Low-level API

For user-friendliness we recommend using the high-level API described in Quick Start and most of the rest of these docs. However, if for some reason you don’t want to use this API, then you have the option of digging deeper, into a lower level API that is exposed by ScannerBit. Below is a short description of how this API can be used.

First, you need to set some flags for dlopen to help than scanner plugin libraries be dynamically loaded correctly (this is done automatically in the high-level API):

import sys
import ctypes
flags = sys.getdlopenflags()
sys.setdlopenflags(flags | ctypes.RTLD_GLOBAL)

Next import the ‘bare-bones’ interface from a few levels down in the package:

# Dig past the extra python wrapping to the direct ScannerBit.so shared library interface level
from pyscannerbit.ScannerBit.python import ScannerBit

Define the log-likelihood function you wish to scan, and (if desired) a prior transformation function:

def loglike(m):
    a = m["model1::x"]
    ScannerBit.print("my_param", 0.5) # Send extra data to the output file at each point
    return -a*a/2.0

def prior(vec, map):
    # tell ScannerBit that the hypergrid dimension is 1
    ScannerBit.ensure_size(vec, 1) # this needs to be the first line!
    map["model1::x"] = 5.0 - 10.0*vec[0]

These look superficially similar to the functions that should be supplied to the high-level API, however please note that there is no sanity/error checking in this low-level API, so mistakes will result in cryptic errors from inside ScannerBit. The model name in e.g. model1::x is also non-optional in this interface, and similarly you must call the ensure_size function in the prior function to tell ScannerBit the dimension of your parameter space.

Next generate a settings dictionary. The structure of this should match the YAML format required by ScannerBit when run via GAMBIT (this is one thing that we will simplify in the nice wrapper….):

settings = {
"Parameters": {
  "model1": {
    "x": None,
"Priors": {
  "x_prior": {
    "prior_type": 'flat',
    "parameters": ['model1::x'],
    "range": [1.0, 40.0],
"Printer": {
  "printer": "hdf5",
  "options": {
    "output_file": "results.hdf5",
    "group": "/",
    "delete_file_on_restart": "true",
"Scanner": {
  "scanners": {
    "twalk": {
      "plugin": "twalk",
      "like": "LogLike",
      "tolerance": 1.003,
      "kwalk_ratio": 0.9,
      "projection_dimension": 4
"KeyValues": {
  "default_output_path": "pyscannerbit_run_data/",
  "likelihood": {
    "model_invalid_for_lnlike_below": -1e6

Run your scan!:

myscan = ScannerBit.scan(True)
myscan.run(inifile=settings, lnlike={"LogLike": like}, prior=prior, restart=True)

If all went well, your scan should begin, and generate HDF5 format output in pyscannerbit_run_data/samples/results.hdf5.