Sorted Spikes Decoding¶

The mechanics of decoding with sorted spikes are largely similar to those of decoding with unsorted spikes. You should familiarize yourself with the clusterless decoding tutorial before proceeding with this one.

The elements we will need to decode with sorted spikes are:

• PositionGroup
• SortedSpikesGroup
• DecodingParameters
• encoding_interval
• decoding_interval

This time, instead of extracting waveform features, we can proceed directly from the SpikeSortingOutput table to specify which units we want to decode. The rest of the decoding process is the same as before.

SortedSpikesGroup¶

In [1]:

from pathlib import Path
import datajoint as dj

dj.config.load(
    Path("../dj_local_conf.json").absolute()
)  # load config for database connection info

from pathlib import Path import datajoint as dj dj.config.load( Path("../dj_local_conf.json").absolute() ) # load config for database connection info

In [2]:

from spyglass.spikesorting.merge import SpikeSortingOutput
import spyglass.spikesorting.v1 as sgs


nwb_copy_file_name = "mediumnwb20230802_.nwb"

sorter_keys = {
    "nwb_file_name": nwb_copy_file_name,
    "sorter": "clusterless_thresholder",
    "sorter_param_name": "default_clusterless",
}

(sgs.SpikeSortingSelection & sorter_keys) * SpikeSortingOutput.CurationV1

from spyglass.spikesorting.merge import SpikeSortingOutput import spyglass.spikesorting.v1 as sgs nwb_copy_file_name = "mediumnwb20230802_.nwb" sorter_keys = { "nwb_file_name": nwb_copy_file_name, "sorter": "clusterless_thresholder", "sorter_param_name": "default_clusterless", } (sgs.SpikeSortingSelection & sorter_keys) * SpikeSortingOutput.CurationV1

[2024-01-17 22:49:07,284][INFO]: Connecting root@localhost:3306
[2024-01-17 22:49:07,353][INFO]: Connected root@localhost:3306

Out[2]:

sorting_id	merge_id	recording_id	sorter	sorter_param_name	nwb_file_name name of the NWB file	interval_list_name descriptive name of this interval list	curation_id
08a302b6-5505-40fa-b4d5-62162f8eef58	485a4ddf-332d-35b5-3ad4-0561736c1844	449b64e3-db0b-437e-a1b9-0d29928aa2dd	clusterless_thresholder	default_clusterless	mediumnwb20230802_.nwb	45f6b9a1-eef3-46eb-866d-d0999afebda6	0
0ca508ee-af4c-4a89-8181-d48bd209bfd4	6acb99b8-6a0c-eb83-1141-5f603c5895e0	328da21c-1d9c-41e2-9800-76b3484b707b	clusterless_thresholder	default_clusterless	mediumnwb20230802_.nwb	686d9951-1c0f-4d5e-9f5c-09e6fd8bdd4c	0
209dc048-6fae-4315-b293-c06fff29f947	f7237e18-4e73-4aee-805b-90735e9147de	aff78f2f-2ba0-412a-95cc-447c3a2f4683	clusterless_thresholder	default_clusterless	mediumnwb20230802_.nwb	719e8a86-fcf1-4ffc-8c1f-ea912f67ad5d	0
21a9a593-f6f3-4b82-99d7-8fc46556eff3	7e3fa66e-727e-1541-819a-b01309bb30ae	2402805a-04f9-4a88-9ccf-071376c8de19	clusterless_thresholder	default_clusterless	mediumnwb20230802_.nwb	d581b117-160e-4311-b096-7781a4de4394	0
406a20e3-5a9f-4fec-b046-a6561f72461e	6d039a63-17ad-0b78-4b1e-f02d5f3dbbc5	f1427e00-2974-4301-b2ac-b4dc29277c51	clusterless_thresholder	default_clusterless	mediumnwb20230802_.nwb	0e848c38-9105-4ea4-b6ba-dbdd5b46a088	0
4131c51b-c56d-41fa-b046-46635fc17fd9	e0e9133a-7a4e-1321-a43a-e8afcb2f25da	9e332d82-1daf-4e92-bb50-12e4f9430875	clusterless_thresholder	default_clusterless	mediumnwb20230802_.nwb	9ed11db5-c42e-491a-8caf-7d9a37a65f13	0
4c5a629a-71d9-481d-ab11-a4cb0fc16087	9959b614-2318-f597-6651-a3a82124d28a	3a2c3eed-413a-452a-83c8-0e4648141bde	clusterless_thresholder	default_clusterless	mediumnwb20230802_.nwb	2b9fbf14-74a0-4294-a805-26702340aac9	0
4d629c07-1931-4e1f-a3a8-cbf1b72161e3	c0eb6455-fc41-c200-b62e-e3ca81b9a3f7	f07bc0b0-de6b-4424-8ef9-766213aaca26	clusterless_thresholder	default_clusterless	mediumnwb20230802_.nwb	5c68f0f0-f577-4905-8a09-e4d171d0a22d	0
554a9a3c-0461-48be-8435-123eed59c228	912e250e-56d8-ee33-4525-c844d810971b	7f128981-6868-4976-ba20-248655dcac21	clusterless_thresholder	default_clusterless	mediumnwb20230802_.nwb	f4b9301f-bc91-455b-9474-c801093f3856	0
7bb007f2-26d3-463f-b7dc-7bd4d271725e	d7d2c97a-0e6e-d1b8-735c-d55dc66a30e1	a9b7cec0-1256-49cf-abf0-8c45fd155379	clusterless_thresholder	default_clusterless	mediumnwb20230802_.nwb	74270cba-36ee-4afb-ab50-2a6cc948e68c	0
80e1f37f-48a7-4087-bd37-7a37b6a2c160	abb92dce-4410-8f17-a501-a4104bda0dcf	3c40ebdc-0b61-4105-9971-e1348bd49bc7	clusterless_thresholder	default_clusterless	mediumnwb20230802_.nwb	0f91197e-bebb-4dc6-ad41-5bf89c3eed28	0
8848c4a8-a2f2-4f3d-82cd-51b13b8bae3c	74e10781-1228-4075-0870-af224024ffdc	257c077b-8f3b-4abb-a631-6b8084d6a1ea	clusterless_thresholder	default_clusterless	mediumnwb20230802_.nwb	e289e03d-32ad-461a-a1cc-c88537343149	0

...

Total: 23

In [3]:

spikesorting_merge_ids = (
    (sgs.SpikeSortingSelection & sorter_keys) * SpikeSortingOutput.CurationV1
).fetch("merge_id")

spikesorting_merge_ids

spikesorting_merge_ids = ( (sgs.SpikeSortingSelection & sorter_keys) * SpikeSortingOutput.CurationV1 ).fetch("merge_id") spikesorting_merge_ids

Out[3]:

array([UUID('485a4ddf-332d-35b5-3ad4-0561736c1844'),
       UUID('6acb99b8-6a0c-eb83-1141-5f603c5895e0'),
       UUID('f7237e18-4e73-4aee-805b-90735e9147de'),
       UUID('7e3fa66e-727e-1541-819a-b01309bb30ae'),
       UUID('6d039a63-17ad-0b78-4b1e-f02d5f3dbbc5'),
       UUID('e0e9133a-7a4e-1321-a43a-e8afcb2f25da'),
       UUID('9959b614-2318-f597-6651-a3a82124d28a'),
       UUID('c0eb6455-fc41-c200-b62e-e3ca81b9a3f7'),
       UUID('912e250e-56d8-ee33-4525-c844d810971b'),
       UUID('d7d2c97a-0e6e-d1b8-735c-d55dc66a30e1'),
       UUID('abb92dce-4410-8f17-a501-a4104bda0dcf'),
       UUID('74e10781-1228-4075-0870-af224024ffdc'),
       UUID('8bbddc0f-d6ae-6260-9400-f884a6e25ae8'),
       UUID('614d796c-0b95-6364-aaa0-b6cb1e7bbb83'),
       UUID('b332482b-e430-169d-8ac0-0a73ce968ed7'),
       UUID('86897349-ff68-ac72-02eb-739dd88936e6'),
       UUID('4a712103-c223-864f-82e0-6c23de79cc14'),
       UUID('cf858380-e8a3-49de-c2a9-1a277e307a68'),
       UUID('cc4ee561-f974-f8e5-0ea4-83185263ac67'),
       UUID('4a72c253-b3ca-8c13-e615-736a7ebff35c'),
       UUID('b92a94d8-ee1e-2097-a81f-5c1e1556ed24'),
       UUID('5c53bd33-d57c-fbba-e0fb-55e0bcb85d03'),
       UUID('0751a1e1-a406-7f87-ae6f-ce4ffc60621c')], dtype=object)

In [4]:

from spyglass.decoding.v1.sorted_spikes import SortedSpikesGroup

SortedSpikesGroup()

from spyglass.decoding.v1.sorted_spikes import SortedSpikesGroup SortedSpikesGroup()

Out[4]:

nwb_file_name name of the NWB file	sorted_spikes_group_name
mediumnwb20230802_.nwb	test_group

Total: 1

In [5]:

SortedSpikesGroup().create_group(
    group_name="test_group",
    nwb_file_name=nwb_copy_file_name,
    keys=[
        {"spikesorting_merge_id": merge_id}
        for merge_id in spikesorting_merge_ids
    ],
)

SortedSpikesGroup & {
    "nwb_file_name": nwb_copy_file_name,
    "sorted_spikes_group_name": "test_group",
}

SortedSpikesGroup().create_group( group_name="test_group", nwb_file_name=nwb_copy_file_name, keys=[ {"spikesorting_merge_id": merge_id} for merge_id in spikesorting_merge_ids ], ) SortedSpikesGroup & { "nwb_file_name": nwb_copy_file_name, "sorted_spikes_group_name": "test_group", }

Out[5]:

nwb_file_name name of the NWB file	sorted_spikes_group_name
mediumnwb20230802_.nwb	test_group

Total: 1

In [6]:

SortedSpikesGroup.SortGroup & {
    "nwb_file_name": nwb_copy_file_name,
    "sorted_spikes_group_name": "test_group",
}

SortedSpikesGroup.SortGroup & { "nwb_file_name": nwb_copy_file_name, "sorted_spikes_group_name": "test_group", }

Out[6]:

nwb_file_name name of the NWB file	sorted_spikes_group_name	spikesorting_merge_id
mediumnwb20230802_.nwb	test_group	0751a1e1-a406-7f87-ae6f-ce4ffc60621c
mediumnwb20230802_.nwb	test_group	485a4ddf-332d-35b5-3ad4-0561736c1844
mediumnwb20230802_.nwb	test_group	4a712103-c223-864f-82e0-6c23de79cc14
mediumnwb20230802_.nwb	test_group	4a72c253-b3ca-8c13-e615-736a7ebff35c
mediumnwb20230802_.nwb	test_group	5c53bd33-d57c-fbba-e0fb-55e0bcb85d03
mediumnwb20230802_.nwb	test_group	614d796c-0b95-6364-aaa0-b6cb1e7bbb83
mediumnwb20230802_.nwb	test_group	6acb99b8-6a0c-eb83-1141-5f603c5895e0
mediumnwb20230802_.nwb	test_group	6d039a63-17ad-0b78-4b1e-f02d5f3dbbc5
mediumnwb20230802_.nwb	test_group	74e10781-1228-4075-0870-af224024ffdc
mediumnwb20230802_.nwb	test_group	7e3fa66e-727e-1541-819a-b01309bb30ae
mediumnwb20230802_.nwb	test_group	86897349-ff68-ac72-02eb-739dd88936e6
mediumnwb20230802_.nwb	test_group	8bbddc0f-d6ae-6260-9400-f884a6e25ae8

...

Total: 23

Model parameters¶

As before we can specify the model parameters. The only difference is that we will use the ContFragSortedSpikesClassifier instead of the ContFragClusterlessClassifier.

In [7]:

from spyglass.decoding.v1.core import DecodingParameters
from non_local_detector.models import ContFragSortedSpikesClassifier


DecodingParameters.insert1(
    {
        "decoding_param_name": "contfrag_sorted",
        "decoding_params": ContFragSortedSpikesClassifier(),
        "decoding_kwargs": dict(),
    },
    skip_duplicates=True,
)

DecodingParameters()

from spyglass.decoding.v1.core import DecodingParameters from non_local_detector.models import ContFragSortedSpikesClassifier DecodingParameters.insert1( { "decoding_param_name": "contfrag_sorted", "decoding_params": ContFragSortedSpikesClassifier(), "decoding_kwargs": dict(), }, skip_duplicates=True, ) DecodingParameters()

Out[7]:

decoding_param_name a name for this set of parameters	decoding_params initialization parameters for model	decoding_kwargs additional keyword arguments
contfrag_clusterless	=BLOB=	=BLOB=
contfrag_clusterless_0.5.13	=BLOB=	=BLOB=
contfrag_sorted	=BLOB=	=BLOB=
contfrag_sorted_0.5.13	=BLOB=	=BLOB=
nonlocal_clusterless_0.5.13	=BLOB=	=BLOB=
nonlocal_sorted_0.5.13	=BLOB=	=BLOB=

Total: 6

1D Decoding¶

As in the clusterless notebook, we can decode 1D position if we specify the track_graph, edge_order, and edge_spacing parameters in the Environment class constructor. See the clusterless decoding tutorial for more details.

Decoding¶

Now we can decode the position using the sorted spikes using the SortedSpikesDecodingSelection table. Here we assume that PositionGroup has been specified as in the clusterless decoding tutorial.

In [8]:

from spyglass.decoding.v1.sorted_spikes import SortedSpikesDecodingSelection

SortedSpikesDecodingSelection()

from spyglass.decoding.v1.sorted_spikes import SortedSpikesDecodingSelection SortedSpikesDecodingSelection()

Out[8]:

nwb_file_name name of the NWB file	sorted_spikes_group_name	position_group_name	decoding_param_name a name for this set of parameters	encoding_interval descriptive name of this interval list	decoding_interval descriptive name of this interval list	estimate_decoding_params whether to estimate the decoding parameters
mediumnwb20230802_.nwb	test_group	test_group	contfrag_sorted	pos 0 valid times	test decoding interval	0

Total: 1

In [9]:

selection_key = {
    "sorted_spikes_group_name": "test_group",
    "position_group_name": "test_group",
    "decoding_param_name": "contfrag_sorted",
    "nwb_file_name": "mediumnwb20230802_.nwb",
    "encoding_interval": "pos 0 valid times",
    "decoding_interval": "test decoding interval",
    "estimate_decoding_params": False,
}

SortedSpikesDecodingSelection.insert1(
    selection_key,
    skip_duplicates=True,
)

selection_key = { "sorted_spikes_group_name": "test_group", "position_group_name": "test_group", "decoding_param_name": "contfrag_sorted", "nwb_file_name": "mediumnwb20230802_.nwb", "encoding_interval": "pos 0 valid times", "decoding_interval": "test decoding interval", "estimate_decoding_params": False, } SortedSpikesDecodingSelection.insert1( selection_key, skip_duplicates=True, )

In [10]:

from spyglass.decoding.v1.sorted_spikes import SortedSpikesDecodingV1

SortedSpikesDecodingV1.populate(selection_key)

from spyglass.decoding.v1.sorted_spikes import SortedSpikesDecodingV1 SortedSpikesDecodingV1.populate(selection_key)

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Encoding models:   0%|          | 0/23 [00:00<?, ?cell/s]

Non-Local Likelihood:   0%|          | 0/23 [00:00<?, ?cell/s]

/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/non_local_detector/models/base.py:780: FutureWarning: the `pandas.MultiIndex` object(s) passed as 'state_bins' coordinate(s) or data variable(s) will no longer be implicitly promoted and wrapped into multiple indexed coordinates in the future (i.e., one coordinate for each multi-index level + one dimension coordinate). If you want to keep this behavior, you need to first wrap it explicitly using `mindex_coords = xarray.Coordinates.from_pandas_multiindex(mindex_obj, 'dim')` and pass it as coordinates, e.g., `xarray.Dataset(coords=mindex_coords)`, `dataset.assign_coords(mindex_coords)` or `dataarray.assign_coords(mindex_coords)`.
  results = xr.Dataset(
/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/xarray/namedarray/core.py:487: UserWarning: Duplicate dimension names present: dimensions {'states'} appear more than once in dims=('states', 'states'). We do not yet support duplicate dimension names, but we do allow initial construction of the object. We recommend you rename the dims immediately to become distinct, as most xarray functionality is likely to fail silently if you do not. To rename the dimensions you will need to set the ``.dims`` attribute of each variable, ``e.g. var.dims=('x0', 'x1')``.
  warnings.warn(

We verify that the results have been inserted into the DecodingOutput merge table.

In [11]:

from spyglass.decoding.decoding_merge import DecodingOutput

DecodingOutput.SortedSpikesDecodingV1 & selection_key

from spyglass.decoding.decoding_merge import DecodingOutput DecodingOutput.SortedSpikesDecodingV1 & selection_key

Out[11]:

merge_id	nwb_file_name name of the NWB file	sorted_spikes_group_name	position_group_name	decoding_param_name a name for this set of parameters	encoding_interval descriptive name of this interval list	decoding_interval descriptive name of this interval list	estimate_decoding_params whether to estimate the decoding parameters
650316f6-e19b-6195-5aea-0f46bc503fa3	mediumnwb20230802_.nwb	test_group	test_group	contfrag_sorted	pos 0 valid times	test decoding interval	0

Total: 1

We can load the results as before:

In [12]:

results = (SortedSpikesDecodingV1 & selection_key).load_results()
results

results = (SortedSpikesDecodingV1 & selection_key).load_results() results

[2024-01-17 22:50:37,762][WARNING]: Skipped checksum for file with hash: 929bf936-5d90-ef32-a736-fb41f4d4932c, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_39518860-b21c-47e4-8a4f-cf7e040e313f.nc
/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/xarray/namedarray/core.py:487: UserWarning: Duplicate dimension names present: dimensions {'states'} appear more than once in dims=('states', 'states'). We do not yet support duplicate dimension names, but we do allow initial construction of the object. We recommend you rename the dims immediately to become distinct, as most xarray functionality is likely to fail silently if you do not. To rename the dimensions you will need to set the ``.dims`` attribute of each variable, ``e.g. var.dims=('x0', 'x1')``.
  warnings.warn(
/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/xarray/namedarray/core.py:487: UserWarning: Duplicate dimension names present: dimensions {'states'} appear more than once in dims=('states', 'states'). We do not yet support duplicate dimension names, but we do allow initial construction of the object. We recommend you rename the dims immediately to become distinct, as most xarray functionality is likely to fail silently if you do not. To rename the dimensions you will need to set the ``.dims`` attribute of each variable, ``e.g. var.dims=('x0', 'x1')``.
  warnings.warn(

Out[12]:

<xarray.Dataset>
Dimensions:                      (state_ind: 26668, dim_0: 26668, time: 451,
                                  states: 2, intervals: 1, state_bins: 26668)
Coordinates:
  * state_ind                    (state_ind) int32 0 0 0 0 0 0 0 ... 1 1 1 1 1 1
  * time                         (time) float64 1.626e+09 ... 1.626e+09
  * states                       (states) object 'Continuous' 'Fragmented'
    environments                 (states) object ...
    encoding_groups              (states) int32 ...
  * state_bins                   (state_bins) object MultiIndex
  * state                        (state_bins) object 'Continuous' ... 'Fragme...
  * x_position                   (state_bins) float64 29.02 29.02 ... 262.7
  * y_position                   (state_bins) float64 0.4493 2.445 ... 224.0
Dimensions without coordinates: dim_0, intervals
Data variables:
    initial_conditions           (dim_0) float64 ...
    discrete_state_transitions   (states, states) float64 ...
    acausal_posterior            (intervals, time, state_bins) float32 ...
    acausal_state_probabilities  (intervals, time, states) float64 ...
Attributes:
    marginal_log_likelihoods:  -16366.834

xarray.Dataset

• Dimensions:
  • state_ind: 26668
  • dim_0: 26668
  • time: 451
  • states: 2
  • intervals: 1
  • state_bins: 26668
• Coordinates: (9)
  • state_ind
    (state_ind)
    int32
    0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1

    array([0, 0, 0, ..., 1, 1, 1], dtype=int32)

  • time
    (time)
    float64
    1.626e+09 1.626e+09 ... 1.626e+09

    array([1.625936e+09, 1.625936e+09, 1.625936e+09, ..., 1.625936e+09,
           1.625936e+09, 1.625936e+09])

  • states
    (states)
    object
    'Continuous' 'Fragmented'

    array(['Continuous', 'Fragmented'], dtype=object)

  • environments
    (states)
    object
    ...

    [2 values with dtype=object]

  • encoding_groups
    (states)
    int32
    ...

    [2 values with dtype=int32]

  • state_bins
    (state_bins)
    object
    MultiIndex

    array([('Continuous', 29.016395273685823, 0.449308551055541),
           ('Continuous', 29.016395273685823, 2.4450014489534446),
           ('Continuous', 29.016395273685823, 4.440694346851348), ...,
           ('Fragmented', 262.70550123169426, 219.97552731982495),
           ('Fragmented', 262.70550123169426, 221.97122021772287),
           ('Fragmented', 262.70550123169426, 223.96691311562074)], dtype=object)

  • state
    (state_bins)
    object
    'Continuous' ... 'Fragmented'

    array(['Continuous', 'Continuous', 'Continuous', ..., 'Fragmented',
           'Fragmented', 'Fragmented'], dtype=object)

  • x_position
    (state_bins)
    float64
    29.02 29.02 29.02 ... 262.7 262.7

    array([ 29.016395,  29.016395,  29.016395, ..., 262.705501, 262.705501,
           262.705501])

  • y_position
    (state_bins)
    float64
    0.4493 2.445 4.441 ... 222.0 224.0

    array([  0.449309,   2.445001,   4.440694, ..., 219.975527, 221.97122 ,
           223.966913])

• Data variables: (4)
  • initial_conditions
    (dim_0)
    float64
    ...

    [26668 values with dtype=float64]

  • discrete_state_transitions
    (states, states)
    float64
    ...

    [4 values with dtype=float64]

  • acausal_posterior
    (intervals, time, state_bins)
    float32
    ...

    [12027268 values with dtype=float32]

  • acausal_state_probabilities
    (intervals, time, states)
    float64
    ...

    [902 values with dtype=float64]

• Indexes: (4)
  • state_ind
    PandasIndex

    PandasIndex(Index([0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
           ...
           1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
          dtype='int32', name='state_ind', length=26668))

  • time
    PandasIndex

    PandasIndex(Index([1625935714.6359036, 1625935714.6692283, 1625935714.7025743,
           1625935714.7358992,  1625935714.769224, 1625935714.8025699,
           1625935714.8358948, 1625935714.8692408, 1625935714.9025657,
           1625935714.9358904,
           ...
           1625935729.3357632,  1625935729.369088,  1625935729.402413,
           1625935729.4357586, 1625935729.4690833, 1625935729.5024297,
           1625935729.5357544,  1625935729.569079,  1625935729.602425,
           1625935729.6357498],
          dtype='float64', name='time', length=451))

  • states
    PandasIndex

    PandasIndex(Index(['Continuous', 'Fragmented'], dtype='object', name='states'))

  • state_bins
    state
    x_position
    y_position
    PandasMultiIndex

    PandasIndex(MultiIndex([('Continuous', 29.016395273685823,  0.449308551055541),
                ('Continuous', 29.016395273685823, 2.4450014489534446),
                ('Continuous', 29.016395273685823,  4.440694346851348),
                ('Continuous', 29.016395273685823,  6.436387244749252),
                ('Continuous', 29.016395273685823,  8.432080142647155),
                ('Continuous', 29.016395273685823, 10.427773040545059),
                ('Continuous', 29.016395273685823, 12.423465938442963),
                ('Continuous', 29.016395273685823, 14.419158836340866),
                ('Continuous', 29.016395273685823,  16.41485173423877),
                ('Continuous', 29.016395273685823, 18.410544632136673),
                ...
                ('Fragmented', 262.70550123169426, 206.00567703453964),
                ('Fragmented', 262.70550123169426,  208.0013699324375),
                ('Fragmented', 262.70550123169426, 209.99706283033544),
                ('Fragmented', 262.70550123169426,  211.9927557282333),
                ('Fragmented', 262.70550123169426, 213.98844862613123),
                ('Fragmented', 262.70550123169426, 215.98414152402916),
                ('Fragmented', 262.70550123169426, 217.97983442192702),
                ('Fragmented', 262.70550123169426, 219.97552731982495),
                ('Fragmented', 262.70550123169426, 221.97122021772287),
                ('Fragmented', 262.70550123169426, 223.96691311562074)],
               name='state_bins', length=26668))

• Attributes: (1)

  marginal_log_likelihoods :

  -16366.834

In [ ]: