cellmil.utils.tools¶

Functions

`close_logger`(logger)	Closing a logger savely
`create_color_gradient`(bins, initial_color, ...)	Create a color gradient from initial_color to final_color.
`end_timer`(start_time[, timed_event])	Prints the time passed from start_time.
`get_cpu_count`()	Get the number of available CPU cores.
`get_size_of_dict`(d)
`module_exists`(*names[, error, ...])	Try to import optional dependencies.
`plot_graph`(data[, title])	Plot a PyTorch Geometric graph using Plotly.
`plot_vector`(vector[, title])	Plot a 1D vector using matplotlib.
`start_timer`()	Returns the number of seconds passed since epoch.
`to_float`(x)	Convert tensor to float32
`to_float_normalized`(x)	Convert tensor to float32 and normalize to [0, 1]
`unflatten_dict`(d[, sep])	Unflatten a flattened dictionary (created a nested dictionary)

cellmil.utils.tools.to_float(x: Tensor) → Tensor[source]¶: Convert tensor to float32

cellmil.utils.tools.to_float_normalized(x: Tensor) → Tensor[source]¶: Convert tensor to float32 and normalize to [0, 1]

cellmil.utils.tools.start_timer() → float[source]¶

Returns the number of seconds passed since epoch. The epoch is the point where the time starts, and is platform dependent.

Returns:: The number of seconds passed since epoch
Return type:: float

cellmil.utils.tools.end_timer(start_time: float, timed_event: str = 'Time usage') → None[source]¶

Prints the time passed from start_time.

Parameters:

start_time (float) – The number of seconds passed since epoch when the timer started
timed_event (str, optional) – A string describing the activity being monitored. Defaults to “Time usage”.

cellmil.utils.tools.module_exists(*names: Union[List[str], str], error: str = 'ignore', warn_every_time: bool = False, __INSTALLED_OPTIONAL_MODULES: Dict[str, bool] = {}) → Optional[Union[Tuple[module | None, ...], module]][source]¶

Try to import optional dependencies. Ref: https://stackoverflow.com/a/73838546/4900327

Parameters:

names (Union(List(str), str)) – The module name(s) to import. Str or list of strings.
error (str, optional) –
What to do when a dependency is not found:
- raise : Raise an ImportError.
- warn: print a warning.
- ignore: If any module is not installed, return None, otherwise, return the module(s).
Defaults to “ignore”.
warn_every_time (bool, optional) – Whether to warn every time an import is tried. Only applies when error=”warn”. Setting this to True will result in multiple warnings if you try to import the same library multiple times. Defaults to False.

Raises:

ImportError – ImportError of Module

Returns:

The imported module(s), if all are found.: None is returned if any module is not found and error!=”raise”.

Return type:

Optional[ModuleType, Tuple[ModuleType…]]

cellmil.utils.tools.close_logger(logger: Logger) → None[source]¶

Closing a logger savely

Parameters:: logger (logging.Logger) – Logger to close

cellmil.utils.tools.get_size_of_dict(d: dict[str, Any]) → int[source]¶

cellmil.utils.tools.unflatten_dict(d: dict[str, Any], sep: str = '.') → dict[str, Any][source]¶

Unflatten a flattened dictionary (created a nested dictionary)

Parameters:

d (dict) – Dict to be nested
sep (str, optional) – Seperator of flattened keys. Defaults to ‘.’.

Returns:

Nested dict

Return type:

dict

cellmil.utils.tools.get_cpu_count() → int[source]¶: Get the number of available CPU cores.

cellmil.utils.tools.create_color_gradient(bins: int, initial_color: list[int], final_color: list[int]) → list[list[int]][source]¶

Create a color gradient from initial_color to final_color.

Parameters:

bins (int) – Number of bins in the gradient.
initial_color (list[int]) – RGB values for the initial color.
final_color (list[int]) – RGB values for the final color.

Returns:

List of RGB colors representing the gradient.

Return type:

list[list[int]]

cellmil.utils.tools.plot_vector(vector: Tensor, title: str = 'Vector Plot') → None[source]¶

Plot a 1D vector using matplotlib.

Parameters:

vector (torch.Tensor) – 1D tensor to plot.
title (str, optional) – Title of the plot. Defaults to “Vector Plot”.

cellmil.utils.tools.plot_graph(data: Data, title: str = 'Graph Visualization') → None[source]¶

Plot a PyTorch Geometric graph using Plotly.

Parameters:

data (Data) – PyTorch Geometric Data object containing graph structure.
title (str, optional) – Title of the plot. Defaults to “Graph Visualization”.