`prolint2.metrics.base`#

Authors:: Daniel P. Ramirez & Besian I. Sejdiu
Year:: 2022
Copyright:: MIT License

class prolint2.metrics.base.BaseContactStore(ts, contact_frames, norm_factor: float = 1.0)[source]#

Base class for storing contact.

apply_function(func: Callable, target_lipid_name=None)[source]#: Apply the given function to the contacts for the given lipid name.

compute(metric: str, target_lipid_name=None)[source]#

Compute a pre-defined metric for all lipids or a specific lipid.

Parameters:

metric (str) – The metric to compute. Must be one of ‘max’, ‘sum’, ‘mean’.
target_lipid_name (str, optional) – The name of the lipid to compute the metric for. If None, the metric will be computed for all lipids.

Returns:

A dictionary of computed metrics for all lipids.

Return type:

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

Examples

>>> cd = AproxContacts(...)
>>> cd.run()
>>> cd.compute('max')
>>> cd.compute('sum', 'DOPC')
>>> cd.compute('median') # raises ValueError. Use `apply_function` instead.

compute_metric(metric: str, target_lipid_name=None)[source]#: Compute the given metric for the given lipid name.

property contacts#: Get the computed contacts all pooled together.

pooled_results()[source]#: Get the computed contacts all pooled together.

property results#: Get the computed contacts per lipid id.

run(lipid_resnames: Optional[Union[str, List]] = None)[source]#: Run the contact calculation for the given lipid resnames. If no resnames are given, all resnames are used.

class prolint2.metrics.base.BaseMetric[source]#: Base class for all metrics classes that act on single frame contact Iterables.

class prolint2.metrics.base.FittingFunction[source]#: Base class for fitting functions.

class prolint2.metrics.base.FittingFunctionMeta(name, bases, dct)[source]#: Metaclass for fitting functions.

class prolint2.metrics.base.Metric(contacts, metrics, output_format: ~prolint2.metrics.formatters.OutputFormat = <prolint2.metrics.formatters.DefaultOutputFormat object>, lipid_type=None, clear=True)[source]#

Base class for metric calculation.

compute(dt=1, totaltime=1)[source]#: Compute the metric for the given contacts.

`prolint2.metrics.aprox_contacts`#

Authors:: Daniel P. Ramirez & Besian I. Sejdiu
Year:: 2022
Copyright:: MIT License

class prolint2.metrics.aprox_contacts.AproxContacts(ts, contact_frames, norm_factor: float = 1.0)[source]#

Compute the duration of lipid contacts. This class is used to compute the duration of lipid contacts.

apply_function(func: Callable, target_lipid_name=None)[source]#

Apply a function to all lipids or a specific lipid.

Parameters:

func (Callable) – The function to apply to the lipid contact durations.
target_lipid_name (str, optional) – The name of the lipid to apply the function to. If None, the function will be applied to all lipids.
conversion_func (Callable, optional) – The function to convert the contact array before calling func. If None, no conversion will be applied.

Returns:

A dictionary of computed metrics for all lipids.

Return type:

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

Example

>>> cd = AproxContacts(...)
>>> cd.run()
>>> cd.apply_function(np.mean)
>>> cd.apply_function(np.max, target_lipid_name='DOPC')
>>> cd.apply_function(lambda x: np.mean(x) / np.max(x), target_lipid_name='DOPC')

compute_metric(metric: str, target_lipid_name=None)[source]#

Compute a pre-defined metric for all lipids or a specific lipid.

Parameters:

metric (str) – The metric to compute. Must be one of ‘max’, ‘sum’, ‘mean’.
target_lipid_name (str, optional) – The name of the lipid to compute the metric for. If None, the metric will be computed for all lipids.

Returns:

A dictionary of computed metrics for all lipids.

Return type:

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

pooled_results(target_lipid_name: Optional[str] = None) → Dict[str, ndarray][source]#

Get the duration of lipid contacts for all lipid types pooled together.

Parameters:: target_lipid_name (str, optional) – A list of lipid residue names to compute durations for. If None, durations will be computed for all lipid types.
Returns:: A dictionary of lipid contact durations for all lipid types.
Return type:: Dict[str, np.ndarray]

run(lipid_resnames: Optional[Union[str, List]] = None) → Dict[str, ndarray][source]#

Compute the duration of lipid contacts for all lipid types.

Parameters:: lipid_resnames (str, optional) – A list of lipid residue names to compute durations for. If None, durations will be computed for all lipid types.
Returns:: A dictionary of lipid contact durations for all lipid types.
Return type:: Dict[str, np.ndarray]

`prolint2.metrics.exact_contacts`#

Authors:: Daniel P. Ramirez & Besian I. Sejdiu
Year:: 2022
Copyright:: MIT License

class prolint2.metrics.exact_contacts.ExactContacts(ts, contact_frames, norm_factor: float = 1.0)[source]#

Compute the duration of lipid contacts. This class is used to compute the duration of lipid contacts.

apply_function(func: Callable, target_lipid_name=None)[source]#

Apply a function to all lipids or a specific lipid.

Parameters:

func (Callable) – The function to apply to the lipid contact durations.
target_lipid_name (str, optional) – The name of the lipid to apply the function to. If None, the function will be applied to all lipids.

Returns:

A dictionary of computed metrics for all lipids.

Return type:

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

Example

>>> cd = ExactContacts(...)
>>> cd.run()
>>> cd.apply_function(np.mean)
>>> cd.apply_function(np.max, target_lipid_name='DOPC')
>>> cd.apply_function(lambda x: np.mean(x) / np.max(x), target_lipid_name='DOPC')

compute_lipid_durations(contact_frame: Dict[int, List[int]], lipid_resname: str) → ndarray[source]#

Compute the duration of lipid contacts.

Parameters:

contact_frame (Dict[int, List[int]]) – A dictionary of contact frames.
lipid_resname (str) – The residue name of the lipid to compute durations for.

Returns:

An array of lipid contact durations.

Return type:

np.ndarray

compute_metric(metric: str, target_lipid_name=None)[source]#

Compute a pre-defined metric for all lipids or a specific lipid.

Parameters:

metric (str) – The metric to compute. Must be one of ‘max’, ‘sum’, ‘mean’.
target_lipid_name (str, optional) – The name of the lipid to compute the metric for. If None, the metric will be computed for all lipids.

Returns:

A dictionary of computed metrics for all lipids.

Return type:

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

pooled_results(target_lipid_name=None)[source]#

Pool results for all lipids.

Parameters:: target_lipid_name (str, optional) – The name of the lipid to compute pooled results for. If None, pooled results will be computed for all lipids.
Returns:: A dictionary of pooled results for all lipids.
Return type:: Dict[str, Dict[str, List[float]]]

run(lipid_resnames: Optional[Union[str, List]] = None) → Dict[str, ndarray][source]#

Compute the duration of lipid contacts for all lipid types.

Parameters:: lipid_resnames (str, optional) – A list of lipid residue names to compute durations for. If None, durations will be computed for all lipid types.
Returns:: A dictionary of lipid contact durations for all lipid types. The output is stored in the self._contacts attribute.
Return type:: Dict[str, np.ndarray]

`prolint2.metrics.metrics`#

Authors:: Daniel P. Ramirez & Besian I. Sejdiu
Year:: 2022
Copyright:: MIT License

class prolint2.metrics.metrics.MaxMetric[source]#

A metric to compute the maximum contact value.

Variables:: name (str) – The name of the metric (set to ‘max’).

compute_metric(contact_array)[source]#: Compute the maximum contact value for a given contact array.

Examples

>>> max_metric = MaxMetric()
>>> result = max_metric.compute_metric(contact_array)

compute_metric(contact_array)[source]#

Compute the maximum contact value for a given contact array.

Parameters:: contact_array (Iterable[int]) – An iterable representing contact data.
Returns:: The maximum contact value.
Return type:: int

Examples

>>> result = max_metric.compute_metric(contact_array)

class prolint2.metrics.metrics.MeanMetric[source]#

A metric to compute the mean of contact values.

Variables:: name (str) – The name of the metric (set to ‘mean’).

compute_metric(contact_array)[source]#: Compute the mean of contact values for a given contact array.

Examples

>>> mean_metric = MeanMetric()
>>> result = mean_metric.compute_metric(contact_array)

compute_metric(contact_array)[source]#

Compute the mean of contact values for a given contact array.

Parameters:: contact_array (Iterable[int]) – An iterable representing contact data.
Returns:: The computed mean value.
Return type:: float

Examples

>>> result = mean_metric.compute_metric(contact_array)

class prolint2.metrics.metrics.SumMetric[source]#

A metric to compute the sum of contact values.

Variables:: name (str) – The name of the metric (set to ‘sum’).

compute_metric(contact_array)[source]#: Compute the sum of contact values for a given contact array.

Examples

>>> sum_metric = SumMetric()
>>> result = sum_metric.compute_metric(contact_array)

compute_metric(contact_array)[source]#

Compute the sum of contact values for a given contact array.

Parameters:: contact_array (Iterable[int]) – An iterable representing contact data.
Returns:: The computed sum of contact values.
Return type:: int

Examples

>>> result = sum_metric.compute_metric(contact_array)

class prolint2.metrics.metrics.UserDefinedMetric(custom_function: Callable[[Iterable[int]], float])[source]#

A user-defined metric class.

Parameters:: custom_function (Callable[[Iterable[int]], float]) – A custom function to compute the metric.
Variables:: name (str) – The name of the metric (set to ‘custom’).

compute_metric(contact_array)[source]#: Compute the custom metric for a given contact array.

Examples

>>> custom_metric = UserDefinedMetric(my_custom_function)
>>> result = custom_metric.compute_metric(contact_array)

compute_metric(contact_array)[source]#

Compute the custom metric for a given contact array.

Parameters:: contact_array (Iterable[int]) – An iterable representing contact data.
Returns:: The computed custom metric.
Return type:: float

Examples

>>> result = custom_metric.compute_metric(contact_array)

prolint2.metrics.metrics.create_metric(contacts, metrics, output_format=None, custom_function: Optional[Callable] = None, metric_registry: Optional[MetricRegistry] = None, lipid_type=None, **kwargs)[source]#

Create and configure a metric computation task.

This function creates a metric computation task based on user-defined parameters. It returns a Metric object that can be used to compute metrics on contact data.

Parameters:

contacts (ContactData) – The contact data to analyze.
metrics (list) – A list of metrics to compute, including ‘max’, ‘sum’, ‘mean’, or ‘custom’.
output_format (str, optional) – The output format for the computed metrics. Supported values include ‘default’, ‘custom’, ‘single’, or ‘dashboard’.
custom_function (Callable, optional) – A custom function to use when the ‘custom’ metric is selected.
metric_registry (MetricRegistry, optional) – A registry containing metric classes.
lipid_type (str, optional) – The type of lipid to consider in the metric computation.
**kwargs – Additional keyword arguments for output format classes.

Returns:

A Metric object configured with the specified parameters.

Return type:

Metric

Raises:

ValueError – If an invalid output format is specified or if ‘single’ format is used with more than one metric.

Examples

>>> metrics = ['max', 'mean']
>>> metric = create_metric(contacts, metrics, output_format='default')
>>> metric.run()
>>> results = metric.compute('max')
>>> results = metric.compute('sum', lipid_type='DOPC')
>>> metric.compute('median')  # Raises ValueError. Use `apply_function` instead.

prolint2.metrics.base#

prolint2.metrics.aprox_contacts#

prolint2.metrics.exact_contacts#

prolint2.metrics.metrics#

`prolint2.metrics.base`#

`prolint2.metrics.aprox_contacts`#

`prolint2.metrics.exact_contacts`#

`prolint2.metrics.metrics`#