing.Repdat_Type])[source]

This function concatenates two repdat files into the executing obj.

Parameters

repdat (List[Repdat] or Repdat) – one or multiple Repdat files.

Return type

None

clean_file_runs(starting_trial: float = 1)[source]
clean_file

Updates the run numbers to be continous sequential. (for example needed for concatenation)

Parameters

starting_trial (int, optional)

count_state_per_position: Dict[int, Dict[str, Union[List, float]]] = None
get_replicaPosition_dependend_nup_ndown(time_window_size: int = - 1, potential_treshold: Optional[float] = None, recalculate: bool = False) → Dict[int, Dict[str, Union[List, pandas.core.frame.DataFrame, dict, float]]][source]

This function is returning the replica position visit counts by all simulation state.

Parameters

  • time_window_size (int,optional) – how many timesteps shall be binned into one bin?

  • potential_treshold (float) – if defined, and there is a time window, in which multiple states are below this threshold, the count is in an undefined state.

  • recalculate (bool) – shall the dict be recalculated?

Returns

returns a dict for all replica positions and the visit counts.

Return type

Dict[int, Dict[str, Union[List or float]]]

get_replicaPosition_dependend_nup_ndown_for_each_state(time_window_size: int = - 1, potential_treshold: Optional[float] = None, recalculate: bool = False) → Dict[int, Dict[str, Union[List, float]]][source]

This function is returning the replica position visit counts by each simulation state, per state.

Parameters

  • time_window_size (int, optional) – how many timesteps shall be binned into one bin?

  • potential_treshold (float, optional) – if defined, and there is a time window, in which multiple states are below this threshold, the count is in an undefined state.

  • recalculate (bool, optional) – shall the dict be recalculated?

Returns

returns a dict for all replica positions and their state visit counts.

Return type

Dict[int, Dict[str, Union[List or float]]]

get_replica_roundtrips(recalculate: bool = False) → Dict[int, int][source]

This function is returning the count of rountrips (RT) for each replica.

Parameters

recalculate (bool) – shall the dict be recalculated?

Returns

returns a dict for all replica and their rountrip counts.

Return type

Dict[int, int]

get_replica_traces(recalculate: bool = False) → pandas.core.frame.DataFrame[source]

returns a replica_traces dictionary. :Parameters: recalculate (bool, optional) – shall the dict be recalculated, if already present?

Returns

dictionary containing all individual replica_traces

Return type

Dict[int, Dict[str,List[float]]]

replica_round_trips: Dict[int, int] = None
transition_traces: Dict[int, Dict[str, List[float]]] = None
write(out_path: str) → str[source]
Parameters

out_path (str) – determines the output path for repdat file

Returns

out_path

Return type

str

pygromos.files.otherfiles.residue_library module

File: gromos residue library

needed if top and pdb resns or atoms are not the same.

Author: Benjamin Ries

class pygromos.files.otherfiles.residue_library.residue_library(in_value: Union[str, Dict] = '/home/mlehner/PyGromosTools/pygromos/data/pdb2g96.lib')[source]

Bases: pygromos.files._basics._general_gromos_file._general_gromos_file

ATOMNAMELIB: pygromos.files.blocks.miscBlocks.ATOMNAMELIB
RESIDUENAMELIB: pygromos.files.blocks.miscBlocks.RESIDUENAMELIB
__init__(in_value: Union[str, Dict] = '/home/mlehner/PyGromosTools/pygromos/data/pdb2g96.lib')[source]

This class represents a file that is used for the gromosPP program - pdb2g96 it contains two blocks for residue naming and atom naming

Parameters

in_value (Union[str, dict])

read_resnlib(path: str)[source]
required_blocks = ['TITLE', 'RESIDUENAMELIB', 'ATOMNAMELIB']
verbose: bool = False

Module contents