config.yaml

TCP:
  host: localhost
  port: 54333
  buffer_size: 8388608 # 8MB
  timeout: 10 # seconds

logging:
  level: INFO
  directory: null
  formatter: '%(asctime)s | %(name)s | %(levelname)-8s | %(message)s'

plots:
  posterior_map_shape: [50,50] #full

scanning:
  max_points: 999 # 0 for unlimited
  duration: 7200 # in seconds
  train_at: [10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000]
  train_every: 0 # 0 for no training
  normalize_values: training # one of init, training, always, fixed or never
  fixed_normalization: [1.0, 1.0] # normalization factors for each task
  merge_unique_positions: true
  base_error: 0.01 # 1% error
  initial_points: hexgrid_2D_13 # grid_3D_27 
  
tasks: # list of tasks to which to reduce the data
  # laplace_filter:
  #   function: laplace_filter
  #   params:
  #     sigma: 5
  #     norm: False
  #     roi: [[145, 190], [10, 140]]
  curvature:
    function: curvature
    params:
      bw: 5
      c1: 0.001
      c2: 0.001
      w: 1
      roi: [[145, 190], [20, 130]]
  # contrast_noise_ratio:
  #   function: contrast_noise_ratio
  #   params: 
  #     signal_roi: [[145, 190], [10, 140]]
  #     bg_roi: [[200,-1], [50, 100]]
  mean: # name of the task found in gp/tasks.py
    function: mean # name of the function
    params: 
      roi: [[145, 190], [20, 130]]

acquisition_function:
  function: acquisition_function_nd # name of the function found in gp/acquisition_functions.py
  params:
    a: 1 # exploration vs exploitation, i.e. variance weight
    weights: null
    norm: 1 # normalization factor for acquisition function
    c: 0 # covariance weight

cost_function:
  function: cost_per_axis # name of the function found in gp/cost_functions.py
  params: 
    speed: [300,300] # um/s, 0 speed is infinite
    weight: [100,100] # 0.01 # weight of the cost function

gp:
  optimizer:
    output_space_dimension: 1 # just leave it at 1

  fvgp:
    init_hyperparameters: [ # initial hyperparameters for the GP, should be 2 + parameter dimensions
      1_000_000, # overall amplitude
      # 100, 
      100, 
      100, 
      0.5 # lengthscale along tasks
    ]
    compute_device: cpu # cpu or cuda:0
    gp_kernel_function: null # default is Matern Kernel
    gp_mean_function: null 
    use_inv: false
    ram_economy: true # shouldnt really do anything without use_inv

  training:
    hyperparameter_bounds: [
      [1_000_000, 1_000_000_000],
      # [1, 50_000],
      [10, 50_000],
      [10, 50_000],
      [0.001, 5000]
    ]
    pop_size: 20 # population size for global optimization
    max_iter: 2 # maximum number of iterations for global optimization
    tolerance: 0.000001 # tolerance for global optimization

  ask:
    n: 1 # number of points to ask per iteration
    bounds: null # bounds of the input space
    method: global # global or local
    pop_size: 20 # population size for global optimization
    max_iter: 10 # maximum number of iterations for global optimization
    tol: 0.000001 # tolerance for global optimization

simulator:
  source_file: D:\data\ARPESdatabase\maps\SGM4\MBT_C_62 # Z006_35_0.h5 # Cleave3_52 
  save_dir: data
  save_to_file: true
  simulate_times: True
  dwell_time: 1 # seconds