simple_metrics

class pyrosetta.bindings.scores.simple_metrics.SimpleMetricCompositeRealDataAccessor(pose)

Bases: SimpleMetricDataAccessorBase

Accessor wrapper for pose SimpleMetric composite real data.

_CustomTypeMetric

alias of CustomTypeMetric

_abc_impl = <_abc._abc_data object>
_clobber_warning(msg)

Issue a ClobberWarning warning with a message.

_custom_type_metrics = {'bool': CustomTypeMetric(type=<class 'bool'>, prefix='[CustomBooleanValueMetric]', encode_func=<class 'str'>, decode_func=<function PoseScoreSerializerBase.bool_from_str>), 'bytes': CustomTypeMetric(type=<class 'bytes'>, prefix='[CustomBinaryValueMetric]', encode_func=<function PoseScoreSerializerBase.to_base64>, decode_func=<function PoseScoreSerializerBase.from_base64>), 'int': CustomTypeMetric(type=<class 'int'>, prefix='[CustomDiscreteValueMetric]', encode_func=<class 'str'>, decode_func=<class 'int'>), 'object': CustomTypeMetric(type=<class 'object'>, prefix='[CustomArbitraryValueMetric]', encode_func=<function PoseScoreSerializerBase.to_base64_pickle>, decode_func=<function PoseScoreSerializerBase.from_base64_pickle>)}
_format_metric(raw_data, as_dict=False)
_get_sm_data_dict(attributes)
_has_reserved_custom_metric_keys()
_maybe_delete_keys_from_sm_data(keys=None, attributes=None)

Cache, clear, and restore all SimpleMetric data except the provided keys in the provided SimpleMetricDataAccessor attributes. This is necessary to delete one or more keys, since clear_sm_data clears all SimpleMetric data.

_maybe_delete_reserved_keys_from_sm_data()

CustomRealValueMetric and CustomStringValueMetric can save the extra default keys “custom_real_valued_metric” or “custom_string_valued_metric” because pose.cache.metrics does not set a ‘custom_type’ parameter when setting SimpleMetric data. This method aims to delete these extra default keys if they were created and we can delete them.

_repr_pretty_(p, cycle)

IPython-display representation.

property _reserved

Reserved scoretype keys that cannot be set or deleted.

property _reserved_custom_metric_keys

Reserved scoretype keys for SimpleMetrics that cannot be set or deleted.

_reserved_custom_metric_keys_warning()

Warn that reserved custom metric keys were added to the data cache.

_reserved_types = (<class 'str'>, <class 'float'>)
property _sm_data_accessor_attrs

Supported SimpleMetricDataAccessor attributes to reset after clear_sm_data.

property _unsupported_sm_data_accessor_attrs

Unsupported SimpleMetricDataAccessor attributes that cannot be reset after clear_sm_data.

_validate_del(key)

Validate that a key can be deleted.

_validate_set(key)

Validate that a key can be set.

property all
apply(metric, key, value)

Apply a SimpleMetric with a key/value pair to the pose.

static bool_from_str(value)
clear() → None.  Remove all items from D.
format_composite_real(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_score_data_from_pose.

format_composite_string(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_string_data_from_pose.

format_per_residue_probabilities(raw_data)

Format per-residue probabilities metrics as nested dictionaries.

The default output format from ScoreMap.add_arbitrary_string_data_from_pose sets values as str objects:

{‘my_metric_1’: ‘ALA:0.0125,ASN:0.000000,…’, ‘my_metric_2’: …}`

Here, we reformat the values into separate dict objects where keys are str objects and values are float objects:

{‘my_metric_1’: {‘ALA’: 0.0125, ‘ASN’: 0.0, …}, ‘my_metric_2’: …}

format_per_residue_real(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_score_data_from_pose.

format_per_residue_string(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_string_data_from_pose.

static from_base64(value)
static from_base64_pickle(value)
static from_pickle(value)
get(k[, d]) → D[k] if k in D, else d.  d defaults to None.
items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
static maybe_decode(value)

Deserialize the input value if it’s serialized.

static maybe_encode(value)

Serialize the input value into a str object if it’s not a str or float object.

pop(k[, d]) → v, remove specified key and return the corresponding value.

If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), remove and return some (key, value) pair

as a 2-tuple; but raise KeyError if D is empty.

pose
setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D
static to_base64(value)
static to_base64_pickle(value)
static to_pickle(value)
update([E, ]**F) → None.  Update D from mapping/iterable E and F.

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

values() → an object providing a view on D's values
class pyrosetta.bindings.scores.simple_metrics.SimpleMetricCompositeStringDataAccessor(pose)

Bases: SimpleMetricDataAccessorBase

Accessor wrapper for pose SimpleMetric composite string data.

_CustomTypeMetric

alias of CustomTypeMetric

_abc_impl = <_abc._abc_data object>
_clobber_warning(msg)

Issue a ClobberWarning warning with a message.

_custom_type_metrics = {'bool': CustomTypeMetric(type=<class 'bool'>, prefix='[CustomBooleanValueMetric]', encode_func=<class 'str'>, decode_func=<function PoseScoreSerializerBase.bool_from_str>), 'bytes': CustomTypeMetric(type=<class 'bytes'>, prefix='[CustomBinaryValueMetric]', encode_func=<function PoseScoreSerializerBase.to_base64>, decode_func=<function PoseScoreSerializerBase.from_base64>), 'int': CustomTypeMetric(type=<class 'int'>, prefix='[CustomDiscreteValueMetric]', encode_func=<class 'str'>, decode_func=<class 'int'>), 'object': CustomTypeMetric(type=<class 'object'>, prefix='[CustomArbitraryValueMetric]', encode_func=<function PoseScoreSerializerBase.to_base64_pickle>, decode_func=<function PoseScoreSerializerBase.from_base64_pickle>)}
_format_metric(raw_data, as_dict=False)
_get_sm_data_dict(attributes)
_has_reserved_custom_metric_keys()
_maybe_delete_keys_from_sm_data(keys=None, attributes=None)

Cache, clear, and restore all SimpleMetric data except the provided keys in the provided SimpleMetricDataAccessor attributes. This is necessary to delete one or more keys, since clear_sm_data clears all SimpleMetric data.

_maybe_delete_reserved_keys_from_sm_data()

CustomRealValueMetric and CustomStringValueMetric can save the extra default keys “custom_real_valued_metric” or “custom_string_valued_metric” because pose.cache.metrics does not set a ‘custom_type’ parameter when setting SimpleMetric data. This method aims to delete these extra default keys if they were created and we can delete them.

_repr_pretty_(p, cycle)

IPython-display representation.

property _reserved

Reserved scoretype keys that cannot be set or deleted.

property _reserved_custom_metric_keys

Reserved scoretype keys for SimpleMetrics that cannot be set or deleted.

_reserved_custom_metric_keys_warning()

Warn that reserved custom metric keys were added to the data cache.

_reserved_types = (<class 'str'>, <class 'float'>)
property _sm_data_accessor_attrs

Supported SimpleMetricDataAccessor attributes to reset after clear_sm_data.

property _unsupported_sm_data_accessor_attrs

Unsupported SimpleMetricDataAccessor attributes that cannot be reset after clear_sm_data.

_validate_del(key)

Validate that a key can be deleted.

_validate_set(key)

Validate that a key can be set.

property all
apply(metric, key, value)

Apply a SimpleMetric with a key/value pair to the pose.

static bool_from_str(value)
clear() → None.  Remove all items from D.
format_composite_real(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_score_data_from_pose.

format_composite_string(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_string_data_from_pose.

format_per_residue_probabilities(raw_data)

Format per-residue probabilities metrics as nested dictionaries.

The default output format from ScoreMap.add_arbitrary_string_data_from_pose sets values as str objects:

{‘my_metric_1’: ‘ALA:0.0125,ASN:0.000000,…’, ‘my_metric_2’: …}`

Here, we reformat the values into separate dict objects where keys are str objects and values are float objects:

{‘my_metric_1’: {‘ALA’: 0.0125, ‘ASN’: 0.0, …}, ‘my_metric_2’: …}

format_per_residue_real(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_score_data_from_pose.

format_per_residue_string(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_string_data_from_pose.

static from_base64(value)
static from_base64_pickle(value)
static from_pickle(value)
get(k[, d]) → D[k] if k in D, else d.  d defaults to None.
items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
static maybe_decode(value)

Deserialize the input value if it’s serialized.

static maybe_encode(value)

Serialize the input value into a str object if it’s not a str or float object.

pop(k[, d]) → v, remove specified key and return the corresponding value.

If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), remove and return some (key, value) pair

as a 2-tuple; but raise KeyError if D is empty.

pose
setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D
static to_base64(value)
static to_base64_pickle(value)
static to_pickle(value)
update([E, ]**F) → None.  Update D from mapping/iterable E and F.

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

values() → an object providing a view on D's values
class pyrosetta.bindings.scores.simple_metrics.SimpleMetricDataAccessor(pose)

Bases: SimpleMetricDataAccessorBase

Accessor wrapper for pose SimpleMetric data.

_CustomTypeMetric

alias of CustomTypeMetric

_abc_impl = <_abc._abc_data object>
_clobber_warning(msg)

Issue a ClobberWarning warning with a message.

_custom_type_metrics = {'bool': CustomTypeMetric(type=<class 'bool'>, prefix='[CustomBooleanValueMetric]', encode_func=<class 'str'>, decode_func=<function PoseScoreSerializerBase.bool_from_str>), 'bytes': CustomTypeMetric(type=<class 'bytes'>, prefix='[CustomBinaryValueMetric]', encode_func=<function PoseScoreSerializerBase.to_base64>, decode_func=<function PoseScoreSerializerBase.from_base64>), 'int': CustomTypeMetric(type=<class 'int'>, prefix='[CustomDiscreteValueMetric]', encode_func=<class 'str'>, decode_func=<class 'int'>), 'object': CustomTypeMetric(type=<class 'object'>, prefix='[CustomArbitraryValueMetric]', encode_func=<function PoseScoreSerializerBase.to_base64_pickle>, decode_func=<function PoseScoreSerializerBase.from_base64_pickle>)}
_format_metric(raw_data, as_dict=False)
_get_sm_data_dict(attributes)
_has_reserved_custom_metric_keys()
_maybe_delete_keys_from_sm_data(keys=None, attributes=None)

Cache, clear, and restore all SimpleMetric data except the provided keys in the provided SimpleMetricDataAccessor attributes. This is necessary to delete one or more keys, since clear_sm_data clears all SimpleMetric data.

_maybe_delete_reserved_keys_from_sm_data()

CustomRealValueMetric and CustomStringValueMetric can save the extra default keys “custom_real_valued_metric” or “custom_string_valued_metric” because pose.cache.metrics does not set a ‘custom_type’ parameter when setting SimpleMetric data. This method aims to delete these extra default keys if they were created and we can delete them.

_repr_pretty_(p, cycle)

IPython-display representation.

property _reserved

Reserved scoretype keys that cannot be set or deleted.

property _reserved_custom_metric_keys

Reserved scoretype keys for SimpleMetrics that cannot be set or deleted.

_reserved_custom_metric_keys_warning()

Warn that reserved custom metric keys were added to the data cache.

_reserved_types = (<class 'str'>, <class 'float'>)
property _sm_data_accessor_attrs

Supported SimpleMetricDataAccessor attributes to reset after clear_sm_data.

property _unsupported_sm_data_accessor_attrs

Unsupported SimpleMetricDataAccessor attributes that cannot be reset after clear_sm_data.

_validate_del(key)

Validate that a key can be deleted.

_validate_set(key)

Validate that a key can be set.

property all

Get all SimpleMetric data (with clobber warnings).

This method aims to mimic data override precedences used in the legacy pose.scores dictionary:

  1. pose.energies().active_total_energies()

  2. ScoreMap.get_arbitrary_score_data_from_pose(pose)

  3. ScoreMap.get_arbitrary_string_data_from_pose(pose)

Data override precedences as defined in ScoreMap::add_arbitrary_score_data_from_pose:

  1. Per-residue real metrics

  2. Composite real metrics

  3. Real metrics

Data override precedences as defined in ScoreMap::add_arbitrary_string_data_from_pose:

  1. Per-residue probabilities metrics

  2. Per-residue string metrics

  3. Composite string metrics

  4. String metrics

apply(metric, key, value)

Apply a SimpleMetric with a key/value pair to the pose.

static bool_from_str(value)
clear()

Clear pose SimpleMetric data.

property composite_real
property composite_string
format_composite_real(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_score_data_from_pose.

format_composite_string(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_string_data_from_pose.

format_per_residue_probabilities(raw_data)

Format per-residue probabilities metrics as nested dictionaries.

The default output format from ScoreMap.add_arbitrary_string_data_from_pose sets values as str objects:

{‘my_metric_1’: ‘ALA:0.0125,ASN:0.000000,…’, ‘my_metric_2’: …}`

Here, we reformat the values into separate dict objects where keys are str objects and values are float objects:

{‘my_metric_1’: {‘ALA’: 0.0125, ‘ASN’: 0.0, …}, ‘my_metric_2’: …}

format_per_residue_real(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_score_data_from_pose.

format_per_residue_string(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_string_data_from_pose.

static from_base64(value)
static from_base64_pickle(value)
static from_pickle(value)
get(k[, d]) → D[k] if k in D, else d.  d defaults to None.
items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
static maybe_decode(value)

Deserialize the input value if it’s serialized.

static maybe_encode(value)

Serialize the input value into a str object if it’s not a str or float object.

property per_residue_probabilities
property per_residue_real
property per_residue_string
pop(k[, d]) → v, remove specified key and return the corresponding value.

If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), remove and return some (key, value) pair

as a 2-tuple; but raise KeyError if D is empty.

pose
property real
setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D
property string
static to_base64(value)
static to_base64_pickle(value)
static to_pickle(value)
update([E, ]**F) → None.  Update D from mapping/iterable E and F.

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

values() → an object providing a view on D's values
class pyrosetta.bindings.scores.simple_metrics.SimpleMetricDataAccessorBase(pose)

Bases: PoseCacheAccessorBase, MutableMapping

Base methods for accessor wrapper for pose SimpleMetric data.

_CustomTypeMetric

alias of CustomTypeMetric

_abc_impl = <_abc._abc_data object>
_clobber_warning(msg)

Issue a ClobberWarning warning with a message.

_custom_type_metrics = {'bool': CustomTypeMetric(type=<class 'bool'>, prefix='[CustomBooleanValueMetric]', encode_func=<class 'str'>, decode_func=<function PoseScoreSerializerBase.bool_from_str>), 'bytes': CustomTypeMetric(type=<class 'bytes'>, prefix='[CustomBinaryValueMetric]', encode_func=<function PoseScoreSerializerBase.to_base64>, decode_func=<function PoseScoreSerializerBase.from_base64>), 'int': CustomTypeMetric(type=<class 'int'>, prefix='[CustomDiscreteValueMetric]', encode_func=<class 'str'>, decode_func=<class 'int'>), 'object': CustomTypeMetric(type=<class 'object'>, prefix='[CustomArbitraryValueMetric]', encode_func=<function PoseScoreSerializerBase.to_base64_pickle>, decode_func=<function PoseScoreSerializerBase.from_base64_pickle>)}
_format_metric(raw_data, as_dict=False)
_get_sm_data_dict(attributes)
_has_reserved_custom_metric_keys()
_maybe_delete_keys_from_sm_data(keys=None, attributes=None)

Cache, clear, and restore all SimpleMetric data except the provided keys in the provided SimpleMetricDataAccessor attributes. This is necessary to delete one or more keys, since clear_sm_data clears all SimpleMetric data.

_maybe_delete_reserved_keys_from_sm_data()

CustomRealValueMetric and CustomStringValueMetric can save the extra default keys “custom_real_valued_metric” or “custom_string_valued_metric” because pose.cache.metrics does not set a ‘custom_type’ parameter when setting SimpleMetric data. This method aims to delete these extra default keys if they were created and we can delete them.

_repr_pretty_(p, cycle)

IPython-display representation.

property _reserved

Reserved scoretype keys that cannot be set or deleted.

property _reserved_custom_metric_keys

Reserved scoretype keys for SimpleMetrics that cannot be set or deleted.

_reserved_custom_metric_keys_warning()

Warn that reserved custom metric keys were added to the data cache.

_reserved_types = (<class 'str'>, <class 'float'>)
property _sm_data_accessor_attrs

Supported SimpleMetricDataAccessor attributes to reset after clear_sm_data.

property _unsupported_sm_data_accessor_attrs

Unsupported SimpleMetricDataAccessor attributes that cannot be reset after clear_sm_data.

_validate_del(key)

Validate that a key can be deleted.

_validate_set(key)

Validate that a key can be set.

apply(metric, key, value)

Apply a SimpleMetric with a key/value pair to the pose.

static bool_from_str(value)
clear() → None.  Remove all items from D.
format_composite_real(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_score_data_from_pose.

format_composite_string(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_string_data_from_pose.

format_per_residue_probabilities(raw_data)

Format per-residue probabilities metrics as nested dictionaries.

The default output format from ScoreMap.add_arbitrary_string_data_from_pose sets values as str objects:

{‘my_metric_1’: ‘ALA:0.0125,ASN:0.000000,…’, ‘my_metric_2’: …}`

Here, we reformat the values into separate dict objects where keys are str objects and values are float objects:

{‘my_metric_1’: {‘ALA’: 0.0125, ‘ASN’: 0.0, …}, ‘my_metric_2’: …}

format_per_residue_real(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_score_data_from_pose.

format_per_residue_string(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_string_data_from_pose.

static from_base64(value)
static from_base64_pickle(value)
static from_pickle(value)
get(k[, d]) → D[k] if k in D, else d.  d defaults to None.
items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
static maybe_decode(value)

Deserialize the input value if it’s serialized.

static maybe_encode(value)

Serialize the input value into a str object if it’s not a str or float object.

pop(k[, d]) → v, remove specified key and return the corresponding value.

If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), remove and return some (key, value) pair

as a 2-tuple; but raise KeyError if D is empty.

pose
setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D
static to_base64(value)
static to_base64_pickle(value)
static to_pickle(value)
update([E, ]**F) → None.  Update D from mapping/iterable E and F.

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

values() → an object providing a view on D's values
class pyrosetta.bindings.scores.simple_metrics.SimpleMetricPerResidueProbabilitiesDataAccessor(pose)

Bases: SimpleMetricDataAccessorBase

Accessor wrapper for pose SimpleMetric per-residue probabilities data.

_CustomTypeMetric

alias of CustomTypeMetric

_abc_impl = <_abc._abc_data object>
_clobber_warning(msg)

Issue a ClobberWarning warning with a message.

_custom_type_metrics = {'bool': CustomTypeMetric(type=<class 'bool'>, prefix='[CustomBooleanValueMetric]', encode_func=<class 'str'>, decode_func=<function PoseScoreSerializerBase.bool_from_str>), 'bytes': CustomTypeMetric(type=<class 'bytes'>, prefix='[CustomBinaryValueMetric]', encode_func=<function PoseScoreSerializerBase.to_base64>, decode_func=<function PoseScoreSerializerBase.from_base64>), 'int': CustomTypeMetric(type=<class 'int'>, prefix='[CustomDiscreteValueMetric]', encode_func=<class 'str'>, decode_func=<class 'int'>), 'object': CustomTypeMetric(type=<class 'object'>, prefix='[CustomArbitraryValueMetric]', encode_func=<function PoseScoreSerializerBase.to_base64_pickle>, decode_func=<function PoseScoreSerializerBase.from_base64_pickle>)}
_format_metric(raw_data, as_dict=False)
_get_sm_data_dict(attributes)
_has_reserved_custom_metric_keys()
_maybe_delete_keys_from_sm_data(keys=None, attributes=None)

Cache, clear, and restore all SimpleMetric data except the provided keys in the provided SimpleMetricDataAccessor attributes. This is necessary to delete one or more keys, since clear_sm_data clears all SimpleMetric data.

_maybe_delete_reserved_keys_from_sm_data()

CustomRealValueMetric and CustomStringValueMetric can save the extra default keys “custom_real_valued_metric” or “custom_string_valued_metric” because pose.cache.metrics does not set a ‘custom_type’ parameter when setting SimpleMetric data. This method aims to delete these extra default keys if they were created and we can delete them.

_repr_pretty_(p, cycle)

IPython-display representation.

property _reserved

Reserved scoretype keys that cannot be set or deleted.

property _reserved_custom_metric_keys

Reserved scoretype keys for SimpleMetrics that cannot be set or deleted.

_reserved_custom_metric_keys_warning()

Warn that reserved custom metric keys were added to the data cache.

_reserved_types = (<class 'str'>, <class 'float'>)
property _sm_data_accessor_attrs

Supported SimpleMetricDataAccessor attributes to reset after clear_sm_data.

property _unsupported_sm_data_accessor_attrs

Unsupported SimpleMetricDataAccessor attributes that cannot be reset after clear_sm_data.

_validate_del(key)

Validate that a key can be deleted.

_validate_set(key)

Validate that a key can be set.

property all
apply(metric, key, value)

Apply a SimpleMetric with a key/value pair to the pose.

static bool_from_str(value)
clear() → None.  Remove all items from D.
format_composite_real(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_score_data_from_pose.

format_composite_string(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_string_data_from_pose.

format_per_residue_probabilities(raw_data)

Format per-residue probabilities metrics as nested dictionaries.

The default output format from ScoreMap.add_arbitrary_string_data_from_pose sets values as str objects:

{‘my_metric_1’: ‘ALA:0.0125,ASN:0.000000,…’, ‘my_metric_2’: …}`

Here, we reformat the values into separate dict objects where keys are str objects and values are float objects:

{‘my_metric_1’: {‘ALA’: 0.0125, ‘ASN’: 0.0, …}, ‘my_metric_2’: …}

format_per_residue_real(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_score_data_from_pose.

format_per_residue_string(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_string_data_from_pose.

static from_base64(value)
static from_base64_pickle(value)
static from_pickle(value)
get(k[, d]) → D[k] if k in D, else d.  d defaults to None.
items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
static maybe_decode(value)

Deserialize the input value if it’s serialized.

static maybe_encode(value)

Serialize the input value into a str object if it’s not a str or float object.

pop(k[, d]) → v, remove specified key and return the corresponding value.

If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), remove and return some (key, value) pair

as a 2-tuple; but raise KeyError if D is empty.

pose
setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D
static to_base64(value)
static to_base64_pickle(value)
static to_pickle(value)
update([E, ]**F) → None.  Update D from mapping/iterable E and F.

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

values() → an object providing a view on D's values
class pyrosetta.bindings.scores.simple_metrics.SimpleMetricPerResidueRealDataAccessor(pose)

Bases: SimpleMetricDataAccessorBase

Accessor wrapper for pose SimpleMetric per-residue real data.

_CustomTypeMetric

alias of CustomTypeMetric

_abc_impl = <_abc._abc_data object>
_clobber_warning(msg)

Issue a ClobberWarning warning with a message.

_custom_type_metrics = {'bool': CustomTypeMetric(type=<class 'bool'>, prefix='[CustomBooleanValueMetric]', encode_func=<class 'str'>, decode_func=<function PoseScoreSerializerBase.bool_from_str>), 'bytes': CustomTypeMetric(type=<class 'bytes'>, prefix='[CustomBinaryValueMetric]', encode_func=<function PoseScoreSerializerBase.to_base64>, decode_func=<function PoseScoreSerializerBase.from_base64>), 'int': CustomTypeMetric(type=<class 'int'>, prefix='[CustomDiscreteValueMetric]', encode_func=<class 'str'>, decode_func=<class 'int'>), 'object': CustomTypeMetric(type=<class 'object'>, prefix='[CustomArbitraryValueMetric]', encode_func=<function PoseScoreSerializerBase.to_base64_pickle>, decode_func=<function PoseScoreSerializerBase.from_base64_pickle>)}
_format_metric(raw_data, as_dict=False)
_get_sm_data_dict(attributes)
_has_reserved_custom_metric_keys()
_maybe_delete_keys_from_sm_data(keys=None, attributes=None)

Cache, clear, and restore all SimpleMetric data except the provided keys in the provided SimpleMetricDataAccessor attributes. This is necessary to delete one or more keys, since clear_sm_data clears all SimpleMetric data.

_maybe_delete_reserved_keys_from_sm_data()

CustomRealValueMetric and CustomStringValueMetric can save the extra default keys “custom_real_valued_metric” or “custom_string_valued_metric” because pose.cache.metrics does not set a ‘custom_type’ parameter when setting SimpleMetric data. This method aims to delete these extra default keys if they were created and we can delete them.

_repr_pretty_(p, cycle)

IPython-display representation.

property _reserved

Reserved scoretype keys that cannot be set or deleted.

property _reserved_custom_metric_keys

Reserved scoretype keys for SimpleMetrics that cannot be set or deleted.

_reserved_custom_metric_keys_warning()

Warn that reserved custom metric keys were added to the data cache.

_reserved_types = (<class 'str'>, <class 'float'>)
property _sm_data_accessor_attrs

Supported SimpleMetricDataAccessor attributes to reset after clear_sm_data.

property _unsupported_sm_data_accessor_attrs

Unsupported SimpleMetricDataAccessor attributes that cannot be reset after clear_sm_data.

_validate_del(key)

Validate that a key can be deleted.

_validate_set(key)

Validate that a key can be set.

property all
apply(metric, key, value)

Apply a SimpleMetric with a key/value pair to the pose.

static bool_from_str(value)
clear() → None.  Remove all items from D.
format_composite_real(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_score_data_from_pose.

format_composite_string(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_string_data_from_pose.

format_per_residue_probabilities(raw_data)

Format per-residue probabilities metrics as nested dictionaries.

The default output format from ScoreMap.add_arbitrary_string_data_from_pose sets values as str objects:

{‘my_metric_1’: ‘ALA:0.0125,ASN:0.000000,…’, ‘my_metric_2’: …}`

Here, we reformat the values into separate dict objects where keys are str objects and values are float objects:

{‘my_metric_1’: {‘ALA’: 0.0125, ‘ASN’: 0.0, …}, ‘my_metric_2’: …}

format_per_residue_real(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_score_data_from_pose.

format_per_residue_string(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_string_data_from_pose.

static from_base64(value)
static from_base64_pickle(value)
static from_pickle(value)
get(k[, d]) → D[k] if k in D, else d.  d defaults to None.
items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
static maybe_decode(value)

Deserialize the input value if it’s serialized.

static maybe_encode(value)

Serialize the input value into a str object if it’s not a str or float object.

pop(k[, d]) → v, remove specified key and return the corresponding value.

If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), remove and return some (key, value) pair

as a 2-tuple; but raise KeyError if D is empty.

pose
setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D
static to_base64(value)
static to_base64_pickle(value)
static to_pickle(value)
update([E, ]**F) → None.  Update D from mapping/iterable E and F.

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

values() → an object providing a view on D's values
class pyrosetta.bindings.scores.simple_metrics.SimpleMetricPerResidueStringDataAccessor(pose)

Bases: SimpleMetricDataAccessorBase

Accessor wrapper for pose SimpleMetric per-residue string data.

_CustomTypeMetric

alias of CustomTypeMetric

_abc_impl = <_abc._abc_data object>
_clobber_warning(msg)

Issue a ClobberWarning warning with a message.

_custom_type_metrics = {'bool': CustomTypeMetric(type=<class 'bool'>, prefix='[CustomBooleanValueMetric]', encode_func=<class 'str'>, decode_func=<function PoseScoreSerializerBase.bool_from_str>), 'bytes': CustomTypeMetric(type=<class 'bytes'>, prefix='[CustomBinaryValueMetric]', encode_func=<function PoseScoreSerializerBase.to_base64>, decode_func=<function PoseScoreSerializerBase.from_base64>), 'int': CustomTypeMetric(type=<class 'int'>, prefix='[CustomDiscreteValueMetric]', encode_func=<class 'str'>, decode_func=<class 'int'>), 'object': CustomTypeMetric(type=<class 'object'>, prefix='[CustomArbitraryValueMetric]', encode_func=<function PoseScoreSerializerBase.to_base64_pickle>, decode_func=<function PoseScoreSerializerBase.from_base64_pickle>)}
_format_metric(raw_data, as_dict=False)
_get_sm_data_dict(attributes)
_has_reserved_custom_metric_keys()
_maybe_delete_keys_from_sm_data(keys=None, attributes=None)

Cache, clear, and restore all SimpleMetric data except the provided keys in the provided SimpleMetricDataAccessor attributes. This is necessary to delete one or more keys, since clear_sm_data clears all SimpleMetric data.

_maybe_delete_reserved_keys_from_sm_data()

CustomRealValueMetric and CustomStringValueMetric can save the extra default keys “custom_real_valued_metric” or “custom_string_valued_metric” because pose.cache.metrics does not set a ‘custom_type’ parameter when setting SimpleMetric data. This method aims to delete these extra default keys if they were created and we can delete them.

_repr_pretty_(p, cycle)

IPython-display representation.

property _reserved

Reserved scoretype keys that cannot be set or deleted.

property _reserved_custom_metric_keys

Reserved scoretype keys for SimpleMetrics that cannot be set or deleted.

_reserved_custom_metric_keys_warning()

Warn that reserved custom metric keys were added to the data cache.

_reserved_types = (<class 'str'>, <class 'float'>)
property _sm_data_accessor_attrs

Supported SimpleMetricDataAccessor attributes to reset after clear_sm_data.

property _unsupported_sm_data_accessor_attrs

Unsupported SimpleMetricDataAccessor attributes that cannot be reset after clear_sm_data.

_validate_del(key)

Validate that a key can be deleted.

_validate_set(key)

Validate that a key can be set.

property all
apply(metric, key, value)

Apply a SimpleMetric with a key/value pair to the pose.

static bool_from_str(value)
clear() → None.  Remove all items from D.
format_composite_real(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_score_data_from_pose.

format_composite_string(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_string_data_from_pose.

format_per_residue_probabilities(raw_data)

Format per-residue probabilities metrics as nested dictionaries.

The default output format from ScoreMap.add_arbitrary_string_data_from_pose sets values as str objects:

{‘my_metric_1’: ‘ALA:0.0125,ASN:0.000000,…’, ‘my_metric_2’: …}`

Here, we reformat the values into separate dict objects where keys are str objects and values are float objects:

{‘my_metric_1’: {‘ALA’: 0.0125, ‘ASN’: 0.0, …}, ‘my_metric_2’: …}

format_per_residue_real(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_score_data_from_pose.

format_per_residue_string(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_string_data_from_pose.

static from_base64(value)
static from_base64_pickle(value)
static from_pickle(value)
get(k[, d]) → D[k] if k in D, else d.  d defaults to None.
items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
static maybe_decode(value)

Deserialize the input value if it’s serialized.

static maybe_encode(value)

Serialize the input value into a str object if it’s not a str or float object.

pop(k[, d]) → v, remove specified key and return the corresponding value.

If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), remove and return some (key, value) pair

as a 2-tuple; but raise KeyError if D is empty.

pose
setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D
static to_base64(value)
static to_base64_pickle(value)
static to_pickle(value)
update([E, ]**F) → None.  Update D from mapping/iterable E and F.

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

values() → an object providing a view on D's values
class pyrosetta.bindings.scores.simple_metrics.SimpleMetricRealDataAccessor(pose)

Bases: SimpleMetricDataAccessorBase

Accessor wrapper for pose SimpleMetric real data.

_CustomTypeMetric

alias of CustomTypeMetric

_abc_impl = <_abc._abc_data object>
_clobber_warning(msg)

Issue a ClobberWarning warning with a message.

_custom_type_metrics = {'bool': CustomTypeMetric(type=<class 'bool'>, prefix='[CustomBooleanValueMetric]', encode_func=<class 'str'>, decode_func=<function PoseScoreSerializerBase.bool_from_str>), 'bytes': CustomTypeMetric(type=<class 'bytes'>, prefix='[CustomBinaryValueMetric]', encode_func=<function PoseScoreSerializerBase.to_base64>, decode_func=<function PoseScoreSerializerBase.from_base64>), 'int': CustomTypeMetric(type=<class 'int'>, prefix='[CustomDiscreteValueMetric]', encode_func=<class 'str'>, decode_func=<class 'int'>), 'object': CustomTypeMetric(type=<class 'object'>, prefix='[CustomArbitraryValueMetric]', encode_func=<function PoseScoreSerializerBase.to_base64_pickle>, decode_func=<function PoseScoreSerializerBase.from_base64_pickle>)}
_format_metric(raw_data, as_dict=False)
_get_sm_data_dict(attributes)
_has_reserved_custom_metric_keys()
_maybe_delete_keys_from_sm_data(keys=None, attributes=None)

Cache, clear, and restore all SimpleMetric data except the provided keys in the provided SimpleMetricDataAccessor attributes. This is necessary to delete one or more keys, since clear_sm_data clears all SimpleMetric data.

_maybe_delete_reserved_keys_from_sm_data()

CustomRealValueMetric and CustomStringValueMetric can save the extra default keys “custom_real_valued_metric” or “custom_string_valued_metric” because pose.cache.metrics does not set a ‘custom_type’ parameter when setting SimpleMetric data. This method aims to delete these extra default keys if they were created and we can delete them.

_repr_pretty_(p, cycle)

IPython-display representation.

property _reserved

Reserved scoretype keys that cannot be set or deleted.

property _reserved_custom_metric_keys

Reserved scoretype keys for SimpleMetrics that cannot be set or deleted.

_reserved_custom_metric_keys_warning()

Warn that reserved custom metric keys were added to the data cache.

_reserved_types = (<class 'str'>, <class 'float'>)
property _sm_data_accessor_attrs

Supported SimpleMetricDataAccessor attributes to reset after clear_sm_data.

property _unsupported_sm_data_accessor_attrs

Unsupported SimpleMetricDataAccessor attributes that cannot be reset after clear_sm_data.

_validate_del(key)

Validate that a key can be deleted.

_validate_set(key)

Validate that a key can be set.

property all
apply(metric, key, value)

Apply a SimpleMetric with a key/value pair to the pose.

static bool_from_str(value)
clear() → None.  Remove all items from D.
format_composite_real(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_score_data_from_pose.

format_composite_string(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_string_data_from_pose.

format_per_residue_probabilities(raw_data)

Format per-residue probabilities metrics as nested dictionaries.

The default output format from ScoreMap.add_arbitrary_string_data_from_pose sets values as str objects:

{‘my_metric_1’: ‘ALA:0.0125,ASN:0.000000,…’, ‘my_metric_2’: …}`

Here, we reformat the values into separate dict objects where keys are str objects and values are float objects:

{‘my_metric_1’: {‘ALA’: 0.0125, ‘ASN’: 0.0, …}, ‘my_metric_2’: …}

format_per_residue_real(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_score_data_from_pose.

format_per_residue_string(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_string_data_from_pose.

static from_base64(value)
static from_base64_pickle(value)
static from_pickle(value)
get(k[, d]) → D[k] if k in D, else d.  d defaults to None.
items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
static maybe_decode(value)

Deserialize the input value if it’s serialized.

static maybe_encode(value)

Serialize the input value into a str object if it’s not a str or float object.

pop(k[, d]) → v, remove specified key and return the corresponding value.

If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), remove and return some (key, value) pair

as a 2-tuple; but raise KeyError if D is empty.

pose
setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D
static to_base64(value)
static to_base64_pickle(value)
static to_pickle(value)
update([E, ]**F) → None.  Update D from mapping/iterable E and F.

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

values() → an object providing a view on D's values
class pyrosetta.bindings.scores.simple_metrics.SimpleMetricStringDataAccessor(pose)

Bases: SimpleMetricDataAccessorBase

Accessor wrapper for pose SimpleMetric string data.

_CustomTypeMetric

alias of CustomTypeMetric

_abc_impl = <_abc._abc_data object>
_clobber_warning(msg)

Issue a ClobberWarning warning with a message.

_custom_type_metrics = {'bool': CustomTypeMetric(type=<class 'bool'>, prefix='[CustomBooleanValueMetric]', encode_func=<class 'str'>, decode_func=<function PoseScoreSerializerBase.bool_from_str>), 'bytes': CustomTypeMetric(type=<class 'bytes'>, prefix='[CustomBinaryValueMetric]', encode_func=<function PoseScoreSerializerBase.to_base64>, decode_func=<function PoseScoreSerializerBase.from_base64>), 'int': CustomTypeMetric(type=<class 'int'>, prefix='[CustomDiscreteValueMetric]', encode_func=<class 'str'>, decode_func=<class 'int'>), 'object': CustomTypeMetric(type=<class 'object'>, prefix='[CustomArbitraryValueMetric]', encode_func=<function PoseScoreSerializerBase.to_base64_pickle>, decode_func=<function PoseScoreSerializerBase.from_base64_pickle>)}
_format_metric(raw_data, as_dict=False)
_get_sm_data_dict(attributes)
_has_reserved_custom_metric_keys()
_maybe_delete_keys_from_sm_data(keys=None, attributes=None)

Cache, clear, and restore all SimpleMetric data except the provided keys in the provided SimpleMetricDataAccessor attributes. This is necessary to delete one or more keys, since clear_sm_data clears all SimpleMetric data.

_maybe_delete_reserved_keys_from_sm_data()

CustomRealValueMetric and CustomStringValueMetric can save the extra default keys “custom_real_valued_metric” or “custom_string_valued_metric” because pose.cache.metrics does not set a ‘custom_type’ parameter when setting SimpleMetric data. This method aims to delete these extra default keys if they were created and we can delete them.

_repr_pretty_(p, cycle)

IPython-display representation.

property _reserved

Reserved scoretype keys that cannot be set or deleted.

property _reserved_custom_metric_keys

Reserved scoretype keys for SimpleMetrics that cannot be set or deleted.

_reserved_custom_metric_keys_warning()

Warn that reserved custom metric keys were added to the data cache.

_reserved_types = (<class 'str'>, <class 'float'>)
property _sm_data_accessor_attrs

Supported SimpleMetricDataAccessor attributes to reset after clear_sm_data.

property _unsupported_sm_data_accessor_attrs

Unsupported SimpleMetricDataAccessor attributes that cannot be reset after clear_sm_data.

_validate_del(key)

Validate that a key can be deleted.

_validate_set(key)

Validate that a key can be set.

property all
apply(metric, key, value)

Apply a SimpleMetric with a key/value pair to the pose.

static bool_from_str(value)
clear() → None.  Remove all items from D.
format_composite_real(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_score_data_from_pose.

format_composite_string(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_string_data_from_pose.

format_per_residue_probabilities(raw_data)

Format per-residue probabilities metrics as nested dictionaries.

The default output format from ScoreMap.add_arbitrary_string_data_from_pose sets values as str objects:

{‘my_metric_1’: ‘ALA:0.0125,ASN:0.000000,…’, ‘my_metric_2’: …}`

Here, we reformat the values into separate dict objects where keys are str objects and values are float objects:

{‘my_metric_1’: {‘ALA’: 0.0125, ‘ASN’: 0.0, …}, ‘my_metric_2’: …}

format_per_residue_real(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_score_data_from_pose.

format_per_residue_string(raw_data)

Mimics formatting in ScoreMap.add_arbitrary_string_data_from_pose.

static from_base64(value)
static from_base64_pickle(value)
static from_pickle(value)
get(k[, d]) → D[k] if k in D, else d.  d defaults to None.
items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
static maybe_decode(value)

Deserialize the input value if it’s serialized.

static maybe_encode(value)

Serialize the input value into a str object if it’s not a str or float object.

pop(k[, d]) → v, remove specified key and return the corresponding value.

If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), remove and return some (key, value) pair

as a 2-tuple; but raise KeyError if D is empty.

pose
setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D
static to_base64(value)
static to_base64_pickle(value)
static to_pickle(value)
update([E, ]**F) → None.  Update D from mapping/iterable E and F.

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

values() → an object providing a view on D's values