Nina's Algorithm

Contents

• Code structure/graph
• Input requirements and functionality
• Output/ control
• Run
• Function connectivity
• Detailed functionality of each module

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Code structure

1. [Optimisation algorithm.ipynb] - File to run code
   1. Calls [factor_vae_bias_sub]
   2. [factor_vae_bias_sub] will call [vae_bias_sub]
   3. FactorVAE is the parent class of VAE

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Input requirements and functionality

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Output/ control

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Run

run_random()

​ Will run algorithm

run_random(savedir, window_small=30, res_low=10, res_high=20, n_walk=30,it=10, window_large_init=30,
               gates_init=[-1150.9539658,-808.79439154,-1815.3845539,-1127.79332466,-1406.53212482,-682.98769082],
               starting_c5_c9_init=[-575,-945],res_save=128)

gates_init=[c3, c4, c6, c7, c8, c10], starting_c5_c9_init=[c5, c9]

• Loads vae and vae2

  • Only vae2 is used - Based on dataset with translational augmentation
  • c5 and c9 are plungers (x and y axis)

• Will run random()

  • for i in range(n_walk):

    • Each i in range(n_walk) runs it=60 steps from the starting position

  • in random dv is the step size

    • Pass in as variable
    • Diminishing step size

  • idx_init

    • 6 options, modifying one of 3 gates by + dv or -dv

  idx_init=[np.array([0,-dv]),np.array([0,dv]),np.array([1,-dv]),np.array([1,dv]),np.array([5,-dv]),np.array([5,dv])]

  • 0,1,5 define the indices of the control gates within the complete gate list defined bygates_init in run_random()

• for j in range(it):

  • Will take it = 60 actions

• Extract new gate voltages

  •     gates_prop,delta_v, idx,gate_idx, idx_tot, gates_tot,gates=get_new_param(gates,gates_tot, idx,idx_tot)

    • get_new_param()

      • if len(idx)==0:
                if len(idx_tot)==0:

        • idx is list of actions left
        • of idx_tot == 0 the entire list is exhausted and then just returns to initial point - has not found within branch a more perfect triangle

      •  else:

        • random action selected from possible action list

• Low res scan to recenter

• 5c9_list_prop=blob_detection(data_low,c5c9_list,res_low=res_low,window_large=window_large)

  • Returns proposed values for plungers of the centre of triangle
  • If blob detection fails
    • Check threshold and min_sigma, mas_sigma which are hardcoded into blob detection
      • Uses blobs (log) not blobs_2, could comment out blobs_2

• High res scan to be scored

• score_vae,score_vae_trans,score_tot=predict_mod(vae,vae2, data_high)

  • predict_mod uses vae's trained on 32 x 32 so res_high should always be defined as 32
  • normalise()
    • Normalises current based upon whole plot (from Dom's) - hardcoded
    • If you normalise just in region of triangle then a feint triangle would be scored highly
    • vae is very sensitive to normalisation

• Lower scores are better as they represent the euclidean distance with variance term from targets

• idx=[x for x in idx if not np.array_equal(x,np.array([gate_idx,-delta_v]))]

  • removes reverse of selected action from list of possible actions within branch

• can comment out data_save

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To run on server

savedir="//media//oxml//DA12F77512F754CB//Pygor_results//optimization//gamma//" #output directory 

pygor = Pygor.Experiment(mode='jupyter', xmlip="http://129.67.86.107:8000/RPC2",savedir=savedir) # Access pygor where http://129.67.86.107:8000/RPC2 is ip address of Triton 4

run_random(window_small=17, res_low=30, res_high=32, n_walk=3,it=60,gates_init=

[-1356.597083799452, -655.7552004185842, -757.39513943, -1910.53470352, -1315.74489394, -610.0010767257405],  starting_c5_c9_init=[-626, -782] ,savedir=savedir,res_save=128)

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Function Connectivity

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Modular Overview