synthesis_workflow.fit_utils¶

Some functions used to fit path distances with depth.

Functions¶

`_get_tmd_feature`(→ numpy.array)	Returns a list of features using tmd.
`get_path_distances`(→ numpy.array)	Returns path distances using tmd.
`get_projections`(→ numpy.array)	Returns projections using tmd.
`fit_function`(→ float)	The function used to fit data.
`clean_outliers`(→ Tuple[numpy.array, numpy.array])	Returns data without outliers.
`fit_path_distance_to_extent`(→ Tuple[float, float])	Returns slope and intercept of a linear fit.
`get_path_distance_from_extent`(→ float)	Returns a path distance for an input extent according to fitted function.

Module Contents¶

_get_tmd_feature(input_population: tmd.Population.Population.Population, feature: str, neurite_type: str = 'apical_dendrite') → numpy.array¶: Returns a list of features using tmd.

get_path_distances(input_population: tmd.Population.Population.Population, neurite_type: str = 'apical_dendrite') → numpy.array¶

Returns path distances using tmd.

Parameters:: input_population – the population of neurons
Returns:: list of path distances

get_projections(input_population: tmd.Population.Population.Population, neurite_type: str = 'apical_dendrite') → numpy.array¶

Returns projections using tmd.

Parameters:: input_population – the population of neurons
Returns:: list of projections

fit_function(x: float, slope: float) → float¶: The function used to fit data.

clean_outliers(x: Sequence[float], y: Sequence[float], outlier_percentage: int = 90) → Tuple[numpy.array, numpy.array]¶

Returns data without outliers.

Parameters:

x – the X-axis coordinates
y – the Y-axis coordinates
outlier_percentage – the percentage used to find and remove outliers

Returns:

cleaned X and Y coordinates

fit_path_distance_to_extent(input_population: tmd.Population.Population.Population, outlier_percentage: int = 90, neurite_type: str = 'apical_dendrite') → Tuple[float, float]¶

Returns slope and intercept of a linear fit.

Returns the two parameters (slope, intercept) for the linear fit of path length (Y-variable) to total extents (X-variable). Removes outliers up to outlier_percentage for a better fit.

Parameters:

input_population – the population of neurons
outlier_percentage – the percentage used to find and remove outliers
neurite_type – neurite_type to make the fit

Returns: slope and intercept of the fit

get_path_distance_from_extent(slope: float, intercept: float, extent: float) → float¶

Returns a path distance for an input extent according to fitted function.

The function is given by the equation: Path = slope * extent + intercept

Parameters:

slope – the slope of the function
intercept – the intercept of the function
extent – the point where the function is evaluated

Returns: function value evaluated at x = extent