ronswanson package

Subpackages

• ronswanson.utils package
  • Submodules
  • ronswanson.utils.cartesian_product module
  • ronswanson.utils.check_complete module
  • ronswanson.utils.color module
  • ronswanson.utils.colormap_generator module
  • ronswanson.utils.configuration module
  • ronswanson.utils.file_open module
  • ronswanson.utils.hdf5_utils module
  • ronswanson.utils.logging module
  • ronswanson.utils.package_data module
  • ronswanson.utils.quailty_checks module
  • Module contents

Submodules

ronswanson.band_simulation module

class ronswanson.band_simulation.BandSimulation(simulation_id: int, parameter_set: Dict[str, float], energy_grid: EnergyGrid, out_file: str)

Bases: Simulation

__init__(simulation_id: int, parameter_set: Dict[str, float], energy_grid: EnergyGrid, out_file: str) → None

Generic simulation class

Parameters:

• simulation_id (int) –

• parameter_set (Dict[str, float]) –

• energy_grid (np.ndarray) –

• out_file (str) –

Returns:

ronswanson.database module

class ronswanson.database.Database(grid_points: ndarray, parameter_names: List[str], energy_grid: ndarray, run_time: ndarray, values: ndarray, meta_data: Optional[Dict[str, ndarray]] = None)

Bases: object

__init__(grid_points: ndarray, parameter_names: List[str], energy_grid: ndarray, run_time: ndarray, values: ndarray, meta_data: Optional[Dict[str, ndarray]] = None) → None

Databse of parameters and simulated values

Parameters:

• grid_points (Dict[str, np.ndarray]) –

• parameter_names (List[str]) –

• energy_grid (np.ndarray) –

• values (Dict[str, np.ndarray]) –

Returns:

property n_entries: int

the number of entries in the database

Returns:

property n_parameters: int

the number of parameters in the database

Returns:

property grid_points: ndarray

property values: ndarray

property parameter_ranges: Dict[str, ndarray]

property parameter_names: List[str]

the names of the parameters

property run_time: ndarray

an array of runtimes for all points in the grid

Returns:

property energy_grid: ndarray

The corresponding energy grid of the simulation

Returns:

property meta_data: Optional[Dict[str, ndarray]]

at(i: int) → ValueContainer

return the parameters and values at an index

classmethod from_file(file_name: str, output: int = 0) → Database

open a database from a file. The output argument specifies which value to collect. For example, a simulation may save photon and electron distributions from a solution.

replace_nan_inf_with(value: float = 0.0) → None

Replace NaN and inf values with a float

Parameters:

value (float) –

Returns:

to_3ml(name: str, desc: str, overwrite: bool = False, **kwargs) → TemplateModel

construct a table model from the database. parameter sub-selections are passed as kwargs of dictionaries:

selections = dict(param1=dict(vmin=1, vmax=2))

Parameters:

• name (str) –

• desc (str) –

• overwrite (bool) –

Returns:

check_for_missing_parameters(parameter_grid: ParameterGrid, create_new_grid: bool = False) → None

Search a parameter grid for missing values

Parameters:

• parameter_grid (ParameterGrid) –

• create_new_grid (bool) –

Returns:

plot_runtime(colorscale: str = 'plasma')

show a parallel plot of the run time

Parameters:

colorscale –

Returns:

plot_meta_data(meta_number: int = 0, colorscale: str = 'plasma', as_log: bool = False)

parallel plot of the meta data

Parameters:

• meta_number (int) –

• colorscale (str) –

• as_log (bool) –

Returns:

ronswanson.grids module

class ronswanson.grids.EnergyGrid(vmin: Union[float, NoneType] = None, vmax: Union[float, NoneType] = None, scale: Union[str, NoneType] = None, n_points: Union[int, NoneType] = None, values: Union[numpy.ndarray, NoneType] = None, custom: bool = False)

Bases: object

vmin: Optional[float] = None

vmax: Optional[float] = None

scale: Optional[str] = None

n_points: Optional[int] = None

values: Optional[ndarray] = None

custom: bool = False

property grid: ndarray

classmethod from_dict(d: Dict[str, Any]) → Parameter

to_dict() → Dict[str, Any]

__init__(vmin: Optional[float] = None, vmax: Optional[float] = None, scale: Optional[str] = None, n_points: Optional[int] = None, values: Optional[ndarray] = None, custom: bool = False) → None

class ronswanson.grids.Parameter(name: str, vmin: Union[float, NoneType] = None, vmax: Union[float, NoneType] = None, scale: Union[str, NoneType] = None, n_points: Union[int, NoneType] = None, values: Union[numpy.ndarray, NoneType] = None, custom: bool = False)

Bases: object

name: str

vmin: Optional[float] = None

vmax: Optional[float] = None

scale: Optional[str] = None

n_points: Optional[int] = None

values: Optional[ndarray] = None

custom: bool = False

property grid: ndarray

classmethod from_dict(name: str, d: Dict[str, Any]) → Parameter

to_dict() → Dict[str, Any]

__init__(name: str, vmin: Optional[float] = None, vmax: Optional[float] = None, scale: Optional[str] = None, n_points: Optional[int] = None, values: Optional[ndarray] = None, custom: bool = False) → None

class ronswanson.grids.ParameterGrid(parameter_list: List[ronswanson.grids.Parameter], energy_grid: List[ronswanson.grids.EnergyGrid])

Bases: object

parameter_list: List[Parameter]

energy_grid: List[EnergyGrid]

full_grid: Optional[ndarray]

property n_points: int

property n_parameters: int

classmethod from_dict(d: Dict[str, Dict[str, Any]]) → ParameterGrid

classmethod from_yaml(file_name: str) → ParameterGrid

property parameter_names: List[str]

to_dict() → Dict[str, Dict[str, Any]]

write(file_name: str) → None

at_index(i: int, as_array: bool = False) → Union[Dict[str, float], ndarray]

return the ith set of parameters

Parameters:

i (int) –

Returns:

__init__(parameter_list: List[Parameter], energy_grid: List[EnergyGrid]) → None

ronswanson.script_generator module

class ronswanson.script_generator.PythonGenerator(file_name: str, database_file: str, parameter_file: str, base_dir: str, import_line: str, n_procs: int, n_nodes: Optional[int] = None, linear_exceution: bool = False, has_complete_params: bool = False, current_size: int = 0, clean: bool = True)

Bases: ScriptGenerator

__init__(file_name: str, database_file: str, parameter_file: str, base_dir: str, import_line: str, n_procs: int, n_nodes: Optional[int] = None, linear_exceution: bool = False, has_complete_params: bool = False, current_size: int = 0, clean: bool = True) → None

Generate the python script that will be run

Parameters:

• file_name (str) –

• database_file (str) –

• parameter_file (str) –

• base_dir (str) –

• import_line (str) –

• n_procs (int) –

• n_nodes (Optional[int]) –

• linear_exceution (bool) –

Returns:

class ronswanson.script_generator.PythonGatherGenerator(file_name: str, database_file_name: str, current_size: int, n_outputs: int, num_meta_parameters: Optional[int] = None, clean: bool = True)

Bases: ScriptGenerator

__init__(file_name: str, database_file_name: str, current_size: int, n_outputs: int, num_meta_parameters: Optional[int] = None, clean: bool = True) → None

class ronswanson.script_generator.SLURMGenerator(file_name: str, n_procs: int, n_procs_to_use: int, n_nodes: int, hrs: int, min: int, sec: int, node_start: int = 0)

Bases: ScriptGenerator

__init__(file_name: str, n_procs: int, n_procs_to_use: int, n_nodes: int, hrs: int, min: int, sec: int, node_start: int = 0) → None

class ronswanson.script_generator.SLURMGatherGenerator(file_name: str, n_procs: int, n_nodes: int, hrs: int, min: int, sec: int)

Bases: ScriptGenerator

__init__(file_name: str, n_procs: int, n_nodes: int, hrs: int, min: int, sec: int) → None

ronswanson.simulation module

class ronswanson.simulation.Timer

Bases: object

A timing context manager

class ronswanson.simulation.Simulation(simulation_id: int, parameter_set: Dict[str, float], energy_grid: List[EnergyGrid], out_file: str, num_outputs: int = 1, num_meta_parameters: Optional[int] = None)

Bases: object

__init__(simulation_id: int, parameter_set: Dict[str, float], energy_grid: List[EnergyGrid], out_file: str, num_outputs: int = 1, num_meta_parameters: Optional[int] = None) → None

Generic simulation class

Parameters:

• simulation_id (int) –

• parameter_set (Dict[str, float]) –

• energy_grid (np.ndarray) –

• out_file (str) –

Returns:

run() → None

run this simulation :returns:

ronswanson.simulation.gather(file_name: str, current_size: int = 0, clean: bool = True) → None

ronswanson.simulation_builder module

class ronswanson.simulation_builder.SLURMTime(hrs: int = 0, min: int = 10, sec: int = 0)

Bases: object

hrs: int = 0

min: int = 10

sec: int = 0

__init__(hrs: int = 0, min: int = 10, sec: int = 0) → None

class ronswanson.simulation_builder.JobConfig(time: ronswanson.simulation_builder.SLURMTime, n_cores_per_node: int)

Bases: object

time: SLURMTime

n_cores_per_node: int

__init__(time: SLURMTime, n_cores_per_node: int) → None

class ronswanson.simulation_builder.GatherConfig(time: ronswanson.simulation_builder.SLURMTime, n_cores_per_node: int, n_gather_per_core: int)

Bases: JobConfig

n_gather_per_core: int

__init__(time: SLURMTime, n_cores_per_node: int, n_gather_per_core: int) → None

time: SLURMTime

n_cores_per_node: int

class ronswanson.simulation_builder.SimulationConfig(time: ronswanson.simulation_builder.SLURMTime, n_cores_per_node: int, n_mp_jobs: int, run_per_node: Union[int, NoneType] = None, use_nodes: bool = False, max_nodes: Union[int, NoneType] = None, linear_execution: bool = False, num_meta_parameters: Union[int, NoneType] = None)

Bases: JobConfig

n_mp_jobs: int

run_per_node: Optional[int] = None

use_nodes: bool = False

max_nodes: Optional[int] = None

linear_execution: bool = False

num_meta_parameters: Optional[int] = None

__init__(time: SLURMTime, n_cores_per_node: int, n_mp_jobs: int, run_per_node: Optional[int] = None, use_nodes: bool = False, max_nodes: Optional[int] = None, linear_execution: bool = False, num_meta_parameters: Optional[int] = None) → None

time: SLURMTime

n_cores_per_node: int

class ronswanson.simulation_builder.JobConfigStructure(time: Union[ronswanson.simulation_builder.SLURMTime, NoneType] = None, n_cores_per_node: Union[int, NoneType] = None)

Bases: object

time: Optional[SLURMTime] = None

n_cores_per_node: Optional[int] = None

__init__(time: Optional[SLURMTime] = None, n_cores_per_node: Optional[int] = None) → None

class ronswanson.simulation_builder.GatherConfigStructure(time: Union[ronswanson.simulation_builder.SLURMTime, NoneType] = None, n_cores_per_node: Union[int, NoneType] = None, n_gather_per_core: int = '???')

Bases: JobConfigStructure

n_gather_per_core: int = '???'

__init__(time: Optional[SLURMTime] = None, n_cores_per_node: Optional[int] = None, n_gather_per_core: int = '???') → None

class ronswanson.simulation_builder.SimulationConfigStructure(time: Union[ronswanson.simulation_builder.SLURMTime, NoneType] = None, n_cores_per_node: Union[int, NoneType] = None, n_mp_jobs: int = '???', run_per_node: Union[int, NoneType] = None, use_nodes: bool = False, max_nodes: Union[int, NoneType] = None, linear_execution: bool = False)

Bases: JobConfigStructure

n_mp_jobs: int = '???'

run_per_node: Optional[int] = None

use_nodes: bool = False

max_nodes: Optional[int] = None

linear_execution: bool = False

__init__(time: Optional[SLURMTime] = None, n_cores_per_node: Optional[int] = None, n_mp_jobs: int = '???', run_per_node: Optional[int] = None, use_nodes: bool = False, max_nodes: Optional[int] = None, linear_execution: bool = False) → None

class ronswanson.simulation_builder.YAMLStructure(import_line: str = '???', parameter_grid: str = '???', out_file: str = '???', clean: bool = True, simulation: ronswanson.simulation_builder.SimulationConfigStructure = SimulationConfigStructure(time=None, n_cores_per_node=None, n_mp_jobs='???', run_per_node=None, use_nodes=False, max_nodes=None, linear_execution=False), gather: Union[ronswanson.simulation_builder.GatherConfigStructure, NoneType] = None, num_meta_parameters: Union[int, NoneType] = None, finish_missing: bool = False)

Bases: object

import_line: str = '???'

parameter_grid: str = '???'

out_file: str = '???'

clean: bool = True

simulation: SimulationConfigStructure = SimulationConfigStructure(time=None, n_cores_per_node=None, n_mp_jobs='???', run_per_node=None, use_nodes=False, max_nodes=None, linear_execution=False)

gather: Optional[GatherConfigStructure] = None

num_meta_parameters: Optional[int] = None

finish_missing: bool = False

__init__(import_line: str = '???', parameter_grid: str = '???', out_file: str = '???', clean: bool = True, simulation: SimulationConfigStructure = SimulationConfigStructure(time=None, n_cores_per_node=None, n_mp_jobs='???', run_per_node=None, use_nodes=False, max_nodes=None, linear_execution=False), gather: Optional[GatherConfigStructure] = None, num_meta_parameters: Optional[int] = None, finish_missing: bool = False) → None

class ronswanson.simulation_builder.SimulationBuilder(parameter_grid: ParameterGrid, out_file: str, import_line: str, simulation_config: SimulationConfig, gather_config: Optional[GatherConfig] = None, num_meta_parameters: Optional[int] = None, clean: bool = True, finish_missing: bool = False)

Bases: object

The simulation builder class constructs the scripts needed for building the table model database

__init__(parameter_grid: ParameterGrid, out_file: str, import_line: str, simulation_config: SimulationConfig, gather_config: Optional[GatherConfig] = None, num_meta_parameters: Optional[int] = None, clean: bool = True, finish_missing: bool = False)

TODO describe function

Parameters:

• parameter_grid (ParameterGrid) –

• out_file (str) –

• import_line (str) –

• simulation_config (SimulationConfig) –

• gather_config (Optional[GatherConfig]) –

• num_meta_parameters (Optional[int]) –

• clean (bool) –

Returns:

classmethod from_yaml(file_name: str) → SimulationBuilder

Create a simulation setup from a yaml file.

Module contents

Top-level package for ronswanson.

class ronswanson.Simulation(simulation_id: int, parameter_set: Dict[str, float], energy_grid: List[EnergyGrid], out_file: str, num_outputs: int = 1, num_meta_parameters: Optional[int] = None)

Bases: object

__init__(simulation_id: int, parameter_set: Dict[str, float], energy_grid: List[EnergyGrid], out_file: str, num_outputs: int = 1, num_meta_parameters: Optional[int] = None) → None

Generic simulation class

Parameters:

• simulation_id (int) –

• parameter_set (Dict[str, float]) –

• energy_grid (np.ndarray) –

• out_file (str) –

Returns:

run() → None

run this simulation :returns:

class ronswanson.ParameterGrid(parameter_list: List[ronswanson.grids.Parameter], energy_grid: List[ronswanson.grids.EnergyGrid])

Bases: object

parameter_list: List[Parameter]

energy_grid: List[EnergyGrid]

full_grid: Optional[ndarray]

property n_points: int

property n_parameters: int

classmethod from_dict(d: Dict[str, Dict[str, Any]]) → ParameterGrid

classmethod from_yaml(file_name: str) → ParameterGrid

property parameter_names: List[str]

to_dict() → Dict[str, Dict[str, Any]]

write(file_name: str) → None

at_index(i: int, as_array: bool = False) → Union[Dict[str, float], ndarray]

return the ith set of parameters

Parameters:

i (int) –

Returns:

__init__(parameter_list: List[Parameter], energy_grid: List[EnergyGrid]) → None

class ronswanson.SimulationBuilder(parameter_grid: ParameterGrid, out_file: str, import_line: str, simulation_config: SimulationConfig, gather_config: Optional[GatherConfig] = None, num_meta_parameters: Optional[int] = None, clean: bool = True, finish_missing: bool = False)

Bases: object

The simulation builder class constructs the scripts needed for building the table model database

__init__(parameter_grid: ParameterGrid, out_file: str, import_line: str, simulation_config: SimulationConfig, gather_config: Optional[GatherConfig] = None, num_meta_parameters: Optional[int] = None, clean: bool = True, finish_missing: bool = False)

TODO describe function

Parameters:

• parameter_grid (ParameterGrid) –

• out_file (str) –

• import_line (str) –

• simulation_config (SimulationConfig) –

• gather_config (Optional[GatherConfig]) –

• num_meta_parameters (Optional[int]) –

• clean (bool) –

Returns:

classmethod from_yaml(file_name: str) → SimulationBuilder

Create a simulation setup from a yaml file.

class ronswanson.Database(grid_points: ndarray, parameter_names: List[str], energy_grid: ndarray, run_time: ndarray, values: ndarray, meta_data: Optional[Dict[str, ndarray]] = None)

Bases: object

__init__(grid_points: ndarray, parameter_names: List[str], energy_grid: ndarray, run_time: ndarray, values: ndarray, meta_data: Optional[Dict[str, ndarray]] = None) → None

Databse of parameters and simulated values

Parameters:

• grid_points (Dict[str, np.ndarray]) –

• parameter_names (List[str]) –

• energy_grid (np.ndarray) –

• values (Dict[str, np.ndarray]) –

Returns:

property n_entries: int

the number of entries in the database

Returns:

property n_parameters: int

the number of parameters in the database

Returns:

property grid_points: ndarray

property values: ndarray

property parameter_ranges: Dict[str, ndarray]

property parameter_names: List[str]

the names of the parameters

property run_time: ndarray

an array of runtimes for all points in the grid

Returns:

property energy_grid: ndarray

The corresponding energy grid of the simulation

Returns:

property meta_data: Optional[Dict[str, ndarray]]

at(i: int) → ValueContainer

return the parameters and values at an index

classmethod from_file(file_name: str, output: int = 0) → Database

open a database from a file. The output argument specifies which value to collect. For example, a simulation may save photon and electron distributions from a solution.

replace_nan_inf_with(value: float = 0.0) → None

Replace NaN and inf values with a float

Parameters:

value (float) –

Returns:

to_3ml(name: str, desc: str, overwrite: bool = False, **kwargs) → TemplateModel

construct a table model from the database. parameter sub-selections are passed as kwargs of dictionaries:

selections = dict(param1=dict(vmin=1, vmax=2))

Parameters:

• name (str) –

• desc (str) –

• overwrite (bool) –

Returns:

check_for_missing_parameters(parameter_grid: ParameterGrid, create_new_grid: bool = False) → None

Search a parameter grid for missing values

Parameters:

• parameter_grid (ParameterGrid) –

• create_new_grid (bool) –

Returns:

plot_runtime(colorscale: str = 'plasma')

show a parallel plot of the run time

Parameters:

colorscale –

Returns:

plot_meta_data(meta_number: int = 0, colorscale: str = 'plasma', as_log: bool = False)

parallel plot of the meta data

Parameters:

• meta_number (int) –

• colorscale (str) –

• as_log (bool) –

Returns:

ronswanson.update_logging_level(level)

ronswanson.show_configuration() → Tree