Score multi-session events using the replay score from Davidson et al.



In [1]:

    
import numpy as np
import os
import pandas as pd
import warnings

import nelpy as nel

warnings.filterwarnings("ignore")









    



/opt/conda/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:106: MatplotlibDeprecationWarning: The mpl_toolkits.axes_grid module was deprecated in version 2.1. Use mpl_toolkits.axes_grid1 and mpl_toolkits.axisartist provies the same functionality instead.

Load experimental data from Google Cloud bucket



In [2]:

    
import gcsfs
import pandas as pd

fs = gcsfs.GCSFileSystem(project='polar-program-784', token='cloud')
print(fs.ls('kemerelab-data/diba'))

with fs.open('kemerelab-data/diba/gor01vvp01pin01-metadata.h5', 'rb') as fid:
    with pd.HDFStore('gor01vvp01pin01-metadata.h5', mode="r", driver="H5FD_CORE",
            driver_core_backing_store=0,
            driver_core_image=fid.read()
            ) as store:
        df = store['Session_Metadata']
        df2 = store['Subset_Metadata']









    



['kemerelab-data/diba/', 'kemerelab-data/diba/gor01vvp01-metadata.h5', 'kemerelab-data/diba/gor01vvp01_processed_speed.nel', 'kemerelab-data/diba/gor01vvp01pin01-metadata.h5', 'kemerelab-data/diba/gor01vvp01pin01_processed_speed.nel']



In [3]:

    
# %%timeit # beware - this will run it 7 times to get a time! 36 s for 1.4 GB file

with fs.open('kemerelab-data/diba/gor01vvp01pin01_processed_speed.nel', 'rb') as fid:
    jar = nel.load_pkl('',fileobj=fid) # currently requires a specific nelpy branch

exp_data = jar.exp_data
aux_data = jar.aux_data
#del jar

Define subset of sessions to score



In [4]:

    
# restrict sessions to explore to a smaller subset
min_n_placecells = 20
min_n_PBEs = 30 # 27 total events ==> minimum 21 events in training set

df2_subset = df2[(df2.n_PBEs >= min_n_PBEs) & (df2.n_placecells >= min_n_placecells)]

sessions = df2_subset['time'].values.tolist()
segments = df2_subset['segment'].values.tolist()

print('Evaluating subset of {} sessions'.format(len(sessions)))

df2_subset.sort_values(by=['n_PBEs', 'n_placecells'], ascending=[0,0])









    



Evaluating subset of 18 sessions






    Out[4]:







  
    
      
      animal
      month
      day
      time
      track
      segment
      duration
      n_cells
      n_placecells
      n_PBEs
      Notes
      prescreen_z
    
  
  
    
      54
      gor01
      6
      9
      22-24-40
      two
      short
      1620.0
      203
      61
      301
      NaN
      NaN
    
    
      1
      gor01
      6
      7
      16-40-19
      two
      short
      1330.0
      117
      46
      277
      NaN
      NaN
    
    
      28
      pin01
      11
      1
      12-58-54
      unknown
      long
      1670.0
      49
      21
      238
      NaN
      NaN
    
    
      29
      pin01
      11
      1
      12-58-54
      unknown
      short
      925.0
      49
      26
      222
      NaN
      NaN
    
    
      0
      gor01
      6
      7
      16-40-19
      two
      long
      1180.0
      117
      43
      150
      NaN
      NaN
    
    
      42
      gor01
      6
      9
      1-22-43
      one
      long
      1012.0
      203
      62
      117
      NaN
      NaN
    
    
      53
      gor01
      6
      9
      22-24-40
      two
      long
      912.0
      203
      66
      103
      NaN
      NaN
    
    
      43
      gor01
      6
      9
      1-22-43
      one
      short
      617.0
      203
      71
      91
      NaN
      NaN
    
    
      18
      gor01
      6
      8
      21-16-25
      two
      long
      720.0
      171
      82
      57
      NaN
      NaN
    
    
      60
      vvp01
      4
      9
      17-29-30
      one
      short
      490.0
      68
      32
      46
      NaN
      NaN
    
    
      26
      vvp01
      4
      9
      16-40-54
      two
      long
      861.0
      41
      25
      42
      NaN
      NaN
    
    
      19
      gor01
      6
      8
      21-16-25
      two
      short
      457.0
      171
      64
      37
      NaN
      NaN
    
    
      24
      gor01
      6
      12
      16-53-46
      two
      long
      470.0
      81
      36
      36
      NaN
      NaN
    
    
      49
      vvp01
      4
      25
      14-28-51
      one
      long
      385.0
      80
      32
      36
      NaN
      NaN
    
    
      21
      vvp01
      4
      16
      15-12-23
      one
      short
      347.0
      74
      24
      34
      NaN
      NaN
    
    
      59
      vvp01
      4
      9
      17-29-30
      one
      long
      800.0
      68
      34
      33
      NaN
      NaN
    
    
      6
      gor01
      6
      12
      15-55-31
      one
      long
      660.0
      97
      40
      32
      NaN
      NaN
    
    
      66
      gor01
      6
      13
      15-22-3
      two
      long
      530.0
      82
      37
      31
      NaN
      NaN

Parallel scoring

NOTE: it is relatively easy (syntax-wise) to score each session as a parallel task, but since the Bayesian scoring takes such a long time to compute, we can be more efficient (higher % utilization) by further parallelizing over events, and not just over sessions. This further level of parallelization makes the bookkeeping a little ugly, so I provide the code for both approaches here.



In [5]:

    
# It would be easy enough to set up the dask hooks for by_session, but right now it's only done for by_event.
# So don't change these or it won't work.
parallelize_by_session = False # won't work currently with Dask
parallelize_by_event = True

n_jobs = 20 # set this equal to number of cores (or number of workers if using the dask-distribued joblib backend)
n_shuffles = 2 # 5000
n_samples = 100 # 35000
w=3 # single sided bandwidth (0 means only include bin who's center is under line, 3 means a total of 7 bins)



In [6]:

    
from dask.distributed import Client
# client = Client('dask-scheduler:8786') # fill in value from dask-kubernetes startup if connecting remotely
client = Client() # Use this to create a local scheduluer
client









    Out[6]:







Client

  Scheduler: tcp://127.0.0.1:33341
  
Dashboard: http://127.0.0.1:8787



Cluster

  Workers: 16
  Cores: 16
  Memory: 63.34 GB



In [7]:

    
# Not set up for dask!!!

if parallelize_by_session:
    from joblib import Parallel, delayed 


    # A function that can be called to do work:
    def work_sessions(arg):    

        # Split the list to individual variables:
        ii, bst, tc = arg    

        scores, shuffled_scores, percentiles = nel.analysis.replay.score_Davidson_final_bst_fast(bst=bst,
                                                                                            tuningcurve=tc,
                                                                                            w=w,
                                                                                            n_shuffles=n_shuffles,
                                                                                            n_samples=n_samples)

        return (ii, scores, shuffled_scores, percentiles)

    # List of instances to pass to work():
    parallel_sessions = [(ii, aux_data[session][segment]['PBEs'], aux_data[session][segment]['tc']) for (ii, (session, segment)) in enumerate(zip(sessions, segments))]

    parallel_results = Parallel(n_jobs=n_jobs, verbose=51)(map(delayed(work_sessions), parallel_sessions))

    # standardize parallel results
    idx = [result[0] for result in parallel_results]

    # check that parallel results came back IN ORDER:
    if nel.utils.is_sorted(idx):
        print('parallel results are ordered...')
    else:
        raise ValueError('results are not ordered! handle it here before proceeding...')

    scores_bayes = [result[1] for result in parallel_results]
    scores_bayes_shuffled = [result[2] for result in parallel_results]
    scores_bayes_percentile = [result[3] for result in parallel_results]

    results = dict()
    for ii, (session, segment) in enumerate(zip(sessions, segments)):
        try:
            results[session][segment] = dict()
        except KeyError:
            results[session] = dict()    
            results[session][segment] = dict()
        results[session][segment]['scores_bayes'] = scores_bayes[ii]
        results[session][segment]['scores_bayes_shuffled'] = scores_bayes_shuffled[ii]
        
        results[session][segment]['scores_bayes_percentile'] = scores_bayes_percentile[ii]

    print('done packing results')



In [9]:

    
# Set up for Dask
if parallelize_by_event:
    import time
    t0 = time.time()
    
    #from joblib import Parallel, delayed 
    import distributed.joblib
    from joblib import Parallel, parallel_backend, delayed

    # A function that can be called to do work:
    def work_events(arg):    
        # Split the list to individual variables:
        session, segment, ii, bst, tc = arg
        scores, shuffled_scores, percentiles = nel.analysis.replay.score_Davidson_final_bst_fast(bst=bst,
                                                                                            tuningcurve=tc,
                                                                                            w=w,
                                                                                            n_shuffles=n_shuffles,
                                                                                            n_samples=n_samples)

        t01 = time.time()
        return (session, segment, ii, scores, shuffled_scores, percentiles)

    # List of instances to pass to work():
    # unroll all events:
    parallel_events = []
    for session, segment in zip(sessions, segments):
        for nn in range(aux_data[session][segment]['PBEs'].n_epochs):
            parallel_events.append((session, segment, nn, aux_data[session][segment]['PBEs'][nn], aux_data[session][segment]['tc']))

# Option 1: Just use joblib - you need the "from joblib import Parallel, delayed" uncommented. 
#           (and not distributed.joblib?)
    #parallel_results = Parallel(n_jobs=n_jobs, verbose=51)(map(delayed(work_events), parallel_events))
    
# Option 2: Use dask-distrubuted for the joblib backend
#     NOTE fill in the scheduler ip returned from looking at "client" in the cell above
    with parallel_backend('dask.distributed', scheduler_host='tcp://127.0.0.1:33341'):
        parallel_results = Parallel(n_jobs=n_jobs, verbose=51)(map(delayed(work_events), parallel_events))

# Option 3: Use the dask-distributed client commands
    # parallel_results_future = client.map(work_events, parallel_events)
    # parallel_results = client.gather(parallel_results_future)
    
# Option 4: Use deep-dask parallelism
    # To be implemented. The key, apparently, in wrapping the "score_" function with delayed is to use the
    # "traverse=False" option. See https://github.com/dask/dask/issues/2785
    
    # standardize parallel results
    bdries_ = [aux_data[session][segment]['PBEs'].n_epochs for session, segment in zip(sessions, segments) ]
    bdries = np.cumsum(np.insert(bdries_,0,0))
    bdries

    sessions_ = np.array([result[0] for result in parallel_results])
    segments_ = np.array([result[1] for result in parallel_results])
    idx = [result[2] for result in parallel_results]

    scores_bayes_evt = np.array([float(result[3]) for result in parallel_results])
    scores_bayes_shuffled_evt = np.array([result[4].squeeze() for result in parallel_results])
    scores_bayes_percentile_evt = np.array([float(result[5]) for result in parallel_results])

    results = {}
    for nn in range(len(bdries)-1):
        session = np.unique(sessions_[bdries[nn]:bdries[nn+1]])
        if len(session) > 1:
            raise ValueError("parallel results in different format / order than expected!")
        session = session[0]
        segment = np.unique(segments_[bdries[nn]:bdries[nn+1]])
        if len(segment) > 1:
            raise ValueError("parallel results in different format / order than expected!")
        segment = segment[0]
        try:
            results[session][segment]['scores_bayes'] = scores_bayes_evt[bdries[nn]:bdries[nn+1]]
        except KeyError:
            try:
                results[session][segment] = dict()
                results[session][segment]['scores_bayes'] = scores_bayes_evt[bdries[nn]:bdries[nn+1]]
            except KeyError:
                results[session] = dict()
                results[session][segment] = dict()
                results[session][segment]['scores_bayes'] = scores_bayes_evt[bdries[nn]:bdries[nn+1]]

        results[session][segment]['scores_bayes_shuffled'] = scores_bayes_shuffled_evt[bdries[nn]:bdries[nn+1]]
        results[session][segment]['scores_bayes_percentile'] = scores_bayes_percentile_evt[bdries[nn]:bdries[nn+1]]

    print('done packing results')

t1 = time.time()    
print('Elapsed time ', t1 - t0)









    



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[Parallel(n_jobs=20)]: Done 1113 tasks      | elapsed:   10.6s
[Parallel(n_jobs=20)]: Done 1114 tasks      | elapsed:   10.6s
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[Parallel(n_jobs=20)]: Done 1120 tasks      | elapsed:   10.6s
[Parallel(n_jobs=20)]: Done 1121 tasks      | elapsed:   10.7s
[Parallel(n_jobs=20)]: Done 1122 tasks      | elapsed:   10.7s
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[Parallel(n_jobs=20)]: Done 1137 tasks      | elapsed:   10.9s
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[Parallel(n_jobs=20)]: Done 1149 tasks      | elapsed:   11.0s
[Parallel(n_jobs=20)]: Done 1150 tasks      | elapsed:   11.0s
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[Parallel(n_jobs=20)]: Done 1160 tasks      | elapsed:   11.1s
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[Parallel(n_jobs=20)]: Done 1170 tasks      | elapsed:   11.2s
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[Parallel(n_jobs=20)]: Done 1179 tasks      | elapsed:   11.3s
[Parallel(n_jobs=20)]: Done 1180 tasks      | elapsed:   11.3s
[Parallel(n_jobs=20)]: Done 1181 tasks      | elapsed:   11.3s
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[Parallel(n_jobs=20)]: Done 1184 tasks      | elapsed:   11.3s
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[Parallel(n_jobs=20)]: Done 1186 tasks      | elapsed:   11.4s
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[Parallel(n_jobs=20)]: Done 1190 tasks      | elapsed:   11.4s
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[Parallel(n_jobs=20)]: Done 1199 tasks      | elapsed:   11.5s
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[Parallel(n_jobs=20)]: Done 1201 tasks      | elapsed:   11.6s
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[Parallel(n_jobs=20)]: Done 1240 tasks      | elapsed:   12.0s
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[Parallel(n_jobs=20)]: Done 1257 tasks      | elapsed:   12.1s
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[Parallel(n_jobs=20)]: Done 1260 tasks      | elapsed:   12.1s
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[Parallel(n_jobs=20)]: Done 1290 tasks      | elapsed:   12.5s
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[Parallel(n_jobs=20)]: Done 1300 tasks      | elapsed:   12.5s
[Parallel(n_jobs=20)]: Done 1301 tasks      | elapsed:   12.6s
[Parallel(n_jobs=20)]: Done 1302 tasks      | elapsed:   12.6s
[Parallel(n_jobs=20)]: Done 1303 tasks      | elapsed:   12.6s
[Parallel(n_jobs=20)]: Done 1304 tasks      | elapsed:   12.6s
[Parallel(n_jobs=20)]: Done 1305 tasks      | elapsed:   12.6s
[Parallel(n_jobs=20)]: Done 1306 tasks      | elapsed:   12.6s
[Parallel(n_jobs=20)]: Done 1307 tasks      | elapsed:   12.6s
[Parallel(n_jobs=20)]: Done 1308 tasks      | elapsed:   12.7s
[Parallel(n_jobs=20)]: Done 1309 tasks      | elapsed:   12.7s
[Parallel(n_jobs=20)]: Done 1310 tasks      | elapsed:   12.7s
[Parallel(n_jobs=20)]: Done 1311 tasks      | elapsed:   12.7s
[Parallel(n_jobs=20)]: Done 1312 tasks      | elapsed:   12.7s
[Parallel(n_jobs=20)]: Done 1313 tasks      | elapsed:   12.7s
[Parallel(n_jobs=20)]: Done 1314 tasks      | elapsed:   12.7s
[Parallel(n_jobs=20)]: Done 1315 tasks      | elapsed:   12.7s
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[Parallel(n_jobs=20)]: Done 1317 tasks      | elapsed:   12.7s
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[Parallel(n_jobs=20)]: Done 1319 tasks      | elapsed:   12.7s
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[Parallel(n_jobs=20)]: Done 1321 tasks      | elapsed:   12.7s
[Parallel(n_jobs=20)]: Done 1322 tasks      | elapsed:   12.7s
[Parallel(n_jobs=20)]: Done 1323 tasks      | elapsed:   12.7s
[Parallel(n_jobs=20)]: Done 1324 tasks      | elapsed:   12.8s
[Parallel(n_jobs=20)]: Done 1325 tasks      | elapsed:   12.8s
[Parallel(n_jobs=20)]: Done 1326 tasks      | elapsed:   12.8s
[Parallel(n_jobs=20)]: Done 1327 tasks      | elapsed:   12.8s
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[Parallel(n_jobs=20)]: Done 1329 tasks      | elapsed:   12.8s
[Parallel(n_jobs=20)]: Done 1330 tasks      | elapsed:   12.8s
[Parallel(n_jobs=20)]: Done 1331 tasks      | elapsed:   12.8s
[Parallel(n_jobs=20)]: Done 1332 tasks      | elapsed:   12.8s
[Parallel(n_jobs=20)]: Done 1333 tasks      | elapsed:   12.8s
[Parallel(n_jobs=20)]: Done 1334 tasks      | elapsed:   12.8s
[Parallel(n_jobs=20)]: Done 1335 tasks      | elapsed:   12.8s
[Parallel(n_jobs=20)]: Done 1336 tasks      | elapsed:   12.8s
[Parallel(n_jobs=20)]: Done 1337 tasks      | elapsed:   12.9s
[Parallel(n_jobs=20)]: Done 1338 tasks      | elapsed:   12.9s
[Parallel(n_jobs=20)]: Done 1339 tasks      | elapsed:   12.9s
[Parallel(n_jobs=20)]: Done 1340 tasks      | elapsed:   12.9s
[Parallel(n_jobs=20)]: Done 1341 tasks      | elapsed:   12.9s
[Parallel(n_jobs=20)]: Done 1342 tasks      | elapsed:   12.9s
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[Parallel(n_jobs=20)]: Done 1344 tasks      | elapsed:   12.9s
[Parallel(n_jobs=20)]: Done 1345 tasks      | elapsed:   12.9s
[Parallel(n_jobs=20)]: Done 1346 tasks      | elapsed:   13.0s
[Parallel(n_jobs=20)]: Done 1347 tasks      | elapsed:   13.0s
[Parallel(n_jobs=20)]: Done 1348 tasks      | elapsed:   13.0s
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[Parallel(n_jobs=20)]: Done 1350 tasks      | elapsed:   13.0s
[Parallel(n_jobs=20)]: Done 1351 tasks      | elapsed:   13.0s
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[Parallel(n_jobs=20)]: Done 1359 tasks      | elapsed:   13.1s
[Parallel(n_jobs=20)]: Done 1360 tasks      | elapsed:   13.1s
[Parallel(n_jobs=20)]: Done 1361 tasks      | elapsed:   13.1s
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[Parallel(n_jobs=20)]: Done 1363 tasks      | elapsed:   13.1s
[Parallel(n_jobs=20)]: Done 1364 tasks      | elapsed:   13.1s
[Parallel(n_jobs=20)]: Done 1365 tasks      | elapsed:   13.1s
[Parallel(n_jobs=20)]: Done 1366 tasks      | elapsed:   13.1s
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[Parallel(n_jobs=20)]: Done 1370 tasks      | elapsed:   13.2s
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[Parallel(n_jobs=20)]: Done 1374 tasks      | elapsed:   13.3s
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[Parallel(n_jobs=20)]: Done 1380 tasks      | elapsed:   13.3s
[Parallel(n_jobs=20)]: Done 1381 tasks      | elapsed:   13.3s
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[Parallel(n_jobs=20)]: Done 1387 tasks      | elapsed:   13.4s
[Parallel(n_jobs=20)]: Done 1388 tasks      | elapsed:   13.4s
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[Parallel(n_jobs=20)]: Done 1390 tasks      | elapsed:   13.4s
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[Parallel(n_jobs=20)]: Done 1399 tasks      | elapsed:   13.4s
[Parallel(n_jobs=20)]: Done 1400 tasks      | elapsed:   13.4s
[Parallel(n_jobs=20)]: Done 1401 tasks      | elapsed:   13.7s
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[Parallel(n_jobs=20)]: Done 1418 tasks      | elapsed:   13.8s
[Parallel(n_jobs=20)]: Done 1419 tasks      | elapsed:   13.9s
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[Parallel(n_jobs=20)]: Done 1641 tasks      | elapsed:   16.2s
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[Parallel(n_jobs=20)]: Done 1645 tasks      | elapsed:   16.3s
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[Parallel(n_jobs=20)]: Done 1650 tasks      | elapsed:   16.3s
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[Parallel(n_jobs=20)]: Done 1664 tasks      | elapsed:   16.4s
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[Parallel(n_jobs=20)]: Done 1666 tasks      | elapsed:   16.4s
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[Parallel(n_jobs=20)]: Done 1668 tasks      | elapsed:   16.4s
[Parallel(n_jobs=20)]: Done 1669 tasks      | elapsed:   16.5s
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[Parallel(n_jobs=20)]: Done 1682 tasks      | elapsed:   16.7s
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done packing results
Elapsed time  29.060096263885498

Save results to disk



In [ ]:

    
jar = nel.ResultsContainer(results=results, description='gor01 and vvp01 speed restricted results for best 20 candidate sessions')
jar.save_pkl('score_bayes_all_sessions.nel')

	animal	month	day	time	track	segment	duration	n_cells	n_placecells	n_PBEs	Notes	prescreen_z
54	gor01	6	9	22-24-40	two	short	1620.0	203	61	301	NaN	NaN
1	gor01	6	7	16-40-19	two	short	1330.0	117	46	277	NaN	NaN
28	pin01	11	1	12-58-54	unknown	long	1670.0	49	21	238	NaN	NaN
29	pin01	11	1	12-58-54	unknown	short	925.0	49	26	222	NaN	NaN
0	gor01	6	7	16-40-19	two	long	1180.0	117	43	150	NaN	NaN
42	gor01	6	9	1-22-43	one	long	1012.0	203	62	117	NaN	NaN
53	gor01	6	9	22-24-40	two	long	912.0	203	66	103	NaN	NaN
43	gor01	6	9	1-22-43	one	short	617.0	203	71	91	NaN	NaN
18	gor01	6	8	21-16-25	two	long	720.0	171	82	57	NaN	NaN
60	vvp01	4	9	17-29-30	one	short	490.0	68	32	46	NaN	NaN
26	vvp01	4	9	16-40-54	two	long	861.0	41	25	42	NaN	NaN
19	gor01	6	8	21-16-25	two	short	457.0	171	64	37	NaN	NaN
24	gor01	6	12	16-53-46	two	long	470.0	81	36	36	NaN	NaN
49	vvp01	4	25	14-28-51	one	long	385.0	80	32	36	NaN	NaN
21	vvp01	4	16	15-12-23	one	short	347.0	74	24	34	NaN	NaN
59	vvp01	4	9	17-29-30	one	long	800.0	68	34	33	NaN	NaN
6	gor01	6	12	15-55-31	one	long	660.0	97	40	32	NaN	NaN
66	gor01	6	13	15-22-3	two	long	530.0	82	37	31	NaN	NaN