schrodinger.application.matsci.genetic_optimization.genetic_optimization module

Classes and functions for the genetic optimization module.

Copyright Schrodinger, LLC. All rights reserved.

class schrodinger.application.matsci.genetic_optimization.genetic_optimization.PropertyInfo(key, units, is_positive, class_evaluator, class_kwargs)

Bases: tuple

__contains__

Return key in self.

__init__

Initialize self. See help(type(self)) for accurate signature.

__len__

Return len(self).

class_evaluator

Alias for field number 3

class_kwargs

Alias for field number 4

count(value) → integer -- return number of occurrences of value
index(value[, start[, stop]]) → integer -- return first index of value.

Raises ValueError if the value is not present.

is_positive

Alias for field number 2

key

Alias for field number 0

units

Alias for field number 1

class schrodinger.application.matsci.genetic_optimization.genetic_optimization.ClassEvaluator(structs, properties)

Bases: object

Manage a class evaluator.

SEPARATOR = '_'
OUT_EXT = '.out'
__init__(structs, properties)

Create an instance.

Parameters:
  • structs (list of schrodinger.structure.Structure) – contains input structures
  • properties (list) – contains Property
getBaseName(struct, aproperty)

Get the base name.

Parameters:
Return type:

str

Returns:

the base name

runIt()

Run it.

Raises:RuntimeError – for any issue
class schrodinger.application.matsci.genetic_optimization.genetic_optimization.StructureEvaluator(structs, properties)

Bases: schrodinger.application.matsci.genetic_optimization.genetic_optimization.ClassEvaluator

Manage structure evaluation.

SMARTS_PATTERN_SEPARATOR = '_'
SMARTS_PROP = 'smarts'
MOL_WEIGHT_PROP = 'molecular_weight'
NATOMS_PROP = 'natoms'
NELEMENTS_PROP = 'nelements'
PROPERTIES = {'molecular_weight', 'natoms', 'nelements', 'smarts'}
SMARTS_KEY = 'i_matsci_SMARTS_property_%s'
MOL_WEIGHT_KEY = 'r_m_Molecular_weight'
NATOMS_KEY = 'i_m_Number_of_atoms'
NELEMENTS_KEY = 'i_m_Number_of_elements'
NO_UNITS = 'None'
MOL_WEIGHT_UNITS = 'g/mol'
PATTERNS = 'patterns'
__init__(structs, properties)

See parent class for documentation.

static getInfo(key, units, patterns=None)

Return a PropertyInfo.

Parameters:
  • key (str) – the property key
  • units (str) – the property units
  • patterns (list) – the SMARTS patterns
Return type:

PropertyInfo

Returns:

the property information

runIt()

Run it.

OUT_EXT = '.out'
SEPARATOR = '_'
getBaseName(struct, aproperty)

Get the base name.

Parameters:
Return type:

str

Returns:

the base name

class schrodinger.application.matsci.genetic_optimization.genetic_optimization.CanvasKPLS(structs, properties)

Bases: schrodinger.application.matsci.genetic_optimization.genetic_optimization.ClassEvaluator

Manage Canvas KPLS jobs.

KPLS_EXT = 'kpls.tar.gz'
FP_TEXT_FILE = 'fpInfo.txt'
MODEL_OPTION = 'kpls_model'
KEY = 'r_matsci_KPLS_%s/%s'
DIR = 'genetic_optimization/canvas_kpls_models'
PATH = '/scr/buildbot/savedbuilds/2019-3/NB/build-143/mmshare-v4.7/data/genetic_optimization/canvas_kpls_models'
UNITS = 'unknown'
SEPARATOR = '_kpls_'
FP_EXT = '.fp'
VALUE_PATTERN = re.compile('\\s*1\\s+unknown.*\\s+(-?\\d+\\.?\\d*)$')
ALLOWED_FP_TYPES = ['linear', 'maccs', 'radial', 'molprint2D', 'torsion', 'pairwise', 'triplet', 'quartet', 'dendritic']
SINGLE_PRECISION = 32
DOUBLE_PRECISION = 64
BIT_EXT = '-bit'
__init__(structs, properties)

See parent class for documentation.

static getInfo(name, units, model_file)

Return a PropertyInfo.

Parameters:
  • name (str) – the property name
  • units (str) – the property units
  • model_file (str) – the Canvas KPLS model file
Return type:

PropertyInfo

Returns:

the property information

getModelFile(aproperty)

Return the model file from the given property.

Parameters:aproperty (Property) – the property
Return type:str
Returns:the model file
copyCanvasKPLSModelFiles()

Copy the Canvas KPLS model files to the CWD.

makeMaestroInfile(struct, aproperty)

Make Maestro infile.

Parameters:
Return type:

str

Returns:

the Maestro input file name

makeFingerPrintInfile(mae_infile, name)

Make fingerprint infile.

Parameters:
  • mae_infile (str) – the Maestro input file name
  • name (str) – the property name
Raises:

RuntimeError – if canvasFPGen fails

Return type:

str

Returns:

the Canvas fingerprint input file name

getPropertyValue(property_outfile)

Get the property value.

Parameters:property_outfile (str) – the Canvas KPLS output file
Raises:RuntimeError – if property output file doesn’t exist or doesn’t contain the property value
Return type:float
Returns:the property value
runCanvasKPLS(struct)

Run the Canvas KPLS.

Parameters:struct (schrodinger.structure.Structure) – the structure on which to run Canvas KPLS
Raises:RuntimeError – if canvasKPLS fails
runIt()

Run it.

makeFpOptionsDict()

Make the fingerprint options dictionary.

OUT_EXT = '.out'
getBaseName(struct, aproperty)

Get the base name.

Parameters:
Return type:

str

Returns:

the base name

class schrodinger.application.matsci.genetic_optimization.genetic_optimization.Jaguar(structs, properties)

Bases: schrodinger.application.matsci.genetic_optimization.genetic_optimization.ClassEvaluator

Manage Jaguar jobs.

JAGUAR_OPTIONS = 'jaguar_options'
TPP = 'tpp'
JAGUAR_OUTPUT_ATTR = 'jaguar_output_attr'
IN_EXT = '.in'
__init__(structs, properties)

See parent class for documentation.

getQueue()

Return a loaded JobDJ to run the jobs.

Return type:queue.JobDJ
Returns:the queue
postProcess()

Post process the jobs and set the final results.

Raises:RuntimeError – if there is a problem
runIt()

Run it.

Raises:RuntimeError – if there is a problem
OUT_EXT = '.out'
SEPARATOR = '_'
getBaseName(struct, aproperty)

Get the base name.

Parameters:
Return type:

str

Returns:

the base name

class schrodinger.application.matsci.genetic_optimization.genetic_optimization.GlassTransitionTemperature(structs, properties)

Bases: schrodinger.application.matsci.genetic_optimization.genetic_optimization.CanvasKPLS

Manage glass transition temperature jobs.

UNITS = 'C'
KEY = 'r_matsci_KPLS_Tg/C'
PROP = 'kpls_tg'
FILE = 'Tg250.kpls.tar.gz'
static getInfo()

Return a PropertyInfo.

Return type:PropertyInfo
Returns:the property information
ALLOWED_FP_TYPES = ['linear', 'maccs', 'radial', 'molprint2D', 'torsion', 'pairwise', 'triplet', 'quartet', 'dendritic']
BIT_EXT = '-bit'
DIR = 'genetic_optimization/canvas_kpls_models'
DOUBLE_PRECISION = 64
FP_EXT = '.fp'
FP_TEXT_FILE = 'fpInfo.txt'
KPLS_EXT = 'kpls.tar.gz'
MODEL_OPTION = 'kpls_model'
OUT_EXT = '.out'
PATH = '/scr/buildbot/savedbuilds/2019-3/NB/build-143/mmshare-v4.7/data/genetic_optimization/canvas_kpls_models'
SEPARATOR = '_kpls_'
SINGLE_PRECISION = 32
VALUE_PATTERN = re.compile('\\s*1\\s+unknown.*\\s+(-?\\d+\\.?\\d*)$')
__init__(structs, properties)

See parent class for documentation.

copyCanvasKPLSModelFiles()

Copy the Canvas KPLS model files to the CWD.

getBaseName(struct, aproperty)

Get the base name.

Parameters:
Return type:

str

Returns:

the base name

getModelFile(aproperty)

Return the model file from the given property.

Parameters:aproperty (Property) – the property
Return type:str
Returns:the model file
getPropertyValue(property_outfile)

Get the property value.

Parameters:property_outfile (str) – the Canvas KPLS output file
Raises:RuntimeError – if property output file doesn’t exist or doesn’t contain the property value
Return type:float
Returns:the property value
makeFingerPrintInfile(mae_infile, name)

Make fingerprint infile.

Parameters:
  • mae_infile (str) – the Maestro input file name
  • name (str) – the property name
Raises:

RuntimeError – if canvasFPGen fails

Return type:

str

Returns:

the Canvas fingerprint input file name

makeFpOptionsDict()

Make the fingerprint options dictionary.

makeMaestroInfile(struct, aproperty)

Make Maestro infile.

Parameters:
Return type:

str

Returns:

the Maestro input file name

runCanvasKPLS(struct)

Run the Canvas KPLS.

Parameters:struct (schrodinger.structure.Structure) – the structure on which to run Canvas KPLS
Raises:RuntimeError – if canvasKPLS fails
runIt()

Run it.

class schrodinger.application.matsci.genetic_optimization.genetic_optimization.RefractiveIndex(structs, properties)

Bases: schrodinger.application.matsci.genetic_optimization.genetic_optimization.CanvasKPLS, schrodinger.application.matsci.genetic_optimization.genetic_optimization.Jaguar

Manage refractive index jobs.

UNITS = 'none'
KEY = 'r_matsci_Refractive_Index_298K/none'
PROP = 'refractive_index'
FILE = '01a_kpls5_amorphous_density_HT.kpls.tar.gz'
MASS_DENSITY_KEY = 'r_matsci_Mass_Density/g/cm^3'
ISOTROPIC_POLARIZABILITY_KEY = 'r_matsci_Isotropic_Polarizability/bohr^3'
__init__(structs, properties)

See parent classes for documentation.

static getInfo(jaguar_options=None, tpp=1)

Return a PropertyInfo.

Parameters:
  • jaguar_options (dict) – contain Jaguar options
  • tpp (int) – the threads per process
Return type:

PropertyInfo

Returns:

the property information

setRefractiveIndexProperty(struct, mass_density, polarizability)

Set the refractive index property on the given structure.

Parameters:
  • struct (schrodinger.structure.Structure) – the structure
  • mass_density (float) – the mass density in g/cm^3
  • polarizability (float) – the isotropically averaged polarizability in atomic units of bohr^3
runIt()

Run it.

ALLOWED_FP_TYPES = ['linear', 'maccs', 'radial', 'molprint2D', 'torsion', 'pairwise', 'triplet', 'quartet', 'dendritic']
BIT_EXT = '-bit'
DIR = 'genetic_optimization/canvas_kpls_models'
DOUBLE_PRECISION = 64
FP_EXT = '.fp'
FP_TEXT_FILE = 'fpInfo.txt'
IN_EXT = '.in'
JAGUAR_OPTIONS = 'jaguar_options'
JAGUAR_OUTPUT_ATTR = 'jaguar_output_attr'
KPLS_EXT = 'kpls.tar.gz'
MODEL_OPTION = 'kpls_model'
OUT_EXT = '.out'
PATH = '/scr/buildbot/savedbuilds/2019-3/NB/build-143/mmshare-v4.7/data/genetic_optimization/canvas_kpls_models'
SEPARATOR = '_kpls_'
SINGLE_PRECISION = 32
TPP = 'tpp'
VALUE_PATTERN = re.compile('\\s*1\\s+unknown.*\\s+(-?\\d+\\.?\\d*)$')
copyCanvasKPLSModelFiles()

Copy the Canvas KPLS model files to the CWD.

getBaseName(struct, aproperty)

Get the base name.

Parameters:
Return type:

str

Returns:

the base name

getModelFile(aproperty)

Return the model file from the given property.

Parameters:aproperty (Property) – the property
Return type:str
Returns:the model file
getPropertyValue(property_outfile)

Get the property value.

Parameters:property_outfile (str) – the Canvas KPLS output file
Raises:RuntimeError – if property output file doesn’t exist or doesn’t contain the property value
Return type:float
Returns:the property value
getQueue()

Return a loaded JobDJ to run the jobs.

Return type:queue.JobDJ
Returns:the queue
makeFingerPrintInfile(mae_infile, name)

Make fingerprint infile.

Parameters:
  • mae_infile (str) – the Maestro input file name
  • name (str) – the property name
Raises:

RuntimeError – if canvasFPGen fails

Return type:

str

Returns:

the Canvas fingerprint input file name

makeFpOptionsDict()

Make the fingerprint options dictionary.

makeMaestroInfile(struct, aproperty)

Make Maestro infile.

Parameters:
Return type:

str

Returns:

the Maestro input file name

postProcess()

Post process the jobs and set the final results.

Raises:RuntimeError – if there is a problem
runCanvasKPLS(struct)

Run the Canvas KPLS.

Parameters:struct (schrodinger.structure.Structure) – the structure on which to run Canvas KPLS
Raises:RuntimeError – if canvasKPLS fails
schrodinger.application.matsci.genetic_optimization.genetic_optimization.get_script_property_info_dict()

Return a (name, PropertyInfo) dict for script based properties.

Return type:dict
Returns:contains (name, PropertyInfo)
schrodinger.application.matsci.genetic_optimization.genetic_optimization.get_property_info(name, jaguar_options=None, tpp=None, patterns=None)

Return a PropertyInfo for the given name and properties.

Parameters:
  • name (str) – the property name
  • jaguar_options (dict) – contains Jaguar options
  • tpp (int) – the threads per process
  • patterns (list) – the SMARTS patterns
Return type:

PropertyInfo or None

Returns:

the PropertyInfo

schrodinger.application.matsci.genetic_optimization.genetic_optimization.get_random_csearch_seed()

Return a random csearch seed.

Return type:int
Returns:the seed
exception schrodinger.application.matsci.genetic_optimization.genetic_optimization.PropertySyntaxError

Bases: Exception

__init__

Initialize self. See help(type(self)) for accurate signature.

args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

exception schrodinger.application.matsci.genetic_optimization.genetic_optimization.UnknownPropertySuboptionError

Bases: Exception

__init__

Initialize self. See help(type(self)) for accurate signature.

args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

class schrodinger.application.matsci.genetic_optimization.genetic_optimization.Property(index=1, key=None, name=None, units=None, minimax=None, target=None, comparator=None, error=None, weight=1.0, positive=None, summarize=None, class_kwargs=None)

Bases: object

Manage a property to be used in a genetic optimization.

MAX = 'max'
MIN = 'min'
EQUALS = 'eq'
GREATER_THAN = 'gt'
LESS_THAN = 'lt'
SUB_OPTIONS = ['index', 'key', 'name', 'units', 'minimax', 'target', 'comparator', 'error', 'weight', 'positive', 'patterns', 'summarize', 'kpls_model', 'class_kwargs']
__init__(index=1, key=None, name=None, units=None, minimax=None, target=None, comparator=None, error=None, weight=1.0, positive=None, summarize=None, class_kwargs=None)

Create an instance.

Parameters:
  • index (int) – a numeric index used to refer to this Property instance, a default of 1 is used
  • key (str) – the schrodinger.structure.Structure property key to be optimized
  • name (str) – specify a name for the property, this name will be, for example used in any *log files, etc.
  • units (str) – enter the units that the property is in, for example eV, nm, etc.
  • minimax (str) – to minimize or maximize this property then set this option to the class constants MIN or MAX
  • target (float) – if instead of maximizing or minimizing the property, the genetic optimization is supposed to handle a specific value then enter that value using this option.
  • comparator (str) – specify here how the target value and computed values are to be compared, i.e. either the class constants EQUALS for =, GREATER_THAN for >, or LESS_THAN for <.
  • error (float) – if equality to a target value has been specified then this option allows the user to control the error bounds of the target value, if not specified then a default of 10% of the specified target value will be used.
  • weight (float) – specify the weight to use for this property, if the genetic optimization is to be run on several properties then the weight allows the user to bias the solution. This option can also be used to control a situation where more than a single property is desired and where those properties are quantified using different physical units such that the numbers might be orders of magnitude apart from one another, for example comparing eV and nm. A default of 1.0 is used.
  • positive (bool) – True if this property can only take on positive values, for example as in the area of a surface, False otherwise, for example as in temperature in Celcius. The default is False.
  • summarize (bool) – if True then print a summary of this property, False otherwise
  • class_kwargs (OrderedDict or None) – contains kwargs for class based evaluation of this property
setAttributes(index=1, key=None, name=None, units=None, minimax=None, target=None, comparator=None, error=None, weight=1.0, positive=None, summarize=None, class_kwargs=None)

Set some attributes for this class.

Parameters:
  • index (int) – a numeric index used to refer to this Property instance, a default of 1 is used
  • key (str) – the schrodinger.structure.Structure property key to be optimized
  • name (str) – specify a name for the property, this name will be, for example used in any *log files, etc.
  • units (str) – enter the units that the property is in, for example eV, nm, etc.
  • minimax (str) – to minimize or maximize this property then set this option to the class constants MIN or MAX
  • target (float) – if instead of maximizing or minimizing the property, the genetic optimization is supposed to handle a specific value then enter that value using this option.
  • comparator (str) – specify here how the target value and computed values are to be compared, i.e. either the class constants EQUALS for =, GREATER_THAN for >, or LESS_THAN for <.
  • error (float) – if equality to a target value has been specified then this option allows the user to control the error bounds of the target value, if not specified then a default of 10% of the specified target value will be used.
  • weight (float) – specify the weight to use for this property, if the genetic optimization is to be run on several properties then the weight allows the user to bias the solution. This option can also be used to control a situation where more than a single property is desired and where those properties are quantified using different physical units such that the numbers might be orders of magnitude apart from one another, for example comparing eV and nm. A default of 1.0 is used.
  • positive (bool) – True if this property can only take on positive values, for example as in the area of a surface, False otherwise, for example as in temperature in Celcius. The default is False.
  • summarize (bool) – if True then print a summary of this property, False otherwise
  • class_kwargs (OrderedDict or None) – contains kwargs for class based evaluation of this property
setClassKwargs(class_kwargs)

Set the class kwargs.

Parameters:class_kwargs (OrderedDict or None) – contains kwargs for class based evaluation of this property
parsePropertyString(property_string)

Parse the attributes of this class from a string representation of the property specifications. For example, ‘index=1 key=r_matsci_Reduction_Potential_(eV) name=reduction units=eV target=1.28 comparator=eq error=0.05 weight=0.5’ or ‘index=2 key=r_matsci_Oxidation_Potential_(eV) name=oxidation units=eV minimax=max weight=2.5’

Parameters:

property_string (str) – the string representation of the property specifications

Raises:
checkProperty()

Check this property instance.

isScriptProperty()

Return True if this property is a script property, False otherwise.

Return type:bool
Returns:return True if this property is a script property, False otherwise
static getPropertyStrings(property_lists)

Return property strings from the given property lists.

Parameters:property_lists (list) – contains lists of property specifications
Return type:list
Returns:contains string representations of the property specifications
static getKwargs(property_string, option_substrings, add_relative_paths=None)

Return kwargs of the given property options from the given property string.

Parameters:
  • property_string (str) – the string representation of the property specifications, containing options as ‘<option_substring>=<value>’
  • option_substrings (list or str) – contains the option substrings for the needed values, a single occurence or list of occurences may be passed
  • add_relative_paths (list) – contains options for which relative paths should be added, such relative paths might be needed for correctly parallelizing the evaluation stage of the genetic optimization as they will be needed to copy otherwise shared files into local subdirectories
Return type:

dict, str, or None

Returns:

the extracted dictionary of kwargs or single kwarg depending on the input option_substrings or None if nothing is found

static rmKwargs(property_string, option_substrings)

Return a copy of the given property string with all of the given property option substrings removed.

Parameters:
  • property_string (str) – the string representation of the property specifications, containing options as ‘<option_substring>=<value>’
  • option_substrings (list) – contains the option substrings to be removed
Return type:

str

Returns:

the string representation of the property specifications less the options substrings that were to be removed

static addKwargs(property_string, kwargs)

Add the given options to the given property string.

Parameters:
  • property_string (str) – the string representation of the property specifications, containing options as ‘<option_substring>=<value>’
  • kwargs (dict) – key-value option pairs to add to the property string
Return type:

str

Returns:

the string representation of the property specifications containin the new options

schrodinger.application.matsci.genetic_optimization.genetic_optimization.set_title_to_stoichiometry(astructure, toappend=None, separation='.')

Set the structure title to be the stoichiometry of the structure.

Parameters:
  • astructure (schrodinger.structure.Structure) – the structure
  • toappend (str) – a string to append to the stoichiometry
  • separation (str) – used to separate the stoichiometry and the toappend str
class schrodinger.application.matsci.genetic_optimization.genetic_optimization.StructureGenome

Bases: pyevolve.GenomeBase.GenomeBase

Manage a genome. The genome, aka chromosome, is the solution to the problem trying to be solved via genetic optimization. It is referred to as being composed of genes that are manipulated by the crossover and mutation operators. In our genetic optimization module this genome is basically just a schrodinger.structure.Structure object.

__init__()

Create an instance.

copy(genome)

Copy the current genome to the provided genome.

Parameters:genome (StructureGenome) – a new genome instance to which to copy the current genome
clone()

Clone the current genome.

Return type:StructureGenome
Returns:genome
updateStructureProperties(index, generation)

Update some structure properties.

Parameters:
  • index (int) – the index of this individual
  • generation (int) – this generation
resetParentProperties()

Reset the crossover and mutation parent structure properties.

removeProperties()

Remove some structure properties.

optimizeGeometry()

Optimize the geometry of this genome’s structure using OPLS.

addPreviousFreezerFile(freezer_file)

Add the given file to the list of previous freezer files.

Parameters:freezer_file (str) – the name of the file to be added
evaluate(**args)

Evaluate the score of this individual.

Parameters:args (dict) – dictionary of genetic optimization parameters created and used by pyevolve
crossover
evaluator
fitness
getFitnessScore()

Get the Fitness Score of the genome

Return type:genome fitness score
getParam(key, nvl=None)

Gets an internal parameter

Example:
>>> genome.getParam("rangemax")
100

Note

All the individuals of the population shares this parameters and uses the same instance of this dict.

Parameters:
  • key – the key of param
  • nvl – if the key doesn’t exist, the nvl will be returned
getRawScore()

Get the Raw Score of the genome

Return type:genome raw score
initializator
initialize(**args)

Called to initialize genome

Parameters:args – this parameters will be passed to the initializator
internalParams
mutate(**args)

Called to mutate the genome

Parameters:args – this parameters will be passed to the mutator
Return type:the number of mutations returned by mutation operator
mutator
resetStats()

Clear score and fitness of genome

score
setParams(**args)

Set the internal params

Example:
>>> genome.setParams(rangemin=0, rangemax=100, gauss_mu=0, gauss_sigma=1)

Note

All the individuals of the population shares this parameters and uses the same instance of this dict.

Parameters:args – this params will saved in every chromosome for genetic op. use
schrodinger.application.matsci.genetic_optimization.genetic_optimization.from_initial_population(genome, **args)

Draw a unique genome from the initial population.

Parameters:
  • genome (StructureGenome) – a genome
  • args (dict) – dictionary of genetic optimization parameters created and used by pyevolve
schrodinger.application.matsci.genetic_optimization.genetic_optimization.get_num_simple_bonds(astructure)

Return the number of simple bonds in the provided structure. The definition of a simple bond follows from that used in the reaction channel module and is an acyclic single order bond that may involve a hydrogen atom.

Parameters:astructure (schrodinger.structure.Structure) – the structure for which to get the number of simple bonds
Return type:int
Returns:the number of simple bonds
schrodinger.application.matsci.genetic_optimization.genetic_optimization.combine_two_structures(astructure, bstructure, offset=10.0)

Combine two structure objects into a single structure object using somewhat arbitrary placement.

Parameters:
  • astructure (schrodinger.structure.Structure) – the first of the structures to be combined
  • bstructure (schrodinger.structure.Structure) – the second of the structures to be combined
  • offset (float) – the final distance between the structures will be the sum of the molecular VDW radii plus this offset in Angstrom
Return type:

schrodinger.structure.Structure

Returns:

the combined structure object

schrodinger.application.matsci.genetic_optimization.genetic_optimization.bond_crossover(genome, **args)

Perform a crossover operation by swapping molecular fragments at two randomly choosen bonds, i.e. a double displacement reaction channel.

Parameters:
  • genome (StructureGenome) – a genome
  • args (dict) – dictionary of genetic optimization parameters created and used by pyevolve
Return type:

tuple

Returns:

tuple containing the sister and brother StructureGenome

schrodinger.application.matsci.genetic_optimization.genetic_optimization.get_element_mutator_dict(astructure)

Return a dictionary where the keys contain the indicies of the mutatable atoms and the values contain those elements that the keyed atom may be mutated to.

Parameters:astructure (schrodinger.structure.Structure) – the structure to be mutated
Return type:dict
Returns:keys are atom indicies of those atoms that are mutatable and values are those elements that the atom can be mutated to
schrodinger.application.matsci.genetic_optimization.genetic_optimization.get_isoelectronic_mutator_indicies(astructure)

Return a list of atom indicies that can be mutated by the isoelectronic mutator.

Parameters:astructure (schrodinger.structure.Structure) – the structure to be mutated
Return type:list
Returns:mutatable indicies
schrodinger.application.matsci.genetic_optimization.genetic_optimization.get_child_like_parent(parent_st, children_sts, definition)

Return the child structure that is most like the provided parent.

Parameters:
  • parent_st (schrodinger.structure.Structure) – the parent structure
  • children_sts (list of schrodinger.structure.Structure) – the children structures
  • definition (two-element list) – each sublist contains two atom indicies describing the reactive bonds in parent and fragment structures which created the children
Return type:

schrodinger.structure.Structure

Returns:

the sought child structure

schrodinger.application.matsci.genetic_optimization.genetic_optimization.elemental_mutator(genome, **args)

Perform a random elemental mutation to an element in the same column (as known as group) of the periodic table. Note that hydrogen and the halogens are considered to belong to the same column.

Parameters:
  • genome (StructureGenome) – a genome
  • args (dict) – dictionary of genetic optimization parameters created and used by pyevolve
Return type:

int

Returns:

the number of mutations applied, appears to never be used in PyEvolve

schrodinger.application.matsci.genetic_optimization.genetic_optimization.fragment_mutator(genome, **args)

Randomly mutate the genome by swapping a molecular fragement on one side of a bond by a similar fragment from a library.

Parameters:
  • genome (StructureGenome) – a genome
  • args (dict) – dictionary of genetic optimization parameters created and used by pyevolve
Return type:

int

Returns:

the number of mutations applied, appears to never be used in PyEvolve

schrodinger.application.matsci.genetic_optimization.genetic_optimization.isoelectronic_mutator(genome, **args)

Perform a random isoelectronic mutation from the following sets of series CH3X, NH2X, OHX, and FX, CH2XY, NHXY, OXY, and CHXYZ and NXYZ, where X, Y, and Z are non-H-bonds.

Parameters:
  • genome (StructureGenome) – a genome
  • args (dict) – dictionary of genetic optimization parameters created and used by pyevolve
Return type:

int

Returns:

the number of mutations applied, appears to never be used in PyEvolve

schrodinger.application.matsci.genetic_optimization.genetic_optimization.get_loggable_float(afloat, num_decimal='%.2f', field_width=10)

Return a float as a string with the specified format.

Parameters:
  • afloat (float) – a float to convert to a string
  • num_decimal (str) – the format of the string representation
  • field_width (int) – the field width of the final string
Return type:

str

Returns:

the float as a string

schrodinger.application.matsci.genetic_optimization.genetic_optimization.uniquify_titles_callback(ga_obj)

Callback to uniquify titles of the individuals.

Parameters:ga_obj (GSimpleGA.GSimpleGA) – the entire current state of the genetic optimization
schrodinger.application.matsci.genetic_optimization.genetic_optimization.prepare_next_generation_dirs_callback(ga_obj)

Callback to update the generation property of the genomes and to create a subdirectory to hold the next series of evaluations.

Parameters:ga_obj (GSimpleGA.GSimpleGA) – the entire current state of the genetic optimization
schrodinger.application.matsci.genetic_optimization.genetic_optimization.manage_skips_callback(ga_obj)

Callback to manage skips in the evaluation.

Parameters:ga_obj (GSimpleGA.GSimpleGA) – the entire current state of the genetic optimization
schrodinger.application.matsci.genetic_optimization.genetic_optimization.manage_failures_callback(ga_obj)

Callback to manage failures in the evaluation.

Parameters:ga_obj (GSimpleGA.GSimpleGA) – the entire current state of the genetic optimization
schrodinger.application.matsci.genetic_optimization.genetic_optimization.logging_summary_callback(ga_obj)

Callback to log progress.

Parameters:ga_obj (GSimpleGA.GSimpleGA) – the entire current state of the genetic optimization
schrodinger.application.matsci.genetic_optimization.genetic_optimization.molecule_history_callback(ga_obj)

Callback to append all structures from all generations to individual log files.

Parameters:ga_obj (GSimpleGA.GSimpleGA) – the entire current state of the genetic optimization
schrodinger.application.matsci.genetic_optimization.genetic_optimization.first_property(ga_obj)

Terminate when the first property has been matched.

Parameters:ga_obj (GSimpleGA.GSimpleGA) – the entire current state of the genetic optimization
Return type:bool
Returns:True to terminate, False otherwise
schrodinger.application.matsci.genetic_optimization.genetic_optimization.all_properties(ga_obj)

Terminate when all properties have been matched.

Parameters:ga_obj (GSimpleGA.GSimpleGA) – the entire current state of the genetic optimization
Return type:bool
Returns:True to terminate, False otherwise
schrodinger.application.matsci.genetic_optimization.genetic_optimization.unproductive(ga_obj)

Terminate if the maximum number of unproductive generations has been reached.

Parameters:ga_obj (GSimpleGA.GSimpleGA) – the entire current state of the genetic optimization
Return type:bool
Returns:True to terminate, False otherwise
class schrodinger.application.matsci.genetic_optimization.genetic_optimization.Skip(genome, msg)

Bases: object

Manage a skip.

__init__(genome, msg)

Create an instance.

Parameters:
updateTitle(title)

Update the title of this job.

Parameters:title (str) – a title to use for reference
class schrodinger.application.matsci.genetic_optimization.genetic_optimization.Failure(genome, msg)

Bases: schrodinger.application.matsci.genetic_optimization.genetic_optimization.Skip

Manage a failure.

__init__(genome, msg)

Create an instance.

Parameters:
updateTitle(title)

Update the title of this job.

Parameters:title (str) – a title to use for reference
class schrodinger.application.matsci.genetic_optimization.genetic_optimization.CheckInput

Bases: object

Manage checking user input.

checkMaeFile(input_file, logger=None)

Check that a file exists and is *mae.

Parameters:
  • input_file (str) – the name of the input file
  • logger (logging.Logger) – output logger
checkOperators(operators, logger=None)

Check the operators.

Parameters:
  • operators (list) – contains tuples of the operator functions and their weights
  • logger (logging.Logger) – output logger
checkRates(crossover_rate, mutation_rate, logger=None)

Check the specified rates of crossover and mutation.

Parameters:
  • crossover_rate (float) – the rate of crossover as a percentage
  • mutation_rate (float) – the rate of mutation as a percentage
  • logger (logging.Logger) – output logger
checkInitialPopulation(initial_population, crossover_names, mutator_names, crossover_rate, mutation_rate, no_open_shell, logger=None)

Check the initial population.

Parameters:
  • initial_population (list) – the initial population of schrodinger.structure.Structure
  • crossover_names (list) – contains the function names of the crossover operators to be used
  • mutator_names (list) – contains the function names of the mutation operators to be used
  • crossover_rate (float) – the rate of crossover
  • mutation_rate (float) – the rate of mutation
  • no_open_shell (bool) – if True then check for open shell structures otherwise do not
  • logger (logging.Logger) – output logger
checkPopulationParam(population, num_structures_given, logger=None)

Check the population parameter.

Parameters:
  • population (int) – the size of the population to use in the genetic optimization
  • num_structures_given (int) – the number of structures provided to the genetic optimization
  • logger (logging.Logger) – output logger
checkFragmentLibs(fragment_libs, logger=None)

Check the specified fragment libraries.

Parameters:
  • fragment_libs (list) – strings specifying fragment libraries to be used
  • logger (logging.Logger) – output logger
Return type:

list

Returns:

valid user provided fragment files

checkProperties(properties, logger=None)

Check the list of properties.

Parameters:
  • properties (list) – contains Property instances
  • logger (logging.Logger) – output logger
checkGenerations(generations, logger=None)

Check the specified number of generations.

Parameters:
  • generations (int) – the number of generations
  • logger (logging.Logger) – output logger
checkSelection(selection, logger=None)

Check the specified selection protocol.

Parameters:
  • selection (str) – the selection protocol to use.
  • logger (logging.Logger) – output logger
checkTournamentSize(tournament_size, population, logger=None)

Check the specified tournament size.

Parameters:
  • tournament_size (int) – the size of tournament to use in tournament based selection
  • population (int) – the size of population to use
  • logger (logging.Logger) – output logger
checkTerminationParams(terminators, num_unproductive, logger=None)

Check the termination parameters.

Parameters:
  • terminators (list) – the list of terminators to use
  • num_unproductive (int) – used when the unproductive termination option is active, it is the generation number on which to exit if the score hasn’t improved
  • logger (logging.Logger) – output logger
Return type:

list and int

Returns:

valid terminators and valid num_unproductive

checkScaling(scaling, properties, logger=None)

Check the scaling.

Parameters:
  • scaling (str) – the scaling protocol to use in the genetic optimization
  • properties (list) – the properties to be optimized
  • logger (logging.Logger) – output logger
checkElitism(elitism, population, logger=None)

Check the elitism.

Parameters:
  • elitism (int) – the number of elite individuals to use
  • population (int) – the size of population to use
  • logger (logging.Logger) – output logger
checkTpp(tpp_ga, population, eval_kwargs, logger=None)

Check the threads per processor.

Parameters:
  • tpp_ga (int) – the threads per processor for the genetic optimization
  • population (int) – the size of population to use
  • eval_kwargs (dict) – the kwargs for the evaluation function
  • logger (logging.Logger) – output logger
checkNodeFile(logger=None)

Check the hosts in the node file, specified by the SCHRODINGER_NODEFILE envvar, that have been allocated by the queue manager.

Parameters:logger (logging.Logger) – output logger
checkConformationalSearch(conformational_search, logger=None)

Check the conformational search.

Parameters:
  • conformational_search (bool or str) – specifies whether a conformational search is to be performed, if a string is given specifies a file used to set options
  • logger (logging.Logger) – output logger
checkFreezers(freezers, pop_size, input_size, logger=None)

Check the freezers.

Parameters:
  • freezers (list) – collection of freezers to use
  • pop_size (int) – the size of the population
  • input_size (int) – the number of structures given
  • logger (logging.Logger) – output logger
Return type:

list

Returns:

collection of freezers to use

checkInoculate(inoculate, logger=None)

Check the inoculate.

Parameters:
  • inoculate (list) – circumstances in which to inoculate
  • logger (logging.Logger) – output logger
__init__

Initialize self. See help(type(self)) for accurate signature.

schrodinger.application.matsci.genetic_optimization.genetic_optimization.print_bad_jobs(all_bad_jobs, logger, bad_type='skip')

Log bad jobs, i.e. skips and failures.

Parameters:
  • all_bad_jobs (dict) – a collection of bad subjobs, keys are genetic optimization generation and values are a list of Skip or Failure objects for bad subjobs
  • logger (logging.Logger) – output logger
  • bad_type (str) – specifies either ‘skip’ or ‘fail’ type
class schrodinger.application.matsci.genetic_optimization.genetic_optimization.GeneticOptimization(initial_population, properties, structure_score_threshold=-50.0, eval_kwargs={}, crossovers=None, mutators=None, fragment_libs=['optoelectronics'], script_evaluator=None, generations=10, population=8, crossover_rate=90.0, mutation_rate=90.0, selection='roulette_wheel', tournament_size=2, terminators=['unproductive', 'all_properties'], num_unproductive=6, scaling='sigma_truncation', elitism=1, random_seed=None, no_minimize=False, file_base_name='genopt', tpp_ga=1, no_open_shell=False, props_to_remove=None, jobbe=None, conformational_search=False, freezers=['remainder', 'previous'], inoculate=['no_child', 'bad_structure'], class_evaluators=None, logger=None)

Bases: object

Manage the genetic optimization.

MSGWIDTH = 80
__init__(initial_population, properties, structure_score_threshold=-50.0, eval_kwargs={}, crossovers=None, mutators=None, fragment_libs=['optoelectronics'], script_evaluator=None, generations=10, population=8, crossover_rate=90.0, mutation_rate=90.0, selection='roulette_wheel', tournament_size=2, terminators=['unproductive', 'all_properties'], num_unproductive=6, scaling='sigma_truncation', elitism=1, random_seed=None, no_minimize=False, file_base_name='genopt', tpp_ga=1, no_open_shell=False, props_to_remove=None, jobbe=None, conformational_search=False, freezers=['remainder', 'previous'], inoculate=['no_child', 'bad_structure'], class_evaluators=None, logger=None)

Create an instance.

Parameters:
  • initial_population (list) – the initial population of schrodinger.structure.Structure
  • properties (list of Property) – the properties to be optimized, including structural properties as well as more physical calculable observables
  • structure_score_threshold (float) – if structure-based properties are being sought and if the base evaluator will be used then subjobs on structures with structure scores below this value will not be launched but rather such structures treated as skips
  • eval_kwargs (dict) – a dictionary that will be available in all evaluator functions
  • crossovers (list) – contains two-element tuples each of which holds a crossover operator to be used in the optimization along with a weight
  • mutators (list) – contains two-element tuples each of which holds a mutation operator to be used in the optimization along with a weight
  • fragment_libs (list) – strings specifying fragment libraries to be used, can be either module constants from FRAGMENT_LIBS.keys() (or ALL if all of those are desired) or the names of Maestro files (including the file extensions) containing fragments collected by the user
  • script_evaluator (method) – the evaluator function to be called to score individuals during the optimization, takes a StructureGenome and returns a JobDJ
  • generations (int) – the number of generations for which to run the optimization
  • population (int) – the population size to use in the optimization, can be less-than-or-equal-to the length of initial_population
  • crossover_rate (float) – the rate of crossover as a percentage
  • mutation_rate (float) – the rate of mutation as a percentage
  • selection (str) – the selection protocol used to select individuals to the gene pool for the upcoming generation
  • tournament_size (int) – the size of tournament to use if using tournament based selection, unused if a tournament based selection is not being used
  • terminators (list) – list of strings that specify the termination protocols to be used to terminate the optimization, typically more than one is specified only if the unproductive protocol is being used
  • num_unproductive (int) – if the unproductive protocol is being used to terminate the optimization then this integer specifies how many unproductive cycles are allowed before terminating, unused if a different termination protocol is used
  • scaling (str) – specifies the scaling protocol to use, scaling scales the raw scores of the individuals to produce fitness scores to ease selection in cases where raw scores are nearly equal
  • elitism (int) – specify the number of elite individuals guaranteed to be added to the gene pool for the upcoming generation, zero disables elitism
  • random_seed (None or int) – the random seed, if None then system time will be used
  • no_minimize (bool) – specify that the offspring structures generated by the crossover and mutation operators not be geometry optimized prior to selection
  • file_base_name (str) – base name to use for output and generation log files
  • tpp_ga (int) – a threads-per-processor value, used to parallelize the scoring of the individuals during the optimization, this option simply forks out tpp_ga simultaneous processes that reside on the same node, if your evaluation function supports parallelization and you wish to utilize a second level of parallelization of each individual evaluation then pass those arguments in eval_kwargs
  • no_open_shell (bool) – if True then do not allow the processing of open shell molecules, False otherwise
  • props_to_remove (list) – a list of structure property keys to be removed prior to the evaluation stage
  • jobbe (schrodinger.job.jobcontrol._Backend) – the jobcontrol backend of the driver job
  • conformational_search (bool or str) – specifies whether a Macromodel conformational search will be performed prior to evaluation, when a string it specifies a simplified Macromodel input file containing extra options
  • freezers (list) – a collection of freezers containing structures that are used to swap out individuals from the population
  • inoculate (list) – the list of circumstances under which to use the structure freezers
  • class_evaluators (dict) – keys are the evaluator classes to be called to score individuals during the optimization, each must inherit ClassEvaluator, values are lists of Property to be passed to the class evaluator
  • logger (logging.Logger) – output logger
setRootLoggerForPyEvolve()

Set up the root logger for PyEvolve.

setOperatorNames()

Set the operator names.

checkInputParams()

Check the input parameters.

printProperties()

Log the set of sought properties and their details.

printParams()

Log the parameters.

initializeGenome()

Initialize a genome.

Return type:StructureGenome
Returns:a genome
initializeGA(genome)

Initialize the genetic optimization.

Parameters:genome (StructureGenome) – a genome
setMonomerGrowAtoms()

Set the monomer grow atoms using the mark monomer module convention rather than the polymer builder module convention.

runIt()

Run the components of the genetic optimization.

schrodinger.application.matsci.genetic_optimization.genetic_optimization.get_output_file_name(basename)

Get the output file name from the basename.

Parameters:basename (str) – base name to use
Return type:str
Returns:output_file_name, name of output file
schrodinger.application.matsci.genetic_optimization.genetic_optimization.get_generation_log_file_name(basename, generation)

Get the generation log file name.

Parameters:
  • basename (str) – base name to use
  • generation (int) – the generation
Return type:

str

Returns:

generation_log_file_name, name of generation log file

schrodinger.application.matsci.genetic_optimization.genetic_optimization.thread_safe_evaluator(evaluator)

Decorator to make evaluator functions thread-safe, i.e. to make using the my_random module safe, meaning usability and reproducibility, with the multiprocessing module.

Parameters:evaluator (function) – the function to decorate
Return type:function
Returns:the decorated function
schrodinger.application.matsci.genetic_optimization.genetic_optimization.get_structure_score(astructure, properties, conformational_search, seed=None)

Return the structure score for the provided structure.

Parameters:
  • astructure (schrodinger.structure.Structure) – the structure to score
  • properties (list of Property) – the properties used in scoring
  • conformational_search (bool or str) – specifies whether a Macromodel conformational search will be performed prior to evaluation, when a string it specifies a simplified Macromodel input file containing extra options
  • seed (int or None) – random seed used in conformational search or None if conformational search is not being done
Return type:

float

Returns:

the structure score

schrodinger.application.matsci.genetic_optimization.genetic_optimization.structure_evaluator(genome)

This is the structure evaulator.

Parameters:genome (StructureGenome) – a genome
Return type:float
Returns:the score for this individual
schrodinger.application.matsci.genetic_optimization.genetic_optimization.base_evaluator(genome)

This is the base evaulator used to wrap all other evaluators.

Parameters:genome (StructureGenome) – a genome
Return type:float
Returns:the score for this individual
schrodinger.application.matsci.genetic_optimization.genetic_optimization.optoelectronics_evaluator(genome)

Run an optoelectronics job.

Parameters:genome (StructureGenome) – a genome
Return type:queue.JobDJ
Returns:the JobDJ object for this individual, it is run in the base evaluator
schrodinger.application.matsci.genetic_optimization.genetic_optimization.apply_uniform_operator_weights(operators)

Set the operator weights uniformly.

Parameters:operators (list) – a list of two-element tuples, each tuple contains first an operator function and second a weight
Return type:list
Returns:list of two-element tuples of operators and uniform weights
schrodinger.application.matsci.genetic_optimization.genetic_optimization.structure_is_open_shell(astructure, ignore_charge=True)

Return True if the provided structure is open shell, i.e. has an odd number of electrons.

Parameters:
  • astructure (schrodinger.structure.Structure) – the structure in question
  • ignore_charge (bool) – if True then ignore any structure.formal_charge settings
Return type:

bool

Returns:

True if the provided structure is open shell, False otherwise

schrodinger.application.matsci.genetic_optimization.genetic_optimization.get_element_histogram(astructure)

Return a dictionary where keys are elements and values are the numbers of atoms of a given element.

Parameters:astructure (schrodinger.structure.Structure) – the structure in question
Return type:dict
Returns:dictionary with element histogram, keys are elements (strs) and values are numbers (ints)
schrodinger.application.matsci.genetic_optimization.genetic_optimization.remove_basename_ext(stoich_ext)

Remove the basename extension from the given string and return the remainder which is the stoichiometry. Do this instead of having to recompute the stoichiometry which can be expensive.

Parameters:stoich_ext (str) – contains the stoichiometry and basename extension
Return type:str
Returns:stoichiometry
schrodinger.application.matsci.genetic_optimization.genetic_optimization.get_low_energy_conformers(astructure_in, macromodel_options_file=None, remove_files=False, overwrite=False, seed=None)

Return the lowest energy conformers from a Macromodel conformational search.

Parameters:
  • astructure_in (schrodinger.structure.Structure) – the structure to search for conformations
  • macromodel_options_file (str or None) – the name of a simplified Macromodel input file that contains any options to use in addition to those used by default in a conformational search or None if there are none and you just want to use the defaults
  • remove_files (bool) – if the job is successful, specifies whether to remove all files created for it after it finishes
  • overwrite (bool) – if True then the coordinates of the input structure will be overwritten by those of the lowest energy conformer and that structure alone returned by this function
  • seed (int or None) – used to seed the random number generator used in the Macromodel conformational search, should be in CONF_SEARCH_SEED_RANGE, if None then if a CONFSEARCH_SEED has been specified in macromodel_options_file it will be used, otherwise a random int in CONF_SEARCH_SEED_RANGE will be used
Return type:

list of schrodinger.structure.Structure, int

Returns:

the structures of the lowest energy conformers sorted by increasing energy and the seed used in the conformational search (same as input if input was given either as seed or in macromodel_options_file)

schrodinger.application.matsci.genetic_optimization.genetic_optimization.get_random_structure(structure_libs, tries_from_libs=3, structure_score_threshold=None, properties=None, conformational_search=False, seed=None)

From the given dictionary of libraries return a random structure.

Parameters:
  • structure_libs (dict) – keys are strings specifying the types of libraries to be used and can be module constants from FREEZER_CHOICES.keys(), values are lists of libraries by type and can be either module constants from FRAGMENT_LIBS.keys(), ALL, or the names of Maestro files (including the file extensions)
  • tries_from_libs (int) – the number of times to try before giving up
  • structure_score_threshold (float or None) – specifies that a structure with a structure score greater-than-or-equal-to this threshold is sought, the best of the considered structures will be returned and will contain several structure properties related to the scoring
  • properties (list of Property or None) – the properties used in structure scoring
  • conformational_search (bool or str) – specifies whether a Macromodel conformational search will be performed prior to evaluation, when a string it specifies a simplified Macromodel input file containing extra options
  • seed (int or None) – if not None specifies that random should be reseeded with the given value
Return type:

schrodinger.structure.Structure or None

Returns:

the random structure or None if one couldn’t be found

schrodinger.application.matsci.genetic_optimization.genetic_optimization.get_freezer_structure(structure_libs, tries_from_libs=3, structure_score_threshold=None, properties=None, conformational_search=False, inoculate='no_child', crossover_applied=None, mutation_applied=None, basename_ext=None, seed=None)

Return a random structure from the freezer and update that structure’s properties.

Parameters:
  • structure_libs (dict) – keys are strings specifying the types of libraries to be used and can be module constants from FREEZER_CHOICES.keys(), values are lists of libraries by type and can be either module constants from FRAGMENT_LIBS.keys(), ALL, or the names of Maestro files (including the file extensions)
  • tries_from_libs (int) – the number of times to try before giving up
  • structure_score_threshold (float or None) – specifies that a structure with a structure score greater-than-or-equal-to this threshold is sought, the best of the considered structures will be returned and will contain several structure properties related to the scoring
  • properties (list of Property or None) – the properties used in structure scoring
  • conformational_search (bool or str) – specifies whether a Macromodel conformational search will be performed prior to evaluation, when a string it specifies a simplified Macromodel input file containing extra options
  • inoculate (str) – specify the reason for drawing from the freezer, which is an inoculate option from INOCULATE_CHOICES
  • crossover_applied (str or None) – specify the intended crossover operator or None if there isn’t to be one
  • mutation_applied (str or None) – specify the intended mutation operator or None if there isn’t to be one
  • basename_ext (str or None) – specify an extension to append to the stoichiometry which is used to set the title of the returned structure
  • seed (int or None) – if not None specifies that random should be reseeded with the given value
Return type:

schrodinger.structure.Structure or None

Returns:

the random structure or None if one couldn’t be found

schrodinger.application.matsci.genetic_optimization.genetic_optimization.hack_for_multiprocessing()
class schrodinger.application.matsci.genetic_optimization.genetic_optimization.CanvasKPLSModelFile

Bases: object

Manage a Canvas KPLS model file.

__init__

Initialize self. See help(type(self)) for accurate signature.

static getFpOptions(model_file)

Return fingerprint options obtained from the given Canvas KPLS model file.

Parameters:model_file (str) – the name of the Canvas KPLS model file
Return type:int, str, int or None
Returns:contains (1) precision, (2) fingerprint type, and (3) atom type if present
Raises:RuntimeError – if there is anything wrong with the Canvas KPLS model file
static checkCanvasKPLSModelFile(model_file)

Check the given Canvas KPLS model file.

Parameters:model_file (str) – the name of the Canvas KPLS model file
Raises:RuntimeError – if there is anything wrong with the Canvas KPLS model file
static getValidCanvasKPLSModelFiles(property_lists)

Return file names of any valid Canvas KPLS model files.

Parameters:property_lists (list) – contains lists of property specifications
Return type:list
Returns:file names of valid Canvas KPLS model files