Statistics

Calculate statistics on grain volumes and aggregate and summarise the data.

Functions:

Name	Description
area_pores	Extract area of objects in each layer.
calculate_pdf	Calculate the scaled probability density function for an array.
centroid_pores	Extract centroid of objects in each layer.
classify_pore_size	Classify pore sizes in a dataframe into colors.
count_pores	Count the number of region_properties, each of which represents a pore for all layers.
create_statistics_dictionary	Extract statistics of objects from each layer.
feret_diameter_maximum_pores	Extract the maximum feret diameter of objects in each layer.
full_width_half_max	Calculate the full-width half max.
sum_area_by_layer	Sum the area of pores on each layer.
summarise_pores	Summarise pore types by image, layer and color.

area_pores

area_pores(sliced_region_properties)

Extract area of objects in each layer.

Parameters:

Name	Type	Description	Default
sliced_region_properties	list[Any]	A list (one for each layer) of lists which contain the region_props for each object within that layer.	required

Returns:

Type	Description
list[list[float]]	A list with the same length as the number of layers, each item is a list of the area of objects within that layer.

calculate_pdf

calculate_pdf(array, xmin=None, xmax=None)

Calculate the scaled probability density function for an array.

Parameters:

Name	Type	Description	Default
array	list[float]	Array of data points to be summarised.	required
xmin	int | float	Minimum value.	None
xmax	int | float	Maximum value.	None

Returns:

Type	Description
dict[str, NDArray]	Dictionary of x and y values for the PDF.

centroid_pores

centroid_pores(sliced_region_properties)

Extract centroid of objects in each layer.

Parameters:

Name	Type	Description	Default
sliced_region_properties	list[Any]	A list (one for each layer) of lists which contain the region_props for each object within that layer.	required

Returns:

Type	Description
list[list[float]]	A list with the same length as the number of layers, each item is a list of the centroid of objects within that layer.

classify_pore_size

classify_pore_size(
    df, area_thresholds, pore_colors, area_val="area"
)

Classify pore sizes in a dataframe into colors.

Parameters:

Name	Type	Description	Default
df	DataFrame	Dataframe with areas to be classified.	required
area_thresholds	dict[str, int]	Dictionary of thresholds, there should be three values for low, medium and high thresholds resulting in four categories.	required
pore_colors	list[str]	Colors to use for the four categories.	required
area_val	str	Column name containing the area data, default is area and is unlikely to need changing.	'area'

Returns:

Type	Description
DataFrame	Dataframe with additional column with text categorisation of pore area.

count_pores

count_pores(sliced_region_properties)

Count the number of region_properties, each of which represents a pore for all layers.

Parameters:

Name	Type	Description	Default
sliced_region_properties	list[Any]	A list of region properties found on each layer.	required

Returns:

Type	Description
NDArray[int32]	A list of the number of region properties detected in each layer.

create_statistics_dictionary

create_statistics_dictionary(
    sliced_region_properties,
    feret_maximum=False,
    centroid=False,
)

Extract statistics of objects from each layer.

Parameters:

Name	Type	Description	Default
sliced_region_properties	list[list[Any]]	List of lists of region properties, the top level is layer, the nesting within it is each of the objects within the layer.	required
feret_maximum	bool	Whether to extract the maximum feret distance.	False
centroid	bool	Whether to extract the centroid coordinates of the region.	False

Returns:

Type	Description
dict[int, dict[int, Any]]	Dictionary of statistics, top-level is the layer/slice through the image, and nested within are statistics for each pore.

feret_diameter_maximum_pores

feret_diameter_maximum_pores(sliced_region_properties)

Extract the maximum feret diameter of objects in each layer.

Parameters:

Name	Type	Description	Default
sliced_region_properties	list[Any]	A list (one for each layer) of lists which contain the region_props for each object within that layer.	required

Returns:

Type	Description
list[list[float]]	A list with the same length as the number of layers, each item is a list of the maximum feret diameter of objects within that layer.

full_width_half_max

full_width_half_max(pdf)

Calculate the full-width half max.

We are interested in the layers that cover the full-width half-max of the number of pores in an image. And therefore extract the indices the calculated PDF (y_pdf)

Parameters:

Name	Type	Description	Default
pdf	NDArray	Array probability density function for which peak and full-width half-max are to be calculated.	required

Returns:

Type	Description
tuple[int, int]	Dictionary of the lower and upper layers for the full-width half-max range.

sum_area_by_layer

sum_area_by_layer(areas, min_size=None)

Sum the area of pores on each layer.

Parameters:

Name	Type	Description	Default
areas	list[list[float]]	A list of areas of pores on each layer.	required
min_size	float	Minimum size to include in calculation.	None

Returns:

Type	Description
list[float]	A list with the total area per slice.

summarise_pores

summarise_pores(df, pore_colors)

Summarise pore types by image, layer and color.

Parameters:

Name	Type	Description	Default
df	DataFrame	Pandas dataframe to aggregate, typically will be AFMSlicer.statistics. Must have columns image, layer, pore_color and counter.	required
pore_colors	list[str]	List of pore color columns that should be present in the resulting dataframe.	required

Returns:

Type	Description
DataFrame	Aggregated data frame of counts of pore_color by image/layer with counts of each pore_color.