Line-by-line Lasers

Laser

Class for data collected line-by-line. Line-by-line data is collected in multiple lines, with each line performed as a continuous ablation in one direction. The lines are then stacked to form an image.

class pewlib.laser.Laser(data, calibration=None, config=None, info=None)

Class for line-by-line laser data.

Parameters:

data (ndarray) – structured array of elemental data
calibration (dict[str, Calibration] | None) – dict mapping elements to calibrations, optional
config (Config | None) – laser parameters
info (dict[str, str] | None) – dict (str, str) of additional info

add(element, data, calibration=None)

Adds a new element.

Parameters:

element (str) – element name
data (ndarray) – array
calibration (Calibration | None) – calibration for data, optional

Return type:

None

property elements: tuple[str, ...]: Elements stored.

property extent: tuple[float, float, float, float]: Image extent in μm

classmethod from_list(elements, datas, config=None, info={})

Creates class from a list of names and unstructured arrays.

Return type:: Laser

get(element=None, calibrate=False, extent=None, **kwargs)

Get elemental data.

If element is None then all elements are returned in a structured array.

Parameters:

element (str | None) – element name, optional
calibrate (bool | None) – apply calibration
extent (tuple[float, float, float, float] | None) – trim to extent, μm

Return type:

ndarray

Returns:

structured if element is None else unstructured

remove(names)

Remove element(s).

Return type:: None

rename(names)

Change the name of element(s).

Parameters:: names (dict[str, str]) – dict mapping old to new names
Return type:: None

Config

class pewlib.config.Config(spotsize=35.0, speed=140.0, scantime=0.25)

Class for the rastered parameters of image.

Parameters:

spotsize (float) – laser-spot diameter, μm
speed (float) – laser movement speed, μm/s
scantime (float) – MS acquisition time, s

data_extent(shape)

Extent of data in μm.

Return type:: tuple[float, float, float, float]

get_pixel_height()

Pixel height in μm.

Return type:: float

get_pixel_width()

Pixel width in μm.

Return type:: float

class pewlib.config.SpotConfig(spotsize=100.0, spotsize_y=None)

Class for the spotwise parameters of image.

Parameters:: spotsize (float) – x, y distance between laser-spots, μm

get_pixel_height()

Pixel height in μm.

Return type:: float

get_pixel_width()

Pixel width in μm.

Return type:: float

Calibration

class pewlib.calibration.Calibration(intercept=0.0, gradient=1.0, unit='', rsq=None, error=None, points=None, weights='Equal')

Class for calibration storage and calculations.

Weights can be automatically generated by passing weighting string to weights.

Parameters:

intercept (float) – of line-of-best-fit
gradient (float) – of line-of-best-fit
unit (str) – calibration units, eg. ‘ppm’
rsq (float | None) – r² of line-of-best-fit
error (float | None) – error in line-of-best-fit
points (ndarray | None) – array of (x, y)
weights (str | tuple[str, ndarray]) – weighting {‘Equal’, ‘x’, ‘1/x’, ‘1/(x^2)’, ‘y’, ‘1/y’, ‘1/(y^2)’} or (name, array) of weights for linear-regression, same length as points

classmethod from_points(points, unit='', weights='Equal')

Create a Calibration from points.

Calulates linear-regression params from points.

Return type:: Calibration

to_array(size=None)

Convert to a Numpy array. Points and weights are trimmed or padded with nan to ‘size’ if passed.

Return type:: ndarray

pewlib.calibration.weighted_linreg(x, y, w=None)

Weighted linear regression.

Uses polyfit with sqrt(weights) for intercept and gradient.

Parameters:

x (ndarray) – 1d-array
y (ndarray) – array, same size as x
w (ndarray | None) – weights, same size as x

Return type:

tuple[float, float, float, float]

Returns:

gradient intercept r² error, S(y,x) the (unweighted) residual standard deviation