bsmart.scans.Contour2D

Basic scan to find contours in two variables

You can specify the contour either via the option ‘Function’ (as a function of variables and observables, e.g. “mh” or “mh-125.0”). If not it will create likelihoods based on the observables. In both cases you need to specify a ‘Threshold’ for the contour value.

Note this comes along with an upgrade to the BSMplots to allow contour plots in ‘csv’ mode. You just add ‘Contours’:[125.0] etc to the options in the plot and make sure three variables are specified.

Information

BSMArt Name: Contour2D

Requires:

• scikit-image

• scipy

• numpy

• pandas

Settings:

• Bad value: Float

• N0: Initial grid points

• Threshold: Float

• Function: Contour function

• Input_CSV_File: Path

class bsmart.scans.Contour2D.NewScan(inputs, log)

Bases: Scan

FirstGrid()

initialise()

This is called during init, but allows us to override certain things like the invalid_return_value

min_distance(pt)

postprocess(Point, observables, data_point, temp_dir, log, lock=None)

Get the function as a product of the likelihoods

run()

bsmart.scans.Contour2D.distance(pt1, pt2)

bsmart.scans.Contour2D.removeclose(pts, threshold)